Biology subjects

Zaucha, J.

Publications and source records attributed to Zaucha, J..

5 recordsLinked to original sources

bioRxiv2025

Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery

Bimodal protein expression, characterized by the distribution of protein expression with two modes, is linked to phenotypic variation across various biological systems. Whereas previous studies focused on RNA expression data, we developed a bimodality model tailored for proteomics to enhance the identification of cancer-associated biomarkers and targets, facilitating precision oncology. We analyzed proteomics data from various cancer types and identified 2401 tumor-associated bimodal proteins. These proteins were evaluated for pathway enrichment, revealing significant associations with critical cancer pathways, such as metabolism of non-essential amino acids, interaction between the extracellular matrix and its receptors on the cell surface, and central carbon metabolism in cancer. Utilizing an AI-enhanced knowledge graph, we further delineated common patterns among pan-cancer tumor-associated bimodal proteins. A case study on the bimodal expression of TROP2 in colon adenocarcinoma highlighted upregulation of MYC and WNT/{beta}-catenin signaling pathways and down-regulation of inflammatory and interferon-related pathways in the TROP2-high group. The biological difference between TROP2-high and TROP2-low groups underscored its significance in determining cancer heterogeneity and differences in cancer vulnerability, which can inform treatment decisions. Our findings show the value of proteomics in uncovering novel biomarkers and advancing precision medicine, setting a precedent for further multi-omics integration and clinical validation.

bioinformatics↗

bioRxiv2023

Hypothesis-free phenotype prediction within a genetics-first framework

Cohort-wide sequencing studies have revealed that the largest category of variants is those deemed rare, even for the subset located in coding regions (99% of known coding variants are seen in less than 1% of the population1-3). Our understanding of how rare genetic variants influence disease and organism-level phenotypes has achieved limited progress, partly explained by the intrinsic difficulty in statistically evaluating the biological significance of rare events. Here we show that discoveries can instead be made through a knowledge-based approach using protein domains and ontologies (function and phenotype) that considers all coding variants regardless of allele frequency. We describe an ab initio, genetics-first method making molecular knowledge-based interpretations for exome-wide non-synonymous variants for phenotypes at the organism and cellular level. By using this reverse approach, we identify plausible novel genetic causes for developmental disorders that have eluded other established methods and present novel molecular hypotheses for the causal genetics of 40 phenotypes generated from a direct-to-consumer genotype cohort. This system offers a chance to extract further discovery from genetic data after standard tools have been applied.

genomics↗

bioRxiv2020

Quantitative secretome analysis establishes the cell type-resolved mouse brain secretome

To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the high-performance secretome-protein-enrichment-with-click-sugars method (hiSPECS). To demonstrate its broad utility, hiSPECS was used to identify the secretory response of brain slices upon LPS-induced neuroinflammation and to establish the cell type-resolved mouse brain secretome resource using primary astrocytes, microglia, neurons and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically-cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer-linked protease BACE1. hiSPECS and the brain secretome resource can be widely exploited to systematically study protein secretion, brain function and to identify cell type-specific biomarkers for CNS diseases.

neuroscience↗

bioRxiv2020

Detection of epistasis between ACTN3 and SNAP-25 with an insight towards gymnastic aptitude identification

In this study, we performed an analysis of the impact of performance enhancing polymorphisms (PEPs) on gymnastic aptitude while considering epistatic effects. Seven PEPs (rs1815739, rs8192678, rs4253778, rs6265, rs5443, rs1076560, rs362584) were considered in a case (gymnasts) - control (sedentary individuals) setting. The study sample comprised of two athletes sets: 27 elite (aged 24.8 {+/-} 2.1 years) and 46 sub-elite (aged 19.7 {+/-} 2.4 years) sportsmen as well as a control group of 245 sedentary individuals (aged 22.5 {+/-} 2.1 years). The DNA was derived from saliva and PEP alleles were determined by PCR, RT-PCR. Following Multifactor Dimensionality Reduction, logistic regression models were built. The synergistic effect for rs1815739 x rs362584 reached 5.43%. The rs1815739 x rs362584 epistatic regression model exhibited a good fit to the data (Chi-squared = 33.758, p {approx} 0) achieving a significant improvement in sportsmen identification over naive guessing. The area under the receiver operating characteristic curve was 0.715 (Z-score = 38.917, p {approx} 0). In contrast, the additive ACTN3 - SNAP-25 logistic regression model has been verified as non-significant. We demonstrate that a gene involved in the differentiation of muscle architecture - ACTN3 and a gene, which plays an important role in the nervous system - SNAP-25 interact. From the perspective originally established by the Berlin Academy of Science in 1751, the matter of communication between the brain and muscles via nerves adopts molecular manifestations. Further in-vitro investigations are required to explain the molecular details of the rs1815739 - rs362584 interaction.

genetics↗

bioRxiv2020

Deep learning model can predict water binding sites on the surface of proteins using limited-resolution data

The surfaces of proteins are generally hydrophilic but there have been reports of sites that exhibit an exceptionally high affinity for individual water molecules. Not only do such molecules often fulfil critical biological functions, but also, they may alter the binding of newly designed drugs. In crystal structures, sites consistently occupied in each unit cell yield electron density clouds that represent water molecule presence. These are recorded in virtually all high-resolution structures obtained through X-ray diffraction. In this work, we utilized the wealth of data from the RCSB Protein Data Bank to train a residual deep learning model named hotWater to identify sites on the surface of proteins that are most likely to bind water, the so-called water hot spots. The model can be used to score existing water molecules from a PDB file to provide their ranking according to the predicted binding strength or to scan the surface of a protein to determine the most likely water hot-spots de novo. This is computationally much more efficient than currently used molecular dynamics simulations. Based on testing the model on three example proteins, which have been resolved using both high-resolution X-ray crystallography (providing accurate positions of trapped waters) as well as low-resolution X-ray diffraction, NMR or CryoEM (where structure refinement does not yield water positions), we were able to show that the hotWater method is able to recover in the "water-free" structures many water binding sites known from the high-resolution structures. A blind test on a newly solved protein structure with waters removed from the PDB also showed good prediction of the crystal water positions. This was compared to two known algorithms that use electron density and was shown to have higher recall at resolutions >2.6 [A]. We also show that the algorithm can be applied to novel proteins such as the RNA polymerase complex from SARS-CoV-2, which could be of use in drug discovery. The hotWater model is freely available at (https://pypi.org/project/hotWater/).

bioinformatics↗