A nonalcoholic fatty liver disease cirrhosis model in gerbil:the dynamic relationship between hepatic lipid metabolism and cirrhosis

Nonalcoholic fatty liver disease (NAFLD) usually takes decades to develop into cirrhosis, which limits the longitudinal study of NAFLD. This work aims at developing a NAFLD-caused cirrhosis model in gerbil and examining the dynamic relationship between hepatic lipid metabolism and cirrhosis. We fed gerbil a high-fat and high-cholesterol diet (HFHCD) for 24 weeks, and recorded the gerbil's phenotype at 3, 6, 9, 12, 15, 18, 21, 24 weeks. The model's pathological process, lipid metabolism, oxidative stress, liver collagen deposition and presence of relevant cytokines were tested and evaluated during the full-time frame of disease onset. The gerbil model can induce nonalcoholic steatohepatitis (NASH) within 9 weeks, and can develop cirrhosis after 21 weeks induction. The model's lipids metabolism disorder is accompanied with the liver damage development. During the NAFLD progression, triglycerides (TG) and free fatty acids (FFA) have presented distinct rise and fall tendency, and the turning points are at the fibrosis stage. Besides that, the ratios of total cholesterol (CHO) to high-density lipoprotein cholesterol (HDL-C) exhibited constant growth tendency, and have a good linear relationship with hepatic stellate cells (HSC) (R-2 = 0.802, P <0.001). The gerbil NAFLD cirrhosis model has been developed and possesses positive correlation between lipids metabolism and cirrhosis. The compelling rise and fall tendency of TG and FFA indicated that the fibrosis progression can lead to impairment in lipoprotein synthesis and engender decreased TG level. CHO/HDL-C ratios can imply the fibrosis progress and be used as a blood indicator for disease prediction and prevention

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

eBLOCKs: Enumerating conserved protein blocks to achieve maximal sensitivity and specificity

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