Citations

Significance The skin and intestinal barrier are controlled by signaling scissors, termed ADAM17 (a disintegrin and metalloprotease 17), that reside in the membrane on the surface of cells. The main purpose of these signaling scissors is to liberate growth factors from their membrane anchor, allowing them to activate their receptors, including the epidermal growth factor receptor (EGFR). The ADAM17/EGFR signaling axis is tightly regulated, yet little is known about the underlying mechanism. Here we use genetic, cell biological, and biochemical approaches to identify two membrane proteins termed iRhoms 1 and 2 (inactive Rhomboid-like proteins) as crucial upstream regulators of ADAM17-dependent EGFR signaling. This uncovers the iRhoms as attractive novel targets to treat ADAM17/EGFR-dependent diseases such as cancer.

Mouse butyrylcholinesterase (mBChE) and an mBChE-based cocaine hydrolase (mCocH, i.e. the A199S/S227A/S287G/A328W/Y332G mutant) have been characterized for their catalytic activities against cocaine, i.e. naturally occurring (−)-cocaine, in comparison with the corresponding human BChE (hBChE) and an hBChE-based cocaine hydrolase (hCocH, i.e. the A199S/F227A/S287G/A328W/Y332G mutant). It has been demonstrated that mCocH and hCocH have improved the catalytic efficiency of mBChE and hBChE against (−)-cocaine by ~8- and ~2000-fold respectively, although the catalytic efficiencies of mCocH and hCocH against other substrates, including acetylcholine (ACh) and butyrylthiocholine (BTC), are close to those of the corresponding wild-type enzymes mBChE and hBChE. According to the kinetic data, the catalytic efficiency (kcat/KM) of mBChE against (−)-cocaine is comparable with that of hBChE, but the catalytic efficiency of mCocH against (−)-cocaine is remarkably lower than that of hCocH by ~250-fold. The remarkable difference in the catalytic activity between mCocH and hCocH is consistent with the difference between the enzyme–(−)-cocaine binding modes obtained from molecular modelling. Further, both mBChE and hBChE demonstrated substrate activation for all of the examined substrates [(−)-cocaine, ACh and BTC] at high concentrations, whereas both mCocH and hCocH showed substrate inhibition for all three substrates at high concentrations. The amino-acid mutations have remarkably converted substrate activation of the enzymes into substrate inhibition, implying that the rate-determining step of the reaction in mCocH and hCocH might be different from that in mBChE and hBChE.

Abstract Motivation: RNA-seq has become the method of choice to quantify genes and exons, discover novel transcripts and detect fusion genes. However, reliable variant identification from RNA-seq data remains challenging because of the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost uses several attributes unique in the process of RNA library preparation, sequencing and RNA-seq data analyses. It uses a boosting method to train a model of ‘good quality’ variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow, which integrates tools of variant calling, annotation and filtering. Results: RVboost consistently outperforms the variant quality score recalibration from the Genome Analysis Tool Kit and the RNA-seq variant-calling pipeline SNPiR in 12 RNA-seq samples using ground-truth variants from paired exome sequencing data. Several RNA-seq–specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first six base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction. Availability and implementation: The RVboost package is implemented to readily run in Mac or Linux environments. The software and user manual are available at http://bioinformaticstools.mayo.edu/research/rvboost/. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Objectives Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality. Methods By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32 415 adults with a cancer diagnosis at Vanderbilt and 79 258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index. Results Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR. Conclusions EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Background Classification of risk of ischemic stroke is important for medical care and public health reasons. Whether addition of biomarkers adds to predictive power of the Framingham Stroke Risk or other traditional risk factors has not been studied in older women. Methods The Hormones and Biomarkers Predicting Stroke Study is a case-control study of blood biomarkers assayed in 972 ischemic stroke cases and 972 controls, nested in the Women's Health Initiative Observational Study of 93 676 postmenopausal women followed for an average of eight-years. We evaluated additive predictive value of two commercially available biomarkers: C-reactive protein and lipoprotein-associated phospholipase A 2 to determine if they added to risk prediction by the Framingham Stroke Risk Score or by traditional risk factors, which included lipids and other variables not included in the Framingham Stroke Risk Score. As measures of additive predictive value, we used the C-statistic, net reclassification improvement, category-less net reclassification improvement, and integrated discrimination improvement index. Results Addition of C-reactive protein to Framingham risk models or additional traditional risk factors overall modestly improved prediction of ischemic stroke and resulted in overall net reclassification improvement of 6·3%, (case net reclassification improvement = 3·9%, control net reclassification improvement = 2·4%). In particular, high-sensitivity C-reactive protein was useful in prediction of cardioembolic strokes (net reclassification improvement = 12·0%; 95% confidence interval 4·3–19·6%) and in strokes occurring in less than three-years (net reclassification improvement = 7·9%, 95% confidence interval 0·8–14·9%). Lipoprotein-associated phospholipase A 2 was useful in risk prediction of large artery strokes (net reclassification improvement = 19·8%, 95% confidence interval 7·4–32·1%) and in early strokes (net reclassification improvement = 5·8%, 95% confidence interval 0·4–11·2%). Conclusions C-reactive protein and lipoprotein-associated phospholipase A 2 can improve prediction of certain subtypes of ischemic stroke in older women, over the Framingham stroke risk model and traditional risk factors, and may help to guide surveillance and treatment of women at risk.