Studies of lipids in CKD, including ESRD, have been limited to measures of conventional lipid profiles. We aimed to systematically identify 17 different lipid classes and associate the abundance thereof with alterations in acylcarnitines, a metric of -oxidation, across stages of CKD. From the Clinical Phenotyping Resource and Biobank Core (CPROBE) cohort of 1235 adults, we selected a panel of 214 participants: 36 with stage 1 or 2 CKD, 99 with stage 3 CKD, 61 with stage 4 CKD, and 18 with stage 5 CKD. Among participants, 110 were men (51.4%), 64 were black (29.9%), and 150 were white (70.1%), and the mean (SD) age was 60 (16) years old. We measured plasma lipids and acylcarnitines using liquid chromatography-mass spectrometry. Overall, we identified 330 different lipids across 17 different classes. Compared with earlier stages, stage 5 CKD associated with a higher abundance of saturated C16-C20 free fatty acids (FFAs) and long polyunsaturated complex lipids. Long-chain-to-intermediate-chain acylcarnitine ratio, a marker of efficiency of-oxidation, exhibited a graded decrease from stage 2 to 5 CKD (<0.001). Additionally, multiple linear regression revealed that the long-chain-to-intermediate-chain acylcarnitine ratio inversely associated with polyunsaturated long complex lipid subclasses and the C16-C20 FFAs but directly associated with short complex lipids with fewer double bonds. We conclude that increased abundance of saturated C16-C20 FFAs coupled with impaired -oxidation of FFAs and inverse partitioning into complex lipids may be mechanisms underpinning lipid metabolism changes that typify advancing CKD.

and has a patent titled "Biomarkers for CKD progression" (encompassing urinary EGF as biomarker of CKD progression) issued. Role of funding source: Financial support of the project.

Exercise provides a robust physiological stimulus that evokes cross-talk among multiple tissues that when repeated regularly (i.e., training) improves physiological capacity, benefits numerous organ systems, and decreases the risk for premature mortality. However, a gap remains in identifying the detailed molecular signals induced by exercise that benefits health and prevents disease. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this gap and generate a molecular map of exercise. Preclinical and clinical studies will examine the systemic effects of endurance and resistance exercise across a range of ages and fitness levels by molecular probing of multiple tissues before and after acute and chronic exercise. From this multi-omic and bioinformatic analysis, a molecular map of exercise will be established. Altogether, MoTrPAC will provide a public database that is expected to enhance our understanding of the health benefits of exercise and to provide insight into how physical activity mitigates disease.

Introduction Human studies report conflicting results on the predictive power of serum lipids on progression of chronic kidney disease (CKD). We aimed to systematically identify the lipids that predict progression to end-stage kidney disease. Methods From the Chronic Renal Insufficiency Cohort, 79 patients with CKD stage 2 to 3 who progressed to ESKD over 6 years of follow up were selected and frequency-matched by age, sex, race, and diabetes with 121 non-progressors with less than 25% decline in estimated glomerular filtration rate (eGFR) during the follow up. The patients were randomly divided into Training and Test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples. Results We identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the Training set. From the top 10 lipids, the abundance of diacylglycerols (DAGs) and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol (MAG) 16:0 was significantly higher in progressors. Using logistic regression models a multi-marker panel consisting of DAGs, and MAG independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of eGFR and urine protein-creatinine ratio (UPCR) as compared to that of the base model was 0.92 (95% Confidence Interval [CI]: 0.88–0.97), and 0.83 (95% CI: 0.76–0.90, P<0.01), respectively; an observation which was validated in the Test subset. Conclusion We conclude that a distinct panel of lipids may improve prediction of progression of CKD beyond eGFR and UPCR when added to the base model.

In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography -mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.

Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage.

Multi-tyrosine kinase inhibitors (MTKIs) have thus far had limited success in the treatment of castration-resistant prostate cancer (CRPC). Here, we report a phase I-cleared orally bioavailable MTKI, ESK981, with a novel autophagy inhibitory property that decreased tumor growth in diverse preclinical models of CRPC. The anti-tumor activity of ESK981 was maximized in immunocompetent tumor environments where it upregulated CXCL10 expression through the interferon gamma pathway and promoted functional T cell infiltration, which resulted in enhanced therapeutic response to immune checkpoint blockade. Mechanistically, we identify the lipid kinase PIKfyve as the direct target of ESK981. PIKfyve-knockdown recapitulated ESK981’s anti-tumor activity and enhanced the therapeutic benefit of immune checkpoint blockade. Our study reveals that targeting PIKfyve via ESK981 turns tumors from cold into hot through inhibition of autophagy, which may prime the tumor immune microenvironment in advanced prostate cancer patients and be an effective treatment strategy alone or in combination with immunotherapies.

Regular exercise promotes whole-body health and prevents disease, yet the underlying molecular mechanisms throughout a whole organism are incompletely understood. Here, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome, and immunome in whole blood, plasma, and 18 solid tissues in Rattus norvegicus over 8 weeks of endurance exercise training. The resulting data compendium encompasses 9466 assays across 19 tissues, 25 molecular platforms, and 4 training time points in young adult male and female rats. We identified thousands of shared and tissue- and sex- specific molecular alterations. Temporal multi-omic and multi-tissue analyses demonstrated distinct patterns of tissue remodeling, with widespread regulation of immune, metabolism, heat shock stress response, and mitochondrial pathways. These patterns provide biological insights into the adaptive responses to endurance training over time. For example, exercise training induced heart remodeling via altered activity of the Mef2 family of transcription factors and tyrosine kinases. Translational analyses revealed changes that are consistent with human endurance training data and negatively correlated with disease, including increased phospholipids and decreased triacylglycerols in the liver. Sex differences in training adaptation were widespread, including those in the brain, adrenal gland, lung, and adipose tissue. Integrative analyses generated novel hypotheses of disease relevance, including candidate mechanisms that link training adaptation to non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health, and tissue injury and recovery. The data and analysis results presented in this study will serve as valuable resources for the broader community and will be provided in an easily accessible public repository (https://motrpac-data.org/).