A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease.

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies. IMPORTANCE: Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

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.

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.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease.

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

Imaging mass cytometry reveals tissue-specific cellular immune phenotypes in the mouse knee following ACL injury.

Objective: To develop an imaging mass cytometry method for identifying complex cell phenotypes, inter-cellular interactions, and population changes in the synovium and infrapatellar fat pad (IFP) of the mouse knee following a non-invasive compression injury. Design: Fifteen male C57BL/6 mice were fed a high-fat diet for 8 weeks prior to random assignment to sham, 0.88 â€‹mm, or 1.7 â€‹mm knee compression displacement at 24 weeks of age. 2-weeks after loading, limbs were prepared for histologic and imaging mass cytometry analysis, focusing on myeloid immune cell populations in the synovium and IFP. Results: 1.7 â€‹mm compression caused anterior cruciate ligament (ACL) rupture, development of post-traumatic osteoarthritis, and a 2- to 3-fold increase in cellularity of synovium and IFP tissues compared to sham or 0.88 â€‹mm compression. Imaging mass cytometry identified 11 myeloid cell subpopulations in synovium and 7 in IFP, of which approximately half were elevated 2 weeks after ACL injury in association with the vasculature. Notably, two monocyte/macrophage subpopulations and an MHC IIhi population were elevated 2-weeks post-injury in the synovium but not IFP. Vascular and immune cell interactions were particularly diverse in the synovium, incorporating 8 unique combinations of 5 myeloid cell populations, including a monocyte/macrophage population, an MHC IIhi population, and 3 different undefined F4/80+ myeloid populations. Conclusions: Developing an imaging mass cytometry method for the mouse enabled us to identify a diverse array of synovial and IFP vascular-associated myeloid cell subpopulations. These subpopulations were differentially elevated in synovial and IFP tissues 2-weeks post injury, providing new details on tissue-specific immune regulation.

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.

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.

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.

Integrative meta-omics in Galaxy and beyond.

BACKGROUND: 'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities. RESULTS: In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO. CONCLUSION: Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

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.

Effects of Leptin and Body Weight on Inflammation and Knee Osteoarthritis Phenotypes in Female Rats.

Leptin is a proinflammatory adipokine that contributes to obesity-associated osteoarthritis (OA), especially in women. However, the extent to which leptin causes knee OA separate from the effect of increased body weight is not clear. We hypothesized that leptin is necessary to induce knee OA in obese female rats but not sufficient to induce knee OA in lean rats lacking systemic metabolic inflammation. The effect of obesity without leptin signaling was modeled by comparing female lean Zucker rats to pair fed obese Zucker rats, which possess mutant fa alleles of the leptin receptor gene. The effect of leptin without obesity was modeled in female F344BN F1 hybrid rats by systemically administering recombinant rat leptin versus saline for 23 weeks via osmotic pumps. Primary OA outcomes included cartilage histopathology and subchondral bone micro-computed tomography. Secondary outcomes included targeted cartilage proteomics, serum inflammation, and synovial fluid inflammation following an acute intra-articular challenge with interleukin-1ß (IL-1ß). Compared to lean Zucker rats, obese Zucker rats developed more severe tibial osteophytes and focal cartilage lesions in the medial tibial plateau, with modest changes in proximal tibial epiphysis trabecular bone structure. In contrast, exogenous leptin treatment, which increased plasma leptin sixfold without altering body weight, caused mild generalized cartilage fibrillation and reduced Safranin O staining compared to vehicle-treated animals. Leptin also significantly increased subchondral and trabecular bone volume and bone mineral density in the proximal tibia. Cartilage metabolic and antioxidant enzyme protein levels were substantially elevated with leptin deficiency and minimally suppressed with leptin treatment. In contrast, leptin treatment induced greater changes in systemic and local inflammatory mediators compared to leptin receptor deficiency, including reduced serum IL-6 and increased synovial fluid IL-1ß. In conclusion, rat models that separately elevate leptin or body weight develop distinct OA-associated phenotypes, revealing how obesity increases OA pathology through both leptin-dependent and independent pathways. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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.

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.

Household Flood Severity and Migration Extent in Central Java: Analysis of the Indonesian Family Life Survey.

Central Java, Indonesia, is prone to river and coastal flooding due to climate changes and geological factors. Migration is one possible adaptation to flooding, but research is limited due to lack of longitudinal spatially granular datasets on migration and metrics to identify flood-affected households. The available literature indicates social and economic barriers may limit mobility from flood prone areas. The Indonesian Family Life Survey (IFLS) provides self-reported data on household experiences with natural disasters among 1501 Central Java households followed over two waves (2007 and 2014). We examined how the severity of flooding, defined by household-level impacts captured by the IFLS (death, injury, financial loss, or relocation of a household member), influenced the extent of household movement in Central Java using a generalized ordered logit/partial proportional odds model. Households severely impacted by floods had 75% lower odds of moving farther away compared to those that did not experience floods. The most severely impacted households may be staying within flood-affected areas in Central Java. Public health, nutrition, and economic surveys should include modules focused on household experiences, impacts, and adaptations to facilitate the study of how climate changes are impacting these outcomes.

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.

Age-Associated Changes in Knee Osteoarthritis, Pain-Related Behaviors, and Dorsal Root Ganglia Immunophenotyping of Male and Female Mice.

OBJECTIVE: Osteoarthritis (OA) is a leading cause of chronic pain, yet OA pain management remains poor. Age is the strongest predictor of OA development, and mechanisms driving OA pain are unclear. We undertook this study to characterize age-associated changes in knee OA, pain-related behaviors, and dorsal root ganglion (DRG) molecular phenotypes in mice of both sexes. METHODS: Male or female C57BL/6 mice 6 or 20 months of age were evaluated for histopathologic knee OA, pain-related behaviors, and L3-L5 DRG immune characterization via flow cytometry. DRG gene expression in older mice and humans was also examined. RESULTS: Male mice at 20 months of age had worse cartilage degeneration than 6-month-old mice. Older female mouse knees showed increased cartilage degeneration but to a lesser degree than those of male mice. Older mice of both sexes had worse mechanical allodynia, knee hyperalgesia, and grip strength compared to younger mice. For both sexes, DRGs from older mice showed decreased CD45+ cells and a significant increase in F4/80+ macrophages and CD11c+ dendritic cells. Older male mouse DRGs showed increased expression of Ccl2 and Ccl5, and older female mouse DRGs showed increased Cxcr4 and Ccl3 expression compared to 6-month-old mouse DRGs, among other differentially expressed genes. Human DRG analysis from 6 individuals >80 years of age revealed elevated CCL2 in men compared to women, whereas CCL3 was higher in DRGs from women. CONCLUSION: We found that aging in male and female mice is accompanied by mild knee OA, mechanical sensitization, and changes to immune cell populations in the DRG, suggesting novel avenues for development of OA therapies.

Lung proteome and metabolome endotype in HIV-associated obstructive lung disease.

Purpose: Obstructive lung disease is increasingly common among persons with HIV, both smokers and nonsmokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease. Methods: Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status and antiretroviral treatment. 6414 proteins were measured using SomaScan® aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1 s % predicted (FEV1 % pred). Significant proteins were entered into a pathway over-representation analysis. We also constructed protein-driven endotypes using K-means clustering and performed over-representation analysis on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort. Results: After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found 575 proteins that were significantly correlated with FEV1 % pred after multiple comparisons adjustment. We identified two protein-driven endotypes, one of which was associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma. Conclusion: Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV1 % pred; however, we identified a unique protein endotype associated with insulin and apoptotic 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.

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.

Potential for surprising heat and drought events in wheat-producing regions of USA and China.

Previous analyses of the possibility of global breadbasket failures have extrapolated risks based on historical relationships between climate and yields. However, climate change is causing unprecedented events globally, which could exceed critical thresholds and reduce yields, even if there is no historical precedent. This means that we are likely underestimating climate risks to our food system. In the case of wheat, parts of the USA and China show little historical relationship between yields and temperature, but extreme temperatures are now possible that exceed critical physiological thresholds in wheat plants. UNprecedented Simulated Extreme ENsemble (UNSEEN) approaches use large ensembles to generate plausible unprecedented events, which can inform our assessment of the risk to crops. We use the UNSEEN approach with a large ensemble of archived seasonal forecasts to generate thousands of plausible events over the last 40 years and compare the results with historically observed extreme temperature and precipitation. In the US midwest, extreme temperatures that would have happened approximately 1-in-100-years in 1981 now have a return period of 1-in-6 years, while in China, the current return period is on the order of 1-in-16 years. This means that in the US midwest, extreme temperatures that used to have a 1% chance to occur in 1981 now have a 17% chance to occur in any given year, while in China, the chance increased from 1% to 6%. Record-breaking years exceeding critical thresholds for enzymes in the wheat plant are now more likely than in the past, and these record-breaking hot years are associated with extremely dry conditions in both locations. Using geopotential height and wind anomalies from the UNSEEN ensemble, we demonstrate that strong winds over land pull dry air towards the regions these during extremely hot and dry unseen events. We characterize plausible extremes from the UNSEEN ensemble that can be used to help imagine otherwise unforeseen events, including a compound event in which high impacts co-occur in both regions, informing adaptation planning in these regions. Recent temperature extremes, especially in the US midwest, are unlikely to be a good proxy for what to expect in the next few years of today's climate, and local stakeholders might perceive their risk to be lower than it really is. We find that there is a high potential for surprise in these regions if people base risk analyses solely on historical datasets.