Biology subjects

Saksager, A.

Publications and source records attributed to Saksager, A..

2 recordsLinked to original sources

bioRxiv2025

Evidence of altered antigen processing in autoimmune disease revealed by comparative immunopeptidomics

Autoimmune disease is traditionally attributed to failure of immune tolerance toward normally presented self-antigens. Although strong HLA associations implicate antigen presentation in disease risk, whether altered antigen processing contributes directly to autoimmunity remains unclear. To address this question, we analysed experimentally verified human T-cell autoantigens across twenty autoimmune diseases and classified them according to their natural presentation on HLA-DR in healthy individuals as either tolerant (detectably presented on MHC) or cryptic (not detectably presented). These classes exhibited distinct molecular and clinical associations: cryptic autoantigens were enriched in membrane proteins with tissue-specific functions and were predominantly linked to organ-specific autoimmune diseases, whereas tolerant autoantigens were largely extracellular, enriched in immune-related functions, and associated with systemic autoimmunity. Analysis of immunopeptidomics datasets from rheumatoid arthritis and multiple sclerosis further revealed disease-associated shifts in peptide flanking residues and increased relative solvent accessibility compared with healthy donors, consistent with altered proteolytic processing. Cryptic proteins were markedly overrepresented among multiple sclerosis-derived ligands. Together, these findings define two mechanistically distinct routes to autoimmune activation: one driven by altered antigen processing that exposes previously unseen self-proteins, and another driven by breakdown of tolerance toward normally presented self-antigens. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/693959v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@9249f5org.highwire.dtl.DTLVardef@35a55borg.highwire.dtl.DTLVardef@1fb0beforg.highwire.dtl.DTLVardef@cc97b3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

An Automatized Workflow to Study Mechanistic Indicators for Driver Gene Prediction with Moonlight

Prediction of tumor suppressors and oncogenes, also called driver genes, is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed changes in gene expression to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene or mutations in the regulator itself. In this work, we developed new functionalities and release Moonlight2, to provide the user with the mutation-based mechanistic indicator to streamline the analyses of this second layer of evidence. The function analyzes mutation information in a cancer cohort to classify them into driver and passenger mutations. Moreover, the function estimates the potential effect of a mutation on the transcriptional, translational, or protein structure/function level. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 and the newly developed function to a case study on Basal-like breast cancer subtype using data from The Cancer Genome Atlas. We found six oncogenes (SF3B4, EBNA1BP2, KRTCAP2, ZBTB8OS, RUNX2, and POLR2J) and ten tumor suppressor genes (KIF26B, NR5A2, ARHGAP25, EMCN, ARL15, PCOLCE, TPK1, TEK, KIR2DL4, and GMFG) containing a driver mutation in their promoter region, possibly explaining their deregulation. The Moonlight2R source code is available at https://github.com/ELELAB/Moonlight2R.

bioinformatics↗