Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue.

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training (ExT) and sex on its molecular landscape is not fully established. Utilizing an integrative multi-omics approach, and leveraging data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we show profound sexual dimorphism in the scWAT of sedentary rats and in the dynamic response of this tissue to ExT. Specifically, the scWAT of sedentary females displays -omic signatures related to insulin signaling and adipogenesis, whereas the scWAT of sedentary males is enriched in terms related to aerobic metabolism. These sex-specific -omic signatures are preserved or amplified with ExT. Integration of multi-omic analyses with phenotypic measures identifies molecular hubs predicted to drive sexually distinct responses to training. Overall, this study underscores the powerful impact of sex on adipose tissue biology and provides a rich resource to investigate the scWAT response to ExT.

The mitochondrial multi-omic response to exercise training across rat tissues.

Mitochondria have diverse functions critical to whole-body metabolic homeostasis. Endurance training alters mitochondrial activity, but systematic characterization of these adaptations is lacking. Here, the Molecular Transducers of Physical Activity Consortium mapped the temporal, multi-omic changes in mitochondrial analytes across 19 tissues in male and female rats trained for 1, 2, 4, or 8 weeks. Training elicited substantial changes in the adrenal gland, brown adipose, colon, heart, and skeletal muscle. The colon showed non-linear response dynamics, whereas mitochondrial pathways were downregulated in brown adipose and adrenal tissues. Protein acetylation increased in the liver, with a shift in lipid metabolism, whereas oxidative proteins increased in striated muscles. Exercise-upregulated networks were downregulated in human diabetes and cirrhosis. Knockdown of the central network protein 17-beta-hydroxysteroid dehydrogenase 10 (HSD17B10) elevated oxygen consumption, indicative of metabolic stress. We provide a multi-omic, multi-tissue, temporal atlas of the mitochondrial response to exercise training and identify candidates linked to mitochondrial dysfunction.

A Novel Liver Cancer-Selective Histone Deacetylase Inhibitor Is Effective Against Hepatocellular Carcinoma and Induces Durable Responses with Immunotherapy.

Hepatocellular cancer (HCC) progression is facilitated by gene-silencing chromatin histone hypoacetylation due to histone deacetylases (HDACs) activation. However, inhibiting HDACs - an effective treatment for lymphomas - has shown limited success in solid tumors. We report the discovery of a class of HDAC inhibitors (HDACi) that demonstrates exquisite selective cytotoxicity against human HCC cells. The lead compound STR-V-53 ( 3 ) showed favorable safety profile in mice and robustly suppressed tumor growth in orthotopic xenograft models of HCC. When combined with the anti-HCC drug sorafenib, STR-V-53 showed greater in vivo efficacy. Moreover, STR-V-53 combined with anti-PD1 therapy increased the CD8 + to regulatory T-cell (Treg) ratio and survival in an orthotopic HCC model in immunocompetent mice. This combination therapy resulted in durable responses in 40% of the mice. Collectively, our data demonstrate that the novel HDACi STR-V-53 is an effective anti-HCC agent that can induce profound responses when combined with standard immunotherapy.

Integrative Analysis of Cytokine and Lipidomics Datasets Following Mild Traumatic Brain Injury in Rats.

Traumatic brain injury (TBI) is a significant source of disability in the United States and around the world and may lead to long-lasting cognitive deficits and a decreased quality of life for patients across injury severities. Following the primary injury phase, TBI is characterized by complex secondary cascades that involve altered homeostasis and metabolism, faulty signaling, neuroinflammation, and lipid dysfunction. The objectives of the present study were to (1) assess potential correlations between lipidome and cytokine changes after closed-head mild TBI (mTBI), and (2) examine the reproducibility of our acute lipidomic profiles following TBI. Cortices from 54 Sprague Dawley male and female rats were analyzed by ultra-high-performance liquid chromatography mass spectrometry (LC-MS) in both positive and negative ionization modes and multiplex cytokine analysis after single (smTBI) or repetitive (rmTBI) closed-head impacts, or sham conditions. Tissue age was a variable, given that two cohorts (n = 26 and n = 28) were initially run a year-and-a-half apart, creating inter-batch variations. We annotated the lipidome datasets using an in-house data dictionary based on exact masses of precursor and fragment ions and removed features with statistically significant differences between sham control batches. Our results indicate that lipids with high-fold change between injury groups moderately correlate with the cytokines eotaxin, IP-10, and TNF-α. Additionally, we show a significant decrease in the pro-inflammatory markers IL-1ß and IP-10, TNF-α, and RANTES in the rmTBI samples relative to the sham control. We discuss the major challenges in correlating high dimensional lipidomic data with functional cytokine profiles and the implications for understanding the biological significance of two related but disparate analysis modes in the study of TBI, an inherently heterogeneous neurological disorder.

Spatial Lipidomics Maps Brain Alterations Associated with Mild Traumatic Brain Injury.

Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing ultrahigh resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.994 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation, oxidative stress and disrupted sodium-potassium pumps are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women.

BACKGROUND: Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention. METHODS: Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches. RESULTS: We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set. CONCLUSIONS: This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women. IMPACT: Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Comparing Brain and Blood Lipidome Changes following Single and Repetitive Mild Traumatic Brain Injury in Rats.

Traumatic brain injury (TBI) is a major health concern in the United States and globally, contributing to disability and long-term neurological problems. Lipid dysregulation after TBI is underexplored, and a better understanding of lipid turnover and degradation could point to novel biomarker candidates and therapeutic targets. Here, we investigated overlapping lipidome changes in the brain and blood using a data-driven discovery approach to understand lipid alterations in the brain and serum compartments acutely following mild TBI (mTBI) and the potential efflux of brain lipids to peripheral blood. The cortices and sera from male and female Sprague-Dawley rats were analyzed via ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in both positive and negative ion modes following single and repetitive closed head impacts. The overlapping lipids in the data sets were identified with an in-house data dictionary for investigating lipid class changes. MS-based lipid profiling revealed overall increased changes in the serum compartment, while the brain lipids primarily showed decreased changes. Interestingly, there were prominent alterations in the sphingolipid class in the brain and blood compartments after single and repetitive injury, which may suggest efflux of brain sphingolipids into the blood after TBI. Genetic algorithms were used for predictive panel selection to classify injured and control samples with high sensitivity and specificity. These overlapping lipid panels primarily mapped to the glycerophospholipid metabolism pathway with Benjamini-Hochberg adjusted q-values less than 0.05. Collectively, these results detail overlapping lipidome changes following mTBI in the brain and blood compartments, increasing our understanding of TBI-related lipid dysregulation while identifying novel biomarker candidates.

Divergent Skeletal Muscle Metabolomic Signatures of Different Exercise Training Modes Independently Predict Cardiometabolic Risk Factors.

We investigated the link between enhancement of SI (by hyperinsulinemic-euglycemic clamp) and muscle metabolites after 12 weeks of aerobic (high-intensity interval training [HIIT]), resistance training (RT), or combined training (CT) exercise in 52 lean healthy individuals. Muscle RNA sequencing revealed a significant association between SI after both HIIT and RT and the branched-chain amino acid (BCAA) metabolic pathway. Concurrently with increased expression and activity of branched-chain ketoacid dehydrogenase enzyme, many muscle amino metabolites, including BCAAs, glutamate, phenylalanine, aspartate, asparagine, methionine, and γ-aminobutyric acid, increased with HIIT, supporting the substantial impact of HIIT on amino acid metabolism. Short-chain C3 and C5 acylcarnitines were reduced in muscle with all three training modes, but unlike RT, both HIIT and CT increased tricarboxylic acid metabolites and cardiolipins, supporting greater mitochondrial activity with aerobic training. Conversely, RT and CT increased more plasma membrane phospholipids than HIIT, suggesting a resistance exercise effect on cellular membrane protection against environmental damage. Sex and age contributed modestly to the exercise-induced changes in metabolites and their association with cardiometabolic parameters. Integrated transcriptomic and metabolomic analyses suggest various clusters of genes and metabolites are involved in distinct effects of HIIT, RT, and CT. These distinct metabolic signatures of different exercise modes independently link each type of exercise training to improved SI and cardiometabolic risk. ARTICLE HIGHLIGHTS: We aimed to understand the link between skeletal muscle metabolites and cardiometabolic health after exercise training. Although aerobic, resistance, and combined exercise training each enhance muscle insulin sensitivity as well as other cardiometabolic parameters, they disparately alter amino and citric acid metabolites as well as the lipidome, linking these metabolomic changes independently to the improvement of cardiometabolic risks with each exercise training mode. These findings reveal an important layer of the unique exercise mode-dependent changes in muscle metabolism, which may eventually lead to more informed exercise prescription for improving SI.

Investigation of Four Cases of Stevens-Johnson Syndrome among Participants in a Mass Drug Administration Campaign with Sulfadoxine-Pyrimethamine and Primaquine in Haiti, 2020.

In 2018, a mass drug administration (MDA) campaign for malaria elimination was piloted in Haiti. The pilot treated 36,338 people with sulfadoxine-pyrimethamine (SP) and primaquine; no severe adverse events were detected. In 2020, another MDA campaign using the same medications was implemented to mitigate an upsurge in malaria cases during the COVID-19 pandemic. Four cases of Stevens-Johnson syndrome (SJS) were identified among the 42,249 people who took the medications. Three of these individuals required hospitalization; all survived. In addition to SP ingestion, an investigation of potential causes for increased SJS cases identified that all four cases had human leukocyte antigens A*29 and/or B*44:03, another known risk factor for SJS. Additionally, three of the four case individuals had antibodies to SARS-CoV-2, and the fourth may have been exposed around the same time. These findings raise the possibility that recent SARS-CoV-2 infection may have contributed to the increased risk for SJS associated with SP exposure during the 2020 campaign.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer.

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Cryptic Chemical Variation in a Marine Red Alga as Revealed by Nontargeted Metabolomics.

Many marine algae occupy habitats that are dark, deep, or encrusted on other organisms and hence are frequently overlooked by natural product chemists. However, exploration of less-studied organisms can lead to new opportunities for drug discovery. Genetic variation at the individual, species, genus, and population levels as well as environmental influences on gene expression enable expansion of the chemical repertoire associated with a taxonomic group, enabling natural product exploration using innovative analytical methods. A nontargeted LC-MS and 1H NMR spectroscopy-based metabolomic study of 32 collections of representatives of the calcareous red algal genus Peyssonnelia from coral reef habitats in Fiji and the Solomon Islands revealed significant correlations between natural products' chemistry, phylogeny, and biomedically relevant biological activity. Hierarchical cluster analysis (HCA) of LC-MS data in conjunction with NMR profiling and MS/MS-based molecular networking revealed the presence of at least four distinct algal chemotypes within the genus Peyssonnelia. Two Fijian collections were prioritized for further analysis, leading to the isolation of three novel sulfated triterpene glycosides with a rearranged isomalabaricane carbon skeleton, guided by the metabolomic data. The discovery of peyssobaricanosides A-C (15-17) from two Fijian Peyssonnelia collections, but not from closely related specimens collected in the Solomon Islands that were otherwise chemically and phylogenetically very similar, alludes to population-level variation in secondary metabolite production. Our study reinforces the significance of exploring unusual ecological niches and showcases marine red algae as a chemically rich treasure trove.

Antiandrogen-Equipped Histone Deacetylase Inhibitors Selectively Inhibit Androgen Receptor (AR) and AR-Splice Variant (AR-SV) in Castration-Resistant Prostate Cancer (CRPC).

BACKGROUND: Epigenetic modification influences androgen receptor (AR) activation, often resulting in prostate cancer (PCa) development and progression. Silencing histone-modifying enzymes (histone deacetylases-HDACs) either genetically or pharmacologically suppresses PCa proliferation in preclinical models of PCa; however, results from clinical studies were not encouraging. Similarly, PCa patients eventually become resistant to androgen ablation therapy (ADT). Our goal is to develop dual-acting small molecules comprising antiandrogen and HDAC-inhibiting moieties that may overcome the resistance of ADT and effectively suppress the growth of castration-resistant prostate cancer (CRPC). METHODS: Several rationally designed antiandrogen-equipped HDAC inhibitors (HDACi) were synthesized, and their efficacy on CRPC growth was examined both in vitro and in vivo. RESULTS: While screening our newly developed small molecules, we observed that SBI-46 significantly inhibited the proliferation of AR+ CRPC cells but not AR- CRPC and normal immortalized prostate epithelial cells (RWPE1) or normal kidney cells (HEK-293 and VERO). Molecular analysis confirmed that SBI-46 downregulated the expressions of both AR+ and AR-splice variants (AR-SVs) in CRPC cells. Further studies revealed the downregulation of AR downstream (PSA) events in CRPC cells. The oral administration of SBI-46 abrogated the growth of C4-2B and 22Rv1 CRPC xenograft tumors that express AR or both AR and AR-SV in xenotransplanted nude mice models. Further, immunohistochemical analysis confirmed that SBI-46 inhibits AR signaling in xenografted tumor tissues. CONCLUSION: These results demonstrate that SBI-46 is a potent agent that inhibits preclinical models of CRPC by downregulating the expressions of both AR and AR-SV. Furthermore, these results suggest that SBI-46 may be a potent compound for treating CRPC.

Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue.

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training and sex on its molecular landscape has not been fully established. Utilizing an integrative multi-omics approach with data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we identified profound sexual dimorphism in the dynamic response of rat scWAT to endurance exercise training. Despite similar cardiorespiratory improvements, only male rats reduced whole-body adiposity, scWAT adipocyte size, and total scWAT triglyceride abundance with training. Multi-omic analyses of adipose tissue integrated with phenotypic measures identified sex-specific training responses including enrichment of mTOR signaling in females, while males displayed enhanced mitochondrial ribosome biogenesis and oxidative metabolism. Overall, this study reinforces our understanding that sex impacts scWAT biology and provides a rich resource to interrogate responses of scWAT to endurance training.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study.

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer.

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer. Teaser: Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

The mitochondrial multi-omic response to exercise training across tissues.

Mitochondria are adaptable organelles with diverse cellular functions critical to whole-body metabolic homeostasis. While chronic endurance exercise training is known to alter mitochondrial activity, these adaptations have not yet been systematically characterized. Here, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) mapped the longitudinal, multi-omic changes in mitochondrial analytes across 19 tissues in male and female rats endurance trained for 1, 2, 4 or 8 weeks. Training elicited substantial changes in the adrenal gland, brown adipose, colon, heart and skeletal muscle, while we detected mild responses in the brain, lung, small intestine and testes. The colon response was characterized by non-linear dynamics that resulted in upregulation of mitochondrial function that was more prominent in females. Brown adipose and adrenal tissues were characterized by substantial downregulation of mitochondrial pathways. Training induced a previously unrecognized robust upregulation of mitochondrial protein abundance and acetylation in the liver, and a concomitant shift in lipid metabolism. The striated muscles demonstrated a highly coordinated response to increase oxidative capacity, with the majority of changes occurring in protein abundance and post-translational modifications. We identified exercise upregulated networks that are downregulated in human type 2 diabetes and liver cirrhosis. In both cases HSD17B10, a central dehydrogenase in multiple metabolic pathways and mitochondrial tRNA maturation, was the main hub. In summary, we provide a multi-omic, cross-tissue atlas of the mitochondrial response to training and identify candidates for prevention of disease-associated mitochondrial dysfunction.

Cook like a Boss Online: an adapted intervention during the COVID-19 pandemic that effectively improved children's perceived cooking competence, movement competence and wellbeing.

BACKGROUND: The COVID-19 pandemic has further exacerbated physical inactivity, poor dietary intake and reduced mental wellbeing, contributing factors to non-communicable diseases in children. Cooking interventions are proposed as having a positive influence on children's diet quality. Motor skills have been highlighted as essential for performance of cooking skills, and this movement may contribute to wellbeing. Additionally, perceived competence is a motivator for behaviour performance and thus important for understanding intervention effectiveness. Therefore, this research aimed to assess the effectiveness of an adapted virtual theory-based cooking intervention on perceived cooking competence, perceived movement competence and wellbeing. METHODS: The effective theory-driven and co-created 'Cook Like A Boss' was adapted to a virtual five day camp-styled intervention, with 248 children across the island of Ireland participating during the pandemic. Pre- and post-intervention assessments of perceived cooking competence, perceived movement competence and wellbeing using validated measurements were completed through online surveys. Bivariate Correlations, paired samples t-tests and Hierarchical multiple regression modelling was conducted using SPSS to understand the relationships between the variables and the effect of the intervention. RESULTS: 210 participants had matched survey data and were included in analysis. Significant positive correlations were shown between perceived cooking competence, perceived movement competence and wellbeing (P < 0.05). Children's perceived cooking competence (P < 0.001, medium to large effect size), perceived movement competence (P < 0.001, small to medium effect size) and wellbeing (P = 0.013, small effect size) all significantly increased from pre to post intervention. For the Hierarchical regression, the final model explained 57% of the total variance in participants' post-intervention perceived cooking competence. Each model explained a significant amount of variance (P < 0.05). Pre-intervention perceived cooking competence, wellbeing, age and perceived movement competence were significant predictors for post-intervention perceived cooking competence in the final model. CONCLUSION: The 'Cook Like A Boss' Online intervention was an adapted virtual outreach intervention. It provides initial evidence for the associations between perceived cooking competence, perceived movement and wellbeing as well as being effective in their improvement. This research shows the potential for cooking to be used as a mechanism for targeting improvements in not only diet quality but also movement and wellbeing. TRIAL REGISTRATION: NCT05395234. Retrospectively registered on 26th May 2022.

Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression.

The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70-80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R2 of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)-MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.