Biology subjects

Schlatzer, D.

Publications and source records attributed to Schlatzer, D..

6 recordsLinked to original sources

bioRxiv2024

Cooperativity of c-MYC with Krüppel-Like Factor 6 Splice Variant 1 induces phenotypic plasticity and promotes prostate cancer progression and metastasis

Metastasis remains a major cause of morbidity and mortality in men with prostate cancer, and the functional impact of the genetic alterations, alone or in combination, driving metastatic disease remains incompletely understood. The proto-oncogene c-MYC, commonly deregulated in prostate cancer. Transgenic expression of c-MYC is sufficient to drive the progression to prostatic intraepithelial neoplasia and ultimately to moderately differentiated localized primary tumors, however, c-MYC-driven tumors are unable to progress through the metastatic cascade, suggesting that a "second-hit" is necessary in the milieu of aberrant c-MYC-driven signaling. Here, we identified cooperativity between c-MYC and KLF6-SV1, an oncogenic splice variant of the KLF6 gene. Transgenic mice that co-expressed KLF6-SV1 and c-MYC developed progressive and metastatic prostate cancer with a histological and molecular phenotype like human prostate cancer. Silencing c-MYC expression significantly reduced tumor burden in these mice supporting the necessity for c-MYC in tumor maintenance. Unbiased global proteomic analysis of tumors from these mice revealed significantly enriched vimentin, a dedifferentiation and pro-metastatic marker, induced by KLF6-SV1. c-MYC-positive tumors were also significantly enriched for KLF6-SV1 in human prostate cancer specimens. Our findings provide evidence that KLF6-SV1 is an enhancer of c-MYC-driven prostate cancer progression and metastasis, and a correlated genetic event in human prostate cancer with potential translational significance.

cancer biology↗

bioRxiv2023

Making Proteomics Accessible: RokaiXplorer for interactive analysis of phospho-proteomic data

In this work, we present RokaiXplorer, an intuitive web tool designed to address the scarcity of user-friendly solutions for proteomics and phospho-proteomics data analysis and visualization. RokaiXplorer streamlines data processing, analysis, and visualization through an interactive online interface, making it accessible to researchers without specialized training in proteomics or data science. With its comprehensive suite of modules, RokaiXplorer facilitates the analysis of phosphosites, proteins, kinases, and gene ontology terms. The tool offers functionalities such as data normalization, statistical testing, enrichment analysis, subgroup analysis, report generator, and multiple visualizations, including volcano plots, bar plots, heatmaps, tables, and interactive networks. Additionally, RokaiXplorer allows researchers to effortlessly deploy their own data browsers, fostering the sharing of research data and findings interactively. By providing simplicity, efficiency, and multi-level data analysis within a single application, it is poised to become a valuable resource for the scientific community working with phospho-proteomic data. Access RokaiXplorer at: http://explorer.rokai.io

systems biology↗

bioRxiv2023

Exploring Temporal and Sex-Linked Dysregulation in Alzheimer's Disease Phospho-Proteome

This study aims to characterize dysregulation of phosphorylation for the 5XFAD mouse model of Alzheimers disease (AD). Employing global phosphoproteome measurements, we analyze temporal (3, 6, 9 months) and sex-dependent effects on mouse hippocampus tissue to unveil molecular signatures associated with AD initiation and progression. Our results indicate 1.9 to 4.4 times higher phosphorylation prevalence compared to protein expression across all time points, with approximately 4.5 times greater prevalence in females compared to males at 3 and 9 months. Moreover, our findings reveal consistent phosphorylation of known AD biomarkers APOE and GFAP in 5XFAD mice, alongside novel candidates BIG3, CLCN6 and STX7, suggesting their potential as biomarkers for AD pathology. In addition, we identify PDK1 as a significantly dysregulated kinase at 9 months in females, and the regulation of gap junction activity as a key pathway associated with Alzheimers disease across all time points. AD-Xplorer, the interactive browser of our dataset, enables exploration of AD-related changes in phosphorylation, protein expression, kinase activities, and pathways. AD-Xplorer aids in biomarker discovery and therapeutic target identification, emphasizing temporal and sex-specific nature of significant phosphoproteomic signatures. Available at: https://yilmazs.shinyapps.io/ADXplorer HighlightsO_LIPhosphorylation-level dysregulation surpasses protein expression C_LIO_LIHigher phospho-dysregulation in females, starting as early as 3-month time point C_LIO_LINovel candidates BIG3, CLCN6, and STX7 exhibit consistent phospho-dysregulation C_LIO_LIDeveloped AD-Xplorer: Online tool to explore Alzheimers disease phospho-proteome C_LI In BriefThis study investigates dysregulation of phospho-proteome in an Alzheimers disease (AD) mouse model, identifying consistent phosphorylation of established AD biomarkers APOE and GFAP, along with novel candidate biomarkers BIG3, CLCN6, and STX7. In addition, the study observes significant PDK1 dysregulation at 9 months, particularly in females. AD-Xplorer, our interactive tool for exploring temporal and sex-linked phosphorylation changes, protein expression, kinase activities, and pathway enrichment, empowers researchers to gain deeper insights into AD mechanisms and uncover novel biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2022

Hypo-responsiveness of human alveolar macrophages to IFN-γ is not due to attenuated STAT1 signaling

Alveolar macrophages (AM) perform a primary defense mechanism in the lung through phagocytosis of inhaled particles and microorganisms. AM are known to be relatively immunosuppressive consistent with the aim to limit alveolar inflammation and maintain effective gas exchange in the face of these constant challenges. How AM respond to T cell derived cytokine signals, which are critical to the defense against inhaled pathogens, is less well understood. For example, successful containment of Mycobacterium tuberculosis (Mtb) in lung macrophages is highly dependent on IFN-{gamma} secreted by Th-1 lymphocytes, however, the proteomic IFN-{gamma} response profile in AM remains mostly unknown. In this study, we measured IFN-{gamma} induced protein abundance changes in human AM and autologous blood monocytes (MN). AM cells were activated by IFN-{gamma} stimulation resulting in STAT1 phosphorylation and production of MIG/CXCL9 chemokine. However, the global proteomic response to IFN-{gamma} in AM was dramatically limited in comparison to that of MN (9 AM vs 89 MN differentially abundant proteins). AM hypo-responsiveness was not explained by reduced JAK-STAT1 signaling nor increased SOCS1 expression. These findings suggest that AM have a tightly regulated response to IFN-{gamma} which may prevent excessive pulmonary inflammation but may also provide a niche for the initial survival and growth of Mtb and other intracellular pathogens in the lung.

immunology↗

bioRxiv2020

Robust Inference of Kinase Activity using Functional Networks

Mass spectrometry enables high-throughput screening of phospho-proteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimers disease and Parkinsons disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.

systems biology↗

bioRxiv2018

CoPhosK: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation Analysis

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naive Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing a to comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations. Author SummaryKinases play an important role in cellular regulation and have emerged as an important class of drug targets for many diseases, particularly cancers. Comprehensive identification of the links between kinases and their substrates enhances our ability to understand the underlying mechanism of diseases and signalling networks to drive drug discovery. Most of the current computational methods for prediction of kinase-substrate associations use static information such as sequence motifs and physical interactions to generate predictions. However, phosphorylation is a dynamic process and these static predictions may overlook unique features of cellular context, where kinases may be rewired. In this manuscript, we propose a computational method, CoPhosK, which uses the mass spectrometry based phosphoproteomics data to predict the kinase for all identified phosphosites in the experiment. We show that our approach complements and extends existing approaches.

bioinformatics↗