Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

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.

Recent Advances in Mass Spectrometry-Based Protein Interactome Studies

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.

POLD3 as Controller of Replicative DNA Repair

Multiple modes of DNA repair need DNA synthesis by DNA polymerase enzymes. The eukaryotic B-family DNA polymerase complexes delta (Polδ) and zeta (Polζ) help to repair DNA strand breaks when primed by homologous recombination or single-strand DNA annealing. DNA synthesis by Polδ and Polζ is mutagenic, but is needed for the survival of cells in the presence of DNA strand breaks. The POLD3 subunit of Polδ and Polζ is at the heart of DNA repair by recombination, by modulating polymerase functions and interacting with other DNA repair proteins. We provide the background to POLD3 discovery, investigate its structure, as well as function in cells. We highlight unexplored structural aspects of POLD3 and new biochemical data that will help to understand the pivotal role of POLD3 in DNA repair and mutagenesis in eukaryotes, and its impact on human health.

Discovery and significance of protein-protein interactions in health and disease

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.

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.

Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Introducing dysfunctional Protein-Protein Interactome (dfPPI) - A platform for systems-level protein-protein interaction (PPI) dysfunction investigation in disease

Protein-protein interactions (PPIs) play a crucial role in cellular function and disease manifestation, with dysfunctions in PPI networks providing a direct link between stressors and phenotype. The dysfunctional Protein-Protein Interactome (dfPPI) platform, formerly known as epichaperomics, is a newly developed chemoproteomic method aimed at detecting dynamic changes at the systems level in PPI networks under stressor-induced cellular perturbations within disease states. This review provides an overview of dfPPIs, emphasizing the novel methodology, data analytics, and applications in disease research. dfPPI has applications in cancer research, where it identifies dysfunctions integral to maintaining malignant phenotypes and discovers strategies to enhance the efficacy of current therapies. In neurodegenerative disorders, dfPPI uncovers critical dysfunctions in cellular processes and stressor-specific vulnerabilities. Challenges, including data complexity and the potential for integration with other omics datasets are discussed. The dfPPI platform is a potent tool for dissecting disease systems biology by directly informing on dysfunctions in PPI networks and holds promise for advancing disease identification and therapeutics.

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.

Targeting PRMT3 impairs methylation and oligomerization of HSP60 to boost anti-tumor immunity by activating cGAS/STING signaling

Immune checkpoint blockade (ICB) has emerged as a promising therapeutic option for hepatocellular carcinoma (HCC), but resistance to ICB occurs and patient responses vary. Here, we uncover protein arginine methyltransferase 3 (PRMT3) as a driver for immunotherapy resistance in HCC. We show that PRMT3 expression is induced by ICB-activated T cells via an interferon-gamma (IFNγ)-STAT1 signaling pathway, and higher PRMT3 expression levels correlate with reduced numbers of tumor-infiltrating CD8+ T cells and poorer response to ICB. Genetic depletion or pharmacological inhibition of PRMT3 elicits an influx of T cells into tumors and reduces tumor size in HCC mouse models. Mechanistically, PRMT3 methylates HSP60 at R446 to induce HSP60 oligomerization and maintain mitochondrial homeostasis. Targeting PRMT3-dependent HSP60 methylation disrupts mitochondrial integrity and increases mitochondrial DNA (mtDNA) leakage, which results in cGAS/STING-mediated anti-tumor immunity. Lastly, blocking PRMT3 functions synergize with PD-1 blockade in HCC mouse models. Our study thus identifies PRMT3 as a potential biomarker and therapeutic target to overcome immunotherapy resistance in HCC.

Progress in mass spectrometry approaches to profiling protein-protein interactions in the studies of the innate immune system

Understanding protein-protein interactions (PPIs) is pivotal for deciphering the intricacies of biological processes. Dysregulation of PPIs underlies a spectrum of diseases, including cancer, neurodegenerative disorders, and autoimmune conditions, highlighting the imperative of investigating these interactions for therapeutic advancements. This review delves into the realm of mass spectrometry-based techniques for elucidating PPIs and their profound implications in biological research. Mass spectrometry in the PPI research field not only facilitates the evaluation of protein-protein interaction modulators but also discovers unclear molecular mechanisms and sheds light on both on- and off-target effects, thus aiding in drug development. Our discussion navigates through six pivotal techniques: affinity purification mass spectrometry (AP-MS), proximity labeling mass spectrometry (PL-MS), cross-linking mass spectrometry (XL-MS), size exclusion chromatography coupled with mass spectrometry (SEC-MS), limited proteolysis-coupled mass spectrometry (LiP-MS), and thermal proteome profiling (TPP).

Biochemical profiling of the protein encoded by grass carp reovirus genotype II

In this study, we obtained the whole genome sequence of GCRV-DY197 and investigated the localization, post-translational modifications, and host interactions of the 11 viral proteins encoded by GCRV-DY197 in grass carp ovary (GCO) cells. The whole genome sequence is 24,704 kb and contains 11 segments (S1-S11). Subcellular localization showed that the VP1, VP2, VP3, VP5, VP56, and VP35 proteins were localized in both cytoplasm and nucleus, whereas the NS79, VP4, VP41, VP6, and NS38 proteins were localized in the cytoplasm. The NS79 and NS38 proteins were phosphorylated, and the ubiquitination modification sites were identified in VP41 and NS38. An interaction network containing 9 viral proteins and 140 host proteins was also constructed. These results offer a theoretical basis for an in-depth understanding of the biochemical characteristics and pathogenic mechanism of GCRV-II.

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

A Multiplexed SEC-MS Approach to Systematically Study the Interplay Between Protein Assembly-States and Phosphorylation Events

Protein molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly-states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly-states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Applying SEC-MX to HEK293 and HCT116 cells, we generated a proof-of-concept dataset mapping thousands of phosphopeptides and their assembly-states. Our analysis revealed intricate relationships between phosphorylation events and assembly-states and generated testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

[Applications of high performance liquid chromatography-mass spectrometry in proteomics]

Proteomics profiling plays an important role in biomedical studies. Proteomics studies are much more complicated than genome research, mainly because of the complexity and diversity of proteomic samples. High performance liquid chromatography-mass spectrometry (HPLC-MS) is a fundamental tool in proteomics research owing to its high speed, resolution, and sensitivity. Proteomics research targets from the peptides and individual proteins to larger protein complexes, the molecular weight of which gradually increases, leading to sustained increases in structural and compositional complexity and alterations in molecular properties. Therefore, the selection of various separation strategies and stationary-phase parameters is crucial when dealing with the different targets in proteomics research for in-depth proteomics analysis. This article provides an overview of commonly used chromatographic-separation strategies in the laboratory, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), hydrophobic interaction chromatography (HIC), ion-exchange chromatography (IEC), and size-exclusion chromatography (SEC), as well as their applications and selectivity in the context of various biomacromolecules. At present, no single chromatographic or electrophoretic technology features the peak capacity required to resolve such complex mixtures into individual components. Multidimensional liquid chromatography (MDLC), which combines different orthogonal separation modes with MS, plays an important role in proteomics research. In the MDLC strategy, IEC, together with RPLC, remains the most widely used separation mode in proteomics analysis; other chromatographic methods are also frequently used for peptide/protein fractionation. MDLC technologies and their applications in a variety of proteomics analyses have undergone great development. Two strategies in MDLC separation systems are mainly used in proteomics profiling: the "bottom-up" approach and the "top-down" approach. The "shotgun" method is a typical "bottom-up" strategy that is based on the RPLC or MDLC separation of whole-protein-sample digests coupled with MS; it is an excellent technique for identifying a large number of proteins. "Top-down" analysis is based on the separation of intact proteins and provides their detailed molecular information; thus, this technique may be advantageous for analyzing the post-translational modifications (PTMs) of proteins. In this paper, the "bottom-up" "top-down" and protein-protein interaction (PPI) analyses of proteome samples are briefly reviewed. The diverse combinations of different chromatographic modes used to set up MDLC systems are described, and compatibility issues between mobile phases and analytes, between mobile phases and MS, and between mobile phases in different separation modes in multidimensional chromatography are analyzed. Novel developments in MDLC techniques, such as high-abundance protein depletion and chromatography arrays, are further discussed. In this review, the solutions proposed by researchers when encountering compatibility issues are emphasized. Moreover, the applications of HPLC-MS combined with various sample pretreatment methods in the study of exosomal and single-cell proteomics are examined. During exosome isolation, the combined use of ultracentrifugation and SEC can yield exosomes of higher purity. The use of SEC with ultra-large-pore-size packing materials (200 nm) enables the isolation of exosomal subgroups, and proteomics studies have revealed significant differences in protein composition and function between these subgroups. In the field of single-cell proteomics, researchers have addressed challenges related to reducing sample processing volumes, preventing sample loss, and avoiding contamination during sample preparation. Innovative methods and improvements, such as the utilization of capillaries for sample processing and microchips as platforms to minimize the contact area of the droplets, have been proposed. The integration of these techniques with HPLC-MS shows some progress. In summary, this article focuses on the recent advances in HPLC-MS technology for proteomics analysis and provides a comprehensive reference for future research in the field of proteomics.

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.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer

NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.

Tryptophanyl-Transfer RNA Synthetase Is Involved in a Negative Feedback Loop Mitigating Interferon-γ-Induced Gene Expression

Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes responsible for linking a transfer RNA (tRNA) with its cognate amino acid present in all the kingdoms of life. Besides their aminoacyl-tRNA synthetase activity, it was described that many of these enzymes can carry out non-canonical functions. They were shown to be involved in important biological processes such as metabolism, immunity, development, angiogenesis and tumorigenesis. In the present work, we provide evidence that tryptophanyl-tRNA synthetase might be involved in a negative feedback loop mitigating the expression of certain interferon-γ-induced genes. Mining the available TCGA and Gtex data, we found that WARS was highly expressed in cutaneous melanoma (SKCM) compared to other cancers and is of good prognosis for this particular cancer type. WARS expression correlates with genes involved in antigen processing and presentation but also transcription factors involved in IFN-γ signaling such as STAT1. In addition, WARS was found in complex with STAT1 in A375 cells treated with IFN-γ. Finally, we showed that knocking down WARS expression during IFN-γ stimulation further increases the expression of GBP2, APOL1, ISG15, HLA-A and IDO1.

Advancing Biomarker Discovery and Therapeutic Targets in Duchenne Muscular Dystrophy: A Comprehensive Review

Mounting evidence underscores the intricate interplay between the immune system and skeletal muscles in Duchenne muscular dystrophy (DMD), as well as during regular muscle regeneration. While immune cell infiltration into skeletal muscles stands out as a prominent feature in the disease pathophysiology, a myriad of secondary defects involving metabolic and inflammatory pathways persist, with the key players yet to be fully elucidated. Steroids, currently the sole effective therapy for delaying onset and symptom control, come with adverse side effects, limiting their widespread use. Preliminary evidence spotlighting the distinctive features of T cell profiling in DMD prompts the immuno-characterization of circulating cells. A molecular analysis of their transcriptome and secretome holds the promise of identifying a subpopulation of cells suitable as disease biomarkers. Furthermore, it provides a gateway to unraveling new pathological pathways and pinpointing potential therapeutic targets. Simultaneously, the last decade has witnessed the emergence of novel approaches. The development and equilibrium of both innate and adaptive immune systems are intricately linked to the gut microbiota. Modulating microbiota-derived metabolites could potentially exacerbate muscle damage through immune system activation. Concurrently, genome sequencing has conferred clinical utility for rare disease diagnosis since innovative methodologies have been deployed to interpret the functional consequences of genomic variations. Despite numerous genes falling short as clinical targets for MD, the exploration of Tdark genes holds promise for unearthing novel and uncharted therapeutic insights. In the quest to expedite the translation of fundamental knowledge into clinical applications, the identification of novel biomarkers and disease targets is paramount. This initiative not only advances our understanding but also paves the way for the design of innovative therapeutic strategies, contributing to enhanced care for individuals grappling with these incapacitating diseases.

Mass spectrometry-based methods for investigating the dynamics and organization of the surfaceome: exploring potential clinical implications

INTRODUCTION

Cell-surface proteins are extremely important for many cellular events, such as regulating cell-cell communication and cell-matrix interactions. Aberrant alterations in surface protein expression, modification (especially glycosylation), and interactions are directly related to human diseases. Systematic investigation of surface proteins advances our understanding of protein functions, cellular activities, and disease mechanisms, which will lead to identifying surface proteins as disease biomarkers and drug targets.

AREAS COVERED

In this review, we summarize mass spectrometry (MS)-based proteomics methods for global analysis of cell-surface proteins. Then, investigations of the dynamics of surface proteins are discussed. Furthermore, we summarize the studies for the surfaceome interaction networks. Additionally, biological applications of MS-based surfaceome analysis are included, particularly highlighting the significance in biomarker identification, drug development, and immunotherapies.

EXPERT OPINION

Modern MS-based proteomics provides an opportunity to systematically characterize proteins. However, due to the complexity of cell-surface proteins, the labor-intensive workflow, and the limit of clinical samples, comprehensive characterization of the surfaceome remains extraordinarily challenging, especially in clinical studies. Developing and optimizing surfaceome enrichment methods and utilizing automated sample preparation workflow can expand the applications of surfaceome analysis and deepen our understanding of the functions of cell-surface proteins.

Interactions of Histone Deacetylase 6 with DNA Damage Repair Factors Strengthen its Utility as a Combination Drug Target in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is the deadliest gynecologic malignancy in women. The low survival rate is largely due to drug resistance. Approximately 80% of patients who initially respond to treatment relapse and become drug-resistant. The lack of effective second-line therapeutics remains a substantial challenge for BRCA-1/2 wild-type HGSOC patients. Histone Deacetylases (HDACs) are promising targets in HGSOC treatment; however, the mechanism and efficacy of HDAC inhibitors are understudied in HGSOC. In order to consider HDACs as a treatment target, an improved understanding of their function within HGSOC is required. This includes elucidating HDAC6-specific protein-protein interactions. In this study, we carried out substrate trapping followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to elucidate HDAC6 catalytic domain (CD)-specific interactors in the context of BRCA-1/2 wild-type HGSOC. Overall, this study identified new HDAC6 substrates that may be unique to HGSOC. The HDAC6-CD1 mutant condition contained the largest number of significant proteins compared to the CD2 mutant and the CD1/2 mutant conditions, suggesting the HDAC6-CD1 domain has catalytic activity that is independent of CD2. Among the identified substrates were proteins involved in DNA damage repair including PARP proteins. These findings further justify the use of HDAC inhibitors as a combination treatment with platinum chemotherapy agents and PARP inhibitors in HGSOC.

Local glycolysis fuels actomyosin contraction during axonal retraction

In response to repulsive cues, axonal growth cones can quickly retract. This requires the prompt activity of contractile actomyosin, which is formed by the non-muscle myosin II (NMII) bound to actin filaments. NMII is a molecular motor that provides the necessary mechanical force at the expense of ATP. Here, we report that this process is energetically coupled to glycolysis and is independent of cellular ATP levels. Induction of axonal retraction requires simultaneous generation of ATP by glycolysis, as shown by chemical inhibition and genetic knock-down of GAPDH. Co-immunoprecipitation and proximal-ligation assay showed that actomyosin associates with ATP-generating glycolytic enzymes and that this association is strongly enhanced during retraction. Using microfluidics, we confirmed that the energetic coupling between glycolysis and actomyosin necessary for axonal retraction is localized to the growth cone and near axonal shaft. These results indicate a tight coupling between on-demand energy production by glycolysis and energy consumption by actomyosin contraction suggesting a function of glycolysis in axonal guidance.

ADAR1 links R-loop homeostasis to ATR activation in replication stress response

Unscheduled R-loops are a major source of replication stress and DNA damage. R-loop-induced replication defects are sensed and suppressed by ATR kinase, whereas it is not known whether R-loop itself is actively involved in ATR activation and, if so, how this is achieved. Here, we report that the nuclear form of RNA-editing enzyme ADAR1 promotes ATR activation and resolves genome-wide R-loops, a process that requires its double-stranded RNA-binding domains. Mechanistically, ADAR1 interacts with TOPBP1 and facilitates its loading on perturbed replication forks by enhancing the association of TOPBP1 with RAD9 of the 9-1-1 complex. When replication is inhibited, DNA-RNA hybrid competes with TOPBP1 for ADAR1 binding to promote the translocation of ADAR1 from damaged fork to accumulate at R-loop region. There, ADAR1 recruits RNA helicases DHX9 and DDX21 to unwind R-loops, simultaneously allowing TOPBP1 to stimulate ATR more efficiently. Collectively, we propose that the tempo-spatially regulated assembly of ADAR1-nucleated protein complexes link R-loop clearance and ATR activation, while R-loops crosstalk with blocked replication forks by transposing ADAR1 to finetune ATR activity and safeguard the genome.

To Break or Not to Break: The Role of TOP2B in Transcription

Transcription and its regulation pose challenges related to DNA torsion and supercoiling of the DNA template. RNA polymerase tracking the helical groove of the DNA introduces positive helical torsion and supercoiling upstream and negative torsion and supercoiling behind its direction of travel. This can inhibit transcriptional elongation and other processes essential to transcription. In addition, chromatin remodeling associated with gene activation can generate or be hindered by excess DNA torsional stress in gene regulatory regions. These topological challenges are solved by DNA topoisomerases via a strand-passage reaction which involves transiently breaking and re-joining of one (type I topoisomerases) or both (type II topoisomerases) strands of the phosphodiester backbone. This review will focus on one of the two mammalian type II DNA topoisomerase enzymes, DNA topoisomerase II beta (TOP2B), that have been implicated in correct execution of developmental transcriptional programs and in signal-induced transcription, including transcriptional activation by nuclear hormone ligands. Surprisingly, several lines of evidence indicate that TOP2B-mediated protein-free DNA double-strand breaks are involved in signal-induced transcription. We discuss the possible significance and origins of these DSBs along with a network of protein interaction data supporting a variety of roles for TOP2B in transcriptional regulation.

Recent Advances in Molecular and Cellular Functions of S100A10

S100A10 (p11, annexin II light chain, calpactin light chain) is a multifunctional protein with a wide range of physiological activity. S100A10 is unique among the S100 family members of proteins since it does not bind to Ca2+, despite its sequence and structural similarity. This review focuses on studies highlighting the structure, regulation, and binding partners of S100A10. The binding partners of S100A10 were collated and summarized.

EWSR1's visual modalities are defined by its association with nucleic acids and RNA polymerase II

We report systematic analysis of endogenous EWSR1's cellular organization. We demonstrate that EWSR1, which contains low complexity and nucleic acid binding domains, is present in cells in faster and slower-recovering fractions, indicative of a protein undergoing both rapid exchange and longer-term interactions. The employment of complementary high-resolution imaging approaches shows EWSR1 exists in in two visual modalities, a distributed state which is present throughout the nucleoplasm, and a concentrated state consistent with the formation of foci. Both EWSR1 visual modalities localize with nascent RNA. EWSR1 foci concentrate in regions of euchromatin, adjacent to protein markers of transcriptional activation, and significantly colocalize with phosphorylated RNA polymerase II. Interestingly, EWSR1 and FUS, another FET protein, exhibit distinct spatial organizations. Our results contribute to bridging the gap between our understanding of the biophysical and biochemical properties of FET proteins, including EWSR1, their functions as transcriptional regulators, and the participation of these proteins in tumorigenesis and neurodegenerative disease.

Uncharacterized Proteins CxORFx: Subinteractome Analysis and Prognostic Significance in Cancers

Functions of about 10% of all the proteins and their associations with diseases are poorly annotated or not annotated at all. Among these proteins, there is a group of uncharacterized chromosome-specific open-reading frame genes (CxORFx) from the 'Tdark' category. The aim of the work was to reveal associations of CxORFx gene expression and ORF proteins' subinteractomes with cancer-driven cellular processes and molecular pathways. We performed systems biology and bioinformatic analysis of 219 differentially expressed CxORFx genes in cancers, an estimation of prognostic significance of novel transcriptomic signatures and analysis of subinteractome composition using several web servers (GEPIA2, KMplotter, ROC-plotter, TIMER, cBioPortal, DepMap, EnrichR, PepPSy, cProSite, WebGestalt, CancerGeneNet, PathwAX II and FunCoup). The subinteractome of each ORF protein was revealed using ten different data sources on physical protein-protein interactions (PPIs) to obtain representative datasets for the exploration of possible cellular functions of ORF proteins through a spectrum of neighboring annotated protein partners. A total of 42 out of 219 presumably cancer-associated ORF proteins and 30 cancer-dependent binary PPIs were found. Additionally, a bibliometric analysis of 204 publications allowed us to retrieve biomedical terms related to ORF genes. In spite of recent progress in functional studies of ORF genes, the current investigations aim at finding out the prognostic value of CxORFx expression patterns in cancers. The results obtained expand the understanding of the possible functions of the poorly annotated CxORFx in the cancer context.

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

Messenger RNAs (mRNAs) interact with RNA-binding proteins (RBPs) in diverse ribonucleoprotein complexes (RNPs) during distinct life-cycle stages for their processing and maturation. While substantial attention has focused on understanding RNA regulation by assigning proteins, particularly RBPs, to specific RNA substrates, there has been considerably less exploration leveraging protein-protein interaction (PPI) methodologies to identify and study the role of proteins in mRNA life-cycle stages. To address this gap, we generated an RNA-aware RBP-centric PPI map across the mRNA life-cycle by immunopurification (IP-MS) of ~100 endogenous RBPs across the life-cycle in the presence or absence of RNase, augmented by size exclusion chromatography (SEC-MS). Aside from confirming 8,700 known and discovering 20,359 novel interactions between 1125 proteins, we determined that 73% of our IP interactions are regulated by the presence of RNA. Our PPI data enables us to link proteins to life-cycle stage functions, highlighting that nearly half of the proteins participate in at least two distinct stages. We show that one of the most highly interconnected proteins, ERH, engages in multiple RNA processes, including via interactions with nuclear speckles and the mRNA export machinery. We also demonstrate that the spliceosomal protein SNRNP200 participates in distinct stress granule-associated RNPs and occupies different RNA target regions in the cytoplasm during stress. Our comprehensive RBP-focused PPI network is a novel resource for identifying multi-stage RBPs and exploring RBP complexes in RNA maturation.

High-Throughput Proteome Profiling of Plasma and Native Plasma Complexes Using Native Chromatography

We describe a high-throughput method for co-fractionation mass spectrometry (CF-MS) profiling for native plasma protein profiling. CF-MS allows the profiling of endogenous protein complexes between samples. Proteins often interact with other proteins and form macromolecular complexes that are different in disease states as well as cell states and cell types. This protocol describes an example for the sample preparation of 954 individual size exclusion chromatography (SEC) fractions, derived from 18 plasma samples that were separated into 53 fractions. Eighteen plasma samples were chosen based on the TMTpro multiplexing, but this methodology can be adapted for fewer or larger numbers of samples as appropriate. Our automated sample preparation method allows for high-throughput native plasma profiling, and we provide detailed methods for both a label-free and an isobaric labeling approach, discuss the merits of each approach, and detail the advantages of combining these strategies for comprehensive native plasma proteome profiling.

Specific S100 Proteins Bind Tumor Necrosis Factor and Inhibit Its Activity

Tumor necrosis factor (TNF) inhibitors (anti-TNFs) represent a cornerstone of the treatment of various immune-mediated inflammatory diseases and are among the most commercially successful therapeutic agents. Knowledge of TNF binding partners is critical for identification of the factors able to affect clinical efficacy of the anti-TNFs. Here, we report that among eighteen representatives of the multifunctional S100 protein family, only S100A11, S100A12 and S100A13 interact with the soluble form of TNF (sTNF) in vitro. The lowest equilibrium dissociation constants (K_d) for the complexes with monomeric sTNF determined using surface plasmon resonance spectroscopy range from 2 nM to 28 nM. The apparent K_d values for the complexes of multimeric sTNF with S100A11/A12 estimated from fluorimetric titrations are 0.1-0.3 µM. S100A12/A13 suppress the cytotoxic activity of sTNF against Huh-7 cells, as evidenced by the MTT assay. Structural modeling indicates that the sTNF-S100 interactions may interfere with the sTNF recognition by the therapeutic anti-TNFs. Bioinformatics analysis reveals dysregulation of TNF and S100A11/A12/A13 in numerous disorders. Overall, we have shown a novel potential regulatory role of the extracellular forms of specific S100 proteins that may affect the efficacy of anti-TNF treatment in various diseases.