Patent Application
20210269860
The present invention, in some embodiments thereof, relates to methods of using probiotics in mammalian subjects. More specifically, the invention relates to personalized predictions as to whether a subject is responsiveness to a probiotic based on the gut microbiome.
Dietary supplementation with commensal microorganisms, collectively termed probiotics, is a constantly growing market, estimated to exceed 35 billion USD globally in 2015. In 2012, in the US alone, 1.6% of the adult population (3.9 million adults) consumed prebiotics or probiotics supplements, a fourfold increase in comparison to the rates in 2007, making probiotics the third most commonly consumed dietary supplement after vitamin and mineral preparations. Claimed rationales for probiotics consumption by healthy individuals vary from alleviation of gastrointestinal (GI) symptoms, ‘fortification’ of the immune system and protection against infectious diseases, prevention of weight gain, mental and behavioral augmentation and promotion of wellbeing. A recent survey demonstrated that over 60% of healthcare providers prescribed probiotics to their patients, mostly for the maintenance of ‘bowel health’, prevention of antibiotic-associated diarrhea or upon patient request.
Nevertheless, despite the popularity of probiotic products, their efficacy under homeostatic conditions remains controversial, with only a few controlled clinical studies pointing to beneficial outcomes, while others failing to establish sustained modulation of the microbiome, or objective physiological consequences.
Collectively, evidence for health-promoting activity of exogenously administered commensals remains inconclusive. As such, probiotics are often classified by regulatory authorities as dietary supplements, emphasizing their safety and lack of impact on food taste, rather than evidence-based proofs of beneficial effects. This confusing situation results in a multitude of non-evidence-based probiotics preparations introduced to the general public in their purified forms or integrated into a variety of foods, ranging from infant formulas, milk products, to pills, powders and candy-like articles, in the absence of concrete proof of efficacy. Medical authorities, such as the European Food Safety Authority or the US Food and drug administration, have therefore declined to approve probiotics formulations as medical intervention modalities.
Several major challenges limit a comprehensive assessment of probiotics effects on the mammalian host. The first stems from common utilization of 16S rDNA analysis as means of microbiome and probiotics characterization in most studies. This methodology, when utilized alone, enables to assess only taxonomic changes in relative abundance, mostly at the genus level, while being agnostic in distinguishing between similar endogenous and probiotics strains, or in quantifying impacts on microbiome function. A second limitation stems from significant inter-individual human microbiome variability, mediated by factors such as age, diet, antibiotic usage, consumption of food supplements, underlying medical conditions and disturbances to circadian activity. This variability may drive individualized probiotics-mediated colonization and host effects, as suggested by long-term stool colonization of Bifidobacterium longum AH1206 that was noted in only 30% of individuals consuming this probiotic (Maldonado-Gomez, M. X. et al. Cell Host Microbe 20, 515-526, (2016)). A third limitation stems from universal reliance on stool microbiome assessment, as a surrogate marker of GI mucosal probiotics impacts on the host and its microbiome.
Goossens, D. A., et al. Aliment Pharmacol Ther 23, 255-263, (2006) discloses a human study utilizing only culture-based techniques in individuals undergoing surveillance colonoscopy in which they failed to detect efficient probiotics gut colonization.
Clinical trial NCT03218579 examines the extent of rehabilitation of the composition and functioning of the intestinal bacteria in healthy people after the consumption of antibiotics.
According to an aspect of some embodiments 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.
According to an aspect of some embodiments 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.
According to an aspect of some embodiments of the present invention there is provided a method of maintaining the health of a subject comprising administering a probiotic to a subject who is deemed responsive to probiotic treatment according to the methods described herein, thereby maintaining the health of the subject.
According to an aspect of some embodiments of the present invention there is provided a method of treating a disease of a subject for which an antibiotic is therapeutic comprising:
(a) assessing whether the subject is suitable for probiotic treatment according to the methods described herein;
(b) administering to the subject an antibiotic which is suitable for treating the disease; and subsequently
(c) administering to the subject a probiotic if the subject is deemed suitable for probiotic treatment; or administering to the subject a fecal transplant if the subject is deemed not suitable for probiotic treatment, thereby treating the disease.
According to an aspect of the present invention there is provided a method of predicting a signature of a microbiome of a GI location of a subject, the method comprising determining an amount and/or activity of at least one genus or order of bacteria of a fecal sample of the subject, the genus or order being set forth in Table N, wherein the amount and/or activity is predicative of the signature of the microbiome of a GI location of the subject.
According to an aspect of the present invention there is provided a method of predicting a signature of a microbiome of a GI location of a subject, the method comprising determining an amount and/or activity of at least one species of bacteria of a fecal sample of the subject, the species being set forth in Table O, wherein the amount and/or activity is predicative of the signature of the microbiome of a GI location of the subject.
According to an aspect of the present invention there is provided a predicting a signature of a microbiome of a GI location of a subject, the method comprising determining an amount and/or activity of at least one KO annotation of bacteria of a fecal sample of the subject, the KO annotation being set forth in Table P, wherein the amount and/or activity is predicative of the signature of the microbiome of a GI location of the subject.
According to an aspect of the present invention there is provided a method of predicting a signature of a microbiome of a GI location of a subject, the method comprising determining an amount and/or activity of bacteria utilizing at least one KEGG pathway of a fecal sample of the subject, the KEGG pathway being set forth in Table Q, wherein the amount and/or activity is predicative of the signature of the microbiome of a GI location of the subject.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing feces of the subject.
According to further features in the described preferred embodiments, the gut microbiome comprises a mucosal gut microbiome or a lumen gut microbiome.
According to further features in the described preferred embodiments, the 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.
According to further features in the described preferred embodiments, the candidate subject does not have a chronic disease.
According to further features in the described preferred embodiments, the signature of the gut microbiome is a presence or level of microbes of the microbiome.
According to further features in the described preferred embodiments, the signature of the gut microbiome is a presence or level of genes of microbes of the microbiome.
According to further features in the described preferred embodiments, the signature of the gut microbiome is a presence or level of a product generated by microbes of the microbiome.
According to further features in the described preferred embodiments, the signature of the gut microbiome is an alpha diversity.
According to further features in the described preferred embodiments, the product is selected from the group consisting of a mRNA, a polypeptide, a carbohydrate and a metabolite.
According to further features in the described preferred embodiments, the microbes of the microbiome are of an identical species to the microbes of the probiotic.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing feces of the subject.
According to further features in the described preferred embodiments, the microbes of the 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.
According to further features in the described preferred embodiments, the microbes of the microbiome utilize at least one pathway set forth in Table B.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the lower gastrointestinal tract (LGI) mucosal microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the LGI mucosal microbiome are selected from the group consisting of bacteria of the genus Odoribacter, bacteria of the genus Bacteroides, bacteria of the genus Bifidobacterium, bacteria of the family Rikenellaceae and a species set forth in Table C.
According to further features in the described preferred embodiments, the microbes of the LGI mucosal microbiome utilize at least one pathway set forth in Table D.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the rectal microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the rectal microbiome are selected from the group consisting of bacteria of the genus Streptococcus, bacteria of the genus Odoribacter, bacteria of the genus Bifidobacterium, bacteria of the genus Bacteroides, bacteria of the family Rikenellaceae and bacteria of the species Barnesiella_intestinihominis.
According to further features in the described preferred embodiments, the microbes of the rectal microbiome utilize at least one pathway listed in Table E.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the sigmoid colon (SM) microbiome of the subject.
According to further features in the described preferred embodiments, the SM microbiome are selected from the group consisting of bacteria of the family Rikenellaceae and bacteria of the species listed in Table F.
According to further features in the described preferred embodiments, the microbes of the SM microbiome utilize at least one pathway listed in Table G.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the descending colon (DC) microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the DC microbiome are selected from the group consisting of bacteria of the genus Bacteroides, bacteria of the genus Odoribacter, bacteria of the family Rikenellaceae and bacteria of the species set forth in Table H.
According to further features in the described preferred embodiments, the microbes of the DC microbiome utilize at least one pathway listed in Table I.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the transverse colon (TC) microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the TC microbiome are selected from the group consisting of Bacteria of the genus Odoribacter, bacteria of the genus Dorea, bacteria of the family Rikenellaceae and bacteria of the species set forth in Table J.
According to further features in the described preferred embodiments, the microbes of the TC microbiome utilize at least one pathway listed in Table K.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the ascending colon (AC) microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the AC microbiome are selected from the group consisting of Bacteria of the genus Odoribacter, bacteria of the family Rikenellaceae and bacteria of the species set forth in Table L.
According to further features in the described preferred embodiments, the microbes of the AC microbiome utilize a fatty acid degradation pathway.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the cecum (Ce) microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the Ce microbiome are selected from the group consisting of Bacteria of the genus Odoribacter, bacteria of the family Rikenellaceae and bacteria of the species Barnesiella_intestinihominis.
According to further features in the described preferred embodiments, the microbes of the Ce microbiome utilize a propanoate metabolism Kegg pathway or the primary bile acid biosynthesis Kegg pathway.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the ileum (Ti) microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the Ti microbiome are selected from the group consisting of bacteria of the genus Faecalibacterium, bacteria of the family Rikenellaceae, bacteria of the genus Bifidobacterium, bacteria of the family Ruminococcaceae.
According to further features in the described preferred embodiments, the microbes of the Ti microbiome utilize a limonene and pinene degradation Kegg pathway or the valine, leucine and isoleucine degradation Kegg pathway.
According to further features in the described preferred embodiments, the determining the signature is effected by analyzing the fundus (GO microbiome of the subject.
According to further features in the described preferred embodiments, the microbes of the Gf microbiome are of the genus Actinobacillus.
According to further features in the described preferred embodiments, the microbes of the Gf microbiome utilize a Kegg pathway set forth in Table M.
According to further features in the described preferred embodiments, the fecal transplant is an autologous fecal transplant.
According to further features in the described preferred embodiments, the predicting is based on the level and/or activity of no more than 10 bacterial genii or orders in the fecal sample.
According to further features in the described preferred embodiments, the predicting is based on the level and/or activity of no more than 10 bacterial species in the fecal sample.
According to further features in the described preferred embodiments, the predicting is based on the level and/or activity of no more than KO annotations in the fecal sample.
According to further features in the described preferred embodiments, the predicting is based on the level and/or activity of no more than 10 KEGG pathways in the fecal sample.
According to further features in the described preferred embodiments, the GI location is selected from the group consisting of the mucosa of the lower gastrointestinal tract, the rectum; the sigmoid colon; the distal colon; the transverse colon; the ascending colon; the cecum; the ileum; the jejunum; the duodenum; the antrum; and the fundus.
Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.
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.
In the drawings:
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.
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.
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.
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.
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.
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.
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.
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.
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.
As used herein the term “subject” refers to a mammalian subject (e.g. mouse, cow, dog, cat, horse, monkey, human), preferably human.
In one embodiment, the candidate subject is a healthy subject.
In another embodiment, the candidate subject has an infection. In still another embodiment, the candidate subject has recovered from an infection following antibiotic treatment.
In another embodiment, the candidate subject does not have a chronic disease.
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.
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.
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.
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.
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).
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).
As used herein, the term “microbiome” refers to the totality of microbes (bacteria, fungae, protists), their genetic elements (genomes) in a defined environment.
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.
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.
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.
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.
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.
Quantifying Microbial Levels:
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.
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.
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.
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.).
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).
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.
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.).
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.
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.
According to one embodiment, the sequencing method allows for quantitating the amount of microbe—e.g. by deep sequencing such as Illumina deep sequencing.
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.
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.
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.
Examples of microbial products include, but are not limited to mRNAs, polypeptides, carbohydrates and metabolites.
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.
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.
According to a particular embodiment, the metabolite is one that alters the composition or function of the microbiome.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Thus, the fractional percentage of microbes (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total may be statistically similar.
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%.
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%.
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.
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.
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.
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.
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.
1. Bacteria of the genus Bifidobacterium
2. Bacteria of the genus Dialister
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.
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.
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).
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.
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).
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.
1. Bacteria of the genus Odoribacter
2. Bacteria of the genus Bacteroides
3. Bacteria of the genus Bifidobacterium
4. Bacteria of the family Rikenellaceae
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)
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.
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).
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.
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).
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.
1. Bacteria of the genus Streptococcus
2. Bacteria of the genus Odoribacter
3. Bacteria of the genus Bifidobacterium
4. Bacteria of the genus Bacteroides
5. Bacteria of the family Rikenellaceae
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).
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.
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).
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.
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).
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.
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).
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.
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).
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.
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).
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.
1. Bacteria of the genus Bacteroides
2. Bacteria of the genus Odoribacter
3. Bacteria of the family Rikenellaceae
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).
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.
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).
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.
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).
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.
1. Bacteria of the genus Odoribacter
2. Bacteria of the genus Dorea
3. Bacteria of the family Rikenellaceae
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).
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.
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).
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.
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).
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.
1. Bacteria of the genus Odoribacter
2. Bacteria of the family Rikenellaceae
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).
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.
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).
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.
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).
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.
1. Bacteria of the genus Odoribacter
2. Bacteria of the family Rikenellaceae
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).
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.
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).
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.
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).
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.
1. Bacteria of the genus Faecalibacterium
2. Bacteria of the family Rikenellaceae
3. Bacteria of the genus Bifidobacterium
4. Bacteria of the family Ruminococcaceae
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).
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.
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).
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.
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).
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.
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).
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.
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.
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.
In other embodiments, the microbial signature of the gut microbiome comprises a metabolite signature.
In other embodiments, the microbial signature of the gut microbiome comprises a bacterial signature.
In still other embodiments, the microbial signature refers to the relative abundance of genes or metabolites belonging to a particular pathway.
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.
It will be appreciated that the signature may comprise additional taxa of microbes other than species, including families, strains, genus, order etc.
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.
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.
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.
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.
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.
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.
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.
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.).
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.
Use of machine learning is particularly, but not exclusively, advantageous when the database includes multidimensional entries.
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.
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.
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.
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.
Following is an overview of some machine learning procedures suitable for the present embodiments.
Association rule algorithm is a technique for extracting meaningful association patterns among features.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A decision tree is a decision support algorithm that forms a logical pathway of steps involved in considering the input to make a decision.
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.
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).
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”.
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.
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.
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.
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.
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.
Instance-based algorithms generate a new model for each instance, instead of basing predictions on trees or networks generated (once) from a training set.
The term “instance”, in the context of machine learning, refers to an example from a database.
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.
Once a subject has been determined to be “responsive to a probiotic”, the present invention further contemplates treating the subject with a probiotic.
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.
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.
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.
In one embodiment, the disease is a chronic disease. In another embodiment, the disease is an acute disease.
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.
According to a particular embodiment, the probiotic composition is formulated in a food product, functional food or nutraceutical.
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.
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.
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%.
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.
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).
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.
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, 1×106, 2×106, 3×106, 4×106, 5×106, 6×106, 7×106, 8×106, 9×106, 1×107, 1×108, 1×109, 1×1010, 1×1011, 1×1012, 1×1013 or more bacteria.
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.
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.
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:
(a) assessing whether the subject is suitable for probiotic treatment according to the method described herein;
(b) administering to the subject an antibiotic which is suitable for treating the disease; and subsequently
(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.
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.
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.
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.
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.
Preferably, the fecal transplant is derived from a healthy subject. In a particular embodiment, the fecal transplant is an autologous fecal transplant.
An autologous fecal transplant is derived from the subject being treated prior to antibiotic administration and preferably prior to disease onset.
Methods of determining the amount of particular bacteria are provided herein above.
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.
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.
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.
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.
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.
As used herein the term “about” refers to ±10%
The terms “comprises”, “comprising”, “includes”, “including”, “having” and their conjugates mean “including but not limited to”.
The term “consisting of” means “including and limited to”.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Lactobacillus acidophilus
Lactobacillus rhamnosus
Lactobacillus casei
Lactobacillus casei subsp. paracasei
Lactobacillus plantarum
Bifidobacterium longum subsp. infantis
Bifidobacterium bifidum
Bifidobacterium breve
Bifidobacterium longum subsp. longum
Lactococcus lactis
Streotococcus thermophilus
L. acidophilus
88
L. acidophilus
88
L. rhamnosus
89
L. rhamnosus
89
L. casei qPCR
89
L. casei qPCR
89
L. paracasei
89
L. paracasei
89
L. plantarum
90
L. plantarum
90
B. infantis qPCR
91
B. infantis qPCR
91
B. bifidum qPCR
91
B. bifidum qPCR
91
B. breve qPCR
91
B. breve qPCR
91
B. longum qPCR
91
B. longum qPCR
91
L. lactis qPCR
92
L. lactis qPCR
92
S. thermophilus
89
S. thermophilus
89
93
93
93
93
93
94
95
96
97
98
99
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.
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.
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).
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.
All participants received payment for their participation in the study upon discharge from their last endoscopic session.
Drugs and Biological Preparations
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
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.
Gut Microbiome Sampling
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.
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.
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
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.
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.
Nucleic Acid Extraction
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.
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.
Nucleic Acid Processing and Library Preparation
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).
L. acidophilus
L. acidophilus
L. rhamnosus
L. rhamnosus
L. casei qPCR89
L. casei qPCR89
L. paracasei qPCR89
L. plantarum qPCR90
L. plantarum qPCR90
B. infantis qPCR91
B. infantis qPCR91
B. bifidum qPCR91
B. bifidum qPCR91
B. breve qPCR91
B. breve qPCR91
B. longum qPCR91
B. longum qPCR91
L. lactis qPCR92
L. lactis qPCR92
S. thermophilus
S. thermophilus
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).
Whole genome shotgun sequencing: 100 ng of purified DNA was sheared with a Covaris E220X sonicator. Illumina compatible libraries were prepared as described60, 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.
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 H2O) 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 H2O, 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 H2O) 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 H2O). 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).
Analyses
16S rDNA analysis: The 2×250 bp reads were processed using the QIIME (Quantitative Insights Into Microbial Ecology, www(dot)qiime(dot)org) analysis pipeline94. 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.
Metagenomic analysis: Data from the sequencer was converted to fastq files with bcl2fastq. Reads were then QC trimmed using Trimmomatic95 with parameters PE -threads 10 -phred33 -validatePairs ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 MINLEN:50. We used MetaPhlAn296 for taxonomic analysis with parameters: —ignore_viruses —ignore_archaea —ignore_eukaryotes.
Host sequences were removed by aligning the reads against human genome reference hg19 using bowtie297 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 catalogue100 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.
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 annotations101. Each sample was scaled to 1M. KEGG Pathway analysis was conducted using EMPANADA98.
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-UD102. Assemblies were manually improved using a mini-assembly approach82. Genomes were recovered based on similarity to reference genomes and connectivity between scaffolds as deduced from the mini-assembly analysis.
Bifidobacterium breve
Bifidobacterium bifidum
Bifidobacterium longum
Lactococcus lactis
Lactobacillus acidophilus
Lactobacillus casei
Lactobacillus paracasei
Lactobacillus plantarum
Lactobacillus rhamnosus
Streptococcus thermophilus
Strain-Level Analysis Probiotic Strains in Human Samples.
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 bowtie297 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.
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.
Determining strain-specific genes: we clustered each probiotic genome's proteins with other genomes available for the its species using USEARCH104 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.
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.
RNAseq Analysis
Data normalization: Initially, we normalized the sequenced data as previously described105. 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.
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 GOrilla99 with a p-value threshold of 10−3 and a false-discovery rate (FDR) threshold of q<0.05.
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.
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 <105 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.
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 physiology32,35,39,49-52,77,78. 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 (
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,
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 preparation79,80, 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 (
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 (
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 (
Given the redundancy in microbial genes and pathways encoded by different microbiome members81, and at different bio-geographical locations along the GI tract75, 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,
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 (
To assess the degree of murine GI colonization by the probiotics, we administered the contents of one pill daily by oral gavage (4*109 CFU kg−1 day−1) to male 10-week-old SPF WT mice (N=10), with an additional group of untreated mice (N=10) serving as controls (
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 (
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 (
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 (
While no consistent probiotics-induced alterations of the UGI luminal (PERMANOVA P=0.2) and mucosal (PERMANOVA P=0.59) microbiome were observed (
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.
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 (
Surprisingly, when each participant was analyzed independently compared to its own baseline, the gastrointestinal mucosal load of probiotics strains considerably varied, with both qPCR (
Importantly, both the relative (
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 (
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 (
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 (
Finally, as the effect of probiotics on the human GI microbiome remains inconclusive47, 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,
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,
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 (
1 Clarke, T. C., Black, L. I., Stussman, B. J., Barnes, P. M. & Nahin, R. L. Trends in the use of complementary health approaches among adults: United States, 2002-2012. Natl Health Stat Report, 1-16 (2015).
2 Pinto-Sanchez, M. I. et al. Probiotic Bifidobacterium longum NCC3001 Reduces Depression Scores and Alters Brain Activity: A Pilot Study in Patients With Irritable Bowel Syndrome. Gastroenterology 153, 448-459 e448, doi:10.1053/j.gastro.2017.05.003 (2017).
Reagents and resources: see Table 1 of Example 1
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.
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.
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.
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.
All participants received payment for their participation in the study upon discharge from their last endoscopic session.
Drugs and Biological Preparations
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.
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).
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.
Gut Microbiome Sampling
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.
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.
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
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 described75. 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.
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.
Nucleic Acid Extraction
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.
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.
Nucleic Acid Processing and Library Preparation
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).
16S rDNA Sequencing—as in Example 1.
Whole genome shotgun sequencing: 100 ng of purified DNA was sheared with a Covaris E220X sonicator. Illumina compatible libraries were prepared as described75, and sequenced on the Illumina NextSeq platform with a read length of 80 bp to a depth of XXX±XXX reads (mean±SD).
RNA-Seq
Ribosomal RNA was selectively depleted by RnaseH (New England Biolabs, M0297) according to a modified version of a published method76. 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 H2O) 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 H2O, 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 H2O) 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 H2O). 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).
Analyses
16S rDNA analysis: The 2×250 bp reads were processed using the QIIMEapor69 (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.
Metagenomic analysis: Data from the sequencer was converted to fastq files with bcl2fastq. Reads were then QC trimmed using Trimmomatic70 with parameters PE -threads 10 -phred33 -validatePairs ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 MINLEN:50. We used MetaPhlAn271 for taxonomic analysis with parameters: —ignore_viruses —ignore_archaea —ignore_eukaryotes.
Host sequences were removed by aligning the reads against human genome reference hg19 using bowtie272 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 catalogue77 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.
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 105 bacterial hits. An entry's score was defined by its length, divided by the gene length. Entries scores were summarized according to KO annotations78. Each sample was scaled to 1M. KEGG Pathway analysis was conducted using EMPANADA73.
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-UD79. Assemblies were manually improved using a mini-assembly approach51. Genomes were recovered based on similarity to reference genomes and connectivity between scaffolds as deduced from the mini-assembly analysis.
Bifidobacterium breve
Bifidobacterium bifidum
Bifidobacterium longum
Lactococcus lactis
Lactobacillus acidophilus
Lactobacillus casei
Lactobacillus paracasei
Lactobacillus plantarum
Lactobacillus rhamnosus
Streptococcus thermophilus
Strain-Level Analysis Probiotic Strains in Human Samples.
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.
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.
Determining strain-specific genes: we clustered each probiotic genome's proteins with other genomes available for the species using USEARCH81 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.
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.
RNAseq Analysis
Data normalization: Initially, we normalized the sequenced data as previously described82. 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.
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 10−3 and a false-discovery rate (FDR) threshold of q<0.05.
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 <105 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.
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 effects31. 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.
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,
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,
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,
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,
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,
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 (
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,
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 (
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,
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 (
Expectedly, antibiotics treatment in humans triggered a profound fecal microbial depletion
(
Fecal 16S rDNA analysis demonstrated that all probiotics-related genera were found in stools prior to probiotics supplementation (
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,
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,
In agreement, mucosal qPCR analysis indicated a significant probiotics colonization of the gastric fundus (Two-Way ANOVA P=0.03,
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 (
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.
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 (
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,
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 (
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.
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 (
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 (
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 (
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 (
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.
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.
In addition, the priority document of this application is hereby incorporated herein by reference in its entirety.
This application claims the benefit of priority from U.S. Provisional Patent Application Nos. 62/695,068 and 62/695,067 filed on Jul. 8 2019, the contents of which are incorporated herein by reference in their entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IL2019/050760 | 7/8/2019 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62695067 | Jul 2018 | US | |
| 62695068 | Jul 2018 | US |
