Methods to establish and restore normal gut microbiota function of subject in need thereof

The present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota. The present invention also provides composition and methods for preventing and/or treating a disease in a subject in need thereof.

FIELD OF THE INVENTION

The present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota. The present invention also provides composition and methods for preventing and/or treating a disease in a subject in need thereof.

REFERENCE TO SEQUENCE LISTING

A paper copy of the sequence listing and a computer readable form of the same sequence listing are appended below and herein incorporated by reference. The information recorded in computer readable form is identical to the written sequence listing, according to 37 C.F.R. 1.821(f).

BACKGROUND OF THE INVENTION

Systematic analyses of the gut microbiota in different healthy and unhealthy populations have been undertaken in the scientific community for many years. The gut microbiota comprises a complex community whose composition is in flux in infants and is generally stable in adults. Disease, illness, and diet, among other factors, have been shown to affect the proportional representation of the bacterial species comprising the gut microbiota. Accordingly, the gut microbiota is viewed as both a diagnostic and therapeutic target. There remains a need in the art, therefore, for methods to define microbiota maturity using bacterial taxonomic biomarkers that are highly discriminatory for age as a way to characterize the health status of the gut microbiota.

SUMMARY OF THE INVENTION

In an aspect, the present disclosure encompasses a method to determine the maturity of a subject's gut microbiota, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) calculating the maturity of the subject's gut microbiota, wherein the calculation for maturity is defined as relative maturity, and relative maturity=(microbiota age of the subject)−(microbiota age of a healthy subject of a similar chronological age. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.

In another aspect, the present disclosure encompasses a method to determine the maturity of a subject's gut microbiota, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) calculating the maturity of the subject's gut microbiota, wherein the calculation for maturity is defined as relative maturity, and relative maturity=(microbiota age of the subject)−(microbiota age of a healthy subject of a similar chronological age. In certain embodiments, the group comprises the bacterial taxa listed in Table B.

In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar chronological age. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.

In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar chronological age. In certain embodiments, the group comprises the bacterial taxa listed in Table B.

In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and wherein the classification model assigns the subject to a category. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.

In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and wherein the classification model assigns the subject to a category. In certain embodiments, the group comprises the bacterial taxa listed in Table B.

In another aspect, the present disclosure encompasses a method for identifying an effect of a therapy, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.

In another aspect, the present disclosure encompasses a method for identifying an effect of a therapy, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. In certain embodiments, the group comprises the bacterial taxa listed in Table B.

In another aspect, the present disclosure encompasses method for identifying an effect of a therapy, the method comprising (a) identifying a set of age-discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising (i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time; (ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age-discriminatory bacterial taxa present in the subject's gut microbiota; and (iii) selecting the minimum number of bacterial taxa from step (ii) that is needed to produce a prediction that is substantially similar to or better than the prediction of step (ii); calculating a relative abundance for each bacterial taxon in the set of age-discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and applying the relative abundances from step (b) to the prediction of gut microbiota age from step (a)(iii) to determine the microbiota age of the subject's gut microbiota before and after therapy; wherein the therapy has an effect when the microbiota age of the subject changes after therapy.

In another aspect, the present disclosure encompasses a composition comprising at least one of the bacterial taxa listed in Table A. The present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising at least one of the bacterial taxa listed in Table A.

In another aspect, the present disclosure encompasses a composition comprising a combination of bacterial taxa, the combination comprising of at least two bacterial taxa listed in Table B. The present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising a combination of bacterial taxa, wherein the combination comprises at least two bacterial taxa listed in Table B.

In another aspect, the present disclosure encompasses a composition comprising a combination of bacterial taxa listed, wherein the combination is selected from combinations listed in Table D. The present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising a combination of bacterial taxa, wherein the combination is selected from combinations listed in Table D.

Other aspects and iterations of the invention are described more thoroughly below.

DETAILED DESCRIPTION

Applicants have discovered that maturation of the gut microbiota in healthy subjects (i.e. normal maturation of the gut microbiota) can be characterized or defined by a minimal number of bacterial taxa whose relative abundance changes over time. Accordingly, the present invention provides a method to identify a set of bacterial taxa that define normal maturation of the gut microbiota. The set of bacterial taxa can also be used to characterize the maturity of a test subject's gut microbiota. Determining whether the maturation of a test subject's gut microbiota corresponds to the test subject's chronological age may be used to guide treatment decisions (e.g. an immature state may indicate a need to initiate or continue therapy) or evaluate the effectiveness of a therapy (e.g. no improvement or a worsening would indicate the therapy is not effective).

Applicants have also discovered a set of bacterial taxa that strongly correlate with normal maturation of the gut microbiota in healthy children and recovery from diseases that perturb a normal/healthy configuration of the gut microbiota in adults. Accordingly, the present invention provides methods for preventing and/or treating a disease in a subject in need thereof by administering a composition comprising one or more of those bacterial taxa. Useful combinations of bacterial taxa are disclosed herein.

The term “subject,” as used herein, refers to a mammal, including, but not limited to, a dog, a cat, a rat, a mouse, a hamster, a mouse, a cow, a horse, a goat, a sheep, a pig, a camel, a non-human primate, and a human. In a preferred embodiment, a subject is a human.

As used herein, a “healthy subject” is a subject with no known or diagnosed disease. The health of a subject may also be assessed by anthropometric measurements. For humans, the World Health Organization (WHO) Department of Nutrition for Health and Development has published a set of reference standards for individuals of about 19 years of age or less (WHO child growth standards growth velocity based on weight, length and head circumference: methods and development; World Health Organization, 2009; or current edition). Similar standards are known in the art for other subjects. The terms “healthy subject” and “normal subject” may be used interchangeably.

As used herein, a “subject in need of treatment” or a “subject in need thereof” is a subject in need of prophylaxis against, or treatment for, a disease, preferably a gastrointestinal disease. In some embodiments, a subject in need of treatment may be a healthy subject. For example, a healthy subject may have an increased risk of developing a disease relative to the population at large. In other embodiments, a subject in need of treatment may have a disease. In certain embodiments, a subject may have an immature microbiota and, therefore, may be in need of treatment. The phrase “microbiota immaturity” is described in detail in Section 1(C).

As used herein, the term “gut microbiota” refers to microbes that have colonized and inhabit the gastrointestinal tract of a subject. While various aspects of the present invention are exemplified with bacteria, the invention is applicable to all microbes including, but not limited to, archaea, bacteria, fungi, protists and viruses. A subject's gut microbiota may be naturally acquired or artificially established. Means by which a subject naturally acquires its gut microbiota are well known. Such examples may include, but are not limited to, exposure during birth, environmental exposure, consumption of foods, and coprophagy. Means by which a subject's gut microbiota may be artificially established are also well known. For example, artificially established gut microbial communities can be established in gnotobiotic animals by inoculating an animal with a defined or undefined consortium of microbes. Typically, a naturally acquired gut microbiota is comprised of both culturable and unculturable components. An artificially acquired gut microbiota may be similarly comprised of both culturable and unculturable components, or may consist of only culturable components. The phrase “culturable components” refers to the microbes comprising the gut microbiota that may be cultured in vitro using techniques known in the art. Culture collections of gut microbial communities are described in detail in PCT/US2012/028600, incorporated herein in its entirety by reference.

As used herein, the phrase “normal maturation of the gut microbiota” refers to the ordered change in the relative abundances of bacterial taxa in the gut microbiota over time, as determined from a group of healthy subjects.

As used herein, the phrase “chronological age of a subject” refers to the amount of time a subject has lived.

I. Normal Maturation of the Gut Microbiota

Applicants have discovered that the proportional representation of a minimal number of bacterial taxa defines a healthy gut microbiota as it assembles (i.e. matures). The changes in the relative abundance of these bacterial taxa over time are consistent across substantially all healthy subjects within the same age range. Accordingly, the present invention provides a method to accurately characterize the maturity of a subject's gut microbiota using a limited amount of information. This is exemplified in the Examples using a group of subjects between 0 and about 2 years of age. However, without wishing to be bound by theory, the gut microbiota of a human relatively stabilizes after about 2-3 years provided there are no insults. When there is an insult, the gut microbiota regresses to an immature state that can be discriminated using the relative abundances of these same bacterial taxa.

A. Methods to Define Normal Maturation of the Gut Microbiota Using a Limited Number of Bacterial Taxa

In an aspect, the present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. The relative abundances of these limited number of bacterial taxa, referred to herein as “age-discriminatory bacterial taxa”, in gut microbiota samples obtained from healthy subjects changes over time in a consistent way across substantially all healthy subjects within the same age range. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota.

A method for identifying a group of age-discriminatory taxa that define normal maturation of the gut microbiota is described in detail in the Examples. Briefly, a method for identifying a group of age-discriminatory bacterial taxa comprises (a) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the healthy subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the subject's gut microbiome was determined from a plurality of gut microbiota samples obtained at intervals of time, (b) applying a regression analysis to model the relative abundance of the bacterial taxa comprising each healthy subject's gut microbiota against the amount of time the subject has lived (i.e. the chronological age of the healthy subject) at the time the gut microbiota sample was collected, and (c) selecting the minimum number of bacterial taxa needed for the model to predict gut microbiota age.

Methods for profiling the relative abundances of bacterial taxa in biological samples, including biological samples of gut microbiota, are well known in the art. Suitable methods may be sequencing-based or array-based. An exemplary method is detailed in the Examples. Briefly, the bacterial component of a gut microbiota sample is characterized by sequencing a nucleic acid suitable for taxonomic classification and assigning the sequencing reads to operational taxonomic units (OTUs) with ≥97% nucleotide sequence identity to a database of annotated and representative sequences. An example of such a database is Greengenes version 4feb2011; however any suitable database may be used. After OTUs are defined, a representative sequence from each OTU can be selected and compared to a reference set. If a match is identified in the reference set, that OTU can be given an identity. Relative abundance of a bacterial taxon may be defined by the number of sequencing reads that can be unambiguously assigned to each taxon after adjusting for genome uniqueness.

Generally speaking, a suitable nucleic acid used for taxonomic classification is universally distributed among the gut microbial population being queried allowing for the analysis of phylogenetic relationships among distant taxa, and has both a conserved region and at least one region subject to variation. The presence of at least one variable region allows sufficient diversification to provide a tool for classification, while the presence of conserved regions enables the design of suitable primers for amplification (if needed) and/or probes for hybridization for various taxa at different taxonomic levels ranging from individual strains to whole phyla. While any suitable nucleic acid known in the art may be used, one skilled in the art will appreciate that selection of a nucleic acid or region of a nucleic acid to amplify may differ by environment. In some embodiments, a nucleic acid queried is a small subunit ribosomal RNA gene. For bacterial and archaeal populations, at least the V1, V2, V3, V4, V5, V6, V7, V8 and/or V9 regions of the 16S rRNA gene are suitable, though other suitable regions are known in the art. Guidance for selecting a suitable 16S rRNA region to amplify can be found throughout the art, including Guo F et al. PLOS One 8(10) e76185, 2013; Soergel DAW et al. ISME Journal 6: 1440, 2012; and Hamady M et al. Genome Res. 19:1141, 2009, each hereby incorporated by reference in its entirety.

As used herein, “gut microbiota sample” refers to a biological sample comprising a plurality of heterogeneous nucleic acids produced by a subject's gut microbiota. Fecal samples are commonly used in the art to sample gut microbiota. Methods for obtaining a fecal sample from a subject are known in the art and include, but are not limited to, rectal swab and stool collection. Suitable fecal samples may be freshly obtained or may have been stored under appropriate temperatures and conditions known in the art. Methods for extracting nucleic acids from a fecal sample are also well known in the art. The extracted nucleic acids may or may not be amplified prior to being used as an input for profiling the relative abundances of bacterial taxa, depending upon the type and sensitivity of the downstream method. When amplification is desired, nucleic acids may be amplified via polymerase chain reaction (PCR). Methods for performing PCR are well known in the art. Selection of nucleic acids or regions of nucleic acids to amplify are discussed above. The nucleic acids comprising the nucleic acid sample may also be fluorescently or chemically labeled, fragmented, or otherwise modified prior to sequencing or hybridization to an array as is routinely performed in the art.

Gut microbiota samples may be obtained from a healthy subject at any suitable interval of time, varying from minutes to hours apart, days to weeks apart, or even weeks to months apart. Gut microbiota samples may be obtained multiple times a day, week, month or year. The duration of sampling can also vary. For example, the duration of sampling may be for about a month, about 6 months, about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, about 10 years, about 11 years, about 12 years, about 13 years, about 14 years, about 15 years, about 16 years, about 17 years, about 18 years, about 19 years, about 20 years, about 30 years, or more.

The number of healthy subjects from which gut microbiota samples are obtained can and will vary. Generally, a suitable number is the number of healthy subjects needed to give the model produced by the regression analysis an acceptable degree of statistical significance. For example, the number of subject may be 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more subjects.

Any suitable machine learning algorithm may be used to regress relative abundances of bacterial taxa against the amount of time the healthy subject has lived at the time the gut microbiota sample was collected. Preferably, the algorithm is able to detect both linear and nonlinear relationships between the bacterial taxa and chronologic age. In an exemplary embodiment, the Random Forests machine learning algorithm is used. The regression analysis produces a model that is a prediction of the gut microbiota age based on the microbial taxa present in the subject's gut microbiota sample.

Before selecting a minimal number of bacterial taxa required to discriminate different periods of post-natal life, the importance of the bacterial taxa are first determined. An “importance score” for the bacterial taxa may be an output of the learning algorithm. Generally, a ranked list of all bacterial taxa, in order of “age-discriminatory importance” may be determined by considering those taxa, whose relative abundance values when permuted leads to a meaningful increase in the error. Bacterial taxa ranked higher on the list (e.g. 1, 2, 3) have a larger increase in error than those lower on the list (e.g. 61, 62, 63). To select a minimal number of bacterial taxa, the predictive performance of sets comprising increasing numbers of the top-ranking bacterial taxa may be evaluated, and when minimal improvements in the predictive performance are observed after adding a new member to the set, then a minimal number of bacterial taxa have been identified. An example of a minimal improvement may be when the root mean-squared error of prediction remains below about 4 or aobut 5 months.

These steps produce a “sparse model” that is a prediction of the gut microbiota age based only on the relative abundance of age-discriminatory bacterial taxa present in a sample of the subject's gut microbiota. Additional bacterial taxa may be added to the sparse model to improve the model's performance, though the overall contribution of the additional bacterial taxa is usually minimal.

B. Useful Groups of Bacterial Taxa to Define Normal Maturation of the Gut Microbiota

In another aspect, the present invention provides a group of bacterial taxa that define normal maturation of the gut microbiota (i.e. a group of age-discriminatory taxa). The phrase “normal maturation of the gut microbiota” is defined above. A group of bacterial taxa that define normal maturation of the gut microbiota can be used to characterize the maturity of a subject's gut microbiota. Stated another way, a group of bacterial taxa that define normal maturation of the gut microbiota can be used to determine whether the maturity of a subject's gut microbiota corresponds to what would be predicted by the subject's chronological age. Such information can be used to guide treatment decisions (e.g. an immature state may indicate a need to initiate or continue therapy) or evaluate the effectiveness of a therapy (e.g. no improvement or a worsening would indicate the therapy is not effective).

In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 12 bacterial taxa listed in Table B; and (b) at least 12 bacterial taxa listed in Table C. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 16 bacterial taxa listed in Table B; and (b) at least 8 bacterial taxa listed in Table C. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 20 bacterial taxa listed in Table B; and (b) at least 4 bacterial taxa listed in Table C. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises the bacterial taxa listed in Table B. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota consists of the bacterial taxa listed in Table B. A bacterial isolate can be identified as belonging to a bacterial taxon listed in Table B or Table C if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1-24 (Table A), or 25-60 (Table B). For example, the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1-60.

In another aspect, the present invention provides a method to determine the maturity of a subject's gut microbiota. The method may comprise: (a) calculating a relative abundance for each bacterial taxon in a group bacterial taxa, from a fecal sample obtained from the subject; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; and (c) calculating the maturity of the subject's gut microbiota using the subject's microbiota age. In some embodiments, the calculation for maturity is defined as relative maturity, and relative maturity=(microbiota age of the subject)−(microbiota age of a healthy subject of a similar chronological age). In other embodiments, the calculation for maturity is defined as a Microbiota-for-Age Z score (MAZ), wherein MAZ=((microbiota age of the subject)−(median microbiota age of a healthy subject of a similar chronological age))/(standard deviation of microbiota age of healthy subjects of the similar chronological age). In each embodiment above, the group of bacterial taxa can be a group described above in Section I(B), which are incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section I(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section I(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 110 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.

In another aspect, the present invention provides a method to classify a subject. For example, because the relative abundances of the age-discriminatory taxa change in a consistent way that corresponds to the chronological age of healthy subjects, it is also possible to classify the maturity of a subject's gut microbiota as perturbed if the abundances of the age-discriminatory taxa are not as predicted by the subject's chronological age. Within the classification of “perturbed maturation, a subject may an immature gut microbiota (i.e. the subject's microbiota age is less than predicted by chronological age alone), or a subject may have a gut microbiota that matured faster than normal (i.e. the subject's microbiota age is less than predicted by chronological age alone).

A method to classify a subject may comprise: (a) calculating a relative abundance for each bacterial taxon in a group bacterial taxa, from a fecal sample obtained from the subject; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject of a similar age. The group of bacterial taxa can be a group described above in Section I(B), which are incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section I(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section I(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 110 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.

Alternatively, a method to classify a subject may comprise: (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject; (b) using the relative abundances of the bacterial taxa from step (a) and chronologic age of the subject to indicate the health status of the subject's gut microbiota (e.g. healthy or unhealthy; normal maturation of the gut microbiota or perturbed maturation of the gut microbiota) using a classification model; wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality undernourished subjects. For example, the datasets may contain measurements from at least 4, 5, 6, 7, 8, 9, 10, 11, 12, or more healthy subjects and at least 4, 5, 6, 7, 8, 9, 10, 11, 12, or more undernourished subjects. The undernourished subjects may be subjects with acute malnutrition, moderate acute malnutrition, or at risk for moderate acute malnutrition, as defined in Section II. The measurements may at least include relative abundances of the same bacterial taxa from step (a) above from samples of the health and undernourished subjects' gut microbiota obtained over time for each subject, and the chronological age of the subject at the time the gut microbiota sample was collected. Additional measurements may optionally include MUAC measurements (as defined in Section II), WHZ measurements ((as defined in Section II), and other anthropometric measurements, as well as measurements such as a response to therapy. The group of bacterial taxa can be a group described above in Section I(B), which are incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section I(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for bacterial taxon are described in Section I(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.

D. Method for Identifying the Effect of a Therapy

In another aspect, the present invention provides a method for identifying an effect of a therapy.

In some embodiments, a method for identifying an effect of a therapy may comprise (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. When the effect is positive, the microbiota age of the subject increases. When the effect is negative, the microbiota age of the subject decreases. The group of bacterial taxa can be a group described above in Section I(B), which are incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section I(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section I(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 110 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.

In other embodiments, a method for identifying an effect of a therapy may comprise (a) identifying a set of age-discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising (i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time; (ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age-discriminatory bacterial taxa present in the subject's gut microbiota; and (iii) selecting the minimum number of bacterial taxa from step (ii) that is needed to produce a prediction that is substantially similar to or better than the prediction of step (ii); calculating a relative abundance for each bacterial taxon in the set of age-discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and applying the relative abundances from step (b) to the prediction of gut microbiota age from step (a)(iii) to determine the microbiota age of the subject's gut microbiota before and after therapy; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. When the effect is positive, then the microbiota age of the subject increases. When the effect is negative, then the microbiota age of the subject decreases. Methods for calculating a relative abundance for a bacterial taxon are described in Section I(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 110 years of age or less.

II. Method of Preventing and/or Treating a Disease

In another aspect, the present invention provides a method for preventing and/or treating a disease in a subject in need thereof. As described in detail in the Examples, Applicants have discovered that certain bacterial taxa associated with normal maturation of the gut microbiota in healthy subjects (i.e. age-discriminatory taxa) are also associated with repair of the gut microbiota (i.e. recovery-indicative taxa) in subjects whose gut communities have been affected by a variety of insults (e.g. malnutrition or gastrointestinal disease). Accordingly, a method for preventing and/or treating a disease in a subject in need thereof may comprise administering to the subject a therapeutically effective amount of one or more age-indicative taxa associated with repair of the gut microbiota.

As used herein, “preventing” a disease refers to reducing the onset of symptoms or complications of a disease, condition or disorder (collectively, a “disease”), or the disease itself. Preventing a disease may involve reducing in treated subjects (e.g. a normal subject) (1) the incidence, development or formation of disease, (2) the development, duration, and/or severity of symptoms of disease, (3) death rates, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. For an infectious disease (e.g. enteropathogenic infection), preventing a disease may also involve increasing the median infectious disease dose (ID50) by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 100%, or more. Preventing a disease may also involve increasing the (relative) microbiota maturity, increasing bacterial diversity, increasing MAZ score.

As used herein, “treating” describes the management and care of a patient for the purpose of combating a disease in a subject. Treating a disease may involve reducing in treated subjects (1) the infectious burden (e.g. viral, bacterial, patristic load), (2) the duration and/or severity of symptoms of disease, (3) death rates, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating a disease may also involve increasing the (relative) microbiota maturity, increasing bacterial diversity, increasing MAZ score.

As used herein, “therapeutically effective amount” means an amount of one or more bacterial taxa of the invention that will elicit a desired biological or medical response of a tissue, system, or subject that is being sought by a researcher or clinician. In one aspect, the biological or medical response is prevention or treatment of a gastrointestinal disease. In another aspect, the biological or medical response is prevention or treatment of a gastrointestinal disease caused by an enteropathogenic infection. In another aspect, the biological or medical response is treatment or prevention of acute malnutrition. In another aspect, the biological or medical response is treatment or prevention of acute diarrheal disease. In another aspect, the biological or medical response is treatment or prevention of acute diarrheal disease caused by an enteropathogenic infection. In another aspect, the biological or medical response is treatment or prevention of chronic diarrheal disease.

As used herein, the phrase “bacterial taxa of the invention” refers to age-discriminatory bacterial taxa associated with repair of the gut microbiota. Methods for identifying age-discriminatory bacterial taxa are described in detail in Section I. In some embodiments, one or more bacterial taxa of the invention are selected from the group listed in Table A. In other embodiments, one or more bacterial taxa of the invention are selected from the group listed in Table B. In other embodiments, one or more bacterial taxa of the invention are selected from the group listed in Table C. Preferred combinations of bacterial taxa of the invention include, but are not limited to the combinations listed in Table D, or further described in Section III. Preferred combinations of bacterial taxa of the invention may also be identified by testing for a therapeutic effect in an appropriate animal model. Combinations may also be selected by identifying bacterial taxa associated with repair of the gut microbiota that are under-represented in a subject's gut microbiota as compared to a healthy subject. Section III also describes in further detail formulations comprising bacterial taxa of the invention for administration to a subject. Bacterial taxa of the invention are preferably administered orally or rectally. A bacterial taxon administered to a subject is isolated and biologically pure. A bacterial isolate can be identified as belonging to a bacterial taxon listed in Tables A-D if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1-60. For example, the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1-60. The duration of therapy can and will vary, depending at least in part upon the disease and the severity of the disease. In subjects with an immature gut microbiota, one or more bacterial taxa of the invention may be administered until the microbiota age of the subject is similar to the microbiota age of a healthy subject of the same chronological age.

Acute malnutrition results from decreased food consumption and/or illness resulting in sudden weight loss. It is associated with greater risk of medical complications and infections, increased risk of death from illness and infections, and micronutrient deficiencies. Non-limiting examples of micronutrient deficiencies associated with acute malnutrition are iron deficiency, iodine deficiency, and vitamin A deficiency. The most common way to assess malnutrition, particularly in humans of about 19 years of age or less, is through anthropometric measurements. It is usually diagnosed in one of three ways: by weighing a subject and measuring the subject's height; by measuring the circumference of the subject's mid-upper arm (MUAC); and/or by checking for oedema in the subject's lower legs or feet. Acute malnutrition is divided into two types: severe acute malnutrition (SAM) and moderate acute malnutrition (MAM). A subject is classified as having SAM if the subject's weight-for-height Z-scores (WHZ) is below three standard deviations (−3 s.d.) from the median of the World Health Organization (WHO) reference growth standards. A subject with a WHZ between −2 s.d. and −3 s.d. from the median of the WHO reference growth standards is categorized as having MAM. If a subject is between about six months and about five years of age, a MUAC measurement of less than 12.5 cm also indicates that a subject is suffering from moderate acute malnutrition. Finally, the presence of oedema in both feet and lower legs of a subject is a sign of SAM. WHO reference growth standards are available from the WHO. See for example, World Health Organization Department of Nutrition for Health and Development: WHO child growth standards growth velocity based on weight, length and head circumference: methods and development; World Health Organization, 2009, or the current edition.

In some embodiments, the subject to be treated is a subject with acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. Treating acute malnutrition may involve reducing in treated subjects the duration and/or severity of symptoms of acute malnutrition, death rates associated with acute malnutrition, or a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating acute malnutrition may also involve increasing a suitable anthropometric measurement in a treated subject including, but not limited to, a subject's WHZ or MUAC. For each aspect, the amount of increase may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects or as compared to the treated subject prior to administration of the combination of the invention. In certain embodiments, a subject's WHZ may improve to less than −3.0 s.d., less than −2.5 s.d., less than −2.0 s.d., less than −1.5 s.d., less than −1.0 s.d., or less than −0.5 s.d. from the median of the WHO reference growth standards. Treating acute malnutrition may also result in no worsening (i.e. decrease) in a subject's WHZ or MUAC. Treating acute malnutrition may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1, 2, 3, or 4 months. In other embodiments, the duration of therapy is about 1, 2, 3, 4, 5, 6, 7, or 8 weeks. In still other embodiments, the duration of therapy is about 4 to about 12 weeks, about 6 to about 10 weeks, or about 6 to about 8 weeks. Alternatively, a subject may be administered one or more bacterial taxa of the invention for as long as acute malnutrition persists. Useful combinations are described in further detail below. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include therapeutic foods, probiotics, antibiotics and vaccines. In exemplary embodiments, a subject with acute malnutrition is administered a therapeutically effective amount of one or more bacterial taxa of the invention and a therapeutic food. As used herein, “therapeutic food” refers to a food designed for specific, usually nutritional, therapeutic purposes as a form of dietary supplement. Therapeutic foods are usually made of a mixture of protein, carbohydrate, lipid and vitamins and minerals, and are usually produced by grinding all ingredients together, mixing them and packaging without water. The term “therapeutic foods” includes ready-to-use-therapeutic foods (RUTFs). Generally speaking, RUTFs are homogenous mixtures of lipid-rich and water-soluble foods. The lipids used in formulating RUTFs are in a viscous liquid form, and the other ingredients (e.g. protein, carbohydrate, vitamins, minerals) are mixed through the lipids. Non-limiting examples of therapeutic foods include F-75, F-100, K-Mix 2, Citadel spread, Plumpy'nut, Medika Mamba, Ensure, Fortisip, Energyzip, TwoCal, BP-100, and eeZee.

In other embodiments, the subject to be treated is a subject at risk for acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. A subject at risk for acute malnutrition may have limited access to nutritious foods and/or may have frequent exposure to infectious diseases. A subject at risk for acute malnutrition may also be a subject with a WHZ between 0 s.d. and −2 s.d., between −1 s.d and −2 s.d, or between −1.5 s.d and −2 s.d from the median of the WHO reference growth standards. Treating a subject at risk for acute malnutrition may prevent acute malnutrition in the subject. For example, preventing acute malnutrition may involve reducing in treated subjects (1) the incidence, development or formation of acute malnutrition, (2) the development, duration, and/or severity of symptoms of malnutrition, if malnutrition does develop, (3) death rates associated with acute malnutrition, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing acute malnutrition may also involve increasing a suitable anthropometric measurement in a treated subject including, but not limited to, a subject's WHZ or MUAC. For each aspect, the amount of increase may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects or as compared to the treated subject prior to administration of the combination of the invention. In certain embodiments, a subject's WHZ may improve to less than −1.5 s.d., less than −1.0 s.d., or less than −1.5 s.d. from the median of the WHO reference growth standards. Preventing acute malnutrition may also result in no worsening (i.e. decrease) in a subject's WHZ or MUAC. Preventing acute malnutrition may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1, 2, 3, or 4 months. In other embodiments, the duration of therapy is about 1, 2, 3, 4, 5, 6, 7, or 8 weeks. In still other embodiments, the duration of therapy is about 4 to about 12 weeks, about 6 to about 10 weeks, or about 6 to about 8 weeks. Alternatively, a subject may be administered one or more bacterial taxa of the invention for as long as the subject remains at risk. Useful combinations are described in further detail below. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include therapeutic foods, antibiotics and/or vaccines.

As used herein, acute diarrhea is defined as three or more stools per day of decreased form (e.g. loose and/or water) from the normal, lasting for less than 14 days; persistent diarrhea is defined as three or more stools per day of decreased form from the normal, lasting for more than 14 days but less than 1 month; and chronic diarrhea is defined as three or more stools per day of decreased form from the normal, lasting for a month or more. Associated symptoms of acute diarrhea, persistent diarrhea, and chronic diarrhea may include abdominal cramps or pain, fever, nausea, vomiting, fatigue, urgency, weight loss, and/or malnutrition. Acute diarrhea, persistent diarrhea, and chronic diarrhea are themselves symptoms. Causes of acute diarrhea are well known in the art, and non-limiting examples include a food allergy, antibiotic use, an enteropathogen infection, and radiation therapy. Causes of persistent diarrhea are well known in the art, and non-limiting examples include a food allergy, antibiotic use, an enteropathogen infection, colon resection, colon cancer, ulcerative colitis, necrotizing enterocolitis, Crohn's disease and radiation therapy. Causes of chronic diarrhea are well known in the art, and non-limiting examples include a food allergy, use of a pharmacological agent, an enteropathogen infection, colon resection, colon cancer, ulcerative colitis, necrotizing enterocolitis, Crohn's disease, and radiation therapy. Pharmacological agents known to cause diarrhea are well known in the art and may include, but are not limited to, an antibiotic, an anti-TNF agent, chemotherapy agents, antacids, proton pump inhibitors, (e.g. asomeprazole, esomeprazole, lansoprazole, rabeprazole, patoprazole, cimetidine, ranitidine, naiziatidine), drugs that suppress the immne system (e.g. mycophenolate), antidepressants, blood pressure medications, digitalis, diuretics, cholesterol-lowering agents, lithium, theophylline, thyroid hormone, and colchicine.

In some embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. Treating acute diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1, 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 11, 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of aVibrio choleraeinfection.

In some embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of a bacterial isolate that is a member of the taxonRuminococcus obeumOTU ID 178122 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. In other embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount ofRuminococcus obeumATCC29714 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. Treating acute diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1, 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 11, 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations comprisingRuminococcus obeumATCC29714 or a bacterial isolate that is a member of the taxonRuminococcus obeumOTU ID 178122 are described in further detail in Section III. A composition comprisingRuminococcus obeumATCC29714 or a bacterial isolate that is a member of the taxonRuminococcus obeumOTU ID 178122 may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of aVibrio choleraeinfection.

In some embodiments, the subject to be treated is a subject at risk for acute diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. A subject at risk for acute diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea. A subject at risk for acute diarrhea may be a subject that is malnourished. Treating a subject at risk for acute diarrhea may prevent acute diarrhea in the subject. For example, preventing an acute diarrhea may involve reducing in treated subjects (1) the incidence, development or formation of the acute diarrhea, (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing an acute diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects. For example, the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less. Preventing acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1, 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 11, 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of aVibrio choleraeinfection.

In some embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of a bacterial isolate that is a member of the taxonRuminococcus obeumOTU ID 178122 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. In some embodiments, the subject to be treated is a subject at risk for acute diarrhea and a therapeutically effective amount ofRuminococcus obeumATCC29714 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. A subject at risk for acute diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea. A subject at risk for acute diarrhea may be a subject that is malnourished. Treating a subject at risk for acute diarrhea may prevent acute diarrhea in the subject. For example, preventing an acute diarrhea may involve reducing in treated subjects (1) the incidence, development or formation of the acute diarrhea, (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing an acute diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects. For example, the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less. Preventing acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1, 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 11, 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations comprisingRuminococcus obeumATCC29714 or a bacterial isolate that is a member of the taxonRuminococcus obeumOTU ID 178122 are described in further detail in Section III. A composition comprisingRuminococcus obeumATCC29714 or a bacterial isolate that is a member of the taxonRuminococcus obeumOTU ID 178122 may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of aVibrio choleraeinfection.

In some embodiments, the subject to be treated is a subject with chronic diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. Treating chronic diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an chronic diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an chronic diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1, 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 11, 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, chronic diarrhea is a symptom of aC. difficileinfection, Crohn's disease, ulcerative colitis, necrotizing enterocolitis, traveler's diarrhea, colon cancer, chemotherapy, radiation therapy, or use of pharmacological agent, including but not limited to an antibiotics, or an anti-TNF agent.

In some embodiments, the subject to be treated is a subject at risk for chronic diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. A subject at risk for chronic diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea. A subject at risk for chronic diarrhea may be a subject that is malnourished. Treating a subject at risk for chronic diarrhea may prevent acute diarrhea in the subject. For example, preventing an chronic diarrhea may involve reducing in treated subjects (1) the incidence, development or formation of the acute diarrhea, (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing an chronic diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects. For example, the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less. Preventing chronic diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1, 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 11, 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, chronic diarrhea is a symptom of aC. difficileinfection, Crohn's disease, ulcerative colitis, necrotizing enterocolitis, traveler's diarrhea, colon cancer, radiation therapy, or use of pharmacological agent, including but not limited to an antibiotics, or an anti-TNF agent.

In another aspect, the present invention provides a combination comprising 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 bacterial taxa selected from the bacterial taxa listed in Table B, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. ≥97% sequence identity to the SEQ ID NO. provided).

In another aspect, the present invention provides a combination identified below in Table D, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. ≥97% sequence identity to the SEQ ID NO. provided).

A bacterial taxon of the invention, or a combination of bacterial taxa of the invention, is formulated to maintain a suitable level of viable cells during the formulation's shelf life and upon administration to a subject. Each bacterial taxon may be present in a wide range of amounts provided that the composition or combination delivers the effect described. The total amount of bacteria per unit dose is dependent, in part, upon the dosage form and excipients. Non-limiting examples of suitable amounts include from about 102to about 1012colony forming units (cfu) of each bacterial strain per unit dose. In some embodiments, the amount of each bacterial strain is between about 105and about 1012cfu per unit dose, between about 106and about 1012cfu per unit dose, between about 107and about 1012cfu per unit dose, or between about 108and about 1012cfu per unit dose. In other embodiments, the amount of each bacterial strain is between about 105and about 1011cfu per unit dose, between about 106 and about 1011cfu per unit dose, between about 107and about 1011cfu per unit dose, or between about 108 and about 1011cfu per unit dose. In other embodiments, the amount of each bacterial strain is between about 105and about 1010cfu per unit dose, between about 106 and about 1010cfu per unit dose, between about 107and about 1010cfu per unit dose, or between about 108and about 1010cfu per unit dose. In other embodiments, the amount of each bacterial taxon is between about 105and about 109cfu per unit dose, between about 106and about 109cfu per unit dose, between about 107and about 109cfu per unit dose, or between about 108and about 109cfu per unit dose. Generally, a bacterial strain may be provided as a frozen or freeze-dried culture, or as bacterial spores.

A bacterial taxon of the invention, or a combination of bacterial taxa of the invention, may be formulated into a formulation for oral or rectal administration comprising one or more bacterial taxa of the invention and one more excipients. Non-limiting examples of excipients include binders, diluents, fillers, disintegrants, effervescent disintegration agents, preservatives, antioxidants, flavor-modifying agents, lubricants and glidants, dispersants, coloring agents, pH modifiers, chelating agents, and release-controlling polymers. Bacterial taxa of the invention, or a combination of bacterial taxa of the invention, may be formulated in unit dosage form as a solid, semi-solid, liquid, capsule, or powder. These formulations are a further aspect of the invention. Usually the amount of a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, is between 0.1-95% by weight of the formulation, or between 1 and 50% by weight of the formulation. Methods of formulating compositions are discussed in, for example, Hoover, John E., Remington's Pharmaceutical Sciences, Mack Publishing Co., Easton, Pa. (1975), and Liberman, H. A. and Lachman, L., Eds., Pharmaceutical Dosage Forms, Marcel Decker, New York, N.Y. (1980).

In the preparation of formulations in dosage units for oral administration, a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, may be mixed with a solid, powdered carrier, e.g. lactose, saccharose, sorbitol, mannitol, starch, amylopectin, cellulose derivatives or gelatin, and optionally with lubricants (e.g. magnesium stearate, calcium stearate, sodium steryl fumarate and polyethylene glycol waxes), release-controlling polymers and other excipients. The mixture is then processed into granules or pressed into tablets. Since the viability of the a bacterial strain may be negatively impacted by acidic media, the above-mentioned granules or tablets may be coated with an enteric coating which protects the combination of the invention from acid degradation as long as the dosage form remains in the stomach. The enteric coating is chosen among pharmaceutically acceptable enteric-coating materials e.g. beeswax, shellac or anionic film-forming polymers such as cellulose acetate phthalate, hydroxypropyl methylcellulose phthalate, partly methyl esterified methacrylic acid polymers and the like, if preferred in combination with a suitable plasticizer. To this coating various dyes may be added in order to distinguish among tablets or granules with different combinations or with different amounts of the combination present.

Alternatively, the pharmaceutical compositions may be incorporated into a food product or powder for mixing with a liquid, or administered orally after only mixing with a non-foodstuff liquid.

Soft gelatine capsules may also be prepared with capsules comprising a combination of the invention and vegetable oil, fat, or other suitable vehicle for soft gelatine capsules. Soft gelatine capsules are preferably enteric coated as described above. Hard gelatine capsules may contain enteric-coated granules of a combination of the invention. Hard gelatine capsules may also contain a combination of the invention in combination with a solid powdered carrier e.g. lactose, saccharose, sorbitol, mannitol, potato starch, corn starch, amylopectin, cellulose derivatives or gelatine; the hard gelatine capsules are preferably enteric coated as described above.

Dosage units for rectal administration may be prepared in the form of suppositories which comprise a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, mixed with a non-irritating excipient that is solid at room temperature but liquid at rectal temperature and therefore will melt in the rectum to release the drug. Non-limiting examples of suitable excipients for rectal suppository embodiments include cocoa butter, beeswax, and polyethylene glycols. Alternatively, a dosage unit for rectal administration may be prepared in the form of a gelatine rectal capsule which comprises a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, in a mixture with a vegetable oil, paraffin oil or other suitable vehicle for gelatine rectal capsules. In yet another alternative, a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, may be prepared in the form of a ready-made enema, or in the form of a dry enema formulation to be reconstituted in a suitable solvent just prior to administration.

Liquid preparations for oral administration may be prepared in the form of syrups or suspensions, e.g. solutions or suspensions containing from 0.2% to 20% by weight of a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, and the remainder consisting of sugar or sugar alcohols and a mixture of ethanol, water, glycerol, propylene glycol and polyethylene glycol. If desired, such liquid preparations may contain colouring agents, flavouring agents, saccharine and carboxymethyl cellulose and thickening agent. Liquid preparations for oral administration may also be prepared in the form of a dry powder to be reconstituted with a suitable solvent prior to use.

The dosing regimen involving compositions in accordance with the present disclosure can be varied to achieve a desired result, such as may be determined empirically for a given individual subject, or by extrapolation from data obtained from administering a composition of the invention to a clinical or other test population. The desired dose may be presented in multiple (e.g., two, three, four, five, six, or more) sub-doses administered at appropriate intervals throughout the day.

EXAMPLES

The following examples illustrate various iterations of the invention.

Severe acute malnutrition and moderate acute malnutrition (MAM) are typically defined by anthropometric measurements: children are classified as having SAM if their weight-for-height Z-scores (WHZ)3are below three standard deviations (—3 s.d.) from the median of the World Health Organization (WHO) reference growth standards, whereas those with WHZ between—2 and—3 s.d. are categorized as having MAM. SAM and MAM typically develop between 3 and 24 months after birth4. A standardized treatment protocol for SAM and its complications has been developed in Bangladesh1. The result has been a reduction in mortality rate, although the extent to which this protocol results in long-term restoration of normal growth and development needs to be ascertained through longitudinal studies5,6. There is similar lack of clarity about the long-term efficacy of nutritional interventions for MAM7,8.

Food is a major factor that shapes the proportional representation of organisms present in the gut microbial community (microbiota), and its gene content (microbiome). The microbiota and microbiome in turn have an important role in extracting and metabolizing dietary ingredients9-14. To investigate the hypothesis that healthy postnatal development (maturation) of the gut microbiota is perturbed in malnutrition12, we monitored 50 healthy Bangladeshi children monthly during the first 2 years after birth (25 singletons, 11 twin pairs, 1 set of triplets; 996 faecal samples collected monthly; see Methods for Examples 1-9 and Tables 1 and 2). By identifying bacterial taxa that discriminate the microbiota of healthy children at different chronologic ages, we were able to test our hypothesis by studying 6 to 20-month-old children presenting with SAM, just before, during, and after treatment with two very different types of food intervention, as well as children with MAM. The results provide a different perspective about malnutrition; one involving disruption of a microbial facet of our normal human postnatal development.

To characterize gut microbiota maturation across unrelated healthy Bangladeshi children living in separate households, faecal samples were collected at monthly intervals up to 23.4±0.5 months of age in a training set of 12 children who exhibited consistently healthy anthropometric scores (WHZ, —0.32±0.98 (mean±s.d.) 22.7±1.5 faecal samples per child; Table 3a). The bacterial component of their faecal microbiota samples was characterized by V4-16S rRNA sequencing (Table 4) and assigning the resulting reads to operational taxonomic units (OTUs) sharing 97% nucleotide sequence identity (see Methods for Examples 1-9; a 97%-identity OTU is commonly construed as representing a species-level taxon). The relative abundances of 1,222 97%-identity OTUs that passed our filtering criterion15were regressed against the chronologic age of each child at the time of faecal sample collection using the Random Forests machine learning algorithm16. The regression explained 73% of the variance related to chronologic age. The significance of the fit was established by comparing fitted to null models in which age labels of samples were randomly permuted with respect to their 16S rRNA microbiota profiles (P=0.0001, 9,999 permutations). Ranked lists of all bacterial taxa, in order of ‘age-discriminatory importance’, were determined by considering those taxa, whose relative abundance values when permuted have a larger marginal increase in mean squared error, to be more important (see Methods for Examples 1-9). Tenfold cross-validation was used to estimate age-discriminatory performance as a function of the number of top-ranking taxa according to their feature importance scores. Minimal improvement in predictive performance was observed when including taxa beyond the top 24 (see Table 5 for the top 60). The 24 most age-discriminatory taxa identified by Random Forests are shown inFIG. 1Ain rank order of their contribution to the predictive accuracy of the model and were selected as inputs to a sparse 24-taxon model.

To test the extent to which this sparse model could be applied, we applied it, with no further parameter optimization, to additional monthly faecal samples collected from two other healthy groups of children: 13 singletons (WHZ, —0.4±0.8 (mean±s.d.)) and 25 children from a birth-cohort study of twins and triplets, (WHZ, —0.5±0.7 (mean±s.d.)), all born and raised in Mirpur, Bangladesh (Table 3b,c). We found that the model could be applied to both groups (r2=0.71 and 0.68, respectively), supporting the consistency of the observed taxonomic signature of microbiota maturation across different healthy children living in this geographic locale (FIGS. 1B-Dand F-H).

Two metrics of microbiota maturation were defined by applying the sparse model to the 13 healthy singletons and 25 members of twin pairs and triplets that had been used for model validation. The first metric, relative microbiota maturity, was calculated as follows:
relative microbiota maturity=(microbiota age of child)−(microbiota age of healthy children of similar chronologic age)
where microbiota age values for healthy children were interpolated across the first 2 years of life using a spline fit (FIG. 1B-D). The second metric, microbiota-for-age Z score, was calculated as follows:
MAZ=(microbiota age−median microbiota age of healthy children of same chronologic age)

(s.d. of microbiota age of healthy children of the same chronologic age) where MAZ is the microbiota-for-age Z-score, and median and s.d. of microbiota age were computed for each month up to 24 months. The MAZ accounts for the variance of predictions of microbiota age as a function of different host age ranges (when considered in discrete monthly bins) (seeFIG. 3for the calculation of each metric, and Example 8 for discussion of how this approach defines immaturity as a specific recognizable state rather than as a lack of maturity).

To study the influences of genetic and environmental factors on these microbiota maturation indices, we examined their distribution in healthy Bangladeshi twins and triplets. Monozygotic twins were not significantly more correlated in their maturity profiles compared to dizygotic twins, and within the set of triplets, the two monozygotic siblings were not more correlated than their fraternal sibling (monozygotic pairs, 0.1±0.5 (Spear-man's Rho±s.d.); dizygotic pairs, 0.33±0.3; in the case of the triplets, values for the monozygotic pair and fraternal sibling were 0.1; and 0.24±0.3, respectively). Maturity was significantly decreased in faecal samples obtained during and 1 month after diarrhoeal episodes (P<0.001 and P<0.01, respectively) but not beyond that period (FIG. 4). There was no discernible effect of recent antibiotic usage (1 week before sampling) on relative microbiota maturity, whereas intake of infant formula was associated with significantly higher maturity values (Table 6). Family membership explained 29% of the total variance in relative microbiota maturity measurements (log-likelihood ratio=102.1,P<0.0001; linear mixed model) (see Example 3, Tables 7 and 8, andFIG. 5for analyses of faecal microbiota variation in mother-infant dyads and fathers).

To investigate the effects of SAM on microbiota maturity, 64 children with SAM who had been admitted to the Nutritional Rehabilitation Unit of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka Hospital, were enrolled in a study to investigate the configuration of their faecal microbiota before, during and after treatment with either an imported, internationally used ready-to-use therapeutic food (RUTF; Plumpy'Nut) or a locally produced, lower-cost nutritional food combination (Khichuri-Halwa). Children ranged in age from 6 to 20 months of age at the time of enrollment and were randomly assigned to either of the treatment arms. At enrollment, WHZ averaged—4.2±0.7 (mean±s.d.) (see Tables 9 and 10 for patient metadata andFIG. 2Afor study design). In the initial ‘acute phase’ of treatment, infection control was achieved with parenteral administration of ampicillin and gentamicin for 2 and 7 days, respectively, and oral amoxicillin for 5 days (from days 3 to 7 of the antibiotic treatment protocol). Children with SAM were initially stabilized by being fed the milk-based gruel, ‘suji’, followed by randomization to either an imported peanut-based RUTF intervention or an intervention with locally produced, rice-and-lentil-based therapeutic foods (Khichuri and Halwa; see Methods for Examples 1-9 and Table 11 for compositions of all foods used during nutritional rehabilitation). During this second ‘nutritional rehabilitation phase’ (1.3±0.7 weeks long) children received 150-250 kcal kg−1body weight per day of RUTF or Khichuri-Halwa (3-5 g protein kg−1per day), plus micronutrients including iron. Children were discharged from the hospital after the completion of this second phase; during the ‘post-intervention phase’, periodic follow-up examinations were performed to monitor health status. Faecal samples were obtained during the acute phase before treatment with Khichuri-Halwa or RUTF, then every 3 days during the nutritional rehabilitation phase, and monthly thereafter during the post-intervention follow-up period.

There was no significant difference in the rate of weight gain between the RUTF and Khichuri-Halwa groups (10.9±4.6 versus 10.4±5.4 g kg−1body weight per day (mean±s.d.); Student's t-test, P=0.7). The mean WHZ at the completion of nutritional rehabilitation was significantly improved in both treatment groups (−3.1±0.7(mean±s.d.) RUTF, P<0.001; and −2.7±1.6 Khichuri-Halwa, P<0.0001), but not significantly different between groups (P=0.15). During follow-up, WHZ remained significantly lower compared to healthy children (−2.1±1.2, Khichuri-Halwa; −2.4±0.8 RUTF versus −0.5±1.1 for healthy, P<0.0001;FIG. 6A). Children in both treatment arms also remained markedly below normal height and severely underweight throughout the follow-up period (FIG. 6B,C).

The Random Forests model derived from healthy children was used to define relative microbiota maturity for children with SAM at the time of enrollment, during treatment, at the end of either nutritional intervention, and during the months of follow-up. The results revealed that compared to healthy children, children with SAM had significant microbiota immaturity at the time that nutritional rehabilitation was initiated and at cessation of treatment (Dunnett's post-hoc test, P<0.0001 for both groups;FIG. 2B). Within 1 month of follow-up, both groups had improved significantly. However, improvement in this metric was short-lived for the RUTF and Khichuri-Halwa groups, with regression to significant immaturity relative to healthy children beyond 4 months after treatment was stopped (FIG. 2Band Table 12). MAZ, like relative microbiota maturity, indicated a transient improvement after RUTF intervention that was not durable beyond 4 months. In the Khichuri-Halwa group, relative microbiota maturity and MAZ improved following treatment, but subsequently regressed, exhibiting significant differences relative to healthy children at 2-3 months, and >4 months after cessation of treatment (FIG. 2Band Table 12).

Both food interventions had non-durable effects on other microbiota parameters. The reduced bacterial diversity associated with SAM persisted after Khichuri-Halwa and only transiently improved with RUTF (FIG. 7and Table 12). We identified a total of 220 bacterial taxa that were significantly different in their proportional representation in the faecal microbiota of children with SAM compared to healthy children; 165 of these 220 97%-identity OTUs were significantly diminished in the microbiota of children with SAM during the longer term follow-up period in both treatment groups (FIGS. 8 and 9and Table 13).

Although the majority of children in both treatment arms of the SAM study were unable to provide faecal samples before the initiation of antibiotic treatment due to the severity of the illness, a subset of nine children each provided one or two faecal samples (n=12) before administration of parenteral ampicillin and gentamicin, and oral amoxicillin. Microbiota immaturity was manifest at this early time-point before antibiotics in these nine children (relative microbiota maturity: −5.15±0.9 months versus −0.03±0.1 for the 38 reference healthy controls; Mann-Whitney, P<0.0001). Sampling these nine children after treatment with parenteral and oral antibiotics but before initiation of RUTF or Khichuri-Halwa (6±3.6 days after hospital admission) showed that there was no significant effect on microbiota maturity (Wilcoxon matched-pairs rank test, P=1). When pre-antibiotic faecal samples from these nine children were compared to samples collected at the end of all treatment interventions (dietary and antibiotic, 20±9 days after admission), no significant differences in relative microbiota maturity (Wilcoxon, P=0.7), MAZ, bacterial diversity (or WHZ) were found (FIG. 10A-D). This is not to say that these interventions were without effects on overall community composition: opposing changes in the relative abundance of Streptococcaceae and Enterobacteriaceae were readily apparent (FIG. 10E,F; note that the Random Forests model classified both the microbiota of children with SAM sampled before and at the conclusion of all treatment interventions as immature, indicating lack of a generic immature state). Although these findings indicate that the relative microbiota immaturity associated with SAM was not solely attributable to the antibiotics used to treat these children, we could not, in cases where children were unable to provide pre-intervention faecal samples, measure the effects of other antibiotics, consumed singly or in various combinations during the acute infection control and nutritional rehabilitation phases, on their metrics of microbiota maturation (see Example 7 and Table 14 for further evidence indicating antibiotic use in the follow-up period did not correlate with the persistence of microbiota immaturity in children with SAM).

SAM affects approximately 4% of children in developing countries. MAM is more prevalent, particularly in South Central Asia, where it affects approximately 19% (30 million children)7. Epidemiological studies indicate that periods of MAM are associated with progression to SAM, and with stunting which affects >40% of children under the age of five in Bangladesh17. Therefore, we extended our study to children from the singleton cohort at 18 months of age, when all had transitioned to solid foods (n=10 children with WHZ lower than −2 s.d., the threshold for MAM; 23 children with healthy WHZ; Table 15). The relationship between relative microbiota maturity, MAZ and WHZ was significant (Spearman's Rho=0.62 and 0.63, P<0.001, respectively;FIG. 11A,B). Comparing children with MAM to those defined as healthy revealed significantly lower relative microbiota maturity, MAZ and differences in the relative abundances of age-discriminatory taxa in the malnourished group (FIGS. 11D-LandFIGS. 12A,B). These results suggest that microbiota immaturity may be an additional pathophysiological component of moderately malnourished states.

In conclusion, definition of microbiota maturity using bacterial taxonomic biomarkers that are highly discriminatory for age in healthy children has provided a way to characterize malnourished states, including whether responses to food interventions endure for prolonged periods of time beyond the immediate period of treatment. RUTF and Khichuri-Halwa produced improvements in microbiota maturity indices that were not sustained. Addressing the question of how to achieve durable responses in children with varying degrees of malnutrition may involve extending the period of administration of existing or new types of food interventions7. One testable hypothesis is that a population's microbiota conditioned for generations on a diet will respond more favourably to nutrient supplementation based on food groups represented in that diet. Next-generation probiotics using gut-derived taxa may also be required in addition to food-based interventions. The functional roles (niches) of the age-discriminatory taxa identified by our Random Forests model need to be clarified since they themselves maybe therapeutic candidates and/or form the basis for low cost field-based diagnostic assessments.

Systematic analyses of microbiota maturation in different healthy and malnourished populations living in different locales, representing different lifestyles andculturaltraditions11,18, may yield a taxonomy-based model that is generally applicable to many countries and types of diagnostic and therapeutic assessments. Alternatively, these analyses may demonstrate a need for geographic specificity when constructing such models (and diagnostic tests or therapeutic regimens). Two observations are notable in this regard. First, expansion of our sparse model from 24 to 60 taxa yielded similar results regarding the effects of diarrhoea in healthy individuals, MAM and SAM (and its treatment with RUTF and Khichuri-Halwa) on microbiota maturity (see Example 8). Second, we applied the model that we used for Bangladeshi children to healthy children in another population at high risk for malnutrition. The results show that the model generalizes (r2=0.6) to a cohort of 47 Malawian twins and triplets, aged 0.4-25.1 months, who were concordant for healthy status in a previous study11(WHZ, −0.23±0.97(mean±s.d.); Table 16). Age-discriminatory taxa identified in healthy Bangladeshi children show similar age-dependent changes in their representation in the microbiota of healthy Malawian children, as assessed by the Spearman rank correlation metric (FIG. 12C,D).

The question of whether microbiota immaturity associated with SAM and MAM is maintained during and beyond childhood also underscores the need to determine the physiologic, metabolic and immunologic consequences of this immaturity, and how they might contribute to the associated morbidities and sequelae of malnutrition, including increased risk for diarrhoeal disease, stunting, impaired vaccine responses, and cognitive abnormalities2,19. Our study raises a testable hypothesis: namely, that assessments of microbiota maturation, including in the context of the maternal-infant dyad, will provide a more comprehensive view of normal human development and of developmental disorders, and generate new directions for preventive medicine. Testing this hypothesis will require many additional clinical studies but answers may also arise from analyses of gut microbiota samples that have already been stored from previous studies.

The study population resided in the Mirpur slum of Dhaka, Bangladesh (23.8042° N 90.3667° E) in a catchment area consisting of 9,250 households. Most of the homes in this community consist of one main room (˜220 square feet), composed of concrete floors and tin roofs with bamboo, metal, and in a few cases cement walls. The average number of household members ranges from 4-10 people, and average monthly family income is 4,000-10,000 Bangladeshi Taka (50 to 130 USD). Infants do not wear diapers, nor do they typically wear any clothing on the bottom halves of their bodies. The importance of hand washing before eating or child feeding is widely understood but rarely practiced due to lack of access to clean water. Families prepare food either on the floor or on a ground-level cement slab located at the entrance of the home; this slab typically straddles an open drain containing wastewater running along the street. Since few families have refrigerators, most food is stored on shelves or under the bed. Households may consist of more than one biological family: in these cases all individuals in the household share a gas stove and cooking area immediately outside the main room, although food, cooking pots and utensils are used separately by each biological family. All individuals share a common ‘bathroom,’ a small space containing a latrine, and sometimes the water pump, located next to the main room. The common practice is to wash the perianal area by hand with water contained in a small, special container called bodna in Bangla.

Fecal Microbiota Variation within and Between Family Members During the First Year of Postnatal Life

There are few reports of time-series studies charting assembly of the gut microbiota in healthy USA infants and even fewer studies in infants from non-Western populations. The results published to date have revealed pronounced intra—as well as interpersonal variation during the first year of life11,29-31. In contrast, the gut microbiota of healthy USA adults is quite stable over time, with signatures of within-individual and within-family similarity evident throughout sampling periods15.

To obtain a view of gut microbiota development in Bangladeshi infants and children as a function of time after birth and family structure, we collected monthly fecal samples from 11 twin pairs and 1 set of triplets and their parents. The first fecal sample was obtained from infants at the time of their enrollment (4±3 days of age). Monthly samples were subsequently obtained from each of these 25 infants and from their mothers while samples were collected from their fathers every three months. Families were followed for a total of 520±159 days (mean±SD). The duration of exclusive breastfeeding was 28±23 days (mean±SD). Diarrhoea occurred for 11±12 days (2±3% of the total number of days followed during the study).

Distances (degree of similarity) between all pairs of fecal microbiota samples in this birth cohort were computed using the Hellinger metric, an abundance-based ecological metric, as well as the phylogeny-based unweighted UniFrac metric where distance is calculated based on the degree to which any two communities share branch length on a bacterial tree of life32. In the case of triplets, we performed all possible pairwise comparisons (self-self; all three possible pairwise comparisons among siblings; each sibling against unrelated age-matched individuals; each sibling against their mother or father).

We had previously noted that genetically unrelated co-habiting adults in the USA have more similar overall bacterial phylogenetic configurations in their fecal microbiota than unrelated adults living separately11. Comparing the difference (distance) of a Bangladeshi mother's microbiota during her first month post-partum to her microbiota three months later revealed a larger shift in overall structure compared to fathers sampled during the same three-month interval [P=0.01 (Hellinger); P=0.04 (unweighted UniFrac);FIG. 5C,D], thus obscuring a microbial manifestation of their co-habitation. This signature of co-habitation emerged 10 months postpartum, at a time when the preceding marked temporal variation of the mother's microbiota had diminished (P=0.006 for difference between co-habiting spouses at 10 months postpartum versus non-co-habiting adults in the cohort as measured by the abundance based Hellinger metric; P=0.08 using the presence/absence unweighted UniFrac phylogenetic metric; seeFIG. 5E,F).

During the first postnatal month, the bacterial configuration of the fecal microbiota of infants was more similar to mothers compared to fathers (P<0.001 Hellinger metric; P=0.07 with unweighted UniFrac;FIG. 5G,H). Co-twins were more similar to one another than unrelated age-matched twins during the first postpartum year (P<0.001 Kruskall-Wallis;FIG. 51,J). An analysis of sources of variation in the microbiota of the twins and triplets over the course of the entire study revealed that age alone captured 19% and 37.7% of variance (Hellinger and unweighted UniFrac metrics, respectively) in contrast to dietary factors (presence/absence of ‘breast milk’, ‘formula’, ‘solid foods’) which explained only 2.5% and 3.8%, respectively (see Table 7 for partitioning of variance by metric and metadata; PERMANOVA as implemented withadonisfunction in R package vegan)33.

We identified increases in the proportional representation of 97%-identity OTUs in the microbiota of mothers during the perinatal period, including a number of the age-discriminatory taxonomic biomarkers, notably Bifidobacteria inFIGS. 5A,B and Table 8. This latter feature is not unique to Bangladesh: a recent study of 80 Finnish mother-infant pairs sampled at 1 and 6 months post-partum demonstrated that if a mother was positive forB. bifidum1 month following delivery, the likelihood of her child being colonized was significantly higher34).

Transient Reduction of Gut Microbiota Diversity in Healthy Twins and Triplets Associated with Diarrhea

In addition to changes in the relative proportions of specific bacterial taxa incorporated into our Random Forests model-derived MAZ and relative microbiota maturity metrics, the developing gut microbiota of infants/children is also characterized by an increase in total community bacterial diversity as judged by the Shannon Diversity Index (SDI). SDI is an ecological measure of within-sample (alpha) diversity that incorporates both the concept of total community size as well as the evenness of the abundance of its members. Across the 50 healthy Bangladeshi children sampled, SDI increased linearly with age (0.11 units per month of life with an intercept of 1.6±0.1 units at birth; mixed model; P<0.0001). In twins and triplets, diarrhoea (n=36 episodes) was the only significant clinical parameter associated with a reduction in SDI (−0.44±0.1; P<0.01). This reduction showed a similar time course of recovery as relative microbiota maturity, persisting for one month (−0.35±0.2 SDI; P<0.05) followed by subsequent recovery (FIG. 4; Table 6c).

Persistent Reductions in Diversity Associated with SAM

As with measurements of microbiota maturity, the RUTF group showed significant improvement in SDI values between 1-3 months following cessation of treatment, followed by regression to a persistent lower than healthy SDI beyond 3 months. In the case of Khichuri-Halwa, improvement in SDI was only significant at 3-4 months of follow-up. SAM children in both treatment groups exhibited significant reductions in diversity compared to healthy Bangladeshi children at all phases of treatment and recovery, except for 1-3 months post-RUTF and 3-4 months post-Khichuri-Halwa (FIG. 7; Table 12).

Two Hundred and Twenty Bacterial Taxa that are Significantly Different in their Proportional Representation in Microbiota of Children with SAM Compared to Healthy at Multiple Treatment Phases Across Both Groups

During the acute phase, prior to nutritional rehabilitation, 116 97%-identity OTUs were significantly altered in SAM. The majority were lower in relative abundance compared to healthy children. The four 97% identity OTUs with the largest reductions in abundance during the acute phase included three classified as belonging to the genusBifidobacterium(B. longum, two unassigned to a species) andFaecalibacterium prausnitzii, of which two are age-discriminatory taxa (FDR-corrected P<0.05). Taxa that were enriched in children diagnosed with SAM compared to healthy children included those belonging to the family Enterobacteriaceae (generaEscherichiaandKlebsiella) as well asEnterococcus faecalis(FDR-corrected P<0.05).

In children with SAM, taxa that remain depleted throughout the follow-up period included members of the bacterial familiesRuminococcaceae, Veillonellaceae and Prevotellaceae. Taxa enriched in the microbiota of children with SAM after the therapeutic food interventions belonged predominantly to the genusStreptococcus, including 97% ID OTUs identified asStreptococcuslutentiensis,Streptococcus thermophilus(also age-discriminatory) and other as yet unknownStreptococcusspecies (FDR-corrected P<0.01; seeFIG. 8andFIG. 9for a heatmap depiction of all 97% ID OTUs whose representation in the fecal microbiota is significantly altered in SAM relative to healthy before, during and after the nutritional rehabilitation period; also see Table 13).

Assessing the Effects of Antibiotics on Microbiota Maturity During the Follow-Up Period in Children with SAM

As noted in Example 1, we compared antibiotic use during the post-intervention periods for the two treatment arms. The results indicate that (i) the frequency of antibiotic consumption during this period was comparable to that of healthy children in our training and validation sets (P=0.5, one-way ANOVA); (ii) there was no significant difference in antibiotic use between treatment arms (P=1; Fisher's exact test; Table 10); (iii) there was no significant association between recent antibiotic intake (defined as occurring seven or fewer days before collection of a fecal sample) and relative microbiota maturity values [difference in maturity values for samples with versus those without recent antibiotic intake: −0.37±0.8 (mean±SEM) P=0.6 (ANOVA of linear mixed model; n=100 samples for the 22 children in the post RUTF arm); +0.17±0.9; P=0.9 (n=103 samples, 25 children in the Khichuri-Halwa arm)]. Similarly, we found that diarrhoea was not significantly associated with differences in maturity values in either arm during the post-intervention period (Table 14).

Expanding the Sparse Random Forests-Based Model from 24 to 60 Taxa

It is logical to ask the following questions about our approach for defining microbiota maturity. First, are we defining “immaturity” entirely as a lack of maturity, rather than a specific, recognizable state in and of itself. Ours is a ‘positive’ composition-based classification. For example, theBifidobacterium longumOTU inFIG. 1Aranks 5thin terms of its feature importance score in the 24-taxon Random Forests model. In samples from healthy infants less than 6 months old, this OTU is highly represented [relative abundance=52.7±30% (mean±SD); >1% in 94% of samples from the training and validation sets). The remaining seven 97%-identity OTUs that comprise the cluster of early age-discriminatory taxa shown in FIG. F-H together represent 6.35±8% of the microbiota and are present at >1% abundance in 84% of samples.

Second, was there an outlet for samples containing very few or none the 24 taxa selected by the model? For example, were they deemed unclassifiable, or classified as “other”, or were all samples “forced” onto the maturity scale? How were samples with low feature signal having few to no age-discriminant taxa classified? Only one of the 589 fecal samples in the SAM study had undetectable levels of the 24 age-discriminatory taxa. In the SAM cohort, only 10% of fecal samples (60/589) had an aggregate relative abundance of the 24 age-discriminatory taxa that was less than 10%. In healthy children, this was true for 36/960 samples.

When we expanded our model to include the top 60 age-discriminatory taxa, we found that <1% of SAM samples and none of the healthy samples had an aggregate relative abundance of the 60 age-discriminatory taxa that was less than 10%. Note that in expanding the model, we excluded OTUs that were deemed chimeric when using default BLAST thresholds to the Greengenes reference as implemented in QIIME. The performance of the 24 and 60 taxa models were similar. Predictions made by the two models when they were applied to the healthy validation datasets (all 724 samples considered), and when they were applied to the SAM datasets (all 589 samples considered), showed a strong correlation (r2=0.98 and 0.93, respectively). Both yielded similar results for our analysis of (i) the effects of diarrhoea in healthy twins/triplets (microbiota immaturity was transient), (ii) the SAM trial (the effects of RUTF and Khichuri-Halwa produced transient non-durable improvements compared to healthy controls; antibiotics did not have a significant effect on microbiota maturity measurements either during the acute phase or during the post-intervention; note that the top 60 model includes Enterobacteriaceae and Streptococcaceae OTUs that are highly enriched in children with SAM relative to healthy); and (iii) the MAM study (a significant difference was observed between 18 month old healthy controls versus children with MAM) (data not shown).

Processed 16S rRNA datasets are available here, http://gordonlab.wustl.edu/Subramanian_6_14/Nature_2014_Processed_16 S_rRNA_da tasets.html, as a BIOM-formatted OTU table, along with split libraries of data generated from faecal samples and ‘Mock’ communities, mapping file, and an augmented reference sequence set (Greengenes version 4feb2011 plus de novo OTUs picked from sequences generated in the present study).

Methods for Examples 1-9

Summary

All subjects lived in Dhaka, Bangladesh (see Anthropologic Study below and Example 2 for anthropologic assessment of Mirpur, an urban slum in Bangladesh, where most subjects resided). Informed consent was obtained and studies were conducted using protocols approved by the ICDDR,B, Washington University, and University of Virginia institutional review boards (IRBs). Linear mixed models were applied to test hypotheses in repeated measurements of relative abundance of 97%-identity OTUs and maturation metrics in time-series profiling of faecal microbiota20. To account for similarity between observations from repeated sampling of the same individuals and families, we fit random intercepts for each subject in the case of adults and singletons, nested these intercepts within each family in the case of twins and triplets, and included age as a fixed-effect covariate, while testing the significance of associations between the microbiota and specified host and environmental factors. Differences between microbiota maturation metrics in each treatment phase of SAM were compared to values at enrollment in each treatment group, and to healthy children within the same age range (excluding samples from children used to train the Random Forests model), using analysis of variance (ANOVA) of linear mixed models followed by Dunnett's post-hoc comparisons.

Full details of the design of this now-complete birth cohort study have been described previously21. Faecal microbiota samples were profiled from 25 children who had consistently healthy anthropometric measures based on quarterly (every 3 months) measurements (Table 1). The WHZ threshold used for ‘healthy’ (on average above −2 s.d.) was based on median weight and height measurements obtained from age- and gender-matched infants and children by the Multi-Centre Growth Reference study of the World Health Organization3. Clinical parameters, including diarrhoeal episodes and antibiotic consumption associated with each of their faecal samples are provided in Supplementary Table 2 of Subramanian et al,Nature2014; S10:417-421, which is hereby incorporated by reference in its entirety.

A second group studied from this singleton cohort consisted of 33 children sampled cross-sectionally at 18 months, including those who were incorporated as healthy reference controls, and those with a WHZ <−2 who were classified with MAM (Table 15).

Twins and Triplets Birth Cohort.

Mothers with multiple pregnancy, identified by routine clinical and sonographic assessment at the Radda Maternal Child Health and Family Planning (MCH-FP) Clinic in Dhaka, were enrolled in a prospective longitudinal study (n=11 mothers with twins, 1 mother with triplets). The zygosity of twin pairs and triplets was determined using plasma DNA and a panel of 96 polymorphic single-nucleotide polymorphisms (SNPs) (Center for Inherited Disease Research, Johns Hopkins University). Four twin pairs were monozygotic, six were dizygotic, and the set of triplets consisted of a monozygotic pair plus one fraternal sibling (Table 1; note that one of the 11 twin pairs could not be tested for zygosity because plasma samples were not available). Information about samples from healthy twins, triplets and their parents, including clinical parameters associated with each faecal sample, is provided in Table 2 and in Supplementary Table 2 of Subramanian et al,Nature2014; S10:417-421, which is hereby incorporated by reference in its entirety.

The three healthy Bangladeshi groups used for model training and validation had the following WHZ scores: −0.32 6 1 (mean 6 s.d.; 12 singletons randomized to the training set), −0.4460.8 (13 singletons randomized to one of the two validation sets), and −0.466 0.7 (twins and triplets in the other validation set) (Table 3). The average number of diarrhoeal episodes in the singleton training set, the singleton validation set, and the twin and triplet validation set (4, 4.6 and 1.7, respectively) was comparable to values reported in previous surveys of another cohort of 0-2-year-old Bangladeshi children (4.25 per child per year)22.

There were no significant differences in the number of diarrheal episodes per year per child and the number of diarrhoeal days per year per child between the singleton training and validation sets (Student's t-test, P 5 0.5). Moreover, across all training and validation sets, neither of these diarrheal parameters correlated with mean age-adjusted Shannon diversity indices (Spearman's Rho, −0.18 and −0.12, P 5 0.22 and 0.4, respectively). The fraction of faecal samples collected from each child where oral antibiotics had been consumed within the prior 7 days was not significantly different between the training and two validation sets (one-way ANOVA, P 5 0.14; see Table 3).

Severe Acute Malnutrition Study.

Sixty-four children in the Nutritional Rehabilitation Unit of ICDDR,B, Dhaka Hospital suffering from SAM (defined as having a WHZ less than −3 s.d. and/or bilateral pedal oedema) were enrolled in a randomized interventional trial to compare an imported peanut-based RUTF, Plumpy'Nut (Nutriset Plumpyfield, India) and locally produced Khichuri-Halwa (clinical trial NCT01331044). Initially, children were stabilized by rehydration and feeding ‘suji’, which contains whole bovine milk powder, rice powder, sugar and soybean oil (approximately 100 kcal kg−1body weight per day, including 1.5 g protein kg−1per day). Children were then randomized to the Khichuri-Halwa or RUTF groups. Khichuri consists of rice, lentils, green leafy vegetables and soybean oil; Halwa consists of wheat flour (atta), lentils, molasses and soybean oil. Children randomized to the Khichuri-Halwa treatment arm also received milk suji ‘100’ during their nutritional rehabilitation phase (a form of suji with a higher contribution of calories from milk powder compared to suji provided during the acute phase). RUTF is a ready-to-use paste that does not need to be mixed with water; it consists of peanut paste mixed with dried skimmed milk, vitamins and minerals (energy density, 5.4 kcalg−1). Khichuri and Halwa are less energy-dense than RUTF (1.45 kcal g-1 and 2.4 kcal g−1, respectively, see Table 11 for a list of ingredients for all foods used during nutritional rehabilitation).

The primary outcome measurement, rate of weight gain (g kg−1per day), along with improvement in WHZ after nutritional rehabilitation are reported by child in Table 9. Faecal samples were collected before randomization to the RUTF and Khichuri-Halwa treatment arms, every 3 days during nutritional rehabilitation and once a month during the follow-up period (information associated with each faecal sample is provided in Supplementary Table 11 of Subramanian et al,Nature2014; S10:417-421, which is hereby incorporated by reference in its entirety).

To obtain additional information about household practices in the Mirpur slum of Dhaka, in-depth semi-structured interviews and observations were conducted over the course of 1 month in nine households (n=30 individuals). This survey, approved by the Washington University and ICDDR,B IRBs, involved three ICDDR,B field research assistants, and three senior scientific staff in the ICDDR,B Centre for Nutrition and Food Security, plus two anthropologists affiliated with Washington University in St. Louis. Parameters that might affect interpretation of metagenomic analyses of gut microbial-community structure were noted, including information about daily food preparation, food storage, personal hygiene and childcare practices.

Characterization of the Bacterial Component of the Gut Microbiota by V4-16S rRNA Sequencing.

Faecal samples were frozen at −20° C. within 30 min of their collection and subsequently stored at −80° C. before extraction of DNA. DNA was isolated by bead-beating in phenol and chloroform, purified further (QIAquick column), quantified (Qubit) and subjected to polymerase chain reaction (PCR) using primers directed at variable region 4 (V4) of bacterial 16S rRNA genes. Bacterial V4-16S rRNA data sets were generated by multiplex sequencing of amplicons prepared from 1,897 faecal DNA samples (26,580 6 26,312 (mean 6 s.d.) reads per sample, paired-end 162- or 250-nucleotide reads; Illumina MiSeq platform; Table 4). Reads of 250 nucleotides in length were trimmed to 162 nucleotides, then all reads were processed using previously described custom scripts, and overlapped to 253-nucleotide fragments spanning the entire V4 amplicon15. ‘Mock’ communities, consisting of mixtures of DNAs isolated from 48 sequenced bacterial members of the human gut microbiota combined in one equivalent and two intentionally varied combinations, were included as internal controls in the Illumina MiSeq runs. Data from the mock communities were used for diversity and precision-sensitivity analyses employing methods described previously15,23.

Reads with ≥97% nucleotide sequence identity (97%-identity) across all studies were binned into operational taxonomic units (OTUs) using QIIME (v 1.5.0), and matched to entries in the Greengenes reference database (version 4feb2011)24,25. Reads that did not map to the Greengenes database were clustered de novo with UCLUST at 97%-identity and retained in further analysis. A total of 1,222 97%-identity OTUs were found to be present at or above a level of confident detection (0.1% relative abundance) in at least two faecal samples from all studies. Taxonomy was assigned based on the naive Bayesian RDP classifier version 2.4 using 0.8 as the minimum confidence threshold for assigning a level of taxonomic classification to each 97%-identity OTU.

Definition of Gut-Microbiota Maturation in Healthy Children Using Random Forests.

Random Forests regression was used to regress relative abundances of OTUs in the time-series profiling of the microbiota of healthy singletons against their chronologic age using default parameters of the R implementation of the algorithm (R package ‘randomForest’, ntree 5 10,000, using default mtry of p/3 where p is the number of input 97%-identity OTUs (features))26. The Random Forests algorithm, due to its non-parametric assumptions, was applied and used to detect both linear and nonlinear relationships between OTUs and chronologic age, thereby identifying taxa that discriminate different periods of postnatal life in healthy children. A rarefied OTU table at 2,000 sequences per sample served as input data. Ranked lists of taxa in order of Random Forests reported ‘feature importance’ were determined over 100 iterations of the algorithm. To estimate the minimal number of top ranking age-discriminatory taxa required for prediction, the rfcv function implemented in the ‘randomForest’ package was applied over 100 iterations. A sparse model consisting of the top 24 taxa was then trained on the training set of 12 healthy singletons (272 faecal samples). Without any further parameter optimization, this model was validated in other healthy children (13 singletons, 25 twins and triplets) and then applied to samples from children with SAM and MAM. A smoothing spline function was fit between microbiota age and chronologic age of the host (at the time of faecal sample collection) for healthy children in the validation sets to which the sparse model was applied.

Alpha Diversity Comparisons.

Estimates of within-sample diversity were made at a rarefaction depth of 2,000 reads per sample. A linear regression was fit between the Shannon diversity index (SDI) and postnatal age in the 50 healthy children using a mixed model (see the additional details regarding statistical methods, below). An estimate of the coefficient for the slope of SDI with age and intercept was extracted, residuals of this regression were defined as a DSDI metric, and associations of this metric with clinical parameters were tested in the cohort of healthy twins and triplets. To test for differences in SDI as a function of health status and chronologic age in malnourished children, we compared the distribution of age-adjusted ΔSDIs in children with SAM between treatment phases.

Detection of Associations of Bacterial Taxa with Nutritional Status and Other Parameters.

Relative abundances of 97%-identity OTUs were used in linear mixed models as response variables to test for associations with clinical metadata as predictors. For each comparison, we restricted our analysis to 97%-identity OTUs and bacterial families whose relative abundance values reached a level of confident detection (0.1%) in a minimum of 1% of samples in each comparison. Pseudocounts of 1 were added to 97%-identity OTUs to account for variable depth of sequencing between samples, and relative abundances were arcs in-square-root-transformed to approximate homoscedasticity when applying linear models. P values of associations of factors with the relative abundances of bacterial taxa were computed using ANOVA type III (tests of fixed effects), subjected to Benjamini-Hochberg false discovery rate (FDR) correction.

Clinical microscopy was performed for all faecal samples collected at monthly intervals from the singleton birth cohort and from healthy twins and triplets, and screened forEntamoeba histolytica, Entamoeba dispar, Escherichia coli, Blastocystis hominis, Trichomonas hominis, Blastocystis hominis, Coccidian-like bodies,Giardia lamblia, Ascaris lumbricoides, Trichuris Tricuria, Ancylostoma duodenale/Necator americanus, Hymenolepsis nana, Endolimax nana, lodamoebabutschliiandChilomastixmesnili. The effects of enteropathogens, detected by microscopy on relative microbiota maturity, MAZ and SDI were included in our analysis of multiple environmental factors inFIG. 4and Table 6. In cases in which children presented with SAM plus diarrhoea, faecal samples collected before nutritional rehabilitation were cultured forVibrio cholerae, Shigella flexneri, Shigella boydi, Shigella sonnei, Salmonella enterica, Aeromonas hydrophilaandHafnia alvae. See Tables 9 and 17 for results of enteropathogen testing.

Additional Details Regarding Statistical Methods.

Linear mixed models were applied to test for associations of microbiota metrics (relative microbiota maturity, MAZ and SDI) with genetic and environmental factors in twins and triplets. Log-likelihood ratio tests and F tests were used to perform backward elimination of non-significant random and fixed effects27. Relative microbiota maturity, MAZ and SDI were defined at different phases of treatment and at defined periods of follow-up (<1 month, 1-2, 3-4 and >4 months after completion of the RUTF or Khichuri-Halwa nutritional intervention) in children with SAM relative to healthy children. ‘Treatment phase’ was specified as a categorical multi-level factor in a univariate mixed model with random by-child intercepts. Dunnett's post-hoc comparison procedure was performed to compare each treatment phase relative to healthy controls and relative to samples collected at enrollment in each food intervention group.

REFERENCES FOR EXAMPLES 1-9

Members of the Human Gut Microbiota Involved in Recovery fromVibrio choleraeInfection

We used an approved protocol for recruiting Bangladeshi adults living in Dhaka Municipal Corporation area for this study. Of the 1,153 patients with acute diarrhoea who were screened, seven passed all entry criteria (Methods for Examples 10-17) and were enrolled (Tables 18 and 19). Faecal samples collected at monthly intervals during the first 2 postnatal years from 50 healthy children living in the Mirpur area of Dhaka city, plus samples obtained at approximately 3-month intervals over a 1-year period from 12 healthy adult males also living Mirpur, allowed us to compare recovery of the microbiota from cholera with the normal process of assembly of the gut community in infants and children, and with unperturbed communities from healthy adult controls.

Using the standard treatment protocol of the International Centre for Diarrhoeal Disease Research, Bangladesh, study participants with acute cholera received a single oral dose of azithromycin and were given oral rehydration therapy for the duration of their hospital stay. Patients were discharged after their first solid stool. We divided the diarrhoeal period (from the first diarrhoeal stool after admission to the first solid stool) into four proportionately equal time bins: diarrhoeal phase 1 (D-Ph1) to D-Ph4. Every diarrhoeal stool was collected from every participant. Faecal samples were also collected every day for the first week after discharge (recovery phase 1, R-Ph1), weekly during the next 3 weeks (R-Ph2), and monthly for the next 2 months (R-Ph3). For each individual, we selected a subset of samples from D-Ph1 to D-Ph3 (Methods for Examples 10-17), plus all samples from D-Ph4 to R-Ph3, for analysis of bacterial composition by sequencing PCR amplicons generated from variable region 4 (V4) of the 16S ribosomal RNA (rRNA) gene (FIG. 15Aand Supplementary Table 3 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety). Reads sharing 97% nucleotide sequence identity were grouped into operational taxonomic units (97%-identity OTUs; Methods for Example 10-17).

We identified a total of 1,733 97%-identity OTUs assigned to 343 different species after filtering and rarefaction (Methods for Example 10-17).V. choleraedominated the microbiota of the seven patients with cholera during D-Ph1 (mean maximum relative abundance 55.6%), declining markedly within hours after initiation of oral rehydration therapy. The microbiota then became dominated by either an unidentifiedStreptococcusspecies (maximum relative abundance 56.2-98.6%) or byFusobacteriumspecies (19.4-65.1% in patients B-E). In patient G, dominance of the community passed from aCampylobacterspecies (58.6% maximum) to aStreptococcusspecies (98.6% maximum) (Table 20 and Supplementary Table 4 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety). Of the 343 species, 47.9±6.6% (mean±s.d.) were observed throughout both the diarrhoeal and recovery phases, suggesting that microbiota composition during the recovery phase may reflect an outgrowth from reservoirs of bacteria retained during disruption by diarrhoea (FIG. 16).

Indicator species analysis4(Methods for Example 10-17) was used to identify 260 bacterial species consistently associated with the diarrhoeal or recovery phases across members of the study group, and in a separate analysis for each subject (Table 21). The relative abundance of each of the discriminatory species in each faecal sample was compared with the mean weighted phylogenetic (UniFrac) distance between that microbiota sample and all microbiota samples collected from the reference cohort of healthy Bangladeshi adults. The results revealed 219 species with significant indicator value assignments to diarrhoeal or recovery phases, and relative abundances with statistically significant Spearman's rank correlation values to community UniFrac distance to healthy control microbiota (Table 22 andFIG. 16F-N). Not surprisingly, the abundance ofV. choleraedirectly correlated with increased distance to a healthy microbiota.StreptococcusandFusobacteriumspecies, which bloomed during the early phases of diarrhoea, were also significantly and positively correlated with distance from a healthy adult microbiota. Increases in the relative abundances of species in the generaBacteroides, Prevotella,Ruminococcus/Blautia, andFaecalibacterium(for example,Bacteroides vulgatus, Prevotella copri, R obeum,andFaecalibacterium prausnitzii) were strongly correlated with a shift in community structure towards a healthy adult configuration (FIGS. 16F-Nand Table 22).

Previously we used Random Forests, a machine-learning algorithm, to identify a collection of age-discriminatory bacterial taxa that together define different stages in the postnatal assembly/maturation of the gut microbiota in healthy Bangladeshi children living in the same area as the adult patients with cholera3. Of those 60 most age-discriminatory 97%-identity OTUs representing 40 different species, 31 species were present in adult patients with cholera. Intriguingly, they followed a similar progression of changing representation during diarrhoea to recovery as they do during normal maturation of the healthy infant gut microbiota (FIG. 16F-N). Twenty-seven of the 31 species were significantly associated with recovery from diarrhoea by indicator species analysis (seeFIG. 17, 18, 19for OTU-level and community-wide analyses). These 27 species, which serve as indicators and are potential mediators of restoration of the gut microbiota after cholera, guided construction of a gnotobiotic mouse model that examined the molecular mechanisms by which some of these taxa might affectV. choleraeinfection and promote restoration.

We assembled an artificial community of 14 sequenced human gut bacterial species (Table 23) that included (1) five species that directly correlated with gut microbiota recovery from cholera and with normal maturation of the infant gut microbiota (R. obeum, Ruminococcus torques, F. prausnitzii, Dorea longicatena, Collinsella aerofaciens), (2) six species significantly associated with recovery from cholera by indicator species analysis (Bacteroides ovatus, Bacteroides vulgatus, Bacteroides caccae, Bacteroides uniformis, Parabacteroides distasonis, Eubacterium rectale), and (3) three prominent members of the adult human gut microbiota that have known capacity to process dietary and host glycans (Bacteroides cellulosilyticus, Bacteroides thetaiotaomicron, Clostridium scindens6,7,8; as noted inFIG. 20and Table 24 and Supplementary Table 8 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety, shotgun sequencing of diarrhoeal- and recovery-phase human faecal DNA samples revealed that genes encoding enzymes involved in carbohydrate metabolism were the largest category of identified genes specifying known enzymes that changed in relative abundance within the faecal microbiome during the course of cholera). One group of mice was directly inoculated with approximately 109colony-forming units (c.f.u.) ofV. choleraeat the same time they received the 14-member community to simulate the rapidly expandingV. choleraepopulation during diarrhoea (‘D1 invasion’ group). A separate group was gavaged with the community alone and then invaded 14 days later withV. cholerae(‘D14invasion’ group) (FIG. 15C,D).

V. choleraelevels remained at a high level in the D1 invasion group over the first week (maximum 46.3% relative abundance), and then declined rapidly to low levels (<1%). Introduction ofV. choleraeinto the established 14-member community produced much lower levels of V. cho/eraeinfection (range of mean abundances measured daily over the 3 days after gavage of the enteropathogen, 1.2-2.7%; Table 25). Control experiments demonstrated thatV. choleraewas able to colonize at high levels for at least 7 days when it was introduced alone into germ-free recipients (109-1010c.f.u. per milligram wet weight of faeces;FIG. 13A,B). Together, these data suggest that a member or members of the artificial human gut microbiota had the ability to restrictV. choleraecolonization.

Changes in relative abundances of the 14 community members in faecal samples in response toV. choleraewere consistent for most species across the D1 invasion and D14invasion mice (Table 25). We focused on one member,R. obeum, because its relative abundance increased significantly after introduction ofV. choleraein both the D1 invasion and D14invasion groups (FIG. 21Aand Table 25) and because it is a prominent age-discriminatory taxon in the Random Forests model of gut microbiota maturation in healthy Bangladeshi children3(FIG. 18J). Mice were mono-colonized with eitherR. obeumorV. choleraefor 7 days and then the other species was introduced (FIG. 15E,F). WhenR. obeumwas present,V. choleraelevels declined by 1-3 logs (FIG. 13A,B). Germ-free mice were also colonized with the defined 14-member community or the same community withoutR. obeumfor 2 weeks, andV. choleraewas then introduced by gavage (FIG. 15G,H). V. choleraelevels 1 day after gavage were 100-fold higher in the community that lackedR. obeum; these differences were sustained over time (50-fold higher after 7 days; P<0.01, unpaired Mann-Whitney U-test;FIG. 13A,B).

Having established thatR. obeumrestrictsV. choleraecolonization, we used microbial RNA sequencing (RNA-seq) of faecal RNAs to determine the effect ofR. obeumon expression of knownV. choleraevirulence factors in mono- and co-colonized mice. Co-colonization led to reduced expression of tcpA (a primary colonization factor in humans9′10), rtxA and hlyA (encode accessory toxins11,12), and VC1447-VC1448 (RtxA transporters) (threefold to fivefold changes; P<0.05 compared withV. choleraemono-colonized controls, Mann-Whitney U-test; see Table 26 for other regulated genes that could impact colonization, plusFIG. 22for an ultra-performance liquid chromatography mass spectrometry (UPLC-MS) analysis of bile acids reported to effectV. choleraegene regulation13).

Two quorum-sensing pathways are known to regulateV. choleraecolonization/virulence14,15,16,17: an intra-species mechanism involving cholera autoinducer-1, and an inter-species mechanism involving autoinducer-218,19. Quorum sensing disrupts expression ofV. choleraevirulence determinants through a signalling pathway that culminates in production of the LuxR-family regulator HapR15,16. Repression of quorum sensing inV. choleraeis important for virulence factor expression and infection20,21,22.The luxS gene encodes the S-ribosylhomocysteine lyase responsible for Al-2 synthesis. Homologues of luxS are widely distributed among bacteria18,19, including 8 of the 14 species in the artificial human gut community (Table 27 andFIG. 23). RNA-seq of the faecal meta-transcriptomes of D1 invasion mice colonized with the 14-member artificial community plusV. cholerae, and mice harbouring the 14-member consortium withoutV. cholerae, revealed that of predicted luxS homologues in the community, only expression ofR. obeumluxS (RUMOBE02774) increased significantly in response toV. cholerae(P<0.05, Mann-Whitney U-test;FIG. 13C). Moreover,R. obeumluxS transcript levels directly correlated withV. choleraelevels (FIG. 21D).

In addition to luxS, theR. obeumstrain represented in the artificial community contains homologues of IsrABCK that are responsible for import and phosphorylation of Al-2 in Gram-negative bacteria23, as well as homologues of two genes, luxR and luxQ, that play a role in Al-2 sensing and downstream signalling in other organisms24. Expression of all theseR. obeumgenes was detected in vivo, consistent withR. obeumhaving a functional Al-2 signalling system (FIG. 21B,C). (See Example 17 for results showing thatR. obeumAl-2 production is stimulated byV. choleraein vitro and in co-colonized animals (FIG. 21E-G), plus (1) a genome-wide analysis of the effects ofV. choleraeonR. obeumtranscription in co-colonized mice (Table 26c) and (2) a community-wide view of the transcriptional responses of the 14-member consortium toV. cholerae(Table 28).)

Quorum sensing downregulates theV. choleraetcp operon that encodes components of the toxin co-regulated pilus (TCP) biosynthesis pathway required for infection of humans9,10. To confirm thatR. obeumLuxS could signal through Al-2 pathways, we clonedR. obeumandV. choleraeluxS downstream of the arabinose-inducible PBADpromoter in plasmids that were maintained in anEscherichia colistrain unable to produce its own Al-2 (DH5a)25. High tcp expression can be induced inV. choleraeafter slow growth in AKI medium without agitation followed by rapid growth under aerobic conditions26. Addition of culture supernatants harvested from theE. colistrains expressingR. obeumorV. choleraeluxS caused a two- to threefold reduction in tcp induction inV. cholerae(P<0.05, unpaired Student's t-test; replicated in four independent experiments). Supernatants from a controlE. colistrain with the plasmid vector lacking luxS had no effect (FIG. 14A). These findings are consistent with our in vivo RNA-seq results and provide direct evidence thatR. obeumAl-2 regulates expression ofV. choleraevirulence factor.

Germ-free mice were then colonized withV. choleraeandE. colibearing either the PBAD-R. obeumluxS plasmid or the vector control. Mice that receivedE. coliexpressingR. obeumluxS showed a significantly lower level ofV. choleraecolonization 8 h after gavage than mice that receivedE. coliwith vector alone (FIG. 14B; there was no statistically significant difference in levels ofE. colibetween the two groups (data not shown)). Together, these results establish a direct causal relationship betweenR. obeum-mediated restriction ofV. choleraecolonization andR. obeumAl-2 synthesis.

SeveralV. choleraemutants were used to determine whether knownV. choleraeAl-2 signalling pathways are required for the observed effects ofR. obeumonV. choleraecolonization. LuxP is critical for sensing Al-2 inV. cholerae. Co-colonization experiments in gnotobiotic mice revealed that levels of isogenic ΔluxP or wild-type luxP+V. choleraestrains were not significantly different as a function of the presence ofR. obeum(FIG. 24), suggesting thatR. obeummodulatesV. choleraelevels through other quorum-sensing regulatory genes. The luxO and hapRgenes encode central regulators linking knownV. choleraequorum-signalling and virulence regulatory pathways. Deletion of luxO typically results in increased hapR expression15. However, our RNA-seq analysis had shown that both luxO and hapR are repressed in the presence ofR. obeum(six- to sevenfold, P<0.0001; Mann-Whitney U-test), as are two important downstream activators of virulence repressed by HapR16, encoded by aphA and aphB. These findings provide additional evidence thatR. obeumoperates to regulate virulence through a novel regulatory pathway.

The quorum-sensing transcriptional regulator VqmA was upregulated more than 25-fold whenV. choleraewas introduced into mice mono-colonized withR. obeum(FIG. 14Cand Table 26). When germ-free mice were gavaged withR. obeumand a mixture of ΔvqmA (ΔlacZ)27and wild-typeV. cholerae(lacZ+) strains, the ΔvqmA mutant exhibited an early competitive advantage (FIG. 14D), suggesting thatR. obeummay be able to affect early colonization ofV. choleraethrough VqmA. VqmA is able to bind to and activate the hapR promoter directly27. Since RNA-seq showed that hapR activation did not occur in gnotobiotic mice despite high levels of vqmAexpression (FIG. 24Band Table 26), we postulate that the role played by VqmA inR. obeummodulation ofVibriovirulence genes involves an uncharacterized mechanism rather than the known pathway passing through HapR.

We have identified a set of bacterial species that strongly correlate with a process in which the perturbed gut bacterial community in adult patients with cholera is restored to a configuration found in healthy Bangladeshi adults. Several of these species are also associated with the normal assembly/maturation of the gut microbiota in Bangladeshi infants and children, raising the possibility that some of these taxa may be useful for ‘repair’ of the gut microbiota in individuals whose gut communities have been ‘wounded’ through a variety of insults, including enteropathogen infections. Translating these observations to a gnotobiotic mouse model containing an artificial human gut microbiota composed of recovery- and age-indicative taxa established that one of these species,R. obeum, reducesV. choleraecolonization. As an entrenched member of the gut microbiota in Bangladeshi individuals,R. obeumcould function to increase median infectious dose (ID50) forV. choleraein humans and thus help to determine whether exposure to a given dose of this enteropathogen results in diarrhoeal illness. The modest effects ofR. obeumAl-2 onV. choleraevirulence gene expression in our adult gnotobiotic mouse model may reflect the possibility that we have only identified a small fraction of the microbiota's full repertoire of virulence-suppressing mechanisms. Culture collections generated from the faecal microbiota of Bangladeshi subjects are a logical starting point for ‘second-generation’ artificial communities containingR. obeumisolates that have evolved in this population, and for testing whether the observed effects ofR. obeumgeneralize across many different strains from different populations. Moreover, the strategy described in this report could be used to mine the gut microbiota of Bangladeshi or other populations where diarrhoeal disease is endemic for additional species that use quorum-related and/or other mechanisms to limit colonization byV. choleraeand potentially other enteropathogens.

Patient Selection

Of the 1153 patients screened (Table 18), 796 (69%) were disqualified because of immediate prior antibiotic usage. Another 100 were disqualified because their diarrhea began more than 24 h before enrollment, while 132 were excluded due to lack of a permanent address for follow-up. Of the 11 adults (all males) who entered the study, four could not be contacted by staff after discharge from the hospital; their samples were not included in our analysis, leaving a total of seven individuals (A-G; see Table 19 for clinical metadata). The seven patients included in the study experienced diarrhea for 8.0±2.6 hours (mean±SD) prior to hospital admission and for 41.5±16.7 hours during their hospital stay. They had an average of 1.5±1.0 diarrheal stools per hour (Table 19).

For ethical reasons, we could not withhold treatment with azithromycin (or for that matter oral rehydration therapy). Therefore, our study design did not allow us to isolate the nature and effect sizes of various elements of the treatment protocol on the temporal patterns of change in the gut microbiota during the acute and recovery phases of infection, versus those produced by the diarrheal disease per se.

Identifying Community-Wide Changes in Representation of 97%-Identity OTU and Species During Diarrhea and Recovery Phases

Across all seven individuals, 58.6% of 97%-identity OTUs with a species-level taxonomic assignment associated with recovery (see below) were also detected during both diarrhea and recovery phases (FIG. 15, 16). For individuals C and E, where higher time-resolution analysis was performed, 41.1% and 29.9% of species-level taxa were identified in both diarrhea and recovery samples respectively, with 6.9% and 11.1% of the identified species detected only in recovery phase samples (see Supplementary Table 4c of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety).

Phylogenetic diversity (PD) of the cholera fecal microbiota decreased markedly during D-Ph2/D-Ph3, approaching the PD of healthy Bangladeshi children, before rising during R-Ph1-R-Ph3 in a temporal pattern that paralleled the UniFrac measurements of similarity to reference healthy adult controls (FIG. 19E).

Indicator species analysis was performed on the set of 236 fecal specimens selected from the diarrheal and recovery phases of subjects A-G. The statistical significance of associations was defined using permutation tests in which permutations were constrained within subjects: a bacterial species was considered significantly associated if it had a FDR-adjusted P<0.05. This approach identified 260 bacterial species consistently associated with either the diarrheal or recovery phases. 219 of these 260 species also had a significant correlation to community UniFrac distance to healthy control microbiota (FDR-adjusted P<0.05; Tables 21, 22,FIG. 16F-N). For species with positive correlations, higher relative abundances in a given microbiota state correlated to an increased difference to healthy fecal microbiota. Interpersonal differences in the distribution of 97%-identity OTUs comprising these species were also evident (FIGS. 16E, 17).

Of the 31 age-discriminatory species-level bacterial taxa in the developing gut microbiota of healthy Bangladeshi children, 24 includingR. obeumandF. prausnitzii, also had a significant Spearman rank correlation value between their relative abundance in each fecal sample and the mean weighted UniFrac distance between that sample and all healthy adult Bangladeshi microbiota (Tables 21, 22, seeFIGS. 16, 17, 18) for 97%-identity OTU analysis). In addition, Spearman rank correlations revealed that (i) 23 of the 27 species had relative abundances that significantly correlated with chronologic age in healthy children and with time following onset of acute diarrhea, and (ii) the direction of change (increase or decrease) was concordant between the two datasets for all 23 species (Table 20).

Changes in Relative Abundance of Genes Encoding ECs in Fecal Microbiomes Sampled During Diarrhea and Recovery

Shotgun sequencing of DNA prepared from diarrhea and recovery phase human fecal samples, followed by binning reads into assignable KEGG Enzyme Commission numbers (ECs), revealed that genes encoding enzymes involved in carbohydrate metabolism comprised the largest category of ECs that changed in relative abundance within the fecal microbiome during the course of cholera (seeFIG. 20and Table 24 and Supplementary Table 8 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety for EC-based abundance analysis, including Spearman rank correlations of EC abundance as a function of time across D-Ph1 through R-Ph3). These results led us to includeBacteroides cellulosilyticus, Bacteroides thetaiotaiotaomicron, andClostridium scindensin the artificial community of human gut symbionts, even though they did not satisfy the criteria described above as recovery phase-indicative or significantly correlated to normal maturation of the Bangladeshi infant microbiota.

Mice were first mono-colonized withR. obeumorV. choleraefor 7 days, followed by introduction of the other organism (FIG. 15C,D). RNA-Seq was performed using fecal samples collected from both groups of mice on the day prior to and 2 days after co-colonization (Table 28). An analysis of changes across the entireR. obeumtranscriptome 2d after introduction ofV. choleraerevealed few (n=7) functionally annotated transcripts with significant differences in expression (P<0.05 after multiple-hypothesis testing in DESee; see Table 26c). LuxS serves a dual role: production of the precursor Al-2 molecule [(S)-4,5-dihydroxyl-2,3-pentanedione] and participation in the pathway that re-generates homocysteine for use in the activated methyl cycle; in the absence of luxS, homocysteine is produced via oxaloacetate involving aspartate and glutamate as intermediates41,42Consistent with this, threeR. obeumgenes encoding ECs involved in glutamate biosynthesis were significantly down-regulated after introduction ofV. cholerae.

R. obeumincreased the expression of severalV. choleraegenes whose functions could impact its colonization, including five that encode products involved in iron acquisition and transport [VCO365 (bacterioferritin), VCO364 (bacterioferritin-associated ferredoxin), VC0608 (iron(III) transporter), VC0750 (hesB family protein)] plus five genes thought to be involved in cell wall modification (VCO246, VCO247, VCO245, VCO259, VCO249 in Table 26b; modifications inV. choleraeLPS have been reported to be important in colonization of mice17). Cholera toxin gene (ctxA, ctxB) expression was below the limits of reliable detection in each of theV. choleraetreatment groups, consistent with previous reports that it is not required for colonization of adult mice11-12.

Bile Acids and Regulation of Virulence Genes

V. choleraesenses host signals, including bile acids, in order to coordinately up-regulate expression of colonization factor genes13,43-47and down-regulate expression of anti-colonization factors such as the mannose-sensitive hemagglutinin pilus48. The gut microbiota could, in principle, affect colonization by modulating levels of host-derived signals that impactV. cholerae, or the microbiota could produce signaling molecules that directly modulateV. choleraepathogenesis. To explore these possibilities, we used ultra-performance liquid chromatography-mass spectroscopy (UPLC-MS) to characterize the representation of bile acid species in fecal samples collected from mice colonized with the 14-member community and this community minusR. obeum, (n=5-6 animals/group). We detected 10 bile acid species, and expressed each of their levels as a proportion of the aggregate levels of all 10 bile acids. Comparing fecal samples collected from mice after colonization with the 14- and 13-member communities, we found that the presence or absence ofR. obeumdid not have a statistically significant effect on levels of the primary bile acids taurocholic acid, tauro-beta-muricholic acid, and beta-muricholic acid that together comprise >80% of the measured pool (P>0.05, unpaired Mann-Whitney U test). One primary bile acid, alpha muricholic acid, and a secondary bile acid, urosodeoxycholic acid, were affected, but in aggregate they represent minor constituents (<11%) of the measured pool (FIG. 22). While we could not rule out that the effect ofR. obeumon these minor bile acid species impactsV. choleraecolonization/virulence, given the observed lack of change in the predominant bile acid species, we turned our attention to classes of microbial factors known to affect virulence (see Example 10).

R. obeumAl-2 Production is Stimulated byV. choleraeIn Vitro and in Co-Colonized Animals

In mice initially mono-colonized withR. obeumfor 7d,R. obeumluxS expression increased more than 2-fold 2d following introduction ofV. cholerae(P<0.01, unpaired Mann-Whitney U test;FIG. 21E). Using the BB170 Al-2 assay24, we measured fecal Al-2 levels from mice mono-colonized withR. obeumor co-colonized withR. obeumand ΔluxSV. cholerae(MM883)14, and confirmed that Al-2 levels were modestly but significantly higher in the co-colonized group (FIG. 21F). When these bacteria were co-cultured in vitro under anaerobic conditions,R. obeumAl-2 signal increased significantly [2.8±0.1-fold (mean±SEM), P<0.01, unpaired Mann-Whitney;FIG. 21G]. Furthermore, we induced expression of clonedR. obeumandV. choleraeluxS genes using an arabinose-inducible PBAD promoter in an Al-2-deficientE. colistrain (DH5a)25and observed that supernatants from these strains were able to induce increased BB170 bioluminescence over vector controls [7.2±1.1 and 8.8±2.4-fold (mean±SEM), respectively].

Methods for Examples 10-17

Human Studies

Subject recruitment. Protocols for recruitment, enrollment, and consent, procedures for sampling the faecal microbiota of healthy Bangladeshi adults and children, and the faecal microbiota of adults during and after cholera infection, plus the subsequent de-identification of these samples, were approved by the Human Studies Committees of the International Centre for Diarrhoeal Disease Research, Bangladesh, and Washington University School of Medicine in St. Louis.

Enrollment into the adult cholera study was based on the following criteria: residency in the Dhaka Municipal Corporation area, a positive stool test forV. choleraeas judged by dark-field microscopy, diarrhoea for no more than 24 h before enrollment, and a permanent address that allowed follow-up faecal sampling after discharge from Dhaka Hospital (International Centre for Diarrhoeal Disease Research, Bangladesh). Non-prescription antibiotic usage is prevalent in Bangladesh28,29. Since a history of previous antibiotic consumption could be a confounder when interpreting the effects of cholera on the gut microbiota, we excluded individuals if they had received antibiotics in the 7 days preceding admission to the hospital. Since this was an observational study with no experimental treatment arm, blinding for study inclusion was not necessary. See Table 18 for the number of individuals screened for inclusion in the study, the number of potential subjects excluded from the study and the reasons for their exclusion, and the number of subjects enrolled who satisfied all criteria for inclusion.

The healthy adults were fathers in a cohort of healthy twins, triplets, and their parents living in Mirpur that is described in ref. 3. Fathers were sampled every 3 months during the first 2 years of their offspring's postnatal life. Histories of diarrhoea and antibiotic use were not available for these fathers. However, histories of diarrhoea and antibiotic use in their healthy children were known: 46 of the 49 paternal faecal samples used were obtained during periods when none of their children had diarrhoea; 36 of these 49 samples were collected at a time when there had been no antibiotic use by their children in the preceding 7 days.

DNA extraction from human faecal samples, sequencing, and analysis. All diarrhoeal stools were collected from each participant (one sterilized bowl per sample), frozen immediately at −80° C., then subjected to the same bead beating and phenol chloroform extraction procedure for DNA purification that was applied to the formed frozen faecal samples collected from these individuals during the recovery phases (and previously to a wide range of samples collected from individuals representing different ages, cultural traditions, geographical locations, and physiological and disease states3,30).

DNA was isolated from all frozen faecal samples from D-Ph1 to D-Ph4, from the period of frequent sampling during the first week following discharge (recovery phase 1; R-Ph1), the period of less frequent sampling during weeks 2-3 (R-Ph2), and from weeks 4 to 12 of recovery (R-Ph3) (n=1,053 samples in total). For analyses involving healthy adult and child control groups, samples were excluded from our analysis where antibiotic use or diarrhoea was known to have occurred in the 7 days before sample collection.

For each participant in the cholera study, we selected one sample with high DNA yield (≥2 μg) from each 2-hour period during D-Ph1 to D-Ph3. An additional 7±2 samples (mean±s.d.) that had been collected during the approximately 5-h period before the rate of diarrhoea began to decrease at the beginning of D-Ph3 were included. All faecal samples collected after this time point (that is, from the remainder of D-Ph3 to R-Ph3), were also included in our analysis (n=19.7±7.4 total samples (mean±s.d.) per individual in the diarrhoeal phase, and 14±3.3 total samples per individual in the recovery phase). Two patients (C and E) were chosen for additional sequencing of all their diarrhoeal samples (n=100 and 50, respectively; see Supplementary Table 3b of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety).

The V4 region of bacterial 16S rRNA genes represented in each selected faecal microbiota sample was amplified by PCR using primers containing sample-specific barcode identifiers. Amplicons were purified, pooled, and paired-end sequenced with an Illumina MiSeq instrument (250 nucleotide paired-end reads; 86,315±2,043 (mean±s.e.m.) assembled reads per sample; see Supplementary Table 3 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety). Healthy control samples were analysed using the same sequencing platform and chemistry (n=293 total samples).

Sequences were assembled, then de-multiplexed and analysed using the QIIME software package31and custom Perl scripts. For analysis of diarrhoeal and recovery phase samples, rarefaction was performed to 49,000 reads per sample. For analyses including samples from healthy adults and children, samples were rarefied to 7,900 reads per sample. Reads sharing 97% nucleotide sequence identity were grouped into operational taxonomic units (97%-identity OTUs). To ensure that we retained less abundant bacterial taxa in our analysis of the faecal samples of patients with cholera, a 97%-identity OTU was called ‘distinct and reliable’ if it appeared at 0.1% relative abundance in at least one faecal sample. Taxonomic assignments of OTUs to species level were made using the Ribosomal Database Project version 2.4 classifier32and a manually curated Greengenes database33.

Indicator species analysis4was used to classify bacterial species as highly associated with either diarrhoeal phases or recovery. This approach is used in studies of macroecosystems to identify species that associate with different environmental groupings; it assigns for each species an indicator value that is a product of two components: (1) the species' specificity, which is the probability that a sample in which the species is found came from a given group; and (2) the species' fidelity, which is the proportion of samples from a given group that contains the species. We performed indicator species analysis in the set of 236 faecal specimens, selected from the seven patients according to the subsampling scheme described above, to identify bacterial species consistently associated with the diarrhoeal or recovery phases across members of the study group; statistical significance was defined using permutation tests in which permutations were constrained within subjects. We also conducted a separate indicator species analysis for each subject, using each individual's replicate diarrhoeal and recovery phase samples as the groupings.

For analyses of variation across communities, we used UniFrac5, a metric that measures the overall degree of phylogenetic similarity of any two communities based on the degree to which they share branch length on a bacterial tree of life; low pairwise UniFrac distance values indicate that communities are more similar to one another. Unifrac distances were calculated using the QIIME software package31.

The gut microbiomes of study participants were characterized by paired-end 2×250 nucleotide shotgun sequencing of faecal DNA using an Illumina MiSeq instrument (mean 216,698 reads per sample; Supplementary Table 3 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety). Paired sequences were assembled into single reads using the SHERA software package34, and annotated by mapping to version 58 of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database35using UBLAST36.

All experiments involving animals used protocols approved by the Washington University Animal Studies Committee. Germ-free male C57BL/6J mice were maintained in flexible plastic film gnotobiotic isolators and fed an autoclaved, low-fat, plant polysaccharide-rich mouse chow (B&K, catalogue number 7378000, Zeigler Bros) ad libitum. Mice were 5-8 weeks old at time of gavage. The number of mice used in each experiment is reported in the text, relevant figure legends, and summarized inFIG. 15.

Bacterial strains and plasmids. Table 23 lists the sequenced human gut-derived bacterial strains used to generate the artificial communities and their sources. Since all Bangladeshi faecal samples were devoted to DNA extraction, we were unable to utilize strains that originated from culture collections generated from study participants' faecal biospecimens. Thus, the strains incorporated into the artificial community were from public repositories, represented multiple individuals, and were typically not accompanied by information about donor health status or living conditions.

A Ptcp-lux reporter strain was constructed by introducing Ptcp-lux (pJZ376) intoV. choleraeC6706 via conjugation from SM10λpir. PBAD-luxS expression vectors were produced by first amplifying the luxS sequences ofV. choleraeC6706 andR. obeumATCC2917 using PCR and the primers described in Table 29. Amplicons were then cloned into pBAD202 (TOPO TA Expression Kit; Life Technologies), and introduced intoE. coliDH5a by electroporation.

All cultures ofV. choleraeC6706, the isogenic ΔluxS mutant (MM883), andE. colistrains containing luxS expression vectors were grown aerobically in Luria Broth (LB) medium with appropriate antibiotics (Table 29). All members of the 14-member artificial human gut microbiota, includingR. obeumATCC29174, were propagated anaerobically in MegaMedium37.

Colonization of gnotobiotic mice. All animal experiments involved administration of known consortia of bacterial species; as such, no blinding to group allocation was performed. The order of administration of microbial species to given groups of recipient mice was intentionally varied, as described inFIG. 15C-H.

Mono-colonized animals received either 200 μl of overnight cultures ofR. obeumstrain ATCC29174 orV. choleraestrain C6706. AllV. choleraecolonization studies in mice used the current pandemic El Tor biotype (strain C6706). Mice receiving the defined 13- or 14-member communities of sequenced human bacterial symbionts were gavaged with 200 μl of an equivalent mixture of bacteria assembled from overnight monocultures of each strain (D600 nm≈0.4 per strain; grown in MegaMedium). In the case of mice that received mixtures ofV. choleraeandE. colistrains withR. obeumluxS-expressing plasmids (or vector controls), theE. colistrains were first grown overnight in LB medium containing 50 μg ml−1kanamycin. Two millilitres of the culture were removed and cell pellets were obtained by centrifugation, washed three times with 2 ml LB medium to remove antibiotics, and re-suspended in 6 ml LB medium containing 0.1% arabinose. The suspension ofE. colicells was then incubated at 37° C. for 90 min, and mixed withV. choleraeC6706 such that each mouse was gavaged with ˜50 μl and ˜2.5 μl of overnight cultures of each organism, respectively. All gavages involvingV. choleraewere preceded by a gavage of 100 μl sterile 1 M sodium bicarbonate to neutralize gastric pH. Colonization levels ofV. choleraewere determined by serial dilution plating of faecal homogenates on selective medium.

Competitive index assays were performed with mice gavaged with 50 μl aliquots of cultures of mutant and wild-typeV. choleraeC6706 strains that had been grown to D600 nm=0.3. For experiments involving competitive index calculations as a function of the presence ofR. obeum,100 μl of an overnightR. obeumculture was co-inoculated with the mixture ofV. choleraestrains. Faecal samples from recipient gnotobiotic mice were subjected to dilution plating and aerobic growth on LB agar with the LacZ substrate Xgal; blue-white screening was used to determine colonization levels of the individualV. choleraestrains.

Community profiling by shotgun sequencing (COPRO-seq). Shotgun sequencing of faecal community DNA was used to define the relative abundance of species in the artificial communities; experimental and computational tools for COPRO-seq have been described previously8.

Microbial RNA-seq analysis of faecal samples collected from mice colonized with the 14-member artificial community with and withoutV. cholerae. Faecal samples were collected from colonized gnotobiotic mice and immediately snap-frozen in liquid nitrogen. RNA was extracted using bead-beating in phenol/chloroform/isoamyl alcohol followed by further purification using MEGACIear (Life Technologies). Purified RNA was depleted of 16S rRNA, 5S rRNA, and transfer RNA as previously described8or by using a RiboZero kit (Epicentre). Complementary DNA (cDNA) libraries were generated and sequenced (50 nucleotide unidirectional reads; Illumina GA-llx, HiSeq 2000 or MiSeq instruments; see Supplementary Table 3 of Hsiao et al,Nature2014; Epub, which is hereby incorporated by reference in its entirety). Reads were mapped to the genomes of members of the artificial community using Bowtie38.

To profile transcriptional responses toV. cholerae, all cDNA reads that mapped to the genomes of the 14 consortium members were binned based on enzyme classification level annotations from KEGG. ShotgunFunctionalizeR39was then used compare the faecal meta-transcriptomes of ‘D14invasion’ animals sampled 4 days after gavage of the 14-member community to the faecal meta-transcriptomes of D1 invasion mice sampled 4 days after gavage of the 14-member community plusV. cholerae. A mean twofold or greater difference in expression between the conditions, with an adjusted P value less than 0.0001 (ShotgunFunctionalizeR) was considered significant. This approach of binning to enzyme classifications mitigates issues with low-abundance transcripts being insufficiently profiled owing to limitations in sequencing depth8.

Owing to the higher sequencing depth achieved forR. obeumandV. choleraein mono- and co-colonization experiments, reads were mapped to reference genomes using Bowtie, and changes at the single transcript level were analysed using DESeq40(Table 27). Transcripts that satisfied the criteria of (1) having greater than twofold differential expression after DESeq normalization, (2) an adjusted P value less than 0.05, and (3) a minimum mean count value more than 10 were retained.

Al-2 assays. Previously frozen faecal pellets from gnotobiotic mice were re-suspended in AB medium24by agitation with a rotary bead-beater (25 mg faecal pellet per millilitre of medium). Al-2 assays were performed using theV. harveyiBB170 bioassay strain24, with reported results representative of at least two independent experiments, each with five technical repeats.V. harveyiBB170 cultures were grown aerobically overnight in AB medium, and diluted 1:500 in this medium for use in the Al-2 bioassay24. Luminescence was measured using a BioTek Synergy 2 instrument after 4 h of growth at 30° C. with agitation (300 r.p.m. using a rotatory incubator).

For in vitro measurements ofR. obeumAl-2 production, a 100 μl aliquot from an overnight monoculture of the bacterium grown in MegaMedium without glucose was diluted 1:20 in fresh MegaMedium without glucose. In addition, cells pelleted from 100 μl of an overnight culture ofV. choleraeΔluxS (MM883 (ref. 14)) grown in LB medium were added toR. obeumthat had also been diluted 1:20 in MegaMedium without glucose. The resulting mono- and co-cultures were incubated anaerobically at 37° C. for 16 h. Cells were pelleted by centrifugation, and supernatants were harvested and then added toV. harveyiBB170 cultures for Al-2 bioassay.

UPLC-MS. Procedures for UPLC-MS of bile acids have been described in ref. 37.

REFERENCES FOR EXAMPLES 10-17

TABLE 3Characteristics of children in training and validationsets used for Random Forests age-discriminatory model(a) Training SetCharacteristicsTrainingWeight-for-Height Z score−0.32 ± 1Male/Female7/5Number of fecal samples collected per child22.7 ± 1.5Age at first fecal sample collection (days)6 ± 1Age at last fecal sample collection (days)712 ± 15Mean sampling interval (days)33 ± 3.0Months of exclusive breastfeeding3.2 ± 2.3Age of first introduction of solid food (months)6.3 ± 2.3Number of diarrhoeal episodes per year4.0 ± 1.9% Days with diarrhoea during sampling period3.9 ± 1.7Fraction of samples collected where antibiotics0.2 ± 0.1had been consumed within prior 7 days(b) ValidationCharacteristicsValidationWeight-for-Height Z score−0.44 ± 0.8Male/Female9/4Number of fecal samples collected per child21.2 ± 2.2Age at first fecal sample collection (days)7 ± 4Age at last fecal sample collection (days)709 ± 9Mean sampling interval (days)35.1 ± 4.3Months of exclusive breastfeeding3.6 ± 2.3Age of first introduction of solid food (months)6.4 ± 6.4Number of diarrhoeal episodes per year4.6 ± 2.4% Days with diarrhoea during sampling period4.3 ± 2.7Fraction of samples collected where antibiotics0.2 ± 0.2had been consumed within prior 7 days(c) Validation - Twins & TripletsValidation -CharacteristicsTwins & TripletsWeight-for-Height Z score−0.46 ± 0.7Male/Female7/18Number of fecal samples collected per child17.9 ± 5.3Age at first fecal sample collection (days)13 ± 12Age at last fecal sample collection (days)497 ± 147Mean sampling interval (days)29 ± 3.1Months of exclusive breastfeeding0.7 ± 2.3Age of first introduction of solid food (months)7.0 ± 1.9Number of diarrhoeal episodes per year1.7 ± 1.3% Days with diarrhoea during sampling period2.2 ± 2.6Fraction of samples collected where antibiotics0.1 ± 0.1had been consumed within prior 7 daysMean values ± SD are shown

TABLE 7Identification of factors affecting variance in fecal microbiota configuration of healthy twins and tripletsHellinger Distance MetricUnweighted UniFrac Distance MetricSumsOfSumsOfFactorSqsMeanSqsF. ModelR2p-valueSqsMeanSqsF. ModelR2p-valueChronologic Age0.62120.6212108.3640.190330.0010.799630.79963290.790.377210.001Diet at timeBreastmilk0.05490.05499.5680.016810.0010.031170.0311711.3360.014710.052of fecalFormula0.00490.00490.8480.001490.880.009390.009393.4150.004430.184sampleSolid foods0.02050.02053.5840.006290.080.039220.0392214.2630.01850.001collectiondiarrhoea0.010.011.6810.002950.180.012550.012554.5630.005920.006Antibiotic Usage0.0020.0020.2940.000520.880.004210.004211.5330.001990.154PERMANOVA was implemented using the R vegan package's ‘adonis function’. We tested for significant associations in a linear model between microbiota variation (as measured by Hellinger or unweighted Unifrac metrics) and the indicated factors. Permutations were constrained within each twin-pair and the set of triplets and interactions between factors were not considered.

TABLE 14Relative microbiota maturity, Microbiota-for-Age Z-score and age-adjusted Shannon Diversity Index in relation to antibiotic usageand diarrhoea during the post-intervention follow-up periodInterventionArmEstimateStd. Errorp valueRelative microbiotamaturityAntibioticsRUTF−0.370.810.65diarrhoea−0.922.030.65Chronologic Age−0.180.120.13AntibioticsKhichuri-0.170.920.85diarrhoeaHalwa0.311.570.84Chronologic Age0.110.130.41Microbiota-for-Age ZscoreAntibioticsRUTF0.150.310.64diarrhoea0.270.530.61Chronologic Age−0.030.040.43AntibioticsKhichuri-0.070.250.77diarrhoeaHalwa−0.420.610.49Chronologic Age−0.120.03<0.001Age-adjusted ShannonDiversity IndexAntibiotics0.020.220.91diarrhoeaRUTF0.070.470.88Chronologic Age−0.050.020.03AntibioticsKhichuri-−0.320.190.09diarrhoeaHalwa−0.490.320.13Chronologic Age0.000.020.89Estimates of beta coefficients, standard error and p-values from linear mixed models with random by-child intercepts and chronologic age as a fixed effect covariate for each intervention arm

TABLE 27Distribution of homologs of AI-2 system genes in members of the artificial communityBacteroidescellulosilyticusBacteroidesBacteroidesBacteroidesBacteroidesBacteroidesColinsellaGeneWH2caccaeovatusthetaiotaomicronuniformisvulgatusaerofaciensluxS0000101luxP0000000luxQ7531584831282luxO2131111luxU0000000luxR1281589105lsrR0000000lsrA0000000lsrB0000000lsrC0000000lsrD0000000lsrK0100000lsrF1000000ClostridiumDoreaEubacteriumFaecalibacteriumParabacteroidesRuminococcusRuminococcusGenescindenslongicatenarectaleprausnitziidistasonisobeumtorquesluxS1111011luxP0000000luxQ001526131luxO0100100luxU0000000luxR1121520lsrR2000000lsrA1000030lsrB1000030lsrC0000020lsrD1000010lsrK6000020lsrF1000100Based on best BLASTp hit to KEGG database (10−5 e-value cutoff)