Biology subjects

Wheeler, C.

Publications and source records attributed to Wheeler, C..

3 recordsLinked to original sources

bioRxiv2026

Mutation spectra across 3,034 Saccharomyces cerevisiae genomes distinguish the Georgian Wine population

Mutation spectra vary among populations, and a comprehensive view of this variation requires broad sampling and fine-scale population resolution. Here, we profile mutation spectra from single-nucleotide polymorphisms and small insertions and deletions in a uniformly processed collection of 3,034 globally sampled Saccharomyces cerevisiae genomes, and an extended dataset including the previously sampled global 1,011-genome collection. Major mutation spectrum patterns reported from the 1,011 genomes remain evident in the extended dataset, including the pronounced mutation-spectrum differentiation of African Beer strains. At finer population resolution, we identify additional variation among wine-associated populations, including a Georgian Wine-associated signal that is strongest among rare variants and is therefore consistent with a comparatively recent change in mutation processes. We further apply a sequence-context-aware classification to characterize small-indel profiles across the 3,034 natural yeast genomes. Together, our analyses show that expanded sampling and refined population assignments provide a more comprehensive view of mutation-spectrum variation within S. cerevisiae.

evolutionary biology↗

bioRxiv2025

The discovery and patterns of the underlying long-standing mild-effect mutator alleles in S. cerevisiae populations

Most mutations are neutral or deleterious, and mutator alleles that substantially increase the mutation rate of an organism are believed to be short-lived in natural populations. Theory suggests that mutator alleles of modest effect have the potential to segregate nearly neutrally, but this prediction has been difficult to test experimentally. Here, we report strong genomic signatures consistent with the transmission and long-term maintenance of at least one natural mutator allele in Saccharomyces cerevisiae isolates. Specifically, such signatures are consistent with segregation of mutator alleles that disproportionately increase A>C mutations in the African Beer strains of S. cerevisiae. Remarkably, the mutation spectra of African Beer strains deviate from that of other S. cerevisiae natural isolates more than some Saccharomyces species differ from each other. Furthermore, computational analysis suggests mutator allele introgression from the African Beer population into a subset of French Dairy yeast, motivating us to experimentally measure the de novo mutation rates and spectra of these strains. We observed a consistent but weak enrichment of A>C and A>G de novo mutations in the African Beer population, with the A>C enrichment also evident in the French Dairy subset. Unexpectedly, other de novo mutation types varied more substantially among strains, including one outlier African Beer strain (AFL) with an excess of C>A mutations. Overall, our measurements reveal that de novo mutation spectra show greater variability among populations compared to the mutation spectra of rare polymorphisms, which in turn show greater variability than the spectra of all polymorphisms. Together, while additional mutator alleles may segregate in natural populations, mild enrichment of A>C and A>G types in African Beer strains reflects mutator alleles with weak effects that can persist and accumulate mutations through evolution.

evolutionary biology↗

bioRxiv2022

Exogenous sequences in tumors and immune cells (exotic): a tool for estimating the microbe abundances in tumor RNAseq data

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is unclear. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool to carefully identify the tumor microbiome within RNAseq datasets and then applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas (TCGA). We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We identify associations with survival and clinical variables that are highly cancer-specific and relatively few associations with immune composition. Finally, we explore potential mechanisms by which microbes and tumors may interact using a network approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The exotic tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNAseq datasets. Statement of SignificanceThe intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNAseq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.

cancer biology↗