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Extracellular vesicles (EVs) are membrane-bound vesicles that are released by cells into their extracellular environment, have selective enrichment of specific proteins and RNA, and can mediate intercellular communication. In this review we highlight recent observations of the role of EVs in liver injury, viral hepatitis, alcoholic or nonalcoholic liver disease, biliary tract disease, and liver cancers. Potential applications as markers of diseases or for therapeutic applications are outlined to emphasize the new opportunities that are arising from the study of EVs.

AbstractIntroductionWe hypothesized that common Alzheimer's disease (AD)‐associated variants within the triggering receptor expressed on myeloid (TREM) gene cluster influence disease through gene expression.MethodsExpression microarrays on temporal cortex and cerebellum from ∼400 neuropathologically diagnosed subjects and two independent RNAseq replication cohorts were used for expression quantitative trait locus analysis.ResultsA variant within a DNase hypersensitive site 5′ of TREM2, rs9357347‐C, associates with reduced AD risk and increased TREML1 and TREM2 levels (uncorrected P = 6.3 × 10−3 and 4.6 × 10−2, respectively). Meta‐analysis on expression quantitative trait locus results from three independent data sets (n = 1006) confirmed these associations (uncorrected P = 3.4 × 10−2 and 3.5 × 10−3, Bonferroni‐corrected P = 6.7 × 10−2 and 7.1 × 10−3, respectively).DiscussionOur findings point to rs9357347 as a functional regulatory variant that contributes to a protective effect observed at the TREM locus in the International Genomics of Alzheimer's Project genome‐wide association study meta‐analysis and suggest concomitant increase in TREML1 and TREM2 brain levels as a potential mechanism for protection from AD.

AbstractAlzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.

BackgroundFunctional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.ResultsThis article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi‐dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms.ConclusionsTo better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi‐dimensional OMICs data will generate novel insights into system‐level gene regulations and serves as a foundation for further hypothesis‐driven research.