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7.16A: Detecting Uncultured Microorganisms - Biology

7.16A: Detecting Uncultured Microorganisms - Biology


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Using metagenomics, the microbial constituents of the world can be identified by culturing each individual species.

LEARNING OBJECTIVES

Recognize the methods used to detect uncultured microorganisms

Key Points

  • Many organisms that can cause diseases are not culturable, so a unicellular culture cannot be obtained.
  • Initial studies of microbial fauna showed that they are more diverse than expected and contained many microbes that were not culturable.
  • Advances in molecular biological techniques allow the sequencing of all or at least many of the genomes of microbes found in a sample.

Key Terms

  • shotgun sequencing: A DNA sequencing technique in which a large number of small fragments of a long DNA strand are generated at random, sequenced, and reassembled to form a sequence of the original strand.
  • culturable: Able to be cultured (grown in a suitable environment).
  • high-throughput sequencing: Technologies that parallelize the sequencing process, producing thousands or millions of sequences at once.

Identification of bacteria in the laboratory is particularly relevant in medicine, where the correct treatment is determined by the bacterial species causing an infection. Consequently, the need to identify human pathogens was a major impetus for the development of techniques to identify bacteria.

Early studies have shown that the microbial life around us in the air, sea, and soil is very diverse and only a small fraction of the species are known. One limitation of identifying human pathogens or conventional sequencing begins with a culture of identical cells as a source of DNA. However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be cultured and thus cannot be sequenced. These early studies focused on 16S ribosomal RNA sequences which are relatively short, often conserved within a species, and generally different between species. Many 16S rRNA sequences have been found which do not belong to any known cultured species, indicating that there are numerous non-isolated organisms out there. These surveys of ribosomal RNA (rRNA) genes taken directly from the environment revealed that cultivation based methods find less than 1% of the bacterial and archaeal species in a sample.

The discovery of such diversity led to the field of metagenomics, which is the study of metagenomes, genetic material recovered directly from environmental samples. Rather than culturing a microbe, this approach takes a sample and identifies the different species in it by sequencing all the species simultaneously. However, recovery of DNA sequences longer than a few thousand base pairs from environmental samples was very difficult until recent advances in molecular biological techniques. More specifically, the construction of libraries in bacterial artificial chromosomes (BACs) provided better vectors for molecular cloning.

Advances in bioinformatics, refinements of DNA amplification, and the proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples. These advances have allowed the adaptation of shotgun sequencing to metagenomic samples. The approach, used to sequence many cultured microorganisms and the human genome, randomly shears DNA, sequences many short sequences, and reconstructs them into a consensus sequence.

Shotgun sequencing and screens of clone libraries reveal genes present in environmental samples. This can be helpful in understanding the ecology of a community, particularly if multiple samples are compared to each other. This was further followed by high-throughput sequencing which did the same process as the shotgun sequencing but at a much bigger scale in terms of the amount of DNA that could sequenced from one sample. This provides information both on which organisms are present and what metabolic processes are possible in the community. Using metagenomics, and the resultant sequencing of uncultured microbes, metagenomics has the potential to advance knowledge in a wide variety of fields. It can also be applied to solve practical challenges in medicine, engineering, agriculture, and sustainability.


Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses

Current understanding of the carbon cycle in methanogenic environments involves trophic interactions such as interspecies H2 transfer between organotrophs and methanogens. However, many metabolic processes are thermodynamically sensitive to H2 accumulation and can be inhibited by H2 produced from co-occurring metabolisms. Strategies for driving thermodynamically competing metabolisms in methanogenic environments remain unexplored.

Results

To uncover how anaerobes combat this H2 conflict in situ, we employ metagenomics and metatranscriptomics to revisit a model ecosystem that has inspired many foundational discoveries in anaerobic ecology—methanogenic bioreactors. Through analysis of 17 anaerobic digesters, we recovered 1343 high-quality metagenome-assembled genomes and corresponding gene expression profiles for uncultured lineages spanning 66 phyla and reconstructed their metabolic capacities. We discovered that diverse uncultured populations can drive H2-sensitive metabolisms through (i) metabolic coupling with concurrent H2-tolerant catabolism, (ii) forgoing H2 generation in favor of interspecies transfer of formate and electrons (cytochrome- and pili-mediated) to avoid thermodynamic conflict, and (iii) integration of low-concentration O2 metabolism as an ancillary thermodynamics-enhancing electron sink. Archaeal populations support these processes through unique methanogenic metabolisms—highly favorable H2 oxidation driven by methyl-reducing methanogenesis and tripartite uptake of formate, electrons, and acetate.

Conclusion

Integration of omics and eco-thermodynamics revealed overlooked behavior and interactions of uncultured organisms, including coupling favorable and unfavorable metabolisms, shifting from H2 to formate transfer, respiring low-concentration O2, performing direct interspecies electron transfer, and interacting with high H2-affinity methanogenesis. These findings shed light on how microorganisms overcome a critical obstacle in methanogenic carbon cycles we had hitherto disregarded and provide foundational insight into anaerobic microbial ecology.


Abstract

Publicly available sequence databases of the small subunit ribosomal RNA gene, also known as 16S rRNA in bacteria and archaea, are growing rapidly, and the number of entries currently exceeds 4 million. However, a unified classification and nomenclature framework for all bacteria and archaea does not yet exist. In this Analysis article, we propose rational taxonomic boundaries for high taxa of bacteria and archaea on the basis of 16S rRNA gene sequence identities and suggest a rationale for the circumscription of uncultured taxa that is compatible with the taxonomy of cultured bacteria and archaea. Our analyses show that only nearly complete 16S rRNA sequences give accurate measures of taxonomic diversity. In addition, our analyses suggest that most of the 16S rRNA sequences of the high taxa will be discovered in environmental surveys by the end of the current decade.


New perspective on uncultured bacterial phylogenetic division OP11

Organisms belonging to the OP11 candidate phylogenetic division of Bacteria have been detected only in rRNA-based sequence surveys of environmental samples. Preliminary studies indicated that such organisms represented by the sequences are abundant and widespread in nature and highly diverse phylogenetically. In order to document more thoroughly the phylogenetic breadth and environmental distribution of this diverse group of organisms, we conducted further molecular analyses on environmental DNAs. Using PCR techniques and primers directed toward each of the five described subdivisions of OP11, we surveyed 17 environmental DNAs and analyzed rRNA gene sequences in 27 clonal libraries from 14 environments. Ninety-nine new and unique sequences were determined completely, and approximately 200 additional clones were subjected to partial sequencing. Extensive phylogenetic comparisons of the new sequences to those representing other bacterial divisions further resolved the phylogeny of the bacterial candidate division OP11 and identified two new candidate bacterial divisions, OP11-derived 1 (OD1) and Sulphur River 1 (SR1). The widespread environmental distribution of representatives of the bacterial divisions OD1, OP11, and SR1 suggests potentially conspicuous biogeochemical roles for these organisms in their respective environments. The information on environmental distribution offers clues for attempts to culture landmark representatives of these novel bacterial divisions, and the sequences are specific molecular signatures that provide for their identification in other contexts.

Figures

Bootstrap consensus tree showing the…

Bootstrap consensus tree showing the well-supported phylogenetic relationships for the bacterial divisions OP11,…


Sulfide-driven denitrification: detecting active microorganisms in fed-batch enrichment cultures by DNA stable isotope probing

A microbial community was enriched in the anoxic compartment of a pilot-scale bioreactor that was operated for 180 days, fed with sewage and designed for organic matter, nitrogen and sulfide removal by coupling anaerobic digestion, nitrification and mixotrophic denitrification. Denitrification occurred with endogenous electron donors, mainly sulfide and residual organic matter, coming from the anaerobic compartment. The microorganisms involved in denitrification with sulfide as electron donor were identified by DNA-stable isotope probing with [U- 13 C]-labelled CO2 and NaHCO3. Complete denitrification occurred every two days, and the applied NO3 − /S 2− ratio was 1.6. Bacteria belonging to the Sulfurimonas denitrificans was identified as a chemoautotrophic denitrifier, and those related to Georgfuchisa toluolica, Geothrix fermentans and Ferritrophicum radicicola were most probably associated with heterotrophic denitrification using endogenous cells and/or intermediate metabolites. This study showed that DNA-SIP was a suitable technique to identify the active microbiota involved in sulfide-driven denitrification in a complex environment, which may contribute to improve design and operation of bioreactors aiming for carbon–nitrogen-sulfur removal.


DISCUSSION

Culture-independent molecular studies have suggested a relationship between gut microbiota and inflammatory bowel disease 7, 11 , allergies 8, 10 and obesity 9, 12 . Because the functions of many genes present in sequence databases are undefined or incorrectly annotated, metagenomic data alone does not represent all cultivated gut bacteria. Cultivation of hitherto uncultured bacteria, which comprise over 70% of gut bacteria 4 , will expand our capabilities to perform more comprehensive microbiological studies (e.g., analysis of bioactivity of individual species and studies using isolates in gnotobiotic animals). These studies will provide more detailed information about the relationships between gut microbiota and human diseases. Therefore, developing culture methods that overcome every limitation of conventional culture methods is critical to advancing microbial research.

Cocultivation with other bacteria using a membrane filter can result in supply of various unknown growth factors that are critically important for cell growth 25 .

Compared with 1.5% agar medium, soft agar contributes to maintaining ideal growth conditions because molecules diffuse faster in the latter. For example, methylene blue dye diffuses faster in 0.4% agar medium than in 1.5% agar (data not shown). Therefore, growth factors provided by a supporter strain can reach the target strain faster in soft agar than in solid plate medium. Further, metabolic products and secreted substances can be removed more quickly, which reduces metabolite-induced inhibition of growth and activity of bacteria 26 . Moreover, bacteria in soft agar medium can form single colonies and are easier to isolate.

This system is simple and can be easily and economically applied to conventional instrumentation and various culture conditions (e.g., aerobic and anaerobic cultures).

The nutrient composition of TYG medium is simpler than that of conventional culture media nutrient-rich media can induce metabolic imbalance and kill a proportion of bacteria 27 . In contrast, the most conventional culture media (e.g., EG medium) used to isolate gut bacteria contain multiple sugars, beef extract, blood, vitamins and fatty acids to promote cell growth 19, 28, 29 . However, the small intestine absorbs most nutrients from digested food, the remaining nutrients accounting for only approximately 6–7% of the large intestine's contents. Therefore, the actual nutritional conditions provided by digested food in the human gut are likely inadequate for gut microbiota to thrive without symbiosis, mutualism, commensalism, or a combination of all of these. For example, over 70% of colony counts of a 14 day stool culture on EG agar were obtained after 2 days of incubation. Although this medium accelerates the growth of certain bacteria, it may artificially supplement the substances originally provided by other bacteria in the human gut. Thus, undetected interbacterial interactions of isolates from EG agar are likely attributable to replenished substances required for symbiosis.

In contrast, colony counts in TYG agar continued to increase over 14 days. Gaps in bacterial growth occur reportedly in symbiotic cross-feeding studies of Bifidobacterium species and butyric acid-producing bacteria 17, 18 . In a medium containing fructo-oligosaccharides as the sole energy source, Bifidobacterium strains function as substrate utilizers, grow first, and are then replaced by butyrate-producing bacteria that degrade substrates, metabolites or both (e.g., lactate). Considering these reports and the transition of colony counts on EG and TYG plates, we surmise that the discrepancy in bacterial growth time detected here may indicate interbacterial interactions similar to the cross-feeding observed between strains that grow quickly or slowly on TYG agar. Specifically, the less complex nutrient composition of TYG agar may facilitate detection of interbacterial interactions and prevent bacterial death caused by metabolic imbalance.

To assess this hypothesis, we cocultivated neighboring colonies on TYG agar plates. Using filter cocultivation, Parabacteroides sp. BL157, which is difficult to subculture, grew and was isolated. Further, we detected interactions between Parabacteroides sp. BL157 and K. pneumoniae BL175 as well as between Sutterella sp. BL252 and B. fragilis BL539. Specifically, we found that the growth of Parabacteroides sp. BL157 was accelerated by the growth of neighboring K. pneumoniae BL175 and that the growth of B. fragilis BL539 partially depended on the presence of Sutterella sp. BL252.

The growth of Parabacteroides sp. BL157 and B. fragilis BL539 was supported by K. pneumoniae BL175 and Sutterella sp. BL252 however, the details of the interaction between Parabacteroides sp. BL157 and K. pneumoniae BL175, and between B. fragilis BL539 and Sutterella sp. BL252 are not definitive a further study is required. Certain interbacterial interactions are mediated through bacterial metabolites. For example, supernatants from a Bacillus strain accelerate the growth of S. thermophilum 15 . Further, supernatants from Sphingomonas spp. support the growth of Catellibacterium nectariphilum 30 . Moreover, Porphyromonas gingivalis grows in the oral cavity while utilizing succinic acid generated by Treponema denticola, and T. denticola utilizes isobutyric acid generated by P. gingivalis 31 . Here a Parabacteroides sp. BL157/B. fragilis BL539 colony grew near but did not overlap a K. pneumoniae BL175/Sutterella sp. BL252 colony, suggesting that they communicate through substance(s) generated by supporting strains. Moreover, it has been reported that both B. fragilis and K. pneumoniae produce autoinducer-2, an interspecific signaling molecule that is related to quorum sensing and formation of biofilm colonies 32, 33 . Therefore, interspecific signaling may also be related to the interbacterial interactions detected in these studies.

Phascolarctobacterium sp. BL377, which was initially colonized in this study, cannot be subcultured in monoculture in the presence of a highly growth-stimulating medium (i.e., EG medium). Here we found that B. dorei BL376 and other Bacteroides strains supported the growth of Phascolarctobacterium sp. BL377. The Bacteroides strains examined in this study were found to generate succinic acid as a metabolite (data not shown). In addition, some of Phascolarctobacterium species reportedly utilize succinic acid 34, 35 . Therefore, common substance(s) generated by the Bacteroides species studied here (e.g., succinate) may also influence the growth of BL377.

Although complex interbacterial communication among gut bacteria has been suggested by a genomic survey that did not involve culture 16 , there is little information concerning the multiple growth dependence of viable gut bacteria. In the present study, growth of B. fragilis, which promoted the growth of Phascolarctobacterium sp. BL377, was stimulated by Sutterella sp. BL252, suggesting that multistep interbacterial interactions of viable gut bacteria occurred in the coculture system described here.

Although the name of isolates in these studies were selected based on homology of 16 S rRNA sequences, the homology of 16 S rRNA gene sequences suggests that strains BL157, BL252 and BL377 likely represent novel genus or species. Thus, the name of these isolates should be changed to denote novel species. Thus, the proposition to systematically designate these isolates as novel species is in progress.

Using the cocultivation technique designed here, we succeeded in isolating several growth-dependent gut bacteria. However, this technique requires further optimization, possibly using multilayer culture, high-throughput multi-well culture, and replicating the complex ecosystem of the gut. A clue to solving these issues may be found in other innovative culturing methods 21-24 . Thus, a combination of this filter system and other advanced methods will result in a powerful approach to cultivation and to understanding the ecology of hitherto uncultured bacteria.


Footnotes

Author contributions: Y.H. and V.K.S. contributed equally to this work Y.H., Y.S., A.T., M.H., and M.O. designed research Y.H. and A.T. performed research Y.H., V.K.S., T.P., S.N., and A.T. analyzed data and Y.H., T.D.T., T.K., M.H., and M.O. wrote the paper.

The authors declare no conflict of interest.

Data deposition: The sequences reported in this paper have been deposited in the DNA Data Bank of Japan [accession nos. AP009510 (chromosome), AP009511–3 (plasmids), and AB360878–AB360905 (others)].


2. Materials and Methods

2.1 Sampling Procedure

The sampling site, the Diyarbakır region, is located at the boundary of the Anatolian plate and the Middle Eastern oil region in south-eastern Turkey. A total of 20 crude oil samples (B1, B6, B8, B14, B23, B32, B56, GK8, GS6, GS15, M3, K2, K3, K32, K35, K44, S4, S15, Y18 and Y30) consisting of an oil/water mixture were collected from the production wells of Diyarbakır oil fields (Figure 1). These wells produced oils withdrawn from the oil sandstone deposits (depths from 1600 m to 2620 m, API gravity from 24.3° to 42.3°, water content around 94%, an average pH of 7.0 and salinity from 2966 mg l −1 to 26,961 mg l −1 ). The samples were aseptically taken at the wellhead and put into sterile 500 ml serum bottles sealed with rubber stoppers and aluminium caps. The samples were shipped at ambient temperature. Upon arrival at the laboratory, the samples were immediately analysed. All samples were treated within 48 h after collection. Decantation was used to separate produced water from the oil/water mixture.

Fig. 1.

Sampling locations in Diyarbakır region. Produced water samples were collected from 20 different oil wells © Maphill / Creative Commons Attribution-NoDerivatives (CC BY-ND)

2.2 DNA Extraction

Bacteria in the produced water samples were collected by filtration over 0.20 μm pore size polyamide filters (Sartolon ® , Sartorius AG, Germany). Genomic DNA was extracted with the UltraClean ® Microbial DNA isolation kit (MO BIO Laboratories Inc, USA) according to the manufacturer’s protocol.

2.3 Polymerase Chain Reaction Amplification

Extracted DNA was used as the template for PCR amplification of partial 16S rRNA fragments. Primer pair consisting of 341F with a GC clamp and 907R was used for DGGE analysis (26). A 40-base GC clamp was used to prevent complete denaturation of the fragment during DGGE (27).

Due to the low DNA yield, a two-step PCR strategy was used. At the first step, a real-time PCR (quantitative PCR, qPCR) approach was applied to the produced water samples. The reaction mixture in a final volume of 22.5 μl contained 0.2 μl of each primer, 12.5 μl iQ TM SYBR ® Green Supermix (Bio-Rad Laboratories Inc, USA), 9.6 μl RNase-Free Water (Qiagen, Germany) and 0.5 μl DNA template. qPCR was performed in iCycler iQ TM Real-Time PCR Detection System (Bio-Rad Laboratories Inc, USA) using the following conditions: 5 min at 95°C 40 cycles of 95°C for 30 s, 57°C for 40 s, 72°C for 40 s and 80°C for 25 s and a final 72°C for 10 min. In the qPCR method, after each cycle, a signal was formed. By observing the signals for each sample, PCR products could be detected. The reaction was terminated when the desired amount of product was reached. At the second step, a conventional PCR approach was applied to the qPCR products. Reaction mixture in a final volume of 25 μl contained 0.2 μl of each primer, 12.5 μl Taq PCR Master Mix (Qiagen, Germany), 9.6 μl RNase-Free Water (Qiagen, Germany) and 0.5 μl DNA template. The PCR was performed in TGradient thermocycler (Biometra, Germany) using the following conditions: 5 min at 95°C 12 cycles of 95°C for 30 s, 57°C for 40 s and 72°C for 40 s and a final 74°C for 30 min.

2.4 Denaturing Gradient Gel Electrophoresis

The DCode TM system (Bio-Rad Laboratories, USA) was used for DGGE analysis. 25 μl of each PCR product (200–300 ng) were loaded onto 6% polyacrylamide gels (w/v) containing gradients of 20% to 70% denaturants (urea/formamide). The gels were run for 16 h at 100 V and 60°C in 1× Tris-acetate-EDTA buffer. After completion of electrophoresis, the gels were stained with SYBR ® Gold Nucleic Acid Gel Stain (Invitrogen TM , Thermo Fisher Scientific, USA) for 20 min, visualised and photographed. Selected predominant DGGE bands were excised, eluted in 40 μl of 1× Tris buffer (pH 8) for 2 d at 4°C and re-amplified with 25 cycles as described above. Reaction mixture in a final volume of 25 μl contained 0.125 μl of primer 341F, 0.125 μl of primer 907R, 12.5 μl of Taq PCR Master Mix, 9.75 μl of ultra-pure water and 0.5 μl of template. The PCR products were quantified on a 1.5% (w/v) agarose gel and then sequenced by Macrogen Inc (Seoul, South Korea).

2.5 Comparative Sequence Analysis

The resulting sequences were first aligned and edited using CodonCode Aligner software (CodonCode Corp, USA). Then they were compared to sequences stored in the database GenBank ® using the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST ® ) (28, 29). All obtained partial 16S rRNA gene sequences were deposited in GenBank ® database under the following accession numbers: KF720792 - KF720796, KF720798, KF720801 - KF720802, KF720804, KF720806 - KF720808, KF720810 - KF720811, KF720814, KF720818, KF720820, KF720823, KF720825 - KF720826, KF720828, KF720830 - KF720832, KF720839, KF720844, KF720852, KF720855, KF720858, KF720872, KF720877, KF720882 - KF720884, KF720886 - KF720889, KF720891, KF720893 - KF720894, KF720896 and KF720903.


Acknowledgments

We thank Dr. Nikolai Chernych for his technical assistance during the isolation and purification of metagenomics DNA. We also thank the Department of Energy Joint Genome Institute for sequencing the metagenomes.

Funding

CDV and GM were supported by the ERC Advanced Grant PARASOL (no. 322551). A-SA and RG were supported by the research grant 17-04828S from the Grant Agency of the Czech Republic. MM was supported by the Czech Academy of Sciences (Postdoc program PPPLZ application number L200961651). DYS was supported by the SIAM/Gravitation Program (Dutch Ministry of Education and Science, grant 24002002) and by the Russian Science Foundation (grant 16–14-00121). Sequencing was performed by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, as part of the Community Sequencing Program (contract no. DE-AC02- 05CH11231).

Availability of data and materials

The raw sequence reads of the five metagenomes have been deposited to the NCBI Sequence Read Archive (see Additional file 1: Table S6 for accession numbers and read and contig statistics). The final 871 MAGs described in this paper have been deposited as Whole Genome Shotgun projects at DDBJ/EMBL/GenBank, and accession numbers are listed in Additional file 4 (BioProject ID PRJNA434545). All versions described in this paper are version XXXX01000000. The cleaned and dereplicated amplicon sequence datasets are available in FigShare (https://figshare.com/s/7684627445e3621aba24). Maximum likelihood trees based on the concatenated alignment of 16 ribosomal proteins, basis for Figs. 2 and 3, in newick format (.tre file) and complementary datasets (used to plot completeness, contamination, genome recovery size, G + C mol% and RPKG in iTOL), as well as K number assignments for the predicted proteins of all MAGs (KEGG-orthologues, Ghost Koala) and the fully annotated CPR MAGs supporting the conclusions of this article are also available in FigShare (https://figshare.com/s/7684627445e3621aba24).


Integrative HMP. (iHMP) Research Network Consortium. The Integrative Human Microbiome Project. Nature. 2019569:641–8.

Integrative HMP. (iHMP) Research Network Consortium. After the Integrative Human Microbiome Project, what’s next for the microbiome community? Nature. 2019569:599.

Proctor L. Priorities for the next 10 years of human microbiome research. Nature. 2019569:623–5.

Douillard FP, de Vos WM. Biotechnology of health-promoting bacteria. Biotechnol Adv. 2019. https://doi.org/10.1016/j.biotechadv.2019.03.008.

Stenman LK, Burcelin R. Establishing a causal link between gut microbes, body weight gain and glucose metabolism in humans–towards treatment with probiotics. Benef Microbes. 2016 http://www.wageningenacademic.com/doi/abs/10.3920/BM2015.0069.

Brunkwall L, Orho-Melander M. The gut microbiome as a target for prevention and treatment of hyperglycaemia in type 2 diabetes: from current human evidence to future possibilities. Diabetologia. 2017. https://doi.org/10.1007/s00125-017-4278-3.

Morotomi M, Nagai F, Watanabe Y. Description of Christensenella minuta gen. nov., sp. nov., isolated from human faeces, which forms a distinct branch in the order Clostridiales, and proposal of Christensenellaceae fam. nov. Int J Syst Evol Microbiol. 201162:144–9.

Lau SKP, McNabb A, Woo GKS, Hoang L, Fung AMY, Chung LMW, et al. Catabacter hongkongensis gen. nov., sp. nov., isolated from blood cultures of patients from Hong Kong and Canada. J Clin Microbiol. 200745:395–401.

Rajilić-Stojanović M, de Vos WM. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol Rev. 201438:996–1047.

Parte AC. LPSN - List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. Int J Syst Evol Microbiol. 201868:1825–9.

Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 201836:996–1004.

Alonso BL. Irigoyen von Sierakowski A, Sáez Nieto JA, Rosel AB. First report of human infection by Christensenella minuta, a Gram-negative, strickly anaerobic rod that inhabits the human intestine. Anaerobe. 201744:124–5.

Yang Y, Gu H, Sun Q, Wang J. Effects of Christensenella minuta lipopolysaccharide on RAW264.7 macrophages activation. Microb Pathog. 2018. https://doi.org/10.1016/j.micpath.2018.10.005.

Rosa BA, Hallsworth-Pepin K, Martin J, Wollam A, Mitreva M. Genome sequence of Christensenella minuta DSM 22607T. Genome Announc. 20175. https://doi.org/10.1128/genomeA.01451-16.

Choi YJ, Won EJ, Kim SH, Shin MG, Shin JH, Suh SP. First case report of bacteremia due to Catabacter hongkongensis in a Korean patient. Ann Lab Med. 201737:84–7.

Lau SKP, Fan RYY, Lo H-W, Ng RHY, Wong SSY, Li IWS, et al. High mortality associated with Catabacter hongkongensis bacteremia. J Clin Microbiol. 201250:2239–43.

Elsendoorn A, Robert R, Culos A, Roblot F, Burucoa C. Catabacter hongkongensis Bacteremia with fatal septic shock. Emerg Infect Dis. 201117:1330–1.

Lau SKP, Teng JLL, Huang Y, Curreem SOT, Tsui SKW, Woo PCY. Draft genome sequence of Catabacter hongkongensis type strain HKU16T, isolated from a patient with bacteremia and intestinal obstruction. Genome Announc. 20153. https://doi.org/10.1128/genomeA.00531-15.

Ndongo S, Khelaifia S, Fournier P-E, Raoult D. Christensenella massiliensis, a new bacterial species isolated from the human gut 2016. https://doi.org/10.1016/j.nmni.2016.04.014.

Ndongo S, Dubourg G, Khelaifia S, Fournier PE, Raoult D. Christensenella timonensis, a new bacterial species isolated from the human gut. New Microbes New Infect. 201613:32–3.

Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, et al. Human genetics shape the gut microbiome. Cell. 2014159. https://doi.org/10.1016/j.cell.2014.09.053.

Upadhyaya B, McCormack L, Fardin-Kia AR, Juenemann R, Nichenametla S, Clapper J, et al. Impact of dietary resistant starch type 4 on human gut microbiota and immunometabolic functions. Sci Rep. 20166:28797.

Hansen EE, Lozupone CA, Rey FE, Wu M, Guruge JL, Narra A, et al. Pan-genome of the dominant human gut-associated archaeon, Methanobrevibacter smithii, studied in twins. Proc Natl Acad Sci U S A. 2011108(Suppl 1):4599–606.

Bennett DC, Tun HM, Kim JE, Leung FC, Cheng KM. Characterization of cecal microbiota of the emu (Dromaius novaehollandiae). Vet Microbiol. 2013166:304–10.

Crisol-Martínez E, Stanley D, Geier MS, Hughes RJ, Moore RJ. Sorghum and wheat differentially affect caecal microbiota and associated performance characteristics of meat chickens. PeerJ. 20175:e3071.

Wilkinson N, Hughes RJ, Aspden WJ, Chapman J, Moore RJ, Stanley D. The gastrointestinal tract microbiota of the Japanese quail. Coturnix japonica. Appl Microbiol Biotechnol. 2016100:4201–9.

Videvall E, Song SJ, Bensch HM, Strandh M, Engelbrecht A, Serfontein N, et al. The development of gut microbiota in ostriches and its association with juvenile growth. bioRxiv. 2018:270017. https://doi.org/10.1101/270017.

Youngblut ND, Reischer GH, Walters W, Schuster N, Walzer C, Stalder G, et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat Commun. 201910:2200.

Zhang J, Shi H, Wang Y, Li S, Cao Z, Ji S, et al. Effect of dietary forage to concentrate ratios on dynamic profile changes and interactions of ruminal microbiota and metabolites in Holstein heifers. Front Microbiol. 20178:2206.

Wang X, Martin GB, Wen Q, Liu S, Zhang J, Yu Y, et al. Linseed oil and heated linseed grain supplements have different effects on rumen bacterial community structures and fatty acid profiles in cashmere kids. J Anim Sci. 2019. https://doi.org/10.1093/jas/skz079.

Kamke J, Kittelmann S, Soni P, Li Y, Tavendale M, Ganesh S, et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome. 20164:56.

He J, Yi L, Hai L, Ming L, Gao W, Ji R. Characterizing the bacterial microbiota in different gastrointestinal tract segments of the Bactrian camel. Sci Rep. 20188:654.

Samsudin AA, Evans PN, Wright A-DG, Al JR. Molecular diversity of the foregut bacteria community in the dromedary camel (Camelus dromedarius). Environ Microbiol. 201113:3024–35.

Li Z, Si H, Nan W, Wang X, Zhang T, Li G. Bacterial community and metabolome shifts in the cecum and colon of captive sika deer (Cervus nippon) from birth to post weaning. FEMS Microbiol Lett. 2019. https://doi.org/10.1093/femsle/fnz010.

Quan J, Cai G, Ye J, Yang M, Ding R, Wang X, et al. A global comparison of the microbiome compositions of three gut locations in commercial pigs with extreme feed conversion ratios. Sci Rep. 20188:4536.

Lu C, Zhou J, Li Y, Zhang D, Wang Z, Li Y, et al. Structural modulation of gut microbiota in Bama minipigs in response to treatment with a “growth-promoting agent”, salbutamol. Appl Microbiol Biotechnol. 2017. https://doi.org/10.1007/s00253-017-8329-y.

Gebreselassie EE, Jackson MI, Yerramilli M, Jewell DE. Anti-aging food that improves markers of health in senior dogs by modulating gut microbiota and metabolite profiles. bioRxiv. 2018:324327. https://doi.org/10.1101/324327.

Ramadan Z, Xu H, Laflamme D, Czarnecki-Maulden G, Li QJ, Labuda J, et al. Fecal microbiota of cats with naturally occurring chronic diarrhea assessed using 16S rRNA gene 454-pyrosequencing before and after dietary treatment. J Vet Intern Med. 201428:59–65.

Shiffman ME, Soo RM, Dennis PG, Morrison M, Tyson GW, Hugenholtz P. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for Eucalyptus digestion. PeerJ. 20175:e4075.

Wang C, Zhu Y, Li F, Huang L. The effect of Lactobacillus isolates on growth performance, immune response, intestinal bacterial community composition of growing Rex Rabbits. J Anim Physiol Anim Nutr. 2017. https://doi.org/10.1111/jpn.12629.

Hansen NCK, Avershina E, Mydland LT, Næsset JA, Austbø D, Moen B, et al. High nutrient availability reduces the diversity and stability of the equine caecal microbiota. Microb Ecol Health Dis. 201526:27216.

McKenzie VJ, Song SJ, Delsuc F, Prest TL, Oliverio AM, Korpita TM, et al. The effects of captivity on the mammalian gut microbiome. Integr Comp Biol. 2017. https://doi.org/10.1093/icb/icx090.

Zhang X, Yasuda K, Gilmore RA, Westmoreland SV, Platt DM, Miller GM, et al. Alcohol-induced changes in the gut microbiome and metabolome of rhesus macaques. Psychopharmacology. 2019. https://doi.org/10.1007/s00213-019-05217-z.

Yuan C, Graham M, Subramanian S. Microbiota-metabolites interactions in non-human primate gastrointestinal tract. bioRxiv. 2018:454496. https://doi.org/10.1101/454496.

Allan N, Knotts TA, Pesapane R, Ramsey JJ, Castle S, Clifford D, et al. Conservation implications of shifting gut microbiomes in captive-reared endangered voles intended for reintroduction into the wild. Microorganisms. 20186. https://doi.org/10.3390/microorganisms6030094.

Connor KL, Chehoud C, Altrichter A, Chan L, DeSantis TZ, Lye SJ. Maternal metabolic, immune, and microbial systems in late pregnancy vary with malnutrition in mice. Biol Reprod. 201898:579–92.

Tillmann S, Abildgaard A, Winther G, Wegener G. Altered fecal microbiota composition in the Flinders sensitive line rat model of depression. Psychopharmacology. 2018. https://doi.org/10.1007/s00213-018-5094-2.

Tsukinowa E, Karita S, Asano S, Wakai Y, Oka Y, Furuta M, et al. Fecal microbiota of a dugong (Dugong dugong) in captivity at Toba Aquarium. J Gen Appl Microbiol. 200854:25–38.

Suzuki A, Ueda K, Segawa T, Suzuki M. Fecal microbiota of captive Antillean manatee Trichechus manatus manatus. FEMS Microbiol Lett. 2019. https://doi.org/10.1093/femsle/fnz134.

Baldo L, Riera JL, Mitsi K, Pretus JL. Processes shaping gut microbiota diversity in allopatric populations of the endemic lizard Podarcis lilfordi from Menorcan islets (Balearic Islands). FEMS Microbiol Ecol. 201894. https://doi.org/10.1093/femsec/fix186.

Kohl KD, Brun A, Magallanes M, Brinkerhoff J, Laspiur A, Acosta JC, et al. Gut microbial ecology of lizards: insights into diversity in the wild, effects of captivity, variation across gut regions, and transmission. Mol Ecol. 2016. https://doi.org/10.1111/mec.13921.

Yuan ML, Dean SH, Longo AV, Rothermel BB, Tuberville TD, Zamudio KR. Kinship, inbreeding and fine-scale spatial structure influence gut microbiota in a hindgut-fermenting tortoise. Mol Ecol. 201524:2521–36.

Huang S, Zhang H. The impact of environmental heterogeneity and life stage on the hindgut microbiota of Holotrichia parallela larvae (Coleoptera: Scarabaeidae). PLoS One. 20138:e57169.

Ayayee PA, Keeney G, Sabree ZL, Muñoz-Garcia A. Compositional differences among female-associated and embryo-associated microbiota of the viviparous Pacific Beetle cockroach. Diploptera punctata. FEMS Microbiol Ecol. 201793. https://doi.org/10.1093/femsec/fix052.

Richards C, Otani S, Mikaelyan A, Poulsen M. Pycnoscelus surinamensis cockroach gut microbiota respond consistently to a fungal diet without mirroring those of fungus-farming termites. PLoS One. 201712:e0185745.

Zakrzewski M, Simms LA, Brown A, Appleyard M, Irwin J, Waddell N, et al. IL23R-protective coding variant promotes beneficial bacteria and diversity in the ileal microbiome in healthy individuals without inflammatory bowel disease. J Crohns Colitis. 2018. https://doi.org/10.1093/ecco-jcc/jjy188.

Huang YJ, Kim E, Cox MJ, Brodie EL, Brown R, Wiener-Kronish JP, et al. A persistent and diverse airway microbiota present during chronic obstructive pulmonary disease exacerbations. OMICS. 201014:9–59.

Burns MB, Montassier E, Abrahante J, Priya S, Niccum DE, Khoruts A, et al. Colorectal cancer mutational profiles correlate with defined microbial communities in the tumor microenvironment. PLoS Genet. 201814:e1007376.

Moreno-Indias I, Sánchez-Alcoholado L, García-Fuentes E, Cardona F, Queipo-Ortuņo MI, Tinahones FJ. Insulin resistance is associated with specific gut microbiota in appendix samples from morbidly obese patients. Am J Transl Res. 20168:5672–84.

Brooks AW, Priya S, Blekhman R, Bordenstein SR. Gut microbiota diversity across ethnicities in the United States. PLoS Biol. 201816:e2006842.

Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009457:480–4.

Turpin W, Espin-Garcia O, Xu W, Silverberg MS, Kevans D, Smith MI, et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat Genet. 201648:1413–7.

Obregon-Tito AJ, Tito RY, Metcalf J, Sankaranarayanan K, Clemente JC, Ursell LK, et al. Subsistence strategies in traditional societies distinguish gut microbiomes. Nat Commun. 20156:6505.

Escobar JS, Klotz B, Valdes BE, Agudelo GM. The gut microbiota of Colombians differs from that of Americans, Europeans and Asians. BMC Microbiol. 201414:311.

Org E, Blum Y, Kasela S, Mehrabian M, Kuusisto J, Kangas AJ, et al. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort. Genome Biol. 201718:70.

Lim MY, You HJ, Yoon HS, Kwon B, Lee JY, Lee S, et al. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut. 2016. https://doi.org/10.1136/gutjnl-2015-311326.

Oki K, Toyama M, Banno T, Chonan O, Benno Y, Watanabe K. Comprehensive analysis of the fecal microbiota of healthy Japanese adults reveals a new bacterial lineage associated with a phenotype characterized by a high frequency of bowel movements and a lean body type. BMC Microbiol. 201616:284.

Ayeni FA, Biagi E, Rampelli S, Fiori J, Soverini M, Audu HJ, et al. Infant and adult gut microbiome and metabolome in rural Bassa and urban settlers from Nigeria. Cell Rep. 201823:3056–67.

Gomez A, Petrzelkova KJ, Burns MB, Yeoman CJ, Amato KR, Vlckova K, et al. Gut microbiome of coexisting BaAka Pygmies and Bantu reflects gradients of traditional subsistence patterns. Cell Rep. 201614:2142–53.

Morton ER, Lynch J, Froment A, Lafosse S, Heyer E, Przeworski M, et al. Variation in rural African gut microbiota is strongly correlated with colonization by Entamoeba and Subsistence. PLoS Genet. 201511:e1005658.

Barrett HL, Gomez-Arango LF, Wilkinson SA, McIntyre HD, Callaway LK, Morrison M, et al. A vegetarian diet is a major determinant of gut microbiota composition in early pregnancy. Nutrients. 201810. https://doi.org/10.3390/nu10070890.

Deschasaux M, Bouter KE, Prodan A, Levin E, Groen AK, Herrema H, et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat Med. 2018. https://doi.org/10.1038/s41591-018-0160-1.

Chi L, Mahbub R, Gao B, Bian X, Tu P, Ru H, et al. Nicotine alters the gut microbiome and metabolites of gut-brain interactions in a sex-specific manner. Chem Res Toxicol. 201730:2110–9.

Davis DJ, Hecht PM, Jasarevic E, Beversdorf DQ, Will MJ, Fritsche K, et al. Sex-specific effects of docosahexaenoic acid (DHA) on the microbiome and behavior of socially-isolated mice. Brain Behav Immun. 2016. https://doi.org/10.1016/j.bbi.2016.09.003.

Kong F, Hua Y, Zeng B, Ning R, Li Y, Zhao J. Gut microbiota signatures of longevity. Curr Biol. 201626:R832–3.

Wang F, Yu T, Huang G, Cai D, Liang X, Su H, et al. Gut microbiota community and its assembly associated with age and diet in Chinese centenarians. J Microbiol Biotechnol. 201525:1195–204.

Biagi E, Franceschi C, Rampelli S, Severgnini M, Ostan R, Turroni S, et al. Gut microbiota and extreme longevity. Curr Biol. 2016. https://doi.org/10.1016/j.cub.2016.04.016.

Kim B-S, Choi CW, Shin H, Jin S-P, Bae J-S, Han M, et al. Comparison of the gut microbiota of centenarians in longevity villages of South Korea with those of other age groups. J Microbiol Biotechnol. 2019. https://doi.org/10.4014/jmb.1811.11023.

Anand R, Song Y, Garg S, Girotra M, Sinha A, Sivaraman A, et al. Effect of aging on the composition of fecal microbiota in donors for FMT and its impact on clinical outcomes. Dig Dis Sci. 2017. https://doi.org/10.1007/s10620-017-4449-6.

Estaki M, Pither J, Baumeister P, Little JP, Gill SK, Ghosh S, et al. Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions. Microbiome. 20164:42.

Jackson MA, Bonder MJ, Kuncheva Z, Zierer J, Fu J, Kurilshikov A, et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ. 20186:e4303.

Shin J-H, Park YH, Sim M, Kim S-A, Joung H, Shin D-M. Serum level of sex steroid hormone is associated with diversity and profiles of human gut microbiome. Res Microbiol. 2019. https://doi.org/10.1016/j.resmic.2019.03.003.

Goodrich JK, Davenport ER, Beaumont M, Jackson MA, Knight R, Ober C, et al. Genetic Determinants of the Gut Microbiome in UK Twins. Cell Host Microbe. 201619:731–43.

Beaumont M, Goodrich JK, Jackson MA, Yet I, Davenport ER, Vieira-Silva S, et al. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol. 201617:189.

Xie H, Guo R, Zhong H, Feng Q, Lan Z, Qin B, et al. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst. 2016. https://doi.org/10.1016/j.cels.2016.10.004.

Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012486:222–7.

Goodrich JK, Davenport ER, Waters JL, Clark AG, Ley RE. Cross-species comparisons of host genetic associations with the microbiome. Science. 2016352:532–5.

Wacklin P, Tuimala J, Nikkilä J, Tims S, Mäkivuokko H, Alakulppi N, et al. Faecal microbiota composition in adults is associated with the FUT2 gene determining the secretor status. PLoS One. 20149:e94863.

Davenport ER, Goodrich JK, Bell JT, Spector TD, Ley RE, Clark AG. ABO antigen and secretor statuses are not associated with gut microbiota composition in 1,500 twins. BMC Genomics. 201617:941.

Turpin W, Bedrani L, Espin-Garcia O, Xu W, Silverberg MS, Smith MI, et al. FUT2 genotype and secretory status are not associated with fecal microbial composition and inferred function in healthy subjects. Gut Microbes. 20189:357–68.

Le Gall G, Guttula K, Kellingray L, Tett AJ, Ten Hoopen R, Kemsley KE, et al. Metabolite quantification of faecal extracts from colorectal cancer patients and healthy controls. Oncotarget. 20189:33278–89.

Yazici C, Wolf PG, Kim H, Cross T-WL, Vermillion K, Carroll T, et al. Race-dependent association of sulfidogenic bacteria with colorectal cancer. Gut. 2017. https://doi.org/10.1136/gutjnl-2016-313321.

Peters BA, Shapiro JA, Church TR, Miller G, Trinh-Shevrin C, Yuen E, et al. A taxonomic signature of obesity in a large study of American adults. Sci Rep. 20188:9749.

López-Contreras BE, Morán-Ramos S, Villarruel-Vázquez R, Macías-Kauffer L, Villamil-Ramírez H, León-Mimila P, et al. Composition of gut microbiota in obese and normal-weight Mexican school-age children and its association with metabolic traits. Pediatr Obes. 201813:381–8.

Ferrer M, Ruiz A, Lanza F, Haange S-B, Oberbach A, Till H, et al. Microbiota from the distal guts of lean and obese adolescents exhibit partial functional redundancy besides clear differences in community structure. Environ Microbiol. 201315:211–26.

Fu J, Bonder MJ, Cenit MC, Tigchelaar EF, Maatman A, Dekens JAM, et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ Res. 2015117:817–24.

Kummen M, Holm K, Anmarkrud JA, Nygård S, Vesterhus M, Høivik ML, et al. The gut microbial profile in patients with primary sclerosing cholangitis is distinct from patients with ulcerative colitis without biliary disease and healthy controls. Gut. 2016. https://doi.org/10.1136/gutjnl-2015-310500.

Stanislawski MA, Dabelea D, Wagner BD, Sontag MK, Lozupone CA, Eggesbø M. Pre-pregnancy weight, gestational weight gain, and the gut microbiota of mothers and their infants. Microbiome. 20175:113.

Yun Y, Kim H-N, Kim SE, Heo SG, Chang Y, Ryu S, et al. Comparative analysis of gut microbiota associated with body mass index in a large Korean cohort. BMC Microbiol. 201717:151.

Alemán JO, Bokulich NA, Swann JR, Walker JM, De Rosa JC, Battaglia T, et al. Fecal microbiota and bile acid interactions with systemic and adipose tissue metabolism in diet-induced weight loss of obese postmenopausal women. J Transl Med. 201816:244.

Walters WA, Xu Z, Knight R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett. 2014588:4223–33.

Hibberd AA, Yde CC, Ziegler ML, Honoré AH, Saarinen MT, Lahtinen S, et al. Probiotic or synbiotic alters the gut microbiota and metabolism in a randomised controlled trial of weight management in overweight adults. Benef Microbes. 201910(2):121–35.

Guzman-Castaneda SJ, Ortega-Vega EL, de la Cuesta-Zuluaga J, Velasquez-Mejia EP, Rojas W, Bedoya G, et al. Gut microbiota composition explains more variance in the host cardiometabolic risk than genetic ancestry. bioRxiv. 2018:394726. https://doi.org/10.1101/394726.

He Y, Wu W, Wu S, Zheng H-M, Li P, Sheng H-F, et al. Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis. Microbiome. 20186:172.

Gomez-Arango LF, Barrett HL, McIntyre HD, Callaway LK, Morrison M, Dekker Nitert M, et al. Increased systolic and diastolic blood pressure is associated with altered gut microbiota composition and butyrate production in early pregnancy. Hypertension. 201668:974–81.

Yanai H, Tomono Y, Ito K, Furutani N, Yoshida H, Tada N. The underlying mechanisms for development of hypertension in the metabolic syndrome. Nutr J. 20087:10.

Lippert K, Kedenko L, Antonielli L, Kedenko I, Gemeier C, Leitner M, et al. Gut microbiota dysbiosis associated with glucose metabolism disorders and the metabolic syndrome in older adults. Benef Microbes. 20178(4):545–56.

Bowyer RCE, Jackson MA, Pallister T, Skinner J, Spector TD, Welch AA, et al. Use of dietary indices to control for diet in human gut microbiota studies. Microbiome. 20186:77.

Maskarinec G, Hullar MAJ, Monroe KR, Shepherd JA, Hunt J, Randolph TW, et al. Fecal microbial diversity and structure are associated with diet quality in the multiethnic cohort adiposity phenotype study. J Nutr. 2019. https://doi.org/10.1093/jn/nxz065.

Klimenko NS, Tyakht AV, Popenko AS, Vasiliev AS, Altukhov IA, Ischenko DS, et al. Microbiome responses to an uncontrolled short-term diet intervention in the frame of the citizen science project. Nutrients. 201810. https://doi.org/10.3390/nu10050576.

De Filippis F, Pellegrini N, Vannini L, Jeffery IB, La Storia A, Laghi L, et al. High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota and associated metabolome. Gut. 201665:1812–21.

Azcarate-Peril MA, Ritter AJ, Savaiano D, Monteagudo-Mera A, Anderson C, Magness ST, et al. Impact of short-chain galactooligosaccharides on the gut microbiome of lactose-intolerant individuals. Proc Natl Acad Sci U S A. 2017. https://doi.org/10.1073/pnas.1606722113.

David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014505:559–63.

Roager HM, Hansen LBS, Bahl MI, Frandsen HL, Carvalho V, Gøbel RJ, et al. Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut. Nat Microbiol. 20161:16093.

Beaumont M, Portune KJ, Steuer N, Lan A, Cerrudo V, Audebert M, et al. Quantity and source of dietary protein influence metabolite production by gut microbiota and rectal mucosa gene expression: a randomized, parallel, double-blind trial in overweight humans. Am J Clin Nutr. 2017106:1005–19.

Manor O, Zubair N, Conomos MP, Xu X, Rohwer JE, Krafft CE, et al. A multi-omic association study of trimethylamine N-oxide. Cell Rep. 201824:935–46.

Jiminez JA, Uwiera TC, Abbott DW, Uwiera RRE, Inglis GD. Impacts of resistant starch and wheat bran consumption on enteric inflammation in relation to colonic bacterial community structures and short-chain fatty acid concentrations in mice. Gut Pathog. 20168:67.

Zheng J, Cheng G, Li Q, Jiao S, Feng C, Zhao X, et al. Chitin oligosaccharide modulates gut microbiota and attenuates high-fat-diet-induced metabolic syndrome in mice. Mar Drugs. 201816. https://doi.org/10.3390/md16020066.

Ferrario C, Statello R, Carnevali L, Mancabelli L, Milani C, Mangifesta M, et al. How to feed the mammalian gut microbiota: bacterial and metabolic modulation by dietary fibers. Front Microbiol. 20178:1749.

Mancabelli L, Milani C, Lugli GA, Turroni F, Cocconi D, van Sinderen D, et al. Identification of universal gut microbial biomarkers of common human intestinal diseases by meta-analysis. FEMS Microbiol Ecol. 2017. https://doi.org/10.1093/femsec/fix153.

Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 201415:382–92.

Imhann F, Vich Vila A, Bonder MJ, Fu J, Gevers D, Visschedijk MC, et al. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut. 2016. https://doi.org/10.1136/gutjnl-2016-312135.

Palm NW, de Zoete MR, Cullen TW, Barry NA, Stefanowski J, Hao L, et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell. 2014158:1000–10.

Pascal V, Pozuelo M, Borruel N, Casellas F, Campos D, Santiago A, et al. A microbial signature for Crohn’s disease. Gut. 2017. https://doi.org/10.1136/gutjnl-2016-313235.

Lee T, Clavel T, Smirnov K, Schmidt A, Lagkouvardos I, Walker A, et al. Oral versus intravenous iron replacement therapy distinctly alters the gut microbiota and metabolome in patients with IBD. Gut. 201766:863–71.

Wright EK, Kamm MA, Wagner J, Teo S-M, Cruz PD, Hamilton AL, et al. Microbial factors associated with postoperative Crohn’s disease recurrence. J Crohns Colitis. 201711:191–203.

Kennedy NA, Lamb CA, Berry SH, Walker AW, Mansfield J, Parkes M, et al. The impact of NOD2 variants on fecal microbiota in Crohn’s disease and controls without gastrointestinal disease. Inflamm Bowel Dis. 201824:583–92.

Pérez-Brocal V, García-López R, Nos P, Beltrán B, Moret I, Moya A. Metagenomic analysis of Crohn’s disease patients identifies changes in the virome and microbiome related to disease status and therapy, and detects potential interactions and biomarkers. Inflamm Bowel Dis. 201521:2515–32.

Papa E, Docktor M, Smillie C, Weber S, Preheim SP, Gevers D, et al. Non-invasive mapping of the gastrointestinal microbiota identifies children with inflammatory bowel disease. PLoS One. 20127:e39242.

Rajilic-Stojanovic M, Shanahan F, Guarner F, de Vos WM. Phylogenetic analysis of dysbiosis in ulcerative colitis during remission. Inflamm Bowel Dis. 201319:481.

Jalanka-Tuovinen J, Salojärvi J, Salonen A, Immonen O, Garsed K, Kelly FM, et al. Faecal microbiota composition and host-microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome. Gut. 201463:1737–45.

De Palma G, Lynch MDJ, Lu J, Dang VT, Deng Y, Jury J, et al. Transplantation of fecal microbiota from patients with irritable bowel syndrome alters gut function and behavior in recipient mice. Sci Transl Med. 20179. https://doi.org/10.1126/scitranslmed.aaf6397.

Pozuelo M, Panda S, Santiago A, Mendez S, Accarino A, Santos J, et al. Reduction of butyrate- and methane-producing microorganisms in patients with irritable bowel syndrome. Sci Rep. 20155:12693.

Hollister EB, Cain KC, Shulman RJ, Jarrett ME, Burr RL, Ko C, et al. Relationships of microbiome markers with extraintestinal, psychological distress and gastrointestinal symptoms, and quality of life in women with irritable bowel syndrome. J Clin Gastroenterol. 2018. https://doi.org/10.1097/MCG.0000000000001107.

Tigchelaar EF, Bonder MJ, Jankipersadsing SA, Fu J, Wijmenga C, Zhernakova A. Gut microbiota composition associated with stool consistency. Gut. 201565. https://doi.org/10.1136/gutjnl-2015-310328.

Jalanka J, Major G, Murray K, Singh G, Nowak A, Kurtz C, et al. The effect of Psyllium husk on intestinal microbiota in constipated patients and healthy controls. Int J Mol Sci. 201920. https://doi.org/10.3390/ijms20020433.

Pedrosa Carrasco AJ, Timmermann L, Pedrosa DJ. Management of constipation in patients with Parkinson’s disease. NPJ Parkinsons Dis. 20184:6.

Wiesel PH, Norton C, Glickman S, Kamm MA. Pathophysiology and management of bowel dysfunction in multiple sclerosis. Eur J Gastroenterol Hepatol. 200113:441–8.

Barichella M, Severgnini M, Cilia R, Cassani E, Bolliri C, Caronni S, et al. Unraveling gut microbiota in Parkinson’s disease and atypical parkinsonism. Mov Disord. 2018. https://doi.org/10.1002/mds.27581.

Hill-Burns EM, Debelius JW, Morton JT, Wissemann WT, Lewis MR, Wallen ZD, et al. Parkinson’s disease and Parkinson's disease medications have distinct signatures of the gut microbiome. Mov Disord. 2017. https://doi.org/10.1002/mds.26942.

Petrov VA, Saltykova IV, Zhukova IA, Alifirova VM, Zhukova NG, Dorofeeva YB, et al. Analysis of gut microbiota in patients with Parkinson’s disease. Bull Exp Biol Med. 2017. https://doi.org/10.1007/s10517-017-3700-7.

Tremlett H, Fadrosh DW, Faruqi AA, Zhu F, Hart J, Roalstad S, et al. Gut microbiota in early pediatric multiple sclerosis: a case-control study. Eur J Neurol. 201623:1308–21.

Chang C-J, Lin T-L, Tsai Y-L, Wu T-R, Lai W-F, Lu C-C, et al. Next generation probiotics in disease amelioration. J Food Drug Anal. 2019. https://doi.org/10.1016/j.jfda.2018.12.011.