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The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets. The number of publicly available proteomics datasets is growing rapidly, but a standardized approach for describing the associated metadata is lacking. Here, the authors propose a format and a software pipeline to present and validate metadata, and integrate them into ProteomeXchange repositories.

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry–based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset. MassIVE.quant is a data repository and data resource for reproducible quantitative mass spectrometry–based proteomics.

Endoplasmic-reticulum resident inositol-requiring enzyme 1α (IRE1) supports protein homeostasis via its cytoplasmic kinase-RNase module. Known cancer dependency on IRE1 entails its enzymatic activation of the transcription factor XBP1s and of regulated RNA decay. We discovered surprisingly that some cancer cell lines require IRE1 but not its enzymatic activity. IRE1 knockdown but not enzymatic IRE1 inhibition or XBP1 disruption attenuated cell cycle progression and tumor growth. IRE1 silencing led to activation of TP53 and CDKN1A/p21 in conjunction with increased DNA damage and chromosome instability, while decreasing heterochromatin as well as DNA and histone H3K9me3 methylation. Immunoelectron microscopy detected some endogenous IRE1 protein at the nuclear envelope. Thus, cancer cells co-opt IRE1 either enzymatically or nonenzymatically, which has significant implications for IRE1’s biological role and therapeutic targeting.

The current management of colorectal cancer (CRC) would greatly benefit from non‐invasive prognostic biomarkers indicative of clinicopathological tumor characteristics. Here, we employed targeted proteomic profiling of 80 glycoprotein biomarker candidates across plasma samples of a well‐annotated patient cohort with comprehensive CRC characteristics. Clinical data included 8‐year overall survival, tumor staging, histological grading, regional localization, and molecular tumor characteristics. The acquired quantitative proteomic dataset was subjected to the development of biomarker signatures predicting prognostic clinical endpoints. Protein candidates were selected into the signatures based on significance testing and a stepwise protein selection, each within 10‐fold cross‐validation. A six‐protein biomarker signature of patient outcome could predict survival beyond clinical stage and was able to stratify patients into groups of better and worse prognosis. We further evaluated the performance of the signature on the mRNA level and assessed its prognostic value in the context of previously published transcriptional signatures. Additional signatures predicting regional tumor localization and disease dissemination were also identified. The integration of rich clinical data, quantitative proteomic technologies, and tailored computational modeling facilitated the characterization of these signatures in patient circulation. These findings highlight the value of a simultaneous assessment of important prognostic disease characteristics within a single measurement. Synopsis Prognostic signatures comprising a handful of secreted proteins extracted non‐invasively from a blood sample, are shown to predict outcome and other clinical features of colorectal cancer patients. Predictive models of prognostic endpoints were developed from a cohort of 202 patients with multiplexed proteomic measurements of 88 glycoprotein biomarker candidates. The outcome signature was able to predict patient survival beyond the clinical stage and stratify patients of localized disease into low and high risk prognostic groups. The predictive ability of the outcome signature was confirmed at the transcript level. Recently defined transcriptional CRC subtypes with good and poor prognosis could be predicted with the outcome signature. Graphical Abstract Prognostic signatures comprising a handful of secreted proteins extracted non‐invasively from a blood sample, are shown to predict outcome and other clinical features of colorectal cancer patients.

Non‐invasive detection of colorectal cancer with blood‐based markers is a critical clinical need. Here we describe a phased mass spectrometry‐based approach for the discovery, screening, and validation of circulating protein biomarkers with diagnostic value. Initially, we profiled human primary tumor tissue epithelia and characterized about 300 secreted and cell surface candidate glycoproteins. These candidates were then screened in patient systemic circulation to identify detectable candidates in blood plasma. An 88‐plex targeting method was established to systematically monitor these proteins in two large and independent cohorts of plasma samples, which generated quantitative clinical datasets at an unprecedented scale. The data were deployed to develop and evaluate a five‐protein biomarker signature for colorectal cancer detection. Synopsis A five‐protein biomarker signature discovered and validated by mass spectrometry can accurately predict colorectal cancer (CRC) diagnosis non‐invasively from a blood sample. Secreted protein biomarker candidates discovered in tumor tissue were detected in the circulation of healthy and CRC subjects. A five‐protein predictive diagnostic signature was developed in a cohort of 100 healthy and 100 CRC subjects, and independently validated in an external cohort of 67 healthy and 202 CRC subjects. The protein biomarker signature was found to be more effective for CRC subjects with larger tumors. Graphical Abstract A five‐protein biomarker signature discovered and validated by mass spectrometry can accurately predict colorectal cancer (CRC) diagnosis non‐invasively from a blood sample.

RIP1 kinase-mediated inflammatory and cell death pathways have been implicated in the pathology of acute and chronic disorders of the nervous system. Here, we describe a novel animal model of RIP1 kinase deficiency, generated by knock-in of the kinase-inactivating RIP1(D138N) mutation in rats. Homozygous RIP1 kinase-dead (KD) rats had normal development, reproduction and did not show any gross phenotypes at baseline. However, cells derived from RIP1 KD rats displayed resistance to necroptotic cell death. In addition, RIP1 KD rats were resistant to TNF-induced systemic shock. We studied the utility of RIP1 KD rats for neurological disorders by testing the efficacy of the genetic inactivation in the transient middle cerebral artery occlusion/reperfusion model of brain injury. RIP1 KD rats were protected in this model in a battery of behavioral, imaging, and histopathological endpoints. In addition, RIP1 KD rats had reduced inflammation and accumulation of neuronal injury biomarkers. Unbiased proteomics in the plasma identified additional changes that were ameliorated by RIP1 genetic inactivation. Together these data highlight the utility of the RIP1 KD rats for target validation and biomarker studies for neurological disorders.

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.

Wheat leaf rust caused by Puccinia triticina is a destructive fungal disease causing considerable grain yield loss. In this study, we developed a novel assay to test the rust resistance of detached wheat leaves on defined media with retarded senescence. We observed that salicylic and jasmonic acid confer leaf rust resistance to a susceptible Keumkang wheat (Triticum aestivium L.). Transcription analysis revealed that atchi8 was highly expressed with an increased chitinase activity in the salicylic acid-treated leaves, while expression of PR-9, atpodL, and PR-5 increased in the jasmonic acid-treated leaves. Additionally, the metabolic profile suggested that the phenylalanine pathway might link flavonoid production to leaf rust resistance in the salicylic acid-treated leaves, while the alanine, aspartate, and glutamate metabolism might control the production of other amino acids to enhance pathogen stress response in the jasmonic acid-treated leaves. Finally, all identified genes and metabolites could be potential targets for screening chemical compounds for leaf rust resistance. Future studies on the underlying mechanisms of leaf rust resistance obtained by exogenous treatment of salicylic and jasmonic acids remain necessary.

Most current therapies that target plasma membrane receptors function by antagonizing ligand binding or enzymatic activities. However, typical mammalian proteins comprise multiple domains that execute discrete but coordinated activities. Thus, inhibition of one domain often incompletely suppresses the function of a protein. Indeed, targeted protein degradation technologies, including proteolysis-targeting chimeras 1 (PROTACs), have highlighted clinically important advantages of target degradation over inhibition 2 . However, the generation of heterobifunctional compounds binding to two targets with high affinity is complex, particularly when oral bioavailability is required 3 . Here we describe the development of proteolysis-targeting antibodies (PROTABs) that tether cell-surface E3 ubiquitin ligases to transmembrane proteins, resulting in target degradation both in vitro and in vivo. Focusing on zinc- and ring finger 3 (ZNRF3), a Wnt-responsive ligase, we show that this approach can enable colorectal cancer-specific degradation. Notably, by examining a matrix of additional cell-surface E3 ubiquitin ligases and transmembrane receptors, we demonstrate that this technology is amendable for ‘on-demand’ degradation. Furthermore, we offer insights on the ground rules governing target degradation by engineering optimized antibody formats. In summary, this work describes a strategy for the rapid development of potent, bioavailable and tissue-selective degraders of cell-surface proteins. Membrane-bound E3 ubiquitin ligases RNF43 and ZNRF3 are overexpressed in colorectal cancer, and can be repurposed using proteolysis-targeting antibodies (PROTABs) to selectively degrade cell-surface receptors in tumours.

Background Neurofibrillary tangles (NFTs) are pathological hallmarks of Alzheimer's Disease (AD), consisting of aggregated tau protein. Tau is frequently used as a biomarker in AD clinical trials, as it provides diagnostic and prognostic information and serves as a pharmacodynamic marker to assess effects of tau‐targeting treatments. Tau PET tracers specifically bind to NFTs, allowing for tau pathology imaging. However, PET imaging requires specialized infrastructure and is costly, limiting its widespread use in trials. Our study assessed the relationship between cerebrospinal fluid (CSF) soluble tau species and tau PET imaging. The aim was to identify fluid biomarkers that could serve as more accessible alternatives for assessing tau pathology in clinical trials. Methods CSF tau species were measured at baseline in a subset of 53 prodromal‐to‐moderate AD participants enrolled in two anti‐Tau Ph II trials (n = 26, n = 27). Tau peptides were measured by data‐independent acquisition mass spectrometry (DIA‐MS). Total tau, pTau181, and pTau205 were measured using Elecsys immunoassays. N‐term and Mid‐domain tau peptides were measured by targeted LC‐MS. pTau217 was measured using a Simoa assay and later with an Elecsys assay. Trial participants also completed [18F]GTP1 imaging. Standardized uptake value ratios (SUVR) were reported from the whole cortical gray matter and meta temporal regions using inferior cerebellar gray matter as the reference area. 46 pathology confirmed AD brains from the Arizona Study of Aging and Neurodegenerative Disorders at Banner Sun Health Research Institute were used to determine correlations between tau peptides and NFT burden in the fusiform gyrus. Tau peptides were measured by DIA‐MS; NFT burden was measured by quantifying AT8‐positive gray matter areas of adjacent brain sections. CSF and brain Tau species were correlated to [18F]GTP1 SUVR and %AT8‐positive area, respectively. Results The analysis demonstrated CSF pTau205, pTau217, and a proteomic peptide from the 2N4R isoform MTBR region had highest correlations with [18F]GTP1 SUVR across both target regions. The same MTBR peptide was highly correlated with the %AT8 positive area in the fusiform gyrus. Conclusion A proteomic peptide from the MTBR region was identified to be the highest correlated tau peptide in AD CSF and in the brain with tau pathology.