Meta-Analysis of Transcriptomes in Insects Showing Density-Dependent Polyphenism

Abstract

With increasing public data, a statistical analysis approach called meta-analysis, which combines transcriptome results obtained from multiple studies, has succeeded in providing novel insights into targeted biological processes. Locusts and aphids are representative of insect groups that exhibit density-dependent plasticity. Although the physiological mechanisms underlying density-dependent polyphenism have been identified in aphids and locusts, the underlying molecular mechanisms remain largely unknown. In this study, we performed a meta-analysis of public transcriptomes to gain additional insights into the molecular underpinning of density-dependent plasticity. We collected RNA sequencing data of aphids and locusts from public databases and detected differentially expressed genes (DEGs) between crowded and isolated conditions. Gene set enrichment analysis was performed to reveal the characteristics of the DEGs. DNA replication (GO:0006260), DNA metabolic processes (GO:0006259), and mitotic cell cycle (GO:0000278) were enriched in response to crowded conditions. To date, these processes have scarcely been the focus of research. The importance of the oxidative stress response and neurological system modifications under isolated conditions has been highlighted. These biological processes, clarified by meta-analysis, are thought to play key roles in the regulation of density-dependent plasticity.

Danger Analysis: Risk-Averse on/Off-Target Assessment for Crispr Editing Without a Reference Genome

“…Finally, our pipeline evaluated the phenotypic risk of deleterious off-targets. Various studies have used Gene Ontology (GO) analysis to assess phenotypic effects( 20,23,[31][32][33][34] ). However, this study also considered off-target frequency because deleterious off-targets with many mismatches were expected to occur ( 5,45 ).…”

“…Phenotypic risk can be assessed by phenotype analysis using gene ontology (GO) annotation( 29,30 ). GO has been widely used for several decades( 20,23,[31][32][33][34] ). Recently, many RNA sequencing (RNA-seq) data and mapped genes have been annotated with GO terms to characterize the transcriptome phenotype under a specific condition of the organism.…”

Meta-analysis of public RNA sequencing data revealed potential key genes associated with reproductive division of labor in social Hymenoptera and termites

“…The 212 identified genes were ranked based on their QW-score values (Table S4: https://doi.org/10.6084/m9.figshare.21524286.v4). These genes were annotated by the corresponding homologs of other species ( D. melanogaster, H. sapiens, Mus musculu s, Bombyx mori, Bombus terrestis , and Nasonia vitripennis ) using the annotation table created by Yokoi et al 2022 [67] (Table S4: https://doi.org/10.6084/m9.figshare.21524286.v4). We focused on the top 10 and bottom 10 genes in Table S4 because they showed abrupt score changes (Figure 2 and Table 1).…”

“…A calculation method developed for the meta-analysis of RNA sequencing data was employed [33,[36][37][38][39] as described below. Pairs of the queen and worker RNA sequencing data (Table S2: https://doi.org/10.6084/m9.figshare.21524286.v1) were used to calculate the queen/worker ratio of TPM for each transcript (referred to as the QW ratio).…”

“…Additionally, it is possible to identify master regulatory genes by combining transcriptome data from a wide range of species. We developed meta-analysis methods for publicly available transcriptome data from humans, plants, and insects [33,[36][37][38][39]. In this study, we used a meta-analysis method to identify regulatory genes related to the reproductive division of labor in ants, bees, wasps, and termites.…”