CD11b maintains West Nile virus replication through modulation of immune response in human neuroblastoma cells.

BACKGROUND: West Nile virus (WNV) is a rapidly spreading mosquito-borne virus accounted for neuroinvasive diseases. An insight into WNV-host factors interaction is necessary for development of therapeutic approaches against WNV infection. CD11b has key biological functions and been identified as a therapeutic target for several human diseases. The purpose of this study was to determine whether CD11b was implicated in WNV infection. METHODS: SH-SY5Y cells with and without MEK1/2 inhibitor U0126 or AKT inhibitor MK-2206 treatment were infected with WNV. CD11b mRNA levels were assessed by real-time PCR. WNV replication and expression of stress (ATF6 and CHOP), pro-inflammatory (TNF-α), and antiviral (IFN-α, IFN-ß, and IFN-γ) factors were evaluated in WNV-infected SH-SY5Y cells with CD11b siRNA transfection. Cell viability was determined by MTS assay. RESULTS: CD11b mRNA expression was remarkably up-regulated by WNV in a time-dependent manner. U0126 but not MK-2206 treatment reduced the CD11b induction by WNV. CD11b knockdown significantly decreased WNV replication and protected the infected cells. CD11b knockdown markedly increased TNF-α, IFN-α, IFN-ß, and IFN-γ mRNA expression induced by WNV. ATF6 mRNA expression was reduced upon CD11b knockdown following WNV infection. CONCLUSION: These results demonstrate that CD11b is involved in maintaining WNV replication and modulating inflammatory as well as antiviral immune response, highlighting the potential of CD11b as a target for therapeutics for WNV infection.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

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-scale 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.

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 mass 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 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 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.

Engagement of AKT and ERK signaling pathways facilitates infection of human neuronal cells with West Nile virus.

West Nile virus (WNV) is an important neurotropic virus that accounts for the emergence of human arboviral encephalitis and meningitis. The interaction of WNV with signaling pathways plays a key role in controlling WNV infection. We have investigated the roles of the AKT and ERK pathways in supporting WNV propagation and modulating the inflammatory response following WNV infection. WNV established a productive infection in neuronal cell lines originated from human and mouse. Expression of IL-11 and TNF-α was markedly up-regulated in the infected human neuronal cells, indicating elicitation of inflammation response upon WNV infection. WNV incubation rapidly activated signaling cascades of AKT (AKT-S6-4E-BP1) and ERK (MEK-ERK-p90RSK) pathways. Treatment with AKT inhibitor MK-2206 or MEK inhibitor U0126 abrogated WNV-induced AKT or ERK activation. Strong activation of AKT and ERK signaling pathways could be detectable at 24 h after WNV infection, while such activation was abolished at 48 h post infection. U0126 treatment or knockdown of ERK expression significantly increased WNV RNA levels and viral titers and efficiently decreased IL-11 production induced by WNV, suggesting the involvement of ERK pathway in WNV propagation and IL-11 induction. MK-2206 treatment enhanced WNV RNA replication accompanied with a moderate decrease in IL-11 production. These results demonstrate that engagement of AKT and ERK signaling pathways facilitates viral infection and may be implicated in WNV pathogenesis.

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.

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.

Inhibition of tick-borne encephalitis virus in cell cultures by ribavirin.

Tick-borne encephalitis virus (TBEV) belonging to arboviruses is a major member of zoonotic pathogens. TBEV infection causes severe human encephalitis without specific antiviral drugs. Due to its use of antiviral drug against a wide range of viruses, we investigated antiviral effect of ribavirin against TBEV in susceptible human cell lines A549 and SH-SY5Y. Ribavirin displayed minor cytotoxicity on multiple cell lines. Ribavirin obviously impaired TBEV replication and protected the infected cells from cytopathic effect. Importantly, ribavirin markedly inhibited TBEV propagation, as evidenced by impairment of TBEV production and viral RNA replication. Treatment with ribavirin (co-treatment and post-treatment) led to a dose-dependent reduction in TBEV titers as well as the viral RNA levels. Antiviral protein myxovirus resistance A mRNA expression was significantly up-regulated and signal transducer and activator of transcription 3 was activated in TBEV-infected A549 cells upon the ribavirin treatment. Induction of inflammatory cytokine tumor necrosis factor alpha by TBEV was decreased in A549 cells with the treatment of ribavirin, whereas interleukin 1 beta release appeared to be unaffected. These results suggest that ribavirin might represent a promising safe and effective antiviral drug against TBEV.

ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.

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.

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.

Associations of physical activity with sarcopenia and sarcopenic obesity in middle-aged and older adults: the Louisiana osteoporosis study.

BACKGROUND: This study examined the associations between physical activity, obesity, and sarcopenia in middle-aged and older adults. METHODS: We analyzed the data of 8, 919 study participants aged between 45 to 97 (mean age = 57.2 ± 8.8) from a Southern state in the United States. Self-reported physical activity was classified to regular exercise ≥ 3 times/week, < 3 times/week, and no regular exercise. Associations between physical activity, obesity and sarcopenia were explored with generalized linear models and ordinal logistic regressions stratified by age (middle-aged and older adults) and gender adjusting for covariates. RESULTS: In middle-aged and older adults, all examined obesity related traits (e.g., body mass index, waist circumference) were inversely associated with physical activity levels (p < 0.01) in both genders. Exercising ≥ 3 times/week was negatively associated with lean mass indicators (e.g., appendicular lean mass) in middle-aged and older females (p < 0.01), while the negative associations become positive after adjusting for weight. Positive associations between physical activity and grip strength were only found in middle-aged males (p < 0.05). Ordinal logistic regression revealed that those exercising ≥ 3 times/week were less likely to have obesity, sarcopenia, and sarcopenia obesity in all groups (p < 0.01), except for sarcopenia in older males and females (p > 0.05). Positive associations of exercising < 3 times/week with sarcopenia and sarcopenia obesity were only found in middled adults. CONCLUSION: The associations of exercise frequency with obesity and sarcopenia vary considerably across gender and age groups. Exercise programs need to be individualized to optimize health benefits. Future research exploring physical activity strategies to balance weight reduction and lean mass maintaining is warranted in middle-aged and especially older adults.

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.

A multiethnic whole genome sequencing study to identify novel loci for bone mineral density.

At present, there have only been a few DNA sequencing-based studies to explore the genetic determinants of bone mineral density (BMD). We carried out the largest whole genome sequencing analysis to date for femoral neck and spine BMD (n = 4981), with one of the highest average sequencing depths implemented thus far at 22×, in a multiethnic sample (58% Caucasian and 42% African American) from the Louisiana Osteoporosis Study (LOS). The LOS samples were combined with summary statistics from the GEFOS consortium and several independent samples of various ethnicities to perform GWAS meta-analysis (n = 44 506). We identified 31 and 30 genomic risk loci for femoral neck and spine BMD, respectively. The findings substantiate many previously reported susceptibility loci (e.g. WNT16 and ESR1) and reveal several others that are either novel or have not been widely replicated in GWAS for BMD, including two for femoral neck (IGF2 and ZNF423) and one for spine (SIPA1). Although we were not able to uncover ethnicity specific differences in the genetic determinants of BMD, we did identify several loci which demonstrated sex-specific associations, including two for women (PDE4D and PIGN) and three for men (TRAF3IP2, NFIB and LYSMD4). Gene-based rare variant association testing detected MAML2, a regulator of the Notch signaling pathway, which has not previously been suggested, for association with spine BMD. The findings provide novel insights into the pathophysiological mechanisms of osteoporosis.

A transcriptome-wide association study to detect novel genes for volumetric bone mineral density.

Transcriptome-wide association studies (TWAS) systematically investigate the association of genetically predicted gene expression with disease risk, providing an effective approach to identify novel susceptibility genes. Osteoporosis is the most common metabolic bone disease, associated with reduced bone mineral density (BMD) and increased risk of osteoporotic fractures, whereas genetic factors explain approximately 70% of the variance in phenotypes associated with bone. BMD is commonly assessed using dual-energy X-ray absorptiometry (DXA) to obtain measurements (g/cm2) of areal BMD. However, quantitative computed tomography (QCT) measured 3D volumetric BMD (vBMD) (g/cm3) has important advantages compared with DXA since it can evaluate cortical and trabecular microstructural features of bone quality, which can be used to directly predict fracture risk. Here, we performed the first TWAS for volumetric BMD (vBMD) by integrating genome-wide association studies (GWAS) data from two independent cohorts, namely the Framingham Heart Study (FHS, n = 3298) and the Osteoporotic Fractures in Men (MrOS, n = 4641), with tissue-specific gene expression data from the Genotype-Tissue Expression (GTEx) project. We first used stratified linkage disequilibrium (LD) score regression approach to identify 12 vBMD-relevant tissues, for which vBMD heritability is enriched in tissue-specific genes of the given tissue. Focusing on these tissues, we subsequently leveraged GTEx expression reference panels to predict tissue-specific gene expression levels based on the genotype data from FHS and MrOS. The associations between predicted gene expression levels and vBMD variation were then tested by MultiXcan, an innovative TWAS method that integrates information available across multiple tissues. We identified 70 significant genes associated with vBMD, including some previously identified osteoporosis-related genes such as LYRM2 and NME8, as well as some novel loci such as DNAAF2 and SPAG16. Our findings provide novel insights into the pathophysiological mechanisms of osteoporosis and highlight several novel vBMD-associated genes that warrant further investigation.

The mediating effect of skeletal muscle index on the relationship between menarcheal age and bone mineral density in premenopausal women by race/ethnicity.

OBJECTIVE: To examine the contribution of skeletal mass index (SMI) as a mediator in the relationship between menarcheal age and hip/spine bone mineral density (BMD) in premenopausal women by race/ethnicity. METHODS: The data of 4,329 participants (age ≥ 18; mean age=35.7 ±â€Š9.5) of Whites (n = 2,543), African Americans (n = 1,236), and Asians (n = 550) enrolled from October 2011 to January 2019 from the Louisiana Osteoporosis Study were analyzed. After adjustment for physiological and behavioral factors, multivariable linear regression analyses were conducted to evaluate each component of the proposed mediation models, and mediation was verified by the bootstrapping resampling approach. RESULTS: Premenopausal women with early menarcheal age tended to have higher SMI and BMD than women with normal menarcheal age among all races/ethnicities included. Women with late menarcheal age were, however, more likely to have a lower SMI than their counterparts with normal menarcheal age (r = -0.212, 95% CI = [-0.321 to -0.103] for White women; r = -0.181, 95% CI = [-0.410 to -0.008] for African-American women; r = -0.174, 95% CI = [-0.343 to -0.006] for Asian women). Similar results were found for both spine and hip BMD. SMI fully mediated the difference in BMD due to different menarcheal ages among Whites, African Americans, and Asian women with early menarcheal age; however, no mediating effects were observed for Asian women with late menarcheal age. CONCLUSIONS: SMI, as a full mediator, affected the relationship between menarcheal age and BMD among premenopausal women, and the mediating effects varied by race/ethnicity. To prevent or slow down the loss of hip/spine BMD and the development of osteoporosis, measures aiming at minimizing the risk for muscle mass loss should be recommended, especially for White and African-American women with late menarcheal age.

Mendelian Randomization Identifies CpG Methylation Sites With Mediation Effects for Genetic Influences on BMD in Peripheral Blood Monocytes.

Osteoporosis is mainly characterized by low bone mineral density (BMD) and is an increasingly serious public health concern. DNA methylation is a major epigenetic mechanism that may contribute to the variation in BMD and may mediate the effects of genetic and environmental factors of osteoporosis. In this study, we performed an epigenome-wide DNA methylation analysis in peripheral blood monocytes of 118 Caucasian women with extreme BMD values. Further, we developed and implemented a novel analytical framework that integrates Mendelian randomization with genetic fine mapping and colocalization to evaluate the causal relationships between DNA methylation and BMD phenotype. We identified 2,188 differentially methylated CpGs (DMCs) between the low and high BMD groups and distinguished 30 DMCs that may mediate the genetic effects on BMD. The causal relationship was further confirmed by eliminating the possibility of horizontal pleiotropy, linkage effect and reverse causality. The fine-mapping analysis determined 25 causal variants that are most likely to affect the methylation levels at these mediator DMCs. The majority of the causal methylation quantitative loci and DMCs reside within cell type-specific histone mark peaks, enhancers, promoters, promoter flanking regions and CTCF binding sites, supporting the regulatory potentials of these loci. The established causal pathways from genetic variant to BMD phenotype mediated by DNA methylation provide a gene list to aid in designing future functional studies and lead to a better understanding of the genetic and epigenetic mechanisms underlying the variation of BMD.

Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally performed for single-trait/disease and failed to assess the pleiotropic role of the identified variants. To identify novel functional loci and the pleiotropic relationship between CAD and T2D, the targeted cFDR analysis on CpG-SNPs was performed by integrating two independent large and multi-centered GWASs with summary statistics of T2D (26,676 cases and 132,532 controls) and CAD (60,801 cases and 123,504 controls). Applying the cFDR significance threshold of 0.05, we observed a pleiotropic enrichment between T2D and CAD by incorporating pleiotropic effects into a conditional analysis framework. We identified 79 novel CpG-SNPs for T2D, 61 novel CpG-SNPs for CAD, and 18 novel pleiotropic loci for both traits. Among these novel CpG-SNPs, 33 of them were annotated as methylation quantitative trait locus (meQTL) in whole blood, and ten of them showed expression QTL (eQTL), meQTL, and metabolic QTL (metaQTL) effects simultaneously. To the best of our knowledge, we performed the first targeted cFDR analysis on CpG-SNPs, and our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.