PERSON-SPECIFIC ASSESSMENT OF PROBIOTICS RESPONSIVENESS
20210269860 · 2021-09-02
Assignee
- Yeda Research And Development Co. Ltd. (Rehovot, IL)
- The Medical Research, Infrastructure And Health Services Fund Of The Tel Aviv Medical Center (Tel-Aviv, IL)
Inventors
Cpc classification
G16H50/20
PHYSICS
G16B10/00
PHYSICS
A61K31/496
HUMAN NECESSITIES
G16H50/30
PHYSICS
A61K2300/00
HUMAN NECESSITIES
A61K2300/00
HUMAN NECESSITIES
A23L33/135
HUMAN NECESSITIES
Y02A90/10
GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
A61K45/06
HUMAN NECESSITIES
Y02A50/30
GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
C12Q1/04
CHEMISTRY; METALLURGY
G01N2560/00
PHYSICS
C12Q2600/106
CHEMISTRY; METALLURGY
A61P1/00
HUMAN NECESSITIES
G16H10/40
PHYSICS
A61K31/496
HUMAN NECESSITIES
International classification
A61K45/06
HUMAN NECESSITIES
A61P1/00
HUMAN NECESSITIES
Abstract
A method of assessing whether a candidate subject is suitable for probiotic treatment is disclosed. The method comprises determining a signature of the gut microbiome of the candidate subject, wherein when the signature of the microbiome of the candidate subject is statistically significantly similar to a signature of a gut microbiome of a control subject known to be responsive to probiotic treatment, it is indicative that the subject is suitable for probiotic treatment.
Claims
1. A method of assessing whether a candidate subject is suitable for probiotic treatment comprising determining a signature of the gut microbiome of the candidate subject, wherein when said signature of the microbiome of the candidate subject is statistically significantly similar to a signature of a gut microbiome of a control subject known to be responsive to probiotic treatment, it is indicative that the subject is suitable for probiotic treatment.
2. The method of claim 1, wherein said determining said signature is effected by analyzing feces of the subject.
3. The method of claim 1, wherein said gut microbiome comprises a mucosal gut microbiome or a lumen gut microbiome.
4. The method of claim 1, wherein said probiotic comprises at least one of the bacterial species selected from the group consisting of B. bifidum, L. rhamnosus, L. lactis, L. casei, B. breve, S. thermophilus, B. longum, L. paracasei, L. plantarum and B. infantis.
5. The method of claim 1, wherein the candidate subject does not have a chronic disease.
6. The method of claim 1, wherein said signature of said gut microbiome is a presence or level of microbes of said microbiome.
7. The method of claim 1, wherein said signature of said gut microbiome is a presence or level of genes of microbes of said microbiome.
8. (canceled)
9. The method of claim 1, wherein said signature of said gut microbiome is an alpha diversity.
10. (canceled)
11. The method of claim 6, wherein said microbes of said microbiome are of an identical species to said microbes of the probiotic.
12. The method of claim 6, wherein said determining said signature is effected by analyzing feces of the subject.
13. The method of claim 12, wherein said microbes of said microbiome are of the species selected from the group consisting of those set forth in Table A and/or are of the genus Bifidobacterium or Dialister.
14. The method of claim 12, wherein said microbes of said microbiome utilize at least one pathway set forth in Table B.
15-43. (canceled)
44. A method of treating a disease of a subject for which an antibiotic is therapeutic comprising: (a) administering to the subject an antibiotic which is suitable for treating the disease; and subsequently (b)administering to the subject a an autologous fecal transplant, thereby treating the disease.
45-54. (canceled)
55. The method of claim 44, wherein the autologous fecal transplant is derived from the subject when he is healthy.
56. The method of claim 44, wherein the disease is a chronic disease.
57. The method of claim 44, wherein the disease is not a bacterial disease.
58. The method of claim 44, wherein the subject is deemed unsuitable for probiotic treatment.
59. The method of claim 58, wherein the subject is deemed unsuitable for probiotic treatment by determining a signature of the gut microbiome of the subject, wherein when said signature of the microbiome of the subject is statistically significantly similar to a signature of a gut microbiome of a control subject known to be non-responsive to probiotic treatment, it is indicative that the subject is not suitable for probiotic treatment.
60. A method of treating a subject having a disease associated with an antibiotics-perturbed gut comprising administering to the subject a therapeutically effective amount of an autologous fecal transplant thereby treating the subject having the disease associated with the antibiotics-perturbed gut.
61. The method of claim 60, wherein the autologous fecal transplant is derived from the subject when he is healthy.
62. The method of claim 60, wherein the disease is a chronic disease.
63. The method of claim 60, wherein the disease is not a bacterial disease.
Description
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0067] Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.
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DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
[0108] The present invention, in some embodiments thereof, relates to methods of using probiotics in mammalian subjects. More specifically, the invention relates to personalized predictions based on the gut microbiome as to whether a subject is responsiveness to a probiotic based on the gut microbiome.
[0109] Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details set forth in the following description or exemplified by the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.
[0110] Probiotics supplements are commonly consumed as means of life quality improvement and disease prevention. However, evidence of probiotics colonization efficacy, upon encountering the adult well-entrenched mucosal-associated gut microbiome, remains sparse and controversial.
[0111] In Example 1, the present inventors profiled the homeostatic mucosal, luminal and fecal microbiome along the entirety of the gastrointestinal tract of mice and humans. They demonstrate that solely relying on stool sampling as a proxy of mucosal GI composition and function yields inherently limited conclusions. Whilst the abundance of particular bacterial species in the stool mirror their abundance along other locations in the GI tract, many do not.
[0112] In contrast, direct gastrointestinal sampling in mice and humans, before and during an 11-strain probiotic consumption showed that probiotics readily pass through the gastrointestinal tract into stool, but encounter along the way a substantial microbiome-mediated mucosal colonization resistance, the level of which significantly impacted probiotics effects on the indigenous mucosal microbiome composition, function, and host gene expression profile. In humans, a person-, strain- and region-specific variability in gut mucosal colonization resistance significantly correlated with baseline host transcriptional and microbiome characteristics, but not with stool levels of probiotics during consumption.
[0113] Identification of such baseline microbial and host factors potentially enables prediction of a probiotics responsiveness or resistant state. The results obtained call for consideration of a transition from an empiric ‘one size fits all’ probiotics regiment design, to one which is based on the individual. Such a measurement-based approach would enable integration of person-specific features in tailoring particular probiotics interventions for a particular person at a given clinical context. Thus, the present invention can be used to devise more effective means of colonizing and impacting the host gut mucosa.
[0114] In Example 2, the present inventors addressed the issue as to whether probiotics efficiently reconstitute the indigenous human gut mucosal microbiome. They compared the effects of the probiotic cocktail described above with autologous fecal microbiome transplantation (aFMT) on post-antibiotic reconstitution of the mucosal gut microbiome, via a sequential invasive multi-omics assessment of the human gut before and during probiotics supplementation. In the antibiotics-perturbed gut, these probiotics feature enhanced colonization in humans and to a lesser degree in mice. Importantly, probiotics in this setting induce a markedly delayed mucosal microbiome reconstitution compared to spontaneous recovery or aFMT. As such, post-antibiotic probiotics-induced benefits may be offset by a delayed indigenous microbiome recovery.
[0115] These results highlight a need for development of personalized, targeted and aFMT-based approaches achieving post-antibiotic mucosal protection, without compromising microbiome recolonization in the perturbed host.
[0116] Thus, according to a first aspect of the present invention, there is provided a method of assessing whether a candidate subject is suitable for probiotic treatment comprising determining a signature of the gut microbiome of the candidate subject, wherein when the signature of the microbiome of the candidate subject is statistically significantly similar to a signature of a gut microbiome of a control subject known to be responsive to probiotic treatment, it is indicative that the subject is suitable for probiotic treatment.
[0117] As used herein the term “subject” refers to a mammalian subject (e.g. mouse, cow, dog, cat, horse, monkey, human), preferably human.
[0118] In one embodiment, the candidate subject is a healthy subject.
[0119] In another embodiment, the candidate subject has an infection. In still another embodiment, the candidate subject has recovered from an infection following antibiotic treatment.
[0120] In another embodiment, the candidate subject does not have a chronic disease.
[0121] The term “probiotic” as used herein, refers to one or more microorganisms which, when administered appropriately, can confer a health benefit on the host or subject and/or reduction of risk and/or symptoms of a disease, disorder, condition, or event in a host organism.
[0122] In some embodiments, probiotics comprise bacteria. Some non-limiting examples of known probiotics include: Akkermansia muciniphila, Anaerostipes caccae, Bifidobacterium adolescentis, Bifidobacterium bifidum, Bifidobacterium infantis, Bifidobacterium longum, Butyrivibrio fibrisolvens, Clostridium acetobutylicum, Clostridium aminophilum, Clostridium beijerinckii, Clostridium butyricum, Clostridium colinum, Clostridium indolis, Clostridium orbiscindens, Enterococcus faecium, Eubacterium hallii, Eubacterium rectale, Faecalibacterium prausnitzii, Fibrobacter succinogenes, Lactobacillus acidophilus, Lactobacillus brevis, Lactobacillus bulgaricus, Lactobacillus casei, Lactobacillus caucasicus, Lactobacillus fermentum, Lactobacillus helveticus, Lactobacillus lactis, Lactobacillus plantarum, Lactobacillus reuteri, Lactobacillus rhamnosus, Oscillospira guilliermondii, Roseburia cecicola, Roseburia inulinivorans, Ruminococcus flavefaciens, Ruminococcus gnavus, Ruminococcus obeum, Streptococcus cremoris, Streptococcus faecium, Streptococcus infantis, Streptococcus mutans, Streptococcus thermophilus, Anaerofustis stercorihominis, Anaerostipes hadrus, Anaerotruncus colihominis, Clostridium sporogenes, Clostridium tetani, Coprococcus, Coprococcus eutactus, Eubacterium cylindroides, Eubacterium dolichum, Eubacterium ventriosum, Roseburia faeccis, Roseburia hominis, Roseburia intestinalis, and any combination thereof.
[0123] The probiotic may comprise one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten or more bacterial species.
[0124] According to a particular embodiment, the probiotic comprises at least one of the following species of bacteria: B. bifidum, L. rhamnosus, L. lactis, L. casei, B. breve, S. thermophilus, B. longum, L. paracasei, L. plantarum and B. infantis.
[0125] A control subject may be classified as being a “responder” to a probiotic if there is a statistically significant elevation in the absolute abundance of that probiotic strain in his GI mucosa (e.g. as determined by Mann-Whitney test).
[0126] A control subject may be classified as being a “non-responder” to a probiotic if there is no statistically significant elevation in the absolute abundance of that probiotic strain in his GI mucosa (e.g. as determined by Mann-Whitney test).
[0127] As used herein, the term “microbiome” refers to the totality of microbes (bacteria, fungae, protists), their genetic elements (genomes) in a defined environment.
[0128] According to a particular embodiment, the microbiome is a gut microbiome (i.e. microbiota of the digestive track). In one embodiment, the environment is the small intestine. In another embodiment, the environment is the large intestine. The microbiome may be of the lumen or the mucosa of the small intestine or large intestine. In still another embodiment, the gut microbiome is a fecal microbiome.
[0129] In some embodiments, a microbiota sample is collected by any means that allows recovery of the microbes and without disturbing the relative amounts of microbes or components or products thereof of a microbiome. In some embodiments, the microbiota sample is a fecal sample. In other embodiments, the microbiota sample is retrieved directly from the gut—e.g. by endoscopy from the lower gastrointestinal (GI) tract or from the upper GI tract. The microbiota sample may be of the lumen of the GI tract or the mucosa of the GI tract.
[0130] According to one embodiment, the microbiome sample (e.g. fecal sample) is frozen and/or lyophilized prior to analysis. According to another embodiment, the sample may be subjected to solid phase extraction methods.
[0131] In some embodiments, the presence, level, and/or activity of between 5 and 10 species of microbes are measured. In some embodiments, the presence, level, and/or activity of between 5 and 20 species of microbes are measured. In some embodiments, the presence, level, and/or activity of between 5 and 50 species of microbes are measured. In some embodiments, the presence, level, and/or activity of between 5 and 100 species of microbes are measured. In some embodiments, the presence, level, and/or activity of between 5 and 500 species of microbes are measured. In some embodiments, the presence, level, and/or activity of between 5 and 1000 species of microbes are measured. In some embodiments, the presence, level, and/or activity of between 50 and 500 species of microbes (e.g. bacteria) are measured. In some embodiments, the presence, level, and/or activity of substantially all species/classes/families of bacteria within the microbiome are measured. In still more embodiments, the presence, level, and/or activity of substantially all the bacteria within the microbiome are measured.
[0132] Measuring a level or presence of a microbe may be effected by analyzing for the presence of microbial component or a microbial by-product. Thus, for example the level or presence of a microbe may be effected by measuring the level of a DNA sequence. In some embodiments, the level or presence of a microbe may be effected by measuring 16S rRNA gene sequences or 18S rRNA gene sequences. In other embodiments, the level or presence of a microbe may be effected by measuring RNA transcripts. In still other embodiments, the level or presence of a microbe may be effected by measuring proteins. In still other embodiments, the level or presence of a microbe may be effected by measuring metabolites.
[0133] Quantifying Microbial Levels:
[0134] It will be appreciated that determining the abundance of microbes may be affected by taking into account any feature of the microbiome. Thus, the abundance of microbes may be affected by taking into account the abundance at different phylogenetic levels; at the level of gene abundance; gene metabolic pathway abundances; sub-species strain identification; SNPs and insertions and deletions in specific bacterial regions; growth rates of bacteria, the diversity of the microbes of the microbiome, as further described herein below.
[0135] In some embodiments, determining a level or set of levels of one or more types of microbes or components or products thereof comprises determining a level or set of levels of one or more DNA sequences. In some embodiments, one or more DNA sequences comprises any DNA sequence that can be used to differentiate between different microbial types. In certain embodiments, one or more DNA sequences comprises 16S rRNA gene sequences. In certain embodiments, one or more DNA sequences comprises 18S rRNA gene sequences. In some embodiments, 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, 100, 1,000, 5,000 or more sequences are amplified.
[0136] 16S and 18S rRNA gene sequences encode small subunit components of prokaryotic and eukaryotic ribosomes respectively. rRNA genes are particularly useful in distinguishing between types of microbes because, although sequences of these genes differs between microbial species, the genes have highly conserved regions for primer binding. This specificity between conserved primer binding regions allows the rRNA genes of many different types of microbes to be amplified with a single set of primers and then to be distinguished by amplified sequences.
[0137] In some embodiments, a microbiota sample (e.g. fecal sample) is directly assayed for a level or set of levels of one or more DNA sequences. In some embodiments, DNA is isolated from a microbiota sample and isolated DNA is assayed for a level or set of levels of one or more DNA sequences. Methods of isolating microbial DNA are well known in the art. Examples include but are not limited to phenol-chloroform extraction and a wide variety of commercially available kits, including QIAamp DNA Stool Mini Kit (Qiagen, Valencia, Calif.).
[0138] In some embodiments, a level or set of levels of one or more DNA sequences is determined by amplifying DNA sequences using PCR (e.g., standard PCR, semi-quantitative, or quantitative PCR) and then sequencing. In some embodiments, a level or set of levels of one or more DNA sequences is determined by amplifying DNA sequences using quantitative PCR. These and other basic DNA amplification procedures are well known to practitioners in the art and are described in Ausebel et al. (Ausubel F M, Brent R, Kingston R E, Moore D, Seidman J G, Smith J A, Struhl K (eds). 1998. Current Protocols in Molecular Biology. Wiley: New York).
[0139] In some embodiments, DNA sequences are amplified using primers specific for one or more sequence that differentiate(s) individual microbial types from other, different microbial types. In some embodiments, 16S rRNA gene sequences or fragments thereof are amplified using primers specific for 16S rRNA gene sequences. In some embodiments, 18S DNA sequences are amplified using primers specific for 18S DNA sequences.
[0140] In some embodiments, a level or set of levels of one or more 16S rRNA gene sequences is determined using phylochip technology. Use of phylochips is well known in the art and is described in Hazen et al. (“Deep-sea oil plume enriches indigenous oil-degrading bacteria.” Science, 330, 204-208, 2010), the entirety of which is incorporated by reference. Briefly, 16S rRNA genes sequences are amplified and labeled from DNA extracted from a microbiota sample. Amplified DNA is then hybridized to an array containing probes for microbial 16S rRNA genes. Level of binding to each probe is then quantified providing a sample level of microbial type corresponding to 16S rRNA gene sequence probed. In some embodiments, phylochip analysis is performed by a commercial vendor. Examples include but are not limited to Second Genome Inc. (San Francisco, Calif.).
[0141] In some embodiments, determining a level or set of levels of one or more types of microbes comprises determining a level or set of levels of one or more microbial RNA molecules (e.g., transcripts). Methods of quantifying levels of RNA transcripts are well known in the art and include but are not limited to northern analysis, semi-quantitative reverse transcriptase PCR, quantitative reverse transcriptase PCR, and microarray analysis.
[0142] Methods for sequence determination are generally known to the person skilled in the art. Preferred sequencing methods are next generation sequencing methods or parallel high throughput sequencing methods. For example, a bacterial genomic sequence may be obtained by using Massively Parallel Signature Sequencing (MPSS). An example of an envisaged sequence method is pyrosequencing, in particular 454 pyrosequencing, e.g. based on the Roche 454 Genome Sequencer. This method amplifies DNA inside water droplets in an oil solution with each droplet containing a single DNA template attached to a single primer-coated bead that then forms a clonal colony. Pyrosequencing uses luciferase to generate light for detection of the individual nucleotides added to the nascent DNA, and the combined data are used to generate sequence read-outs. Yet another envisaged example is Illumina or Solexa sequencing, e.g. by using the Illumina Genome Analyzer technology, which is based on reversible dye-terminators. DNA molecules are typically attached to primers on a slide and amplified so that local clonal colonies are formed. Subsequently one type of nucleotide at a time may be added, and non-incorporated nucleotides are washed away. Subsequently, images of the fluorescently labeled nucleotides may be taken and the dye is chemically removed from the DNA, allowing a next cycle. Yet another example is the use of Applied Biosystems' SOLiD technology, which employs sequencing by ligation. This method is based on the use of a pool of all possible oligonucleotides of a fixed length, which are labeled according to the sequenced position. Such oligonucleotides are annealed and ligated. Subsequently, the preferential ligation by DNA ligase for matching sequences typically results in a signal informative of the nucleotide at that position. Since the DNA is typically amplified by emulsion PCR, the resulting bead, each containing only copies of the same DNA molecule, can be deposited on a glass slide resulting in sequences of quantities and lengths comparable to Illumina sequencing. A further method is based on Helicos' Heliscope technology, wherein fragments are captured by polyT oligomers tethered to an array. At each sequencing cycle, polymerase and single fluorescently labeled nucleotides are added and the array is imaged. The fluorescent tag is subsequently removed and the cycle is repeated. Further examples of sequencing techniques encompassed within the methods of the present invention are sequencing by hybridization, sequencing by use of nanopores, microscopy-based sequencing techniques, microfluidic Sanger sequencing, or microchip-based sequencing methods.
[0143] According to one embodiment, the sequencing method allows for quantitating the amount of microbe—e.g. by deep sequencing such as Illumina deep sequencing.
[0144] As used herein, the term “deep sequencing” refers to a sequencing method wherein the target sequence is read multiple times in the single test. A single deep sequencing run is composed of a multitude of sequencing reactions run on the same target sequence and each, generating independent sequence readout.
[0145] In some embodiments, determining a level or set of levels of one or more types of microbes comprises determining a level or set of levels of one or more microbial polypeptides. Methods of quantifying polypeptide levels are well known in the art and include but are not limited to Western analysis and mass spectrometry.
[0146] As mentioned herein above, as well as (or instead of) analyzing the level of microbes, the present invention also contemplates analyzing the level of microbial products.
[0147] Examples of microbial products include, but are not limited to mRNAs, polypeptides, carbohydrates and metabolites.
[0148] In some embodiments, the presence, level, and/or activity of metabolites of at least ten species of microbes are measured. In other embodiments, the presence, level, and/or activity of metabolites of between 5 and 50 species of microbes are measured. In other embodiments, the presence, level, and/or activity of metabolites of between 5 and 20 species of microbes are measured. In other embodiments, the presence, level, and/or activity of metabolites of between 5 and 100 species of microbes are measured. In some embodiments, the presence, level, and/or activity of metabolites of between 100 and 1000 or more species of microbes are measured. In other embodiments, the presence, level, and/or activity of metabolites of all bacteria within the microbiome are analyzed. In other embodiments, the presence, level, and/or activity of metabolites of all microbes within the microbiome are measured.
[0149] As used herein, a “metabolite” is an intermediate or product of metabolism. The term metabolite is generally restricted to small molecules and does not include polymeric compounds such as DNA or proteins. A metabolite may serve as a substrate for an enzyme of a metabolic pathway, an intermediate of such a pathway or the product obtained by the metabolic pathway.
[0150] According to a particular embodiment, the metabolite is one that alters the composition or function of the microbiome.
[0151] In preferred embodiments, metabolites include but are not limited to sugars, organic acids, amino acids, fatty acids, hormones, vitamins, oligopeptides (less than about 100 amino acids in length), as well as ionic fragments thereof. Cells can also be lysed in order to measure cellular products present within the cell. In particular, the metabolites are less than about 3000 Daltons in molecular weight, and more particularly from about 50 to about 3000 Daltons.
[0152] The metabolite of this aspect of the present invention may be a primary metabolite (i.e. essential to the microbe for growth) or a secondary metabolite (one that does not play a role in growth, development or reproduction, and is formed during the end or near the stationary phase of growth.
[0153] Representative examples of metabolic pathways in which the metabolites of the present invention are involved include, without limitation, citric acid cycle, respiratory chain, photosynthesis, photorespiration, glycolysis, gluconeogenesis, hexose monophosphate pathway, oxidative pentose phosphate pathway, production and β-oxidation of fatty acids, urea cycle, amino acid biosynthesis pathways, protein degradation pathways such as proteasomal degradation, amino acid degrading pathways, biosynthesis or degradation of: lipids, polyketides (including, e.g., flavonoids and isoflavonoids), isoprenoids (including, e.g., terpenes, sterols, steroids, carotenoids, xanthophylls), carbohydrates, phenylpropanoids and derivatives, alkaloids, benzenoids, indoles, indole-sulfur compounds, porphyrines, anthocyans, hormones, vitamins, cofactors such as prosthetic groups or electron carriers, lignin, glucosinolates, purines, pyrimidines, nucleosides, nucleotides and related molecules such as tRNAs, microRNAs (miRNA) or mRNAs.
[0154] Representative examples of metabolites that may be analyzed according to this aspect of the present invention include, but are not limited to bile acid components such as ursodeoxycholate, glycocholate, phenylacetate and heptanoate and flavonoids such as apigenin and naringenin.
[0155] In some embodiments, levels of metabolites are determined by mass spectrometry. In some embodiments, levels of metabolites are determined by nuclear magnetic resonance spectroscopy, as further described herein below. In some embodiments, levels of metabolites are determined by enzyme-linked immunosorbent assay (ELISA). In some embodiments, levels of metabolites are determined by colorimetry. In some embodiments, levels of metabolites are determined by spectrophotometry, as further described herein below.
[0156] According to one embodiment of this aspect of the present invention two microbiomes can be statistically significantly similar when they comprise at least 50% of the same microbial species, at least 60% of the same microbial species, at least 70% of the same microbial species, at least 80% of the same microbial species, at least 90% of the same microbial species, at least 91% of the same microbial species, at least 92% of the same microbial species, at least 93% of the same microbial species, at least 94% of the same microbial species, at least 95% of the same microbial species, at least 96% of the same microbial species, at least 97% of the same microbial species, at least 98% of the same microbial species, at least 99% of the same microbial species or 100% of the same microbial species.
[0157] According to one embodiment of this aspect of the present invention two microbiomes can be statistically significantly similar when they comprise at least 50% of the same microbial genus, at least 60% of the same microbial genus, at least 70% of the same microbial genus, at least 80% of the same microbial genus, at least 90% of the same microbial genus, at least 91% of the same microbial genus, at least 92% of the same microbial genus, at least 93% of the same microbial genus, at least 94% of the same microbial genus, at least 95% of the same microbial genus, at least 96% of the same microbial genus, at least 97% of the same microbial genus, at least 98% of the same microbial genus, at least 99% of the same microbial genus or 100% of the same microbial genus.
[0158] Additionally, or alternatively, microbiomes may be statistically similar when the relative quantity (e.g. occurrence) of at least five microbes of interest is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 10% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 20% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 30% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 40% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 50% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 60% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 70% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 80% of microbial bacterial species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the relative amount of at least 90% of microbial bacterial species is identical.
[0159] Additionally, or alternatively, microbiomes may be statistically significant similar when the quantity (e.g. occurrence) in the microbiome of at least five microbe of interest is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 10% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 20% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 30% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 40% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 50% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 60% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 70% of their species are identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 80% of their species is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 90% of their species is identical.
[0160] According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 10% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 20% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 30% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 40% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 50% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 60% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 70% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 80% of their genus is identical. According to another embodiment, microbiomes may be statistically significantly similar when the absolute amount of at least 90% of their genus is identical.
[0161] Thus, the fractional percentage of microbes (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total may be statistically similar.
[0162] According to another embodiment, in order to classify a microbe as belonging to a particular genus, family, order, class or phylum, it must comprise at least 90% sequence homology, at least 91% sequence homology, at least 92% sequence homology, at least 93% sequence homology, at least 94% sequence homology, at least 95% sequence homology, at least 96% sequence homology, at least 97% sequence homology, at least 98% sequence homology, at least 99% sequence homology to a reference microbe known to belong to the particular genus. According to a particular embodiment, the sequence homology is at least 95%.
[0163] According to another embodiment, in order to classify a microbe as belonging to a particular species, it must comprise at least 90% sequence homology, at least 91% sequence homology, at least 92% sequence homology, at least 93% sequence homology, at least 94% sequence homology, at least 95% sequence homology, at least 96% sequence homology, at least 97% sequence homology, at least 98% sequence homology, at least 99% sequence homology to a reference microbe known to belong to the particular species. According to a particular embodiment, the sequence homology is at least 97%.
[0164] In determining whether a nucleic acid or protein is substantially homologous or shares a certain percentage of sequence identity with a sequence of the invention, sequence similarity may be defined by conventional algorithms, which typically allow introduction of a small number of gaps in order to achieve the best fit. In particular, “percent identity” of two polypeptides or two nucleic acid sequences is determined using the algorithm of Karlin and Altschul (Proc. Natl. Acad. Sci. USA 87:2264-2268, 1993). Such an algorithm is incorporated into the BLASTN and BLASTX programs of Altschul et al. (J. Mol. Biol. 215:403-410, 1990). BLAST nucleotide searches may be performed with the BLASTN program to obtain nucleotide sequences homologous to a nucleic acid molecule of the invention. Equally, BLAST protein searches may be performed with the BLASTX program to obtain amino acid sequences that are homologous to a polypeptide of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST is utilized as described in Altschul et al. (Nucleic Acids Res. 25:3389-3402, 1997). When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., BLASTX and BLASTN) are employed. See www(dot)ncbi(dot)nlm(dot)nih(dot)gov for more details.
[0165] The present embodiments encompass the recognition that microbial signatures can be relied upon as proxy for microbiome composition and/or activity. Microbial signatures comprise data points that are indicators of microbiome composition and/or activity. Thus, according to the present invention, changes in microbiomes can be detected and/or analyzed through detection of one or more features of microbial signatures.
[0166] Thus, in some embodiments only the microbes (or activity thereof) of a microbial signature are measured. In other embodiments, additional microbes are measured (e.g. all the bacteria of the microbiome are sequenced), but the analysis for the prediction relies on those microbes of the microbial signature.
[0167] In some embodiments, a microbial signature includes information relating to absolute amount of five or more types of microbes, and/or products thereof. In some embodiments, a microbial signature includes information relating to relative amounts of five, ten, twenty, fifty, one hundred or more species of microbes and/or products thereof. In some embodiments, a microbial signature includes information relating to relative amounts of two, three, four, five, ten, twenty, fifty, one hundred or more genus of microbes and/or products thereof.
[0168] In the fecal microbiome, the present inventors have found that levels of the following genii of microbes are indicative as to whether a subject is a responder or not.
[0169] 1. Bacteria of the genus Bifidobacterium
[0170] 2. Bacteria of the genus Dialister
[0171] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of Bifidobacterium in the feces signifies a responder (i.e. permissive), whereas a higher abundance (i.e. above a predetermined level) of Dialister in the feces is indicative of a responder.
[0172] Furthermore, in the fecal microbiome, the present inventors have found that the species of microbes listed in Table A are indicative as to whether a subject is a responder or not.
TABLE-US-00001 TABLE A s_Lachnospiraceae_bacterium_5_1_63FAA s_Bacteroides_vulgatus s_Bacteroides_caccae s_Alistipes_onderdonkii s_Lachnospiraceae_bacterium_1_1_57FAA s_Parabacteroides_unclassified s_Parabacteroides_johnsonii s_Bifidobacterium_pseudocatenulatum s_Megasphaera_unclassified
[0173] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of the species listed in Table A in the feces signifies a responder (i.e. permissive).
[0174] Furthermore, in the fecal microbiome, the present inventors have found that the level of microbes utilizing a Kegg pathway listed in Table B are indicative as to whether a subject is a responder or not.
TABLE-US-00002 TABLE B ko00670 One carbon pool by folate ko00360 Phenylalanine metabolism ko00030 Pentose phosphate pathway ko00052 Galactose metabolism ko00010 Glycolysis/Gluconeogenesis ko00040 Pentose and glucuronate interconversions ko00960 Tropane, piperidine and pyridine alkaloid biosynthesis ko00363 Bisphenol degradation ko00260 Glycine, serine and threonine metabolism ko00190 Oxidative phosphorylation ko00340 Histidine metabolism ko00330 Arginine and proline metabolism ko00983 Drug metabolism - other enzymes * ko00770 Pantothenate and CoA biosynthesis ko00562 Inositol phosphate metabolism ko00521 Streptomycin biosynthesis * ko00523 Polyketide sugar unit biosynthesis * ko00910 Nitrogen metabolism ko00633 Nitrotoluene degradation ko00440 Phosphonate and phosphinate metabolism ko00750 Vitamin B6 metabolism
[0175] More specifically, the present inventors showed that increase abundance in the feces (i.e. levels above a predetermined level) of bacteria utilizing a Kegg pathway listed in Table B in which no * appear signifies resistance to probiotic (i.e. non-permissive), whereas lower abundance in the feces (i.e. levels below a predetermined level) of the species listed in Table B in which an * appear signifies a resistance to probiotic (i.e. non-permissive).
[0176] In the microbiome of the mucosa of the lower gastrointestinal tract (LGIM), the present inventors have found that levels of the following genii of microbes are indicative as to whether a subject is a responder or not.
[0177] 1. Bacteria of the genus Odoribacter
[0178] 2. Bacteria of the genus Bacteroides
[0179] 3. Bacteria of the genus Bifidobacterium
[0180] 4. Bacteria of the family Rikenellaceae
[0181] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of these genii in the LGIM microbiome signifies a responder (i.e. permissive)
[0182] Furthermore, in the LGIM microbiome, the present inventors have found that the species of microbes listed in Table C are indicative as to whether a subject is a responder or not.
TABLE-US-00003 TABLE C s_Barnesiella_intestinihominis s_Bacteroides_caccae s_Coprobacter_fastidiosus s_Bacteroides_coprophilus
[0183] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of the species listed in Table C in the LGIM microbiome signifies a responder (i.e. permissive).
[0184] Furthermore, in the LGIM microbiome, the present inventors have found that the level of microbes utilizing a Kegg pathway listed in Table D are indicative as to whether a subject is a responder or not.
TABLE-US-00004 TABLE D ko00071 Fatty acid degradation ko00311 Penicillin and cephalosporin biosynthesis ko00531 Glycosaminoglycan degradation ko05111 Biofilm formation - Vibrio cholera * ko00640 Propanoate metabolism * ko00440 Phosphonate and phosphinate metabolism ko00120 Primary bile acid biosynthesis Ko03018 RNA degradation
[0185] More specifically, the present inventors showed that increase abundance in the LGIM microbiome (i.e. levels above a predetermined level) of bacteria utilizing a Kegg pathway listed in Table D in which no * appear signifies resistance to probiotic (i.e. non-permissive), whereas lower abundance in the LGIM microbiome (i.e. levels below a predetermined level) of bacteria utilizing a Kegg pathway listed in Table D in which an * appear signifies a resistance to probiotic (i.e. non-permissive).
[0186] In the microbiome of the rectum, the present inventors have found that levels of the following genii of microbes are indicative as to whether a subject is a responder or not.
[0187] 1. Bacteria of the genus Streptococcus
[0188] 2. Bacteria of the genus Odoribacter
[0189] 3. Bacteria of the genus Bifidobacterium
[0190] 4. Bacteria of the genus Bacteroides
[0191] 5. Bacteria of the family Rikenellaceae
[0192] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of all these genii except Streptococcus in the rectal microbiome signifies a responder (i.e. permissive). Lower abundance (i.e. levels below a predetermined level) of Streptococcus in the rectal microbiome signifies resistance (i.e. non-permissive).
[0193] Furthermore, in the rectal microbiome, the present inventors have found that the level of the species Barnesiella intestinihominis is indicative as to whether a subject is a responder or not.
[0194] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of Barnesiella intestinihominis in the rectal microbiome signifies a responder (i.e. permissive).
[0195] Furthermore, in the rectal microbiome, the present inventors have found that the level of microbes utilizing a Kegg pathway listed in Table E are indicative as to whether a subject is a responder or not.
TABLE-US-00005 TABLE E ko00640 Propanoate metabolism ko00660 C5-Branched dibasic acid metabolism
[0196] More specifically, the present inventors showed that lower abundance in the rectal microbiome (i.e. levels below a predetermined level) of bacteria utilizing the pathways listed in Table E signifies a resistance to probiotic (i.e. non-permissive).
[0197] In the sigmoid colon (SC) microbiome, the present inventors have found that levels of the Rikenellaceae family of microbes are indicative as to whether a subject is a responder or not.
[0198] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of Rikenellaceae in the SC signifies a responder (i.e. permissive).
[0199] Furthermore, in the SC microbiome, the present inventors have found that the level of species of microbes listed in Table F are indicative as to whether a subject is a responder or not.
TABLE-US-00006 TABLE F s_Barnesiella_intestinihominis s_Bacteroides_caccae
[0200] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of the species listed in Table F in the SC microbiome signifies a responder (i.e. permissive).
[0201] Furthermore, in the SC microbiome, the present inventors have found that the level of microbes utilizing a Kegg pathway listed in Table G are indicative as to whether a subject is a responder or not.
TABLE-US-00007 TABLE G ko00040 Pentose and glucuronate interconversions ko01040 Biosynthesis of unsaturated fatty acids ko00281 Geraniol degradation ko00071 Fatty acid degradation ko00960 Tropane, piperidine and pyridine alkaloid biosynthesis ko00120 Primary bile acid biosynthesis ko00440 Phosphonate and phosphinate metabolism ko00473 D-Alanine metabolism ko00380 Tryptophan metabolism ko00740 Riboflavin metabolism ko00311 Penicillin and cephalosporin biosynthesis ko03410 Base excision repair ko03060 Protein export ko02020 Two-component system ko00785 Lipoic acid metabolism ko00500 Starch and sucrose metabolism ko00330 Arginine and proline metabolism ko00730 Thiamine metabolism ko03440 Homologous recombination ko00230 Purine metabolism ko00790 Folate biosynthesis ko00360 Phenylalanine metabolism ko03018 RNA degradation ko00630 Glyoxylate and dicarboxylate metabolism ko00620 Pyruvate metabolism ko00052 Galactose metabolism ko03430 Mismatch repair ko00061 Fatty acid biosynthesis ko00511 Other glycan degradation ko00290 Valine, leucine and isoleucine biosynthesis ko00531 Glycosaminoglycan degradation ko00750 Vitamin B6 metabolism ko00908 Zeatin biosynthesis
[0202] More specifically, the present inventors showed that increase abundance in the SC microbiome (i.e. levels above a predetermined level) of bacteria utilizing a Kegg pathway listed in Table G signifies resistance to probiotic (i.e. non-permissive).
[0203] In the descending colon (DC) microbiome, the present inventors have found that levels of the following genii of microbes are indicative as to whether a subject is a responder or not.
[0204] 1. Bacteria of the genus Bacteroides
[0205] 2. Bacteria of the genus Odoribacter
[0206] 3. Bacteria of the family Rikenellaceae
[0207] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of these genii/family in the DC signifies a responder (i.e. permissive).
[0208] Furthermore, in the DC microbiome, the present inventors have found that the levels of species of microbes listed in Table H are indicative as to whether a subject is a responder or not.
TABLE-US-00008 TABLE H s_Barnesiella_intestinihominis s_Escherichia_coli
[0209] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of Barnesiella intestinihominis in the DC signifies a responder (i.e. permissive), whereas lower abundance (i.e. levels below a predetermined level) of Escherichia_coli signifies a non-responder (i.e. resistant).
[0210] Furthermore, in the DC microbiome, the present inventors have found that the levels of microbes utilizing a Kegg pathway listed in Table I are indicative as to whether a subject is a responder or not.
TABLE-US-00009 TABLE I ko00311 Penicillin and cephalosporin biosynthesis ko00740 Riboflavin metabolism ko00562 Inositol phosphate metabolism ko00650 Butanoate metabolism ko00531 Glycosaminoglycan degradation ko00480 Glutathione metabolism ko00071 Fatty acid degradation ko00040 Pentose and glucuronate interconversions ko00640 Propanoate metabolism * ko00790 Folate biosynthesis ko00053 Ascorbate and aldarate metabolism
[0211] More specifically, the present inventors showed that increase abundance in the DC microbiome (i.e. levels above a predetermined level) of bacteria utilizing a Kegg pathway listed in Table I in which no * appear signifies resistance to probiotic (i.e. non-permissive), whereas lower abundance in the DI (i.e. levels below a predetermined level) of the species listed in Table I in which an * appear signifies a resistance to probiotic (i.e. non-permissive).
[0212] In the transverse colon (TC) microbiome, the present inventors have found that levels of the following genii of microbes are indicative as to whether a subject is a responder or not.
[0213] 1. Bacteria of the genus Odoribacter
[0214] 2. Bacteria of the genus Dorea
[0215] 3. Bacteria of the family Rikenellaceae
[0216] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of these genii/family in the TC microbiome signifies a responder (i.e. permissive).
[0217] Furthermore, in the TC microbiome, the present inventors have found that the levels of species of microbes listed in Table J are indicative as to whether a subject is a responder or not.
TABLE-US-00010 TABLE J s_Bacteroides_massiliensis s_Bacteroides_cellulosilyticus s_Dorea_unclassified
[0218] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of S. Dorea in the TC microbiome signifies a responder (i.e. permissive), whereas lower abundance (i.e. levels below a predetermined level) of Bacteroides_cellulosilyticus or s_Bacteroides_massiliensis in the TC microbiome signifies resistance (i.e. non-permissive).
[0219] Furthermore, in the TC microbiome, the present inventors have found that the level of microbes utilizing a Kegg pathway listed in Table K are indicative as to whether a subject is a responder or not.
TABLE-US-00011 TABLE K ko00640 Propanoate metabolism ko02060 Phosphotransferase system (PTS) ko05111 Biofilm formation - Vibrio cholerae ko00363 Bisphenol degradation
[0220] More specifically, the present inventors showed that lower abundance in the TC microbiome (i.e. levels below a predetermined level) of the species utilizing the Kegg pathway listed in Table K signifies a resistance to probiotic (i.e. non-permissive).
[0221] In the ascending colon (AC) microbiome, the present inventors have found that levels of the following genii/family of microbes are indicative as to whether a subject is a responder or not.
[0222] 1. Bacteria of the genus Odoribacter
[0223] 2. Bacteria of the family Rikenellaceae
[0224] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of these genii/family in the AC microbiome signifies a responder (i.e. permissive).
[0225] Furthermore, in the AC microbiome, the present inventors have found that the levels of species of microbes listed in Table L are indicative as to whether a subject is a responder or not.
TABLE-US-00012 TABLE L s_Alistipes_onderdonkii s_Odoribacter_unclassified s_Roseburia_intestinalis s_Bacteroides_caccae s_Bacteroides_salyersiae s_Eubacterium_ramulus
[0226] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of the above species in the AC microbiome signifies a responder (i.e. permissive).
[0227] Furthermore, in the AC microbiome, the present inventors have found that the levels of microbes utilizing fatty acid degradation Kegg pathway are indicative as to whether a subject is a responder or not.
[0228] More specifically, the present inventors showed that lower abundance in the AC microbiome (i.e. levels below a predetermined level) of microbes utilizing the fatty acid degradation Kegg pathway signifies a responder to probiotic (i.e. permissive).
[0229] In the cecum (Ce) microbiome, the present inventors have found that levels of the following genii/family of microbes are indicative as to whether a subject is a responder or not.
[0230] 1. Bacteria of the genus Odoribacter
[0231] 2. Bacteria of the family Rikenellaceae
[0232] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of these genii/family in the Ce microbiome signifies a responder (i.e. permissive).
[0233] Furthermore, in the Ce microbiome, the present inventors have found that the levels of species of Barnesiella_intestinihominis are indicative as to whether a subject is a responder or not.
[0234] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of the above species in the Ce microbiome signifies a responder (i.e. permissive).
[0235] Furthermore, in the Ce microbiome, the present inventors have found that the microbes utilizing propanoate metabolism Kegg pathway or the primary bile acid biosynthesis Kegg pathway are indicative as to whether a subject is a responder or not.
[0236] More specifically, the present inventors showed that lower abundance in the Ce microbiome (i.e. levels below a predetermined level) of microbes utilizing the primary bile acid biosynthesis pathway signifies a responder to probiotic (i.e. permissive), whereas lower abundance in the Ce microbiome (i.e. levels below a predetermined level) of microbes utilizing the propanoate metabolism Kegg pathway signifies a resistance to probiotic (i.e. non-permissive).
[0237] In the ileum (Ti) microbiome, the present inventors have found that levels of the following genii/family of microbes are indicative as to whether a subject is a responder or not.
[0238] 1. Bacteria of the genus Faecalibacterium
[0239] 2. Bacteria of the family Rikenellaceae
[0240] 3. Bacteria of the genus Bifidobacterium
[0241] 4. Bacteria of the family Ruminococcaceae
[0242] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of these genii/family in the Ti microbiome signifies a responder (i.e. permissive).
[0243] Furthermore, in the Ti microbiome, the present inventors have found that the levels of microbes utilizing limonene and pinene degradation Kegg pathway or the valine, leucine and isoleucine degradation Kegg pathway are indicative as to whether a subject is a responder or not.
[0244] More specifically, the present inventors showed that lower abundance in the Ti microbiome (i.e. levels below a predetermined level) of microbes utilizing these pathways signifies a responder to probiotic (i.e. permissive).
[0245] In the fundus (GF) microbiome, the present inventors have found that levels of the genus Actinobacillus are indicative as to whether a subject is a responder or not.
[0246] More specifically, the present inventors showed that lower abundance (i.e. levels below a predetermined level) of this genus in the GF microbiome signifies resistance (i.e. non-permissive).
[0247] Furthermore, in the GF microbiome, the present inventors have found that the level of microbes utilizing a Kegg pathway listed in Table M are indicative as to whether a subject is a responder or not.
TABLE-US-00013 TABLE M ko00710 Carbon fixation in photosynthetic organisms ko00910 Nitrogen metabolism * ko00051 Fructose and mannose metabolism *
[0248] More specifically, the present inventors showed that increase abundance in the GF microbiome (i.e. levels above a predetermined level) of bacteria utilizing a Kegg pathway listed in Table M in which no * appear signifies resistance to probiotic (i.e. non-permissive), whereas lower abundance in the GF (i.e. levels below a predetermined level) of the species listed in Table M in which an * appear signifies a resistance to probiotic (i.e. non-permissive).
[0249] Thus, according to a particular embodiment, the microbial signature comprises the absolute or relative amount of at least one, two, three, four, five, six, seven, eight, nine or ten or more of any of the bacterial species/genus/family/pathway listed in Tables A-M.
[0250] In one embodiment, the bacterial signature comprises the relative or absolute amount of the bacterial species that are provided as the probiotic. The present inventors have shown that a relatively low level of such species in a subject indicates that the subject is more likely to be a responder to such species in a probiotic.
[0251] In other embodiments, the microbial signature of the gut microbiome comprises a microbe diversity—for example alpha diversity. The present inventors have shown that the alpha diversity of responders was higher than that of non-responders at baseline.
[0252] In other embodiments, the microbial signature of the gut microbiome comprises a metabolite signature.
[0253] In other embodiments, the microbial signature of the gut microbiome comprises a bacterial signature.
[0254] In still other embodiments, the microbial signature refers to the relative abundance of genes or metabolites belonging to a particular pathway.
[0255] Preferably, the signature relates to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300 (e.g. 1-10, 1-20, 1-30, 1-40, 50, 10-100, 10-50, 20-50, 20-100) microbial species or product thereof.
[0256] It will be appreciated that the signature may comprise additional taxa of microbes other than species, including families, strains, genus, order etc.
[0257] As mentioned, the method is carried out by analyzing the microbes of a microbiome signature of the subject and comparing its microbial composition to the microbial composition of a microbiome of control subject known to be responsive to a probiotic. Additionally, the microbiome of the subject may be compared with a control subject known to be non-responsive to a probiotic. Measuring the microbial composition of the control subject may be carried out prior to, at the same time as, or following measuring the microbial composition of the test subject. Preferably, the microbiome (or signature thereof) of a plurality of control subject is measured. The data from such measurements may be stored in a database, as further described herein below.
[0258] When the test microbiome and the control microbiome from a subject known to be responsive have a statistically significant similar signature, then the likelihood of being responsive to the probiotic is increased as compared to a subject having a microbiome which is not statistically significantly similar to that of the responsive subject. Alternatively, a comparison can be made with a control subject known not to be response to a probiotic. When the two microbiomes have a statistically significant similar signature, then the likelihood of being responsive to the probiotic is decreased as compared to a subject having a microbiome which is statistically significantly similar to that of the non-responsive subject.
[0259] In another embodiment, the method is carried out by analyzing the metabolites of the metabolome of the subject and comparing its metabolite composition to the metabolite composition of a metabolome of a probiotic-responsive subject. When the two metabolomes have a statistically significant similar signature, then the likelihood of being responsive to a probiotic is increased as compared to a subject having a metabolome, which is not statistically significantly similar to that of the responsive subject.
[0260] According to still another embodiment, two microbiome signatures can be classified as being similar, if the number of genes belonging to a particular pathway expressed by both microbes is similar.
[0261] According to still another embodiment, two microbiome signatures can be classified as being similar, if the expression level of genes belonging to a particular pathway in both microbes is similar.
[0262] According to still another embodiment, two microbiome signatures can be classified as being similar, if the amount of a product generated by both microbes is similar.
[0263] The prediction of this aspect of the present invention may be made using an algorithm (e.g. a machine learning algorithm) which takes into account the relevance (i.e. weight) of particular microbes and/or products thereof in the composition. The algorithm may be built using gut microbiome data of a population of subjects classified according to their responsiveness to a probiotic.
[0264] The database may include other parameters relating to the subjects, for example the weight of the subject, the health of the subject, the blood chemistry of the subject, the genetic profile of the subject, the BMI of the subject, the eating habits of the subject and/or the health of the subject (e.g. diabetic, pre-diabetic, other metabolic disorder, hypertension, cardiac disorder etc.).
[0265] As used, herein the term “machine learning” refers to a procedure embodied as a computer program configured to induce patterns, regularities, or rules from previously collected data to develop an appropriate response to future data, or describe the data in some meaningful way.
[0266] Use of machine learning is particularly, but not exclusively, advantageous when the database includes multidimensional entries.
[0267] The database can be used as a training set from which the machine learning procedure can extract parameters that best describe the dataset. Once the parameters are extracted, they can be used to predict the likelihood of a subject responding to a probiotic treatment.
[0268] In machine learning, information can be acquired via supervised learning or unsupervised learning. In some embodiments of the invention the machine learning procedure comprises, or is, a supervised learning procedure. In supervised learning, global or local goal functions are used to optimize the structure of the learning system. In other words, in supervised learning there is a desired response, which is used by the system to guide the learning.
[0269] In some embodiments of the invention the machine learning procedure comprises, or is, an unsupervised learning procedure. In unsupervised learning there are typically no goal functions. In particular, the learning system is not provided with a set of rules. One form of unsupervised learning according to some embodiments of the present invention is unsupervised clustering in which the data objects are not class labeled, a priori.
[0270] Representative examples of “machine learning” procedures suitable for the present embodiments, including, without limitation, clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors analysis, ensemble learning algorithms, probabilistic models, graphical models, regression methods, gradient ascent methods, singular value decomposition methods and principle component analysis. Among neural network models, the self-organizing map and adaptive resonance theory are commonly used unsupervised learning algorithms. The adaptive resonance theory model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter.
[0271] Following is an overview of some machine learning procedures suitable for the present embodiments.
[0272] Association rule algorithm is a technique for extracting meaningful association patterns among features.
[0273] The term “association”, in the context of machine learning, refers to any interrelation among features, not just ones that predict a particular class or numeric value. Association includes, but it is not limited to, finding association rules, finding patterns, performing feature evaluation, performing feature subset selection, developing predictive models, and understanding interactions between features.
[0274] The term “association rules” refers to elements that co-occur frequently within the databases. It includes, but is not limited to association patterns, discriminative patterns, frequent patterns, closed patterns, and colossal patterns.
[0275] A usual primary step of association rule algorithm is to find a set of items or features that are most frequent among all the observations. Once the list is obtained, rules can be extracted from them.
[0276] The aforementioned self-organizing map is an unsupervised learning technique often used for visualization and analysis of high-dimensional data. Typical applications are focused on the visualization of the central dependencies within the data on the map. The map generated by the algorithm can be used to speed up the identification of association rules by other algorithms. The algorithm typically includes a grid of processing units, referred to as “neurons”. Each neuron is associated with a feature vector referred to as observation. The map attempts to represent all the available observations with optimal accuracy using a restricted set of models. At the same time, the models become ordered on the grid so that similar models are close to each other and dissimilar models far from each other. This procedure enables the identification as well as the visualization of dependencies or associations between the features in the data.
[0277] Feature evaluation algorithms are directed to the ranking of features or to the ranking followed by the selection of features based on their impact on the likelihood of the subject to respond to probiotic administration.
[0278] The term “feature” in the context of machine learning refers to one or more raw input variables, to one or more processed variables, or to one or more mathematical combinations of other variables, including raw variables and processed variables. Features may be continuous or discrete.
[0279] Information gain is one of the machine learning methods suitable for feature evaluation. The definition of information gain requires the definition of entropy, which is a measure of impurity in a collection of training instances. The reduction in entropy of the target feature that occurs by knowing the values of a certain feature is called information gain. Information gain may be used as a parameter to determine the effectiveness of a feature in explaining the likelihood of the subject under analysis to respond to a probiotic. Symmetrical uncertainty is an algorithm that can be used by a feature selection algorithm, according to some embodiments of the present invention. Symmetrical uncertainty compensates for information gain's bias towards features with more values by normalizing features to a [0,1] range.
[0280] Subset selection algorithms rely on a combination of an evaluation algorithm and a search algorithm. Similarly to feature evaluation algorithms, subset selection algorithms rank subsets of features. Unlike feature evaluation algorithms, however, a subset selection algorithm suitable for the present embodiments aims at selecting the subset of features with the highest impact on the likelihood of the subject under analysis to respond to an antibiotic, while accounting for the degree of redundancy between the features included in the subset. The benefits from feature subset selection include facilitating data visualization and understanding, reducing measurement and storage requirements, reducing training and utilization times, and eliminating distracting features to improve classification.
[0281] Two basic approaches to subset selection algorithms are the process of adding features to a working subset (forward selection) and deleting from the current subset of features (backward elimination). In machine learning, forward selection is done differently than the statistical procedure with the same name. The feature to be added to the current subset in machine learning is found by evaluating the performance of the current subset augmented by one new feature using cross-validation. In forward selection, subsets are built up by adding each remaining feature in turn to the current subset while evaluating the expected performance of each new subset using cross-validation. The feature that leads to the best performance when added to the current subset is retained and the process continues. The search ends when none of the remaining available features improves the predictive ability of the current subset. This process finds a local optimum set of features.
[0282] Backward elimination is implemented in a similar fashion. With backward elimination, the search ends when further reduction in the feature set does not improve the predictive ability of the subset. The present embodiments contemplate search algorithms that search forward, backward or in both directions. Representative examples of search algorithms suitable for the present embodiments include, without limitation, exhaustive search, greedy hill-climbing, random perturbations of subsets, wrapper algorithms, probabilistic race search, schemata search, rank race search, and Bayesian classifier.
[0283] A decision tree is a decision support algorithm that forms a logical pathway of steps involved in considering the input to make a decision.
[0284] The term “decision tree” refers to any type of tree-based learning algorithms, including, but not limited to, model trees, classification trees, and regression trees.
[0285] A decision tree can be used to classify the databases or their relation hierarchically. The decision tree has tree structure that includes branch nodes and leaf nodes. Each branch node specifies an attribute (splitting attribute) and a test (splitting test) to be carried out on the value of the splitting attribute, and branches out to other nodes for all possible outcomes of the splitting test. The branch node that is the root of the decision tree is called the root node. Each leaf node can represent a classification (e.g., whether a particular portion of the group database matches a particular portion of the subject-specific database) or a value (e.g., a predicted the likelihood of the subject to respond to a probiotic). The leaf nodes can also contain additional information about the represented classification such as a confidence score that measures a confidence in the represented classification (i.e., the likelihood of the classification being accurate). For example, the confidence score can be a continuous value ranging from 0 to 1, which a score of 0 indicating a very low confidence (e.g., the indication value of the represented classification is very low) and a score of 1 indicating a very high confidence (e.g., the represented classification is almost certainly accurate).
[0286] Support vector machines are algorithms that are based on statistical learning theory. A support vector machine (SVM) according to some embodiments of the present invention can be used for classification purposes and/or for numeric prediction. A support vector machine for classification is referred to herein as “support vector classifier,” support vector machine for numeric prediction is referred to herein as “support vector regression”.
[0287] An SVM is typically characterized by a kernel function, the selection of which determines whether the resulting SVM provides classification, regression or other functions. Through application of the kernel function, the SVM maps input vectors into high dimensional feature space, in which a decision hyper-surface (also known as a separator) can be constructed to provide classification, regression or other decision functions. In the simplest case, the surface is a hyper-plane (also known as linear separator), but more complex separators are also contemplated and can be applied using kernel functions. The data points that define the hyper-surface are referred to as support vectors.
[0288] The support vector classifier selects a separator where the distance of the separator from the closest data points is as large as possible, thereby separating feature vector points associated with objects in a given class from feature vector points associated with objects outside the class. For support vector regression, a high-dimensional tube with a radius of acceptable error is constructed which minimizes the error of the data set while also maximizing the flatness of the associated curve or function. In other words, the tube is an envelope around the fit curve, defined by a collection of data points nearest the curve or surface.
[0289] An advantage of a support vector machine is that once the support vectors have been identified, the remaining observations can be removed from the calculations, thus greatly reducing the computational complexity of the problem. An SVM typically operates in two phases: a training phase and a testing phase. During the training phase, a set of support vectors is generated for use in executing the decision rule. During the testing phase, decisions are made using the decision rule. A support vector algorithm is a method for training an SVM. By execution of the algorithm, a training set of parameters is generated, including the support vectors that characterize the SVM. A representative example of a support vector algorithm suitable for the present embodiments includes, without limitation, sequential minimal optimization.
[0290] The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is a shrinkage and/or selection algorithm for linear regression. The LASSO algorithm may minimize the usual sum of squared errors, with a regularization, that can be an L1 norm regularization (a bound on the sum of the absolute values of the coefficients), an L2 norm regularization (a bound on the sum of squares of the coefficients), and the like. The LASSO algorithm may be associated with soft-thresholding of wavelet coefficients, forward stagewise regression, and boosting methods. The LASSO algorithm is described in the paper: Tibshirani, R, Regression Shrinkage and Selection via the Lasso, J. Royal. Statist. Soc B., Vol. 58, No. 1, 1996, pages 267-288, the disclosure of which is incorporated herein by reference.
[0291] A Bayesian network is a model that represents variables and conditional interdependencies between variables. In a Bayesian network, variables are represented as nodes, and nodes may be connected to one another by one or more links. A link indicates a relationship between two nodes. Nodes typically have corresponding conditional probability tables that are used to determine the probability of a state of a node given the state of other nodes to which the node is connected. In some embodiments, a Bayes optimal classifier algorithm is employed to apply the maximum a posteriori hypothesis to a new record in order to predict the probability of its classification, as well as to calculate the probabilities from each of the other hypotheses obtained from a training set and to use these probabilities as weighting factors for future predictions about the likelihood of a subject to respond to a probiotic. An algorithm suitable for a search for the best Bayesian network, includes, without limitation, global score metric-based algorithm. In an alternative approach to building the network, Markov blanket can be employed. The Markov blanket isolates a node from being affected by any node outside its boundary, which is composed of the node's parents, its children, and the parents of its children.
[0292] Instance-based algorithms generate a new model for each instance, instead of basing predictions on trees or networks generated (once) from a training set.
[0293] The term “instance”, in the context of machine learning, refers to an example from a database.
[0294] Instance-based algorithms typically store the entire database in memory and build a model from a set of records similar to those being tested. This similarity can be evaluated, for example, through nearest-neighbor or locally weighted methods, e.g., using Euclidian distances. Once a set of records is selected, the final model may be built using several different algorithms, such as the naive Bayes.
[0295] Once a subject has been determined to be “responsive to a probiotic”, the present invention further contemplates treating the subject with a probiotic.
[0296] Thus, according to another aspect of the present invention, there is provided a method of treating a disease comprising administering a therapeutically effective amount of a probiotic to a subject in need thereof, the subject being deemed responsive to probiotic treatment according to the methods described herein thereby treating the disease.
[0297] As used herein, the term “treating” includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetical symptoms of a condition or substantially preventing the appearance of clinical or aesthetical symptoms of a condition.
[0298] Diseases, which may be treated with probiotics, include, but are not limited to allergic diseases (atopic dermatitis, possibly allergic rhinitis), gastrointestinal diseases such as colitis, inflammatory bowel disease and Diarrheal diseases, bacterial vaginosis, urinary tract infections, prevention of dental caries or respiratory infections.
[0299] In one embodiment, the disease is a chronic disease. In another embodiment, the disease is an acute disease.
[0300] The probiotic microorganism may be in any suitable form, for example in a powdered dry form. In addition, the probiotic microorganism may have undergone processing in order for it to increase its survival. For example, the microorganism may be coated or encapsulated in a polysaccharide, fat, starch, protein or in a sugar matrix. Standard encapsulation techniques known in the art can be used. For example, techniques discussed in U.S. Pat. No. 6,190,591, which is hereby incorporated by reference in its entirety, may be used.
[0301] According to a particular embodiment, the probiotic composition is formulated in a food product, functional food or nutraceutical.
[0302] In some embodiments, a food product, functional food or nutraceutical is or comprises a dairy product. In some embodiments, a dairy product is or comprises a yogurt product. In some embodiments, a dairy product is or comprises a milk product.
[0303] In some embodiments, a dairy product is or comprises a cheese product. In some embodiments, a food product, functional food or nutraceutical is or comprises a juice or other product derived from fruit. In some embodiments, a food product, functional food or nutraceutical is or comprises a product derived from vegetables. In some embodiments, a food product, functional food or nutraceutical is or comprises a grain product, including but not limited to cereal, crackers, bread, and/or oatmeal. In some embodiments, a food product, functional food or nutraceutical is or comprises a rice product. In some embodiments, a food product, functional food or nutraceutical is or comprises a meat product.
[0304] Prior to administration, the subject may be pretreated with an agent which reduces the number of naturally occurring microbes in the microbiome (e.g. by antibiotic treatment). According to a particular embodiment, the treatment significantly eliminates the naturally occurring gut microflora by at least 20%, 30% 40%, 50%, 60%, 70%, 80% or even 90%.
[0305] In some embodiments, administering comprises any means of administering an effective (e.g., therapeutically effective) or otherwise desirable amount of a composition to an individual. In some embodiments, administering a composition comprises administration by any route, including for example parenteral and non-parenteral routes of administration. Parenteral routes include, e.g., intraarterial, intracerebroventricular, intracranial, intramuscular, intraperitoneal, intrapleural, intraportal, intraspinal, intrathecal, intravenous, subcutaneous, or other routes of injection. Non-parenteral routes include, e.g., buccal, nasal, ocular, oral, pulmonary, rectal, transdermal, or vaginal. Administration may also be by continuous infusion, local administration, sustained release from implants (gels, membranes or the like), and/or intravenous injection.
[0306] In some embodiments, a composition is administered in an amount and/or according to a dosing regimen that is correlated with a particular desired outcome (e.g., with a particular change in microbiome composition and/or signature that correlates with an outcome of interest).
[0307] Particular doses or amounts to be administered in accordance with the present invention may vary, for example, depending on the nature and/or extent of the desired outcome, on particulars of route and/or timing of administration, and/or on one or more characteristics (e.g., weight, age, personal history, genetic characteristic, lifestyle parameter, etc., or combinations thereof). Such doses or amounts can be determined by those of ordinary skill. In some embodiments, an appropriate dose or amount is determined in accordance with standard clinical techniques. Alternatively or additionally, in some embodiments, an appropriate dose or amount is determined through use of one or more in vitro or in vivo assays to help identify desirable or optimal dosage ranges or amounts to be administered.
[0308] In some particular embodiments, appropriate doses or amounts to be administered may be extrapolated from dose-response curves derived from in vitro or animal model test systems. The effective dose or amount to be administered for a particular individual can be varied (e.g., increased or decreased) over time, depending on the needs of the individual. In some embodiments, where bacteria are administered, an appropriate dosage comprises at least about 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 or more bacterial cells. In some embodiments, the present invention encompasses the recognition that greater benefit may be achieved by providing numbers of bacterial cells greater than about 1000 or more (e.g., than about 1500, 2000, 2500, 3000, 35000, 4000, 4500, 5000, 5500, 6000, 7000, 8000, 9000, 10,000, 15,000, 20,000, 25,000, 30,000, 40,000, 50,000, 75,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1×10.sup.6, 2×10.sup.6, 3×10.sup.6, 4×10.sup.6, 5×10.sup.6, 6×10.sup.6, 7×10.sup.6, 8×10.sup.6, 9×10.sup.6, 1×10.sup.7, 1×10.sup.8, 1×10.sup.9, 1×10.sup.10, 1×10.sup.11, 1×10.sup.12, 1×10.sup.13 or more bacteria.
[0309] Since probiotics are contemplated for health maintenance, and not necessarily for treatment of a disease, once a subject has been determined to be “responsive to a probiotic”, the present invention further contemplates providing the subject with the probiotic for health-promoting benefits.
[0310] Knowledge as to whether a subject is responsive to a probiotic is also useful to determine whether it is advantageous to treat that subject with a probiotic following antibiotic administration.
[0311] Thus, according to another aspect of the present invention, there is provided a method of treating a disease of a subject for which an antibiotic is therapeutic comprising:
[0312] (a) assessing whether the subject is suitable for probiotic treatment according to the method described herein;
[0313] (b) administering to the subject an antibiotic which is suitable for treating the disease; and subsequently
[0314] (c) administering to the subject a probiotic if the subject is deemed suitable for probiotic treatment; or administering to the subject an autologous fecal transplant if the subject is deemed not suitable for probiotic treatment, thereby treating the disease.
[0315] In one embodiment, the disease is a bacterial disease. In another embodiment, the disease is not a bacterial disease. In one embodiment, the disease is chronic. In another embodiment, the disease is acute.
[0316] Examples of diseases which may be treated using antibiotics include but are not limited to acne, appendicitis, atrial septal defect, bacterial arthritis, bacterial vaginosis, balance disorder, Bartholin's cyst, bursitis, pressure ulcer, bronchitis, conductive hearing loss, croup, cystic fibrosis, Granuloma inguinale, duodenitis, dermatitis, emphysema, endocarditis, enteritis, gastritis, Glomerulonephritis, Gonorrhea, cardiovascular disease, Hidradenitis suppurativa, laryngitis, Livedo reticularis, Lymphogranuloma venereum, marasmus, mastoiditis, meningitis, myocarditis, nephrotic syndrome, Neurogenic bladder dysfunction, Non-gonococcal urethritis, noonan syndrome, osteomyelitis, Onychocryptosis, otitis externa, otitis media, Patent ductus arteriosus, pelvic inflammatory disease, perforated eardrum, pericarditis, peritonitis, pharyngitis, pilonidal cyst, pleurisy, Prepatellar bursitis, Pyelonephritis, sepsis, Stevens-Johnson syndrome, Streptococcal pharyngitis, syphilis, tonsillitis, Trichomoniasis, tuberculosis, Ureterocele, urethral syndrome, urethritis, urinary tract infection and vertigo.
[0317] Examples of antibiotics contemplated by the present invention include, but are not limited to Daptomycin; Gemifloxacin; Telavancin; Ceftaroline; Fidaxomicin; Amoxicillin; Ampicillin; Bacampicillin; Carbenicillin; Cloxacillin; Dicloxacillin; Flucloxacillin; Mezlocillin; Nafcillin; Oxacillin; Penicillin G; Penicillin V; Piperacillin; Pivampicillin; Pivmecillinam; Ticarcillin; Aztreonam; Imipenem; Doripenem; Meropenem; Ertapenem; Clindamycin; Lincomycin; Pristinamycin; Quinupristin; Cefacetrile (cephacetrile); Cefadroxil (cefadroxyl); Cefalexin (cephalexin); Cefaloglycin (cephaloglycin); Cefalonium (cephalonium); Cefaloridine (cephaloridine); Cefalotin (cephalothin); Cefapirin (cephapirin); Cefatrizine; Cefazaflur; Cefazedone; Cefazolin (cephazolin); Cefradine (cephradine); Cefroxadine; Ceftezole; Cefaclor; Cefamandole; Cefmetazole; Cefonicid; Cefotetan; Cefoxitin; Cefprozil (cefproxil); Cefuroxime; Cefuzonam; Cefcapene; Cefdaloxime; Cefdinir; Cefditoren; Cefetamet; Cefixime; Cefmenoxime; Cefodizime; Cefotaxime; Cefpimizole; Cefpodoxime; Cefteram; Ceftibuten; Ceftiofur; Ceftiolene; Ceftizoxime; Ceftriaxone; Cefoperazone; Ceftazidime; Cefclidine; Cefepime; Cefluprenam; Cefoselis; Cefozopran; Cefpirome; Cefquinome; Fifth Generation; Ceftobiprole; Ceftaroline; Not Classified; Cefaclomezine; Cefaloram; Cefaparole; Cefcanel; Cefedrolor; Cefempidone; Cefetrizole; Cefivitril; Cefmatilen; Cefmepidium; Cefovecin; Cefoxazole; Cefrotil; Cefsumide; Cefuracetime; Ceftioxide; Azithromycin; Erythromycin; Clarithromycin; Dirithromycin; Roxithromycin; Telithromycin; Amikacin; Gentamicin; Kanamycin; Neomycin; Netilmicin; Paromomycin; Streptomycin; Tobramycin; Flumequine; Nalidixic acid; Oxolinic acid; Piromidic acid; Pipemidic acid; Rosoxacin; Ciprofloxacin; Enoxacin; Lomefloxacin; Nadifloxacin; Norfloxacin; Ofloxacin; Pefloxacin; Rufloxacin; Balofloxacin; Gatifloxacin; Grepafloxacin; Levofloxacin; Moxifloxacin; Pazufloxacin; Sparfloxacin; Temafloxacin; Tosufloxacin; Besifloxacin; Clinafloxacin; Gemifloxacin; Sitafloxacin; Trovafloxacin; Prulifloxacin; Sulfamethizole; Sulfamethoxazole; Sulfisoxazole; Trimethoprim-Sulfamethoxazole; Demeclocycline; Doxycycline; Minocycline; Oxytetracycline; Tetracycline; Tigecycline; Chloramphenicol; Metronidazole; Tinidazole; Nitrofurantoin; Vancomycin; Teicoplanin; Telavancin; Linezolid; Cycloserine 2; Rifampin; Rifabutin; Rifapentine; B acitracin; Polymyxin B; Viomycin; Capreomycin.
[0318] As used herein, the term “fecal transplant” refers to fecal bacteria isolated from a subject and thereby processed by the hand of man, which is transplanted into a recipient. In a particular embodiment, the fecal transplant is manmade processed fecal material (fecal filtrate) having reduced volume and/or fecal aroma relative to unprocessed fecal material. In a more particular embodiment, the fecal transplant is a fecal bacterial sample. The term fecal transplant may also be used to refer to the process of transplantation of fecal bacteria isolated from a healthy individual into a recipient. It is also referred to as fecal microbiota transplantation (FMT), stool transplant or bacteriotherapy.
[0319] Preferably, the fecal transplant is derived from a healthy subject. In a particular embodiment, the fecal transplant is an autologous fecal transplant.
[0320] An autologous fecal transplant is derived from the subject being treated prior to antibiotic administration and preferably prior to disease onset.
[0321] Methods of determining the amount of particular bacteria are provided herein above.
[0322] The present inventors have also found that the human fecal microbiome is a limited indicator of gut mucosal-associated microbiome composition and metagenomic function and particular taxa are more indicative than others.
[0323] Thus, for example Table N provides a list of bacterial genii or orders whose abundance in the stool is indicative of the abundance at particular locations along the GI tract.
TABLE-US-00014 TABLE N orders of bacteria whose abundance in the stool mirror the abundance of that bacteria in microbiomes of different locations of the GI tract location Genus/order LGIM g_Akkermansia LGIM g_Ruminococcus LGIM g_Faecalibacterium LGIM g_Prevotella LGIM o_Clostridiales UGIM g_Akkermansia Re g_Sutterella Re o_Clostridiales Re g_Faecalibacterium Re g_Prevotella SC g_Ruminococcus SC g_Faecalibacterium SC o_Clostridiales SC g_Prevotella DC g_Sutterella DC g_Ruminococcus DC g_Faecalibacterium DC g_Prevotella DC o_Clostridiales TC o_Clostridiales TC g_Prevotella AC g_Sutterella AC g_Faecalibacterium AC g_Prevotella AC o_Clostridiales Ce g_Sutterella Ce g_Faecalibacterium Ce g_[Ruminococcus] Ce o_Clostridiales Ce g_Prevotella TI g_Faecalibacterium TI g_Prevotella TI o_Clostridiales TI g_Streptococcus Je g_Bacteroides Je g_Akkermansia Du g_Bacteroides Du g_Akkermansia GA g_Akkermansia GF g_Akkermansia LGIM—mucosa of the lower GI; Re—rectum; SC—sigmoid colon; DC—distal colon; TC—transverse colon; AC—ascending colon; Ce—cecum; TI—ileum; Je—jejunum; Du—duodenum; GA—antrum; GF—fundus; g—genus; o—order
[0324] In addition, Table O provides a list of bacterial species whose abundance in the stool is indicative of the abundance at particular locations along the GI tract.
TABLE-US-00015 TABLE O Species of bacteria whose abundance in the stool mirror the abundance of that bacteria in microbiomes of different locations of the GI tract location species LGIM s_Subdoligranulum_unclassified LGIM s_Bacteroides_dorei LGIM s_Bamesiella_intestinihominis LGIM s_Ruminococcus_torques LGIM s_Bacteroides_coprocola LGIM s_Bacteroides_caccae LGIM s_Bacteroides_uniformis LGIM s_Faecalibacterium_prausnitzii UGIM s_Bacteroides_dorei UGIM s_Bacteroides_vulgatus Re s_Bamesiella_intestinihominis Re s_Bacteroides_dorei Re s_Bacteroides_coprocola Re s_Bacteroides_uniformis Re s_Bacteroides_caccae Re s_Ruminococcus_torques Re s_Faecalibacterium_prausnitzii SC s_Bacteroides_dorei SC s_Bacteroides_coprocola SC s_Bacteroides_caccae SC s_Bamesiella_intestinihominis SC s_Bacteroides_uniformis SC s_Ruminococcus_torques SC s_Faecalibacterium_prausnitzii DC s_Bacteroides_caccae DC s_Bacteroides_coprocola DC s_Prevotella_copri DC s_Barnesiella_intestinihominis DC s_Ruminococcus_torques DC s_Bacteroides_uniformis DC s_Faecalibacterium_prausnitzii DC s_Coprococcus_comes TC s_Bacteroides_coprocola TC s_Bacteroides_caccae TC s_Barnesiella_intestinihominis TC s_Bacteroides_uniformis TC s_Faecalibacterium_prausnitzii TC s_Alistipes_putredinis TC s_Ruminococcus_torques AC s_Bacteroides_dorei AC s_Subdoligranulum_unclassified AC s_Bacteroides_coprocola AC s_Bacteroides_caccae AC s_Faecalibacterium_prausnitzii AC s_Barnesiella_intestinihominis AC s_Coprococcus_comes Ce s_Bacteroides_dorei Ce s_Bacteroides_vulgatus Ce s_Bacteroides_coprocola Ce s_Ruminococcus_torques Ce s_Bacteroides_caccae Ce s_Alistipes_putredinis Ce s_Barnesiella_intestinihominis Ce s_Faecalibacterium_prausnitzii TI s_Bacteroides_vulgatus TI s_Bacteroides_uniformis TI s_Bacteroides_dorei TI s_Bacteroides_caccae TI s_Alistipes_putredinis TI s_Barnesiella_intestinihominis TI s_Ruminococcus_torques TI s_Faecalibacterium_prausnitzii GA s_Bacteroides_dorei GA s_Bacteroides_vulgatus GA s_Prevotella_copri GF s_Bacteroides_vulgatus LGIM—mucosa of the lower GI; Re—rectum; SC—sigmoid colon; DC—distal colon; TC—transverse colon; AC—ascending colon; Ce—cecum; TI—ileum; Je—jejunum; Du—duodenum; GA—antrum; GF—fundus
[0325] In addition, Table P provides a list of KO annotations whose abundance in the stool is indicative of the abundance at particular locations along the GI tract.
TABLE-US-00016 TABLE P KO annotations whose abundance in the stool mirror the abundance of that bacteria in microbiomes of different locations of the GI tract Organ feature LGIM K01190 LGIM K03088 LGIM K07495 LGIM K07165 LGIM K07114 LGIM K03296 LGIM K02014 Re K07114 SC K01238 SC K07165 SC K03088 SC K01190 SC K07114 SC K02014 SC K03296 DC K07165 DC K01238 DC K07114 DC K03296 DC K02014 DC K03088 DC K01190 TC K07484 TC K00754 TC K00936 TC K01190 TC K03088 TC K04763 TC K00540 TC K07495 TC K01238 AC K07495 AC K03088 AC K01190 AC K02014 AC K03296 AC K07114 Ce K07484 Ce K07165 Ce K01238 Ce K07495 Ce K02014 Ce K07114 Ce K03296 Ce K01190 Ce K03088 LGIM—mucosa of the lower GI; Re—rectum; SC—sigmoid colon; DC—distal colon; TC—transverse colon; AC—ascending colon; Ce—cecum;
[0326] In addition, Table Q provides a list of KEGG pathways whose abundance in the stool is indicative of the abundance at particular locations along the GI tract.
TABLE-US-00017 TABLE Q KEGG pathways whose abundance in the stool mirror the abundance of that bacteria in microbiomes of different locations of the GI tract Organ feature LGIM ko01053 LGIM ko00480 LGIM ko00281 LGIM ko00363 LGIM ko00350 LGIM ko00785 LGIM ko00380 LGIM ko04146 LGIM ko00310 LGIM ko05111 LGIM ko00511 LGIM ko00121 LGIM ko00540 LGIM ko00280 LGIM ko00053 LGIM ko00311 LGIM ko00984 Re ko00280 Re ko00785 Re ko05111 Re ko00531 SC ko00071 SC ko00020 SC ko00650 SC ko00360 SC ko00531 SC ko04146 SC ko00281 SC ko00440 SC ko00052 SC ko00480 SC ko00130 SC ko01040 SC ko00350 SC ko00363 SC ko00380 SC ko00121 SC ko00785 SC ko00310 SC ko00053 SC ko00540 SC ko00280 SC ko00984 SC ko00311 SC ko00511 SC ko05111 DC ko00650 DC ko01040 DC ko00052 DC ko00440 DC ko00480 DC ko00350 DC ko00280 DC ko00281 DC ko00790 DC ko00130 DC ko01053 DC ko00380 DC ko00020 DC ko00785 DC ko00984 DC ko04146 DC ko00511 DC ko00121 DC ko00053 DC ko00540 DC ko00311 DC ko05111 TC ko00071 TC ko00311 TC ko04614 TC ko00061 TC ko00908 TC ko00540 TC ko00633 TC ko00130 TC ko00020 TC ko00310 TC ko03018 TC ko00281 TC ko00740 TC ko00053 TC ko00350 TC ko00040 TC ko00360 TC ko01040 TC ko00780 TC ko00480 TC ko00984 TC ko00440 TC ko00790 TC ko00650 TC ko00280 TC ko00562 TC ko05111 TC ko04146 TC ko00363 TC ko00121 TC ko00052 TC ko00380 AC ko00380 AC ko01040 AC ko00480 AC ko00640 AC ko00650 AC ko00020 AC ko00281 AC ko00130 AC ko00633 AC ko00984 AC ko00531 AC ko01053 AC ko04146 AC ko00311 AC ko00363 AC ko00052 AC ko00540 AC ko00121 AC ko00511 AC ko05111 AC ko00053 AC ko00785 AC ko00280 Ce ko00363 Ce ko00910 Ce ko00780 Ce ko00061 Ce ko00360 Ce ko00531 Ce ko00633 Ce ko00350 Ce ko00785 Ce ko00020 Ce ko00562 Ce ko01040 Ce ko00790 Ce ko01053 Ce ko00480 Ce ko00121 Ce ko00380 Ce ko00130 Ce ko00281 Ce ko00511 Ce ko05111 Ce ko00650 Ce ko00052 Ce ko00440 Ce ko00310 Ce ko00311 Ce ko00053 Ce ko04146 Ce ko00540 Ce ko00280 Ce ko00984 TI ko00785 TI ko00531 LGIM—mucosa of the lower GI; Re—rectum; SC—sigmoid colon; DC—distal colon; TC—transverse colon; AC—ascending colon; Ce—cecum; TI—ileum;
[0327] As used herein the term “about” refers to ±10%
[0328] The terms “comprises”, “comprising”, “includes”, “including”, “having” and their conjugates mean “including but not limited to”.
[0329] The term “consisting of” means “including and limited to”.
[0330] The term “consisting essentially of” means that the composition, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.
[0331] As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.
[0332] Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
[0333] Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.
[0334] As used herein the term “method” refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.
[0335] When reference is made to particular sequence listings, such reference is to be understood to also encompass sequences that substantially correspond to its complementary sequence as including minor sequence variations, resulting from, e.g., sequencing errors, cloning errors, or other alterations resulting in base substitution, base deletion or base addition, provided that the frequency of such variations is less than 1 in 50 nucleotides, alternatively, less than 1 in 100 nucleotides, alternatively, less than 1 in 200 nucleotides, alternatively, less than 1 in 500 nucleotides, alternatively, less than 1 in 1000 nucleotides, alternatively, less than 1 in 5,000 nucleotides, alternatively, less than 1 in 10,000 nucleotides.
[0336] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.
[0337] Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.
EXAMPLES
[0338] Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the invention in a non limiting fashion. Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, “Molecular Cloning: A laboratory Manual” Sambrook et al., (1989); “Current Protocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., “Current Protocols in Molecular Biology”, John Wiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide to Molecular Cloning”, John Wiley & Sons, New York (1988); Watson et al., “Recombinant DNA”, Scientific American Books, New York; Birren et al. (eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis, J. E., ed. (1994); “Culture of Animal Cells—A Manual of Basic Technique” by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; “Current Protocols in Immunology” Volumes I-III Coligan J. E., ed. (1994); Stites et al. (eds), “Basic and Clinical Immunology” (8th Edition), Appleton & Lange, Norwalk, Conn. (1994); Mishell and Shiigi (eds), “Selected Methods in Cellular Immunology”, W. H. Freeman and Co., New York (1980); available immunoassays are extensively described in the patent and scientific literature, see, for example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578; 3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533; 3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and 5,281,521; “Oligonucleotide Synthesis” Gait, M. J., ed. (1984); “Nucleic Acid Hybridization” Hames, B. D., and Higgins S. J., eds. (1985); “Transcription and Translation” Hames, B. D., and Higgins S. J., eds. (1984); “Animal Cell Culture” Freshney, R. I., ed. (1986); “Immobilized Cells and Enzymes” IRL Press, (1986); “A Practical Guide to Molecular Cloning” Perbal, B., (1984) and “Methods in Enzymology” Vol. 1-317, Academic Press; “PCR Protocols: A Guide To Methods And Applications”, Academic Press, San Diego, Calif. (1990); Marshak et al., “Strategies for Protein Purification and Characterization—A Laboratory Course Manual” CSHL Press (1996); all of which are incorporated by reference as if fully set forth herein. Other general references are provided throughout this document. The procedures therein are believed to be well known in the art and are provided for the convenience of the reader. All the information contained therein is incorporated herein by reference.
Example 1
Person-Specific Microbiome-Mediated Gut Mucosal Colonization Resistance to Empiric Probiotics in the Naive Host
Materials and Methods
[0339]
TABLE-US-00018 TABLE 1 Reagents and Resources REAGENT or RESOURCE SOURCE IDENTIFIER Bacterial and Virus Strains Lactobacillus acidophilus ATCC 4356 Lactobacillus rhamnosus Clinical isolate Lactobacillus casei ATCC 393 Lactobacillus casei subsp. paracasei ATCC BAA- 52 Lactobacillus plantarum ATCC 8014 Bifidobacterium longum subsp. infantis ATCC 15697 Bifidobacterium bifidum ATCC 29521 Bifidobacterium breve ATCC 15700 Bifidobacterium longum subsp. longum ATCC 15707 Lactococcus lactis Isolated from Bio 25 Supherb Streotococcus thermophilus ATCC BAA- 491 Biological Samples Chemicals, Peptides, and Recombinant Proteins Bio 25 Supherb Supherb Ltd, Nazareth Ilit, Israel Critical Commercial Assays NextSeq 500/550 High Output v2 kit (150 cycles) illumina FC-404-2002 Was used for Metagenome shotgun sequencing NextSeq 500/550 High Output v2 kit (75 cycles) illumina FC-404-2005 Was used for RNA-Seq MiSeq Reagent Kit v2 (500-cycles) illumina MS-102-2003 RNeasy mini kit Qiagen RNAeasy 74104 PowerSoil DNA Isolation Kit (MOBIO Laboratories) Qiagen DNeasy Power Lyzer PowerSoil, 12855-100 NEBNext Ultra Directional RNA Library Prep Kit for New England E7420S Illumina Biolabs NEB Next Multiplex Oligos for Illumina New England E7600S Biolabs Experimental Models: Organisms/Strains C57BL/6J01aHsd males 8-9 weeks of age Envigo, Israel Germ-free Swiss-Webster males 8-9 weeks of age Weizmann institute of Science Sequence-Based Reagents Miseq Illumina sequencing primers Read 1- TATGGTAATTGTGTGCCAGCMGCCGCGGTAA (SEQ ID NO: 1) Read 2- AGTCAGTCAGCCGGACTACHVGGGTWTCTAA T (SEQ ID NO: 2) Index primer - ATTAGAWACCCBDGTAGTCCGGCTGACTGAC T (SEQ ID NO: 3) qPCR primers LAC- L. acidophilus .sup.88 F:CTTTGACTCAGGCAATTGCTCGTGAAGGTAT qPCR G (SEQ ID NO: 4) LAC- L. acidophilus .sup.88 R:CAACTTCTTTAGATGCTGAAGAAACAGCAG qPCR CTACG (SEQ ID NO: 5) LRH-F:GTGCTTGCATCTTGATTTAATTTT L. rhamnosus .sup.89 (SEQ ID NO: 6) qPCR LRH-R:TGCGGTTCTTGGATCTATGCG L. rhamnosus .sup.89 (SEQ ID NO: 7) qPCR LCA-F:GTGCTTGCACTGAGATTCGACTTA L. casei qPCR .sup.89 (SEQ ID NO: 8) LCA-R:TGCGGTTCTTGGATCTATGCG L. casei qPCR .sup.89 (SEQ ID NO: 9) LPA-F:GTGCTTGCACCGAGATTCAACATG L. paracasei .sup.89 (SEQ ID NO: 10) qPCR LPA-R:TGCGGTTCTTGGATCTATGCG L. paracasei .sup.89 (SEQ ID NO: 11) qPCR LPL-F:TTACATTTGAGTGAGTGGCGAACT L. plantarum .sup.90 (SEQ ID NO: 12) qPCR LPL-R:AGGTGTTATCCCCCGCTTCT L. plantarum .sup.90 (SEQ ID NO: 13) qPCR BIN-F:CGC GAG CAA AAC AAT GGT T B. infantis qPCR .sup.91 (SEQ ID NO: 14) BIN-R:AAC GAT CGA AAC GAA CAA TAG AGT B. infantis qPCR .sup.91 T (SEQ ID NO: 15) BBI-F:GTT GAT TTC GCC GGA CTC TTC B. bifidum qPCR .sup.91 (SEQ ID NO: 16) BBI-R:GCA AGC CTA TCG CGC AAA B. bifidum qPCR .sup.91 (SEQ ID NO: 17) BBR-F:GTG GTG GCT TGA GAA CTG GAT AG B. breve qPCR .sup.91 (SEQ ID NO: 18) BBR-R:CAA AAC GAT CGA AAC AAA CAC TAA B. breve qPCR .sup.91 A (SEQ ID NO: 19) BLO-F:TGG AAG ACG TCG TTG GCT TT B. longum qPCR .sup.91 (SEQ ID NO: 20) BLO-R:ATC GCG CCA GGC AAA A B. longum qPCR .sup.91 (SEQ ID NO: 21) LLA-F:TGA ACC ACA ATG GGT TGC TA L. lactis qPCR .sup.92 (SEQ ID NO: 22) LLA-R:TCG ACT GGA AGA AGG AGT GG L. lactis qPCR .sup.92 (SEQ ID NO: 23) STH-F:TTATTTGAAAGGGGCAATTGCT S. thermophilus .sup.89 (SEQ ID NO: 24) qPCR STH-R:GTGAACTTTCCACTCTCACAC S. thermophilus .sup.89 (SEQ ID NO: 25) qPCR qPCR primers for 16S gene 111-967F-PP:CNACGCGAAGAACCTTANC Total 16S qPCR .sup.93 (SEQ ID NO: 26) 112-967F-UC3:ATACGCGARGAACCTTACC Total 16S qPCR .sup.93 (SEQ ID NO: 27) 113-967F-AQ:CTAACCGANGAACCTYACC Total 16S qPCR .sup.93 (SEQ ID NO: 28) 114-967F-S :CAACGCGMARAACCTTACC Total 16S qPCR .sup.93 (SEQ ID NO: 29) 115- 1046R-S :CGACRRCCATGCANCACCT Total 16S qPCR .sup.93 (SEQ ID NO: 30) Software and Algorithms QIIME .sup.94 Trimmomatic .sup.95 MetaPhlAn2 .sup.96 Bowtie2 .sup.97 EMPANADA .sup.98 RNASeq analysis software GOrilla (Gene .sup.99 Ontology enRIchment anaLysis and visuaLizAtion tool)
Experimental Model and Subject Details
[0340] Clinical trial: The human trial was approved by the Tel Aviv Sourasky Medical Center Institutional Review Board (IRB approval numbers TLV-0553-12, TLV-0658-12 and 0196-13-TLV) and Weizmann Institute of Science Bioethics and Embryonic Stem Cell Research oversight committee (IRB approval numbers 421-1, 430-1 and 444-1), and was reported to clinical trials (Identifier: NCT03218579). Written informed consent was obtained from all subjects. No changes were done to the study protocol and methods after the trial commenced.
[0341] Exclusion and inclusion criteria (human cohorts): All subjects fulfilled the following inclusion criteria: males and females, aged 18-70, who are currently not following any diet regime or dietitian consultation and are able to provide informed consent. Exclusion criteria included: (i) pregnancy or fertility treatments; (ii) usage of antibiotics or antifungals within three months prior to participation; (iii) consumption of probiotics in any form within one month prior to participation, (iv) chronically active inflammatory or neoplastic disease in the three years prior to enrollment; (v) chronic gastrointestinal disorder, including inflammatory bowel disease and celiac disease; (vi) active neuropsychiatric disorder; (vii) myocardial infarction or cerebrovascular accident in the 6 months prior to participation; (viii) coagulation disorders; (ix) chronic immunosuppressive medication usage; (x) pre-diagnosed type I or type II diabetes mellitus or treatment with anti-diabetic medication. Adherence to inclusion and exclusion criteria was validated by medical doctors.
TABLE-US-00019 TABLE 2 Participants details Age Weight Height BMI # Sex Group (years) (Kg) (cm) (kg/m2) Smoking Diet 1 F No intervention 40 50 158 20.03 Never Omnivore 2 M No intervention 46 100 191 27.41 Never Vegetarian 3 M No intervention 32 63 178 19.88 Never Omnivore 4 F No intervention 45 59 159 23.34 Never Omnivore 5 M No intervention 58 76 175 24.82 Never Omnivore 6 M No intervention 58 100 184 29.54 Never Omnivore 7 F No intervention 40 65 160 25.39 Never Omnivore 8 F No intervention 66 64 164 23.8 Never Omnivore 9 F No intervention 25 60 172 20.28 Past Omnivore 10 F No intervention 27 66 170 22.84 Never Omnivore 11 M Probiotics 19 80 186 23.12 Past Omnivore 12 F Probiotics 35 50 168 17.72 Never Vegetarian 13 M Probiotics 47 84 187 24.02 Never Vegetarian 14 F Probiotics 23 60 170 20.76 Never Vegan 15 F Probiotics 25 37 149 16.67 Never Vegan 16 M Probiotics 35 77 172 26.03 Present Vegetarian 17 M Probiotics 65 80 176 25.83 Never Omnivore 18 F Probiotics 64 67 164 24.91 Past Omnivore 19 M Probiotics 43 69 176 22.28 Past Omnivore 20 M Probiotics 39 62 180 19.14 Never Omnivore 21 M Placebo 29 67 190 18.56 Never Omnivore 22 F Placebo 32 70 162 26.67 Never Vegetarian 23 M Placebo 35 78 175 25.47 Never Omnivore 24 F Placebo 65 82 167 29.40 Never Omnivore 25 F Placebo 40 50 158 20.03 Never Omnivore 26 F Validation 51 68 168 24.09 Never Omnivore 27 F Validation 52 51 167 18.29 Past Omnivore 28 M Validation 50 70 172 23.66 Present Omnivore 29 M Validation 48 85 187 24.31 Past Omnivore
[0342] Human Study Design: Twenty-nine healthy volunteers were recruited for this study between the years 2016 and 2018. Upon enrollment, participants were required to fill up medical, lifestyle and food frequency questionnaires, which were reviewed by medical doctors before the acceptance to participate in the study. Two cohorts were recruited, a naive cohort (n=10) and a case-control cohort (n=19), subdivided into 2 interventions of probiotics (n=14) and placebo pills (n=5). For the latter cohort, the study design consisted of four phases, baseline (7 days), intervention (28 days) and follow-up (28 days). During the 4-week intervention phase (days 1 thru 28), participants from the probiotics arm were instructed to consume a commercial probiotic supplement (Bio-25) bidaily; participants from the placebo arm were instructed to consume a similar-looking pill bidaily (see “Drugs and biological preparations”). In the case-control cohort stool samples were collected daily during the baseline phase and during the first week of intervention, and then weekly throughout the rest of the intervention and follow-up phases. Ten participants in the probiotics arm and the entire placebo arm underwent two endoscopic examinations, one immediately before the intervention, at the end of the baseline phase (day 0), and another three weeks through the intervention phase (day 21). Participants in the naive cohort underwent a single endoscopic examination; and four participants in the probiotics arm (“validation arm”) underwent only a single colonoscopy three weeks through the intervention phase (day 21).
[0343] The trial was completed as planned. All 29 subjects completed the trial and there were no dropouts or withdrawals. Adverse effects were mild and did not tamper with the study protocol. They included minor bleeding following endoscopic mucosal sampling and throat pain and hoarseness following the endoscopic examination.
[0344] All participants received payment for their participation in the study upon discharge from their last endoscopic session.
[0345] Drugs and Biological Preparations
[0346] Probiotics: During the probiotics phase participants were treated by oral Supherb Bio-25 twice daily, which is described by the manufacturer to contain at least 25 billion active bacteria of the following species: B. bifidum, L. rhamnosus, L. lactis, L. casei, B. breve, S. thermophilus, B. longum, L. paracasei, L. plantarum and B. infantis. According to the manufacturer, the pills underwent double coating to ensure their survival under stomach acidity condition and their proliferation in the intestines. Validation of the aforementioned strains quantity and viability was performed as part of the study, see
[0347] Placebo pills: Placebo pills (Trialog, Inc.) were composed of a hydroxypropylmethyl cellulose (HPMC) capsule, filled with 600 mg microcrystalline cellulose PH.EU (MCC). Placebo pill manufacturing process was approved for pharmaceutical use by the Israeli Ministry of Health, and underwent a microbial burden examination prior to administration. Placebo and probiotic pills were labeled identically to maintain blinding.
[0348] Gut Microbiome Sampling
[0349] Stool sampling: Participants were requested to self-sample their stool on pre-determined intervals using a swab following detailed printed instructions. Collected samples were immediately stored in a home freezer (−20° C.) for no more than 7 days and transferred in a provided cooler to our facilities, where they were stored at −80° C.
[0350] Endoscopic examination: Forty-eight hours prior to the endoscopic examination, participants were asked to follow a pre-endoscopy diet. 20 hours prior to the examination diet was restricted to clear liquids. All participants underwent a sodium picosulfate (Pico Salax)-based bowel preparation. Participants were equipped with two fleet enemas, which they were advised to use in case of unclear stools. The examination was performed using a Pentax 90i endoscope (Pentax Medical) under light sedation with propofol-midazolam.
[0351] Luminal content was aspirated from the stomach, duodenum, jejunum, terminal ileum, cecum and descending colon into 15 ml tubes by the endoscope suction apparatus and placed immediately liquid nitrogen. Brush cytology (US Endoscopy) was used to scrape the gut lining to obtain mucosal content from the gastric fundus, gastric antrum, duodenal bulb, jejunum, terminal ileum, cecum, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. Brushes were placed in a screw cap micro tube and were immediately stored in liquid nitrogen. Biopsies from the gut epithelium were obtained from the stomach, duodenum, jejunum, terminal ileum, cecum and descending colon and were immediately stored in liquid nitrogen. By the end of each session, all samples were transferred to Weizmann Institute of Science and stored in −80° C. In the two endoscopic examinations arm the endoscopies were scheduled in sessions 3 weeks apart
[0352] Mouse study design; C57BL/6 male mice were purchased from Harlan Envigo and allowed to acclimatize to the animal facility environment for 2 weeks before used for experimentation. Germ-free Swiss-Webster mice were born in the Weizmann Institute germ-free facility, kept in gnotobiotic isolators and routinely monitored for sterility. In all experiments, age- and gender-matched mice were used. Mice were 8-9 weeks of age and weighed 20 gr at average at the beginning of experiments. All mice were kept at a strict 24 hr light-dark cycle, with lights being turned on from 6 am to 6 pm. Each experimental group consisted of two cages to control for cage effect. For probiotics consumption, a single pill (Supherb Bio-25) was dissolved in 10 mL of sterile PBS and immediately fed to mice by oral gavage during the dark phase. For FMT experiments, 200 mg of stored human stool samples were resuspended in sterile PBS under anaerobic conditions (Coy Laboratory Products, 75% N2, 20% CO2, 5% H2), vortexed for 3 minutes and allowed to settle by gravity for 2 min. Samples were immediately transferred to the animal facility in Hungate anaerobic culture tubes and the supernatant was administered to germ-free mice by oral gavage. Mice were allowed to conventionalize for three days prior to probiotics treatment, as previously described. Stool was collected on pre-determined days at the beginning of the dark phase, and immediately snap-frozen and transferred for storage at −80° C. until further processing. Upon the termination of experiments, mice were sacrificed by CO2 asphyxiation, and laparotomy was performed by employing a vertical midline incision. After the exposure and removal of the digestive tract, it was dissected into eight parts: the stomach; beginning at the pylorus, the proximal 4 cm of the small intestine was collected as the duodenum; the following third of the small intestine was collected as the proximal and distal jejunum; the ileum was harvested as the distal third of the small intestine; the cecum; lastly, the colon was divided into its proximal and distal parts. For each section, the content within the cavity was extracted and collected for luminal microbiome isolation, and the remaining tissue was rinsed three times with sterile PBS and collected for mucosal microbiome isolation. During each time point, each group was handled by a different researcher in one biological hood to minimize cross-contamination. All animal studies were approved by the Weizmann Institute of Science Institutional Animal Care and Use committee (IACUC), application number 29530816-2.
[0353] Bacterial cultures: Bacterial strains used in this study are listed in Key Resource Table. Lactobacillus strains were grown in De Man, Rogosa and Sharpe (MRS) broth or agar, Bifidobacterium strains in modified Bifidobacterium agar or modified reinforced clostridial broth, Lactococcus and Streptococcus were grown in liquid or solid M17 medium. Liquid or solid Brain-Heart Infusion (BHI) was used for non-selective growth of probiotic bacteria. Cultures were grown under anaerobic conditions (Coy Laboratory Products, 75% N2, 20% CO2, 5% H2) in 37° C. without shaking. All growth media were purchased from BD. For enumeration of viable bacteria from the probiotics pill, a single pill (Supherb Bio-25) was dissolved in 10 mL of sterile PBS and serially diluted on all growth media.
[0354] Nucleic Acid Extraction
[0355] DNA purification: DNA was isolated from endoscopic samples, both luminal content and mucosal brushes, using PowerSoil DNA Isolation Kit (MOBIO Laboratories). DNA was isolated from stool swabs using PowerSoil DNA Isolation Kit (MOBIO Laboratories) optimized for an automated platform.
[0356] RNA Purification: Gastrointestinal biopsies obtained from the participants were purified using RNAeasy kit (Qiagen, 74104) according to the manufacturer's instructions. Most of the biopsies were kept in RNAlater solution (ThermoFisher, AM7020) and were immediately frozen at liquid nitrogen.
[0357] Nucleic Acid Processing and Library Preparation
[0358] 16S qPCR Protocol for Quantification of Bacterial DNA: DNA templates were diluted to 1 ng/ul before amplifications with the primer sets (indicated in Table 3) using the Fast Sybr™ Green Master Mix (ThermoFisher) in duplicates. Amplification conditions were: Denaturation 95° C. for 3 minutes, followed by 40 cycles of Denaturation 95° C. for 3 seconds; annealing 64° C. for 30 seconds followed by meting curve. Duplicates with >2 cycle difference were excluded from analysis. The CT value for any sample not amplified after 40 cycles was defined as 40 (threshold of detection).
TABLE-US-00020 TABLE 3 Primers used in qPCR analysis. Sequence Target & reference LAC-F:CTTTGACTCAGGCAATTGCTCGTGAAGGTATG L. acidophilus (SEQ ID NO: 31) qPCR.sup.88 LAC-R:CAACTTCTTTAGATGCTGAAGAAACAGCAGCTACG L. acidophilus (SEQ ID NO: 32) qPCR.sup.88 LRH-F:GTGCTTGCATCTTGATTTAATTTT (SEQ ID NO: 33) L. rhamnosus qPCR.sup.89 LRH-R:TGCGGTTCTTGGATCTATGCG (SEQ ID NO: 34) L. rhamnosus qPCR.sup.89 LCA-F:GTGCTTGCACTGAGATTCGACTTA (SEQ ID NO: 35) L. casei qPCR.sup.89 LCA-R:TGCGGTTCTTGGATCTATGCG (SEQ ID NO: 36) L. casei qPCR.sup.89 LPA-F:GTGCTTGCACCGAGATTCAACATG (SEQ ID NO: 37) L. paracasei qPCR.sup.89 LPA-R:TGCGGTTCTTGGATCTATGCG (SEQ ID NO: 38) L. paracasei qPCR.sup.89 LPL-F:TTACATTTGAGTGAGTGGCGAACT (SEQ ID NO: 39) L. plantarum qPCR.sup.90 LPL-R:AGGTGTTATCCCCCGCTTCT (SEQ ID NO: 40) L. plantarum qPCR.sup.90 BIN-F:CGC GAG CAA AAC AAT GGT T (SEQ ID NO: 41) B. infantis qPCR.sup.91 BIN-R:AAC GAT CGA AAC GAA CAA TAG AGT T (SEQ ID NO: B. infantis qPCR.sup.91 42) BBI-F:GTT GAT TTC GCC GGA CTC TTC (SEQ ID NO: 43) B. bifidum qPCR.sup.91 BBI-R:GCA AGC CTA TCG CGC AAA (SEQ ID NO: 44) B. bifidum qPCR.sup.91 BBR-F:GTG GTG GCT TGA GAA CTG GAT AG (SEQ ID NO: 45) B. breve qPCR.sup.91 BBR-R:CAA AAC GAT CGA AAC AAA CAC TAA A (SEQ ID NO: B. breve qPCR.sup.91 46) BLO-F:TGG AAG ACG TCG TTG GCT TT (SEQ ID NO: 47) B. longum qPCR.sup.91 BLO-R:ATC GCG CCA GGC AAA A (SEQ ID NO: 48) B. longum qPCR.sup.91 LLA-F:TGA ACC ACA ATG GGT TGC TA (SEQ ID NO: 49) L. lactis qPCR.sup.92 LLA-R:TCG ACT GGA AGA AGG AGT GG (SEQ ID NO: 50) L. lactis qPCR.sup.92 STH-F:TTATTTGAAAGGGGCAATTGCT (SEQ ID NO: 51) S. thermophilus qPCR.sup.89 STH-R:GTGAACTTTCCACTCTCACAC (SEQ ID NO: 52) S. thermophilus qPCR.sup.89 qPCR primers for 16S gene.sup.93 111-967F-PP:CNACGCGAAGAACCTTANC (SEQ ID NO: 53) Total 16S qPCR 112-967F-UC3:ATACGCGARGAACCTTACC (SEQ ID NO: 54) Total 16S qPCR 113-967F-AQ:CTAACCGANGAACCTYACC (SEQ ID NO: 55) Total 16S qPCR 114-967F-S:CAACGCGMARAACCTTACC (SEQ ID NO: 56) Total 16S qPCR 115-1046R-S:CGACRRCCATGCANCACCT (SEQ ID NO: 57) Total 16S qPCR
[0359] 16S rDNA Sequencing: For 16S amplicon pyrosequencing, PCR amplification was performed spanning the V4 region using the primers 515F/806R of the 16S rRNA gene and subsequently sequenced using 2×250 bp paired-end sequencing (Illumina MiSeq). Custom primers were added to Illumina MiSeq kit resulting in 253 bp fragment sequenced following paired end joining to a depth of 110,998±66,946 reads (mean±SD).
TABLE-US-00021 Read1: (SEQ ID NO: 58) TATGGTAATTGTGTGCCAGCMGCCGCGGTAA Read2: (SEQ ID NO: 59) AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT Index sequence primer: (SEQ ID NO: 60) ATTAGAWACCCBDGTAGTCCGGCTGACTGACTATTAGAA
[0360] Whole genome shotgun sequencing: 100 ng of purified DNA was sheared with a Covaris E220X sonicator. Illumina compatible libraries were prepared as described.sup.60, and sequenced on the Illumina NextSeq platform with a read length of 80 bp to a depth of 5,041,171±3,707,376 (mean±SD) reads for stool samples and 2,000,661±4,196,093 (mean±SD) for endoscopic samples.
[0361] RNA-Seq: Ribosomal RNA was selectively depleted by RnaseH (New England Biolabs, M0297) according to a modified version of a published method (pubmed ID:23685885). Specifically, a pool of 50 bp DNA oligos (25 nM, IDT, indicated in Table 4) that is complementary to murine rRNA18S and 28S, was resuspended in 75 μl of 10 mM Tris pH 8.0. Total RNA (100-1000 ng in 10 μl H.sub.2O) were mixed with an equal amount of rRNA oligo pool, diluted to 2 μl and 3 μl 5×rRNA hybridization buffer (0.5 M Tris-HCl, 1 M NaCl, titrated with HCl to pH 7.4) was added. Samples were incubated at 95° C. for 2 minutes, then the temperature was slowly decreased (−0.1° C./s) to 37° C. RNAseH enzyme mix (2 μl of 10 U RNAseH, 2 μl 10×RNAseH buffer, 1 μl H.sub.2O, total 5 μl mix) was prepared 5 minutes before the end of the hybridization and preheated to 37° C. The enzyme mix was added to the samples when they reached 37° C. and they were incubated at this temperature for 30 minutes. Samples were purified with 2.2×SPRI beads (Ampure XP, Beckmann Coulter) according to the manufacturers' instructions. Residual oligos were removed with DNAse treatment (ThermoFisher Scientific, AM2238) by incubation with 5 μl DNAse reaction mix (1 μl Trubo DNAse, 2.5 μl Turbo DNAse 10× buffer, 1.5 μl H.sub.2O) that was incubated at 37° C. for 30 minutes. Samples were again purified with 2.2×SPRI beads and suspended in 3.6 μl priming mix (0.3 μl random primers of New England Biolab, E7420, 3.3 μl H.sub.2O). Samples were subsequently primed at 65° C. for 5 minutes. Samples were then transferred to ice and 2 μl of the first strand mix was added (1 μl 5× first strand buffer, NEB E7420; 0.125 μl RNAse inhibitor, NEB E7420; 0.25 μl ProtoScript II reverse transcriptase, NEB E7420; and 0.625 μl of 0.2 μl/ml Actinomycin D, Sigma, A1410). The first strand synthesis and all subsequent library preparation steps were performed using NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB, E7420) according to the manufacturers' instructions (all reaction volumes reduced to a quarter).
TABLE-US-00022 TABLE 4 DNA oligos used for rRNA depletion Oligo name Sequence AG9327_18_1 TAATGATCCTTCCGCAGGTTCACCTACGGAAACCTTGTTA CGACTTTTAC (SEQ ID NO: 61) AG9328_18_2 TTCCTCTAGATAGTCAAGTTCGACCGTCTTCTCAGCGCTC CGCCAGGGCC (SEQ ID NO: 62) AG9329_18_3 GTGGGCCGACCCCGGCGGGGCCGATCCGAGGGCCTCACT AAACCATCCAA (SEQ ID NO: 63) AG9330_18_4 TCGGTAGTAGCGACGGGCGGTGTGTACAAAGGGCAGGG ACTTAATCAACG (SEQ ID NO: 64) AG9331_18_5 CAAGCTTATGACCCGCACTTACTCGGGAATTCCCTCGTTC ATGGGGAATA (SEQ ID NO: 65) AG9332_18_6 ATTGCAATCCCCGATCCCCATCACGAATGGGGTTCAACG GGTTACCCGCG (SEQ ID NO: 66) AG9333_18_7 CCTGCCGGCGTAGGGTAGGCACACGCTGAGCCAGTCAGT GTAGCGCGCGT (SEQ ID NO: 67) AG9334_18_8 GCAGCCCCGGACATCTAAGGGCATCACAGACCTGTTATT GCTCAATCTCG (SEQ ID NO: 68) AG9335_18_9 GGTGGCTGAACGCCACTTGTCCCTCTAAGAAGTTGGGGG ACGCCGACCGC (SEQ ID NO: 69) AG9336_18_10 TCGGGGGTCGCGTAACTAGTTAGCATGCCAGAGTCTCGT TCGTTATCGGA (SEQ ID NO: 70) AG9337_18_11 ATTAACCAGACAAATCGCTCCACCAACTAAGAACGGCCA TGCACCACCAC (SEQ ID NO: 71) AG9338_18_12 CCACGGAATCGAGAAAGAGCTATCAATCTGTCAATCCTG TCCGTGTCCGG (SEQ ID NO: 72) AG9339_18_13 GCCGGGTGAGGTTTCCCGTGTTGAGTCAAATTAAGCCGC AGGCTCCACTC (SEQ ID NO: 73) AG9340_18_14 CTGGTGGTGCCCTTCCGTCAATTCCTTTAAGTTTCAGCTT TGCAACCATA (SEQ ID NO: 74) AG9341_18_15 CTCCCCCCGGAACCCAAAGACTTTGGTTTCCCGGAAGCT GCCCGGCGGGT (SEQ ID NO: 75) AG9342_18_16 CATGGGAATAACGCCGCCGCATCGCCGGTCGGCATCGTT TATGGTCGGAA (SEQ ID NO: 76) AG9343_18_17 CTACGACGGTATCTGATCGTCTTCGAACCTCCGACTTTCG TTCTTGATTA (SEQ ID NO: 77) AG9344_18_18 ATGAAAACATTCTTGGCAAATGCTTTCGCTCTGGTCCGTC TTGCGCCGGT (SEQ ID NO: 78) AG9345_18_19 CCAAGAATTTCACCTCTAGCGGCGCAATACGAATGCCCC CGGCCGTCCCT (SEQ ID NO: 79) AG9346_18_20 CTTAATCATGGCCTCAGTTCCGAAAACCAACAAAATAGA ACCGCGGTCCT (SEQ ID NO: 80) AG9347_18_21 ATTCCATTATTCCTAGCTGCGGTATCCAGGCGGCTCGGGC CTGCTTTGAA (SEQ ID NO: 81) AG9348_18_22 CACTCTAATTTTTTCAAAGTAAACGCTTCGGGCCCCGCGG GACACTCAGC (SEQ ID NO: 82) AG9349_18_23 TAAGAGCATCGAGGGGGCGCCGAGAGGCAAGGGGCGGG GACGGGCGGTGG (SEQ ID NO: 83) AG9350_18_24 CTCGCCTCGCGGCGGACCGCCCGCCCGCTCCCAAGATCC AACTACGAGCT (SEQ ID NO: 84) AG9351_18_25 TTTTAACTGCAGCAACTTTAATATACGCTATTGGAGCTGG AATTACCGCG (SEQ ID NO: 85) AG9352_18_26 GCTGCTGGCACCAGACTTGCCCTCCAATGGATCCTCGTTA AAGGATTTAA (SEQ ID NO: 86) AG9353_18_27 AGTGGACTCATTCCAATTACAGGGCCTCGAAAGAGTCCT GTATTGTTATT (SEQ ID NO: 87) AG9354_18_28 TTTCGTCACTACCTCCCCGGGTCGGGAGTGGGTAATTTGC GCGCCTGCTG (SEQ ID NO: 88) AG9355_18_29 CCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTC CGGAATCGAA (SEQ ID NO: 89) AG9356_18_30 CCCTGATTCCCCGTCACCCGTGGTCACCATGGTAGGCAC GGCGACTACCA (SEQ ID NO: 90) AG9357_18_31 TCGAAAGTTGATAGGGCAGACGTTCGAATGGGTCGTCGC CGCCACGGG (SEQ ID NO: 91) AG9358_18 32 GCGTGCGATCGGCCCGAGGTTATCTAGAGTCACCAAAGC CGCCGGCGCCC (SEQ ID NO: 92) AG9359_18_33 GCCCCCCGGCCGGGGCCGGAGAGGGGCTGACCGGGTTG GTTTTGATCTGA (SEQ ID NO: 93) AG9360_18_34 TAAATGCACGCATCCCCCCCGCGAAGGGGGTCAGCGCCC GTCGGCATGTA (SEQ ID NO: 94) AG9361_18_35 TTAGCTCTAGAATTACCACAGTTATCCAAGTAGGAGAGG AGCGAGCGACC (SEQ ID NO: 95) AG9362_18_36 AAAGGAACCATAACTGATTTAATGAGCCATTCGCAGTTT CACTGTACCGG (SEQ ID NO: 96) AG9363_18_37 CCGTGCGTACTTAGACATGCATGGCTTAATCTTTGAGACA AGCATATGCT (SEQ ID NO: 97) AG9364_18_38 TGGCTTAATCTTTGAGACAAGCATATGCTACTGGCAGGA TCAACCAGGTA (SEQ ID NO: 98) AG9466_5.8_1 AAGCGACGCTCAGACAGGCGTAGCCCCGGGAGGAACCC GGGGCCGCAAGT (SEQ ID NO: 99) AG9467_5.8_2 GCGTTCGAAGTGTCGATGATCAATGTGTCCTGCAATTCAC ATTAATTCTC (SEQ ID NO: 100) AG9468_5.8_3 GCAGCTAGCTGCGTTCTTCATCGACGCACGAGCCGAGTG ATCCACCGCTA (SEQ ID NO: 101) AG9469_16_1 AAACCCTGTTCTTGGGTGGGTGTGGGTATAATACTAAGTT GAGATGATAT (SEQ ID NO: 102) AG9470_16_2 CATTTACGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATT TCTCTTGTCCT (SEQ ID NO: 103) AG9471_16_3 TTCGTACAGGGAGGAATTTGAANGTAGATAGAAACCGAC CTGGATTACTC (SEQ ID NO: 104) AG9472_16_4 CGGTCTGAACTCAGATCACGTAGGACTTTAATCGTTGAA CAAACGAACCT (SEQ ID NO: 105) AG9473_16_5 TTAATAGCGGCTGCACCATCGGGATGTCCTGATCCAACA TCGAGGTCGTA (SEQ ID NO: 106) AG9474_16_6 AACCCTATTGTTGATATGGACTCTAGAATAGGATTGCGCT GTTATCCCTA (SEQ ID NO: 107) AG9475_16_7 GGGTAACTTGTTCCGTTGGTCAAGTTATTGGATCAATTGA GTATAGTAGT (SEQ ID NO: 108) AG9476_16_8 TCGCTTTGACTGGTGAAGTCTTAGCATGTACTGCTCGGAG GTTGGGTTCT (SEQ ID NO: 109) AG9477_16_9 GCTCCGAGGTCGCCCCAACCGAAATTTTTAATGCAGGTTT GGTAGTTTAG (SEQ ID NO: 110) AG9478_16_10 GACCTGTGGGTTTGTTAGGTACTGTTTGCATTAATAAATT AAAGCTCCAT (SEQ ID NO: 111) AG9479_16_11 AGGGTCTTCTCGTCTTGCTGTGTTATGCCCGCCTCTTCAC GGGCAGGTCA (SEQ ID NO: 112) AG9480_16_12 ATTTCACTGGTTAAAAGTAAGAGACAGCTGAACCCTCGT GGAGCCATTCA (SEQ ID NO: 113) AG9481_16_13 TACAGGTCCCTATTTAAGGAACAAGTGATTATGCTACCTT TGCACGGTTA (SEQ ID NO: 114) AG9482_16_14 GGGTACCGCGGCCGTTAAACATGTGTCACTGGGCAGGCG GTGCCTCTAAT (SEQ ID NO: 115) AG9483_16_15 ACTGGTGATGCTAGAGGTGATGTTTTTGGTAAACAGGCG GGGTAAGATTT (SEQ ID NO: 116) AG9484_16_16 GCCGAGTTCCTTTTACTTTTTTTAACCTTTCCTTATGAGCA TGCCTGTGT (SEQ ID NO: 117) AG9485_16_17 TGGGTTGACAGTGAGGGTAATAATGACTTGTTGGTTGATT GTAGATATTG (SEQ ID NO: 118) AG9486_16_18 GGCTGTTAATTGTCAGTTCAGTGTTTTAATCTGACGCAGG CTTATGCGGA (SEQ ID NO: 119) AG9487_16_19 GGAGAATGTTTTCATGTTACTTATACTAACATTAGTTCTT CTATAGGGTG (SEQ ID NO: 120) AG9488_16_20 ATAGATTGGTCCAATTGGGTGTGAGGAGTTCAGTTATAT GTTTGGGATTT (SEQ ID NO: 121) AG9489_16_21 TTTAGGTAGTGGGTGTTGAGCTTGAACGCTTTCTTAATTG GTGGCTGCTT (SEQ ID NO: 122) AG9490_16_22 TTAGGCCTACTATGGGTGTTAAATTTTTTACTCTCTCTAC AAGGTTTTTT (SEQ ID NO: 123) AG9491_16_23 CCTAGTGTCCAAAGAGCTGTTCCTCTTTGGACTAACAGTT AAATTTACAA (SEQ ID NO: 124) AG9492_16_24 GGGATTTAGAGGGTTCTGTGGGCAAATTTAAAGTTGAAC TAAGATTCTA (SEQ ID NO: 125) AG9493_16_25 TCTTGGACAACCAGCTATCACCAGGCTCGGTAGGTTTGTC GCCTCTACCT (SEQ ID NO: 126) AG9494_16_26 ATAAATCTTCCCACTATTTTGCTACATAGACGGGTGTGCT CTTTTAGCTG (SEQ ID NO: 127) AG9495_ 16_27 TTCTTAGGTAGCTCGTCTGGTTTCGGGGGTCTTAGCTTTG GCTCTCCTTG (SEQ ID NO: 128) AG9496_16_28 CAAAGTTATTTCTAGTTAATTCATTATGCAGAAGGTATAG GGGTTAGTCC (SEQ ID NO: 129) AG9497_16_29 TTGCTATATTATGCTTGGTTATAATTTTTCATCTTTCCCTT GCGGTACTA (SEQ ID NO: 130) AG9498_16_30 TATCTATTGCGCCAGGTTTCAATTTCTATCGCCTATACTTT ATTTGGGTA (SEQ ID NO: 1301) AG9499_16_31 AATGGTTTGGCTAAGGTTGTCTGGTAGTAAGGTGGAGTG GGTTTGGGGCT (SEQ ID NO: 132) AG9500_12_1 GTTCGTCCAAGTGCACTTTCCAGTACACTTACCATGTTAC GACTTGTCTC (SEQ ID NO: 133) AG9501_12_2 CTCTATATAAATGCGTAGGGGTTTTAGTTAAATGTCCTTT GAAGTATACT (SEQ ID NO: 134) AG9502_12_3 TGAGGAGGGTGACGGGCGGTGTGTACGCGCTTCAGGGCC CTGTTCAACTA (SEQ ID NO: 135) AG9503_12_4 AGCACTCTACTCTTAGTTTACTGCTAAATCCACCTTCGAC CCTTAAGTTT (SEQ ID NO: 136) AG9504_12_5 CATAAGGGCTATCGTAGTTTTCTGGGGTAGAAAATGTAG CCCATTTCTTG (SEQ ID NO: 137) AG9505_12_6 CCACCTCATGGGCTACACCTTGACCTAACGTCTTTACGTG GGTACTTGCG (SEQ ID NO: 138) AG9506_12_7 CTTACTTTGTAGCCTTCATCAGGGTTTGCTGAAGATGGCG GTATATAGGC (SEQ ID NO: 139) AG9507_12_8 TGAGCAAGAGGTGGTGAGGTTGATCGGGGTTTATCGATT ACAGAACAGGC (SEQ ID NO: 140) AG9508_12_9 TCCTCTAGAGGGATATGAAGCACCGCCAGGTCCTTTGAG TTTTAAGCTGT (SEQ ID NO: 141) AG9509_12_10 GGCTCGTAGTGTTCTGGCGAGCAGTTTTGTTGATTTAACT GTTGAGGTTT (SEQ ID NO: 142) AG9510_12_11 AGGGCTAAGCATAGTGGGGTATCTAATCCCAGTTTGGGT CTTAGCTATTG (SEQ ID NO: 143) AG9511_12_12 TGTGTTCAGATATGTTAAAGCCACTTTCGTAGTCTATTTT GTGTCAACTG (SEQ ID NO: 144) AG9512_12_13 GAGTTTTTTACAACTCAGGTGAGTTTTAGCTTTATTGGGG AGGGGGTGAT (SEQ ID NO: 145) AG9513_12_14 CTAAAACACTCTTTACGCCGGCTTCTATTGACTTGGGTTA ATCGTGTGAC (SEQ ID NO: 146) AG9514_12_15 CGCGGTGGCTGGCACGAAATTGACCAACCCTGGGGTTAG TATAGCTTAGT (SEQ ID NO: 147) AG9515_12_16 TAAACTTTCGTTTATTGCTAAAGGTTAATCACTGCTGTTT CCCGTGGG (SEQ ID NO: 148) AG9516_12_17 TGTGGCTAGGCTAAGCGTTTTGAGCTGCATTGCTGCGTGC TTGATGCTTG (SEQ ID NO: 149) AG9517_12_18 TTCCTTTTGATCGTGGTGATTTAGAGGGTGAACTCACTGG AACGGGGATG (SEQ ID NO: 150) AG9518_12_ 19 CTTGCATGTGTAATCTTACTAAGAGCTAATAGAAAGGCT AGGACCAAACC (SEQ ID NO: 151) AG9519_5_1 AAAGCCTACAGCACCCGGTATTCCCAGGCGGTCTCCCAT CCAAGTACTAA (SEQ ID NO: 152) AG9520_5_2 CCAGGCCCGACCCTGCTTAGCTTCCGAGATCAGACGAGA TCGGGCGCGTT (SEQ ID NO: 153) AG9521_5_3 TTCCGAGATCAGACGAGATCGGGCGCGTTCAGGGTGGTA TGGCCGTAGAC (SEQ ID NO: 154)
[0362] Analyses
[0363] 16S rDNA analysis: The 2×250 bp reads were processed using the QIIME (Quantitative Insights Into Microbial Ecology, www(dot)qiime(dot)org) analysis pipeline.sup.94. In brief, fasta quality files and a mapping file indicating the barcode sequence corresponding to each sample were used as inputs, paired reads were first assembled into longer reads based on sequence similarity, the assembled reads were then split to samples according to the barcodes, Sequences sharing 97% nucleotide sequence identity in the 16S rRNA region were binned into operational taxonomic units (97% ID OTUs). Each OTU was assigned a taxonomical classification by applying the Uclust algorithm against the Greengenes database, and an OTU table was created.
[0364] Metagenomic analysis: Data from the sequencer was converted to fastq files with bcl2fastq. Reads were then QC trimmed using Trimmomatic.sup.95 with parameters PE -threads 10 -phred33 -validatePairs ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 MINLEN:50. We used MetaPhlAn2.sup.96 for taxonomic analysis with parameters: —ignore_viruses —ignore_archaea —ignore_eukaryotes.
[0365] Host sequences were removed by aligning the reads against human genome reference hg19 using bowtie2.sup.97 with parameters: -D 5 -R 1 -N 0 -L 22 -i 5,0,2.50. The resulting non-host reads were then mapped to the integrated gene catalogue.sup.100 using bowtie2 with parameters: —local -D 25 -R 3 -N 1 -L 19 -i 5,1,0.25 -k 5 allowing to a single read to match up to five different entries.
[0366] Further filtering of the bacterial reads consisted of retaining only records with minimal base quality of 26. The bacterial quality filtered resulting bam files were then subsampled to 100,000 bacterial hits. An entry's score was defined by its length, divided by the gene length. Entries scores were summarized according to KO annotations.sup.101. Each sample was scaled to 1M. KEGG Pathway analysis was conducted using EMPANADA.sup.98.
[0367] Probiotics strain identification by unique genomic sequences: Recovery of genomes for probiotic strains from pill metagenomics samples: Genomes for 9 of the 11 probiotic strains were recovered at >93% completeness and <4% contamination from metagenomics samples of the probiotics pill (Table 5). For one of the species (B. longum) only part of the genome was recovered due to strain heterogeneity. The samples were assembled in multiple cycles using IDBA-UD.sup.102. Assemblies were manually improved using a mini-assembly approach.sup.82. Genomes were recovered based on similarity to reference genomes and connectivity between scaffolds as deduced from the mini-assembly analysis.
TABLE-US-00023 TABLE 5 statics for genomes recovered from metagenomics samples of probiotics pill used in the study. Completeness and contamination were evaluated using CheckM.sup.103. #Scaf- Complete- Contam- Species Size folds ness ination Bifidobacterium breve 2,051,417 128 93.66 0.69 Bifidobacterium bifidum 2,196,275 11 99.54 0.12 Bifidobacterium longum 1,200,324 180 46.03 0.96 Lactococcus lactis 2,472,057 36 100 0 Lactobacillus acidophilus 1,963,581 22 98.94 0 Lactobacillus casei 2,968,946 33 94.64 1.72 Lactobacillus paracasei 3,038,895 92 98.79 3.56 Lactobacillus plantarum 3,299,766 31 99.38 2.79 Lactobacillus rhamnosus 2,921,071 29 99.02 0 Streptococcus thermophilus 1,789,952 74 99.89 0.29
[0368] Strain-Level Analysis Probiotic Strains in Human Samples.
[0369] Identifying reads that belong to the probiotic strains in each sample: All human reads were first removed from all samples by mapping against the human genome (assembly GRCh38.p7) using bowtie2.sup.97 with the -very_sensitive flag. Next, the non-human reads were mapped against all probiotics genomes recovered from the pill using bowtie2 to identify reads that potentially belong to these strains. Finally, the reads were mapped against a database of genomes for all species in the orders Lactobacillales and Bifidobacteriales to which the probiotic strains belong, including the probiotic genomes. Only reads that received their best hit from one of the probiotics strains were further analyzed.
[0370] Determining presence of probiotic species: we counted the number of genes in each probiotic genome whose coverage is greater than 0. A probiotic species was determined to be present in a sample if at least 400 of its genes were detected, with the threshold being set based on comparison to MetaPhlAn2 results and an analysis of gene number distribution across the different samples.
[0371] Determining strain-specific genes: we clustered each probiotic genome's proteins with other genomes available for the its species using USEARCH.sup.104 with 90% identity threshold. All genes in clusters whose size was <10% of the number of genomes analyzed were determined to be strain specific. The analysis could be applied to the genomes of B. bifidum, B. breve, B. longum, L. acidophilus, L. casei, L. lactis, L. paracasei, L. plantarum and S. thermophilus. For B. longum, it is not possible to determine which of the probiotic strains is present.
[0372] Determining samples with probiotic strains: For each strain that passed the 400-genes threshold from step 3 we compared the fraction of strain-specific genes detected with the fraction of all genes on the genome that were detected. The probiotic strain was determined to be present if at least 65% of the total number of genes were detected and the difference between the fraction of the total and strain-specific genes that were found was 20% or less.
[0373] RNAseq Analysis
[0374] Data normalization: Initially, we normalized the sequenced data as previously described.sup.105. Briefly, genes with mean TPM<1 across all samples were filtered out from the analysis, and a value of 0.001 was added to remaining TPM values to avoid zero-values in downstream calculations. Then, sample median normalization was performed based on all constitutive gene reads with positive counts for all samples. Thus, all TPM values in each sample were scaled by the median TPM of constitutive reads in that sample, divided by the median TPM across all samples. We then performed a per-gene normalization by dividing each expression value by the median value of that gene across all samples. Finally, expression data was log-transformed (base 2). The above normalization steps were performed separately to data acquired from each of the different experimental batches, determined by the presence or absence of RNAlater solution for sample preservation.
[0375] Comparison of expression levels before and after treatment with probiotics: To account for inter-personal differences and reduce noise, we compared the effects of probiotics treatment on host expression patterns using a repeated-measures design. Thus, for each individual, in each biopsy region, the relative fold-changes (log, base 2) in expression levels of each gene were calculated between samples taken at baseline and after treatment with probiotics. Then, for each individual, genes were ranked from low to high, and sorted by their median rank across all available samples. These sorted lists were subsequently used for gene ontology (GO) enrichment analysis using GOrilla.sup.99 with a p-value threshold of 10.sup.−3 and a false-discovery rate (FDR) threshold of q<0.05.
[0376] Comparison of expression levels between probiotics persistent and resistant individuals: For each gene, median relative expression was calculated in probiotics-persistent and resistant individuals within each biopsy region and experimental batch. Then, genes were sorted by the ratio (log, base 2) between median relative expression levels across probiotics-persistent compared to resistant individuals. Finally, to combine findings from both experimental batches, we intersected the top and the bottom 10% of the genes across the two batches. Intersected lists were used as target sets for GOrilla GO enrichment analysis as described above, with the entire set of genes that passed the initial filtering as a background set.
[0377] Quantification and statistical analysis: The following statistical analyses were applied unless specifically stated otherwise: For 16S data, rare OTUs (<0.1% in relative abundance) were filtered out, and samples were then rarefied to a depth of 10,000 reads (5000 in mouse tissues). For metagenomic data, samples with <10.sup.5 assigned bacterial reads (after host removal) were excluded from further analysis. In the remaining samples, rare KEGG orthologous (KO) genes (<10-5) were removed. Beta diversity was calculated on OTUs (16S) or species (metagenomics) relative abundances using UniFrac distances or Bray-Curtis dissimilarity (R Vegan package, www(dot)CRAN.R-project(dot)org/package=vegan) respectively. PCA for KOs and functional bacterial pathways were calculated using Spearman's rank correlation coefficient. Alpha diversity was calculated on OTUs (16S) using the observed species index. For 16S data, measurements of alpha and beta diversity were calculated using QIIME tools v 1.9.1. In order to determine the effect of treatment on microbiota taxonomic composition and functional capacity repeated-measures Kruskal Wallis with Dunn's test was used. In order to compare the effect of treatment over time between two groups or more two-way ANOVA with Dunnet's test, or permutation tests performed by switching labels between participants, including all their assigned samples, were used. Mann-Whitney and Wilcoxon tests were used to conduct pairwise comparisons between two treatment arms or two groups of participants. Permutational multivariate ANOVA (Adonis PERMANOVA with 10,000 permutations) based on sample distances was used to test for changes in the community composition and function. To analyze qPCR data, two way ANOVA with Sidak or Dunnett test was used. The threshold of significance was determined to be 0.05 both for p and q-values. Statistically significant findings were marked according to the following cutoffs: *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. Data were plotted with GraphPad Prism version 7.0c. Statistical details for all experiments, including sample size, the statistical test used dispersion and precision measures and statistical significance, are specified in the result section and denoted in figure legends.
Results
Murine Stool Microbiome Configuration Only Partially Correlates with the Gut Mucosa Microbiome
[0378] Most evidence supporting beneficial effects of probiotic microorganisms stem from animal and human studies extrapolating from stool microbiome analysis to potential probiotics impacts on host physiology.sup.32,35,39,49-52,77,78. To assess whether stool microbiome represents an accurate marker of upper and lower GI mucosal microbiome architecture, we began our investigation by performing a comparative analysis of lumen and mucosa-associated microbiome samples collected from multiple regions of the upper gastrointestinal (UGI) and lower gastrointestinal (LGI) tract of 10 naïve 10-week-old male wild type (WT) C57Bl/6 mice (
[0379] Unweighted UniFrac distances based on 16S rDNA sequencing separated both luminal and mucosal GI samples from stool samples collected from the same mice during the 4 weeks prior to dissection (One-way ANOVA and Tukey post-hoc P<0.0001,
Human Fecal Microbiome is a Limited Indicator of Gut Mucosal-Associated Microbiome Composition and Metagenomic Function
[0380] Similar to mice, studies on the human GI microbiome rely almost exclusively on stool sampling, despite insufficient evidence that these samples accurately reflect the microbial gut mucosal composition or function. We therefore sought to investigate the potential of stool samples as markers for the mucosal GI microbial community by directly sampling throughout the GI tract. To account for mucosal microbiome-altering impacts of bowel preparation.sup.79,80, we sampled along the UGI and LGI tract 2 healthy female adult participants (aged 25 and 27, BMI 20.3 and 22.8) undergoing two consecutive colonoscopies, the first performed in the absence of any form of bowel preparation, followed by a second procedure three weeks later performed using a routine Picolax bowel preparation protocol (
[0381] We began by characterizing the gut microbiome of a cohort in healthy human adults at different bio-geographical regions and directly compared these to stool microbiome configuration of the same individuals. To this aim, 25 healthy participants aged 20-66 (mean age 41.32±14.28, 13 females, mean BMI 23.1±3.5) underwent a multi-omic microbiome characterization at multiple gut mucosal and luminal regions spanning the LGI and UGI (
[0382] Expectedly, microbiome load varied throughout the GI tract. qPCR-based amplification of the 16S gene demonstrated stool samples to harbor the highest bacterial load compared to more proximal GI regions, with a gradient starting from the sparsely populated UGI regions, which were significantly less colonized than their most distal region (TI) and the LGI (
[0383] Given the redundancy in microbial genes and pathways encoded by different microbiome members.sup.81, and at different bio-geographical locations along the GI tract.sup.75, we next set out to determine whether the different regions of the human GI tract display variation in microbial-encoded functions, and whether such variation is reflected in stool. Mapping whole DNA shotgun metagenomic sequencing reads to KEGG orthologous genes (KOs) revealed that, like microbial composition, microbial functions display a dissimilarity gradient throughout the GI tract, starting from stool, to LGI, TI and UGI samples, with all regions significantly different from stool (Kruskal-Wallis P<0.0001,
Probiotics Strains are Present and Viable in the Administered Supplement
[0384] To study the effects of commonly consumed probiotics on the mammalian gut, we focused on a commercial probiotics preparation that includes 11 strains belonging to the four major Gram-positive bacterial genera used for this purpose: Lactobacillus, Bifidobacterium, Lactococcus and Streptococcus. Specifically, the preparation contained the following 11 strains: Lactobacillus acidophilus (abbreviated henceforth as LAC), Lactobacillus casei (LCA), Lactobacillus casei sbsp. paracasei (LPA), Lactobacillus plantarum (LPL), Lactobacillus rhamnosus (LRH), Bifidobacterium longum (BLO), Bifidobacterium bifidum (BBI), Bifidobacterium breve (BBR), Bifidobacterium longum sbsp. infantis (BIN), Lactococcus lactis (LLA) and Streptococcus thermophilus (STH). In order to determine the presence and viability of these 11 strains in the supplement, we first analyzed 16S rDNA amplicons obtained from the supplement pill with and without culturing. All four genera (and no others), but only 4/11 species (BBI, BLO, LAC, LCA) were identified by 16s rDNA analysis in the pill (
Murine Microbiome-Driven Colonization Resistance Limits Probiotics Mucosal Colonization and Impact on the Indigenous Microbiome
[0385] To assess the degree of murine GI colonization by the probiotics, we administered the contents of one pill daily by oral gavage (4*10.sup.9 CFU kg.sup.−1 day.sup.−1) to male 10-week-old SPF WT mice (N=10), with an additional group of untreated mice (N=10) serving as controls (
[0386] 16S rDNA-based compositional analysis of luminal and mucosal samples collected throughout the GI tract did not indicate any significant differences between the probiotics and control groups in any region for any of the four probiotics genera (
[0387] We hypothesized that this limited colonization of probiotics at the mucosal regions may result from colonization resistance of the murine microbiome to the supplemented strains. To address this possibility, we inoculated GF mice with an identical probiotics preparation by oral gavage and housed them in sterile isocages for 14 days before dissecting their GI tract (
[0388] We next assessed the impact of the above low level probiotic colonization in the murine indigenous microbiome configuration. Both unweighted and weighted UniFrac distances of fecal samples (rarefied to 20000 reads) to baseline indicated no differences between the probiotics and control groups (Unweighted PERMANOVA P=0.35, weighted P=0.75) at early time points, with several later time-points becoming significantly different between the groups due to a drift observed only in the control group (
[0389] While no consistent probiotics-induced alterations of the UGI luminal (PERMANOVA P=0.2) and mucosal (PERMANOVA P=0.59) microbiome were observed (
[0390] Taken together, these findings suggest that despite daily administration, human-targeted probiotics feature low-level murine mucosal colonization, mediated by resistance exerted by the indigenous murine gut microbiome. Even at these low colonization levels, probiotics induced significant modulation of the LGI mucosal microbiome, which was not observed in stool samples.
Inter-Individual Differences in Probiotics Colonization of the Human GI Tract
[0391] In contrast to inbred mice, humans display considerable person-to-person variation in gut microbiome composition, which may be more permissive to colonization with exogenous probiotics bacteria. To test this notion, we conducted a placebo-controlled trial, in which 15 healthy volunteers (see inclusion and exclusion criteria in methods) received either an identical 11-strain probiotics preparation or a cellulose placebo bi-daily for a 4-week period. Stool was sampled at multiple time points before, during, and after the administration of probiotics or placebo; colonoscopy and deep enteroscopy were performed prior to intervention and three weeks after the initiation of probiotics or placebo consumption in all participants (
[0392] Surprisingly, when each participant was analyzed independently compared to its own baseline, the gastrointestinal mucosal load of probiotics strains considerably varied, with both qPCR (
[0393] Importantly, both the relative (
Baseline Personalized Host and Mucosal Microbiome Features are Associated with Probiotics Persistence
[0394] We next set out to identify factors that may dictate or mark the extent to which probiotics colonize the human GI mucosa. Interestingly, we observed a significant inverse correlation between initial levels of a given probiotics strain in a given GI region and its fold change, i.e. low abundant strains were more likely to expand than those already present in high loads (
[0395] To determine whether these compositional and functional microbiome differences between permissive and resistant individuals impact colonization capacity of probiotics, we conventionalized two groups of GF mice with stool samples from either a permissive or a resistant participant. Probiotics were administered to the conventionalized mice daily by oral gavage for 4 weeks, after which the load of probiotics in the GI tract lumen and mucosa was quantified by qPCR (
[0396] In order to identify host factors that may affect permissiveness or resistance to probiotics colonization, we performed a global gene expression analysis through RNA sequencing of transcripts collected from stomach, duodenum, jejunum, terminal ileum and descending colon biopsies before probiotics supplementation. Two clusters of genes that were higher in permissive vs resistant and vice versa were visible in the stomach (
Probiotics Differentially Affect Human Responders and Non-Responders
[0397] Finally, as the effect of probiotics on the human GI microbiome remains inconclusive.sup.47, we sought to determine probiotics impact on microbiome composition and function and the host transcriptome, and whether these follow personalized patterns. We compared stool samples collected during and after probiotics supplementation to each participant's baseline, using 16S rDNA and MetaPhlAn2 compositional analysis, and shotgun metagenomic functional mapping to KOs and KEGG pathways. Stool microbiome composition was distinct from baseline during the probiotic exposure period (days 4-28 to baseline, Friedman & Dunn's P=0.0044 for 16s rDNA and MetaPhlAn2 analyses,
[0398] We next hypothesized that differential probiotics colonization between participants may result in differential effects on the microbiome, which can be obscured when all individuals are considered together. Indeed, during probiotic supplementation, compositional changes were pronounced in stools of permissive than in resistant participants, as evident by higher distances to baseline (unweighted UniFrac incremental AUC Mann-Whitney P=0.038,
[0399] Probiotics also differentially affected the host GI transcriptome. Following initiation of probiotic consumption, all significant baseline ileum host pathways that distinguished permissive from resistant individuals (
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Example 2
Post-Antibiotic Gut Mucosal Microbiome Reconstitution is Impaired by Probiotics and Improved by Autologous FMT
[0505] Reagents and resources: see Table 1 of Example 1
[0506] Clinical trial: The human trial was approved by the Tel Aviv Sourasky Medical Center Institutional Review Board (IRB approval numbers TLV-0553-12 and TLV-0658-12) and Weizmann Institute of Science Bioethics and Embryonic Stem Cell Research oversight committee (IRB approval numbers 421-1 and 430-1), and was reported to clinical trials (Identifier: NCT03218579). Written informed consent was obtained from all subjects. No changes were done to the study protocol and methods after the trial commenced.
[0507] Exclusion and inclusion criteria (human cohorts): All subjects fulfilled the following inclusion criteria: males and females, aged 18-70, who are currently not following any diet regime or dietitian consultation and are able to provide informed consent. Exclusion criteria included: (i) pregnancy or fertility treatments; (ii) usage of antibiotics or antifungals within three months prior to participation; (iii) consumption of probiotics in any form within one month prior to participation, (iv) chronically active inflammatory or neoplastic disease in the three years prior to enrollment; (v) chronic gastrointestinal disorder, including inflammatory bowel disease and celiac disease; (vi) active neuropsychiatric disorder; (vii) myocardial infarction or cerebrovascular accident in the 6 months prior to participation; (viii) coagulation disorders; (ix) chronic immunosuppressive medication usage; (x) pre-diagnosed type I or type II diabetes mellitus or treatment with anti-diabetic medication. Adherence to inclusion and exclusion criteria was validated by medical doctors.
TABLE-US-00024 TABLE 6 Participants details Age Weight Height BMI #Participant Sex Group (years) (Kg) (cm) (kg/m2) Smoking Diet 1 M No intervention 46 100 191 27.41 Never Vegetarian 2 M No intervention 32 63 178 19.88 Never Omnivore 3 F No intervention 45 59 159 23.34 Never Omnivore 4 M No intervention 58 76 175 24.82 Never Omnivore 5 M No intervention 58 100 184 29.54 Never Omnivore 6 F No intervention 40 65 160 25.39 Never Omnivore 7 F No intervention 66 64 164 23.8 Never Omnivore 8 F No intervention 25 60 172 20.28 Past Omnivore 9 F No intervention 27 66 170 22.84 Never Omnivore 10 M No intervention 19 80 186 23.12 Past Omnivore 11 F No intervention 35 50 168 17.72 Never Vegetarian 12 M No intervention 47 84 187 24.02 Never Vegetarian 13 F No intervention 23 60 170 20.76 Never Vegan 14 F No intervention 25 37 149 16.67 Never Vegan 15 M No intervention 35 77 172 26.03 Present Vegetarian 16 M No intervention 65 80 176 25.83 Never Omnivore 17 F No intervention 64 67 164 24.91 Past Omnivore 18 M No intervention 43 69 176 22.28 Past Omnivore 19 M No intervention 39 62 180 19.14 Never Omnivore 20 M No intervention 29 67 190 18.56 Never Omnivore 21 F No intervention 40 49.5 158 19.83 Never Omnivore 22 No intervention 32 70 162 26.67 Never Vegetarian 23 No intervention 35 78 175 25.47 Never Omnivore 24 No intervention 65 82 167 29.40 Never Omnivore 25 No intervention 40 49.5 158 19.83 Never Omnivore 26 M Probiotics 29 55 168 19.49 Never Omnivore 27 M Probiotics 27 71 179 22.16 Past Omnivore 28 F Probiotics 32 70 177 22.34 Never Vegan 29 M Probiotics 28 71 174 23.45 Present Omnivore 30 F Probiotics 25 59 170 20.42 Never Vegan 31 F Probiotics 27 58 170 20.07 Never Omnivore 32 Probiotics 26 80 183 23.89 Present Omnivore 33 Probiotics 60 173 20.05 Never Omnivore 34 M aFMT 28 63 175 20.57 Past Omnivore 35 F aFMT 46 78 158 31.24 Past Omnivore 36 F aFMT 46 59 159 23.34 Never Omnivore 37 F aFMT 32 85 175 27.76 Present Omnivore 38 M aFMT 31 62 172 20.96 Never Omnivore 39 M aFMT 30 73 169 25.56 Past Omnivore 40 M Spontaneous 41 74 175 24.16 Never Vegetarian recovery 41 M Spontaneous 45 80 180 24.69 Past Omnivore recovery 42 M Spontaneous 40 82 183 24.49 Past Omnivore recovery 43 M Spontaneous 30 66 170 22.84 Past Omnivore recovery 44 M Spontaneous 36 73 167 26.18 Never Omnivore recovery 45 F Spontaneous 25 53 163 19.95 Never Omnivore recovery 46 M Spontaneous 35 78 180 24.07 Never Omnivore recovery
[0508] Human Study Design: Forty-six healthy volunteers were recruited for this study between the years 2014 and 2018. Upon enrollment, participants were required to fill up medical, lifestyle and food frequency questionnaires, which were reviewed by medical doctors before the acceptance to participate in the study. Two cohorts were recruited, a naive cohort (n=25) and an antibiotics-treated cohort (n=21), subdivided into 3 interventions of probiotics (n=8), autologous fecal microbiome transplantation (aFMT, n=6) and spontaneous reconstitution (n=7). For the latter, the study design consisted of four phases, baseline (7 days), antibiotics (7 days), intervention (28 days) and follow-up (28 days). During the 4-week intervention phase (days 1 thru 28), participants from the probiotics arm were instructed to consume a commercial probiotic supplement (Bio-25) bidaily; participants from the aFMT arm received an intraduodenal infusion of processed microbiome (on day 0), which had been obtained prior to the antibiotic therapy; and participants from the spontaneous reconstitution group did not undergo any treatment. Stool samples were collected daily during the baseline and antibiotics phases, daily during the first week of intervention and then weekly throughout the rest of the intervention and follow-up phases. Participants in the antibiotics cohort underwent two endoscopic examinations, one at the end of the antibiotics phase (day 0) and another three weeks through the intervention phase (day 21). Participants in the naive cohort underwent a single endoscopic examination, and ten of which collected daily stool samples on the seven days prior to the endoscopy.
[0509] The trial was completed as planned. All 46 subjects completed the trial and there were no dropouts or withdrawals. Adverse effects were mild and did not tamper with the study protocol. They included weakness, headaches, abdominal discomfort, anorexia, regurgitation, nausea and oral thrush during the administration of antibiotics and a minor corneal laceration during the endoscopic procedure.
[0510] All participants received payment for their participation in the study upon discharge from their last endoscopic session.
[0511] Drugs and Biological Preparations
[0512] Antibiotics: During the antibiotics phase participants were required to consume oral ciprofloxacin 500 mg bidaily and oral metronidazole 500 mg tridaily for a period of 7 days. This is a broad-spectrum antibiotic regimen is commonly prescribed for treatment of gastrointestinal infections and inflammatory bowel disease exacerbation.
[0513] Probiotics: During the probiotics phase participants were treated by oral Supherb Bio-25 twice daily, which is described by the manufacturer to contain at least 25 billion active bacteria of the following species: B. bifidum, L. rhamnosus, L. lactis, L. casei subsp. casei, B. breve, S. thermophilus, B. longum subsp. longum, L. casei subsp. paracasei, L. plantarum and B. longum subsp. infantis. According to the manufacturer, the pills underwent double coating to ensure their survival under stomach acidity condition and their proliferation in the intestines. Validation of the aforementioned species quantity and viability was performed as part of the study (story 1 ref).
[0514] Autologous fecal microbiome transplantation (FMT): Participants assigned to the FMT study arm were requested to attend the bacteriotherapy unit of TASMC and deposit a fresh stool sample of at least 350 g. Sample promptly underwent embedding in glycerol, homogenization, filtering and was transferred to storage at −80° C. Sample was thawed 30 minutes prior to the endoscopic procedure and placed in syringes. A volume of 150 ml of the preparation was given as an intraduodenal infusion at the end of the first (post-antibiotics) endoscopic examination. The average fecal content was 70.02±22.28 gr per 150 ml suspension.
[0515] Gut Microbiome Sampling
[0516] Stool sampling: Participants were requested to self-sample their stool on pre-determined intervals (as previously described) using a swab following detailed printed instructions. Collected samples were immediately stored in a home freezer (−20° C.) for no more than 7 days and transferred in a provided cooler to our facilities, where they were stored at −80° C.
[0517] Endoscopic examination: Forty-eight hours prior to the endoscopic examination, participants were asked to follow a pre-endoscopy diet. 20 hours prior to the examination diet was restricted to clear liquids. All participants underwent a sodium picosulfate (Pico Salax)-based bowel preparation. Participants were equipped with two fleet enemas, which they were advised to use in case of unclear stools. The examination was performed using a Pentax 90i endoscope (Pentax Medical) under light sedation with propofol-midazolam.
[0518] Luminal content was aspirated from the stomach, duodenum, jejunum, terminal ileum, cecum and descending colon into 15 ml tubes by the endoscope suction apparatus and placed immediately liquid nitrogen. Brush cytology (US Endoscopy) was used to scrape the gut lining to obtain mucosal content from the gastric fundus, gastric antrum, duodenal bulb, jejunum, terminal ileum, cecum, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. Brushes were placed in a screw cap micro tube and were immediately stored in liquid nitrogen. Biopsies from the gut epithelium were obtained from the stomach, duodenum, jejunum, terminal ileum, cecum and descending colon and were immediately stored in liquid nitrogen. By the end of each session, all samples were transferred to Weizmann Institute of Science and stored in −80° C. In the two endoscopic examinations arm the endoscopies were scheduled in sessions 3 weeks apart
[0519] Mouse study design: C57BL/6 male mice were purchased from Harlan Envigo and allowed to acclimatize to the animal facility environment for 2 weeks before used for experimentation. Germ-free Swiss-Webster mice were born in the Weizmann Institute germ-free facility, kept in gnotobiotic isolators and routinely monitored for sterility. In all experiments, age- and gender-matched mice were used. Every experimental group consisted of two cages per group (N=5 in each cage). Mice were 8-9 weeks of age and weighed 20 gr at average at the beginning of experiments. All mice were kept at a strict 24 hr light-dark cycle, with lights turned on from 6 am to 6 pm. Each experimental group consisted of two cages to control for cage effect. For antibiotic treatment, mice were given a combination of ciprofloxacin (0.2 g/l) and metronidazole (1 g/l) in their drinking water for two weeks as previously described.sup.75. Both antibiotics were obtained from Sigma Aldrich. For probiotics consumption, a single pill (Supherb Bio-25) was dissolved in 10 mL of sterile PBS and immediately fed to mice by oral gavage during the dark phase. For auto-FMT, fecal pellets were collected prior to antibiotics administration and snap-frozen in liquid nitrogen; during the day of FMT, the pellets from each mouse were separately resuspended in sterile PBS under anaerobic conditions (Coy Laboratory Products, 75% N2, 20% CO2, 5% H2), vortexed for 3 minutes and allowed to settle by gravity for 2 min. Samples were immediately transferred to the animal facility in Hungate anaerobic culture tubes and the supernatant was administered to the mice by oral gavage. Stool was collected on pre-determined days at the beginning of the dark phase, and immediately snap-frozen and transferred for storage at −80° C. until further processing. Upon the termination of experiments, mice were sacrificed by CO2 asphyxiation, and laparotomy was performed by employing a vertical midline incision. After the exposure and removal of the digestive tract, it was dissected into eight parts: the stomach; beginning at the pylorus, the proximal 4 cm of the small intestine was collected as the duodenum; the following third of the small intestine was collected as the proximal and distal jejunum; the ileum was harvested as the distal third of the small intestine; the cecum; lastly, the colon was divided into its proximal and distal parts. For each section, the content within the cavity was extracted and collected for luminal microbiome isolation, and the remaining tissue was rinsed three times with sterile PBS and collected for mucosal microbiome isolation. During each time point, each group was handled by a different researcher in one biological hood to minimize cross-contamination. All animal studies were approved by the Weizmann Institute of Science Institutional Animal Care and Use committee (IACUC), application number 29530816-2.
[0520] Bacterial cultures: Bacterial strains used in this study are listed in Table 1 herein above. For culturing of bacteria from the probiotics pill, the following liquid media were used: De Man, Rogosa and Sharpe (MRS), modified reinforced clostridial (RC), M17, Brain-Heart Infusion (BHI), or chopped meat carbohydrate medium (CM). All growth media were purchased from BD. Cultures were grown under anaerobic conditions (Coy Laboratory Products, 75% N2, 20% CO2, 5% H2) in 37° C. without shaking. For fecal microbiome cultures, ˜200 mg of frozen human feces was vortexed in 5 ml of BHI under anaerobic conditions. 200 ul of the supernatant were transferred to fresh 5 mL of BHI for initiation of growth. Stationary phase probiotics cultures were filtered using a 0.22 uM filter and added to the fecal culture. For pure Lactobacillus cultures, each strain was grown in liquid MRS under anaerobic conditions.
[0521] Nucleic Acid Extraction
[0522] DNA purification: DNA was isolated from endoscopic samples, both luminal content and mucosal brushes, using PowerSoil DNA Isolation Kit (MOBIO Laboratories). DNA was isolated from stool swabs using PowerSoil DNA Isolation Kit (MOBIO Laboratories) optimized for an automated platform.
[0523] RNA Purification: Gastrointestinal biopsies obtained from the participants were purified using RNAeasy kit (Qiagen, 74104) according to the manufacturer's instructions. Most of the biopsies were kept in RNAlater solution (ThermoFisher, AM7020) and were immediately frozen at liquid nitrogen.
[0524] Nucleic Acid Processing and Library Preparation
[0525] 16S qPCR Protocol for Quantification of Bacterial DNA: DNA templates were diluted to 1 ng/ul before amplifications with the primer sets (indicated in Table 3) using the Fast Sybr™ Green Master Mix (ThermoFisher) in duplicates. Amplification conditions were: Denaturation 95° C. for 3 minutes, followed by 40 cycles of Denaturation 95° C. for 3 seconds; annealing 64° C. for 30 seconds followed by meting curve. Duplicates with >2 cycle difference were excluded from analysis. The CT value for any sample not amplified after 40 cycles was defined as 40 (threshold of detection).
[0526] 16S rDNA Sequencing—as in Example 1.
[0527] Whole genome shotgun sequencing: 100 ng of purified DNA was sheared with a Covaris E220X sonicator. Illumina compatible libraries were prepared as described.sup.75, and sequenced on the Illumina NextSeq platform with a read length of 80 bp to a depth of XXX±XXX reads (mean±SD).
[0528] RNA-Seq
[0529] Ribosomal RNA was selectively depleted by RnaseH (New England Biolabs, M0297) according to a modified version of a published method.sup.76. Specifically, a pool of 50 bp DNA oligos (25 nM, IDT, indicated in Table 4) that is complementary to murine rRNA18S and 28S, was resuspended in 75 μl of 10 mM Tris pH 8.0. Total RNA (100-1000 ng in 10 μl H.sub.2O) were mixed with an equal amount of rRNA oligo pool, diluted to 2 μl and 3 μl 5×rRNA hybridization buffer (0.5 M Tris-HCl, 1 M NaCl, titrated with HCl to pH 7.4) was added. Samples were incubated at 95° C. for 2 minutes, then the temperature was slowly decreased (−0.1° C./s) to 37° C. RNAseH enzyme mix (2 μl of 10 U RNAseH, 2 μl 10×RNAseH buffer, 1 μl H.sub.2O, total 5 μl mix) was prepared 5 minutes before the end of the hybridization and preheated to 37° C. The enzyme mix was added to the samples when they reached 37° C. and they were incubated at this temperature for 30 minutes. Samples were purified with 2.2×SPRI beads (Ampure XP, Beckmann Coulter) according to the manufacturers' instructions. Residual oligos were removed with DNAse treatment (ThermoFisher Scientific, AM2238) by incubation with 5 μl DNAse reaction mix (1 μl Trubo DNAse, 2.5 μl Turbo DNAse 10× buffer, 1.5 μl H.sub.2O) that was incubated at 37° C. for 30 minutes. Samples were again purified with 2.2×SPRI beads and suspended in 3.6 μl priming mix (0.3 μl random primers of New England Biolab, E7420, 3.3 μl H.sub.2O). Samples were subsequently primed at 65° C. for 5 minutes. Samples were then transferred to ice and 2 μl of the first strand mix was added (1 μl 5× first strand buffer, NEB E7420; 0.125 μl RNAse inhibitor, NEB E7420; 0.25 μl ProtoScript II reverse transcriptase, NEB E7420; and 0.625 μl of 0.2 μl /ml Actinomycin D, Sigma, A1410). The first strand synthesis and all subsequent library preparation steps were performed using NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB, E7420) according to the manufacturers' instructions (all reaction volumes reduced to a quarter).
[0530] Analyses
[0531] 16S rDNA analysis: The 2×250 bp reads were processed using the QIIMEapor.sup.69 (Quantitative Insights Into Microbial Ecology) analysis pipeline. In brief, fasta quality files and a mapping file indicating the barcode sequence corresponding to each sample were used as inputs, paired reads were first assembled into longer reads based on sequence similarity, the assembled reads were then split to samples according to the barcodes, Sequences sharing 97% nucleotide sequence identity in the 16S rRNA region were binned into operational taxonomic units (97% ID OTUs). Each OTU was assigned a taxonomical classification by applying the Uclust algorithm against the Greengenes database, and an OTU table was created.
[0532] Metagenomic analysis: Data from the sequencer was converted to fastq files with bcl2fastq. Reads were then QC trimmed using Trimmomatic.sup.70 with parameters PE -threads 10 -phred33 -validatePairs ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 MINLEN:50. We used MetaPhlAn2.sup.71 for taxonomic analysis with parameters: —ignore_viruses —ignore_archaea —ignore_eukaryotes.
[0533] Host sequences were removed by aligning the reads against human genome reference hg19 using bowtie2.sup.72 with parameters: -D 5 -R 1 -N 0 -L 22 -i 5,0,2.50. The resulting non-host reads were then mapped to the integrated gene catalogue.sup.77 using bowtie2 with parameters: —local -D 25 -R 3 -N 1 -L 19 -i 5,1,0.25 -k 5 allowing to a single read to match up to five different entries.
[0534] Further filtering of the bacterial reads consisted of retaining only records with minimal base quality of 26. The bacterial quality filtered resulting bam files were then subsampled to 10.sup.5 bacterial hits. An entry's score was defined by its length, divided by the gene length. Entries scores were summarized according to KO annotations.sup.78. Each sample was scaled to 1M. KEGG Pathway analysis was conducted using EMPANADA.sup.73.
[0535] Probiotics strain identification by unique genomic sequences: Recovery of genomes for probiotic strains from pill metagenomics samples: Genomes for 9 of the 11 probiotic strains were recovered at >93% completeness and <4% contamination from metagenomics samples of the probiotics pill (Table 7). For one of the species (B. longum) only part of the genome was recovered due to strain heterogeneity. The samples were assembled in multiple cycles using IDBA-UD.sup.79. Assemblies were manually improved using a mini-assembly approach.sup.51. Genomes were recovered based on similarity to reference genomes and connectivity between scaffolds as deduced from the mini-assembly analysis.
TABLE-US-00025 TABLE 7 statics for genomes recovered from metagenomics samples of probiotics pill used in the study. Completeness and contamination were evaluated using CheckM.sup.80. # Scaf- Complete- Contam- Species Size folds ness ination Bifidobacterium breve 2,051,417 128 93.66 0.69 Bifidobacterium bifidum 2,196,275 11 99.54 0.12 Bifidobacterium longum 1,200,324 180 46.03 0.96 Lactococcus lactis 2,472,057 36 100 0 Lactobacillus acidophilus 1,963,581 22 98.94 0 Lactobacillus casei 2,968,946 33 94.64 1.72 Lactobacillus paracasei 3,038,895 92 98.79 3.56 Lactobacillus plantarum 3,299,766 31 99.38 2.79 Lactobacillus rhamnosus 2,921,071 29 99.02 0 Streptococcus thermophilus 1,789,952 74 99.89 0.29
[0536] Strain-Level Analysis Probiotic Strains in Human Samples.
[0537] Identifying reads that belong to the probiotic strains in each sample: All human reads were first removed from all samples by mapping against the human genome (assembly GRCh38.p7) using bowtie2 with the -very_sensitive flag. Next, the non-human reads were mapped against all probiotics genomes recovered from the pill using bowtie2 to identify reads that potentially belong to these strains. Finally, the reads were mapped against a database of genomes for all species in the orders Lactobacillales and Bifidobacteriales to which the probiotic strains belong, including the probiotic genomes. Only reads that received their best hit from one of the probiotics strains were further analyzed.
[0538] Determining presence of probiotic species: we counted the number of genes in each probiotic genome whose coverage is greater than 0. A probiotic species was determined to be present in a sample if at least 400 of its genes were detected, with the threshold being set based on comparison to MetaPhlAn2 results and an analysis of gene number distribution across the different samples.
[0539] Determining strain-specific genes: we clustered each probiotic genome's proteins with other genomes available for the species using USEARCH.sup.81 with 90% identity threshold. All genes in clusters whose size was <10% of the number of genomes analyzed were determined to be strain specific. The analysis could be applied to the genomes of B. bifidum, B. breve, B. longum, L. acidophilus, L. casei, L. lactis, L. paracasei, L. plantarum and S. thermophilus. For B. longum, it is not possible to determine which of the probiotic strains is present.
[0540] Determining samples with probiotic strains: For each strain that passed the 400-genes threshold from step 3 we compared the fraction of strain-specific genes detected with the fraction of all genes on the genome that were detected. The probiotic strain was determined to be present if at least 65% of the total number of genes were detected and the difference between the fraction of the total and strain-specific genes that were found was 20% or less.
[0541] RNAseq Analysis
[0542] Data normalization: Initially, we normalized the sequenced data as previously described.sup.82. Briefly, genes with mean TPM<1 across all samples were filtered out from the analysis, and a value of 0.001 was added to remaining TPM values to avoid zero-values in downstream calculations. Then, sample median normalization was performed based on all constitutive gene reads with positive counts for all samples. Thus, all TPM values in each sample were scaled by the median TPM of constitutive reads in that sample, divided by the median TPM across all samples. We then performed a per-gene normalization by dividing each expression value by the median value of that gene across all samples. Finally, expression data was log-transformed (base 2). The above normalization steps were performed separately to data acquired from each of the different experimental batches, determined by the presence or absence of RNAlater solution for sample preservation.
[0543] Comparison of expression levels before and after treatment with probiotics: To account for inter-personal differences and reduce noise, we compared the effects of probiotics treatment on host expression patterns using a repeated-measures design. Thus, for each individual, in each biopsy region, the relative fold-changes (log, base 2) in expression levels of each gene were calculated between samples taken at baseline and after treatment with probiotics. Then, for each individual, genes were ranked from low to high, and sorted by their median rank across all available samples. These sorted lists were subsequently used for gene ontology (GO) enrichment analysis using GOrilla with a p-value threshold of 10.sup.−3 and a false-discovery rate (FDR) threshold of q<0.05.
[0544] Quantification and statistical analysis: The following statistical analyses were applied unless specifically stated otherwise: For 16S data, rare OTUs (<0.1% in relative abundance) were filtered out, and samples were then rarefied to a depth of 10,000 reads (5000 in mouse tissues). For metagenomic data, samples with <10.sup.5 assigned bacterial reads (after host removal) were excluded from further analysis. In the remaining samples, rare KEGG orthologous (KO) genes (<10-5) were removed. Beta diversity was calculated on OTUs (16S) or species (metagenomics) relative abundances using UniFrac distances or Bray-Curtis dissimilarity (R Vegan package, www(dot)CRAN.R-project(dot)org/package=vegan) respectively. PCA for KOs and functional bacterial pathways were calculated using Spearman's rank correlation coefficient. Alpha diversity was calculated on OTUs (16S) using the observed species index. For 16S data, measurements of alpha and beta diversity were calculated using QIIME tools v 1.9.1. In order to determine the effect of treatment on microbiota taxonomic composition and functional capacity repeated-measures Kruskal Wallis with Dunn's test was used. In order to compare the effect of treatment over time between two groups or more two-way ANOVA with Dunnet's test, or permutation tests performed by switching labels between participants, including all their assigned samples, were used. Mann-Whitney and Wilcoxon tests were used to conduct pairwise comparisons between two treatment arms or two groups of participants. Permutational multivariate ANOVA (Adonis PERMANOVA with 10,000 permutations) based on sample distances was used to test for changes in the community composition and function. To analyze qPCR data, Two way ANOVA with Sidak or Dunnett test was used. The threshold of significance was determined to be 0.05 both for p and q-values. Statistically significant findings were marked according to the following cutoffs: *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. Data were plotted with GraphPad Prism version 7.0c. Statistical details for all experiments, including sample size, the statistical test used, dispersion and precision measures and statistical significance, are specified in the result section and denoted in figure legends.
Results
Experimental Setup in Mice
[0545] Under homeostatic conditions (see Example 1), administration of a multi-strain probiotic preparation was associated with limited colonization in mice, and with person-specific gut mucosal colonization resistance in humans. To study gut mucosal colonization and resistance patterns to probiotics under microbiome-perturbing conditions, we chose the antibiotic treatment setting, in which probiotics are commonly recommended as means of preventing or ameliorating antibiotics-associated adverse effects.sup.31. In this setting, antibiotics are postulated to provide a ‘freed niche’ potentially enabling probiotics to serve as ‘place holders’ in counteracting antibiotics-induced adverse effects on the indigenous microbiome and mammalian host. However, neither the probiotic mucosal colonization capacity in this context, nor their impact on reconstitution of the indigenous gut mucosal microbiome, have been globally and directly explored to date.
[0546] To study the mucosal colonization capacity of probiotics, and their impact on the indigenous mucosal microbiome as compared to aFMT or watchful waiting, we supplemented the drinking water of male adult WT C57Bl/6 mice (N=40) with a wide-spectrum antibiotics regimen of ciprofloxacin and metronidazole for two weeks. The immediate impact of antibiotic treatment on gut mucosal microbiome configuration was assessed in one group of mice, sacrificed after the two-week antibiotic exposure (“antibiotics”, N=10,
Antibiotic Treatment Marginally Enhances Probiotic Gut Mucosal Colonization in Mice
[0547] We began our investigation by assessing the fecal and mucosal colonization of probiotics following wide-spectrum antibiotic treatment in mice. 16S rDNA rarefied to 10000 reads indicated three of the four genera comprising the probiotics mix to be present in stool samples even prior to antibiotic administration (Lactobacillus, Bifidobacterium and Streptococcus,
[0548] Like in stool, 16S-rDNA assessment of mucosal colonization did not detect significant elevation in the relative abundance of any of the probiotics genera in any of the regions (FIGS. 29E-H). A pooled qPCR analysis for all administered probiotic species indicated significantly higher abundance in the lumen of the LGI (Kruskal-Wallis & Dunn's P<0.0001 vs. each group,
Probiotics Delay, and aFMT Improves the Post-Antibiotic Reconstitution of the Indigenous Murine Microbiome
[0549] We next determined the impact of probiotics on reconstitution of the indigenous murine fecal and mucosal gut microbiome community following antibiotic treatment. Expectedly, antibiotic treatment resulted in a dramatic reduction in stool alpha-diversity (>66% reduction, Two-Way ANOVA & Dunnett P=0.0001 for all groups,
[0550] Delayed murine probiotics-induced microbiome reconstitution was also reflected in the kinetics of return to fecal baseline pre-antibiotics composition, as expressed by UniFrac distances. While all treatment groups were dramatically shifted from baseline stool composition upon antibiotic treatment, aFMT returned to baseline by day 28 (P=0.83,
[0551] Consistent with the findings in stool, the number of observed species in the probiotics group was comparable to the group dissected immediately after two weeks of antibiotics, and significantly lower compared to the control, aFMT, and spontaneous recovery groups in both the lumen and the mucosa of the LGI (
[0552] To ascertain that the delayed return to homeostatic indigenous microbiome configuration following probiotics treatment was not a unique feature of the studied vivarium, we performed the same set of interventions on mice housed in a different SPF animal facility with distinct baseline fecal microbiome (26 OTUs significantly differentially represented, FDR-corrected Mann-Whitney P<0.05,
[0553] Collectively, four weeks of spontaneous recovery following a wide-spectrum antibiotics treatment in mice partially restored baseline gut mucosal configuration and bacterial richness and load. Watchful waiting was superior, in its rate of induction of indigenous microbiome reconstitution, to consumption of probiotics, which demonstrated little improvement of the post-antibiotics microbiome configuration and delayed the restoration of homeostatic composition and richness of the pre-antibiotic gut mucosal microbiome (
Human Experimental Design
[0554] We next set out to determine how post-antibiotic probiotics or aFMT treatment would affect the human luminal and mucosa-associated microbiome reconstitution. To this aim, we conducted a prospective longitudinal interventional study in 21 healthy human volunteers not consuming probiotics (Table 6), who were given an oral broad-spectrum antibiotic treatment of ciprofloxacin and metronidazole at standard dosages for a period of 7 days (days −7 through −1,
[0555] Endoscopic examinations were performed twice in each of the 21 participants. A first colonoscopy and deep endoscopy were performed after completion of the weeklong antibiotic course, thereby characterizing the post-antibiotics dysbiosis throughout the gastrointestinal tract. A second colonoscopy and deep endoscopy were performed three weeks later (day 21), to assess the degree of mucosal and luminal reconstitution in each of the three treatment arms (
Probiotics in Antibiotics Perturbed Humans are Continuously Shed in Stool, and Colonize the LGI Mucosa
[0556] Expectedly, antibiotics treatment in humans triggered a profound fecal microbial depletion
[0557] (
[0558] Fecal 16S rDNA analysis demonstrated that all probiotics-related genera were found in stools prior to probiotics supplementation (
[0559] Fecal species-specific qPCR, the most sensitive method, revealed a significant fecal expansion during probiotics administration of the 11-probiotic species when considered together (Two-Way ANOVA & Dunnett P=0.0001), with 7/11 species being significantly elevated from baseline when separately analyzed (BBR, BIN, LAC, LCA, LLA, LPL and LRH,
[0560] Given the above continuous shedding in stool, we assumed that the post-antibiotic gut mucosal colonization of probiotics is also enhanced as compared to that observed during homeostasis (Example 1). 16S rDNA analysis of luminal and mucosal GI samples collected before and after 3 weeks of probiotics, indicated no significant increases in the relative abundance of probiotic genera in the GI lumen (range 0.001-48, Two-Way ANOVA & Sidak P>0.05,
[0561] In agreement, mucosal qPCR analysis indicated a significant probiotics colonization of the gastric fundus (Two-Way ANOVA P=0.03,
[0562] To determine whether antibiotics-treated individuals feature a person-specific, microbiome related colonization permissiveness/resistance to probiotics, similar to our observations under homeostatic conditions (Example 1), we calculated qPCR-based individual fold changes in the probiotic load between the first and last day of probiotics supplementation (
[0563] Collectively in the antibiotics-perturbed gut, reversal of colonization resistance to probiotics enables incremental gut colonization by exogenously administered probiotic strains, mainly in the proximal large intestine, leading to long-term probiotic fecal shedding, indicative of stable colonization and active proliferation. Probiotic species belonging to Bifidobacterium were colonized at higher numbers compared to the other tested probiotics species.
Probiotics Delay, while aFMT Improves the Post-Antibiotic Reconstitution of the Indigenous Human Fecal Microbiome
[0564] We next assessed the contribution of the three post-antibiotic treatment arms to reconstitution of the indigenous fecal microbiome in humans. We first utilized fecal 16s rDNA analysis, to calculate the unweighted UniFrac distances between stools collected during antibiotics treatment or during the reconstitution period to that of baseline stool microbiome configuration (
[0565] We then quantified species and functional KEGG orthologs (KOs) that were more than two-fold distinct in their fecal abundances between baseline (pre-antibiotics) and the end of reconstitution in the three arms; aFMT had the fewest number of fecal species distinct between baseline and endpoint (29 species,
[0566] Of the species altered in fecal RA by antibiotics, we identified 20 that returned to baseline comparable levels in the aFMT and spontaneous recovery groups, but not in the probiotics group (
[0567] Together, while probiotics species colonized the mucosa of the antibiotics-perturbed human gut, they delayed the stool microbiome compositional, functional and diversity-related reconstitution to a pre-antibiotic configuration. This delayed fecal reconstitution persisted even after probiotic cessation. In contrast, aFMT induced a rapid and nearly complete fecal microbiome reconstitution, as compared to either the watchful waiting or probiotics-administered groups.
Probiotics Delay the Post-Antibiotic Reconstitution of the Indigenous Human Mucosal Microbiome
[0568] We next assessed whether the above probiotics- and aFMT-induced impacts on stool microbiome re-colonization could be documented in the gut mucosal level. We focused on the LGI, given the preferential probiotic post-antibiotic colonization at this region (
[0569] Collectively, enhanced post-antibiotic probiotics colonization in the LGI mucosa was associated with a compositional and functional persistence of post-antibiotic dysbiosis, reflected in both stool and LGI lumen and mucosa. This delayed return of the indigenous gut microbiome towards pre-antibiotic microbiome composition and function is in line with similar observations in mice (
Reversion of Antibiotics-Associated GI Transcriptomic Landscape is Delayed by Probiotics
[0570] Given the differential impact of probiotics and aFMT, as compared to watchful waiting, or the recovery of mucosal gut microbiome composition and function, we next sought to characterize the effect of the three post-antibiotics interventions on the host. To this aim, we performed a global gene expression analysis through RNA sequencing of transcripts collected from stomach, duodenum, jejunum, terminal ileum, cecum and descending colon biopsies immediately after the antibiotics period and after three weeks of reconstitution (
Probiotics-Secreted Molecules Inhibit Human Microbiome In Vitro Growth
[0571] Finally, we explored potential direct probiotic-mediated mechanisms contributing to the inhibition of indigenous microbiome restoration. To this aim, we utilized a host-free, contact-independent system of probiotics-human microbiome culture. We began by culturing the probiotics pill content in five enriching growth media, differentially supporting the growth of distinct members of the probiotics consortium (
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[0654] Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.
[0655] All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting.
[0656] In addition, the priority document of this application is hereby incorporated herein by reference in its entirety.