Molecular & Cellular Proteomics最新文献

Understanding dysregulated genes and pathways in cancer is critical for precision oncology. Integrating mass spectrometry-based proteomic data with transcriptomic data presents unique opportunities for systematic analyses of dysregulated genes and pathways in pan-cancer. Here, we compiled a comprehensive set of datasets, encompassing proteomic data from 2404 samples and transcriptomic data from 7752 samples across 13 cancer types. Comparisons between normal or adjacent normal tissues and tumor tissues identified several dysregulated pathways including mRNA splicing, interferon pathway, fatty acid metabolism, and complement coagulation cascade in pan-cancer. Additionally, pan-cancer upregulated and downregulated genes (PCUGs and PCDGs) were also identified. Notably, RRM2 and ADH1B, two genes which belong to PCUGs and PCDGs, respectively, were identified as robust pan-cancer diagnostic biomarkers. TNM stage-based comparisons revealed dysregulated genes and biological pathways involved in cancer progression, among which the dysregulation of complement coagulation cascade and epithelial-mesenchymal transition are frequent in multiple types of cancers. A group of pan-cancer continuously upregulated and downregulated proteins in different tumor stages (PCCUPs and PCCDPs) were identified. We further constructed prognostic risk stratification models for corresponding cancer types based on dysregulated genes, which effectively predict the prognosis for patients with these cancers. Drug prediction based on PCUGs and PCDGs as well as PCCUPs and PCCDPs revealed that small molecule inhibitors targeting CDK, HDAC, MEK, JAK, PI3K, and others might be effective treatments for pan-cancer, thereby supporting drug repurposing. We also developed web tools for cancer diagnosis, pathologic stage assessment, and risk evaluation. Overall, this study highlights the power of combining proteomic and transcriptomic data to identify valuable diagnostic and prognostic markers as well as drug targets and treatments for cancer.

Metaproteomics, the study of collective proteomes in environmental communities, plays a crucial role in understanding microbial functionalities affecting ecosystems and human health. Pathway analysis offers structured insights into the biochemical processes within these communities. However, no existing tool effectively combines pathway analysis with peptide- or protein-level data. We here introduce PathwayPilot, a web-based application designed to improve metaproteomic data analysis by integrating pathway analysis with peptide- and protein-level data, filling a critical gap in current metaproteomics bioinformatics tools. By allowing users to compare functional annotations across different samples or multiple organisms within a sample, PathwayPilot provides valuable insights into microbial functions. In the re-analysis of a study examining the effects of caloric restriction on gut microbiota, the tool successfully identified shifts in enzyme expressions linked to short-chain fatty acid biosynthesis, aligning with its original findings. PathwayPilot's user-friendly interface and robust capabilities make it a significant advancement in metaproteomics, with the potential for widespread application in microbial ecology and health sciences. All code is open source under the Apache2 license and is available at https://pathwaypilot.ugent.be.

Genotype-phenotype correlations of rare diseases are complicated by low patient number, high phenotype variability, and compound heterozygosity. Mutations may cause instability of single proteins, and affect protein complex formation or overall robustness of a specific process in a given cell. Ciliopathies offer an interesting case for studying genotype-phenotype correlations as they have a spectrum of severity and include diverse phenotypes depending on different mutations in the same protein. For instance, mutations in the intraflagellar transport protein IFT140 cause a vast spectrum of ciliopathies ranging from isolated retinal dystrophy to severe skeletal abnormalities and multi-organ diseases such as Mainzer-Saldino and Jeune syndrome. Here, the quantitative effects of 23 missense mutations in IFT140, which forms part of the crucial IFT-A complex of the ciliary machinery, were analyzed using affinity purification coupled with mass spectrometry (AP-MS). A subset of 10 mutations led to a significant and domain-specific reduction in IFT140-IFT-A complex interaction indicating complex formation issues and potentially hampering its molecular function. Knockout of IFT140 led to loss of cilia, as shown before. However, phenotypically only mild effects concerning cilia assembly were observed for two out of four tested IFT140 missense mutations. Therefore, our results demonstrate the utility of AP-MS in discerning pathogenic MMs from polymorphisms, and we postulate that reduced function is tolerated by the evolutionarily highly conserved IFT-A system.

Mass spectrometry-based proteomics has revolutionized bacterial identification and elucidated many molecular mechanisms underlying bacterial growth, community formation, and drug resistance. However, most research has been focused on a few model bacteria, overlooking bacterial diversity. In this study, we present the most extensive bacterial proteomic resource to date, covering 303 species, 119 genera, and five phyla with over 636,000 unique expressed proteins, confirming the existence of over 38,700 hypothetical proteins. Accessible via the public resource ProteomicsDB, this dataset enables quantitative exploration of proteins within and across species. Additionally, we developed MS2Bac, a bacterial identification algorithm that queries NCBI's bacterial proteome space in two iterations. MS2Bac achieved over 99% species-level and 89% strain-level accuracy, surpassing methods like MALDI-TOF and FTIR, as demonstrated with food-derived bacterial isolates. MS2Bac also effectively identified bacteria in clinical samples, highlighting the potential of MS-based proteomics as a routine diagnostic tool.

Accurate genome duplication requires a tightly regulated DNA replication program that relies on the fine regulation of origin firing. While the molecular steps involved in origin firing have been determined predominantly in budding yeast, the complexity of this process in human cells has yet to be fully elucidated. Here, we describe a straightforward proteomics approach to systematically analyze protein recruitment to the chromatin during induced origin firing in human cells. Using a specific inhibitor against CHK1 kinase, we induced a synchronized wave of dormant origin firing (DOF) and assessed the S phase chromatin proteome at different time points. We provide time-resolved loading dynamics of 3269 proteins, including the core replication machinery and origin firing factors. This dataset accurately represents known temporal dynamics of proteins on the chromatin during the activation of replication forks and the subsequent DNA damage due to the hyperactivation of excessive replication forks. Finally, we used our dataset to identify the condensin II subunit NCAPH2 as a novel factor required for efficient origin firing and replication. Overall, we provide a comprehensive resource to interrogate the protein recruitment dynamics of replication origin firing events in human cells.

Intercellular communication is fundamental to multicellular life and a core determinant of outcomes during viral infection, where the common goals of virus and host for persistence and replication are generally at odds. Hosts rely on encoded innate and adaptive immune responses to detect and clear viral pathogens, while viruses can exploit or disrupt these pathways and other intercellular communication processes to enhance their spread and promote pathogenesis. While virus-induced signaling can result in systemic changes to the host, striking alterations are observed within the cellular microenvironment directly surrounding a site of infection, termed the virus microenvironment (VME). Mechanisms employed by viruses to condition their VMEs are emerging and are critical for understanding the biology and pathologies of viral infections. Recent advances in experimental approaches, including proteomic methods, have enabled study of the VME in unprecedented detail. In this review article, we provide a primer on proteomic approaches used to study how viral infections alter intercellular communication, highlighting the ways in which these approaches have been implemented and the exciting biology they have uncovered. First, we consider the different molecules secreted by an infected cell, including proteins, either soluble or contained within extracellular vesicles, and metabolites. We further discuss the modalities of interactions facilitated by alteration at the cell surface of infected cells, including immunopeptide presentation and interactions with the extracellular matrix. Finally, we review spatial profiling approaches that have allowed distinguishing how specific subpopulations of cells within a VME respond to infection and alter their protein composition, discussing valuable insights these methods have offered.

Single cell proteomics was performed on human induced pluripotent stem cells (iPSCs), iPSC-derived cardiomyocytes, and adult cardiomyocytes. Over 700 proteins could be simultaneously measured in each cell revealing unique subpopulations. A sub-set of iPSCs expressed higher levels of Lin28a and Tra-1-60 towards the outer edge of cell colonies. In the cardiomyocytes, two distinct populations were found that exhibited complementary metabolic profiles. Cardiomyocytes from iPSCs showed a glycolysis profile while adult cardiomyocytes were enriched in proteins involved with fatty acid metabolism. Interestingly, rare single cells also co-expressed markers of both cardiac and neuronal lineages, suggesting there maybe a novel hybrid cell type in the human heart.

Signaling pathways often convergence on transcription factors and other DNA-binding proteins that regulate chromatin structure and gene expression, thereby governing a broad range of essential cellular functions. However, the repertoire of DNA-binding proteins is incompletely understood even for the best-characterized pathways. Here, we optimized a strategy for the isolation of Proteins on Chromatin (iPOC) exploiting tagged nucleoside analogs to label the DNA and capture associated proteins, thus enabling the comprehensive, sensitive, and unbiased characterization of the DNA-bound proteome. We then applied iPOC to investigate chromatome changes upon perturbation of the cancer-relevant PI3K-AKT-mTOR pathway. Our results show distinct dynamics of the DNA-bound proteome upon selective inhibition of PI3K, AKT, or mTOR, and we provide evidence how this signaling cascade regulates the DNA-bound status of SUZ12, thereby modulating H3K27me3 levels. Collectively, iPOC is a powerful approach to study the composition of the DNA-bound proteome operating downstream of signaling cascades, thereby both expanding our knowledge of the mechanism of action of the pathway and unveiling novel chromatin modulators that can potentially be targeted pharmacologically.

Protein phosphorylation plays a crucial role in regulating diverse biological processes. Perturbations in protein phosphorylation are closely associated with downstream pathway dysfunctions, whereas alterations in protein expression could serve as sensitive indicators of pathological status. However, there are currently few methods that can accurately identify the regulatory links between protein phosphorylation and expression, given issues like reverse causation and confounders. Here, we present Phoslink, a causal inference model to infer causal effects between protein phosphorylation and expression, integrating prior evidence and multiomics data. We demonstrated the feasibility and advantages of our method under various simulation scenarios. Phoslink exhibited more robust estimates and lower false discovery rate than commonly used Pearson and Spearman correlations, with better performance than canonical instrumental variable selection methods for Mendelian randomization. Applying this approach, we identified 345 causal links involving 109 phosphosites and 310 proteins in 79 lung adenocarcinoma (LUAD) samples. Based on these links, we constructed a causal regulatory network and identified 26 key regulatory phosphosites as regulators strongly associated with LUAD. Notably, 16 of these regulators were exclusively identified through phosphosite-protein causal regulatory relationships, highlighting the significance of causal inference. We explored potentially druggable phosphoproteins and provided critical clues for drug repurposing in LUAD. We also identified significant mediation between protein phosphorylation and LUAD through protein expression. In summary, our study introduces a new approach for causal inference in phosphoproteomics studies. Phoslink demonstrates its utility in potential drug target identification, thereby accelerating the clinical translation of cancer proteomics and phosphoproteomic data.

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.