Biology subjects

B. Jesse Shapiro

Publications and source records attributed to B. Jesse Shapiro.

3 recordsLinked to original sources

bioRxiv2016

Origins of pandemic clones from environmental gene pools

Some microbes can transition from an environmental lifestyle to a pathogenic one1-3. This ecological switch typically occurs through the acquisition of horizontally acquired virulence genes4,5. However, the genomic features that must be present in a population prior to the acquisition of virulence genes and emergence of pathogenic clones remain unknown. We hypothesized that virulence adaptive polymorphisms (VAPs) circulate in environmental populations and are required for this transition. We developed a comparative genomic framework for identifying VAPs, using Vibrio cholerae as a model. We then characterized several environmental VAP alleles to show that, while some of them reduced the ability of clinical strains to colonize a mammalian host, other alleles conferred efficient host colonization. These results show that VAPs are present in environmental bacterial populations prior to the emergence of virulent clones. We propose a scenario in which VAPs circulate in the environment, they become selected and enriched under certain ecologicalconditions, and finally a genomic background containing several VAPs acquires virulence factors that allows for its emergence as a pathogenic clone.

Microbiology↗

bioRxiv2016

Characterizing and predicting cyanobacterial blooms in an 8-year amplicon sequencing time-course

Cyanobacterial blooms occur in lakes worldwide, producing toxins that pose a serious public health threat. Eutrophication caused by human activities and warmer temperatures both contribute to blooms, but it is still difficult to predict precisely when and where blooms will occur. One reason that prediction is so difficult is that blooms can be caused by different species or genera of cyanobacteria, which may interact with other bacteria and respond to a variety of environmental cues. Here we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterize the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time-course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community - including non-cyanobacteria - prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloom events are predictable. We used probabilistic assemblages of OTUs to characterize the bloom-associated community, and to classify samples into bloom or non-bloom categories, achieving up to 92% classification accuracy (86% after excluding cyanobacterial sequences). Finally, using symbolic regression, we were able to predict the start date of a bloom with 78-92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables.

Microbiology↗

bioRxiv2016

How clonal are bacteria over time?

Bacteria and archaea reproduce clonally (vertical descent), but exchange genes by recombination (horizontal transfer). Recombination allows adaptive mutations or genes to spread rapidly within (or even between) species, and reduces the burden of deleterious mutations. Clonality - defined here as the balance between vertical and horizontal inheritance - is therefore a key microbial trait, determining how quickly a population can adapt and the size of its gene pool. Here, I discuss whether clonality varies over time and if it can be considered a stable trait of a given population. I show that, in some cases, clonality is clearly not static. For example, non-clonal (highly recombining) populations can give rise to clonal expansions, often of pathogens. However, an analysis of time-course metagenomic data from a lake suggests that a bacterial populations past clonality (as measured by its genetic diversity) is a good predictor of its future clonality. Clonality therefore appears to be relatively - but not completely - stable over evolutionary time.

Evolutionary Biology↗