1887

Abstract

Ethnicity is consistently reported as a strong determinant of human gut microbiota. However, the bulk of these studies are from Western countries, where microbiota variations are mainly driven by relatively recent migration events. Malaysia is a multicultural society, but differences in gut microbiota persist across ethnicities. We hypothesized that migrant ethnic groups continue to share fundamental gut traits with the population in the country of origin due to shared cultural practices despite subsequent geographical separation. To test this hypothesis, the 16S rRNA gene amplicons from 16 studies comprising three major ethnic groups in Malaysia were analysed, covering 636 Chinese, 248 Indian and 123 Malay individuals from four countries (China, India, Indonesia and Malaysia). A confounder-adjusted permutational multivariate analysis of variance (PERMANOVA) detected a significant association between ethnicity and the gut microbiota (PERMANOVA =0.005, pseudo-=2.643, =0.001). A sparse partial least squares – discriminant analysis model trained using the gut microbiota of individuals from China, India and Indonesia (representation of Chinese, Indian and Malay ethnic group, respectively) showed a better-than-random performance in classifying Malaysian of Chinese descent, although the performance for Indian and Malay were modest (true prediction rate, Chinese=0.60, Indian=0.49, Malay=0.44). Separately, differential abundance analysis singled out as being elevated in Indians. We postulate that despite the strong influence of geographical factors on the gut microbiota, cultural similarity due to a shared ethnic origin drives the presence of a shared gut microbiota composition. The interplay of these factors will likely depend on the circumstances of particular groups of migrants.

Funding
This study was supported by the:
  • Tropical Medicine and Biology (Award TMB Grant for Malaysian Microbiome in Health and Disease Project)
    • Principle Award Recipient: SadequrRahman
  • Monash University Malaysia (Award LG-2017-01-SCI)
    • Principle Award Recipient: SuiMae Lee
  • Ministry of Education (MY) (Award FRGS/1/2019/SKK01/MUSM/01/1)
    • Principle Award Recipient: SadequrRahman
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2021-08-31
2021-12-01
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References

  1. Mancabelli L, Milani C, Lugli GA, Turroni F, Cocconi D et al. Identification of universal gut microbial biomarkers of common human intestinal diseases by meta-analysis. FEMS Microbiol Ecol 2017; 93: [View Article]
    [Google Scholar]
  2. Zhou Y, Xu ZZ, He Y, Yang Y, Liu L et al. Gut microbiota offers universal biomarkers across ethnicity in inflammatory bowel disease diagnosis and infliximab response prediction. mSystems 2018; 3:00117–e00188 [View Article]
    [Google Scholar]
  3. Yu J, Feng Q, Wong SH, Zhang D, Liang QY et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 2017; 66:70–78 [View Article]
    [Google Scholar]
  4. Quigley EMM, Gajula P. Recent advances in modulating the microbiome. F1000Res 2020; 9: [View Article]
    [Google Scholar]
  5. Kelly CR, Khoruts A, Staley C, Sadowsky MJ, Abd M et al. Effect of fecal microbiota transplantation on recurrence in multiply recurrent Clostridium difficile infection. Ann Intern Med 2016; 165:609–616 [View Article]
    [Google Scholar]
  6. Rowland I, Gibson G, Heinken A, Scott K, Swann J et al. Gut microbiota functions: metabolism of nutrients and other food components. Eur J Nutr 2018; 57:1–24 [View Article]
    [Google Scholar]
  7. Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y et al. Population-level analysis of gut microbiome variation. Science 2016; 352:560–564 [View Article]
    [Google Scholar]
  8. Fortenberry JD. The uses of race and ethnicity in human microbiome research. Trends Microbiol 2013; 21:165–166 [View Article]
    [Google Scholar]
  9. Gaulke CA, Sharpton TJ. The influence of ethnicity and geography on human gut microbiome composition. Nat Med 2018; 24:1495–1496 [View Article]
    [Google Scholar]
  10. Vangay P, Johnson AJ, Ward TL, Al-Ghalith GA, Shields-Cutler RR et al. USUs immigration westernizes the human gut microbiome. Cell 2018; 175:962–972 [View Article]
    [Google Scholar]
  11. Peters BA, Yi SS, Beasley JM, Cobbs EN, Choi HS et al. US nativity and dietary acculturation impact the gut microbiome in a diverse US population. The ISME Journal 2020; 14:1639–1650 [View Article]
    [Google Scholar]
  12. Deschasaux M, Bouter KE, Prodan A, Levin E, Groen AK et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat Med 2018; 24:1526–1531 [View Article]
    [Google Scholar]
  13. Brooks AW, Priya S, Blekhman R, Bordenstein SR. Gut microbiota diversity across ethnicities in the United States. PLoS Biol 2018; 16:e2006842 [View Article]
    [Google Scholar]
  14. Chong CW, Ahmad AF, Lim YAL, Teh CSJ, Yap IKS et al. Effect of ethnicity and socioeconomic variation to the gut microbiota composition among pre-adolescent in Malaysia. Sci Rep 2015; 5:13338 [View Article]
    [Google Scholar]
  15. Dwiyanto J, Hussain MH, Reidpath D, Ong KS, Qasim A et al. Ethnicity influences the gut microbiota of individuals sharing a geographical location: a cross-sectional study from a middle-income country. Sci Rep 2021; 11:2618 [View Article]
    [Google Scholar]
  16. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009; 339:b2535 [View Article]
    [Google Scholar]
  17. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34:i884–i890 [View Article]
    [Google Scholar]
  18. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal 2011; 17:
    [Google Scholar]
  19. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 2016; 13:581–583 [View Article]
    [Google Scholar]
  20. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E et al. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucleic Acids Res 2014; 42:D643–D648 [View Article]
    [Google Scholar]
  21. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8:e61217 [View Article]
    [Google Scholar]
  22. Calle ML. Statistical analysis of metagenomics data. Genomics & informatics 2019; 17:
    [Google Scholar]
  23. Kassambara A. ggpubr: “ggplot2” Based publication ready plots; 2020 https://CRAN.R-project.org/package=ggpubr accessed 13 May 2021
  24. Quinn TP, Richardson MF, Lovell D, propr CTM. An R-package for identifying proportionally abundant features using compositional data analysis. Sci Rep 2017; 7:16252 [View Article]
    [Google Scholar]
  25. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P et al. Vegan: Community Ecology Package; 2019 https://CRAN.R-project.org/package=vegan accessed 13 May 2021
  26. Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 2017; 13:e1005752 [View Article] [PubMed]
    [Google Scholar]
  27. Lê Cao K-A, Boitard S, Besse P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 2011; 12:253 [View Article]
    [Google Scholar]
  28. Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDex) analysis for mixed population RNA-seq. PLoS One 2013; 8:e67019 [View Article]
    [Google Scholar]
  29. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016; 32:2847–2849 [View Article]
    [Google Scholar]
  30. Wei T, Simko V. R Package “Corrplot”: Visualisation of a Correlation Matrix (Version 0.84) 2017
    [Google Scholar]
  31. Kim S. ppcor: An R package for a fast calculation to semi-partial correlation coefficients. Commun Stat Appl Methods 2015; 22:665–674 [View Article]
    [Google Scholar]
  32. Hu Y-J, Satten GA. Testing hypotheses about the microbiome using the linear decomposition model (LDM. Bioinformatics 2020; 36:4106–4115 [View Article]
    [Google Scholar]
  33. Bian G, Gloor GB, Gong A, Jia C, Zhang W et al. The gut microbiota of healthy aged chinese is similar to that of the healthy young. mSphere 2017; 2:e00327–00317 [View Article]
    [Google Scholar]
  34. Duan Y, Chen Z, Tan L, Wang X, Xue Y et al. Gut resistomes, microbiota and antibiotic residues in Chinese patients undergoing antibiotic administration and healthy individuals. Sci Total Environ 2020; 705:135674 [View Article] [PubMed]
    [Google Scholar]
  35. Gaike AH, Paul D, Bhute S, Dhotre DP, Pande P et al. The gut microbial diversity of newly diagnosed diabetics but not of prediabetics is significantly different from that of healthy nondiabetics. mSystems 2020; 5:e00578–00519 [View Article]
    [Google Scholar]
  36. Khine WWT, Rahayu ES, See TY, Kuah S, Salminen S et al. Indonesian children fecal microbiome from birth until weaning was different from microbiomes of their mothers. Gut Microbes 2020 1–19 [View Article]
    [Google Scholar]
  37. Kumbhare S, Patangia D, Mongad DS, Bora A, Bavdekar AR et al. Gut microbial diversity during pregnancy and early infancy: an exploratory study in the Indian population. FEMS Microbiol Lett 2020; 367: [View Article]
    [Google Scholar]
  38. Lappan R, Classon C, Kumar S, Singh OP, de Almeida R et al. Meta-taxonomic analysis of prokaryotic and eukaryotic gut flora in stool samples from visceral leishmaniasis cases and endemic controls in Bihar State India. PLoS Negl Trop Dis 2019; 13:e0007444 [View Article]
    [Google Scholar]
  39. Parker EPK, Praharaj I, John J, Kaliappan SP, Kampmann B et al. Changes in the intestinal microbiota following the administration of azithromycin in a randomised placebo-controlled trial among infants in south India. Sci Rep 2017; 7:9168 [View Article]
    [Google Scholar]
  40. Schneider D, Thürmer A, Gollnow K, Lugert R, Gunka K et al. Gut bacterial communities of diarrheic patients with indications of Clostridioides difficile infection. Scientific Data 2017; 4:170152 [View Article]
    [Google Scholar]
  41. Sun Y, Chen Q, Lin P, Xu R, He D et al. Characteristics of gut microbiota in patients with Rheumatoid arthritis in Shanghai, China. Front Cell Infect Microbiol 2019; 9:369 [View Article]
    [Google Scholar]
  42. Tang M, Frank DN, Tshefu A, Lokangaka A, Goudar SS et al. Different gut microbial profiles in sub-saharan african and south asian women of childbearing age are primarily associated with dietary intakes. Front Microbiol 2019; 10:1848 [View Article]
    [Google Scholar]
  43. Weng YJ, Gan HY, Li X, Huang Y, Li ZC et al. Correlation of diet, microbiota and metabolite networks in inflammatory bowel disease. J Dig Dis 2019; 20:447–459 [View Article]
    [Google Scholar]
  44. Winglee K, Howard AG, Sha W, Gharaibeh RZ, Liu J et al. Recent urbanisation in China is correlated with a Westernized microbiome encoding increased virulence and antibiotic resistance genes. Microbiome 2017; 5:121 [View Article]
    [Google Scholar]
  45. Yin Y, Fan B, Liu W, Ren R, Chen H et al. Investigation into the stability and culturability of Chinese enterotypes. Sci Rep 2017; 7:7947 [View Article]
    [Google Scholar]
  46. Zeng B, Zhang S, Xu H, Kong F, Yu X et al. Gut microbiota of Tibetans and Tibetan pigs varies between high and low altitude environments. Microbiol Res 2020; 235:126447 [View Article]
    [Google Scholar]
  47. Zhou C-H, Meng Y-T, Xu J-J, Fang X, Zhao J-L et al. Altered diversity and composition of gut microbiota in Chinese patients with chronic pancreatitis. Pancreatology 2020; 20:16–24 [View Article]
    [Google Scholar]
  48. Lokmer A, Aflalo S, Amougou N, Lafosse S, Froment A et al. Response of the human gut and saliva microbiome to urbanisation in Cameroon. Sci Rep 2020; 10:2856 [View Article]
    [Google Scholar]
  49. Chua EG, Loke MF, Gunaletchumy SP, Gan HM, Thevakumar K et al. The influence of modernization and disease on the gastric microbiome of orang asli. Myanmars Med J Malaysia 2019; 7:174
    [Google Scholar]
  50. Chee-Beng T. Chinese identities in Malaysia. Asian J Soc Sci 1997; 25:103–116 [View Article]
    [Google Scholar]
  51. Walker AR. South Asians in Malaysia and Singapore. Ember M, Ember C, Skoggard I. eds In Encyclopedia of Diasporas: Immigrant and Refugee Cultures Around the World Boston, MA: Springer US; 2005 pp 1105–1119
    [Google Scholar]
  52. Singh A. Indian diaspora as a factor in India–Malaysia relations. Diaspora Studies 2014; 7:130–140 [View Article]
    [Google Scholar]
  53. Winstedt R. Indian influence in the Malay world. J R Asiat Soc 1944; 2:186–196
    [Google Scholar]
  54. Lee RLM. Malaysian identities and mélange food cultures. J Intercult Stud 2017; 38:139–154 [View Article]
    [Google Scholar]
  55. Zheng J, Wittouck S, Salvetti E, Franz CMAP, Harris HMB et al. A taxonomic note on the genus Lactobacillus: Description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. Int J Syst Evol Microbiol 2020; 70:2782–2858
    [Google Scholar]
  56. Goodrich JK, Davenport ER, Beaumont M, Jackson MA, Knight R et al. Genetic Determinants of the Gut Microbiome in UK Twins. Cell Host Microbe 2016; 19:731–743 [View Article]
    [Google Scholar]
  57. Blekhman R, Goodrich JK, Huang K, Sun Q, Bukowski R et al. Host genetic variation impacts microbiome composition across human body sites. Genome Biol 2015; 16:191 [View Article]
    [Google Scholar]
  58. Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 2018; 555:210–215 [View Article]
    [Google Scholar]
  59. Xu J, Lawley B, Wong G, Otal A, Chen L et al. Ethnic diversity in infant gut microbiota is apparent before the introduction of complementary diets. Gut Microbes 2020; 11:1362–1373 [View Article]
    [Google Scholar]
  60. Ussar S, Griffin NW, Bezy O, Fujisaka S, Vienberg S et al. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab 2015; 22:516–530 [View Article] [PubMed]
    [Google Scholar]
  61. Khine WWT, Zhang Y, Goie GJY, Wong MS, Liong M et al. Gut microbiome of pre-adolescent children of two ethnicities residing in three distant cities. Sci Rep 2019; 9:7831 [View Article]
    [Google Scholar]
  62. Embong AM, Jusoh JS, Hussein J, Mohammad R. Tracing the Malays in the Malay land. Procedia Soc Behav Sci 2016; 219:235–240 [View Article]
    [Google Scholar]
  63. Hugo G. Indonesian labour migration to Malaysia: Trends and policy implications. Asian J Soc Sci 1993; 21:36–70 [View Article]
    [Google Scholar]
  64. He Y, Wu W, Zheng H-M, Li P, McDonald D et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat Med 2018; 24:1532–1535 [View Article]
    [Google Scholar]
  65. Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 2018; 555:623–628 [View Article]
    [Google Scholar]
  66. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 2015; 528:262–266 [View Article]
    [Google Scholar]
  67. Langille MG, Ravel J, Fricke WF. “Available upon request”: not good enough for microbiome data!. Microbiome 2018; 6:8 [View Article]
    [Google Scholar]
  68. Drewnowski A, Mognard E, Gupta S, Ismail MN, Karim NA et al. Socio-cultural and economic drivers of plant and animal protein consumption in Malaysia: The SCRIPT study. Nutrients 2020; 12:1530 [View Article]
    [Google Scholar]
  69. Sathyamala C. Meat-eating in India: Whose food, whose politics, and whose rights?. Pol Futures Educ 2019; 17:878–891 [View Article]
    [Google Scholar]
  70. Sun L, Chen J, Li M, Liu Y, Zhao G. Effect of star anise (I llicium verum) on the volatile compounds of Stewed chicken. J Food Process Eng 2014; 37:131–145 [View Article]
    [Google Scholar]
  71. Ji Y, Li S, Ho C-T. Chemical composition, sensory properties and application of Sichuan pepper (Zanthoxylum genus. Food Sci Hum Well 2019; 8:115–125
    [Google Scholar]
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