A fast SEQUEST cross correlation algorithm

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data

Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants. While direct application of cross-correlation to mass spectral data has been shown to aid in the identification of many other types of compounds, this type of analysis has not been demonstrated in the literature for the purpose of LC-MS based identification of microbial strains. A method of identifying microbial strains is presented here that applies the principle of 2D cross-correlation to LC-MS data. For a set of N = 30 yeast isolate samples representing 5 yeast strains (K-97, S-33, T-58, US-05, WB-06), high-resolution LC-MS-Orbitrap data were collected. Reference spectra were then generated for each strain from the combined data of each sample of that strain. Sample strains were then predicted by computing the 2D cross-correlation of each sample against the reference spectra, followed by application of correction factors measuring the asymmetry of the 2D correlation functions.

Discovery of immunotherapy targets for pediatric solid and brain tumors by exon-level expression

Immunotherapy with chimeric antigen receptor T cells for pediatric solid and brain tumors is constrained by available targetable antigens. Cancer-specific exons present a promising reservoir of targets; however, these have not been explored and validated systematically in a pan-cancer fashion. To identify cancer specific exon targets, here we analyze 1532 RNA-seq datasets from 16 types of pediatric solid and brain tumors for comparison with normal tissues using a newly developed workflow. We find 2933 exons in 157 genes encoding proteins of the surfaceome or matrisome with high cancer specificity either at the gene (n = 148) or the alternatively spliced isoform (n = 9) level. Expression of selected alternatively spliced targets, including the EDB domain of fibronectin 1, and gene targets, such as COL11A1, are validated in pediatric patient derived xenograft tumors. We generate T cells expressing chimeric antigen receptors specific for the EDB domain or COL11A1 and demonstrate that these have antitumor activity. The full target list, explorable via an interactive web portal ( https://cseminer.stjude.org/ ), provides a rich resource for developing immunotherapy of pediatric solid and brain tumors using gene or AS targets with high expression specificity in cancer.

Target-decoy false discovery rate estimation using Crema

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

moPepGen: Rapid and Comprehensive Proteoform Identification

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteoform diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively enumerates proteoforms in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it detects and quantifies previously unobserved noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient identification and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Pseudomonas aeruginosa heme metabolites biliverdin IXβ and IXδ are integral to lifestyle adaptations associated with chronic infection

Pseudomonas aeruginosa is a versatile opportunistic pathogen requiring iron for its survival and virulence within the host. The ability to switch to heme as an iron source and away from siderophore uptake provides an advantage in chronic infection. We have recently shown the extracellular heme metabolites biliverdin IXβ (BVIXβ) and BVIXδ positively regulate the heme-dependent cell surface signaling cascade. We further investigated the role of BVIXβ and BVIXδ in cell signaling utilizing allelic strains lacking a functional heme oxygenase (hemOin) or one reengineered to produce BVIXα (hemOα). Compared to PAO1, both strains show a heme-dependent growth defect, decreased swarming and twitching, and less robust biofilm formation. Interestingly, the motility and biofilm defects were partially rescued on addition of exogenous BVIXβ and BVIXδ. Utilizing liquid chromatography-tandem mass spectrometry, we performed a comparative proteomics and metabolomics analysis of PAO1 versus the allelic strains in shaking and static conditions. In shaking conditions, the hemO allelic strains showed a significant increase in proteins involved in quorum sensing, phenazine production, and chemotaxis. Metabolite profiling further revealed increased levels of Pseudomonas quinolone signal and phenazine metabolites. In static conditions, we observed a significant repression of chemosensory pathways and type IV pili biogenesis proteins as well as several phosphodiesterases associated with biofilm dispersal. We propose BVIX metabolites function as signaling and chemotactic molecules integrating heme utilization as an iron source into the adaptation of P. aeruginosa from a planktonic to sessile lifestyle.

IMPORTANCE

The opportunistic pathogen Pseudomonas aeruginosa causes long-term chronic infection in the airways of cystic fibrosis patients. The ability to scavenge iron and to establish chronic infection within this environment coincides with a switch to utilize heme as the primary iron source. Herein, we show the heme metabolites biliverdin beta and delta are themselves important signaling molecules integrating the switch in iron acquisition systems with cooperative behaviors such as motility and biofilm formation that are essential for long-term chronic infection. These significant findings will enhance the development of viable multi-targeted therapeutics effective against both heme utilization and cooperative behaviors essential for survival and persistence within the host.

Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies

Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.

Proteomic Analysis to Identify Prospective Biomarkers of Treatment Outcome After Microvascular Decompression for Trigeminal Neuralgia: A Preliminary Study

Trigeminal neuralgia (TN) is a severe neuropathic facial pain disorder, often caused by vascular or neuronal compression of the trigeminal nerve. In such cases, microvascular decompression (MVD) surgery can be used to treat TN, but pain relief is not guaranteed. The molecular mechanisms that affect treatment response to MVD are not well understood. In this exploratory study, we performed label-free quantitative proteomic profiling of plasma and cerebrospinal fluid samples from patients undergoing MVD for TN, then compared the proteomic profiles of patients graded as responders (n = 7) versus non-responders (n = 9). We quantified 1,090 proteins in plasma and 1,087 proteins in the cerebrospinal fluid, of which 12 were differentially regulated in the same direction in both sample types. Functional analyses of differentially regulated proteins in protein-protein interaction networks suggested pathways of the immune system, axon guidance, and cellular stress response to be associated with response to MVD. These findings suggest potential biomarkers of response to MVD, as well as possible mechanisms of variable treatment success in TN patients. PERSPECTIVE: This exploratory study evaluates proteomic profiles in plasma and cerebrospinal fluid of patients undergoing microvascular decompression surgery for trigeminal neuralgia. Differential expression of proteins between surgery responders versus non-responders may serve as biomarkers to predict surgical success and provide insight into surgical mechanisms of pain relief in trigeminal neuralgia.

HDPairFinder: A data processing platform for hydrogen/deuterium isotopic labeling-based nontargeted analysis of trace-level amino-containing chemicals in environmental water

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.

Empowering peptidomics: utilizing computational tools and approaches

Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biological functions. This area includes peptide identification and characterization, database construction, de novo sequencing, functional annotation, omics data integration and systems biology. Artificial intelligence techniques, such as machine learning and natural language processing, aid in the interpretation of peptide sequence data and the generation of biological insights. By using bioinformatics approaches, peptidomics researchers can accelerate peptide discovery, understand their functions and gain insights into complex molecular interactions.

The heme-responsive PrrH sRNA regulates Pseudomonas aeruginosa pyochelin gene expression

Pseudomonas aeruginosa is an opportunistic pathogen that requires iron for growth and virulence, yet this nutrient is sequestered by the innate immune system during infection. When iron is limiting, P. aeruginosa expresses the PrrF1 and PrrF2 small RNAs (sRNAs), which post-transcriptionally repress expression of nonessential iron-containing proteins, thus sparing this nutrient for more critical processes. The genes for the PrrF1 and PrrF2 sRNAs are arranged in tandem on the chromosome, allowing for the transcription of a longer heme-responsive sRNA, termed PrrH. While the functions of PrrF1 and PrrF2 have been extensively studied, the role of PrrH in P. aeruginosa physiology and virulence is not well understood. In this study, we performed transcriptomic and proteomic studies to identify the PrrH regulon. In shaking cultures, the pyochelin synthesis proteins were increased in two distinct prrH mutants compared to the wild type, while the mRNAs for these proteins were not affected by the prrH mutation. We identified complementarity between the PrrH sRNA and the sequence upstream of the pchE mRNA, suggesting the potential for PrrH to directly regulate the expression of genes for pyochelin synthesis. We further showed that pchE mRNA levels were increased in the prrH mutants when grown in static but not shaking conditions. Moreover, we discovered that controlling for the presence of light was critical for examining the impact of PrrH on pchE expression. As such, our study reports on the first likely target of the PrrH sRNA and highlights key environmental variables that will allow for future characterization of PrrH function. IMPORTANCE In the human host, iron is predominantly in the form of heme, which Pseudomonas aeruginosa can acquire as an iron source during infection. We previously showed that the iron-responsive PrrF small RNAs (sRNAs) are critical for mediating iron homeostasis during P. aeruginosa infection; however, the function of the heme-responsive PrrH sRNA remains unclear. In this study, we identified genes for pyochelin siderophore biosynthesis, which mediates uptake of inorganic iron, as a novel target of PrrH regulation. This study therefore highlights a novel relationship between heme availability and siderophore biosynthesis in P. aeruginosa.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Statistical Framework for Identifying Differences in Similar Mass Spectra: Expanding Possibilities for Isomer Identification

Isomeric molecules are important analytes in many biological and chemical arenas, yet their similarity poses challenges for many analytical methods, including mass spectrometry (MS). Tandem-MS provides significantly more information about isomers than intact mass analysis, but highly similar fragmentation patterns are common and include cases where no unique m/z peaks are generated between isomeric pairs. However, even in such situations, differences in peak intensity can exist and potentially contain additional information. Herein, we present a framework for comparing mass spectra that differ only in terms of peak intensity and include calculation of a statistical probability that the spectra derive from different analytes. This framework allows for confident identification of peptide isomers by collision-induced dissociation, higher-energy collisional dissociation, electron-transfer dissociation, and radical-directed dissociation. The method successfully identified many types of isomers including various d/l amino acid substitutions, Leu/Ile, and Asp/IsoAsp. The method can accommodate a wide range of changes in instrumental settings including source voltages, isolation widths, and resolution without influencing the analysis. It is shown that quantification of the composition of isomeric mixtures can be enabled with calibration curves, which were found to be highly linear and reproducible. The analysis can be implemented with data collected by either direct infusion or liquid-chromatography MS. Although this framework is presented in the context of isomer characterization, it should also prove useful in many other contexts where similar mass spectra are generated.

Integrative Proteomics and Metabolomics Analysis Reveals the Role of Small Signaling Peptide Rapid Alkalinization Factor 34 (RALF34) in Cucumber Roots

The main role of RALF small signaling peptides was reported to be the alkalization control of the apoplast for improvement of nutrient absorption; however, the exact function of individual RALF peptides such as RALF34 remains unknown. The Arabidopsis RALF34 (AtRALF34) peptide was proposed to be part of the gene regulatory network of lateral root initiation. Cucumber is an excellent model for studying a special form of lateral root initiation taking place in the meristem of the parental root. We attempted to elucidate the role of the regulatory pathway in which RALF34 is a participant using cucumber transgenic hairy roots overexpressing CsRALF34 for comprehensive, integrated metabolomics and proteomics studies, focusing on the analysis of stress response markers. CsRALF34 overexpression resulted in the inhibition of root growth and regulation of cell proliferation, specifically in blocking the G2/M transition in cucumber roots. Based on these results, we propose that CsRALF34 is not part of the gene regulatory networks involved in the early steps of lateral root initiation. Instead, we suggest that CsRALF34 modulates ROS homeostasis and triggers the controlled production of hydroxyl radicals in root cells, possibly associated with intracellular signal transduction. Altogether, our results support the role of RALF peptides as ROS regulators.

Alterations in cytoskeletal and Ca2+ cycling regulators in atria lacking the obscurin Ig58/59 module

Introduction

Obscurin (720-870 kDa) is a giant cytoskeletal and signaling protein that possesses both structural and regulatory functions in striated muscles. Immunoglobulin domains 58/59 (Ig58/59) of obscurin bind to a diverse set of proteins that are essential for the proper structure and function of the heart, including giant titin, novex-3, and phospholamban (PLN). Importantly, the pathophysiological significance of the Ig58/59 module has been further underscored by the discovery of several mutations within Ig58/59 that are linked to various forms of myopathy in humans. We previously generated a constitutive deletion mouse model, Obscn-ΔIg58/59, that expresses obscurin lacking Ig58/59, and characterized the effects of this deletion on cardiac morphology and function through aging. Our findings demonstrated that Obscn-ΔIg58/59 male animals develop severe arrhythmia, primarily manifesting as episodes of junctional escape and spontaneous loss of regular p-waves, reminiscent of human atrial fibrillation, accompanied by significant atrial enlargement that progresses in severity with aging.

Methods and Results

To comprehensively characterize the molecular alterations responsible for these pathologies, we performed proteomic and phospho-proteomic analyses in aging Obscn-ΔIg58/59 atria. Our studies revealed extensive and novel alterations in the expression and phosphorylation profile of major cytoskeletal proteins, Ca²⁺ regulators, and Z-disk associated protein complexes in the Obscn-ΔIg58/59 atria through aging.

Discussion

These studies implicate obscurin, particularly the Ig58/59 module, as an essential regulator of the Z-disk associated cytoskeleton and Ca²⁺ cycling in the atria and provide new molecular insights into the development of atrial fibrillation and remodeling.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Proteostasis and lysosomal quality control deficits in Alzheimer's disease neurons

The role of proteostasis and organelle homeostasis dysfunction in human aging and Alzheimer's disease (AD) remains unclear. Analyzing proteome-wide changes in human donor fibroblasts and their corresponding transdifferentiated neurons (tNeurons), we find aging and AD synergistically impair multiple proteostasis pathways, most notably lysosomal quality control (LQC). In particular, we show that ESCRT-mediated lysosomal repair defects are associated with both sporadic and PSEN1 familial AD. Aging- and AD-linked defects are detected in fibroblasts but highly exacerbated in tNeurons, leading to enhanced neuronal vulnerability, unrepaired lysosomal damage, inflammatory factor secretion and cytotoxicity. Surprisingly, tNeurons from aged and AD donors spontaneously develop amyloid-β inclusions co-localizing with LQC markers, LAMP1/2-positive lysosomes and proteostasis factors; we observe similar inclusions in brain tissue from AD patients and APP-transgenic mice. Importantly, compounds enhancing lysosomal function broadly ameliorate these AD-associated pathologies. Our findings establish cell-autonomous LQC dysfunction in neurons as a central vulnerability in aging and AD pathogenesis.

Modeling Lower-Order Statistics to Enable Decoy-Free FDR Estimation in Proteomics

One of the chief objectives in mass spectrometry-based peptide identification in proteomics is the statistical validation of top-scoring peptide-spectrum matches (PSMs) in the form of false discovery rate (FDR) estimation. Existing methods construct a null model that captures the characteristics of incorrect target PSMs to estimate the FDR, most often with the help of decoys. Decoy-based methods, however, increase the computational cost and rely on the difficult-to-verify assumption that decoy PSMs constitute a sufficient and representative sample of the population of possible incorrect target PSMs. On the other hand, the possibility of FDR estimation assisted by the plentiful non-top-scoring PSMs, which are almost always incorrect, has been scarcely explored. In this work, we propose a novel decoy-free procedure for developing null models for top-scoring PSMs using the transformed e-value (TEV) score and the distributions of non-top-scoring target PSMs. The method relies on a theoretically derivable relationship between the parameters of the distributions of lower-order statistics of the TEV score and a necessary empirical optimization to fit a single parameter to actual data. The framework was tested on multiple different data sets and two search engines. We present evidence that our method is comparable to and occasionally outperforms popular decoy-free and decoy-based methods in FDR estimation.

Improved Analysis of Cross-Linking Mass Spectrometry Data with Kojak 2.0, Advanced by Integration into the Trans-Proteomic Pipeline

Fragmentation ion spectral analysis of chemically cross-linked proteins is an established technology in the proteomics research repertoire for determining protein interactions, spatial orientation, and structure. Here we present Kojak version 2.0, a major update to the original Kojak algorithm, which was developed to identify cross-linked peptides from fragment ion spectra using a database search approach. A substantially improved algorithm with updated scoring metrics, support for cleavable cross-linkers, and identification of cross-links between 15N-labeled homomultimers are among the newest features of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic Pipeline, enabling access to dozens of additional tools within that suite. In particular, the PeptideProphet and iProphet tools for validation of cross-links improve the sensitivity and accuracy of correct cross-link identifications at user-defined thresholds. These new features improve the versatility of the algorithm, enabling its use in a wider range of experimental designs and analysis pipelines. Kojak 2.0 remains open-source and multiplatform.

HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search

Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.

The heme-responsive PrrH sRNA regulates Pseudomonas aeruginosa pyochelin gene expression

Pseudomonas aeruginosa is an opportunistic pathogen that requires iron for growth and virulence, yet this nutrient is sequestered by the innate immune system during infection. When iron is limiting, P. aeruginosa expresses the PrrF1 and PrrF2 small regulatory RNAs (sRNAs), which post-transcriptionally repress expression of non-essential iron-containing proteins thus sparing this nutrient for more critical processes. The genes for the PrrF1 and PrrF2 sRNAs are arranged in tandem on the chromosome, allowing for the transcription of a longer heme-responsive sRNA, termed PrrH. While the functions of PrrF1 and PrrF2 have been studied extensively, the role of PrrH in P. aeruginosa physiology and virulence is not well understood. In this study, we performed transcriptomic and proteomic studies to identify the PrrH regulon. In shaking cultures, the pyochelin synthesis proteins were increased in two distinct prrH mutants compared to wild type, while the mRNAs for these proteins were not affected by prrH mutation. We identified complementarity between the PrrH sRNA and sequence upstream of the pchE mRNA, suggesting potential for PrrH to directly regulate expression of genes for pyochelin synthesis. We further showed that pchE mRNA levels were increased in the prrH mutants when grown in static but not shaking conditions. Moreover, we discovered controlling for the presence of light was critical for examining the impact of PrrH on pchE expression. As such, our study reports on the first likely target of the PrrH sRNA and highlights key environmental variables that will allow for future characterization of PrrH function.

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.

Targeted Inhibition of Select Extracellular Signal-regulated Kinases 1 and 2 Functions Mitigates Pathological Features of Asthma in Mice

ERK1/2 (extracellular signal-regulated kinases 1 and 2) regulate the activity of various transcription factors that contribute to asthma pathogenesis. Although an attractive drug target, broadly inhibiting ERK1/2 is challenging because of unwanted cellular toxicities. We have identified small molecule inhibitors with a benzenesulfonate scaffold that selectively inhibit ERK1/2-mediated activation of AP-1 (activator protein-1). Herein, we describe the findings of targeting ERK1/2-mediated substrate-specific signaling with the small molecule inhibitor SF-3-030 in a murine model of house dust mite (HDM)-induced asthma. In 8- to 10-week-old BALB/c mice, allergic asthma was established by repeated intranasal HDM (25 μg/mouse) instillation for 3 weeks (5 days/week). A subgroup of mice was prophylactically dosed with 10 mg/kg SF-3-030/DMSO intranasally 30 minutes before the HDM challenge. Following the dosing schedule, mice were evaluated for alterations in airway mechanics, inflammation, and markers of airway remodeling. SF-3-030 treatment significantly attenuated HDM-induced elevation of distinct inflammatory cell types and cytokine concentrations in BAL and IgE concentrations in the lungs. Histopathological analysis of lung tissue sections revealed diminished HDM-induced pleocellular peribronchial inflammation, mucus cell metaplasia, collagen accumulation, thickening of airway smooth muscle mass, and expression of markers of cell proliferation (Ki-67 and cyclin D1) in mice treated with SF-3-030. Furthermore, SF-3-030 treatment attenuated HDM-induced airway hyperresponsiveness in mice. Finally, mechanistic studies using transcriptome and proteome analyses suggest inhibition of HDM-induced genes involved in inflammation, cell proliferation, and tissue remodeling by SF-3-030. These preclinical findings demonstrate that function-selective inhibition of ERK1/2 signaling mitigates multiple features of asthma in a murine model.

Protein Changes After 6 weeks of Walking and the Relationship to Pain in Adults with Knee Osteoarthritis

Background: Knee osteoarthritis (KOA) affects 22.9% of individuals over the age of 40 and causes significant pain and disability. Pain is the most prevalent and troublesome symptom of KOA leading patients to seek medical interventions for relief. Knee osteoarthritis pain has both peripheral and central mechanisms that vary by individual. Non-pharmacological pain management strategies such as walking is the first step in reducing KOA pain. However, initiation of a walking regime can induce knee pain for some and the mechanism by which habitual walking reduces KOA pain is unclear. Purpose: The purpose of this study was to use a discovery proteomics approach and quantitative sensory testing (QST) to determine the molecular changes that occur after habitual walking and their relationship to pain sensitivity. Research Design and Study Sample: We conducted a pre-test/post-test study using QST to measure neurophysiological parameters at the knee and contralateral forearm and examined platelet protein signatures before and after 6 weeks of walking 3 days per week for 30 minutes among six adults with KOA and six healthy controls. Results: Knee pain sensitivity did not change significantly after 6 weeks of walking among either KOA or healthy participants. However, forearm pressure pain sensitivity decreased for both groups after walking, indicating reduction in central pain pathways. Protein signatures showed downregulation of immune and inflammatory, pathways among KOA participants after walking which were upregulated in healthy controls. Conclusion: These differences may contribute differences in centralized pain thresholds seen between KOA and healthy participants.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity

Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.

Inflammatory and Immune Protein Pathways Possible Mechanisms for Pain Following Walking in Knee Osteoarthritis

BACKGROUND

Knee osteoarthritis affects nearly 30% of adults aged 60 years or older and causes significant pain and disability. Walking is considered a "gold standard" treatment option for reducing knee osteoarthritis pain and maintaining joint mobility but does not reduce pain for all adults with knee osteoarthritis pain and may induce pain-particularly when starting a walking routine. The mechanism by which walking is helpful for knee osteoarthritis pain is unclear. Quantitative sensory testing has revealed that knee osteoarthritis pain has both peripheral and central components, which vary by individual.

OBJECTIVE

The purpose of this study was to better understand the mechanisms underlying the value of walking for knee pain.

METHODS

We conducted a pretest/posttest study using quantitative sensory testing to measure neurophysiological parameters and examined systemic protein signatures. Adults with knee osteoarthritis and healthy controls underwent quantitative sensory testing and blood draw for platelet proteomics before and after a 30-minute walk at 100 steps per minute.

RESULTS

A single 30-minute walk moderately increased pressure pain sensitivity at the affected knee among persons with knee osteoarthritis. Healthy adults showed no difference in pain sensitivity. Protein signatures among participants with knee osteoarthritis indicated changes in inflammatory and immune pathways, including the complement system and SAA1 protein that coincided with changes in pain with walking and differed from healthy participants.

DISCUSSION

One goal of developing individualized interventions for knee osteoarthritis pain is to elucidate the mechanisms by which self-management interventions affect pain. The addition of therapies that target the complement system or SAA1 expression may improve the pain sensitivity after a moderate walk for adults with knee osteoarthritis.

Pyruvate-Driven Oxidative Phosphorylation is Downregulated in Sepsis-Induced Cardiomyopathy: A Study of Mitochondrial Proteome

BACKGROUND

Sepsis-induced cardiomyopathy (SIC) is a major contributing factor for morbidity and mortality in sepsis. Accumulative evidence has suggested that cardiac mitochondrial oxidative phosphorylation is attenuated in sepsis, but the underlying molecular mechanisms remain incompletely understood.

METHODS

Adult male mice of 9 to 12 weeks old were subjected to sham or cecal ligation and puncture procedure. Echocardiography in vivo and Langendorff-perfused hearts were used to assess cardiac function 24 h after the procedures. Unbiased proteomics analysis was performed to profile mitochondrial proteins in the hearts of both sham and SIC mice. Seahorse respirator technology was used to evaluate oxygen consumption in purified mitochondria.

RESULTS

Of the 665 mitochondrial proteins identified in the proteomics assay, 35 were altered in septic mice. The mitochondrial remodeling involved various energy metabolism pathways including subunits of the electron transport chain, fatty acid catabolism, and carbohydrate oxidative metabolism. We also identified a significant increase of pyruvate dehydrogenase (PDH) kinase 4 (PDK4) and inhibition of PDH activity in septic hearts. Furthermore, compared to sham mice, mitochondrial oxygen consumption of septic mice was significantly reduced when pyruvate was provided as a substrate. However, it was unchanged when PDH was bypassed by directly supplying the Complex I substrate NADH, or by using the Complex II substrate succinate, or using Complex IV substrate, or by providing the beta-oxidation substrate palmitoylcarnitine, neither of which require PDH for mitochondrial oxygen consumption.

CONCLUSIONS

These data demonstrate a broad mitochondrial protein remodeling, PDH inactivation and impaired pyruvate-fueled oxidative phosphorylation during SIC, and provide a molecular framework for further exploration.

Role of cellular retinol-binding protein, type 1 and retinoid homeostasis in the adult mouse heart: A multi-omic approach

The main active metabolite of Vitamin A, all-trans retinoic acid (RA), is required for proper cellular function and tissue organization. Heart development has a well-defined requirement for RA, but there is limited research on the role of RA in the adult heart. Homeostasis of RA includes regulation of membrane receptors, chaperones, enzymes, and nuclear receptors. Cellular retinol-binding protein, type 1 (CRBP1), encoded by retinol-binding protein, type 1 (Rbp1), regulates RA homeostasis by delivering vitamin A to enzymes for RA synthesis and protecting it from non-specific oxidation. In this work, a multi-omics approach was used to characterize the effect of CRBP1 loss using the Rbp1^-/- mouse. Retinoid homeostasis was disrupted in Rbp1^-/- mouse heart tissue, as seen by a 33% and 24% decrease in RA levels in the left and right ventricles, respectively, compared to wild-type mice (WT). To further inform on the effect of disrupted RA homeostasis, we conducted high-throughput targeted metabolomics. A total of 222 metabolite and metabolite combinations were analyzed, with 33 having differential abundance between Rbp1^-/- and WT hearts. Additionally, we performed global proteome profiling to further characterize the impact of CRBP1 loss in adult mouse hearts. More than 2606 unique proteins were identified, with 340 proteins having differential expression between Rbp1^-/- and WT hearts. Pathway analysis performed on metabolomic and proteomic data revealed pathways related to cellular metabolism and cardiac metabolism were the most disrupted in Rbp1^-/- mice. Together, these studies characterize the effect of CRBP1 loss and reduced RA in the adult heart.

Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data

Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the existing parallel algorithms is Ω ( m n + 2 r q p ) , where m and n are the dimensions of the theoretical database matrix, q and r are dimensions of spectra, and p is the number of processors. We further prove that communication-optimal strategy with fast-memory M = m n + 2 q r p can achieve Ω ( 2 m n q p ) but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of provable, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems.

Developmental Timing of Trauma in Women Predicts Unique Extracellular Vesicle Proteome Signatures

BACKGROUND

Exposure to traumatic events is a risk factor for negative physical and mental health outcomes. However, the underlying biological mechanisms that perpetuate these lasting effects are not known.

METHODS

We investigated the impact and timing of sexual trauma, a specific type of interpersonal violence, experienced during key developmental windows of childhood, adolescence, or adulthood on adult health outcomes and associated biomarkers, including circulating cell-free mitochondrial DNA levels and extracellular vesicles (EVs), in a predominantly Black cohort of women (N = 101).

RESULTS

Significant changes in both biomarkers examined, circulating cell-free mitochondrial DNA levels and EV proteome, were specific to developmental timing of sexual trauma. Specifically, we identified a large number of keratin-related proteins from EVs unique to the adolescent sexual trauma group. Remarkably, the majority of these keratin proteins belong to a 17q21 gene cluster, which suggests a potential local epigenetic regulatory mechanism altered by adolescent trauma to impact keratinocyte EV secretion or its protein cargo. These results, along with changes in fear-potentiated startle and skin conductance detected in these women, suggest that sexual violence experienced during the specific developmental window of adolescence may involve unique programming of the skin, the body's largest stress organ.

CONCLUSIONS

Together, these descriptive studies provide novel insight into distinct biological processes altered by trauma experienced during specific developmental windows. Future studies will be required to mechanistically link these biological processes to health outcomes.

Proteomic Changes in the Monolayer and Spheroid Melanoma Cell Models of Acquired Resistance to BRAF and MEK1/2 Inhibitors

Extracellular signal-regulated kinase-1/2 (ERK1/2) pathway inhibitors are important therapies for treating many cancers. However, acquired resistance to most protein kinase inhibitors limits their ability to provide durable responses. Approximately 50% of malignant melanomas contain activating mutations in BRAF, which promotes cancer cell survival through the direct phosphorylation of the mitogen-activated protein kinase MAPK/ERK 1/2 (MEK1/2) and the activation of ERK1/2. Although the combination treatment with BRAF and MEK1/2 inhibitors is a recommended approach to treat melanoma, the development of drug resistance remains a barrier to achieving long-term patient benefits. Few studies have compared the global proteomic changes in BRAF/MEK1/2 inhibitor-resistant melanoma cells under different growth conditions. The current study uses high-resolution label-free mass spectrometry to compare relative protein changes in BRAF/MEK1/2 inhibitor-resistant A375 melanoma cells grown as monolayers or spheroids. While approximately 66% of proteins identified were common in the monolayer and spheroid cultures, only 6.2 or 3.6% of proteins that significantly increased or decreased, respectively, were common between the drug-resistant monolayer and spheroid cells. Drug-resistant monolayers showed upregulation of ERK-independent signaling pathways, whereas drug-resistant spheroids showed primarily elevated catabolic metabolism to support oxidative phosphorylation. These studies highlight the similarities and differences between monolayer and spheroid cell models in identifying actionable targets to overcome drug resistance.

Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease

One of the potential benefits of using data-independent acquisition (DIA) proteomics protocols is that information not originally targeted by the study may be present and discovered by subsequent analysis. Herein, we reanalyzed DIA data originally recorded for global proteomic analysis to look for isomerized peptides, which occur as a result of spontaneous chemical modifications to long-lived proteins. Examination of a large set of human brain samples revealed a striking relationship between Alzheimer's disease (AD) status and isomerization of aspartic acid in a peptide from tau. Relative to controls, a surprising increase in isomer abundance was found in both autosomal dominant and sporadic AD samples. To explore potential mechanisms that might account for these observations, quantitative analysis of proteins related to isomerization repair and autophagy was performed. Differences consistent with reduced autophagic flux in AD-related samples relative to controls were found for numerous proteins, including most notably p62, a recognized indicator of autophagic inhibition. These results suggest, but do not conclusively demonstrate, that lower autophagic flux may be strongly associated with loss of function in AD brains. This study illustrates that DIA data may contain unforeseen results of interest and may be particularly useful for pilot studies investigating new research directions. In this case, a promising target for future investigations into the therapy and prevention of AD has been identified.

Dysregulated retinoic acid signaling in airway smooth muscle cells in asthma

Vitamin A deficiency has been shown to exacerbate allergic asthma. Previous studies have postulated that retinoic acid (RA), an active metabolite of vitamin A and high-affinity ligand for RA receptor (RAR), is reduced in airway inflammatory condition and contributes to multiple features of asthma including airway hyperresponsiveness and excessive accumulation of airway smooth muscle (ASM) cells. In this study, we directly quantified RA and examined the molecular basis for reduced RA levels and RA-mediated signaling in lungs and ASM cells obtained from asthmatic donors and in lungs from allergen-challenged mice. Levels of RA and retinol were significantly lower in lung tissues from asthmatic donors and house dust mite (HDM)-challenged mice compared to non-asthmatic human lungs and PBS-challenged mice, respectively. Quantification of mRNA and protein expression revealed dysregulation in the first step of RA biosynthesis consistent with reduced RA including decreased protein expression of retinol dehydrogenase (RDH)-10 and increased protein expression of RDH11 and dehydrogenase/reductase (DHRS)-4 in asthmatic lung. Proteomic profiling of non-asthmatic and asthmatic lungs also showed significant changes in the protein expression of AP-1 targets consistent with increased AP-1 activity. Further, basal RA levels and RA biosynthetic capabilities were decreased in asthmatic human ASM cells. Treatment of human ASM cells with all-trans RA (ATRA) or the RARγ-specific agonist (CD1530) resulted in the inhibition of mitogen-induced cell proliferation and AP-1-dependent transcription. These data suggest that RA metabolism is decreased in asthmatic lung and that enhancing RAR signaling using ATRA or RARγ agonists may mitigate airway remodeling associated with asthma.

Understanding the Limit of Open Search in the Identification of Peptides With Post-translational Modifications - A Simulation-Based Study

Peptide identification from tandem mass spectrometry data is a fundamental task in computational proteomics. Traditional algorithms perform well when facing unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, open search methods have been proposed to identify peptides with PTMs. While the performance of these new methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and the quality parameters of tandem mass spectrometry data as well as the number of PTMs in peptides. In this paper, we have proposed an analytical model derived from simulated data to describe the relationship between the probability of obtaining correct results and the spectrum quality as well as the number of PTMs. The proposed model is verified using 1,464,146 real experimental spectra. The consistent trend observed in both simulated data and real data reveals the necessary conditions to effectively apply open search methods. Source code of our study is available at http://bioinformatics.ust.hk/PST.html.

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an optimal feature extraction in its kernels for scoring mass spectrometry (MS)/MS spectra to increase the number of spectrum annotations with high confidence. Experimental results using publicly available data sets show that Slider can annotate slightly more spectra than the state-of-the-art methods (BoltzMatch, Res-EV, Prosit), albeit 2-10 times faster. More interestingly, Slider provides only 2-4% fewer spectrum annotations with low-resolution fragmentation information than other methods with high-resolution information. This means that Slider can exploit nearly as much information from the context of low-resolution spectrum peaks as the high-resolution fragmentation information can provide for other scoring methods. Thus, Slider can be an optimal choice for practitioners using old spectrometers with low-resolution detectors.

Effect of Radiation on the Essential Nutrient Homeostasis and Signaling of Retinoids in a Non-human Primate Model with Minimal Bone Marrow Sparing

ABSTRACT

High-dose radiation exposure results in hematopoietic (H) and gastrointestinal (GI) acute radiation syndromes (ARS) followed by delayed effects of acute radiation exposure (DEARE), which include damage to lung, heart, and GI. Whereas DEARE includes inflammation and fibrosis in multiple tissues, the molecular mechanisms contributing to inflammation and to the development of fibrosis remain incompletely understood. Reports that radiation dysregulates retinoids and proteins within the retinoid pathway indicate that radiation disrupts essential nutrient homeostasis. An active metabolite of vitamin A, retinoic acid (RA), is a master regulator of cell proliferation, differentiation, and apoptosis roles in inflammatory signaling and the development of fibrosis. As facets of inflammation and fibrosis are regulated by RA, we surveyed radiation-induced changes in retinoids as well as proteins related to and targets of the retinoid pathway in the non-human primate after high dose radiation with minimal bone marrow sparing (12 Gy PBI/BM2.5). Retinoic acid was decreased in plasma as well as in lung, heart, and jejunum over time, indicating a global disruption of RA homeostasis after IR. A number of proteins associated with fibrosis and with RA were significantly altered after radiation. Together these data indicate that a local deficiency of endogenous RA presents a permissive environment for fibrotic transformation.

Complementary Lipidomic, Proteomic, and Mass Spectrometry Imaging Approach to the Characterization of the Acute Effects of Radiation in the Non-human Primate Mesenteric Lymph Node after Partial-body Irradiation with Minimal Bone Marrow Sparing

ABSTRACT

Radiation sequelae is complex and characterized by multiple pathologies, which occur over time and nonuniformly throughout different organs. The study of the mesenteric lymph node (MLN) due to its importance in the gastrointestinal system is of particular interest. Other studies have shown an immediate post-irradiation reduction in cellularity due to the known effects of irradiation on lymphoid cell populations, but the molecular and functional mechanisms that lead to these cellular alterations remain limited. In this work, we show the use of lipidomic, proteomic, and mass spectrometry imaging in the characterization of the effects of acute radiation exposure on the MLN at different time points after ionizing radiation (IR) from 4 d to 21 d after 12 Gy partial body irradiation with 2.5% bone marrow sparing. The combined analyses showed a dysregulation of the lipid and protein composition in the MLN after IR. Protein expression was affected in numerous pathways, including pathways regulating lipids such as LXR/RXR activation and acute phase response. Lipid distribution and abundance was also affected by IR in the MLN, including an accumulation of triacylglycerides, a decrease in polyunsaturated glycerophospholipids, and changes in polyunsaturated fatty acids. Those changes were observed as early as 4 d after IR and were more pronounced for lipids with a higher concentration in the nodules and the medulla of the MLN. These results provide molecular insight into the MLN that can inform on injury mechanism in a non-human primate model of the acute radiation syndrome of the gastrointestinal tract. Those findings may contribute to the identification of therapeutic targets and the development of new medical countermeasures.

Multi-omic Analysis of Non-human Primate Heart after Partial-body Radiation with Minimal Bone Marrow Sparing

ABSTRACT

High-dose radiation exposure results in hematopoietic and gastrointestinal acute radiation syndromes followed by delayed effects of acute radiation exposure, which encompasses multiple organs, including heart, kidney, and lung. Here we sought to further characterize the natural history of radiation-induced heart injury via determination of differential protein and metabolite expression in the heart. We quantitatively profiled the proteome and metabolome of left and right ventricle from non-human primates following 12 Gy partial body irradiation with 2.5% bone marrow sparing over a time period of 3 wk. Global proteome profiling identified more than 2,200 unique proteins, with 220 and 286 in the left and right ventricles, respectively, showing significant responses across at least three time points compared to baseline levels. High-throughput targeted metabolomics analyzed a total of 229 metabolites and metabolite combinations, with 18 and 22 in the left and right ventricles, respectively, showing significant responses compared to baseline levels. Bioinformatic analysis performed on metabolomic and proteomic data revealed pathways related to inflammation, energy metabolism, and myocardial remodeling were dysregulated. Additionally, we observed dysregulation of the retinoid homeostasis pathway, including significant post-radiation decreases in retinoic acid, an active metabolite of vitamin A. Significant differences between left and right ventricles in the pathology of radiation-induced injury were identified. This multi-omic study characterizes the natural history and molecular mechanisms of radiation-induced heart injury in NHP exposed to PBI with minimal bone marrow sparing.

Acute Proteomic Changes in Non-human Primate Kidney after Partial-body Radiation with Minimal Bone Marrow Sparing

Near total body exposure to high-dose ionizing radiation results in organ-specific sequelae, including acute radiation syndromes and delayed effects of acute radiation exposure. Among these sequelae are acute kidney injury and chronic kidney injury. Reports that neither oxidative stress nor inflammation are dominant mechanisms defining radiation nephropathy inspired an unbiased, discovery-based proteomic interrogation in order to identify mechanistic pathways of injury. We quantitatively profiled the proteome of kidney from non-human primates following 12 Gy partial body irradiation with 2.5% bone marrow sparing over a time period of 3 wk. Kidney was analyzed by liquid chromatography-tandem mass spectrometry. Out of the 3,432 unique proteins that were identified, we found that 265 proteins showed significant and consistent responses across at least three time points post-irradiation, of which 230 proteins showed strong upregulation while 35 proteins showed downregulation. Bioinformatics analysis revealed significant pathway and upstream regulator perturbations post-high dose irradiation and shed light on underlying mechanisms of radiation damage. These data will be useful for a greater understanding of the molecular mechanisms of injury in well-characterized animal models of partial body irradiation with minimal bone marrow sparing. These data may be potentially useful in the future development of medical countermeasures.

A large portion of the astrocyte proteome is dedicated to perivascular endfeet, including critical components of the electron transport chain

The perivascular astrocyte endfoot is a specialized and diffusion-limited subcellular compartment that fully ensheathes the cerebral vasculature. Despite their ubiquitous presence, a detailed understanding of endfoot physiology remains elusive, in part due to a limited understanding of the proteins that distinguish the endfoot from the greater astrocyte body. Here, we developed a technique to isolate astrocyte endfeet from brain tissue, which was used to study the endfoot proteome in comparison to the astrocyte somata. In our approach, brain microvessels, which retain their endfoot processes, were isolated from mouse brain and dissociated, whereupon endfeet were recovered using an antibody-based column astrocyte isolation kit. Our findings expand the known set of proteins enriched at the endfoot from 10 to 516, which comprised more than 1/5th of the entire detected astrocyte proteome. Numerous critical electron transport chain proteins were expressed only at the endfeet, while enzymes involved in glycogen storage were distributed to the somata, indicating subcellular metabolic compartmentalization. The endfoot proteome also included numerous proteins that, while known to have important contributions to blood-brain barrier function, were not previously known to localize to the endfoot. Our findings highlight the importance of the endfoot and suggest new routes of investigation into endfoot function.

Acute Proteomic Changes in Lung after Radiation: Toward Identifying Initiating Events of Delayed Effects of Acute Radiation Exposure in Non-human Primate after Partial Body Irradiation with Minimal Bone Marrow Sparing

ABSTRACT

Radiation-induced lung injury is a delayed effect of acute radiation exposure resulting in pulmonary pneumonitis and fibrosis. Molecular mechanisms that lead to radiation-induced lung injury remain incompletely understood. Using a non-human primate model of partial body irradiation with minimal bone marrow sparing, lung was analyzed from animals irradiated with 12 Gy at timepoints every 4 d up to 21 d after irradiation and compared to non-irradiated (sham) controls. Tryptic digests of lung tissues were analyzed by liquid chromatography-tandem mass spectrometry followed by pathway analysis. Out of the 3,101 unique proteins that were identified, we found that 252 proteins showed significant and consistent responses across at least three time points post-irradiation, of which 215 proteins showed strong up-regulation while 37 proteins showed down-regulation. Canonical pathways affected by irradiation, changes in proteins that serve as upstream regulators, and proteins involved in key processes including inflammation, fibrosis, and retinoic acid signaling were identified. The proteomic profiling of lung conducted here represents an untargeted systems biology approach to identify acute molecular events in the non-human primate lung that could potentially be initiating events for radiation-induced lung injury.

High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry Data

Database peptide search algorithms deduce peptides from mass spectrometry (MS) data. There has been substantial effort in improving their computational efficiency to achieve larger and more complex systems biology studies. However, modern serial and high-performance computing (HPC) algorithms exhibit sub-optimal performance mainly due to their ineffective parallel designs (low resource utilization), and high overhead costs. We present an HPC framework, called HiCOPS, for efficient acceleration of the database peptide search algorithms on distributed-memory supercomputers. HiCOPS provides, on average, more than 10-fold improvement in speed, and superior parallel performance over several existing HPC database search software. We also formulate a mathematical model for performance analysis and optimization, and report near-optimal results for several key metrics including strong-scale efficiency, hardware utilization, load-balance, inter-process communication and I/O overheads. The core parallel design, techniques, and optimizations presented in HiCOPS are search-algorithm independent and can be extended to efficiently accelerate the existing and future algorithms and software.

Communication-avoiding micro-architecture to compute Xcorr scores for peptide identification

Database algorithms play a crucial part in systems biology studies by identifying proteins from mass spectrometry data. Many of these database search algorithms incur huge computational costs by computing similarity scores for each pair of sparse experimental spectrum and candidate theoretical spectrum vectors. Modern MS instrumentation techniques which are capable of generating high-resolution spectrometry data require comparison against an enormous search space, further emphasizing the need of efficient accelerators. Recent research has shown that the overall cost of scoring, and deducing peptides is dominated by the communication costs between different hierarchies of memory and processing units. However, these communication costs are seldom considered in accelerator-based architectures leading to inefficient DRAM accesses, and poor data-utilization due to irregular memory access patterns. In this paper, we propose a novel communication-avoiding micro-architecture to compute cross-correlation based similarity score by utilizing efficient local cache, and peptide pre-fetching to minimize DRAM accesses, and a custom-designed peptide broadcast bus to allow input reuse. An efficient bus arbitration scheme was designed, and implemented to minimize synchronization cost and exploit parallelism of processing elements. Our simulation results show that the proposed micro-architecture performs on average 24x better than a CPU implementation running on a 3.6 GHz Intel i7-4970 processor with 16GB memory.

The Functional Consequences of the Novel Ribosomal Pausing Site in SARS-CoV-2 Spike Glycoprotein RNA

The SARS-CoV-2 Spike glycoprotein (S protein) acquired a unique new 4 amino acid -PRRA- insertion sequence at amino acid residues (aa) 681-684 that forms a new furin cleavage site in S protein as well as several new glycosylation sites. We studied various statistical properties of the -PRRA- insertion at the RNA level (CCUCGGCGGGCA). The nucleotide composition and codon usage of this sequence are different from the rest of the SARS-CoV-2 genome. One of such features is two tandem CGG codons, although the CGG codon is the rarest codon in the SARS-CoV-2 genome. This suggests that the insertion sequence could cause ribosome pausing as the result of these rare codons. Due to population variants, the Nextstrain divergence measure of the CCU codon is extremely large. We cannot exclude that this divergence might affect host immune responses/effectiveness of SARS-CoV-2 vaccines, possibilities awaiting further investigation. Our experimental studies show that the expression level of original RNA sequence "wildtype" spike protein is much lower than for codon-optimized spike protein in all studied cell lines. Interestingly, the original spike sequence produces a higher titer of pseudoviral particles and a higher level of infection. Further mutagenesis experiments suggest that this dual-effect insert, comprised of a combination of overlapping translation pausing and furin sites, has allowed SARS-CoV-2 to infect its new host (human) more readily. This underlines the importance of ribosome pausing to allow efficient regulation of protein expression and also of cotranslational subdomain folding.

INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity-based scores helps to overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity-based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity-based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator. We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity-based scores when analyzing low-intensity spectra or analytes with very similar physicochemical properties that require vast search spaces. Overall, the end-to-end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

Unsupervised Reconstruction of Analyte-Specific Mass Spectra Based on Time-Domain Morphology with a Modified Cross-Correlation Approach

Concomitant species that appear at the same or very similar times in a mass-spectral analysis can clutter a spectrum because of the coexistence of many analyte-related ions (e.g., molecular ions, adducts, fragments). One method to extract ions stemming from the same origin is to exploit the chemical information encoded in the time domain, where the individual temporal appearances inside the complex structures of chronograms or chromatograms differ with respect to analytes. By grouping ions with very similar or identical time-domain structures, single-component mass spectra can be reconstructed, which are much easier to interpret and are library-searchable. While many other approaches address similar objectives through the Pearson's correlation coefficient, we explore an alternative method based on a modified cross-correlation algorithm to compute a metric that describes the degree of similarity between features inside any two ion chronograms. Furthermore, an automatic workflow was devised to be capable of categorizing thousands of mass-spectral peaks into different groups within a few seconds. This approach was tested with direct mass-spectrometric analyses as well as with a simple, fast, and poorly resolved LC-MS analysis. Single-component mass spectra were extracted in both cases and were identified based on accurate mass and a mass-spectral library search.

Characterization of SARS-CoV-2 proteins reveals Orf6 pathogenicity, subcellular localization, host interactions and attenuation by Selinexor

BACKGROUND

SARS-CoV-2 causes COVID-19 which has a widely diverse disease profile. The mechanisms underlying its pathogenicity remain unclear. We set out to identify the SARS-CoV-2 pathogenic proteins that through host interactions cause the cellular damages underlying COVID-19 symptomatology.

METHODS

We examined each of the individual SARS-CoV-2 proteins for their cytotoxicity in HEK 293 T cells and their subcellular localization in COS-7 cells. We also used Mass-Spec Affinity purification to identify the host proteins interacting with SARS-CoV-2 Orf6 protein and tested a drug that could inhibit a specific Orf6 and host protein interaction.

RESULTS

We found that Orf6, Nsp6 and Orf7a induced the highest toxicity when over-expressed in human 293 T cells. All three proteins showed membrane localization in COS-7 cells. We focused on Orf6, which was most cytotoxic and localized to the endoplasmic reticulum, autophagosome and lysosomal membranes. Proteomics revealed Orf6 interacts with nucleopore proteins (RAE1, XPO1, RANBP2 and nucleoporins). Treatment with Selinexor, an FDA-approved inhibitor for XPO1, attenuated Orf6-induced cellular toxicity in human 293 T cells.

CONCLUSIONS

Our study revealed Orf6 as a highly pathogenic protein from the SARS-CoV-2 genome, identified its key host interacting proteins, and Selinexor as a drug candidate for directly targeting Orf6 host protein interaction that leads to cytotoxicity.