Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF.

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome.

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Open Modification Searching of SARS-CoV-2-Human Protein Interaction Data Reveals Novel Viral Modification Sites.

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification-mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus-host protein-protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

Proteomic Assessment of C57BL/6 Hippocampi after Non-Selective Pharmacological Inhibition of Nitric Oxide Synthase Activity: Implications of Seizure-like Neuronal Hyperexcitability Followed by Tauopathy.

Nitric oxide (NO) is a small gaseous signaling molecule responsible for maintaining homeostasis in a myriad of tissues and molecular pathways in neurology and the cardiovasculature. In recent years, there has been increasing interest in the potential interaction between arterial stiffness (AS), an independent cardiovascular risk factor, and neurodegenerative syndromes given increasingly epidemiological study reports. For this reason, we previously investigated the mechanistic convergence between AS and neurodegeneration via the progressive non-selective inhibition of all nitric oxide synthase (NOS) isoforms with N(G)-nitro-L-arginine methyl ester (L-NAME) in C57BL/6 mice. Our previous results showed progressively increased AS in vivo and impaired visuospatial learning and memory in L-NAME-treated C57BL/6 mice. In the current study, we sought to further investigate the progressive molecular signatures in hippocampal tissue via LC-MS/MS proteomic analysis. Our data implicate mitochondrial dysfunction due to progressive L-NAME treatment. Two weeks of L-NAME treatment implicates altered G-protein-coupled-receptor signaling in the nerve synapse and associated presence of seizures and altered emotional behavior. Furthermore, molecular signatures implicate the cerebral presence of seizure-related hyperexcitability after short-term (8 weeks) treatment followed by ribosomal dysfunction and tauopathy after long-term (16 weeks) treatment.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2.

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

Diagnostic accuracy of the Barr and Blethyn radiological scoring systems for childhood constipation assessed using colonic transit time as the gold standard.

BACKGROUND: Constipation is a common childhood symptom and abdominal radiography is advocated in diagnosis and management. OBJECTIVE: To assess the reproducibility and diagnostic accuracy of the Barr and Blethyn systems for quantifying constipation on abdominal radiographs in children. MATERIALS AND METHODS: Radiographs were scored by three observers of increasing radiological experience (student, junior doctor, consultant). Abdominal radiographs produced during measurement of colonic transit time (CTT) were classified as constipated or normal based on the value of the transit time, and were scored using both systems by observers blinded to the CTT. Abdominal radiographs obtained in children for reasons other than constipation were classed as normal and similarly scored. Reproducibility was measured using the kappa statistic. Diagnostic accuracy was measured using the area under the curve (AUC) for the receiver operator characteristic (ROC) curve. RESULTS: Using either system, scores were higher for constipated children (P<0.01). The consultant produced higher scores than the other observers (P<0.01). Interobserver reproducibility was moderate with the best kappa value only 0.48. The best correlation between score and CTT was 0.51 (junior doctor scores). Diagnostic accuracy of the scores was only moderate, with the largest AUC for a ROC curve of 0.84 for the consultant using the Barr score. CONCLUSIONS: Scoring of abdominal radiographs in the assessment of childhood constipation should be abandoned because it is dependent on the experience of the observer, is poorly reproducible, and does not accurately discriminate between constipated children and children without constipation.

Patient outcome after treatment in a community-based crisis stabilization unit.

Community-based residential treatment for acute psychiatric crisis has been proposed as an alternative to inpatient hospitalization, but there is a dearth of adequate outcome studies. We examined naturalistic symptomatic and treatment outcomes in patients admitted to a residential crisis treatment program. The 24-item Brief Psychiatric Rating Scale score dropped from moderately ill (40.5 +/- SD 8.25 points) on admission to mildly ill at discharge (28.7 +/- 11.37 points, t = 10.02, P < 0.0001). Beck's Depression Inventory also improved greatly, from a significant level of depression of 29.5 +/- 11.41 points on admission, to a nearly euthymic level of 10.1 +/- 8.60 points at discharge (a difference of 19.4 +/- 12.10 points, t = 12.5, P < 0.0001). The current study is limited by the lack of a matched comparison group of hospitalized patients. Nonetheless, community-based crisis stabilization units appear to be cost-effective alternatives to inpatient hospitalization for selected patients.