Multiple early factors anticipate post-acute COVID-19 sequelae.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

A learned embedding for efficient joint analysis of millions of mass spectra.

Computational methods that aim to exploit publicly available mass spectrometry repositories rely primarily on unsupervised clustering of spectra. Here we trained a deep neural network in a supervised fashion on the basis of previous assignments of peptides to spectra. The network, called 'GLEAMS', learns to embed spectra in a low-dimensional space in which spectra generated by the same peptide are close to one another. We applied GLEAMS for large-scale spectrum clustering, detecting groups of unidentified, proximal spectra representing the same peptide. We used these clusters to explore the dark proteome of repeatedly observed yet consistently unidentified mass spectra.

Detecting Modifications in Proteomics Experiments with Param-Medic.

Searching tandem mass spectra against a peptide database requires accurate knowledge of various experimental parameters, including machine settings and details of the sample preparation protocol. In some cases, such as in reanalysis of public data sets, this experimental metadata may be missing or inaccurate. We describe a method for automatically inferring the presence of various types of modifications, including stable-isotope and isobaric labeling and tandem mass tags as well as the enrichment of phosphorylated peptides, directly from a given set of mass spectra. We demonstrate the sensitivity and specificity of the proposed approach, and we provide open-source Python and C++ implementations in a new version of the software tool Param-Medic.

Param-Medic: A Tool for Improving MS/MS Database Search Yield by Optimizing Parameter Settings.

In shotgun proteomics analysis, user-specified parameters are critical to database search performance and therefore to the yield of confident peptide-spectrum matches (PSMs). Two of the most important parameters are related to the accuracy of the mass spectrometer. Precursor mass tolerance defines the peptide candidates considered for each spectrum. Fragment mass tolerance or bin size determines how close observed and theoretical fragments must be to be considered a match. For either of these two parameters, too wide a setting yields randomly high-scoring false PSMs, whereas too narrow a setting erroneously excludes true PSMs, in both cases, lowering the yield of peptides detected at a given false discovery rate. We describe a strategy for inferring optimal search parameters by assembling and analyzing pairs of spectra that are likely to have been generated by the same peptide ion to infer precursor and fragment mass error. This strategy does not rely on a database search, making it usable in a wide variety of settings. In our experiments on data from a variety of instruments including Orbitrap and Q-TOF acquisitions, this strategy yields more high-confidence PSMs than using settings based on instrument defaults or determined by experts. Param-Medic is open-source and cross-platform. It is available as a standalone tool ( http://noble.gs.washington.edu/proj/param-medic/ ) and has been integrated into the Crux proteomics toolkit ( http://crux.ms ), providing automatic parameter selection for the Comet and Tide search engines.

An Alignment-Free "Metapeptide" Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing.

In principle, tandem mass spectrometry can be used to detect and quantify the peptides present in a microbiome sample, enabling functional and taxonomic insight into microbiome metabolic activity. However, the phylogenetic diversity constituting a particular microbiome is often unknown, and many of the organisms present may not have assembled genomes. In ocean microbiome samples, with particularly diverse and uncultured bacterial communities, it is difficult to construct protein databases that contain the bulk of the peptides in the sample without losing detection sensitivity due to the overwhelming number of candidate peptides for each tandem mass spectrum. We describe a method for deriving "metapeptides" (short amino acid sequences that may be represented in multiple organisms) from shotgun metagenomic sequencing of microbiome samples. In two ocean microbiome samples, we constructed site-specific metapeptide databases to detect more than one and a half times as many peptides as by searching against predicted genes from an assembled metagenome and roughly three times as many peptides as by searching against the NCBI environmental proteome database. The increased peptide yield has the potential to enrich the taxonomic and functional characterization of sample metaproteomes.

Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a dismal prognosis. However, while most patients die within the first year of diagnosis, very rarely, a few patients can survive for >10 years. Better understanding the molecular characteristics of the pancreatic adenocarcinomas from these very-long-term survivors (VLTS) may provide clues for personalized medicine and improve current pancreatic cancer treatment. To extend our previous investigation, we examined the proteomes of individual pancreas tumor tissues from a group of VLTS patients (survival ≥10 years) and short-term survival patients (STS, survival <14 months). With a given analytical sensitivity, the protein profile of each pancreatic tumor tissue was compared to reveal the proteome alterations that may be associated with pancreatic cancer survival. Pathway analysis of the differential proteins identified suggested that MYC, IGF1R and p53 were the top three upstream regulators for the STS-associated proteins, and VEGFA, APOE and TGFß-1 were the top three upstream regulators for the VLTS-associated proteins. Immunohistochemistry analysis using an independent cohort of 145 PDAC confirmed that the higher abundance of ribosomal protein S8 (RPS8) and prolargin (PRELP) were correlated with STS and VLTS, respectively. Multivariate Cox analysis indicated that 'High-RPS8 and Low-PRELP' was significantly associated with shorter survival time (HR=2.69, 95% CI 1.46-4.92, P=0.001). In addition, galectin-1, a previously identified protein with its abundance aversely associated with pancreatic cancer survival, was further evaluated for its significance in cancer-associated fibroblasts. Knockdown of galectin-1 in pancreatic cancer-associated fibroblasts dramatically reduced cell migration and invasion. The results from our study suggested that PRELP, LGALS1 and RPS8 might be significant prognostic factors, and RPS8 and LGALS1 could be potential therapeutic targets to improve pancreatic cancer survival if further validated.

Quantitative glycoproteomics analysis reveals changes in N-glycosylation level associated with pancreatic ductal adenocarcinoma.

Glycosylation plays an important role in epithelial cancers, including pancreatic ductal adenocarcinoma. However, little is known about the glycoproteome of the human pancreas or its alterations associated with pancreatic tumorigenesis. Using quantitative glycoproteomics approach, we investigated protein N-glycosylation in pancreatic tumor tissue in comparison with normal pancreas and chronic pancreatitis tissue. The study lead to the discovery of a roster of glycoproteins with aberrant N-glycosylation level associated with pancreatic cancer, including mucin-5AC (MUC5AC), carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), insulin-like growth factor binding protein (IGFBP3), and galectin-3-binding protein (LGALS3BP). Pathway analysis of cancer-associated aberrant glycoproteins revealed an emerging phenomenon that increased activity of N-glycosylation was implicated in several pancreatic cancer pathways, including TGF-ß, TNF, NF-kappa-B, and TFEB-related lysosomal changes. In addition, the study provided evidence that specific N-glycosylation sites within certain individual proteins can have significantly altered glycosylation occupancy in pancreatic cancer, reflecting the complexity of the molecular mechanisms underlying cancer-associated glycosylation events.

Statistical inference from multiple iTRAQ experiments without using common reference standards.

Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or "masterpool", in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.

Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study.

Metabolomic profiles were used to characterise the effects of consuming a high-phytochemical diet compared with a diet devoid of fruits and vegetables (F&V) in a randomised trial and cross-sectional study. In the trial, 8 h fasting urine from healthy men (n 5) and women (n 5) was collected after a 2-week randomised, controlled trial of two diet periods: a diet rich in cruciferous vegetables, citrus and soya (F&V), and a fruit- and vegetable-free (basal) diet. Among the ions found to differentiate the diets, 176 were putatively annotated with compound identifications, with forty-six supported by MS/MS fragment evidence. Metabolites more abundant in the F&V diet included markers of the dietary intervention (e.g. crucifers, citrus and soya), fatty acids and niacin metabolites. Ions more abundant in the basal diet included riboflavin, several acylcarnitines and amino acid metabolites. In the cross-sectional study, we compared the participants based on the tertiles of crucifers, citrus and soya from 3 d food records (n 36) and FFQ (n 57); intake was separately divided into the tertiles of total fruit and vegetable intake for FFQ. As a group, ions individually differential between the experimental diets differentiated the observational study participants. However, only four ions were significant individually, differentiating the third v. first tertile of crucifer, citrus and soya intake based on 3 d food records. One of these ions was putatively annotated: proline betaine, a marker of citrus consumption. There were no ions significantly distinguishing tertiles by FFQ. The metabolomic assessment of controlled dietary interventions provides a more accurate and stronger characterisation of the diet than observational data.

T cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibodies and disease severity.

BACKGROUNDMeasuring the immune response to SARS-CoV-2 enables assessment of past infection and protective immunity. SARS-CoV-2 infection induces humoral and T cell responses, but these responses vary with disease severity and individual characteristics.METHODSA T cell receptor (TCR) immunosequencing assay was conducted using small-volume blood samples from 302 individuals recovered from COVID-19. Correlations between the magnitude of the T cell response and neutralizing antibody (nAb) titers or indicators of disease severity were evaluated. Sensitivity of T cell testing was assessed and compared with serologic testing.RESULTSSARS-CoV-2-specific T cell responses were significantly correlated with nAb titers and clinical indicators of disease severity, including hospitalization, fever, and difficulty breathing. Despite modest declines in depth and breadth of T cell responses during convalescence, high sensitivity was observed until at least 6 months after infection, with overall sensitivity ~5% greater than serology tests for identifying prior SARS-CoV-2 infection. Improved performance of T cell testing was most apparent in recovered, nonhospitalized individuals sampled > 150 days after initial illness, suggesting greater sensitivity than serology at later time points and in individuals with less severe disease. T cell testing identified SARS-CoV-2 infection in 68% (55 of 81) of samples with undetectable nAb titers (<1:40) and in 37% (13 of 35) of samples classified as negative by 3 antibody assays.CONCLUSIONThese results support TCR-based testing as a scalable, reliable measure of past SARS-CoV-2 infection with clinical value beyond serology.TRIAL REGISTRATIONSpecimens were accrued under trial NCT04338360 accessible at clinicaltrials.gov.FUNDINGThis work was funded by Adaptive Biotechnologies, Frederick National Laboratory for Cancer Research, NIAID, Fred Hutchinson Joel Meyers Endowment, Fast Grants, and American Society for Transplantation and Cell Therapy.

Investigating neoplastic progression of ulcerative colitis with label-free comparative proteomics.

Patients with extensive ulcerative colitis (UC) have an increased risk of colorectal cancer. Although UC patients generally undergo lifelong colonoscopic surveillance to detect dysplasia or cancer in the colon, detection of cancer in this manner is expensive and invasive. An objective biomarker of dysplasia would vastly improve the clinical management of cancer risk in UC patients. In the current study, accurate mass and time methods with ion intensity-based label-free proteomics are applied to profile individual rectal and colon samples from UC patients with dysplasia or cancer (UC progressors) compared to rectal samples from patients that are dysplasia/cancer free (UC nonprogressors) to identify a set of proteins in the rectum mucosa that differentiate the two groups. In addition to the identification of proteins in UC dysplastic colon tissue, we for the first time identified differentially expressed proteins in nondysplastic rectal tissue from UC progressors. This provides a candidate pool of biomarkers for dysplasia/cancer that could be detected in a random nondysplastic rectal biopsy. Mitochondrial proteins, cytoskeletal proteins, RAS superfamily, proteins relating to apoptosis and metabolism were important protein clusters differentially expressed in the nondysplastic and dysplastic tissues of UC progressors, suggesting their importance in the early stages of UC neoplastic progression. Among the differentially expressed proteins, immunohistochemistry analysis confirmed that TRAP1 displayed increased IHC staining in UC progressors, in both dysplastic and nondysplastic tissue, and CPS1 showed a statistically significant difference in IHC staining between the nonprogressor and progressor groups. Furthermore, rectal CPS1 staining could be used to predict dysplasia or cancer in the colon with 87% sensitivity and 45% specificity, demonstrating the feasibility of using surrogate biomarkers in rectal biopsies to predict dysplasia and/or cancer in the colon.

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling.

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.

Brain-specific proteins decline in the cerebrospinal fluid of humans with Huntington disease.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

MRMer, an interactive open source and cross-platform system for data extraction and visualization of multiple reaction monitoring experiments.

Multiple reaction monitoring (MRM) mass spectrometry identifies and quantifies specific peptides in a complex mixture with very high sensitivity and speed and thus has promise for the high throughput screening of clinical samples for candidate biomarkers. We have developed an interactive software platform, called MRMer, for managing highly complex MRM-MS experiments, including quantitative analyses using heavy/light isotopic peptide pairs. MRMer parses and extracts information from MS files encoded in the platform-independent mzXML data format. It extracts and infers precursor-product ion transition pairings, computes integrated ion intensities, and permits rapid visual curation for analyses exceeding 1000 precursor-product pairs. Results can be easily output for quantitative comparison of consecutive runs. Additionally MRMer incorporates features that permit the quantitative analysis experiments including heavy and light isotopic peptide pairs. MRMer is open source and provided under the Apache 2.0 license.

Gut Microbial Protein Expression in Response to Dietary Patterns in a Controlled Feeding Study: A Metaproteomic Approach.

Although the gut microbiome has been associated with dietary patterns linked to health, microbial metabolism is not well characterized. This ancillary study was a proof of principle analysis for a novel application of metaproteomics to study microbial protein expression in a controlled dietary intervention. We measured the response of the microbiome to diet in a randomized crossover dietary intervention of a whole-grain, low glycemic load diet (WG) and a refined-grain, high glycemic load diet (RG). Total proteins in stools from 9 participants at the end of each diet period (n = 18) were analyzed by LC MS/MS and proteins were identified using the Human Microbiome Project (HMP) human gut microbiome database and UniProt human protein databases. T-tests, controlling for false discovery rate (FDR) <10%, were used to compare the Gene Ontology (GO) biological processes and bacterial enzymes between the two interventions. Using shotgun proteomics, more than 53,000 unique peptides were identified including microbial (89%) and human peptides (11%). Forty-eight bacterial enzymes were statistically different between the diets, including those implicated in SCFA production and degradation of fatty acids. Enzymes associated with degradation of human mucin were significantly enriched in the RG diet. These results illustrate that the metaproteomic approach is a valuable tool to study the microbial metabolism of diets that may influence host health.

Metaproteomics reveal that rapid perturbations in organic matter prioritize functional restructuring over taxonomy in western Arctic Ocean microbiomes.

We examined metaproteome profiles from two Arctic microbiomes during 10-day shipboard incubations to directly track early functional and taxonomic responses to a simulated algal bloom and an oligotrophic control. Using a novel peptide-based enrichment analysis, significant changes (p-value < 0.01) in biological and molecular functions associated with carbon and nitrogen recycling were observed. Within the first day under both organic matter conditions, Bering Strait surface microbiomes increased protein synthesis, carbohydrate degradation, and cellular redox processes while decreasing C1 metabolism. Taxonomic assignments revealed that the core microbiome collectively responded to algal substrates by assimilating carbon before select taxa utilize and metabolize nitrogen intracellularly. Incubations of Chukchi Sea bottom water microbiomes showed similar, but delayed functional responses to identical treatments. Although 24 functional terms were shared between experimental treatments, the timing, and degree of the remaining responses were highly variable, showing that organic matter perturbation directs community functionality prior to alterations to the taxonomic distribution at the microbiome class level. The dynamic responses of these two oceanic microbial communities have important implications for timing and magnitude of responses to organic perturbations within the Arctic Ocean and how community-level functions may forecast biogeochemical gradients in oceans.

Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels.

T cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection. Here, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides (class II data in a forthcoming study). Then, at the population level, we performed T-cell repertoire sequencing on 1,815 samples (from 1,521 COVID-19 subjects) as well as 3,500 controls to identify shared "public" T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells. Collectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for at least several months after recovery. As an application of these data, we trained a classifier to diagnose SARS-CoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3-7 = 85.1% [95% CI = 79.9-89.7]; Day 8-14 = 94.8% [90.7-98.4]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 95.4% [92.1-98.3]). These results demonstrate an approach to reliably assess the adaptive immune response both soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points. This blood-based molecular approach to characterizing the cellular immune response has applications in clinical diagnostics as well as in vaccine development and monitoring.

Proteomics on Fixed Tissue Specimens - A Review.

The vast majority of clinical tissue samples are formalin-fixed and paraffin-preserved. This type of preservation has been considered an obstacle to protein extraction from these tissues. However, these are the very tissue samples that have associated patient histories, diagnoses and outcomes - ideal samples in the quest to translate bench research into clinical applications. Thus, until recently, these valuable specimens have been unavailable for proteomic analysis.Over the last decade, researchers have been exploring efficient methods to undo protein cross-linking caused by standard tissue fixatives and extract proteins from archived tissue specimens. These methods have been applied in different clinical proteomic studies. In this report, we attempt to review the development of these techniques, summarize the proteomic findings, and discuss the impact on future clinical proteomics.

Growth phase proteomics of the heterotrophic marine bacterium Ruegeria pomeroyi.

The heterotrophic marine bacterium, Ruegeria pomeroyi, was experimentally cultured under environmentally realistic carbon conditions and with a tracer-level addition of 13C-labeled leucine to track bacterial protein biosynthesis through growth phases. A combination of methods allowed observation of real-time bacterial protein production to understand metabolic priorities through the different growth phases. Over 2000 proteins were identified in each experimental culture from exponential and stationary growth phases. Within two hours of the 13C-labeled leucine addition, R. pomeroyi significantly assimilated the newly encountered substrate into new proteins. This dataset provides a fundamental baseline for understanding growth phase differences in molecular physiology of a cosmopolitan marine bacterium.

Open-source platform for the analysis of liquid chromatography-mass spectrometry (LC-MS) data.

The analysis of protein mixtures by liquid chromatography-mass spectrometry (LCMS) requires tools for viewing and navigating LC-MS data, locating peptides in LC-MS data, and eliminating low-quality peptides. msInspect, an open source platform, can carry out these steps for single experiments and can align and normalize peptide features in comparative studies with multiple LC-MS runs. In addition, msInspect can analyze quantitative studies with and without isotopic labels to generate peptide arrays.