Palmitic acid in type 2 diabetes mellitus promotes atherosclerotic plaque vulnerability via macrophage Dll4 signaling.

Patients with Type 2 Diabetes Mellitus are increasingly susceptible to atherosclerotic plaque vulnerability, leading to severe cardiovascular events. In this study, we demonstrate that elevated serum levels of palmitic acid, a type of saturated fatty acid, are significantly linked to this enhanced vulnerability in patients with Type 2 Diabetes Mellitus. Through a combination of human cohort studies and animal models, our research identifies a key mechanistic pathway: palmitic acid induces macrophage Delta-like ligand 4 signaling, which in turn triggers senescence in vascular smooth muscle cells. This process is critical for plaque instability due to reduced collagen synthesis and deposition. Importantly, our findings reveal that macrophage-specific knockout of Delta-like ligand 4 in atherosclerotic mice leads to reduced plaque burden and improved stability, highlighting the potential of targeting this pathway. These insights offer a promising direction for developing therapeutic strategies to mitigate cardiovascular risks in patients with Type 2 Diabetes Mellitus.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Metabolomic profiles associated with physical activity in White and African American adult men.

BACKGROUND: Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations. OBJECTIVE: This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men. METHODS: Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking. RESULTS: Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups. CONCLUSIONS: Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Coptidis Rhizoma processed with Evodia Rutaecarpa improves the effect on ulcerative colitis by increasing intestinal energy metabolites alpha-ketoglutarate and Lactobacillus reuteri.

BACKGROUND: Evodia Rutaecarpa-processed Coptidis Rhizoma (ECR) is a traditional Chinese medicine for the treatment of ulcerative colitis (UC) in China. However, the mechanisms underlying the ECR processing are not elucidated. PURPOSE: Coptidis Rhizoma (CR) regulates the gut microbiota in the treatment of gastrointestinal diseases. This study explored the mechanism of action of ECR before and after processing in UC in view of the regulation of gut microecology. STUDY DESIGN: A preclinical experimental investigation was performed using a mouse model of UC to examine the regulatory effect of ECR and its mechanisms through gut microbiota analysis and metabolomic assays. METHODS: Mice received 4% dextran sulfate sodium to establish a UC model and treated with ECR and CR. Colonic histopathology and inflammatory changes were observed. Gut microbiota was analyzed using 16 s rRNA sequencing. Transplants of Lactobacillus reuteri were used to explore the correlation between ECR processing and the gut microbiota. The expression of mucin-2, Lgr5, and PCNA in colonic epithelial cells was measured using immunofluorescence. Wnt3a and ß-catenin levels were detected by western blotting. The metabolites in the colon tissue were analyzed using a targeted energy metabolomic assay. The effect of energy metabolite α-ketoglutarate (α-KG) on L. reuteri growth and UC were verified in mice. RESULTS: ECR improved the effects on UC in mice compared to CR, including alleviating colonic injury and inflammation, and modulating gut microbiota by increasing L. reuteri level. L. reuteri dose-dependently alleviated colonic injury, increased mucin-2 level, and promoted colonic epithelial regeneration by increasing Lgr5 and PCNA expression. This was consistent with the results before and after ECR processing. L. reuteri promoted epithelial regeneration by upregulating Wnt/ß-catenin pathway. Moreover, ECR increased metabolites levels (especially α-KG) to promote energy metabolism in the colon tissue compared to CR. α-KG treatment increased L. reuteri level and alleviated mucosal damage in UC mice. It promoted L. reuteri growth by increasing the energy metabolic status by enhancing α-KG dehydrogenase activity. CONCLUSION: ECR processing improves the therapeutic effects of UC via the α-KG-L. reuteri-epithelial regeneration axis.

omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Single-cell RNA sequencing reveals in vivo osteoimmunology interactions between the immune and skeletal systems.

Background: While osteoimmunology interactions between the immune and skeletal systems are known to play an important role in osteoblast development, differentiation and bone metabolism related disease like osteoporosis, such interactions in either bone microenvironment or peripheral circulation in vivo at the single-cell resolution have not yet been characterized. Methods: We explored the osteoimmunology communications between immune cells and osteoblastic lineage cells (OBCs) by performing CellphoneDB and CellChat analyses with single-cell RNA sequencing (scRNA-seq) data from human femoral head. We also explored the osteoimmunology effects of immune cells in peripheral circulation on skeletal phenotypes. We used a scRNA-seq dataset of peripheral blood monocytes (PBMs) to perform deconvolution analysis. Then weighted gene co-expression network analysis (WGCNA) was used to identify monocyte subtype-specific subnetworks. We next used cell-specific network (CSN) and the least absolute shrinkage and selection operator (LASSO) to analyze the correlation of a gene subnetwork identified by WGCNA with bone mineral density (BMD). Results: We constructed immune cell and OBC communication networks and further identified L-R genes, such as JAG1 and NOTCH1/2, with ossification related functions. We also found a Mono4 related subnetwork that may relate to BMD variation in both older males and postmenopausal female subjects. Conclusions: This is the first study to identify numerous ligand-receptor pairs that likely mediate signals between immune cells and osteoblastic lineage cells. This establishes a foundation to reveal advanced and in-depth osteoimmunology interactions to better understand the relationship between local bone microenvironment and immune cells in peripheral blood and the impact on bone phenotypes.

A variability in response of osteoclasts to zoledronic acid is mediated by smoking-associated modification in the DNA methylome.

BACKGROUND: Clinical trials have shown zoledronic acid as a potent bisphosphonate in preventing bone loss, but with varying potency between patients. Human osteoclasts ex vivo reportedly displayed a variable sensitivity to zoledronic acid > 200-fold, determined by the half-maximal inhibitory concentration (IC50), with cigarette smoking as one of the reported contributors to this variation. To reveal the molecular basis of the smoking-mediated variation on treatment sensitivity, we performed a DNA methylome profiling on whole blood cells from 34 healthy female blood donors. Multiple regression models were fitted to associate DNA methylation with ex vivo determined IC50 values, smoking, and their interaction adjusting for age and cell compositions. RESULTS: We identified 59 CpGs displaying genome-wide significance (p < 1e-08) with a false discovery rate (FDR) < 0.05 for the smoking-dependent association with IC50. Among them, 3 CpGs have p < 1e-08 and FDR < 2e-03. By comparing with genome-wide association studies, 15 significant CpGs were locally enriched (within < 50,000 bp) by SNPs associated with bone and body size measures. Furthermore, through a replication analysis using data from a published multi-omics association study on bone mineral density (BMD), we could validate that 29 out of the 59 CpGs were in close vicinity of genomic sites significantly associated with BMD. Gene Ontology (GO) analysis on genes linked to the 59 CpGs displaying smoking-dependent association with IC50, detected 18 significant GO terms including cation:cation antiporter activity, extracellular matrix conferring tensile strength, ligand-gated ion channel activity, etc. CONCLUSIONS: Our results suggest that smoking mediates individual sensitivity to zoledronic acid treatment through epigenetic regulation. Our novel findings could have important clinical implications since DNA methylation analysis may enable personalized zoledronic acid treatment.

Xianglian pill modulates gut microbial production of succinate and induces regulatory T cells to alleviate ulcerative colitis in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Xianglian pill (XLP), a traditional Chinese formula, is widely used as treatment for ulcerative colitis (UC) in China. However, the mechanism of its therapeutic effect is still unclear. AIM OF THE STUDY: Our previous studies showed a low oral bioavailability and a predominant distribution of major XLP ingredients in the gut. In the present study, we aimed to explore the mechanism of action of XLP on UC with respect to the regulation of gut microecology. MATERIALS AND METHODS: UC model rats established using 5% dextran sulfate sodium were treated with XLP. After the treatment period, bodyweight, colon length, histopathology, and inflammatory changes were evaluated. Further, changes in gut microbiota structure were detected via 16S rRNA sequencing, and microbial metabolites in feces were analyzed via a metabolomic assay. Antibiotic intervention and fecal microbiota transplantation were also employed to explore the involvement of gut microbiota, while the level of regulatory T cells (Tregs) in mesenteric lymph nodes was determined via flow cytometry. Transcriptome sequencing was also performed to determine colonic gene changes. RESULTS: XLP alleviated colonic injury, inflammation, and gut microbial dysbiosis in UC model rats and also changed microbial metabolite levels. Particularly, it significantly decreased succinate level in the tyrosine pathway. We also observed that fecal microbiota derived from XLP-treated rats conferred resilience to UC model rats. However, this therapeutic effect of XLP on UC was inhibited by succinate. Moreover, XLP increased the level of anti-inflammatory cellular Tregs via gut microbiota. However, this beneficial effect was counteracted by succinate supplementation. Further, XLP induced the differentiation of Treg possibly by the regulation of the PHD2/HIF-1α pathway via decreasing microbial succinate production. CONCLUSIONS: Our findings indicated that XLP exerts its therapeutic effects on UC mainly via the gut microbiota-succinate-Treg differentiation axis.

Five-year remission without disease progression in a patient with relapsed/refractory multiple myeloma with extramedullary disease treated with LCAR-B38M chimeric antigen receptor T cells in the LEGEND-2 study: a case report.

BACKGROUND: Multiple myeloma remains incurable despite treatment advancements over the last 20 years. LCAR-B38M Cells in Treating Relapsed/Refractory Multiple Myeloma was a phase 1, first-in-human, investigator-initiated study in relapsed/refractory multiple myeloma conducted at four sites in China. The study used LCAR-B38M chimeric antigen receptor-T cells expressing two B-cell maturation antigen-targeting single-domain antibodies designed to confer avidity, and a CD3ζ signaling domain with a 4-1BB costimulatory domain to optimize T-cell activation and proliferation. This chimeric antigen receptor construct is identical to ciltacabtagene autoleucel. In the LEGEND-2 study (n = 57, Xi'an site), overall response rate was 88%; median (95% CI) progression-free survival and overall survival were 19.9 (9.6-31.0) and 36.1 (26.4-not evaluable) months, respectively; and median follow-up was 25 months. This case study reports on a patient with relapsed/refractory multiple myeloma (λ light chain type) who was treated with LCAR-B38M chimeric antigen receptor T cells in the LEGEND-2 study (Xi'an site); he had received five prior lines of treatment and had extensive extramedullary lesions. CASE PRESENTATION: The patient, a 56-year-old Asian male, received cyclophosphamide (500 mg daily × 3 days) as lymphodepletion therapy and a total dose of 0.5 × 106 chimeric antigen receptor + T cells/kg split into three infusions (days 1, 24, and 84 from June to August 2016). He experienced grade 2 cytokine release syndrome after the first infusion; all symptoms resolved with treatment. No cytokine release syndrome occurred following the second and third infusions. His λ light chain levels decreased and normalized 20 days after the first infusion, and extramedullary lesions were healed as of January 2018. He has sustained remission for 5 years and received no other multiple myeloma treatments after LCAR-B38M chimeric antigen receptor T cell infusion. As of 30 October 2020, the patient is still progression-free and has maintained minimal residual disease-negative (10-4) complete response status for 52 months. CONCLUSIONS: This case provides support that treatment with LCAR-B38M chimeric antigen receptor T cells can result in long-term disease remission of 5 or more years without disease progression in a heavily pretreated patient with extensive extramedullary disease and no other treatment options.

An autoencoder-based deep learning method for genotype imputation.

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

Metagenomic study of the gut microbiota associated with cow milk consumption in Chinese peri-/postmenopausal women.

Cow milk consumption (CMC) and alterations of gut bacterial composition are proposed to be closely related to human health and disease. Our research aims to investigate the changes in human gut microbial composition in Chinese peri-/postmenopausal women with different CMC habits. A total of 517 subjects were recruited and questionnaires about their CMC status were collected; 394 subjects were included in the final analyses. Fecal samples were used for studying gut bacterial composition. All the subjects were divided into a control group (n = 248) and a CMC group (n = 146) according to their CMC status. Non-parametric tests and LEfSe at different taxonomic levels were used to reveal differentially abundant taxa and functional categories across different CMC groups. Relative abundance (RA) of one phylum (p_Actinobacteria), three genera (g_Bifidobacterium, g_Anaerostipes, and g_Bacteroides), and 28 species diversified significantly across groups. Specifically, taxa g_Anaerostipes (p < 0.01), g_Bacteroides (p < 0.05), s_Anaerostipes_hadrus (p < 0.01), and s_Bifidobacterium_pseudocatenulatum (p < 0.01) were positively correlated with CMC levels, but p_Actinobacteria (p < 0.01) and g_Bifidobacterium (p < 0.01) were negatively associated with CMC levels. KEGG module analysis revealed 48 gut microbiome functional modules significantly (p < 0.05) associated with CMC, including Vibrio cholerae pathogenicity signature, cholera toxins (p = 9.52e-04), and cephamycin C biosynthesis module (p = 0.0057), among others. In conclusion, CMC was associated with changes in gut microbiome patterns including beta diversity and richness of some gut microbiota. The alterations of certain bacteria including g_Anaerostipes and s_Bifidobacterium_pseudocatenulatum in the CMC group should be important for human health. This study further supports the biological value of habitual cow milk consumption.

Single nucleotide mutation changes the capability of CCN3 in osteosarcoma cell invasion and differentiation.

This study aimed to identify significant mutations in CCN3 gene in osteosarcoma, and to explore the influence of this gene on cell invasion and differentiation and the underlying mechanism. Sanger sequencing was used to identify CCN3 gene sequence in human osteosarcoma cell lines, peripheral blood mononuclear cells (PBMC), and osteosarcoma tissues. Wild-type and mutant CCN3 (mCCN3) were ectopically expressed by lentivirus in human osteosarcoma cell lines. Tumor cell invasion was measured by trans-well assay. Osteogenic differentiation was induced by osteogenic differentiating medium and evaluated based on alkaline phosphatase activity and collagen type I alpha 1 chain and osteocalcin expression. Western blotting was used to detect protein levels of CCN3 and mCCN3 in cytoplasmic, nuclear and secreted fractions of cells. A G-to-A single nucleotide mutation in the coding region of CCN3 was found in both osteosarcoma cells and tissues. The frequency of this mutation in osteosarcoma tissue was much higher than that in para-carcinoma tissue and PBMC of healthy people. This nucleotide mutation decreased nuclear glycosylated full length protein level of CCN3 and affected osteosarcoma cell invasion and differentiation. A lower nuclear ratio of glycosylated/non-glycosylated isoforms accounted for the different behavior of mCCN3 compared with CCN3. The G-to-A mutation identified in CCN3 resulted in differential glycosylated full-length protein levels and altered the functional role of CCN3 in osteosarcoma cell invasion and differentiation.

A bi-directional Mendelian randomization study of the sarcopenia-related traits and osteoporosis.

Both sarcopenia and osteoporosis are common geriatric diseases causing huge socioeconomic burdens, and clinically, they often occur simultaneously. Observational studies have found a controversial correlation between sarcopenia and osteoporosis and their causal relationship is not clear. Therefore, we performed a bi-directional two-sample Mendelian randomization (MR) analysis to assess the potential causal relationship between sarcopenia-related traits (hand grip strength, lean mass, walking pace) and osteoporosis. Our analysis was performed by applying genetic variants obtained from the UK Biobank and the GEnetic Factors for OSteoporosis (GEFOS) datasets. We used inverse-variance weighted (IVW) and several sensitivity analyses to estimate and cross-validate the potential causal relationship in this study. We found that bone mineral density (BMD) was causally positively associated with left-hand grip strength (ß = 0.017, p-value = 0.001), fat-free mass (FFM; right leg FFM, ß = 0.014, p-value = 0.003; left arm FFM, ß = 0.014, p-value = 0.005), but not walking pace. Higher hand grip strength was potentially causally associated with increased LS-BMD (right-hand grip strength, ß = 0.318, p-value = 0.001; left-hand grip strength, ß = 0.358, p-value = 3.97 × 10-4). In conclusion, osteoporosis may be a risk factor for sarcopenia-related traits and muscle strength may have a site-specific effect on BMD.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans.

BACKGROUND: Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels. SUBJECTS AND METHODS: Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans. RESULTS: By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases. CONCLUSIONS: The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Pathway-based metabolomics study of sarcopenia-related traits in two US cohorts.

We aimed to validate two metabolites, aspartic acid and glutamic acid, which were associated with sarcopenia-related traits, muscle mass and strength, in our previous untargeted metabolomics study and to identify novel metabolites from five metabolic pathways involving these two metabolites. We included a discovery cohort of 136 white women aged 20-40 years (used for the previous untargeted metabolomics analysis) and a validation cohort of 174 subjects aged ≥ 60 years, including men and women of white and black. A targeted LC-MS assay successfully detected 12 important metabolites from these pathways. Aspartic acid was associated with muscle mass and strength in the discovery cohort, but not in the validation cohort. However, glutamic acid was associated with these sarcopenia traits in both cohorts. Additionally, N-acetyl-L-aspartic acid and carnosine were the newly identified metabolites that were associated with muscle strength in the discovery and validation cohorts, respectively. We did not observe any significant sex and race differences in the associations of these metabolites with sarcopenia traits in the validation cohort. Our findings indicated that glutamic acid might be consistently associated with sarcopenia-related traits across age, sex, and race. They also suggested that age-specific metabolites and metabolic pathways might be involved in muscle regulation.

Rivaroxaban Suppresses Atherosclerosis by Inhibiting FXa-Induced Macrophage M1 Polarization-Mediated Phenotypic Conversion of Vascular Smooth Muscle Cells.

Background: Factor Xa (FXa) is a mediator initiating and accelerating atherosclerosis (AS). Both macrophage and vascular smooth muscle cells (VSMCs) participate in AS progression. This study was aimed to investigate the mechanisms underlying the effects of the FXa inhibitor rivaroxaban on AS. Methods: Rivaroxaban was administered to AS mice. Primary macrophages were exposed to FXa, treated with rivaroxaban, and transfected with siRNA silencing protease-activated receptor 2 (PAR2), hypoxia-inducible factor 1α (HIF1α), delta-like receptor 4 (Dll4), and Akt. Interaction between macrophages and VSMCs was assessed by co-culturing systems. Atherosclerotic lesions were evaluated by oil red O stain. Fluorescent staining was used to determine the cell phenotypes. Secretions of inflammatory cytokines and collagen were assessed by ELISA and Sircol assays. Western blotting was used to evaluate the protein expression and phosphorylation levels. Results: Rivaroxaban reduced lesion area, accumulation of M1 macrophages, and contractile-synthetic phenotypic conversion of VSMCs in atherosclerotic plaques. FXa exposure induced polarization of macrophages toward M1 and Dll4 high expression, which were inhibited by PAR2, Akt1, and HIF1α silencing. Rivaroxaban treatment inhibited PAR2/Akt/HIF1α signaling activation and Dll4 expression in FXa-exposed macrophages. By cell-to-cell contact, M1 macrophages induced Notch signaling activation in VSMCs which committed contractile-synthetic conversion. Rivaroxaban treatment and Dll4 silencing incapacitated macrophage in inducing phenotypic conversion of VSMCs upon cell-to-cell contact. Conclusion: Rivaroxaban suppresses AS by inhibiting FXa-induced PAR2/Akt/HIF1α signaling activation-mediated macrophage M1 polarization and high Dll4 expression. These macrophages facilitated VSMCs to perform contractile-synthetic phenotypic conversion upon macrophage-VSMCs cell-to-cell contact.

A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets, with application to adolescent brain development and osteoporosis.

In recent years, a comprehensive study of complex disease with multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary information of disease, analysis of multi-view data with complex interactions is challenging for a deeper and holistic understanding of biological systems. In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets (GKMAHCE). This generalized semi-parametric (a mixed-effect linear model) approach includes the marginal and joint Hadamard product of features from different views of data. The proposed kernel machine approach considers multi-view data as predictor variables to allow a more thorough and comprehensive modeling of a complex trait. We applied GKMAHCE approach to both synthesized datasets and real multi-view datasets from adolescent brain development and osteoporosis study. Our experiments demonstrate that the proposed method can effectively identify higher-order composite effects and suggest that corresponding features (genes, region of interests, and chemical taxonomies) function in a concerted effort. We show that the proposed method is more generalizable than existing ones. To promote reproducible research, the source code of the proposed method is available at.

Identification of novel functional CpG-SNPs associated with Type 2 diabetes and birth weight.

Genome-wide association studies (GWASs) have identified hundreds of genetic loci for type 2 diabetes (T2D) and birth weight (BW); however, a large proportion of the total trait heritability remains unexplained. The previous studies were generally focused on individual traits and largely failed to identify the majority of the variants that play key functional roles in the etiology of the disease. Here, we aim to identify novel functional loci for T2D, BW and the pleiotropic variants shared between them by performing a targeted conditional false discovery rate (cFDR) analysis that integrates two independent GWASs with summary statistics for T2D (n = 26,676 cases and 132,532 controls) and BW (n = 153,781) which entails greater statistical power than individual trait analyses. In this analysis, we considered CpG-SNPs, which are SNPs that may influence DNA methylation status, and are therefore considered to be functionally important. We identified 103 novel CpG-SNPs for T2D, 182 novel CpG-SNPs for BW (cFDR < 0.05), and 52 novel pleiotropic loci for both (conjunction cFDR [ccFDR] < 0.05). Among the identified novel CpG-SNPs, 33 were annotated as methylation quantitative trait loci (meQTLs) in whole blood, and 145 displayed at least some effects on meQTL, metabolic QTL (metaQTL), and/or expression QTL (eQTL). These findings may provide further insights into the shared biological mechanisms and functional genetic determinants that overlap between T2D and BW, thereby providing novel potential targets for treatment/intervention development.

Activation of RAGE-dependent endoplasmic reticulum stress associates with exacerbated postmyocardial infarction ventricular arrhythmias in diabetes.

Association between receptor for advanced glycation end products (RAGE) and postmyocardial infarction (MI) ventricular arrhythmias (VAs) in diabetes was investigated. Correlation between premature ventricular contractions (PVCs) and serum advanced glycation end products (AGEs) content was analyzed in a cohort consisting of 101 patients with ST-segment elevated MI (STEMI). MI diabetic rats were treated with anti-receptor for AGE (RAGE) antibody. Electrocardiography was used to record VAs. Myocytes were isolated from adjacent area around infracted region. Immunofluorescent stains were used to evaluate the association between FKBP12.6 (FK506-bindingprotein 12.6) and ryanodine receptor 2 (RyR2). Calcium sparks were evaluated by confocal microscope. Protein expression and phosphorylation were assessed by Western blotting. Calcineurin (CaN) enzymatic activity and RyR2 channel activity were also determined. In the cohort study, significantly increased amount of PVC was found in STEMI patients with diabetes (P < 0.05). Serum AGE concentration was significantly positively correlated with PVC amount in patients with STEMI (r = 0.416, P < 0.001). Multivariate analysis showed that serum AGE concentration was independently and positively related to frequent PVCs (adjusted hazard ratio, 1.86; 95% CI, 1.09-3.18, P = 0.022). In the animal study, increased glucose-regulated protein 78 (GRP78) expression, protein kinase RNA-like ER kinase (PERK) phosphorylation, CaN enzymatic activity, FKBP12.6-RyR2 disassociation, RyR2 channel opening, and endoplasmic reticulum (ER) calcium releasing were found in diabetic MI animals, which were attenuated by anti-RAGE antibody treatment. This RAGE blocking also significantly lowered the VA amount in diabetic MI animals. Activation of RAGE-dependent ER stress-mediated PERK/CaN/RyR2 signaling participated in post-MI VAs in diabetes.NEW & NOTEWORTHY In this study, we proposed a possible mechanism interpreting the clinical scenario that after myocardial infarction (MI) patients were more vulnerable to ventricular arrhythmias (VAs) when complicated with diabetes. A cohort study revealed that advanced glycation end products (AGEs) accumulated in patients with diabetes and closely associated post-MI VAs. In vivo and in vitro studies indicated that receptor for AGEs (RAGE)-dependent endoplasmic reticulum (ER) stress protein kinase RNA-like ER kinase (PERK) pathway triggered VAs, via ER calcium releasing, through calcineurin/RyR2 mechanism.