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Vaz, C.

Publications and source records attributed to Vaz, C..

3 recordsLinked to original sources

Distinct regulatory networks control toxin gene expression in elapid and viperid snakes

Venom systems are ideal models to study genetic regulatory mechanisms that underpin evolutionary novelty. Snake venom glands are thought to share a common origin, but there are major distinctions between venom toxins from the medically significant snake families Elapidae and Viperidae, and toxin gene regulation in elapids is largely unexplored. Here, we used high-throughput RNA-sequencing to profile gene expression and microRNAs between active (milked) and resting (unmilked) venom glands in an elapid (Eastern Brown Snake, Pseudonaja textilis), in addition to comparative genomics, to identify cis- and trans- acting regulation of venom production in an elapid in comparison to viperids (Crotalus viridis and C. tigris). Although there is conservation in high-level mechanistic pathways regulating venom production, there are histone methylation, transcription factor, and microRNA regulatory differences between these two snake families. Histone methyltransferases (KMT2A, KMT2C and KMT2D) and transcription factor (TF) specificity protein 1 (Sp1) were highly upregulated in the milked elapid venom gland, whereas nuclear factor I (NFI) TFs were upregulated after viperid venom milking. Sp1 and NFI cis-regulatory elements were common to toxin gene promoter regions, but many unique elements were also present between elapid and viperid toxins. microRNA profiles were distinctive between milked and unmilked venom glands for both snake families, and microRNAs were predicted to target different toxin transcripts. Our comparative transcriptomic and genomic analyses between toxin genes and isoforms in elapid and viperid snakes suggests independent toxin evolution between these two snake families, demonstrating multiple toxin genes and regulatory mechanisms converged to underpin a highly venomous phenotype.

evolutionary biology↗

Short-term diet intervention alters the small non-coding RNA (sncRNA) landscape of human sperm

Offspring health outcomes are often linked with epigenetic alterations triggered by maternal nutrition and intrauterine environment. Strong experimental data also link paternal preconception nutrition with pathophysiology in the offspring, but the mechanism(s) routing the effects of paternal exposures remain elusive. Animal experimental models have highlighted small non-coding RNAs (sncRNAs) as potential regulators of paternal effects, though less is known about the existence of similar mechanisms in human sperm. Here, we first characterised the baseline sncRNA landscape of human sperm, and then studied the effects of a 6-week diet intervention on their expression profile. Baseline profiling identified 5tRFs, miRNAs and piRNAs to be the most abundant sncRNA subtypes, primarily expressed from regulatory elements like UTRs, CpG-rich regions and promoters. Expression of a subset of these sncRNAs varied with age, BMI and sperm quality of the donor. Diet intervention enriched in vitamin D and omega-3 fatty acids showed a marked increase of these nutrients in circulation and altered the sperm sncRNA expression. These included 3 tRFs, 15 miRNAs and 112 piRNAs, with gene targets involved in fatty acid metabolism, vitamin D response (LXR/RXR activation, TGF-beta and Wnt signaling), and transposable elements. These findings provide evidence that human sperms are sensitive to alterations in exposures such as diet, and sncRNAs capture the epigenetic imprint of this change. Hence changes to paternal nutrition during preconception may improve sperm quality and offspring health outcomes. To benefit future research, we developed iDad_DB, an open access database of baseline and diet-altered sncRNA in human male germline.

bioinformatics↗

GrapeTree: Visualization of core genomic relationships among 100,000 bacterial pathogens

O_LICurrent methods struggle to reconstruct and visualise the genomic relationships of [≥]100,000 bacterial genomes.\nC_LIO_LIGrapeTree facilitates the analyses of allelic profiles from 10,000s of core genomes within a web browser window.\nC_LIO_LIGrapeTree implements a novel minimum spanning tree algorithm to reconstruct genetic relationships despite missing data together with a static \"GrapeTree Layout\" algorithm to render interactive visualisations of large trees.\nC_LIO_LIGrapeTree is a stand-along package for investigating Newick trees plus associated metadata and is also integrated into EnteroBase to facilitate cutting edge navigation of genomic relationships among >160,000 genomes from bacterial pathogens.\nC_LIO_LIThe GrapeTree package was released under the GPL v3.0 Licence.\nC_LI

bioinformatics↗