Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

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.

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.

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.

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.

Identification of a 1p21 independent functional variant for abdominal obesity.

OBJECTIVES: Aiming to uncover the genetic basis of abdominal obesity, we performed a genome-wide association study (GWAS) meta-analysis of trunk fat mass adjusted by trunk lean mass (TFMadj) and followed by a series of functional investigations. SUBJECTS: A total of 11,569 subjects from six samples were included into the GWAS meta-analysis. METHODS: Meta-analysis was performed by a weighted fixed-effects model. In silico replication analysis was performed in the UK-Biobank (UKB) sample (N = 331,093) and in the GIANT study (N up to 110,204). Cis-expression QTL (cis-eQTL) analysis, dual-luciferase reporter assay and electrophoresis mobility shift assay (EMSA) were conducted to examine the functional relevance of the identified SNPs. At last, differential gene expression analysis (DGEA) was performed. RESULTS: We identified an independent SNP rs12409479 at 1p21 (MAF = 0.07, p = 7.26 × 10-10), whose association was replicated by the analysis of TFM in the UKB sample (one-sided p = 3.39 × 10-3), and was cross-validated by the analyses of BMI (one-sided p = 0.03) and WHRadj (one-sided p = 0.04) in the GIANT study. Cis-eQTL analysis demonstrated that allele A at rs12409479 was positively associated with PTBP2 expression level in subcutaneous adipose tissue (N = 385, p = 4.15 × 10-3). Dual-luciferase reporter assay showed that the region repressed PTBP2 gene expression by downregulating PTBP2 promoter activity (p < 0.001), and allele A at rs12409479 induced higher luciferase activity than allele G did (p = 4.15 × 10-3). EMSA experiment implied that allele A was more capable of binding to unknown transcription factors than allele G. Lastly, DGEA showed that the level of PTBP2 expression was higher in individuals with obesity than in individuals without obesity (N = 20 and 11, p = 0.04 and 9.22 × 10-3), suggesting a regulatory role in obesity development. CONCLUSIONS: Taken together, we hypothesize a regulating path from rs12409479 to trunk fat mass development through its allelic specific regulation of PTBP2 gene expression, thus providing some novel insight into the genetic basis of abdominal obesity.

Genome-wide association study of lncRNA polymorphisms with bone mineral density.

Recent studies suggested that long noncoding RNAs (lncRNAs) were widely transcribed in the genome, but their potential roles in the genetic complexity of human disorders required further exploration. The purpose of the present study was to explore genetic polymorphisms of lncRNAs associated with bone mineral density (BMD) and its potential value. Based on the lncRNASNP database, 55,906 lncSNPs were selected to conduct a genome-wide association study meta-analysis among 11,140 individuals of seven independent studies for BMDs at femoral neck (FN), lumbar spine, and total hip (HIP). Promising results were replicated in Genetic Factors for Osteoporosis Consortium (GEFOS Sequencing, n = 32,965). We found two lncRNA loci that were significantly associated with BMD. MEF2C antisense RNA 1 (MEF2C-AS1) located at 5q14.3 was significantly associated with FN-BMD after Bonferroni correction, and the strongest association signal was detected at rs6894139 (P = 3.03 × 10-9 ). LOC100506136 rs6465531 located at 7q21.3 showed significant association with HIP-BMD (P = 7.43 × 10-7 ). MEF2C-AS1 rs6894139 was replicated in GEFOS Sequencing with P-value of 1.43 × 10-23 . Our results illustrated the important role of polymorphisms in lncRNAs in determining variations of BMD and provided justification and evidence for subsequent functional studies.

Bivariate genome-wide association analyses identified genetic pleiotropic effects for bone mineral density and alcohol drinking in Caucasians.

Several studies indicated bone mineral density (BMD) and alcohol intake might share common genetic factors. The study aimed to explore potential SNPs/genes related to both phenotypes in US Caucasians at the genome-wide level. A bivariate genome-wide association study (GWAS) was performed in 2069 unrelated participants. Regular drinking was graded as 1, 2, 3, 4, 5, or 6, representing drinking alcohol never, less than once, once or twice, three to six times, seven to ten times, or more than ten times per week respectively. Hip, spine, and whole body BMDs were measured. The bivariate GWAS was conducted on the basis of a bivariate linear regression model. Sex-stratified association analyses were performed in the male and female subgroups. In males, the most significant association signal was detected in SNP rs685395 in DYNC2H1 with bivariate spine BMD and alcohol drinking (P = 1.94 × 10-8). SNP rs685395 and five other SNPs, rs657752, rs614902, rs682851, rs626330, and rs689295, located in the same haplotype block in DYNC2H1 were the top ten most significant SNPs in the bivariate GWAS in males. Additionally, two SNPs in GRIK4 in males and three SNPs in OPRM1 in females were suggestively associated with BMDs (of the hip, spine, and whole body) and alcohol drinking. Nine SNPs in IL1RN were only suggestively associated with female whole body BMD and alcohol drinking. Our study indicated that DYNC2H1 may contribute to the genetic mechanisms of both spine BMD and alcohol drinking in male Caucasians. Moreover, our study suggested potential pleiotropic roles of OPRM1 and IL1RN in females and GRIK4 in males underlying variation of both BMD and alcohol drinking.

Inhibition of STAT Pathway Impairs Anti-Hepatitis C Virus Effect of Interferon Alpha.

BACKGROUND/AIMS: Signal transducer and activator of transcription (STAT) pathway plays an important role in antiviral efficacy of interferon alpha (IFN-α). IFN-α is the main therapeutic against hepatitis C virus (HCV) infection. We explored effects of IFN-α on HCV replication and antiviral gene expression by targeting STAT. METHODS: In response to IFN-α, STAT status, HCV replication, and antiviral gene expression were analyzed in human hepatoma Huh7.5.1 cells before and after cell culture-derived HCV infection. RESULTS: IFN-α treatment induced expression and phosphorylation of STAT1 and STAT2 in Huh7.5.1 cells. Pretreatment of Huh7.5.1 cells with a mAb to IFN alpha receptor (IFNAR) 2 decreased IFN-α-dependent phosphorylation of STAT1 and STAT2, whereas pretreatment with an IFNAR1 mAb increased such phosphorylation, suggesting that IFNAR mediates IFN-α-triggered STAT signaling. During HCV infection, STAT1 and STAT2 phosphorylation could be rescued by IFN-α and IFN-α-induced phosphorylation of STAT1 and STAT2 was impaired. Inhibition of STAT pathway by Jak inhibitor I significantly enhanced HCV RNA replication and viral protein expression. Antiviral genes coding for IFN regulatory factor 9 and IFN-stimulated gene 15 were up-regulated by IFN-α during HCV infection but such up-regulation was abrogated by Jak inhibitor I. CONCLUSION: These results establish that activation of STAT pathway is essential for anti-HCV efficacy of IFN-α. Impairment of IFN-α-triggered STAT signaling by HCV may account for evading IFN-α response.

Interferon alpha antagonizes STAT3 and SOCS3 signaling triggered by hepatitis C virus.

We aimed to investigate regulation of signal transducer and activator of transcription 3 (STAT3) and suppressor of cytokine signaling 3 (SOCS3) by interferon alpha (IFN-α) and to analyze the relationship between STAT3 and SOCS3 during hepatitis C virus (HCV) infection. Changes in STAT3 and SOCS3 were analyzed at both mRNA and protein levels in human hepatoma cells infected with HCV (J6/JFH1). At 72h of HCV infection, STAT3 expression was decreased with sustained phosphorylation, and IFN-α increased such decrease and phosphorylation. HCV increased SOCS3 expression, while IFN-α impaired such increase, indicating different regulation of STAT3 and SOCS3 by IFN-α. IFN-α-induced expression and phosphorylation of upstream kinases of the JAK/STAT pathway, Tyk2 and Jak1, were suppressed by HCV. Moreover, knockdown of STAT3 by RNA interference led to decreases in HCV RNA replication and viral protein expression, without affecting either the expression of Tyk2 and Jak1 or the SOCS3 induction in response to IFN-α. These results show that IFN-α antagonizes STAT3 and SOCS3 signaling triggered by HCV and that STAT3 regulation correlates inversely with SOCS3 induction by IFN-α, which may be important in better understanding the complex interplay between IFN-α and signal molecules during HCV infection.

Involvement of ERK pathway in interferon alpha-mediated antiviral activity against hepatitis C virus.

Interferon alpha (IFN-α) is the key component of the therapy for hepatitis C virus (HCV) infection. IFN-α exerts anti-HCV activity by targeting certain signaling pathways. Using infectious HCV culture system in human hepatoma Huh7.5.1 cells, we analyzed functional relevance of extracellular signal-regulated kinase (ERK) pathway for IFN-α-mediated anti-HCV activity. IFN-α treatment resulted in activation of ERK pathway by increasing phosphorylation of c-Raf, MEK, and ERK1/2 in Huh7.5.1 cells, whereas HCV impaired such activation. IFN-α-dependent ERK1/2 phosphorylation was blocked by MEK inhibitor U0126. Pharmacological inhibition of ERK1/2 by U0126 or siRNA-mediated knockdown of ERK1/2 resulted in suppressive effects on HCV RNA levels and expression of HCV nonstructural protein 3 and envelope protein 2, establishing an important role for ERK pathway in HCV replication. Moreover, induction of a set of antiviral genes by IFN-α was enhanced in HCV-infected Huh7.5.1 cells due to the ERK1/2 knockdown, suggesting that impairment of ERK signaling may potentiate HCV inhibition by IFN-α. These results demonstrate that ERK pathway is involved in IFN-α-mediated antiviral activity against HCV.

Small nucleolar RNA 113-1 suppresses tumorigenesis in hepatocellular carcinoma.

BACKGROUND: Emerging evidence suggests that small nucleolar RNAs (snoRNAs) are involved in tumorigenesis. The roles of small nucleolar RNA 113-1 (SNORD113-1) on the development of hepatocellular carcinoma (HCC) remain unknown. METHODS: The expression of SNORD113-1 was measured in 112 HCC tumor tissues using quantitative RT-PCR and compared with expression levels from with paired non-tumor tissues. The effects of SNORD113-1 on HCC tumorigenesis were investigated in HepG2 and Huh7 cells as well as a xenograft nude mouse model. CpG methylation within the promoter region of the SNORD113-1 gene was identified using Sodium bisulfite sequencing. Cancer pathway reporter investigate the mechanism by which SNORD113-1 suppressed tumorigenesis. RESULTS: SNORD113-1 expression was significantly downregulated in HCC tumors compared with adjacent non-tumor tissues, and downregulation of SNORD113-1 in HCC tumors was significantly associated with worse survival of patients. In addition, CpG methylation at the promoter region of the SNORD113-1 gene was higher in HCC tumors than adjacent non-tumor tissues. Functionally, SNORD113-1 suppressed cancer cell growth in HepG2 and Huh7 cells and in a xenograft nude mouse model. Furthermore, SNORD113-1 inactivated the phosphorylation of ERK1/2 and SMAD2/3 in MAPK/ERK and TGF-ß pathways. CONCLUSIONS: SNORD113-1 functions as a tumor suppressor role in HCC and may be important as a potential diagnostic and therapeutic target for HCC.

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.

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.

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.

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

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.

Interferon alpha and ribavirin collaboratively regulate p38 mitogen-activated protein kinase signaling in hepatoma cells.

Signaling events triggered by interferon alpha (IFN-α) and ribavirin are involved in anti-hepatitis C virus (HCV) action. The p38 mitogen-activated protein kinase (MAPK) pathway plays an important role in HCV pathogenesis. Effects of IFN-α and ribavirin on p38 MAPK signaling were investigated in human hepatoma cells. Type I IFN receptor 2 (IFNAR2) mediated IFN-α-induced p38 MAPK phosphorylation. Also, p38 MAPK phosphorylation was enhanced by ribavirin. Treatment for 48 h with a combination of IFN-α and ribavirin increased p38 MAPK phosphorylation, whereas the treatment for 72 h reduced p38 MAPK phosphorylation. Cell culture-derived HCV (HCVcc) infection dramatically increased p38 MAPK phosphorylation and such phosphorylation was inhibited by IFN-α or ribavirin. Moreover, siRNA-mediated knockdown of p38 MAPK resulted in enhancement of ribavirin-dependent HCV RNA replication. These results suggest that regulation of p38 MAPK signaling by IFN-α and ribavirin might contribute to anti-HCV action.

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.