AP-1/C-FOS and AP-1/FRA2 differentially regulate early and late adipogenic differentiation of mesenchymal stem cells.

Obesity is defined as an abnormal accumulation of adipose tissue in the body and is a major global health problem due to increased morbidity and mortality. Adipose tissue is made up of adipocytes, which are fat-storing cells, and the differentiation of these fat cells is known as adipogenesis. Several transcription factors (TFs) such as CEBPß, CEBPα, PPARγ, GATA, and KLF have been reported to play a key role in adipogenesis. In this study, we report one more TF AP-1, which is found to be involved in adipogenesis. Human mesenchymal stem cells  were differentiated into adipocytes, and the expression pattern of different subunits of AP-1 was examined during adipogenesis. It was observed that C-FOS was predominantly expressed at an early stage (Day 2), whereas FRA2 expression peaked at later stages (Days 6 and 8) of adipogenesis. Chromatin immunoprecipitation-sequencing analysis revealed that C-FOS binds mainly to the promoters of WNT1, miR-30a, and ANAPC7 and regulates their expression during mitotic clonal expansion. In contrast, FRA2 binds to the promoters of CIDEA, NOTCH1, ARAF, and MYLK, regulating their expression and lipid metabolism. Data obtained clearly indicate that the differential expression of C-FOS and FRA2 is crucial for different stages of adipogenesis. This also raises the possibility of considering AP-1 as a therapeutic target for treating obesity and related disorders.

Comprehensive profiling of rRNA-derived small RNAs in Arabidopsis thaliana using rsRNAfinder pipeline.

Ribosomal RNA (rRNA) gives rise to non-random small RNA fragments known as ribosomal-derived small RNAs (rsRNAs), which despite their biological importance, have been relatively understudied in comparison to other short non-coding RNAs. There exists a compelling necessity to develop a methodology for the identification, categorization, and quantification of rsRNAs from small RNA sequencing (sRNA-seq) data sets, considering the unique characteristics of ribosomal RNA (rRNA). To bridge this gap, we introduce 'rsRNAfinder' a specialized pipeline designed within the Snakemake framework. This analytical approach enables robust identification of rsRNAs using sRNA-seq datasets from Arabidopsis thaliana. Our methodology constitutes an integrated bioinformatic pipeline designed for different kinds of analysis.1.sRNA-seq data analysis: It performs in-depth analysis of reference-aligned sRNA-seq data, facilitating rsRNA annotation and quantification.2.Parametric reporting: Our pipeline provides comprehensive reports encompassing key parameters such as rsRNA size distributions, strandedness, genomic origin, and source rRNA origin.3.Illustrative validation: We have demonstrated the utility of our approach by conducting comprehensive rsRNA annotation in Arabidopsis thaliana. This validation reveals unique rsRNAs originating from all rRNA types, each of them distinguished by distinct identity, abundance, and length.