STAT1 modulates tissue wasting or overgrowth downstream from PDGFRß.

Platelet-derived growth factor (PDGF) acts through two conserved receptor tyrosine kinases: PDGFRα and PDGFRß. Gain-of-function mutations in human PDGFRB have been linked recently to genetic diseases characterized by connective tissue wasting (Penttinen syndrome) or overgrowth (Kosaki overgrowth syndrome), but it is unclear whether PDGFRB mutations alone are responsible. Mice with constitutive PDGFRß signaling caused by a kinase domain mutation (D849V) develop lethal autoinflammation. Here we used a genetic approach to investigate the mechanism of autoinflammation in Pdgfrb+/D849V mice and test the hypothesis that signal transducer and activator of transcription 1 (STAT1) mediates this phenotype. We show that Pdgfrb+/D849V mice with Stat1 knockout (Stat1-/-Pdgfrb+/D849V ) are rescued from autoinflammation and have improved life span compared with Stat1+/-Pdgfrb+/D849V mice. Furthermore, PDGFRß-STAT1 signaling suppresses PDGFRß itself. Thus, Stat1-/-Pdgfrb+/D849V fibroblasts exhibit increased PDGFRß signaling, and mice develop progressive overgrowth, a distinct phenotype from the wasting seen in Stat1+/-Pdgfrb+/D849V mice. Deletion of interferon receptors (Ifnar1 or Ifngr1) does not rescue wasting in Pdgfrb+/D849V mice, indicating that interferons are not required for autoinflammation. These results provide functional evidence that elevated PDGFRß signaling causes tissue wasting or overgrowth reminiscent of human genetic syndromes and that the STAT1 pathway is a crucial modulator of this phenotypic spectrum.

Sex-specific effects of injury and beta-adrenergic activation on metabolic and inflammatory mediators in a murine model of post-traumatic osteoarthritis.

OBJECTIVE: Metabolic processes are intricately linked to the resolution of innate inflammation and tissue repair, two critical steps for treating post-traumatic osteoarthritis (PTOA). Based on lipolytic and immunoregulatory actions of norepinephrine, we hypothesized that intra-articular ß-adrenergic receptor (ßAR) stimulation would suppress PTOA-associated inflammation in the infrapatellar fat pad (IFP) and synovium. DESIGN: We used the ßAR agonist isoproterenol to perturb intra-articular metabolism 3.5 weeks after applying a non-invasive single-load compression injury to knees of 12-week-old male and female mice. We examined the acute effects of intra-articular isoproterenol treatment relative to saline on IFP histology, multiplex gene expression of synovium-IFP tissue, synovial fluid metabolomics, and mechanical allodynia. RESULTS: Injured knees developed PTOA pathology characterized by heterotopic ossification, articular cartilage loss, and IFP atrophy and fibrosis. Isoproterenol suppressed the upregulation of pro-fibrotic genes and downregulated the expression of adipose genes and pro-inflammatory genes (Adam17, Cd14, Icam1, Csf1r, and Casp1) in injured joints of female (but not male) mice. Analysis of published single-cell RNA-seq data identified elevated catecholamine-associated gene expression in resident-like synovial-IFP macrophages after injury. Injury substantially altered synovial fluid metabolites by increasing amino acids, peptides, sphingolipids, phospholipids, bile acids, and dicarboxylic acids, but these changes were not appreciably altered by isoproterenol. Intra-articular injection of either isoproterenol or saline increased mechanical allodynia in female mice, whereas neither substance affected male mice. CONCLUSIONS: Acute ßAR activation altered synovial-IFP transcription in a sex and injury-dependent manner, suggesting that women with PTOA may be more sensitive than men to treatments targeting sympathetic neural signaling pathways.

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.

Families' experiences accessing care after genomic sequencing in the pediatric cancer context: "It's just been a big juggle".

Access to genomic sequencing (GS) and resulting recommendations have not been well described in pediatric oncology. GS results may provide a cancer predisposition syndrome (CPS) diagnosis that warrants screening and specialist visits beyond cancer treatment, including testing or surveillance for family members. The Texas KidsCanSeq (KCS) Study evaluated implementation of GS in a diverse pediatric oncology population. We conducted semi-structured interviews (n = 20) to explore experiences of KCS patients' families around learning about a CPS diagnosis and following up on recommended care. We used qualitative content analysis to develop themes and subthemes across families' descriptions of their experiences accessing care and to understand which factors presented barriers and/or facilitators. We found participants had difficulty differentiating which follow-up care recommendations were made for their child's current cancer treatment versus the CPS. In families' access to follow-up care for CPS, organizational factors were crucial: travel time and distance were common hardships, while coordination of care to streamline multiple appointments with different providers helped facilitate CPS care. Financial factors also impacted families' access to CPS-related follow-up care: having financial assistance and insurance were facilitators for families, while costs and lack of insurance posed as barriers for patients who lost coverage during transitions from pediatric to adult care, and for adult family members who had no coverage. Factors related to beliefs and perceptions, specifically perceiving the risk as less salient to them and feeling overwhelmed with the patient's cancer care, presented barriers to follow-up care primarily for family members. Regarding social factors, competing life priorities made it difficult for families to access follow-up care, though having community support alleviated these barriers. We suggest interventions to improve coordination of cancer treatment and CPS-related care and adherence to surveillance protocols for families as children age, such as care navigators and integrating longitudinal genetic counseling into hereditary cancer centers.

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.

Improving school lunch menus with multi-objective optimisation: nutrition, cost, consumption and environmental impacts.

OBJECTIVE: To support school foods programmes by evaluating the relationship between nutritional quality, cost, student consumption and the environmental impacts of menus. DESIGN: Using linear programming and data from previously served menu items, the relationships between the nutritional quality, cost, student consumption and the environmental impacts of lunch menus were investigated. Optimised lunch menus with the maximum potential student consumption and nutritional quality and lowest costs and environmental impacts were developed and compared with previously served menus (baseline). SETTING: Boston Public Schools (BPS), Boston Massachusetts, USA. PARTICIPANTS: Menu items served on the 2018-2019 BPS lunch menu (n 142). RESULTS: Using single-objective models, trade-offs were observed between most interests, but the use of multi-objective models minimised these trade-offs. Compared with the current weekly menus offered, multi-objective models increased potential caloric intake by up to 27 % and Healthy Eating Index scores by up to 19 % and reduced costs and environmental impacts by up to 13 % and 71 %, respectively. Improvements were made by reducing the frequency of beef and cheese entrées and increasing the frequency of fish and legume entrées on weekly menus. CONCLUSIONS: This work can be extrapolated to monthly menus to provide further direction for school districts, and the methods can be employed with different recipes and constraints. Future research should test the implementation of optimised menus in schools and consider the broader implications of implementation.

Circulating tumor DNA sequencing of pediatric solid and brain tumor patients: An institutional feasibility study.

The potential of circulating tumor DNA (ctDNA) analysis to serve as a real-time "liquid biopsy" for children with central nervous system (CNS) and non-CNS solid tumors remains to be fully elucidated. We conducted a study to investigate the feasibility and potential clinical utility of ctDNA sequencing in pediatric patients enrolled on an institutional clinical genomics trial. A total of 240 patients had tumor DNA profiling performed during the study period. Plasma samples were collected at study enrollment from 217 patients and then longitudinally from a subset of patients. Successful cell-free DNA extraction and quantification occurred in 216 of 217 (99.5%) of these initial samples. Twenty-four patients were identified whose tumors harbored 30 unique variants that were potentially detectable on a commercially-available ctDNA panel. Twenty of these 30 mutations (67%) were successfully detected by next-generation sequencing in the ctDNA from at least one plasma sample. The rate of ctDNA mutation detection was higher in patients with non-CNS solid tumors (7/9, 78%) compared to those with CNS tumors (9/15, 60%). A higher ctDNA mutation detection rate was also observed in patients with metastatic disease (9/10, 90%) compared to non-metastatic disease (7/14, 50%), although tumor-specific variants were detected in a few patients in the absence of radiographic evidence of disease. This study illustrates the feasibility of incorporating longitudinal ctDNA analysis into the management of relapsed or refractory patients with childhood CNS or non-CNS solid tumors.

RELATIONSHIP BETWEEN REPRODUCTIVE AND BONE BIOMARKERS AND OSTEOARTHRITIS IN ZOO ASIAN (ELEPHAS MAXIMUS) AND AFRICAN (LOXODONTA AFRICANA) ELEPHANTS.

Osteoarthritis (OA) is common in zoo Asian (Elephas maximus) and African (Loxodonta africana) elephants. This study investigated the relationship between confirmed or suspected OA with ovarian cyclicity, gonadotropins, progestagens, luteinizing hormone (LH), follicle-stimulating hormone (FSH), and collagen type I (CTX-I) in zoo elephants. In Asian elephants, odds of having confirmed or suspected OA decreased with cycling (OR = 0.22, P = 0.016; OR = 0.29, P = 0.020, respectively), however, not when adjusted for age (odds ratio [OR] = 0.31, P = 0.112; OR = 0.58, P = 0.369, respectively). In African elephants, none of the models between confirmed OA and cycling status were significant (P > 0.060), while the odds of having suspected OA decreased with cycling (OR = 0.12, P = 0.001), even after adjusting for age (OR = 0.15, P = 0.005). Progestagens (Asian elephants P > 0.096; African elephants P > 0.415), LH (Asian P > 0.129; African P > 0.359), and FSH (Asian P > 0.738; African P > 0.231) did not differ with confirmed or suspected OA status, unadjusted. CTX-I concentrations were not related to OA status (P > 0.655). This study concluded hormonal changes may not have a strong impact on OA, so additional investigation into other serologic biomarkers is warranted.

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.

PFKFB3-dependent glucose metabolism regulates 3T3-L1 adipocyte development.

Proliferation and differentiation of preadipocytes, and other cell types, is accompanied by an increase in glucose uptake. Previous work showed that a pulse of high glucose was required during the first 3 days of differentiation in vitro, but was not required after that. The specific glucose metabolism pathways required for adipocyte differentiation are unknown. Herein, we used 3T3-L1 adipocytes as a model system to study glucose metabolism and expansion of the adipocyte metabolome during the first 3 days of differentiation. Our primary outcome measures were GLUT4 and adiponectin, key proteins associated with healthy adipocytes. Using complete media with 0 or 5 mM glucose, we distinguished between developmental features that were dependent on the differentiation cocktail of dexamethasone, insulin, and isobutylmethylxanthine alone or the cocktail plus glucose. Cocktail alone was sufficient to activate the capacity for 2-deoxglucose uptake and glycolysis, but was unable to support the expression of GLUT4 and adiponectin in mature adipocytes. In contrast, 5 mM glucose in the media promoted a transient increase in glucose uptake and glycolysis as well as a significant expansion of the adipocyte metabolome and proteome. Using genetic and pharmacologic approaches, we found that the positive effects of 5 mM glucose on adipocyte differentiation were specifically due to increased expression of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), a key regulator of glycolysis and the ancillary glucose metabolic pathways. Our data reveal a critical role for PFKFB3 activity in regulating the cellular metabolic remodeling required for adipocyte differentiation and maturation.

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.