Citations

With the epidemic of obesity, nonalcoholic fatty liver disease (NAFLD) has become the most common pediatric liver disease. The influence of a perinatal obesity‐inducing diet (OID) on the development and progression of NAFLD in offspring is important but incompletely studied. Hence, we fed breeding pairs of C57BL/6J mice during gestation and lactation (perinatally) either chow or an OID rich in fat, fructose, and cholesterol (FFC). The offspring were weaned to either chow or an FFC diet, generating four groups: perinatal (p)Chow‐Chow, pChow‐FFC, pFFC‐Chow, and pFFC‐FFC. Mice were sacrificed at 10 weeks of age. We examined the whole‐liver transcriptome by RNA sequencing (RNA‐seq) and whole‐liver genome methylation by reduced representation bisulfite sequencing (RRBS). Our results indicated that the pFFC‐FFC mice had a significant increase in hepatic steatosis, injury, inflammation, and fibrosis, as assessed histologically and biochemically. We identified 189 genes that were differentially expressed and methylated in the pFFC‐FFC mice versus the pChow‐FFC mice. Gene set enrichment analysis identified hepatic fibrosis/hepatic stellate cell activation as the top canonical pathway, suggesting that the differential DNA methylation events in the mice exposed to the FFC diet perinatally were associated with a profibrogenic transcriptome. To verify that this finding was consistent with perinatal nutritional reprogramming of the methylome, we exposed pFFC‐Chow mice to an FFC diet in adulthood. These mice developed significant hepatic steatosis, injury, inflammation, and more importantly fibrosis when compared to the appropriate controls.Conclusion: Perinatal exposure to an OID primes the immature liver for an accentuated fibrosing nonalcoholic steatohepatitis (NASH) phenotype, likely through nutritional reprogramming of the offspring methylome. These data have potential clinical implications for monitoring children of obese mothers and risk stratification of children with NAFLD.

AbstractNOTCH1 is the prototype of the NOTCH family of single‐pass transmembrane receptors and regulates many basic processes during embryonic development and human pathogenesis. In core to NOTCH1 activation are proteolytic cleavages that release its intracellular domain (NICD1), which in turn translocates to the nucleus to regulate gene transcription. Macroautophagy (hereafter autophagy) has been shown to promote the degradation of NOTCH1, but the underlying mechanisms remain elusive. Here, we show that autophagy promotes the degradation of NOTCH1 by p62‐dependent binding between NICD1 and LC3, a component of the autophagosomes that execute autophagy. Strikingly, deleting any of the structural NICD1 domains fails to block the degradation of NICD1 by autophagy, and p62 binds to almost all these domains independently, indicating that p62 binds to multiple sites on NICD1 to promote its degradation. Intriguingly, inhibition of autophagy induces the accumulation of NICD1 in not only the cytoplasm but also the nucleus and increases the transcriptional activity of NICD1, and such regulation of nuclear NICD1 by autophagy is unique to NICD1 and not observed for all other NICDs (NICD2‐4). Collectively, our results suggest that autophagy tightly controls nuclear NOTCH1 activity through multiple p62 binding sites, and that modulating autophagy activity may be useful for treating NOTCH1 related human diseases. © 2018 IUBMB Life, 70(10):985–994, 2018

Abstract Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10−6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10−7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10−3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis. Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419–30. ©2018 AACR.

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer’s disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer’s Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer’s Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.

Abstract Lung cancer remains the leading cause of cancer-related death, partly due to the fact that most patients are diagnosed with metastatic disease, which is the main cause of death, yet effective therapeutics are lacking. Utilizing mouse models of metastatic lung adenocarcinomas, we report that the transcription factor GATA3 and its cofactor FOG2 play critical roles in lung adenocarcinoma metastasis by promoting the expression of the lysyl hydroxylase (LH) family members, which in turn regulate the tumorigenicity and motility of lung tumor cells through both EMT and EMT-independent mechanisms. In vivo, LHs are elevated in human lung adenocarcinomas and are associated with worse patient survival. Collectively, our data suggest that targeting LHs may be useful for treating metastatic lung cancer. Citation Format: Wei Liu, Ting Zhang, Lixia Guo, Yuanyuan Wang, Yanan Yang. Role of lysyl hydroxylases in metastatic lung cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Modeling Cancer in Mice: Technology, Biology, and Beyond; 2017 Sep 24-27; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(10 Suppl):Abstract nr B07.