The focus of my independent research over the last few years has been on how we (the microbiome research community) can use whole-genome shotgun sequencing (WGS) data to efficiently identify what genetic elements within microbes are consistently enriched in the microbiome of humans with particular health of disease states.
A lot of that work is focused on the tractability of the various computational methods that we need in order to perform this process: de novo assembly, gene de-duplication, read mapping, alignment de-duplication, co-abundance clustering, etc. In a couple of cases I’ve worked with collaborators to improve those individual components, but we’ve also spent a lot of time on making all of those pieces work together as part of a cohesive whole (using Nextflow).
I’ve been working with my collaborators (Kevin Barry, Amy Willis, Jonathan Golob, and Caroline Kasman) to put together a demonstration of this approach, and I’m happy to say that this has all come together in the form of a preprint which was published this week:
Gene-level metagenomics identifies genome islands associated with immunotherapy response
I’ll use subsequent posts to talk more about the ideas behind this approach to analyzing the microbiome, but for now I’ll just say that I’m extremely excited that we are able to analyze previously-published datasets and identify new gene-level microbiome associations. In this case, we compared the stool microbiome of individuals being treated for metastatic melanoma on the basis of whether they responded to immune checkpoint inhibitor (ICI) therapy. With this approach we identified specific “genome islands” (localized regions of the genome) whose presence in gut bacteria was consistently associated with ICI response across two independent cohorts.
Needless to say, I think this is an extremely exciting finding and I’m looking forward to pushing forward with this research, both on the methods and the microbiome-ICI association. Follow this space for future developments!