Biology subjects

Csosz, E.

Publications and source records attributed to Csosz, E..

6 recordsLinked to original sources

bioRxiv2026

Unveiling Cerebrospinal Fluid Protein Biomarkers in Pediatric Acute Lymphoblastic Leukemia Using Proximity Extension Assay

BackgroundAcute Lymphoblastic Leukemia (ALL) is a highly heterogeneous pediatric malignancy. Despite high survival rates, relapse and the involvement of central nervous system (CNS) remains a significant clinical challenge. Traditional clinical parameters often lack the precision required for early detection and risk stratification. This study utilizes high-throughput proteomics and machine learning (ML) to identify molecular signatures in cerebrospinal fluid (CSF) that characterize disease effect and treatment response. Methods82 CSF samples from 41 pediatric ALL patients at diagnosis (VD) and remission (VR) were analyzed. Proteomic profiling of 276 proteins was performed using Olinks Proximity Extension Assay. Differentially abundant proteins were identified (q-value< 0.05, |Log_2FC| > 0.5) using the Wilcoxon rank-sum test. Three ML algorithms - Random Forest, LASSO, and SVM-RFE - were integrated to select the differentially abundant proteins in VR and VD and between CNS involvement levels. To validate the data Pan-Cancer Atlas analysis was done using two different platforms. ResultsIn VR, we observed significant alterations in the expression of key proteins compared to VD, with ADGRG1 and KYNU showing a marked increase, while CCL17, CD5, CD27, CXCL9, CXCL11, FASLG, GZMA, and TNFRSF9 were significantly downregulated. Furthermore, our analysis identified distinct protein signatures associated with CNS involvement: CCL4, CTSC, CXCL10, CXCL9, and MMP7 were differentially abundant at the VD, whereas CAIX, CASP-8, HAGH, CXCL9, MMP7, MCP-2, and VWC2 at the VR. ConclusionIntegrating Olink proteomics with ML identified molecular signatures in ALL that have the potential to be further developed to a biomarker panel for monitoring treatment response and guiding personalized therapeutic strategies shifting the focus toward the Precision One Health approaches.

biochemistry↗

bioRxiv2026

Development of a Xylene-Free Sample Preparation Protocol for Quantitative Proteomics of Clinically Relevant Formaldehyde-Fixed Paraffin-Embedded Needle Biopsy Samples

Fresh-frozen tissues are considered the gold standard for proteomic analyses due to superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with efficient reversal of formaldehyde-induced crosslinks. In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Among the tested methods, a chaotropic-, reductant-, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Distinct proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research.

molecular biology↗

bioRxiv2026

Machine learning uncovers circulating biomarkers and molecular heterogeneity in obesity and type 2 diabetes

Obesity and Type 2 Diabetes (T2D) are heterogeneous metabolic disorders whose molecular diversity is incompletely defined. We analyzed circulating proteomic profiles from 129 individuals belonging to Control, Obesity, and T2D groups and applied complementary machine-learning approaches, including random forest, multinomial logistic regression with LASSO regularization, support vector machines, and ensemble voting to identify proteins distinguishing the clinical groups. Convergent model outputs revealed a partially overlapping panel of discriminative proteins. Model performance was evaluated in an independent dataset from the Human Protein Atlas (n=834) comprising healthy individuals, patients with Obesity, T2D, or other metabolic diseases. Unsupervised clustering further identified multiple proteomic subgroups within each clinical category, indicating substantial intragroup heterogeneity. Bootstrap random forest with null-model benchmarking highlighted stable cluster-discriminative proteins. These findings demonstrate that integrating circulating proteomics with machine learning can resolve molecular heterogeneity in Obesity and T2D and nominate candidate biomarkers for metabolic disease stratification.

biochemistry↗

bioRxiv2026

DIA-NN EasyFilter workflow for the fast and user-friendly critical assessment and visualization of DIA-NN proteomics analysis outcome

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics, particularly data-independent acquisition (DIA), has become widely adopted across in One Health approaches for biological and clinical research for quantitative protein characterization. Among the many computational tools available, DIA-NN has demonstrated superior performance; however, the primary output of the current versions is provided as a compact, compressed PARQUET file that can be difficult to interrogate without programming expertise. To address this limitation, we developed DIA-NN EasyFilter (DEF), a fast, user-friendly, KNIME-based workflow for comprehensive protein filtering, and visualization. DEF integrates chromatographic peak-based filtering, curated contaminant libraries, and quantity-quality assessment, along with interactive modules for qualitative and quantitative data exploration. The workflow is optimized for efficient execution within the KNIME local desktop environment and is designed to support end-users in improving accuracy and interpretability without requiring coding skills. We provide detailed description on how to run DEF and demonstrate the utility and robustness of DEF using published large-scale proteomics datasets, showing high comparability across studies regardless of instrument platform or dataset size. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/710308v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@ce9f1dorg.highwire.dtl.DTLVardef@13042faorg.highwire.dtl.DTLVardef@17d3907org.highwire.dtl.DTLVardef@2b3aee_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Unraveling the Regenerative Proteomic Signature of Helix aspersa's Slime in Human Dermal Fibroblasts by Data-driven Proteomics Approach

Proteins orchestrate essential cellular processes, including metabolism, communication, survival, and regeneration, making proteomic profiling a powerful strategy to elucidate complex biological responses. Snail slime (SnS) has emerged as a bioactive material with documented pro-healing, antioxidant, and anti-inflammatory properties; however, its effects at the proteome level in normal human dermal fibroblasts (NHDFs) remain unexplored. In this study, an LC-MS-based proteomic approach (Data are available via ProteomeXchange with identifier PXD075292) combined with network and Gene Ontology enrichment analyses was employed to investigate SnS-induced molecular reprogramming in NHDFs, followed by functional assays. Results show that SnS is well tolerated for up to 72 h, confirming its cytocompatibility, followed by proteomic analysis revealing enrichment of biological processes related to apoptosis regulation, oxidative stress response, wound healing, cell migration, and anti-aging. Network analysis identified AKT, PI3K, SRC, and KRAS family members as key hub proteins, indicating convergence on central signaling pathways controlling survival, redox balance, and migratory activity. Functional assays demonstrated a time-dependent, controlled modulation of apoptosis consistent with cellular turnover, alongside a hormetic redox response characterized by transient ROS signaling followed by enhanced antioxidant capacity. Importantly, SnS significantly accelerated fibroblast migration, achieving complete wound closure within 24 h. Collectively, these findings demonstrate that SnS induces coordinated proteomic and functional reprogramming that integrates redox modulation, controlled apoptosis, and enhanced migration, providing a mechanistic basis for its pro-healing and anti-aging effects and supporting its potential as a regenerative biomaterial.

cell biology↗

bioRxiv2023

Human Abdominal Subcutaneous-Derived Active Beige Adipocytes Carrying FTO rs1421085 Obesity-Risk Alleles Exert Lower Thermogenic Capacity

White adipocytes store lipids, have a large lipid droplet and few mitochondria. Brown and beige adipocytes, which produce heat, are characterized by high expression of uncoupling protein (UCP) 1, multilocular lipid droplets, and large amounts of mitochondria. The rs1421085 T-to-C single-nucleotide polymorphism (SNP) of the human FTO gene interrupts a conserved motif for ARID5B repressor, resulting in adipocyte type shift from beige to white. We obtained abdominal subcutaneous adipose tissue from donors carrying FTO rs1421085 TT (risk-free) or CC (obesity-risk) genotypes, isolated and differentiated their preadipocytes into beige adipocytes (driven by the PPAR{gamma} agonist rosiglitazone for 14 days), and activated them with dibutyryl-cAMP for 4 hours. Then, either the same culture conditions were applied for additional 14 days (active beige adipocytes) or it was replaced by a white differentiation medium (inactive beige adipocytes). White adipocytes were differentiated by their medium for 28 days. RNA-sequencing was performed to investigate the gene expression pattern of adipocytes carrying different FTO alleles and found that active beige adipocytes had higher brown adipocyte content and browning capacity compared to white or inactive beige ones when the cells were obtained from risk-free TT but not from obesity-risk CC genotype carriers. Active beige adipocytes carrying FTO CC had lower thermogenic gene (e.g., UCP1, PM20D1, CIDEA) expression and thermogenesis measured by proton leak respiration as compared to TT carriers. In addition, active beige adipocytes with CC alleles exerted lower expression of ASC1 neutral amino acid transporter (encoded by SLC7A10) and less consumption of Ala, Ser, Cys, and Gly as compared to risk-free carriers. We did not observe any influence of the FTO rs1421085 SNP on white and inactive beige adipocytes highlighting its exclusive and critical effect when adipocytes were activated for thermogenesis.

cell biology↗