Citations

The large diversity and volume of extracellular RNA (exRNA) data that will form the basis of the exRNA Atlas generated by the Extracellular RNA Communication Consortium pose a substantial data integration challenge. We here present the strategy that is being implemented by the exRNA Data Management and Resource Repository, which employs metadata, biomedical ontologies and Linked Data technologies, such as Resource Description Framework to integrate a diverse set of exRNA profiles into an exRNA Atlas and enable integrative exRNA analysis. We focus on the following three specific data integration tasks: (a) selection of samples from a virtual biorepository for exRNA profiling and for inclusion in the exRNA Atlas; (b) retrieval of a data slice from the exRNA Atlas for integrative analysis and (c) interpretation of exRNA analysis results in the context of pathways and networks. As exRNA profiling gains wide adoption in the research community, we anticipate that the strategies discussed here will increasingly be required to enable data reuse and to facilitate integrative analysis of exRNA data.

Ten ongoing studies designed to test the possibility that extracellular RNAs may serve as biomarkers in human disease are described. These studies, funded by the NIH Common Fund Extracellular RNA Communication Program, examine diverse extracellular body fluids, including plasma, serum, urine and cerebrospinal fluid. The disorders studied include hepatic and gastric cancer, cardiovascular disease, chronic kidney disease, neurodegenerative disease, brain tumours, intracranial haemorrhage, multiple sclerosis and placental disorders. Progress to date and the plans for future studies are outlined.

MRS provides a valuable tool for the non‐invasive detection of brain γ‐aminobutyric acid (GABA) in vivo. GABAergic dysfunction has been observed in the aging cerebellum. The study of cerebellar GABA changes is of considerable interest in understanding certain age‐related motor disorders. However, little is known about the reproducibility of GABA MRS in an aged population. Therefore, this study aimed to explore the feasibility and reproducibility of GABA MRS in the aged cerebellum at 3.0 T and to examine the effect of differing tissue composition on GABA measurements. MRI and 1H MRS examinations were performed on 10 healthy elderly volunteers (mean age, 75.2 ± 6.5 years) using a 3.0‐T Siemens Tim Trio scanner. Among them, five subjects were scanned twice to assess the short‐term reproducibility. The MEGA‐PRESS (Mescher–Garwood point‐resolved spectroscopy) J‐editing sequence was used for GABA detection in two volumes of interest (VOIs) in the left and right cerebellar dentate. MRS data processing and quantification were performed with LCModel 6.3‐0L using two separate basis sets, generated from density matrix simulations using published values for chemical shifts and J couplings. Raw metabolite levels from LCModel outputs were corrected for cerebrospinal fluid contamination and relaxation. GABA‐edited spectra yielded robust and stable GABA measurements with averaged intra‐individual coefficients of variation for corrected GABA+ between 4.0 ± 2.8% and 13.4 ± 6.3%, and inter‐individual coefficients of variation between 12.6% and 24.2%. In addition, there was a significant correlation between GABA+ obtained with the two LCModel basis sets. Overall, our results demonstrated the feasibility and reproducibility of cerebellar GABA‐edited MRS at 3.0 T in an elderly population. This information might be helpful for studies using this technique to study GABA changes in normal or diseased aging brain, e.g. for power calculations and the interpretation of longitudinal observations. Copyright © 2015 John Wiley & Sons, Ltd.

AbstractMicroRNA‐200 (miR‐200) has emerged as a regulator of the PI3K/AKT pathway and cancer cell growth. It was reported that miR‐200 can activate PI3K/AKT by targeting FOG2 (friend of GATA 2), which directly binds to the p85α regulatory subunit of PI3K. We found that miR‐200 was elevated in early stage lung adenocarcinomas when compared with normal lung tissues, and the expression of miR‐200 promoted the tumor spheroid growth of lung adenocarcinoma cells. We show that AKT activation was essential for such oncogenic action of miR‐200. However, depletion of FOG2 had little effect on AKT activation. By performing a reverse‐phase protein array, we found that miR‐200 not only activated AKT but also concomitantly inactivated S6K and increased IRS‐1, an S6K substrate that is increased on S6K inactivation. Depletion of IRS‐1 partially inhibited the miR‐200‐dependent AKT activation. Taken together, our results suggest that miR‐200 may activate AKT in lung adenocarcinoma cells through a FOG2‐independent mechanism involving IRS‐1. Our findings also provide evidence that increased miR‐200 expression may contribute to early lung tumorigenesis and that AKT inhibitors may be useful for the treatment of miR‐200‐dependent tumor cell growth. © 2015 IUBMB Life, 67(9):720–725, 2015

Acute decompensated heart failure (ADHF) is a prominent condition whose frequency is increasing, especially in the context of an aging population. The ability to discern the cause of undifferentiated dyspnea in acute presentations is an increasing struggle. In this review, we seek to explore the various biomarkers that have been studied for the diagnosis of ADHF and their respective clinical utilities as the most recent data would suggest. Natriuretic peptides, galectin-3, soluble ST2, adrenomedullin, cardiac troponin-T, neutrophil gelatinase-associated lipocalin, and copeptin are reviewed. Of these biomarkers, natriuretic peptides are the most thoroughly evaluated and show the most promise for diagnosis or exclusion of acute heart failure, though other biomarkers can add significant utility in the form of prognostication.

Abstract Summary: One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of ‘omics’ features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package ‘glmgraph’ that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available. Availability and implementation: ‘glmgraph’ is implemented in R and C++ Armadillo and publicly available under CRAN. Contact: chen.jun2@mayo.edu or hongzhe@upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.