Cited by 111Gut microbiota and healthMicrobial Community Ecology and PhysiologyOral microbiology and periodontitis research

The DOI matches its Crossref record; 93 of the 95 references it cites were found in a registry or an index; 7 of the 74 sources its claims cite were checked for support.

Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana

Open access

Abstract

BACKGROUND: Gut microbiota from individuals in rural, non-industrialized societies differ from those in individuals from industrialized societies. Here, we use 16S rRNA sequencing to survey the gut bacteria of seven non-industrialized populations from Tanzania and Botswana. These include populations practicing traditional hunter-gatherer, pastoralist, and agropastoralist subsistence lifestyles and a comparative urban cohort from the greater Philadelphia region. RESULTS: We find that bacterial diversity per individual and within-population phylogenetic dissimilarity differs between Botswanan and Tanzanian populations, with Tanzania generally having higher diversity per individual and lower dissimilarity between individuals. Among subsistence groups, the gut bacteria of hunter-gatherers are phylogenetically distinct from both agropastoralists and pastoralists, but that of agropastoralists and pastoralists were not significantly different from each other. Nearly half of the Bantu-speaking agropastoralists from Botswana have gut bacteria that are very similar to the Philadelphian cohort. Based on imputed metagenomic content, US samples have a relative enrichment of genes found in pathways for degradation of several common industrial pollutants. Within two African populations, we find evidence that bacterial composition correlates with the genetic relatedness between individuals. CONCLUSIONS: Across the cohort, similarity in bacterial presence/absence compositions between people increases with both geographic proximity and genetic relatedness, while abundance weighted bacterial composition varies more significantly with geographic proximity than with genetic relatedness.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

In a 16S rRNA survey of gut bacteria from 126 people in rural Tanzania, rural Botswana, and Philadelphia, bacterial diversity and composition differed more by country/region than by subsistence practice, and a subset of Botswanan Bantu agropastoralists had microbiota similar to the US cohort. Similarity in bacterial presence/absence increased with geographic proximity and host genetic relatedness, while abundance-weighted composition tracked geography more than relatedness.

Key findings

  • Tanzanian populations generally had higher within-individual bacterial diversity and lower within-population phylogenetic dissimilarity than Botswanan populations; the Sandawe had the highest Shannon diversity and the US cohort the lowest. Hunter-gatherers were phylogenetically distinct from both agropastoralists and pastoralists, while agropastoralists and pastoralists were not significantly different from each other.
  • Prevotellaceae was the most abundant bacterial family in 70.2% of Africans, whereas Bacteroidaceae was most abundant in 50% of US samples. Twenty-three Africans had Prevotellaceae abundances within one standard deviation of the US mean (12.4%); 19 were from Botswana and 12 were Bantu. Nearly half of Bantu-speaking agropastoralists from Botswana had gut bacteria very similar to the Philadelphia cohort, and 12 low-Prevotellaceae Bantu were not statistically different from US samples by weighted UniFrac (PERMANOVA p = 0.12).
  • Imputed metagenomes showed 146 KEGG pathways significantly differentially enriched between US and African cohorts and 148 between Botswana and Tanzania; US samples had relative enrichment of pathways for degradation of industrial pollutants, and DDT degradation was enriched in Botswanan but not Tanzanian or US samples.
  • Within-population bacterial composition correlated with host genetic relatedness in the Hadza (both unweighted and weighted UniFrac) and Maasai (unweighted UniFrac). Across the cohort, unweighted UniFrac similarity increased with both geographic proximity and genetic relatedness, while weighted UniFrac varied significantly only with geographic separation.

Methods

  • Stool samples from 126 participants were analyzed by 16S rRNA V1–V2 sequencing: 60 from Tanzania, 54 from Botswana, and 12 from Philadelphia; 114 African adults were recruited, and US samples came from prior studies. Samples were processed with the same laboratory and computational pipelines.
  • Reads were clustered into operational taxonomic units at 97% identity using QIIME/Swarm, taxonomically assigned with Greengenes, and analyzed with UniFrac, PERMANOVA (50,000 permutations), PCoA, ANCOM, and PICRUSt. qPCR quantified 16S rRNA gene copies and screened for *Entamoeba histolytica*.
  • A subset of 97 African individuals was genotyped on the Illumina Omni5M Exome array; genetic relatedness was estimated using identity-by-descent and a genetic relationship matrix, and linear models tested UniFrac distances as functions of geographic distance and host relatedness.

Limitations

  • The paper states that individual- and population-level dietary information was not available; PICRUSt imputation may not capture gene content in diverged strains, so shotgun sequencing is needed to verify functional differences; the US cohort is the only urban/industrialized population; larger sample sizes are needed to identify factors causing sex differences; and the BMI–diversity correlation cannot be separated from other environmental or cultural covariates.
  • The paper also notes that cross-study microbiome comparisons are difficult because of different amplicons, protocols, reagents, and primers, and that seasonal variation may affect taxa such as Prevotellaceae.
  • Evident from the text: 16S V1–V2 amplicon data limit taxonomic resolution, with 1,044 unassigned OTUs removed and African samples having more unassigned OTUs; several populations are small (e.g., Herero n = 8, US n = 12), and the design is cross-sectional, limiting causal inference.

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Integrity signals

Registry

Crossref · 10.1186/s13059-018-1616-9

Cites

93 of 93 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1 Map of the sampled population groups
Fig. 1 Map of the sampled population groupsCC BY · PubMed Central
Fig. 2 The relative abundance per individual for the ten most common taxa, shown for the bacterial taxonomic rank of a Order and b Family. c The population distribution of the relative proportion of Bacteroidales per total of Bacteroidales and Clostridiales
Fig. 2 The relative abundance per individual for the ten most common taxa, shown for the bacterial taxonomic rank of a Order and b Family. c The population distribution of the relative proportion of Bacteroidales per total of Bacteroidales and ClostridialesCC BY · PubMed Central
Fig. 3 Within group mean α and β diversity. a Shannon index. b Unweighted UniFrac distance distribution within group. c Weighted UniFrac distance distribution within group. The within-population mean Shannon diversity versus unweighted and weighted UniFrac distances are shown in d and e, respectively
Fig. 3 Within group mean α and β diversity. a Shannon index. b Unweighted UniFrac distance distribution within group. c Weighted UniFrac distance distribution within group. The within-population mean Shannon diversity versus unweighted and weighted UniFrac distances are shown in d and e, respectivelyCC BY · PubMed Central
Fig. 4 PERMANOVA tests of the phylogenetic difference between pairs of groups, based on unweighted UniFrac (panels a, b, c) and weighted UniFrac (panels d, e, f). Shown are groups defined by country of origin (panels a, d), subsistence practice (panels b, e), and sex (panels c, f). The subsistence practices are abbreviated as US = western (Philadelphian), HG = hunter-gatherers (Hadza, San), AP = agropastoralists (Bantu agropastoralists, Burunge, Sandawe), and PA = pastoralists (Herero, Maasai). Bar in red denote pairs where the F-statistic p value is < 0.05
Fig. 4 PERMANOVA tests of the phylogenetic difference between pairs of groups, based on unweighted UniFrac (panels a, b, c) and weighted UniFrac (panels d, e, f). Shown are groups defined by country of origin (panels a, d), subsistence practice (panels b, e), and sex (panels c, f). The subsistence practices are abbreviated as US = western (Philadelphian), HG = hunter-gatherers (Hadza, San), AP = agropastoralists (Bantu agropastoralists, Burunge, Sandawe), and PA = pastoralists (Herero, Maasai). Bar in red denote pairs where the F-statistic p value is < 0.05CC BY · PubMed Central
Fig. 5 Principle coordinate analysis (PCoA) for weighted UniFrac distances. a The first two principle coordinates for all individuals in the study, where marker shape and color denote the population of origin. Sidebar (panels b and c) show the abundances of Prevotellaceae (Prev.), Bacteroidaceae (Bact.), and Ruminococcaceae (Rumi.) aligned to the first two principal coordinates. d Box-and-whisker distributions between each African population and the US samples, over all pairs of individuals
Fig. 5 Principle coordinate analysis (PCoA) for weighted UniFrac distances. a The first two principle coordinates for all individuals in the study, where marker shape and color denote the population of origin. Sidebar (panels b and c) show the abundances of Prevotellaceae (Prev.), Bacteroidaceae (Bact.), and Ruminococcaceae (Rumi.) aligned to the first two principal coordinates. d Box-and-whisker distributions between each African population and the US samples, over all pairs of individualsCC BY · PubMed Central
Fig. 6 Box-and-whisker plots of relative abundances distributions per group for the taxa that varied significantly among groups by the ANCOM tests, where individuals are grouped by a traditional or industrial lifestyle, b country of origin, c population, and d traditional subsistence strategy
Fig. 6 Box-and-whisker plots of relative abundances distributions per group for the taxa that varied significantly among groups by the ANCOM tests, where individuals are grouped by a traditional or industrial lifestyle, b country of origin, c population, and d traditional subsistence strategyCC BY · PubMed Central

Verification

Claims the paper makes

42 Own result · 35 Not checked yet · 23 Own interpretation · 10 No basis given · 7 Own method · 1 Partly supported · accuracy not yet measured

Verify all 67 references — free account

Show all 69 claims

  • Probably not a substantial claim. 28
  • It may state two separate claims as one. 34
49 of 49
  • The paper relies on this without support.

    The range of gut microbiome compositions among African populations with diverse subsistence practices remains largely unknown.

    “Although there have been several studies of microbiome diversity within African populations [22–24, 27, 29–31], the range of gut microbiome compositions among African populations with diverse subsistence practices remains largely unknown.”
  • The paper relies on this without support.

    PICRUSt imputation accurately estimates metagenomic content.

    “We infer that the regional differences in bacterial abundances may lead to regional differences in functional pathway abundances, depending upon the accuracy of gene content imputation.”
  • The authors state this as an assumption.

    Estimation of the identity-by-descent fraction assumes a panmictic population.

    “Estimation of the identity-by-descent fraction assumes a panmictic population and is, therefore, reasonably suited for use as a within-population relatedness metric.”
  • The authors state this as an assumption.

    The bacterial strains in each sample have the same gene content as the database strains used for PICRUSt analysis.

    “PICRUSt relies on the assumption that the bacterial strains in each sample have the same gene content as database strains used for the analysis, which can be inaccurate when strains vary substantially in gene content.”
4 of 4

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Citations

Cites

Research works printed but not linked: 7

  1. Yatsuka H. Hunter-gatherers and their neighbors in Asia, Africa, and South America. Senri Ethnol Stud. 2016;94:81–105.
  2. Silberbauer G. Report to the government of Bechuanaland on the bushmen survey. Gaborone: Bechuanaland Government Printer; 1965.
  3. Silberbauer G. The G/wi Bushmen. In: Bicchieri M, editor. Hunt Gatherers Today. New York: Holt, Rinehart and Winston; 1972. p. 271–326.
  4. Tanaka J. The san, hunter-gatherers of the Kalahari. Tokyo: University of Tokyo Press; 1980.
  5. Pennington RL. Economic stratification and health among the Herero of Botswana. In: Leonard WR, Crawford MH, editors. Hum Biol Pastor Popul. Cambridge: Cambridge University Press; 2002.
  6. Hansen MEB, Rubel MA, Bailey AG, Ranciaro A, Thompson SR, Campbell MC, Beggs W, Dave JR, Mokone GG, Mpoloka SW, Nyambo T, Abnet C, Chanock SJ, Bushman FD, Tishkoff SA. Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana. Bioproject, Accession PRJNA395034. 2017. https://www.ncbi.nlm.nih.gov/bioproject/395034 .
  7. Tishkoff SA. Genetics of pigmentation in Eastern and Southern African populations study. dbGAP, Accession phs001396.v1.p1. 2017. https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001396.v1.p1 .

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Metadata

Authors
Hansen, M. E. B., Rubel, M. A., Bailey, A. G., Ranciaro, A., Thompson, S. R., Campbell, M. C., Beggs, W., Dave, J. R., Mokone, G. G., Mpoloka, S. W., Nyambo, T., Abnet, C., Chanock, S. J., Bushman, F. D., & Tishkoff, S. A.
Affiliations
  • University of Pennsylvania (US)
  • Queen Mary University of London (GB)
  • Howard University (US)
  • Geisinger College of Health Sciences (US)
  • University of Botswana (BW)
  • Muhimbili University of Health and Allied Sciences (TZ)
  • National Cancer Institute (US)
Corresponding author
Tishkoff, Sarah A.
Venue
Genome Biology
Published
2019-01-22
Volume
20
Issue
1
Pages
16-16
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
OpenAlex
W2917203974
Cited by
110
References
95
Citations by year
2026: 7 · 2025: 11 · 2024: 14 · 2023: 8 · 2022: 12 · 2021: 34 · 2020: 16 · 2019: 8
FWCI
5.79
Type
journal article
Language
en
Open access
Locations
  • Genome biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
Funders
  • Lewis and Clark Fund
  • University of Pennsylvania
  • National Institutes of Health (5T32AI007532-18, DP1 ES022577-04, 1R01DK104339-01, 1R01GM113657-01, T32ES019851-02)
  • National Science Foundation (BCS-1540432, BCS-1317217)
  • Center of Excellence in Environmental Toxicology
  • Wenner-Gren Foundation (9299)
  • Leakey Foundation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
Keywords
gut microbiota, population structure, hunter-gatherers, pastoralists, agro-pastoralists, subsistence lifestyle, rural population, bacterial diversity, phylogenetic dissimilarity, Bantu peoples, industrial pollutants, genetic relatedness, geographic proximity, urban comparison, Tanzania, Botswana
MeSH terms
Eubacteriales, Clostridiales, Gastrointestinal Microbiome, Adolescent, Adult, Aged, Aged, 80 and over, Agriculture, Animals, Bacteroidaceae, Botswana, Cattle, Female, Humans, Male, Middle Aged, Rural Population, Tanzania, Philadelphia, Cohort Studies, Population Groups, Metagenome, Young Adult, Diet, Paleolithic

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