TO with health. . This study established that these

TOSTUDY THE  ,STRUCTURE,FUNCTION OF ANHEALTHY HUMAN MICROBIAME INTRODUCTION:-The diversity ofmicrobes within given body habitat can be defined as the number and abundancedistribution of distinct types of organisms, for example, and high diversity inthe vagina to bacterial vaginosis locations in the United States, we havedefined the microbial communities at each body habitat, encountering 81–99% ofpredicted genera and saturating the range of overall community configurations .The uniqueness of each individual’s microbial community thus seems to be stableover time, which may be another feature of the human microbiome specificallyassociated with health.

. This study established that these patterns of alphadiversity differed markedly from comparisons between samples from the samehabitat among subjects . For example, the saliva had among the highest medianalpha diversities of operational taxonomic units (OTUs, roughly species levelclassification, but one of the lowest beta diversities so although eachindividual’s saliva was ecologically rich, members of the population sharedsimilar organisms. Conversely, the antecubital fossae (skin) were intermediatein alpha diversity.

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The vagina had the lowest alpha diversity, with quite lowbeta diversity at the genus level but very high among OTUs due to the presenceof distinct Lactobacillus spp. The primary patterns of variation in communitystructure followed the major body habitat groups (oral, skin, gut and vaginal),defining as a result the complete range of population-wide between-subjectvariation in human microbiome habitats . Oral and stool communities wereespecially diverse in terms of community membership, which has been linked toseveral human diseases: low diversity in the gut inflammatory bowel diseasesexpanding prior observations , and vaginal sites harboured particularly simplecommunities Within-subject variation over time was consistently lower thanbetween-subject variation, both in organismal composition and in metabolicfunction .

 CARRIAGEOF SPECIFIC MICROBES Inter-individual variation in the microbiomeproved to be specific, functionally relevant and personalized. of the oralcavity. The genus dominates the oropharynx16, with different species abundantwithin each sampled body habitat even at the species level, marked differencesin carriage within each habitat among individuals . As the ratio of pan- tocore-genomes is high in many human-associated microbes17, this variation inabundance could be due to selective pressures acting on pathways differentiallypresent among Streptococcus species or strains. Indeed, we observed extensivestrain-level genomic variation within microbial species in this population,enriched for host-specific structural variants around genomic islands. Evenwith respect to the single, gene losses associated with these events werecommon, for example differentially eliminating S. mitis carriage of the V-typeATPase or choline binding proteins cbp6 and cbp12 among subsets of the hostpopulation.

For this large study involving microbiome samples collected fromhealthy volunteers at two distinct geographic, These losses were easilyobservable by comparison to reference isolate genomes, and these initialfindings indicate that microbial strain- and host-specific gene gains andpolymorphisms may be similarly ubiquitousTheHuman Gut Microbiome:  Toolkit behind theScienceThe widespreadapplication of 16S rRNA gene sequencing for detection of bacterial pathogensand microbial ecology has provided a robust technical platform for theevaluation of the bacterial composition of the human microbiome to the humanfecal microbiome of 242 healthy adults. In the Human Microbiome Project, 18different body sites were sampled and sequenced. Stool specimens were thesingle specimen type used to study the intestinal microbiome.

Previouslypublished studies demonstrated the variation in composition of the gutmicrobiome among locations . Sequencing of 2 primary targets within bacterial16S rRNA genes yielded valuable compositional data pertaining within thegastrointestinal tract in different mammalian species. For example, 16S rRNAgene sequencing has been deployed to study the maturation of murine cecalmicrobiota, and these studies demonstrated the existence of a large number ofyet-unidentified bacteria that inhabit the mammalian intestine.. Thus, it isessential to develop robust experimental models of the human microbiome todelineate important mechanistic processes in the development of human diseasestates. Gut Microbiome: Composition to Function andMetabolismThe gut microbialcommunity includes approximately bacteria that normally reside in thegastrointestinal tract, reaching a microbial cell number that greatly exceedsthe number of human cells of the body.

The collective genome of thesemicroorganisms contains millions of genes compared in the human genome. Thismicrobial “factory” contributes to a broad range of biochemical and metabolicfunctions that the human body could not otherwise perform. Althoughdiet-induced changes in gut microbiota occur within a short time frame (1–3–4days after a diet switch), the changes are readily reversible.

In animalmodels, the ratio of the most prominent intestinal bacterial phyla,and isaltered in response to dietary changes. Disruption of the energy equilibriumleads to weight gain. Mouse model studies have demonstrated the relationshipbetween energy equilibrium, diet, and the composition of the gut microbiome.Transplantation of the gut microbiota from obese donors resulted in increasedadiposity in recipients compared to a similar transfer from lean donors. This extensive samplingof the human microbiome across many subjects and body habitats provides aninitial characterization of the normal microbiota of healthy adults in aWestern population.

The large sample size and consistent sampling of many sitesfrom the same individuals allows for the first time an understanding of therelationships among microbes, and between the microbiome and clinicalparameters, that underpin the basis for individual variation—variation that mayultimately be critical for understanding microbiome-based disorders. Clinicalstudies of the microbiome will be able to leverage the resulting extensivecatalogues of taxa, pathways and genes1 , although they must also still includecarefully matched internal controls. The uniqueness of each individual’smicrobiome even in this reference population argues for future studies toconsider prospective within-subjects designs where possible.

The HMP’s uniquecombination of organismal and functional data across body habitats,encompassing both 16S and metagenomic profiling, together with detailedcharacterization of each subject, has allowed us and subsequent studies to movebeyond the observation of variability in the human microbiome to ask how andwhy these microbial communities vary so extensively.How large a role does hostimmunity or genetics play in shaping patterns of diversity, and how do thepatterns observed in this North American population compare to those around theworld? Future studies building on the gene and organism catalogues establishedby the Human Microbiome Project, including increasingly detailed investigationsof metatranscriptomes and metaproteomes, will help to unravel these openquestions and allow us to more fully understand the links between the humanmicrobiome, health and disease. The Gut Microbiome and Body Metabolism: Obesity and InflammationThe incidence of overweight and obesity has reached epidemicproportions. Data reported by the CDC and the National Health and NutritionExamination Survey indicated that, in 2008, an estimated 1.5 billion adultswere overweight, and more than 200 million men and almost 300 million womenwere obese by these criteria. Worldwide obesity has more than doubled in thelast 2 decades. Obesity is associated with a cluster of metabolic and systemicdisorders such as insulin resistance, type 2 diabetes, fatty liver disease,atherosclerosis, and hypertension. The major cause of obesity is a positiveenergetic balance resulting from an increased energy intake from the diet and adecreased energy output associated with low physical activity.

In addition toalterations in diet and physical activity resulting in obesity, geneticdifferences contribute to obesity and cause differences in energy storage andexpenditure. Furthermore, growing evidence suggests that the gut microbiotarepresents an important factor contributing to the host response to nutrients.A landmark study by Turnbaugh et al.

was one of the first studies to show howthe gene content in the gut microbiota contributes to obesity. The microbiomesobtained from the distal gut of genetically obese leptin-deficient mice (ob/ob)and their lean littermates (ob/+ and +/+) were compared. In this study,investigators reported that the microbiota in the ob/ob mice contained genesencoding enzymes that hydrolyze indigestible dietary polysaccharides. Increasedamounts of fermentation end products (such as acetate and butyrate) anddecreased calories were found in the feces of obese mice. These data suggestthat the gut microbiota in this mouse model promoted the extraction ofadditional calories from the diet.Fig: human microbian and its importanceA Comprehensive Human-Associated Microbial CensusSequencing ofthe 16S rRNA gene is an effective method for interrogating the taxonomiccomposition of microbial communities. This gene is ubiquitous within theprokaryotic domain and can be effectively PCR-amplified from even previously unknownorganisms.

The analysis of microbial communities through the sequencing of 16SrRNA gene was common long before the influx of high throughput sequencing (HTS)data , making this gene one of the most highly represented within GenBank. HTS approachesto 16S rRNA sequence analysis typically include targeted Illumina or 454 readsof up to a few hundred nucleotides, each targeting uniquely identifiable variableregions of the gene that can be used as unique microbial identifiers .The HMPplanned to comprehensively characterize the taxonomic composition of themicrobiome by averaging 5,000 454 FLX 16S rRNA gene sequences from all 300subjects, 18 body sites, and multiple time points. This design, combined withmore than a 1,000-fold increase in sequencing throughput over the course of theHMP, forced the consortium to develop novel tools for processing large 16S rRNAgene datasets, tackling issues specific to 454 sequence data quality, and addressingnovel biological questions that were previously inaccessible due to limited samplesizes. An interesting question addressed by thesedata is the presence or absence of stable community configurations in differenthuman body sites, such as enterotypes in the gut. Identifying groups of highlysimilar microbial communities among many samples is a difficult unsupervised machinelearning problem, akin to that of clustering or discovering molecular subtypesin cancer gene expression data.

Work to better understand the topic is ongoing,and the HMP’s survey of many body sites offered the chance to contrastcommunity organization within distinct ecologies. The vaginal microbiome, forexample, has been observed to occupy one of five main states characterized bydiffering Lactobacillus spp. abundances. This proved to be the case in the HMPas well, in contrast to a more complex continuum of community configurationsoccupied by the gut microbiota, particularly when meta-analyzed with the MetaHITcohort. As the presence of community types in distinct ecosystems may beinfluenced by environmental factors that can themselves vary continuously, suchas diet, care must be taken in future computational efforts to reproduciblyidentify microbial community types within habitats where they do occur.  Putting the Pieces Together:Metagenomic SequenceAssembly The taxonomiccomposition of the human microbiome is thus one step in understanding the rolemicrobes play in our health, and it is well complemented by sequencing ofmicrobial communities’ entire genomic contents to catalog their biologicalfunctions. Thus, the HMP carried out extensive deep sequencing on a subset ofits subjects and body sites using the Illumina platform.

While portions of the HMP’s16S rRNA gene analysis were based on extensions of established experimental andcomputational approaches, this approach to whole-metagenome sequencing was aforay into new territory.The sequencingtechnology itself was (and still is) rapidly evolving, and metagenomic datasetsof comparable size, read length, and ecological diversity did not previously exist.In the relatively short period between an initial pilot which were alreadydifficult to interpret in microbial communities containing hundreds orthousands of taxa. It thus necessitated development of a scalable end-to-endshotgun pre-processing and quality control pipeline, including duplicate readremoval, quality and length trimming, host sequence removal, and whole-samplequality control. In the end, the HMP generated over 8 Tbp of raw sequence data,representing two lanes of paired-end Illumina sequencing for each of over 700samples (targeting 10 Gbp/ sample) as well as a small collection of samples,which were also sequenced with the Roche/454 instrument to investigate theimpact of longer reads on metagenome assembly. The design of thiswhole-metagenome sequencing experiment warrants a brief discussion. As the HMPwas started, little information was available about the genomic diversity ofthe communities being assayed.

The use of Illumina sequencing in metagenomicsprojects was still being debated, the main argument against this technologybeing the very short length of the reads being generated (just 100 bp comparedto close to 400 bp achievable by Roche/454 and over 1,000 bp routinely achievedthrough Sanger sequencing). As detailed below, the feasibility of assembling theresulting data into large enough chunks to enable meaningful analyses was by nomeans obvious. At the same time, analyzing the reads themselves, rather thanassembled contigs, was considered insufficiently accurate, although both assemblyand read-based analyses ultimately proved successful. The choice of depth ofsequencing, ”just” two lanes of the instrument, was chosen to be sufficient togenerate roughly 1-fold coverage of the Escherichia coli genome within gutmicrobiome samples estimated to occur in most individuals at 0.1%–5% relativeabundance. The human distal gut was the body site for which the most prior knowledgewas available due to extensive studies of the fecal microbiome, particularly dueto insights from the MetaHIT project—a European-led study aimed at characterizingthe human gut microbiome in health and disease.  References1.

      Huber M, Knottnerus JA, Green,L., van der Horst H, Jadad AR, Kromhout D, Smid H. (2011). “How shouldwe define health?” (PDF). BMJ. 343:d4163. doi:10.1136/bmj.

d4163. PMID 21791490.2.      World HealthOrganization.

 WHO definitionof Health, Preamble to the Constitution of the World HealthOrganization as adopted by the International Health Conference, New York, 19–22June 1946; signed on 22 July 1946 by the representatives of 61 States (OfficialRecords of the World Health Organization, no. 2, p. 100) and entered into forceon 7 April 1948. In Grad,Frank P.

(2002). “The Preamble of the Constitution of the World HealthOrganization”. Bulletin of the World Health Organization. 80 (12):982.

3.      World HealthOrganization. (2006). Constitution ofthe World Health Organization – Basic Documents,Forty-fifth edition, Supplement, October 2006.4.

     JadadAR, O’Grady L: How should health be defined? (2008). “How should health bedefined?”. BMJ (Clinical research ed.). 337: a2900.

 doi:10.1136/bmj.a2900. PMID 19073663.

5.     CallahanD. (1973).

“The WHO definition of ‘health'”. The Hastings CenterStudies. 1 (3): 77–87. doi:10.2307/3527467.

 JSTOR 3527467.6.      TaylorS, Marandi A (2008). “How should health be defined?”.

 BMJ. 337:a290. doi:10.1136/bmj.a290.

 PMID 18614520.7.      BellieniCV, Buonocore G (2009). “Pleasing desires or pleasing wishes? A newapproach to pain definition”. Ethics Med. 25 (1): 7.

8.      Sport,Disability and an Original Definition of Health. Zenit.org (February27, 2013).

9.     “What Is Physical Health? – Definition, Components& Examples – Video & Lesson Transcript | Study.com”. study.com.

Retrieved 2017-11-14. 

TO with health. . This study established that these

TOSTUDY THE  ,STRUCTURE,FUNCTION OF ANHEALTHY HUMAN MICROBIAME INTRODUCTION:-The diversity ofmicrobes within given body habitat can be defined as the number and abundancedistribution of distinct types of organisms, for example, and high diversity inthe vagina to bacterial vaginosis . For this large study involving microbiomesamples collected from healthy volunteers at two distinct geographic locationsin the United States, we have defined the microbial communities at each bodyhabitat, encountering 81–99% of predicted genera and saturating the range ofoverall community configurations . The uniqueness of each individual’smicrobial community thus seems to be stable over time, which may be anotherfeature of the human microbiome specifically associated with health. . Thisstudy established that these patterns of alpha diversity differed markedly fromcomparisons between samples from the same habitat among subjects . For example,the saliva had among the highest median alpha diversities of operationaltaxonomic units (OTUs, roughly species level classification, but one of thelowest beta diversities so although each individual’s saliva was ecologicallyrich, members of the population shared similar organisms. Conversely, theantecubital fossae (skin) were intermediate in alpha diversity. The vagina hadthe lowest alpha diversity, with quite low beta diversity at the genus levelbut very high among OTUs due to the presence of distinct Lactobacillus spp.

Theprimary patterns of variation in community structure followed the major bodyhabitat groups (oral, skin, gut and vaginal), defining as a result the completerange of population-wide between-subject variation in human microbiome habitats. Oral and stool communities were especially diverse in terms of communitymembership, which has been linked to several human diseases: low diversity inthe gut inflammatory bowel diseases expanding prior observations , and vaginalsites harboured particularly simple communities Within-subject variation overtime was consistently lower than between-subject variation, both in organismalcomposition and in metabolic function .  CARRIAGEOF SPECIFIC MICROBES Inter-individual variation in the microbiomeproved to be specific, functionally relevant and personalized. of the oralcavity. The genus dominates the oropharynx16, with different species abundantwithin each sampled body habitat even at the species level, marked differencesin carriage within each habitat among individuals .

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As the ratio of pan- tocore-genomes is high in many human-associated microbes17, this variation inabundance could be due to selective pressures acting on pathways differentiallypresent among Streptococcus species or strains. Indeed, we observed extensivestrain-level genomic variation within microbial species in this population,enriched for host-specific structural variants around genomic islands. Evenwith respect to the single, gene losses associated with these events werecommon, for example differentially eliminating S. mitis carriage of the V-typeATPase or choline binding proteins cbp6 and cbp12 among subsets of the hostpopulation. These losses were easily observable by comparison to referenceisolate genomes, and these initial findings indicate that microbial strain- andhost-specific gene gains and polymorphisms may be similarly ubiquitousTheHuman Gut Microbiome:  Toolkit behind theScienceThe widespreadapplication of 16S rRNA gene sequencing for detection of bacterial pathogensand microbial ecology has provided a robust technical platform for theevaluation of the bacterial composition of the human microbiome to the humanfecal microbiome of 242 healthy adults. In the Human Microbiome Project, 18different body sites were sampled and sequenced.

Stool specimens were thesingle specimen type used to study the intestinal microbiome. Previouslypublished studies demonstrated the variation in composition of the gutmicrobiome among locations . Sequencing of 2 primary targets within bacterial16S rRNA genes yielded valuable compositional data pertaining within thegastrointestinal tract in different mammalian species. For example, 16S rRNAgene sequencing has been deployed to study the maturation of murine cecalmicrobiota, and these studies demonstrated the existence of a large number ofyet-unidentified bacteria that inhabit the mammalian intestine..

Thus, it isessential to develop robust experimental models of the human microbiome todelineate important mechanistic processes in the development of human diseasestates. Gut Microbiome: Composition to Function andMetabolismThe gut microbialcommunity includes approximately bacteria that normally reside in thegastrointestinal tract, reaching a microbial cell number that greatly exceedsthe number of human cells of the body. The collective genome of thesemicroorganisms contains millions of genes compared in the human genome. Thismicrobial “factory” contributes to a broad range of biochemical and metabolicfunctions that the human body could not otherwise perform.

Althoughdiet-induced changes in gut microbiota occur within a short time frame (1–3–4days after a diet switch), the changes are readily reversible. In animalmodels, the ratio of the most prominent intestinal bacterial phyla,and isaltered in response to dietary changes. Disruption of the energy equilibriumleads to weight gain. Mouse model studies have demonstrated the relationshipbetween energy equilibrium, diet, and the composition of the gut microbiome.Transplantation of the gut microbiota from obese donors resulted in increasedadiposity in recipients compared to a similar transfer from lean donors.

This extensive samplingof the human microbiome across many subjects and body habitats provides aninitial characterization of the normal microbiota of healthy adults in aWestern population. The large sample size and consistent sampling of many sitesfrom the same individuals allows for the first time an understanding of therelationships among microbes, and between the microbiome and clinicalparameters, that underpin the basis for individual variation—variation that mayultimately be critical for understanding microbiome-based disorders. Clinicalstudies of the microbiome will be able to leverage the resulting extensivecatalogues of taxa, pathways and genes1 , although they must also still includecarefully matched internal controls. The uniqueness of each individual’smicrobiome even in this reference population argues for future studies toconsider prospective within-subjects designs where possible. The HMP’s uniquecombination of organismal and functional data across body habitats,encompassing both 16S and metagenomic profiling, together with detailedcharacterization of each subject, has allowed us and subsequent studies to movebeyond the observation of variability in the human microbiome to ask how andwhy these microbial communities vary so extensively.

How large a role does hostimmunity or genetics play in shaping patterns of diversity, and how do thepatterns observed in this North American population compare to those around theworld? Future studies building on the gene and organism catalogues establishedby the Human Microbiome Project, including increasingly detailed investigationsof metatranscriptomes and metaproteomes, will help to unravel these openquestions and allow us to more fully understand the links between the humanmicrobiome, health and disease. The Gut Microbiome and Body Metabolism: Obesity and InflammationThe incidence of overweight and obesity has reached epidemicproportions. Data reported by the CDC and the National Health and NutritionExamination Survey indicated that, in 2008, an estimated 1.

5 billion adultswere overweight, and more than 200 million men and almost 300 million womenwere obese by these criteria. Worldwide obesity has more than doubled in thelast 2 decades. Obesity is associated with a cluster of metabolic and systemicdisorders such as insulin resistance, type 2 diabetes, fatty liver disease,atherosclerosis, and hypertension. The major cause of obesity is a positiveenergetic balance resulting from an increased energy intake from the diet and adecreased energy output associated with low physical activity.

In addition toalterations in diet and physical activity resulting in obesity, geneticdifferences contribute to obesity and cause differences in energy storage andexpenditure. Furthermore, growing evidence suggests that the gut microbiotarepresents an important factor contributing to the host response to nutrients.A landmark study by Turnbaugh et al.was one of the first studies to show howthe gene content in the gut microbiota contributes to obesity. The microbiomesobtained from the distal gut of genetically obese leptin-deficient mice (ob/ob)and their lean littermates (ob/+ and +/+) were compared. In this study,investigators reported that the microbiota in the ob/ob mice contained genesencoding enzymes that hydrolyze indigestible dietary polysaccharides. Increasedamounts of fermentation end products (such as acetate and butyrate) anddecreased calories were found in the feces of obese mice. These data suggestthat the gut microbiota in this mouse model promoted the extraction ofadditional calories from the diet.

Fig: human microbian and its importanceA Comprehensive Human-Associated Microbial CensusSequencing ofthe 16S rRNA gene is an effective method for interrogating the taxonomiccomposition of microbial communities. This gene is ubiquitous within theprokaryotic domain and can be effectively PCR-amplified from even previously unknownorganisms. The analysis of microbial communities through the sequencing of 16SrRNA gene was common long before the influx of high throughput sequencing (HTS)data , making this gene one of the most highly represented within GenBank. HTS approachesto 16S rRNA sequence analysis typically include targeted Illumina or 454 readsof up to a few hundred nucleotides, each targeting uniquely identifiable variableregions of the gene that can be used as unique microbial identifiers .The HMPplanned to comprehensively characterize the taxonomic composition of themicrobiome by averaging 5,000 454 FLX 16S rRNA gene sequences from all 300subjects, 18 body sites, and multiple time points. This design, combined withmore than a 1,000-fold increase in sequencing throughput over the course of theHMP, forced the consortium to develop novel tools for processing large 16S rRNAgene datasets, tackling issues specific to 454 sequence data quality, and addressingnovel biological questions that were previously inaccessible due to limited samplesizes.

An interesting question addressed by thesedata is the presence or absence of stable community configurations in differenthuman body sites, such as enterotypes in the gut. Identifying groups of highlysimilar microbial communities among many samples is a difficult unsupervised machinelearning problem, akin to that of clustering or discovering molecular subtypesin cancer gene expression data. Work to better understand the topic is ongoing,and the HMP’s survey of many body sites offered the chance to contrastcommunity organization within distinct ecologies. The vaginal microbiome, forexample, has been observed to occupy one of five main states characterized bydiffering Lactobacillus spp. abundances. This proved to be the case in the HMPas well, in contrast to a more complex continuum of community configurationsoccupied by the gut microbiota, particularly when meta-analyzed with the MetaHITcohort. As the presence of community types in distinct ecosystems may beinfluenced by environmental factors that can themselves vary continuously, suchas diet, care must be taken in future computational efforts to reproduciblyidentify microbial community types within habitats where they do occur.  Putting the Pieces Together:Metagenomic SequenceAssembly The taxonomiccomposition of the human microbiome is thus one step in understanding the rolemicrobes play in our health, and it is well complemented by sequencing ofmicrobial communities’ entire genomic contents to catalog their biologicalfunctions.

Thus, the HMP carried out extensive deep sequencing on a subset ofits subjects and body sites using the Illumina platform. While portions of the HMP’s16S rRNA gene analysis were based on extensions of established experimental andcomputational approaches, this approach to whole-metagenome sequencing was aforay into new territory.The sequencingtechnology itself was (and still is) rapidly evolving, and metagenomic datasetsof comparable size, read length, and ecological diversity did not previously exist.In the relatively short period between an initial pilot which were alreadydifficult to interpret in microbial communities containing hundreds orthousands of taxa.

It thus necessitated development of a scalable end-to-endshotgun pre-processing and quality control pipeline, including duplicate readremoval, quality and length trimming, host sequence removal, and whole-samplequality control. In the end, the HMP generated over 8 Tbp of raw sequence data,representing two lanes of paired-end Illumina sequencing for each of over 700samples (targeting 10 Gbp/ sample) as well as a small collection of samples,which were also sequenced with the Roche/454 instrument to investigate theimpact of longer reads on metagenome assembly. The design of thiswhole-metagenome sequencing experiment warrants a brief discussion. As the HMPwas started, little information was available about the genomic diversity ofthe communities being assayed. The use of Illumina sequencing in metagenomicsprojects was still being debated, the main argument against this technologybeing the very short length of the reads being generated (just 100 bp comparedto close to 400 bp achievable by Roche/454 and over 1,000 bp routinely achievedthrough Sanger sequencing). As detailed below, the feasibility of assembling theresulting data into large enough chunks to enable meaningful analyses was by nomeans obvious. At the same time, analyzing the reads themselves, rather thanassembled contigs, was considered insufficiently accurate, although both assemblyand read-based analyses ultimately proved successful.

The choice of depth ofsequencing, ”just” two lanes of the instrument, was chosen to be sufficient togenerate roughly 1-fold coverage of the Escherichia coli genome within gutmicrobiome samples estimated to occur in most individuals at 0.1%–5% relativeabundance. The human distal gut was the body site for which the most prior knowledgewas available due to extensive studies of the fecal microbiome, particularly dueto insights from the MetaHIT project—a European-led study aimed at characterizingthe human gut microbiome in health and disease.  References1.      Huber M, Knottnerus JA, Green,L., van der Horst H, Jadad AR, Kromhout D, Smid H.

(2011). “How shouldwe define health?” (PDF). BMJ. 343:d4163.

 doi:10.1136/bmj.d4163. PMID 21791490.2.      World HealthOrganization. WHO definitionof Health, Preamble to the Constitution of the World HealthOrganization as adopted by the International Health Conference, New York, 19–22June 1946; signed on 22 July 1946 by the representatives of 61 States (OfficialRecords of the World Health Organization, no. 2, p.

100) and entered into forceon 7 April 1948. In Grad,Frank P. (2002). “The Preamble of the Constitution of the World HealthOrganization”. Bulletin of the World Health Organization. 80 (12):982.

3.      World HealthOrganization. (2006). Constitution ofthe World Health Organization – Basic Documents,Forty-fifth edition, Supplement, October 2006.4.     JadadAR, O’Grady L: How should health be defined? (2008). “How should health bedefined?”. BMJ (Clinical research ed.

). 337: a2900. doi:10.1136/bmj.

a2900. PMID 19073663.5.     CallahanD. (1973). “The WHO definition of ‘health'”.

 The Hastings CenterStudies. 1 (3): 77–87. doi:10.2307/3527467. JSTOR 3527467.6.      TaylorS, Marandi A (2008).

“How should health be defined?”. BMJ. 337:a290. doi:10.1136/bmj.a290.

 PMID 18614520.7.      BellieniCV, Buonocore G (2009). “Pleasing desires or pleasing wishes? A newapproach to pain definition”. Ethics Med. 25 (1): 7.8.

      Sport,Disability and an Original Definition of Health. Zenit.org (February27, 2013).9.     “What Is Physical Health? – Definition, Components& Examples – Video & Lesson Transcript | Study.com”. study.

com. Retrieved 2017-11-14. 

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