Large-scale Proteomic Profiling Research Articles

Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of Frontotemporal Dementia.

Diagnosis of Frontotemporal dementia (FTD) and the specific underlying neuropathologies (frontotemporal lobar degeneration; FTLD-Tau and FTLD-TDP) is challenging, and thus fluid biomarkers are needed to improve diagnostic accuracy. We used proximity extension assays to analyze 665 proteins in cerebrospinal fluid (CSF) samples from a multicenter cohort including patients with FTD (n = 189), Alzheimer's Disease dementia (AD; n = 232), and cognitively unimpaired individuals (n = 196). In a subset, FTLD neuropathology was determined based on phenotype or genotype (FTLD-Tau = 87 and FTLD-TDP = 68). Forty three proteins were differentially regulated in FTD compared to controls and AD, reflecting axon development, regulation of synapse assembly, and cell-cell adhesion mediator activity pathways. Classification analysis identified a 14- and 13-CSF protein panel that discriminated FTD from controls (AUC: 0.96) or AD (AUC: 0.91). Custom multiplex panels confirmed the highly accurate discrimination between FTD and controls (AUCs > 0.96) or AD (AUCs > 0.88) in three validation cohorts, including one with autopsy confirmation (AUCs > 0.90). Six proteins were differentially regulated between FTLD-TDP and FTLD-Tau, but no reproducible classification model could be generated (AUC: 0.80). Overall, this study introduces novel FTD-specific biomarker panels with potential use in diagnostic setting.

The maintenance of oocytes in the mammalian ovary involves extreme protein longevity

Women are born with all of their oocytes. The oocyte proteome must be maintained with minimal damage throughout the woman’s reproductive life, and hence for decades. Here we report that oocyte and ovarian proteostasis involves extreme protein longevity. Mouse ovaries had more extremely long-lived proteins than other tissues, including brain. These long-lived proteins had diverse functions, including in mitochondria, the cytoskeleton, chromatin and proteostasis. The stable proteins resided not only in oocytes but also in long-lived ovarian somatic cells. Our data suggest that mammals increase protein longevity and enhance proteostasis by chaperones and cellular antioxidants to maintain the female germline for long periods. Indeed, protein aggregation in oocytes did not increase with age and proteasome activity did not decay. However, increasing protein longevity cannot fully block female germline senescence. Large-scale proteome profiling of ~8,890 proteins revealed a decline in many long-lived proteins of the proteostasis network in the aging ovary, accompanied by massive proteome remodeling, which eventually leads to female fertility decline.

Large-Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis.

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as "the great imitator," NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients.

Conventional and genetic associations of adiposity with 1463 proteins in relatively lean Chinese adults

Adiposity is associated with multiple diseases and traits, but little is known about the causal relevance and mechanisms underlying these associations. Large-scale proteomic profiling, especially when integrated with genetic data, can clarify mechanisms linking adiposity with disease outcomes. We examined the associations of adiposity with plasma levels of 1463 proteins in 3977 Chinese adults, using measured and genetically-instrumented BMI. We further used two-sample bi-directional MR analyses to assess if certain proteins influenced adiposity, along with other (e.g. enrichment) analyses to clarify possible mechanisms underlying the observed associations. Overall, the mean (SD) baseline BMI was 23.9 (3.3) kg/m2, with only 6% being obese (i.e. BMI ≥ 30 kg/m2). Measured and genetically-instrumented BMI was significantly associated at FDR < 0.05 with levels of 1096 (positive/inverse: 826/270) and 307 (positive/inverse: 270/37) proteins, respectively, with FABP4, LEP, IL1RN, LSP1, GOLM2, TNFRSF6B, and ADAMTS15 showing the strongest positive and PON3, NCAN, LEPR, IGFBP2 and MOG showing the strongest inverse genetic associations. These associations were largely linear, in adiposity-to-protein direction, and replicated (> 90%) in Europeans of UKB (mean BMI 27.4 kg/m2). Enrichment analyses of the top > 50 BMI-associated proteins demonstrated their involvement in atherosclerosis, lipid metabolism, tumour progression and inflammation. Two-sample bi-directional MR analyses using cis-pQTLs identified in CKB GWAS found eight proteins (ITIH3, LRP11, SCAMP3, NUDT5, OGN, EFEMP1, TXNDC15, PRDX6) significantly affect levels of BMI, with NUDT5 also showing bi-directional association. The findings among relatively lean Chinese adults identified novel pathways by which adiposity may increase disease risks and novel potential targets for treatment of obesity and obesity-related diseases.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Associations of circulating proteins with lipoprotein profiles: proteomic analyses from the OmniHeart randomized trial and the Atherosclerosis Risk in Communities (ARIC) Study

BackgroundWithin healthy dietary patterns, manipulation of the proportion of macronutrient can reduce CVD risk. However, the biological pathways underlying healthy diet-disease associations are poorly understood. Using an untargeted, large-scale proteomic profiling, we aimed to (1) identify proteins mediating the association between healthy dietary patterns varying in the proportion of macronutrient and lipoproteins, and (2) validate the associations between diet-related proteins and lipoproteins in the Atherosclerosis Risk in Communities (ARIC) Study.MethodsIn 140 adults from the OmniHeart trial, a randomized, cross-over, controlled feeding study with 3 intervention periods (carbohydrate-rich; protein-rich; unsaturated fat-rich dietary patterns), 4,958 proteins were quantified at the end of each diet intervention period using an aptamer assay (SomaLogic). We assessed differences in log2-transformed proteins in 3 between-diet comparisons using paired t-tests, examined the associations between diet-related proteins and lipoproteins using linear regression, and identified proteins mediating these associations using a causal mediation analysis. Levels of diet-related proteins and lipoprotein associations were validated in the ARIC study (n = 11,201) using multivariable linear regression models, adjusting for important confounders.ResultsThree between-diet comparisons identified 497 significantly different proteins (protein-rich vs. carbohydrate-rich = 18; unsaturated fat-rich vs. carbohydrate-rich = 335; protein-rich vs. unsaturated fat-rich dietary patterns = 398). Of these, 9 proteins [apolipoprotein M, afamin, collagen alpha-3(VI) chain, chitinase-3-like protein 1, inhibin beta A chain, palmitoleoyl-protein carboxylesterase NOTUM, cathelicidin antimicrobial peptide, guanylate-binding protein 2, COP9 signalosome complex subunit 7b] were positively associated with lipoproteins [high-density lipoprotein (HDL)-cholesterol (C) = 2; triglyceride = 5; non-HDL-C = 3; total cholesterol to HDL-C ratio = 1]. Another protein, sodium-coupled monocarboxylate transporter 1, was inversely associated with HDL-C and positively associated with total cholesterol to HDL-C ratio. The proportion of the association between diet and lipoproteins mediated by these 10 proteins ranged from 21 to 98%. All of the associations between diet-related proteins and lipoproteins were significant in the ARIC study, except for afamin.ConclusionsWe identified proteins that mediate the association between healthy dietary patterns varying in macronutrients and lipoproteins in a randomized feeding study and an observational study.Trial registrationNCT00051350 at clinicaltrials.gov.

1237-P: Proteomic Indicators of Metabolic Health in Diabetes and Social Vulnerability

Introduction: Understanding the health impacts of socioeconomic deprivation (SED) and its interaction with type 2 diabetes is important for patient care and effective public health initiatives. Large-scale proteomic profiling using aptamer-based technology to measure 7,000 proteins has facilitated the development of blood-based proteomic signatures for 10 cardiometabolic SomaSignal Tests (SST), including a test that predicts the 4-year risk of a cardiovascular event (CV risk). Methods: The cross-sectional PsoBid study recruited participants from socioeconomically deprived and affluent areas of Glasgow, Scotland with similar age ranges and equal proportions of male and female, to identify relevant health disparities. We used regression analyses to estimate the association between SSTs and socioeconomic status (affluent (n = 257, 50.7%), deprived (n = 250, 49.3%)) and prevalent diabetes (n = 27 (5.3%), 7 (2.7%) affluent, 20 (8.0%) SV). Results: Diabetes was associated with SST predicted increased visceral fat, lean body mass, liver fat, body fat percentage, resting energy rate, and CV risk score, and worse proteomic predictors of cardiorespiratory fitness, glucose tolerance, alcohol impact, and kidney health (p&amp;lt;0.05). SED was associated with SST predicted increased visceral adiposity and CV risk score, and decreased cardiorespiratory fitness, glucose tolerance, and kidney health (p&amp;lt;0.05). The association between diabetes and CV risk score was highest among socioeconomically deprived participants (interaction p-value: 0.01). Conclusion: These results demonstrate that participants with diabetes and SED have proteomic phenotypes consistent with decreased cardiometabolic health compared to participants without diabetes or SV and SED is associated with worse predicted cardiovascular risk in patients with diabetes. Proteomics may be useful in identifying and monitoring the health effects of diabetes and SED or the impact of relevant interventions. Disclosure M.Simpson: Employee; SomaLogic Operating CO., Inc. R.Ostroff: Employee; SomaLogic. S.A.Williams: Employee; SomaLogic. N.Sattar: Advisory Panel; Amgen Inc., Boehringer Ingelheim and Eli Lilly Alliance, Novartis, Roche Diagnostics, Consultant; Afimmune Limited, Hanmi Pharm. Co., Ltd., Pfizer Inc., Other Relationship; Abbott, AstraZeneca, Boehringer Ingelheim Pharma GmbH&amp;Co.KG, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Research Support; AstraZeneca, Boehringer Ingelheim Pharma GmbH&amp;Co.KG, Novartis, Roche Diagnostics.

Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease

Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts—including Mendelian randomization, colocalization analyses, and proteome-wide association studies—can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.

Plasma proteomic changes in response to exercise training are associated with cardiorespiratory fitness adaptations.

Regular exercise leads to widespread salutary effects, and there is increasing recognition that exercise-stimulated circulating proteins can impart health benefits. Despite this, limited data exist regarding the plasma proteomic changes that occur in response to regular exercise. Here, we perform large-scale plasma proteomic profiling in 654 healthy human study participants before and after a supervised, 20-week endurance exercise training intervention. We identify hundreds of circulating proteins that are modulated, many of which are known to be secreted. We highlight proteins involved in angiogenesis, iron homeostasis, and the extracellular matrix, many of which are novel, including training-induced increases in fibroblast activation protein (FAP), a membrane-bound and circulating protein relevant in body-composition homeostasis. We relate protein changes to training-induced maximal oxygen uptake adaptations and validate our top findings in an external exercise cohort. Furthermore, we show that FAP is positively associated with survival in 3 separate, population-based cohorts.

IProMix: A Mixture Model for Studying the Function of ACE2 based on Bulk Proteogenomic Data

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and uses a nonparametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in nonasymptotic settings. We apply iProMix to the proteomic data of 110 (tumor-adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies. Supplementary materials for this article are available online.

Proteomic Profiling of Plasma Biomarkers Associated With Return to Sport Following Concussion: Findings From the NCAA and Department of Defense CARE Consortium.

ObjectiveTo investigate the plasma proteomic profiling in identifying biomarkers related to return to sport (RTS) following a sport-related concussion (SRC).MethodsThis multicenter, prospective, case-control study was part of a larger cohort study conducted by the NCAA-DoD Concussion Assessment, Research, and Education (CARE) Consortium, athletes (n = 140) with blood collected within 48 h of injury and reported day to asymptomatic were included in this study, divided into two groups: (1) recovery <14-days (n = 99) and (2) recovery ≥14-days (n = 41). We applied a highly multiplexed proteomic technique that uses DNA aptamers assay to target 1,305 proteins in plasma samples from concussed athletes with <14-days and ≥14-days.ResultsWe identified 87 plasma proteins significantly dysregulated (32 upregulated and 55 downregulated) in concussed athletes with recovery ≥14-days relative to recovery <14-days groups. The significantly dysregulated proteins were uploaded to Ingenuity Pathway Analysis (IPA) software for analysis. Pathway analysis showed that significantly dysregulated proteins were associated with STAT3 pathway, regulation of the epithelial mesenchymal transition by growth factors pathway, and acute phase response signaling.ConclusionOur data showed the feasibility of large-scale plasma proteomic profiling in concussed athletes with a <14-days and ≥ 14-days recovery. These findings provide a possible understanding of the pathophysiological mechanism in neurobiological recovery. Further study is required to determine whether these proteins can aid clinicians in RTS decisions.

SimPLIT: Simplified Sample Preparation for Large-Scale Isobaric Tagging Proteomics.

Large scale proteomic profiling of cell lines can reveal molecular signatures attributed to variable genotypes or induced perturbations, enabling proteogenomic associations and elucidation of pharmacological mechanisms of action. Although isobaric labeling has increased the throughput of proteomic analysis, the commonly used sample preparation workflows often require time-consuming steps and costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost-effective one-pot reaction workflow in a 96-well plate format (SimPLIT) that minimizes processing steps and demonstrates improved reproducibility compared to alternative approaches. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent cleanup step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. We showcase the applicability of the workflow in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in any proteomics laboratory for medium-to-large scale TMT-based studies for deep profiling of cell lines.

Isolation, profiling, and tracking of extracellular vesicle cargo in Caenorhabditis elegans

Extracellular vesicles (EVs) may mediate intercellular communication by carrying protein and RNA cargo. The composition, biology, and roles of EVs in physiology and pathology have been primarily studied in the context of biofluids and in cultured mammalian cells. The experimental tractability of C.elegans makes for a powerful invivo animal system to identify and study EV cargo from its cellular source. We developed an innovative method to label, track, and profile EVs using genetically encoded, fluorescent-tagged EV cargo and conducted a large-scale isolation and proteomic profiling. Nucleic acid binding proteins (∼200) are overrepresented in our dataset. By integrating our EV proteomic dataset with single-cell transcriptomic data, we identified and validated ciliary EV cargo: CD9-like tetraspanin (TSP-6), ectonucleotide pyrophosphatase/phosphodiesterase (ENPP-1), minichromosome maintenance protein (MCM-3), and double-stranded RNA transporter SID-2. C.elegans EVs also harbor RNA, suggesting that EVs may play a role in extracellular RNA-based communication.

From the Editor’s Desk...

From the Editor’s Desk...

ProteoSushi: A Software Tool to Biologically Annotate and Quantify Modification-Specific, Peptide-Centric Proteomics Data Sets.

Large-scale proteomic profiling of protein post-translational modifications has provided important insights into the regulation of cell signaling and disease. These modification-specific proteomics workflows nearly universally enrich modified peptides prior to mass spectrometry analysis, but protein-centric proteomic software tools have many limitations evaluating and interpreting these peptide-centric data sets. We, therefore, developed ProteoSushi, a software tool tailored to analysis of each modified site in peptide-centric proteomic data sets that is compatible with any post-translational modification or chemical label. ProteoSushi uses a unique approach to assign identified peptides to shared proteins and genes, minimizing redundancy by prioritizing shared assignments based on UniProt annotation score and optional user-supplied protein/gene lists. ProteoSushi simplifies quantitation by summing or averaging intensities for each modified site, merging overlapping peptide charge states, missed cleavages, spectral matches, and variable modifications into a single value. ProteoSushi also annotates each PTM site with the most up-to-date biological information available from UniProt, such as functional roles or known modifications, the protein domain in which the site resides, the protein's subcellular location and function, and more. ProteoSushi has a graphical user interface for ease of use. ProteoSushi's flexibility and combination of analysis features streamlines peptide-centric data processing and knowledge mining of large modification-specific proteomics data sets.

Proteome and phospho-proteome profiling for deeper phenotype characterization of colorectal cancer heterogeneity.

e15536 Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.

Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice.

Performing large-scale plasma proteome profiling is challenging due to limitations imposed by lengthy preparation and instrument time. We present a fully automated multiplexed proteome profiling platform (AutoMP3) using the Hamilton Vantage liquid handling robot capable of preparing hundreds to thousands of samples. To maximize protein depth in single-shot runs, we combined 16-plex Tandem Mass Tags (TMTpro) with high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro) and real-time search (RTS). We quantified over 40 proteins/min/sample, doubling the previously published rates. We applied AutoMP3 to investigate the naked mole-rat plasma proteome both as a function of the circadian cycle and in response to ultraviolet (UV) treatment. In keeping with the lack of synchronized circadian rhythms in naked mole-rats, we find few circadian patterns in plasma proteins over the course of 48 h. Furthermore, we quantify many disparate changes between mice and naked mole-rats at both 48 h and one week after UV exposure. These species differences in plasma protein temporal responses could contribute to the pronounced cancer resistance observed in naked mole-rats. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD022891.

Influence of short-term exposure to high light on photosynthesis and proteins involved in photo-protective processes in tomato leaves

Plants in natural environments have to cope with fluctuations in light conditions on different timescales, ranging from seconds to days. Toward this, plants employ both adaptive and protective mechanisms. Here, we studied the response of low-light-grown tomato plants to short-term high light (HL) exposure followed by a recovery period using large-scale proteomic profiling, photosynthetic- and biochemical measurements. We found that following a 6-h HL treatment, plants exhibited photosystem II photoinhibition, a tendency toward a reduced electron transport and induction of non-photochemical quenching (NPQ). The proteomic data showed that key enzymes of the Mg-branch of the tetrapyrrole pathway, leading to chlorophyll biosynthesis, were significantly down-regulated by HL. Nonetheless, no changes in chlorophyll content or in the levels of most photosynthetic proteins, including all identified photosystem and light-harvesting antenna proteins, were detected. Notably, we observed that following a recovery period (24 h), plants were characterized by higher electron transport and an increased NPQ capacity, indicative of an acclimation that resulted in better photosynthetic performance. Our proteomic analysis also revealed prominent up-regulation of several enzymes of the flavonoid pathway, as well as of enzymatic components of other plant antioxidant systems. In accord with these, we found significant increases in the content of flavonoids and ascorbate, and in the activity of ascorbate peroxidase following the HL and recovery. Together, the results highlight the key alterations involved in photo-protection and detoxification of reactive oxygen species which take part in the short-term response to HL.

Quantitative proteomic profiling of tumor-associated vascular endothelial cells in colorectal cancer.

ABSTRACTTo investigate the global proteomic profiles of vascular endothelial cells (VECs) in the tumor microenvironment and antiangiogenic therapy for colorectal cancer (CRC), matched pairs of normal (NVECs) and tumor-associated VECs (TVECs) were purified from CRC tissues by laser capture microdissection and subjected to iTRAQ-based quantitative proteomics analysis. Here, 216 differentially expressed proteins (DEPs) were identified and used for bioinformatics analysis. Interestingly, these proteins were implicated in epithelial mesenchymal transition (EMT), ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, angiogenesis and HIF-1 signaling pathway, which may play important roles in CRC angiogenesis. Among these DEPs we found that Tenascin-C (TNC) was upregulated in TVECs of CRC and correlated with CRC multistage carcinogenesis and metastasis. Furthermore, the reduction of tumor-derived TNC could attenuate human umbilical vein endothelial cell (HUVEC) proliferation, migration and tube formation through ITGB3/FAK/Akt signaling pathway. Based on the present work, we provided a large-scale proteomic profiling of VECs in CRC with quantitative information, a certain number of potential antiangiogenic targets and a novel vision in the angiogenesis bio-mechanism of CRC.

Quantitative Proteomics of the 2016 WHO Neisseria gonorrhoeae Reference Strains Surveys Vaccine Candidates and Antimicrobial Resistance Determinants.

The sexually transmitted disease gonorrhea (causative agent: Neisseria gonorrhoeae) remains an urgent public health threat globally because of its reproductive health repercussions, high incidence, widespread antimicrobial resistance (AMR), and absence of a vaccine. To mine gonorrhea antigens and enhance our understanding of gonococcal AMR at the proteome level, we performed the first large-scale proteomic profiling of a diverse panel (n = 15) of gonococcal strains, including the 2016 World Health Organization (WHO) reference strains. These strains show all existing AMR profiles - established through phenotypic characterization and reference genome publication - and are intended for quality assurance in laboratory investigations. Herein, these isolates were subjected to subcellular fractionation and labeling with tandem mass tags coupled to mass spectrometry and multi-combinatorial bioinformatics. Our analyses detected 904 and 723 common proteins in cell envelope and cytoplasmic subproteomes, respectively. We identified nine novel gonorrhea vaccine candidates. Expression and conservation of new and previously selected antigens were investigated. In addition, established gonococcal AMR determinants were evaluated for the first time using quantitative proteomics. Six new proteins, WHO_F_00238, WHO_F_00635c, WHO_F_00745, WHO_F_01139, WHO_F_01144c, and WHO_F_01126, were differentially expressed in all strains, suggesting that they represent global proteomic AMR markers, indicate a predisposition toward developing or compensating gonococcal AMR, and/or act as new antimicrobial targets. Finally, phenotypic clustering based on the isolates' defined antibiograms and common differentially expressed proteins yielded seven matching clusters between established and proteome-derived AMR signatures. Together, our investigations provide a reference proteomics data bank for gonococcal vaccine and AMR research endeavors, which enables microbiological, clinical, or epidemiological projects and enhances the utility of the WHO reference strains.