Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease.

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

Pseudouridine and N-formylmethionine associate with left ventricular mass index: Metabolome-wide association analysis of cardiac remodeling.

BACKGROUND: Heart failure (HF) is the fastest growing form of cardiovascular disease both nationally and globally, underlining a need to phenotype subclinical HF intermediaries to improve primary prevention. OBJECTIVES: We aimed to identify novel metabolite associations with left ventricular (LV) remodeling, one upstream HF intermediary, among a community-based cohort of individuals. METHODS: We examined 1052 Bogalusa Heart Study participants (34.98% African American, 57.41% female, aged 33.6-57.5 years). Measures of LV mass and relative wall thickness (RWT) were obtained using two-dimensional-guided echocardiographic measurements via validated eqs. LV mass was indexed to height2.7 to calculate left ventricular mass index (LVMI). Untargeted metabolomic analysis of fasting serum samples was conducted. In combined and ethnicity-stratified analyses, multivariable linear and multinomial logistic regression models tested the associations of metabolites with the continuous LVMI and RWT and categorical LV geometry phenotypes, respectively, after adjusting for demographic and traditional cardiovascular disease risk factors. RESULTS: Pseudouridine (B = 1.38; p = 3.20 × 10-5) and N-formylmethionine (B = 1.65; 3.30 × 10-6) were significantly associated with LVMI in the overall sample as well significant in Caucasians, with consistent effect direction and nominal significance (p < .05) in African Americans. Upon exclusion of individuals with self-report myocardial infarction or congestive HF, we similarly observed a 1.33 g/m2.7 and 1.52 g/m2.7 higher LVMI for each standard deviation increase in pseudouridine and N-formylmethionine, respectively. No significant associations were observed for metabolites with RWT or categorical LV remodeling outcomes. CONCLUSIONS: The current analysis identified novel associations of pseudouridine and N-formylmethionine with LVMI, suggesting that mitochondrial-derived metabolites may serve as early biomarkers for LV remodeling and subclinical HF.

Novel associations between blood metabolites and kidney function among Bogalusa Heart Study and Multi-Ethnic Study of Atherosclerosis participants.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health challenge given its high global prevalence and associated risks of cardiovascular disease and progression to end stage renal disease. Although it is known that numerous metabolic changes occur in CKD patients, identifying novel metabolite associations with kidney function may enhance our understanding of the physiologic pathways relating to CKD. OBJECTIVES: The objective of this study was to elucidate novel metabolite associations with kidney function among participants of two community-based cohorts with carefully ascertained metabolomics, kidney function, and covariate data. METHODS: Untargeted ultrahigh-performance liquid chromatography-tandem mass spectrometry was used to detect and quantify blood metabolites. We used multivariate adjusted linear regression to examine associations between single metabolites and creatinine-based estimated glomerular filtration rate (eGFRcr) among 1243 Bogalusa Heart Study (BHS) participants (median eGFRcr: 94.4, 5th-95th percentile: 66.0-119.6 mL/min/1.73 m2). Replication, determined by statistical significance and consistent effect direction, was tested using gold standard measured glomerular filtration rate (mGFR) among 260 Multi-Ethnic Study of Atherosclerosis (MESA) participants (median mGFR: 72.0, 5th-95th percentile: 43.5-105.0 mL/min/1.73 m2). All analyses used Bonferroni-corrected alpha thresholds. RESULTS: Fifty-one novel metabolite associations with kidney function were identified, including 12 from previously unrelated sub-pathways: N6-carboxymethyllysine, gulonate, quinolinate, gamma-CEHC-glucuronide, retinol, methylmalonate, 3-hydroxy-3-methylglutarate, 3-aminoisobutyrate, N-methylpipecolate, hydroquinone sulfate, and glycine conjugates of C10H12O2 and C10H14O2(1). Significant metabolites were generally inversely associated with kidney function and smaller in mass-to-charge ratio than non-significant metabolites. CONCLUSION: The 51 novel metabolites identified may serve as early, clinically relevant, kidney function biomarkers.

MicroRNA-216a induces endothelial senescence and inflammation via Smad3/IκBα pathway.

Vascular endothelial senescence contributes to atherosclerosis and coronary artery disease (CAD), but the mechanisms are yet to be clarified. We identified that microRNA-216a (miR-216a) significantly increased in senescent endothelial cells. The replicative senescence model of human umbilical vein endothelial cells (HUVECs) was established to explore the role of miR-216a in endothelial ageing and dysfunction. Luciferase assay indicated that Smad3 was a direct target of miR-216a. Stable expression of miR-216a induced a premature senescence-like phenotype in HUVECs with an impairment in proliferation and migration and led to an increased adhesion to monocytes by inhibiting Smad3 expression and thereafter modulating the degradation of NF-κB inhibitor alpha (IκBα) and activation of adhesion molecules. Conversely, inhibition of endogenous miR-216a in senescent HUVECs rescued Smad3 and IκBα expression and inhibited monocytes attachment. Plasma miR-216a was significantly higher in old CAD patients (>50 years) and associated with increased 31% risk for CAD (odds ratio 1.31, 95% confidence interval 1.03-1.66; P = .03) compared with the matched healthy controls (>50 years). Taken together, our data suggested that miR-216a promotes endothelial senescence and inflammation as an endogenous inhibitor of Smad3/IκBα pathway, which might serve as a novel target for ageing-related atherosclerotic diseases.

Effects of tea intake on blood pressure: a meta-analysis of randomised controlled trials.

The effect of tea intake on blood pressure (BP) is controversial. We performed a meta-analysis of randomised controlled trials to determine the changes in systolic and diastolic BP due to the intake of black and green tea. A systematic search was conducted in MEDLINE, EMBASE and the Cochrane Controlled Trials Register up to May 2014. The weighted mean difference was calculated for net changes in systolic and diastolic BP using fixed-effects or random-effects models. Previously defined subgroup analyses were performed to explore the influence of study characteristics. A total of twenty-five eligible studies with 1476 subjects were selected. The acute intake of tea had no effects on systolic and diastolic BP. However, after long-term tea intake, the pooled mean systolic and diastolic BP were lower by - 1·8 (95 % CI - 2·4, - 1·1) and - 1·4 (95 % CI - 2·2, - 0·6) mmHg, respectively. When stratified by type of tea, green tea significantly reduced systolic BP by 2·1 (95 % CI - 2·9, - 1·2) mmHg and decreased diastolic BP by 1·7 (95 % CI - 2·9, - 0·5) mmHg, and black tea showed a reduction in systolic BP of 1·4 (95 % CI - 2·4, - 0·4) mmHg and a decrease in diastolic BP of 1·1 (95 % CI - 1·9, - 0·2) mmHg. The subgroup analyses showed that the BP-lowering effect was apparent in subjects who consumed tea more than 12 weeks (systolic BP - 2·6 (95 % CI - 3·5, - 1·7) mmHg and diastolic BP - 2·2 (95 % CI - 3·0, - 1·3) mmHg, both P< 0·001). The present findings suggest that long-term ( ≥ 12 weeks) ingestion of tea could result in a significant reduction in systolic and diastolic BP.

Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction.

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Leveraging machine learning models for peptide-protein interaction prediction.

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer Learning.

Ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes often exhibit promiscuous substrate preferences that cannot be reduced to simple rules. Large language models are promising tools for predicting such peptide fitness landscapes. However, state-of-the-art protein language models are trained on relatively few peptide sequences. A previous study comprehensively profiled the peptide substrate preferences of LazBF (a two-component serine dehydratase) and LazDEF (a three-component azole synthetase) from the lactazole biosynthetic pathway. We demonstrated that masked language modeling of LazBF substrate preferences produced language model embeddings that improved downstream classification models of both LazBF and LazDEF substrates. Similarly, masked language modelling of LazDEF substrate preferences produced embeddings that improved the performance of classification models of both LazBF and LazDEF substrates. Our results suggest that the models learned functional forms that are transferable between distinct enzymatic transformations that act within the same biosynthetic pathway. Our transfer learning method improved performance and data efficiency in data-scarce scenarios. We then fine-tuned models on each data set and showed that the fine-tuned models provided interpretable insight that we anticipate will facilitate the design of substrate libraries that are compatible with desired RiPP biosynthetic pathways.

Deep mutational scanning reveals sequence to function constraints for SWEET family transporters.

Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.

Sequence controlled secondary structure is important for the site-selectivity of lanthipeptide cyclization.

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides that are generated from precursor peptides through a dehydration and cyclization process. ProcM, a class II lanthipeptide synthetase, demonstrates high substrate tolerance. It is enigmatic that a single enzyme can catalyze the cyclization process of many substrates with high fidelity. Previous studies suggested that the site-selectivity of lanthionine formation is determined by substrate sequence rather than by the enzyme. However, exactly how substrate sequence contributes to site-selective lanthipeptide biosynthesis is not clear. In this study, we performed molecular dynamic simulations for ProcA3.3 variants to explore how the predicted solution structure of the substrate without enzyme correlates to the final product formation. Our simulation results support a model in which the secondary structure of the core peptide is important for the final product's ring pattern for the substrates investigated. We also demonstrate that the dehydration step in the biosynthesis pathway does not influence the site-selectivity of ring formation. In addition, we performed simulation for ProcA1.1 and 2.8, which are well-suited candidates to investigate the connection between order of ring formation and solution structure. Simulation results indicate that in both cases, C-terminal ring formation is more likely which was supported by experimental results. Our findings indicate that the substrate sequence and its solution structure can be used to predict the site-selectivity and order of ring formation, and that secondary structure is a crucial factor influencing the site-selectivity. Taken together, these findings will facilitate our understanding of the lanthipeptide biosynthetic mechanism and accelerate bioengineering efforts for lanthipeptide-derived products.

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning.

Excipients are major components of drugs and are used to improve drug attributes such as stability and appearance. Excipients approved by the U.S. Food and Drug Administration (FDA) are regarded as safe for humans in allowed concentrations, but their potential interactions with drug targets have not been investigated systematically, which might influence a drug's efficacy. Deep learning models have been used for the identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models to data sets that include ligands binding to G-protein-coupled receptors (GPCRs) and kinases. The predicted results suggest that one-shot learning could be used for predicting ligand-protein interactions, and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning can be used to make accurate predictions for excipient-protein interactions, and these methods could be used for selecting excipients with limited drug-protein interactions.

Inhibition of lung microbiota-derived proapoptotic peptides ameliorates acute exacerbation of pulmonary fibrosis.

Idiopathic pulmonary fibrosis is an incurable disease of unknown etiology. Acute exacerbation of idiopathic pulmonary fibrosis is associated with high mortality. Excessive apoptosis of lung epithelial cells occurs in pulmonary fibrosis acute exacerbation. We recently identified corisin, a proapoptotic peptide that triggers acute exacerbation of pulmonary fibrosis. Here, we provide insights into the mechanism underlying the processing and release of corisin. Furthermore, we demonstrate that an anticorisin monoclonal antibody ameliorates lung fibrosis by significantly inhibiting acute exacerbation in the human transforming growth factorß1 model and acute lung injury in the bleomycin model. By investigating the impact of the anticorisin monoclonal antibody in a general model of acute lung injury, we further unravel the potential of corisin to impact such diseases. These results underscore the role of corisin in the pathogenesis of acute exacerbation of pulmonary fibrosis and acute lung injury and provide a novel approach to treating this incurable disease.

Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.

BACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure. METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants. RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P<5×10-8). Among them, a rare intergenic variant at novel locus, LOC100506274, was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; P=4.99×10-8) but not stage-2 analysis (P=0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; P=4.18×10-7) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; P=7.28×10-23). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (P<1×10-6 and P<1×10-4, respectively). DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.

BACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10-11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10-9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10-9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10-9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10-9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

MicroRNA-216a Promotes Endothelial Inflammation by Smad7/IκBα Pathway in Atherosclerosis.

BACKGROUND: The endothelium is the first line of defence against harmful microenvironment risks, and microRNAs (miRNAs) involved in vascular inflammation may be promising therapeutic targets to modulate atherosclerosis progression. In this study, we aimed to investigate the mechanism by which microRNA-216a (miR-216a) modulated inflammation activation of endothelial cells. Methods. A replicative senescence model of human umbilical vein endothelial cells (HUVECs) was established, and population-doubling levels (PDLs) were defined during passages. PDL8 HUVECs were transfected with miR-216a mimics/inhibitor or small interfering RNA (siRNA) of SMAD family member 7 (Smad7). Real-time PCR and Western blot assays were performed to detect the regulatory role of miR-216a on Smad7 and NF-κB inhibitor alpha (IκBα) expression. The effect of miR-216a on adhesive capability of HUVECs to THP-1 cells was examined. MiR-216a and Smad7 expression in vivo were measured using human carotid atherosclerotic plaques of the patients who underwent carotid endarterectomy (n = 41). RESULTS: Luciferase assays showed that Smad7 was a direct target of miR-216a. Smad7 mRNA expression, negatively correlated with miR-216a during endothelial aging, was downregulated in senescent PDL44 cells, compared with young PDL8 HUVECs. MiR-216a markedly increased endothelial inflammation and adhesive capability to monocytes in PDL8 cells by promoting the phosphorylation and degradation of IκBα and then activating NF-κB signalling pathway. The effect of miR-216a on endothelial cells was consistent with that blocked Smad7 by siRNAs. When inhibiting endogenous miR-216a, the Smad7/IκBα expression was rescued, which led to decreased endothelial inflammation and monocytes recruitment. In human carotid atherosclerotic plaques, Smad7 level was remarkably decreased in high miR-216a group compared with low miR-216a group. Moreover, miR-216a was negatively correlated with Smad7 and IκBα levels and positively correlated with interleukin 1 beta (IL1ß) expression in vivo. CONCLUSION: In summary, our findings suggest a new mechanism of vascular endothelial inflammation involving Smad7/IκBα signalling pathway in atherosclerosis.

Novel Findings From a Metabolomics Study of Left Ventricular Diastolic Function: The Bogalusa Heart Study.

Background Diastolic dysfunction is one important causal factor for heart failure with preserved ejection fraction, yet the metabolic signature associated with this subclinical phenotype remains unknown. Methods and Results Ultra-high-performance liquid chromatography-tandem mass spectroscopy was used to conduct untargeted metabolomic analysis of fasting serum samples in 1050 white and black participants of the BHS (Bogalusa Heart Study). After quality control, 1202 metabolites were individually tested for association with 5 echocardiographic measures of left ventricular diastolic function using multivariable-adjusted linear regression. Measures of left ventricular diastolic function included the ratio of peak early filling velocity to peak late filling velocity, ratio of peak early filling velocity to mitral annular velocity, deceleration time, isovolumic relaxation time, and left atrial maximum volume index (LAVI). Analyses adjusted for multiple cardiovascular disease risk factors and used Bonferroni-corrected alpha thresholds. Eight metabolites robustly associated with left ventricular diastolic function in the overall population and demonstrated consistent associations in white and black study participants. N-formylmethionine (B=0.05; P=1.50×10-7); 1-methylhistidine (B=0.05; P=1.60×10-7); formiminoglutamate (B=0.07; P=5.60×10-7); N2, N5-diacetylornithine (B=0.05; P=1.30×10-7); N-trimethyl 5-aminovalerate (B=0.04; P=5.10×10-6); 5-methylthioadenosine (B=0.04; P=1.40×10-5); and methionine sulfoxide (B=0.04; P=3.80×10-6) were significantly associated with the natural log of the ratio of peak early filling velocity to mitral annular velocity. Butyrylcarnitine (B=3.18; P=2.10×10-6) was significantly associated with isovolumic relaxation time. Conclusions The current study identified novel findings of metabolite associations with left ventricular diastolic function, suggesting that the serum metabolome, and its underlying biological pathways, may be implicated in heart failure with preserved ejection fraction pathogenesis.

An untargeted metabolomics study of blood pressure: findings from the Bogalusa Heart Study.

OBJECTIVE: To identify novel and confirm previously reported metabolites associated with SBP, DBP, and hypertension in a biracial sample of Bogalusa Heart Study (BHS) participants. METHODS: We employed untargeted, ultra-high performance liquid chromatography tandem mass spectroscopy metabolomics profiling among 1249 BHS participants (427 African-Americans and 822 whites) with BP and covariable data collected during the 2013 to 2016 visit cycle. A total of 1202 metabolites were tested for associations with continuous and binary BP phenotypes using multiple linear and logistic regression models, respectively, in overall and race-stratified analyses. RESULTS: A total of 24 novel metabolites robustly associated with BP, achieving Bonferroni-corrected P less than 4.16 × 10 in the overall analysis and consistent effect sizes across race groups. The identified metabolites included three amino acid and nucleotide metabolites from histidine, pyrimidine, or tryptophan metabolism sub-pathways, seven cofactor and vitamin or xenobiotic metabolites from the ascorbate and aldarate metabolism, bacterial/fungal, chemical, and food component sub-pathways, 10 lipid metabolites from the eicosanoid, phosphatidylcholine, phosphatidylethanolamine, and sphingolipid metabolism sub-pathways, and four still unnamed metabolites. Six previously described metabolites were robustly confirmed by our study (Bonferroni-corrected P < 4.95 × 10 and consistent effect directions across studies). Furthermore, previously reported metabolites for SBP, DBP, and hypertension demonstrated 5.92-fold, 4.77-fold, and 4.54-fold enrichment for nominally significant signals in the BHS (P = 3.08 × 10, 5.93 × 10, and 2.30 × 10, respectively). CONCLUSION: In aggregate, our study provides new information about potential molecular mechanisms underlying BP regulation. We also demonstrate reproducibility of findings across studies despite differences in study populations and metabolite profiling methods.

Race modifies the association between animal protein metabolite 1-methylhistidine and blood pressure in middle-aged adults: the Bogalusa Heart Study.

OBJECTIVE: Dietary factors mediate racial disparities in hypertension. However, the physiological mechanisms underlying this relationship are incompletely understood. We sought to assess the association between 1-methylhistidine (1-MH), a metabolite marker of animal protein consumption, and blood pressure (BP) in a community-based cohort of black and white middle-aged adults. METHODS: This analysis consisted of 655 participants of the Bogalusa Heart Study (25% black, 61% women, aged 34-58 years) who were not taking antihypertensive medication. Fasting serum 1-MH was measured using liquid chromatography-tandem mass spectroscopy. Animal food intakes were quantified by food-frequency questionnaires. Multivariable linear regression assessed the association between 1-MH and BP in combined and race-stratified analyses, adjusting for demographic, dietary, and cardiometabolic factors. RESULTS: A significant dose--response relationship was observed for the association of red meat (P-trend <0.01) and poultry (P-trend = 0.03) intake with serum 1-MH among all individuals. Serum 1-MH, per standard deviation increase, had a significant positive association with SBP (ß=3.4 ±â€Š1.6 mmHg, P = 0.04) and DBP (ß=2.0 ±â€Š1.1 mmHg, P = 0.05) in black participants, whereas no appreciable association was observed in white participants. Among a subgroup of black participants with repeat outcome measures (median follow-up = 3.0 years), one standard deviation increase in 1-MH conferred a 3.1 and 2.2 mmHg higher annual increase in SBP (P = 0.03) and DBP (P = 0.03), respectively. CONCLUSION: Serum 1-MH associates with higher SBP and DBP in blacks, but not whites. These results suggest a utility for further assessing the role of dietary 1-MH among individuals with hypertension to help minimize racial disparities in cardiovascular health.

Serum metabolites associate with physical performance among middle-aged adults: Evidence from the Bogalusa Heart Study.

Age-related declines in physical performance predict cognitive impairment, disability, chronic disease exacerbation, and mortality. We conducted a metabolome-wide association study of physical performance among Bogalusa Heart Study participants. Bonferroni corrected multivariate-adjusted linear regression was employed to examine cross-sectional associations between single metabolites and baseline gait speed (N=1,227) and grip strength (N=1,164). In a sub-sample of participants with repeated assessments of gait speed (N=282) and grip strength (N=201), significant metabolites from the cross-sectional analyses were tested for association with change in physical performance over 2.9 years of follow-up. Thirty-five and seven metabolites associated with baseline gait speed and grip strength respectively, including six metabolites that associated with both phenotypes. Three metabolites associated with preservation or improvement in gait speed over follow-up, including: sphingomyelin (40:2) (P=2.6×10-4) and behenoyl sphingomyelin (d18:1/22:0) and ergothioneine (both P<0.05). Seven metabolites associated with declines in gait speed, including: 1-carboxyethylphenylalanine (P=8.8×10-5), and N-acetylaspartate, N-formylmethionine, S-adenosylhomocysteine, N-acetylneuraminate, N2,N2-dimethylguanosine, and gamma-glutamylphenylalanine (all P<0.05). Two metabolite modules reflecting sphingolipid and bile acid metabolism associated with physical performance (minimum P=7.6×10-4). These results add to the accumulating evidence suggesting an important role of the human metabolome in physical performance and specifically implicate lipid, nucleotide, and amino acid metabolism in early physical performance decline.

Long-term stimulation of angiotensin II induced endothelial senescence and dysfunction.

The role of angiotensin II (Ang II) in hypertension has been clarified, but recent studies show that aging-associated arterial changes and those with hypertension as well as atherosclerosis may have some common pathogenesis. This study aimed to clarify the effects of Ang II on endothelial senescence by establishing a replicative senescence model of human umbilical vein endothelial cells (HUVECs) in vitro. The population-doubling level (PDL) was calculated, PDL5 and PDL25 respectively referred to cells cultured for 2 days and 30 days. Compared with Ang II-treated young PDL5 cells, chronic stimulation of Ang II significantly promoted the senescence-associated ß-galactosidase activity and expression of senescence-related genes p16 and p21, slowed down cell growth rate, and decreased expression of longevity-related genes sirtuin1 as well as telomerase activity in senescent PDL25 cells (all P < 0.05). Moreover, expression of pro-inflammatory cytokines and adhesion molecules were up-regulated in Ang II-treated PDL25 cells (all P < 0.05). Ang II-induced senescent progression and inflammation were attenuated by angiotensin receptor blocker valsartan. In young PDL5 cells, Ang II promoted the endothelial viability including cell proliferation, migration, angiogenesis and cell adhesion to monocytes; however, chronic stimulation of Ang II suppressed the cell viability, promoted cell adhesion and apoptosis in senescent PDL25 cells, which could be ameliorated by short-term valsartan, but long-term valsartan had no effects. In addition, Ang II-induced senescent features could be partly recovered if Ang II was stopped at PDL20. These findings suggested that chronic stimulation of Ang II can accelerate the endothelial senescence process which is implicated in aging-related atherosclerosis.