Method for detecting a pathology

Abstract

This application relates to an in vitro method for detecting a pathology from a biological sample taken from a subject. In one particular aspect, this application relates to a method for detecting the presence of a pathology or predicting the risk of a pathological condition from a biological sample taken from a subject. In another particular aspect, the present invention relates to detecting the probability of premature birth from a biological sample taken from a pregnant woman. Figure for the abstract: Fig. 3

Description

Title of the invention: Method for detecting a pathology

[0001] The present application relates to an in vitro method for detecting a pathology from a biological sample taken from a subject. According to a particular aspect, the present application relates to a method for detecting the presence of a pathology or for predicting a risk of a pathological condition, from a biological sample taken from a subject. According to a particular aspect, the present invention relates to the detection of the probability of premature delivery from a biological sample taken from a pregnant woman. The present method is therefore in the field of in vitro diagnosis and personalized medicine.

[0002] Preterm birth is a major cause of morbidity and mortality in newborns. A proportion of spontaneous preterm births appear to result from an inflammatory reaction following an infection of the genital tract. However, a large proportion of preterm births remain without an identified cause. Despite various studies relating to the vaginal microbiota and the occurrence of preterm birth, there is currently a need for a reliable clinical method for predicting the occurrence of preterm birth. Currently, clinicians unfortunately do not have any reliable tool to predict the risk of preterm birth.

[0003] Patent EP 3 161 167 describes a method for assessing a risk of premature delivery based on the detection, in a vaginal or cervical sample obtained by swabbing from a pregnant woman, of the quantity of the following bacteria: Vimonas micra, Ureaplasma urealyticum or Ureaplasma parvum, Atopobium vaginae, Peptoniphilus lacrimalis, Megasphaera cerevisiae and Parvibacter caecicola, compared to a reference level. The quantification of the bacteria is carried out by amplification of a small region of the 16S rDNA by quantitative polymerase chain reaction (PCR) (qPCR).

[0004] Patent EP 2 972 308 B9 describes a serum or plasma peptide biomarker, produced by human cells, and not by the microbiota, the detection of which is used in a method for assessing a risk of premature delivery.

[0005] International application WO 2020 / 227053 describes a method for determining the risk of premature birth comprising determining the abundance of Sac-charibacteria TM7-H1 and optionally BVAB1, Sneathia amnii and Prevotella in a vaginal sample from a pregnant woman, from the nucleotide sequence of a small portion of the 16S rDNA of the microorganisms.

[0006] The complexity of microbiotas makes it difficult to determine specific and predictive microbial signatures characteristic of a pathological state. This situation is made all the more complex by the very strong inter-indigenous variations individual. To date, several microbiota analysis techniques exist. However, current approaches do not allow for precise characterization of microbiotas.

[0007] Genes expressing the small subunit of rRNA, i.e. genes called "16S rDNA" for prokaryotic microorganisms, such as bacteria and archaea, and "18S rDNA" for eukaryotes, including yeasts, are used to enable the description of the structure of the microbiota (Chakoory et al., 2022).

[0008] The publications of Park et al in 2021 and 2022 describe a method for predicting the probability of premature birth from the detection of a limited number of microorganisms present in the vaginal microbiome.

[0009] In the 2021 publication by Park et al., the method comprises the simultaneous quantification by qPCR amplification of specific small fragments of DNA from each of the following 10 microorganisms: Lactobacillus crispatus, Lactobacillus iners, Weissella koreensis, Bacteroides fragilis, Prevotella bivia, Prevotell amnii, Prevotella salivae, Ureaplasma urealyticum, Ureaplasma parvum, Gardnerella vaginalis.

[0010] In the 2022 publication by Park et al., based on a sequencing approach of a small V3-V4 region of the 16S rDNA gene and literature studies, predictions are made based on a selection of 10 bacteria (Lactobacillus crispatus, Lactobacillus fomicalis, Lactobacillus gasseri, Lactobacillus iners, Lactobacillus jensenii, Gardnerella vaginalis, Ureaplasma parvum, Atropobium vaginae, Prevotella timonensis and Peptoniphilus grossensis) as well as 7 additional bacteria based on previous work by other authors (Bifidobacterium breve, Dialister proprionicifaciens, Lactobacillus paracasei, Mobiluncus curtisii, Prevotella disiens, Staphylococcus aureus, Streptococcus anginosus).

[0011] A method comprising the amplification of fragments of a size less than 300 base pairs of the gene expressing the 16S rRNA has several limitations: the biases likely to be generated during the amplification step carried out by PCR can alter the vision of the real diversity of the microbiota, the short length of the fragment only provides a low taxonomic resolution.

[0012] A method comprising a direct metagenomic sequencing step (in English "shotgun") followed by an assembly step to generate complete genomes (in English: Metagenome Assembled Genomes or MAGs) and a step of affiliation of the MAGs leads to an identification restricted to the dominant species.

[0013] Another method comprises a direct metagenomic sequencing step ("shotgun") followed by an affiliation of the raw unassembled reads of a size less than 300 base pairs of a part of the gene expressing the 16S rRNA. The affiliation of these small sequences leads to a low resolution of microbial identification and to an overestimation of the diversity, in particular by false positive detection.

[0014] Therefore, there is a need to develop a method to predict the probability of premature delivery more reliably. Monitoring pregnant women would make it possible to identify women at risk and anticipate the care of newborns.

[0015] The inventors have now developed a method for detecting a pathology from a biological sample taken from a subject, said detection method is based on the analysis of the microbiota present in said biological sample. More particularly, the inventors have developed a method for predicting the occurrence of premature delivery whose reliability is greater than 69% and can in particular achieve a reliability of 85%. Such a degree of reliability is unequaled among the methods for diagnosing premature delivery to date.

[0016] A method according to the invention comprises a step of reconstructing at least a part, preferably the major part, of the sequence of the gene expressing the 16S rRNA and / or the sequence of the gene expressing the 18S rRNA of the microorganisms present in the biological sample, followed by a step of taxonomic classification of said reconstructed genes. The analysis of the classification of the reconstructed genes expressing the 16S rRNA and / or the 18S rRNA of said microorganisms allows the precise identification, at the genus and possibly the species level, and the measurements of relative abundances of a plurality of microorganisms present in the biological sample.

[0017] A method according to the invention uses all of the metagenomic data of the microbiota which then allows the complete reconstruction of the 16S and / or 18S rDNAs and a precise affiliation of the microorganisms of the microbial community at the genus or species level, or even the identification of new microorganisms.

[0018] A method according to the invention makes it possible to transmit simple predictive information to a clinician. A method according to the invention therefore has the advantages of high accuracy in prediction, combined with high speed and high throughput. According to a particular aspect, a method according to the invention also has the advantage of not absolutely requiring an obstetric examination at the time the method is carried out.

[0019] According to a particular aspect, a method according to the invention has the advantage, from a simple sample of vaginal microbiota during pregnancy, in the 1st trimester and / or in the 2nd trimester and / or 3rd trimester, and its direct metagenomic sequencing, of determining with high certainty the probability of a premature birth or a full-term birth.

[0020] This approach can be used in the context of personalized medicine to assess the relevance of more precise clinical monitoring and / or the use of therapeutic treatment, monitoring and treatment methods are well known.

[0021] Microorganisms could also be identified as being able to play the role of probiotics or for the development of new treatments. Description of the invention

[0022] The subject of the present invention is an in vitro method for determining a pathology, from a biological sample taken from a patient, said method comprising the following steps: (a) isolation of nucleic acid from a plurality of microorganisms present in said biological sample, b) determination by sequencing of the nucleotide sequence of at least one nucleic acid chosen from: a gene fragment expressing 16S ribosomal RNA (rRNA), a gene fragment expressing 18S rRNA, a 16S rRNA fragment and / or an 18S rRNA fragment, of a plurality of microorganisms, to generate a plurality of nucleotide sequences, c) organizing a plurality of nucleotide sequences determined during step b) to reconstruct the nucleotide sequence of at least one fragment of the genes expressing 16S rRNA, of the genes expressing 18S rRNA, of the 16S rRNA and / or of the 18S rRNA of a plurality of microorganisms, d) from the results of step c), determining the identity, taxonomic classification, and relative abundance of a plurality of microorganisms present in said sample, and e) from the characteristics determined during step d), determination, by a previously trained classification model, of a pathology.

[0023] The present invention relates more particularly to an in vitro method for determining a pathology, from a biological sample taken from a subject, said method comprising the following steps: (a) isolation of nucleic acid from a plurality of microorganisms present in said biological sample, b) determination by sequencing of the nucleotide sequence of at least one gene fragment expressing 16S ribosomal RNA (rRNA) and / or one gene fragment expressing 18S rRNA of a plurality of microorganisms to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined during step b) to reconstruct the nucleotide sequence of at least one fragment of the genes expressing 16S rRNA and / or of the genes expressing 18S rRNA of a plurality of microorganisms, d) from the results of step c), determining the identity, taxonomic classification, and relative abundance of a plurality of microorganisms present in said sample, and e) from the characteristics determined during step d), determination, by a previously trained classification model, of a pathology.

[0024] The term "nucleic acid" means the nucleic acid molecules present in the biological sample, in particular deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), in particular ribosomal RNA (rRNA) and even more particularly 16S rRNA and 18S rRNA. The term "gene expressing 16S rRNA" means the DNA nucleotide sequence comprising the nucleotide sequence encoding 16S rRNA. A gene expressing 16S rRNA is also called "16S rDNA". The term "gene expressing 18S rRNA" means the DNA nucleotide sequence comprising the DNA nucleotide sequence encoding 18S rRNA. A gene expressing 18S rRNA is also called "18S rDNA". According to a particular aspect, a method according to the invention comprises determining by sequencing the nucleotide sequence of at least one gene fragment expressing 16S ribosomal RNA (rRNA) and / or a gene fragment expressing 18S rRNA.According to another particular aspect, a method according to the invention comprises the determination by sequencing of the nucleotide sequence of at least one 16S rRNA fragment and / or one 18S rRNA fragment. The isolation of the nucleic acid molecules from the biological sample is carried out according to any technique well known to those skilled in the art.

[0025] “Pathology” means a disease, a risk of an adverse event, a biological imbalance or discomfort. “Microbiota” means a community of microorganisms present in a given sample. “Subject” means an individual, in particular a human being or an animal, preferably an mammal.

[0026] According to a particular aspect of the method of the invention, the nucleic acid of at least 30% of the microbiota present in the biological sample is analyzed in a method according to the invention. More particularly, the nucleic acid of at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99% or 100% of the microbiota present in the biological sample is analyzed.

[0027] According to another particular aspect, the nucleotide sequence of the gene expressing the 16S rRNA or of the gene expressing the 18S rRNA of at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99% or 100% of the microbiota present in the biological sample is analyzed.

[0028] "Sequencing" means any known method for determining the nucleotide sequence of a nucleic acid. Among these methods, direct metagenomic sequencing, known as "shotgun", is preferred. The use of sequencing data from gene capture approaches by hybridization is also preferred.

[0029] The nucleotide sequences of the genes expressing the 16S rRNA of microorganisms and genes expressing 18S rRNA from microorganisms are at least partially known and are listed in specialized databases that are publicly accessible. Among these databases, the SILVA database is accessible on the website "arb-silva.de". Another example of databases is the “Greengenes”. The person skilled in the art can easily determine whether a given nucleotide sequence is derived from a known or unknown microorganism, or from the human host.

[0030] By "identity determination" is meant the determination of the genus of a microorganism present in the sample and optionally the determination of the species of a microorganism present in the sample. The determination of the identity of a microorganism is carried out from the nucleotide sequence of the 16S rDNA and / or the 18S rDNA.

[0031] By "determination of the taxonomic classification" is meant the organization of a plurality of microorganisms, based on knowledge of the identity of said microorganisms, into hierarchical categories, in other words into taxonomic ranks, these categories consisting of belonging to the domain of life (least precise rank) to the definition of the species (most precise rank). The taxonomic classification is carried out by comparing the 16S rDNA sequences and / or the 18S rDNA sequences determined with, respectively, 16S rDNA sequences and / or 18S rDNA sequences contained in databases. Among the databases that can be used, the SILVA database may be mentioned in particular.

[0032] By "determination of the relative abundance" is meant the determination for each of the microorganisms considered for the method according to the invention, of the abundance of the microorganism relative to the total abundance of microorganisms considered for the method according to the invention.

[0033] The present invention more particularly relates to an in vitro method for determining a probability of premature delivery of a pregnant woman, from a biological sample taken from said pregnant woman, said method comprising the following steps: (a) isolation of nucleic acid from a plurality of microorganisms present in said biological sample, b) determination by sequencing of the nucleotide sequence of at least one gene fragment expressing 16S rRNA and / or one gene fragment expressing 18S rRNA of a plurality of microorganisms to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined during step b) to reconstruct the nucleotide sequence of at least one fragment of the genes expressing 16S rRNA and / or a fragment of the genes expressing 18S rRNA of a plurality of microorganisms, d) from the results of step c), determination of the identity of a plurality of microorganisms present in the sample, of the taxonomic classification, of the and of the relative abundance of a plurality of microorganisms present in said sample, and determination of at least one clinical data characteristic of the pregnancy, and (e) based on the characteristics determined in step (d), determination, by a previously trained classification model, of the probability of premature delivery.

[0034] “Premature delivery” means delivery occurring before the 37th week of gestation.

[0035] More particularly, a method according to the invention comprises determining the nucleotide sequence of at least 20% of the 16S rDNA and / or the 18S rDNA of a plurality of microorganisms derived from the biological sample. Even more particularly, a method according to the invention comprises determining the nucleotide sequence of at least 20% of the 16S rDNA or the 16S rRNA of a plurality of microorganisms derived from the biological sample and the nucleotide sequence of at least 20% of the 18S rDNA or the 18S rRNA of a plurality of eukaryotic cells derived from the biological sample.

[0036] By "a fragment of at least 20%" is meant a fragment of at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, at least 99% or 100% of the nucleotide sequence in question.

[0037] More particularly, in a method according to the present invention, during step c) of reconstructing at least one nucleotide sequence, at least 70% of the length of the gene expressing the 16S rRNA and / or at least 70% of the length of the 16S rRNA is reconstructed. The length of a 16S rDNA gene being approximately 1500 base pairs on average, a nucleotide sequence of at least 70% of the length of the gene comprises approximately 1050 base pairs, on average.

[0038] More particularly, in a method according to the present invention, during step d) of determining at least one clinical datum characteristic of the pregnancy, said clinical datum is chosen from: - the gestation period, - the age of the pregnant woman, - the ethnicity of the pregnant woman, and - a combination of these clinical data. The duration of gestation can notably be expressed in number of weeks of gestation or designated by the period at which the biological sample is taken.

[0039] This period is notably chosen from: the first trimester of pregnancy, the second trimester of pregnancy, the third trimester of pregnancy.

[0040] The age of the pregnant woman, in a method according to the invention, can be defined in number of years or by her belonging to an age group. More particularly, the age of the pregnant woman can be attributed to one of the following two groups: "less than 35 years" and "equal to or greater than 35 years".

[0041] By "ethnicity" is meant a group of people who are brought together by a certain number of characteristics. In a method according to the invention, the characteristic "ethnicity" is notably chosen from the group consisting of: "African-American", "American-Indian", "Black", "White", "Caucasian", "Hispanic", "Asian", "Multi-ethnicity".

[0042] A biological sample used in a method according to the invention is chosen from: a vaginal sample, a cervical sample and any other biological sample. Preferably, in a method according to the invention, the sample is a vaginal sample. Said sample is carried out in a conventional and well-known manner by a specialist.

[0043] In a method according to the invention, step e) of determining a probability of premature delivery is carried out by means of a previously trained classification model.

[0044] By “classification model” we mean in particular a classification model comprising: - a previously trained neural network, in particular during supervised learning, in particular a deep neural network, - a machine learning algorithm and - a training dataset. The classification model may consist of a computer program. The computer program may be written in any computer language, such as for example: C, C++, JAVA, Python, etc. The said classification model therefore potentially executes a technical function, consisting here of steps of the classification process. The execution of the said program by a computer produces a digital object having technical characteristics.

[0045] By "pre-trained" is meant a process allowing the neural network to learn to associate in a weighted manner a microorganism or a group of microorganisms and clinical data of the pregnancy with a premature delivery or a full-term delivery. This process is carried out using samples for which the nature of the delivery, premature or full-term, and the clinical data of pregnancy are known.

[0046] For the implementation of a method according to the invention, step e) of determining a probability of premature delivery is carried out by means of a previously trained classification model comprising an algorithm, said algorithm being chosen from the group consisting of: a neural network (NN), a decision tree, K-nearest neighbors (KNN), a random forest (RF), a naive Bayesian classification (NF), an extreme gradient boosting (XGBoost) algorithm, a logistic regression and a support vector machine (SVM).

[0047] In one embodiment of the method according to the invention for determining a probability of premature delivery, the method further comprises a step of determining a first profile of a plurality of microorganisms, said first profile being characteristic of a high probability of premature delivery. By "high probability of premature delivery" is meant a probability greater than 50% of premature delivery, preferably a probability greater than or equal to 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or equal to 100%.

[0048] According to this particular embodiment, from the results of step d) of a method according to the inventions, a first profile of a plurality of microorganisms statistically associated with a high probability of premature delivery is determined.

[0049] A first profile of microorganisms statistically associated with a high probability of premature delivery, obtained by a method according to the invention, is characterized in particular by the presence of microorganisms of the genus: - Anaerococcus, - Peptoniphilus, - Prevotella, in particular Prevotella bivia, - Gardnerella in particular Gardnerella vaginalis, - Sneathia in particular Sneathia amnii. Indeed, these microorganisms are present or present in greater quantity in biological samples statistically associated with a high probability of premature delivery.

[0050] In one embodiment of the method according to the invention for determining a probability of premature delivery, said method further comprises a step of determining a second profile of a plurality of microorganisms characteristic of a high probability of delivery at term, that is to say after a duration equal to or greater than 37 weeks of gestation. By "high probability of delivery at term" is meant a probability greater than 50%.

[0051] According to this particular embodiment, from the results of step d) of a method according to the inventions, a second profile of a plurality of microorganisms statistically associated with a high probability of delivery at term is determined.

[0052] Said second profile of a plurality of microorganisms statistically associated with a high probability of delivery at term, obtained by a method according to the invention, is characterized in particular by the presence of microorganisms of the Christensenellaceae family and of the genus: - Bacteroides, or - Lactobacillus, in particular Lactobacillus crispatus. Indeed, these microorganisms are present or present in greater quantity in biological samples statistically associated with a high probability of delivery at term.

[0053] The present invention also relates to a classification model previously trained on a training data set to determine, according to a method according to the invention, a pathology.

[0054] The present invention also relates to a classification model previously trained on a training data set to determine, according to a method according to the invention, the probability of premature delivery.

[0055] According to a third aspect, the invention relates to the use of a classification means according to the invention for determining a pathology.

[0056] According to a third aspect, the invention relates to the use of a classification means according to the invention for determining a probability of premature delivery.

[0057] According to a fourth aspect, the invention relates to the use of a computer tool capable of ensuring the precise determination of the structures of microbial communities. Said computer tool makes it possible in particular to determine the identity of the microorganisms and their relative abundance, for the determination of a probability of premature delivery. An example of such a computer tool is RiboTaxa (Chakoory et al., 2022).

[0058] The present invention is further explained by the following figures and examples.

[0059] [Fig.l]: This figure shows an overview of the steps followed for the general pathology detection process, starting with the training of the neural network from the sequencing data of a mixed population comprising healthy subjects and subjects with a pathology, followed by the process of predicting the risk of a pathological condition using the trained and optimized model.

[0060] [Fig.2]: This figure illustrates the steps of training the neural network and the adjustment of hyperparameters, necessary for optimizing the prediction of the risk of premature delivery.

[0061] [Fig.3]: this figure illustrates the prediction performances obtained by the network of deep neurons based on the input data provided. The input data are the data from direct metagenomic sequencing (Fettweis cohort) processed by RiboTaxa or MetaPhlAn3. MetaPhlAn3 uses the high-quality reads from direct metagenomic sequencing to compare them to a reference genome database of microorganisms accessible at the address: segatalab.cibio.unitn.it / data / Pasolli_et_al.html and determine the taxonomic composition of the analyzed microbiota (from domain to species) and the relative abundances of the identified microorganisms (TSV file). Examples

[0062] Example 1: Determination of the structure of vaginal microbiotas used for training deep neural networks Materials and methods:

[0063] The inventors selected the following five recent studies using the keywords “vaginal microbiome”, “shotgun metagenomics” and “preterm birth”: Feehily et al., 2020; Fettweiss et al, 2019; Goltsman et al, 2018; Pace et al, 2021; Tortelli et al. 2021. Publicly available metagenomic sequencing data obtained by shotgun sequencing of vaginal microbiota and associated metadata were imported by the inventors. All of these data were carefully selected by the inventors.

[0064] Raw data and metadata for the Feehily et al. cohort were downloaded from the European Nucleotide Archive under BioProject PRJEB34536 (61.49 Gb). Raw data (2.77 Tb) and metadata for the Fettweis et al. cohort were received after data access approval by the National Institute of Health. Raw data and metadata for the Goltsman et al. cohort were downloaded from the Sequence Read Archive (SRA) under BioProject PRJNA288562 (115.53 Gb). Raw data and metadata for the Pace et al. cohort were downloaded from SRA under BioProject PRJNA451212 (15.92 GB) and metadata were received from the study authors. Raw data and metadata for the Tortelli et al. cohort were downloaded from the European Nucleotide Archive under BioProject PRJEB34536 (61.49 Gb). were downloaded from SRA under BioProject PRJNA639592 (8.52 Gb).

[0065] The five studies mentioned above were selected based on the availability of data from Illumina direct metagenomic (“shotgun”) sequencing of vaginal samples. For each cohort, the following sample metadata were retained and are presented in Table 1: - term birth (TB) or premature birth phenotype (PTB for preterm birth), - time of sample collection: 1st trimester of pregnancy i.e. 1-13 weeks of gestation, 2nd trimester of pregnancy i.e. 14-26 weeks of gestation, 3rd trimester of pregnancy: >= 27 weeks of gestation, delivery, 4 weeks postpartum), - age of participants (less than 35 years old, greater than or equal to 35 years old), - ethnic group (African-American, American-Indian, Asian, Black, Caucasian, Hispanic, Multi-ethnic, White) and - the participant's identifier (ID).

[0066] A total of 1298 samples were retrieved, 8 samples were rejected due to incomplete description of the nature of the participant's delivery. Table 1 represents the data of the general properties of the individual studies included for training the deep neural network, these represent a number of samples or a number of participants, TB represents a term birth (in English Term Birth) and PTB a preterm birth (in English PreTerm Birth).

[0067] [Tables 1] 1 -J 3 ■ 4 5 Authors of V study Feebily Fettweis and sd Ocltsman nZ Face £7 nf Tortelii s? «d Participants 45 231 IC 78 î$3 EehaaiilSoos (toial) 4$ $33 96 HS 195 Edtsntillioa* pcoer sanc e & premature S 1 57 36 19 41 Ratio gSoSœi d: births lireui âtnr fi IOVk 18% ÎS% 21% Ech sut: lions taken in first quarter - 86 TB 28 PTB 11 TB 12 PTB - -42TB 13 PTB Samples taken in second quarter 4: TB 8 GDP 20S TB 68 PTB 22 TB 15 PTB t.ü IB 10 P TB -41 TB 11 PTB Samples taken in third quarter 3 SS TE 57 PTB 27 TB 9PTE g? TB & PTB 71 TB 17 PTB- Samples for which the quarter is unknown, has 3 TE 2 PTB ...Ethnicity Samples "African American" - 44ÎTE ■ 102 PTS 11 TB 2 P TB - Ethnicity Sample a Hispanic - 20 TB 12 PTB 10 TB U PTB SJ TB 17 PTB 45 TB 11 PTB Ethnic Sample x Caucasian a 2 TB 1 PTB- 172 TB 17 PTB - 2:TB QPTB - Ethnicity Sample a White x 35 TB 7 PTB * 30 TB 22 PTB - 7$ TE 1SPTH Ethnicity Sample a Black a 2 TB 6 PTB * - - 15 TE ë ETE Ethnicity Sample & Muhi-eïhiue » - 38 TE 11 PTB 9 TS 5 PTB - 1 TS ■2- PiE ichsntilSoiks d ethnie x -Asian ai TB G PTB 3 TB 3 PTB- n ts {•PTB ■ 5 TB OPTE 12 TE 2 PTB Ech^ntiBonr d'ethnie •>: American. indiemÀttièric&in Native a 8 TB 5 PTB- ■ - 9 TB 2 PTB Ethnic songs the • TB 36 PTB 71 TB 1 PTB 12£ TB 33 PTB Samples of participants more or less likely to.35 years 13 TB 2 PT B 25 TB 8 PTB 28 TB S PTB 28 TB 4 PTB 26 TB K PTB Samples of participants of unknown age x -to 10- TE C PTB .

[0068] RiboTaxa chaining was used to obtain the precise, species-level structure of the microbiota of each sample. RiboTaxa uses raw metagenomic data to reconstruct complete or nearly complete 16S and / or 18S rDNA gene sequences using the SILVA SSU Ref NR99 138.1 database (Quast C. et al., 2013). The RiboTaxa software is freely available at github.com / oschakoory / RiboTaxa under the GNU AGP 3.0 license.

[0069] For each sample, raw shotgun sequencing reads were provided as input to RiboTaxa. The sequences were processed to remove Illumina adapters, known Illumina artifacts, and to adjust the quality of both ends at Q20 before performing 16S and / or 18S rDNA gene reconstruction. The Q20 quality score represents an error rate of 1 in 100, meaning that every 100 base pair sequencing read may contain one error, with a corresponding calling accuracy of 99%. Resulting reads containing more than one "N", or with quality scores averaging less than 20 across the read, or a length less than 60 base pairs after base trimming, were rejected.

[0070] For the reconstruction of the 16S / 18S rDNA gene, the default parameters were used except for the parameters described in the following Table 2.

[0071] [Table 2]: Cohort Parameter A Parameter B Parameter C max read length insert mean insert stddev Feehiiy et al. 300 120 300 Fettweis et al. 301 120 300 Goitsman et al. 151 146 100 Race et al. 151 100 142 Tortelfi et al. 75 75 50

[0072] These parameters depend exclusively on the sequencing length applied in the five studies. For each cohort: - parameter A “max_read_length” represents the longest read size of the input dataset, - parameter B “insert_mean” represents the average size of the inserts of the paired reads and - the C parameter “insert_stddev” represents the standard deviation of the size distribution of inserts of paired-end reads.

[0073] The parameters "insert_mean" and "insert_stddev" were estimated using the script "mean_size.py", accessible at: gist.github.com / timoast / af73c0e9fac00187ee49.

[0074] The reconstructed 16S / 18S rDNA sequences were then classified at different taxonomic levels, from the living domain to the species and, after discarding the human eukaryotic sequences (human 18S rDNA), the relative abundances were calculated by RiboTaxa.

[0075] All the taxonomic classification result tables with corresponding relative abundances were grouped into a single table containing all species-level profiles using RiboTaxa's RiboTaxa_group_taxonomy.sh script and used as input for neural network training including deep neural networks. Results

[0076] The processing of metagenomic sequencing data by RiboTaxa bioinformatics chaining of the five studies generated input data for learning by neural networks including deep neural networks, composed of a table containing the structures of the vaginal microbiotas (said structure including the determination of the microbial species present and their relative abundance) of each sample associated with the metadata: nature of delivery (TB or PTB), trimester of pregnancy, age group, ethnicity.

[0077] A quality control of the raw reads of the shotgun metagenomic sequences is performed by RiboTaxa using the BBMap and FastQC tools. The BBTools software is freely available at: sourceforge.net / projects / bbmap. This software takes as input the raw reads from the sequencing (in FASTQ format), and performs a quality control of these reads (parameters described in the paragraph above) to retain only the high-quality reads (in FASTQ format). The output data is therefore the high-quality sequencing data. The FastQC software is freely available at: bioinformatics.babraham.ac.uk / projects / fastqc. This software allows to graphically visualize the quality of the sequencing data (HTML file) before and after cleaning the sequencing data, by analyzing the quality information present in the FASTQ file of the raw or high-quality reads.FastQC therefore takes sequencing data as input and provides an HTML file as output containing graphical representations of the quality of the sequencing data.

[0078] The high-quality reads are then processed by the MetaRib tool (Xue et al., 2020) to perform a guided assembly of the high-quality reads (input data) using as references the 16S or 18S rDNA sequences from the SILVA database (SILVA SSU Ref NR99 138.1) and reconstruct the gene expressing the 16S rRNA or 18S rRNA. MetaRib thus makes it possible to obtain complete to near-complete 16S or 18S rDNA sequences with a minimum length of 1045 bases (in FASTA format) from the high-quality sequencing data. The MetaRib software is freely available at: github.com / yxxue / MetaRib. The input data for MetaRib are therefore high-quality sequencing data from BBMap and a reference database containing genes expressing 16S and / or 18S rRNA. The script 'run_metarib.py' allows you to align the reads corresponding to 16S and / or 18S rDNA to a reference database to reconstruct the 16S and / or 18S rDNA gene fragments. The output is a FASTA format file that contains the reconstructed gene fragments expressing 16S and / or 18S rRNA.

[0079] In parallel, high-quality reads are processed by SortMeRNA (Kopylova et al., 2012) to specifically extract all reads corresponding to rDNA 16S or 18S rDNA which will be used by EMIRGE (Miller et al., 2011) to reconstruct the gene expressing 16S / 18S rRNA similar to MetaRib.

[0080] SortMeRNA takes as input i) high quality reads (in FASTQ format) and ii) a reference database containing genes expressing 16S and / or 18S rRNA. The "SortmeRNA" function allows to filter the reads corresponding to 16S and / or 18S rDNA by aligning them with the reference sequences of the SILVA database to retain only the high quality reads corresponding to a portion of 16S / 18S rDNA (FASTQ format). The SortMeRNA software is freely available at: github.com / sortmema / sortmerna

[0081] EMIRGE uses the reads from SortMeRNA (in FASTQ format) to perform their assembly guided by the reference sequences from the SILVA database (SILVA SSU Ref NR99 138.1). EMIRGE allows to obtain complete to near-complete 16S or 18S rDNA sequences, with a minimum length of 1045 bases (in FASTA format) from high-quality sequencing data filtered by SortMeRNA. The EMIRGE software is freely available at: github.com / csmiller / EMIRGE. The EMIRGE input data are therefore the high-quality sequencing data, filtered by SortMeRNA, and a reference database containing genes expressing 16S and / or 18S rRNA. The script "emirge_amplicon.py" allows to align the reads corresponding to 16S and / or 18S rDNA on a reference database to reconstruct the fragments of the 16S and / or 18S rDNA gene.The output is a FASTA format file that contains the reconstructed fragments of the gene expressing 16S and / or 18S rRNA.

[0082] The dual reconstruction approach (EMIRGE and MetaRib) makes it possible to maximize the reconstruction of genes expressing 16S / 18S rRNA and to describe the structure of vaginal microbiotas as precisely as possible. Although both assemblers (EMIRGE and MetaRib) require a reference database (here SILVA, which is the most complete and of high quality), it is possible to reconstruct sequences very distant from the reference sequences, which thus makes it possible to identify microorganisms that would not be identified by other approaches (quantitative PCR, classic analyses of metagenomic data, PCR amplification of a portion of the gene expressing 16S rRNA then sequencing).

[0083] RiboTaxa calculates the abundance of each reconstructed 16S / 18S rDNA sequence. To do this, the number of high-quality reads constituting a fragment of the reconstructed 16S / 18S rDNA sequences is determined using BBMap, by aligning the high-quality raw reads with the 16S / 18S rDNA sequences reconstructed by MetaRib and EMIRGE. The output file ('Abundances' file, in TSV format) contains the identifier of the reconstructed 16S / 18S rDNA sequences and the number of high-quality reads counted for each of them.

[0084] Before taxonomic classification, all 16S / 18S rDNA sequences reconstructed by EMIRGE and MetaRib are clustered using a sequence similarity threshold greater than or equal to 97% using VSEARCH (Rognes et al., 2016) to avoid sequence duplication. For this, the function 'vsearch -cluster_fast' uses the output files of MetaRib and EMIRGE (in FASTA format) and produces two files, one containing the 16S / 18S rDNA sequences representative of a group of sequences sharing at least 97% identity (in FASTA format) and the other listing the sequences belonging to each of the groups (in TSV format). The VSEARCH software is freely available at: github.com / torognes / vsearch.

[0085] Finally, the Skleam classifier from QIIME2 (Bokulich et al., 2018) is used to classify the representative 16S / 18S rDNA sequences of each group at the different taxonomic levels from the living domain to the species. For this, the file containing the representative 16S / 18S rDNA sequences (FASTA format) obtained with VSEARCH is used by the function 'qihne feature-classifier classify-sklearn' to produce a file containing the taxonomic classification (from the living domain to the species) of each of the representative 16S / 18S rDNA sequences ('Taxonomies' file, TSV format). The Sklearn software is freely available at: github.com / qiime2 / q2-feature-classifier.

[0086] A final step consists of eliminating the human 18S rDNA considered as contaminant and calculating the relative abundances of each of the identified microorganisms. For this, the file obtained with Sklearn ('Taxonomies' file) is used as input data by the 'PostRiboTaxa.sh' script. This script identifies the affiliated sequences as human 18S rDNA to eliminate them from the 'Abundances' file obtained with BBMap and calculate the relative abundance (in percentage) of each of the remaining representative 16S / 18S rDNA sequences. Finally, the taxonomic information is aggregated to the species level using the RiboTaxa_group_taxonomy.sh script, in that the relative abundances of all sequences affiliated with the same species are added together and only the taxonomy at the species level is retained. Thus the output file (in TSV format) contains the identified species along with their relative abundance.

[0087] A precise description of the vaginal microbiota is obtained for each sample, this description includes an identification at the species level and the relative abundance of each microorganism.

[0088] Example 2: Use of artificial intelligence on data classified by RiboTaxa Materials and methods:

[0089] Species-level profiles as well as the metadata file containing information on ethnicity, age, phenotype and time of collection of samples were used to train the deep neural network (DNN). Before the training step, categorical data (ethnicity, age, time of sample collection, phenotype) were converted into vectors using "one hot encoding", i.e., all elements of the vector are converted to 0 except the categorical variable which is converted to 1. Microbial abundances were normalized.

[0090] For training the neural network, the preprocessed dataset is split with a ratio of 8:2 to obtain 80% training data and 20% testing data. For training, K-Fold cross-validation is applied with the training data. These are divided into K subsets of almost equal size; K1 subsets are used for training the model and the remaining subset for validating the produced model. In this way K models are built, with each time a redistribution of the K subsets and the definition of new hyperparameters. The best combination of hyperparameters for each model is selected by averaging the accuracy metric of the K models. The optimized model is then trained into a final classification model using the training dataset and tested on the testing data. Results

[0091] Vaginal microbiota input data combined with metadata enabled deep neural network learning to distinguish term births from premature births. Diagnostic reliability reached 85%. Bibliographic references

[0092] - Bokulich NA et al. "Optimizing taxonomy classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifierplugin.” Microbiome 6: 90-107. (2018). - Chakoory O. et al. “RiboTaxa: combined approaches for rRNA genes taxonomy resolution down to the species level from metagenomics data revealing novelties.” NAR Genomics Bioinformatics. 4, lqac070 (2022). - Feehily C. et al. “Sholgun sequencing of the vaginal microbiome reveals both a species and functional potential signature of preterm birth.” NPJ Biofilms Mi-crobiomes 6, 50 (2020). - Fettweis J. M. et al. “The vaginal microbiome andpreterm birth.” Nat. Med. 25, 1012-1021 (2019). - Goltsman D. S. A. et al. “Metagenomic analysis with strain-level resolution reveals fine- scale variation in the human pregnancy microbiome.” Genome Res. 28, 1467-1480 (2018). - Kopylova E. et al. "SortMeRNA: fast and accurate filtering of ribosomal RNAs in me-tatranscriptomic data.” Bioinformatics 28.24 (2012): 3211-3217. - Miller C.S. et al. "EMIRGE: reconstruction offull-length ribosomal genesfrom microbial community short read sequencing data.” Genome biology 12.5 (2011): 1-14. - Pace R. M. et al. "Complex species and strain ecology ofthe vaginal microbiome from pregnancy to postpartum and association with preterm birth.” Med 2, 1027-1049.e7 (2021). - Park et al. “Prédiction of preterm based on machine leaming using bacterial risk score in cervicovaginal fluid” Am. J. Reprod. Immunol., 2021, sep 86(3). - Park et al. “Predicting preterm birth through vaginal microbiota, cervical length and WBC using a machine leaming mode!', Front. Microbiol., 02 Aug. 2022. - Quast C. et al., 2013). “The SILVA ribosomal RNA gene databaseproject: improved data processing and web-based tools.” Nucleic Acids Res. 41, D590-D596 (2013). - Rognes T. et al. "VSEARCH: a versatile open-source tool for metagenomics.” PeerJ 4 (2016): e2584. - Tortelli B. A. et al. “The structure and diversity of strain-level variation in vaginal bâcle ria" Microb. Genomics 7, (2021). - Xue Y. et al. "Reconstructing ribosomal genesfrom large scale total RNA meta-transcriptomic data.” Bioinformatics 36.11 (2020): 3365-3371.

Claims

Claims

1. An in vitro method for determining a pathology, from a biological sample taken from a subject, said method comprising the following steps: a) isolation of the nucleic acid from a plurality of microorganisms present in said biological sample, b) determination by sequencing of the nucleotide sequence of at least one gene fragment expressing 16S ribosomal RNA (rRNA) and / or a gene fragment expressing 18S rRNA from a plurality of microorganisms to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined during step b) to reconstruct the nucleotide sequence of at least one part of the gene expressing 16S rRNA and / or of the gene expressing 18S rRNA from a plurality of microorganisms, d) from the results of step c), determination of the identity,of the taxonomic classification and the relative abundance of a plurality of microorganisms present in said sample, e) from the characteristics determined during step d), determination, by a previously trained classification model, of said pathology.,

2. In vitro method according to claim 1, wherein said pathology is a probability of premature delivery, from a biological sample taken from a pregnant woman, said method comprising steps a), b), c) and d) and comprising a step e) in which is carried out, from the characteristics determined during step d) and at least one clinical data characteristic of the pregnancy of said pregnant woman, the determination, by a previously trained classification model, of the probability of premature delivery.

3. Method according to any one of the preceding claims, characterized in that step c) comprises organizing a plurality of nucleotide sequences to reconstruct the sequence of at least 70% of the length of the nucleotide sequence of a gene expressing 16S rRNA and / or of the nucleotide sequence of a gene expressing 18S rRNA.

4. Method according to any one of the preceding claims 2 or 3, characterized in that said at least one clinical data characteristic of said pregnant woman is chosen from: the age of the woman, the ethnicity of the woman, the trimester of pregnancy, or a combination of any of these characteristics.

5. Method according to any one of the preceding claims, characterized in that the classification model comprises: - a previously trained neural network, in particular during supervised learning, - a machine learning algorithm, and - a training data set.

6. Method according to the preceding claim, characterized in that said classification model comprises an algorithm chosen from the group consisting of: a neural network, a decision tree, K-nearest neighbors, a random forest, a naive Bayesian classification, an “Extreme Gradient Boosting” algorithm, a logistic regression and a support vector machine.

7. Method according to any one of the preceding claims 2 to 6, characterized in that it further comprises a step of determining a first profile of a plurality of microorganisms, said first profile being characteristic of a high probability of premature delivery.

8. Method according to the preceding claim, characterized in that said first profile of a plurality of microorganisms comprises at least microorganisms of the genus Anaerococcus, Peptoniphilus, Prevotella, Gardnerella and Sneathia.

9. Method according to any one of the preceding claims 2 to 8, characterized in that it further comprises a step of determining a second profile of a plurality of microorganisms, said second profile being characteristic of a high probability of delivery at term.

10. Method according to the preceding claim, characterized in that said second profile of a plurality of microorganisms comprises at least microorganisms of the Christensenellaceae family and of the Bacteroides and Lactobacillus genus.

11. Use of a computer tool for determining microbial community structures for, from a nucleotide sequence of at least a part of a gene expressing 16S rRNA and / or a gene expressing 18S rRNA of a plurality of microorganisms, reconstructed by organizing a plurality of nucleotide sequences, the plurality of nucleotide sequences being generated from the result of a se- quenching the nucleotide sequence of at least one gene fragment expressing 16S ribosomal RNA (rRNA) and / or one gene fragment expressing 18S rRNA of a plurality of microorganisms: - determine the identity of the microorganisms in the microbial community; - determine a relative abundance of microorganisms in the microbial community; and - determine the probability of premature delivery based on the determined identity and relative abundance of microorganisms.

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