Biology subjects

Zararsiz, G.

Publications and source records attributed to Zararsiz, G..

3 recordsLinked to original sources

bioRxiv2025

Multi-Omics Integration Reveals Lipid Metabolic Reprogramming as a Driver of Cystogenesis in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

Autosomal dominant polycystic kidney disease (ADPKD) is a most common hereditary kidney disorder and a major contributor to end-stage kidney disease (ESKD), but its molecular progression mechanisms are unclear. We performed metabolomic and transcriptomic profiling on a longitudinal cohort of 254 and 47 ADPKD patients, respectively, to identify molecular alterations and then applied Multi-Omics Factor Analysis (MOFA) to uncover coordinated signatures. Disease progression was associated with a marked shift in dysregulated lipids profile, increased acylcarnitines, and increased glycolytic metabolites, suggesting a shift from fatty acid oxidation (FAO) toward glycolysis. Transcriptomic analysis revealed enrichment of PPAR signaling, cytoskeletal remodeling, and cystogenesis, providing a mechanistic link between metabolic alterations and structural dysregulation. Integrative analysis identified a central axis where inflammation, metabolic reprogramming, and cytoskeletal remodeling converges. Our integrated multi-omics analysis defines a molecular framework where transcriptomic dysregulation drives metabolic reprogramming contribute to cystogenesis, providing potential biomarkers and insights to guide precision therapy in ADPKD progression. Lay SummaryThe clinical heterogeneity of ADPKD limits current prognostic tools for early disease progression, often delaying therapeutic intervention. This study integrates longitudinal clinical data with multi-omics profiling (whole-blood transcriptomics and plasma metabolomics) in a large ADPKD cohort using Multi-Omics Factor Analysis. This approach identified coordinated molecular signatures linking aberrant gene expression and metabolic reprogramming directly to disease progression, hypertension, and mortality status. These findings uncover novel mechanistic drivers of ADPKD and suggest biomarkers to enhance early risk stratification, supporting the development of precision medicine strategies.

bioinformatics↗

bioRxiv2018

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their trans criptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the predictive power of GERAS to identify genome-wide molecular factors that correlate with aging. We show that one of these factors, junb, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling to detect pro-aging factors and candidate genes associated with aging.

bioinformatics↗

bioRxiv2017

Quaternary structure evaluation tool for protein assemblies

The Protein Data Bank (PDB) is the single worldwide archive of experimentally-determined three-dimensional (3D) structures of proteins and nucleic acids. As of January 2017, the PDB housed more than 125,000 structures and was growing by more than 11,000 structures annually. Since the 3D structure of a protein is vital to understand the mechanisms of biological processes, diseases, and drug design, correct oligomeric assembly information is of critical importance. For example, it makes a difference if the protein is normally a dimer and not a monomer or a trimer or a tetramer or a hexamer in nature. Unfortunately, the biologically relevant oligomeric form of a 3D structure is not directly obtainable by X-ray crystallography. Instead, this information may be provided by the PDB Depositor as metadata coming from additional experiments, be inferred by sequence-sequence comparisons with similar proteins of known oligomeric state, or predicted using software, such as PISA (Proteins, Interfaces, Structures and Assemblies) or EPPIC (Evolutionary Protein Protein Interface Classifier). Despite significant efforts by professional PDB Biocurators during data deposition, there remain a number of structures in the archive with incorrect quaternary structure descriptions (or annotations). Further investigation is, therefore, needed to evaluate the correctness of quaternary structure annotations. In this study, we aim to identify the most probable oligomeric states for proteins represented in the PDB. Our approach evaluated the performance of four independent prediction methods, including text mining of primary publications, inference from homologous protein structures, and two computational methods (PISA and EPPIC). Aggregating predictions to give consensus results outperformed all four of the independent prediction methods, yielding 86% correct, 9% incorrect, and 5% inconclusive predictions, when tested with a well-curated benchmark dataset. We have developed a freely-available web-based tool to make this approach accessible to researchers and PDB Biocurators (http://quatstruct.rcsb.org).

bioinformatics↗