Identification of differentially expressed mRNA/lncRNA modules in acutely regorafenib-treated sorafenib-resistant Huh7 hepatocellular carcinoma cells.

The multikinase inhibitor sorafenib is the standard first-line treatment for advanced hepatocellular carcinoma (HCC), but many patients become sorafenib-resistant (SR). This study investigated the efficacy of another kinase inhibitor, regorafenib (Rego), as a second-line treatment. We produced SR HCC cells, wherein the PI3K-Akt, TNF, cAMP, and TGF-beta signaling pathways were affected. Acute Rego treatment of these cells reversed the expression of genes involved in TGF-beta signaling but further increased the expression of genes involved in PI3K-Akt signaling. Additionally, Rego reversed the expression of genes involved in nucleosome assembly and epigenetic gene expression. Weighted gene co-expression network analysis (WGCNA) revealed four differentially expressed long non-coding RNA (DElncRNA) modules that were associated with the effectiveness of Rego on SR cells. Eleven putative DElncRNAs with distinct expression patterns were identified. We associated each module with DEmRNAs of the same pattern, thus obtaining DElncRNA/DEmRNA co-expression modules. We discuss the potential significance of each module. These findings provide insights and resources for further investigation into the potential mechanisms underlying the response of SR HCC cells to Rego.

Comparative study of exoelectrogenic utilization preferences and hydrogen conversion among major fermentation products in microbial electrolysis cells.

This study comparatively investigated the exoelectrogenic utilization and hydrogen conversion of major dark fermentation products (acetate, propionate, butyrate, lactate, and ethanol) from organic wastes in dual-chamber microbial electrolysis cells (MECs) alongside their mixture as a simulated dark fermentation effluent (DFE). Acetate-fed MECs showed the highest hydrogen yield (1,465 mL/g chemical oxygen demand), near the theoretical maximum yield, with the highest coulombic efficiency (105%) and maximum current density (7.9 A/m2), followed by lactate-fed, propionate-fed, butyrate-fed, mixture-fed, and ethanol-fed MECs. Meanwhile, the highest hydrogen production rate (514 mL/L anolyte∙d) was observed in ethanol-fed MECs despite their lower coulombic efficiency. Butyrate was the least favored substrate, followed by propionate, leading to significantly delayed startup and reaction. The active anodic microbial community structure varied considerably among the MECs utilizing different substrates, particularly between Geobacter and Acetobacterium dominance. The results highlight the substantial effect of the DFE composition on its utilization and current-producing bioanode development.

Gene-Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors.

Asthma is a complex heterogeneous disease caused by gene-environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene-environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.