Drifting into speculation: I think these data support the picture that gut microbes can spread much faster than human genes. Strong geographic barriers can be still be very important (as in the Tsimane & Hadza case) but the magnitude of this effect
stands out compared to other contemporary pop'ns.

Interestingly, of the subset of species shared w/ industrialized pop'ns, some show evidence of recent transmission, while others suggest a more ancient association. Among these, the genetic isolation between Tsimane & Hadza strains was generally larger than between comparable industrialized pop'ns

We developed several methods to detect & date the onset of genetic isolation between these microbial pop'ns, which leverage the fact that gut bacteria constantly exchange DNA w/ each other. In most species, we found patterns of divergence & gene flow consistent w/ prehistoric co-migration w/ humans.

We were surprised to see right off the bat that - despite their long history of geographic separation - most of the microbial species in the Tsimane microbiome were also found in the Hadza (and most of these are either rare or absent in industrialized populations).

Benefiting from decades of work by the Tsimane Life History Project (tsimane.anth.ucsb.edu) and a previous set of samples collected by Melanie Martin, @danielsprockett.bsky.social & co, we used deep metagenomic sequencing to reconstruct ~12k microbial genomes from ~80 Tsimane adults.

Here we tried to get around this challenge by analyzing two geographically distant populations – the Tsimane of Bolivia and the Hadza of Tanzania – whose ancestors have been separated for more than 10,000 years prior to recent human globalization.

Why am I so excited about this work? There’s been a lot of interest in understanding how gut bacteria might have evolved (or co-evolved) w/ humans. But the plasticity of the microbiome can make it difficult to distinguish long-term associations from more recent acquisitions from the environment.

I think this emphasizes the need for more concrete models of pleiotropy to help us know when these verbal models should even apply in principle (let alone in expts like @oliviamghosh.bsky.social's). In that vein, was very excited about the new work that @djhelam1.bsky.social‬ talked about at NITMB

I think it would be super cool to explore some of these assumptions and models theoretically, but (spoiler alert) we don't actually find evidence for the pleiotropic expansion model in our data! So perhaps these assumptions are not borne out in reality (in our system at least).

There are some implicit assumptions that must be true for this pleiotropic expansion model to work. First, just to clarify, the fact that a box is colorful does not mean it has a positive effect on fitness, it just means it is relevant. So a mutant's affect on a trait can be good in E1, bad in E2