Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities

11-Plex DiLeu Isobaric Labeling Enables Quantitative Assessment of Brain Region Protein Association Networks Impacted by the Gut Microbiome

Gut microbiota can regulate host brain functions and influence various physiological and pathological processes through the brain-gut axis. To systematically elucidate the intervention of different gut environments on different brain regions, we implemented an integrated approach that combines 11-plex DiLeu isobaric tags with a "BRIDGE" normalization strategy to comparatively analyze the proteome of six brain regions in germ-free (GF)- and conventionally raised (ConvR)-mice. A total of 5945 proteins were identified and 5656 were quantifiable, while 1906 of them were significantly changed between GF- and ConvR-mice; 281 proteins were filtered with FC greater than 1.2 in at least one brain region, of which heatmap analysis showed clear protein profile disparities, both between brain regions and gut microbiome conditions. Gut microbiome impact is most overt in the hypothalamus and the least in the thalamus region. Collectively, this approach allows an in-depth investigation of the induced protein changes by multiple gut microbiome environments in a brain region-specific manner. This comprehensive proteomic work improves the understanding of the brain region protein association networks impacted by the gut microbiome and highlights the critical roles of the brain-gut axis.

Glioblastoma extracellular vesicles modulate immune PD-L1 expression in accessory macrophages upon radiotherapy

Glioblastoma (GBM) is the most aggressive brain tumor, presenting major challenges due to limited treatment options. Standard care includes radiation therapy (RT) to curb tumor growth and alleviate symptoms, but its impact on GBM is limited. In this study, we investigated the effect of RT on immune suppression and whether extracellular vesicles (EVs) originating from GBM and taken up by the tumor microenvironment (TME) contribute to the induced therapeutic resistance. We observed that (1) ionizing radiation increases immune-suppressive markers on GBM cells, (2) macrophages exacerbate immune suppression in the TME by increasing PD-L1 in response to EVs derived from GBM cells which is further modulated by RT, and (3) RT increases CD206-positive macrophages which have the most potential in inducing a pro-oncogenic environment due to their increased uptake of tumor-derived EVs. In conclusion, RT affects GBM resistance by immuno-modulating EVs taken up by myeloid cells in the TME.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression

Co-expression of two or more genes at the single-cell level is usually associated with functional co-regulation. While mRNA co-expression-measured as the correlation in mRNA levels-can be influenced by both transcriptional and post-transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post-transcriptional regulation of co-expression. To enhance our understanding, we integrate four datasets spanning single-cell gene expression data, single-cell promoter activity data and individual transcript half-lives. Confirming expectations, we find that positive co-expression necessitates promoter coordination and similar mRNA half-lives. Surprisingly, negative co-expression is favored by differences in mRNA half-lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half-lives, which additional stochastic simulations suggest might give rise to the observed co-expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

GPCR signaling contributes to immune characteristics of microenvironment and process of EBV-induced lymphomagenesis

Epstein-Barr virus (EBV) is the oncogenic driver of multiple cancers. However, the underlying mechanism of virus-cancer immunological interaction during disease pathogenesis remains largely elusive. Here we reported the first comprehensive proteogenomic characterization of natural killer/T-cell lymphoma (NKTCL), a representative disease model to study EBV-induced lymphomagenesis, incorporating genomic, transcriptomic, and in-depth proteomic data. Our multi-omics analysis of NKTCL revealed that EBV gene pattern correlated with immune-related oncogenic signaling. Single-cell transcriptome further delineated the tumor microenvironment as immune-inflamed, -deficient, and -desert phenotypes, in association with different setpoints of cancer-immunity cycle. EBV interacted with transcriptional factors to provoke GPCR interactome (GPCRome) reprogramming. Enhanced expression of chemokine receptor-1 (CCR1) on malignant and immunosuppressive cells modulated virus-cancer interaction on microenvironment. Therapeutic targeting CCR1 showed promising efficacy with EBV eradication, T-cell activation, and lymphoma cell killing in NKTCL organoid. Collectively, our study identified a previously unknown GPCR-mediated malignant progression and translated sensors of viral molecules into EBV-specific anti-cancer therapeutics.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Multiomics analyses reveal dynamic bioenergetic pathways and functional remodeling of the heart during intermittent fasting

Intermittent fasting (IF) has been shown to reduce cardiovascular risk factors in both animals and humans, and can protect the heart against ischemic injury in models of myocardial infarction. However, the underlying molecular mechanisms behind these effects remain unclear. To shed light on the molecular and cellular adaptations of the heart to IF, we conducted comprehensive system-wide analyses of the proteome, phosphoproteome, and transcriptome, followed by functional analysis. Using advanced mass spectrometry, we profiled the proteome and phosphoproteome of heart tissues obtained from mice that were maintained on daily 12- or 16 hr fasting, every-other-day fasting, or ad libitum control feeding regimens for 6 months. We also performed RNA sequencing to evaluate whether the observed molecular responses to IF occur at the transcriptional or post-transcriptional levels. Our analyses revealed that IF significantly affected pathways that regulate cyclic GMP signaling, lipid and amino acid metabolism, cell adhesion, cell death, and inflammation. Furthermore, we found that the impact of IF on different metabolic processes varied depending on the length of the fasting regimen. Short IF regimens showed a higher correlation of pathway alteration, while longer IF regimens had an inverse correlation of metabolic processes such as fatty acid oxidation and immune processes. Additionally, functional echocardiographic analyses demonstrated that IF enhances stress-induced cardiac performance. Our systematic multi-omics study provides a molecular framework for understanding how IF impacts the heart's function and its vulnerability to injury and disease.

Integrative analysis of cancer dependency data and comprehensive phosphoproteomics data revealed the EPHA2-PARD3 axis as a cancer vulnerability in KRAS-mutant colorectal cancer

Colorectal cancer (CRC), a common malignant tumour of the gastrointestinal tract, is a life-threatening cancer worldwide. Mutations in KRAS and BRAF, the major driver mutation subtypes in CRC, activate the RAS pathway, contribute to tumorigenesis in CRC and are being investigated as potential therapeutic targets. Despite recent advances in clinical trials targeting KRASG12C or RAS downstream signalling molecules for KRAS-mutant CRC, there is a lack of effective therapeutic interventions. Therefore, understanding the unique molecular characteristics of KRAS-mutant CRC is essential for identifying molecular targets and developing novel therapeutic interventions. We obtained in-depth proteomics and phosphoproteomics quantitative data for over 7900 proteins and 38 700 phosphorylation sites in cells from 35 CRC cell lines and performed informatic analyses, including proteomics-based coexpression analysis and correlation analysis between phosphoproteomics data and cancer dependency scores of the corresponding phosphoproteins. Our results revealed novel dysregulated protein-protein associations enriched specifically in KRAS-mutant cells. Our phosphoproteomics analysis revealed activation of EPHA2 kinase and downstream tight junction signalling in KRAS-mutant cells. Furthermore, the results implicate the phosphorylation site Y378 in the tight junction protein PARD3 as a cancer vulnerability in KRAS-mutant cells. Together, our large-scale phosphoproteomics and proteomics data across 35 steady-state CRC cell lines represent a valuable resource for understanding the molecular characteristics of oncogenic mutations. Our approach to predicting cancer dependency from phosphoproteomics data identified the EPHA2-PARD3 axis as a cancer vulnerability in KRAS-mutant CRC.

Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

Global and tissue-specific aging effects on murine proteomes

Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Rapid adaptation to CDK2 inhibition exposes intrinsic cell-cycle plasticity

CDK2 is a core cell-cycle kinase that phosphorylates many substrates to drive progression through the cell cycle. CDK2 is hyperactivated in multiple cancers and is therefore an attractive therapeutic target. Here, we use several CDK2 inhibitors in clinical development to interrogate CDK2 substrate phosphorylation, cell-cycle progression, and drug adaptation in preclinical models. Whereas CDK1 is known to compensate for loss of CDK2 in Cdk2-/- mice, this is not true of acute inhibition of CDK2. Upon CDK2 inhibition, cells exhibit a rapid loss of substrate phosphorylation that rebounds within several hours. CDK4/6 activity backstops inhibition of CDK2 and sustains the proliferative program by maintaining Rb1 hyperphosphorylation, active E2F transcription, and cyclin A2 expression, enabling re-activation of CDK2 in the presence of drug. Our results augment our understanding of CDK plasticity and indicate that co-inhibition of CDK2 and CDK4/6 may be required to suppress adaptation to CDK2 inhibitors currently under clinical assessment.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

Higher-order modular regulation of the human proteome

Operons are transcriptional modules that allow bacteria to adapt to environmental changes by coordinately expressing the relevant set of genes. In humans, biological pathways and their regulation are more complex. If and how human cells coordinate the expression of entire biological processes is unclear. Here, we capture 31 higher-order co-regulation modules, which we term progulons, by help of supervised machine-learning on proteomics data. Progulons consist of dozens to hundreds of proteins that together mediate core cellular functions. They are not restricted to physical interactions or co-localisation. Progulon abundance changes are primarily controlled at the level of protein synthesis and degradation. Implemented as a web app at www.proteomehd.net/progulonFinder, our approach enables the targeted search for progulons of specific cellular processes. We use it to identify a DNA replication progulon and reveal multiple new replication factors, validated by extensive phenotyping of siRNA-induced knockdowns. Progulons provide a new entry point into the molecular understanding of biological processes.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

A NuRD for all seasons

The nucleosome-remodeling and deacetylase (NuRD) complex is an essential transcriptional regulator in all complex animals. All seven core subunits of the complex exist as multiple paralogs, raising the question of whether the complex might utilize paralog switching to achieve cell type-specific functions. We examine the evidence for this idea, making use of published quantitative proteomic data to dissect NuRD composition in 20 different tissues, as well as a large-scale CRISPR knockout screen carried out in >1000 human cancer cell lines. These data, together with recent reports, provide strong support for the idea that distinct permutations of the NuRD complex with tailored functions might regulate tissue-specific gene expression programs.

Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition

Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.

Neutrophil profiles of pediatric COVID-19 and multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a delayed-onset, COVID-19-related hyperinflammatory illness characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigenemia, cytokine storm, and immune dysregulation. In severe COVID-19, neutrophil activation is central to hyperinflammatory complications, yet the role of neutrophils in MIS-C is undefined. Here, we collect blood from 152 children: 31 cases of MIS-C, 43 cases of acute pediatric COVID-19, and 78 pediatric controls. We find that MIS-C neutrophils display a granulocytic myeloid-derived suppressor cell (G-MDSC) signature with highly altered metabolism that is distinct from the neutrophil interferon-stimulated gene (ISG) response we observe in pediatric COVID-19. Moreover, we observe extensive spontaneous neutrophil extracellular trap (NET) formation in MIS-C, and we identify neutrophil activation and degranulation signatures. Mechanistically, we determine that SARS-CoV-2 immune complexes are sufficient to trigger NETosis. Our findings suggest that hyperinflammatory presentation during MIS-C could be mechanistically linked to persistent SARS-CoV-2 antigenemia, driven by uncontrolled neutrophil activation and NET release in the vasculature.

Andrographolide suppresses the malignancy of triple-negative breast cancer by reducing THOC1-promoted cancer stem cell characteristics

Triple-negative breast cancers (TNBCs) are difficult to cure and currently lack of effective treatment strategies. Cancer stem cells (CSCs) are highly associated with the poor clinical outcome of TNBCs. Thoc1 is a core component of the THO complex (THOC) that regulates the elongation, processing and nuclear export of mRNA. The function of thoc1 in TNBC and whether Thoc1 serves as a drug target are poorly understood. In this study, we demonstrated that thoc1 expression is elevated in TNBC cell lines and human TNBC patient tissues. Knockdown of thoc1 decreased cancer stem cell populations, reduced mammosphere formation, impaired THOC function, and downregulated the expression of stemness-related proteins. Moreover, the thoc1-knockdown 4T1 cells showed less lung metastasis in an orthotopic breast cancer mouse model. Overexpression of Thoc1 promoted TNBC malignancy and the mRNA export of stemness-related genes. Furthermore, treatment of TNBC cells with the natural compound andrographolide reduced the expression of Thoc1 expression, impaired homeostasis of THOC, suppressed CSC properties, and delayed tumor growth in a 4T1-implanted orthotopic mouse model. Andrographolide also reduced the activity of NF-κB, an upstream transcriptional regulator of Thoc1. Notably, thoc1 overexpression attenuates andrographolide-suppressed cellular proliferation. Altogether, our results demonstrate that THOC1 promotes cancer stem cell characteristics of TNBC, and andrographolide is a potential natural compound for eliminating CSCs of TNBCs by downregulating the NF-κB-thoc1 axis.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

PARP1-SNAI2 transcription axis drives resistance to PARP inhibitor, Talazoparib

The synthetic lethal association between BRCA deficiency and poly (ADP-ribose) polymerase (PARP) inhibition supports PARP inhibitor (PARPi) clinical efficacy in BRCA-mutated tumors. PARPis also demonstrate activity in non-BRCA mutated tumors presumably through induction of PARP1-DNA trapping. Despite pronounced clinical response, therapeutic resistance to PARPis inevitably develops. An abundance of knowledge has been built around resistance mechanisms in BRCA-mutated tumors, however, parallel understanding in non-BRCA mutated settings remains insufficient. In this study, we find a strong correlation between the epithelial-mesenchymal transition (EMT) signature and resistance to a clinical PARPi, Talazoparib, in non-BRCA mutated tumor cells. Genetic profiling demonstrates that SNAI2, a master EMT transcription factor, is transcriptionally induced by Talazoparib treatment or PARP1 depletion and this induction is partially responsible for the emerging resistance. Mechanistically, we find that the PARP1 protein directly binds to SNAI2 gene promoter and suppresses its transcription. Talazoparib treatment or PARP1 depletion lifts PARP1-mediated suppression and increases chromatin accessibility around SNAI2 promoters, thus driving SNAI2 transcription and drug resistance. We also find that depletion of the chromatin remodeler CHD1L suppresses SNAI2 expression and reverts acquired resistance to Talazoparib. The PARP1/CHD1L/SNAI2 transcription axis might be therapeutically targeted to re-sensitize Talazoparib in non-BRCA mutated tumors.

CD4+ T helper 2 cells suppress breast cancer by inducing terminal differentiation

Cancer immunology research is largely focused on the role of cytotoxic immune responses against advanced cancers. Herein, we demonstrate that CD4⁺ T helper (Th2) cells directly block spontaneous breast carcinogenesis by inducing the terminal differentiation of the cancer cells. Th2 cell immunity, stimulated by thymic stromal lymphopoietin, caused the epigenetic reprogramming of the tumor cells, activating mammary gland differentiation and suppressing epithelial–mesenchymal transition. Th2 polarization was required for this tumor antigen–specific immunity, which persisted in the absence of CD8⁺ T and B cells. Th2 cells directly blocked breast carcinogenesis by secreting IL-3, IL-5, and GM-CSF, which signaled to their common receptor expressed on breast tumor cells. Importantly, Th2 cell immunity permanently reverted high-grade breast tumors into low-grade, fibrocystic-like structures. Our findings reveal a critical role for CD4⁺ Th2 cells in immunity against breast cancer, which is mediated by terminal differentiation as a distinct effector mechanism for cancer immunoprevention and therapy.

Protein-protein interaction network of E. coli K-12 has significant high-dimensional cavities: New insights from algebraic topological studies

As a model system, Escherichia coli (E. coli) has been used to study various life processes. A dramatic paradigm shift has occurred in recent years, with the study of single proteins moving towards the study of dynamically-interacting proteins, especially protein-protein interaction (PPI) networks. However, despite the importance of PPI networks, little is known about the intrinsic nature of the network structure, especially high-dimensional topological properties . By introducing general hypergeometric distribution, here we reconstruct a statistically-reliable combined PPI network of E. coli (E.coli-PPI-Network) from several datasets. Unlike traditional graph analysis, algebraic topology was introduced to analyze the topological structures of the E.coli-PPI-Network, including high-dimensional cavities and cycles. Random networks with the same node and edge number (RandomNet), or scale-free networks with the same degree distribution (RandomNet-SameDD) were produced as controls. We discovered that the E.coli-PPI-Network had special algebraic typological structures, exhibiting more high-dimensional cavities and cycles, compared to RandomNets or, importantly, RandomNet-SameDD. Based on these results, we defined degree of involved q dimensional cycles of proteins (q-DC_protein ) in the network, a novel concept which relies on the integral structure of the network and is different from traditional node degree or hubs. Finally, top proteins ranked by their 1-DC_protein were identified. In conclusion, by introducing mathematical and computer technologies, we discovered novel algebraic topological properties of the E.coli-PPI-Network, which has special high-dimensional cavities and cycles, and thereby revealed certain intrinsic rules of information flow underlining bacteria biology.

Phosphoproteomic analysis of thrombin- and p38 MAPK-regulated signaling networks in endothelial cells

Endothelial dysfunction is a hallmark of inflammation and is mediated by inflammatory factors that signal through G protein–coupled receptors including protease-activated receptor-1 (PAR1). PAR1, a receptor for thrombin, signals via the small GTPase RhoA and myosin light chain intermediates to facilitate endothelial barrier permeability. PAR1 also induces endothelial barrier disruption through a p38 mitogen-activated protein kinase–dependent pathway, which does not integrate into the RhoA/MLC pathway; however, the PAR1-p38 signaling pathways that promote endothelial dysfunction remain poorly defined. To identify effectors of this pathway, we performed a global phosphoproteome analysis of thrombin signaling regulated by p38 in human cultured endothelial cells using multiplexed quantitative mass spectrometry. We identified 5491 unique phosphopeptides and 2317 phosphoproteins, four distinct dynamic phosphoproteome profiles of thrombin-p38 signaling, and an enrichment of biological functions associated with endothelial dysfunction, including modulators of endothelial barrier disruption and a subset of kinases predicted to regulate p38-dependent thrombin signaling. Using available antibodies to detect identified phosphosites of key p38-regulated proteins, we discovered that inhibition of p38 activity and siRNA-targeted depletion of the p38α isoform increased basal phosphorylation of extracellular signal–regulated protein kinase 1/2, resulting in amplified thrombin-stimulated extracellular signal–regulated protein kinase 1/2 phosphorylation that was dependent on PAR1. We also discovered a role for p38 in the phosphorylation of α-catenin, a component of adherens junctions, suggesting that this phosphorylation may function as an important regulatory process. Taken together, these studies define a rich array of thrombin- and p38-regulated candidate proteins that may serve important roles in endothelial dysfunction.

Comprehensive proteomic profiling of plasma and serum phosphatidylserine-positive extracellular vesicles reveals tissue-specific proteins

Extracellular vesicles (EVs) are ubiquitously secreted by almost all tissues and carry many cargoes, including proteins, RNAs, and lipids, which are related to various biological processes. EVs are shed from tissues into the blood and expected to be used as biomarkers for diseases. Here, we isolated EVs from EDTA plasma and serum of six healthy subjects by an affinity capture isolation method, and a total of 4,079 proteins were successfully identified by comprehensive EV proteomics. Our reliable and detailed catalog of the differential expression profiles of EV proteins in plasma and serum between healthy individuals could be useful as a reference for biomarker discovery. Furthermore, tissue-specific protein groups co-regulated between blood EVs from healthy individuals were identified. These EV proteins are expected to be used for more specific and sensitive enrichment of tissue-specific EVs and for screening and monitoring of disease without diagnostic imaging in patient blood in the future.

•

Catalog of EV proteome created by state-of-the-art proteome analysis technologies

•

Plasma and serum EV proteome profiles showed a difference in healthy individuals

•

Novel standard reference proteins in plasma and serum EVs were identified

•

Tissue-specific EV marker candidates were presented by the informatics approach

Aptamer-Based Analysis and Manipulation of the Protein Activity in Living Cells

Directly analyzing and precisely manipulating the activity of target proteins without altering their natural structure and expression would be essential to decoding many protein-dominant cellular processes. To meet this goal, we used streptavidin as the carrier to develop an aptamer-based nanoplatform for monitoring the activation process of specific proteins in living cells. Our results showed that this nanoplatform could efficiently enter the cellular cytoplasm and specifically report the presence of RelA in the activated state. Meanwhile, with incorporation of a photoresponsive module, this aptamer-based nanoplatform was able to manipulate the nuclear translocation behavior of active RelA, enabling control over related downstream signaling events.

Dynamics of huntingtin protein interactions in the striatum identifies candidate modifiers of Huntington disease

Huntington disease (HD) is a monogenic neurodegenerative disorder with one causative gene, huntingtin (HTT). Yet, HD pathobiology is multifactorial, suggesting that cellular factors influence disease progression. Here, we define HTT protein-protein interactions (PPIs) perturbed by the mutant protein with expanded polyglutamine in the mouse striatum, a brain region with selective HD vulnerability. Using metabolically labeled tissues and immunoaffinity purification-mass spectrometry, we establish that polyglutamine-dependent modulation of HTT PPI abundances and relative stability starts at an early stage of pathogenesis in a Q140 HD mouse model. We identify direct and indirect PPIs that are also genetic disease modifiers using in-cell two-hybrid and behavioral assays in HD human cell and Drosophila models, respectively. Validated, disease-relevant mHTT-dependent interactions encompass mediators of synaptic neurotransmission (SNAREs and glutamate receptors) and lysosomal acidification (V-ATPase). Our study provides a resource for understanding mHTT-dependent dysfunction in cortico-striatal cellular networks, partly through impaired synaptic communication and endosomal-lysosomal system. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Decoding the cellular effects of genetic variation through interaction proteomics

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

Neutrophil Profiles of Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children

Multisystem Inflammatory Syndrome in Children (MIS-C) is a delayed-onset, COVID-19-related hyperinflammatory systemic illness characterized by SARS-CoV-2 antigenemia, cytokine storm and immune dysregulation; however, the role of the neutrophil has yet to be defined. In adults with severe COVID-19, neutrophil activation has been shown to be central to overactive inflammatory responses and complications. Thus, we sought to define neutrophil activation in children with MIS-C and acute COVID-19. We collected samples from 141 children: 31 cases of MIS-C, 43 cases of acute pediatric COVID-19, and 67 pediatric controls. We found that MIS-C neutrophils display a granulocytic myeloid-derived suppressor cell (G-MDSC) signature with highly altered metabolism, which is markedly different than the neutrophil interferon-stimulated gene (ISG) response observed in pediatric patients during acute SARS-CoV-2 infection. Moreover, we identified signatures of neutrophil activation and degranulation with high levels of spontaneous neutrophil extracellular trap (NET) formation in neutrophils isolated from fresh whole blood of MIS-C patients. Mechanistically, we determined that SARS-CoV-2 immune complexes are sufficient to trigger NETosis. Overall, our findings suggest that the hyperinflammatory presentation of MIS-C could be mechanistically linked to persistent SARS-CoV-2 antigenemia through uncontrolled neutrophil activation and NET release in the vasculature.

ONE SENTENCE SUMMARY

Circulating SARS-CoV-2 antigen:antibody immune complexes in Multisystem Inflammatory Syndrome in Children (MIS-C) drive hyperinflammatory and coagulopathic neutrophil extracellular trap (NET) formation and neutrophil activation pathways, providing insight into disease pathology and establishing a divergence from neutrophil signaling seen in acute pediatric COVID-19.

B cells support the repair of injured tissues by adopting MyD88-dependent regulatory functions and phenotype

Exogenously applied mature naïve B220+ /CD19+ /IgM+ /IgD+ B cells are strongly protective in the context of tissue injury. However, the mechanisms by which B cells detect tissue injury and aid repair remain elusive. Here, we show in distinct models of skin and brain injury that MyD88-dependent toll-like receptor (TLR) signaling through TLR2/6 and TLR4 is essential for the protective benefit of B cells in vivo, while B cell-specific deletion of MyD88 abrogated this effect. The B cell response to injury was multi-modal with simultaneous production of both regulatory cytokines, such as IL-10, IL-35, and transforming growth factor beta (TGFβ), and inflammatory cytokines, such as tumor necrosis factor alpha (TNFα), IL-6, and interferon gamma. Cytometry analysis showed that this response was time and environment-dependent in vivo, with 20%-30% of applied B cells adopting an immune modulatory phenotype with high co-expression of anti- and pro-inflammatory cytokines after 18-48 h at the injury site. B cell treatment reduced the expression of TNFα and increased IL-10 and TGFβ in infiltrating immune cells and fibroblasts at the injury site. Proteomic analysis further showed that B cells have a complex time-dependent homeostatic effect on the injured microenvironment, reducing the expression of inflammation-associated proteins, and increasing proteins associated with proliferation, tissue remodeling, and protection from oxidative stress. These findings chart and validate a first mechanistic understanding of the effects of B cells as an immunomodulatory cell therapy in the context of tissue injury.

Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors

Macrophages undergoing M1- versus M2-type polarization differ significantly in their cell metabolism and cellular functions. Here, global quantitative time-course proteomics and phosphoproteomics paired with transcriptomics provide a comprehensive characterization of temporal changes in cell metabolism, cellular functions, and signaling pathways that occur during the induction phase of M1- versus M2-type polarization. Significant differences in, especially, metabolic pathways are observed, including changes in glucose metabolism, glycosaminoglycan metabolism, and retinoic acid signaling. Kinase-enrichment analysis shows activation patterns of specific kinases that are distinct in M1- versus M2-type polarization. M2-type polarization inhibitor drug screens identify drugs that selectively block M2- but not M1-type polarization, including mitogen-activated protein kinase kinase (MEK) and histone deacetylase (HDAC) inhibitors. These datasets provide a comprehensive resource to identify specific signaling and metabolic pathways that are critical for macrophage polarization. In a proof-of-principle approach, we use these datasets to show that MEK signaling is required for M2-type polarization by promoting peroxisome proliferator-activated receptor-γ (PPARγ)-induced retinoic acid signaling.

A multiplexed, automated immuno-matrix assisted laser desorption/ionization mass spectrometry assay for simultaneous and precise quantitation of PTEN and p110α in cell lines and tumor tissues

The PI3-kinase/AKT/mTOR pathway plays a central role in cancer signaling. While p110α is the catalytic α-subunit of PI3-kinase and a major drug target, PTEN is the main negative regulator of the PI3-kinase/AKT/mTOR pathway. PTEN is often down-regulated in cancer, and there are conflicting data on PTEN's role as breast cancer biomarker. PTEN and p110α protein expression in tumors is commonly analyzed by immunohistochemistry, which suffers from poor multiplexing capacity, poor standardization, and antibody crossreactivity, and which provides only semi-quantitative data. Here, we present an automated, and standardized immuno-matrix-assisted laser desorption/ionization mass spectrometry (iMALDI) assay that allows precise and multiplexed quantitation of PTEN and p110α concentrations, without the limitations of immunohistochemistry. Our iMALDI assay only requires a low-cost benchtop MALDI-TOF mass spectrometer, which simplifies clinical translation. We validated our assay's precision and accuracy, with simultaneous enrichment of both target proteins not significantly affecting the precision and accuracy of the quantitation when compared to the PTEN- and p110α-singleplex iMALDI assays (<15% difference). The multiplexed assay's linear range is from 0.6-20 fmol with accuracies of 90-112% for both target proteins, and the assay is free of matrix-related interferences. The inter-day reproducibility over 5-days was high, with an overall CV of 9%. PTEN and p110α protein concentrations can be quantified down to 1.4 fmol and 0.6 fmol per 10 μg of total tumor protein, respectively, in various tumor tissue samples, including fresh-frozen breast tumors and colorectal cancer liver metastases, and patient-derived xenograft (PDX) tumors.

ggVolcanoR: A Shiny app for customizable visualization of differential expression datasets

Volcano and other analytical plots (e.g., correlation plots, upset plots, and heatmaps) serve as important data visualization methods for transcriptomic and proteomic analyses. Customizable generation of these plots is fundamentally important for a better understanding of dysregulated expression data and is therefore instrumental for the ensuing pathway analysis and biomarker identification. Here, we present an R-based Shiny application, termed ggVolcanoR, to allow for customizable generation and visualization of volcano plots, correlation plots, upset plots, and heatmaps for differential expression datasets, via a user-friendly interactive interface in both local executable version and web-based application without requiring programming expertise. Compared to currently existing packages, ggVolcanoR offers more practical options to optimize the generation of publication-quality volcano and other analytical plots for analyzing and comparing dysregulated genes/proteins across multiple differential expression datasets. In addition, ggVolcanoR provides an option to download the customized list of the filtered dysregulated expression data, which can be directly used as input for downstream pathway analysis. The source code of ggVolcanoR is available at https://github.com/KerryAM-R/ggVolcanoR and the webserver of ggVolcanoR 1.0 has been deployed and is freely available for academic purposes at https://ggvolcanor.erc.monash.edu/.

Interpretation of cancer mutations using a multiscale map of protein systems

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations-sometimes not observed by genomics-represent cancer vulnerabilities that may be targeted in precision oncology.

Synaptic proteins associated with cognitive performance and neuropathology in older humans revealed by multiplexed fractionated proteomics

Alzheimer's disease (AD) is defined by the presence of abundant amyloid-β (Aβ) and tau neuropathology. While this neuropathology is necessary for AD diagnosis, it is not sufficient for causing cognitive impairment. Up to one third of community dwelling older adults harbor intermediate to high levels of AD neuropathology at death yet demonstrate no significant cognitive impairment. Conversely, there are individuals who exhibit dementia with no gross explanatory neuropathology. In prior studies, synapse loss correlated with cognitive impairment. To understand how synaptic composition changes in relation to neuropathology and cognition, multiplexed liquid chromatography mass-spectrometry was used to quantify enriched synaptic proteins from the parietal association cortex of 100 subjects with contrasting levels of AD pathology and cognitive performance. 123 unique proteins were significantly associated with diagnostic category. Functional analysis showed enrichment of serotonin release and oxidative phosphorylation categories in normal (cognitively unimpaired, low neuropathology) and "resilient" (unimpaired despite AD pathology) individuals. In contrast, frail individuals, (low pathology, impaired cognition) showed a metabolic shift towards glycolysis and increased presence of proteasome subunits.

Integrated multi-omics analysis of RB-loss identifies widespread cellular programming and synthetic weaknesses

Inactivation of RB is one of the hallmarks of cancer, however gaps remain in our understanding of how RB-loss changes human cells. Here we show that pRB-depletion results in cellular reprogramming, we quantitatively measured how RB-depletion altered the transcriptional, proteomic and metabolic output of non-tumorigenic RPE1 human cells. These profiles identified widespread changes in metabolic and cell stress response factors previously linked to E2F function. In addition, we find a number of additional pathways that are sensitive to RB-depletion that are not E2F-regulated that may represent compensatory mechanisms to support the growth of RB-depleted cells. To determine whether these molecular changes are also present in RB1^-/- tumors, we compared these results to Retinoblastoma and Small Cell Lung Cancer data, and identified widespread conservation of alterations found in RPE1 cells. To define which of these changes contribute to the growth of cells with de-regulated E2F activity, we assayed how inhibiting or depleting these proteins affected the growth of RB1^-/- cells and of Drosophila E2f1-RNAi models in vivo. From this analysis, we identify key metabolic pathways that are essential for the growth of pRB-deleted human cells.

Multisystem inflammatory syndrome in children is driven by zonulin-dependent loss of gut mucosal barrier

BACKGROUNDWeeks after SARS-CoV-2 infection or exposure, some children develop a severe, life-threatening illness called multisystem inflammatory syndrome in children (MIS-C). Gastrointestinal (GI) symptoms are common in patients with MIS-C, and a severe hyperinflammatory response ensues with potential for cardiac complications. The cause of MIS-C has not been identified to date.METHODSHere, we analyzed biospecimens from 100 children: 19 with MIS-C, 26 with acute COVID-19, and 55 controls. Stools were assessed for SARS-CoV-2 by reverse transcription PCR (RT-PCR), and plasma was examined for markers of breakdown of mucosal barrier integrity, including zonulin. Ultrasensitive antigen detection was used to probe for SARS-CoV-2 antigenemia in plasma, and immune responses were characterized. As a proof of concept, we treated a patient with MIS-C with larazotide, a zonulin antagonist, and monitored the effect on antigenemia and the patient's clinical response.RESULTSWe showed that in children with MIS-C, a prolonged presence of SARS-CoV-2 in the GI tract led to the release of zonulin, a biomarker of intestinal permeability, with subsequent trafficking of SARS-CoV-2 antigens into the bloodstream, leading to hyperinflammation. The patient with MIS-C treated with larazotide had a coinciding decrease in plasma SARS-CoV-2 spike antigen levels and inflammatory markers and a resultant clinical improvement above that achieved with currently available treatments.CONCLUSIONThese mechanistic data on MIS-C pathogenesis provide insight into targets for diagnosing, treating, and preventing MIS-C, which are urgently needed for this increasingly common severe COVID-19-related disease in children.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

E2F/Dp inactivation in fat body cells triggers systemic metabolic changes

The E2F transcription factors play a critical role in controlling cell fate. In Drosophila, the inactivation of E2F in either muscle or fat body results in lethality, suggesting an essential function for E2F in these tissues. However, the cellular and organismal consequences of inactivating E2F in these tissues are not fully understood. Here, we show that the E2F loss exerts both tissue-intrinsic and systemic effects. The proteomic profiling of E2F-deficient muscle and fat body revealed that E2F regulates carbohydrate metabolism, a conclusion further supported by metabolomic profiling. Intriguingly, animals with E2F-deficient fat body had a lower level of circulating trehalose and reduced storage of fat. Strikingly, a sugar supplement was sufficient to restore both trehalose and fat levels, and subsequently rescued animal lethality. Collectively, our data highlight the unexpected complexity of E2F mutant phenotype, which is a result of combining both tissue-specific and systemic changes that contribute to animal development.

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein-protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

iDRiP for the systematic discovery of proteins bound directly to noncoding RNA

More than 90% of the human genome is transcribed into noncoding RNAs, but their functional characterization has lagged behind. A major bottleneck in the understanding of their functions and mechanisms has been a dearth of systematic methods for identifying interacting protein partners. There now exist several methods, including identification of direct RNA interacting proteins (iDRiP), chromatin isolation by RNA purification (ChIRP), and RNA antisense purification, each previously applied towards identifying a proteome for the prototype noncoding RNA, Xist. iDRiP has recently been modified to successfully identify proteomes for two additional noncoding RNAs of interest, TERRA and U1 RNA. Here we describe the modified protocol in detail, highlighting technical differences that facilitate capture of various noncoding RNAs. The protocol can be applied to short and long RNAs in both cultured cells and tissues, and requires ~1 week from start to finish. Here we also perform a comparative analysis between iDRiP and ChIRP. We obtain partially overlapping profiles, but find that iDRiP yields a greater number of specific proteins and fewer mitochondrial contaminants. With an increasing number of essential long noncoding RNAs being described, robust RNA-centric protein capture methods are critical for the probing of noncoding RNA function and mechanism.

Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response

Defective autophagy is strongly associated with chronic inflammation. Loss-of-function of the core autophagy gene Atg16l1 increases risk for Crohn's disease in part by enhancing innate immunity through myeloid cells such as macrophages. However, autophagy is also recognized as a mechanism for clearance of certain intracellular pathogens. These divergent observations prompted a re-evaluation of ATG16L1 in innate antimicrobial immunity. In this study, we found that loss of Atg16l1 in myeloid cells enhanced the killing of virulent Shigella flexneri (S.flexneri), a clinically relevant enteric bacterium that resides within the cytosol by escaping from membrane-bound compartments. Quantitative multiplexed proteomics of murine bone marrow-derived macrophages revealed that ATG16L1 deficiency significantly upregulated proteins involved in the glutathione-mediated antioxidant response to compensate for elevated oxidative stress, which simultaneously promoted S.flexneri killing. Consistent with this, myeloid-specific deletion of Atg16l1 in mice accelerated bacterial clearance in vitro and in vivo. Pharmacological induction of oxidative stress through suppression of cysteine import enhanced microbial clearance by macrophages. Conversely, antioxidant treatment of macrophages permitted S.flexneri proliferation. These findings demonstrate that control of oxidative stress by ATG16L1 and autophagy regulates antimicrobial immunity against intracellular pathogens.

Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches

Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

Leveraging proteomics in orphan disease research: pitfalls and potential

Introduction: The term 'orphan diseases' includes conditions meeting prevalence-based or commercial viability criteria: they affect a small number of individuals and are considered an unviable market for drug development. Proteomics is an important technology to study them, providing information on mechanisms and evolution, biomarkers, and effects of therapeutic interventions.Areas covered: Herein, we review how proteomics and bioinformatic tools could be applied to the study of rare diseases and discuss pitfalls and potential.Expert opinion: Research in the field of rare diseases has to face many challenges, and implementation plans should foresee highly specialized collaborative consortia to create multidisciplinary frameworks for data sharing, advancing research, supporting clinical studies, and accelerating drug development. The integration of different technologies will allow better knowledge of disease pathophysiology, and the inclusion of proteomics and other omics technologies in this context will be pivotal to this aim.Several aspects of rare diseases, often perceived as limiting factors, might actually be advantages for a precision medicine approach: the limited number of patients, the collaboration with patient societies, and the availability of curated clinical registries could allow the development of homogeneous clinical databases and ultimately a better control over the data to be analyzed.

An integrated landscape of protein expression in human cancer

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.