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.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics.

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation.

Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.

Enzymatic Synthesis Assisted Discovery of Proline-Rich Macrocyclic Peptides in Marine Sponges.

Proline-rich macrocyclic peptides (PRMPs) are natural products present in geographically and phylogenetically dispersed marine sponges. The large diversity and low abundance of PRMPs in sponge metabolomes precludes isolation and structure elucidation of each individual PRMP congener. Here, using standards developed via biomimetic enzymatic synthesis of PRMPs, a mass spectrometry-based workflow to sequence PRMPs was developed and validated to reveal that the diversity of PRMPs in marine sponges is much greater than that has been realized by natural product isolation-based strategies. Findings are placed in the context of diversity-oriented transamidative macrocyclization of peptide substrates in sponge holobionts.

Chemical encoding of risk perception and predator detection among estuarine invertebrates.

An effective strategy for prey to survive in habitats rich in predators is to avoid being noticed. Thus, prey are under selection pressure to recognize predators and adjust their behavior, which can impact numerous community-wide interactions. Many animals in murky and turbulent aquatic environments rely on waterborne chemical cues. Previous research showed that the mud crab, Panopeus herbstii, recognizes the predatory blue crab, Callinectus sapidus, via a cue in blue crab urine. This cue is strongest if blue crabs recently preyed upon mud crabs. Subsequently, mud crabs suppress their foraging activity, reducing predation by blue crabs. Using NMR spectroscopy- and mass spectrometry-based metabolomics, chemical variation in urine from blue crabs fed different diets was related to prey behavior. We identified the urinary metabolites trigonelline and homarine as components of the cue that mud crabs use to detect blue crabs, with concentrations of each metabolite dependent on the blue crab's diet. At concentrations found naturally in blue crab urine, trigonelline and homarine, alone as well as in a mixture, alerted mud crabs to the presence of blue crabs, leading to decreased foraging by mud crabs. Risk perception by waterborne cues has been widely observed by ecologists, but the molecular nature of these cues has not been previously identified. Metabolomics provides an opportunity to study waterborne cues where other approaches have historically failed, advancing our understanding of the chemical nature of a wide range of ecological interactions.

Early Detection of Cystic Fibrosis Acute Pulmonary Exacerbations by Exhaled Breath Condensate Metabolomics.

The most common cause of death in cystic fibrosis (CF) patients is progressive lung function decline, which is punctuated by acute pulmonary exacerbations (APEs). A major challenge is to discover biomarkers for detecting an oncoming APE and allow for pre-emptive clinical interventions. Metabolic profiling of exhaled breath condensate (EBC) samples collected from CF patients before, during, and after APEs and under stable conditions (n = 210) was performed using ultraperformance liquid chromatography (UPLC) coupled to Orbitrap mass spectrometry (MS). Negative ion mode MS data showed that classification between metabolic profiles from "pre-APE" (pending APE before the CF patient had any signs of illness) and stable CF samples was possible with good sensitivities (85.7 and 89.5%), specificities (88.4 and 84.1%), and accuracies (87.7 and 85.7%) for pediatric and adult patients, respectively. Improved classification performance was achieved by combining positive with negative ion mode data. Discriminant metabolites included two potential biomarkers identified in a previous pilot study: lactic acid and 4-hydroxycyclohexylcarboxylic acid. Some of the discriminant metabolites had microbial origins, indicating a possible role of bacterial metabolism in APE progression. The results show promise for detecting an oncoming APE using EBC metabolites, thus permitting early intervention to abort such an event.

Lipidome signatures of metastasis in a transgenic mouse model of sonic hedgehog medulloblastoma.

Medulloblastoma (MB), the most common malignant pediatric brain tumor, has high propensity to metastasize. Currently, the standard treatment for MB patients includes radiation therapy administered to the entire brain and spine for the purpose of treating or preventing against metastasis. Due to this aggressive treatment, the majority of long-term survivors will be left with permanent and debilitating neurocognitive impairment, for the 30-40% patients that fail to respond to treatment, all will relapse with terminal metastatic disease. An understanding of the underlying biology that drives MB metastasis is lacking, and is critically needed in order to develop targeted therapeutics for its prevention. To examine the metastatic biology of sonic hedgehog (SHH) MB, the human MB subgroup with the worst clinical outcome in children, we first generated a robust SmoA1-Math-GFP mouse model that reliably reproduces human SHH MB whereby metastases can be visualized under fluorescence microscopy. Lipidome alterations associated with metastasis were then investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) under positive ionization mode to primary tumor samples collected from mice without (n = 18) and with (n = 7) metastasis. Thirty-four discriminant lipids associated with SHH MB metastasis were successfully annotated, including ceramides (Cers), sphingomyelins (SMs), triacylglycerols (TGs), diacylglycerols (DGs), phosphatidylcholines (PCs), and phosphatidic acids (PAs). This study provides deeper insights into dysregulations of lipid metabolism associated with SHH MB metastatic progression, and thus serves as a guide toward novel targeted therapies.

Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model.

High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70-80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy.

Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

International Ring Trial of a High Resolution Targeted Metabolomics and Lipidomics Platform for Serum and Plasma Analysis.

A challenge facing metabolomics in the analysis of large human cohorts is the cross-laboratory comparability of quantitative metabolomics measurements. In this study, 14 laboratories analyzed various blood specimens using a common experimental protocol provided with the Biocrates AbsoluteIDQ p400HR kit, to quantify up to 408 metabolites. The specimens included human plasma and serum from male and female donors, mouse and rat plasma, as well as NIST SRM 1950 reference plasma. The metabolite classes covered range from polar (e.g., amino acids and biogenic amines) to nonpolar (e.g., diacyl- and triacyl-glycerols), and they span 11 common metabolite classes. The manuscript describes a strict system suitability testing (SST) criteria used to evaluate each laboratory's readiness to perform the assay, and provides the SST Skyline documents for public dissemination. The study found approximately 250 metabolites were routinely quantified in the sample types tested, using Orbitrap instruments. Interlaboratory variance for the NIST SRM-1950 has a median of 10% for amino acids, 24% for biogenic amines, 38% for acylcarnitines, 25% for glycerolipids, 23% for glycerophospholipids, 16% for cholesteryl esters, 15% for sphingolipids, and 9% for hexoses. Comparing to consensus values for NIST SRM-1950, nearly 80% of comparable analytes demonstrated bias of <50% from the reference value. The findings of this study result in recommendations of best practices for system suitability, quality control, and calibration. We demonstrate that with appropriate controls, high-resolution metabolomics can provide accurate results with good precision across laboratories, and the p400HR therefore is a reliable approach for generating consistent and comparable metabolomics data.

Flow Injection-Traveling-Wave Ion Mobility-Mass Spectrometry for Prostate-Cancer Metabolomics.

Flow injection-traveling-wave ion mobility-mass spectrometry (FI-TWIM-MS) was applied to the nontargeted metabolic profiling of serum extracts from 61 prostate-cancer (PCa) patients and 42 controls with an analysis speed of 6 min per sample, including a 3 min wash run. Comprehensive data mining of the mobility-mass domain was used to discriminate species with various charge states and filter matrix salt-cluster ions. Specific criteria were developed to ensure correct grouping of adducts, in-source fragments, and impurities in the data set. Endogenous metabolites were identified with high confidence using FI-TWIM-MS/MS and collision-cross-section (CCS) matching with chemical standards or CCS databases. PCa patient samples were distinguished from control samples with good accuracies (88.3-89.3%), sensitivities (88.5-90.2%), and specificity (88.1%) using supervised multivariate classification methods. Although largely underutilized in metabolomics studies, FI-TWIM-MS proved advantageous in terms of analysis speed, separation of ions in complex mixtures, improved signal-to-noise ratio, and reduction of spectral congestion. Results from this study showcase the potential of FI-TWIM-MS as a high-throughput metabolic-profiling tool for large-scale metabolomics studies.

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 high 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 0.99 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 and oxidative stress 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.

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.

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.

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.

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.

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.

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.