Microbiota(Redirected from Microbiome)
A microbiota is an "ecological community of commensal, symbiotic and pathogenic microorganisms" found in and on all multicellular organisms studied to date from plants to animals. A microbiota includes bacteria, archaea, protists, fungi and viruses. Microbiota have been found to be crucial for immunologic, hormonal and metabolic homeostasis of their host. The synonymous term microbiome describes either the collective genomes of the microorganisms that reside in an environmental niche or the microorganisms themselves.
- the ability to perform genomic and gene expression analyses of single cells and of entire microbial communities in the disciplines of metagenomics and metatranscriptomics
- databases accessible to researchers across multiple disciplines
- methods of mathematical analysis suitable for complex data sets
Types of host relationshipsEdit
Commensalism, a concept developed by Pierre-Joseph van Beneden (1809-1894), a Belgian professor at the University of Louvain during the nineteenth century  is central to the microbiome, where microbiota colonize a host in a non-harmful coexistence. The relationship with their host is called mutualistic when organisms perform tasks that are known to be useful for the host,:700 parasitic, when disadvantageous to the host. Other authors define a situation as mutualistic where both benefit, and commensal, where the unaffected host benefits the symbiont. A nutrient exchange may be bidirectional or unidirectional, may be context dependent and may occur in diverse ways. Microbiota that are expected to be present, and that under normal circumstances do not cause disease, are deemed normal flora or normal microbiota.
Acquisition and changeEdit
The initial acquisition of microbiota in animals from mammalians to marine sponges is at birth, and may even occur through the germ cell line. In plants, the colonizing process can be initiated below ground in the root zone, around the germinating seed, the spermosphere, or originate from the above ground parts, the phyllosphere and the flower zone or anthosphere. The stability of the rhizosphere microbiota over generations depends upon the plant type but even more on the soil composition, i.e. living and non living environment.
Microbiota by hostEdit
Consensus exists among evolutionary biologists that one should not separate an organism's genes from the context of its resident microbes.
Humans are colonized by many microorganisms; the traditional estimate was that humans live with ten times more non-human cells than human cells; more recent estimates have lowered this to 3:1 and even to about 1:1.
The Human Microbiome Project sequenced the genome of the human microbiota, focusing particularly on the microbiota that normally inhabit the skin, mouth, nose, digestive tract, and vagina. It reached a milestone in 2012 when it published initial results.
- Amphibians have microbiota on their skin. Some species are able to carry a fungus named Batrachochytrium dendrobatidis, which in others can cause a deadly infection Chytridiomycosis depending on their microbiome, resisting pathogen colonization or inhibiting their growth with antimicrobial skin peptides.
- In mammals, herbivores such as cattle depend on their rumen microbiome to convert cellulose into proteins, short chain fatty acids, and gases. Culture methods cannot provide information on all microorganisms present. Comparative metagenomic studies yielded the surprising result that individual cattle possess markedly different community structures, predicted phenotype, and metabolic potentials, even though they were fed identical diets, were housed together, and were apparently functionally identical in their utilization of plant cell wall resources.
- Mice have become the most studied mammalian regarding their microbiomes. The gut microbiota have been studied in relation to allergic airway disease, obesity, gastrointestinal diseases and diabetes. Perinatal shifting of microbiota through low dose antibiotics can have long-lasting effects on future susceptibility to allergic airway disease. The frequency of certain subsets of microbes has been linked to disease severity. The presence of specific microbes early in postnatal life, instruct future immune responses. In gnotobiotic mice certain gut bacteria were found to transmit a particular phenotype to recipient germ-free mice, that promoted accumulation of colonic regulatory T cells, and strains that modulated mouse adiposity and cecal metabolite concentrations. This combinatorial approach enables a systems-level understanding of microbial contributions to human biology. But also other mucoide tissues as lung and vagina have been studied in relation to diseases such as asthma, allergy and vaginosis.
- Insects have their own microbiomes. For example, leaf-cutter ants form huge underground colonies harvesting hundreds of kilograms of leaves each year and are unable to digest the cellulose in the leaves directly. They maintain fungus gardens as the colony's primary food source. While the fungus itself does not digest cellulose, a microbial community containing a diversity of bacteria is doing so. Analysis of the microbial population's genome revealed many genes with a role in cellulose digestion. This microbiome's predicted carbohydrate-degrading enzyme profile is similar to that of the bovine rumen, but the species composition is almost entirely different. Gut microbiota of the fruit fly can affect the way its gut looks, by impacting epithelial renewal rate, cellular spacing, and the composition of different cell types in the epithelium. When the moth Spodoptera exigua is infected with baculovirus immune-related genes are downregulated and the amount of its gut microbiota increases.
- Plants are attractive hosts for microorganisms since they provide a variety of nutrients. Microorganisms on plants can be epiphytes (found on the plants) or endophytes (found inside plant tissue). Oomycetes and fungi have, through convergent evolution, developed similar morphology and occupy similar ecological niches. They develop hyphae, threadlike structures that penetrate the host cell. In mutualistic situations the plant often exchanges hexose sugars for inorganic phosphate from the fungal symbiont. It is speculated that such very ancient associations have aided plants when they first colonized land. Plant-growth promoting bacteria (PGPB) provide the plant with essential services such as nitrogen fixation, solubilization of minerals such as phosphorus, synthesis of plant hormones, direct enhancement of mineral uptake, and protection from pathogens. PGPBs may protect plants from pathogens by competing with the pathogen for an ecological niche or a substrate, producing inhibitory allelochemicals, or inducing systemic resistance in host plants to the pathogen
Microbiota have been found to be crucial for immunologic, hormonal and metabolic homeostasis of their host.
The symbiotic relationship between a host and its microbiota shapes the immune system of mammalians, insects, plants and aquatic organisms. In many animals, the immune system and microbiota engage in "cross-talk", exchanging chemical signals. This allows the immune system to recognize the types of bacteria that are harmful to the host and combat them, while allowing the helpful bacteria to carry out their functions; in turn, the microbiota influence immune reactivity and targeting. Bacteria can be transferred from mother to child through direct contact and after birth, or through indirect contact through eggs, coprophagy, and several other pathways. As the infant microbiome is established, commensal bacteria quickly populate the gut, prompting a range of immune responses and "programming" the immune system with long-lasting effects. The bacteria are able to stimulate lymphoid tissue associated with the gut mucosa, which enables the tissue to produce antibodies for pathogens that may enter the gut.
The human microbiome plays a role in the activation of toll-like receptors (TLRs) in the intestines, a type of pattern recognition receptor host cells use to recognize dangers and repair damage. Pathogens can influence this coexistence leading to immune dysregulation including and susceptibility to diseases, immune tolerance and autoimmune diseases.
Intestinal microbiota can interact with thyroid-related micronutrients and the metabolism of endogenous iodothyronines such as Triiodothyronine and exogenous iodothyronines, which affects even phyla without thyroid follicles, such as jellyfish, insects, and sea urchins, as iodothyronine-induced metamorphosis is an ancestral feature of all chordates.
Microbiota can affect a fruit fly´s sex preference in mating. Infecting flies with pure cultures of Lactobacillus plantarum established a certain mating preference as L. plantarum can change the fly´s levels of cuticular hydrocarbon sex pheromones.
When adult germ-free mice are colonized with the gut flora of obese mice, they can gain weight dramatically with an increased metabolism of monosaccharides and short-chain fatty acids. The gut flora of obese mice has less Bacteroidetes than Firmicutes and is thought to be more efficient at extracting energy from food.
Co-evolution of microbiotaEdit
Organisms evolve within eco-systems so that the change of one organism affects the change of others. Co-evolution (also called "hologenome theory") proposes that an object of natural selection is not the individual organism, but the organism together with its associated organisms, including its microbial communities.
Coral reefs. The hologenome theory originated in studies on coral reefs. Coral reefs are the largest structures created by living organisms, and contain abundant and highly complex microbial communities. Over the past several decades, major declines in coral populations have occurred. Climate change, water pollution and over-fishing are three stress factors that have been described as leading to disease susceptibility. Over twenty different coral diseases have been described, but of these, only a handful have had their causative agents isolated and characterized. Coral bleaching is the most serious of these diseases. In the Mediterranean Sea, the bleaching of Oculina patagonica was first described in 1994 and shortly determined to be due to infection by Vibrio shiloi. From 1994 to 2002, bacterial bleaching of O. patagonica occurred every summer in the eastern Mediterranean. Surprisingly, however, after 2003, O. patagonica in the eastern Mediterranean has been resistant to V. shiloi infection, although other diseases still cause bleaching. The surprise stems from the knowledge that corals are long lived, with lifespans on the order of decades, and do not have adaptive immune systems. Their innate immune systems do not produce antibodies, and they should seemingly not be able to respond to new challenges except over evolutionary time scales.
The puzzle of how corals managed to acquire resistance to a specific pathogen led to a 2007 proposal, that a dynamic relationship exists between corals and their symbiotic microbial communities. It is thought that by altering its composition, the holobiont can adapt to changing environmental conditions far more rapidly than by genetic mutation and selection alone. Extrapolating this hypothesis to other organisms, including higher plants and animals, led to the proposal of the "hologenome theory of evolution".
As of 2007[update] the hologenome theory was still being debated. A major criticism has been the claim that V. shiloi was misidentified as the causative agent of coral bleaching, and that its presence in bleached O. patagonica was simply that of opportunistic colonization. If this is true, the basic observation leading to the theory would be invalid. The theory has gained significant popularity as a way of explaining rapid changes in adaptation that cannot otherwise be explained by traditional mechanisms of natural selection. Within the hologenome theory, the holobiont has not only become the principal unit of natural selection but also the result of other step of integration that it is also observed at the cell (symbiogenesis, endosymbiosis) and genomic levels.
Targeted amplicon sequencingEdit
Targeted amplicon sequencing relies on having some expectations about the composition of the community that is being studied. In target amplicon sequencing a phylogenetically informative marker is targeted for sequencing. Such a marker should be present in ideally all the expected organisms. It should also evolve in such a way that it is conserved enough that primers can target genes from a wide range of organisms while evolving quickly enough to allow for finer resolution at the taxonomic level. A common marker for human microbiome studies is the gene for bacterial 16S rRNA (i.e. "16S rDNA", the sequence of DNA which encodes the ribosomal RNA molecule). Since ribosomes are present in all living organisms, using 16S rDNA allows for DNA to be amplified from many more organisms than if another marker were used. The 16S rDNA gene contains both slowly evolving regions and fast evolving regions; the former can be used to design broad primers while the latter allow for finer taxonomic distinction. However, species-level resolution is not typically possible using the 16S rDNA. Primer selection is an important step, as anything that cannot be targeted by the primer will not be amplified and thus will not be detected. Different sets of primers have been shown to amplify different taxonomic groups due to sequence variation.
Targeted studies of eukaryotic and viral communities are limited and subject to the challenge of excluding host DNA from amplification and the reduced eukaryotic and viral biomass in the human microbiome.
After the amplicons are sequenced, molecular phylogenetic methods are used to infer the composition of the microbial community. This is done by clustering the amplicons into operational taxonomic units (OTUs) and inferring phylogenetic relationships between the sequences. Due to the complexity of the data, distance measures such as UniFrac distances are usually defined between microbiome samples, and downstream multivariate methods are carried out on the distance matrices. An important point is that the scale of data is extensive, and further approaches must be taken to identify patterns from the available information. Tools used to analyze the data include VAMPS, QIIME and mothur.
Metagenomics is also used extensively for studying microbial communities. In metagenomic sequencing, DNA is recovered directly from environmental samples in an untargeted manner with the goal of obtaining an unbiased sample from all genes of all members of the community. Recent studies use shotgun Sanger sequencing or pyrosequencing to recover the sequences of the reads. The reads can then be assembled into contigs. To determine the phylogenetic identity of a sequence, it is compared to available full genome sequences using methods such as BLAST. One drawback of this approach is that many members of microbial communities do not have a representative sequenced genome, but this applies to 16S rRNA amplicon sequencing as well and is a fundamental problem. With shotgun sequencing, it can be resolved by having a high coverage (50-100x) of the unknown genome, effectively doing a de novo genome assembly. As soon as there is a complete genome of an unknown organism available it can be compared phylogenetically and the organism put into its place in the tree of life, by creating new taxa.
Despite the fact that metagenomics is limited by the availability of reference sequences, one significant advantage of metagenomics over targeted amplicon sequencing is that metagenomics data can elucidate the functional potential of the community DNA. Targeted gene surveys cannot do this as they only reveal the phylogenetic relationship between the same gene from different organisms. Functional analysis is done by comparing the recovered sequences to databases of metagenomic annotations such as KEGG. The metabolic pathways that these genes are involved in can then be predicted with tools such as MG-RAST, CAMERA and IMG/M.
RNA and protein-based approachesEdit
Metatranscriptomics studies have been performed to study the gene expression of microbial communities through methods such as the pyrosequencing of extracted RNA. Structure based studies have also identified non-coding RNAs (ncRNAs) such as ribozymes from microbiota. Metaproteomics is an approach that studies the proteins expressed by microbiota, giving insight into its functional potential.
The Human Microbiome Project launched in 2008 was a United States National Institutes of Health initiative to identify and characterize microorganisms found in both healthy and diseased humans. The five-year project, best characterized as a feasibility study with a budget of $115 million tested how changes in the human microbiome are associated with human health or disease.
The Earth Microbiome Project (EMP) is an initiative to collect natural samples and analyze the microbial community around the globe. Microbes are highly abundant, diverse and have an important role in the ecological system. Yet as of 2010[update], it was estimated that the total global environmental DNA sequencing effort had produced less than 1 percent of the total DNA found in a liter of seawater or a gram of soil, and the specific interactions between microbes are largely unknown. The EMP aims to process as many as 200,000 samples in different biomes, generating a complete database of microbes on earth to characterize environments and ecosystems by microbial composition and interaction. Using these data, new ecological and evolutionary theories can be proposed and tested.
The Brazilian Microbiome Project aims to assemble a Brazilian Microbiome Consortium/Database. This is the first attempt to collect and collate information about Brazilian microbial genetic and functional diversity in a systematic and holistic manner. New sequence data have been generated from samples collected in all Brazilian regions.
Microbial DNA inhabiting a person's human body can uniquely identify the person. A person's privacy may be compromised if the person anonymously donated microbe DNA data. Their medical condition and identity could be revealed.
- Lederberg, J; McCray, AT (2001). "'Ome Sweet 'Omics—a genealogical treasury of words". Scientist. 15: 8.
- NIH HMP Working Group; Peterson, J; Garges, S; et al. (2009). "The NIH Human Microbiome Project". Genome Res. 19 (12): 2317–2323. doi:10.1101/gr.096651.109. PMC 2792171. PMID 19819907.
- Backhed, F.; Ley, R. E.; Sonnenburg, J. L.; Peterson, D. A.; Gordon, J. I. (2005). "Host-Bacterial Mutualism in the Human Intestine". Science. 307 (5717): 1915–1920. Bibcode:2005Sci...307.1915B. doi:10.1126/science.1104816. PMID 15790844.
- Turnbaugh, P. J.; Ley, R. E.; Hamady, M.; Fraser-Liggett, C. M.; Knight, R.; Gordon, J. I. (2007). "The Human Microbiome Project". Nature. 449 (7164): 804–810. Bibcode:2007Natur.449..804T. doi:10.1038/nature06244. PMC 3709439. PMID 17943116.
- Ley, R. E.; Peterson, D. A.; Gordon, J. I. (2006). "Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine". Cell. 124 (4): 837–848. doi:10.1016/j.cell.2006.02.017. PMID 16497592.
- Salvucci, E. (2016). "Microbiome, holobiont and the net of life". Critical Reviews in Microbiology. 42 (3): 485–494. doi:10.3109/1040841X.2014.962478. PMID 25430522.
- Guerrero, R.; Margulis, Lynn; Berlanga, M. (2013). "Symbiogenesis: The holobiont as a unit of evolution". International microbiology : the official journal of the Spanish Society for Microbiology. 16 (3): 133–43. doi:10.2436/20.1501.01.188. PMID 24568029.
- Mendes, R.; Raaijmakers, J.M. (2015). "Cross-kingdom similarities in microbiome functions". The ISME Journal. 9 (9): 1905–1907. doi:10.1038/ismej.2015.7. PMC 4542044. PMID 25647346.
- Bosch, T. C. G.; McFall-Ngai, M. J. (2011). "Metaorganisms as the new frontier". Zoology. 114 (4): 185–190. doi:10.1016/j.zool.2011.04.001. PMC 3992624. PMID 21737250.
- Poreau B., Biologie et complexité : histoire et modèles du commensalisme. PhD Dissertation, University of Lyon, France, 2014.
- Sherwood, Linda; Willey, Joanne; Woolverton, Christopher (2013). Prescott's Microbiology (9th ed.). New York: McGraw Hill. pp. 713–721. ISBN 9780073402406. OCLC 886600661.
- Quigley, E. M. (Sep 2013). "Gut bacteria in health and disease". Gastroenterol Hepatol (N Y). 9 (9): 560–9. PMC 3983973. PMID 24729765.
- Remy W, Taylor TN, Hass H, Kerp H (1994). "Four hundred-million-year-old vesicular arbuscular mycorrhizae". Proc. Natl. Acad. Sci. USA. 91 (25): 11841–3. Bibcode:1994PNAS...9111841R. doi:10.1073/pnas.91.25.11841. PMC 45331. PMID 11607500.
- Compant S, Duffy B, Nowak J, Clément C, Barka EA (2005). "Use of Plant Growth-Promoting Bacteria for Biocontrol of Plant Diseases: Principles, Mechanisms of Action, and Future Prospects". Appl Environ Microbiol. 71 (9): 4951–9. doi:10.1128/AEM.71.9.4951-4959.2005. PMC 1214602. PMID 16151072.
- Tkacz, Andrzej; Cheema, Jitender; Chandra, Govind; Grant, Alastair; Poole, Philip S. (Nov 2015). "Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition". ISME J. 9 (11): 2349–2359. doi:10.1038/ismej.2015.41. PMC 4611498. PMID 25909975.
- American Academy of Microbiology FAQ: Human Microbiome January 2014
- Judah L. Rosner for Microbe Magazine, Feb 2014. Ten Times More Microbial Cells than Body Cells in Humans?
- Alison Abbott for Nature News. Jan 8 2016 Scientists bust myth that our bodies have more bacteria than human cells
- Sender, R; Fuchs, S; Milo, R (Jan 2016). "Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans". Cell. 164 (3): 337–40. doi:10.1016/j.cell.2016.01.013. PMID 26824647.
- "NIH Human Microbiome Project defines normal bacterial makeup of the body". NIH News. 13 June 2012.
- Bataille, A; Lee-Cruz, L; Tripathi, B; Kim, H; Waldman, B (Jan 2016). "Microbiome Variation Across Amphibian Skin Regions: Implications for Chytridiomycosis Mitigation Efforts". Microb Ecol. 71 (1): 221–32. doi:10.1007/s00248-015-0653-0. PMID 26271741.
- Woodhams DC, Rollins-Smith LA, Alford RA, Simon MA, Harris RN (2007). "Innate immune defenses of amphibian skin: antimicrobial peptides and more". Animal Conservation. 10 (4): 425–8. doi:10.1111/j.1469-1795.2007.00150.x.
- Brulc JM; Antonopoulos DA; Miller MEB; et al. (2009). "Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases". Proc. Natl. Acad. Sci. USA. 106 (6): 1948–53. Bibcode:2009PNAS..106.1948B. doi:10.1073/pnas.0806191105. PMC 2633212. PMID 19181843.
- Russell SL, Gold MJ; et al. (May 2012). "Early life antibiotic-driven changes in microbiota enhance susceptibility to allergic asthma". EMBO Rep. 13 (5): 440–7. doi:10.1038/embor.2012.32. PMC 3343350. PMID 22422004.
- Russell SL, Gold MJ, et al. (Aug 2014). "Perinatal antibiotic-induced shifts in gut microbiota have differential effects on inflammatory lung diseases". J Allergy Clin Immunol. 135 (1): 100–9. doi:10.1016/j.jaci.2014.06.027. PMID 25145536.
- Turnbaugh PJ, et al. (Dec 2006). "An obesity-associated gut microbiome with increased capacity for energy harvest" (PDF). Nature. 444 (7122): 1027–31. Bibcode:2006Natur.444.1027T. doi:10.1038/nature05414. PMID 17183312.
- Faith JJ, Ahern PP, Ridaura VK, et al. (Jan 2014). "Identifying gut microbe-host phenotype relationships using combinatorial communities in gnotobiotic mice". Sci. Transl. Med. 6 (220): 220. doi:10.1126/scitranslmed.3008051. PMC 3973144. PMID 24452263.
- Barfod, KK; Roggenbuck, M; Hansen, LH; Schjørring, S; Larsen, ST; Sørensen, SJ; Krogfelt, KA (2013). "The murine lung microbiome in relation to the intestinal and vaginal bacterial communities". BMC Microbiol. 13: 303. doi:10.1186/1471-2180-13-303.
- Suen; Scott JJ; Aylward FO; et al. (2010). Sonnenburg, Justin, ed. "An Insect Herbivore Microbiome with High Plant Biomass-Degrading Capacity". PLoS Genet. 6 (9): e1001129. doi:10.1371/journal.pgen.1001129. PMC 2944797. PMID 20885794.
- Broderick, Nichole A.; Buchon, Nicolas; Lemaitre, Bruno (2014). "Microbiota-Induced Changes in Drosophila melanogaster Host Gene Expression and Gut Morphology". mBio. 5 (3): e01117–14. doi:10.1128/mBio.01117-14. PMC 4045073. PMID 24865556.
- Jakubowska, Agata K.; Vogel, Heiko; Herrero, Salvador (May 2013). "Increase in Gut Microbiota after Immune Suppression in Baculovirus-infected Larvae". PLoS Pathog. 9 (5): e1003379. doi:10.1371/journal.ppat.1003379. PMC 3662647. PMID 23717206.
- Berlec, Aleš (2012-09-01). "Novel techniques and findings in the study of plant microbiota: Search for plant probiotics". Plant Science. 193–194: 96–102. doi:10.1016/j.plantsci.2012.05.010. PMID 22794922.
- Whipps, J.m.; Hand, P.; Pink, D.; Bending, G.d. (2008-12-01). "Phyllosphere microbiology with special reference to diversity and plant genotype". Journal of Applied Microbiology. 105 (6): 1744–1755. doi:10.1111/j.1365-2672.2008.03906.x. ISSN 1365-2672. PMID 19120625.
- Chibucos MC, Tyler BM (2009). "Common themes in nutrient acquisition by plant symbiotic microbes, described by the Gene Ontology". BMC Microbiology. 9(Suppl 1): S6. doi:10.1186/1471-2180-9-S1-S6.
- Kloepper, J. W (1993). "Plant growth-promoting rhizobacteria as biological control agents". In Metting, F. B. Jr. Soil microbial ecology: applications in agricultural and environmental management. New York: Marcel Dekker Inc. pp. 255–274. ISBN 0-8247-8737-4.
- Bloemberg, G. V.; Lugtenberg, B. J. J. (2001). "Molecular basis of plant growth promotion and biocontrol by rhizobacteria". Current Opinion in Plant Biology. 4 (4): 343–350. doi:10.1016/S1369-5266(00)00183-7. PMID 11418345.
- Round, June L.; O'Connell, Ryan M.; Mazmanian, Sarkis K. (2010). "Coordination of tolerogenic immune responses by the commensal microbiota". Journal of Autoimmunity. 34 (3): J220–J225. doi:10.1016/j.jaut.2009.11.007. PMC 3155383. PMID 19963349.
- Baird AH, Bhagooli R, Ralph PJ, Takahashi S (2009). "Coral bleaching: the role of the host" (PDF). Trends in Ecology and Evolution. 24 (1): 16–20. doi:10.1016/j.tree.2008.09.005. PMID 19022522.
- Cahenzli, Julia; Balmer, Maria L.; McCoy, Kathy D. (2012). "Microbial-immune cross-talk and regulation of the immune system". Immunology. 138 (1): 12–22. doi:10.1111/j.1365-2567.2012.03624.x. PMC 3533697. PMID 22804726.
- Rosenberg, Eugene; Zilber-Rosenberg, Ilana (2016). "Microbes Drive Evolution of Animals and Plants: the Hologenome Concept" (PDF). MBio. 7 (2): e01395–15. doi:10.1128/mbio.01395-15. PMC 4817260. PMID 27034283.
- Nikoopour, E; Singh, B (2014). "Reciprocity in microbiome and immune system interactions and its implications in disease and health". Inflamm Allergy Drug Targets. 13 (2): 94–104. doi:10.2174/1871528113666140330201056. PMID 24678760.
- Virili C, Centanni M. "With a little help from my friends" - The role of microbiota in thyroid hormone metabolism and enterohepatic recycling. Mol Cell Endocrinol. 2017 Feb 4. doi:10.1016/j.mce.2017.01.053. PMID 28167127
- Iodine Metabolism and Thyroid-Related Functions in Organisms Lacking Thyroid Follicles: Are Thyroid Hormones also Vitamins? - November 08, 2016
- Denver, Robert J. "Chordate Metamorphosis: Ancient Control by Iodothyronines" (PDF). Current Biology. 18 (13): R567–9. doi:10.1016/j.cub.2008.05.024.
- Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I (2007). "The role of microorganisms in coral health, disease and evolution". Nature Reviews Microbiology. 5 (5): 355–362. doi:10.1038/nrmicro1635. PMID 17384666.
- Leggat W, Ainsworth T, Bythell J, Dove S, Gates R, Hoegh-Guldberg O, Iglesias-Prieto R, Yellowlees D (2007). "The hologenome theory disregards the coral holobiont". Nature Reviews Microbiology. 5 (10): Online Correspondence. doi:10.1038/nrmicro1635-c1.
- Ainsworth TD, Fine M, Roff G, Hoegh-Guldberg O (2008). "Bacteria are not the primary cause of bleaching in the Mediterranean coral Oculina patagonica". The ISME Journal. 2 (1): 67–73. doi:10.1038/ismej.2007.88. PMID 18059488.
- Kuczynski, J.; Lauber, C. L.; Walters, W. A.; Parfrey, L. W.; Clemente, J. C.; Gevers, D.; Knight, R. (2011). "Experimental and analytical tools for studying the human microbiome". Nature Reviews Genetics. 13 (1): 47–58. doi:10.1038/nrg3129. PMC 5119550. PMID 22179717.
- Marchesi, J. R. (2010). "Prokaryotic and Eukaryotic Diversity of the Human Gut". Advances in Applied Microbiology Volume 72. Advances in Applied Microbiology. 72. pp. 43–62. doi:10.1016/S0065-2164(10)72002-5. ISBN 9780123809896. PMID 20602987.
- Vestheim, H.; Jarman, S. N. (2008). "Blocking primers to enhance PCR amplification of rare sequences in mixed samples – a case study on prey DNA in Antarctic krill stomachs". Frontiers in Zoology. 5: 12. doi:10.1186/1742-9994-5-12. PMC 2517594. PMID 18638418.
- "VAMPS: The Visualization and Analysis of Microbial Population Structures". Bay Paul Center, MBL, Woods Hole. Retrieved 11 March 2012.
- Caporaso, J. G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F. D.; Costello, E. K.; Fierer, N.; Peña, A. G.; Goodrich, J. K.; Gordon, J. I.; Huttley, G. A.; Kelley, S. T.; Knights, D.; Koenig, J. E.; Ley, R. E.; Lozupone, C. A.; McDonald, D.; Muegge, B. D.; Pirrung, M.; Reeder, J.; Sevinsky, J. R.; Turnbaugh, P. J.; Walters, W. A.; Widmann, J.; Yatsunenko, T.; Zaneveld, J.; Knight, R. (2010). "QIIME allows analysis of high-throughput community sequencing data". Nature Methods. 7 (5): 335–336. doi:10.1038/nmeth.f.303. PMC 3156573. PMID 20383131.
- Schloss, P. D.; Westcott, S. L.; Ryabin, T.; Hall, J. R.; Hartmann, M.; Hollister, E. B.; Lesniewski, R. A.; Oakley, B. B.; Parks, D. H.; Robinson, C. J.; Sahl, J. W.; Stres, B.; Thallinger, G. G.; Van Horn, D. J.; Weber, C. F. (2009). "Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities". Applied and Environmental Microbiology. 75 (23): 7537–7541. doi:10.1128/AEM.01541-09. PMC 2786419. PMID 19801464.
- Turnbaugh, P. J.; Hamady, M.; Yatsunenko, T.; Cantarel, B. L.; Duncan, A.; Ley, R. E.; Sogin, M. L.; Jones, W. J.; Roe, B. A.; Affourtit, J. P.; Egholm, M.; Henrissat, B.; Heath, A. C.; Knight, R.; Gordon, J. I. (2008). "A core gut microbiome in obese and lean twins". Nature. 457 (7228): 480–484. Bibcode:2009Natur.457..480T. doi:10.1038/nature07540. PMC 2677729. PMID 19043404.
- Qin, J.; Li, R.; Raes, J.; Arumugam, M.; Burgdorf, K. S.; Manichanh, C.; Nielsen, T.; Pons, N.; Levenez, F.; Yamada, T.; Mende, D. R.; Li, J.; Xu, J.; Li, S.; Li, D.; Cao, J.; Wang, B.; Liang, H.; Zheng, H.; Xie, Y.; Tap, J.; Lepage, P.; Bertalan, M.; Batto, J. M.; Hansen, T.; Le Paslier, D.; Linneberg, A.; Nielsen, H. B. R.; Pelletier, E.; Renault, P. (2010). "A human gut microbial gene catalogue established by metagenomic sequencing". Nature. 464 (7285): 59–65. Bibcode:2010Natur.464...59.. doi:10.1038/nature08821. PMC 3779803. PMID 20203603.
- Tringe, S. G.; Von Mering, C.; Kobayashi, A.; Salamov, A. A.; Chen, K.; Chang, H. W.; Podar, M.; Short, J. M.; Mathur, E. J.; Detter, J. C.; Bork, P.; Hugenholtz, P.; Rubin, E. M. (2005). "Comparative Metagenomics of Microbial Communities". Science. 308 (5721): 554–557. Bibcode:2005Sci...308..554T. doi:10.1126/science.1107851. PMID 15845853.
- Wooley, J. C.; Godzik, A.; Friedberg, I. (2010). Bourne, Philip E., ed. "A Primer on Metagenomics". PLoS Computational Biology. 6 (2): e1000667. Bibcode:2010PLSCB...6E0667W. doi:10.1371/journal.pcbi.1000667. PMC 2829047. PMID 20195499.
- Muller, J.; Szklarczyk, D.; Julien, P.; Letunic, I.; Roth, A.; Kuhn, M.; Powell, S.; Von Mering, C.; Doerks, T.; Jensen, L. J.; Bork, P. (2009). "EggNOG v2.0: Extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations". Nucleic Acids Research. 38 (Database issue): D190–D195. doi:10.1093/nar/gkp951. PMC 2808932. PMID 19900971.
- Kanehisa, M.; Goto, S.; Furumichi, M.; Tanabe, M.; Hirakawa, M. (2009). "KEGG for representation and analysis of molecular networks involving diseases and drugs". Nucleic Acids Research. 38 (Database issue): D355–D360. doi:10.1093/nar/gkp896. PMC 2808910. PMID 19880382.
- Meyer, F.; Paarmann, D.; d'Souza, M.; Olson, R.; Glass, E. M.; Kubal, M.; Paczian, T.; Rodriguez, A.; Stevens, R.; Wilke, A.; Wilkening, J.; Edwards, R. A. (2008). "The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes". BMC Bioinformatics. 9: 386. doi:10.1186/1471-2105-9-386. PMC 2563014. PMID 18803844.
- Sun, S.; Chen, J.; Li, W.; Altintas, I.; Lin, A.; Peltier, S.; Stocks, K.; Allen, E. E.; Ellisman, M.; Grethe, J.; Wooley, J. (2010). "Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: The CAMERA resource". Nucleic Acids Research. 39 (Database issue): D546–D551. doi:10.1093/nar/gkq1102. PMC 3013694. PMID 21045053.
- Markowitz, V. M.; Ivanova, N. N.; Szeto, E.; Palaniappan, K.; Chu, K.; Dalevi, D.; Chen, I. M. A.; Grechkin, Y.; Dubchak, I.; Anderson, I.; Lykidis, A.; Mavromatis, K.; Hugenholtz, P.; Kyrpides, N. C. (2007). "IMG/M: A data management and analysis system for metagenomes". Nucleic Acids Research. 36 (Database issue): D534–D538. doi:10.1093/nar/gkm869. PMC 2238950. PMID 17932063.
- Shi, Y.; Tyson, G. W.; Delong, E. F. (2009). "Metatranscriptomics reveals unique microbial small RNAs in the ocean's water column". Nature. 459 (7244): 266–269. Bibcode:2009Natur.459..266S. doi:10.1038/nature08055. PMID 19444216.
- Jimenez, R. M.; Delwart, E.; Luptak, A (2011). "Structure-based Search Reveals Hammerhead Ribozymes in the Human Microbiome". Journal of Biological Chemistry. 286 (10): 7737–7743. doi:10.1074/jbc.C110.209288. PMC 3048661. PMID 21257745.
- Maron, PA; Ranjard, L.; Mougel, C.; Lemanceau, P. (2007). "Metaproteomics: A New Approach for Studying Functional Microbial Ecology". Microbial Ecology. 53 (3): 486–493. doi:10.1007/s00248-006-9196-8. PMID 17431707.
- "NIH Human Microbiome Project". US National Institutes of Health, Department of Health and Human Services, US Government. 2016. Retrieved 14 June 2016.
- Gilbert, J. A.; Meyer, F.; Antonopoulos, D.; et al. (2010). "Meeting Report: The Terabase Metagenomics Workshop and the Vision of an Earth Microbiome Project". Standards in Genomic Sciences. 3 (3): 243–248. doi:10.4056/sigs.1433550. PMC 3035311. PMID 21304727.
- Gilbert, J. A.; O'Dor, R.; King, N.; Vogel, T. M. (2011). "The importance of metagenomic surveys to microbial ecology: Or why Darwin would have been a metagenomic scientist". Microbial Informatics and Experimentation. 1 (1): 5. doi:10.1186/2042-5783-1-5. PMC 3348666. PMID 22587826.
- Brazilian Microbiome Project
- magazine, Ewen. "Microbial DNA in Human Body Can Be Used to Identify Individuals". Retrieved 2015-05-17.
- "Microbiomes raise privacy concerns". Retrieved 2015-05-17.
- Yong, Ed. "Can The Microbes You Leave Behind Be Used to Identify You?". National Geographic. Retrieved 2015-05-17.
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