Gut, oral and skin microbiome of Indian patrilineal families reveal perceptible association with age

Abstract

The human microbiome plays a key role in maintaining host homeostasis and is influenced by age, geography, diet, and other factors. traditionally, india has an established convention of extended family arrangements wherein three or more generations, bound by genetic relatedness, stay in the same household. in the present study, we have utilized this unique family arrangement to understand the association of age with the microbiome. We characterized stool, oral and skin microbiome of 54 healthy individuals from six joint families by 16S rRNA gene-based metagenomics. In total, 69 (1.03%), 293 (2.68%) and 190 (8.66%) differentially abundant OTUs were detected across three generations in the gut, skin and oral microbiome, respectively. Age-associated changes in the gut and oral microbiome of patrilineal families showed positive correlations in the abundance of phyla proteobacteria and fusobacteria, respectively. Genera Treponema and Fusobacterium showed a positive correlation with age while Granulicatella and Streptococcus showed a negative correlation with age in the oral microbiome. Members of genus Prevotella illustrated high abundance and prevalence as a core otUs in the gut and oral microbiome. in conclusion, this study highlights that precise and perceptible association of age with microbiome can be drawn when other causal factors are kept constant.

Prakriti phenotypes as a stratifier of gut microbiome: A new frontier in personalized medicine?

“…Chaudhari et al. [ 35 ] analyzed the association of age with gut microbiome by studying 54 genetically linked individuals (encompassing samples from 6 joint families) with similar diet, ethnicity and geographical locations. They observed an increase in the members of Proteobacteria and decrease in genus Bacteroides with increasing age.…”

“…[ 30 ] Chaudhari et al. [ 35 ] Geography Rural population in Pune (VHDSS) Rural and urban sea level Ballabhgarh areas, Haryana and rural high altitude areas of Leh, Ladakh Ahmedabad, Gujarat Rural population in Pune (VHDSS) Bhopal (LOC1) and Kerala (LOC2) Rural population in Pune (VHDSS) Samples analyzed (male + female) 113 (50 + 63) 84 (45 + 39) 80 (NA) 18 (8 + 10) 110 (58 + 62) 50 (NA) Sequencing platform Roche GS FLX Roche GS FLX Illumina Miseq Illumina MiSeq Illumina NextSeq 500 Illumina MiSeq Variable region V2–V6 V1–V5 V3–V4 V3–V4 V3 V3–V4 Core microbiome estimation method Presence in >50% samples Presence in >50% samples (abundance ≥ 0.01%) Bootstrapping procedure NA MetaHIT algorithm Presence in >95% samples (abundance ≥ 0.1%) Association parameters Prakriti phenotype Geography, Diet, Cooking oil Geography (country), Diet Prakriti phenotype Geography (country) Age Core members described 22 54 52 NA 19 a 6 b Top 2 Phyla Bacteroidetes and Firmicutes Firmicutes and Bacteroidetes Bacteroidetes and Firmicutes Bacteroidetes and Firmicutes Bacteroidetes to Firmicutes ratio in LOC1 > LOC2 Bacteroidetes and Firmicutes a For comparison with other studies, the list was curated by maintaining genera identity and only genera found associated with Indian gut microbiome (log odds ratio > 0.5) was considered. b All microbes that passed the “core microbiome” criteria were included irrespective of their individual presence in the sub-cohorts as defined in the study.…”

“…All these studies, in spite of their inherent differences, have essentially attempted to stratify the gut microbiome composition based on population taking into due consideration the variance caused by geography, diet and age. However, there are some differences between the core microbiome (gut microbiota members present across all samples within a study irrespective of the variables) described by these five studies which could be attributed to the following reasons [ [30] , [31] , [32] , 35 , 36 ]. While the study design for all the reports involved 16S rRNA profiling for taxonomic assignments, the hypervariable regions profiled varied among the five studies.…”

“…[ 32 ] and Chaudhari et al. [ 35 ] analyzed a similar population subset from Pune (Vadu Health and Demographic Surveillance System (VHDSS) participants) and reported a similar assemblage of core microbiome ( Fig. 1 ) but with varying abundance.…”

“…While Chaudhari et al. [ 35 ] considered only microbes in >95% of samples with abundance of at least 0.1% for their analysis, Chauhan et al. [ 32 ] considered all those microbes present in >50% of samples irrespective of abundance.…”