Triptolide induced cell death through apoptosis and autophagy in murine leukemia WEHI‐3 cells in vitro and promoting immune responses in WEHI‐3 generated leukemia mice in vivo

Triptolide, a traditional Chinese medicine, obtained from Tripterygium wilfordii Hook F, has anti‐inflammatory, antiproliferative, and proapoptotic properties. We investigated the potential efficacy of triptolide on murine leukemia by measuring the triptolide‐induced cytotoxicity in murine leukemia WEHI‐3 cells in vitro. Results indicated that triptolide induced cell morphological changes and induced cytotoxic effects through G0/G1 phase arrest, induction of apoptosis. Flow cytometric assays showed that triptolide increased the production of reactive oxygen species, Ca²⁺ release and mitochondrial membrane potential (ΔΨ_m), and activations of caspase‐8, ‐9, and ‐3. Triptolide increased protein levels of Fas, Fas‐L, Bax, cytochrome c, caspase‐9, Endo G, Apaf‐1, PARP, caspase‐3 but reduced levels of AIF, ATF6α, ATF6β, and GRP78 in WEHI‐3 cells. Triptolide stimulated autophagy based on an increase in acidic vacuoles, monodansylcadaverine staining for LC‐3 expression and increased protein levels of ATG 5, ATG 7, and ATG 12. The in vitro data suggest that the cytotoxic effects of triptolide may involve cross‐talk between cross‐interaction of apoptosis and autophagy. Normal BALB/c mice were i.p. injected with WEHI‐3 cells to generate leukemia and were oral treatment with triptolide at 0, 0.02, and 0.2 mg/kg for 3 weeks then animals were weighted and blood, liver, spleen samples were collected. Results indicated that triptolide did not significantly affect the weights of animal body, spleen and liver of leukemia mice, however, triptolide significant increased the cell populations of T cells (CD3), B cells (CD19), monocytes (CD11b), and macrophage (Mac‐3). Furthermore, triptolide increased the phagocytosis of macrophage from peripheral blood mononuclear cells (PBMC) but not effects from peritoneum. Triptolide promoted T and B cell proliferation at 0.02 and 0.2 mg/kg treatment when cells were pretreated with Con A and LPS stimulation, respectively; however, triptolide did not significant affect NK cell activities in vivo. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 550–568, 2017.     

Imaging in glaucoma

Structural assessment using the imaging technologies discussed herein provides reproducible quantitative measurements of posterior segment ocular structures. These measurements have been found to provide useful data for glaucoma detection in various regions of the posterior segment. Further studies are needed to evaluate the utility of these technologies for pre-perimetric glaucoma detection and for monitoring glaucoma progression over an extended period.     

Improving GWAS discovery and genomic prediction accuracy in biobank data

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R² was 47% in a UK Biobank holdout sample, which was 76% of the estimated hSNP2. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.

As biobank datasets increase in size, it is important to understand the factors limiting the prediction of phenotype from genotype. Alongside others, we have recently shown that genomic prediction accuracy can be improved through the use of random-effects models that incorporate prior knowledge of genomic annotations and allow for differences in the variance explained by single-nucleotide polymorphism (SNP) markers, depending upon their linkage disequilibrium (LD) and their minor allele frequency (MAF) (1–8). These improvements in prediction accuracy should also translate into greater genome-wide association study (GWAS) discovery power. Mixed-linear models of association (MLMA) are commonly applied in GWASs in a two-step approach, where a random-effects model is first used to estimate leave-one-chromosome-out (LOCO) genetic values, and these are then used in a second marginal regression coefficient estimation step. Theory suggests that the test statistics obtained in the MLMA second step depend upon the accuracy of the LOCO genomic predictors produced from the first step. Current MLMA implementations use a blocked ridge regression model (9), a Restricted Maximum Likelihood (REML) genomic relationship model (10), or a Bayesian spike-and-slab model (11) within the first step.Here, we improve the computational implementation of our recently developed Bayesian grouped mixture of regressions model (GMRM), which estimates genetic marker effects jointly, but with independent marker inclusion probabilities and independent hSNP2 parameters across LD, MAF, and functional annotation groups (Materials and Methods). This allows us to apply the model to 21 traits in the UK Biobank to test for prediction accuracy improvements over existing approaches. We then extend the model to provide MLMA SNP marker association estimates to test whether improved prediction accuracy translates to improved GWAS discovery compared to existing MLMA approaches.     

Mammographic density and risk of breast cancer by age and tumor characteristics

Introduction

Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models.

Methods

Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (<55, 55–64, and ≥65 years).

Results

MD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma in situ (DCIS) compared to women ages 55–64 and ≥65 years (P_{age-interaction} = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P’s < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55–64 and ≥65 years (P_{age-interaction} = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group.

Conclusion

MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.     

Bone‐induced c‐kit expression in prostate cancer: A driver of intraosseous tumor growth

Loss of BRCA2 function stimulates prostate cancer (PCa) cell invasion and is associated with more aggressive and metastatic tumors in PCa patients. Concurrently, the receptor tyrosine kinase c‐kit is highly expressed in skeletal metastases of PCa patients and induced in PCa cells placed into the bone microenvironment in experimental models. However, the precise requirement of c‐kit for intraosseous growth of PCa and its relation to BRCA2 expression remain unexplored. Here, we show that c‐kit expression promotes migration and invasion of PCa cells. Alongside, we found that c‐kit expression in PCa cells parallels BRCA2 downregulation. Gene rescue experiments with human BRCA2 transgene in c‐kit‐transfected PCa cells resulted in reduction of c‐kit protein expression and migration and invasion, suggesting a functional significance of BRCA2 downregulation by c‐kit. The inverse association between c‐kit and BRCA2 gene expressions in PCa cells was confirmed using laser capture microdissection in experimental intraosseous tumors and bone metastases of PCa patients. Inhibition of bone‐induced c‐kit expression in PCa cells transduced with lentiviral short hairpin RNA reduced intraosseous tumor incidence and growth. Overall, our results provide evidence of a novel pathway that links bone‐induced c‐kit expression in PCa cells to BRCA2 downregulation and supports bone metastasis.