Displaying all 7 publications

  1. Lou Y, Shi J, Guo D, Qureshi AK, Song L
    Saudi J Biol Sci, 2017 May;24(4):803-807.
    PMID: 28490949 DOI: 10.1016/j.sjbs.2015.06.025
    Human glioma is a highly fatal tumor with a significant feature of immune suppression. The functions of PD-L1 refer to co-simulation and immune regulation. To investigate expression and functional activity of PD-L1 in human glioma cell in vivo and in vitro. Expressions of PD-L1mRNA and protein in the human glioma cell line were analyzed with quantitative RT-PCR and flow cytometer; and then expression of PD-L1 in tissue specimens of 10 glioma patients was treated with immunohistochemical analysis; glioma cell and allogeneic CD4(+) and CD8(+) T cells were co-cultured, and cytokine IFN-γ, IL-2 and IL-10 in cultured supernatant fluid were determined with ELISA; upon blocking the interaction between glioma cell and the immune cell with PD-L1 monoclonal antibody (5H1), surface markers on immune cells were analyzed using flow cytometer. All human glioma cell lines constitutively expressed PD-L1, and IFN-γ induced glioma cell to highly express PD-L1. It was shown through immunohistochemical analysis that glioma specimen expressed PD-L1, while expression of PD-L1 was not observed in normal tissue and normal human brain near the tumor location. The release of IFN-γ and IL-2 was inhibited, while IL-10 was increased slightly. Glioma cell may escape from immune recognition and injury with the help of PD-L1, which is a significant pathogenic mechanism of glioma.
  2. Maier R, Moser G, Chen GB, Ripke S, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Coryell W, et al.
    Am. J. Hum. Genet., 2015 Feb 05;96(2):283-94.
    PMID: 25640677 DOI: 10.1016/j.ajhg.2014.12.006
    Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.
  3. Peyrot WJ, Lee SH, Milaneschi Y, Abdellaoui A, Byrne EM, Esko T, et al.
    Mol. Psychiatry, 2015 Jun;20(6):735-43.
    PMID: 25917368 DOI: 10.1038/mp.2015.50
    An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.
  4. Cai Q, Zhang B, Sung H, Low SK, Kweon SS, Lu W, et al.
    Nat. Genet., 2014 Aug;46(8):886-90.
    PMID: 25038754 DOI: 10.1038/ng.3041
    In a three-stage genome-wide association study among East Asian women including 22,780 cases and 24,181 controls, we identified 3 genetic loci newly associated with breast cancer risk, including rs4951011 at 1q32.1 (in intron 2 of the ZC3H11A gene; P=8.82×10(-9)), rs10474352 at 5q14.3 (near the ARRDC3 gene; P=1.67×10(-9)) and rs2290203 at 15q26.1 (in intron 14 of the PRC1 gene; P=4.25×10(-8)). We replicated these associations in 16,003 cases and 41,335 controls of European ancestry (P=0.030, 0.004 and 0.010, respectively). Data from the ENCODE Project suggest that variants rs4951011 and rs10474352 might be located in an enhancer region and transcription factor binding sites, respectively. This study provides additional insights into the genetics and biology of breast cancer.
  5. Shi J, Zhang Y, Zheng W, Michailidou K, Ghoussaini M, Bolla MK, et al.
    Int. J. Cancer, 2016 Sep 15;139(6):1303-1317.
    PMID: 27087578 DOI: 10.1002/ijc.30150
    Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2)  = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.
  6. Guo X, Long J, Zeng C, Michailidou K, Ghoussaini M, Bolla MK, et al.
    Cancer Epidemiol. Biomarkers Prev., 2015 Nov;24(11):1680-91.
    PMID: 26354892 DOI: 10.1158/1055-9965.EPI-15-0363
    BACKGROUND: A recent association study identified a common variant (rs9790517) at 4q24 to be associated with breast cancer risk. Independent association signals and potential functional variants in this locus have not been explored.

    METHODS: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium.

    RESULTS: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10(-4); OR, 1.04; 95% confidence interval (CI), 1.02-1.07] and rs77928427 (P = 1.86 × 10(-4); OR, 1.04; 95% CI, 1.02-1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r(2) ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor-binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue.

    CONCLUSION: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2.

    IMPACT: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk.

  7. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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