CORUM: the comprehensive resource of mammalian protein complexes-2022

Effects of In Vitro Hemolysis and Repeated Freeze-Thaw Cycles in Protein Abundance Quantification Using the SomaScan and Olink Assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11 K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supporting Information files to be used as resources for the growing user communities of both platforms.

A comprehensive review and in silico analysis of the role of survivin (BIRC5) in hepatocellular carcinoma hallmarks: A step toward precision

Hepatocellular carcinoma (HCC) is a complex malignancy driven by the dysregulation of multiple cellular pathways. Survivin, a key member of the inhibitor of apoptosis (IAP) family, plays a central role in HCC tumorigenesis and progression. Despite significant research, a comprehensive understanding of the contributions of survivin to the hallmarks of cancer, its molecular network, and its potential as a therapeutic target remains incomplete. In this review, we integrated bioinformatics analysis with an extensive literature review to provide deeper insights into the role of survivin in HCC. Using bioinformatics tools such as the Human Protein Atlas, GEPIA, STRING, TIMER, and Metascape, we analyzed survivin expression and its functional associations and identified the top 20 coexpressed genes in HCC. These include TK1, SPC25, SGO2, PTTG1, PRR11, PLK1, NCAPH, KPNA2, KIF2C, KIF11, HJURP, GTSE1, FOXM1, CEP55, CENPA, CDCA3, CDC45, CCNB2, CCNB1 and CTD-2510F5.4. Our findings also revealed significant protein-protein interactions among these genes, which were enriched in pathways associated with the FOXM1 oncogenic signaling cascade, and biological processes such as cell cycle regulation, mitotic checkpoints, and diseases such as liver neoplasms. We also discussed the involvement of survivin in key oncogenic pathways, including the PI3K/AKT, WNT/β-catenin, Hippo, and JAK/STAT3 pathways, and its role in modulating cell cycle checkpoints, apoptosis, and autophagy. Furthermore, we explored its interactions with the tumor microenvironment, particularly its impact on immune modulation through myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages, and natural killer cell function in HCC. Additionally, we highlighted its involvement in alkylglycerone phosphate synthase (AGPS)-mediated lipid reprogramming and identified important gaps in the survivin network that warrant further investigation. This review also examined the role of survivin in cancer stemness, inflammation, and virally mediated hepatocarcinogenesis. We evaluated its potential as a diagnostic, prognostic, predictive, and pharmacodynamic biomarker in HCC, emphasizing its relevance in precision medicine. Finally, we summarized emerging survivin-targeted therapeutics and ongoing clinical trials, underscoring the need for novel strategies to effectively target survivin in HCC.

Multimodal cell maps as a foundation for structural and functional genomics

Human cells consist of a complex hierarchy of components, many of which remain unexplored1,2. Here we construct a global map of human subcellular architecture through joint measurement of biophysical interactions and immunofluorescence images for over 5,100 proteins in U2OS osteosarcoma cells. Self-supervised multimodal data integration resolves 275 molecular assemblies spanning the range of 10-8 to 10-5 m, which we validate systematically using whole-cell size-exclusion chromatography and annotate using large language models3. We explore key applications in structural biology, yielding structures for 111 heterodimeric complexes and an expanded Rag-Ragulator assembly. The map assigns unexpected functions to 975 proteins, including roles for C18orf21 in RNA processing and DPP9 in interferon signalling, and identifies assemblies with multiple localizations or cell type specificity. It decodes paediatric cancer genomes4, identifying 21 recurrently mutated assemblies and implicating 102 validated new cancer proteins. The associated Cell Visualization Portal and Mapping Toolkit provide a reference platform for structural and functional cell biology.

Effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification using the SomaScan and Olink assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supplementary Data files to be used as resources for the growing user communities of both platforms.

Affinity Purification Mass Spectrometry on the Orbitrap-Astral Mass Spectrometer Enables High-Throughput Protein-Protein Interaction Mapping

Classical proteomics experiments offer high-throughput protein quantification but lack direct evidence of the spatial organization of the proteome, including protein-protein interaction (PPIs) networks. While affinity purification mass spectrometry (AP-MS) is the method of choice for generating these networks, technological impediments have stymied the throughput of AP-MS sample collection and therefore constrained the rate and scale of experiments that can be performed. Here, we build on advances in mass spectrometry hardware that have rendered high-flow liquid chromatography separations a viable solution for faster throughput quantitative proteomics. We describe our methodology using the Orbitrap-Astral mass spectrometer with 7 min, high-flow separations to analyze 216 AP-MS samples in ∼29 h. We show that the ion-focusing advancements, rapid mass analysis, and sensitive ion detection facilitate narrow-bin data-independent acquisition on a chromatographically practical timescale. Further, we highlight several aspects of state-of-the-art confidence-scoring software that warrant reinvestigation given the analytical characteristics of the Orbitrap-Astral mass spectrometer through comparisons with an enrichment-based thresholding technique. With our data, we generated an interaction map between 998 human proteins and 59 viral proteins. These results hold promise in expediting the throughput of AP-MS experiments, enabling more high-powered PPI studies.

Theranostic Target NSUN2, a C(5)-Methyltransferase, Phospho-Regulatory Network Uncovered with Systematic Assembly of 805 Datasets

The RNA cytosine C(5)-methyltransferase NSUN2 is involved in RNA modification and regulates gene expression and genomic stability. Beyond multiple sequence/copy number variations, NSUN2 displays altered phosphoprotein expression in various cancers and developmental disorders, thereby making it a prime molecular target of relevance to both therapeutics and diagnostics, that is, theranostics. Despite its key role in kinase-regulated pathways and broader biological processes, the phospho-regulatory network of NSUN2 remains largely unexplored. We report here a systematic assembly of 805 phosphoproteomics datasets from the literature, among which 239 datasets showed differential regulation of NSUN2 phosphopeptides and 40 ensembled phosphosites in NSUN2. Significantly, the phosphorylation sites Ser456, Ser743, and Ser751 represented NSUN2 in ∼50% of these datasets. This is notable given that the functional roles of these phosphosites have been previously underappreciated and underrepresented in the scientific literature. Therefore, we implemented a codetection/coregulation approach based on the phosphosites in other proteins that are codifferentially regulated with phosphopeptides of NSUN2. This approach led to our identification of 55 interactors, 4 potential kinases, and 7 other methylases whose phosphopeptides were codifferentially regulated with NSUN2 phosphopeptides. To the best of our knowledge, this study provides the first phosphosite-centric regulatory network model of NSUN2 to employ theranostic strategies for targeting NSUN2 in cancers and other disorders.

Orchestrating Intracellular Calcium Signaling Cascades by Phosphosite-Centric Regulatory Network: A Comprehensive Analysis on Kinases CAMKK1 and CAMKK2

Intracellular calcium signaling is a cornerstone in cell biology and a key molecular target for human health and disease. Calcium/calmodulin dependent protein kinase kinases, CAMKK1 and CAMKK2 are serine/threonine kinases that contribute to the regulation of intracellular calcium signals in response to diverse stimuli. CAMKK1 generally has stable dynamics, whereas CAMKK2 dysregulation triggers oncogenicity and neurological disorders. To differentiate the phosphosignaling hierarchy associated with predominant phosphosites of CAMKK1 and CAMKK2, we assembled and analyzed the global cellular phosphoproteome datasets. We found that predominant phosphosites in CAMKK1 and CAMKK2 are located outside the kinase domain, and their phosphomotifs are highly homologous. Further, we employed a coregulation analysis approach to these predominant phosphosites, to infer the co-occurrence patterns of phosphorylations within CAMKKs and the coregulation patterns of other protein phosphosites with CAMKK sites. We report herein that independent phosphorylations at CAMKK2 S100 and S511 increase their enzymatic activity in the presence of calcium/calmodulin. In addition, the study unveils kinase-substrate associations such as RPS6KB1 as a novel high-confidence upstream kinase of both CAMKK1 S74 and CAMKK2 S100. Further, CAMKK2 was identified as a primary orchestrator in mediating intracellular calcium signaling cascades compared to CAMKK1 based on coregulation patterns of phosphosites from proteins involved in the calcium signaling pathway. These molecular details shed promising insights into the pathophysiology of several diseases such as cancers and psychiatric disorders associated with kinase activity dysregulations of CAMKK2 and further open the avenue for novel PTM-directed therapeutic strategies to regulate CAMKK2.

Gene and transcript expression patterns, coupled with isoform switching and long non-coding RNA dynamics in adipose tissue, underlie the longevity of Ames dwarf mice

Our study investigates gene expression in adipose tissue of Ames dwarf (df/df) mice, whose deficiency in growth hormone is linked to health and extended lifespan. Recognizing adipose tissue influence on metabolism, aging, and related diseases, we aim to understand its contribution to the health and longevity of df/df mice. We have identified gene and transcript expression patterns associated with critical biological functions, including metabolism, stress response, and resistance to cancer. Intriguingly, we identified genes that, despite maintaining unchanged expression levels, switch between different isoforms, impacting essential cellular functions such as tumor suppression, oncogenic activity, ATP transport, and lipid biosynthesis and storage. The isoform switching is associated with changes in protein domains, retention of introns, initiation of nonsense-mediated decay, and emergence of intrinsically disordered regions. Moreover, we detected various alternative splicing events that may drive these structural alterations. We also found changes in the expression of long non-coding RNAs (lncRNAs) that may be involved in the aging process and disease resistance by regulating crucial genes in survival and metabolism. Through weighted gene co-expression network analysis, we have linked four lncRNAs with 29 genes, which contribute to protein complexes such as the Mili-Tdrd1-Tdrd12 complex. Beyond safeguarding DNA integrity, this complex also has a wider impact on gene regulation, chromatin structure, and metabolic control. Our detailed investigation provides insight into the molecular foundations of the remarkable health and longevity of df/df mice, emphasizing the significance of adipose tissue in aging and identifying new avenues for health-promoting therapeutic strategies.

Distinct deregulation trends of transcriptional protein complexes in aging naive T cells

The impact of aging on T cell subsets, specifically CD4+ and CD8+ T cells, leading to immune system dysfunction has been the focus of scientific investigation due to its potential to reverse age-associated deterioration. Transcriptomic and epigenomic studies have identified the primary regulators in T cell aging. However, comprehending the underlying dynamic mechanisms requires studying these proteins with their interactors. Here, we integrated single-cell RNA sequencing data of naive CD4+ and CD8+ T cells obtained from 3 different age groups with protein-protein and domain-domain interaction networks to predict and compare the transcriptional protein complexes and identify their capacity to explain age-associated variances. Our novel approach revealed significant effects of aging on the repertoire of complexes, which remains unchanged in naive CD4+ T cells, while in naive CD8+ T cells, it diminishes. In both cell types, there was major deregulation of complexes with the same composition, involving a range of transcription factors. This aging-associated deregulation is characterized by a specific set of protein complexes in naive CD4+ T cells, but this pattern is not observed in naive CD8+ T cells. SMAD3 and BCL11A complexes emerge as key markers in defining a trajectory in aging naive CD4+ T cells. These complexes can accurately distinguish between 3 different age groups, indicating their potential as targets. The direct link between SMAD3 and FOS complexes whose regulatory role has been previously implicated in aging and MBD3 as the novel key link between SMAD3 and BCL11A complexes implicates a coordinated mechanism in age-associated deregulation.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Immunotherapies, including checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy, have revolutionized cancer treatment; however, many patients remain unresponsive to these treatments or relapse following treatment. CRISPR screenings have been used to identify novel single gene targets that can enhance immunotherapy effectiveness, but the identification of combinational targets remains a challenge. Here, we introduce a computational approach that uses sgRNA set enrichment analysis to identify cancer-intrinsic paralog pairs for enhancing immunotherapy using genome-wide screens. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features to predict paralog gene pairs that influence immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using CRISPR double knockout (DKO). These data and analyses collectively provide a sensitive approach to identifying previously undetected paralog gene pairs that can significantly affect cancer immunotherapy response, even when individual genes within the pair have limited effect.

A versatile information retrieval framework for evaluating profile strength and similarity

In profiling assays, thousands of biological properties are measured across many samples, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.

CoPPIs algorithm: a tool to unravel protein cooperative strategies in pathophysiological conditions

We present here the co-expressed protein-protein interactions algorithm. In addition to minimizing correlation-causality imbalance and contextualizing protein-protein interactions to the investigated systems, it combines protein-protein interactions and protein co-expression networks to identify differentially correlated functional modules. To test the algorithm, we processed a set of proteomic profiles from different brain regions of controls and subjects affected by idiopathic Parkinson's disease or carrying a GBA1 mutation. Its robustness was supported by the extraction of functional modules, related to translation and mitochondria, whose involvement in Parkinson's disease pathogenesis is well documented. Furthermore, the selection of hubs and bottlenecks from the weightedprotein-protein interactions networks provided molecular clues consistent with the Parkinson pathophysiology. Of note, like quantification, the algorithm revealed less variations when comparing disease groups than when comparing diseased and controls. However, correlation and quantification results showed low overlap, suggesting the complementarity of these measures. An observation that opens the way to a new investigation strategy that takes into account not only protein expression, but also the level of coordination among proteins that cooperate to perform a given function.

CellGAT: A GAT-Based Method for Constructing a Cell Communication Network Integrating Multiomics Information

The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein-protein interactions, including ligand-receptor; receptor-receptor, and extracellular matrix-receptor interactions. Currently, computational methods for inferring ligand-receptor communication primarily depend on gene expression data of ligand-receptor pairs and spatial information of cells. Some approaches integrate protein complexes; transcription factors; or pathway information to construct cell communication networks. However, few methods consider the critical role of protein-protein interactions (PPIs) in intercellular communication networks, especially when predicting communication between different cell types in the absence of cell type information. These methods often rely on ligand-receptor pairs that lack PPI evidence, potentially compromising the accuracy of their predictions. To address this issue, we propose CellGAT, a framework that infers intercellular communication by integrating gene expression data of ligand-receptor pairs, PPI information, protein complex data, and experimentally validated pathway information. CellGAT not only builds a priori models but also uses node embedding algorithms and graph attention networks to build cell communication networks based on scRNA-seq (single-cell RNA sequencing) datasets and includes a built-in cell clustering algorithm. Through comparisons with various methods, CellGAT accurately predicts cell-cell communication (CCC) and analyzes its impact on downstream pathways; neighboring cells; and drug interventions.

Functional proteoform group deconvolution reveals a broader spectrum of ibrutinib off-targets

Proteome-wide profiling has revealed that targeted drugs can have complex protein interaction landscapes. However, it's a challenge to profile drug targets while systematically accounting for the dynamic protein variations that produce populations of multiple proteoforms. We address this problem by combining thermal proteome profiling (TPP) with functional proteoform group detection to refine the target landscape of ibrutinib. In addition to known targets, we implicate additional specific functional proteoform groups linking ibrutinib to mechanisms in immunomodulation and cellular processes like Golgi trafficking, endosomal trafficking, and glycosylation. Further, we identify variability in functional proteoform group profiles in a CLL cohort, linked to treatment status and ex vivo response and resistance. This offers deeper insights into the impacts of functional proteoform groups in a clinical treatment setting and suggests complex biological effects linked to off-target engagement. These results provide a framework for interpreting clinically observed off-target processes and adverse events, highlighting the importance of functional proteoform group-level deconvolution in understanding drug interactions and their functional impacts with potential applications in precision medicine.

Progress toward a comprehensive brain protein interactome

Protein-protein interactions (PPIs) in the brain play critical roles across all aspects of the central nervous system, from synaptic transmission, glial development, myelination, to cell-to-cell communication, and more. Understanding these interactions is crucial for deciphering neurological mechanisms and the underlying biochemical machinery affected in neurological disorders. Recently, advances in proteomics techniques have significantly enhanced our ability to study interactions among the proteins expressed in the brain. Here, we review some of the high-throughput studies characterizing brain PPIs, using affinity purification, proximity labeling, co-fractionation, and chemical cross-linking mass spectrometry methods, as well as yeast two-hybrid assays. We present the current state of the field, discuss challenges, and highlight promising future directions.

Covalent targeting of splicing in T cells

Despite significant interest in therapeutic targeting of splicing, few chemical probes are available for the proteins involved in splicing. Here, we show that elaborated stereoisomeric acrylamide EV96 and its analogues lead to a selective T cell state-dependent loss of interleukin 2-inducible T cell kinase (ITK) by targeting one of the core splicing factors SF3B1. Mechanistic investigations suggest that the state-dependency stems from a combination of differential protein turnover rates and extensive ITK mRNA alternative splicing. We further introduce the most comprehensive list to date of proteins involved in splicing and leverage cysteine- and protein-directed activity-based protein profiling with electrophilic scout fragments to demonstrate covalent ligandability for many classes of splicing factors and splicing regulators in T cells. Taken together, our findings show how chemical perturbation of splicing can lead to immune state-dependent changes in protein expression and provide evidence for the broad potential to target splicing factors with covalent chemistry.

Potential Adaptive Introgression From Dogs in Iberian Grey Wolves (Canis lupus)

Invading species along with increased anthropogenization may lead to hybridization events between wild species and closely related domesticates. As a consequence, wild species may carry introgressed alleles from domestic species, which is generally assumed to yield adverse effects in wild populations. The opposite evolutionary consequence, adaptive introgression, where introgressed genes are positively selected in the wild species, is possible but has rarely been documented. Grey wolves (Canis lupus) are widely distributed across the Holarctic and frequently coexist with their close relative, the domestic dog (C. familiaris). Despite ample opportunity, hybridization rarely occurs in most populations. Here we studied the geographically isolated grey wolves of the Iberian Peninsula, who have coexisted with a large population of loosely controlled dogs for thousands of years in a human-modified landscape. We assessed the extent and impact of dog introgression on the current Iberian grey wolf population by analysing 150 whole genomes of Iberian and other Eurasian grey wolves as well as dogs originating from across Europe and western Siberia. We identified almost no recent introgression and a small (< 5%) overall ancient dog ancestry. Using a combination of single scan statistics and ancestry enrichment estimates, we identified positive selection on six genes (DAPP1, NSMCE4A, MPPED2, PCDH9, MBTPS1, and CDH13) for which wild Iberian wolves carry alleles introgressed from dogs. The genes with introgressed and positively selected alleles include functions in immune response and brain functions, which may explain some of the unique behavioural phenotypes in Iberian wolves such as their reduced dispersal compared to other wolf populations.

CORUM in 2024: protein complexes as drug targets

CORUM (https://mips.helmholtz-muenchen.de/corum/) is a public database that offers comprehensive information about mammalian protein complexes, including their subunits, functions and associations with human diseases. The newly released CORUM 5.0, encompassing 7193 protein complexes, is the largest dataset of manually curated mammalian protein complexes publicly available. This update represents the most significant upgrade to the database in >15 years. At present, the molecular processes in cells that are influenced by drugs are only incompletely understood. In this latest release, we have begun systematically investigating the impact of drugs on protein complexes. Our studies are based on a dataset from DrugCentral comprising 725 protein drug targets with approved drugs and known mechanisms of action. To date, we have identified 1975 instances from the literature where a drug affects the formation and/or function of a protein complex. Numerous examples highlight the crucial role of understanding drug-protein complex relationships in drug efficacy. The expanded dataset and the inclusion of drug effects on protein complexes are expected to significantly enhance the utility and application potential of CORUM 5.0 in fields such as network medicine and pharmacological research.

PerturbDB for unraveling gene functions and regulatory networks

Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-based genetic screens with single-cell RNA sequencing readouts for high-content phenotypic screens. Despite the rapid accumulation of Perturb-Seq datasets, there remains a lack of a user-friendly platform for their efficient reuse. Here, we developed PerturbDB (http://research.gzsys.org.cn/perturbdb), a platform to help users unveil gene functions using Perturb-Seq datasets. PerturbDB hosts 66 Perturb-Seq datasets, which encompass 4 518 521 single-cell transcriptomes derived from the knockdown of 10 194 genes across 19 different cell lines. All datasets were uniformly processed using the Mixscape algorithm. Genes were clustered by their perturbed transcriptomic phenotypes derived from Perturb-Seq data, resulting in 421 gene clusters, 157 of which were stable across different cellular contexts. Through integrating chemically perturbed transcriptomes with Perturb-Seq data, we identified 552 potential inhibitors targeting 1409 genes, including an mammalian target of rapamycin (mTOR) signaling inhibitor, retinol, which was experimentally verified. Moreover, we developed a 'Cancer' module to facilitate the understanding of the regulatory role of genes in cancer using Perturb-Seq data. An interactive web interface has also been developed, enabling users to visualize, analyze and download all the comprehensive datasets available in PerturbDB. PerturbDB will greatly drive gene functional studies and enhance our understanding of the regulatory roles of genes in diseases such as cancer.

Emergent properties of the lysine methylome reveal regulatory roles via protein interactions and histone mimicry

AIMS

Epigenomics has significantly advanced through the incorporation of Systems Biology approaches. This study aims to investigate the human lysine methylome as a system, using a data-science approach to reveal its emergent properties, particularly focusing on histone mimicry and the broader implications of lysine methylation across the proteome.

METHODS

We employed a data-science-driven OMICS approach, leveraging high-dimensional proteomic data to study the lysine methylome. The analysis focused on identifying sequence-based recognition motifs of lysine methyltransferases and evaluating the prevalence and distribution of lysine methylation across the human proteome.

RESULTS

Our analysis revealed that lysine methylation impacts 15% of the known proteome, with a notable bias toward mono-methylation. We identified sequence-based recognition motifs of 13 lysine methyltransferases, highlighting candidates for histone mimicry. These findings suggest that the selective inhibition of individual lysine methyltransferases could have systemic effects rather than merely targeting histone methylation.

CONCLUSIONS

The lysine methylome has significant mechanistic value and should be considered in the design and testing of therapeutic strategies, particularly in precision oncology. The study underscores the importance of considering non-histone proteins involved in DNA damage and repair, cell signaling, metabolism, and cell cycle pathways when targeting lysine methyltransferases.

Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics

Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry

T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. This study utilizes a high-throughput, reproducible, and robust integrated ion-exchange chromatography-tandem mass tag (IEC-TMT) platform, coupled with a complex-centric quantification algorithm, to thoroughly profile phosphotyrosine (pTyr) protein complex changes during T cell exhaustion. The platform's high reproducibility is evidenced by >0.94 correlation and a median coefficient of variation of 0.25 among quantified complexes in HeLa cell biological replicates. This high-throughput approach allowed analysis of 312 fractions within 2 days, identifying 268 pTyr protein complexes from the T cell exhaustion model. Robust quantification of 28 complexes revealed 12 exhibiting significant abundance alterations in exhausted T cells, notably impacting lysosomal and endoplasmic reticulum-associated complexes. RTN4, a subunit of the newly identified PPI204 protein complex, is upregulated in exhausted T cells. Its knockdown reversed T cell exhaustion, enhancing antitumor immunity. These findings provide novel insights into the molecular mechanisms of T cell exhaustion and propose RTN4 as a potential therapeutic target.

Protocol for mapping differential protein-protein interaction networks using affinity purification-mass spectrometry

Proteins congregate into complexes to perform diverse cellular functions. Protein complexes are remodeled by protein-coding mutations or cellular signaling changes, driving phenotypic outcomes in health and disease. We present an affinity purification-mass spectrometry (AP-MS) proteomics protocol to express affinity-tagged "bait" proteins in mammalian cells, identify and quantify purified protein interactors, and visualize differential protein-protein interaction networks between pairwise conditions. Our protocol possesses general applicability to various cell types and biological areas. For complete details on the use and execution of this protocol, please refer to Bouhaddou et al.1.

The PRMT5-splicing axis is a critical oncogenic vulnerability that regulates detained intron splicing

Protein arginine methyltransferase 5 (PRMT5) is a promising cancer target, yet it's unclear which PRMT5 roles underlie this vulnerability. Here, we establish that PRMT5 inhibition induces a special class of unspliced introns, called detained introns (DIs). To interrogate the impact of DIs, we depleted CLNS1A, a PRMT5 cofactor that specifically enables Sm protein methylation. We found that many, but not all, cell lines are CLNS1A-dependent and established that loss of viability is linked to loss of Sm protein methylation and DI upregulation. Finally, we discovered that PRMT5-regulated DIs, and the impacted genes, are highly conserved across human, and also mouse, cell lines but display little interspecies conservation. Despite this, human and mouse DIs have convergent impacts on proliferation by affecting essential components of proliferation-regulating complexes. Together, these data argue that the PRMT5-splicing axis, including appropriate DI splicing, underlies cancer's vulnerability to PRMT5 inhibitors.

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition

Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.

ICoN: integration using co-attention across biological networks

Motivation

Molecular interaction networks are powerful tools for studying cellular functions. Integrating diverse types of networks enhances performance in downstream tasks such as gene module detection and protein function prediction. The challenge lies in extracting meaningful protein feature representations due to varying levels of sparsity and noise across these heterogeneous networks.

Results

We propose ICoN, a novel unsupervised graph neural network model that takes multiple protein-protein association networks as inputs and generates a feature representation for each protein that integrates the topological information from all the networks. A key contribution of ICoN is exploiting a mechanism called "co-attention" that enables cross-network communication during training. The model also incorporates a denoising training technique, introducing perturbations to each input network and training the model to reconstruct the original network from its corrupted version. Our experimental results demonstrate that ICoN surpasses individual networks across three downstream tasks: gene module detection, gene coannotation prediction, and protein function prediction. Compared to existing unsupervised network integration models, ICoN exhibits superior performance across the majority of downstream tasks and shows enhanced robustness against noise. This work introduces a promising approach for effectively integrating diverse protein-protein association networks, aiming to achieve a biologically meaningful representation of proteins.

Availability and implementation

The ICoN software is available under the GNU Public License v3 at https://github.com/Murali-group/ICoN.

The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration

Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA-associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, a network-based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.

A Comprehensive and Robust Multiplex-DIA Workflow Profiles Protein Turnover Regulations Associated with Cisplatin Resistance

Measuring protein turnover is essential for understanding cellular biological processes and advancing drug discovery. The multiplex DIA mass spectrometry (DIA-MS) approach, combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC), has proven to be a reliable method for analyzing protein turnover and degradation kinetics. Previous multiplex DIA-MS workflows have employed various strategies, including leveraging the highest isotopic labeling channels of peptides to enhance the detection of isotopic MS signal pairs or clusters. In this study, we introduce an improved and robust workflow that integrates a novel machine learning strategy and channel-specific statistical filtering, enabling dynamic adaptation to systematic or temporal variations in channel ratios. This allows comprehensive profiling of protein turnover throughout the pSILAC experiment without relying solely on the highest channel signals. Additionally, we developed KdeggeR , a data processing and analysis package optimized for pSILAC-DIA experiments, which estimates and visualizes peptide and protein degradation rates and dynamic profiles. Our integrative workflow was benchmarked on both 2-channel and 3-channel standard DIA datasets and across two mass spectrometry platforms, demonstrating its broad applicability. Finally, applying this workflow to an aneuploid cancer cell model before and after cisplatin resistance development demonstrated a strong negative correlation between transcript regulation and protein degradation for major protein complex subunits. We also identified specific protein turnover signatures associated with cisplatin resistance.

Improved protein interaction models predict differences in complexes between human cell lines

The interactions of proteins to form complexes play a crucial role in cell function. Data on protein-protein or pairwise interactions (PPI) typically come from a combination of sample separation and mass spectrometry. Since 2010, several extensive, high-throughput mass spectrometry-based experimental studies have dramatically expanded public repositories for PPI data and, by extension, our knowledge of protein complexes. Unfortunately, challenges of limited overlap between experiments, modality-oriented biases, and prohibitive costs of experimental reproducibility continue to limit coverage of the human protein assembly map, both underscoring the need for and spurring the development of relevant computational approaches. Here, we present a new method for predicting the strength of protein interactions. It addresses two important issues that have limited past PPI prediction approaches: incomplete feature sets and incomplete proteome coverage. For a given collection of protein pairs, we fused data from heterogeneous sources into a feature matrix and identified the minimal set of feature partitions for which a non-empty set of protein pairs had complete values. For each such feature partition, we trained a classifier to predict PPI probabilities. We then calculated an overall prediction for a given protein pair by weighting the probabilities from all models that applied to that pair. Our approach accurately identified known and highly probable PPI, far exceeding the performance of current approaches and providing more complete proteome coverage. We then used the predicted probabilities to assemble complexes using previously-described graph-based tools and clustering algorithms and again obtained improved results. Lastly, we used features for three human cell lines to predict PPI and complex scores and identified complexes predicted to differ between those cell lines.

Unbiased complexome profiling and global proteomics analysis reveals mitochondrial impairment and potential changes at the intercalated disk in presymptomatic R14Δ/+ mice hearts

Phospholamban (PLN) is a sarco-endoplasmic reticulum (SER) membrane protein that regulates cardiac contraction/relaxation by reversibly inhibiting the SERCA2a Ca2+-reuptake pump. The R14Δ-PLN mutation causes severe cardiomyopathy that is resistant to conventional treatment. Protein complexes and higher-order supercomplexes such as intercalated disk components and Ca+2-cycling domains underlie many critical cardiac functions, a subset of which may be disrupted by R14Δ-PLN. Complexome profiling (CP) is a proteomics workflow for systematic analysis of high molecular weight (MW) protein complexes and supercomplexes. We hypothesize that R14Δ-PLN may alter a subset of these assemblies, and apply CP workflows to explore these changes in presymptomatic R14Δ/+ mice hearts. Ventricular tissues from presymptomatic 28wk-old WT and R14Δ/+ mice were homogenized under non-denaturing conditions, fractionated by size-exclusion chromatography (SEC) with a linear MW-range exceeding 5 MDa, and subjected to quantitative data-independent acquisition mass spectrometry (DIA-MS) analysis. Unfortunately, current workflows for the systematic analysis of CP data proved ill-suited for use in cardiac samples. Most rely upon curated protein complex databases to provide ground-truth for analysis; however, these are derived primarily from cancerous or immortalized cell lines and, consequently, cell-type specific complexes (including cardiac-specific machinery potentially affected in R14Δ-PLN hearts) are poorly covered. We thus developed PERCOM: a novel CP data-analysis strategy that does not rely upon these databases and can, furthermore, be implemented on widely available spreadsheet software. Applying PERCOM to our CP dataset resulted in the identification of 296 proteins with disrupted elution profiles. Hits were significantly enriched for mitochondrial and intercalated disk (ICD) supercomplex components. Changes to mitochondrial supercomplexes were associated with reduced expression of mitochondrial proteins and maximal oxygen consumption rate. The observed alterations to mitochondrial and ICD supercomplexes were replicated in a second cohort of "juvenile" 9wk-old mice. These early-stage changes to key cardiac machinery may contribute to R14Δ-PLN pathogenesis.

Benchmarking machine learning methods for synthetic lethality prediction in cancer

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

Large-scale map of RNA-binding protein interactomes across the mRNA life cycle

mRNAs interact with RNA-binding proteins (RBPs) throughout their processing and maturation. While efforts have assigned RBPs to RNA substrates, less exploration has leveraged protein-protein interactions (PPIs) to study proteins in mRNA life-cycle stages. We generated an RNA-aware, RBP-centric PPI map across the mRNA life cycle in human cells by immunopurification-mass spectrometry (IP-MS) of ∼100 endogenous RBPs with and without RNase, augmented by size exclusion chromatography-mass spectrometry (SEC-MS). We identify 8,742 known and 20,802 unreported interactions between 1,125 proteins and determine that 73% of the IP-MS-identified interactions are RNA regulated. Our interactome links many proteins, some with unknown functions, to specific mRNA life-cycle stages, with nearly half associated with multiple stages. We demonstrate the value of this resource by characterizing the splicing and export functions of enhancer of rudimentary homolog (ERH), and by showing that small nuclear ribonucleoprotein U5 subunit 200 (SNRNP200) interacts with stress granule proteins and binds cytoplasmic RNA differently during stress.

Evolution of translational control and the emergence of genes and open reading frames in human and non-human primate hearts

Evolutionary innovations can be driven by changes in the rates of RNA translation and the emergence of new genes and small open reading frames (sORFs). In this study, we characterized the transcriptional and translational landscape of the hearts of four primate and two rodent species through integrative ribosome and transcriptomic profiling, including adult left ventricle tissues and induced pluripotent stem cell-derived cardiomyocyte cell cultures. We show here that the translational efficiencies of subunits of the mitochondrial oxidative phosphorylation chain complexes IV and V evolved rapidly across mammalian evolution. Moreover, we discovered hundreds of species-specific and lineage-specific genomic innovations that emerged during primate evolution in the heart, including 551 genes, 504 sORFs and 76 evolutionarily conserved genes displaying human-specific cardiac-enriched expression. Overall, our work describes the evolutionary processes and mechanisms that have shaped cardiac transcription and translation in recent primate evolution and sheds light on how these can contribute to cardiac development and disease.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

Interaction of chikungunya virus glycoproteins with macrophage factors controls virion production

Despite their role as innate sentinels, macrophages can serve as cellular reservoirs of chikungunya virus (CHIKV), a highly-pathogenic arthropod-borne alphavirus that has caused large outbreaks among human populations. Here, with the use of viral chimeras and evolutionary selection analysis, we define CHIKV glycoproteins E1 and E2 as critical for virion production in THP-1 derived human macrophages. Through proteomic analysis and functional validation, we further identify signal peptidase complex subunit 3 (SPCS3) and eukaryotic translation initiation factor 3 subunit K (eIF3k) as E1-binding host proteins with anti-CHIKV activities. We find that E1 residue V220, which has undergone positive selection, is indispensable for CHIKV production in macrophages, as its mutation attenuates E1 interaction with the host restriction factors SPCS3 and eIF3k. Finally, we show that the antiviral activity of eIF3k is translation-independent, and that CHIKV infection promotes eIF3k translocation from the nucleus to the cytoplasm, where it associates with SPCS3. These functions of CHIKV glycoproteins late in the viral life cycle provide a new example of an intracellular evolutionary arms race with host restriction factors, as well as potential targets for therapeutic intervention.

Computational tools to predict context-specific protein complexes

Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.

Advances in spatial proteomics: Mapping proteome architecture from protein complexes to subcellular localizations

Proteins are responsible for most intracellular functions, which they perform as part of higher-order molecular complexes, located within defined subcellular niches. Localization is both dynamic and context specific and mislocalization underlies a multitude of diseases. It is thus vital to be able to measure the components of higher-order protein complexes and their subcellular location dynamically in order to fully understand cell biological processes. Here, we review the current range of highly complementary approaches that determine the subcellular organization of the proteome. We discuss the scale and resolution at which these approaches are best employed and the caveats that should be taken into consideration when applying them. We also look to the future and emerging technologies that are paving the way for a more comprehensive understanding of the functional roles of protein isoforms, which is essential for unraveling the complexities of cell biology and the development of disease treatments.

The proteomic landscape and temporal dynamics of mammalian gastruloid development

Gastrulation is the highly coordinated process by which the early embryo breaks symmetry, establishes germ layers and a body plan, and sets the stage for organogenesis. As early mammalian development is challenging to study in vivo, stem cell-derived models have emerged as powerful surrogates, e.g. human and mouse gastruloids. However, although single cell RNA-seq (scRNA-seq) and high-resolution imaging have been extensively applied to characterize such in vitro embryo models, a paucity of measurements of protein dynamics and regulation leaves a major gap in our understanding. Here, we sought to address this by applying quantitative proteomics to human and mouse gastruloids at four key stages of their differentiation (naïve ESCs, primed ESCs, early gastruloids, late gastruloids). To the resulting data, we perform network analysis to map the dynamics of expression of macromolecular protein complexes and biochemical pathways, including identifying cooperative proteins that associate with them. With matched RNA-seq and phosphosite data from these same stages, we investigate pathway-, stage- and species-specific aspects of translational and post-translational regulation, e.g. finding peri-gastrulation stages of human and mice to be discordant with respect to the mitochondrial transcriptome vs. proteome, and nominating novel kinase-substrate relationships based on phosphosite dynamics. Finally, we leverage correlated dynamics to identify conserved protein networks centered around congenital disease genes. Altogether, our data (https://gastruloid.brotmanbaty.org/) and analyses showcase the potential of intersecting in vitro embryo models and proteomics to advance our understanding of early mammalian development in ways not possible through transcriptomics alone.

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Differential Contributions of Interferon Classes to Host Inflammatory Responses and Restricting Virus Progeny Production

Fundamental to mammalian intrinsic and innate immune defenses against pathogens is the production of Type I and Type II interferons, such as IFN-β and IFN-γ, respectively. The comparative effects of IFN classes on the cellular proteome, protein interactions, and virus restriction within cell types that differentially contribute to immune defenses are needed for understanding immune signaling. Here, a multilayered proteomic analysis, paired with biochemical and molecular virology assays, allows distinguishing host responses to IFN-β and IFN-γ and associated antiviral impacts during infection with several ubiquitous human viruses. In differentiated macrophage-like monocytic cells, we classified proteins upregulated by IFN-β, IFN-γ, or pro-inflammatory LPS. Using parallel reaction monitoring, we developed a proteotypic peptide library for shared and unique ISG signatures of each IFN class, enabling orthogonal confirmation of protein alterations. Thermal proximity coaggregation analysis identified the assembly and maintenance of IFN-induced protein interactions. Comparative proteomics and cytokine responses in macrophage-like monocytic cells and primary keratinocytes provided contextualization of their relative capacities to restrict virus production during infection with herpes simplex virus type-1, adenovirus, and human cytomegalovirus. Our findings demonstrate how IFN classes induce distinct ISG abundance and interaction profiles that drive antiviral defenses within cell types that differentially coordinate mammalian immune responses.

WeSA: a web server for improving analysis of affinity proteomics data

Protein-protein interaction experiments still yield many false positive interactions. The socioaffinity metric can distinguish true protein-protein interactions from noise based on available data. Here, we present WeSA (Weighted SocioAffinity), which considers large datasets of interaction proteomics data (IntAct, BioGRID, the BioPlex) to score human protein interactions and, in a statistically robust way, flag those (even from a single experiment) that are likely to be false positives. ROC analysis (using CORUM-PDB positives and Negatome negatives) shows that WeSA improves over other measures of interaction confidence. WeSA shows consistently good results over all datasets (up to: AUC = 0.93 and at best threshold: TPR = 0.84, FPR = 0.11, Precision = 0.98). WeSA is freely available without login (wesa.russelllab.org). Users can submit their own data or look for organized information on human protein interactions using the web server. Users can either retrieve available information for a list of proteins of interest or calculate scores for new experiments. The server outputs either pre-computed or updated WeSA scores for the input enriched with information from databases. The summary is presented as a table and a network-based visualization allowing the user to remove those nodes/edges that the method considers spurious.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Integrated Platform for Large-Scale Quantitative Profiling of Phosphotyrosine Signaling Complexes Based on Cofractionation/Mass Spectrometry and Complex-Centric Algorithm

The scarcity and dynamic nature of phosphotyrosine (pTyr)-modified proteins pose a challenge for researching protein complexes with pTyr modification, which are assembled through multiple protein-protein interactions. We developed an integrated complex-centric platform for large-scale quantitative profiling of pTyr signaling complexes based on cofractionation/mass spectrometry (CoFrac-MS) and a complex-centric algorithm. We initially constructed a trifunctional probe based on pTyr superbinder (SH2-S) for specifically binding and isolation of intact pTyr protein complexes. Then, the CoFrac-MS strategy was employed for the identification of pTyr protein complexes by integrating ion exchange chromatography in conjunction with data independent acquisition mass spectrometry. Furthermore, we developed a novel complex-centric algorithm for quantifying protein complexes based on the protein complex elution curve. Utilizing this algorithm, we effectively quantified 216 putative protein complexes. We further screened 21 regulated pTyr protein complexes related to the epidermal growth factor signal. Our study engenders a comprehensive framework for the intricate examination of pTyr protein complexes and presents, for the foremost occasion, a quantitative landscape delineating the composition of pTyr protein complexes in HeLa cells.

Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19.

IMPORTANCE

A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

The KAT module of the SAGA complex maintains the oncogenic gene expression program in MYCN-amplified neuroblastoma

Pediatric cancers are frequently driven by genomic alterations that result in aberrant transcription factor activity. Here, we used functional genomic screens to identify multiple genes within the transcriptional coactivator Spt-Ada-Gcn5-acetyltransferase (SAGA) complex as selective dependencies for MYCN-amplified neuroblastoma, a disease of dysregulated development driven by an aberrant oncogenic transcriptional program. We characterized the DNA recruitment sites of the SAGA complex in neuroblastoma and the consequences of loss of SAGA complex lysine acetyltransferase (KAT) activity on histone acetylation and gene expression. We demonstrate that loss of SAGA complex KAT activity is associated with reduced MYCN binding on chromatin, suppression of MYC/MYCN gene expression programs, and impaired cell cycle progression. Further, we showed that the SAGA complex is pharmacologically targetable in vitro and in vivo with a KAT2A/KAT2B proteolysis targeting chimeric. Our findings expand our understanding of the histone-modifying complexes that maintain the oncogenic transcriptional state in this disease and suggest therapeutic potential for inhibitors of SAGA KAT activity in MYCN-amplified neuroblastoma.

Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO

The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

IB-DNQ and Rucaparib dual treatment alters cell cycle regulation and DNA repair in triple negative breast cancer cells

Background

Triple negative breast cancer (TNBC), characterized by the lack of three canonical receptors, is unresponsive to commonly used hormonal therapies. One potential TNBC-specific therapeutic target is NQO1, as it is highly expressed in many TNBC patients and lowly expressed in non-cancer tissues. DNA damage induced by NQO1 bioactivatable drugs in combination with Rucaparib-mediated inhibition of PARP1-dependent DNA repair synergistically induces cell death.

Methods

To gain a better understanding of the mechanisms behind this synergistic effect, we used global proteomics, phosphoproteomics, and thermal proteome profiling to analyze changes in protein abundance, phosphorylation and protein thermal stability.

Results

Very few protein abundance changes resulted from single or dual agent treatment; however, protein phosphorylation and thermal stability were impacted. Histone H2AX was among several proteins identified to have increased phosphorylation when cells were treated with the combination of IB-DNQ and Rucaparib, validating that the drugs induced persistent DNA damage. Thermal proteome profiling revealed destabilization of H2AX following combination treatment, potentially a result of the increase in phosphorylation. Kinase substrate enrichment analysis predicted altered activity for kinases involved in DNA repair and cell cycle following dual agent treatment. Further biophysical analysis of these two processes revealed alterations in SWI/SNF complex association and tubulin / p53 interactions.

Conclusions

Our findings that the drugs target DNA repair and cell cycle regulation, canonical cancer treatment targets, in a way that is dependent on increased expression of a protein selectively found to be upregulated in cancers without impacting protein abundance illustrate that multi-omics methodologies are important to gain a deeper understanding of the mechanisms behind treatment induced cancer cell death.

Oncogene EVI1 drives acute myeloid leukemia via a targetable interaction with CTBP2

Acute myeloid leukemia (AML) driven by the activation of EVI1 due to chromosome 3q26/MECOM rearrangements is incurable. Because transcription factors such as EVI1 are notoriously hard to target, insight into the mechanism by which EVI1 drives myeloid transformation could provide alternative avenues for therapy. Applying protein folding predictions combined with proteomics technologies, we demonstrate that interaction of EVI1 with CTBP1 and CTBP2 via a single PLDLS motif is indispensable for leukemic transformation. A 4× PLDLS repeat construct outcompetes binding of EVI1 to CTBP1 and CTBP2 and inhibits proliferation of 3q26/MECOM rearranged AML in vitro and in xenotransplant models. This proof-of-concept study opens the possibility to target one of the most incurable forms of AML with specific EVI1-CTBP inhibitors. This has important implications for other tumor types with aberrant expression of EVI1 and for cancers transformed by different CTBP-dependent oncogenic transcription factors.