Biology subjects

del Sol Mesa, A.

Publications and source records attributed to del Sol Mesa, A..

4 recordsLinked to original sources

bioRxiv2023

Transcriptomic reprogramming screen identifies SRSF1 as rejuvenation factor

Aging is a complex process that manifests through the time-dependent functional decline of a biological system. Age-related changes in epigenetic and transcriptomic profiles have been successfully used to measure the aging process1,2. Moreover, modulating gene regulatory networks through interventions such as the induction of the Yamanaka factors has been shown to reverse aging signatures and improve cell function3,4. However, this intervention has safety and efficacy limitations for in vivo rejuvenation5,6, underscoring the need for identifying novel age reversal factors. Here, we discovered SRSF1 as a new rejuvenation factor that can improve cellular function in vitro and in vivo. Using a cDNA overexpression screen with a transcriptomic readout we identified that SRSF1 induction reprograms the cell transcriptome towards a younger state. Furthermore, we observed beneficial changes in senescence, proteasome function, collagen production, and ROS stress upon SRSF1 overexpression. Lastly, we showed that SRSF1 can improve wound healing in vitro and in vivo and is linked to organismal longevity. Our study provides a proof of concept for using transcriptomic reprogramming screens in the discovery of age reversal interventions and identifies SRSF1 as a promising target for cellular rejuvenation.

genomics↗

bioRxiv2023

Epigenome-wide profiling in the dorsal raphe nucleus highlights cell-type-specific changes in TNXB in Alzheimer's disease

Recent studies have demonstrated that the dorsal raphe nucleus (DRN) is among the first brain regions affected in Alzheimers disease. Hence, in this study we conducted the first comprehensive epigenetic analysis of the DRN in AD, targeting both bulk tissue and single isolated cells. The Illumina Infinium MethylationEPIC BeadChip array was used to analyze the bulk tissue, assessing differentially modified positions (DMoPs) and regions (DMoRs) associated with Braak stage. The strongest Braak stage-associated DMoR in TNXB was targeted in a second patient cohort utilizing single laser-capture microdissected serotonin-positive (5-HT+) and -negative (5-HT-) cells isolated from the DRN. Our study revealed previously identified epigenetic loci, including TNXB and PGLYRP1, and novel loci, including RBMXL2, CAST, GNAT1, MALAT1, and DNAJB13. Strikingly, we found that the methylation profile of TNXB depends both on disease phenotype and cell type analyzed, emphasizing the significance of single cell(-type) neuroepigenetic studies in AD.

neuroscience↗

bioRxiv2022

RNetDys: identification of disease-related impaired regulatory interactions due to SNPs

The dysregulation of regulatory mechanisms due to Single Nucleotide Polymorphisms (SNPs) can lead to diseases and does not affect all cell (sub)types equally. Current approaches to study the impact of SNPs in diseases lack mechanistic insights. Indeed, they do not account for the regulatory landscape to decipher cell (sub)type specific regulatory interactions impaired due to disease-related SNPs. Therefore, characterizing the impact of disease-related SNPs in cell (sub)type specific regulatory mechanisms would provide novel therapeutical targets, such as promoter and enhancer regions, for the development of gene-based therapies directed at preventing or treating diseases. We present RNetDys, a pipeline to decipher cell (sub)type specific regulatory interactions impaired by disease-related SNPs based on multi-OMICS data. RNetDys leverages the information obtained from the generated cell (sub)type specific GRNs to provide detailed information on impaired regulatory elements and their regulated genes due to the presence of SNPs. We applied RNetDys in five disease cases to study the cell (sub)type differential impairment due to SNPs and leveraged the GRN information to guide the characterization of dysregulated mechanisms. We were able to validate the relevance of the identified impaired regulatory interactions by verifying their connection to disease-related genes. In addition, we showed that RNetDys identifies more precisely dysregulated interactions linked to disease-related genes than expression Quantitative Trait Loci (eQTL) and provides additional mechanistic insights. RNetDys is a pipeline available at https://github.com/BarlierC/RNetDys.git

bioinformatics↗

bioRxiv2022

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells. Here we present ChemPert (https://chempert.uni.lu/), a database consisting of 82270 transcriptional signatures across 167 non-cancer cell types, enabling more accurate predictions of perturbation responses and drugs compared to cancer databases in non-cancer cells. In particular, ChemPert correctly predicted drug effects for treating non-alcoholic steatohepatitis and novel drugs for osteoarthritis. Overall, ChemPert provides a valuable resource for drug discovery in non-cancer diseases.

bioinformatics↗