MiR-143-5p inhibits proliferation, invasion, and epithelial to mesenchymal transition of colorectal cancer cells by downregulation of HMGA2

Purpose: To investigate the regulatory effect and molecular mechanism of miR-143-5p in colorectal cancer (CRC) progression. Methods: Expression of miR-143-5p in CRC cell lines SW620 and HCT116 was determined by quantitative real-time polymerase chain reaction (qRT-PCR). Stable miR-143-5p overexpression was mediated by lentivirus. The effects of miR-143-5p on proliferation, migration, invasion, and epithelial- mesenchymal transition (EMT) of SW620 and HCT116 cells were assessed by colony formation assay, CCK-8, Transwell assay, wound healing assay, and western blot. Target prediction was performed for miR-143-5p, and a dual luciferase assay was used to verify the targeting relationship. Results: Compared to CRC cells transfected with negative controls, cell proliferation, migration and invasion, and EMT were inhibited in miR-143-5p-overexpressing cells. Expression of HMGA2 (high- mobility Group AT-Hook 2), a target gene of miR-143-5p, was repressed by miR-143-5p. Rescue experiments confirmed that upregulation of HMGA2 due to mIR-143-5p overexpression reversed inhibition of CRC cell proliferation, invasion and EMT. Conclusion: MiR-143-5p inhibits the malignant progression of CRC by regulating HMGA2 expression and is expected to provide new therapeutic approaches for clinical treatment of CRC

Application of Bayesian classification with singular value decomposition method in genome-wide association studies

To analyze multiple single-nucleotide polymorphisms simultaneously when the number of markers is much larger than the number of studied individuals, as is the situation we have in genome-wide association studies (GWAS), we developed the iterative Bayesian variable selection method and successfully applied it to the simulated rheumatoid arthritis data provided by the Genetic Analysis Workshop 15 (GAW15). One drawback for applying our iterative Bayesian variable selection method is the relatively long running time required for evaluation of GWAS data. To improve computing speed, we recently developed a Bayesian classification with singular value decomposition (BCSVD) method. We have applied the BCSVD method here to the rheumatoid arthritis data distributed by GAW16 Problem 1 and demonstrated that the BCSVD method works well for analyzing GWAS data

Time series study on the effect of low air pollution level NO2 On the death of residents from cardiovascular and cerebrovascular diseases

Objective: To explore the impact of low-level atmospheric nitrogen dioxide (NO2) on the death risk of cardiovascular and cerebrovascular diseases in Enshi City, so as to provide scientific basis for locating sensitive populations and formulating population health policiesmethods the monitoring of air pollutants, meteorological factors and death data of residents from cardiovascular and cerebrovascular diseases in Enshi City from 2015 to 2018 were collected. The generalized additive model based on Poisson distribution was used to analyze the impact of low air pollution level NO2 on the death risk of cardiovascular and cerebrovascular diseases in Enshi City, and subgroup analysis was carried out on age, gender and seasonresults the average concentrations of major gaseous pollutants in Enshi from 2015 to 2018 were NO2 (21.40 μg/m3), sulfur dioxide (so, 9.68 μg/m3). Carbon oxide (CO, 0.88 mg/m3) and ozone (O, 61.21 μg/m3). The results of single pollutant model analysis show that every increase of NO2 concentration in the total population μg/m, the risk of death from cardiovascular and cerebrovascular diseases on the same day (lag0) will increase by 0.33% (-0.06%~0.72%) (P>0.05); In the female population, every 1% increase in NO2 concentration μg/m, the death risk of cardiovascular and cerebrovascular diseases with cumulative lag of 1 day (lag01) will increase by 0.92% (0.26%~1.56%) (P < 0.05); In the cold season, every increase of NO2 concentration μg/m, the death risk of cardiovascular and cerebrovascular diseases in the whole population on the same day (lag0) will increase by 0.62% (0.12%~1.12%) (P < 0.05). The results of the two pollutant model show that after controlling other gaseous pollutants (SO2, Co or O3), the impact of NO2 on the death risk of cardiovascular and cerebrovascular diseases in women and the whole population in cold season still exists. Conclusion: Low pollution level of NO2 in Enshi City will increase the death risk of cardiovascular and cerebrovascular diseases among women and the whole population in cold season. Attention should be paid to the health protection of special populations in low pollution areas and in special seasons

Heritability patterns in hand osteoarthritis: the role of osteophytes

Abstract Introduction The objective of the present study was to assess heritability of clinical and radiographic features of hand osteoarthritis (OA) in affected patients and their siblings. Methods A convenience sample of patients with clinical and radiographic hand OA and their siblings were evaluated by examination and radiography. Radiographs were scored for hand OA features by radiographic atlas. The heritability of hand OA phenotypes was assessed for clinical and radiographic measures based on anatomic locations and radiographic characteristics. Phenotypic data were transformed to reduce non-normality, if necessary. A variance components approach was used to calculate heritability. Results One hundred and thirty-six probands with hand OA and their sibling(s) were enrolled. By anatomic location, the highest heritability was seen with involvement of the first interphalangeal joint (h 2 = 0.63, P = 0.00004), the first carpometacarpal joint (h 2 = 0.38, P = 0.01), the distal interphalangeal joints (h 2 = 0.36, P = 0.02), and the proximal interphalangeal joints (h 2 = 0.30, P = 0.03) with osteophytes. The number and severity of joints with osteophyte involvement was heritable overall (h 2 = 0.38, P = 0.008 for number and h 2 = 0.35, P = 0.01 for severity) and for all interphalangeal joints (h 2 = 0.42, P = 0.004 and h 2 = 0.33, P = 0.02). The severity of carpometacarpal joint involvement was also heritable (h 2 = 0.53, P = 0.0006). Similar results were obtained when the analysis was limited to the Caucasian sample. Conclusions In a population with clinical and radiographic hand OA and their siblings, the presence of osteophytes was the most sensitive biomarker for hand OA heritability. Significant heritability was detected for anatomic phenotypes by joint location, severity of joint involvement with osteophytes as well as for overall number and degree of hand OA involvement. These findings are in agreement with the strong genetic predisposition for hand OA reported by others. The results support phenotyping based on severity of osteophytes and a joint-specific approach. More specific phenotypes may hold greater promise in the study of genetics in hand OA

Application of Bayesian regression with singular value decomposition method in association studies for sequence data

Genetic association studies usually involve a large number of single-nucleotide polymorphisms (SNPs) (k) and a relative small sample size (n), which produces the situation that k is much greater than n. Because conventional statistical approaches are unable to deal with multiple SNPs simultaneously when k is much greater than n, single-SNP association studies have been used to identify genes involved in a disease’s pathophysiology, which causes a multiple testing problem. To evaluate the contribution of multiple SNPs simultaneously to disease traits when k is much greater than n, we developed the Bayesian regression with singular value decomposition (BRSVD) method. The method reduces the dimension of the design matrix from k to n by applying singular value decomposition to the design matrix. We evaluated the model using a Markov chain Monte Carlo simulation with Gibbs sampler constructed from the posterior densities driven by conjugate prior densities. Permutation was incorporated to generate empirical p-values. We applied the BRSVD method to the sequence data provided by Genetic Analysis Workshop 17 and found that the BRSVD method is a practical method that can be used to analyze sequence data in comparison to the single-SNP association test and the penalized regression method

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p