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McCoy, C.

Publications and source records attributed to McCoy, C..

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Transcriptional variation and divergence of host-finding behaviour in Steinernema carpocapsae infective juveniles

Steinernema carpocapsae is an entomopathogenic nematode that employs nictation and jumping behaviours to find host insects. We aimed to investigate the transcriptional basis of variant host-finding behaviours in the infective juvenile (IJ) stage of three S. carpocapsae strains (ALL, Breton and UK1). RNA-seq analysis revealed that whilst up to 28% of the S. carpocapsae transcriptome was differentially expressed (P<0.0001) between strains, remarkably few of the most highly differentially expressed genes (>2 log2 fold change, P<0.0001) were from neuronal gene families. S. carpocapsae Breton displays increased chemotaxis toward the laboratory host Galleria mellonella, relative to the other strains. This correlates with the up-regulation of four srsx chemosensory GPCR genes, and a sodium transporter gene, asic-2, relative to both ALL and UK1 strains. The UK1 strain exhibits a decreased nictation phenotype relative to ALL and Breton strains, which correlates with co-ordinate up-regulation of neuropeptide like protein 36 (nlp-36), and down-regulation of an srt family GPCR gene, and a distinct asic-2-like sodium channel paralogue. To further investigate the link between transcriptional regulation and behavioural variation, we sequenced microRNAs across IJs of each strain. We have identified 283 high confidence microRNA genes, yielding 321 isomiR variants in S. carpocapsae, and find that up to 36% of microRNAs are differentially expressed (P<0.0001) between strains. Many of the most highly differentially expressed microRNAs (>2 log2 fold, P<0.0001) are predicted to regulate a variety of neuronal genes that may contribute to variant host-finding behaviours. We have also found evidence for differential gene isoform usage between strains, which alters predicted microRNA interactions, and could contribute to the diversification of behaviour. These data provide deeper insight to the transcriptional landscape of behavioural variation in S. carpocapsae, underpinning efforts to functionally dissect the parasite host-finding apparatus.\n\nAuthor summarySteinernema carpocapsae is a lethal parasite of insects. In order to find and invade a host insect, the S. carpocapsae infective juvenile will typically stand upright, waving its anterior in the air as it searches for host-specific cues. When the infective juvenile senses insect volatile compounds and movement (both signals are required), it will attempt to jump towards the source of those stimuli. Whilst the jumping behaviour is unique to Steinernema species nematodes, nictation is a host-finding behaviour shared with other important parasites of medical and veterinary importance. We have found that different strains of S. carpocpsae use modified host-finding strategies, and that these behavioural differences correlate with gene expression patterns, identifying genes that may be crucial in regulating aspects of host-finding. We also assessed the complement of microRNAs, which are small non-coding RNAs that regulate target gene expression. We found a surprising difference in the abundance of shared microRNAs between strains of S. carpocapsae; these differences also reveal expression differences that correlate with behavioural variation. Predicted microRNA target genes suggest that microRNA variation could significantly influence the behaviour of nematodes. Broadly, this study provides insight to the relationship between gene expression and behaviour, paving the way for detailed studies on gene function.

animal behavior and cognition

Effective Online Bayesian Phylogenetics Via Sequential Monte Carlo With Guided Proposals

AO_SCPLOWBSTRACTC_SCPLOWModern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phy-logenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this paper we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop guided proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely-used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.

bioinformatics