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Joyce, R.

Publications and source records attributed to Joyce, R..

2 recordsLinked to original sources

Gain and loss of gut symbionts during stingless bee diversification is linked to host group size

Microbiomes typically vary between closely related host species, but how and why they diverge during host diversification remain poorly understood. Bees provide a well-established model for the role of sociality in microbiome evolution: solitary species have generalist gut bacterial symbionts, while eusocial honey bees and bumble bees have host-specific "core" gut bacterial symbionts. Curiously however, some species of stingless bees--the most diverse clade of eusocial bees--lack the core symbionts Snodgrassella and Gilliamella. Here, we tested whether group size, a fundamental trait of social organisms, explains symbiont gain and loss in stingless bees. Using specimens from a global collection, we performed 16S rRNA gene sequencing and qPCR on 187 adult workers representing 29 species and 18 genera. Maximum reported colony sizes for these species range from [~]100 to over [~]100,000 individuals. Our phylogenetic models indicate positive feedbacks between group size and the symbionts Snodgrassella and Gilliamella. We suggest that challenges faced by large groups (e.g., disease risk) may select for symbionts that supply needed functionality (e.g., pathogen protection). Our findings highlight group size as a potentially widespread driver of microbiome evolution in social hosts.

evolutionary biology↗

Using a Whole Genome Co-expression Network to Inform the Functional Characterisation of Predicted Genomic Elements from Mycobacterium tuberculosis Transcriptomic Data

A whole genome co-expression network was created using Mycobacterium tuberculosis transcriptomic data from publicly available RNA-sequencing experiments covering a wide variety of experimental conditions. The network includes expressed regions with no formal annotation, including putative short RNAs and untranslated regions of expressed transcripts, along with the protein-coding genes. These unannotated expressed transcripts were among the best-connected members of the module sub-networks, making up more than half of the hub elements in modules that include protein-coding genes known to be part of regulatory systems involved in stress response and host adaptation. This dataset provides a valuable resource for investigating the role of non-coding RNA, and conserved hypothetical proteins, in transcriptomic remodelling. Based on their connections to genes with known functional groupings and correlations with replicated host conditions, predicted expressed transcripts can be screened as suitable candidates for further experimental validation.

bioinformatics↗