Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients.

During the COVID-19 pandemic, impaired immunity and medical interventions resulted in cases of secondary infections. The clinical difficulties and dangers associated with secondary infections in patients necessitate the exploration of their microbiome. Metaproteomics is a powerful approach to study the taxonomic composition and functional status of the microbiome under study. In this study, the mass spectrometry (MS)-based data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. We have established a robust bioinformatics workflow within the Galaxy platform, which includes (a) generation of a tailored database of the common respiratory tract pathogens, (b) database search using multiple search algorithms, and (c) verification of the detected microbial peptides. The microbial peptides detected in this study, belong to several opportunistic pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Rhizopus microsporus, and Syncephalastrum racemosum. Microbial proteins with a role in stress response, gene expression, and DNA repair were found to be upregulated in severe patients compared to negative patients. Using parallel reaction monitoring (PRM), we confirmed some of the microbial peptides in fresh clinical samples. MS-based clinical metaproteomics can serve as a powerful tool for detection and characterization of potential pathogens, which can significantly impact the diagnosis and treatment of patients.

A Galaxy of informatics resources for MS-based proteomics.

INTRODUCTION: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED: The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples.

BACKGROUND: Clinical bronchoalveolar lavage fluid (BALF) samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, mass spectrometry (MS)-based proteomic analysis of BALF is challenged by the dynamic range of protein abundance, and potential for interfering contaminants. A robust, MS-based proteomics compatible sample preparation workflow for BALF samples, including those of small and large volume, would be useful for many researchers. RESULTS: We have developed a workflow that combines high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, that is compatible with either qualitative or quantitative MS-based proteomic analysis. The workflow includes a value-added collection of endogenous peptides for peptidomic analysis of BALF samples, if desired, as well as amenability to offline semi-preparative or microscale fractionation of complex peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We demonstrate the effectiveness of this workflow on BALF samples collected from COPD patients, including for smaller sample volumes of 1-5 mL that are commonly available from the clinic. We also demonstrate the repeatability of the workflow as an indicator of its utility for quantitative proteomic studies. CONCLUSIONS: Overall, our described workflow consistently provided high quality proteins and tryptic peptides for MS analysis. It should enable researchers to apply MS-based proteomics to a wide-variety of studies focused on BALF clinical specimens.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS.

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Mass Spectrometry-Based Strategies for Assessing Human Exposure Using Hemoglobin Adductomics.

Hemoglobin (Hb) adducts are widely used in human biomonitoring due to the high abundance of hemoglobin in human blood, its reactivity toward electrophiles, and adducted protein stability for up to 120 days. In the present paper, we compared three methods of analysis of hemoglobin adducts: mass spectrometry of derivatized N-terminal Val adducts, mass spectrometry of N-terminal adducted hemoglobin peptides, and limited proteolysis mass spectrometry . Blood from human donors was incubated with a selection of contact allergens and other electrophiles, after which hemoglobin was isolated and subjected to three analysis methods. We found that the FIRE method was able to detect and reliably quantify N-terminal adducts of acrylamide, acrylic acid, glycidic acid, and 2,3-epoxypropyl phenyl ether (PGE), but it was less efficient for 2-methyleneglutaronitrile (2-MGN) and failed to detect 1-chloro-2,4-dinitrobenzene (DNCB). By contrast, bottom-up proteomics was able to determine the presence of adducts from all six electrophiles at both the N-terminus and reactive hemoglobin side chains. Limited proteolysis mass spectrometry, studied for four contact allergens (three electrophiles and a metal salt), was able to determine the presence of covalent hemoglobin adducts with one of the three electrophiles (DNCB) and coordination complexation with the nickel salt. Together, these approaches represent complementary tools in the study of the hemoglobin adductome.

An overview of technologies for MS-based proteomics-centric multi-omics.

INTRODUCTION: Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems. AREAS COVERED: Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics. EXPERT COMMENTARY: Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Proteomics uses mass spectrometry to identify as many of the proteins in a system of interest as possible, making it extremely useful in biomedical research and basic biological research. Unlike next-generation DNA/genome sequencing, proteomics directly measures the changes in gene translation in response to a disease state, injury, etc. However, when proteomics data is coupled to and examined together with other forms of 'omics' data, such as transcriptomics, genomics, and metabolomics, a full biological picture emerges that can demonstrate the underlying regulatory networks of living systems and how they respond to positive and negative stimuli. This integration is called multi-omics and represents a powerful paradigm shift in systems biology. To be fully compatible with other 'omics datasets, proteomics must be as complete and accurate as possible; in addition, the task of integrating multiple different kinds of datasets can be daunting to novice researchers. With this in mind, we reviewed in this manuscript the technologies that allow for the generation of the best possible proteomics for multi-omics analysis, in addition to the software tools needed to integrate proteomics data with other 'omics data. Together, we believe this review will enable other researchers to begin applying multi-omics approaches to answer their research questions.

Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms.

In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.

Updates on metaQuantome Software for Quantitative Metaproteomics.

metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.

Evaluation of the potential of Pap test fluid and cervical swabs to serve as clinical diagnostic biospecimens for the detection of ovarian cancer by mass spectrometry-based proteomics.

BACKGROUND: The purpose of this study was to determine whether the residual fixative from a liquid-based Pap test or a swab of the cervix contained proteins that were also found in the primary tumor of a woman with high grade serous ovarian cancer. This study is the first step in determining the feasibility of using the liquid-based Pap test or a cervical swab for the detection of ovarian cancer protein biomarkers. METHODS: Proteins were concentrated by acetone precipitation from the cell-free supernatant of the liquid-based Pap test fixative or eluted from the cervical swab. Protein was also extracted from the patient's tumor tissue. The protein samples were digested into peptides with trypsin, then the peptides were run on 2D-liquid chromatography mass spectrometry (2D-LCMS). The data was searched against a human protein database for the identification of peptides and proteins in each biospecimen. The proteins that were identified were classified for cellular localization and molecular function by bioinformatics integration. RESULTS: We identified almost 5000 proteins total in the three matched biospecimens. More than 2000 proteins were expressed in each of the three biospecimens, including several known ovarian cancer biomarkers such as CA125, HE4, and mesothelin. By Scaffold analysis of the protein Gene Ontology categories and functional analysis using PANTHER, the proteins were classified by cellular localization and molecular function, demonstrating that the Pap test fluid and cervical swab proteins are similar to each other, and also to the tumor extract. CONCLUSIONS: Our results suggest that Pap test fixatives and cervical swabs are a rich source of tumor-specific biomarkers for ovarian cancer, which could be developed as a test for ovarian cancer detection.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19).

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. METHODS: In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). RESULTS: Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. CONCLUSION: We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

Novel 4-Hydroxybenzyl Adducts in Human Hemoglobin: Structures and Mechanisms of Formation.

Humans are exposed to large numbers of electrophiles from their diet, the environment, and endogenous physiological processes. Adducts formed at the N-terminal valine of hemoglobin are often used as biomarkers of human exposure to electrophilic compounds. We previously reported the formation of hemoglobin N-terminal valine adducts (added mass, 106.042 Da) in the blood of human smokers and nonsmokers and identified their structure as 4-hydroxybenzyl-Val. In the present work, mass spectrometry-based proteomics was utilized to identify additional sites for 4-hydroxybenzyl adduct formation at internal nucleophilic amino acid side chains within hemoglobin. Hemoglobin isolated from human blood was treated with para-quinone methide (para-QM) followed by global nanoLC-MS/MS and targeted nanoLC-MS/MS to identify amino acid residues containing the 4-hydroxybenzyl modification. Our experiments revealed the formation of 4-hydroxybenzyl adducts at the αHis20, αTyr24, αTyr42, αHis45, ßSer72, ßThr84, ßThr87, ßSer89, ßHis92, ßCys93, ßCys112, ßThr123, and ßHis143 residues (in addition to N-terminal valine) through characteristic MS/MS spectra. These amino acid side chains had variable reactivity toward para-QM with αHis45, αTyr42, ßCys93, ßHis92, and ßSer72 forming the largest numbers of adducts upon exposure to para-QM. Two additional mechanisms for formation of 4-hydroxybenzyl adducts in humans were investigated: exposure to 4-hydroxybenzaldehyde (4-HBA) followed by reduction and UV-mediated reactions of hemoglobin with tyrosine. Exposure of hemoglobin to a 5-fold molar excess of 4-HBA followed by reduction with sodium cyanoborohydride produced 4-hydroxybenzyl adducts at several amino acid side chains of which αHis20, αTyr24, αTyr42, αHis45, ßSer44, ßThr84, and ßHis92 were verified in targeted mass spectrometry experiments. Similarly, exposure of human blood to ultraviolet radiation produced 4-hydroxybenzyl adducts at αHis20, αTyr24, αTyr42, αHis45, ßSer44, ßThr84, and ßSer89. Overall, our results reveal that 4-hydroxybenzyl adducts form at multiple nucleophilic sites of hemoglobin and that para-QM is the most likely source of these adducts in humans.

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes.

Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes.Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark metaQuantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset.In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization-based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication-quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.

Application of Proteomics in Sarcoidosis.

Sarcoidosis is a multisystem disease with heterogeneity in manifestations and outcomes. System-level studies leveraging "omics" technologies are expected to define mechanisms contributing to sarcoidosis heterogeneous manifestations and course. With improvements in mass spectrometry (MS) and bioinformatics, it is possible to study protein abundance for a large number of proteins simultaneously. Contemporary fast-scanning MS enables the acquisition of spectral data for deep coverage of the proteins with data-dependent or data-independent acquisition MS modes. Studies leveraging MS-based proteomics in sarcoidosis have characterized BAL fluid (BALF), alveolar macrophages, plasma, and exosomes. These studies identified several differentially expressed proteins, including protocadherin-2 precursor, annexin A2, pulmonary surfactant A2, complement factors C3, vitamin-D-binding protein, cystatin B, and amyloid P, comparing subjects with sarcoidosis with control subjects. Other studies identified ceruloplasmin, complement factors B, C3, and 1, and others with differential abundance in sarcoidosis compared with other interstitial lung diseases. Using quantitative proteomics, most recent studies found differences in PI3K/Akt/mTOR, MAP kinase, pluripotency-associated transcriptional factor, and hypoxia response pathways. Other studies identified increased clathrin-mediated endocytosis and Fcγ receptor-mediated phagocytosis pathways in sarcoidosis alveolar macrophages. Although studies in mixed BAL and blood cells or plasma are limited, some of the changes in lung compartment are detected in the blood cells and plasma. We review proteomics for sarcoidosis with a focus on the existing MS data acquisition strategies, bioinformatics for spectral data analysis to infer protein identity and quantity, unique aspects about biospecimen collection and processing for lung-related proteomics, and proteomics studies conducted to date in sarcoidosis.

A Sectioning and Database Enrichment Approach for Improved Peptide Spectrum Matching in Large, Genome-Guided Protein Sequence Databases.

Multiomics approaches focused on mass spectrometry (MS)-based data, such as metaproteomics, utilize genomic and/or transcriptomic sequencing data to generate a comprehensive protein sequence database. These databases can be very large, containing millions of sequences, which reduces the sensitivity of matching tandem mass spectrometry (MS/MS) data to sequences to generate peptide spectrum matches (PSMs). Here, we describe and evaluate a sectioning method for generating an enriched database for those protein sequences that are most likely present in the sample. Our evaluation demonstrates how this method helps to increase the sensitivity of PSMs while maintaining acceptable false discovery rate statistics-offering a flexible alternative to traditional large database searching, as well as previously described two-step database searching methods for large sequence database applications. Furthermore, implementation in the Galaxy platform provides access to an automated and customizable workflow for carrying out the method. Additionally, the results of this study provide valuable insights into the advantages and limitations offered by available methods aimed at addressing challenges of genome-guided, large database applications in proteomics. Relevant raw data has been made available at https://zenodo.org/ using data set identifier "3754789" and https://arcticdata.io/catalog using data set identifier "A2VX06340".

Challenges in Peptide-Spectrum Matching: A Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits.

Workflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide-spectrum matches (PSMs). To improve the robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a liquid chromatography-mass spectrometry (LC-MS) run, decoy PSMs, and length of the peptide. We created a postanalysis Bayesian confidence measure based on score and PMD, called PMD-false discovery rate (FDR). We tested PMD-FDR on four data sets across three types of MS-based proteomics projects: standard (single organism; reference database), proteogenomics (single organism; customized genomic-based database plus reference), and metaproteomics (microorganism community; customized conglomerate database). On a ground-truth data set and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits). PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. Going forward, PMD-FDR should provide detection of high scoring but likely false-hits, aiding applications that rely heavily on accurate PSMs, such as proteogenomics and metaproteomics.

SH3BP4 is a negative regulator of amino acid-Rag GTPase-mTORC1 signaling.

Amino acids stimulate cell growth and suppress autophagy through activation of mTORC1. The activation of mTORC1 by amino acids is mediated by Rag guanosine triphosphatase (GTPase) heterodimers on the lysosome. The molecular mechanism by which amino acids regulate the Rag GTPase heterodimers remains to be elucidated. Here, we identify SH3 domain-binding protein 4 (SH3BP4) as a binding protein and a negative regulator of Rag GTPase complex. SH3BP4 binds to the inactive Rag GTPase complex through its Src homology 3 (SH3) domain under conditions of amino acid starvation and inhibits the formation of active Rag GTPase complex. As a consequence, the binding abrogates the interaction of mTORC1 with Rag GTPase complex and the recruitment of mTORC1 to the lysosome, thus inhibiting amino acid-induced mTORC1 activation and cell growth and promoting autophagy. These results demonstrate that SH3BP4 is a negative regulator of the Rag GTPase complex and amino acid-dependent mTORC1 signaling.

Distinct Cancer-Promoting Stromal Gene Expression Depending on Lung Function.

Rationale: Chronic obstructive pulmonary disease is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that lung stromal cells activate pathological gene expression programs that support oncogenesis.Objectives: To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer.Methods: The study included subjects with and without lung cancer across a spectrum of lung-function values. We conducted a multiomics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome.Measurements and Main Results: Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation modulating protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms that drove these cancer-associated translation programs differed based on lung function. In subjects with normal to mildly impaired lung function, the mammalian target of rapamycin (mTOR) pathway served as an upstream driver, whereas in subjects with severe airflow obstruction, pathways downstream of pathological extracellular matrix emerged. Consistent with a role during cancer initiation, both the mTOR and extracellular matrix gene expression programs paralleled the activation of previously identified procancer secretomes. Furthermore, an in situ examination of lung tissue showed that stromal fibroblasts expressed cancer-associated proteins from two procancer secretomes: one that included IL-6 (in cases of mild or no airflow obstruction), and one that included BMP1 (in cases of severe airflow obstruction).Conclusions: Two distinct stromal gene expression programs that promote cancer initiation are activated in patients with lung cancer depending on lung function. Our work has implications both for screening strategies and for personalized approaches to cancer treatment.

Update on the moFF Algorithm for Label-Free Quantitative Proteomics.

moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF.

QuanTP: A Software Resource for Quantitative Proteo-Transcriptomic Comparative Data Analysis and Informatics.

Next-generation sequencing technologies, coupled to advances in mass-spectrometry-based proteomics, have facilitated system-wide quantitative profiling of expressed mRNA transcripts and proteins. Proteo-transcriptomic analysis compares the relative abundance levels of transcripts and their corresponding proteins, illuminating discordant gene product responses to perturbations. These results reveal potential post-transcriptional regulation, providing researchers with important new insights into underlying biological and pathological disease mechanisms. To carry out proteo-transcriptomic analysis, researchers require software that statistically determines transcript-protein abundance correlation levels and provides results visualization and interpretation functionality, ideally within a flexible, user-friendly platform. As a solution, we have developed the QuanTP software within the Galaxy platform. The software offers a suite of tools and functionalities critical for proteo-transcriptomics, including statistical algorithms for assessing the correlation between single transcript-protein pairs as well as across two cohorts, outlier identification and clustering, along with a diverse set of results visualizations. It is compatible with analyses of results from single experiment data or from a two-cohort comparison of aggregated replicate experiments. The tool is available in the Galaxy Tool Shed through a cloud-based instance and a Docker container. In all, QuanTP provides an accessible and effective software resource, which should enable new multiomic discoveries from quantitative proteo-transcriptomic data sets.