Biology subjects

Jarnuczak, A. F.

Publications and source records attributed to Jarnuczak, A. F..

5 recordsLinked to original sources

bioRxiv2024

Network-driven identification of indisulam neo-substrates for targeted protein degradation

Indisulam, a DCAF15-based molecular glue degrader, induces widespread proteome changes with implications for cell division and chromosome segregation. While RBM39 and RBM23 are two well-characterized indisulam neo-substrates, additional targets likely exist. To identify those degradation targets, we applied a network-based approach to prioritize novel neo-substrates from large-scale omics data. Our approach integrates proteome-wide expression measurements with information from publicly accessible databases into a multiplex heterogeneous network. Utilizing a Random Walk with Restart algorithm, we identified a preliminary list of 30 neo-substrates. These proteins are likely interactors with DCAF15 in the presence of indisulam and are subject to subsequent degradation. Experimental validation of RBM5, one of the shortlisted candidates, confirmed its degradation in a proteasome-dependent manner, supporting its identification as a novel indisulam neo-substrate. Our work employs established network resources and analytical methods to effectively identify direct targets of indisulam molecular glue degrader. This approach is readily adaptable for exploring novel targets across other molecular glue systems, enhancing its applicability and value to the drug discovery community. SummaryMolecular glue neo-substrate identification via network analysis and proteomics profiling

bioinformatics↗

bioRxiv2021

PCSK9 affects expression of key surface proteins in human pancreatic beta cells through intra- and extracellular regulatory circuits

Aims/hypothesisProprotein convertase subtilisin/kexin 9 (PCSK9) is involved in the degradation of LDLR. However, PCSK9 can target other proteins in a cell-type specific manner. While PCSK9 has been detected in pancreatic islets, its expression in insulin-producing pancreatic beta cells is debated. Herein, we studied PCSK9 expression, regulation and function in the human pancreatic beta cell line EndoC-{beta}H1. MethodsWe assessed PCSK9 expression in mouse and human pancreatic islets, and in the pancreatic beta cell line EndoC-{beta}H1. We also studied PCSK9 regulation by cholesterol, lipoproteins, Mevastatin, and by SREBPs transcription factors. To evaluate PCSK9 function in pancreatic beta cells, we performed PCSK9 gain-and loss-of-function experiments in EndoC-{beta}H1 using siPCSK9 or recombinant PCSK9 treatments, respectively. ResultsWe demonstrate that PCSK9 is expressed and secreted by pancreatic beta cells. In EndoC-{beta}H1 cells, PCSK9 expression is regulated by cholesterol and by SREBPs transcription factors. Importantly, PCSK9 knockdown results in multiple transcriptome, proteome and secretome deregulations and impaired insulin secretion. By gain- and loss-of-function experiments, we observed that PCSK9 regulates the expression levels of LDLR and VLDLR through an extracellular mechanism while CD36, PD-L1 and HLA-ABC are regulated through an intracellular mechanism. Conclusions/interpretationCollectively, these results highlight PCSK9 as an important regulator of CD36, PD-L1 and HLA-ABC cell surface expression in pancreatic beta cells. Data availabilityRNA-seq data have been deposited to GEO database with accession number GSE182016. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the following identifiers: PXD027921, PXD027911 and PXD027913.

cell biology↗

bioRxiv2021

Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1

Early diabetes research is hampered by limited availability, variable quality and instability of human pancreatic islets in culture. Little is known about the human {beta} cell secretome, and recent studies question translatability of rodent {beta} cell secretory profiles. Here, we verify representativeness of EndoC-{beta}H1, one of the most widely used human {beta} cell lines, as a translational human {beta} cell model based on omics and characterize the EndoC-{beta}H1 secretome. We profiled EndoC-{beta}H1 cells using RNA-seq, Data Independent Acquisition (DIA) and Tandem Mass Tag proteomics of cell lysate. Omics profiles of EndoC-{beta}H1 cells were compared to human {beta} cells and insulinomas. Secretome composition was assessed by DIA proteomics. Agreement between EndoC-{beta}H1 cells and primary adult human {beta} cells was ~90% for global omics profiles as well as for {beta} cell markers, transcription factors and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-{beta}H1 cells compared to adult {beta} cells. Consistently, similarity was slightly higher with benign non-metastatic insulinomas. EndoC-{beta}H1 secreted 671 proteins in untreated baseline state and 3,278 proteins when stressed with non-targeting control siRNA, including known {beta} cell hormones INS, IAPP, and IGF2. Further, EndoC-{beta}H1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. Unexpectedly, exosomes appeared to be a major mode of secretion in EndoC-{beta}H1 cells. We believe that secretion of exosomes and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/459582v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1976bc0org.highwire.dtl.DTLVardef@233c4eorg.highwire.dtl.DTLVardef@14c3350org.highwire.dtl.DTLVardef@1bcf0d5_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe validate EndoC-{beta}H1 as a translational human {beta} cell model using omics. C_LIO_LIWe present the first unbiased proteomics composition of human {beta} cell line secretome. C_LIO_LIThe secretome of human {beta} cells is more extensive than previously thought. C_LIO_LIUntreated cells secreted 671 proteins and stressed cells secreted 3,278 proteins. C_LIO_LISecretion of exosomes and bioactive peptides constitute directions of future research. C_LI

bioinformatics↗

bioRxiv2019

An integrated landscape of protein expression in human cancer

Using public proteomics datasets, mostly available through the PRIDE database, we assembled a proteomics resource for 191 cancer cell lines and 246 clinical tumour samples, across 13 cancer lineages. We found that baseline protein abundance in cell lines was generally representative of tumours. However, when considering differences in protein expression between tumour subtypes, as exemplified in the breast lineage, many of these changes were no longer recapitulated in the cell line models. Integration of proteomics and transcriptomics data suggested that the level of transcriptional control in cell lines changed significantly depending on their lineage. Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis study including cancer-related proteomics datasets. We anticipate this aggregated dataset will be of significant aid to future studies requiring a reference to baseline protein expression in cancer.

bioinformatics↗

bioRxiv2019

The functional landscape of the human phosphoproteome

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. While tens of thousands of phosphorylation sites have been identified in human cells to date, the extent and functional importance of the phosphoproteome remains largely unknown. Here, we have analyzed 6,801 publicly available phospho-enriched mass spectrometry proteomics experiments, creating a state-of-the-art phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, 59 features indicative of proteomic, structural, regulatory or evolutionary relevance were integrated into a single functional score using machine learning. We demonstrate how this prioritization identifies regulatory phosphosites across different molecular mechanisms and pinpoint genetic susceptibilities at a genomic scale. Several novel regulatory phosphosites were experimentally validated including a role in neuronal differentiation for phosphosites present in the SWI/SNF SMARCC2 complex member. The scored reference phosphoproteome and its annotations identify the most relevant phosphorylations for a given process or disease addressing a major bottleneck in cell signaling studies.

genomics↗