Back

Short- and long-read metagenomics of South African gut microbiomes reveal a transitional composition and novel taxa

Tamburini, F. B.; Maghini, D.; Oduaran, O. H.; Brewster, R.; Hulley, M. R.; Sahibdeen, V.; Norris, S. A.; Tollman, S.; Kahn, K.; Wagner, R.; Wade, A. N.; Wafawanaka, F.; Gomez-Olive, X.; Twine, R.; Lombard, Z.; Hazelhurst, S.; Bhatt, A. S.

2020-10-20 genomics
10.1101/2020.05.18.099820 bioRxiv
Show abstract

Human gut microbiome research focuses on populations living in high-income countries or on the other end of the spectrum, namely non-urban agriculturalist and hunter-gatherer societies. The scarcity of research between these extremes limits our understanding of how the gut microbiota relates to health and disease in the majority of the worlds population. We present the first study evaluating gut microbiome composition in transitioning South African populations using short- and long-read sequencing. We analyzed stool samples from adult females (age 40 - 72) living in rural Bushbuckridge municipality (n=118) or urban Soweto (n=51) and find that these microbiomes are taxonomically intermediate between those of individuals living in high-income countries and traditional communities. We demonstrate that reference collections are incomplete for characterization of microbiomes of individuals living outside high-income countries, resulting in artificially low species-level beta diversity measurements. To improve reference databases, we generated complete genomes of undescribed taxa, including Treponema, Lentisphaerae, and Succinatimonas species. Our results suggest that the gut microbiome in South African populations do not exist along a simple "western-nonwestern" axis and that these populations contain microbial diversity that remains to be described.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.