High glucose-induced downregulation of PTEN-Long is sufficient for proximal tubular cell injury in diabetic kidney disease.

During the progression of diabetic kidney disease, proximal tubular epithelial cells respond to high glucose to induce hypertrophy and matrix expansion leading to renal fibrosis. Recently, a non-canonical PTEN has been shown to be translated from an upstream initiation codon CUG (leucine) to produce a longer protein called PTEN-Long (PTEN-L). Interestingly, the extended sequence present in PTEN-L contains cell secretion/penetration signal. Role of this non-canonical PTEN-L in diabetic renal tubular injury is not known. We show that high glucose decreases expression of PTEN-L. As a mechanism of its function, we find that reduced PTEN-L activates Akt-2, which phosphorylates and inactivate tuberin and PRAS40, resulting in activation of mTORC1 in tubular cells. Antibacterial agent acriflavine and antiviral agent ATA regulate translation from CUG codon. Acriflavine and ATA, respectively, decreased and increased expression of PTEN-L to altering Akt-2 and mTORC1 activation in the absence of change in expression of canonical PTEN. Consequently, acriflavine and ATA modulated high glucose-induced tubular cell hypertrophy and lamininγ1 expression. Importantly, expression of PTEN-L inhibited high glucose-stimulated Akt/mTORC1 activity to abrogate these processes. Since PTEN-L contains secretion/penetration signals, addition of conditioned medium containing PTEN-L blocked Akt-2/mTORC1 activity. Notably, in renal cortex of diabetic mice, we found reduced PTEN-L concomitant with Akt-2/mTORC1 activation, leading to renal hypertrophy and lamininγ1 expression. These results present first evidence for involvement of PTEN-L in diabetic kidney disease.

Exploration and analysis of R-loop mapping data with RLBase.

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. In 2012, Ginno et al. introduced the first R-loop mapping method. Since that time, dozens of R-loop mapping studies have been conducted, yielding hundreds of publicly available datasets. Current R-loop databases provide only limited access to these data. Moreover, no web tools for analyzing user-supplied R-loop datasets have yet been described. In our recent work, we reprocessed 810 R-loop mapping samples, building the largest R-loop data resource to date. We also defined R-loop consensus regions and developed a framework for R-loop data analysis. Now, we introduce RLBase, a user-friendly database that provides the capability to (i) explore hundreds of public R-loop mapping datasets, (ii) explore R-loop consensus regions, (iii) analyze user-supplied data and (iv) download standardized and reprocessed datasets. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/shiny/rlbase/.

Protein N-glycans in Healthy and Sclerotic Glomeruli in Diabetic Kidney Disease.

BACKGROUND: Diabetes is expected to directly impact renal glycosylation, yet to date, there has not been a comprehensive evaluation of alterations in N-glycan composition in the glomeruli of patients with diabetic kidney disease (DKD). METHODS: We used untargeted mass spectrometry imaging to identify N-glycan structures in healthy and sclerotic glomeruli in FFPE sections from needle biopsies of five patients with DKD and three healthy kidney samples. Regional proteomics was performed on glomeruli from additional biopsies from the same patients to compare the abundances of enzymes involved in glycosylation. Secondary analysis of single nuclei transcriptomics (snRNAseq) data was used to inform on transcript levels of glycosylation machinery in different cell types and states. RESULTS: We detected 120 N-glycans, and among them identified twelve of these protein post-translated modifications that were significantly increased in glomeruli. All glomeruli-specific N-glycans contained an N-acetyllactosamine (LacNAc) epitope. Five N-glycan structures were highly discriminant between sclerotic and healthy glomeruli. Sclerotic glomeruli had an additional set of glycans lacking fucose linked to their core, and they did not show tetra-antennary structures that are common in healthy glomeruli. Orthogonal omics analyses revealed lower protein abundance and lower gene expression involved in synthesizing fucosylated and branched N-glycans in sclerotic podocytes. In snRNAseq and regional proteomics analyses, we observed that genes and/or proteins involved in sialylation and LacNAc synthesis were also downregulated in DKD glomeruli, but this alteration remained undetectable by our spatial N-glycomics assay. CONCLUSIONS: Integrative spatial glycomics, proteomics, and transcriptomics revealed protein N-glycosylation characteristic of sclerotic glomeruli in DKD.

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights and Implication for Clinical Decision Making.

BACKGROUND: Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression. SUMMARY: This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes. KEY MESSAGE: Through an extensive analysis of metabolic pathways and small molecule fluctuations, bulk and spatial metabolomics offer unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision making.

Quality-controlled R-loop meta-analysis reveals the characteristics of R-loop consensus regions.

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 810 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate R-loop data quality control method, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called 'R-loop regions' (RL regions). In the process, we identified a stark divergence between RL regions detected by S9.6 and dRNH-based mapping methods, particularly with respect to R-loop size, location, and colocalization with RNA binding factors. Taken together, this work provides a much-needed method to assess R-loop data quality and offers novel context regarding the differences between dRNH- and S9.6-based R-loop mapping approaches.

Understanding Heterogeneity in Individual Responses to Digital Lifestyle Intervention Through Self-Monitoring Adherence Trajectories in Adults With Overweight or Obesity: Secondary Analysis of a 6-Month Randomized Controlled Trial.

BACKGROUND: Achieving clinically significant weight loss through lifestyle interventions for obesity management is challenging for most individuals. Improving intervention effectiveness involves early identification of intervention nonresponders and providing them with timely, tailored interventions. Early and frequent self-monitoring (SM) adherence predicts later weight loss success, making it a potential indicator for identifying nonresponders in the initial phase. OBJECTIVE: This study aims to identify clinically meaningful participant subgroups based on longitudinal adherence to SM of diet, activity, and weight over 6 months as well as psychological predictors of participant subgroups from a self-determination theory (SDT) perspective. METHODS: This was a secondary data analysis of a 6-month digital lifestyle intervention for adults with overweight or obesity. The participants were instructed to perform daily SM on 3 targets: diet, activity, and weight. Data from 50 participants (mean age: 53.0, SD 12.6 y) were analyzed. Group-based multitrajectory modeling was performed to identify subgroups with distinct trajectories of SM adherence across the 3 SM targets. Differences between subgroups were examined for changes in clinical outcomes (ie, body weight, hemoglobin A1c) and SDT constructs (ie, eating-related autonomous motivation and perceived competence for diet) over 6 months using linear mixed models. RESULTS: Two distinct SM trajectory subgroups emerged: the Lower SM group (21/50, 42%), characterized by all-around low and rapidly declining SM, and the Higher SM group (29/50, 58%), characterized by moderate and declining diet and weight SM with high activity SM. Since week 2, participants in the Lower SM group exhibited significantly lower levels of diet (P=.003), activity (P=.002), and weight SM (P=.02) compared with the Higher SM group. In terms of clinical outcomes, the Higher SM group achieved a significant reduction in body weight (estimate: -6.06, SD 0.87 kg; P<.001) and hemoglobin A1c (estimate: -0.38, SD 0.11%; P=.02), whereas the Lower SM group exhibited no improvements. For SDT constructs, both groups maintained high levels of autonomous motivation for over 6 months. However, the Lower SM group experienced a significant decline in perceived competence (P=.005) compared with the Higher SM group, which maintained a high level of perceived competence throughout the intervention (P=.09). CONCLUSIONS: The presence of the Lower SM group highlights the value of using longitudinal SM adherence trajectories as an intervention response indicator. Future adaptive trials should identify nonresponders within the initial 2 weeks based on their SM adherence and integrate intervention strategies to enhance perceived competence in diet to benefit nonresponders. TRIAL REGISTRATION: ClinicalTrials.gov NCT05071287; https://clinicaltrials.gov/study/NCT05071287. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2022.106845.

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study.

BACKGROUND: The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests. RESULTS: Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models. CONCLUSIONS: We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

SUPPRESION OF MITOCHONDRIAL RESPIRATION IS A FEATURE OF CELLULAR GLUCOSE TOXICITY.

Glucose toxicity is central to the myriad complications of diabetes and is now believed to encompass neurodegenerative diseases and cancer as well as microvascular and macrovascular disease. Due to the widespread benefits of SGLT2 inhibitors, which affect glucose uptake in the kidney proximal tubular cell, a focus on cell metabolism in response to glucose has important implications for overall health. We previously found that a -Warburg-type effect underlies diabetic kidney disease and involves metabolic reprogramming. This is now supported by quantitative measurements of superoxide measurement in the diabetic kidney and systems biology analysis of urine metabolites in patients. Further exploration of mechanisms underlying mediators of mitochondrial suppression will be critical in understanding the chronology of glucose-induced toxicity and developing new therapeutics to arrest the systemic glucose toxicity of diabetes.

Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection.

Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles in the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS)-based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near-cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled a matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q Exactive HF Orbitrap MS upgraded with ultrahigh mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass (HR/AM) measurements of proteoforms up to 16.5 kDa directly from tissues using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a considerable increase in duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. The use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps toward high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.

High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study.

INTRODUCTION: Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression. METHODS: Urine samples (N = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites' ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites. RESULTS: Six eGFR slope models selected 9-30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (p < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease. CONCLUSIONS: Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.

A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project.

Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate three-dimensional (3-D) molecular atlases of healthy and diseased kidney biopsies by using multiple state-of-the-art omics and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single-cell level or in 3-D space is a significant challenge that can be a futile exercise if not well controlled. We describe a "follow the tissue" pipeline for generating a reliable and authentic single-cell/region 3-D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation, and harmonization across different omics and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis, and sharing. We established benchmarks for quality control, rigor, reproducibility, and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before their being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multiomics and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.

Restoring mitochondrial superoxide levels with elamipretide (MTP-131) protects db/db mice against progression of diabetic kidney disease.

Exposure to chronic hyperglycemia because of diabetes mellitus can lead to development and progression of diabetic kidney disease (DKD). We recently reported that reduced superoxide production is associated with mitochondrial dysfunction in the kidneys of mouse models of type 1 DKD. We also demonstrated that humans with DKD have significantly reduced levels of mitochondrion-derived metabolites in their urine. Here we examined renal superoxide production in a type 2 diabetes animal model, the db/db mouse, and the role of a mitochondrial protectant, MTP-131 (also called elamipretide, SS-31, or Bendavia) in restoring renal superoxide production and ameliorating DKD. We found that 18-week-old db/db mice have reduced renal and cardiac superoxide levels, as measured by dihydroethidium oxidation, and increased levels of albuminuria, mesangial matrix accumulation, and urinary H2O2 Administration of MTP-131 significantly inhibited increases in albuminuria, urinary H2O2, and mesangial matrix accumulation in db/db mice and fully preserved levels of renal superoxide production in these mice. MTP-131 also reduced total renal lysocardiolipin and major lysocardiolipin subspecies and preserved lysocardiolipin acyltransferase 1 expression in db/db mice. These results indicate that, in type 2 diabetes, DKD is associated with reduced renal and cardiac superoxide levels and that MTP-131 protects against DKD and preserves physiological superoxide levels, possibly by regulating cardiolipin remodeling.

Rationale and design of the Kidney Precision Medicine Project.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE.

Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high-throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. Here, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI data sets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus, facilitating applications of MSI in glycobiology.

Changes in plasma and urine metabolites associated with empagliflozin in patients with type 1 diabetes.

AIM: To examine the impact of the sodium-glucose co-transporter-2 inhibitor, empagliflozin, on plasma and urine metabolites in participants with type 1 diabetes. MATERIAL AND METHODS: Participants (n = 40, 50% male, mean age 24.3 years) with type 1 diabetes and without overt evidence of diabetic kidney disease had baseline assessments performed under clamped euglycaemia and hyperglycaemia, on two consecutive days. Participants then proceeded to an 8-week, open-label treatment period with empagliflozin 25 mg/day, followed by repeat assessments under clamped euglycaemia and hyperglycaemia. Plasma and urine metabolites were first grouped into metabolic pathways using MetaboAnalyst software. Principal component analysis was performed to create a representative value for each sufficiently represented metabolic group (false discovery rate ≤ 0.1) for further analysis. RESULTS: Of the plasma metabolite groups, tricarboxylic acid (TCA) cycle (P < .0001), biosynthesis of unsaturated fatty acids (P = .0045), butanoate (P < .0001), propanoate (P = .0053), and alanine, aspartate and glutamate (P < .0050) metabolites were increased after empagliflozin treatment under clamped euglycaemia. Of the urine metabolite groups, only butanoate metabolites (P = .0005) were significantly increased. Empagliflozin treatment also attenuated the increase in a number of urine metabolites observed with acute hyperglycaemia. CONCLUSIONS: Empagliflozin was associated with increased lipid and TCA cycle metabolites in participants with type 1 diabetes, suggesting a shift in metabolic substrate use and improved mitochondrial function. These effects result in more efficient energy production and may contribute to end-organ protection by alleviating local hypoxia and oxidative stress.

Fatigue characteristics on dialysis and non-dialysis days in patients with chronic kidney failure on maintenance hemodialysis.

BACKGROUND: Fatigue is prevalent in hemodialysis patients who for survival follow a strict dialysis treatment regimen - dialysis and non-dialysis days. As a result, the daily activities, symptom burden, and clinical outcomes of hemodialysis patients vary significantly between dialysis and non-dialysis days. Fatigue is one of the most reported debilitating symptoms by hemodialysis patients with profound negative impact on their quality of life. Prior studies assessed fatigue during the preceding 7 or 30 days and did not discriminate fatigue characteristics between dialysis and non-dialysis days. We aimed to characterize and compare fatigue severity and fatigue interference with daily activities between dialysis and non-dialysis days. METHODS: Hemodialysis patients self-reported fatigue on consecutive dialysis and non-dialysis days using the 9-item Brief Fatigue Inventory. The differences in fatigue characteristics between dialysis and non-dialysis days were analyzed using one-way ANCOVA. RESULTS: Global fatigue burden was worse on a dialysis day compared to a non-dialysis day (P for all < 0.001). Age and education were associated with fatigue, but hemodialysis-related variables were not. A significant inverse association of physical activity with fatigue severity observed on non-dialysis day; there was also a negative association between the normalized protein catabolic rate and fatigue severity on both dialysis and non-dialysis days. The positive association of depression with fatigue severity and fatigue interference were consistent on both dialysis and non-dialysis days. None of these factors, however, explained differences in fatigue characteristics between dialysis and non-dialysis days. CONCLUSIONS: Fatigue, measured in severity and interference, was more pronounced on a dialysis day relative to a non-dialysis day. These differences were not explained by age, sex, education, hemodialysis-related variables, habitual exercise, nutritional status, and or depression. The quantitative measures of fatigue characteristics may facilitate future interventional trials design and better fatigue management for hemodialysis patients.

A role for tubular Na+/H+ exchanger NHE3 in the natriuretic effect of the SGLT2 inhibitor empagliflozin.

Inhibitors of proximal tubular Na+-glucose cotransporter 2 (SGLT2) are natriuretic, and they lower blood pressure. There are reports that the activities of SGLT2 and Na+-H+ exchanger 3 (NHE3) are coordinated. If so, then part of the natriuretic response to an SGLT2 inhibitor is mediated by suppressing NHE3. To examine this further, we compared the effects of an SGLT2 inhibitor, empagliflozin, on urine composition and systolic blood pressure (SBP) in nondiabetic mice with tubule-specific NHE3 knockdown (NHE3-ko) and wild-type (WT) littermates. A single dose of empagliflozin, titrated to cause minimal glucosuria, increased urinary excretion of Na+ and bicarbonate and raised urine pH in WT mice but not in NHE3-ko mice. Chronic empagliflozin treatment tended to lower SBP despite higher renal renin mRNA expression and lowered the ratio of SBP to renin mRNA, indicating volume loss. This effect of empagliflozin depended on tubular NHE3. In diabetic Akita mice, chronic empagliflozin enhanced phosphorylation of NHE3 (S552/S605), changes previously linked to lesser NHE3-mediated reabsorption. Chronic empagliflozin also increased expression of genes involved with renal gluconeogenesis, bicarbonate regeneration, and ammonium formation. While this could reflect compensatory responses to acidification of proximal tubular cells resulting from reduced NHE3 activity, these effects were at least in part independent of tubular NHE3 and potentially indicated metabolic adaptations to urinary glucose loss. Moreover, empagliflozin increased luminal α-ketoglutarate, which may serve to stimulate compensatory distal NaCl reabsorption, while cogenerated and excreted ammonium balances urine losses of this "potential bicarbonate." The data implicate NHE3 as a determinant of the natriuretic effect of empagliflozin.

Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.

MOTIVATION: Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological 'concepts' that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression. RESULTS: We built a partial correlation interaction network, identified highly connected network components, applied network-based gene-set analysis to identify differentially enriched subnetworks, and compared the subnetworks in patients with early-stage versus late-stage CKD. We identified two subnetworks 'triacylglycerols' and 'cardiolipins-phosphatidylethanolamines (CL-PE)' characterized by lower connectivity, and a higher abundance of longer polyunsaturated triacylglycerols in patients with severe CKD (stage ≥4) from the Clinical Phenotyping Resource and Biobank Core. These finding were replicated in an independent cohort, the Chronic Renal Insufficiency Cohort. Using an innovative method for elucidating biological alterations in lipid networks, we demonstrated alterations in triacylglycerols and cardiolipins-phosphatidylethanolamines that precede the clinical outcome of end-stage kidney disease by several years. AVAILABILITY AND IMPLEMENTATION: A complete list of NetGSA results in HTML format can be found at http://metscape.ncibi.org/netgsa/12345-022118/cric_cprobe/022118/results_cric_cprobe/main.html. The DNEA is freely available at https://github.com/wiggie/DNEA. Java wrapper leveraging the cytoscape.js framework is available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: 1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m2 were followed up prospectively for a median of 8 (range, 2-10) years. PREDICTORS: 13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR. OUTCOMES: Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant). ANALYTICAL APPROACH: Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT. RESULTS: During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m2 per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]). LIMITATIONS: Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches. CONCLUSIONS: Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules.

INTRODUCTION: Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported. OBJECTIVES: In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD. METHODS: DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6). RESULTS: Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z's in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls. CONCLUSION: Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.