Effect of biological sex and short-term high-fat diet on cellular proliferation, ribosomal biogenesis, and targeted protein abundance in murine articular cartilage.

Objective: To identify factors contributing to sex-differences in OA risk by evaluating the short-term effect of high-fat (HF) diet on sex-specific changes in cartilage cell proliferation, ribosomal biogenesis, and targeted extra-cellular and cellular protein abundance. Materials and methods: Knee cartilage was harvested to the subchondral bone from 20-week-old female and male C57BL/6J mice fed a low-fat or HF diet for 4 weeks and labeled with deuterium oxide for 1, 3, 5, 7, 15, or 21 days. Deuterium enrichment was quantified in isolated DNA and RNA to measure cell proliferation and ribosomal biogenesis, respectively. Protein concentration was measured using targeted high resolution accurate mass spectrometry. Results: HF diet increased the maximal deuterium incorporation into DNA from approximately 40 to 50%, albeit at a slower rate. These findings, which were magnified in female versus male mice, indicate a greater number of proliferating cells with longer half-lives under HF diet conditions. HF diet caused distinct sex-dependent effects on deuterium incorporation into RNA, increasing the fraction of ribosomes undergoing biogenesis in male mice and doubling the rate of ribosome biogenesis in female mice. HF diet altered cartilage protein abundance similarly in both sexes, except for matrilin-3, which was more abundant in HF versus LF conditions in female mice only. Overall, HF diet treatment had a stronger effect than sex on cartilage protein abundance, with most changes involving extracellular matrix and matrix-associated proteins. Conclusions: Short-term HF diet broadly altered cartilage matrix protein abundance, while sex-dependent effects primarily involved differences in cell proliferation and ribosomal biogenesis.

Novel Approach to Exploring Protease Activity and Targets in HIV-associated Obstructive Lung Disease using Combined Proteomic-Peptidomic Analysis.

Background: Obstructive lung disease (OLD) is increasingly prevalent among persons living with HIV (PLWH). However, the role of proteases in HIV-associated OLD remains unclear. Methods: We combined proteomics and peptidomics to comprehensively characterize protease activities. We combined mass spectrometry (MS) analysis on bronchoalveolar lavage fluid (BALF) peptides and proteins from PLWH with OLD (n=25) and without OLD (n=26) with a targeted Somascan aptamer-based proteomic approach to quantify individual proteases and assess their correlation with lung function. Endogenous peptidomics mapped peptides to native proteins to identify substrates of protease activity. Using the MEROPS database, we identified candidate proteases linked to peptide generation based on binding site affinities which were assessed via z-scores. We used t-tests to compare average forced expiratory volume in 1 second per predicted value (FEV1pp) between samples with and without detection of each cleaved protein and adjusted for multiple comparisons by controlling the false discovery rate (FDR). Findings: We identified 101 proteases, of which 95 had functional network associations and 22 correlated with FEV1pp. These included cathepsins, metalloproteinases (MMP), caspases and neutrophil elastase. We discovered 31 proteins subject to proteolytic cleavage that associate with FEV1pp, with the top pathways involved in small ubiquitin-like modifier mediated modification (SUMOylation). Proteases linked to protein cleavage included neutrophil elastase, granzyme, and cathepsin D. Interpretations: In HIV-associated OLD, a significant number of proteases are up-regulated, many of which are involved in protein degradation. These proteases degrade proteins involved in cell cycle and protein stability, thereby disrupting critical biological functions.

An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease.

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

Metaproteomics for Coinfections in the Upper Respiratory Tract: The Case of COVID-19.

The upper respiratory tract (URT) is home to a diverse range of microbial species. Respiratory infections disturb the microbial flora in the URT, putting people at risk of secondary infections. The potential dangers and clinical effects of bacterial and fungal coinfections with SARS-CoV-2 support the need to investigate the microbiome of the URT using clinical samples. Mass spectrometry (MS)-based metaproteomics analysis of microbial proteins is a novel approach to comprehensively assess the clinical specimens with complex microbial makeup. The coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2) is responsible for the COVID-19 pandemic resulting in a plethora of microbial coinfections impeding therapy, prognosis, and overall disease management. In this chapter, the corresponding workflows for MS-based shotgun proteomics and metaproteomic analysis are illustrated.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.