Identification of covalent modifications regulating immune signaling complex composition and phenotype

Transcriptomic Context of RUNX3 Expression in Monocytes: A Cross-Sectional Analysis

The runt-related transcription factor 3 (RUNX3) regulates the differentiation of monocytes and their response to inflammation. However, the transcriptomic context of RUNX3 expression in blood monocytes remains poorly understood. We aim to learn about RUNX3 from its relationships within transcriptomes of bulk CD14+ cells in adults. This study used immunomagnetically sorted CD14+ cell gene expression microarray data from the Multi-Ethnic Study of Atherosclerosis (MESA, n = 1202, GSE56047) and the Correlated Expression and Disease Association Research (CEDAR, n = 281, E-MTAB-6667) cohorts. The data were preprocessed, subjected to RUNX3-focused correlation analyses and random forest modeling, followed by the gene ontology analysis. Immunity-focused differential ratio analysis with intermediary inference (DRAIMI) was used to integrate the data with protein-protein interaction network. Correlation analysis of RUNX3 expression revealed the strongest positive association for EVL (r_mean = 0.75, p_FDR-MESA = 5.37 × 10^-140, p_FDR-CEDAR = 5.52 × 10^-80), ARHGAP17 (r_mean = 0.74, p_FDR-MESA = 1.13 × 10^-169, p_FDR-CEDAR = 9.20 × 10^-59), DNMT1 (r_mean = 0.74, p_FDR-MESA = 1.10 × 10^-169, p_FDR-CEDAR = 1.67 × 10^-58), and CLEC16A (r_mean = 0.72, p_FDR-MESA = 3.51 × 10^-154, p_FDR-CEDAR = 2.27 × 10^-55), while the top negative correlates were C2ORF76 (r_mean = -0.57, p_FDR-MESA = 8.70 × 10^-94, p_FDR-CEDAR = 1.31 × 10^-25) and TBC1D7 (r_mean = -0.55, p_FDR-MESA = 1.36 × 10^-69, p_FDR-CEDAR = 7.81 × 10^-30). The RUNX3-associated transcriptome signature was involved in mRNA metabolism, signal transduction, and the organization of cytoskeleton, chromosomes, and chromatin, which may all accompany mitosis. Transcriptomic context of RUNX3 expression in monocytes hints at its relationship with cell growth, shape maintenance, and aspects of the immune response, including tyrosine kinases.

Cell-selective proteomics segregates pancreatic cancer subtypes by extracellular proteins in tumors and circulation

Cell-selective proteomics is a powerful emerging concept to study heterocellular processes in tissues. However, its high potential to identify non-cell-autonomous disease mechanisms and biomarkers has been hindered by low proteome coverage. Here, we address this limitation and devise a comprehensive azidonorleucine labeling, click chemistry enrichment, and mass spectrometry-based proteomics and secretomics strategy to dissect aberrant signals in pancreatic ductal adenocarcinoma (PDAC). Our in-depth co-culture and in vivo analyses cover more than 10,000 cancer cell-derived proteins and reveal systematic differences between molecular PDAC subtypes. Secreted proteins, such as chemokines and EMT-promoting matrisome proteins, associated with distinct macrophage polarization and tumor stromal composition, differentiate classical and mesenchymal PDAC. Intriguingly, more than 1,600 cancer cell-derived proteins including cytokines and pre-metastatic niche formation-associated factors in mouse serum reflect tumor activity in circulation. Our findings highlight how cell-selective proteomics can accelerate the discovery of diagnostic markers and therapeutic targets in cancer.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

A proteomic perspective on TNF-mediated signalling and cell death

The tumour necrosis factor (TNF) is the most potent inducer of cell death amongst cytokines. It is crucial for processes including homeostasis, the development of the immune system and fighting infections. However, high levels of TNF due to genetic disorders or persistent infections can contribute to autoinflammatory and autoimmune diseases or life-threatening conditions like sepsis. These diseases generally display increased levels of cell death, which, downstream of the TNF receptor, can either be caspase-dependent (apoptosis) or caspase-independent (necroptosis). Significant efforts have been invested in unravelling and manipulating signalling mechanisms regulating these two different types of cell death. Here I discuss how modern proteomic approaches like phosphoproteomics and secretomics provide a novel perspective on this central cytokine and its effect on inflammation and cell survival.

Decoding the cellular effects of genetic variation through interaction proteomics

It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.