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BACKGROUND: The swift expansion of the invasive malaria vector Anopheles stephensi throughout Africa presents a major challenge to malaria control initiatives. Unlike the native African vectors, An. stephensi thrives in urban settings and has developed resistance to multiple classes of insecticides, including pyrethroids, organophosphates, and carbamates. METHODS: Insecticide susceptibility tests were performed on field-collected An. stephensi mosquitoes from Awash Sebac Kilo, Ethiopia, to assess insecticide resistance levels. Illumina RNA-seq analysis was then employed to compare the transcriptomes of field-resistant populations and susceptible laboratory strains (STE2). RESULTS: An. stephensi populations exhibited high levels of resistance to both deltamethrin (mortality, 39.4 ± 6.0%) and permethrin (mortality, 59.3 ± 26.3%) in WHO tube bioassays. RNA-seq analysis revealed that both field-resistant and field-unexposed populations exhibited increased expressions of genes associated with pyrethroid resistance, including esterases, P450s, and GSTs, compared to the susceptible STE2 strain. Notably, esterase E4 and venom carboxylesterase-6 were significantly overexpressed, up to 70-fold, compared to the laboratory strain. Functional enrichment analysis revealed a significant overrepresentation of genes associated with catalytic activity under molecular functions and metabolic process under biological process. Using weighted gene co-expression network analysis (WGCNA), we identified two co-expression modules (green and blue) that included 48 genes strongly linked to pyrethroid insecticide resistance. A co-expression network was subsequently built based on the weight values within these modules. CONCLUSIONS: This study highlights the role of esterases in the pyrethroid resistance of an An. stephensi population. The identification of candidate genes associated with insecticide resistance will facilitate the development of rapid diagnostic tools to monitor resistance trends.

Resistance training is used to combat skeletal muscle function decline in older adults. Few studies have been designed specific for females, resulting in very limited treatment options for skeletal muscle atrophy in aging women. Here, we analyzed the gene expression profiles of skeletal muscle samples from sedentary young women, sedentary older women, and resistance-trained older women, using microarray data from public database. A total of 45 genes that were differentially expressed during female muscle aging and reversed by resistance training were identified. Functional and pathway enrichment analysis, protein–protein interaction network analysis, and receiver operating characteristic analysis were performed to reveal the key genes and pathways involved in the effects of resistance training on female muscle aging. The collagen genes COL1A1, COL3A1, and COL4A1 were identified important regulators of female muscle aging and resistance training, by modulating multiple signaling pathways, such as PI3 kinase-Akt signaling, focal adhesions, extracellular matrix-receptor interactions, and relaxin signaling. Interestingly, the expression of CDKN1A and TP63 were increased during aging, and further upregulated by resistance training in older women, suggesting they may negatively affect resistance training outcomes. Our findings provide novel insights into the molecular mechanisms of resistance training on female muscle aging and identify potential biomarkers and targets for clinical intervention. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to identify the therapeutic targets and regulatory mechanisms of the antitumor drug gallic acid (GA) in cervical cancer (CC). Methods: HeLa cells were treated with GA and subjected to RNA-sequencing using the DNBSEQ platform. By combining the results of the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) analysis and RNA-seq, the differentially expressed genes (DEGs), including those upregulated and downregulated genes in CC compared with the normal cervix in the GEO and TCGA database, while expressed reversed after treatment with GA, were identified. Subsequently, the function enrichment analysis and protein–protein interaction of the DEGs were conducted. The candidate genes were identified using the Cytoscape software Gentiscape2.2 and MCODE plug-ins. Furthermore, the upstream microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) of the candidate genes were predicted using the online tools of MirDIP, TarBase, and ENCORI. Finally, the regulatory network was constructed using Cytoscape software. Results: CC cells are significantly inhibited by GA. Combining the GEO and TCGA databases and RNA-seq analyses, 127 DEGs were obtained and subjected to functional enrichment analysis. This analysis revealed that 221 biological processes, 82 cellular components, 63 molecular functions, and 36 KEGG pathways were employed to identify three therapeutic candidate genes, including CDC20, DLGAP5, and KIF20A. The upstream 13 miRNAs, 4 lncRNA, and 42 circRNAs were detected and used to construct a lncRNA/circRNA-miRNA-mRNA-pathway regulatory network. Conclusion: This study identified candidate genes and the regulatory networks underlying the therapeutic effects of GA on CC using GA data mining methods, thus establishing a theoretical basis for targeted therapy of CC. [ABSTRACT FROM AUTHOR]