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Integration of a neuronal RNAseq dataset with the draft Gryllus bimaculatus transcriptome refines gene predictions and highlights potential systematic response to injury

Wang, F. F.; Fisher, H. P.; Ledwidge, L. M.; Graber, J. H.; Grindle, R. A.; Rollins, J. A.; Horch, H. W.

2025-07-18 neuroscience
10.1101/2025.07.13.663756 bioRxiv
Show abstract

The cricket Gryllus bimaculatus presents a compelling model for investigating neuroplasticity due to its unusual capability of adult structural reorganization. The molecular pathways underlying these changes are entirely unknown. Here, we reanalyzed RNAseq data, drawn from deafferented neuronal tissue one, three, and seven days post-injury, that was previously used to assemble a de novo transcriptome. In this current analysis, we aligned our original RNAseq data to the publicly available G. bimaculatus draft genome, and used the resulting alignments to refine and update the existing annotations. We identified over 10,000 missing genes and reported a measurable improvement in BUSCO scores. These updated annotations were then used as the basis for a DESeq2 differential expression analysis and subsequent functional enrichment analysis to further explore the potential molecular basis of this compensatory anatomical plasticity. Days one and three showed the largest post-deafferentation expression differences. Overall, more transcripts were upregulated rather than downregulated. Protein-protein interactions enriched for G-protein-related signaling, hormone metabolism, and membrane dynamics were evident. Changes in expression of factors related to small GTPases and nervous system development were particularly intriguing. We also identified a surprising enrichment of GO terms related to muscle contraction in this neuronal-specific transcriptome. Identifying these and other differentially regulated transcripts can be used to design hypotheses around well-conserved molecular mechanisms that may be involved in this unique example of adult structural plasticity in the cricket.

Published in PLOS ONE · training set

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