Integration of lipidomics and metabolomics reveals plasma and urinary profiles associated with pediatric Mycoplasma pneumoniae infections and its severity.

Mycoplasma pneumoniae is a significant contributor to lower respiratory infections in children. However, the lipidomics and metabolics bases of childhood M. pneumoniae infections remain unclear. In this study, lipidomics and metabolomics analyses were conducted using UHPLC-LTQ-Orbitrap XL mass spectrometry and gas chromatography-triple quadrupole mass spectrometry on plasma (n = 65) and urine (n = 65) samples. MS-DIAL software, in combination with LipidBlast and Fiehn BinBase DB, identified 163 lipids and 104 metabolites in plasma samples, as well as 208 metabolites in urine samples. Perturbed lipid species (adjusted p < 0.05) were observed, including lysophosphatidylethanolamines, phosphatidylinositols, phosphatidylcholines, phosphatidylethanol amines, and triglycerides. Additionally, differential metabolites (adjusted p < 0.05) exhibited associations with amino acid metabolism, nucleotide metabolism, and energy metabolism. Thirteen plasma metabolites, namely l-hydroxyproline, 3-phosphoglycerate, citric acid, creatine, inosine, ribitol, α tocopherol, cholesterol, cystine, serine, uric acid, tagatose, and glycine, showed significant associations with disease severity (p < 0.05) and exhibited distinct separation patterns in M. pneumoniae-infected bronchitis and pneumonia, with an area under the curve of 0.927. Nine of them exhibited either positive or negative correlations with neutrophil or lymphocyte percentages. These findings indicated significant systemic metabolic shifts in childhood M. pneumoniae infections, offering valuable insights into the associated metabolic alterations and their relationship with disease severity.

Anti-tumour effect of Huangqin Decoction on colorectal cancer mice through microbial butyrate mediated PI3K/Akt pathway suppression.

Introduction. Huangqin Decoction (HQD), a Chinese herbal formula, is widely used for various diseases, including colorectal cancer (CRC).Hypothesis/Gap Statement. We proposed that microbial butyrate mediated PI3K/Akt pathway suppression might involve the anti-cancer effect of HQD.Aim. This study aimed to evaluate the potential mechanism of HQD against CRC.Methodology. An azoxymethane plus dextran sulphate sodium induced CRC mouse model was used, and the intestinal flora and faecal short-chain fatty acid changes were detected, respectively, after HQD administration with 16S rRNA sequencing and gas chromatography coupled with mass spectrometry. Disease activity index, colon length and levels of inflammatory cytokines were measured to evaluate the effect of HQD on intestinal inflammation. Tumour size, number and histopathology were assessed to reflect the impact of HQD on tumour burden. Apoptosis and PI3K/Akt pathway activity were measured by TUNEL staining and Western-blotting. In vitro, the effects of sodium butyrate (NaB) on the viability of CRC cell lines were detected by the Cell-counting Kit-8. The apoptotic cells were determined by TUNEL staining. Cell migration and invasion were assessed by wound healing assay and Transwell assay, respectively. Western-blotting and immunofluorescent staining were used to test the activity of PI3K/Akt pathway.Results. Animal study showed that HQD could improve the gut dysbiosis, increase the abundance of Clostridium and the level of faecal butyric acid. Then, we found that HQD could attenuate colitis, reduce tumour burden, promote cell apoptosis and suppress PI3K/Akt pathway activity in CRC mice. In vitro experiment revealed that NaB treatment could inhibit cell growth, migration and invasion in CRC cell lines. Additionally, NaB enhanced cellular apoptosis, and reduced phosphorylated PI3K and Akt expressions. Interestingly, addition of 740Y-P, an agonist of PI3K, reversed the NaB effects on CRC cells.Conclusion. Overall, in this study, we revealed that HQD could induce apoptosis through microbial butyrate mediated PI3K/Akt inhibition and perform anti-CRC activity.

[Degradation of SMX with Peracetic Acid Activated by Nano Core-shell Co@NC Catalyst].

Peracetic acid (PAA), as a new oxidant, has attracted increasing attention in the treatment of refractory organic pollution in sewage. In this study, the nano core-shell Co@NC catalyst was prepared via etching and used to activate PAA to degrade sulfamethoxazole (SMX) in sewage. The results indicated that the degradation rate of SMX reached 98%, and its reaction rate constant was 0.80 min-1 under optimal conditions (catalyst dosage=0.02 g·L-1, PAA concentration=0.12 mmol·L-1, pH=7, SMX concentration=10 µmol·L-1). With the increase in PAA concentration and core-shell Co@NC dosage, the degradation efficiency of SMX increased. The study found that the core-shell Co@NC/PAA system had the best degradation effect on SMX under near-neutral conditions (pH 6.0-8.0), and both acidic and alkaline environments were not conducive to SMX degradation. HCO3- and humic acid showed significant inhibition on the degradation of SMX, whereas Cl- showed weak inhibition. In addition, through a free radical quenching experiment and electron paramagnetic resonance (EPR) detection, acetoxy radical (CH3CO2CO3·) were the main active species for the degradation of organic pollutants in the system. Transformation products (TPs) of SMX were analyzed by U-HPLC-Q-Exactive Orbitrap HRMS, and a possible degradation path of SMX was proposed. At the same time, the catalyst recycling experiment showed that the nano core-shell Co@NC catalyst had good stability and reusability.

Spatiotemporal Distribution and Epidemiological Characteristics of Hospital Admissions for Carbon Monoxide Poisoning in Guangdong, China, 2013-2020.

Objective: This study aimed to determine the spatiotemporal distribution and epidemiological characteristics of hospital admissions for carbon monoxide poisoning (COP) in Guangdong, China, from 2013 to 2020. Methods: Data on age- and sex- specific numbers of hospital admissions due to COP in Guangdong (2013-2020) were collected. Daily temperatures were downloaded through the China Meteorological Data Sharing Service System. We analyzed temporal trends through time series decomposition and used spatial autocorrelation analysis to detect spatial clustering. The distributed lag nonlinear model was used to quantify the effects of temperature. Results: There were 48,854 COP admissions over the study period. The sex ratio (male to female) was 1:1.74. The concentration ratios (M) ranged from 0.73-0.82. The highest risk occurred in January (season index = 3.59). Most cases were concentrated in the northern mountainous areas of Guangdong with high-high clustering. COP in the study region showed significant spatial autocorrelation, and the global Moran's Ivalue of average annual hospital admission rates for COP was 0.447 ( P < 0.05). Low temperatures were associated with high hospital admission rates for COP, with a lag lasting 7 days. With a lag of 0 days, the effects of low temperatures [5th (12 °C)] on COP were 2.24-3.81, as compared with the reference temperature [median (24 °C)]. Conclusion: COP in Guangdong province showed significant temporal and spatial heterogeneity. Low temperature was associated with a high risk of COP, and the influence had a lag lasting 7 days.

MetaDecoder: a novel method for clustering metagenomic contigs.

BACKGROUND: Clustering the metagenomic contigs into potential genomes is a key step to investigate the functional roles of microbial populations. Existing algorithms have achieved considerable success with simulated or real sequencing datasets. However, accurately classifying contigs from complex metagenomes is still a challenge. RESULTS: We introduced a novel clustering algorithm, MetaDecoder, which can classify metagenomic contigs based on the frequencies of k-mers and coverages. MetaDecoder was built as a two-layer model with the first layer being a GPU-based modified Dirichlet process Gaussian mixture model (DPGMM), which controls the weight of each DPGMM cluster to avoid over-segmentation by dynamically dissolving contigs in small clusters and reassigning them to the remaining clusters. The second layer comprises a semi-supervised k-mer frequency probabilistic model and a modified Gaussian mixture model for modeling the coverage based on single copy marker genes. Benchmarks on simulated and real-world datasets demonstrated that MetaDecoder can be served as a promising approach for effectively clustering metagenomic contigs. CONCLUSIONS: In conclusion, we developed the GPU-based MetaDecoder for effectively clustering metagenomic contigs and reconstructing microbial communities from microbial data. Applying MetaDecoder on both simulated and real-world datasets demonstrated that it could generate more complete clusters with lower contamination. Using MetaDecoder, we identified novel high-quality genomes and expanded the existing catalog of bacterial genomes. Video Abstract.

DDRS: Detection of drug response SNPs specifically in patients receiving drug treatment.

Detecting SNPs associated with drug efficacy or toxicity is helpful to facilitate personalized medicine. Previous studies usually find SNPs associated with clinical outcome only in patients received a specific treatment. However, without information from patients without drug treatment, it is possible that the detected SNPs are associated with patients' clinical outcome even without drug treatment. Here we aimed to detect drug response SNPs based on data from patients with and without drug treatment through combing the cox proportional-hazards model and pairwise Kaplan-Meier survival analysis. A pipeline named Detection of Drug Response SNPs (DDRS) was built and applied to TCGA breast cancer data including 363 patients with doxorubicin treatment and 321 patients without any drug treatment. We identified 548 doxorubicin associated SNPs. Drug response score derived from these SNPs were associated with drug-resistant level (indicated by IC50) of breast cancer cell lines. Enrichment analyses showed that these SNPs were enriched in active epigenetic regulation markers (e.g., H3K27ac). Compared with random genes, the cis-eQTL genes of these SNPs had a shorter protein-protein interaction distance to doxorubicin associated genes. In addition, linear discriminant analysis showed that the eQTL gene expression levels could be used to predict clinical outcome for patients with doxorubicin treatment (AUC = 0.738). Specifically, we identified rs2817101 as a drug response SNP for doxorubicin treatment. Higher expression level of its cis-eQTL gene GSTA1 is associated with poorer survival. This approach can also be applied to identify new drug associated SNPs in other cancers.

An Intronic Risk SNP rs12454712 for Central Obesity Acts As an Allele-Specific Enhancer To Regulate BCL2 Expression.

Genome-wide association studies (GWAS) have reproducibly associated the single nucleotide polymorphism (SNP) rs12454712 with waist-to-hip ratio adjusted for BMI (WHRadjBMI), but the functional role underlying this intronic variant is unknown. Integrative genomic and epigenomic analyses supported rs12454712 as a functional independent variant. We further demonstrated that rs12454712 acted as an allele-specific enhancer regulating expression of its located gene BCL2 by using dual-luciferase reporter assays and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9. Specifically, the rs12454712-C allele can bind transcription factor ZNF329, which efficiently elevates the enhancer activity and increases BCL2 expression. Knocking down Bcl2 in 3T3-L1 cells led to the downregulation of adipogenic differentiation marker genes and increased cell apoptosis. A significant negative correlation between BCL2 expression in subcutaneous adipose tissues and obesity was observed. Our findings illustrate the molecular mechanisms behind the intronic SNP rs12454712 for central obesity, which would be a potential and promising target for developing appropriate therapies.

Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study.

BACKGROUND: Childhood obesity is reported to be associated with the risk of many diseases in adulthood. However, observational studies cannot fully account for confounding factors. We aimed to systematically assess the causal associations between childhood body mass index (BMI) and various adult traits/diseases using two-sample Mendelian randomization (MR). METHODS: After data filtering, 263 adult traits genetically correlated with childhood BMI (P < 0.05) were subjected to MR analyses. Inverse-variance weighted, MR-Egger, weighted median, and weighted mode methods were used to estimate the causal effects. Multivariable MR analysis was performed to test whether the effects of childhood BMI on adult traits are independent from adult BMI. RESULTS: We identified potential causal effects of childhood obesity on 60 adult traits (27 disease-related traits, 27 lifestyle factors, and 6 other traits). Higher childhood BMI was associated with a reduced overall health rating (ß = - 0.10, 95% CI - 0.13 to - 0.07, P = 6.26 × 10-11). Specifically, higher childhood BMI was associated with increased odds of coronary artery disease (OR = 1.09, 95% CI 1.06 to 1.11, P = 4.28 × 10-11), essential hypertension (OR = 1.12, 95% CI 1.08 to 1.16, P = 1.27 × 10-11), type 2 diabetes (OR = 1.36, 95% CI 1.30 to 1.43, P = 1.57 × 10-34), and arthrosis (OR = 1.09, 95% CI 1.06 to 1.12, P = 8.80 × 10-9). However, after accounting for adult BMI, the detrimental effects of childhood BMI on disease-related traits were no longer present (P > 0.05). For dietary habits, different from conventional understanding, we found that higher childhood BMI was associated with low calorie density food intake. However, this association might be specific to the UK Biobank population. CONCLUSIONS: In summary, we provided a phenome-wide view of the effects of childhood BMI on adult traits. Multivariable MR analysis suggested that the associations between childhood BMI and increased risks of diseases in adulthood are likely attributed to individuals remaining obese in later life. Therefore, ensuring that childhood obesity does not persist into later life might be useful for reducing the detrimental effects of childhood obesity on adult diseases.

An integrative multi-omics network-based approach identifies key regulators for breast cancer.

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

[Degradation of AO7 with Magnetic Fe3O4-CuO Heterogeneous Catalyzed Sodium Percarbonate System].

Magnetically recyclable Fe3O4-CuO was synthesized by a one-step hydrothermal method and characterized by scanning electron microscopy coupled with energy dispersive spectrometer (SEM-EDS) and X-ray diffraction (XRD). The degradation of azo dye acid orange 7 (AO7) by percarbonate (SPC) activated with Fe3O4-CuO was studied. The effects of Fe3O4-CuO catalyst loading, SPC concentration, pH value, and common chloride ions on AO7 degradation in the Fe3O4-CuO/SPC system were evaluated. The main reaction mechanism of AO7 degradation was analyzed. The results show that Fe3O4-CuO could effectively activate SPC to degrade AO7 and the reaction was accelerated with the increase of Fe3O4-CuO dosage. The increase of SPC dosage was favorable for the degradation of AO7, but excessive SPC dosage inhibited the degradation of AO7. Common ions (e.g., Cl-) in dye wastewater could promote the degradation of AO7, and the degradation rate increased with increasing concentration of Cl-. The reaction mainly occurred on the surface of the catalyst, and·OH was identified as the main active species for the degradation of AO7. The catalyst Fe3O4-CuO showed excellent stability owing to the high catalytic activity remaining after 4 cycles of repeated use. The Fe3O4-CuO/SPC system achieved a high mineralization rate in the process of decolorization of AO7.

Modeling circRNA expression pattern with integrated sequence and epigenetic features demonstrates the potential involvement of H3K79me2 in circRNA expression.

MOTIVATION: CircRNAs are an abundant class of non-coding RNAs with widespread, cell-/tissue-specific patterns. Previous work suggested that epigenetic features might be related to circRNA expression. However, the contribution of epigenetic changes to circRNA expression has not been investigated systematically. Here, we built a machine learning framework named CIRCScan, to predict circRNA expression in various cell lines based on the sequence and epigenetic features. RESULTS: The predicted accuracy of the expression status models was high with area under the curve of receiver operating characteristic (ROC) values of 0.89-0.92 and the false-positive rates of 0.17-0.25. Predicted expressed circRNAs were further validated by RNA-seq data. The performance of expression-level prediction models was also good with normalized root-mean-square errors of 0.28-0.30 and Pearson's correlation coefficient r over 0.4 in all cell lines, along with Spearman's correlation coefficient ρ of 0.33-0.46. Noteworthy, H3K79me2 was highly ranked in modeling both circRNA expression status and levels across different cells. Further analysis in additional nine cell lines demonstrated a significant enrichment of H3K79me2 in circRNA flanking intron regions, supporting the potential involvement of H3K79me2 in circRNA expression regulation. AVAILABILITY AND IMPLEMENTATION: The CIRCScan assembler is freely available online for academic use at https://github.com/johnlcd/CIRCScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sex-specific SNP-SNP interaction analyses within topologically associated domains reveals ANGPT1 as a novel tumor suppressor gene for lung cancer.

Genetic interaction has been recognized to be an important cause of the missing heritability. The topologically associating domain (TAD) is a self-interacting genomic region, and the DNA sequences within a TAD physically interact with each other more frequently. Sex differences influence cancer susceptibility at the genetic level. Here, we performed both regular and sex-specific genetic interaction analyses within TAD to identify susceptibility genes for lung cancer in 5204 lung cancer patients and 7389 controls. We found that one SNP pair, rs4262299-rs1654701, was associated with lung cancer in women after multiple testing corrections (combined P = 8.52 × 10-9 ). Single-SNP analyses did not detect significant association signals for these two SNPs. Both identified SNPs are located in the intron region of ANGPT1. We further found that 5% of nonsmall cell lung cancer patients have an alteration in ANGPT1, indicated the potential role of ANGPT1 in the neoplastic progression in lung cancer. The expression of ANGPT1 was significantly down-regulated in patients in lung squamous cell carcinoma and lung adenocarcinoma. We checked the interaction effect on the ANGPT1 expression and lung cancer and found that the minor allele "G" of rs1654701 increased ANGPT1 gene expression and decreased lung cancer risk with the increased dosage of "A" of rs4262299, which consistent with the tumor suppressor function of ANGPT1. Survival analyses found that the high expression of ANGPT1 was individually associated with a higher survival probability in lung cancer patients. In summary, our results suggest that ANGPT1 may be a novel tumor suppressor gene for lung cancer.

Integrating regulatory features data for prediction of functional disease-associated SNPs.

Genome-wide association studies (GWASs) are an effective strategy to identify susceptibility loci for human complex diseases. However, missing heritability is still a big problem. Most GWASs single-nucleotide polymorphisms (SNPs) are located in noncoding regions, which has been considered to be the unexplored territory of the genome. Recently, data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects have shown that many GWASs SNPs in the noncoding regions fall within regulatory elements. In this study, we developed a pipeline named functional disease-associated SNPs prediction (FDSP), to identify novel susceptibility loci for complex diseases based on the interpretation of the functional features for known disease-associated variants with machine learning. We applied our pipeline to predict novel susceptibility SNPs for type 2 diabetes (T2D) and hypertension. The predicted SNPs could explain heritability beyond that explained by GWAS-associated SNPs. Functional annotation by expression quantitative trait loci analyses showed that the target genes of the predicted SNPs were significantly enriched in T2D or hypertension-related pathways in multiple tissues. Our results suggest that combining GWASs and regulatory features data could identify additional functional susceptibility SNPs for complex diseases. We hope FDSP could help to identify novel susceptibility loci for complex diseases and solve the missing heritability problem.

Analysis of risk factors and establishment of a risk prediction model for cardiothoracic surgical intensive care unit readmission after heart valve surgery in China: A single-center study.

BACKGROUND: Valvular heart disease is one of the most frequent and challenging heart diseases worldwide. The incidence of complications and cardiothoracic surgical intensive care unit (CSICU) readmission after cardiac valve surgery is high. Because CSICU readmission is costly and adversely impacts the quality life, reducing the risk of CSICU readmission has become one of the main focuses of health care. OBJECTIVE: To explore the risk factors for CSICU readmission and to establish a risk prediction model for CSICU readmission in heart valve surgical patients. METHODS: A total of 1216 patients who had undergone cardiac valvular surgery between January 2016 and August 2017 at the First Affiliated Hospital of Sun Yat-sen University were assigned as the development and validation data sets. Data from 824 patients in the development data set were retrospectively analyzed to identify potential risk factors with univariate analysis. Multivariate logistic regression was used to determine the independent risk factors of CSICU readmission, which served as the basis for our prediction model. The calibration and discrimination of the model were assessed by the Hosmer-Lemeshow (H-L) test and the area under the receiver operating characteristic (ROC) curve, respectively. RESULTS: Six preoperative variables (age ≥ 65, previous chronic lung disease, prior cardiac surgery, left ventricular ejection fraction (LVEF) ≤ 40%, 40% < LVEF ≤ 50%, and New York Heart Association (NYHA) classification III/IV), two intraoperative variables (multiple valve repair/replacement and cardiopulmonary bypass time ≥ 180 min), and five postoperative variables (cardiac arrest, acute respiratory distress syndrome, pneumonia, deep sternal wound infection, and renal failure) were independent risk factors of CSICU readmission. Our risk prediction model, which was established based on the above-mentioned risk factors, had robust discrimination and calibration in both the development and validation data sets. CONCLUSION: The prediction model established in our study is a simple, objective, and accurate scoring system, which can be used to predict the risk of CSICU readmission and assist researchers with designing intervention strategies to prevent CSICU readmission.

Runs of homozygosity associate with decreased risks of lung cancer in never-smoking East Asian females.

Although genome-wide association studies (GWASs) have identified some risk single-nucleotide polymorphisms in East Asian never-smoking females, the unexplained missing heritability is still required to be investigated. Runs of homozygosity (ROHs) are thought to be a type of genetic variation acting on human complex traits and diseases. We detected ROHs in 8,881 East Asian never-smoking women. The summed ROHs were used to fit a logistic regression model which noteworthily revealed a significant association between ROHs and the decreased risk of lung cancer (P < 0.05). We identified 4 common ROHs regions located at 2p22.1, which were significantly associated with decreased risk of lung cancer (P = 2.00 × 10-4 - 1.35 × 10-4). Functional annotation was conducted to investigate the regulatory function of ROHs. The common ROHs were overlapped with potential regulatory elements, such as active epigenome elements and chromatin states in lung-derived cell lines. SOS1 and ARHGEF33 were significantly up-regulated as the putative target genes of the identified ROHs in lung cancer samples according to the analysis of differently expressed genes. Our results suggest that ROHs could act as recessive contributing factors and regulatory elements to influence the risk of lung cancer in never-smoking East Asian females.

[Degradation of OG with Peroxymonosulfate Activated by a MnFe2O4-graphene Hybrid].

The rG-MnFe2O4 was synthesized by hydrothermal method and characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and Raman spectra. The rG-MnFe2O4 was used to activate peroxymonosulfate (PMS) to decolorize azo dyes, e.g., Orange G, and the effect of PMS dosage, rG-MnFe2O4 loadings, initial pH, and the concentration of Cl- were investigated. The results indicated that the degradation rate of OG was 100% within 27 min with 0.3 g ·L-1 of rG-MnFe2O4 and at a 40:1 of PMS:OG molar ratio. The decolorization efficiency of OG increased with increasing PMS concentration and increasing rG-MnFe2O4 dosage. The initial pH had a significant effect on OG degradation, and pH 5.00 was most favorable for its decolorization. In addition, the addition of Cl- accelerated the decolorization of OG, and the decolorization rate increased with increasing concentration of Cl-. The rG-MnFe2O4 also exhibited an excellent reusability, and its activation of PMS was still observed after five rounds of tests. From the analysis of UV-vis spectra and gas chromatography-mass spectrometry (GC/MS), the naphthalene ring and azo band were found to be destroyed, with p-nitrophenol and phthalic acid as the main degradation products. Finally, a TOC analysis indicated that a certain degree of OG mineralization was obtained in the rG-MnFe2O4/PMS system.

An Osteoporosis Risk SNP at 1p36.12 Acts as an Allele-Specific Enhancer to Modulate LINC00339 Expression via Long-Range Loop Formation.

Genome-wide association studies (GWASs) have reproducibly associated variants within intergenic regions of 1p36.12 locus with osteoporosis, but the functional roles underlying these noncoding variants are unknown. Through an integrative functional genomic and epigenomic analyses, we prioritized rs6426749 as a potential causal SNP for osteoporosis at 1p36.12. Dual-luciferase assay and CRISPR/Cas9 experiments demonstrate that rs6426749 acts as a distal allele-specific enhancer regulating expression of a lncRNA (LINC00339) (∼360 kb) via long-range chromatin loop formation and that this loop is mediated by CTCF occupied near rs6426749 and LINC00339 promoter region. Specifically, rs6426749-G allele can bind transcription factor TFAP2A, which efficiently elevates the enhancer activity and increases LINC00339 expression. Downregulation of LINC00339 significantly increases the expression of CDC42 in osteoblast cells, which is a pivotal regulator involved in bone metabolism. Our study provides mechanistic insight into how a noncoding SNP affects osteoporosis by long-range interaction, a finding that could indicate promising therapeutic targets for osteoporosis.

Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.

Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

Application of Traditional Chinese Medical Herbs in Prevention and Treatment of Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) is a common viral pathogen of the lower respiratory tract, which, in the absence of effective management, causes millions of cases of severe illness per year. Many of these infections develop into fatal pneumonia. In a review of English and Chinese medical literature, recent traditional Chinese medical herb- (TCMH-) based progress in the area of prevention and treatment was identified, and the potential anti-RSV compounds, herbs, and formulas were explored. Traditional Chinese medical herbs have a positive effect on inhibiting viral attachment, inhibiting viral internalization, syncytial formation, alleviation of airway inflammation, and stimulation of interferon secretion and immune system; however, the anti-RSV mechanisms of TCMHs are complicated, which should be further investigated.