Method for modifying the gut microbiome

Abstract

One aspect of the invention relates to a method for modifying the gut microbiome, comprising a first step of receiving phenotypic data, medical data, lifestyle data and metagenomic data for a first set of individuals and for a target individual. From these data, a vector is constructed for each individual. This vector is then used to determine a second set of individuals with maximum similarity to the target individual. Among this second set of individuals, a third set of individuals is determined using criteria of gut microbiome richness and / or of absence of metabolic syndrome and / or of glycaemia and / or of waist circumference and / or of cholesterolaemia. A digital profile is generated for each individual of the third set of individuals and for the target individual. The digital profile comprises a value resulting from an evaluation of a score representing the positive or negative influence of a person's lifestyle on their gut microbiome. Then, data are generated from the lifestyle data of the target individual and digital profiles are generated from these generated lifestyle data. Finally, the digital profiles with maximum similarity with at least one digital profile among the digital profiles generated for each individual of the third set of individuals and having a score greater than the score of the digital profile of the target individual are used to generate at least one recommendation regarding the lifestyle of the target individual.

Description

DESCRIPTION TITLE: Process for modifying the intestinal microbiome TECHNICAL FIELD OF THE INVENTION

[0001] The technical field of the invention is that of metagenomics.

[0002] The present invention relates to a method of modifying an intestinal microbiome of an individual and in particular to a personalized method of modifying an intestinal microbiome of an individual. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] Metagenomics is a methodological process that aims to study the microbiome. The microbiome is the set of microorganisms and their genes. Microorganisms can be, for example, bacteria, viruses, fungi, yeasts, or plankton, living in a specific environment such as the intestine, ocean, soil, or air.

[0004] A person's gut microbiome is home to a very large number of microorganisms. This collection of non-pathogenic bacteria, viruses, parasites, and fungi present in the gut constitutes the gut microbiome.

[0005] The role of the gut microbiome is becoming increasingly well-known. Numerous studies highlight the links between the gut microbiome and the overall functioning of the body. Thus, there is a consensus on the importance of the microbiome for a person's well-being and / or health. It is therefore common to try to positively influence the gut microbiome through lifestyle modification recommendations. An influence will be considered positive, for example, if the richness of the microbiome is increased. These lifestyle modification recommendations can concern many areas such as physical activity, sleep, eating behavior, nutrition, or emotional well-being.

[0006] A better understanding of how the microbiome interacts with its host should therefore help improve the body's overall functioning and health. It is therefore necessary to understand how a person's gut microbiome reacts to different factors.

[0007] A better understanding of the gut microbiome could help us understand the individual response to one or more lifestyle changes such as diet. Indeed, molecules processed by the microbiome are absorbed by the host to affect target organs. These molecules have local effects on the intestine, are retransformed and / or metabolized by microbes, and are excreted in the feces, so they have a wide range of effects on the individual. A diet will be defined here as being able to include a single meal or a multitude of meals over one or more days.

[0008] Gut microbiome modeling allows the prediction of a variation in the gut microbiome to one or more lifestyle changes such as the absorption of one or more foods in a diet. Indeed, by simulating the absorption of one or more foods by the gut microbiome using a gut microbiome model, it is possible to obtain a molecular fingerprint of the activity of the gut microbiome. A molecular fingerprint of the activity of the gut microbiome, under an individual's diet, is a vector of metabolic fluxes comprising a distribution of metabolic fluxes across the reactions of the community model at the microbiome scale.

[0009] In-silico metabolic modeling of microbes allows the translation of functional annotations of (meta)genomic sequences into mathematical models of cellular metabolism. From these mathematical models, the behaviors of systems can be studied under different conditions, also called constraints or factors. To do this, two elements are essential. The first element is the genome-scale metabolic model (commonly called GSMM) or network (GSMN) of the cellular system under study, which is reconstructed from the functional annotations of the genes encoded in the genomic sequences.This GSMN is a repository of all biochemical reactions catalyzed by cellular enzymes and transport reactions carried out by different cellular transport systems that describes the drainage and production of cellular metabolites in the studied organism necessary to support cell growth, e.g. amino acids required for protein synthesis, nucleotides required for DNA replication and transcription, lipids. necessary for the construction of cell membranes, the cofactors necessary for the activity of different enzymes, etc. The second element is the molecular profile of the environment in which the cellular system lives, in the form of different compounds to which the system has access to develop, for example carbon sources such as glucose, maltose, fructose; nitrogen sources such as ammonia, aerobic or anaerobic conditions defined by the presence / absence of oxygen, etc. These molecular profiles are codified in the form of exchange reactions that introduce these compounds into the extracellular environment of the metabolic network at maximum rates defined by the estimated quantities of these compounds in the environment.

[0010] With the biochemical and transport reactions of GSMM and the exchange reactions defining the environmental inputs, a mathematical model of cellular metabolism is constructed by encoding the metabolic network reactions as a stochiometric matrix S where rows represent chemical compounds (m compounds), columns represent reactions (n ​​reactions), and subscripts represent the stochiometric coefficient of each metabolite in each reaction of the network. Stochiometric coefficients are molar ratios in which substrates are transformed into products in a chemical reaction. The matrix S is the most important structural property of metabolic networks. It describes the architecture and topological properties of the system and remains constant under changing conditions that could alter the kinetic parameters or reaction rates.With this matrix S, it is possible to define differential equations for each chemical compound in the network. The differential equations describe the net change in metabolite concentrations as a function of time, which is equal to the differences between the sum of all reaction fluxes that produce the chemical compound and the sum of all reaction fluxes that consume it.

[0011] ^ = s * v

[0012] This system of differential equations incorporates enzyme kinetics through the time derivative, so that the reaction flux vector v will depend on the concentration of the metabolites and a number of kinetic parameters that are unknown for many network reactions in the GSMM. It is in this context that, in order to overcome this lack of knowledge of kinetic information, the metabolic system is placed in a state of equilibrium, which is the most fundamental constraint in the computational modeling of cellular metabolism. This is justified by the fact that the metabolic transitions of internal chemical compounds are faster than cell growth rates and dynamic changes in the organism's environment. Consequently, the metabolic fluxes leading to the formation and degradation of any particular chemical compound must be balanced and their sum must be equal to zero:

[0013] 0 = S * v

[0014] Assuming this steady state, the above system of differential equations is converted into a set of linear equations from which it is possible to calculate the vector of reaction fluxes v that defines the metabolic state of the system under the defined environmental conditions. However, this system of linear equations is generally indeterminate, meaning that there is no unique solution, i.e., no unique vector of reaction fluxes v that satisfies the equilibrium condition, but rather a space of feasible flux distributions, i.e., multiple vectors v, in the N-dimensional space of reaction fluxes that satisfy the equilibrium condition. This space of flux distributions can be more or less wide depending on additional constraints. These additional constraints can be imposed on the behavior of the system in addition to the environmental constraints.These environmental constraints are imposed in the form of maximum and minimum values ​​that different reaction fluxes can reach, derived from experimental data, such as the maximal glucose uptake rates calculated experimentally with Metabolic Flux Analyses (MFAs); the zero flux through reactions catalyzed by enzymes not expressed in transcriptomic or proteomic datasets, etc.

[0015] There are several ways to explore the space of feasible flow distributions, the most common of which is linear optimization of an objective function. These linear optimization approaches include: Flux balance analyses, commonly referred to as "Flux Balance Analyses" and known as FBA, which attempt to find the flux distribution across model reactions that maximizes the objective function, Parsimonious flux balance analyses, commonly referred to as "Parsimonious FBA" and known as pFBA, which attempt to find the flux distribution between model reactions that maximizes the objective function while minimizing the overall total flux between model reactions, Flux Variability Analyses, commonly referred to as FVA, attempt to find the range of achievable flux values ​​for the model's reactions that maximizes the objective function.

[0016] The mentioned mathematical methods (FBA, pFBA, FVA) explore this space of feasible flux distributions using mathematical optimization of linear programming, which allows finding the best flux distribution v that optimizes a given objective function, which is defined in the form of a biomass equation. This biomass equation is usually written as an additional reaction in the model that reflects the cell's needs (amino acids, nucleotides, lipids, cofactors, possibly macromolecules such as DNA, RNA or proteins in some models) to make one gram of cell dry weight, and is usually obtained by reviewing the relevant scientific literature regarding experimental measurements of the biomass constituents of the organisms under study or by adapting the biomass equation of related organisms if experimental information from the organism under study is scarce.In summary, linear programming optimization on a metabolic system, with biomass production maximization as the objective function, yields the maximum growth yield achievable by the system under a given set of environmental constraints. The environmental constraints are defined by the nutritional inputs as well as the reaction flux distribution (v) that drives the cell growth phenotype, from which the consumption rates can be extracted. nutrients or secretion of cellular metabolites that define the metabolic state of the system.

[0017] This approach is based on the strong assumption that cellular systems have optimized their growth performance under a subset of possible environmental conditions during their evolution, so that biomass maximization can be considered a guiding principle of metabolic functioning. This assumption has been experimentally validated in organisms such as Escherichia coli or Helicobacter pylori, for which experimentally measured growth rates have been shown to correlate with biomass production rates in model simulations with FBA and cell biomass as the objective function.

[0018] Currently, it is common to provide standard recommendations, such as advising the use of probiotics or physical activity, in order to positively influence the intestinal microbiome.

[0019] However, each gut microbiome and its response to one or more lifestyle changes are unique. For example, for the same dietary change, the gut microbiome of two people will not evolve in the same way. There is therefore a need to provide a microbiome modification method that takes into account the uniqueness of each individual's microbiome. SUMMARY OF THE INVENTION

[0020] The invention offers a solution to the problems mentioned above, by allowing the generation of personalized recommendations in order to modify an individual's microbiome.

[0021] One aspect of the invention relates to a method of modifying the intestinal microbiome comprising the steps of: Receiving, for each individual of a first set of individuals and for a target individual, said target individual being an individual not included in the first set of individuals, phenotypic data, medical data, lifestyle data, the lifestyle data comprising at least one domain among the practice of one or more physical activities, the quality and quantity of sleep, behavior food, nutrition and perception of emotional well-being and metagenomic data, said metagenomic data comprising a plurality of metagenomic taxa abundances, Construction, for each individual of the first set of individuals and for the target individual, of a vector comprising the received data, Determination, among the first set of individuals, of a second set of individuals maximizing a similarity with the target individual, the similarity being evaluated from the vectors calculated for each individual of the first set of individuals and from the vector constructed for the target individual, Determining, among the second set of individuals, a third set of individuals, each individual of the third set of individuals having: a gut microbiome richness greater than a predefined gut microbiome richness value and / or greater than a gut microbiome richness of the target individual, and an absence of metabolic syndrome or blood sugar, waist circumference and cholesterol levels lower than the target individual, For each individual of the third set of individuals, generating a digital profile from the phenotypic, medical, metagenomic and lifestyle data of said each individual of the third set of individuals, said digital profile of each individual of the third set of individuals further comprising a value resulting from an evaluation of a score representing the positive or negative influence of a person's lifestyle on the intestinal microbiome, Generating a digital profile of the target individual from the phenotypic, medical, metagenomic and lifestyle data of the target individual, said digital profile further comprising a value resulting from an evaluation of a score representing the positive or negative influence of a person's lifestyle on the intestinal microbiome, Generating a lifestyle dataset from the target individual's lifestyle data, Generating a first set of digital profiles, each digital profile of the first set of digital profiles being generated from the generated lifestyle data set, Determining a second set of digital profiles from among the first set of digital profiles, the second set of digital profiles: maximizing a similarity with at least one digital profile from among the digital profiles generated for each individual of the third set of individuals, and having a score higher than the score of the digital profile of the target individual, and Generating at least one recommendation regarding the at least one domain for which data has been received, said recommendation making it possible to reduce the gap between the score of the digital profiles of the second set and the score of the target individual for the at least one domain for which lifestyle data has been received.

[0022] Thanks to the invention, it is possible to provide personalized recommendations to a target individual in order to modify their intestinal microbiome. The personalization of recommendations is carried out by identifying individuals similar to a target individual. Thus, these recommendations will be adapted to the target individual and will allow optimization of the modification of their intestinal microbiome.

[0023] In addition to the characteristics which have just been mentioned in the preceding paragraph, the method for modifying the intestinal microbiome according to one aspect of the invention may have one or more complementary characteristics among the following, considered individually or according to all technically possible combinations: the phenotypic data comprise at least one data item among: a sex, an age, a body weight, a height, a body mass index (BMI), a body mass index category, a waist circumference and one or more data on a menopause status. the medical data includes at least one of: one or more data on a sleep apnea status, one or more data on a diabetes status and a treatment for diabetes, one or more data on a dyslipidemia status, one or more data on a treatment for dyslipidemia, one or more data on a hypertension status, one or more data on a treatment for hypertension, one or more data on a hypertriglyceridemia status, one or more data on a treatment for hypertriglyceridemia, one or more data on a hypercholesterolemia status, one or more data on a treatment for hypercholesterolemia, one or more data on a status of arthritis, one or more data on a treatment for arthritis, one or more data on a status of varicose vein disease, one or more data on a treatment for varicose vein disease,one or more data on the perception of emotional and physical well-being, one or more data on the perception of emotions and data on the perception of sleep quality, one or more data on a type of stool on a Bristol scale., Metagenomic data result from metagenomic sequencing of a fecal sample. Lifestyle data includes at least one of: one or more data on dietary, physical and social behavior, one or more data on smoking status, one or more data on physical activity practices, one or more data on diet and one or more socio-demographic data and one or more geographic data. The determination, among the first set of individuals, of a second set of individuals maximizing a similarity with the target individual consists of a determination of the second set of individuals, among the first set of individuals, minimizing a Gower distance calculated between the vector constructed for the target individual and the vector constructed for each individual of the first set of individuals. generating one or more recommendations involves determining at least one lifestyle modification of the target individual for the at least one domain for which data has been received, the at least one lifestyle modification relating to a lifestyle practice for which the target individual has obtained a score below a predetermined threshold and / or the difference between the target individual's score and the digital profiles of the second set is greater than a predetermined threshold difference. The method further comprises: Obtaining a molecular fingerprint of a metabolic activity of the intestinal microbiome, for the target individual and for each individual among the third set of individuals, by simulating, using a community model at the microbiome scale, a monitoring of a diet of each individual, the diet being determined from data on the diet of the target individual and the molecular fingerprint of the activity of the intestinal microbiome comprising a vector of metabolic flows, said vector of metabolic flows comprising a distribution of metabolic flows through reactions of the community model of the microbiome, Calculation of a composite molecular fingerprint of a community activity at the microbiome scale in the context of diet monitoring from the molecular fingerprint obtained for each individual among the third set of individuals, Calculating one or more differences between the molecular fingerprint of the target individual and the composite molecular fingerprint, and Generation of one or more dietary recommendations to reduce said one or more differences between the molecular fingerprint of the target individual and the composite molecular fingerprint. Obtaining a molecular fingerprint of a metabolic activity of the intestinal microbiome for the target individual and for each individual among the third set of individuals, includes the steps of: Obtaining at least one genome-scale metabolic model for each metagenomic taxon of the plurality of metagenomic taxon abundances and at least one additional exchange reaction, each genome-scale metabolic model comprising exchange reactions defining environmental conditions, an individual biomass of the metagenomic taxon simulating cell growth, and biochemical reactions and transport reactions encoded by protein-coding genes of the genome of the metagenomic taxon, Receiving at least one additional exchange reaction, Construction of a microbiome community model comprising the previously obtained genome-scale metabolic models linked by at least a portion of the exchange reactions comprised in the genome-scale metabolic models, the genome-scale metabolic models exchanging molecules with a common luminal compartment shared by the genome-scale metabolic models of the community model, the community model comprising the additional exchange reactions defining the admission of molecules from the diet into the common luminal compartment and the release of the molecules from the common luminal compartment into a fecal compartment, Calculation of a molecular fingerprint of community activity in the context of diet, the calculation of the molecular fingerprint comprising the use of a method linear optimization with community biomass maximization as the objective function and with the diet dataset as the external constraint, where community biomass is the sum of individual biomasses weighted by metagenomic taxa abundances, and metabolic flux vector comprises a distribution of metabolic fluxes across the microbiome community model reactions. Generating one or more dietary recommendations involves: Obtaining, for each individual of the third set of individuals, a molecular fingerprint of a metabolic activity of the intestinal microbiome, by simulating, using the community model at the scale of the microbiome of the target individual, a monitoring of a diet, said diet being determined from data on the diet of each individual of the third set of individuals, Ranking the individuals of the third set of individuals using the molecular fingerprint obtained for each individual of the third set of individuals, said ranking being based on the similarity of the molecular fingerprint of a metabolic activity of the intestinal microbiome obtained for each individual with the composite molecular fingerprint, and Generating one or more dietary recommendations based on the dietary data of the individuals in the third set of individuals and based on said ranking of the individuals in the third set of individuals.

[0024] A second aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the method according to the invention.

[0025] A third aspect of the invention relates to a computer-readable recording medium comprising instructions which, when executed by a computer, cause the latter to implement the method according to the invention.

[0026] A fourth aspect of the invention relates to a system comprising the means adapted to execute the method according to the invention.

[0027] The invention and its various applications will be better understood by reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0028] The figures are presented for information purposes only and in no way limit the invention. Figure 1 shows a schematic representation of a method for modeling an intestinal microbiome according to the invention. Figure 2 shows a schematic representation of step 110 of obtaining a molecular fingerprint of a metabolic activity of the intestinal microbiome according to a variant of the invention. Figure 3 shows a schematic representation of step 140 - of generating one or more dietary recommendations according to a variant of the invention. Figure 4 shows a schematic representation of a community model obtained by a method of modeling an intestinal microbiome according to the invention. Figures 5A, 5B, and 5C show examples of Gower distances calculated between a target individual and individuals maximizing similarity to the corresponding target individual. Figure 6 shows an example of Gower distance calculated between different individuals of a set of individuals compatible with the method according to the invention. Figure 7 shows an example of the distribution of the median Gower distance between all pairs of subjects in a set of individuals compatible with the method according to the invention. Figure 8 shows an example of results that can be obtained with the method according to the invention. Figure 9 shows characteristics of an example of a first set of individuals compatible with the method according to the invention. DETAILED DESCRIPTION

[0029] Figure 1 shows a schematic representation of a method for modifying the intestinal microbiome of an individual according to the invention. The method can, for example, make it possible to reduce symptoms of discomfort, such as bloating, during the digestion of a meal. The method can also, for example, make it possible to improve the health of an individual, in particular when the intestinal microbiome is negatively influenced, for example, by taking medications such as antibiotics. The method can also make it possible to improve the health of an individual in order to obtain purely aesthetic effects. For example, in order to combat hair loss, the method can aim to increase the abundance of biotin.

[0030] In a variant of the invention, the method according to the invention may be "computer-implemented." By "computer-implemented" is meant that the steps, or at least some of the steps, are executed by at least one computer or processor or any other similar system. Thus, steps are performed by the computer, possibly fully automatically, or semi-automatically. In examples, the triggering of at least some of the steps of these methods may be carried out by user-computer interaction. The level of user-computer interaction required may depend on the intended level of automation and balanced against the need to implement the user's wishes. In examples, this level may be user-defined and / or predefined.

[0031] A typical example of implementing a method according to the invention consists in executing the method with a system adapted for this purpose. A system 200 can be configured to implement all the steps of the method and / or its different embodiments according to the invention. To do this, the system 200 comprises a memory and a computing unit, the memory being configured to store instructions which, when executed by the computing unit, cause the computing unit to implement the steps of the method according to the invention. and / or the various embodiments according to the invention. The system 200 further comprises at least one network interface for communicating with remote entities, i.e. for sending and receiving data to said entities, via at least one network. The entities may be servers or computers storing data. The data may be phenotypic data, medical data, lifestyle data or metagenomic data. The data may be stored by the same server or by different servers / databases.

[0032] A first step of the method for modifying the intestinal microbiome according to the invention consists of receiving 10 for each individual of a first set of individuals and for a target individual, phenotypic data, medical data, lifestyle data and metagenomic data. The lifestyle data relates to at least one area among the practice of one or more physical activities, the quality and quantity of sleep, eating behavior, nutrition and the perception of emotional well-being. These data relate to lifestyle practices which are therefore modifiable. Thus, the generated recommendations 100 will be based on these data in order to modify the lifestyle practices of the target individual. The metagenomic data of an individual comprise a plurality of abundances of metagenomic taxa present in the intestinal microbiome of the individual. The reception 10 of the data can be carried out by the system 200.The term "target individual" refers to an individual not included in the first set of individuals. In addition, the target individual is the individual for whom the recommendations will be generated using the method according to the invention. The first set of individuals may include between five hundred individuals and several hundred thousand individuals. For example, the first set of individuals may include 639 individuals with the characteristics listed in the table below:.

[0033] Additionally, Figure 9 details the statistical characteristics of this example first set of individuals for different numerical variables.

[0034] In a variant of the invention, compatible with the previous variant, the phenotypic data, for each individual of the set of individuals and for the target individual, comprise at least one data item from among: a sex, an age, a body weight, a height, a body mass index (BMI), a body mass index category, and a waist circumference and one or more data items on a menopause status.

[0035] In a second variant of the invention, compatible with the previous variants, the medical data, for each individual of the set of individuals and for the target individual, comprise at least one piece of data from among: one or more pieces of data on a sleep apnea status, one or more pieces of data on a status of a diabetes and diabetes treatment, data on dyslipidemia status, data on dyslipidemia treatment, data on hypertension status, data on hypertension treatment, data on hypertriglyceridemia status, data on hypertriglyceridemia treatment, data on hypercholesterolemia status, data on hypercholesterolemia treatment, data on arthritis status, data on arthritis treatment, data on varicose vein disease status, data on varicose vein disease treatment, data on perception of emotional and physical well-being, data on perception of emotions and data on perception of sleep quality, data on stool type on a Bristol scale.

[0036] In one embodiment of the invention, consistent with the preceding embodiments, the diet data comprises the abundance of at least one molecule included in at least a portion of the individual's diet. The abundance of molecules in the diet data set corresponds to the absolute nutrient composition per person per day. The diet data can be obtained from various dietary data collection methods, such as food frequency questionnaires or 24-hour dietary recalls that collect individuals' dietary habits. The foods in these dietary records are translated into quantitative macro- and micro-molecule profiles. The quantitative macromolecule and micromolecule profile of an individual's diet is referred to as "diet data."The translation of foods into a quantitative profile can be done using integrated food databases such as the United States Department of Agriculture's FoodData Central (https: / / fdc.nal.usda.gov) which contains the molecular breakdown of these different foods, so that personalized molecular profiles of an individual can be defined from their dietary data.

[0037] In a variant of the invention, compatible with the preceding variants, the lifestyle data, for each individual of the set of individuals and for the target individual, comprise at least one data item from among: one or more data items on dietary, physical and social behavior, one or more data items on a status smoking, one or more data on one or more physical activity practices, one or more data on diet and one or more sociodemographic data and one or more geographical data. Sociodemographic data include, for example, housing, civil and family status, language(s) spoken, and level of education. Geographical data may include, for example, place of birth, place of residence or even place of work. Data on the perception of emotional well-being may include the level of stress experienced, the level of anxiety experienced or even the assessment of the feeling of exclusion. These data may be obtained from questionnaires.

[0038] In a variant of the invention, compatible with the preceding variants, certain data among the phenotypic data, the medical data, the metagenomic data and the lifestyle data received in step 10 are expressed as a difference during a period between a time to and a time ti. For example, instead of having data such as the weight of an individual, the data will be the variation in weight of an individual during a period. Similarly, the lifestyle data received in step 50 may be expressed as a difference during the same period. For example, an increase in the time spent practicing a physical activity may be received in step 50. Thus, it will be possible in this variant to identify correlations between the variations in the metagenomic, medical and phenotypic data and the variations in the lifestyle data.

[0039] In a variant of the invention, compatible with the preceding variants, the metagenomic data result from metagenomic sequencing, such as 16S sequencing, of a fecal sample and comprise a plurality of abundances of metagenomic taxa. Metagenomic sequencing of a fecal sample comprises extraction of nucleotides, for example DNA (deoxyribonucleic acid) and / or RNA (ribonucleic acid) from a fecal sample, library construction and sequencing in order to obtain, after taxonomic assignment, abundance tables and therefore the identification and quantification of the species forming the intestinal microbiome. A metagenomic dataset can be obtained from a fecal sample using a sequencing platform such as Nanopore or a high-throughput sequencing platform such as Illumina® NovaSeq® products. A metagenomic dataset is for example example one or more abundance tables including the abundance of at least one taxon present in the fecal samples. A taxon is understood to mean a taxonomic unit, such as the bacterium Akkermansia muciniphila present in a relative abundance of 1.5% (15,000 metagenomic reads classified as A. muciniphila / 1,000,000 total metagenomic reads in the fecal sample). The abundance of taxa may be a relative abundance, for example as a percentage of the microbial composition.

[0040] In a first implementation mode, compatible with the preceding variants, the first set of individuals whose data is received 10 have varied characteristics. By "varied characteristics" is meant data having values ​​presenting a great diversity in different domains. The determination, in steps 30 and 40, of several individuals, having a richness of the intestinal microbiome greater than a richness of the intestinal microbiome greater than a predefined richness value or greater than a richness of the microbiome of the target individual and an absence of a metabolic syndrome or a blood sugar, a waist circumference and a cholesterolemia lower than the target individual, maximizing a similarity with the target individual will make it possible to find among this set of individuals, the individuals necessary for the generation of personalized lifestyle modification recommendations.This implementation mode allows the maximization of the number of individuals present in the set of individuals, since no selection criteria are applied to integrate the set of individuals. In addition, this first set of individuals, due to its great diversity, allows to increase the probability that one or more individuals in the set of individuals is similar to the target individual.

[0041] In a second implementation mode, the individuals of the first set of individuals may constitute a population having one or more similar characteristics. For example, if the target individual has a specific medical characteristic, such as diabetes, the set of individuals could be made up of individuals having the same diabetes. Preferably, the similar characteristic(s) between the individuals of the first group are characteristics that cannot be modified such as age and / or sex, etc. The method according to this second implementation mode could be specifically dedicated to individuals suffering from diabetes.

[0042] In the second implementation mode, the individuals in the set of individuals with one or more similar characteristics can come, after filtering, from the individuals in the set of individuals with varied characteristics as defined in the first implementation mode. For example, among the thousands of individuals with varied characteristics, it is possible to filter all women suffering from diabetes, to obtain a set of individuals with two similar characteristics: sex and diabetic status.

[0043] The method according to the invention comprises a second step 20 of construction, for each individual of the first set of individuals and for the target individual, of a vector of the phenotypic data, the medical data, and the metagenomic data. The term "vector" designates a container of elements ordered and accessible by indices, the size of which is dynamic. Thus, an index is assigned to each data item of the phenotypic, medical, and metagenomic data. In addition, the data can be modified in order to optimize their storage and the determination 40 of individuals maximizing a similarity with the target individual.

[0044] The method according to the invention comprises a third step 30 of determining, among the first set of individuals, a second set of individuals maximizing a similarity with the target individual. The similarity is evaluated from the vectors constructed for each individual of the first set of individuals and from the constructed vector 20 for the target individual. More concretely, the constructed vectors will have a similar architecture, that is to say that the data of the same type will have the same index in the different constructed vectors. For example, the data “weight of a person” will have the same index in the different constructed vectors. Then, the similarity between each data item of the constructed vectors is calculated. Finally, an average similarity is calculated from the different similarities calculated previously. This average similarity corresponds to the similarity between the constructed vectors 30.

[0045] During this step 30, a predefined number of similar individuals to be determined may be predetermined. Thus, this step will for example make it possible to determine a predetermined proportion, for example 10%, 33% or 40%, of the individuals among the individuals of the entire population maximizing the similarity with the target individual among the set of individuals. Alternatively, a minimum similarity threshold can be predetermined. Thus, this step will, for example, make it possible to determine the individuals having a similarity greater than the minimum similarity threshold with the target individual among the set of individuals. These two alternatives can be combined during this step 30 to have a minimum or maximum number of individuals to determine and / or a minimum similarity threshold to respect.

[0046] In a variant of the invention, compatible with the preceding variants, the determination 30, among the first set of individuals, of a second set of individuals maximizing a similarity with the target individual consists of a determination of one or more individuals, among the first set of individuals, minimizing a Gower distance calculated between the vector constructed for the target individual and the vector constructed for each individual of the first set of individuals. For example, the second set of individuals may correspond to 25% of the individuals of the first set of individuals maximizing a similarity with the target individual.

[0047] In a variant of the invention, compatible with the preceding variants, the determination 30, among the first set of individuals, of a second set of individuals maximizing a similarity with the target individual comprises the identification of a predetermined number of individuals, for example ten individuals, among the set of individuals, maximizing a similarity with the target individual. To carry out this identification, the Gower distance is used on the basis of a data set as presented previously. The Gower distance is an index that uses numerical and categorical data to measure the degree of dissimilarity between two samples. The Gower distance varies between a value equal to zero, when two samples are identical, and a value equal to one, when two samples are completely dissimilar. In this variant of the invention, the following formula is used:

[0048]

[0049] With :

[0050] Sj, the partial similarity between data x t and Xj.

[0051] Furthermore, it is possible to note that the similarity between two samples can be calculated differently if the data are numerical or categorical. Several known formulas are compatible with the method according to the invention.

[0052] Figures 5A, 5B and 5C show examples of Gower distances calculated between a target individual and individuals maximizing a similarity with the corresponding target individual. Figure 5A illustrates for example that a first target individual has a Gower distance equal to 0.0982100 with individual 470081101. Figure 5B illustrates for example that a second target individual has a Gower distance equal to 0.0540370 with individual 910072261. Figure 5C illustrates for example that a third target individual has a Gower distance equal to 0.0824474 with individual 550469111. The proximity of the target individuals to the individuals maximizing a similarity is highlighted both by the low values ​​of the Gower distance and by the comparison with the distance matrix of Figure 6, which is an extract of the complete matrix summarized in Figure 7. Figure 6 illustrates the Gower distance between different individuals of the set of individuals identified by an identifier.For example, the Gower distance between individual 100011101 and individual 100036071 is equal to 0.2152812. Figure 6 illustrates a distribution of the median Gower distance between all pairs of subjects in a set of individuals compatible with the invention. Figure 7 therefore shows that the average distance between the samples is preferentially relatively low and therefore that the homogeneity of the set of individuals also allows sufficient diversity to find close matches with the customer. In a variant, compatible with the previous variants, the set of individuals comprises for example between 500 and 1000 individuals, for example 750 individuals.

[0053] The method according to the invention comprises a fourth step 40 of determining, among the second set of individuals, a third set of individuals according to characteristics of their intestinal microbiome. For example, each individual of the third set of individuals has an intestinal microbiome richness greater than a predefined intestinal microbiome richness value and / or greater than the intestinal microbiome richness of the target individual and an absence of metabolic syndrome or a blood sugar, waist circumference and cholesterolemia lower than the target individual. Metabolic syndrome corresponds to the association of several disorders linked to the presence of excess fat inside the belly. These disorders are for example abnormally high blood sugar, cholesterol and blood pressure. In another example, each individual in the third set of individuals has a gut microbiome richness greater than a predefined gut microbiome richness value and / or greater than the gut microbiome richness of the target individual and has experienced weight loss and / or a reduction in their blood sugar and / or a reduction in cholesterol following surgery and / or dietary intervention.

[0054] The method according to the invention comprises a fifth step 50 of generating a digital profile, for each individual of the third set of individuals, from the phenotypic, medical, metagenomic and lifestyle data of said each individual of the third set of individuals. The digital profile also comprises a score representing the positive or negative influence of an individual's lifestyle on their intestinal microbiome. This score is calculated from the lifestyle data and based in particular on the knowledge provided by the scientific literature. In a first example, the higher the calculated score for an individual's digital profile, the more positive the influence of that individual's lifestyle on their intestinal microbiome. In a second example, the higher the calculated score for an individual's digital profile, the more negative the influence of that individual's lifestyle on their intestinal microbiome.

[0055] In a variant of the invention, compatible with the preceding variants, the score(s) may be calculated from prior knowledge. For example, examples of score calculations may be found in the scientific literature: Regarding emotional well-being: “36-ltem Short Form Survey (SF-36) Scoring Instructions” disclosed for example in Ware, JE, Jr., & Sherbourne, CD “The MOS 36-ltem Short-Form Health Survey (SF-36): I. Conceptual Framework and Item Selection,”. Medical Care, 30:473-483, 1992, Regarding physical activity: a sum of Baecke indices calculated from questionnaires (using the “Baecke Physical Activity Questionnaire / Modified Baecke Physical Activity Questionnaire” for example),

[0056] -Regarding eating behavior: the “Uncontrolled eating score” index disclosed for example in Cappelleri, JC, Bushmakin, AG, Gerber, RA, Leidy, NK, Sexton, CC, Lowe, MR, & Karlsson, J. (2009),

[0057] -Regarding sleep: an average of the following questions transformed onto a scale of 0 to 100% can be used: Severity of sleep disturbances: Choice from 0 (non-troublesome disturbances) to 10 (extremely bothersome disturbances) Actual sleep quality: Choice from 0 (poor sleep) to 10 (excellent sleep) Quality of wakefulness during the day: Choice ranging from 0 (daytime sleepiness) to 10 (daytime awake)

[0058] The method according to the invention comprises a sixth step 60 of generating a digital profile of the target individual from the phenotypic, medical, metagenomic and lifestyle data of the target individual.

[0059] The method according to the invention comprises a seventh step 70 of generating a digital lifestyle data set. This data set comprises lifestyle data generated from the lifestyle data received 50 from the target individual. Thus, in order to generate lifestyle data, it is possible to generate random values ​​included in a predetermined value interval. The value interval being for example predetermined based on data from the scientific literature. For example, for lifestyle data concerning the practice of a physical activity, the predetermined data interval can be between 0 and 30 hours per week. In order to generate lifestyle data, it is also possible to generate only data making it possible to improve the score representing the positive influence of a person's lifestyle on the intestinal microbiome.For the same example, of physical activity practice, if the target individual has a practice of two hours per week, then the values ​​of 0 or 1 hour per week will not be generated.

[0060] The method according to the invention comprises an eighth step 80 of generating a first set of digital profiles. The digital profiles of the first set of digital profiles are generated from the phenotypic, medical and metagenomic data of the target individual and the data set of lifestyle generated 70. Thus, these digital profiles are a mixture of "passive" data from the target individual and "active" data generated. Here, the term "active data" describes the data for which the method according to the invention will potentially provide recommendations for modifying them. Conversely, "passive data" is data for which the method does not provide recommendations. The method therefore aims to positively influence, via a modification of the microbiome, the passive data via recommendations for modifying the active data.

[0061] The method according to the invention comprises a ninth step 90 of generating a second set of digital profiles. This second set of digital profiles maximizes a similarity with the digital profile of the target individual and has a score higher than the score of the digital twin of the target individual. During this step 90, a predefined number of similar digital profiles to be determined may be predetermined. Thus, this step will for example make it possible to determine between one and several hundred digital profiles maximizing the similarity with the digital profile of the target individual from among the first set of digital profiles. Alternatively, a minimum similarity threshold may be predetermined. Thus, this step will for example make it possible to determine the individuals having a similarity higher than the minimum similarity threshold with the digital profile of the target individual from among the first set of digital profiles.These two alternatives can be combined during this step to have a minimum or maximum number of digital profiles to determine and / or a minimum similarity threshold to respect.

[0062] In a variant of the invention, compatible with the preceding variants, each digital profile comprises a vector of all the data of the digital profile. The vector therefore comprises phenotypic, medical and metagenomic and lifestyle data. The similarity between two digital profiles is determined in a similar manner as for step 40. Thus, the digital profiles of the second set will be determined by identifying the digital profiles minimizing a Gower distance calculated between the vector constructed for the digital profile of the target individual and the vector constructed for each individual of the first set of digital profiles. More concretely, the vectors of all digital profiles will have a similar architecture, that is to say that the data of the same type will have the same index in the different vectors. For example, the data “weight of a individual >> will have the same index in the different vectors. Then, the similarity between each data of the vectors is calculated. Finally, an average similarity is calculated from the different similarities calculated previously. This average similarity is the similarity between the two digital profiles.

[0063] The method according to the invention comprises a tenth step 100 of generating one or more recommendations for the domain(s) for which data were received in step 50. The recommendations aim to reduce the gap between the score of the digital profiles of the second set and the score of the target individual. The recommendations relate to the domain(s) for which lifestyle data were received in step 50. The right-hand diagram of Figure 8 is an alluvial diagram illustrating an example of results that can be obtained with the method according to the invention for a target individual, called a client in Figure 8. The right-hand diagram of Figure 8 thus shows an example in which, for four variables, the client's score is compared to the scores of the digital profiles of the second set of digital profiles.The left diagram shows the actual values ​​for the target individual and for individuals from the third set of individuals, called champions in Figure 8, who are most similar to the numerical profiles of the second set of numerical profiles in the right diagram of Figure 8. Thus, from these results, it is possible to generate one or more recommendations for the 4 domains illustrated. For example, from the right diagram of Figure 8, it is possible to identify that the sleep domain is an area to improve for client 980021661. Thus, it is possible to recommend for client 980021661 to try to improve the quality of his sleep, for example by increasing his sleep time, going to bed regularly at the same time, limiting exposure to blue light.

[0064] In a variant of the invention, compatible with the preceding variants, the generation 100 of one or more recommendations consists of determining one or more modifications to the lifestyle of the target individual. This modification to the lifestyle of the target individual may concern a lifestyle practice for which the target individual has obtained a particularly low score, for example a score below a predetermined threshold. This modification to the lifestyle of the target individual may also concern a lifestyle practice for which the difference between the score of the target individual and the numerical profiles of the second set is the Tl greater and / or greater than a predetermined threshold difference. These recommendations therefore make it possible to modify the microbiome of the target individual, which will in turn have a beneficial effect on the individual's well-being.

[0065] In a variant of the invention, compatible with the preceding variants, the method comprises four additional steps. The first additional step 110 consists of obtaining a molecular fingerprint of a metabolic activity of the intestinal microbiome, for the target individual and for each individual among the third set of individuals, by simulating, using a community model at the microbiome scale, the monitoring of a diet of each individual. The diet is determined from, respectively, the data on the diet of the target individual and the data on the diet of said each individual among the third set of individuals. Thus, the molecular fingerprint of a metabolic activity of the intestinal microbiome for the target individual is obtained by simulating the monitoring of a diet of the target individual.Similarly, the molecular fingerprint of a metabolic activity of the intestinal microbiome for each individual among the third set of individuals is obtained by simulating the monitoring of a diet for said each individual. The monitoring of a diet is defined as the absorption of food within the framework of this diet. Thus, the absorption of food within the framework of a first diet is simulated at this step 110. The molecular fingerprint of the activity of the intestinal microbiome comprises a vector of metabolic fluxes. The vector of metabolic fluxes comprises a distribution of metabolic fluxes through reactions of the community model of the microbiome.

[0066] In a variant of the invention, compatible with the previous variants, the additional step 110 comprises four sub-steps. Figure 2 illustrates this variant of the invention.

[0067] A first sub-step 11 consists of obtaining at least one genome-scale metabolic model. These genome-scale metabolic models may be requested by the system 200 from a database or a server, after receiving metagenomic data in step 10. The system 200 receives in response the genome-scale metabolic models (GSMM), one for each taxon of the metagenomic dataset. The genome-scale metabolic models may be obtained using a repository called AGORA (htp: / / dx.doi.org / 10.1038 / nbt.3703 or more recently https: / / doi.org / 10.1038 / s41587-022-01628-0) which contains GSMMs for more than 800 microorganisms of the gut microbiome, reconstructions that have received varying degrees of manual processing in terms of reaction content and biomass equation definition that substantially improves its predictive ability compared to automatically generated GSMMs.

[0068] A genome-scale metabolic model of a metagenomic taxon includes genes, reactions, and metabolites. A genome-scale metabolic model allows computational simulation of the taxon's cell growth. It defines environmental conditions, exchange reactions, and an individual biomass of the metagenomic taxon. "Exchange reactions" are pseudo-reactions that make different metabolites available for biochemical and transport reactions encoded by protein-coding genes in the taxon's genome.For example, an exchange reaction for L-Glucose (EX_Glc_L: =>L_Glc[e]) makes available the metabolite L-glucose (L_Glc) in the extracellular environment of GSMM ([e]), from which GSMM-specific transport reactions can transport the metabolite to the cytoplasmic / intracellular compartment (e.g. by an ATP-dependent transport system represented by the reaction L_Glc_ABCtrp: L_Glc[e] + ATP[c] => L_Glc[c] + ADP[c] + Pi[c]), and intracellular reactions can transform the metabolite for a given metabolic purpose (e.g., in the case of L-glucose, the metabolite is channeled by central metabolism reactions that allow the generation of energy (ATP) and precursors for different biomass constituents).The environmental conditions are the nutrients available to simulate cell growth (e.g. glucose in the example above) and the individual taxon biomass is a specific reaction that reflects the cell's needs (amino acids, nucleotides, lipids, cofactors, possibly macromolecules like DNA, RNA or proteins in some models) to make one gram of cell dry weight (cell growth). As an example, the biomass equation for the bacterium A. muciniphila, commonly found in the human gut microbiome, is shown below:.

[0069] 0.0030965 nadp[c] + 35.5403 h2o[c] + 0.21909 glu_L[c] + 0.0030965 ACP[c] + 0.0030965 coa[c] + 0.0030965 thmpp[c] + 40.1102 atp[c] + 0.0030965 nad[c] + 0.13541 gtp[c] + 0.0030965 amet[c] + 0.21909 gln_L[c] + 0.016021 dttpfc] + 0.084104 ctp[c] + 0.016021 datp[c] + 0.016021 dctp[c] + 0.016021 dgtp[c] + 0.0030965 adocbl[c] + 0.20083 asp_L[c] + 0.42793 ala_L[c] + 0.50987 gly[c] + 0.0030965 10fthf[c] + 0.0030965 thf[c] + 0.079264 his_L[c] + 0.37539 leu_L[c] + 0.21107 thr_L[c] + 0.076146 cys_L[c] + 0.0030965 mqn8[c] + 0.2467 arg_L[c] + 0.20083 asn_L[c] + 0.1278 met_L[c] + 0.0030965 ca2[c] + 0.010648 pg180[c] + 0.010648 clpn180[c] + 0.010648 pgai17[c] + 0.010648 clpnai17[c] + 0.010648 pgi17[c] + 0.010648 clpnil 7[c] + 0.0030965 cobalt2[c] + 0.090832 utp[c] + 0.17946 ser_L[c] + 0.0030965 q8[c] + 0.0030965 cu2[c] + 0.28544 lys_L[c] + 0.0030965 fe2[c] + 0.0030965 fe3[c] + 0.0030965 fad[c] + 0.0030965 2dmmq8[c] + 0.0030965 gthrd[c] + 0.15452 phe_L[c] + 0.18435 pro_L[c] + 0.12068 tyr_L[c] + 0.0030965 pheme[c] + 0.025011 colipa[c] + 0.0030965 k[c] + 0.2418 ile_L[c] + 0.025011 udcpdp[c] + 0.0030965 5mthf[c] + 0.0030965 mg2[c] + 0.0030965 mn2[c] + 0.0030965 ptrc[c] + 0.0030965 pydx5p[c] + 0.025011 PGP[c] + 0.010648 pe180[c] + 0.010648 peai17[c] + 0.010648 pei17[c] + 0.0030965 ribflv[c] + 0.0030965 so4[c] + 0.0030965 sheme[c] + 0.0030965 spmd[c] + 0.047202 trp _L[c] + 0.35223 val_L[c] + 0.0030965 zn2[c] + dnarep[c] + proteinsynth[c] + 0.0030965 cl[c] + rnatrans[c] -> 40 h[c] + 39.9969 pi[c] + 0.4846 ppi[c] + 40 adp[c] + 0.0030965 apoACP[c] + 0.0030965 cbi[c] + biomass[c] + 0.0030965 dmbzid[c] + 0.025011 PGPml[c].

[0070] A second sub-step 112 consists of receiving at least one additional exchange reaction, which will be used during the construction 113 of the microbiome-scale community model.

[0071] A third sub-step 113 is to build a microbiome-scale microbiome community model. The microbiome-scale community model is created by linking individual GSMMs through their exchange reactions. Individual GSMMs exchange molecules between their individual cytoplasmic environment and a luminal compartment shared by all GSMMs in the community. This allows molecules to be exchanged between GSMMs and ultimately links GSMMs. Additional exchange reactions allow the release of molecules into an external compartment such as the fecal compartment to be simulated. These additional reactions are linked to the GSMM individual and may be received during step 113 or during a preceding step, or may constitute a step in itself. In another embodiment, this step may be performed from metagenomic taxon abundances and genome-wide metabolic models gathered at higher levels of the taxonomic hierarchy than species-level abundances, e.g., genus, family, order, class, phylum. In a first embodiment, the metagenomic gene abundances are derived from non-redundant gene catalogs of the human gut microbiome and the genome-wide metabolic models are derived from functional annotations of said non-redundant gene catalogs.In another embodiment, the construction of a microbiome community model includes metagenomic taxa specific to the target individual, product of the de-novo genome assembly with the metagenomic dataset generated from the corresponding fecal sample and the reconstruction of the GSMM from the functional annotation of the genome.

[0072] A fourth sub-step 114 is to calculate a molecular fingerprint of the community activity. By "community" is meant the set of taxa of the combined metagenomic data. The metabolic flux vector of the metabolic fingerprint is calculated based on the microbiome-scale community model created as described above. Calculating the metabolic flux vector includes calculating the admissible flux distributions that satisfy the steady-state constraint and using a linear optimization method with maximizing the community biomass as the objective function and with the diet data set as the external constraint. This linear optimization method may be, as described in the prior art, FBA, pFBA, FVA or any other suitable linear optimization method.Community biomass is the sum of the individual biomasses of the taxa included in the received GSMMs from the taxa, weighted by the abundances of the metagenomic taxa. In other words, if a metagenomic taxon is present in greater abundance than another metagenomic taxon, its individual biomass will contribute more to the community biomass. The diet dataset is used as an external constraint, i.e., it defines the input metabolites of the model. community. The resulting metabolic flux vector Vec comprises a distribution of metabolic fluxes across community reactions that maximizes community biomass, which is the molecular driver of community activity under the defined dietary constraints. Below is an example of a metabolic flux vector (Flux column) produced from pFBA simulations on a community model of an individual comprising 402479 reactions from 252 metagenomic taxa identified in the simulated fecal sample under a dietary profile derived from the individual's food frequency questionnaire (FFQ).For simplicity, only 44 reactions are represented, corresponding to the dietary exchange reactions that define the consumption of dietary metabolites by the community (Diet_EX_ reactions), the communityBiomass reaction corresponding to the objective function that is maximized by the pFBA step, a subset of GSMM reactions of two microbial taxa of the community (Acidaminococcus fermetans and Dorea formicigenerans) with their corresponding biomass reaction (biomass400 and biomass137 in Table 1) and a subset of exchange reactions from the lumen to the fecal compartment (EX_ reactions in Table 1).

[0073]

[0173] This variant of the invention comprising sub-steps 111 to 114 allows the source of chemical compounds from the diet to be specifically traced to the fecal compartment via the reaction fluxes in the community model, as illustrated in Figure 4. As shown in Figure 4, it is possible, using the invention, to trace TMA (trimethylamine) and TMAO (trimethylamine oxide) from the fecal compartment to the dietary source (dietary choline (CHOL) being the source of TMA / TMAO in the fecal compartment). For example, with a European diet, two bacteria (S. wadsworthensis_3_1_45B and E. lenta_DSM_2243) can be shown to use luminal TMAO as an electron acceptor in respiration, releasing TMA and decreasing TMAO in the fecal compartment.

[0174] The second additional step 120 consists of calculating a composite molecular fingerprint of the community activity at the microbiome scale in the context of the diet monitoring. In a variant of the invention, compatible with the previous variants, the composite molecular fingerprint can be obtained by calculating the average of the molecular fingerprints of the community activity at the microbiome scale in the context of the diet monitoring obtained 110 for each individual among the third set of individuals. Optionally, the average of the molecular fingerprints can be a weighted average. The weighting can be based on the similarity determined in step 30.For example, the molecular fingerprints of individuals maximizing similarity to the target individual will have a greater weight in calculating the composite molecular fingerprint than the molecular fingerprints of individuals with lower similarity to the target individual.

[0175] The third additional step 130 consists of calculating one or more differences between the molecular fingerprint of the target individual and the composite molecular fingerprint. These differences will then make it possible to generate, in step 140, one or more dietary recommendations. These dietary recommendations aim to reduce the calculated difference(s) 130.

[0176] In a variant of the invention, compatible with the previous variants, the generation of one or more dietary recommendations is carried out in three sub-steps. Figure 3 illustrates this variant of the invention.

[0177] The first sub-step 141 consists of obtaining at least one molecular fingerprint of a metabolic activity of the intestinal microbiome, by simulating, using the community model at the scale of the microbiome of the target individual, the monitoring of the diet, determined from data from a food data catalog.

[0178] The second sub-step 142 consists of classifying the at least one molecular fingerprint obtained 141. The classification is based on the similarity of the at least one molecular fingerprint of a metabolic activity of the intestinal microbiome obtained 141 with the composite molecular fingerprint. In order to determine the molecular fingerprints obtained 141 most similar to the composite fingerprint, a standard score, also called Z score, can be used.

[0179] The third sub-step 143 consists of generating one or more dietary recommendations based on the data from the food data catalog. In addition, these dietary recommendations will be generated based on the ranking of the at least one molecular fingerprint obtained in step 141. For example, the dietary recommendations may be based on the dietary data of a predefined number of molecular fingerprints obtained having the best ranking among all the molecular fingerprints obtained. It is also possible to generate dietary recommendations by combining the dietary data of a predefined number of molecular fingerprints obtained having the best ranking among the molecular fingerprints obtained in step 141.

[0180] In a variant of the invention, compatible with the previous variants, the method according to the invention comprises a step of monitoring the recommendations generated in step 10. Optionally, the dietary recommendations generated in step 143 can also be monitored.

[0181] Unless otherwise specified, the same element appearing in different figures has a single reference.

Claims

CLAIMS

1. A method of modifying the intestinal microbiome comprising the steps of: - Reception (10), for each individual of a first set of individuals and for a target individual, said target individual being an individual not included in the first set of individuals, of phenotypic data, medical data, lifestyle data, said lifestyle data comprising at least one domain among the practice of one or more physical activities, the quality and quantity of sleep, eating behavior, nutrition and the perception of emotional well-being and metagenomic data, said metagenomic data comprising a plurality of abundances of metagenomic taxa, - Construction (20), for each individual of the first set of individuals and for the target individual, of a vector comprising the received data (10), - Determination (30), among the first set of individuals, of a second set of individuals maximizing a similarity with the target individual, the similarity being evaluated from the vectors calculated for each individual of the first set of individuals and from the constructed vector (20) for the target individual, - Determination (40), among the second set of individuals, of a third set of individuals, each individual of the third set of individuals having: - a gut microbiome richness greater than a predefined gut microbiome richness value and / or greater than a gut microbiome richness of the target individual, and / or - an absence of metabolic syndrome and / or blood sugar and / or waist circumference and / or cholesterol levels lower than the target individual, - For each individual of the third set of individuals, generation (50) of a digital profile from the phenotypic, medical, metagenomic and lifestyle data of said each individual of the third set of individuals, said digital profile of each individual of the third set of individuals further comprising a value resulting from an evaluation of a score representing the positive or negative influence of a person's lifestyle on the intestinal microbiome, - Generation (60) of a digital profile of the target individual comprising phenotypic, medical, metagenomic and lifestyle data of the target individual, said digital profile further comprising a value resulting from an evaluation of a score representing the positive or negative influence of the lifestyle of an individual on the intestinal microbiome, - Generation (70) of a lifestyle data set from the lifestyle data of the target individual, - Generating (80) a first set of digital profiles, each digital profile of the first set of digital profiles being generated from the generated lifestyle data set (70), each digital profile further comprising a score representing the positive or negative influence of an individual's lifestyle on the intestinal microbiome, - Determination (90) of a second set of digital profiles from among the first set of digital profiles, the second set of digital profiles: - maximizing a similarity with at least one digital profile among the generated digital profiles (50) for each individual of the third set of target individuals, and - having a score higher than the target individual's digital profile score, and Generation (100) of at least one recommendation concerning the at least one domain for which data has been received (50), said at least one recommendation for reducing the gap between the score of the digital profiles of the second set and the score of the target individual for the at least one domain for which lifestyle data has been received (50).

2. A method according to claim 1 wherein: - Phenotypic data includes at least one of: sex, age, body weight, height, body mass index (BMI), body mass index category, waist circumference and one or more data on menopause status, - The medical data includes at least one of: one or more data on sleep apnea status, one or more data on diabetes status and diabetes treatment, one or more data on dyslipidemia status, one or more data on dyslipidemia treatment, one or more data on hypertension status, one or more data on hypertension treatment, one or more data on hypertriglyceridemia status, one or more data on hypertriglyceridemia treatment, one or more data on hypercholesterolemia status, one or more data on hypercholesterolemia treatment, one or more data on arthritis status, one or more data on arthritis treatment, one or more data on varicose vein disease status, one or more data on varicose vein disease treatment, one or more data on the perception of emotional and physical well-being,one or more data on the perception of emotions and data on the perception of sleep quality, one or more data on a type of stool on a Bristol scale, - Metagenomic data result from metagenomic sequencing of a fecal sample, and - Lifestyle data includes at least one of: one or more data on dietary, physical and social behavior, one or more data on smoking status, one or more data on one or more physical activity practices, one or more data on a diet and one or more sociodemographic data and one or more geographical data.

3. Method according to claim 1 or 2 according to which the determination (30), among the first set of individuals, of a second set of individuals maximizing a similarity with the target individual consists of a determination of the second set of individuals, among the first set of individuals, minimizing a Gower distance calculated between the constructed vector (20) for the target individual and the constructed vector (20) for each individual of the first set of individuals.

4. A method according to any preceding claim wherein generating (100) one or more recommendations comprises determining at least one lifestyle modification of the target individual for the at least one domain for which data has been received (50), the at least one lifestyle modification relating to a lifestyle practice for which the target individual has obtained a score below a predetermined threshold and / or the difference between the score of the target individual and the digital profiles of the second set is greater than a predetermined threshold difference.

5. A method according to any preceding claim further comprising: - Obtaining (110) a molecular fingerprint of a metabolic activity of the intestinal microbiome, for the target individual and for each individual among the third set of individuals, by simulating using a community model at the microbiome scale, a monitoring of a diet of each individual, the diet being determined from respectively the data on the diet of the target individual and the data on the diet of said each individual among the third set of individuals and the molecular fingerprint of the activity of the intestinal microbiome comprising a vector of metabolic flows, said vector of metabolic flows comprising a distribution of the flows metabolic through reactions of the microbiome community model, - Calculation (120) of a composite molecular fingerprint of a community activity at the microbiome scale in the context of diet monitoring from the molecular fingerprint obtained for each individual among the third set of individuals, - Calculation (130) of one or more differences between the molecular fingerprint of the target individual and the composite molecular fingerprint, and - Generation (140) of one or more dietary recommendations making it possible to reduce said one or more differences between the molecular fingerprint of the target individual and the composite molecular fingerprint.

6. The method of claim 5 wherein obtaining (110) a molecular fingerprint of a metabolic activity of the intestinal microbiome for the target individual and for each individual among the third set of individuals, comprises the steps of: - Obtaining (111) at least one genome-wide metabolic model for each metagenomic taxon of the plurality of metagenomic taxon abundances and at least one additional exchange reaction, each genome-wide metabolic model comprising exchange reactions defining environmental conditions, an individual biomass of the metagenomic taxon simulating cell growth, and biochemical reactions and transport reactions encoded by genes encoding proteins of the genome of the metagenomic taxon, - Reception (112) of at least one additional exchange reaction, - Construction (113) of a microbiome community model comprising the previously obtained genome-scale metabolic models linked by at least a portion of the exchange reactions comprised in the genome-scale metabolic models, the genome-scale metabolic models exchanging molecules with a common luminal compartment shared by the genome-scale metabolic models of the community model, the community model including the additional exchange reactions defining the admission of molecules from the diet into the common luminal compartment and the release of the molecules from the common luminal compartment into a fecal compartment, and - Calculating (114) a molecular fingerprint of community activity under the diet, the molecular fingerprint calculation comprising using a linear optimization method with community biomass maximization as the objective function and with the diet dataset as the external constraint, the community biomass being the sum of individual biomasses weighted by the abundances of the metagenomic taxa, the metabolic flux vector comprising a distribution of metabolic fluxes across the reactions of the microbiome community model.

7. A method of modifying the intestinal microbiome according to claim 6 wherein the generation (140) of one or more dietary recommendations comprises: - Obtaining (141) at least one molecular fingerprint of a metabolic activity of the intestinal microbiome, by simulating, using the community model at the scale of the microbiome of the target individual, a monitoring of a diet, said diet being determined from data from a food data catalog, - Ranking (142) the at least one molecular fingerprint obtained (141), said ranking being based on the similarity of the molecular fingerprint of a metabolic activity of the intestinal microbiome obtained (141) with the composite molecular fingerprint, and - Generation (143) of one or more dietary recommendations based on data from the food data catalog and according to said classification (142).

8. A computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the method according to any one of claims 1 to 7.

9. A computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 7.

10. A system (200) comprising means adapted to carry out the method according to any one of claims 1 to 7.

Images