Expression of HIV transgene aggravates kidney injury in diabetic mice

With the widespread use of combination antiretroviral agents, the incidence of HIV-associated nephropathy has decreased. Currently, HIV-infected patients live much longer and often suffer from comorbidities such as diabetes mellitus. Recent epidemiological studies suggest that concurrent HIV infection and diabetes mellitus may have a synergistic effect on the incidence of chronic kidney disease. To address this, we determined whether HIV-1 transgene expression accelerates diabetic kidney injury using a diabetic HIV-1 transgenic (Tg26) murine model. Diabetes was initially induced with low-dose streptozotocin in both Tg26 and wild-type mice on a C57BL/6 background, which is resistant to classic HIV-associated nephropathy. Although diabetic nephropathy is minimally observed on the C57BL/6 background, diabetic Tg26 mice exhibited a significant increase in glomerular injury compared with nondiabetic Tg26 mice and diabetic wild-type mice. Validation of microarray gene expression analysis from isolated glomeruli showed a significant upregulation of proinflammatory pathways in diabetic Tg26 mice. Thus, our study found that expression of HIV-1 genes aggravates diabetic kidney disease

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Integrated multiomics network analysis reveals signaling profiles in lung cancer.</jats:p

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program (http://www.lincsproject.org/) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response

Regulation of Embryonic and Induced Pluripotency by Aurora Kinase-p53 Signaling

SummaryMany signals must be integrated to maintain self-renewal and pluripotency in embryonic stem cells (ESCs) and to enable induced pluripotent stem cell (iPSC) reprogramming. However, the exact molecular regulatory mechanisms remain elusive. To unravel the essential internal and external signals required for sustaining the ESC state, we conducted a short hairpin (sh) RNA screen of 104 ESC-associated phosphoregulators. Depletion of one such molecule, aurora kinase A (Aurka), resulted in compromised self-renewal and consequent differentiation. By integrating global gene expression and computational analyses, we discovered that loss of Aurka leads to upregulated p53 activity that triggers ESC differentiation. Specifically, Aurka regulates pluripotency through phosphorylation-mediated inhibition of p53-directed ectodermal and mesodermal gene expression. Phosphorylation of p53 not only impairs p53-induced ESC differentiation but also p53-mediated suppression of iPSC reprogramming. Our studies demonstrate an essential role for Aurka-p53 signaling in the regulation of self-renewal, differentiation, and somatic cell reprogramming

Patterns and rates of exonic de novo mutations in autism spectrum disorders

Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified1,2. To identify further genetic risk factors, we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n= 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant and the overall rate of mutation is only modestly higher than the expected rate. In contrast, there is significantly enriched connectivity among the proteins encoded by genes harboring de novo missense or nonsense mutations, and excess connectivity to prior ASD genes of major effect, suggesting a subset of observed events are relevant to ASD risk. The small increase in rate of de novo events, when taken together with the connections among the proteins themselves and to ASD, are consistent with an important but limited role for de novo point mutations, similar to that documented for de novo copy number variants. Genetic models incorporating these data suggest that the majority of observed de novo events are unconnected to ASD, those that do confer risk are distributed across many genes and are incompletely penetrant (i.e., not necessarily causal). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5 to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favor of CHD8 and KATNAL2 as genuine autism risk factors

Predicting opioid dependence from electronic health records with machine learning

Abstract Background The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence. Results We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls. Conclusions The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room