Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

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.

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.

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.

Interleukin-36 receptor antagonist attenuates atherosclerosis development by inhibiting NLRP3 inflammasome.

Atherosclerosis is characterized, as an inflammatory disorder in the circulatory system, with increasing tendency toward mortality and morbidity. Thus, developing novel therapeutic targeting inflammation is necessary. Here, we investigated the effects of interleukin-36 receptor antagonist (IL-36RN), a newly identified anti-inflammatory factor, on atherosclerosis. The regulation of NLRP3 inflammasome by IL-36RN was determined in vitro in macrophage cells after oxidized low-density lipoprotein (ox-LDL) stimulation. The IL-1ß and caspase-1 p10 secretion were assessed by enzyme-linked immunosorbent assay and western blot analysis. Finally, the IL-36RN/NLRP3 inflammasome pathway was confirmed in apolipoprotein E-deficient mice. IL-36RN suppressed the expression of NLRP3, the secretion of IL-1ß, and caspase-1 p10 in vitro, while IL-36 pathway stimulation activated the NLRP3 inflammasome, which was inhibited by IL-36RN. In the mouse model of atherosclerosis, IL-36RN delivered by the lentivirus vector inhibited the development of atherosclerosis, and the atheroprotective effects of IL-36RN were attenuated by IL-36 pathway stimulation. Furthermore, the regulation of NLRP3 inflammasome by IL-36RN was also confirmed in vivo. We demonstrated here that IL-36RN exerted atheroprotective functions through IL-36RN/NLRP3 inflammasome pathway.

Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis.

Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.

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.

Novel Prognostic Model for Gastric Cancer using 13 Co-Expression Long Non-Coding RNAs (LncRNAs).

BACKGROUND The established clinical criteria for gastric cancer prognosis are insufficient due to molecular heterogeneity. Therefore, constructing a robust prognostic model is essential to predict gastric cancer patient survival. MATERIAL AND METHODS A comprehensive method, which combined weighted gene co-expression network analysis (WGCNA) with elastic-net Cox regression, was utilized to identify prognostic long non-coding RNAs (lncRNAs) from Gene Expression Omnibus database for overall survival (OS) prediction. Methods using WGCNA or elastic-net Cox regression alone were treated as "contrast" methods. The univariate and multivariate Cox regression was used to identify independent prognostic clinical factors. We performed 3-year and 5-year area under the curve (AUC) of the time-dependent receiver operating characteristic comparison of 3 different methods in gene and clinical-gene models to explore the prediction ability of the comprehensive method. The optimal model identified in the training set were validated in the validation set. Biological information analysis for the optimal model was also explored. RESULTS The clinical-gene model containing 13 co-expression lncRNAs identified by the comprehensive method and 3 clinical factors including molecular subtype, recurrence status and operation type, was the found to be the optimal model in the study, with 0.832 and 0.830 for the 3-year and 5-year AUC in the training set, and 0.764 and 0.778 in the validation set, respectively. Biological information analysis suggested that lipid metabolism played an important role in the occurrence and development of gastric cancer. CONCLUSIONS We constructed a novel prognostic model containing 13 co-expression lncRNAs and 3 clinical factors for gastric cancer patients.

Ear Crease Features Are Associated with Complexity of Coronary Lesions.

BACKGROUND The presence of a Frank's sign ear crease is closely correlated with coronary artery disease (CAD). The SYNTAX score indicates the complexity of coronary lesions. This present investigation sought to identify the correlation between SYNTAX score and several specific ear creases. MATERIAL AND METHODS Four specific types of ear creases - crossing crease not originated from ear hole (CC-NEH), crossing crease originated from ear hole (CC-EH), vertical creases on the face side (VC-F), and vertical creases dividing earlobe and face (VC-EF) - were investigated in patients undergoing coronary angiography. A Frank's sign score system was introduced based on the 4 creases. Demographic data, clinical data, and SYNTAX score were also documented. The association between ear creases and SYNTAX score, as well as the correlation between Frank's score and SYNTAX score, were statistically analyzed. RESULTS CC-NEH had the highest positive predictive value (positive predictive value=0.439), and VC-F had the highest negative predictive value for the detection of intermediate and high SYNTAX score (negative predictive value=1.000). VC-EF and CC-NEH were associated with intermediate and high SYNTAX scores (OR=2.913-7.694, all P<0.05). Only 2.9% of patients with Frank's score=0 had intermediate or high SYNTAX scores, and 52.2% and 50.0% of patients with Frank's sign score=3 and 4 had intermediate or high SYNTAX scores, respectively. The Frank's sign score was significantly and positively correlated with SYNTAX score (r=0.457, P<0.001). CONCLUSIONS Features of specific ear creases and Frank's sign scores were associated with intermediate and high complexity of coronary lesions.

Integrative genomic analysis predicts novel functional enhancer-SNPs for bone mineral density.

Osteoporosis is a skeletal disorder characterized by low bone mineral density (BMD) and deterioration of bone microarchitecture. To identify novel genetic loci underlying osteoporosis, an effective strategy is to focus on scanning of variants with high potential functional impacts. Enhancers play a crucial role in regulating cell-type-specific transcription. Therefore, single-nucleotide polymorphisms (SNPs) located in enhancers (enhancer-SNPs) may represent strong candidate functional variants. Here, we performed a targeted analysis for potential functional enhancer-SNPs that may affect gene expression and biological processes in bone-related cells, specifically, osteoblasts, and peripheral blood monocytes (PBMs), using five independent cohorts (n = 5905) and the genetics factors for osteoporosis summary statistics, followed by comprehensive integrative genomic analyses of chromatin states, transcription, and metabolites. We identified 15 novel enhancer-SNPs associated with femoral neck and lumbar spine BMD, including 5 SNPs mapped to novel genes (e.g., rs10840343 and rs10770081 in IGF2 gene) and 10 novel SNPs mapped to known BMD-associated genes (e.g., rs2941742 in ESR1 gene, and rs10249092 and rs4342522 in SHFM1 gene). Interestingly, enhancer-SNPs rs10249092 and rs4342522 in SHFM1 were tightly linked, but annotated to different enhancers in PBMs and osteoblasts, respectively, suggesting that even tightly linked SNPs may regulate the same target gene and contribute to the phenotype variation in cell-type-specific manners. Importantly, ten enhancer-SNPs may also regulate BMD variation by affecting the serum metabolite levels. Our findings revealed novel susceptibility loci that may regulate BMD variation and provided intriguing insights into the genetic mechanisms of osteoporosis.

hnRNPs and ELAVL1 cooperate with uORFs to inhibit protein translation.

Most of our knowledge about translation regulatory mechanisms comes from studies on lower organisms. However, the translation control system of higher organisms is less understood. Here we find that in 5' untranslated region (5'UTR) of human Annexin II receptor (AXIIR) mRNA, there are two upstream open reading frames (uORFs) acting in a fail-safe manner to inhibit the translation from the main AUG. These uORFs are unfavorable for re-initiation after termination of uORF translation. Heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), hnRNPA0 and ELAV like RNA binding protein 1 (ELAVL1) bind to the 5'UTR of AXIIR mRNA. They focus the translation of uORFs on uORF1 and attenuate leaky scanning that bypasses uORFs. The cooperation between the two uORFs and the three proteins formed a multiple fail-safe system that tightly inhibits the translation of downstream AXIIR. Such cooperation between multiple molecules and elements reflects that higher organism develops a complex translation regulatory system to achieve accurate and flexible gene expression control.

Evidence of peripheral olfactory impairment in the domestic silkworms: insight from the comparative transcriptome and population genetics.

BACKGROUND: The insect olfactory system is a highly specific and sensitive chemical detector, which plays important roles in feeding, mating and finding an appropriate oviposition site. The ecological niche of Bombyx mori has changed greatly since domestication from B. mandarina, and its olfactory response to environmental odorants clearly decreased. However, the mechanisms that result in the olfactory impairment are largely unknown. RESULTS: The antennal transcriptomes were compared between the domestic and wild silkworms. Comparison of the same sex between the domestic and wild silkworms revealed 1410 and 1173 differentially expressed genes (DEGs) in males and females, respectively. To understand the olfactory impairment, we mainly focused on the olfactory-related genes. In total, 30 olfactory genes and 19 odorant-degrading enzymes (ODEs) showed differential expression in the two comparisons, in which 19 and 14 were down-regulated in the domestic silkworm, respectively. Based on population genomic data, the down-regulated odorant receptors (ORs) showed a higher ratio of unique non-synonymous polymorphisms to synonymous polymorphisms (N/S ratio) in the domestic populations than that in the wild silkworms. Furthermore, one deleterious mutation was found in OR30 of the domestic population, which was located in transmembrane helix 6 (TM6). CONCLUSIONS: Our results suggested that down-regulation of the olfactory-related genes and relaxed selection might be the major reasons for olfactory impairment of the domestic silkworm reared completely indoor environment. Reversely, wild silkworm may increase expression and remove deleterious polymorphisms of olfactory-related genes to retain sensitive olfaction.

Integrative functional analysis of super enhancer SNPs for coronary artery disease.

Clinical research in coronary artery disease (CAD) primarily focused on genetic variants located in protein-coding regions. Recently, mutations fall within non-coding regions have been suggested to be essential to the pathogenesis of human complex disease. Super enhancer is a densely spaced cluster of transcriptional enhancers located in non-coding regions, which is critical for regulating cell-type specific gene expression. However, the underlying mechanism of the super enhancer single-nucleotide polymorphisms (SNPs) affecting the risk of CAD remains unclear. By integrating genome-wide association study (GWAS) meta-analysis of CAD and cell/tissue-specific histone modification data set, we identified 366 potential CAD-associated super enhancer SNPs in 67 loci, including 94 SNPs that are involved in regulating chromatin interactive and/or affecting the transcription factors binding affinity. Interestingly, we found 7 novel functional loci (CBFA2T3, ZMIZ1, DIP2B, SCNN1D/ACAP3, TMEM105, CAMK2G, and MAPK1) that CAD-associated super enhancer SNPs were clustered into the same or neighboring super enhancers. Pathway analysis showed a significant enrichment in several well-known signaling and regulatory processes, e.g., cAMP signaling pathway and ErbB signaling pathway, which play a key role in CAD metabolism. Our results highlight the potential functional importance of CAD-associated super enhancer SNPs and provide the targets for further insights on the pathogenesis of CAD.

Liraglutide suppresses non-esterified free fatty acids and soluble vascular cell adhesion molecule-1 compared with metformin in patients with recent-onset type 2 diabetes.

BACKGROUND: It has been suggested that liraglutide could have an impact on glucose and lipid metabolism disorder and adhesion molecule activation, which may play important roles in the vascular damage of diabetes. In this study, we examined the effects of liraglutide versus metformin on non-esterified free fatty acids, beta-cell insulin secretion, and adhesion molecule levels in patients with recent-onset type 2 diabetes mellitus. METHODS: In this study, 60 patients newly diagnosed with type 2 diabetes mellitus (mean age 33.97 ± 5.67 years) were randomly assigned to receive once-daily subcutaneous liraglutide or oral metformin. Before the study and after the 8-week treatment period, a 75 g oral glucose tolerance test was performed. Plasma glucose, lipids and lipoprotein, plasma insulin, glycaemic and insulin responses, non-esterified free fatty acids (NEFA), and soluble vascular cell adhesion molecule-1 (sVCAM-1) levels were evaluated. RESULTS: After 8 weeks, 120 min of NEFA (155 ± 125 vs 99 ± 73 µmol/L, P = 0.026) and the levels of sVCAM-1 (465 ± 136 vs 382 ± 131 ng/ml, P = 0.013) significantly decreased, while the early phase insulin secretion index (24.94 [7.78, 38.89] vs. 31.13 [17.67, 59.09], P = 0.031), fasting plasma insulin (104 [51, 123] vs 113 [54, 171] mIU/L, P = 0.015), 60 min plasma insulin (326 [165, 441] vs 471 [334, 717] mIU/L, P = 0.005), 120 min plasma insulin (401 [193, 560] vs 500 [367, 960] mIU/L, P = 0.047), and insulin area under the curve (AUCins) (648 [321, 742] vs 738 [451, 1118] mIU/L, P = 0.005) remarkably increased for patients in the liraglutide treatment group. The levels of sVCAM-1 dramatically decreased after 8 weeks of liraglutide treatment (503 ± 182 vs 382 ± 131 ng/ml, P = 0.046) compared to that of the metformin treatment group. At the same time, the differences before and after liraglutide treatment in 120 min of NEFA (- 32 [- 96, - 5] vs 5 [- 35, 38] µmol/L, P = 0.033) and AUCins (738 [451, 1118] vs 594 [357, 1216] mIU/L, P = 0.014) were remarkably enhanced compared to that of the metformin therapy. Nevertheless, there were no significant differences in fasting NEFA after liraglutide or metformin treatment. The reduction of 120 min NEFA (ΔNEFA) was positively correlated with the decrease of sVCAM-1 (ΔsVCAM-1) after 8 weeks of liraglutide treatment (r = 0.523, P = 0.003). CONCLUSIONS: Our results demonstrate that liraglutide administration is more effective than metformin in reducing 120 min NEFA and suppressing sVCAM-1 levels for recent-onset type 2 diabetes mellitus. We suggest that this outcome may be because liraglutide is associated with potentiating insulin secretion capacity, inhibiting vascular inflammatory cytokines, and antagonizing atherosclerosis.

Matrine pretreatment improves cardiac function in rats with diabetic cardiomyopathy via suppressing ROS/TLR-4 signaling pathway.

AIM: Matrine is an alkaloid from Sophora alopecuroides L, which has shown a variety of pharmacological activities and potential therapeutic value in cardiovascular diseases. In this study we examined the protective effects of matrine against diabetic cardiomyopathy (DCM) in rats. METHODS: Male SD rats were injected with streptozotocin (STZ) to induce DCM. One group of DCM rats was pretreated with matrine (200 mg·kg(-1)·d(-1), po) for 10 consecutive days before STZ injection. Left ventricular function was evaluated using invasive hemodynamic examination, and myocardiac apoptosis was assessed. Primary rat myocytes were used for in vitro experiments. Intracellular ROS generation, MDA content and GPx activity were determined. Real-time PCR and Western blotting were performed to detect the expression of relevant mRNAs and proteins. RESULTS: DCM rats exhibited abnormally elevated non-fasting blood glucose levels at 4 weeks after STZ injection, and LV function impairment at 16 weeks. The cardiac tissues of DCM rats showed markedly increased apoptosis, excessive ROS production, and activation of TLR-4/MyD-88/caspase-8/caspase-3 signaling. Pretreatment with matrine significantly decreased non-fasting blood glucose levels and improved LV function in DCM rats, which were associated with reducing apoptosis and ROS production, and suppressing TLR-4/MyD-88/caspase-8/caspase-3 signaling in cardiac tissues. Incubation in a high-glucose medium induced oxidative stress and activation of TLR-4/MyD-88 signaling in cultured myocytes in vitro, which were significantly attenuated by pretreatment with N-acetylcysteine. CONCLUSION: Excessive ROS production in DCM activates the TLR-4/MyD-88 signaling, resulting in cardiomyocyte apoptosis, whereas pretreatment with matrine improves cardiac function via suppressing ROS/TLR-4 signaling pathway.

Hydrodynamic delivery of IL-28B (IFN-λ3) gene ameliorates lung inflammation induced by cigarette smoke exposure in mice.

Cigarette smoke (CS) is the principal cause of pulmonary inflammatory response. IL-28 (IFN-λ) is a novel group of class II cytokines targeting the epithelial cells and IL-28 responses prominent in lungs can exert important immunomodulatory effects. We tested the hypothesis that IL-28B may modulate the lung inflammation induced by CS. Groups of mice were exposed to CS two times per day for 11 consecutive days. CS exposure induced lymphocyte, neutrophil and macrophage infiltration and inflammatory cytokine (IL-1ß, tumor necrosis factor-α (TNF)-α, IL-17, and IL-4) in the airways. More importantly, all these CS-induced pathogenic changes were significantly inhibited by hydrodynamic delivery of plasmid DNA encoding mouse IL-28B. Thus, our results suggest that IL-28 cytokines are beneficial for the suppression of CS-mediated airway inflammation and may be a therapeutic target in CS-related diseases.

[TXNDC5 mediates serum starvation-induced proliferation inhibition of HeLa cell].

OBJECTIVE: To investigate the role of TXNDC5 in serum starvation-induced proliferation inhibition of HeLa cell. METHODS: TXNDC5 was either over-expressed or knocked down by small interfering RNA (siRNA) in HeLa cells which were then cultured in conventional medium or serum starvation medium. The protein level of TXNDC5 was evaluated by Western blot analysis. The mRNA level of TXNDC5 was measured by quantitative real-time PCR. Cell growth rate was determined by cell proliferation assay kit (MTS method). Cell cycle distribution and apoptosis were detected by flow cytometry. RESULTS: Serum starvation mildly reduced the mRNA level of TXNDC5 (P<0.05), but dramatically increased the protein level of TXNDC5 in HeLa cells. The stability of TXNDC5 mRNA remained unchanged. Cycloheximide abolished the serum starvation-induced up-regulation of TXNDC5 protein. Over-expression of TXNDC5 had no effect on cell proliferation. However, suppression of TXNDC5 attenuated the proliferation inhibition of HeLa cell induced by serum starvation (P<0.05), increased the proportion of cells in S phase (P<0.05), but had no effect on cell apoptosis. CONCLUSION: TXNDC5 mediates serum starvation-induced proliferation inhibition of HeLa cell.

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.

Introduction: Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods: By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results: By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion: In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

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.