Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics.

The identification of proteoforms by top-down proteomics requires both high quality fragmentation spectra and the neutral mass of the proteoform from which the fragments derive. Intact proteoform spectra can be highly complex and may include multiple overlapping proteoforms, as well as many isotopic peaks and charge states. The resulting lower signal-to-noise ratios for intact proteins complicates downstream analyses such as deconvolution. Averaging multiple scans is a common way to improve signal-to-noise, but mass spectrometry data contains artifacts unique to it that can degrade the quality of an averaged spectra. To overcome these limitations and increase signal-to-noise, we have implemented outlier rejection algorithms to remove outlier measurements efficiently and robustly in a set of MS1 scans prior to averaging. We have implemented averaging with rejection algorithms in the open-source, freely available, proteomics search engine MetaMorpheus. Herein, we report the application of the averaging with rejection algorithms to direct injection and online liquid chromatography mass spectrometry data. Averaging with rejection algorithms demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate. We show that the increase is due to improved spectral quality, particularly in regions surrounding isotopic envelopes.

Identification of Host Proteins Involved in Hepatitis B Virus Genome Packaging.

A critical part of the hepatitis B virus (HBV) life cycle is the packaging of the pregenomic RNA (pgRNA) into nucleocapsids. While this process is known to involve several viral elements, much less is known about the identities and roles of host proteins in this process. To better understand the role of host proteins, we isolated pgRNA and characterized its protein interactome in cells expressing either packaging-competent or packaging-incompetent HBV genomes. We identified over 250 host proteins preferentially associated with pgRNA from the packaging-competent version of the virus. These included proteins already known to support capsid formation, enhance viral gene expression, catalyze nucleocapsid dephosphorylation, and bind to the viral genome, demonstrating the ability of the approach to effectively reveal functionally significant host-virus interactors. Three of these host proteins, AURKA, YTHDF2, and ATR, were selected for follow-up analysis. RNA immunoprecipitation qPCR (RIP-qPCR) confirmed pgRNA-protein association in cells, and siRNA knockdown of the proteins showed decreased encapsidation efficiency. This study provides a template for the use of comparative RNA-protein interactome analysis in conjunction with virus engineering to reveal functionally significant host-virus interactions.

Defining Distinct RNA-Protein Interactomes of SARS-CoV-2 Genomic and Subgenomic RNAs.

Host RNA binding proteins recognize viral RNA and play key roles in virus replication and antiviral mechanisms. SARS-CoV-2 generates a series of tiered subgenomic RNAs (sgRNAs), each encoding distinct viral protein(s) that regulate different aspects of viral replication. Here, for the first time, we demonstrate the successful isolation of SARS-CoV-2 genomic RNA and three distinct sgRNAs (N, S, and ORF8) from a single population of infected cells and characterize their protein interactomes. Over 500 protein interactors (including 260 previously unknown) were identified as associated with one or more target RNA. These included protein interactors unique to a single RNA pool and others present in multiple pools, highlighting our ability to discriminate between distinct viral RNA interactomes despite high sequence similarity. Individual interactomes indicated viral associations with cell response pathways, including regulation of cytoplasmic ribonucleoprotein granules and posttranscriptional gene silencing. We tested the significance of three protein interactors in these pathways (APOBEC3F, PPP1CC, and MSI2) using siRNA knockdowns, with several knockdowns affecting viral gene expression, most consistently PPP1CC. This study describes a new technology for high-resolution studies of SARS-CoV-2 RNA regulation and reveals a wealth of new viral RNA-associated host factors of potential functional significance to infection.

Five-year outcomes of fractionated stereotactic body radiotherapy for oligometastatic prostate cancer from the TRANSFORM phase II trial.

Metastasis-directed therapy (MDT) for oligometastatic prostate cancer (PCa), including stereotactic body radiotherapy (SBRT), has shown promise but is still considered investigational. This is the 5-year analysis of the TRANSFORM trial, the largest prospective cohort of men with oligometastatic PCa treated with SBRT-based MDT. The primary endpoint was 5-year treatment escalation-free survival (TE-FS), defined as freedom from any new cancer therapy other than further SBRT. In total, 199 men received SBRT; 76.4% were hormone-naïve at baseline. The rate of 5-year TE-FS was 21.7% (95% confidence interval [CI]: 15.7%-28.7%) overall and 25.4% (95% CI: 18.1%-33.9%) in the hormone-naïve subgroup. The subgroups with International Society of Urological Pathology Grade Groups 4-5 disease (hazard ratio [HR] = 1.48, 95% CI: 1.05-2.01, p = .026), a higher baseline prostate-specific antigen (PSA) (HR = 1.06, 95% CI: 1.03-1.09, p < .001) and those who received prior androgen deprivation therapy (ADT) (HR = 2.13, 95% CI: 1.40-3.26, p < .001), were at greater risk of treatment escalation. Outcomes for participants with four or five initial lesions were comparable to those with one to three lesions. At last follow-up, 18.9% (95% CI: 13.2%-25.7%) of participants were free from treatment escalation (median follow-up of 67.9 months) and two participants had an undetectable PSA level. No treatment-related grade three or higher adverse events were reported. The findings of this study demonstrate that SBRT-based MDT is an effective option for delaying systemic treatment escalation in the context of oligometastatic PCa. Future randomised trials comparing SBRT-based MDT to standard-of-care ADT-based approaches are required to evaluate the impact of delaying ADT on survival.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe.

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Long-Term Exposure to Outdoor Ultrafine Particles and Black Carbon and Effects on Mortality in Montreal and Toronto, Canada.

INTRODUCTION: Numerous studies support an important relationship between long-term exposure to outdoor fine particulate air pollution (PM2.5) and both nonaccidental and cause-specific mortality. Less is known about the long-term health consequences of other traffic pollutants, including ultrafine particles (UFPs, <0.1 µm) and black carbon (BC), which are often present at elevated concentrations in urban areas but are not currently regulated. Knowledge is lacking largely because these pollutants generally are not monitored by governments and vary greatly over small spatial scales, hindering the evaluation of long-term exposures in population-based studies. METHODS: We aimed to estimate associations between long-term exposures to outdoor UFPs and BC and nonaccidental and cause-specific mortality in Canada's two largest cities, Montreal and Toronto. We considered several approaches to exposure assessment: (1) land use regression (LUR) models based on large-scale year-long mobile monitoring campaigns combined with detailed land use and traffic information; (2) machine learning (i.e., convolutional neural networks [CNN]) models trained by combining mobile monitoring data with aerial images; and (3) the combined use of these two approaches. We also examined exposure models with and without backcasting based on historical trends in vehicle emissions (to capture potential trends in pollutant concentrations over time) and with and without accounting for neighborhood-level mobility patterns (based on travel demand surveys). These exposure models were linked to members of the Canadian Census Health and Environment Cohorts (CanCHEC) residing in Montreal or Toronto (including census years 1991, 1996, 2001, and 2006) with mortality follow-up from 2001 (or cohort entry for the 2006 cohort) to 2016. Cox proportional hazard models were used to estimate associations between long-term exposures to outdoor UFPs and BC, adjusting for sociodemographic factors and co-pollutants identified as potential confounding factors. Concentration-response relationships for outdoor UFPs and BC were also examined for nonaccidental and cause-specific mortality using smoothing splines. RESULTS: Our cohort study included approximately 1.5 million people with 174,200 nonaccidental deaths observed during the follow-up period. Combined LUR and machine learning model predictions performed slightly better than LUR models alone and were used as the main exposure models in all epidemiological analyses. Long-term exposures to outdoor UFP number concentrations were consistently positively associated with nonaccidental and cause-specific mortality. Importantly, hazard ratios (HRs) for outdoor UFP number concentrations were sensitive to adjustment for UFP size: UFP size was inversely related to number concentrations and independently associated with mortality, resulting in underestimation of mortality risk for outdoor UFP number concentrations when UFP size was excluded. HRs for outdoor UFP number concentrations were robust to backcasting and mobility weighting but varied slightly in analyses using LUR and machine learning models alone, with stronger associations typically observed for the machine learning models. Associations between outdoor BC concentrations and mortality were generally weak or null, but a positive association was observed for cardiovascular mortality. CONCLUSIONS: Outdoor UFP number concentrations were consistently associated with increased risks of nonaccidental and cause-specific mortality in Montreal and Toronto. Our results suggest that UFP size should be considered in epidemiological analyses of outdoor UFP number concentrations, as excluding size can lead to an underestimation of health risks. Our results suggest that outdoor UFP number concentrations are positively associated with mortality independent of other outdoor air pollutants, including PM2.5 mass concentrations and oxidant gases (i.e., nitrogen dioxide [NO2] and ozone [O3]). As outdoor UFPs are currently unregulated, interventions targeting these pollutants could significantly affect population health.

Newly synthesized glycoprotein profiling to identify molecular signatures of warm ischemic injury in donor lungs.

Despite recent technological advances such as ex vivo lung perfusion (EVLP), the outcome of lung transplantation remains unsatisfactory with ischemic injury being a common cause for primary graft dysfunction. New therapeutic developments are hampered by limited understanding of pathogenic mediators of ischemic injury to donor lung grafts. Here, to identify novel proteomic effectors underlying the development of lung graft dysfunction, using bioorthogonal protein engineering, we selectively captured and identified newly synthesized glycoproteins (NewS-glycoproteins) produced during EVLP with unprecedented temporal resolution of 4 h. Comparing the NewS-glycoproteomes in lungs with and without warm ischemic injury, we discovered highly specific proteomic signatures with altered synthesis in ischemic lungs, which exhibited close association to hypoxia response pathways. Inspired by the discovered protein signatures, pharmacological modulation of the calcineurin pathway during EVLP of ischemic lungs offered graft protection and improved posttransplantation outcome. In summary, the described EVLP-NewS-glycoproteomics strategy delivers an effective new means to reveal molecular mediators of donor lung pathophysiology and offers the potential to guide future therapeutic development.NEW & NOTEWORTHY This study developed and implemented a bioorthogonal strategy to chemoselectively label, enrich, and characterize newly synthesized (NewS-)glycoproteins during 4-h ex vivo lung perfusion (EVLP). Through this approach, the investigators uncovered specific proteomic signatures associated with warm ischemic injury in donor lung grafts. These signatures exhibit high biological relevance to ischemia-reperfusion injury, validating the robustness of the presented approach.

Cysteine Counting via Isotopic Chemical Labeling for Intact Mass Proteoform Identifications in Tissue.

Top-down proteomics, the tandem mass spectrometric analysis of intact proteoforms, is the dominant method for proteoform characterization in complex mixtures. While this strategy produces detailed molecular information, it also requires extensive instrument time per mass spectrum obtained and thus compromises the depth of proteoform coverage that is accessible on liquid chromatography time scales. Such a top-down analysis is necessary for making original proteoform identifications, but once a proteoform has been confidently identified, the extensive characterization it provides may no longer be required for a subsequent identification of the same proteoform. We present a strategy to identify proteoforms in tissue samples on the basis of the combination of an intact mass determination with a measured count of the number of cysteine residues present in each proteoform. We developed and characterized a cysteine tagging chemistry suitable for the efficient and specific labeling of cysteine residues within intact proteoforms and for providing a count of the cysteine amino acids present. On simple protein mixtures, the tagging chemistry yields greater than 98% labeling of all cysteine residues, with a labeling specificity of greater than 95%. Similar results are observed on more complex samples. In a proof-of-principle study, proteoforms present in a human prostate tumor biopsy were characterized. Observed proteoforms, each characterized by an intact mass and a cysteine count, were grouped into proteoform families (groups of proteoforms originating from the same gene). We observed 2190 unique experimental proteoforms, 703 of which were grouped into 275 proteoform families.

Elucidating the RNA-Protein Interactomes of Target RNAs in Tissue.

RNA-protein interactions are key to many aspects of cellular homeostasis and their identification is important to understanding cellular function. Multiple strategies have been developed for the RNA-centric characterization of RNA-protein complexes. However, these studies have all been done in immortalized cell lines that do not capture the complexity of heterogeneous tissue samples. Here, we develop hybridization purification of RNA-protein complexes followed by mass spectrometry (HyPR-MS) for use in tissue samples. We isolated both polyadenylated RNA and the specific long noncoding RNA MALAT1 and characterized their protein interactomes. These results demonstrate the feasibility of HyPR-MS in tissue for the multiplexed characterization of specific RNA-protein complexes.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization.

We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .

Overview and considerations in bottom-up proteomics.

Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.

How the other half screens: A model for partnerships between student-run free clinics and genetic counseling programs to address disparities in hereditary cancer evaluation.

Genetic medicine is considered a major part of the future of preventative care, offering evidence-based, effective interventions to improve health outcomes and reduce morbidity and mortality, especially regarding hereditary cancer screening. Identification of individuals who would benefit from screening is key to improving their cancer-related healthcare outcomes. However, patients without insurance, of historically underserved races, of lower socioeconomic status, and in rural communities have lower access to such care. Barriers to access lead to populations having higher rates of undetected hereditary cancer, and consequently more severe forms of cancer. With an already-established reach, student-run free clinics can work with genetic counseling training programs to incorporate genetic medicine into their workflow. Such partnerships will (1) make genetic care more accessible with goals of improving patient morbidity, mortality, and health outcomes, (2) offer robust educational experiences for genetic counseling learners, particularly in understanding social determinants of health and barriers to care, and (3) actively combat the growing racial and geographic gaps in genetic care. Our study presents how one student-run free clinic implemented genetic counseling into its primary care workflow to improve access to genetics services. We present two examples of how genetic counseling improved patients' medical care. We also identify obstacles encountered during this program's development, as well as solutions-those we incorporated and possible considerations for other clinics. With the hope that other clinics can use this paper to design similar partnerships, we aim to lessen the gap between sickness and screening.

A Hybrid Spectral Library and Protein Sequence Database Search Strategy for Bottom-Up and Top-Down Proteomic Data Analysis.

Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. Protein sequence databases lack fragment peak intensity information, which can result in poor discrimination between correct and incorrect spectrum assignments. Spectral libraries usually contain fewer peptides than protein sequence databases, which limits the number of peptides that can be identified. Notably, few post-translationally modified peptides are represented in spectral libraries. This is because few search engines can both identify a broad spectrum of PTMs and create corresponding spectral libraries. Also, programs that generate spectral libraries using deep learning approaches are not yet able to accurately predict spectra for the vast majority of PTMs. Here, we address these limitations through use of a hybrid search strategy that combines protein sequence database and spectral library searches to improve identification success rates and sensitivity. This software uses Global PTM Discovery (G-PTM-D) to produce spectral libraries for a wide variety of different PTMs. These features, along with a new spectrum annotation and visualization tool, have been integrated into the freely available and open-source search engine MetaMorpheus.

Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1.

Human immunodeficiency virus type 1 (HIV-1) remains a deadly infectious disease despite existing antiretroviral therapies. A comprehensive understanding of the specific mechanisms of viral infectivity remains elusive and currently limits the development of new and effective therapies. Through in-depth proteomic analysis of HIV-1 virions, we discovered the novel post-translational modification of highly conserved residues within the viral matrix and capsid proteins to the dehydroamino acids, dehydroalanine and dehydrobutyrine. We further confirmed their presence by labeling the reactive alkene, characteristic of dehydroamino acids, with glutathione via Michael addition. Dehydroamino acids are rare, understudied, and have been observed mainly in select bacterial and fungal species. Until now, they have not been observed in HIV proteins. We hypothesize that these residues are important in viral particle maturation and could provide valuable insight into HIV infectivity mechanisms.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses.

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

Fused in sarcoma regulates DNA replication timing and kinetics.

Fused in sarcoma (FUS) encodes an RNA-binding protein with diverse roles in transcriptional activation and RNA splicing. While oncogenic fusions of FUS and transcription factor DNA-binding domains are associated with soft tissue sarcomas, dominant mutations in FUS can cause amyotrophic lateral sclerosis. FUS has also been implicated in genome maintenance. However, the underlying mechanisms of its actions in genome stability are unknown. Here, we applied gene editing, functional reconstitution, and integrated proteomics and transcriptomics to illuminate roles for FUS in DNA replication and repair. Consistent with a supportive role in DNA double-strand break repair, FUS-deficient cells exhibited subtle alterations in the recruitment and retention of double-strand break-associated factors, including 53BP1 and BRCA1. FUS-/- cells also exhibited reduced proliferative potential that correlated with reduced speed of replication fork progression, diminished loading of prereplication complexes, enhanced micronucleus formation, and attenuated expression and splicing of S-phase-associated genes. Finally, FUS-deficient cells exhibited genome-wide alterations in DNA replication timing that were reversed upon re-expression of FUS complementary DNA. We also showed that FUS-dependent replication domains were enriched in transcriptionally active chromatin and that FUS was required for the timely replication of transcriptionally active DNA. These findings suggest that alterations in DNA replication kinetics and programming contribute to genome instability and functional defects in FUS-deficient cells.

Best practices and benchmarks for intact protein analysis for top-down mass spectrometry.

One gene can give rise to many functionally distinct proteoforms, each of which has a characteristic molecular mass. Top-down mass spectrometry enables the analysis of intact proteins and proteoforms. Here members of the Consortium for Top-Down Proteomics provide a decision tree that guides researchers to robust protocols for mass analysis of intact proteins (antibodies, membrane proteins and others) from mixtures of varying complexity. We also present cross-platform analytical benchmarks using a protein standard sample, to allow users to gauge their proficiency.

A Cross-Sectional Study of Disparities in Screening Guideline Concordance Within a Student-Run Clinic.

Examination of screening guideline concordance can help clinics and institutions identify and understand disparities within their own practices. We conducted a study to examine whether screening completion rates within a student-run free clinic (SRFC) reflected, exacerbated, or narrowed population-level disparities in outcomes by race/ethnicity and primary language. We compared completion rates for cervical cancer (n = 114), diabetic retinopathy (n = 91), colorectal cancer (n = 114), and breast cancer (n = 63) by race/ethnicity (Black, n = 37; Hispanic, n = 133; white, n = 54; other, n = 29) and primary language (English, n = 106; Spanish, n = 136; other, n = 11) among patients at Shade tree clinic (STC), an SFRC in Nashville, TN. There were no differences in screening completion rate by race/ethnicity, and Spanish-speaking patients had slightly higher rates of cervical cancer screening [91% (95% confidence interval 84-97%)] than English-speaking patients [72% (57-86%)]. Overall screening rates were comparable to national averages, and in the case of screenings performed within clinic-cervical cancer [82%; (75-89%)] and diabetic retinopathy screening [86% (79-92%)]-exceeded national averages and/or affiliated academic medical center goals. These findings extend the existing literature supporting the ability of SRFCs to provide effective care by also demonstrating one measure of equity in clinic processes, providing a framework for future studies of equity within SRFCs and traditional primary care practices.

Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules.

RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA-protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed to identify protein binders of selected RNA molecules. Amongst these, 'in cell' hybridization methods represent the gold standard in the field because they are designed to reveal the proteins bound to specific RNAs in a cellular context. Here, we compare the technical features of different 'in cell' hybridization approaches with a focus on their advantages, limitations, and current and potential future applications.