Biology subjects

Cetin, H.

Publications and source records attributed to Cetin, H..

4 recordsLinked to original sources

bioRxiv2026

A primary human muscle cell-based assay for detecting myasthenia gravis autoantibody binding and assessing AChR cluster impairment

Myasthenia gravis (MG) is an autoimmune disease caused by pathogenic autoantibodies against proteins at the neuromuscular junction (NMJ). The diagnosis and clinical management of MG patients largely relies on the detection of antigen-specific autoantibodies targeting acetylcholine receptor (AChR) or muscle-specific kinase (MuSK). Yet a subset of patients remains seronegative for known MG autoantibodies, highlighting a critical need for alternative approaches to identify pathogenic NMJ antibodies. We established a new human in vitro model of the NMJ based on primary human muscle cells that recapitulates key features of the NMJ: differentiation to myotubes, expression of key NMJ proteins and formation of postsynaptic AChR clusters in response to agrin stimulation. The model allows new insights into myogenesis and genetic muscle diseases, and the new muscle cell-based assay (CBA) detected autoantibodies in sera from patients with AChR- and MuSK-positive MG with 96.43% sensitivity and 100% specificity, while healthy control sera showed no reactivity. Incubation with patient sera significantly reduced AChR clustering compared to controls, demonstrating functional pathogenic effects. Thus, we established a physiologically relevant human NMJ model that enables detection and functional characterization of neuromuscular autoantibodies. This novel approach addresses a key limitation of current antigen-specific diagnostics and provides a method for improved detection and characterization of MG antibodies, independent of antigen specificity. One Sentence SummaryWe established a postsynaptic human in vitro neuromuscular junction model to assess binding and pathogenicity of MG autoantibodies. Key messagesO_ST_ABSWhat is already known on this topic?C_ST_ABSCurrent diagnosis of myasthenia gravis (MG) relies largely on the detection of antigen-specific autoantibodies against AChR and MuSK, leaving a clinically relevant subset of patients seronegative. What are the new findings?We established a physiologically relevant human in vitro neuromuscular junction model based on primary human muscle cells and developed a novel muscle cell-based assay (CBA) for the detection of neuromuscular autoantibodies. How might this impact on clinical practice or future developments?The CBA detected autoantibodies in patients with AChR- or MuSK-positive MG with high sensitivity and specificity and demonstrated their functional pathogenic effects on AChR clustering. This antigen-independent approach may improve the detection and functional characterization of MG autoantibodies, particularly in patients who are seronegative in current diagnostic assays. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743478v1_ufig1.gif" ALT="Figure 1000"> View larger version (38K): org.highwire.dtl.DTLVardef@c2e1fforg.highwire.dtl.DTLVardef@8336fforg.highwire.dtl.DTLVardef@8fa2b0org.highwire.dtl.DTLVardef@1d94e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Epigenetic dysregulation of Th2 cytokine genes in MuSK myasthenia gravis and its modulation by immunosuppressive therapy

Background and objectivesMyasthenia gravis associated with antibodies against muscle-specific kinase (MuSK-MG) is a well-characterized IgG4-autoimmune disease, however, the mechanisms driving IgG4 predominance remain poorly understood. This study investigated whether promoter DNA methylation of cytokine genes involved in IgG4 class switching is associated with this immune response. MethodsPeripheral blood mononuclear cells were isolated from MuSK-MG patients (n=36), acetylcholine receptor myasthenia gravis (AChR-MG) patients as disease controls (n=7), and sex-matched healthy controls (n=12). Promoter DNA methylation of IL4, IL10, and IL13 was assessed by methylation-sensitive high-resolution melting and relative cytokine mRNA expression by qPCR. Associations with clinical variables, and antibody levels were subsequently evaluated. ResultsMuSK-MG patients showed lower median IL13 promoter methylation compared with healthy controls (p = 0.004). Median IL4 promoter methylation was also reduced in MuSK-MG compared with healthy controls (p < 0.001) and AChR-MG disease controls (p < 0.001), whereas no differences were observed for IL10 promoter methylation. Relative mRNA expression of IL4 (p = 0.0005), IL10 (p = 0.0462), and IL13 (p = 0.0002) was increased in MuSK-MG compared with AChR-MG. Compared with healthy controls, only IL4 expression remained significantly increased (p < 0.0001). Promoter methylation was inversely correlated with relative mRNA expression for IL4 (p < 0.0001), while IL13 showed a similar but non-significant trend (p = 0.054), no association was observed for IL10. Multivariable analysis demonstrated that treatment at sampling was independently associated with lower IL10 and IL13 promoter methylation, whereas no associations were observed with age, sex, disease phase, or disease duration. Promoter methylation did not correlate with total serum IgG4 or anti-MuSK IgG4 levels. DiscussionMuSK-MG is associated with selective hypomethylation of IL4 and IL13 promoters accompanied by increased cytokine gene expression, while IL10 promoter methylation remains unchanged. The association between treatment and IL10 and IL13 promoter methylation suggests that immunosuppressive therapy may influence epigenetic regulation in MuSK-MG. Together, these findings support a role for epigenetic dysregulation of Th2-associated cytokines in the immunological environment associated with IgG4 subclass switch. To our knowledge, this is the first study investigating IL4, IL10, and IL13 promoter DNA methylation in MuSK-MG.

immunology↗

bioRxiv2025

Multi-scale network topology analysis reveals metabolic reprogramming and therapeutic vulnerabilities in the tumor microenvironment

The tumor microenvironment comprises diverse cell populations that coordinate metabolic activities to sustain malignant growth, yet the systems-level organization of these interactions remains poorly understood. Here, we present an integrated framework combining single-cell transcriptomics, genome-scale metabolic modeling, and multi-scale network geometry to decode metabolic coordination in colorectal cancer. We demonstrate that FAP+ cancer-associated fibroblasts and MARCO+ tumor-associated macrophages undergo extensive reprogramming, establishing metabolic division of labor: fibroblasts specialize in amino acid and fatty acid metabolism while macrophages adopt cancer-like nucleotide biosynthesis programs. Systematic knockout analysis identified 19 tumor-selective vulnerabilities in branched-chain amino acid catabolism, with MAOB validated as a prognostic marker through patient survival analysis. To reveal architectural organization, we applied multifractal geometric characterization and Ollivier-Ricci curvature analysis for the first time to flux-weighted metabolic networks derived from context-specific genome-scale models. While conventional network metrics failed to distinguish tumor from normal phenotypes, multifractal analysis successfully separated tissue states through coordinated architectural changes across hierarchical scales. Role transition analysis revealed that 20-25% of metabolites undergo functional reorganization, with prostaglandin and bile acid derivatives emerging as critical communication hubs between stromal populations. Curvature analysis identified pathway-specific geometric remodeling in fatty acid metabolism (fibroblasts) and leukotriene metabolism (macrophages). Our findings establish that metabolic adaptation represents ecosystem-level network reorganization rather than isolated pathway changes, providing a generalizable framework for identifying therapeutic strategies targeting cooperative metabolic networks.

systems biology↗

bioRxiv2025

Cancer-associated fibroblasts drive metabolic heterogeneity in KRAS-mutant colorectal cancer cells

KRAS-mutant colorectal cancer (CRC) is characterized by metabolic reprogramming that can lead to tumor progression and drug resistance. The tumor microenvironment (TME) plays a pivotal role in modulating these metabolic adaptations. In particular, cancer-associated fibroblasts (CAFs), which make up a large portion of the TME, have been shown to strongly contribute to metabolic reprogramming in CRC. This study applies flux sampling, a computational method that explores the full range of feasible metabolic states, combined with representation learning and hierarchical clustering, to a computational model of central carbon metabolism to understand how CAFs influence metabolic adaptations of KRAS-mutant CRC cells following targeted enzyme knockdowns. Focusing on twelve key enzymes involved in glycolysis and the pentose phosphate pathway, knockdowns were simulated under both normal CRC media and CAF-conditioned media (CCM) conditions. Analysis revealed that CCM induces greater metabolic heterogeneity, with knockdown models exhibiting more variable and distinct metabolic states compared to those cultured in normal CRC media. While some enzyme knockdowns produced similar metabolic states, this overlap was less frequent in CCM, indicating that CAF-derived factors diversify the metabolic responses of CRC cells to enzyme perturbations. Pathway-level flux analysis demonstrated media-specific shifts in central carbon metabolism pathways. Importantly, the predicted biomass flux showed that enzyme knockdowns reduced growth across both conditions, but models in the CCM condition indicated CAFs could offer a protective effect against metabolic perturbation. Overall, this study reveals that CCM significantly influences the metabolic state and adaptability of KRAS-mutant CRC cells to enzyme perturbations, emphasizing the importance of including TME components in metabolic modeling and therapeutic development. These findings provide valuable insights into the metabolic adaptability of CRC and suggest that targeting tumor-CAF metabolic interactions may improve treatment strategies. Graphical Abstract Overview of computational workflowModels of interest represent simulated enzyme knockdowns in central carbon metabolism. Flux sampling searches the entire metabolic solution space and results in a distribution of flux values for each reaction within each model. Samples can be organized by knockdown and condition into matrices for input into representation learning. Representation learning is applied to sampling data to identify shared and independent metabolic states. Metabolic states indicate a heterogeneous response to enzyme knockdowns. Overlap of dark and light blue flux distributions, sampling clusters, and metabolic responses exemplify a shared metabolic state separate from to the gray unperturbed state. This workflow provides a low-dimensional representation of metabolic state that captures both the pathway- and reaction-level differences that describe each simulated knockdown. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/679631v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@cb6226org.highwire.dtl.DTLVardef@98d94eorg.highwire.dtl.DTLVardef@e2b20aorg.highwire.dtl.DTLVardef@116cfcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗