Aim: GDF15 levels are a biomarker for metformin use. We performed the functional annotation of noncoding genome-wide association study (GWAS) SNPs for GDF15 levels and the Genotype-Tissue Expressio... Graphical abstract

Long noncoding RNAs (lncRNAs) undergo splicing and have multiple transcribed isoforms. Nevertheless, for lncRNAs, as well as for mRNA, measurements of expression are routinely performed only at the gene level. Metformin is the first-line oral therapy for type 2 diabetes mellitus and other metabolic diseases. However, its mechanism of action remains not thoroughly explained. Transcriptomic analyses using metformin in different cell types reveal that only protein-coding genes are considered. We aimed to characterize lncRNA isoforms that were differentially affected by metformin treatment on multiple human cell types (three cancer, two non-cancer) and to provide insights into the lncRNA regulation by this drug. We selected six series to perform a differential expression (DE) isoform analysis. We also inferred the biological roles for lncRNA DE isoforms using in silico tools. We found the same isoform of an lncRNA (AC016831.6-205) highly expressed in all six metformin series, which has a second exon putatively coding for a peptide with relevance to the drug action. Moreover, the other two lncRNA isoforms (ZBED5-AS1-207 and AC125807.2-201) may also behave as cis-regulatory elements to the expression of transcripts in their vicinity. Our results strongly reinforce the importance of considering DE isoforms of lncRNA for understanding metformin mechanisms at the molecular level.

RNA processing is a highly conserved mechanism that serves as a pivotal regulator of gene expression. Alternative processing generates transcripts that can still be translated but lead to potentially nonfunctional proteins. A plethora of respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strategically manipulate the host’s RNA processing machinery to circumvent antiviral responses. We integrated publicly available omics datasets to systematically analyze isoform-level expression and delineate the nascent peptide landscape of SARS-CoV-2-infected human cells. Our findings explore a suggested but uncharacterized mechanism, whereby SARS-CoV-2 infection induces the predominant expression of unproductive splicing isoforms in key IFN signaling, interferon-stimulated (ISGs), class I MHC, and splicing machinery genes, including IRF7, HLA-B, and HNRNPH1. In stark contrast, cytokine and chemokine genes, such as IL6 and TNF, predominantly express productive (protein-coding) splicing isoforms in response to SARS-CoV-2 infection. We postulate that SARS-CoV-2 employs an unreported tactic of exploiting the host splicing machinery to bolster viral replication and subvert the immune response by selectively upregulating unproductive splicing isoforms from antigen presentation and antiviral response genes. Our study sheds new light on the molecular interplay between SARS-CoV-2 and the host immune system, offering a foundation for the development of novel therapeutic strategies to combat COVID-19.