Journal of Proteomics

A diet derived agent Curcumin (Diferuloylmethane), demonstrated its clinical application in inflammation, infection and cancer conditions. Nevertheless, its impact on the proteome of epithelial cells of non-small cell lung carcinoma (NSCLC) is yet to be explored. We employed a stable isotope labeling method for cell culture (SILAC) based relative quantitative proteomics and informatics analysis to comprehend global proteome change in A549 cells treated with curcumin and/or Lipopolysaccharide (LPS). Pretreated A549 cells were infected with Mycobacterium tuberculosis H37Rv strain to monitor bacterial load. With exposure to curcumin and LPS, out of the 1492 identified proteins, 305 and 346 proteins showed deregulation respectively. The expression of BID and AIFM1 mitochondrial proteins which play critical role in apoptotic pathway were deregulated in curcumin treated cells. Higher mitochondria intensity was observed in curcumin treated A549 cells than LPS treatment. Simultaneous treatment of curcumin and LPS neutralized the effect of LPS. Curcumin and/or LPS pretreated A549 cells infected with H37Rv, showed successful bacterial internalization. LPS treated A549 cells after infection showed increased bacterial load than curcumin compared to non-treated infected cells. However, simultaneous treatment of curcumin and LPS neutralized the effect of LPS. This study generated molecular evidence to deepen our understanding of the anti-inflammatory role of curcumin and may be useful to identify molecular targets for drug discovery.

Background and aimsInflammatory bowel diseases (IBD), including Crohns disease (CD), ulcerative colitis (UC), and IBD-unclassified (IBD-U), are chronic inflammatory disorders of the gastrointestinal tract. Current methods for classification and longitudinal monitoring are invasive, expensive, and often delayed, limiting timely diagnosis and management. This study reports the first application of high-dimensional salivary proteomics integrated with interpretable artificial intelligence/machine learning (AI/ML) to define a minimal protein signature for pediatric IBD classification with the goal of informing therapeutic decision-making. MethodsUnstimulated saliva from pediatric CD, UC, and IBD-U patients was analyzed using Alamar Biosciences NULISAseq Inflammation Panel 250 (250 proteins). Logistic regression with recursive feature elimination identified a minimal discriminative signature. Performance was tested in independent follow-up samples. SHapley Additive exPlanations (SHAP) quantified patient-specific protein contributions and assessed biological similarity of IBD-U to CD and UC. ResultsDifferential abundance analysis between UC and CD revealed 53 significantly different proteins. ML identified a 14-protein signature comprising chemokines/cytokines (CCL1, IFNA1;IFNA13, IL12p70, IL34, TNFSF11/RANKL), receptors/ligands (CD40LG, ICOSLG, IL1R2, IL17RA), structural/tissue-remodeling proteins (CD93, GFAP, SPP1), and growth factors/immune modulators (GDF2, GZMA). The model achieved 96.2% overall accuracy in first-visit samples and 86.4% overall accuracy in follow-up testing. SHAP revealed patient-specific drivers and suggested biological alignment of IBD-U cases toward CD-like or UC-like profiles. ConclusionsThis first-in-field integration of salivary proteomics with interpretable AI/ML demonstrates that accurate, noninvasive classification of pediatric IBD is possible using minimal biomarker sets. This approach establishes a scalable framework for future longitudinal monitoring, and supports earlier and more precise therapeutic interventions.

Prerequisite for a successful proteomics experiment is a high-quality lysis of the sample of interest, resulting in a large number of identified proteins as well as a high coverage of protein sequences. Therefore, the choice of suitable lysis conditions is crucial. Many buffers were previously employed in proteomics studies, yet a comprehensive comparison of lysate preparation conditions was so far missing. In this study, we compared the efficiency of four commonly used lysis buffers, containing the agents NP40, SDS, urea or GdnHCl, in four different types of biological samples (suspension and adherent cell lines, primary mouse cells and mouse liver tissue). After liquid chromatography-mass spectrometry (LC-MS) measurement and MaxQuant analysis, we compared chromatograms, intensities, number of identified proteins and the localization of the identified proteins. Overall, SDS emerged as the most reliable reagent, ensuring stable performance and reproducibility across diverse samples. Furthermore, our data advocated for a dual-sample lysis approach, including that the resulting pellet is lysed again after the initial lysis with a urea lysis buffer and subsequently both lysates are combined for a single LC-MS run to maximize the proteome coverage. However, none of the investigated lysis buffers proved to be superior in every category, indicating that the lysis buffer of choice depends on the proteins of interest and on the biological question. Further, we demonstrated with our systematic studies the establishment of conditions that allows to perform global proteomics and affinity purification-based interactome characterization from the same lysate. In sum our results provide guidance for the best-suited lysis buffer for mass spectrometry-based proteomics depending on the question of interest.

Gliomas account for most brain malignancies, with astrocytomas being the most common subtype. Among these, glioblastoma (GBM) stands out as the most aggressive form, exhibiting a median survival time of just 15 months despite intensive therapy. Current diagnostic practices rely on magnetic resonance imaging (MRI) and histopathological analysis, which often necessitate invasive surgical sampling. This underscores the need for minimally invasive diagnostic tools capable of characterizing glioma progression and guiding treatment strategies. Advances in glioma classification have integrated histological and molecular markers, notably IDH1 mutations, which are prognostically significant, particularly in low-grade gliomas and in the previously defined "secondary GBM" (IDH-mutant astrocytoma grade 4). This study aimed to explore the potential of serum proteomics as a non-invasive diagnostic tool using MALDI-TOF mass spectrometry (MS) combined with machine learning techniques. We analyzed serum samples from 269 patients, employing machine learning models to differentiate between healthy individuals and astrocytoma patients. The MALDI-TOF MS approach achieved a balanced accuracy of 94.5% in distinguishing GBM patients from healthy controls. However, it showed limited efficacy in classifying tumor grades or determining IDH1 mutational status. Further investigation using bottom-up proteomics by GeLC-MS/MS identified potential biomarkers, such as transthyretin, previously associated with high-grade gliomas. These findings highlight the promise of MALDI-TOF MS in identifying serum-based biomarkers for astrocytoma diagnosis. While the results are promising, further validation in independent cohorts is essential to assess the clinical utility of these biomarkers for non-invasive glioma diagnostics and patient monitoring.

The stimulation of cells by inflammatory mediators gives rise to intricate signaling cascades, inducing specific biological functions. The role of kinase activation for the establishment of specific inflammatory functions has only scarcely been established. A time-course analysis of human fetal fibroblasts stimulated with Interleukin-1{beta} (IL-1{beta}) and/or Dexamethasone (Dex) was conducted using mass spectrometry-based proteomics and phosphoproteomics in conjunction with lysolipid and oxylipin profiling. The IL-1{beta} induced proteome alterations indicated metabolic, transcriptional, and translational activation, including inflammatory marker proteins such as NOS1, THBS1, and STING1. The induction of mitochondrial proteins and the formation of numerous lysolipids indicated an increase in beta-oxidation. In addition to the NF-{kappa}B and STAT signaling pathways, which are characteristic of inflammatory activation, the MAP and AKT kinase signaling pathways were found to be strongly induced. Six hours after treatment, the observed signaling events exhibited a notable decline, nearly returning to their initial states after 24 hours. It is noteworthy that nearly all of these signaling activities were also observed in cells treated with Dex alone. Additionally, the proteome exhibited transient alterations, which included proteins otherwise characteristic of an inflammatory response, such as MMP3 and NFKB2. Activation of the kinase PIKFYVE was apparently specific for dexamethasone but constituted a minority of the total phosphorylation events. Only after 24 hours was the induction of proteins characteristic of glucocorticoids, such as TSC22D3 and MAOA, observed. The analysis of the effects of dexamethasone on the background of established inflammatory signaling verified the known inhibitory functions and resulted in the expression of anti-inflammatory proteins and oxylipins, but hardly affected the signaling events involving MAP and AKT kinases. In conclusion, this data demonstrates that a majority of inflammation-associated signaling events in fibroblasts needs to be attributed to resource and stress management rather than the establishment of specific inflammatory effector functions.

The yeast cell wall is a highly dynamic and multifunctional structure that is essential for maintaining cellular integrity, protecting against environmental stresses, and enabling adhesion, signaling, and interactions with the surrounding environment. Its chemical composition and organization are strongly influenced by external factors such as temperature, pH, nutrient availability and their delivery mode. In batch culture systems, yeast cells grow in a closed environment with limited nutrients, leading to well-defined growth phases that reflect major metabolic transitions. While global proteomic changes during these phases have been described, the temporal regulation of cell wall protein (CWP) expression remains insufficiently characterized. In this study, the temporal remodeling of the cell wall proteome of Saccharomyces cerevisiae S288c was examined during batch cultivation in rich medium over 24 hours. A classical proteomics workflow was applied to analyze CWPs from samples collected at multiple time points over the cultivation period. The analysis revealed substantial qualitative and quantitative changes in CWPs expression linked to metabolic shifts between growth phases. Proteins involved in cell wall remodeling and glycoprotein biosynthesis were particularly enriched at the initial sampling point (b-T0h), corresponding to the transition from flask cultivation to bioreactor conditions, and overall CWP abundance was highest during this early growth stage. Time-resolved quantitative, transcription factor, and functional enrichment analyses revealed coordinated regulation of cell wall adaptation. Stationary-phase specific protein markers linked to glucose depletion were identified, offering insight into nutrient-limited remodeling. Comparisons with previous studies showed variability driven by strain differences, culture conditions, and methodological approaches.

Biomarkers are molecular markers found in clinical samples which may aid disease diagnosis or prognosis. High-throughput techniques allow prospecting for such signature molecules by comparing gene expression between normal and sick cells. Cancer-testis antigens (CTAs) are promising candidates for cancer biomarkers due to their limited expression to the testis in normal conditions versus their aberrant expression in various tumors. CTAs are routinely identified by transcriptomics, but a comprehensive characterization of their protein levels in different tissues is still necessary. Mass spectrometry-based proteomics allows the characterization of many cellular types and the production of large amounts of data while computational tools allow the comparison of multiple datasets, and together those may corroborate insights obtained at the transcriptomic level. Here a computational meta-analysis explores the CTAs protein abundance in the proteomic layer of healthy and tumor tissues. The combined datasets present the expression patterns of 17,200 unique proteins, including 241 known CTAs previously described at the transcriptomic level. Those were further ranked as significantly enriched in tumor tissues (22 proteins), exclusive to tumor tissues (42 proteins) or abundant in healthy tissues (32 proteins). This analysis illustrates the possibilities for tumor proteome characterization and the consequent identification of biomarker candidates and/or therapeutic targets.

Insulin resistance impairs the cellular insulin response and frequently precedes metabolic disorders, like type 2 diabetes, which are affecting an increasing number of people globally. Given the critical role of the liver in glucose and lipid metabolism, understanding the molecular mechanisms in hepatic insulin resistance is essential for early preventive treatments. To elucidate changes in insulin signal transduction associated with hepatocellular resistance, we employed a multi-layered mass spectrometry-based proteomics approach focusing on insulin receptor (IR) signaling at the interactome, phosphoproteome, and proteome levels.in a long-term hyperinsulinemia-induced insulin-resistant HepG2 cell line with a knockout of the insulin-like growth factor 1 receptor (IGF1R KO). Analysis of the dynamic insulin-induced IR interactome revealed recruitment of the PI3K complex in both insulin-sensitive and -resistant cells. From the phosphoproteomics dataset, a change in insulin-stimulated signaling responses in insulin resistance was observed and showed attenuated signaling via the metabolic PI3K-AKT pathway but sustained extracellular signal-regulated kinase (ERK) activity. At the proteome level, the ephrin type-A receptor 2 (EphA2) showed an insulin-induced increase in expression. This receptor belongs to the Eph receptor family and participates in various cellular processes, such as cell adhesion, migration, and tissue development. The protein abundance regulation of EphA2 occurred through the ERK signaling pathway and was concordantly independent of insulin resistance. Induction of EphA2 by insulin was confirmed in other cell lines and observed uniquely in cells with high levels of IR compared to IGF1R. The multi-layered proteomics dataset provided insights into insulin signaling in general and in the context of insulin resistance, and it can going forward serve as a resource to generate and test hypotheses, leading to an improved understanding of insulin resistance.

By applying dual proteome profiling to Salmonella enterica serovar Typhimurium (S. Typhimurium) encounters with its epithelial host (here, S. Typhimurium infected human HeLa cells), a detailed interdependent and holistic proteomic perspective on host-pathogen interactions over a time course of infection was obtained. Data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows, especially in identifying the downregulated bacterial proteome response during infection progression infection by permitting quantification of low abundant bacterial proteins at early times of infection at low bacterial infection load. S. Typhimurium invasion and replication specific proteomic signatures in epithelial cells revealed interdependent host/pathogen specific responses besides pointing to putative novel infection markers and signalling responses.

Listeria monocytogenes exhibits two ClpP isoforms (ClpP1/ClpP2) which assemble into a heterooligomeric complex with enhanced proteolytic activity. Herein, we demonstrate that the formation of this complex depends on temperature and reaches a maximum ratio of about 1:1 at heat shock conditions, while almost no complex formation occurred below 4{degrees}C. In order to decipher the role of the two isoforms at elevated temperatures, we constructed L. monocytogenes ClpP1, ClpP2 and ClpP1/2 knockout strains and analyzed their protein regulation in comparison to the wild type (WT) strain via whole proteome mass-spectrometry (MS) at 37 {degrees}C and 42 {degrees}C. While {Delta}clpP1 strain only altered the expression of very few proteins, {Delta}clpP2 and {Delta}clpP1/2 strains revealed the dysregulation of many proteins at both temperatures. These effects were corroborated by crosslinking co-immunoprecipitation MS analysis. Thus, while ClpP1 serves as a mere enhancer of protein degradation in the heterocomplex, ClpP2 is essential for ClpX binding and thus functions as a gatekeeper for substrate entry. Applying an integrated proteomic approach combining whole proteome and co-immunoprecipitation datasets, several putative ClpP2 substrates were identified in the context of different temperatures and discussed with regards to their function in cellular pathways such as the SOS response.

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes including proliferation and migration. Alterations of ECM structure and/or composition has been shown to lead to, or accompany, many pathological processes including cancer and fibrosis. To understand how the ECM contributes to diseases, we first need to obtain a comprehensive characterization of the ECM of tissues and of its changes during disease progression. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundance in the ECM, nor do they permit to achieve the high coverage needed to gain finer biochemical information, including the presence of isoforms or post-translational modifications. In addition, broadly adopted proteomic methods relying on extended trypsin digestion do not provide structural information on ECM proteins, yet, gaining insights into ECM protein structure is critical to better understanding protein functions. Here, we present the optimization of a time-lapsed proteomic method using limited proteolysis of partially denatured samples and the sequential release of peptides to achieve superior sequence coverage as compared to standard ECM proteomic workflow. Exploiting the spatio-temporal resolution of this method, we further demonstrate how 3-dimensional time-lapsed peptide mapping can identify protein regions differentially susceptible to trypsin and can thus identify sites of post-translational modifications, including protein-protein interactions. We further illustrate how this approach can be leveraged to gain insight on the role of the novel ECM protein SNED1 in ECM homeostasis. We found that the expression of SNED1 by mouse embryonic fibroblasts results in the alteration of overall ECM composition and the sequence coverage of certain ECM proteins, raising the possibility that SNED1 could modify accessibility to trypsin by engaging in protein-protein interactions.

Lyme disease, caused by an infection with the spirochete Borrelia burgdorferi, is the most common vector-borne disease in North America. B. burgdorferi strains harbor extensive genomic and proteomic variability and further comparison is key to understanding the spirochetes infectivity and biological impacts of identified sequence variants. To achieve this goal, both transcript and mass spectrometry (MS)-based proteomics was applied to assemble peptide datasets of laboratory strains B31, MM1, B31-ML23, infective isolates B31-5A4, B31-A3, and 297, and other public datasets, to provide a publicly available Borrelia PeptideAtlas (http://www.peptideatlas.org/builds/borrelia/). Included is information on total proteome, secretome, and membrane proteome of these B. burgdorferi strains. Proteomic data collected from 35 different experiment datasets, with a total of 855 mass spectrometry runs, identified 76,936 distinct peptides at a 0.1% peptide false-discovery-rate, which map to 1,221 canonical proteins (924 core canonical and 297 noncore canonical) and covers 86% of the total base B31 proteome. The diverse proteomic information from multiple isolates with credible data presented by the Borrelia PeptideAtlas can be useful to pinpoint potential protein targets which are common to infective isolates and may be key in the infection process.

ObjectiveUlcerative colitis (UC) is a chronic disease with rising incidence and unclear etiology. The application of mass spectrometry-based analysis methods shall support the establishment of systemic molecular biomarker signatures providing status information with regard to individual UC pathomechanisms. DesignUC pathomechanisms were assessed by proteome profiling of human tissue specimen, obtained from five distinct colon locations each of 12 patients. Systemic disease-associated alterations were investigated in a cross-sectional setting by mass spectrometry-based multi-omics analyses comprising proteins, metabolites and eicosanoids of plasma obtained from UC patients during disease and upon remission in comparison to healthy controls. ResultsTissue proteome profiling identified colitis-associated activation of neutrophils, macrophages, B- and T-cells, fibroblasts, endothelial cells and platelets, and indicated hypoxic stress, as well as a general downregulation of mitochondrial proteins accompanying the establishment of apparent wound healing-promoting activities including scar formation. While the immune cells mainly contributed pro-inflammatory proteins, the colitis-associated epithelial cells, fibroblasts, endothelial cells and platelets predominantly formed anti-inflammatory and wound healing-promoting proteins. Blood plasma proteomics indicated chronic inflammation and platelet activation, whereas plasma metabolomics identified disease-associated deregulation of bile acids, eicosanoids and gut microbiome-derived metabolites. Upon remission, several, but not all, molecular candidate biomarker levels recovered to normal levels. These findings may indicate that pathomechanisms related to gut functions, gut microbiome status, microvascular damage and metabolic dysregulation associated with hypoxia may not resolve uniformly during remission. ConclusionsThis study integrates and expands the knowledge about local and systemic effects of UC and identifies biomarker profiles related to molecular UC pathomechanisms.

LC3 (Light Chain 3) is a key player of autophagy, a major stress-responsive proteolysis pathway promoting cellular homeostasis. It coordinates the formation and maturation of autophagosomes and recruits cargo to be further degraded upon autophagosome-lysosome fusion. To orchestrate its functions, LC3 binds to multiple proteins from the autophagosomes inner and outer membranes, but the full extent of these interactions is not known. Moreover, LC3 has been increasingly reported in other cellular locations than the autophagosome, with cellular outcome not fully understood and not all related to autophagy. Furthermore, novel functions of LC3 as well as autophagy can occur in cells growing in a normal medium thus in non-stressed conditions. A better knowledge of the molecule in proximity to LC3 in normal growth conditions will improve the understanding of LC3 function in autophagy and in other cell biology function. Using an APEX2 based proteomic approach, we have detected 407 proteins in proximity to the well-characterised LC3B isoform in non-stress conditions. These include known and novel LC3B proximity proteins, associated with various cell localisation and biological functions. Sixty-nine of these proteins contain a putative LIR (LC3 Interacting Region) including 41 not reported associated to autophagy. Several APEX2 hits were validated by co-immunoprecipitation and co-immunofluorescence. This study uncovers the LC3B global interactome and reveals novel LC3B interactors, irrespective of LC3B localisation and function. This knowledge could be exploited to better understand the role of LC3B in autophagy and non-autophagy cellular processes.

The increasing incidence of coeliac disease is leading to a growing interest in active search for associated factors, even the intrauterine and early life. The exposome approach to disease encompasses a lifecourse perspective from conception onwards has recently been highlighted. Knowledge of early exposure to gluten immunogenic peptides (GIP) in utero could challenge the chronology of early prenatal tolerance or inflammation, rather than after the infants solid diet after birth. We developed an accurate and specific immunoassay to detect GIP in amniotic fluid (AF) and studied their accumulates, excretion dynamics and foetal exposure resulting from AF swallowing. 119 pregnant women with different gluten diets and gestational ages were recruited. GIP were detectable in AF from at least the 16th gestational week in gluten-consuming women. Although no significant differences in GIP levels were observed during gestation, amniotic GIP late pregnancy was not altered by maternal fasting, suggesting closed-loop entailing foetal swallowing of GIP-containing AF and subsequent excretion via the foetal kidneys. The study shows evidence, for the first time, of the fetal exposure to gluten immunogenic peptides, and establish a positive correlation with maternal gluten intake. The results obtained point to a novel physiological concept as they describe a closed-loop circuit entailing fetal swallowing of GIP contained in AF, and its subsequent excretion through the fetal kidneys. The study adds important new information to understanding the coeliac exposome.

Calcium (Ca2+) and reactive oxygen species (ROS) are key secondary messengers in plant stress signaling, yet their interplay in regulating proteome-wide responses remains poorly understood. In this study, we employed label-free quantitative (LFQ) proteomics to investigate Ca2+-dependent and independent changes in the proteome of Arabidopsis thaliana leaves upon oxidative stress induced by hydrogen peroxide (H2O2). To dissect the role of Ca2+ signaling, we inhibited H2O2-induced Ca2+ transients by pretreatment with LaCl3, a plasma membrane Ca2+ channel blocker. We then analysed the proteome of plants treated with H202 or ddH2O after 10 and 30 min of treatment and detected 3724 and 3757 proteins, respectively. From these, 581 proteins showed significant changes in abundance after 10 min and 909 proteins after 30 min. Remarkably, the combined LaCl3 and H2O2 treatment resulted in the highest number of differentially abundant proteins (DAPs), indicating a strong attenuating effect of Ca2+ signaling on the oxidative stress response. Specifically responsive to only H2O2 were 37 and 57 proteins with distinct subsets of strictly Ca2+-dependent, partially Ca2+- dependent, and Ca2+-independent proteins. Notably, Ca2+-independent H2O2-responsive proteins predominantly showed increased abundance, while strictly Ca2+-dependent proteins exhibited decreased abundance, suggesting a role for Ca2+ signaling in protein degradation. Furthermore, three proteins--WLIM1, CYP97C1, and AGAP1--underwent Ca2+-dependent shifts between the two time points, pointing to a dynamic nature of Ca2+-regulated proteomic changes. This study provides novel insights into short-term Ca2+-dependent and independent regulation of the Arabidopsis leaf proteome in response to oxidative stress, identifying key stress-responsive proteins and potential new targets for further research on plant stress resilience mechanisms.

BackgroundCurrent proteomics techniques allow rapid identification and quantification of proteins within any given biological source. In particular, nanoUHPLC/High-Resolution nanoESI-MS/MS enables the characterization of proteins in complex biological samples due to its high sensitivity, accuracy, and scalability. However, LC-MS/MS proteomics might still be susceptible to laboratory and sample-associated contaminants, which can significantly compromise the quality and reliability of data. Therefore, an accurate identification and annotation of such contaminants is crucial for the development of robust proteomics databases and spectral-libraries related search engines. This approach is of special interest in the field of secretome and extracellular vesicles (EVs), membrane-enclosed nanostructures that contain a variety of proteins crucial for cell-to-cell communication and translational applications. ResultsWhen working in ex vivo/in vitro settings, proteins from fetal bovine serum (FBS), commonly employed in standard cell culture media, may interfere with the proteome analysis. To address this issue, we conceived and designed SPROUTS_DB, Serum Protein Repository Of Unwanted Target(ed) Sequences DataBase, a dedicated resource to catalog serum-derived contaminants. Starting from media supplemented with EV-depleted FBS, we simulated cell growth conditions - in the absence of cells - followed by ultracentrifugation. LC-MS/MS analysis of these samples resulted in the identification of a novel set of 1,288 contaminant proteins, which has been deposited in the ProteomeXchange repository (identifier PXD044137). SPROUTS_DB contains primarily soluble proteins, mainly related to the Gene Ontology categories Extracellular Region and Extracellular Space, in line with the nature of the starting sample. In contrast, only a small fraction of the contaminants is classified as membrane-associated proteins, supporting the limited vesicle contamination in the complete medium, due to the use of EV-depleted FBS. Of note, we demonstrated that SPROUTS_DB outperforms existing contaminants databases, ensuring that only peptide spectra relevant to the examined sample are retained and identified as true positive data. ConclusionsConsidering that even proteins from phylogenetically distant organisms share extensive stretches of sequences, SPROUTS_DB is designed to discern contaminants from real sample proteins of interest, minimizing false positive identifications. To the best of our knowledge, SPROUTS_DB is the most updated database of contaminants useful for proteomics investigations of cellular secretomes and EV-containing samples.

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled to mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package, RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biological relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the users interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization of biological signals, previously absent in the DDA mode. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

ObjectiveTo shed light on gut mucosa processes provoked by gluten exposure in coeliac disease, we performed a quantitative proteomic analysis of duodenal tissue from well-treated coeliac patients before and after gluten challenge. DesignWe extracted and digested proteins from formalin-fixed paraffin-embedded tissue of 19 coeliac disease patients who had been challenged orally with gluten for 14 days. Protein identification and quantification was done by label-free quantitative mass spectrometry-based proteomics from total tissue and from laser capture microdissected epithelial cell layer. Proteomics data were compared with clinical, serological and histological data. ResultsAt baseline, all patients were in clinical and mucosal remission (Marsh 0-1) except one (Marsh 3). After challenge, five patients reached Marsh 3 scores. Proteome analysis categorised these five and additionally two patients as responders. Already at baseline, responder patients differed from the remaining patients in their gut tissue protein composition with altered levels of inflammatory and enterocyte function proteins - the same proteins that changed upon gluten challenge. Patients classified as responders from the proteomic analysis also differed from the remaining patients at baseline, with mild crypt hyperplasia and a slight increase in blood inflammatory parameters and gluten specific CD4+ T-cell frequencies. ConclusionDespite clinical and histological remission, coeliac disease patients that develop a mucosal response after 14-day gluten challenge have already at baseline altered protein compositions of their gut tissue with signs of ongoing inflammation. Thus, apparently well-treated coeliac disease is frequently not fully quiescent with presence of low-grade anti-gluten immunity in gut mucosa.

The development of new approaches that allow early assessment of which cases of COVID-19 will likely become critical and the discovery of new therapeutic targets are urgent demands. In this cohort study, we performed proteomic and laboratorial profiling of plasma from 163 patients admitted to Bauru State Hospital (Bauru, SP, Brazil) between May 4th and July 4th, 2020, who were diagnosed with COVID-19 by RT-PCR nasopharyngeal swab samples. Plasma samples were collected upon admission for routine laboratory analyses and shotgun quantitative label-free proteomics. Based on the course of the disease, the patients were further divided into 3 groups: a) mild symptoms, discharged without admission to an intensive care unit (ICU) (n=76); b) severe symptoms, discharged after admission to an ICU (n=56); c) critical, died after admission to an ICU (n=31). White cells and neutrophils were significantly higher in severe and critical patients compared to mild ones. Lymphocytes were significantly lower in critical patients compared to mild ones and platelets were significantly lower in critical patients compared to mild and severe ones. Ferritin, TGO, urea and creatinine were significantly higher in critical patients compared to mild and severe ones. Albumin, CPK, LDH and D-dimer were significantly higher in severe and critical patients compared to mild ones. PCR was significantly higher in severe patients compared to mild ones. Proteomic analysis revealed marked changes between the groups in plasma proteins related to complement activation, blood coagulation, antimicrobial humoral response, acute inflammatory response, and endopeptidase inhibitor activity. Higher levels of IREB2, GELS, POLR3D, PON1 and ULBP6 upon admission to hospital were found in patients with mild symptoms, while higher levels of Gal-10 were found in critical and severe patients. This needs to be validated in further studies. If confirmed, pathways involving these proteins might be potential new therapeutic targets for COVID-19.