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] 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. This method therefore falls within the field of in vitro diagnostics and personalized medicine.

[0002] Premature 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 a genital tract infection. 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. Unfortunately, clinicians currently lack any reliable tool to predict the risk of preterm birth.

[0003] EP 3 161 167 describes a method for assessing the risk of premature birth based on the detection, in a vaginal or cervical sample obtained by swabbing 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, relative to a reference level. The quantification of the bacteria is performed by amplification of a small region of 16S rDNA by quantitative polymerase chain reaction (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 the risk of premature birth.

[0005] International application WO 2020 / 227053 describes a method for determining the risk of premature birth comprising determining the abundance of Saccharibacteria TM7-H1 and optionally of 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 microbiomes makes it difficult to determine the specific and predictive microbial signatures characteristic of a pathological state. This situation is further complicated by significant inter-individual variations. To date, several microbiota analysis techniques exist. However, current approaches do not allow for a precise characterization of microbiomes.

[0007] Genes expressing the small subunit of rRNA, that is to say the genes called "16S rDNA" for prokaryotic microorganisms, such as bacteria and archaea, and "18S rDNA" for eukaryotes, including yeasts, are used to allow 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 includes the simultaneous quantification by qPCR amplification of specific small DNA fragments 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 reviews, predictions are established on the basis of a selection of 10 bacteria (Lactobacillus crispatus, Lactobacillus funicalis, 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 propionicifaciens, 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 16S rRNA has several limitations: the biases likely to be generated during the amplification step carried out by PCR can alter the view of the real diversity of the microbiota, the small length of the fragment provides only 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 MAG) and an affiliation step of the MAGs leads to an identification restricted to dominant species.

[0013] Another method includes a direct metagenomic sequencing step (“shotgun”) followed by an affiliation of the unassembled raw reads of a A portion of the gene expressing 16S rRNA is smaller than 300 base pairs. The affiliation of these small sequences leads to low microbial identification resolution and overestimation of diversity, particularly through the detection of false positives.

[0014] Therefore, there is a need to develop a method for predicting the probability of premature birth 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. This detection method is based on the analysis of the microbiota present in said biological sample. More specifically, the inventors have developed a method for predicting the occurrence of premature birth with a reliability exceeding 69% and, in particular, capable of reaching 85% reliability. Such a degree of reliability is unmatched among methods for diagnosing premature birth to date.

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

[0017] A method according to the invention uses all the metagenomic data of the microbiota which then allows the complete reconstruction of 16S and / or 18S rDNAs and a precise affiliation of microorganisms of the microbial community at the level of the genus or species, 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 offers the advantages of high accuracy in prediction, combined with high speed and the potential for high throughput. In a particular aspect, a method according to the invention also has the advantage of not absolutely requiring an obstetric examination at the time of performing the method.

[0019] According to a particular aspect, a method according to the invention has the advantage, starting from a simple sample of vaginal microbiota during pregnancy, in the 1st trimester and / or the 2nd trimester and / or the 3rd trimester, and its direct metagenomic sequencing, to determine with a high degree of 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, the 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 present invention relates to 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 selected from: a fragment of genes expressing 16S ribosomal RNA (rRNA), a fragment of genes expressing 18S rRNA, a fragment of 16S rRNA and / or a fragment of 18S rRNA, from a plurality of microorganisms, to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined in step b) to reconstruct the nucleotide sequence of at least one fragment of genes expressing 16S rRNA, genes expressing 18S rRNA, 16S rRNA and / or 18S rRNA from a plurality of microorganisms, d) from the results of step c), determination of the identity, taxonomic classification, and relative abundance of a plurality of microorganisms present in said sample, and e) based on the characteristics determined in 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 fragment of genes expressing 16S ribosomal RNA (rRNA) and / or one fragment of genes expressing 18S rRNA from a plurality of microorganisms to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined in step b) to reconstruct the nucleotide sequence of at least one gene fragment expressing 16S rRNA and / or genes expressing 18S rRNA from a plurality of microorganisms, d) from the results of step c), determination of the identity, taxonomic classification, and relative abundance of a plurality of microorganisms present in said sample, and e) from the characteristics determined in step d), determination, by a previously trained classification model, of a pathology.

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

[0025] The term "pathology" means a disease, a risk of an adverse event, a biological imbalance, or discomfort. The term "microbiota" means a community of microorganisms present in a given sample. The term "subject" means an individual, in particular a human being or an animal, preferably an amphibian.

[0026] According to a particular aspect of the process of the invention, the nucleic acid of at least 30% of the microbiota present in the biological sample is analyzed in a process 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 16S rRNA or of the gene expressing 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] The term "sequencing" refers to any known method for determining the nucleotide sequence of a nucleic acid. Among these methods, direct metagenomic sequencing, also known as "shotgun" sequencing, is preferred. The use of sequencing data from gene capture hybridization approaches is also preferred.

[0029] The nucleotide sequences of genes expressing 16S rRNA from microorganisms and of 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 at the website "arb-silva.de". Another example of a database is the "Greengenes" database. A person skilled in the art can easily determine whether a given nucleotide sequence originates 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 identity determination of a microorganism is carried out using the nucleotide sequence of 16S rDNA and / or 18S rDNA.

[0031] By "determination of 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 range from belonging to the domain of living organisms (least precise rank) to the definition of species (most precise rank). Taxonomic classification is carried out by comparing the determined 16S rDNA sequences and / or 18S rDNA sequences with, respectively, 16S rDNA sequences and / or 18S rDNA sequences contained in databases. Among the usable databases, the SILVA database can be notably cited.

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

[0033] The present invention relates more particularly to an in vitro method for determining the probability of premature birth in 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 fragment of genes expressing 16S rRNA and / or one fragment of genes expressing rRNA 18S from a plurality of microorganisms to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined in step b) to reconstruct the nucleotide sequence of at least one fragment of genes expressing 16S rRNA and / or one fragment of genes expressing 18S rRNA from 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, and of the relative abundance of a plurality of microorganisms present in said sample, and determination of at least one clinical feature characteristic of pregnancy, and e) from the characteristics determined in step d), determination, by a previously trained classification model, of the probability of a premature birth.

[0034] Premature birth means a birth 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 18S rDNA of a plurality of microorganisms 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 16S rRNA of a plurality of microorganisms from the biological sample and the nucleotide sequence of at least 20% of the 18S rDNA or 18S rRNA of a plurality of eukaryotic cells from the biological sample.

[0036] By "a fragment of at least 20%", we mean 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 under consideration.

[0037] More particularly, in a process 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 16S rRNA and / or at least 70% of the length of the 16S rRNA is reconstructed. Since the length of a 16S rDNA gene is approximately 1500 base pairs on average, a nucleotide sequence representing at least 70% of the gene length 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 data characteristic of pregnancy, said clinical data 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 be expressed in particular in number of weeks of gestation or designated by the period at which the biological sample is taken.

[0039] This period is chosen in particular 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 assigned to one of the following two groups: "under 35 years" and "equal to or over 35 years".

[0041] By "ethnic group," we mean a group of people who share a number of characteristics. In a method according to the invention, the characteristic "ethnic group" is in particular chosen from the group consisting of: "African American," "Indian American," "Black," "White," "Caucasian," "Hispanic," "Asian," "Multi-ethnic."

[0042] A biological sample used in a process according to the invention is selected from: a vaginal swab, a cervical swab, and any other biological sample. Preferably, in a process according to the invention, the sample is a vaginal swab. Said swab is taken 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] The term "classification model" includes, in particular, a classification model comprising: - a previously trained neural network, particularly during supervised learning, especially a deep neural network, - a machine learning algorithm and - a training dataset. The classification model can consist of a computer program. This program can be written in any programming language, such as C, C++, Java, Python, etc. The classification model thus performs a technical function, consisting of steps in the classification process. The execution of this program by a computer produces a digital object with specific technical characteristics.

[0045] By "pre-trained" is meant a process enabling the neural network to learn to associate, in a weighted manner, a microorganism or group of microorganisms and clinical pregnancy data with premature or full-term delivery. This process is carried out using samples for which the nature of the delivery, premature or full-term, and the clinical pregnancy data 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 pre-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 Bayes 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 the probability of premature birth, 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 birth. By "high probability of premature birth" is meant a probability greater than 50% of premature birth, 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 process according to the inventions, a first profile of a plurality of microorganisms statistically associated with a high probability of premature birth is determined.

[0049] A first profile of microorganisms statistically associated with a high probability of premature birth, obtained by a process 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 quantities in biological samples statistically associated with a high probability of premature birth.

[0050] In one embodiment of the method according to the invention for determining the probability of premature birth, 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 gestational age of 37 weeks or more. "High probability of delivery at term" means a probability greater than 50%.

[0051] According to this particular embodiment, from the results of step d) of a process 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 term delivery, obtained by a process according to the invention, is characterized in particular by the presence of microorganisms of the family Christensenellaceae and of the genus: - Bacteroides, or - Lactobacillus, in particular Lactobacillus crispatus. Indeed, these microorganisms are present or present in greater quantities in biological samples statistically associated with a high probability of full-term delivery.

[0053] The present invention also relates to a classification model previously trained on a training dataset 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 dataset to determine, according to a method according to the invention, the probability of a premature birth.

[0055] According to a third aspect, the invention relates to the use of a classification means according to the invention for the determination of 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 accurately determining the structures of microbial communities. This computer tool makes it possible, in particular, to determine the identity of microorganisms and their relative abundance, for the purpose of determining the probability of premature birth. An example of such a computer tool is RiboTaxa (Chakoory et al., 2022).

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

[0059] [Fig.1]: This figure shows an overview of the steps followed for the general process of detecting a pathology, starting with the training of the neural network from the sequencing data of a mixed population including healthy subjects and subjects with a pathology, followed by the process of predicting the risk of a pathological state 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 birth.

[0061] [Fig.3]: This figure illustrates the prediction performance 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 database of reference microorganism genomes accessible at: 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 microbiomes 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 “premature birth”: Feehily et al., 2020; Fettweiss et al., 2019; Goltsman et al., 2018; Pace et al., 2021; Tortelli et al., 2021. The inventors imported publicly available metagenomic sequencing data obtained by shotgun sequencing of vaginal microbiomes and associated metadata. This dataset was 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 from the National Institutes 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 the 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 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 sequencing (“shotgun”) of vaginal samples. For each cohort, the following sample metadata were retained and are presented in Table 1: - Term Birth (TB) or Preterm Birth (PTB) phenotype, - time of sample collection: 1st trimester of pregnancy (1-13 weeks gestation), 2nd trimester of pregnancy (14-26 weeks gestation), 3rd trimester of pregnancy (≥ 27 weeks gestation), delivery, 4 weeks postpartum), - age of participants (under 35 years, ≥ 35 years), - ethnic group (African American, Indian American, Asian, Black, Caucasian, Hispanic, Multi-ethnic, White) and - participant identifier (ID).

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

[0067] [Tables 1] ■ ■y 3 4 3 Authors of Fever Fever si al. Fenweis Pscs and Parents 49 231' K -S l'ÎS 49 332 95 118 ÎS5 Utilities for birth-health 157 316 Global birth control 19 16% 18% 38% 3594 2I5's fit fowettsii 4b iriatnta! Samples taken in the second trimester. 41 TE S PTB 2® TE 50 PTS 22 TB MPTB ÎSTB 10 PTS 41TB 11 PTB. Bdisntî&ns prélevés 2\tic iiwAiib aire - 386 TB 57 PTS 27 TB 9PTB 89 TB 9FTB 7i TB 17 PTB LcLï'ït L is pc s H t^tLc^be — 3 TBm 3 PTB Africans - Echan - Echan 441 TE 102 PTB - 1113 2 PTS Echsntillcîii-d' ethnie ÿ Hispanique s - 20 TB 12 PTB 10 TB 9 PTB 31 TE-17PT8 45 TE 11 PTB Samples.of ethnicity * Caucasian ;•> 2TB 1 PTB 172 TE 17 PTB ■ 2 TB OPTE - Samples of ethnicity sBknc* 36 TE 7 PTB 30 TB 22PTB - 78 TB 18 PTB Samples of ethnicity v Black 2 TE OPTE - - 16 TH 6 PTB Samples of ethnicity v 5duib--a6irue v - MPTB 9 TB 5 PTB - 1 TB 2 PTB Sample of ethnicity- •y Asian $ 1 TH 0 PTB 3 TB 3 PTB 11TB-OPTB 5 TB OPTE- 12 TB 2 PTB Sample of ethnicity' .y American Indian Nztif a ■ ÛTB - 0-TB 2 PTB Samples -of ethnicity •y unknown $ - 9 TB "PTB - - 2 TB: ■0 PTB Samples of participants aged 35 years 23 TB 6 PTB 640 TB 149 PTS 40 TB 36PTE 71 TB 15 PTB 123 TB 33 PTB Samples of participants aged over ■ùu equal to 3 5 years 13 TE 2 PTB 35 TB SETS 20 TB OPTE 28 TE 4 PTS 25 TE 8 PTB Samples of age-•y sncoïmu * ... 25- TB OPÎB - - .

[0068] RiboTaxa chaining was used to obtain the precise, species-level structure of the microbiota in 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, the raw shotgun sequencing reads were provided as input data to RiboTaxa. The sequences were processed to remove Illumina adaptors, known Illumina artifacts, and to adjust the quality of both ends at Q20 before performing gene reconstruction of 16S and / or 18S rDNA. The Q20 quality score represents an error rate of 1 in 100, meaning that each 100-base-pair sequencing read may contain one error, with a corresponding call accuracy of 99%. Resulting reads containing more than one "N", or with average quality scores below 20 on the read, or a length of less than 60 base pairs after base removal, were rejected.

[0070] For the reconstruction of the 16S / 18S rDNA gene, the default parameters were used with the exception of the parameters described in the following table 2.

[0071] [Table 2]: Cohort Parameter A Parameter B Parameter C max read length insert mean insert standard deviation Feehiîy et al. 300 120 300 Fettweis et al. 301 120 300 Goitsman et al. 151 146 100 Pace et al. 151 100 142 TorteKi 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 from the paired reads and - The C parameter "insert_stddev" represents the standard deviation of the size distribution of the inserts of the paired 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 level and, after discarding human eukaryotic sequences (human 18S rDNA), the relative abundances were calculated by RiboTaxa.

[0075] All the result tables of the taxonomic classifications with the corresponding relative abundances were grouped into a single table containing all the profiles at the species level using the RiboTaxa_group_taxonomy.sh script from RiboTaxa and used as input for learning by neural networks including deep neural networks. Results

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

[0077] A quality control of the raw reads of 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 check on 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 graphical visualization of 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, as output, an HTML file 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 the 16S or 18S rRNA sequences from the SILVA database (SILVA SSU Ref NR99 138.1) as references and to reconstruct the gene expressing 16S 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 is 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 alignment of reads corresponding to 16S and / or 18S rDNA on a reference database to reconstruct the 16S and / or 18S rDNA gene fragments. The output is a FASTA format file containing 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 16S rDNA or 18S rDNA which will be used by EMIRGE (Miller et al., 2011) to reconstruct the gene expressing 16S / 18S rRNA similarly 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 filters the reads corresponding to 16S and / or 18S rDNA by aligning them with the reference sequences in 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 reads from SortMeRNA (in FASTQ format) to perform sequence assembly guided by reference sequences from the SILVA database (SILVA SSU Ref NR99 138.1). EMIRGE allows the generation of 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 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 aligning reads corresponding to 16S and / or 18S rDNA on a reference database to reconstruct fragments of the 16S and / or 18S rDNA gene.The output is a FASTA format file containing the reconstructed fragments of the gene expressing 16S and / or 18S rRNA.

[0082] The dual reconstruction approach (EMIRGE and MetaRib) maximizes the reconstruction of genes expressing 16S / 18S rRNA and allows for the most precise description of the structure of vaginal microbiomes. 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 different from the reference sequences, thus enabling the identification of microorganisms that would not be identified by other approaches (quantitative PCR, classical metagenomic data analyses, PCR amplification of a portion of the gene expressing 16S rRNA followed by 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 raw high-quality 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 grouped using a threshold of Sequence similarity of 97% or greater was achieved using VSEARCH (Rognes et al., 2016) to avoid sequence duplication. To achieve this, the 'vsearch -cluster_fast' function uses the output files from MetaRib and EMIRGE (in FASTA format) and produces two files: one containing representative 16S / 18S rDNA sequences from a cluster of sequences sharing at least 97% identity (in FASTA format), and the other listing the sequences belonging to each cluster (in TSV format). The VSEARCH software is freely available at: github.com / torognes / vsearch.

[0085] Finally, the Sklearn classifier from QIIME2 (Bokulich et al., 2018) is used to classify the representative 16S / 18S rDNA sequences of each group at different taxonomic levels, from the domain of living organisms to the species level. For this purpose, 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 domain of living organisms to the species level) of each of the representative 16S / 18S rDNA sequences (file 'Taxonomies', TSV format). The Sklearn software is freely available at: github.com / qiime2 / q2-feature-classifier.

[0086] A final step consists of removing the human 18S rDNA considered a contaminant and calculating the relative abundances of each of the identified microorganisms. For this, the file obtained with Sklearn (file 'Taxonomies') is used as input data by the script 'PostRiboTaxa.sh'. This script identifies the sequences affiliated as human 18S rDNA in order to remove them from the 'Abundances' file obtained with BBMap and calculates the relative abundance (as a percentage) of each of the remaining representative 16S / 18S rDNA sequences. Finally, the taxonomic information is aggregated at the species level using the script RiboTaxa_group_taxonomy.sh, in that the relative abundances of all sequences affiliated with the same species are summed, and only the species-level taxonomy 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 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 and the metadata file containing information on ethnicity, age, phenotype, and time of sample collection were used to train the deep neural network (DNN; Deep Neural Network). Before the training stage, the 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 in an 8:2 ratio to obtain 80% training data and 20% test data. For training, K-Fold cross-validation is applied to the training data. This data is divided into K subsets of nearly equal size; Kl subsets are used for training the model, and the remaining subset is used for validating the resulting model. In this way, K models are constructed, 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 entire training dataset and tested on the test data. Results

[0091] Vaginal microbiota input data combined with metadata enabled deep neural network learning to distinguish between full-term and preterm births. Diagnostic accuracy reached 85%. Bibliographical 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 Microbiomes 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 metatranscriptomic 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 moder. Front. Microbiol., 02 Aug. 2022. - Quast C. et al., 2013). “The SILVA ribosomal RNA gene databaseproject: improved data processing and web-based toolsP 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 metatranscriptomic data." Bioinformatics 36.11 (2020): 3365-3371.

Claims

1.

2. Demands 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 all 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 from all microorganisms to generate a plurality of nucleotide sequences, c) organization of a plurality of nucleotide sequences determined in step b) to reconstruct the nucleotide sequence of at least a portion of a gene expressing 16S rRNA and / or a gene expressing 18S rRNA from all microorganisms, d) from the results of step c), determination of the identity, taxonomic classification and relative abundance of all microorganisms present in said sample, e) from the features determined in step d), determination, by a classification model previously trained on a training dataset, of said pathology, where the training dataset includes metagenomic data from control biological samples and biological samples from subjects with the pathology for which the identity, taxonomic classification, and relative abundance of all microorganisms present are determined by the reconstruction of 16S / 18S rDNA sequences, and where only those microorganisms not used in training the classification model are excluded from the features determined in step d). An 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, from the characteristics determined in step d) and at least one clinical data point, is performed characteristic of the pregnancy of said pregnant woman, the determination, by a previously trained classification model, of the probability of a premature birth.

3. A 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 the nucleotide sequence of a gene expressing 18S rRNA.

4. A 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 woman's age, the woman's ethnicity, the trimester of pregnancy, or a combination of any one of these characteristics.

5. A 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 dataset.

6. A method according to the preceding claim, characterized in that said classification model comprises an algorithm selected 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. A 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. A method according to the preceding claim, characterized in that said first profile of a plurality of microorganisms comprises at least microorganisms of the genera Anaerococcus, Peptoniphilus, Prevotella, Gardnerella and Sneathia.

9. A 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 the second profile being characteristic of a high probability of delivery at term.

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

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

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