Biology subjects

Vlachogiannis, N. I.

Publications and source records attributed to Vlachogiannis, N. I..

3 recordsLinked to original sources

bioRxiv2026

Pharmacologic METTL3 inhibition attenuates TGF-beta-driven profibrotic signature in systemic sclerosis

Systemic sclerosis (SSc) is a devastating fibrosing disorder affecting the skin and internal organs, associated with the highest mortality among all rheumatic diseases. The mechanisms underlying excessive fibrogenesis in SSc are incompletely defined and treatment options are limited. N6-methyladenosine (m6A), the most abundant internal mRNA modification, is a reversible and pharmacologically tractable determinant of RNA fate. Here we mapped the m6A epitranscriptome of peripheral blood mononuclear cells (PBMCs) from patients with SSc using two orthogonal single-nucleotide-resolution platforms, a MazF-based m6A microarray and nanopore direct RNA sequencing. SSc PBMCs showed coordinated upregulation of the m6A writer complex (METTL3, METTL14, WTAP), a two-fold increase in global m6A, and redistribution of m6A marks from 3' untranslated regions toward coding sequences. Both platforms converged on TGF-b signaling as the most enriched pathway among differentially methylated transcripts, with hypermethylation of TGFB1, JUNB and KLF10 transcripts. Pharmacologic inhibition of METTL3 with STM2457 significantly reversed TGF-b-induced transcriptomic changes in skin fibroblasts preventing their transformation into pathological myofibroblasts. Together, these findings uncover an epitranscriptomic signature driving profibrotic transcriptional alterations in SSc, including enhanced TGF-b signaling, which can be effectively reversed by clinically available enzymatic m6A inhibitors. Our data suggest that targeting m6A methylation may offer a novel therapeutic strategy to mitigate fibrosis in SSc.

molecular biology↗

bioRxiv2025

Exercise induces anti-inflammatory reprogramming in macrophages via Hsp60

Physical activity exerts systemic anti-inflammatory effects and reduces the risk for multiple non-communicable diseases, with 7.2% of all-cause deaths globally being attributed to physical inactivity. However, the cellular and molecular components of the exercise-induced anti-inflammatory effects remain only partly understood. Herein we show that moderate-intensity exercise promotes anti-inflammatory reprograming of macrophages orchestrated by the skeletal muscle cells secretome. Primary bone marrow-derived macrophages (BMDMs) exposed to the secretome of mechanically-loaded myotubes (exercise-conditioned medium, exCM) acquire an anti-inflammatory transcriptional profile and increased reliance on oxidative phosphorylation, as shown by Seahorse real-time cell metabolic analysis, compatible with an M2-like phenotypic switch. Using an unbiased proteomic analysis of the exCM we identify the chaperonin Hsp60 as a key mediator of the anti-inflammatory effects of exercise. Hsp60 expression increases in mechanically loaded myotubes in vitro, in the quadriceps muscle and serum of mice following an 8-week program of moderate-intensity aerobic exercise, as well as in human muscle after resistance training. Importantly, treatment of BMDMs with Hsp60 in vitro recapitulates the exCM-induced transcriptional reprograming, promoting an M2-like phenotype. Taken together, our data highlight Hsp60 as a novel component of the skeletal muscle cell-macrophage crosstalk, providing mechanistic insights into the anti-inflammatory effects of exercise.

physiology↗

bioRxiv2025

STExplorer: Navigating the Micro-Geography of Spatial Omics Data

Spatial transcriptomics (ST) has the potential to provide unprecedented insights into gene expression across tissue architecture, but existing analytical methods often overlook the full complexity of the spatial dimension. We present STExplorer, an R package that adapts well-established computational geography (CG) methods to explore the micro-geography of spatial omics data. By incorporating techniques like Geographically Weighted Principal Component Analysis (GWPCA), Fuzzy Geographically Weighted Clustering (FGWC), Geographically Weighted Regression (GWR), and analyses of observation Spatial Autocorrelation (SA), STExplorer enables the uncovering of spatially resolved patterns that capture the spatial heterogeneity of biological data. STExplorer provides a complete suite of functions for spatial analyses and visualisations, supporting deeper biological understanding and inference. Built on the Bioconductor ecosystem, the package integrates with SpatialFeatureExperiment objects, ensuring compatibility with existing pipelines. It includes preprocessing capabilities such as data import, quality control, gene count normalisation, and variable gene selection, alongside tools for downstream analysis and detailed visualisations that quantify and map spatial heterogeneity and relationships. We demonstrate the utility of STExplorer through applications to spatial transcriptomics datasets, revealing that spatially varying gene expression and relationships are often masked by standard analyses. By bridging bioinformatics and CG, STExplorer provides a novel and informed approach to spatial transcriptomics analysis, with robust tools to address spatial heterogeneity and its associated underlying biology, thereby advancing our understanding of complex tissue biology without reinventing the wheel.

bioinformatics↗