diagnosis

Abstract

The invention methods for determining the presence or absence of infertility in a subject as well as methods for predicting the likelihood of success of in vitro fertilisation.

Description

[0001] DIAGNOSIS

[0002] Field of the Invention

[0003] The invention relates to methods for determining the presence or absence of infertility in a subject as well as methods for predicting the likelihood of success of in vitro fertilisation.

[0004] Background of the Invention

[0005] Reproductive health is not well understood and there is a gap in knowledge related to diagnosing infertility and providing suitable therapies. Currently, the most effective solution to infertility is in vitro fertilisation (IVF), with more than 2.5 million rounds of IVF happening globally every year. However, IVF is an expensive process and more than 70% of IVF rounds fail to result in a live birth. Reasons for failure of IVF are widely accepted to be due to three main conditions: sperm (male factor infertility), oocyte / embryo, and the endometrium.

[0006] The endometrium is the inner epithelial layer of the uterus which thickens throughout the menstrual cycle and is subsequently broken down and shed during menstruation. The interplay of the endometrium with the oocyte is thought to contribute to fertility issues. However, conducting research into the endometrium has been difficult because it is a complex and poorly understood system, with unique vascularisation, development and hormonal and immune landscape. Furthermore, research into the endothelium has been difficult also because of the lack of human-like menstruation in mice, with a mouse model of menstruation having been developed only recently (Liu et al., 2020, Molecular Medicine Reports, 22(6), pp. 4463-4474.). Thus, innovations in the assessment of infertility have largely focused on the male factor and oocyte / embryo conditions, with limited research into the endometrium.

[0007] There has been some innovation with regards to the endometrium, which include the Analysis of Infectious Chronic Endometritis (ALICE) and Endometrial Microbiome Metagenomic Analysis (EMMA) tests from iGenomix which use an endometrial biopsy to assess the microbiome, as well as chronic, low-grade and bacterial infections. These tests have been shown to increase pregnancy outcomes by up to 20%, although the invasive nature, inaccuracy and high cost (in excess of £1500) have likely prevented them from reaching greater scale.

[0008] Recurrent implantation failure (RIF) has also been used as a means to develop tests that measure a woman’s implantation window using Endometrial Receptivity Analysis tests. However, such tests cannot be considered in isolation, without considering other factors of the endometrial landscape.

[0009] Despite the innovation shown in the ALICE, EMMA and RIF tests, there has been some pushback in light of the risks and unreliability of the tests. The ALICE and EMMA kits are highly invasive and come with the risk of uterine perforation. In addition, variability in the biopsy location has led to high variability in outcomes and a number of false negatives results (Williams & Gaddey, Am Fam Physician. 2020 May 1 ; 101 (9): 551 -556.). Furthermore, women with persistent symptoms are not advised to undergo a biopsy as there is a high likelihood of missing lesions. When these considerations are coupled with the fact that a small group of 342 patients was used, with minimal diversity, and factors such as progesterone levels and sample timing were not monitored, the tests have not necessarily provided a fair representation of the population or the endometrial condition. Furthermore, RIF has also been limited by study size and reports of differing success rates across research groups, and not considering the dynamic nature of the endometrium.

[0010] Overall, the lack of ease of getting data from the endometrium in a non-invasive and cheap way, the inability to use mice as accurate animal models until very recently, the limited understanding of reproductive health, and the limited omics and systems biology knowledge and applications in clinical settings have all contributed to the lack of understanding for the endometrium (Koblinsky et al. 2018. Mother and more: a broader perspective on women’s health. In The health of women (pp. 33-62); Wang, X., 2018. Cell Biology and Toxicology, 34, pp.163-166; Egea et al. Journal of Human Reproductive Sciences, 7(2), pp.73-92; Fitzgerald et al. Biology of reproduction, 104(2), pp.282-293; Ruiz-Alonso et al. Biochimica et Biophysica Acta (BB A) -Molecular Basis of Disease, 1822(12), pp.1931-1942.). However, such an understanding is vital to further understand infertility.

[0011] There is an unmet need for an improved method for assessing fertility and predicting the likelihood of success of IVF.

[0012] Summary of the preferred embodiments

[0013] The invention provides a method for determining the presence or absence of infertility in a subject, comprising detecting in a non-invasive sample one or more marker(s) from at least two groups selected from: (a) a reproductive hormone, (b) a bacterium, (c) cortisol, and (d) an adhesion, inflammation or receptivity marker.

[0014] The invention also provides a method for determining the likelihood of success of in vitro fertilisation in a subject, comprising detecting in a non-invasive sample one or more marker(s) from at least two groups selected from: (a) a reproductive hormone, (b) a bacterium, (c) cortisol, and (d) an adhesion, inflammation or receptivity marker.

[0015] The invention further provides a method for determining the presence or absence of infertility in a subject or for determining the likelihood of success of in vitro fertilisation in a subject, comprising detecting in a non-invasive sample two or more marker(s) from at least one groups selected from: (a) a reproductive hormone, (b) a bacterium, (c) cortisol, and (d) an adhesion, inflammation or receptivity marker. Optionally the method comprises a further step of detecting cortisol.

[0016] In some embodiments, the method as described herein comprises quantifying (a) the reproductive hormone, (b) the bacterium, (c) the cortisol and / or (d) the adhesion, inflammation or receptivity marker.

[0017] In some embodiments, the method as described herein comprises quantifying (a) the reproductive hormone, (b) the bacterium, (c) the cortisol and (d) the adhesion, inflammation or receptivity marker.

[0018] In some embodiments, the method as described herein further comprises a step of comparing the concentration of the markers to a reference.

[0019] In some embodiments, the non-invasive sample is menstrual blood, urine or hair. Preferably, the non-invasive sample is menstrual blood.

[0020] In some embodiments, the reproductive hormone, bacterium, and / or adhesion, inflammation or receptivity marker are measured in menstrual blood and / or urine.

[0021] In some embodiments, cortisol is measured in menstrual blood and / or hair.

[0022] In some embodiments, the method as described herein further comprises a step of detecting a heavy metal.

[0023] In some embodiments, the reproductive hormone is selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b and / or oestrogen.

[0024] In some embodiments, the method as described herein comprises a step of quantifying all of the reproductive hormones selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b and oestrogen.

[0025] In some embodiments, the bacterium is from a species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., Corynebacterium spp., as well as the presence of pathogenic bacteria like Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and / or Ureaplasma spp.

[0026] In some embodiments, the bacterium is of Lactobacillus spp. and / or of Bifidobacterium spp. In some embodiments, the method as described herein comprises a step of determining the relative abundance of bacteria from two or more species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and / or Ureaplasma spp.

[0027] In some embodiments, the method comprises a step of determining the relative abundance of one or more bacteria from all of the species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and Ureaplasma spp .

[0028] In some embodiments, the adhesion, inflammation or receptivity marker is selected from the group consisting of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a, interleukin-6, interleukin-8, and / or interleukin-33.

[0029] In some embodiments, the method as described herein comprises a step of quantifying all of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a, interleukin-6, interleukin-8, and interleukin -33.

[0030] In some embodiments, the marker is quantified using an ELISA assay and / or an immunoassay and / or a mass spectrometry assay.

[0031] In some embodiments, the marker is quantified using Maglumi assays, or Luminex assays of Inductively Coupled Plasma Mass Spectrometry (ICP-MS).

[0032] In some embodiments, the heavy metals are selected from the group consisting of arsenic, lead, cadmium, and mercury.

[0033] In some embodiments, the method as described herein comprises a step of detecting or quantifying all of the heavy metals from the group consisting of Arsenic, Lead, Cadmium, and / or Mercury.

[0034] In some embodiments, the heavy metals are detected using inductively coupled plasma mass spectrometry.

[0035] In some embodiments, the markers are quantified in the menstrual serum or the menstrual plasma.

[0036] In some embodiments, the method is performed ex vivo or in vitro.

[0037] The invention provides a kit comprising two or more antibodies selected from the group consisting of: (a) an antibody that can detect one or more reproductive hormones selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b and / or oestrogen; and / or (b) an antibody that can detect one or more bacteria from a species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., as well as the presence of pathogenic bacteria like Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and / or Ureaplasma spp , and / or (c) an antibody that can detect cortisol; and / or (d) an antibody that can detect one or more markers selected from the group consisting of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a, interleukin-6, interleukin-8, and / or interleukin-33, wherein the kit comprises antibodies from at least two of groups a, b, c and d.

[0038] In some embodiments, the antibodies are monoclonal antibodies.

[0039] The invention also provides the use of the kit as described herein in a method of determining the presence or absence of infertility in a subject.

[0040] The invention also provides the use of the kit as described herein for determining the likelihood of success of in vitro fertilisation in a subject.

[0041] The invention further provides a method for preparing a non-invasive sample suitable for use in a method according to the invention, the method comprising (a) centrifuging a non-invasive sample obtained from a patient; (b) separating the supernatant and the pellet; and (c) centrifuging the pellet from step (b). Preferably the non-invasive sample is menstrual blood.

[0042] Also provided is a method for determining the presence or absence of infertility in a subject according to the methods of the invention or for determining the likelihood of success of in vitro fertilisation in a subject according to the methods of the invention, comprising a step of preparing a non-invasive sample according to a method comprising (a) centrifuging a non- invasive sample obtained from a patient; (b) separating the supernatant and the pellet; and (c) centrifuging the pellet from step (b). Preferably the non-invasive sample is menstrual blood.

[0043] Brief Description of the Drawings

[0044] Figure 1: Balance of hormones during the menstrual cycle, indicating that menstrual cycle readings are expected to give accurate, baseline readings (Scott et al.. 1989 Fertility and sterility, 51(4), pp.651-654).

[0045] Figure 2: Diagram of the solution: Menstrual blood is used to assess the endometrial microbiome, as well as hormones and proteins, which are then integrated with population data to give a cohesive model of IVF success, to guide further steps. Figure 3: qPCR amplification curves (FAM) for Lactobacillus gBlock standards. Fluorescence (AR) is plotted vs cycle number without baseline correction.

[0046] Figure 4: qPCR amplification curves (FAM) for Lactobacillus for DNA samples from MB.

[0047] Figure 5 : Standard curve for Lactobacillus DNA quantification generated from gBlock standards (1 ng, 0.1 ng, 0.01 ng; Ct = 5, 9, and 15, respectively) using linear regression.

[0048] Figure 6: Comparison of Ct values for fresh versus 24 h stored menstrual blood samples (neat, then diluted in PBS) from three biological replicates.

[0049] Figure 7: Comparison of Ct values for fresh versus 120 h stored menstrual blood samples in neat (processed with PBS) or ACD from three biological replicates.

[0050] Figure 8: qPCR amplification curves (FAM) for Klebsiella gBlock standards. Fluorescence (AR) is plotted vs cycle number without baseline correction.

[0051] Figure 9: qPCR amplification curves (FAM) for Chlamydia gBlock standards. Fluorescence (AR) is plotted vs cycle number without baseline correction.

[0052] Figure 10: qPCR amplification curves (FAM) for Gardnerella gBlock standards. Fluorescence (AR) is plotted vs cycle number without baseline correction.

[0053] Figure 11: qPCR amplification curves (FAM) for bifidobacterium gBlock standards. Fluorescence (AR) is plotted vs cycle number without baseline correction.

[0054] Figure 12: Relative abundance (%) of the top 10 bacterial genera detected across six freshly processed samples using the double -spinning method. Genera are ranked based on overall abundance across all samples. P12. 1 and P12.2 were obtained from the same woman in two cycles, as were P20.1 and P20.2.

[0055] Figure 13: Distribution of Lactobacillus species across individual samples: a) and b) Lactobacillus composition from P12 in two separate menstrual cycles, c) and d) Lactobacillus composition from P20 in two separate menstrual cycles, e) from Pl 8, and f) from P28.

[0056] Figure 14: UMAP visualisation of genus-level relative abundance profiles from menstrual blood samples processed using different storage and handling conditions. Conditions included single spin from whole menstrual blood (P) and double spin after supernatant (SN) collection (N), with variations in NAC addition (NAC), ACD dilution (ACD), and storage for 24 h at 4 °C either neat (N_40N) or in ACD (A_4ON).

[0057] Figure 15: tSNE visualisation of genus-level relative abundance profiles from menstrual blood samples processed using different storage and handling conditions. Conditions included single spin from whole menstrual blood (P) and double spin after supernatant (SN) collection (N), with variations in NAC addition (NAC), ACD dilution (ACD), and storage for 24 h at 4 °C either neat (N_40N) or in ACD (A_40N).

[0058] Figure 16: Relative abundance (%) of the top 10 bacterial genera detected across all samples processed with different methods from six biological replicates. P= single spin method, N = double spin and PBS diluted, A= double spin and ACD diluted. NAC= double spin with 2% NAC addition. 4ON= 4 degree storage overnight. P12.1 and P12.2 were obtained from the same woman in two cycles, as were P20. 1 and P20.2.

[0059] Figure 17: Comparison of proteins detected in menstrual blood MS-DIE and pregnancy plasma MS-DIE

[0060] Figure 18: Correlation plot of hormone concentration in PBS diluted fresh menstrual blood (MB) supernatants (SN) to age, BMI and smoking status.

[0061] Figure 19: Correlation plot of hormone concentration in ACD diluted fresh menstrual blood supernatant to age, BMI and smoking status.

[0062] Figure 20: Hormone concentrations after 48 h storage in ACD or PBS at room temperature (RT) or 4 °C, relative to fresh samples.

[0063] Figure 21: Hormone concentrations after 120 h storage at 4 °C in ACD or PBS, relative to fresh samples.

[0064] Figure 22: Effects of NAC treatment on lipemic index. Menstrual blood samples were analysed for lipemic index following treatment with 0.1% NAC (n = 3), 0.2% NAC (n = 7), 0.5% NAC (n = 7), 1% NAC (n = 3), or no treatment (PBS only, n = 12).

[0065] Figure 23: Effect of NAC on TSH levels in PB. Geometric mean ratio (GMR) of NAC / PBS is shown for 0. 1% (n = 1), 0.2% (n = 1), and 0.5% (n = 3) NAC treated PB samples.

[0066] Figure 24: Effect of NAC on analyte levels in MB. Geometric mean ratio (GMR) of NAC / PBS is shown for TSH, LH, FSH, cortisol, AMH, and oestradiol in MB samples treated with 0. 1% (n = 3), 0.2% (n = 7), 0.5% (n = 7), and 1% (n = 3) NAC.

[0067] Figure 25: Hormone levels in paired MB and PB samples. Concentrations of E2, cortisol, TSH, LH, and FSH were measured in MB and matched PB samples at baseline and after different concentrations of NAC treatment. Error bars represent variability from duplicate measurements. Data shown are from one biological replicate. Figure 26: The comparison of hormone levels in serum and plasma PB samples. Peripheral blood serum and plasma were compared in two biological replicates. Serum was collected in a no-additive tube, while plasma was tested as EDTA plasma diluted 1:2.5 with PBS.

[0068] Figure 27: Comparison of hormone levels in PB and MB with or without NAC treatment. Box- and-scatter plots are shown for PB and MB samples, with or without NAC treatment. Reference venous ranges for each hormone are indicated. All values are displayed on a log scale.

[0069] Figure 28: PCA plot of metabolite data. PCA plots are shown for (a) all MB and PB samples and (b) samples excluding ACD-diluted samples.

[0070] Figure 29: Comparison of metabolite levels between PBS and ACD diluted MB samples. From two biological replicates.

[0071] Figure 30: Fold change (ACD / PBS) is shown for two MB samples (n = 2). Red indicates higher levels in ACD, and blue indicates higher levels in PBS.

[0072] Figure 31 : Comparison of metabolite levels between MB and PB samples. Data are shown from three paired biological replicates.

[0073] Figure 32: Fold change (ACD / PBS) is shown for paired MB and PB samples (n = 3). Red indicates higher levels in MB, and blue indicates higher levels in PB.

[0074] Figure 33: Effect of hemolysis on metabolite levels in MB samples. Comparisons of hemolyzed MB samples (P6.2 and P31.1) with average of non-hemolyzed MB (PBS-diluted) are shown at the single -metabolite level.

[0075] Figure 34: Comparison of metabolite levels in MB and PB samples across two menstrual cycles. MB and PB samples collected from the same participant across two consecutive menstrual cycles. Coefficients of variation were calculated for individual metabolites.

[0076] Figure 35: Inter-individual variability of metabolite profiles in MB and PB samples.

[0077] Coefficients of variation were calculated to assess inter-individual variability in MB (n = 4) and PB (n = 3) samples. One hemolysed sample was excluded from analysis.

[0078] Brief Description of the Sequences

[0079] SEQ ID NO: 1 - The 16s rRNA sequence of Lactobacillus iners in positive orientation taaattgagagtttgatcctggctcaggacgaacgctggcggcgtgcctaatacatgcaagtcgagcgagtctgccttgaagatcggagtg cttgcactctgtgaaacaagatacaggctagcggcggacgggtgagtaacacgtgggtaacctgcccaagagatcgggataacacctgg aaacagatgctaataccggataacaacagatgatgcctatcaactgtttaaaagatggttctgctatcactcttggatggacctgcggtgcatt agctagttggtagggtaacggcctaccaaggcgatgatgcatagccgagttgagagactgatcggccacattgggactgagacacggcc caaactcctacgggaggcagcagtagggaatcttccacaatggacgcaagtctgatggagcaacgccgcgtgagtgaagaagggtttcg gctcgtaaagctctgttgttggtgaagaaggacaggggtagtaactgacctttgtttgacggtaatcaattagaaagtcacggctaactacgt gccagcagccgcggtaatacgtaggtggcaagcgttgtccggatttattgggcgtaaagcgagtgcaggcggctcgataagtctgatgtg aaagccttcggctcaaccggagaattgcatcagaaactgtcgagcttgagtacagaagaggagagtggaactccatgtgtagcggtgaaa tgcgtagatatatggaagaacaccggtggcgaaggcggctctctggtctgttactgacgctgaggctcgaaagcatgggtagcgaacag gattagataccctggtagtccatgccgtaaacgatgagtgctaagtgttgggaggtttccgcctctcagtgctgcagctaacgcattaagca ctccgcctggggagtacgaccgcaaggttgaaactcaaaggaattgacgggggcccgcacaagcggtggagcatgtggtttaattcgaa gcaacgcgaagaaccttaccaggtcttgacatccatagccagtctaagagattagatgttcccttcggggactatgagacaggtggtgcat ggctgtcgtcagctcgtgtcgtgagatgtgggtaagtcccgcaacgagcgcaaccctgtcatagtgccagcataagtgggcactct aatgagactgccggtgacaaaccggaggaaggtggggatgacgtcaagtcatcatgccccttatgacctgggctacacacgtgctacaat ggacggtacaacgagaagcgaccctgtgaaggcaagcggatctctgaaagccgttctcagtcggattgcaggctgcaactcgcctgcat gaagctggaatcgctagtaatcgcaaatcagcacgttgcggtgaatacgttcccgggccttgtacacaccgcccgtcacaccatgagagt ctgtaacgcccgaagccggcgggataaccgaaaggagtcagccgtctaaggcgggacagatgattagggtgaagtcgtaacaaggtag ccgtaggagaacctgcggctggatcacctccttt

[0080] SEQ ID NO: 2 - The 16s rRNA sequence of Lactobacillus crispatus in positive orientation taaaatgagagtttgatcctggctcaggacgaacgctggcggcgtgcctaatacatgcaagtcgagcgagcggaactaacagatttactc ggtaatgacgttaggaaagcgagcggcggatgggtgagtaacacgtggggaacctgccccatagtctgggataccacttggaaacaggt gctaataccggataagaaagcagatcgcatgatcagcttttaaaaggcggcgtaagctgtcgctatgggatggccccgcggtgcattagct agttggtaaggtaaaggcttaccaaggcgatgatgcatagccgagttgagagactgatcggccacattgggactgagacacggcccaaa ctcctacgggaggcagcagtagggaatcttccacaatggacgcaagtctgatggagcaacgccgcgtgagtgaagaaggttttcggatc gtaaagctctgtgtggtgaagaaggatagaggtagtaactggcctttatttgacggtaatcaaccagaaagtcacggctaactacgtgcc agcagccgcggtaatacgtaggtggcaagcgtgtccggatttatgggcgtaaagcgagcgcaggcggaagaataagtctgatgtgaa agccctcggcttaaccgaggaactgcatcggaaactgtttttctgagtgcagaagaggagagtggaactccatgtgtagcggtggaatgc gtagatatatggaagaacaccagtggcgaaggcggctctctggtctgcaactgacgctgaggctcgaaagcatgggtagcgaacaggat tagataccctggtagtccatgccgtaaacgatgagtgctaagtgtgggaggtttccgcctctcagtgctgcagctaacgcataagcactcc gcctggggagtacgaccgcaaggttgaaactcaaaggaattgacgggggcccgcacaagcggtggagcatgtggtttaattcgaagca acgcgaagaacctaccaggtcttgacatctagtgccatttgtagagatacaaagttccctcggggacgctaagacaggtggtgcatggct gtcgtcagctcgtgtcgtgagatgtgggtaagtcccgcaacgagcgcaaccctgtatagtgccagcataagtgggcactctaatg agactgccggtgacaaaccggaggaaggtggggatgacgtcaagtcatcatgcccctatgacctgggctacacacgtgctacaatggg cagtacaacgagaagcgagcctgcgaaggcaagcgaatctctgaaagctgttctcagttcggactgcagtctgcaactcgactgcacgaa gctggaatcgctagtaatcgcggatcagcacgccgcggtgaatacgttcccgggccttgtacacaccgcccgtcacaccatgggagtctg caatgcccaaagccggtggcctaaccttcgggaaggagccgtctaaggcagggcagatgactggggtgaagtcgtaacaaggtagccg taggagaacctgcggctggatcacctccttt SEQ ID NO: 3 - The 16s rRNA sequence of Lactobacillus crispatus in negative orientation aaaggaggtgatccagccgcaggttctcctacggctacctgtacgacttcaccccagtcatctgccctgcctagacggctcctcccga aggttaggccaccggctttgggcattgcagactcccatggtgtgacgggcggtgtgtacaaggcccgggaacgtattcaccgcggcgtg ctgatccgcgattactagcgatccagcttcgtgcagtcgagttgcagactgcagtccgaactgagaacagctttcagagattcgcttgcctt cgcaggctcgcttctcgtgtactgcccatgtagcacgtgtgtagcccaggtcataaggggcatgatgactgacgtcatccccaccttcct ccggtttgtcaccggcagtctcattagagtgcccaacttaatgctggcaactaataacaagggttgcgctcgttgcgggactaacccaaca tctcacgacacgagctgacgacagccatgcaccacctgtcttagcgtccccgaagggaactttgtatctctacaaatggcactagatgtcaa gacctggtaaggttcttcgcgtgcttcgaataaaccacatgctccaccgctgtgcgggcccccgtcaattcctttgagtttcaacctgcg gtcgtactccccaggcggagtgctaatgcgtagctgcagcactgagaggcggaaacctcccaacactagcactcatcgtttacggcat ggactaccagggtatctaatcctgttcgctacccatgctttcgagcctcagcgtcagtgcagaccagagagccgccttcgccactggtgtt cttccatatatctacgcatccaccgctacacatggagttccactctcctcttctgcactcaagaaaaacagtttccgatgcagttcctcggta agccgagggctttcacatcagactattcttccgcctgcgctcgctttacgcccaataaatccggacaacgctgccacctacgtattaccgc ggctgctggcacgtagtagccgtgactttctggtgataccgtcaaataaaggccagttactacctctatccttcttcaccaacaacagagc tttacgatccgaaaaccttcttcactcacgcggcgtgctccatcagactgcgtccatgtggaagattccctactgctgcctcccgtaggag tttgggccgtgtctcagtcccaatgtggccgatcagtctctcaactcggctatgcatcatcgcctggtaagcctttacctaccaactagctaa tgcaccgcggggccatcccatagcgacagctacgccgccttttaaaagctgatcatgcgatctgctttctatccggtatagcacctgtttc caagtggtatcccagactatggggcaggttccccacgtgtactcacccatccgccgctcgcttcctaacgtcataccgaagtaaatctgtt agttccgctcgctcgactgcatgtattaggcacgccgccagcgttcgtcctgagccaggatcaaactctcatttta

[0081] SEQ ID NO: 4 - The 16s rRNA sequence of Lactobacillus gasseri in positive orientation gaaaatgagagtttgatcctggctcaggacgaacgctggcggcgtgcctaatacatgcaagtcgagcgagcttgcctagatgaatttggtg cttgcaccagatgaaactagatacaagcgagcggcggacgggtgagtaacacgtgggtaacctgcccaagagactgggataacacctg gaaacagatgctaataccggataacaacactagacgcatgtctagagtttaaaagatggttctgctatcactcttggatggacctgcggtgc attagctagttggtaaggtaacggcttaccaaggcaatgatgcatagccgagttgagagactgatcggccacattgggactgagacacgg cccaaactcctacgggaggcagcagtagggaatcttccacaatggacgcaagtctgatggagcaacgccgcgtgagtgaagaagggttt cggctcgtaaagctctgttggtagtgaagaaagatagaggtagtaactggcctttatttgacggtaattacttagaaagtcacggctaactac gtgccagcagccgcggtaatacgtaggtggcaagcgtgtccggatttatgggcgtaaagcgagtgcaggcggttcaataagtctgatgt gaaagccttcggctcaaccggagaattgcatcagaaactgttgaacttgagtgcagaagaggagagtggaactccatgtgtagcggtgga atgcgtagatatatggaagaacaccagtggcgaaggcggctctctggtctgcaactgacgctgaggctcgaaagcatgggtagcgaaca ggattagataccctggtagtccatgccgtaaacgatgagtgctaagtgttgggaggtttccgcctctcagtgctgcagctaacgcattaagc actccgcctggggagtacgaccgcaaggttgaaactcaaaggaattgacgggggcccgcacaagcggtggagcatgtggtttaatcga agcaacgcgaagaaccttaccaggtcttgacatccagtgcaaacctaagagattaggtgttccctcggggacgctgagacaggtggtgc atggctgtcgtcagctcgtgtcgtgagatgttgggttaagtcccgcaacgagcgcaacccttgtcattagttgccatcataagttgggcact ctaatgagactgccggtgacaaaccggaggaaggtggggatgacgtcaagtcatcatgccccttatgacctgggctacacacgtgctaca atggacggtacaacgagaagcgaacctgcgaaggcaagcggatctctgaaagccgttctcagttcggactgtaggctgcaactcgccta cacgaagctggaatcgctagtaatcgcggatcagcacgccgcggtgaatacgttcccgggccttgtacacaccgcccgtcacaccatga gagtctgtaacacccaaagccggtgggataacctttataggagtcagccgtctaaggtaggacagatgatagggtgaagtcgtaacaag gtagccgtaggagaacctgcggctggatcacctccttt

[0082] SEQ ID NO: 5 - The 16s rRNA sequence of Lactobacillus gasseri in negative orientation aaaggaggtgatccagccgcaggttctcctacggctacctgtacgacttcaccctaatcatctgtcctacctagacggctgactcctataa aggtatcccaccggctttgggtgtacagactctcatggtgtgacgggcggtgtgtacaaggcccgggaacgtattcaccgcggcgtgct gatccgcgatactagcgatccagcttcgtgtaggcgagttgcagcctacagtccgaactgagaacggctttcagagatccgcttgcctc gcaggtcgcttctcgtgtaccgtccatgtagcacgtgtgtagcccaggtcataaggggcatgatgactgacgtcatccccacctcctc cggtttgtcaccggcagtctcattagagtgcccaactaatgatggcaactaatgacaagggtgcgctcgtgcgggactaacccaacat ctcacgacacgagctgacgacagccatgcaccacctgtctcagcgtccccgaagggaactcctaatctctaggtttgcactggatgtcaa gacctggtaaggttcttcgcgtgcttcgaataaaccacatgctccaccgctgtgcgggcccccgtcaattcctttgagtttcaacctgcg gtcgtactccccaggcggagtgctaatgcgtagctgcagcactgagaggcggaaacctcccaacactagcactcatcgtttacggcat ggactaccagggtatctaatcctgttcgctacccatgctttcgagcctcagcgtcagtgcagaccagagagccgccttcgccactggtgtt cttccatatatctacgcatccaccgctacacatggagttccactctcctcttctgcactcaagttcaacagtttctgatgcaattctccggtga gccgaaggctttcacatcagactattgaaccgcctgcactcgctttacgcccaataaatccggacaacgctgccacctacgtattaccgc ggctgctggcacgtagtagccgtgactttctaagtaataccgtcaaataaaggccagtactacctctatctttcttcactaccaacagagct tacgagccgaaaccctcttcactcacgcggcgtgctccatcagactgcgtccatgtggaagattccctactgctgcctcccgtaggag tttgggccgtgtctcagtcccaatgtggccgatcagtctctcaactcggctatgcatcatgcctggtaagccgtgcctaccaactagcta atgcaccgcaggtccatccaagagtgatagcagaaccatcttttaaactctagacatgcgtctagtgttgttatccggtattagcatctgtttcc aggtgtatcccagtctctgggcaggtacccacgtgtactcacccgtccgccgctcgctgtatctagtttcatctggtgcaagcaccaaa ttcatctaggcaagctcgctcgactgcatgtattaggcacgccgccagcgttcgtcctgagccaggatcaaactctcattttc

[0083] SEQ ID NO: 6 - The 16s rRNA sequence of Lactobacillus jensenii in positive orientation caaaatgagagtttgatcctggctcaggacgaacgctggcggcgtgcctaatacatgcaagtcgagcgagctgcctatagaagttcttcg gaatggacatagatacaagctagcggcggatgggtgagtaacgcgtgggtaacctgcccttaagtctgggataccatttggaaacagatg ctaataccggataaaagctactttcgcatgaaagaagtttaaaaggcggcgtaagctgtcgctaaaggatggacctgcgatgcattagcta gttggtaaggtaacggcttaccaaggcgatgatgcatagccgagttgagagactgatcggccacattgggactgagacacggcccaaac tcctacgggaggcagcagtagggaatcttccacaatggacgaaagtctgatggagcaacgccgcgtgagtgaagaaggttttcggatcgt aaagctctgttgttggtgaagaaggatagaggtagtaactggcctttatttgacggtaatcaaccagaaagtcacggctaactacgtgccag cagccgcggtaatacgtaggtggcaagcgtgtccggatttatgggcgtaaagcgagcgcaggcggatgataagtctgatgtgaaagc cttcggctcaaccgaagaactgcatcagaaactgtcaatcttgagtgcagaagaggagagtggaactccatgtgtagcggtggaatgcgt agatatatggaagaacaccagtggcgaaggcggctctctggtctgtaactgacgctgaggctcgaaagcatgggtagcgaacaggatta gataccctggtagtccatgccgtaaacgatgagtgctaagtgtgggaggtttccgcctctcagtgctgcagctaacgcataagcactccg cctggggagtacgaccgcaaggttgaaactcaaaggaattgacgggggcccgcacaagcggtggagcatgtggtttaatcgaagcaa cgcgaagaaccttaccaggtctgacatcctttgaccacctaagagattaggttttccctcggggacaaagagacaggtggtgcatggctg tcgtcagctcgtgtcgtgagatgttgggttaagtcccgcaacgagcgcaacccttgttaatagttgccagcattaagttgggcactctattga gactgccggtgacaaaccggaggaaggtggggatgacgtcaagtcatcatgccccttatgacctgggctacacacgtgctacaatgggc agtacaacgagaagcgaacctgtgaaggcaagcggatctcttaaagctgtctcagttcggactgtaggctgcaactcgcctacacgaag ctggaatcgctagtaatcgcggatcagcacgccgcggtgaatacgttcccgggccttgtacacaccgcccgtcacaccatgagagtttgta acacccaaagtcggtgaggtaacctttggagccagccgcctaaggtgggacagatgattagggtgaagtcgtaacaaggtagccgtagg agaacctgcggctggatcacctccttt

[0084] SEQ ID NO: 7 - The 16s rRNA sequence of Lactobacillus jensenii in negative orientation aaaggaggtgatccagccgcaggttctcctacggctacctgtacgacttcaccctaatcatctgtcccaccttaggcggctggctccaaa ggtacctcaccgactttgggtgtacaaactctcatggtgtgacgggcggtgtgtacaaggcccgggaacgtattcaccgcggcgtgctg atccgcgattactagcgattccagcttcgtgtaggcgagttgcagcctacagtccgaactgagaacagctttaagagatccgcttgccttca caggtttgcttctcgtgtactgcccatgtagcacgtgtgtagcccaggtcataaggggcatgatgactgacgtcatccccaccttcctccg gtttgtcaccggcagtctcaatagagtgcccaacttaatgctggcaactataacaagggtgcgctcgtgcgggacttaacccaacatctc acgacacgagctgacgacagccatgcaccacctgtctctttgtccccgaagggaaaacctaatctctaggtggtcaaaggatgtcaagac ctggtaaggttcttcgcgtgcttcgaattaaaccacatgctccaccgctgtgcgggcccccgtcaattcctttgagtttcaacctgcggtcg tactccccaggcggagtgcttaatgcgttagctgcagcactgagaggcggaaacctcccaacacttagcactcatcgtttacggcatggac taccagggtatctaatcctgttcgctacccatgctttcgagcctcagcgtcagtacagaccagagagccgccttcgccactggtgttcttcc atatatctacgcattccaccgctacacatggagttccactctcctctctgcactcaagatgacagtttctgatgcagttctcggtgagccga aggctttcacatcagactatcaatccgcctgcgctcgctttacgcccaataaatccggacaacgcttgccacctacgtattaccgcggctgc tggcacgtagtagccgtgactttctggtgataccgtcaaataaaggccagtactacctctatccttcttcaccaacaacagagctttacga tccgaaaaccttctcactcacgcggcgtgctccatcagactttcgtccatgtggaagattccctactgctgcctcccgtaggagtttgggc cgtgtctcagtcccaatgtggccgatcagtctctcaactcggctatgcatcatcgcctggtaagccgtacctaccaactagctaatgcatc gcaggtccatcctttagcgacagctacgccgccttttaaacttctttcatgcgaaagtagcttttatctggtatagcatctgtttccaaatggta tcccagactaagggcaggtacccacgcgtactcacccatccgccgctagctgtatctatttccatccgaagaacttctataggcaagct cgctcgactgcatgtataggcacgccgccagcgttcgtcctgagccaggatcaaactctcattttg

[0085] SEQ ID NO: 8 - The 16s rRNA sequence of Klebsiella pneumoniae in positive orientation agagtttgatcatggctcagattgaacgctggcggcaggcctaacacatgcaagtcgagcggtagcacagagagcttgctctcgggtgac gagcggcggacgggtgagtaatgtctgggaaactgcctgatggagggggataactactggaaacggtagctaataccgcataacgtcgc aagaccaaagtgggggaccttcgggcctcatgccatcagatgtgcccagatgggattagctagtaggtggggtaacggctcacctaggc gacgatccctagctggtctgagaggatgaccagccacactggaactgagacacggtccagactcctacgggaggcagcagtggggaat atgcacaatgggcgcaagcctgatgcagccatgccgcgtgtgtgaagaaggccttcgggtgtaaagcactttcagcggggaggaagg cgtaaggttaataacctctcgatgacgttacccgcagaagaagcaccggctaactccgtgccagcagccgcggtaatacggagggtgc aagcgttaatcggaattactgggcgtaaagcgcacgcaggcggtctgtcaagtcggatgtgaaatccccgggctcaacctgggaactgc attcgaaactggcaggctagagtctgtagaggggggtagaattccaggtgtagcggtgaaatgcgtagagatctggaggaataccggtg gcgaaggcggccccctggacaaagactgacgctcaggtgcgaaagcgtggggagcaaacaggattagataccctggtagtccacgcc gtaaacgatgtcgatttggaggttgtgcccttgaggcgtggcttccggagctaacgcgttaaatcgaccgcctggggagtacggccgcaa ggttaaaactcaaatgaattgacgggggcccgcacaagcggtggagcatgtggtttaattcgatgcaacgcgaagaacctacctggtctt gacatccacagaactttccagagatggattggtgccttcgggaactgtgagacaggtgctgcatggctgtcgtcagctcgtgttgtgaaatg ttgggttaagtcccgcaacgagcgcaacccttatcctttgttgccagcggtccggccgggaactcaaaggagactgccagtgataaactg gaggaaggtggggatgacgtcaagtcatcatggccctacgaccagggctacacacgtgctacaatggcatatacaaagagaagcgacc tcgcgagagcaagcggacctcataaagtatgtcgtagtccggattggagtctgcaactcgactccatgaagtcggaatcgctagtaatcgt agatcagaatgctacggtgaatacgttcccgggccttgtacacaccgcccgtcacaccatgggagtgggttgcaaaagaagtaggtagct taacctcgggagggcgcttaccactttgtgattcatgactggggtgaagtcgtaacaaggtaaccgtaggggaacctgcggttggatcac ct

[0086] SEQ ID NO: 9 - The 16s rRNA sequence of Klebsiella pneumoniae in negative orientation aggtgatccaaccgcaggttcccctacggttaccttgttacgacttcaccccagtcatgaatcacaaagtggtaagcgccctcccgaaggtt aagctacctacttcttttgcaacccactcccatggtgtgacgggcggtgtgtacaaggcccgggaacgtattcaccgtagcattctgatctac gattactagcgattccgacttcatggagtcgagttgcagactccaatccggactacgacatactttatgaggtccgcttgctctcgcgaggtc gcttctctttgtatatgccattgtagcacgtgtgtagccctggtcgtaagggccatgatgacttgacgtcatccccaccttcctccagtttatcac tggcagtctcctttgagttcccggccggaccgctggcaacaaaggataagggttgcgctcgttgcgggacttaacccaacatttcacaaca cgagctgacgacagccatgcagcacctgtctcacagttcccgaaggcaccaatccatctctggaaagttctgtggatgtcaagaccaggta aggttcttcgcgttgcatcgaattaaaccacatgctccaccgcttgtgcgggcccccgtcaattcatttgagttttaaccttgcggccgtactcc ccaggcggtcgatttaacgcgttagctccggaagccacgcctcaagggcacaacctccaaatcgacatcgtttacggcgtggactaccag ggtatctaatcctgtttgctccccacgctttcgcacctgagcgtcagtctttgtccagggggccgccttcgccaccggtattcctccagatctc tacgcatttcaccgctacacctggaattctacccccctctacaagactctagcctgccagtttcgaatgcagttcccaggttgagcccgggg atttcacatccgacttgacagaccgcctgcgtgcgctttacgcccagtaattccgattaacgcttgcaccctccgtattaccgcggctgctgg cacggagttagccggtgcttcttctgcgggtaacgtcaatcgagaggttattaaccttacgccttcctccccgctgaaagtgctttacaaccc gaaggccttcttcacacacgcggcatggctgcatcaggcttgcgcccattgtgcaatattccccactgctgcctcccgtaggagtctggacc gtgtctcagttccagtgtggctggtcatcctctcagaccagctagggatcgtcgcctaggtgagccgttaccccacctactagctaatcccat ctgggcacatctgatggcatgaggcccgaaggtcccccactttggtcttgcgacgttatgcggtattagctaccgtttccagtagttatcccc ctccatcaggcagtttcccagacattactcacccgtccgccgctcgtcacccgagagcaagctctctgtgctaccgctcgacttgcatgtgtt aggcctgccgccagcgttcaatctgagccatgatcaaactct

[0087] SEQ ID NO: 10 - The 16s rRNA sequence of Prevotella bivia in positive orientation acaatggagagtttgatcctggctcaggatgaacgctagctataggcttaacacatgcaagtcgaggggcagcgaatagatagcttgctatt tatgtcggcgaccggcgcacgggtgagtaacgcgtatccaacctacccataactaagggataacccagcgaaagttggactaataccttat gtattcgtttgatctcatgagattacgaataaagatttatcggttatggatggggatgcgtctgattagcttgttggcggggtaacggcccacc aaggcaacgatcagtaggggttctgagaggaaggtcccccacattggaactgagacacggtccaaactcctacgggaggcagcagtga ggaatattggtcaatggacgcaagtctgaaccagccaagtagcgtgcaggatgacggccctatgggttgtaaactgcttttatatggggata aagtggggaacgtgttcccttttgcaggtaccatatgaataaggaccggctaattccgtgccagcagccgcggtaatacggaaggttcgg gcgttatccggatttattgggtttaaagggagcgtaggccgtttggtaagcgtgttgtgaaatgtagtagctcaacttctagattgcagcgcga actgtcagacttgagtgcgcacaacgtaggcggaattcatggtgtagcggtgaaatgcttagatatcatgaagaactccgattgcgaaggc agcttacgggagcgcaactgacgctgaagctcgaaggtgcgggtatcgaacaggattagataccctggtagtccgcacagtaaacgatg gatgcccgctgttagcacctagtgttagcggctaagcgaaagcattaagcatcccacctggggagtacgccggcaacggtgaaactcaa aggaattgacgggggcccgcacaagcggaggaacatgtggtttaatcgatgatacgcgaggaaccttacccgggcttgaatgcagatg aacgatttagagataatgaggtccttcgggacatctgtgaaggtgctgcatggtgtcgtcagctcgtgccgtgaggtgtcggctaagtgc cataacgagcgcaacccctttctttagttgccatcaggttctgctgggcactctggagatactgccaccgtaaggtgtgaggaaggtgggg atgacgtcaaatcagcacggcccttacgtccggggctacacacgtgttacaatgggtggtacagatagttggtcgtgtgcaaatacgatcta atcctaaaaccattctcagttcggactggggtctgcaacccgaccccacgaagctggattcgctagtaatcgcgcatcagccatggcgcg gtgaatacgttcccgggccttgtacacaccgcccgtcaagccatgaaagccgggggtgcctgaagttcgtgaccgtaaggatcgacctag ggcaaaactggtaattggggctaagtcgtaacaaggtagccgtaccggaaggtgcggctggaacacctccttt

[0088] SEQ ID NO: 11 - The 16s rRNA sequence of Prevotella bivia in negative orientation aaaggaggtgttccagccgcacctccggtacggctacctgtacgactagccccaataccagttttgccctaggtcgatcctacggtc acgaacttcaggcacccccggctttcatggcttgacgggcggtgtgtacaaggcccgggaacgtattcaccgcgccatggctgatgcgc gattactagcgaatccagcttcgtggggtcgggttgcagaccccagtccgaactgagaatggttttaaggattagatcgtatttgcacacga ccaactatctgtaccacccatgtaacacgtgtgtagccccggacgtaagggccgtgctgatttgacgtcatccccaccttcctcacaccta cggtggcagtatctccagagtgcccagcagaacctgatggcaactaaagaaaggggtgcgctcgtatggcactaagccgacacctca cggcacgagctgacgacaaccatgcagcaccttcacagatgtcccgaaagacctcatatctctaaatcgttcatctgcaattcaagcccgg gtaaggttcctcgcgtatcatcgaataaaccacatgttcctccgctgtgcgggccccccgtcaattcctttgagtttcaccgtgccggcgt actcccccaggtgggatgctaatgctttcgctagccgctaacactaggtgctaacagcgggcatccatcgtttactgtgcggactaccag ggtatctaatcctgttcgatacccgcacctcgagcttcagcgtcagtgcgctcccgtaagctgccttcgcaatcggagttcttcatgatatct aagcatttcaccgctacaccatgaattccgcctacgtgtgcgcactcaagtctgacagttcgcgctgcaatctagaagtgagctcctacatt tcacaacacgcttaccaaacggcctacgctccctttaaacccaataaatccggataacgcccgaaccttccgtataccgcggctgctggc acggaatagccagtccttatcatatggtacctgcaaaagggaacacgttccccactttatccccatataaaagcagtttacaacccatagg gccgtcatcctgcacgctactggctggttcagactgcgtccatgaccaatattcctcactgctgcctcccgtaggagtttggaccgtgtct cagttccaatgtgggggaccttcctctcagaacccctactgatcgtgcctggtgggccgtaccccgccaacaagctaatcagacgcatc cccatccataaccgataaatctttattcgtaatctcatgagatcaaacgaatacataaggtatagtccaactttcgctgggtatccctagtat gggtaggtggatacgcgtactcacccgtgcgccggtcgccgacataaatagcaagctatctatcgctgcccctcgactgcatgtgtaa gcctatagctagcgttcatcctgagccaggatcaaactctccatgt

[0089] SEQ ID NO: 12 The 16s rRNA sequence of Prevotella amnii in positive orientation acaatggagagtttgatcctggctcaggatgaacgctagctataggcttaacacatgcaagtcgaggggcagcatatagattgcttgcaatt tatgatggcgaccggcgcacgggtgagtaacgcgtatccaacctacccattactagggaataacccagcgaaagttggcctaatgcccta tgtagtcgtttgatcgcctgagatttcgacgaaagatttatcggtatggatggggatgcgtctgatagctgtggcggggtaaaggcccac caaggcaacgatcagtaggggttctgagaggaaggtcccccacattggaactgagacacggtccaaactcctacgggaggcagcagtg aggaatattggtcaatgggcgagagcctgaaccagccaagtagcgtgcaggatgacggccctatgggttgtaaactgcttttatatgggaa taaagtgagggacgtgtcccttattgcatgtaccatacgaataaggaccggctaattccgtgccagcagccgcggtaatacggaaggtcca ggcgtatccggatttatgggtttaaagggagcgtaggctgtttgtaagcgtgtgtgaaatgtaggagctcaacttttagatgcagcgcg aactggcagacttgagtgcgcacaacgtaggcggaattcatggtgtagcggtgaaatgcttagatatcatgacgaactccgattgcgaagg cagcttacgggagcgcaactgacgctaaagctcgaaggtgcgggtatcgaacaggattagataccctggtagtccgcacagtaaacgat ggatgcccgctgttagcacctagtgttagcggctaagcgaaagcattaagcatcccacctggggagtacgccggcaacggtgaaactca aaggaattgacgggggcccgcacaagcggaggaacatgtggtttaattcgatgatacgcgaggaaccttacccgggcttgaattgcagat gtttat

[0090] SEQ ID NO: 13 The 16s rRNA sequence of Prevotella amnii in negative orientation aaaggaggtgttccagccgcacctccggtacggctacctgtacgactagccccaataccagttttgccctaggtcgatcctacggtc acgaacttcaggcacccccggctttcatggcttgacgggcggtgtgtacaaggcccgggaacgtattcaccgcgccatggctgatgcgc gattactagcgaatccagcttcgtggggtcgggttgcagaccccagtccgaactgagaatggtttttaggattagatgcacttgcgtacaac caactctctgtaccacccatgtaacacgtgtgtagccccggacgtaagggccgtgctgatttgacgtcatccccaccttcctcacacctac ggtggcagtatctctagagtgcccagcactacctgatggcaactaaaaaaaggggtgcgctcgtatggcactaagccgacacctcacg gcacgagctgacgacaaccatgcagcaccttcacaaatgccccgaagggaata

[0091] SEQ ID NO: 14 The 16s rRNA sequence of Bifidobacterium breve in positive orientation ttttttgtggagggttcgattctggctcaggatgaacgctggcggcgtgcttaacacatgcaagtcgaacgggatccatcgggctttgctgg tggtgagagtggcgaacgggngagtaatgcgtgaccgacctgccccatgcaccggaatagctcctggaaacgggtggtaatgccggat gctccatcacaccgcatggtgtgttgggaaagcctttgcggcatgggatggggtcgcgtcctatcagcttgatggcggggtaacggccca ccatggcttcgacgggnagccggcctgagagggcgaccggccacattgggactgagatacggcccagactcctacgggaggcagca gtggggaatatgcacaatgggcgcaagcctnatgcagcgacgcngcgtgagggatggaggccttcgggtgtaaacctcttttgtagg gagcaaggcactttgtgttgagtgtacctttcgaataagcaccggctaactacgtgccagcagccgcggtaatacgtagggtgnnagcgtt atccggaattatgggcgtaaagggctcgtaggcggttcgtcgcgtccggtgtgaaagtccatcgctaacggtggatccgcgccgggta cgggcgggcttgagtgcggtaggggagactggaattcccggtgtaacggtggaatgtgtagatatcgggaagaacaccaatggcgaag gcaggtctctgggcngttactgacgctgaggagcnaaagcgtggggagcgaacaggattagataccctggtagtccacgccgtaaacg gtggatgctggatgtggggtcngttccacgggttccgtgtcggagctaacgcgtaagcatcccgcctggggagtacggccgcaaggct aaaactcaaagaaattgacgggngccngcacaagcggcggagcatgcggattaattcgatgnaacgcgaagaaccttacctgggcttg acatgttcccgacgatcccagagatggggtttcccttcggggngggttcacaggtggtgcatggtcgtcgtcagctcgtgtcgtgagatgtt gggtaagtcccgcaacgagcgcaaccctcgcccnntgtgcnagcggatgtgccgggaactcacggnnnaccgccggggtaactc ggaggaaggtggggatgacgtcagatcatcatgccccttacgtccagggcttcacgcatgctacaatggccggtacaacgggatgcgac agtgcgagctggagcggatccctgaaaaccggtctcagttcggatcgcagtctgcaactcgactgcgtgaaggcggagtcgctagtaatc gcgaatcagcaacgtcgcggtgaatgcgttcccgggccttgtacacaccgcccgtcaagtcatgaaagtgggcagcacccgaagccgg tggcctaacccctgcgggagggagccgtctaaggtgaggctcgtgatgggactaagnngtaacaagnnnnnngtaccggaagnnn nnnnnngatcacctcctttct SEQ ID NO: 15 The 16s rRNA sequence of Bifidobacterium breve in negative orientation aaaggaggtgatccagccgcaccttccggtacggctacctgtacgactagtcccaatcacgagcctcacctagacggctccctcccg caaggggtaggccaccggcttcgggtgctgcccactttcatgactgacgggcggtgtgtacaaggcccgggaacgcattcaccgcga cgtgctgattcgcgattactagcgactccgccttcacgcagtcgagttgcagactgcgatccgaactgagaccggttttcagggatccgct ccagctcgcactgtcgcatcccgtgtaccggccatgtagcatgcgtgaagccctggacgtaaggggcatgatgatctgacgtcatcccc accttcctccgagttaaccccggcggtcccccgtgagtcccggcacaatccgctggcaacacggggcgagggttgcgctcgttgcggg actaacccaacatctcacgacacgagctgacgacgaccatgcaccacctgtgaacccgccccgaagggaaaccccatctctgggatcg tcgggaacatgtcaagcccaggtaaggttcttcgcgtgcatcgaataatccgcatgctccgccgctgtgcgggcccccgtcaatttcttt gagttttagcctgcggccgtactccccaggcgggatgcttaacgcgttagctccgacacggaacccgtggaacgggccccacatccag catccaccgtttacggcgtggactaccagggtatctaatcctgttcgctccccacgctttcgctcctcagcgtcagtaacggcccagagacc tgccttcgccatggtgttcttcccgatatctacacattccaccgtacaccgggaattccagtctcccctaccgcactcaagcccgcccgtac ccggcgcggatccaccgttaagcgatggactttcacaccggacgcgacgaaccgcctacgagccctttacgcccaataattccggataac gctgcaccctacgtataccgcggctgctggcacgtagtagccggtgctattcgaaaggtacactcaacacaaagtgcctgctccctaa caaaagaggtttacaacccgaaggcctccatccctcacgcggcgtcgctgcatcaggcttgcgcccattgtgcaatattccccactgctgc ctcccgtaggagtctgggccgtatctcagtcccaatgtggccggtcgccctctcaggccggctacccgtcgaagccatggtgggccgta ccccgccatcaagctgataggacgcgaccccatcccatgccgcaaaggctttcccaacacaccatgcggtgtgatggagcatccggcatt accacccgtttccaggagctattccggtgcatggggcaggtcggtcacgcattactcacccgttcgccactctcaccaccaggcaaagcc cgatggatcccgttcgactgcatgtgtaagcacgccgccagcgttcatcctgagccagaatcgaaccctccacaaa

[0092] SEQ ID NO: 16 The 16s rRNA sequence of Bifidobacterium bifidum in positive orientation tttttgtggagggttcgattctggctcaggatgaacgctggcggcgtgctaacacatgcaagtcgaacgggatccatcaagctgctggtg gtgagagtggcgaacgggtgagtaatgcgtgaccgacctgccccatgctccggaatagctcctggaaacgggtggtaatgccgnatgtt ccacatgatcgcatgtgatgtgggaaagattctatcggcgtgggatggggtcgngtcctatcagctgtggtgaggtaacggctcaccaa ggcttcgacgggtagccggcctgagagggcgaccggccacattgggactgagatacggcccagactcctacgggaggcagcagtgg ggaatatgcacaatgggcgcaagcctgatgcagcgacgccgcgtgagggatggaggccttcgggtgtaaacctcttttgtttgggagc aagccttcgggtgagtgtacctttcgaataagcgccggctaactacgtgccagcagccgcggtaatacgtagggnnnnagcgttatccg gatttatgggcgtaaagggctcgtaggcggctcgtcgcgtccggtgtgaaagtccatcgctaacggtggatctgcgccgggtacgggc gggctggagtgcggtaggggagactggaattcccggtgtaacggtggaatgtgtagatatcgggaagaacaccgatggcgaaggcag gtctctgggcngtcactgacgctgaggagcnaaagcgtggggagcgaacaggattagataccctggtagtccacgccgtaaacggtgg acgctggatgtggggcacgttccacgtgttccgtgtcggagctaacgcgttaagcgtcccgcctggggagtacggccgcaaggctaaaa ctcaaagaaattgacgggggccngcacaagcggcggagcatgcggattaattcgaacnaacgcgaagaaccttacctgggcttgacat gttcccgacgacgccagagatggcgtttcccttcggggcgggttcacaggtggtgcatggtcgtcgtcagctcgtgtcgtgagatgtggg ttaagtcccgcaacgagcgcaaccctcgccccgtgttgccagcacgttatggtgggaactcacgggnnaccgccggggttaacncgga ggaaggtggggatgacgtcagatcatcatgccccttacgtccagggcttcacgcatgctacaatggccggtacagcgggatgcgacatg gcgacatggagcggatccctgaaaaccggtctcagttcggatcggagcctgcaacccggctccgtgaaggcggagtcgctagtaatcgc ggatcagcaacgccgcggtgaatgcgttcccgggccttgtacacaccgcccgtcaagtcatgaaagtgggcagcacccgaagccggtg gcctaacccctgtgggatggagccgtctaaggtgaggctcgtgntgggactaagnngtaacaagnnnnnngtaccggaagnnnnn nnnngatcacctcctttct

[0093] SEQ ID NO: 17 The 16s rRNA sequence of Bifidobacterium bifidum in negative orientation aaaggaggtgatccagccgcaccttccggtacggctacctgtacgactagtcccaatcacgagcctcacctagacggctccatccca caaggggtaggccaccggcttcgggtgctgcccactttcatgactgacgggcggtgtgtacaaggcccgggaacgcattcaccgcgg cgtgctgatccgcgattactagcgactccgccttcacggagccgggttgcaggctccgatccgaactgagaccggttttcagggatccgc tccatgtcgccatgtcgcatcccgctgtaccggccatgtagcatgcgtgaagccctggacgtaaggggcatgatgatctgacgtcatccc caccttcctccgagttaaccccggcggtcccccgtgagttcccaccataacgtgctggcaacacggggcgagggttgcgctcgttgcgg gacttaacccaacatctcacgacacgagctgacgacgaccatgcaccacctgtgaacccgccccgaagggaaacgccatctctggcgtc gtcgggaacatgtcaagcccaggtaaggtcttcgcgtgcatcgaataatccgcatgctccgccgctgtgcgggcccccgtcaatttctt tgagttttagccttgcggccgtactccccaggcgggacgcttaacgcgttagctccgacacggaacacgtggaacgtgccccacatccag cgtccaccgtttacggcgtggactaccagggtatctaatcctgttcgctccccacgctttcgctcctcagcgtcagtgacggcccagagacc tgccttcgccatcggtgttcttcccgatatctacacattccaccgtacaccgggaattccagtctcccctaccgcactccagcccgcccgta cccggcgcagatccaccgttaagcgatggactttcacaccggacgcgacgagccgcctacgagccctttacgcccaataaatccggata acgctgcgccctacgtataccgcggctgctggcacgtagtagccggcgcttatcgaaaggtacactcacccgaaggctgctcccaa acaaaagaggtttacaacccgaaggcctccatccctcacgcggcgtcgctgcatcaggcttgcgcccattgtgcaatattccccactgctg cctcccgtaggagtctgggccgtatctcagtcccaatgtggccggtcgccctctcaggccggctacccgtcgaagcctggtgagccgta cctcaccaacaagctgataggacgcgaccccatcccacgccgatagaatctttcccacaatcacatgcgatcatgtggaacatccggcatt accacccgtttccaggagctattccggagcatggggcaggtcggtcacgcattactcacccgttcgccactctcaccaccaagcaaagcc cgatggatcccgttcgactgcatgtgtaagcacgccgccagcgttcatcctgagccagaatcgaaccctccacaaa

[0094] SEQ ID NO: 18 The 16s rRNA sequence of Bifidobacterium longum in positive orientation tttgtggagggttcgattctggctcaggatgaangctggcggcgtgctaacacatgcaagtcgaacggcatccatcgggcntgctggt ggtgagagtggcgaacgggngagtaatgcgtgaccnacctgccccatacaccggaatagctcctggaaacgggtggtaatgccgnatg ttccagtgatcgcntngtctctgggaaactttcgcggtatgggatggggtcgcgtcctatcngctgacggcggggtaacggccnaccg tggcttcgacgggtagccggcctgagagggcgaccggccacatgggactgagatacggccnngactcctacgggaggcagcantgg gnaatattgcacaatgggcgcaagcctgatgcagcgacgcngcgtgagggatggaggcttcgggttgtaaacctnttttntcggggagc aagcgtnagtgagtttaccnnttgaataagcaccggctaactacgtgccagcagccgcggtaatacgtagggtgnnagcgttatccggaa tatgggcgtaaagggctcgtaggcggttcgtcgcgtccggtgtgaaagtccatcgctaacggtgnatccgcgccgggtacgggcggg ctgagtgcggtaggggagactggaattcccggtgtaacggtggaatgtgtagatatcgggaagaacaccaatggcgaaggcnggtctc tgggcngttactgacgctgaggagcnaaagcgtggggagcgaacaggattagataccctggtagtccacgccgtaaacggtggatgct ggatgtgcggccggttccacgggttccgtgtcggagctaacgcgttaagcatcccgcctggggagtacggccgnaacgnaaaactcaa agaaattgacgggggcccgcacaagcgcgnanatgcggattaattcgatgcaacgcgaagaacctacctgggcttgacatgttcccga cggtcgtagagatacggctnccctcggggcgggttcacaggtggtgcatggtcgtcgtcagctcgtgtcgtgagatgtgggtaagtcc cgcaacgagcgcaaccctcgccccgtgttgccagcggattatgccgggaactcacgggggaccgccggggttaactcggaggaaggt ggggatgacgtcagatcatcatgccccttacgtccagggcttcacgcatgctacaatggccggtacaacgggatgcgacgcggcgacgc ggagcggatccctgaaaaccggtctcagttcggatcgcagtctgcaactcgactgcgtgaaggcggagtcgctagtaatcgcgaatcag caacgtcgcggtgaatgcgttcccgggccttgtacacaccgcccgncaagtcatgaaagtgggcagcacccgaagcggtggcctaacc cctgtgggatggagccgtctaaggtgaggc

[0095] SEQ ID NO: 19 - The 16s rRNA sequence of Bifidobacterium longum in negative orientation aaaggaggtgatccagccgcaccttccggtacggctacctgtacgactagtcccaatcacgagcctcacctagacggctccatccca caaggggtaggccaccggcttcgggtgctgcccactttcatgactgacgggcggtgtgtacaaggcccgggaacgcattcaccgcga cgtgctgattcgcgattactagcgactccgccttcacgcagtcgagttgcagactgcgatccgaactgagaccggttttcagggatccgct ccgcgtcgccgcgtcgcatcccgttgtaccggccattgtagcatgcgtgaagccctggacgtaaggggcatgatgatctgacgtcatccc caccttcctccgagttaaccccggcggtcccccgtgagttcccggcataatccgctggcaacacggggcgagggttgcgctcgttgcgg gacttaacccaacatctcacgacacgagctgacgacgaccatgcaccacctgtgaacccgccccgaagggaagccgtatctctacgacc gtcgggaacatgtcaagcccaggtaaggtcttcgcgtgcatcgaataatccgcatgctccgccgctgtgcgggcccccgtcaatttctt tgagttttagccttgcggccgtactccccaggcgggatgcttaacgcgttagctccgacacggaacccgtggaacgggccccacatccag catccaccgtttacggcgtggactaccagggtatctaatcctgttcgctccccacgctttcgctcctcagcgtcagtaacggcccagagacc tgccttcgccatggtgttcttcccgatatctacacattccaccgtacaccgggaattccagtctcccctaccgcactcaagcccgcccgtac ccggcgcggatccaccgttaagcgatggactttcacaccggacgcgacgaaccgcctacgagccctttacgcccaataattccggataac gctgcaccctacgtataccgcggctgctggcacgtagtagccggtgctattcaacgggtaaactcactctcgctgctccccgataaaa gaggtttacaacccgaaggcctccatccctcacgcggcgtcgctgcatcaggctgcgcccatgtgcaatattccccactgctgcctccc gtaggagtctgggccgtatctcagtcccaatgtggccggtcgccctctcaggccggctacccgtcgaagccacggtgggccgtacccc gccgtcaagctgataggacgcgaccccatcccataccgcgaaagctttcccagaagaccatgcgatcaactggaacatccggcattacc acccgtttccaggagctattccggtgtatggggcaggtcggtcacgcatactcacccgttcgccactctcaccaccaagcaagctgatgg atcccgtcgacttgcatgtgttaagcacgccgccagcgttcatcctgagccagaatcgaaccctccacaaa

[0096] SEQ ID NO: 20 - The 16s rRNA sequence of Bifidobacterium adolescentis in positive orientation nnnntgtggagggttcgattctggctcaggatnaacgctngcggcgtgctaacacatgcaagtcgaacgggatcggctngagctgct ccggctgtgagagtggcgaacgggtgagtaatgcgtgaccgacctgccccatacaccggaatagctcctggaaacgggtggtaatgcc ggatgctccagtggatgcatgtccttctgggaaagattctatcggtatgggatggggtcgcgtcctatcagctgatggcggggtaacggc ccnccatggcttcgacgggnagccggcctgagagggcgaccggccacattgggactgagatacggcccngactcctacgggaggca gcagtgggnaatattgcacaatgggcgcaagcctaatgcagcgacgccgcgtgcgggatgacggccttcgggttgtaaaccgcttttga ctgggagcaagccttcggggtgagtgtacctttcgaataagcaccggctaactacgtgccagcagccncggtaatacgtagggtgcnag cgtatccggaatatgggcgtaaagggctcgtaggcggttcgtcgcgtccggtgtgaaagtccatcgcttaacggtggntccgcgccgg gtacgggcggnctgagtgcggtagggnagactggaattccnggtgtaacggtggaatgtgtagatatcgggaagaacaccaatggcg aaggcaggtctctgggcngtnactgacgctgaggagcgaaagcgtggggagcgaacaggattagataccctggtagtccacgccgtaa acggtggatgctggatgtggggaccattccacggtctccgtgtcggagccaacgcgttaagcatcccgcctggggagtacggccgcaag gctaaaactcaaagaaattgacgggnnccnncacaagcggcngagcatgcggattaattcgatnnaacgcgaagaacctacctgggc tgacatgttcccgacaggccccagagatgggnnntccttcgggncgggntcacaggtggngcatggtcgtcgtcagctcgtgtcgtga gatgtgggtaagtcccgcaacgagcgcaaccctcgccctgtgtgccagcacgtcgtggtggnaactcacgggngaccgccggggtc aactcggaggaaggtgggnatgacgtcagatcatcatgccccttacgtccagggcttcacgcatgctacaatggccggtacaacgggat gcgacctcgtgagggggagcggatcccttaaaaccggnctcagttcggattggagtctgcaacccgactccatgaaggcggagtcgcta gtaatcgcggatcagcaacgccgcggtnaatgcgttcccgggccttgtacacaccgcccgtcaagccatgaaagtgggtagcacccgaa gccggtggcccnacctttttggggggagccgtctaaggtgagnctcgtgatngg

[0097] SEQ ID NO: 21 - The 16s rRNA sequence of Bifidobacterium adolescentis in negative orientation aaaggaggtgatccagccgcaccttccggtacggctacctgtacgactagtcccaatcacgagtctcacctagacggctccccccaa aaaggttgggccaccggcttcgggtgctacccactttcatgacttgacgggcggtgtgtacaaggcccgggaacgcattcaccgcggcgt tgctgatccgcgatactagcgactccgccttcatggagtcgggttgcagactccaatccgaactgagaccggttttaagggatccgctccc cctcacgaggtcgcatcccgttgtaccggccattgtagcatgcgtgaagccctggacgtaaggggcatgatgatctgacgtcatccccacc ttcctccgagttgaccccggcggtcccccgtgagttcccaccacgacgtgctggcaacacagggcgagggttgcgctcgttgcgggactt aacccaacatctcacgacacgagctgacgacgaccatgcaccacctgtgaacccgccccgaagggaggccccatctctggggctgtcg ggaacatgtcaagcccaggtaaggttcttcgcgtgcatcgaataatccgcatgctccgccgctgtgcgggcccccgtcaatttctttgag ttttagccttgcggccgtactccccaggcgggatgcttaacgcgttggctccgacacggagaccgtggaatggtccccacatccagcatcc accgtttacggcgtggactaccagggtatctaatcctgttcgctccccacgctttcgctcctcagcgtcagtgacggcccagagacctgcct tcgccatggtgttcttcccgatatctacacattccaccgtacaccgggaattccagtctcccctaccgcactcaagcccgcccgtacccgg cgcggatccaccgttaagcgatggactttcacaccggacgcgacgaaccgcctacgagccctttacgcccaataattccggataacgctt gcaccctacgtataccgcggctgctggcacgtagtagccggtgctattcgaaaggtacactcaccccgaagggctgctcccagtcaa aagcggtttacaacccgaaggccgtcatcccgcacgcggcgtcgctgcatcaggctgcgcccatgtgcaatatccccactgctgcctc ccgtaggagtctgggccgtatctcagtcccaatgtggccggtcgccctctcaggccggctacccgtcgaagccatggtgggccgtaccc cgccatcaagctgataggacgcgaccccatcccataccgatagaatctttcccagaaggacatgcatccaactggagcatccggcattac cacccgtttccaggagctattccggtgtatggggcaggtcggtcacgcatactcacccgtcgccactctcacggccggagcaagctcca gccgatcccgttcgacttgcatgtgttaagcacgccgccagcgttcatcctgagccagaatcgaaccctccacaaa

[0098] SEQ ID NO: 22 - The 16s rRNA sequence of Dialister micraerophilus in positive orientation agagtttgatcctggctcaggacgaacgctggcggcgtgcttaacacatgcaagtcgaacgagaggacatgaaaagcttgctttttatgaa atctagtggcaaacgggtgagtaacacgtaaacaacctgcctcaagatggggacaacagacggaaacgactgctaataccgaatacga tccgaaagtcgcatgacatttggatgaaagggtggcctatcgaagaagctatcgcttgaagaggggtttgcgtccgattaggtagttggtga ggtaacggcccaccaagccgacgatcggtagccggtctgagaggatgaacggccacactggaactgagacacggtccagactcctac gggaggcagcagtggggaatcttccgcaatggacgaaagtctgacggagcaacgccgcgtgagtgaagacggccttcgggtgtaaag ctctgtgattcgggacgaaaggccatatgtgaataatatatggaaatgacggtaccgaaaaagcaagccacggctaactacgtgccagca gccgcggtaatacgtaggtggcaagcgtgtccggaatatgggcgtaaagcgcgcgcaggcggtcactaagtccatctagaagtgc ggggcttaaccccgtgatgggatggaaactgggagactggagtatcggagaggaaagtggaatcctagtgtagcggtgaaatgcgtag atattaggaagaacaccggtggcgaaggcgactttctggacgaaaactgacgctgaggcgcgaaagcgtggggagcaaacaggattag ataccctggtagtccacgccgtaaacgatggatactaggtgtaggaggtatcgacccctctgtgccggagttaacgcaataagtatcccg cctgggaagtacgatcgcaagattaaaactcaaaggaattgacgggggcccgcacaagcggtggagtatgtggtttaattcgacgcaac gcgaagaacctaccaagtcttgacattgatcgccatccaagagattggaagttctccttcgggagacgagaaaacaggtggtgcacggc tgtcgtcagctcgtgtcgtgagatgtgggtaagtcccgcaacgagcgcaacccctatcttttgtgccagcacgtagaggtgggaactca gaagagaccgccgcagacaatgcggaggaaggtggggatgacgtcaagtcatcatgccccttatgacttgggctacacacgtactacaa tgggctttaacaaagagcagcgaaaccgcgaggtggagcgaaactcaaaaacaagcccccagttcagatcgcaggctgcaactcgcct gcgtgaagcaggaatcgctagtaatcgcgggtcagcataccgcggtgaatacgttcccgggcctgtacacaccgcccgtcacactatga gagtcggaaacacccgaagccggtgaggtaaccgcaaggagccagccgtcgaaggtggggctgatgattggagtgaagtcgtaacaa ggtaacc

[0099] SEQ ID NO: 23 - The 16s rRNA sequence of Gardnerella vaginalis in positive orientation ttcgtggagggttcgattctggctcaggatgaacgctggcggcgtgctaacacatgcaagtcgaacgggatctgaccagctgctggtg gtgagagtggcgaacgggtgagtaatgcgtgaccaacctgccccatgctccagaatagctctggaaacgggtggtaatgctggatgctc caactgacgcatgtctgtgggaaagtgttagtggcatgggatggggtcgcgtcctatcagctgtaggcggggtaatggcccacctag gcttcgacgggtagccggcctgagagggcggacggccacattgggactgagatacggcccagactcctacgggaggcagcagtggg gaatattgcgcaatgggggaaaccctgacgcagcgacgccgcgtgcgggatgaaggccttcgggttgtaaaccgcttttgattgggagc aagccttttgggtgagtgtacctttcgaataagcgccggctaactacgtgccagcagccgcggtaatacgtagggcgcaagcgttatccgg aatatgggcgtaaagagctgtaggcggttcgtcgcgtctggtgtgaaagcccatcgcttaacggtgggtttgcgccgggtacgggcgg gctagagtgcagtaggggagactggaattctcggtgtaacggtggaatgtgtagatatcgggaagaacaccaatggcgaaggcaggtct ctgggctgttactgacgctgagaagcgaaagcgtggggagcgaacaggattagataccctggtagtccacgccgtaaacggtggacgct ggatgtggggcccattccacgggttctgtgtcggagctaacgcgttaagcgtcccgcctggggagtacggccgcaaggctaaaactcaa agaaattgacgggggcccgcacaagcggcggagcatgcggattaattcgatgcaacgcgaagaacctacctgggcttgacatgtgcct gacgactgcagagatgtggtttcctttcggggcaggttcacaggtggtgcatggtcgtcgtcagctcgtgtcgtgagatgtgggtaagtc ccgcaacgagcgcaaccctcgccctgtgttgccagcgggttatgccgggaactcacgggggaccgccggggttaactcggaggaagg tggggatgacgtcagatcatcatgcccctacgtccagggcttcacgcatgctacaatggccagtacaacgggttgcttcatggtgacatgg tgctaatccctaaaactggtctcagttcggatcgtagtctgcaactcgactacgtgaaggcggagtcgctagtaatcgcgaatcagcaacg tcgcggtgaatgcgttcccgggccttgtacacaccgcccgtcaagtcatgaaagtgggcagcacccgaagccggtggcctaacccttttg ggatggagccgtctaaggtgaggctcgtgattgggactaagtcgtaacaaggtagccgtaccggaaggtgcggctggatcacctccttt

[0100] SEQ ID NO: 24 - The 16s rRNA sequence of Chlamydia trachomatis in positive orientation ctgagaatttgatcttggttcagatgaacgctggcggcgtggatgaggcatgcaagtcgaacggagcaattgtttcgacgattgtttagtgg cggaagggtagtaatgcatagataatttgtccttaacttgggaataacggttggaaacggccgctaataccgaatgtggcgatatttgggca tccgagtaacgttaaagaaggggatcttaggacctttcggttaagggagagtctatgtgatatcagctagttggtggggtaaaggcctacca aggctatgacgtctaggcggattgagagattggccgccaacactgggactgagacactgcccagactcctacgggaggctgcagtcga gaatctttcgcaatggacggaagtctgacgaagcgacgccgcgtgtgtgatgaaggctctagggttgtaaagcactttcgcttgggaataa gagaagacggttaatacccgctggatttgagcgtaccaggtaaagaagcaccggctaactccgtgccagcagctgcggtaatacggagg gtgctagcgttaatcggatttattgggcgtaaagggcgtgtaggcggaaaggtaagttagttgtcaaagatcggggctcaaccccgagtcg gcatctaatactatttttctagaggatagatggagaaaagggaatttcacgtgtagcggtgaaatgcgtagatatgtggaagaacaccagtg gcgaaggcgcttttctaatttatacctgacgctaaggcgcgaaagcaaggggagcaaacaggattagataccctggtagtccttgccgtaa acgatgcatactgatgtggatggtctcaaccccatccgtgtcggagctaacgcgtaagtatgccgcctgaggagtacactcgcaagggt gaaactcaaaagaattgacgggggcccgcacaagcagtggagcatgtggtttaatcgatgcaacgcgaaggaccttacctgggtttgac atgtatatgaccgcggcagaaatgtcgttttccgcaaggacatatacacaggtgctgcatggctgtcgtcagctcgtgccgtgaggtgttgg gttaagtcccgcaacgagcgcaacccttatcgttagttgccagcacttagggtgggaactctaacgagactgcctgggttaaccaggagg aaggcgaggatgacgtcaagtcagcatggcccttatgcccagggcgacacacgtgctacaatggccagtacagaaggtggcaagatcg cgagatggagcaaatcctcaaagctggccccagttcggatgtagtctgcaactcgactacatgaagtcggaatgctagtaatggcgtgtc agccataacgccgtgaatacgtcccgggccttgtacacaccgcccgtcacatcatgggagttggttttaccttaagtcgttgactcaacccg caagggagagaggcgcccaaggtgaggctgatgactaggatgaagtcgtaacaaggtagccctaccggaaggtggggctggatcacc tcctttt

[0101] SEQ ID NO: 25 - The 16s rRNA sequence of Streptococcus cigalcicticie in positive orientation tttaatgagagtttgatcctggctcaggacgaacgctggcggcgtgcctaatacatgcaagtagaacgctgaggtttggtgtttacactagac tgatgagttgcgaacgggtgagtaacgcgtaggtaacctgcctcatagcgggggataactattggaaacgatagctaataccgcataaga gtaattaacacatgttagtatttaaaaggagcaattgcttcactgtgagatggacctgcgttgtattagctagttggtgaggtaaaggctcacc aaggcgacgatacatagccgacctgagagggtgatcggccacactgggactgagacacggcccagactcctacgggaggcagcagta gggaatcttcggcaatggacggaagtctgaccgagcaacgccgcgtgagtgaagaaggttttcggatcgtaaagctctgttgttagagaa gaacgttggtaggagtggaaaatctaccaagtgacggtaactaaccagaaagggacggctaactacgtgccagcagccgcggtaatac gtaggtcccgagcgtgtccggatttatgggcgtaaagcgagcgcaggcggttctttaagtctgaagttaaaggcagtggctaaccatgt acgctttggaaactggaggactgagtgcagaaggggagagtggaattccatgtgtagcggtgaaatgcgtagatatatggaggaacacc ggtggcgaaagcggctctctggtctgtaactgacgctgaggctcgaaagcgtggggagcaaacaggatagataccctggtagtccacg ccgtaaacgatgagtgctaggtgttaggccctttccggggcttagtgccgcagctaacgcattaagcactccgcctggggagtacgaccg caaggttgaaactcaaaggaatgacgggggcccgcacaagcggtggagcatgtggtttaattcgaagcaacgcgaagaaccttaccag gtctgacatccttctgaccggcctagagataggctttctcttcggagcagaagtgacaggtggtgcatggtgtcgtcagctcgtgtcgtga gatgtgggtaagtcccgcaacgagcgcaacccctatgtagtgccatcataagtgggcactctagcgagactgccggtaataaacc ggaggaaggtggggatgacgtcaaatcatcatgcccctatgacctgggctacacacgtgctacaatggttggtacaacgagtcgcaagc cggtgacggcaagctaatctctaaagccaatctcagttcggatgtaggctgcaactcgcctacatgaagtcggaatcgctagtaatcgcg gatcagcacgccgcggtgaatacgttcccgggccttgtacacaccgcccgtcacaccacgagagtttgtaacacccgaagtcggtgagg taaccttttaggagccagccgcctaaggtgggatagatgatggggtgaagtcgtaacaaggtagccgtatcggaaggtgcggctggatc acctccttt SEQ ID NO: 26 The 16s rRNA sequence of Staphylococcus aureus in positive orientation ttttatggagagtttgatcctggctcaggatgaacgctggcggcgtgcctaatacatgcaagtcgagcgaacggacgagaagcttgcttctc tgatgttagcggcggacgggtgagtaacacgtggataacctacctataagactgggataacttcgggaaaccgtagctaataccggataat attttgaaccgcatggttcaaaagtgaaagacggtcttgctgtcacttatagatggatccgcgctgcattagctagttggtaaggtaacggctt accaaggcaacgatgcatagccgacctgagagggtgatcggccacactggaactgagacacggtccagactcctacgggaggcagca gtagggaatcttccgcaatgggcgaaagcctgacggagcaacgccgcgtgagtgatgaaggtcttcggatcgtaaaactctgtattagg gaagaacatatgtgtaagtaactgtgcacatcttgacggtacctaatcagaaagccacggctaactacgtgccagcagccgcggtaatacg taggtggcaagcgttatccggaattatgggcgtaaagcgcgcgtaggcggttttttaagtctgatgtgaaagcccacggctcaaccgtgg agggtcattggaaactggaaaacttgagtgcagaagaggaaagtggaattccatgtgtagcggtgaaatgcgcagagatatggaggaac accagtggcgaaggcgactttctggtctgtaactgacgctgatgtgcgaaagcgtggggatcaaacaggattagataccctggtagtccac gccgtaaacgatgagtgctaagtgttagggggtttcccgccccttagtgctgcagctaacgcattaagcactccgcctggggagtacgacc gcaaggttgaaactcaaaggaattgacggggacccgcacaagcggtggagcatgtggtttaattcgaagcaacgcgaagaaccttacca aatctgacatcctttgacaactctagagatagagccttcccctcgggggacaaagtgacaggtggtgcatggttgtcgtcagctcgtgtcg tgagatgtgggtaagtcccgcaacgagcgcaaccctaagctagtgccatcataagtgggcactctaagtgactgccggtgacaaa ccggaggaaggtggggatgacgtcaaatcatcatgccccttatgatttgggctacacacgtgctacaatggacaatacaaagggcagcga aaccgcgaggtcaagcaaatcccataaagtgttctcagttcggatgtagtctgcaactcgactacatgaagctggaatcgctagtaatcgt agatcagcatgctacggtgaatacgttcccgggtcttgtacacaccgcccgtcacaccacgagagtttgtaacacccgaagccggtggag taaccttttaggagctagccgtcgaaggtgggacaaatgattggggtgaagtcgtaacaaggtagccgtatcggaaggtgcggctggatc acctcctttct

[0102] SEQ ID NO: 27 - The 16s rRNA sequence of Staphylococcus aureus in negative orientation agaaaggaggtgatccagccgcaccttccgatacggctacctgtacgacttcaccccaatcatttgtcccaccttcgacggctagctccta aaaggtactccaccggcttcgggtgtacaaactctcgtggtgtgacgggcggtgtgtacaagacccgggaacgtattcaccgtagcatg ctgatctacgatactagcgatccagcttcatgtagtcgagttgcagactacaatccgaactgagaacaactttatgggatttgcttgacctca cggtttcgctgccctttgtatgtccatgtagcacgtgtgtagcccaaatcataaggggcatgatgatttgacgtcatccccaccttcctccgg tttgtcaccggcagtcaactagagtgcccaactaatgatggcaactaagcttaagggtgcgctcgtgcgggactaacccaacatctca cgacacgagctgacgacaaccatgcaccacctgtcactttgtcccccgaaggggaaagctctatctctagagtgtcaaaggatgtcaaga tttggtaaggttctcgcgtgcttcgaattaaaccacatgctccaccgctgtgcgggtccccgtcaattcctttgagtttcaacctgcggtcg tactccccaggcggagtgcttaatgcgttagctgcagcactaaggggcggaaaccccctaacacttagcactcatcgtttacggcgtggac taccagggtatctaatcctgtttgatccccacgctttcgcacatcagcgtcagttacagaccagaaagtcgccttcgccactggtgttcctcca tatctctgcgcatttcaccgctacacatggaattccactttcctcttctgcactcaagttttccagtttccaatgaccctccacggtgagccgtg ggctttcacatcagactaaaaaaccgcctacgcgcgctttacgcccaataattccggataacgcttgccacctacgtattaccgcggctgct ggcacgtagtagccgtggctttctgataggtaccgtcaagatgtgcacagtactacacatatgttcttccctaataacagagttttacgatc cgaagaccttcatcactcacgcggcgtgctccgtcaggctttcgcccatgcggaagattccctactgctgcctcccgtaggagtctggac cgtgtctcagttccagtgtggccgatcaccctctcaggtcggctatgcatcgtgcctggtaagccgtaccttaccaactagctaatgcagc gcggatccatctataagtgacagcaagaccgtctttcacttttgaaccatgcggttcaaaatatatccggtattagctccggtttcccgaagtt atcccagtctataggtaggtatccacgtgtactcacccgtccgccgctaacatcagagaagcaagcttctcgtccgttcgctcgactgca tgtataggcacgccgccagcgttcatcctgagccaggatcaaactctccataaaa

[0103] SEQ ID NO: 28 - The 16s rRNA sequence of Mycobacterium tuberculosis in positive orientation ttttgtttggagagtttgatcctggctcaggacgaacgctggcggcgtgcttaacacatgcaagtcgaacggaaaggtctctcggagatac tcgagtggcgaacgggtgagtaacacgtgggtgatctgccctgcacttcgggataagcctgggaaactgggtctaataccggataggac cacgggatgcatgtctgtggtggaaagcgctttagcggtgtgggatgagcccgcggcctatcagcttgttggtggggtgacggcctacc aaggcgacgacgggtagccggcctgagagggtgtccggccacactgggactgagatacggcccagactcctacgggaggcagcagt ggggaatattgcacaatgggcgcaagcctgatgcagcgacgccgcgtgggggatgacggccttcgggttgtaaacctctttcaccatcga cgaaggtccgggttctctcggattgacggtaggtggagaagaagcaccggccaactacgtgccagcagccgcggtaatacgtagggtg cgagcgtgtccggaatactgggcgtaaagagctcgtaggtggtttgtcgcgtgttcgtgaaatctcacggctaactgtgagcgtgcgg gcgatacgggcagactagagtactgcaggggagactggaattcctggtgtagcggtggaatgcgcagatatcaggaggaacaccggtg gcgaaggcgggtctctgggcagtaactgacgctgaggagcgaaagcgtggggagcgaacaggattagataccctggtagtccacgcc gtaaacggtgggtactaggtgtgggtttccttcctgggatccgtgccgtagctaacgcataagtaccccgcctggggagtacggccgca aggctaaaactcaaaggaattgacgggggcccgcacaagcggcggagcatgtggattaatcgatgcaacgcgaagaaccttacctgg gtttgacatgcacaggacgcgtctagagataggcgttccctgtggcctgtgtgcaggtggtgcatggctgtcgtcagctcgtgtcgtgaga tgtgggtaagtcccgcaacgagcgcaaccctgtctcatgtgccagcacgtaatggtggggactcgtgagagactgccggggtcaact cggaggaaggtggggatgacgtcaagtcatcatgcccctatgtccagggcttcacacatgctacaatggccggtacaaagggctgcgat gccgcgaggtaagcgaatccttaaaagccggtctcagttcggatcggggtctgcaactcgaccccgtgaagtcggagtcgctagtaatc gcagatcagcaacgctgcggtgaatacgttcccgggccttgtacacaccgcccgtcacgtcatgaaagtcggtaacacccgaagccagt ggcctaaccctcgggagggagctgtcgaaggtgggatcggcgattgggacgaagtcgtaacaaggtagccgtaccggaaggtgcggc tggatcacctcctttct

[0104] SEQ ID NO: 29 - The 16s rRNA sequence of Ureaplasma parvum in positive orientation attttaaagagtttgatcctggctcaggattaacgctggcggcatgcctaatacatgcaaatcgaacgaagccttttaggcttagtggtgaac gggtgagtaacacgtatccaatctaccctaagtggggataactagtcgaaagattagctaataccgaataataacatcaatatcgcatgag aagatgtagaaagtcgctctttgtggcgacgcttttggatgagggtgcgacgtatcagatagttggtgaggtaatggctcaccaagtcaatg acgcgtagctgtactgagaggtagaacagccacaatgggactgagacacggcccatactcctacgggaggcagcagtagggaatttttc acaatgggcgcaagccttatgaagcaatgccgcgtgaacgatgaaggtcttatagattgtaaagttcttttatatgggaagaaacgctaagat aggaaatgattttagtttgactgtaccatttgaataagtatcggctaactatgtgccagcagccgcggtaatacataggatgcaagcgttatcc ggatttactgggcgtaaaacgagcgcaggcgggtttgtaagtttggtataaatctagatgcttaacgtctagctgtatcaaaaactgtaaacc tagagtgtagtagggagtggggaactccatgtggagcggtaaaatgcgtagatatatggaagaacaccggtggcgaaggcgccaactt ggactatcactgacgctaggctcgaaagtgtggggagcaaataggatagataccctagtagtccacaccgtaaacgatcatcataaatg tcggcccgaatgggtcggtgtgtagctaacgcataaatgatgtgcctgggtagtacattcgcaagaatgaaactcaaacggaatgacg gggacccgcacaagtggtggagcatgttgcttaatttgacaatacacgtagaaccttacctaggtttgacatctattgcgatgctatagaaata tagttgaggttaacaatatgacaggtggtgcatggttgtcgtcagctcgtgtcgtgagatgttgggttaagtcccgcaacgagcgcaacccc tttcgtagttacttttctagcgatactgctaccgcaaggtagaggaaggtggggatgacgtcaaatcatcatgcccctatatctagggctgc aaacgtgctacaatggctaatacaaactgctgcaaaatcgtaagatgaagcgaaacagaaaaagttagtctcagttcggatagagggctgc aatcgtcctctgaagtggaatcactagtaatcgcgaatcagacatgtcgcggtgaatacgttctcgggtctgtacacaccgcccgtcaa actatgggagctggtaatatctaaaaccgcaaagctaaccttttggaggcatgcgtctagggtaggatcggtgactggagttaagtcgtaac aaggtatccctacgagaacgtggggatggatcacctccttt

[0105] SEQ ID NO: 30 - The 16s rRNA sequence of Ureciplcisma urealyticum in positive orientation attttaaagagtttgatcctggctcaggattaacgctggcggcatgcctaatacatgcaaatcgaacgaagccttttaggcttagtggtgaac gggtgagtaacacgtatccaacctaccctaagtggggataactagtcgaaagattagctaataccgaataataacatcaatatcgcatga gaagatgtagaaagtcgcgtttgcgacgcttttggatgggggtgcgacgtatcagatagttggtgaggtaatggctcaccaagtcaatgac gcgtagctgtactgagaggtagaacagccacaatgggactgagacacggcccatactcctacgggaggcagcagtagggaatttttcac aatgggcgaaagccttatgaagcaatgccgcgtgaacgatgaaggtcttatagattgtaaagttcttttatatgggaagaaacgctaagata ggaaatgattttagtttgactgtaccatttgaataagtatcggctaactatgtgccagcagccgcggtaatacataggatgcaagcgttatccg gatttactgggcgtaaaacgagcgcaggcgggtttgtaagtttggtataaatctagatgcttaacgtctagctgtatcaaaaactgtaaacct agagtgtagtagggagttggggaactccatgtggagcggtaaaatgcgtagatatatggaagaacaccggtggcgaaggcgccaactg gactatcactgacgctaggctcgaaagtgtggggagcaaataggattagataccctagtagtccacaccgtaaacgatcatcattaaatgt cggctcgaacgagtcggtgttgtagctaacgcattaaatgatgtgcctgggtagtacattcgcaagaatgaaactcaaacggaattgacgg ggacccgcacaagtggtggagcatgttgcttaatttgacaatacacgtagaaccttacctaggtttgacatctatgcgacgctatagaaatat agttgaggttaacaatatgacaggtggtgcatggttgtcgtcagctcgtgtcgtgagatgttgggttaagtcccgcaacgagcgcaacccct ttcgtagtgcttttctagcgatactgctaccgcaaggtagaggaaggtggggatgacgtcaaatcatcatgcccctatatctagggctgca aacgtgctacaatggctaatacaaactgctgcaaaatcgtaagatgaagcgaaacagaaaaagttagtctcagttcggatagagggctgca attcgccctctgaagtggaatcactagtaatcgcgaatcagacatgtcgcggtgaatacgtctcgggtctgtacacaccgcccgtcaaa ctatgggagctggtaatatctaaaaccgcaaagctaaccttttggaggtatgcgtctagggtaggatcggtgactggagttaagtcgtaaca aggtatccctacgagaacgtggggatggatcacctccttt

[0106] SEQ ID NO: 31 - The 16S rRNA sequence of Corynehacterium amycolatum in positive orientation acgaacgctggcggcgtgcttaacacatgcaagtcgaacggtaaggctccagcttgctggggtacacgagtggcgaacgggtgagtaa cacgtgggtgacctgccctgcacttcgggataagcctgggaaactgggtctaataccggataggaccatggtgtggatgctgtggtggaa agttttttcggtgtgggatgggcccgcggcctatcagctgtggtggggtaatggcctaccaaggcggcgacgggtagccggcctgaga gggtggacggccacatgggactgagacacggcccagactcctacgggaggcagcagtggggaatatttgcacaatgggcggaagcc tgatgcagcgacgccgcgtgggggatgacggccttcgggttgtaaactcctttcaccatcgacgaagggtttctgacggtagatggagaa gaagcaccggctaactacgtgccagcagccgcggtaatacgtagggtgcgagcgttgtccggaattactgggcgtaaagagctcgtagg tggtttgtcgcgtcgtcgtgaaattccggggctaactccgggcgtgcaggcgatacgggcataactgagtactgtaggggagactggaa ttcctggtgtagcggtgaaatgcgcagatatcaggaggaacaccggtggcgaaggcgggtctctgggcagtaactgacgctgaggagc gaaagcatggggagcgaacaggattagataccctggtagtccatgccgtaaacggtgggcgctaggtgtgggtttccttccacgggatcc gtgccgtagctaacgcattaagcgccngcctggggagtacggccgcaaggctaaaactcaaaggaattgacgggggccngcacaagc ggcggagcatgtggattaattcgatgcaacgcgaagaaccttacctgggcttgacatatacaggatcgcgccagagatggtgtttcccttgt ggcttgtatacaggtggtgcatggttgtcgtcagctcgtgtcgtgagatgttgggttaagtcccgcaacgagcgcaacccttgtcttatgttgc cagcacgttgtggtggggactcgtaagaaactgccggggttaactcggaggaaggtggggatgacgtcaaatcatcatgccccttatgtc cagggcttcacacatgctacaatggtcggtacagtgggttgccagtccgtgagggcgagctaatcccgtaaagccggtctcagttcggatc ggggtctgcaactcgaccccgtgaagtcggagtcgctagtaatcgcagatcagcaacgctgcggtgaatacgttcccgggccttgtacac accgcccgtcacgtcatgaaagtcggtaacacccgaacgcagtggcctaaact

[0107] Detailed Description of the preferred embodiments

[0108] The invention provides an improved method of determining the presence or absence of infertility in a subject. The methods of the invention are further suitable for determining the likelihood of success of in vitro fertilisation in a subject. This is achieved by detecting in a non- invasive sample one or more marker(s) from at least two groups selected from (a) a reproductive hormone, (b) a bacterium, (c) cortisol, and (d) an adhesion, inflammation or receptivity marker. The method may comprise a further step of detecting a heavy metal.

[0109] In other embodiments, the methods comprise detecting in a non-invasive sample two or more (for example two, three, four, five or more) marker(s) from at least one group selected from: (a) a reproductive hormone, (b) a bacterium, and (c) an adhesion, inflammation or receptivity marker. The method may further comprise a step of detecting cortisol and / or of detecting a heavy metal. Preferably markers from at least two, at least three or at least four groups are detected.

[0110] These methods are useful for determining the presence or absence of infertility in a subject.

[0111] Typically, infertility is assessed on the basis of a woman failing to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse. Accordingly, fertility treatments such as IVF will be available only after this time window has passed which can be a problem especially in older woman. The methods of the invention allow for an early diagnosis of infertility which allows affected women to seek treatment earlier.

[0112] The methods of the invention are also suitable for determining the presence or absence of infertility in a subject where the subject is not currently considering undergoing IVF. For example, a subject may desire to find out about their fertility. The methods of the invention allow for the determination whether a subject suffers from infertility. Depending on the deviation of the presence and / or concentration of the tested markers from the reference the methods may also be used to determine the relative fertility of a subject. For example, where the deviation is large a subject may suffer from a higher rate of infertility compared to a subject whose markers deviate less from the reference.

[0113] The methods of the invention are further suitable for determining the likelihood of success of IVF in a subject. IVF is expensive and can take a significant physical and psychological toll on a patient and it is therefore desirable to maximise the chances of success. For example, if the methods of the invention determine that the chances of success are low a patient can be advised in advance of undergoing IVF which gives them a chance to undergo treatment or make lifestyle changes before starting on an IVF cycle to improve the chances of success. For example, where the methods of the invention show that a patient has a low likelihood of success they may undergo treatment with antibiotics or chelating agents as discussed further below to reduce adverse factors or they may undertake a lifestyle change, for example by reducing their BMI or by stopping to smoke cigarettes. “Determining the likelihood of success” in this context means the probability of IVF success following one, two, three or four rounds of IVF. This can be determined for example based on a statistical analysis of data from patients.

[0114] The methods of the invention may further comprise a step of detecting or quantifying a heavy metal. The heavy metal is preferably selected from the group consisting of arsenic, lead, cadmium, and / or mercury.

[0115] In the methods of the invention the markers are preferably quantified as this allows for a more precise assessment.

[0116] The method may comprise the detection or quantification of at least one marker from each group of (a) a reproductive hormone, (b) a bacterium, (c) cortisol, (d) an adhesion, inflammation or receptivity marker, and (e) a heavy metal. The method may comprise the detection of at least two, at least three, at least four or at least five markers from each group of (a) a reproductive hormone, (b) a bacterium, (c) an adhesion, inflammation or receptivity marker, and (d) a heavy metal, optionally in combination with the detection or quantification of cortisol. Preferably, the at least one marker from each group is quantified.

[0117] The methods of the invention may be practised by detecting or quantifying the markers in the same experiment. The methods may also be practised by analysing different markers separately, for example in separate laboratories. In these embodiments an assessment on fertility or the likelihood of IVF success will be made once the data from the analysis of all markers is available. Reproductive hormones

[0118] In some embodiments, the methods of the invention comprise a step of detecting or quantifying one or more reproductive hormones. The reproductive hormones are preferably selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b, oestrogen. These hormones are preferred as they have all been reported to be correlated to fertility, and have been found in menstrual blood and urine.

[0119] Reproductive hormones may have a variety of effects on fertility.

[0120] Anti-Mullerian Hormone (AMH) concentrations may be measured to monitor the number of eggs in a woman’s ovarian reserve. Lower AMH indicates fewer eggs and a lower ovarian reserve. Accordingly, an AMH concentration which is reduced relative to a reference is indicative of infertility or a decreased chance of IVF success.

[0121] Thyroid-stimulating hormone (TSH) stimulates the thyroid gland to produce thyroxin. Concentrations of TSH outside a normal range can be associated with infertility, as lower concentrations can interfere with egg release from the ovary, whereas higher concentrations have been associated with lower AMH concentrations and consequently a lower ovarian reserve. Thus, a TSH concentration which is which is reduced or higher relative to a reference is indicative of infertility or a decreased chance of IVF success.

[0122] Follicle-stimulating hormone (FSH) triggers the growth of eggs in the ovaries, and prepares the egg for ovulation. High concentrations of FSH can indicate reduced egg count and reduced FSH suggests that eggs are not being produced. Accordingly, an AMH concentration which is higher relative to a reference is indicative of infertility or a decreased chance of IVF success.

[0123] Luteinizing hormone (LH) is involved with release of a mature egg, therefore LH increases when the egg is being prepared for released. As seen with many of the other reproductive hormones, LH concentrations outside the normal range indicate that the ovaries and pituitary gland are not functioning as normal. Similar to LH, a progesterone increase indicates ovulation. As such, low concentrations of progesterone can cause irregular periods and difficulties with conceiving. Therefore, a LH concentration which is lower relative to a reference is indicative of infertility or a decreased chance of IVF success.

[0124] Estradiol 17-b stimulates proliferation of the endometrium and induces LH for ovulation. Therefore, low concentrations may indicate gynaecological complications such as polycystic ovary syndrome whereas high concentrations may indicate a low ovarian reserve. Therefore, an estradiol 17-b concentration which is lower relative to a reference is indicative of infertility or a decreased chance of IVF success.

[0125] TJ Oestrogen is involved in ovulation and thickening the lining of the uterus in preparation for pregnancy. In light of this, high oestrogen can suggest irregular ovulation and low oestrogen can result in difficulty getting pregnant as the uterus lining will not thicken correctly. Thus, an oestrogen concentration which is higher relative to a reference is indicative of infertility or a decreased chance of IVF success.

[0126] In addition to the role as individual markers, the methods of the invention may comprise the quantification of two or more, preferably all, of these reproductive hormones as the relation of the hormone concentrations to each other is informative.

[0127] It is clear that careful regulation of the reproductive hormones is required in order to conceive and maintain a healthy pregnancy. It is therefore important to measure the reproductive hormones to gain an understanding of the endometrial condition and fertility. If there were a hormone imbalance, this can be treated with hormone medication, or lifestyle changes in order to regulate the hormones and increase fertility. In addition, an understanding of the hormone concentrations can be used to avoid ovarian hyperstimulation, and aid in timing operations, implantation during IVF or drug dosages.

[0128] Bacteria

[0129] The methods of the invention may involve a step of detecting or quantifying one or more bacteria. Preferably the one or more bacteria are quantified. The bacteria are preferably selected from a species selected from the group consisting of Lactobacillus spp. Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., as well as the presence of pathogenic bacteria like Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis, Ureaplasma spp. The bacteria found in the endometrium, and the quantities of such bacteria have been shown to affect fertility, and therefore the microbiota of the endometrium is an important factor to monitor in order to understand fertility.

[0130] In circumstances where abnormal concentrations (relative to a reference) of bacteria are detected, or bacteria not normally found in the endometrium are identified, this can lead to implantation failure and miscarriages. Therefore, gaining a greater understanding of the endometrial microbiota could aid in the success of IVF treatment and those conceiving naturally. If abnormal bacteria, or increased concentrations are detected, the bacteria in the endometrium can be treated using antibiotics, probiotics and prebiotics to maintain a healthy endometrial flora (Elnashar, A.M. 2021 Impact of endometrial microbiome on fertility. Middle East Fertility Society Journal 26, 4). Cortisol

[0131] Cortisol is a stress hormone which is released through the activation of the hypothalamuspituitary-adrenal (HP A) axis. It is a steroid hormone in the glucocorticoid class of hormones. It is produced in many animals, mainly by the zona fasciculata of the adrenal cortex in an adrenal gland. In other tissues, it is produced in lower quantities.

[0132] Increased cortisol concentrations have been reported to affect human reproductive function through immunosuppression. However, conflicting results were reported in respect to the effect of cortisol on IVF. The inventors have found that cortisol provides a good indication of infertility and the likely success of IVF in the context of the methods of the invention. In some embodiments, a cortisol concentration which is higher than in a reference sample is indicative of infertility and a low likelihood of IVF success.

[0133] Where cortisol concentrations are found to be outside of an ideal range, they can be regulated through lifestyle changes and drugs, such as supplements and in extreme cases steroids.

[0134] Adhesion, inflammation or receptivity marker

[0135] The methods of the invention may involve a step of detecting or quantifying one or more adhesion, inflammation or receptivity markers. Preferably, the markers are quantified.

[0136] The markers which may be analysed may comprise one or more of ALB, FGA, HBA1, HBB, TF, AHNAK, HBD, AP0A1, F2, C3, AP0A4, SERPINA1, TTR, AP0A2, INS3, PLG, C4B, C4A, SPRR3, ITIH4, FLNA, KRT1, ACTB, ACTG1, APOH, GC, APOC2, PPL, FGB, VIM, SBSN, DSP, TLN1, MYH9, IGHG1, KRT6A, IGKC, HBG1, KRT10, HBG2, CLU, KRT13, PGLYRP2, CFB, AHSG, APOC3, A2M, KRT6B, KRT2, PAEP, PPIA, H2AC21, INS, CP, HP, APOE, S100A9, LYZ, IGHG2, FGG, SLPI, A1BG, SPINK5, AGT, AZGP1, ANXA1, EEF1A1, EEF1A1P5, IVL, INS-IGF2, PLEC, IGHG4, KRT5, TPI1, ALDOA, ZYX, ACTC1, SPP1, VTN, TUBB, ACTA1, EVPL, IGFBP7, TUBB4B, H4C1, APOM, KRT19, IGHG3, CFL1, PRDX6, ACTA2, ACTG2, RBP4, H2AC18, H2AC20, APOCI, KRT9, PPBP, LTF, GSN, CRNN, TAGLN2, CLEC3B, IGFBP3, GAPDH, TUBA1A, H2BC13, H2BC14, H2BC12, H2BC18, H2BC12L, H2BC5, H2BC4, H2BC9, H2BC15, KRT8, KNG1, SERPINC1, WFDC2, HPX, CFD, PKM, HNRNPK, KRT4, LGALS1, MMP10, MACR0H2A1, APOD, H2BC17, H2BC21, H2BU1, H2BC3, H2BC11, PEBP1, LRG1, HSPB1, PFN1, SPRR1B, S100A8, POTEE, TUBB3, KRT14, CAP1, PARK7, GPX3, ITIH2, HSPA1B, HSPA1A, SERPINB3, TUBA4A, HSPA8, PF4, KLKB1, SPRR1A, B2M, MAP4, HPR, PGK1, KRT7, SCEL, ELANE, IGLL5, LMNA, TGM2, CAMP, HBZ, CTSG, LUM, SAA1, CST3, Hl-4, PDLIM1, FN1, 0RM1, COL1A2, IGFBP2, SERPINA3, NME2, KRT16, PTBP1, POTEI, EEF1A2, CFHR2, TUBA3C, TUBA3D, SERPINB4, PI3, BLVRB, KRT15, TPM4, TUBA3E, IGLC1, PRDX5, ECM1, LCN2, CSTB, PRTN3, HNRNPA2B1, POTEKP, CALD1, CFHR1, LGALS3, FABP5, LASPI, NPC2, ZNF185, PABPC1, NME1, IGHM, SERPINF1, H3C1, CAST, THBS1, GSTP1, AMBP, CTSD, SAA4, CTTN, Hl -3, ENO1, Hl -2, COL18A1, LDHA, BASP1, MSN, UBE2N, ACTBL2, 0RM2, HSP90AB1, POTEJ, H3-7, RSU1, AFM, PF4V1, S100A7, RAD23B, COL15A1, IGHA1, SERPING1, CFH, CSTA, HMGB1, HNRNPM, Hl-5, CAI, SAA2, IGFBP4, SOD2, AKR1A1, DEFA3, H3-3A, TUBB6, GRN, SERPINF2, ITIH1, FUBP1, MIF, KHSRP, SFN, RNASE3, DEFA1, SPRR2A, PGLS, MPO, GSTO1, PGAM1, CPB2, PRSS3, OGN, EIF5A, MAPRE1, TXN, NID2, PIGR, C2, APOL1, TUBA8, HRG, TPM2, SPRR2D, IGFBP1, NCL, LMNB1, TIMP1, S100A11, LEFTY2, COL1A1, YWHAZ, PDIA3, CAVIN2, RNASE4, PCBP1, SPRR2B, HSPA5, CSRP1, IGFBP5, H2AZ1, H2AZ2, PRDX1, FAM3C, SH3BGRL, RRBP1, RPL7A, EZR, PCOLCE, HDGF, AZU1, KLK6, STIP1, KRT17, NME2P1, RPL4, TPM3, PABPC3, SRI, KLK10, CNN2, RPS7, ISLR, EEF2, HNRNPA3, APOC4, HNRNPH1, MMP7, KRTDAP, RPL6, CD14, CTSB, DSTN, NSFL1C, S100A6, DDT, ANG, HSPE1, CRISP3, PCMT1, ACTN4, STC1, AK2, PYCARD, TPM1, COL6A3, CCN2, LGALS3BP, HNRNPC, APCS, RAC2, CPN1, HNRNPU, IL1RN, FLNB, RPLP2, IQGAP1, HMGB2, RPS18, CA2, TFF3, PNP, MYL9, UBE2L3, RPS3, F5, CFL2, HMGB1P1, JUP, GM2A, SERPINB1, HSP90B1, KRT78, PSAP, MARCKS, IGKV3-20, ABHD14B, RPSA, MMP3, CLIC1, SNCA, SERPIND1, MANF, NUCB1, PRDX2, IGHV3-7, PLIN3, RPL23A, DCTN2, YWHAQ, TUBB8, CALM1, CALM2, CALM3,SRSF1, TPD52L2, PPA1, PGLYRP1, TMPO, TMPO, SELENBP1, SELENOP, RNASET2, PSME1, RPS3A, RAC1, HSP90AA1, RBMX, DBI, TMOD3, MST1, MYL6, P4HB, GLO1, UBA1, TUBB8B, HP1BP3, YBX1, DDAH2, UBE2NL, TMSB4X, MTPN, HMGN2,RTRAF, MYL12B, MYL12A, MDK, S100A12, HNRNPA1, KRT73, CALML5, MACROH2A2, PCBP2, LTBP2, GLRX, NPM1,C9, LGALS7, IGF2, IGLV1-47, EIF5AL1, RHOA, GCA,CXCL1, IGHV3-23, TUBB1, DBNL, RAB7A, CORO 1 A, SARG, HNRNPD, TAGLN,DES, PDIA6, UBC, UBB, SPON2, MMP9, CBR1, DSG3, NEDD8, YWHAB, DDTL, RPL12, UBA52, RBP1, STMN1, INHBA, HCLS1, VASP, HINT1, FLG, NUDT21,HNRNPCL3 , HNRNPCL4, HNRNPCL1, HNRNPCL2, MSLN, VAT1, LRRC59, KRT77, CRKL, HSPA2, HSPA6, KRT18, HNRNPR, UBE2V1, F10, SH3BGRL3, SHBG, KRT79, IGHV3-74, PZP, PLEK, MNDA, KLK13, BIN2, PLGLB1, CAPS, CD59, TREML1, HNRNPH2, ARPC3, SF3B1, RALY, IGF1JPT2, PRSS1, LTBP1, IGFALS, SERBP1, EEF1D, RPL18, CRYAB, IGLC7,HNRNPL, SYNCRIP, CLTB, DAG1, MYH10, FKBP1A, HNRNPH3, FKBP2, HDLBP, SFPQ, LEFTY1, TP53I3, RPL7, LOXL2, RNASE2, RBMXL1, SNRPA1, F12, HNRNPA1L2, Cllorf54, MASP2, CMPK1, RPS12, RPS9, PPIB, VCL, SCP2, RAP IB, HGFAC, CDC42, UBE2K, EIF5A2, CXCL5, COL4A2, IGLV1-44, XRCC6, RAC3, RNH1, SNRPA, ARHGDIB, CASP14, RAP1A, SRSF7, MATR3, CNN3, YBX3, DSG1, TBCA, LSP1, ESD, UBE2D3, FBLN1, TIMP2, STX7, RPS19, SUB1, IGKV1D-16, DBN1, LCP1, MDH2, SEC22B, IGKV2D-28, IGKV2-28, ILF3, MYH14, FAM25A, FAM25G, FAM25C, FSTL1, RPL10A, PGAM2, ACTN1, SF3B2, VGF, CD5L, SEPTIN9, ANXA2, Hl-O, PPP1R12A, IGLV3-21, HSPA7, C6orfl32,SNRPD2, MTDH, SOD3, S100A16, SPRR2G, UBE2V2, CIS, CKB, SLC9A3R1, RHOC, PDLIM5, HSP90AB2P, UBE2I, FKBP3, IGLV1-51, BANF1, SPINK7, MST1L, CHIT1, CAT, HTRA1, SCGB2A1, AAMDC, PSMD9, C5, STIM1, SPRR2F, CDC37, GCG, UBE2L5, UFM1, CALML3, NIT2, KRT28, RPS20, SCGB1D2, CXCL8, YWHAE, IGKV3D-11, IGKV3-11, RAB11A, RABI IB, LMO7, IGKV1-12, IGKV1D-12, YWHAG, PDCD6IP, S100A7A, PGK2, DMTN, RPL23, IGHV3-30, IGHV3-30-5, S100A4, Hl-1, FUS, FAH, UBE2D2, COX5A, FLNC, EIF4B, CBR3, SERPINH1, CXCL3, COL3A1, CCN1, RHOG, IGKV3D-20, RPS25, FERMT3, ARHGAP1, PABPC4, GPX1, RPS13, RPLPO, RPS5, ILK, APOB, RAN, HNRNPDL, HLA-A, COX5B, RPS16, IGFBP6, USP14, PSMA1, RPL13, ANXA2P2, PLGLA, HDGFL3, ARF4, MMRN1, PPIH, RAB10, CARHSP1, SND1, S100A10, EPRS1, AK1, KRT12, TFPI2, ACPI, CFHR5, RAD23A, AHSP, RARRES2, CBX3, VAPA, YWHAH, THRAP3, F9, IGKV2D-40, IGKV2-40, IGKV3-15, PDCD5, TLN2, ACSL5, WAS, RPS2, HNRNPAB, CTSC, SPARC, EIF4H, SIOOP, CD248, ABRACL, KLK11, GLIPR2, IGLV1-40, EIF4A1, CCT2, EPS8L1, IGHV3-66, JPT1, LDHB, WIPF1, CTSS, DIAPH1, LTBP4, AP2A1, EEF1B2, LYPLA1, TRIM29, AP2B1, MYCBP, STB, CKAP4, KRT20, SRP14, LYPLA2, CCL14, LGALSL, PAFAH1B3, TPD52, GANAB, RPLP0P6, EPB41, PCBP3, DCD, KRT27, C1R, RPL30, IGLV3-10, ARF1, ARF3, RPL17, ATOX1, PAK2, DPYSL2, GLOD4, IGKV3D-15, AGR2, RPS4X, VPS25, OSTF1, ENSA, ERP29, SOD1, TXNDC17, CST6, PCNP, Hl-10, HNRNPA1L3, BID, AIMP1, DMKN, IGKV1-27, EIF4G1, NIDI, SSB, HNRNPF, DEFBI, CAPG, GNG12, HSP90AB3P, LDHAL6A, HMGA1, ELOB, DNAJA2, ATP5F1A, OXSR1, TOLLIP, TES, MMP1, MPIG6B, CDH1, IDH1, ALDOC, PRPH, DDE, TYMP, RPL27, SEC31A, F13A1, U2AF2, IGLV3-27, UGDH, CDA, ARPC1B, APOF, SPTBN1, QARS1, NCF2, PREXI, RAB6A, RAB6B, GFAP, CXCL2, DEK, ANP32A, ST13P5, LDHAL6B, CRIP1, COL5A1, RAB1C, RAB1B, CYCS, SPTB, IGKV2D-30, IGKV2-30, UFC1, DYNLL1, DYNLL2, ASRGL1, XRCC5, RAB1A, TGFB1I1, RPS10, C4BPA, KTN1, IGKV1-5, PDLIM4, ATP5IF1, DUSP3, PDAP1, RAB27B, IMMT, DDX3X, TUBA4B, NONO, HSPB6, HSP90AA2P, DPY30, DDX5, TOMI, C1RL, RPS24, FTH1, ELAVL1, AKR7A2, RAB27A, PRAM1, PTRHD1, HMGB3, COL4A1, HABP2, RAB35, LPP, EFHD2, LAD1, PPP1R14B, WDR44, PGAM4, RNASE1, GRB2, HTRA3, NAP1L1, DDX17, CCL18, PRG4, HLA-C, DDX39B, UQCRFS1, UQCRFS1P1, CNDP2, PEA15, SNX12, IGLV3-9, NDRG1, TRIM28, FERMT2, VAPB, IGKV1-13, IGKV1D-13, NAP1L4, GPI, FHL1, SF1, CHMP4B, C0R01B, EHD1, GP5, CCL5, DNAJB1, IGKV4-1, SCG2, RPL5, MDH1, IGKV2-29, IGKV2D-29, ITGAM, CRIP2, DDX39A, COTL1, IGHV5-10-1, ARF5, PSMB2, HSP90AB4P, DDX1, SRGN, PRKDC, CST1, EIF1, CIRBP, CPN2, MPST, PARP1, RPS6, DDX3Y, AHNAK2, PLS3, JCHAIN, PSMC1, SNRPGP15, SNRPG, LSM6, NIBAN2, RTN4, LBR, RPL8, C6, CYB5A, PSMB7, ALYREF, LMNB2, RPS8, PSMD2, S100A13, TJP2, CCL20, CALR, EHD3, SNRPB, KRT25, ARPC5, ATP6AP1, S100A14, ARFIP1, PFDN5, HMGN3, PCBD1, SNX3, HEBP1, ARG1, DNAJA1, DSC2, HUWE1, MMP14, ACYP1, ATIC, CAVIN3, MELTF, RAB11FIP1, KRT84, CDC5L, DYNC1I2, GFUS, ARPC4, GNLY, CLTA, TPT1, RPS28, EML4, CLTC, LSM7, LMAN2, ITIH3, AIF1, DSC3, SELENOM, SPTA1, SAR1A, RPS23, SET, IGLV3-19, UCHL1, CUTA, BPI, CANX, RPL27A, PPIL1, HRNR, PCSK1N, TIMM13, IFI16, PIP, CST4, PAPLN, ACTN3, MT1X, MT2A, MT1M, MT1E, MT1G, EPPK1, PRSS2, MECP2, EDF1, USP5, ABCB1, LGALS9, HBQ1, IGLV2-8, AGFG1, NANS, TCP1, DNM2, HLA-B, SUMO2, NAMPT, IGLV7-43, PSMB3, IGKV1-9, IGKV1D-8, IGKV1-8, ZNF207, IGKV2D-24, IGKV2-24, RPL22, KHDRBS1, APRT, ACTN2, RAB8A, CILP, LIF, VCP, NEXN, RPL24, COL2A1, CD2AP, LY6D, RBMXL2, SARNP, ATP5PO, PRSS8, MYL6B, EIF3G, SRSF3, INA, SHISA5, PPIAL4E, PPIAL4C, PPIAL4A, PPIAL4F, PPIAL4D, PPIAL4H, COX6B1, DFFB, CTNNB1, IGKV1D-33, IGKV1-33, GPX6, SNX9, ARCN1, ALAD, ANP32B, RPN1, PTBP2, CPLANE1, RPS17, EHD2, HNRNPUL1, SERPINA7, PAM, RPL14, SNUB, CLINT 1, SNX1, GLRX3, STAU1, RPL35, GNAI2, EMILIN2, MYOF, PRSS27, RPL31, DCN, COL14A1, THSD4, STMN2, RHOQ, RPS14, SNRPN, DHX9, IGLV2- 11, PAFAH1B2, WASF2, KRT31, KRT33B, ERO1A, ACR, AK3, PLA2G4F, QSOX1, CDADC1, ENO3, LIMSI, TMPRSS11E, UBE2D4, UBE2D1, ADAMTSL4, PABPC1L, BCAP31, FUBP3, TGM1, B4GALT1, DGKK, EFEMP1, LAMT0R1, TXNDC5, IGHV5-51, CLCA4, IGHV4-31, IGHV4-61, IGHV4-30-4, IGHV4-59, METTL18, NBL1, EIF3A, IAH1, LRRFIP1, HPSE, PXDN, PRKAR2A, FIS1, GNG5, DYNLRB1, CNPY2, RPS15A, PPIE, SEPTIN14, KLK7, SEPTIN6, RPS15, EPS15L1, NCK2, RETN, IGLV6-57, SNRPD1, STRBP, NOP56, MAN2B1, EMILIN 1, FAM120A, URGCP, TJP1, HM0X1, PPIC, TNXB, CAVIN 1, CXCL6, RANBP2, CRISPLD2, GNG2, TSTD1, RPL15, NUTF2, RPL3, PSIP1, RBM25, HNRNPUL2, MARS1, FOLR1, IGLV7-46, CTNND1, SPINT1, PCSK9, LRRC4C, TAF15, RPS21, MAPRE3, LIMS2, CNN1, SCAF1, TSKU, HDGFL2, HLA-E, VAMP8, CIAPIN1, SERPINB5, HY0U1, DNM1L, CCT6A, PDLIM7, SRP9, EIF1AY, EIF1AX, AKR1B1, MARCKSL1, PTP4A2, SYNE2, COROIC, PFDN6, RABB, PON1, SERPINB13, PSMA2, RPL32, HOPX, ATP5PF, LGALS9B, LGALS9C, RTCB, SLC4A1, BAZ2B, KIF2A, DEFA4, IGHV3-49, SP1OO, CAPNS1, YARS1, PA2G4, MMP2, TBC1D10B, NDUFA4, KRT38, RAB3D, STK39, H2BC19P, H2BC20P, ERH, KNTC1, RPL28, TXNDC12, GSPT1, CRABP2, H0XB3, CDH13, SNCB, GNAI3, GNAI1, SLURP2, IGHV2-5, IL16, T0P2B, CTSH, C0X6C, ARHGEF1, HMGN1, IK, REGIA, MAP1S, EEF1G, ACTR3, RPL38, FBL, CUL3, SUM03, PPP2R1A, IGKV1-16, RAB18, MPH0SPH8, CEMIP, CHCHD2, SEC16A, EMD, PLS1, PGD, UBAP2L, COX7A2, EFHD1, MIX23, ITM2B, GMFG, SNX2, KRAS, HRAS, NRAS, C3orf85, ACAT1, ARHGAP18, ANK1, SULF2, TFPI, IGLV3-12, NCF1, NCF1B, ATP5ME, DUSP23, C8A, SNAP23, CFHR3, SEPTIN2, CLIC3, UBQLN1, PLAUR, ARPC5L, C0X4I1, ATP5F1B, TWF2, EIF3J, VPS4B, IL36A, RAB41, ATP6V1G1, TAX1BP3, ATP6V1F, DDRGK1, ARID1A, AUP1, SNRPB2, SRSF2, RPS11, EWSR1, NCF1C, EIF1B, IGHV6-1, FYB1, FABP5P3, PAK3, HSPG2, PTP4A1, NAGK, EVPLL, PLAU, HPCAL1, SRSF9, AGR3, LCAT, MY018B, EIF2S2, TXNL1, VPS29, TOMM70, STK24, SRSF11, HNRNPAO, EIF4A2, RYR2, USP34, SYK, COL4A5, FAU, TPR, GRIK5, SLC25A6, MAP2K1, GAA, GMFB, GNAL, GNAS, GNAS, CHP1, SNRPD3, TGFB1, ARHGAP45, MMP11, SLC35A4, RAB14, PSME3, LOX, HEBP2, SETD1A, ANXA3, MSI1, TALDO1, SETSIP, CHRD, COPS8, HMCN2, MRPL12, ARPP19, SRSF5, LCP2, LSM3, PANXI, MSI2, DNM1, ATR, CST2, ARHGDIA, NITI, KANK2, RBPMS, VPS4A, FAM107B, CNBP, CAPZA1, DRAP1, POSTN, ST13P4, PSMB10, DNAH10, GTPBP2, GNA13, TIA1, SLC25A4, SLC25A5, IMPDH2, VPS13D, CYB5R2, ANGPTL4, CLIC4, LECT2, IST1, PHB1, HINT2, GNG10, ANXA6, HLA-G, SCGB3A1, USP1, S100A2, PDCD10, ATP2B1, TARDBP, CFHR4, GTF2B, RPL29, NUP62, AKR7L, SULT1A1, PRXL2A, PLXND1, AK9, IQUB, RPS4Y2, RPS26, ADAMTS2, SDR9C7, CNNM2, SHC1, ARF6, , PPP6R1, TMA7, H4C7, SF3A3, PRAP1, STOM, LNPK, DLG5, NCF4, CARD16, AIF1L, TBCK, UHRF1BP1L, PDCD6, COMP, PTMS, FLG2, CHCHD2P9, DEFB4A, NPEPPS, IGLV2-18, MACF1, SPTAN1, RHOB, AKR1E2, IGKV3-7, IGKV3D-7, RPLP1, BST1, GAR1, SRSF8, MCFD2, PTMA, MYH7, FAM25E, ALDOB, OPTN, NDUFA2, KLK4, KLK5, CYBB, SERPINA2, BPGM, B4GALT5, GPRC5C, PTH2R, IRF7, PTBP3, IGHV4-38-2, IGHV4-39, IGHV4-34, PRDX4, CRMP1, KIF5B, ELN, IGLL1, PABPC1L2A, PPIAL4G, MOSMO, LINC00477, CEP192, NEK8, GPR61, SHISAL2A, P2RY8, EPG5, NT5C1B, ANKDD1B, SPACA3, HYPK, OASL, ZSCAN21, RPL34, CDNF, CD68, PIK3C2G, ATM, LAMA3, NRG3, ECPAS, NUCKS1, SMYD4, CAMK1, ADAMTSL1, EDEMI, SNRPE, DNTT, LAMTOR3, DNHD1, TBCB, ANKRD17, ANKHD1, DDAH1, WNT8B, NRGN, SEC24B, NKAPL, BNC2, UBN1, AK5, GNA12, UMOD, DENR, PIK3IP1, KRT23, KRT40, KRT34, KRT33A, TMOD2, CROCC, HOXA3, HOXD3, GBP6, WDR61, KIF5C, CLC, PLEKHA7, DKC1, SLC25A31, FRMPD4, GRIP2, SEPTIN14P20, CRAT, DCXR, FNDC3B, SNRNP70, CAP2, EEA1, REGIB, FNDC3A, PREPL, ADORA3, NIN, HNRNPLL, FER1L4, SLC7A7, SLC7A6, CZIB, MRC1, ABCA5, IGLV8-61, TXNDC16, IFRD1, FBLN5, ZNF560, CDH26, XP01, TNS1, GPSM3, PDE4DIP, CDK5RAP2, SLC34A2, EFTUD2, IQGAP3, CCL3, PADI4, SAR1B, NMES1, SPTBN2, TNFRSF12A, ZNF625, ZNF433, SEPTIN11, SEPTIN8, SEPTIN7, SYTL4, MAP1LC3B, MAP1LC3A, MAP1LC3B2, APBA1, LAMT0R5, 0PN1MW2, 0PN1MW3, 0PN1MW, OPN1LW, ADAM17, PRDX3, GABPA, ADAMTS1, ADD3, CHMP4A, PLEKHO2, WFDC13, TMOD1, CD93, DYNLRB2, CCSER1, IGHV2-70, IGHV4-30-2, IGHV4-28, NLRX1, HSP90B2P, CNTNAP4, RPS10P5, PKIG, CHST2, SLC22A10, U2AF1, FOLR2, LGALS8, LUC7L2, PTGDS, CRK, GSDMC, ZNF462, FAM186A, ADIRF, CREG1, ZC3H12D, ANP32C, DPYSL5, XPNPEP3, MIB2, ARHGAP17, CCDC150, CHIA, CAMKID, CDH2, SNTB2, APLP2, ACSL1, TAGLN3, GPX2, AKR1B15, WFDC12, KALRN, MMP17, CCDC169, BBS12, CCDC81, FOLR3, LUZP1, DNM3, TRA2B, DNAJC5G, KHDRBS3, KHDRBS2, SULT1A2, HABP4, RPS26P11, ARHGEF18, CASP1, NRIP1, ATP5F1D, STAT1, TP53RK, ACADS, SHE, PDLIM2, ZFPM1, DNAJC10, LRRCC1, ITPR1, WAPL, SUCO, NDUFB1, GSPT2, RPL37A, GATA6, IDH2, NHSL2, ABCB4, LOXL4, ETFBKMT, KNDC1, UBQLN2, FASTKD5, ABCB6, CCT6B, PSCA, TTC28, MY0M1, NFASC, CD22, SACS, S100A1, CUL9, RABGAP1, TDRD7, IGHV2-70D, USP24, COG8, FAM166B, Cllorf52, ECI2, PAK1, XRN1, SBF1, SEC63, CAMTA2, NCALD, HPCA, CLTCL1, and RFC1.

[0137] The markers which may be analysed may comprise one or more of Leucine, Valine, Isoleucine, 2,3-Butandiol, Lactate, Alanine, Putrescine, Lysine, Acetate, Acetone, Glutamate, Pyruvate, Succinate, Glutamine, Citrate, Glycine, Threonine, Glucose, Rhammose, Fumarate, Tyrosine, Histidine, Phenylalanine, Tryptophan, Hypoxanthine, Formate, Total fatty acids, Polyunsaturated fatty acids, Phosphatidylcholines, Triglycerides, and unsaturated fatty acids.

[0138] The markers which may be analysed may comprise one or more of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a (TNF-a), interleukin-6 (IL-6), interleukin-8 (IL-8), and / or interleukin-33 (IL-33). Preferably, the methods of the invention comprise the analysis of all of these markers. These markers are preferred as they have been reported to be associated with infertility (Oliveira et al., 2015, Brazilian Archives of Biology and Technology, 58, pp.512-520; Paravati et al., 2020, Journal of Molecular Medicine, 98, pp.1713-1725.; Kanaka et al., 2024, Gynecol Obstet Open Acc, 8(189), pp.2577-2236).

[0139] For example, glycosaminoglycans, hyaluronan and heparan sulfate are important for regulating uterine and placenta development during birth. The concentrations increase during pregnancy in order to regulate the organization of macromolecules of the extracellular matrix. Thus, concentrations of glycosaminoglycans hyaluronan and / or heparan sulfate which are reduced relative to a reference are an indication of infertility or a reduced likelihood of success of IVF. CD44 is involved in the migration and adhesion of endometrial cells, therefore lower concentrations of CD44 have been shown to correspond with infertility. Thus, concentrations of CD44 which are reduced relative to a reference are an indication of infertility or a reduced likelihood of success of IVF.

[0140] TNF-a is involved in the regulation of the microenvironment of the foetal -maternal interface. Increased concentrations of TNF-a are associated with abortion of the foetus due to microenvironment dysregulation. Thus, concentrations of CD44 which are increased relative to a reference are an indication of infertility or a reduced likelihood of success of IVF.

[0141] IL-6 and IL-8 are involved in egg implantation. Impaired expression can lead to compromised endometrial receptivity and difficulties with establishing pregnancy. Thus, concentrations of IL- 6 and / or IL-8 which are reduced relative to a reference are an indication of infertility or a reduced likelihood of success of IVF.

[0142] IL-33 plays a role in tissue regulation. When IL-33 concentrations are depleted, this can result in abnormal vascular remodeling in the early stages of pregnancy, which can lead to fertility issues. Thus, concentrations of IL-33 which are reduced relative to a reference are an indication of infertility or a reduced likelihood of success of IVF.

[0143] Heavy metals

[0144] A "heavy metal” is defined as a metal of relatively high density, high relative atomic weight, or high atomic number. The methods of the invention may involve a step of detecting or quantifying one or more heavy metals. The heavy metals are preferably selected from the group consisting of aluminium, iron, copper, zinc, selenium, arsenic, lead, cadmium, and mercury. Heavy metals can cause irregular ovulation, delayed egg maturation, and difficulty for fertilized eggs to implant in the uterus. Long-term exposure can also lead to menstrual irregularities, endometriosis, endometrial cancer, and miscarriage. During pregnancy heavy metals can negatively impact oocyte fertilization and pregnancy rates in women undergoing IVF. Accordingly, the presence of a heavy metal as disclosed herein is an indication of infertility or a reduced likelihood of success of IVF.

[0145] Heavy metals can be reduced through the use of chelating agents (Flora et al., 2010; International Journal of Environmental Research and Public Health, 7(7), pp. 2745-2788) where they are found to be increased relative to a control. In one embodiment, the methods of the invention involve a step of treating the subject with a chelating agent. These are organic compounds capable of linking together metal ions to form complex ring -like structures called chelates. Examples of suitable chelating agents include ethylenediaminetetraacetic acid (EDTA), calcium disodium ethylenediamine tetraacetic acid (CaNa2EDTA), dimercaptopropanesulfonic acid (DPMS), (DMSA) succimer and penicillamine.

[0146] Further markers

[0147] The methods of the invention may further comprise a step of analysing one or more additional factor(s). For example, the methods may comprise a step of assessing the medical history of the subject. The assessment may consider whether a patient is or has been taking medications that may negatively affect fertility. Such medications may include but are not limited to EmbryoGlue, anti-inflammatories, antibiotics, probiotics and steroids. It may also, for example, include monitoring whether AMH has been prescribed in concordance with progesterone. The assessment may also consider chronic diseases or uterine conditions.

[0148] The methods may further contain a step of considering data such as age, smoking, body mass index (BMI) and sleep duration. For example, an age between 18-25, low levels or no smoking, low BMI and 7-9 hours of sleep will be input as negative associations for calculating infertility. Data within these ranges would contribute to a higher fertility score and / or a higher probability of IVF success score.

[0149] Alternatively, an age between 26-40, high levels of smoking, a high BMI outside of the healthy range and greater than 9 hours of sleep or less than 7 hours of sleep would be input as positive associations for calculating infertility. Data within these ranges would contribute to a lower fertility score and / or a lower probability of IVF success score.

[0150] The method may further contain a step of considering data such as race. For example, the probability of IVF success and / or a higher fertility score is greatest for people of white ethnicity, followed by mixed ethnicity, Asian ethnicity and the probability of IVF success and / or a higher fertility score is lowest for people of black ethnicity.

[0151] In addition, as the duration of infertility increases by one year over 4 years of infertility, the fertility score and / or a probability of IVF success score decreases.

[0152] The subject

[0153] The methods of the invention are for determining the presence or absence of infertility in a subject. The methods of the invention are further suitable for assessing the likelihood of success of in vitro fertilisation (IVF) in a subject.

[0154] The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a mammal. In an embodiment, the mammal is a human, such as a female human. The subject will typically be suspected of having infertility which is defined as the failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse.

[0155] Marker detection and quantification

[0156] The skilled person may use any suitable technique known in the art to detect or quantify a marker in a method according to the invention.

[0157] The marker may be detected or quantified by interaction with a ligand or ligands, 1 -D or 2-D gel-based analysis systems, liquid chromatography, combined liquid chromatography and any mass spectrometry techniques including MSMS, ICAT(R) or iTRAQ(R), agglutination tests, thin-layer chromatography, NMR spectroscopy, sandwich immunoassays, enzyme linked immunosorbent assays (ELISAs), radioimmunoassays (RAI), enzyme immunoassays (EIA), lateral flow / immunochromatographic strip tests, Western Blotting, immunoprecipitation, particle -based immunoassays including using gold, silver, or latex particles and magnetic particles or Q-dots, or any other suitable technique known in the art.

[0158] In some embodiments of the methods of the invention, the step of detecting or quantifying the at least one marker comprises performing an ELISA assay to detect the at least one marker or determine the concentration of the at least one marker. In a further embodiment, the ELISA assay is a sandwich ELISA assay. A sandwich ELISA assay comprises steps of capturing the at least one marker to be detected or whose concentration is to be determined using a "capture antibody” already bound to a plate, and detecting how much of the at least one marker has been captured using a "detection antibody”. The detection antibody may be pre-conjugated to a label such as the enzyme HRP (Horse Radish Peroxidase). The ELISA plate may then be exposed to the labelled detection antibody, such that the labelled detection antibody binds to the captured at least one marker. After exposure to the labelled detection antibody, the ELISA plate should then be washed to remove any excess unbound labelled detection antibody. The washed plate can then be exposed to an agent whose properties are changed by the label (of the detection antibody) in a measurable manner. The concentration of the detection antibody may then be determined. For example, if the detection antibody is labelled by e.g. conjugation to HRP, the ELISA plate may be exposed to 3,3',5,5'-Tetramethylbenzidine (TMB) substrate. The concentration of the detection antibody, and therefore the concentration of the at least one marker in the original non-invasive sample, may then be determined by quantitation of the colour change corresponding to the conversion of TMB into a coloured product.

[0159] The ELISA assay may be a qualitative ELISA assay. In the context of the invention, a qualitative ELISA assay is one that is performed without determining a numerical value for the concentration of the at least one marker in the non-invasive sample and / or without determining, or using a previously determined, numerical value for the concentration of the at least one marker as a reference. For example, in a qualitative ELISA assay, the intensity of a coloured product (e.g. a coloured product produced by an enzyme linked to a detection antibody) may be compared to a reference. In this embodiment, the intensity of the coloured product is indicative of the concentration of the at least one marker without the need to determine a numerical value for the concentration of the at least one marker. The reference used in a qualitative ELISA of this type may be a previously determined threshold intensity of the coloured product.

[0160] The methods of the invention preferably comprise a step of comparing the concentration of the markers to a reference. Suitably, the reference may be a concentration of the at least one marker determined for a sample obtained from one or more healthy individual(s). A healthy individual in this context is a subject who is not affected by infertility.

[0161] For some markers a subject will be considered to suffer from infertility if the concentration of at least 2 markers, at least 3 markers, at least 4 markers, at least 5 markers, at least 6 markers, at least 7 markers, at least 8 markers, at least 9 markers, at least 10 markers, at least 11 markers, at least 12 markers, at least 13 markers, at least 14 markers, at least 15 markers, at least 16 markers, at least 17 markers, at least 18 markers, at least 19 markers, at least 20 markers, at least 21 markers, at least 22 markers, at least 23 markers, at least 24 markers, at least 25 markers, at least 26 markers, at least 27 markers, at least 28 markers, at least 29 markers, or at least 30 markers is higher or lower than the reference.

[0162] For some markers the concentration can be at least 1.05 times, at least 1.10 times, at least 1.15 time, at least 1.2 times, at least 1.3 times, at least 1.4 times, at least 1.5 time, at least 1.6 time, at least on 1.7 time, at least 1.8 times, at least 1.9 times, at least 2.0 times, at least 2.1 times at least 2.2 times, at least 2.3 times, at least 2.5 times, at least 3 times, at least 3.5 times, at least 4 times, at least 5 times, at least 10 times, at least 25 times, at least 50 times, at least 75 time, or at least 100 times, higher than the reference.

[0163] For some markers the concentration can be at least 1.05 times, at least 1.10 times, at least 1.15 time, at least 1.2 times, at least 1.3 times, at least 1.4 times, at least 1.5 time, at least 1.6 time, at least on 1.7 time, at least 1.8 times, at least 1.9 times, at least 2.0 times, at least 2.1 times at least 2.2 times, at least 2.3 times, at least 2.5 times, at least 3 times, at least 3.5 times, at least 4 times, at least 5 times, at least 10 times, at least 25 times, at least 50 times, at least 75 time, or at least 100 times, lower than the reference.

[0164] Where the reference used in the methods of the invention is the concentration of the at least one marker from one or more healthy subject(s), the reference may be an average concentration of the at least one marker determined for multiple samples obtained from a single healthy individual. Alternatively, the reference may be an average concentration of the at least one marker determined for multiple samples prepared from multiple healthy individuals. In this context, “one or more samples” may be at least 10, 100, 500, 1000, 10,000, 100,000 or 1,000,000 samples.

[0165] In another embodiment, the reference can be an average concentration of the at least one marker previously determined for one or more samples prepared from a healthy individual. In such embodiments, a numeric comparison may be made by comparing the concentration of the at least one marker determined for the sample obtained in the invention to the reference. The advantage of this is not having to duplicate the analysis by determining a reference in parallel each time a sample from a subject is analysed.

[0166] Suitably the reference may be matched to the subject being analysed e.g. by age e.g. by ethnic background or other such criteria which are well known in the art. For example, the reference may suitably be matched to specific patient sub-groups e.g. younger subjects.

[0167] In some embodiments, the concentration of the at least one marker determined may be compared to a concentration of the at least one marker previously determined from a non- invasive sample obtained from the same subject. In these embodiments, the previously determined concentration of the at least one marker is used as the reference. This can be beneficial in monitoring the subject. In some embodiments, the non -invasive sample is menstrual blood, urine or hair. Preferably, the non-invasive sample is menstrual blood. In some embodiments, the reproductive hormone, bacterium, and / or adhesion, inflammation or receptivity marker are measured in menstrual blood and / or urine. In some embodiments, cortisol is measured in menstrual blood and / or hair.

[0168] In some embodiments, infertility or a low likelihood for IVF success is less likely to be present if the concentration of the markers, analysed according to the methods of the invention, is normal compared to the reference. The concentration of the at least one marker may be said to be normal compared to the reference if there is no statistically significant difference between the concentration of the at least one marker and the reference. For example, the concentration of the at least one marker may be said to be normal compared to the reference if the difference between the concentration of the at least one marker and the reference is less than two, or less than one standard deviations.

[0169] In some embodiments, the subject may be diagnosed as having infertility or a low likelihood of IVF success if the concentration of the at least one marker determined for the sample is higher than the mean value determined for healthy individuals. In such embodiments, the “mean value determined for healthy individuals” is a type of “reference” as defined herein. It should be understood that the various embodiments and characteristics of a “reference” as defined herein may also apply to the “mean value determined for healthy individuals”. In some embodiments, the concentration of the at least one marker is higher than the mean value determined for healthy individuals if it is higher than the mean value determined for healthy individuals plus a multiple of the standard deviation of the mean value determined for healthy individuals, for example, higher than the mean value plus one, two, three, four, or five standard deviations of the mean value determined for healthy individuals.

[0170] Where the methods of the invention comprise a step of quantifying cortisol, this can be done using an immunoassay using an antibody suitable for detecting cortisol. Suitable antibodies will be known to a skilled person and are commercially available. Cortisol may also be measured in a hair sample. This has the advantage that it allows a better analysis of the long-term secretory patterns which are more informative for assessing infertility. In particular high state reactivity and the pulsatile secretion of cortisol mean single measurements reflect short-term levels, ranging from minutes (plasma or saliva) to hours (urine), and provide limited information about long-term cortisol secretion. It is therefore preferred that cortisol is quantified in a hair sample.

[0171] Where the methods of the invention comprise a step of testing for one or more heavy metal(s) this is preferably done through inductively coupled plasma mass spectrometry (ICP-MS), Atomic Absorption Spectroscopy (AAS), Flame Emission Spectroscopy (FES), UV / VIS Spectroscopy, Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) or X-ray Fluorescence Spectrometry (XRF). This is a high throughput method has been developed and validated for the analysis of heavy metals in blood.

[0172] In some embodiments, the bacterium is a species selected from the group consisting of Lactobacillus spp. Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., as well as the presence of pathogenic bacteria like Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis, Ureaplasma spp.

[0173] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Lactobacillus iners. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 1. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 1. In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Lactobacillus crispatus. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 2 or 3. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 2 or 3.

[0174] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Lactobacillus gasseri. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 4 or 5. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 4 or 5.

[0175] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Lactobacillus jensenii. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 6 or 7. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 6 or 7.

[0176] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Klebsiella pneumoniae. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 8 or 9. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 8 or 9.

[0177] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Prevotella bivia. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 10 or 11. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 10 or 11.

[0178] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Prevotella amnii. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 12 or 13. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 12 or 13.

[0179] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Bifidobacterium breve. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 14 or 15. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 14 or 15.

[0180] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Bifidobacterium bifidum. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 16 or 17. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 16 or 17.

[0181] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Bifidobacterium longum. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 18 or 19. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 18 or 19.

[0182] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Bifidobacterium adolesentis. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 20 or 21. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 20 or 21.

[0183] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Dialister micraer ophilus. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%,

[0184] 99.5% or 99.9% identical to SEQ ID NO: 22. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 22.

[0185] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Gardnerella vaginalis. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 23. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 23.

[0186] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Chlamydia trachomatis. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 24. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 24.

[0187] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Streptococcus agalactiae. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 25. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 25.

[0188] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Staphylococcus aureus. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NOs: 26 or 27. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NOs: 26 or 27.

[0189] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Mycobacterium tuberculosis. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 28. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 28.

[0190] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Ureciplcisma parvum. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 29. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 29.

[0191] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Ureciplcisma urealitycum. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 30. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 30.

[0192] In some embodiments, closely related strains may also be used, such as bacterial strains that comprise a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to the 16S rRNA sequence of a bacterial strain of Corynebacterium amycolatum. Preferably, the bacterial strain comprises a 16S rRNA sequence that is at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identical to SEQ ID NO: 31. Preferably, the bacterial strain for use in the invention comprises the 16S rRNA sequence represented by SEQ ID NO: 31.

[0193] Method for 16s rRNA sequencing are known in the art and include, for example, next generation sequencing (Cheng et al., 2023, International Journal of Molecular Sciences, 24(6), p.5633). This is preferred because this method has been widely used to detect the blood and uterine microbiome.

[0194] The table below provides the normal concentration ranges for number of markers that may be measured. The concentration range given as the normal concentration range may be used as the reference in the methods of the invention.

[0195] Preferably, the biomarkers analysed in the methods of the invention is selected from a biomarker of table 1. In some embodiments, (a) the reproductive hormone, and / or (b) the bacterium, and / or (c) the cortisol and / or (d) the adhesion, inflammation or receptivity marker are selected from the markers shown in Table 1. All of the markers in Table 1 may be analysed. Table 1 : Biomarker concentrations in menstrual blood

[0196]

[0197]

[0198]

[0199] In some embodiments, the reference value for AMH is 1-4 ng / ml. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 1 ng / ml, less than 0.9 ng / ml, less than 0.8 ng / ml, less than 0.7 ng / ml, less than 0.6 ng / ml, less than 0.5 ng / ml, less than 0.4 ng / ml, less than 0.3 ng / ml, less than 0.2 ng / ml, less than 0. 1 ng / ml, or less than 0.05 ng / ml indicates infertility.

[0200] In some embodiments, the reference value for TSH is 2 IU / L. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 2 IU / L, greater than 2.5 IU / L, greater than 3 IU / L, greater than 3.5 IU / L, greater than 4 IU / L, greater than 4.5 IU / L greater than 5 IU / L, greater than 5.5 IU / L, or greater than 6 IU / L indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 4.5 IU / L indicates infertility or a low likelihood of IVF success.

[0201] In some embodiments, the reference value for FSH is 8.5 IU / L. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 8.5 IU / L, greater than 9 IU / L, greater than 9.5 IU / L, greater than 10 IU / L, greater than 12.5 IU / L, greater than 15 IU / L, greater than 17.5 IU / L, greater than 20 IU / L, greater than 22.5 IU / L or greater than 25 IU / L indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 20 IU / L indicates infertility or a low likelihood of IVF success.

[0202] In some embodiments, the reference value for LH is 4.5 IU / L. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 4.5 IU / L, greater than 5 IU / L, greater than 5.5 IU / L, greater than 6 IU / L, greater than 6.5 IU / L, greater than 7 IU / L, greater than 7.5 IU / L, greater than 8 IU / L, greater than 8.5 IU / L greater than 9 IU / L, greater than 9.5 IU / L, greater than 10 IU / L, greater than 12.5 IU / L, greater than 15 IU / L, greater than 17.5 IU / L or greater than 20 IU / L indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 15 IU / L indicates infertility or a low likelihood of IVF success.

[0203] In some embodiments, the reference value for progesterone is 0.5ng / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 0.5ng / ml, greater than Ing / ml, greater than 1.5ng / ml, greater than 2ng / ml, greater than 2.5ng / ml, greater than 3ng / ml, greater than 3.5ng / ml, greater than 4ng / ml, greater than 4.5ng / ml or greater than 5ng / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 2ng / ml indicates infertility or a low likelihood of IVF success.

[0204] In some embodiments, the reference value for oestrogen is 50pg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 50 pg / ml, greater than 60 pg / ml, greater than 70 pg / ml, greater than 80 pg / ml, greater than 90 pg / ml, greater than 10 pg / ml, greater than 110 pg / ml greater than 120 pg / ml, greater than 130 pg / ml, greater than 140 pg / ml or greater than 150 pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 120pg / ml indicates infertility or a low likelihood of IVF success. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 50pg / ml, less than 45pg / ml, less than 40pg / ml, less than 35pg / ml, less than 30pg / ml or less than 25pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of less than 50pg / ml indicates infertility or a low likelihood of IVF success. In some embodiments, the reference value for estradiol 17B is 50pg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 50 pg / ml, greater than 60 pg / ml, greater than 70 pg / ml, greater than 80 pg / ml, greater than 90 pg / ml, greater than 100 pg / ml, greater than 110 pg / ml, greater than 120 pg / ml, greater than 130 pg / ml, greater than 140 pg / ml, greater than 150 pg / ml greater than 160 pg / ml or greater than 170 pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 150pg / ml indicates infertility or a low likelihood of IVF success. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 50pg / ml, less than 45pg / ml, less than 40pg / ml, less than 35pg / ml, less than 30pg / ml less than 25pg / ml or less than 20pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of less than 50pg / ml indicates infertility or a low likelihood of IVF success.

[0205] In some embodiments, the reference value for cortisol is 0.1 -0.2 mg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 0.2mg / ml, greater than 0.3mg / ml, greater than 0.4mg / ml, greater than 0.5mg / ml, greater than 0.6mg / ml or greater than 0.7mg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 0.4mg / ml indicates infertility or a low likelihood of IVF success.

[0206] In some embodiments, the reference value for hyaluronan is 50pg / l. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 50 pg / 1, greater than 60 pg / 1, greater than 70 pg / 1, greater than 80 pg / 1, greater than 90 pg / 1, greater than 100 pg / 1, greater than 110 pg / 1 or greater than 120 pg / 1 indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 100 pg / 1 indicates infertility or a low likelihood of IVF success. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 50pg / l, less than 45pg / l, less than 40pg / l, less than 35 pg / 1, less than 30pg / l, less than 25 pg / 1, less than 20pg / l, less than 15 pg / 1 or less than 10pg / l indicates infertility or a low likelihood of IVF success. Preferably, a value of less than 20pg / l indicates infertility or a low likelihood of IVF success.

[0207] In some embodiments, the reference value for heparan sulfate is lOOpg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than lOOpg / ml, greater than 1 lOpg / ml, greater than 120 pg / ml. greater than 130pg / ml, greater than 140pg / ml, greater than 150pg / ml greater than 160pg / ml or greater than 170pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 150pg / ml indicates infertility or a low likelihood of IVF success.

[0208] In some embodiments, the reference value for CD44 is 500 ng / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 500 ng / ml, greater than 550 ng / ml, greater than 600 ng / ml, greater than 650 ng / ml, greater than 700 ng / ml, greater than 750 ng / ml, greater than 800 ng / ml, greater than 850 ng / ml, greater than 900 ng / ml, greater than 950 ng / ml or greater than 1000 ng / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 850ng / ml indicates infertility or a low likelihood of IVF success. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 500ng / ml, less than 400ng / ml, less than 300ng / ml, less than 300ng / ml or less than lOOng / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of less than 400ng / ml indicates infertility or a low likelihood of IVF success.

[0209] In some embodiments, the reference value for B3 integrin is 5 pg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than 5 pg / ml, greater than 6 pg / ml, greater than 7 pg / ml, greater than 8 pg / ml, greater than 9 pg / ml, greater than 10 pg / ml, greater than 11 pg / ml or greater than 12 pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 10 pg / ml indicates infertility or a low likelihood of IVF success. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 5 pg / ml, less than 4.5 pg / ml, less than 4 pg / ml, less than 3.5 pg / ml, less than 3 pg / ml, less than 2.5 pg / ml, less than 2 pg / ml, less than 1.5 pg / ml or less than 1 pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of less than 2 pg / ml indicates infertility or a low likelihood of IVF success.

[0210] In some embodiments, the reference value for TNF -alpha is lOpg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than lOpg / ml, greater than 20pg / ml, greater than 30pg / ml, greater than 40pg / ml, greater than 50pg / ml, greater than 60pg / ml, greater than 70pg / ml, greater than 80pg / ml or greater than 90pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 60pg / ml indicates infertility or a low likelihood of IVF success.

[0211] In some embodiments, the reference value for IL-6 is lOpg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than lOpg / ml, greater than 15pg / ml, greater than 20pg / ml, greater than 25pg / ml or greater than 30pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 15pg / ml indicates infertility or a low likelihood of IVF success.

[0212] In some embodiments, the reference value for IL-8 is lOpg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than lOpg / ml, greater than 15pg / ml, greater than 20pg / ml, greater than 25pg / ml or greater than 30pg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 15pg / ml indicates infertility or a low likelihood of IVF success.

[0213] In some embodiments, the reference value for IL-33 is lOpg / ml. In some embodiments, an increased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of greater than lOpg / ml, greater than 20pg / ml, greater than 30pg / ml, greater than 40pg / ml or greater than 50pg / ml, greater than 60pg / ml, greater than 70pg / ml, greater than 80pg / ml, greater than 90pg / ml or greater than lOOpg / ml indicates infertility or a low likelihood of IVF success. Preferably, a value of greater than 80pg / ml indicates infertility or a low likelihood of IVF success.

[0214] In some embodiments, the reference value for Lactobacillus spp. is 90%. In some embodiments, a decreased concentration when compared to the reference value indicates infertility or a low likelihood of IVF success. In some embodiments, a value of less than 90% indicates infertility or a low likelihood of IVF success.

[0215] In some embodiments, the presence of Klebsiella indicates infertility or a low likelihood of IVF success.

[0216] In some embodiments, the presence of any one of arsenic, lead, cadmium, and mercury indicates infertility or a low likelihood of IVF success. Sensitivity and Specificity

[0217] The methods of the invention allow infertility or a low likelihood of IVF success to be detected with high specificity and high sensitivity. In the field of medical diagnostics and as used herein the term “sensitivity” (also referred to as the true positive rate) refers to a measure of the proportion of actual positives that are correctly identified as such. In other words, the sensitivity of a diagnostic test may be expressed as the number of true positives i.e. individuals correctly identified as having a disease as a proportion of all the individuals having the disease in the test population (i.e. the sum of true positive and false negative outcomes). Thus, a high sensitivity diagnostic test is desirable as it rarely misidentifies individuals having the disease. This means that a negative result obtained by a highly sensitive test has a high likelihood of ruling out the disease.

[0218] In the field of medical diagnostics, and as used herein, the term “specificity” (also referred to as the true negative rate) refers to a measure of the proportion of actual negatives that are correctly identified as such. In other words, the specificity of a diagnostic test may be expressed as the number of true negatives (i.e. healthy individuals correctly identified as not having a disease) as a proportion of all the healthy individuals in the test population (i.e. the sum of true negative and false positive outcomes). Thus, a high specificity diagnostic test is desirable as it rarely misidentifies healthy individuals.

[0219] In the field of medical diagnostics, and as used herein, the term “positive predictive value (PPV)” refers to the proportion of all positive outcomes generated by diagnostic test that are true positive outcomes. Put another way, PPV can be defined as the number of true positive outcomes divided by the sum of true positive outcomes and false positive outcomes. In the field of medical diagnostics, and as used herein, the term “negative predictive value (NPV)” refers to the proportion of all negative outcomes generated by diagnostic test that are true negative outcomes. Put another way, NPV can be defined as the number of true negative outcomes divided by the sum of true negative outcomes and false negative outcomes.

[0220] In the field of medical diagnostics, and as used herein, a “Receiver Operating Characteristic (ROC) curve” refers to a plot of true positive rate (sensitivity) against the false positive rate (1 - specificity) for all possible cut-off values. In the field of medical diagnostics, and as used herein, a “Youdens index” refers to the cut-off point at which the distance between the ROC curve and the line of chance (45 degree line) is highest. These terms are well known in the art and to the skilled person. The specificity and / or sensitivity of a method may be determined by performing said method on samples which are known to be positive samples (e.g. samples from patients having a gynaecological cancer) and / or samples which are known to be negative samples (e.g. samples from healthy individuals). The extent to which the method correctly identifies the known positive samples (i.e. the sensitivity / true positive rate of the method) and / or the known negative samples (z. e. the specificity / true negative rate of the method) can thus be determined.

[0221] In some embodiments of the methods of the invention, the method has a sensitivity for detecting infertility or a low likelihood of IVF success of at least about 40 %, 42 %, 44 %, 46 %, 48 %, or 50 %.

[0222] In some embodiments of the methods of the invention, the method has a sensitivity for detecting infertility or a low likelihood of IVF success of at least about 50 %, 52 %, 54 %, 55 %, 56 %, 57 %, 58 %, 59 %, 60 %, or 61%.

[0223] Samples

[0224] The methods of the invention comprise measuring the biomarkers in a non -invasive sample. As used herein, the term “non-invasive” refers to a step that does not require surgical intervention, such as that required to obtain a tissue sample in order to perform biopsy.

[0225] The sample may be menstrual blood, urine, a vaginal swab, an endometrial swab, a uterine flush sample or hair. Preferably the same is menstrual blood, menstrual plasma or menstrual serum.

[0226] The methods of the invention may comprise a step of obtaining the menstrual blood sample from the subject. The sample may have a volume of about 500pl, 1ml, 2ml, 3ml, 4ml, 5ml, 6ml, 7ml, 8ml, 9ml or 10ml. Preferably the sample has a volume of about 5ml.

[0227] The sample may be collected using a menstrual cup (such as a DivaCup™ or a Mooncup™).

[0228] The cup may be worn until a suitable sample volume is collected. The sample may also be taken from a tampon sample. In these embodiments the tampon may be submerged in a buffer to release the menstrual blood.

[0229] Samples may be taken on any day on which a subject experiences menstrual flow. Preferably samples are taken on day 1-3 of full menstrual flow. Preferably they are taken on day 2 of menstruation.

[0230] The methods of the invention may be performed using the whole menstrual blood. Alternatively, the methods of the invention may be practised using the plasma or serum. The menstrual blood can be separated using standard methods in the art such as centrifugation.

[0231] In some embodiments, the non-invasive sample used in the methods of the present invention is a urine sample. A urine sample can be obtained using any technique known in the art that the skilled person would be aware of. For example, a urine sample may be obtained from a subject by asking the subject to urinate into a collection vessel, such as a sterile container. As used herein, the term “urine sample” encompasses any sample derived from the urine sample originally obtained from the subject. For example, the urine sample may be processed by centrifugation to obtain e.g. a pellet comprising urinary sediment comprising cells. The supernatant may be discarded and the urinary sediment cell pellet re-suspended in a suitable buffer, such as a lysis buffer according to the invention or otherwise described herein.

[0232] The sample may also be a vaginal swab or an endometrial swab.

[0233] In other embodiments, the non-invasive sample used in the methods of the present invention is a vaginal or endometrial swab sample. A vaginal or endometrial swab sample can be obtained using any technique known in the art that the skilled person would be aware of. For example, a vaginal swab or endometrial sample may be obtained from a subject by a suitably trained medical professional inserting a soft endocervical collection brush (referred to as “swab”) into the vagina or the uterus of the subject and rotating it. The swab may be inserted 3-5 cm into the vagina and rotated four times (two towards the left and two towards the right). As used herein, the term “vaginal swab sample” or “endometrial swab sample” encompasses any sample derived from vaginal or endometrial swab sample originally obtained from the subject. For example, the swab sample may be processed by placing the swab into a vessel containing a suitable buffer, such as PBS, such that cells are transferred from the swab into the buffer. After removing the swab from the vessel, the buffer comprising the cells from the swab can be processed by centrifugation to obtain e.g. a pellet comprising urinary sediment comprising cells. The supernatant may be discarded and the urinary sediment cell pellet re-suspended in a suitable buffer, such as a lysis buffer according to the invention or otherwise described herein.

[0234] The sample may also be obtained from a uterine flush.

[0235] In some embodiments the methods of the invention may comprise a step of obtaining a non- invasive sample from a subject.

[0236] The sample may be analysed immediately after collection. It may also be stored prior to analysis, for example at 4°C, -20 °C or -80 °C. In some embodiments the sample may be mixed with a buffer, for example in order to increase the stability of the sample. Suitable buffers will be known to a skilled person and include phospho-buffered saline (PBS) and Citrate- phosphate-dextrose (CPD). These are preferred because they have both been used for menstrual blood and indicate stability in usage (Meledeo et al., 2019, Transfusion, 59(S2), pp.1549-1559).

[0237] The method of the invention may comprise the steps of: (i) wearing a menstrual cup on day 1 and / or day 2 of full flow; (ii) emptying the menstrual cup into a container containing PBS; (iii) diluting the sample by at least 5x, in at least 5 ml of PBS; (iv) spinning the sample at 500 RCF for 5 minutes; and (v) obtaining the supernatant for analysis.

[0238] The methods of the invention may comprise a step of obtaining the urine sample from the subject. The sample may have a volume of about 500pl, 1ml, 2ml, 3ml, 4ml, 5ml, 6ml, 7ml, 8ml, 9ml or 10ml. Preferably the sample has a volume of about 5ml.

[0239] Samples may be taken on any day on which a subject experiences menstrual flow. Preferably samples are taken on day 1-3 of full menstrual flow.

[0240] The sample may be analysed immediately after collection. It may also be stored prior to analysis, for example at 4°C, -20 °C or -80 °C. In some embodiments the sample may be mixed with a buffer, for example in order to increase the stability of the sample. Suitable buffers will be known to a skilled person and include phospho-buffered saline (PBS) and Citrate- phosphate-dextrose (CPD).

[0241] The methods of the invention may comprise a step of obtaining a hair sample from the subject.

[0242] It will be evident to a skilled person which samples can be used to detect or quantify a specific marker. For example, a skilled person will understand that a bacterium cannot be detected in a hair sample.

[0243] In the methods of the invention the reproductive hormone can be detected or quantified in a menstrual blood, menstrual serum, menstrual plasma, or a urine sample.

[0244] The bacterium may be detected or quantified in a menstrual blood, menstrual serum, or menstrual plasma sample.

[0245] Cortisol can be detected or quantified in a menstrual blood, menstrual serum, menstrual plasma, urine or a hair sample.

[0246] The adhesion, inflammation or receptivity marker may be measured or quantified in a menstrual blood, menstrual serum, menstrual plasma, or a urine sample.

[0247] The heavy metal may be detected or quantified in a menstrual blood, menstrual serum, or menstrual plasma sample.

[0248] Preferably all markers are detected or quantified in the sample type, for example menstrual blood. This is preferred as it is more convenient to perform all tests in the same sample. The invention can also be performed, however, using a combination of different sample types as discussed in the preceding paragraphs. For example, the one or more reproductive hormone(s) can be detected or quantified in urine whilst the other markers are detected or quantified in menstrual blood. Where more than one marker from each group is detected or quantified the markers can all be detected or quantified in the same sample type or in different sample types. For example, one reproductive hormone can be detected or quantified in urine and another in menstrual.

[0249] Sample preparation

[0250] An aspect of the invention is that the inventors have surprisingly discovered that the detection of a range of markers, in accordance with the methods of the invention, is particularly useful in determining the presence or absence of infertility in a subject or determining the likelihood of success of in vitro fertilisation. Measuring a range of different markers, which each play a different role in health of the reproductive system, provides a broad understanding of the health of the reproductive system and therefore fertility of the subject and thus provides for an improved diagnostic compared to use of a single type of marker.

[0251] The methods of the invention enable analysis of multiple markers at the same time (which no other test on the market can currently do) due to the sample preparation methods of the invention which allow, for the first time, the analysis of several types of markers from the same sample. The methods of the invention further allow the interactions between the markers to be corrected when they are measured together. A skilled person would not be able to extract and analyse all of the markers of the invention from a single sample, particularly in view of the difficulties in extracting all the markers from the same samples in the prior art. Furthermore, the teachings in the prior art would not enable the interactions between the markers to be corrected, even though it has been recognised as important, this has not previously been achieved, particularly in the context of each other using a multi -omic ML model.

[0252] Currently, in order to analyse the combination of markers of the invention, a patient must have an endometrial biopsy, vaginal swab and peripheral blood draw. The current methods in combination are expensive and invasive. The invention therefore provides a non -invasive and cheaper approach in comparison to current biopsies. In addition, it is more comprehensive than current blood tests and more reflective of the uterine environment. It is expected to have higher specificity and sensitivity relating to determining the presence or absence of infertility or the success of IVF compared to blood tests and biopsies.

[0253] Menstrual blood in particular presents challenges during processing in view of its high viscosity, and compositional heterogeneity (for example due to the presence of blood clots and endometrial tissue fragments), in comparison to peripheral blood samples. The methods of the invention, following the sample preparation workflow designed by the inventors, minimise intrasample variation and address the viscosity and compositional heterogeneity inherent to menstrual blood.

[0254] The invention provides a method for preparing a non-invasive sample suitable for use in a method according to the invention, the method comprising (a) centrifuging a non-invasive sample obtained from a patient; (b) separating the supernatant and the pellet; and (c) centrifuging the pellet from step (b).

[0255] The centrifugation step of step (c) will be performed using a higher g force compared to the centrifugation step of step (a). For example, the g force used in step (c) may be at least 5 times, at least 6 times, at least 7 times, at least 8 times, at least 9 times, at least 10 times, at least 11 times, at least 12 times, at least 13 times, at least 14 times, at least 15 times, at least 16 times or at least 17 times higher compared to the g force used in step (a). Preferably, the g force used in step (c) is 13 times higher compared to the g force used in step (a).

[0256] Centrifugation in step (a) may be carried out at a g force of between 500g and 1500g, between 600g and 1400g, between 700g and 1300g, between 800g and 1200g, between 900g and 1100g or at about 1000g. In a preferred embodiment centrifugation in step (a) is carried out using a g force of about 1000g.

[0257] The centrifugation step (a) may be carried out for between 2-10 minutes, 3-9 minutes, 4-8 minutes or 5-7 minutes. Preferably the sample is centrifuged for about 4 minutes.

[0258] Centrifugation in step (c) may be carried out at a g force of between 8000g - 20000g, between 9000g and 19000g, between 10000g and 18000g, between 11000g and 17000g, between 12000g and 16000g or at about 13000g. In a preferred embodiment centrifugation in step (a) is carried out using a g force of about 13000g.

[0259] The centrifugation step (c) may be carried out for between 10 seconds and 5 minutes, 20 seconds to 4 minutes, 20 seconds to 3 minutes, 20 seconds to 2 minutes, 20 second to 1 minute or for about 30 seconds. Preferably the sample is centrifuged for about 30 seconds.

[0260] A skilled person will understand that the centrifugation steps (a) and (c) effect separation of the sample into a supernatant and a pellet. These can be separated using techniques known to a skilled person, e.g. pipetting, decanting etc.

[0261] The different fractions (supernatant and pellet) obtained after each step will be used for the measurement of different markers in accordance with the invention. Specifically, the supernatant obtained from the centrifugation step (a) is particularly suitable for detecting and or quantifying the reproductive hormone; cortisol; the adhesion, inflammation or receptivity marker; and / or the heavy metal in accordance with the methods of the invention. Bacteria are quantified in the pellet obtained in step (c) of the sample preparation method of the invention.

[0262] In conventional, non-menstrual blood samples, the detection of such markers is associated with technical difficulties. Specifically, these markers are not typically present together in a single sample type, which complicates their simultaneous measurement. By contrast, menstrual blood samples inherently combine tissue and blood, thereby enabling the concurrent detection of these distinct classes of markers. Furthermore, in the absence of the present system, it is difficult to effectively separate the two surfaces required to recover the full complement of analytes of interest. Additionally, previous processing protocols have encountered persistent challenges with sample stability, which has further limited the reproducibility and reliability of analytical outcomes. Finally, NAC enables processing before protocols where hindered by mucus in MB preventing pipetting and leading to variability between samples, and not accurate quantification.

[0263] The collected menstrual blood samples are preferably transferred to a sterile container following collection.

[0264] The samples may be processed immediately, within 30 minutes, within one hour or within two hours following collection. Preferably, the samples are processed within two hours of collection. The samples are preferably stored at -4°C before analysis. Preferably the samples are stored at -4°C and processed within four hours.

[0265] Prior to centrifugation step (a), the sample may be diluted. For example, it may be diluted at a ratio of 1: 1, 1: 1.5, 1:2, 1:2.5, 1:3, 1:3.5, or 1:4. It is preferred to use a ratio of 1:2.5. Preferably the samples are diluted with a medium such as phosphate-buffered saline (PBS). The sample may be diluted with blood collection medium (such as Anticoagulant Citrate Dextrose Solution, Solution A (ACD-A)). Preferably the samples are diluted with blood collection medium (ACD- A) at a ratio of 1:2.5.

[0266] In some embodiments N-acetylcysteine (NAC) is subsequently be added to the samples following step (c). This reduces the viscosity of the sample.

[0267] The supernatant and pellet are preferably prepared using the sample preparation method of the invention but may also be prepared using a single spin centrifugation method. The double spin centrifugation method comprises a first centrifugation step wherein the menstrual blood sample is centrifuged at 1,000 x g for 5 min and the supernatant is collected. Following the first centrifugation step and collection of the supernatant, the remaining pellet undergoes a second centrifugation step at 13,000 x g for 2 min. The remaining pellet is then extracted. The supernatant and pellet can be prepared using a single spin centrifugation method, wherein the method comprises centrifuging the menstrual blood sample at 13,000 x g for 2 min and collecting the supernatant and the pellet.

[0268] Kits

[0269] The invention also provides a kit suitable for use in the methods of the invention. The kit may contain the reagents necessary to perform the methods of the invention. For example, it may contain antibodies to detect at least two markers in accordance with the methods of the invention. The antibodies are preferably monoclonal antibodies.

[0270] A kit according to the invention may further comprise reagents, such a buffers suitable for carrying out the methods of the invention.

[0271] The kit may also contain receptacles suitable for collecting the non-invasive sample. For example, where the sample is menstrual blood the kit may contain a menstrual cup to collect the menstrual blood.

[0272] Uses

[0273] Further aspects of the invention relate to the use of kits of the invention in a method of determining reduced fertility in a subject. The uses and methods of the invention may be in vitro or ex vivo uses or methods.

[0274] Other

[0275] It is to be understood that the different embodiments disclosed may be tailored to the specific needs in the art. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments of the invention only, and is not intended to be limiting.

[0276] The singular forms “a”, “an”, and “the” include plural references unless the content clearly dictates otherwise.

[0277] The word “about” in relation to a value x may be construed to mean x±10% of the value x. For example, where x=l- a value of about x may encompass 0.9-1.1.

[0278] Furthermore, when referring to “>x” herein, this means equal to or greater than x. When referred to “<x” herein, this means less than or equal to x.

[0279] The term “comprises” (comprise, comprising) should be understood to have its normal meaning in the art, i.e. that the stated feature or group of features is included, but that the term does not exclude any other stated feature or group of features from also being present. For example, a lysis buffer comprising a detergent may contain other components. The term “consists of should also be understood to have its normal meaning in the art, i.e. that the stated feature or group of features is included, to the exclusion of further features. For example a lysis buffer consisting of a detergent contains detergent and no other components. A lysis buffer comprising a detergent consisting of polysorbate 80 may comprise components other than detergents but the only detergent in the lysis buffer is polysorbate 80.

[0280] For every embodiment in which "comprises" or "comprising" is used, we anticipate a further embodiment in which “consists of’ or “consisting o is used. Thus, every disclosure of "comprises" should be considered to be a disclosure of “consists of.

[0281] For the purpose of this invention, in order to determine the percent identity of two sequences (such as two polynucleotide or two polypeptide sequences), the sequences are aligned for optimal comparison purposes (e.g. gaps can be introduced in a first sequence for optimal alignment with a second sequence). The nucleotide or amino acid residues at each position are then compared. When a position in the first sequence is occupied by the same nucleotide or amino acid as the corresponding position in the second sequence, then the nucleotides or amino acids are identical at that position. The percent identity between the two sequences is a function of the number of identical positions shared by the sequences (i.e., % identity = number of identical positions / total number of positions in the reference sequence x 100).

[0282] Typically, the sequence comparison is carried out over the length of the reference sequence. For example, if the user wished to determine whether a given (“test”) sequence is 95% identical to SEQ ID NO: 1, SEQ ID NO: 1 would be the reference sequence. To assess whether a sequence is at least 95% identical to SEQ ID NO: 1 (an example of a reference sequence), the skilled person would carry out an alignment over the length of SEQ ID NO: 1, and identify how many positions in the test sequence were identical to those of SEQ ID NO: 1. If at least 95% of the positions are identical, the test sequence is at least 95% identical to SEQ ID NO: 1. If the sequence is shorter than SEQ ID NO: 3, the gaps or missing positions should be considered to be non-identical positions.

[0283] The skilled person is aware of different computer programs that are available to determine the homology or identity between two sequences. For instance, a comparison of sequences and determination of percent identity between two sequences can be accomplished using a mathematical algorithm. In an embodiment, the percent identity between two amino acid or nucleic acid sequences is determined using the Needleman and Wunsch (1970) algorithm which has been incorporated into the GAP program in the Accelrys GCG software package (available at http: / / www.accelrys.com / products / gcg / ), using either a Blosum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. Examples

[0284] The invention will now be described with reference to specific Examples, which should not be construed as in any way limiting.

[0285] Example 1

[0286] Methods

[0287] Recruitment

[0288] A total of 50 participants were recruited between March and August. Among those recruited, 5 individuals completed sample collection over two menstrual cycles, while one participant completed collection over three cycles. The remaining individuals completed sample collection over one menstrual cycle. Two participants provided a paired sample, consisting of both peripheral blood and menstrual blood collected within the same cycle.

[0289] Eligibility for participation required individuals to collect their menstrual blood prior to or on their heaviest flow day and to wear a menstrual cup for a period of four to six hours.

[0290] Participants were excluded if they were using any form of hormonal treatment, including oral contraceptive pills, hormonal implants, or hormonal intrauterine devices. The use of non- hormonal copper coils also led to exclusion, as did the presence of gynaecological conditions, like endometriosis (Table 2).

[0291] Table 2. Participant exclusion and inclusion criteria

[0292] Several individuals were screened but excluded for not meeting the eligibility criteria. The most frequent reasons for ineligibility were current use of hormonal contraception or existing medical conditions incompatible with study parameters. One participant was included despite technically being outside the criteria; she initially reported no hormone use on the questionnaire but later disclosed that she had a copper coil in place. Additionally, participants who collected samples after their heaviest menstrual flow day were included, as some may misremember or miscalculate their cycle timing. Inclusion of these samples is also beneficial for capturing variability across different cycle days and exploring its potential impact on study outcomes.

[0293] The contents of the menstrual cups were transferred into a sterile container. All samples were anonymised and labelled using a pseudonymised identifier, consisting of a coded participant number (P number). Samples were either transferred directly to the laboratory and processed within two hours or stored temporarily at -4°C and processed within four hours.

[0294] For the 46 participants, the mean age was 28.8 ± 5.1 years (mean ± SD), mean weight was 63.5 ± 13.1 kg, and mean height was 165.9 ± 6.7 cm. The average mean period length was 4.44 ± 0.93 days. The average menstrual cup wearing time on the recorded day was 5.45 ± 2.54 hours and overall, 69.6% of collections occurred before or on the participant’s heaviest flow day.

[0295] For participants who provided peripheral blood samples, collection was performed by a trained phlebotomist during a scheduled clinical visit at the Hale Clinic. Approximately 5 mb of peripheral blood was collected into each tube: one VACUETTE tube containing EDTA and one additive-free tube (ref. no. 45501). During the same visit, participants also completed a questionnaire and transferred their menstrual blood into a sterile container provided by the study team. Both the peripheral and menstrual blood samples were then transported to the Genie lab at room temperature within one hour.

[0296] Pre-processing

[0297] Menstrual blood is a complex biological fluid comprising a liquid fraction (supernatant) and a solid cellular fraction (pellet), each of which can be isolated by centrifugation. In contrast to peripheral blood, which is relatively uniform and benefits from established processing protocols, menstrual blood presents additional handling challenges due to its higher viscosity and the presence of blood clots and fragments of endometrial tissue. These features reduce the efficiency of direct centrifugation and require tailored processing steps to achieve reliable separation and analysis.

[0298] The menstrual blood pre-processing workflow discovered by the inventors is designed to minimise intrasample variability and address both the viscosity and compositional heterogeneity inherent to this sample type. In this workflow, the supernatant is allocated for protein / metabolite measurement by chemiluminescent Immunoassay (CLIA) and mass spectrometry, and heavy metal analysis, while the cell pellet is reserved for DNA extraction and downstream microbiome profiling by qPCR and 16s sequencing. To optimise supernatant-pellet separation protocols, several strategies were investigated:

[0299] 1. Dilution with medium: samples were diluted with either phosphate -buffered saline (PBS) or blood collection medium (ACD-A) to reduce viscosity, thereby enabling more consistent aliquoting, improving reproducibility, and facilitating a sharper separation between the supernatant and pellet during centrifugation.

[0300] 2. Addition of NAC (N -Acetyl -L-cysteine): NAC acts as a mucolytic agent by cleaving disulphide bonds within mucin glycoproteins. The inventors hypothesised that, in menstrual blood, NAC treatment would reduce mucus viscosity, promote better separation of serum and cells, and release trapped cells and nucleic acids. This would produce a more homogeneous sample, improving pipetting accuracy and enabling more consistent downstream DNA extraction and amplification. Multiple NAC concentrations have been tested to identify the most effective protocol.

[0301] 3. Centrifugation method for cell pellet collection: A lower-speed spin is used to collect supernatant for protein / metabolite analysis in blood, while a higher-speed spin is applied to collect concentrated cell pellets for DNA extraction. In standard workflows, this requires splitting the menstrual blood sample into separate portions for each procedure. However, because menstrual blood is not homogeneous, splitting may introduce variation between aliquots.

[0302] To address this, a double-spin method was tested in which the whole menstrual blood sample is first centrifuged at 1,000 x g for 5 min to collect supernatant, followed by a second centrifugation of the remaining pellet at 13,000 x g for 2 min to obtain a cell pellet for DNA extraction and microbiome analysis. This approach could allow both supernatant and cell pellets to be derived from the same starting material.

[0303] All aliquots were labelled using a standardised coding system that encodes the participant identifier, sample type, processing method, storage condition, and date of collection.

[0304] Evaluated supernatant extraction protocols

[0305] The sample conditions tested were tested before centrifugation:

[0306] • Neat menstrual blood (undiluted)

[0307] • Menstrual blood diluted in PBS at ratios of 1 :2 and 1:2.5 prior to centrifugation

[0308] • Menstrual blood mixed with ACD-A at a ratio of 1 : 2.5 prior to centrifugation To assess storage stability, both neat menstrual blood and menstrual blood mixed with ACD-A were stored under different temperature and time conditions before processing. Specifically, samples were maintained at room temperature for 48 hours, or at 4 °C for 24, 48, and 120 hours, and then analysed for potential changes in quality.

[0309] Standard Processing Protocol

[0310] 1. Record the participant’s pseudonumber, and receipt time.

[0311] 2. Document the color, viscosity, presence of tissue or clots.

[0312] 3. Transfer the menstrual blood into a sterile 50 mb centrifuge tube to assess volume. If visually less than 3 mb, no need to record volume in graduated tube to avoid sample loss.

[0313] 4. Remove large tissue or clots using sterile tweezers.

[0314] 5. Take a photo of the sample before processing.

[0315] 6. Use sterile wide-bore 1000 pL pipette tips to carefully transfer the menstrual blood into 15 mb centrifuge tubes for processing.

[0316] 7.

[0317] A: PBS Dilution (1:2.5) - Mix 1 mb of menstrual blood with 1.5 mb of sterile PBS (total volume: 2.5 mb).

[0318] B: ACD-A (1:2.5) - Mix 1 mb of menstrual blood in an ACD-A blood tube which contains 1.5 mb of ACD-A solution.

[0319] Notes: Mix by gentle inversion, no pipetting

[0320] 8. Centrifuge all tubes at 1000 x g for 5 minutes at room temperature.

[0321] 9. After centrifugation, observe any signs of hemolysis (e.g., pink / red supernatant: no / mild / moderate / severe hemolysis), and separation (presence of mucus layer), photograph the results before proceeding.

[0322] 10. Carefully transfer supernatant from each condition into a single sterile microcentrifuge tube.

[0323] 11. Aliquot 500 pL per tube, aiming for at least two aliquots per condition, use green caps for supernatant. Storage condition testing

[0324] 1. When additional menstrual blood volume was available after primary processing, samples were stored neat at 4°C for 24 hours, 48 hours, and 120 hours.

[0325] 2. After the designated storage period, visual inspection and photograph for color change, phase separation, and texture.

[0326] 3. Neat samples were diluted 1:2.5 in PBS (i.e., 1 mb menstrual blood + 1.5 mb PBS), then centrifuged at 1000 x g for 5 minutes at room temperature. Samples were aliquoted and stored following the standard processing protocol.

[0327] 4. Record menstrual blood sheet and lab note with picture uploaded.

[0328] In addition to the standard protocols described above, modifications designed to enhance sample separation and consistency were tested. These include:

[0329] NAC (N-Acetyl-L-cysteine) addition: NAC treatment would reduce mucus viscosity, promote better separation of serum and cells, and release trapped cells and nucleic acids.

[0330] Preparation

[0331] 1. Prepare fresh 2% NAC stock solution by dissolving 0.1 g NAC in 5 mL PBS.

[0332] 2. Prepare 5% NAC solution by dissolving 0.25 g NAC in 5mL PBS

[0333] 3. Prepare working concentrations as follows:

[0334] • 1 % NAC: Add 1 mL of 2% NAC to 1 mL PBS

[0335] • 0.5% NAC: Add 1 mL of 2% NAC to 3 mL PBS.

[0336] • 0.2% NAC: Add 500 pL of 2% NAC to 4.5 mL PBS.

[0337] • 0.1% NAC: Add 2 mL of 0.2% NAC to 2 mL PBS.

[0338] (Alternative Step 7) NAC Addition

[0339] • Mix 1 mL menstrual blood with 1.5 mL of each NAC -PBS solution at the following concentrations: 1% NAC, 0.5% NAC, 0.2% NAC, 0.1% NAC.

[0340] • Incubate at room temperature for 5 minutes.

[0341] • Observe for color change and texture change and take photos.

[0342] After completing this step, proceed to Step 8 of the standard processing protocol. ACD addition after storage: ACD addition results in better separation of serum and cells, the inventors hypothesized that it could reduce intrasample variability.

[0343] (Alternative Step 3 of storage condition testing) ACD Addition: Neat samples were diluted 1:2.5 in ACD-A tube, then centrifuged at 1000 x g for 5 minutes at room temperature.

[0344] Protocols for cell pellet collection - Microbiome processing (cell pellet collection and DNA extraction)

[0345] After pre-processing, the menstrual blood cell pellet was processed for microbiome analysis using 16S rRNA sequencing and qPCR supernatant. The analysis was performed following the platform recommended protocols, as described below.

[0346] Double spin: Pellets from post-supernatant collection in section a were transferred into Eppendorf tubes by 1000 uL wide bore tip and centrifuged at 13,000 x g for 2 minutes.

[0347] Single spin: unprocessed MENSTRUAL BLOOD centrifuged at 13,000 x g for 2 minutes

[0348] Protocols for DNA extraction from collected cell pellets

[0349] 1. DNA extraction was performed within 7 days of cell pellet storage using the BiOstic Bacteremia DNA Kit (Qiagen, USA) and DNA was eluted in 20uL elution buffer.

[0350] 2. DNA yield assessed using a DS-11 Spectrophotometer (DeNovix).

[0351] 3. Extracted DNA stored at -20°C until further analysis.

[0352] Microbiome DNA

[0353] PCR analysis

[0354] Instrument: AriaMx Real-Time PCR System (Agilent Technologies)

[0355] Primers and gBlocks: All primers and gBlocks were synthesised by Integrated DNA Technologies (IDT). Specific primer sequences and gBlock details are listed in Table 3 (below). Primers reconstituted into stock solutions at 100 pM following IDT instructions. Working primer solutions prepared by diluting stocks to 10 pM. gBlocks reconstituted into 10 ng / pL stock solutions following IDT instructions. Table 3. Primer and gBlock sequences used

[0356] Reaction Setup:

[0357] 1. Prepare a working solution of DNA at 50 ng / pL.

[0358] 2. Use 1 pL of this working solution per reaction to achieve 50 ng template DNA in the final reaction volume.

[0359] 3. Each sample is run in triplicate.

[0360] 4. Prepare G block working solutions at 1 ng / pL, 0. 1 ng / pL, and 0.01 ng / pL and use 1 pL per reaction.

[0361] 5. Prepare reaction with Luna™ Universal qPCR Master Mix following NEB protocol

[0362] Table 4.

[0363] Cycle setup:

[0364] Use FAM scan mode on the AriaMx and acquire fluorescence during the anneal / extension step.

[0365] Program the run as shown in Table 5.

[0366] Table 5. Cycle information

[0367] Ct values were determined using the maximum slope method, where the change in fluorescence (AR) between cycles was calculated to find the point of fastest amplification. The cycle with the steepest increase in fluorescence was recorded as the Ct, representing the inflection point of the amplification curve.

[0368] Microbiome DNA 16s Sequencing

[0369] 16s Sequencing was outsourced to Source Bioscience, which performed the sequencing using Oxford Nanopore. The initial analysis was then performed by Source Bioscience, who aligned the reads and identified genera and species and their abundances in the samples.

[0370] Protocols for processing of samples from supernatant

[0371] After pre-processing, the menstrual blood supernatant was allocated for hormone measurement by CLIA, heavy metal quantification, and protein profiling by mass spectrometry. Each analysis was performed following the respective platform’s recommended protocols, as described below.

[0372] Mass spectrometry (outsourced analysis) for protein analysis

[0373] Lab: Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge

[0374] Instrument: ZenoTOL 7600 system

[0375] Extraction method:

[0376] 1. standard proteomics method: Acetonitrile (ACN) precipitation followed by solid phase extraction (SPE)

[0377] 2. SPE only

[0378] 3. No extraction

[0379] Protocols for heavy metals analysis - ICP-MS at the London Metallomics Lacility at King’s College, London

[0380] Lor heavy metal analysis, the supernatant standard processing protocol was applied for the preparation of supernatants. In addition, unprocessed whole menstrual blood samples were analysed directly as comparison: 500 pL aliquots of unprocessed menstrual blood were collected into sterile Eppendorf tubes. Control samples included: (i) empty sterile Eppendorf tubes to assess background contamination, and (ii) media controls consisting of 500 pL aliquots of sterile PBS or ACD-A solution to evaluate any trace metal contributions from the diluents.

[0381] Protocols for hormone and cortisol quantification - CLIA (outsourced analysis)

[0382] Company: Salient Labs Limited and Alderley Lighthouse Labs

[0383] Instrument: Maglumi Snibe 2000 chemiluminescence immunoassay analyser. The instrument measures light emitted from chemiluminescent reactions and converts it into quantitative concentrations using calibration curves. Reagents and Kits:

[0384] MAGLUMI® TSH (CLIA) (Snibe diagnostics), MAGLUMI® FSH (CLIA) (Snibe diagnostics), MAGLUMI™ LH (CLIA) (Snibe diagnostics), MAGLUMI™ cortisol (CLIA) (Snibe diagnostics), MAGLUMI™ estradiol (CLIA) (Snibe diagnostics).

[0385] Table 6 - Detection limit

[0386] Example 2 - Microbiome analysis in menstrual blood using qPCR and 16s rRNA sequencing qPCR with Lactobacillus -specific primers (Figure 3) showed amplification for gBlock standards (1 ng, 0. 1 ng, 0.01 ng) with the expected rightward Cq shift as concentration decreased. Primer- only controls showed no amplification. These results confirm a proven primer design and protocol suitable for quantifying Lactobacillus in samples.

[0387] Lactobacillus was detected in DNA extracted from three menstrual blood samples processed fresh in PBS dilution and from matched samples stored at 4 °C for 24 hours in neat form, with both fresh and stored samples diluted in PBS prior to processing. Amplification curves showed clear amplification in all test samples, while weak signals were observed in two of the four primer-only controls, appearing much later in the amplification cycles (Figure 4).

[0388] Ct values for all test samples, primer-only controls, and gBlock standards are shown in Table 7. Quantification was performed using a gBlock-derived standard curve generated from known template quantities (1 ng, 0.1 ng, 0.01 ng) corresponding to Ct values of 5, 9, and 15, respectively by linear regression (Figure 5). Table 7. Ct values and estimated Lactobacillus DNA quantities for all test samples and gBlock standards. N1 = neat fresh processed; N2 = neat stored at 4 °C for 24 h (both diluted in PBS prior to processing).

[0389] Comparison of fresh versus 24 h stored samples (Figure 6) revealed no significant difference in Ct values (paired t-test, P>0.999). Similarly, quantification using the gBlock reference (Table 7) showed no significant differences (paired t-test, P = 0.605).

[0390] In a second set of experiments, menstrual blood (MB) samples from three donors were processed fresh with PBS or ACD, or stored at 4 °C for 120 h either in ACD or neat, then diluted in PBS before processing. Ct values for these samples are shown in Table 8.

[0391] Table 8. Ct values for menstrual blood samples processed fresh with PBS (Nl) or ACD (Al), or stored at 4 °C for 120 h either in ACD (A2) or neat (diluted in PBS before processing; N2) from three biological replicates. G block standards (0. 1 ng and 0.01 ng) included for reference.

[0392]

[0393] No significant differences in Ct values were found among most processing methods or storage conditions (one-way ANOVA, p = 0.53; paired t-tests: PBS vs ACD, p = 0.77; PBS vs PBS 120 h at 4 °C, p = 0.77; ACD vs ACD 120 h at 4 °C, p = 0.14; PBS 120 h vs ACD 120 h, p = 0.41). However, a significant difference was observed between PBS and ACD 120 h samples (p < 0.0001), suggesting that extended storage in ACD may introduce changes. Overall, variation in Ct values was more pronounced between biological replicates than between processing methods or storage conditions (Figure 7).

[0394] Overall, Lactobacillus was detected in all six biological samples tested, with amplification curves consistent with template levels equivalent to gBlock 0.01 ng or higher. Expression was not significantly affected by the processing method or storage length at 4 °C. qPCR using Klebsiella -specific primers detected amplification from both G-block templates, but only at late cycles (Figure 8). The 0.1 ng template produced a Ct of 36, and the 0.01 ng template a Ct of 39. Neither reaction reached a clear plateau within the 40-cycle limit. Low-level amplification was also observed in the primer-only control, suggesting potential primer-dimer formation or low-level contamination. The late amplification and incomplete curves indicate that the cycling protocol was too short for these assay conditions . qPCR using Chlamydia-specific primers detected amplification for G-block templates at 0.1 ng and 0.01 ng, with Ct values of 6 and 10, respectively, and no detectable amplification in the negative controls. However, when the assay was applied to DNA samples from freshly processed MB of three biological replicates, no amplification was observed within 40 cycles, and the curves were indistinguishable from those of the negative controls, indicating the absence of Chlamydia in healthy participants. qPCR using Gardnerella-specific primers detected amplification for G-block templates at 0.1 ng and 0.01 ng, with Ct values of 15 and 22, respectively, and no detectable amplification in the negative controls. However, when the assay was applied to DNA samples from freshly processed MB of three biological replicates, no amplification was observed within 40 cycles, and the curves were indistinguishable from those of the negative controls, indicating the absence of Gardnerella in healthy participants. qPCR using Bifidobacterium-specific primers detected amplification from both G-block templates, (Figure 11). The 0.1 ng template produced a Ct of 23, and the 0.01 ng template a Ct of 26. Neither reaction reached a clear plateau within the 40-cycle limit. Low-level amplification was also observed in the primer-only control. The late amplification and incomplete curves indicate that the cycling protocol was too short for these assay conditions.

[0395] Overall, the preliminary data indicate that the gBlocks and primers are functional for Lactobacillus, Chlamydia and Gardnerella bacterial targets. For Chlamydia and Gardnerella, the G-blocks showed amplification with a clear plateau, and no amplification was observed in the primer-only controls .

[0396] A total of 20 samples were then analyzed from DNA extracted from 5 women, two of whom were sampled during two menstrual cycles (a total of 7 cycles). Among these, six samples were freshly processed using a double -spinning method. Across these six samples, 169 genera and 403 species (excluding Homo sapiens) were detected. The ten genera with the highest relative abundance were Lactobacillus, Ureaplasma, Prevotella, Aerococcus, Parvimonas, Staphylococcus, Vibrio, Gardnerella, Peptoniphilus, and Anaerococcus (Figure 12 and Table 9). In participant P12, the distribution of genera changed markedly between cycles, whereas in P20 it remained relatively stable. Overall, Lactobacillus was the dominant genus, although biological variation was observed.

[0397] Table 9. Relative abundance (%) of top 10 bacterial genera in freshly processed MB samples using double -spinning.

[0398] Within Lactobacillus spp. 32 different species were identified. The most abundant were Lactobacillus crispaius. Lactobacillus iners. Lactobacillus jensenii. Lactobacillus muUeris. and Lactobacillus helveticus. Their distribution profiles remained consistent across the two cycles for the same women (n = 2), but varied substantially between different women (Figure 13). The detailed distribution of Lactobacillus species is presented in Table 10.

[0399] Table 10. Relative abundance (%) of Lactobacillus species across six freshly processed samples from different menstrual cycles. P12.1 and P12.2 were obtained from the same woman in two cycles, as were P20.1 and P20.2. To compare the findings of this application with the EMMA results reported in the referenced study (Moreno et al:, Microbiome. 2022 Jan 4; 10(1): 1), the overall 22 bacterial genera identified in that work were examined (Table 11).

[0400] Table 11. Comparison of bacterial genera detected in the present study with those reported in the EMMA analysis of endometrial biopsy and fluid (Moreno et al., 2022). Sample % refers to the percentage of samples in which the genus was detected (expression level above 0, n=20).

[0401] Of these, 18 genera were also detected in the dataset of this application (Table 12). Propionibacterium, Aiopobium. Micrococcus and Tepidimonas are unique to EMMA results while Bifidobacterium, Escherichia, and Clostridium were present only at low abundance in the dataset of this application, occurring in fewer than two samples following storage or ACD processing, and absent in freshly processed PBS menstrual blood samples. Several highly abundant genera in the dataset of this application (Top 20) were not reported in the EMMA results, including Ureaplasma, Parvimonas, Vibrio, Peptoniphilus, and Limosilactobacillus etc. Table 12. Comparison of genera between datasets Additionally, DNA extracted from the same menstrual blood sample was analysed, but processed under different storage and handling conditions: single spin from whole menstrual blood or double spin after supernatant (SN) collection. For the double-spin method, variations also included NAC addition, ACD / PBS dilution, and storage for 24 h at 4 °C either neat or in ACD. Both UMAP and t-SNE analyses demonstrated clustering of samples according to menstrual cycle, irrespective of the processing method (Figures 14 and 15).

[0402] The comparison was narrowed to the top 10 expressed genera shown in Figure 16 and Table 10. Apart from P12.2, where ACD dilution appeared to reduce Prevotella expression compared with PBS dilution and single-spin processing, the remaining samples showed minimal changes in genus distribution between processing methods. The largest differences were still attributable to biological variation.

[0403] Lastly, paired t-tests revealed no statistically significant differences in bacterial genera abundance, after FDR correction, between samples stored for 24 h at 4 °C and those freshly processed, or between ACD-diluted and non-ACD-diluted samples.

[0404] In summary, the inventors have shown that a range of bacterial genera can be detected from menstrual blood, using optimised primers and qPCR methods as detailed above.

[0405] Example 3 - Protein analysis in menstrual blood using mass spectrometry

[0406] In preliminary analyses, three extraction methods were compared for protein recovery. The standard extraction protocol yielded the highest number of proteins compared with solid-phase extraction (SPE) alone or no extraction, and was therefore selected for subsequent analyses.

[0407] From 15 samples processed using this method, a total of 1,073 proteins were detected. With differential isotopic enrichment (DIE), protein detection increased, resulting in a total of 1,747 proteins identified overall. The ten most abundant proteins were ALB, HBB, HBA1, HBD, APOA1, INS3, INS, INS-IGF2, FGA, and PAEP, comprising predominantly plasma-derived proteins, insulin -related proteins, and one endometrium -enriched protein (PAEP, glycodelin).

[0408] A shortlist of relevant proteins was generated and their expression profiles cross-referenced with the Human Protein Atlas (Table 13). In the Human Protein Atlas, tissue specificity “elevated expression in the endometrium” is a defined category containing 89 genes. 16 proteins in the category of “elevated in the endometrium” were identified including PAEP (Glycodelin), SPON2 (Spondin-2), and MMP10 (Stromelysin -2). In addition, 46 proteins classified as “elevated in vagina” including SERPINB3 (Serpin B3), and 43 proteins “elevated in the cervix” including SLPI (Secretory leukocyte protease inhibitor), COL1A2 (Collagen alpha-2(I) chain), were also detected. Other proteins expressed in most samples and associated with the endometrium and decidualisation included SPP1 (Osteopontin) and VIM (Vimentin).

[0409] Table 13. Shortlisted proteins detected in menstrual blood MS-DIE analysis with elevated expression in the endometrium. Tissue specificity is defined as elevated expression in the indicated reproductive tissue compared to other tissues according to the Human Protein Atlas.

[0410]

[0411] Apart from tissue-specific proteins, plasma-derived proteins represent a major component of the menstrual blood proteome. Albumin was consistently the highest-expressed protein across all samples. The other highly abundant plasma proteins were identified by cross-referencing the menstrual blood dataset with the top 50 proteins by expression level in human plasma (listed in the plasma mass spectrometry result in the Human Protein Atlas). Those plasma proteins that were detected in the menstrual blood samples are listed in Table 14.

[0412] Table 14. Shortlisted of plasma enriched proteins in MB DIA

[0413] To further explore the utility of these tissue-specific proteins in Table 13 and plasma enriched proteins in Table 14, preliminary normalisation analysis based on their relative abundances across menstrual blood (MB) samples was performed. Six MB samples (P18 ACD1, P18_ACD2, P5.2_ACD, P15_ACD, P23_ACD, P12_ACD) were processed using the same workflow (ACD-diluted, freshly processed) from five biological replicates (including one technical duplicate). The method involved summing the total intensity for all proteins assigned to each marker category (Albumin, other blood-dominant proteins, endometrium-elevated proteins, cervix-elevated proteins, and vagina-elevated proteins) and expressing this as a percentage of the total signal across all five groups for each sample.

[0414] Results are summarised in Table 15. Plasma-derived proteins accounted for approximately 60- 90% of the total marker signal, with albumin as the dominant component. Endometriumelevated proteins contributed 1.0-24.0%, cervix-elevated proteins 2.4-10.3%, and vaginaelevated proteins consistently <3.5%. The duplicate samples from P18 (ACD1 and ACD2) displayed noticeably different compositions, likely reflecting substantial variation between aliquots due to MB sample inhomogeneity; nevertheless, both showed a higher proportion of endometrium-elevated proteins compared with the samples from other women.

[0415] Table 15. Relative abundance (%) of plasma-enriched proteins (albumin and others), and tissueelevated protein panels (endometrium, cervix, vagina) in MB samples.

[0416] To define the menstrual blood-specific proteome, the dataset was compared to a reference plasma DIE dataset from women during pregnancy, which contained 1,308 proteins. This comparison revealed 1,210 proteins unique to menstrual blood (Figure 17). Among the 16 markers elevated in endometrium in Table 13, 11 were unique to menstrual blood (Table 16), while ADAMTSL1, PAEP, CNN1, FLNA, PAEP, TAGLN were shared with pregnancy plasma and may potentially originate from the placenta.

[0417] Table 16. List of proteins elevated in endometrium, vagina, and cervix that are unique to the MB MS-DIA dataset when compared to the reference plasma DIA dataset from pregnant women.

[0418] A total of 537 proteins were shared between menstrual blood and pregnancy plasma, the majority of which were abundant blood-derived proteins such as ALB (Albumin), FGA (Fibrinogen alpha chain), HBA1 (Hemoglobin subunit alpha), HBB (Hemoglobin subunit beta), and TF (Serotransferrin).

[0419] These findings confirm that menstrual blood contains proteins originating from the endometrium, cervix, vagina, and blood. Using pregnancy plasma as a reference dataset allowed confirmation of a shortlist of proteins unique to menstrual blood and mostly endometrium and vagina associated. For a more precise characterisation, future studies should include matched peripheral blood and vaginal swab samples from the same individuals to distinguish proteins of local reproductive tract origin from systemic sources and identify markers for normalisation.

[0420] Example 4 - Detection of heavy metals in menstrual blood

[0421] Whole menstrual blood, freshly prepared supernatants (neat, or diluted 1:2.5 in ACD-A or PBS), and reagent controls (ACD-A alone, PBS alone, and empty collection tube) were analysed for aluminium (Al), iron (Fe), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), mercury (Hg), and lead (Pb). Samples were obtained from three independent biological replicates. For diluted samples, concentrations were back-calculated to undiluted- equivalent values. Concentrations of each metal are listed in Table 17.

[0422] Table 17. Mean and standard deviation (SD) of elemental concentrations (pg / L) in whole menstrual blood (MB) (n=5), freshly prepared supernatants (SN) in ACD-A (n=2) or PBS (n=3), and reagent controls (ACD-A alone, PBS alone, and empty collection tube). For diluted samples (SN-ACD and SN-PBS), values are back -calculated to undiluted-equivalent concentrations. “Below LOQ” indicates the concentration was below the analytical limit of quantification.

[0423] Whole menstrual blood contained the highest levels of Fe, Cu, and Zn among all sample types, consistent with the cellular and haemoglobin -rich nature of the material. Fe levels in whole menstrual blood averaged 3.37 x 10e5 pg / L ± 1.24 x 10e5 pg / L (mean ± SD) with substantial variability between biological replicates. Zn and Cu concentrations were comparable to those reported for reference whole peripheral blood. In contrast, Al levels (124 ± 38 pg / L) were markedly higher than typical whole peripheral blood reference values (0-15 pg / L). Concentrations of other toxic elements, including As, Cd, and Pb, remained relatively low and within established reference ranges for whole blood.

[0424] Freshly prepared supernatants showed substantially reduced Fe and Cu compared with whole menstrual blood. The ACD-A supernatant (SN-ACD) showed highest Zn (8060 pg / L) due to the Zn content of ACD-A, but Fe and Cu dropped markedly to 669 and 128 pg / L, respectively. PBS supernatant (SN-PBS) contained very low Zn (64.7 pg / L) and Fe (1,356 pg / L), with similarly reduced Cu (182.6 pg / L). Al levels in both SN-ACD and SN-PBS were higher than in whole menstrual blood, indicating a potential contribution from the diluents themselves.

[0425] Reagent controls reflected the composition of the diluents: ACD-A alone contained substantial Zn (6521 pg / L) but low levels of other metals, while PBS alone showed minimal trace metal content (Zn = 52.78 pg / L; most others at or below LOQ). Both diluents contained measurable Al. The empty tube control contained only trace metals, confirming negligible background from the collection materials.

[0426] Overall, processing to supernatants removed the majority of Fe and Cu associated with the cellular fraction, while Zn levels were strongly influenced by the presence of Zn-containing anticoagulants in dilution media. Toxic metal levels remained low across all conditions. Al detected in diluted MB samples likely originated, at least in part, from the diluents. Example 5 - Detection of hormones in menstrual blood

[0427] A total of 120 menstrual blood-derived samples from two batches were sent for CLIA analysis of TSH, cortisol, estradiol (E2), FSH, and LH.

[0428] Batch 1 contained 40 samples from nine biological replicates across ten menstrual cycles, including freshly processed neat samples, samples diluted in PBS or ACD, and samples stored overnight at 4 °C neat or in ACD. Original aliquots were 200-300 pL; this limited volume prevented complete measurement of all analytes for every sample. With the exception of TSH which had been analysed the first, many hormone measurements could not be obtained from all samples without further dilution. Processing neat samples proved challenging due to high viscosity, which contributed to greater variability.

[0429] Mean hormone concentrations from batch 1 are summarised in Table 18. Given the incomplete dataset and the need of additional dilution of some samples, these results should be interpreted with caution.

[0430] Table 18. Mean concentrations (mean ± SD) of TSH, FSH, LH, E2 and cortisol in MB supernatant prepared under different processing conditions. N / A: No available data

[0431] Following the Batch 1 analysis, the standard processing protocol was revised to increase aliquot volume from 200 pL to 500 pL and to discontinue neat sample processing in view of the high viscosity of said samples.

[0432] A total of 80 samples were included in batch 2, derived from 11 biological replicates. Samples were freshly processed in either ACD or PBS, stored at room temperature for 48 hours, or at 4 °C for 48 hours or 120 hours. Most conditions provided aliquots in duplicate to assess intrasample variation. All samples were analysed undiluted. Two samples had short volume, and therefore readings were unavailable and excluded from analysis. Only freshly processed samples were compared with reference values from venous serum (Table 19), excluding visually haemolysed samples. All measured analytes fell within the reported reference ranges, with variation across biological replicates. Low FSH and LH were consistent with the menstrual phase at the time of sampling. Clear differences were observed between ACD- and PBS-processed samples for several analytes.

[0433] Table 19. Hormone concentrations (mean ± SD) in PBS- and ACD-processed freshly collected samples compared with reference venous serum values. Visually haemolysed samples were excluded.

[0434] Concentrations of each hormone in PBS- and ACD-diluted samples (average of duplicates for each dot) were compared with patient characteristics, as shown in Figure 18 and Figure 19 respectively.

[0435] TSH, LH, and FSH did not show any apparent correlation with the assessed parameters. E2 showed a positive correlation with BMI, and cortisol showed a positive correlation with age. Interestingly, these relationships were more pronounced in the PBS-diluted group. For PBS samples, E2 and BMI displayed a very strong positive association (Pearson r ~ 0.93, p ~ 0.0076; Spearman r = 1.0, p < 0.001), which was statistically significant. Cortisol and age also showed a strong positive trend (Pearson r ~ 0.73, p ~ 0. 10; Spearman r ~ 0.81, p ~ 0.0499), with the Spearman result reaching borderline statistical significance. In the ACD-diluted group, the E2- BMI relationship was weaker (Pearson r ~ 0.66, p ~ 0.106; Spearman r ~ 0.50, p ~ 0.253) and not statistically significant. Similarly, cortisol-age correlations in ACD samples were weak (Pearson r ~ 0.39, p ~ 0.39; Spearman r ~ 0.44, p ~ 0.33) and not significant.

[0436] Previous studies in serum have reported no correlation between E2 levels and BMI (Nolan BJ et al., 2020; Elliott MJ et al., 2020).

[0437] The positive association between cortisol and age observed in this study is consistent with previous reports (MI Stamou et al., 2023; SD Moffat et al., 2020).

[0438] Next, the inventors examined the intrasample variability (%) across all storage conditions and is presented in Table 20. E2 consistently showed the largest variability, never falling below 10% in any condition. Across most analytes, PBS-processed samples exhibited lower variability than those processed in ACD, particularly after storage.

[0439] Excluding E2, samples stored for 48 h at 4 °C in neat form and then diluted in PBS demonstrated very low variability, around 2.0-2.9% for TSH, LH, and cortisol. After 120 h at 4 °C, PBS variability remained low, generally around 2.0-5.6%. ACD-processed samples showed low variability when freshly processed (1.8-16.0%), but variability spiked after prolonged storage, most notably for FSH (42.9% at 48 h RT, 37.2% at 120 h 4 °C).

[0440] Storage at 4 °C consistently resulted in lower variability compared to room temperature for both ACD and neat (PBS-diluted) samples, particularly evident for LH (ACD: 9. 1% at 48 h RT vs 19.2% at 48 h 4 °C; PBS: 3.2% at 48 h RT vs 2.0% at 48 h 4 °C).

[0441] Table 20. Duplicate intrasample variability (%SE) for each analyte across storage conditions. Values calculated as (SD / mean) x 100.

[0442] Figures 20 and 21 illustrate the change in hormone concentrations across storage conditions for both ACD- and PBS-processed samples. In Figure 20 (48 h storage), hormone concentrations in PBS remained relatively stable, particularly for Cortisol, LH, and TSH, with minimal deviation from fresh values after 48 h at 4 °C. In contrast, ACD-processed samples showed greater shifts in concentration after storage, especially at room temperature, where marked changes were observed for FSH and E2.

[0443] Figure 21 (120 h storage at 4 °C) highlights the cumulative effect of prolonged storage. PBS- processed samples maintained concentrations close to fresh levels for most analytes, with only modest declines for E2 and FSH. In ACD-processed samples, however, extended storage led to more pronounced decreases or variability in several hormones, particularly E2 and FSH, confirming their reduced stability in this medium over time. Across both figures, refrigerated storage consistently preserved hormone concentrations more effectively than room temperature storage, and PBS processing was generally more stable than ACD. A total of 96 aliquots derived from 14 biological replicates (including two paired menstrual blood-peripheral blood (MB-PB) samples) were then shipped to Lighthouse fortesting. Samples were packed on dry ice and shipped via one-day courier delivery to Manchester for NAC treatment experiments and stability analysis. FSH, LH, TSH, progesterone, E2, AMH, were tested for haemolysis, lipera and Icterus.

[0444] Raw data were obtained for all but one MB sample, which showed severe hemolysis (hemolysis index 1400) and produced no reportable results. FSH, cortisol, and AMH measurements were all within expected reference ranges. Detection rates were FSH 96%, LH 71%, E2 89%, and progesterone 39%. Adjusting the dilution factor from 1:2.5 to 1:2 may improve detection rates. Progesterone levels were particularly difficult to measure, as even diluted PB samples often failed to yield a signal.

[0445] No significant icterus was observed (only two cases with low reading (5) in one serum sample). One participant had a lipemic index of 500, which was excluded as an outlier. The average lipemic index across samples was 34. NAC treatment approximately doubled the lipemic index, and 1% NAC led to nearly a fivefold increase (Figure 22). Significant increases were observed starting at 0.2% NAC and again at 0.5% NAC (p=0.0016, non-parametric), with values occasionally reaching several hundred. This effect was not seen in PB samples treated with NAC, which remained below 30 and showed a non-significant decreasing trend (p=0.07).

[0446] Duplicate variability was generally lower than that observed in previous testing conducted by Salient Bio (Table 21). Variability for untreated MB samples was approximately 5% across all hormones except E2, which showed instability similar to that seen in PB, suggesting the issue is not related to MB processing. NAC treatment at 5% further reduced variability across all analytes with no significant difference (P 0.32-0.9)

[0447] Table 21 - Variability in analyte levels between duplicate samples

[0448] Example 6 - Assessment ofNAC Effects on Hormone Measurements

[0449] For each donor and condition, duplicate measurements were summarized with the geometric mean. Paired comparisons were performed on the log scale which corresponds to a ratio of NAC / PBS (the geometric mean ratio, GMR). Results were reported with p-values from paired analyses.

[0450] Starting with peripheral blood and focusing on 0.5% NAC (n=3), no significant change was observed in most analytes, apart from TSH which was significantly lower in the NAC-treated samples compared with PBS (paired analysis p=0.04). Across the limited doses available there is a visual tendency toward lower TSH with increasing NAC (Figure 23).

[0451] In menstrual blood, paired analyses including NAC concentration: 0.1% (n=3), 0.2% (n=7), 0.5% (n=8), and 1% (n=3) showed no consistent change in LH, FSH, or TSH at any concentration, and the PB-like TSH reduction was not observed (Figure 24). In contrast, cortisol increased with NAC in a manner consistent with dose response: 0.2% produced a significant rise (p=0.02) and 0.5% was borderline / significant (p=0.05). Estradiol (E2) also increased at 0.5% (p=0.03). AMH showed a fluctuation: higher than PBS at 0.2% (p=0.02) but the effect did not persist at 0.5%. Findings at 1% NAC were inconclusive because of small n and heterogeneous donor responses.

[0452] These NAC-associated increases in menstrual blood do not by themselves imply a deleterious biological effect. NAC can liquefy mucus; some hormones appear partially retained within cervical / vaginal mucus or diluted in menstrual blood. Consistent with this, in one participant the baseline menstrual blood concentrations of E2 and other hormones were approximately 20-40% lower than matched peripheral blood. After NAC treatment, menstrual blood E2 rose modestly yet remained below peripheral blood, whereas cortisol rose above peripheral blood; TSH, LH, and FSH remained largely unchanged (Figure 25).

[0453] Because menstrual blood may be diluted by endometrial fluid and mucus, and because analytes can be sequestered within mucus, menstrual blood values are not directly comparable to peripheral without adjustment. Additional paired menstrual blood-peripheral blood samples (serum) plus NAC treatment are required to establish menstrual blood-specific reference intervals or applying a validated normalization factor will be necessary before drawing clinical conclusions from menstrual blood alone. Peripheral blood plasma and serum readings were compared in two biological replicates (Figure 26). Serum was collected in a no-additive tube, and plasma was also tested in diluted form (1:2.5 with PBS). EDTA plasma showed, on average, 20% lower hormone concentrations than serum, with cortisol showing the largest difference (over 30%). These findings are consistent with a plasma dilution effect reported in the literature. Because serum is the standard reference matrices for these hormones, paired serum collection will be important in future studies, and the use of serum separator tubes could be considered to ensure complete coagulation and consistent results.

[0454] Comparison of NAC treated menstrual blood, menstrual blood and peripheral blood to reference range were shown in Figure 27.

[0455] Control menstrual blood (no NAC treatment, n=12), ALL NAC concentrations (0.1%, 0.2%, 0.5%, and 1%NAC, n=19), and PB samples (n=4). NAC treatments included various concentrations (0.1-1%) at different time points (5-30 minutes), with 0.2% NAC 5-minute treatment being most common (n=5).

[0456] PB samples achieved 100% normal rates for TSH, FSH, LH, Oestradiol, and AMH, with 75% normal for Cortisol, confirming the reliability of the results.

[0457] All NAC treatment improved the percentage of samples within normal reference ranges for 4 of 6 hormones (66.7%). FSH showed the largest accuracy gain (58.3% to 82.4%, +24.0 percentage points), followed by Cortisol (45.5% to 57.9%, +12.4 percentage points) and TSH (50.0% to 57.9%, +7.9 percentage points). AMH demonstrated slight improvement (+3.9 percentage points), while LH remained at 100% normal and Oestradiol decreased from 100% to 88.2% normal.

[0458] All NAC treatment significantly improved analytical precision (coefficient of variation, CV) for 4 of 6 hormones (66.7%). The most substantial precision improvements were observed for TSH (113.5% to 87.6% CV, -25.9 percentage points), FSH (64.2% to 48.9% CV, -15.3 percentage points), and Cortisol (77.4% to 57.1% CV, -20.3 percentage points). AMH showed modest improvement (-2.9 percentage points), while LH and Oestradiol demonstrated reduced precision (+4.2 and +47.5 percentage points, respectively).

[0459] Example 7 - Metabolomic analysis of menstrual blood samples

[0460] A total of 13 samples from 5 biological replicates were sent for metabolomic analysis at KCL. These included menstrual blood diluted in either PBS or ACD, as well as matched PB EDTA plasma samples.

[0461] A total of 31 metabolites are detected and listed in Table 22.

[0462] Principal Component Analysis (PCA)

[0463] PCA of all samples revealed a distinct profde for two ACD samples, which were subsequently excluded to allow clearer visualization of group differences (Figure 28a). After exclusion, PB samples clustered closely, whereas menstrual blood samples appeared more scattered (Figure 28b). Notably, two consecutive menstrual blood samples from the same participant (P29) clustered tightly, as did her matched PB samples. In contrast, samples P31.1 and P6.2 deviated strongly from the main clusters, likely due to severe hemolysis. The remaining menstrual blood samples showed little to no evidence of hemolysis.

[0464] All observations from the PCA were further examined at the single -metabolite level, including comparisons of ACD vs. PBS, peripheral blood vs. menstrual blood, the impact of hemolysis, and variability across consecutive cycles.

[0465] ACD vs. PBS Dilution of menstrual blood

[0466] Two menstrual blood samples diluted in PBS and ACD were compared across metabolites (Figure 29). The most significant changes were observed in citrate and glucose, with ACD- diluted samples showing more than five-fold higher levels compared to PBS. However, the glucose effect appeared participant-specific, being evident only in Pl 2. In addition, fatty acid levels were reduced in ACD-diluted samples (Figure 30). No statistical testing was performed due to the limited sample size (n = 2).

[0467] PB vs. menstrual blood Comparison

[0468] PCA demonstrated distinct clustering between peripheral blood and menstrual blood samples. Single-metabolite analyses from four matched pairs confirmed these differences, with overall trends being consistent across pairs. Sample P6.2 menstrual blood exhibited unusually high levels of several metabolites, likely due to heavy hemolysis, and was therefore excluded from downstream statistical analyses (Figure 31). Paired t-tests revealed that menstrual blood contained significantly lower glucose (P = 0.03) and significantly higher lactate, glutamate, and phenylalanine compared to PB (Figure 32).

[0469] Effect of hemolysis

[0470] Comparisons of haemolyzed menstrual blood samples (P6.2 and P31.1), which deviated from the menstrual blood PBS cluster in PCA, against non -haemolyzed menstrual blood (PBS- diluted) at the single -metabolite level revealed broadly increased concentrations of many metabolites, typically showing 1-2 fold changes (Figure 33). No statistical testing was performed due to the limited sample size (n = 2).

[0471] Cross patient and consecutive menstrual cycle comparison

[0472] Comparison of menstrual blood and PB samples collected from the same participant across consecutive menstrual cycles indicated that menstrual blood samples were more reproducible than PB samples (Figure 34). Menstrual blood showed a mean CV of 22. 1% and a median CV of 11.8%. Several metabolites demonstrated particularly high stability in menstrual blood, with alanine, threonine, glycine, and tryptophan each showing CVs below 5%, and a total of 12 metabolites with CVs under 10%. In contrast, PB samples showed no metabolites with CVs below 5% and only seven metabolites with CVs under 10%. Both peripheral blood and menstrual blood displayed considerable variability in hypoxanthine, with CVs exceeding 100%. Citrate was unstable in peripheral blood but more stable in menstrual blood, whereas pyruvate, glucose, succinate, and acetate were less stable in menstrual blood compared to peripheral blood.

[0473] Comparison of metabolite profdes across different patients revealed distinct patterns of interindividual variability between menstrual blood and peripheral blood samples (Figure 35). Peripheral blood samples demonstrated superior consistency across patients compared to menstrual blood samples. Peripheral blood showed a mean CV of 23.7% and a median CV of 13.3% across different individuals, while menstrual blood samples exhibited substantially higher inter-patient variability with a mean CV of 55.8% and a median CV of 46.2%.

[0474] The contrast between low intra-individual variability and high inter-individual variability in menstrual blood samples suggests that menstrual blood metabolites are highly individualized but remain remarkably consistent within the same woman across consecutive menstrual cycles. This pattern indicates that each woman maintains her own distinct metabolic signature in menstrual blood that is stable over time. In contrast, PB samples showed similar variability patterns both within individuals and across the population (23.7% vs -15.8% mean CV), suggesting more standardised systemic metabolic regulation that is less influenced by individual biological variation and maintains relative consistency both temporally and across different individuals. These results need further validation.

Claims

Claims1. A method for determining the presence or absence of infertility in a subject, comprising detecting in a non-invasive sample one or more marker(s) from at least two groups selected from: a) a reproductive hormone, b) a bacterium, c) cortisol, and d) an adhesion, inflammation or receptivity marker.

2. A method for determining the likelihood of success of in vitro fertilisation in a subject, comprising detecting in a non-invasive sample one or more marker(s) from at least two groups selected from: a) a reproductive hormone, b) a bacterium, c) cortisol, and d) an adhesion, inflammation or receptivity marker.

3. A method for determining the presence or absence of infertility in a subject or for determining the likelihood of success of in vitro fertilisation in a subject, comprising detecting in a non-invasive sample two or more marker(s) from at least one groups selected from: a) a reproductive hormone, b) a bacterium, and c) an adhesion, inflammation or receptivity marker.

4. The method of claim 3, further comprising a step of detecting cortisol.

5. The method of any preceding claim wherein the method comprises quantifying (a) the reproductive hormone, (b) the bacterium, (c) the cortisol and / or (d) the adhesion, inflammation or receptivity marker.

6. The method of claim 5 wherein the method comprises quantifying (a) the reproductive hormone, (b) the bacterium, (c) the cortisol and (d) the adhesion, inflammation or receptivity marker.

7. The method of claim 5 or claim 6 further comprising a step of comparing the concentration of the marker(s) to a reference.

8. The method of any preceding claim wherein (a) the reproductive hormone, and / or (b) the bacterium, and / or (c) the cortisol and / or (d) the adhesion, inflammation or receptivity marker are selected from the markers shown in Table 1.

9. The method of claim 8 wherein all of the markers in Table 1 are analysed.

10. The method of any preceding claim, wherein the non-invasive sample is menstrual blood.

11. The method of any preceding claim wherein (a) the reproductive hormone, (b) the bacterium, and / or (c) the adhesion, inflammation or receptivity marker is measured in menstrual blood.

12. The method of any preceding claim, wherein the cortisol is measured in menstrual blood.

13. The method of any one of claims 5 to 12, wherein the marker is quantified using an ELISA assay and / or an immunoassay and / or a mass spectrometry assay.

14. The method of any preceding claim further comprising a step of detecting or quantifying a heavy metal, optionally wherein the step of quantifying comprises comparing the concentration of the heavy metal to a reference.

15. The method of claim 14 wherein the heavy metal is selected from the group consisting of arsenic, lead, cadmium, and mercury, optionally wherein the method comprises a step of detecting or quantifying all of the heavy metals from the group consisting of arsenic, lead, cadmium, and / or mercury.

16. The method of any one of claims 14 to 15, wherein the heavy metal is detected using Inductively Coupled Plasma Mass Spectrometry.

17. The method of any preceding claim wherein the reproductive hormone is selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b and / or oestrogen, optionally wherein the method comprises a step of quantifying all of the reproductive hormones selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b and oestrogen.

18. The method of any preceding claim wherein the bacterium is from a species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., as well as the presence of pathogenic bacteria like Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and / or Ureaplasma spp. preferably wherein the bacterium is of Lactobacillus spp. and / or Bifidobacterium spp.

19. The method of claim 18, comprising a step determining the relative abundance of bacteria from two or more species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and / or Ureaplasma spp.

20. The method of any one of claims 18 to 19 comprising a step of determining the relative abundance of one or more bacteria from all of the species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and Ureaplasma spp.

21. The method of any one of claims 18 to 20 wherein method comprises a step of detecting a 16s rRNA sequence having at least 95%, 96%, 97%, 98%, 99%, 99.5% or 99.9% identity to any one of SEQ ID NOs 1 to 31.

22. The method of any preceding claim wherein the adhesion, inflammation or receptivity marker is selected from the group consisting of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a, interleukin-6, interleukin-8, and / or interleukin-33, optionally wherein the method comprises a step of quantifying all of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a, interleukin-6, interleukin-8, and interleukin-33.

23. The method of any preceding claim, wherein:(a) the markers are quantified in the menstrual serum or the menstrual plasma; and / or(b) the method is performed ex vivo or in vitro.

24. The method of any preceding claim, wherein the marker(s) are analysed in a single non- invasive sample obtained from the patient.

25. A kit comprising two or more antibodies selected from the group consisting of: a) an antibody that can detect one or more reproductive hormones selected from the group consisting of AMH, TSH, FSH, LH, progesterone, estradiol 17-b and / or oestrogen; and / or b) an antibody that can detect one or more bacteria from a species selected from the group consisting of Lactobacillus spp., Klebsiella spp., Prevotella spp., Bifidobacterium spp., Gardnerella spp. and Dialister spp., as well as the presence of pathogenic bacteria like Chlamydia trachomatis, Streptococcus spp., Staphylococcus spp., Mycobacterium tuberculosis and / or Ureaplasma spp:, and / orc) an antibody that can detect cortisol; and / or d) an antibody that can detect one or more markers selected from the group consisting of hyaluronan, heparan sulfate, CD44, b3 integrin, tumour necrosis factor-a, interleukin-6, interleukin-8, and / or interleukin-33, wherein the kit comprises antibodies from at least two of groups a, b, c and d.

26. The kit of claim 25, wherein the antibodies are monoclonal antibodies.

27. Use of the kit of claim 25 or claim 26 in a method of determining the presence or absence of infertility in a subject or for determining the likelihood of success of in vitro fertilisation in a subject.

28. A method for preparing a non-invasive sample suitable for use in a method according to any one of claims 1-24, the method comprising a) centrifuging a non-invasive sample obtained from a patient; b) separating the supernatant and the pellet; and c) centrifuging the pellet from step (b).

29. The method according to claim 28, wherein the non-invasive sample is menstrual blood.

30. The method according to claim 28 or claim 29, wherein the centrifugation of step (a) uses a centrifugal force of 500 - 1500 g.

31. The method according to claim 30, wherein the centrifugation of step (a) uses a centrifugal force of about 1000 g.

32. The method according to any one of claims 28 to 31, wherein the centrifugation in step (a) is conducted for 2-10 minutes.

33. The method according to claim 32, wherein the centrifugation in step (a) is conducted for 5 minutes.

34. The method according to any one of claims 28 to 33, wherein the centrifugation step in step (c) uses a centrifugal force of 8,000 - 20,000g.

35. The method according to claim 34, wherein the centrifugation step in step (c) uses a centrifugal force of about 13,000 g.

36. The method according to any one of claims 28 to 35, wherein the centrifugation step in step (c) is conducted for 30 seconds to 5 minutes.

37. The method according to claim 36, wherein the centrifugation step in step (c) is conducted for about 2 minutes.

38. The method according to any one of claims 28 to 37, wherein the non-invasive sample is menstrual blood, urine or hair.

39. The method according to claim 38, wherein the non-invasive sample is menstrual blood.

40. The method of any one of claims 28 to 39, further comprising a step of diluting the non- invasive sample prior to centrifugation step (a).

41. The method of claim 40, wherein the non-invasive sample is diluted with phosphate- buffered saline or Anticoagulant Citrate Dextrose Solution, Solution A (ACD-A).

42. A method for determining the presence or absence of infertility in a subject, comprising a step of preparing a non-invasive sample according to the method of any one of claims 28 to 41 and detecting one or more marker(s) according to the method of claim 1 or any one of claims 5 to 24.

43. A method for determining the likelihood of success of in vitro fertilisation in a subject, comprising a step of preparing a non-invasive sample according to the method of any one of claims 28 to 42 and detecting one or more marker(s) according to the method of claim 2 or any one of claims 5 to 24.

44. A method for determining the presence or absence of infertility in a subject or for determining the likelihood of success of in vitro fertilisation in a subject, comprising a step of preparing a non-invasive sample according to the method of any one of claims 28 to 41 and detecting two or more marker(s) according to the method of any one of claims 3 to 24.

45. The method according to any one of claims 42 to 44, wherein the reproductive hormone; cortisol; the adhesion, inflammation or receptivity marker; and / or the heavy metal are detected and / or quantified in the supernatant obtained in step (b) as defined in any one of claims 28 to 41.

46. The method according to any one of claims 42 to 45, wherein the pellet obtained in step (c) as defined in any one of claims 28 to 41 is used to quantify the bacterium.

47. The method according to any of claims 42 to 46, wherein the non-invasive sample is menstrual blood.

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