MicroRNA (miRNA) are important regulators of many biological processes, but the targets for most miRNA are still poorly defined. In this study, we profiled the expression of miRNA during myogenesis, from proliferating myoblasts through to terminally differentiated myotubes. Microarray results identified six significantly differentially expressed miRNA that were more than 2-fold different in myotubes. From this list, miRNA-26a (miR-26a), an up-regulated miRNA, was further examined. Overexpression of miR-26a in murine myogenic C2C12 cells induced creatine kinase activity, an enzyme that markedly increases during myogenesis. Further, myoD and myogenin mRNA expression levels were also up-regulated. These results suggest that increased expression of miR-26a promotes myogenesis. Through a bioinformatics approach, we identified the histone methyltransferase, Enhancer of Zeste homolog 2 (Ezh2), as a potential target of miR-26a. Overexpression of miR-26a suppressed the activity of a luciferase reporter construct fused with the 3-untranslated region of Ezh2. In addition, miR-26a overexpression decreased Ezh2 mRNA expression. These results reveal a model of regulation during myogenesis whereby the up-regulation of miR-26a acts to post-transcriptionally repress Ezh2, a known suppressor of skeletal muscle cell differentiation.

Introduction: Weight gain following antiretroviral therapy (ART) initiation is common, potentially predisposing some persons with HIV (PWH) to cardio-metabolic disease. We assessed relationships between ART drug class and weight change among treatment-na€ ıve PWH initiating ART in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD). Methods: Adult, treatment-na€ ıve PWH in NA-ACCORD initiating integrase strand transfer inhibitor (INSTI), protease inhibitor (PI) or non-nucleoside reverse-transcriptase inhibitor (NNRTI)-based ART on/after 1 January 2007 were followed through 31 December 2016. Multivariate linear mixed effects models estimated weight up to five years after ART initiation, adjusting for age, sex, race, cohort site, HIV acquisition mode, treatment year, and baseline weight, plasma HIV-1 RNA level and CD4 + cell count. Due to shorter follow-up for PWH receiving newer INSTI drugs, weights for specific INSTIs were estimated at two years. Secondary analyses using logistic regression and all covariates from primary analyses assessed factors associated with >10% weight gain at two and five years. Results: Among 22,972 participants, 87% were male, and 41% were white. 49% started NNRTI-, 31% started PI-and 20% started INSTI-based regimens (1624 raltegravir (RAL), 2085 elvitegravir (EVG) and 929 dolutegravir (DTG)). PWH starting INSTI-based regimens had mean estimated five-year weight change of +5.9kg, compared to +3.7kg for NNRTI and +5.5kg for PI. Among PWH starting INSTI drugs, mean estimated two-year weight change was +7.2kg for DTG, +5.8kg for RAL and +4.1kg for EVG. Women, persons with lower baseline CD4 + cell counts, and those initiating INSTI-based regimens had higher odds of >10% body weight increase at two years (adjusted odds ratio = 1.37, 95% confidence interval: 1.20 to 1.56 vs. NNRTI). Conclusions: PWH initiating INSTI-based regimens gained, on average, more weight compared to NNRTI-based regimens. This phenomenon may reflect heterogeneous effects of ART agents on body weight regulation that require further exploration.

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Background Previous studies of cardiovascular disease (CVD) among HIV-infected individuals have been limited by the inability to validate and differentiate atherosclerotic type 1 myocardial infarctions (T1MIs) from other events. We sought to define the incidence of T1MIs and risk attributable to traditional and HIV-specific factors among participants in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), and compare adjusted incidence rates to the general population Atherosclerosis Risk in Communities (ARIC) cohort. Methods We ascertained and adjudicated incident MIs among individuals enrolled in seven NA-ACCORD cohorts between 1995–2014. We calculated incidence rates (IR), adjusted incidence rate ratios (aIRRs), and 95% confidence intervals ([,]) of risk factors for T1MI using Poisson regression. We compared aIRRs of T1MIs in NA-ACCORD with those from ARIC. Results Among 29,169 HIV-infected individuals, the IR for T1MIs was 2.57[2.30–2.86] per 1000 person-years, and the aIRR was significantly higher compared with participants in ARIC (1.30[1.09–1.56]). In multivariable analysis restricted to HIV-infected individuals and including traditional CVD risk factors, the rate of T1MI increased with decreasing CD4 count (≥500 cells/μL: ref; 350–499 cells/μL: aIRR=1.32[0.98–1.77]; 200–349 cells/μL: aIRR=1.37[1.01–1.86]; 100–199 cells/μL: aIRR=1.60[1.09–2.34]; <100 cells/μL: aIRR=2.19[1.44–3.33]). Risk associated with detectable HIV RNA (<400 copies/mL: ref; ≥400 copies/mL: aIRR=1.36 [1.06–1.75]) was significantly increased only when CD4 was excluded. Conclusions The higher incidence of T1MI in HIV-infected individuals and increased risk associated with lower CD4 count and detectable HIV RNA suggest that early suppressive antiretroviral treatment and aggressive management of traditional CVD risk factors are necessary to maximally reduce MI risk.

The risk of ESRD remains high among HIV-infected individuals in care but is declining with improvements in virologic suppression. HIV-infected black persons continue to comprise the majority of cases, as a result of higher viral loads, comorbidities, and genetic susceptibility.

Multimorbidity prevalence has increased among PLWH. Comorbidity prevention and multisubspecialty management of increasingly complex healthcare needs will be vital to ensuring that they receive needed care.

Background Adults with HIV have an increased burden of non-AIDS-defining cancers, myocardial infarction, end-stage liver disease, and end-stage renal disease. The objective of this study was to estimate the population attributable fractions (PAFs) of preventable or modifiable HIV-related and traditional risk factors for non-AIDS-defining cancers, myocardial infarction, end-stage liver disease, and end-stage renal disease outcomes. Methods We included participants receiving care in academic and community-based outpatient HIV clinical cohorts in the USA and Canada from Jan 1, 2000, to Dec 31, 2014, who contributed to the North American AIDS Cohort Collaboration on Research and Design and who had validated non-AIDS-defining c ancers, m yocardial i nfarction, e nd-stage l iver disease, or end-stage renal disease outcomes. Traditional risk factors were tobacco smoking, hypertension, elevated total cholesterol, type 2 diabetes, renal impairment (stage 4 chronic kidney disease), and hepatitis C virus and hepatitis B virus infections. HIV-related risk factors were low CD4 count (<200 cells per µL), detectable plasma HIV RNA (>400 copies per mL), and history of a clinical AIDS diagnosis. PAFs and 95% CIs were estimated to quantify the proportion of outcomes that could be avoided if the risk factor was prevented. Findings In each of the study populations for the four outcomes (1405 of 61 500 had non-AIDS-defining cancer, 347 of 29 515 had myocardial infarctions, 387 of 35 044 had end-stage liver disease events, and 255 of 35 620 had end-stage renal disease events), about 17% were older than 50 years at study entry, about 50% were non-white, and about 80% were men. Preventing smoking would avoid 24% (95% CI 13-35) of these cancers and 37% (7-66) of the myocardial infarctions. Preventing elevated total cholesterol and hypertension would avoid the greatest proportion of myocardial infarctions: 44% (30-58) for cholesterol and 42% (28-56) for hypertension. For liver disease, the PAF was greatest for hepatitis C infection (33%; 95% CI 17-48). For renal disease, the PAF was greatest for hypertension (39%; 26-51) followed by elevated total cholesterol (22%; 13-31), detectable HIV RNA (19; 9-31), and low CD4 cell count (13%; 4-21). Interpretation The substantial proportion of non-AIDS-defining cancers, myocardial infarction, end-stage liver disease, and end-stage renal disease outcomes that could be prevented with interventions on traditional risk factors elevates the importance of screening for these risk factors, improving the effectiveness of prevention (or modification) of these risk factors, and creating sustainable care models to implement such interventions during the decades of life of adults living with HIV who are receiving care.

Even if the structure of a receptor has been determined experimentally, it may not be a conformation to which a ligand would bind when induced fit effects are significant. Molecular docking using such a receptor structure may thus fail to recognize a ligand to which the receptor can bind with reasonable affinity. Here, we examine one way to alleviate this problem by using an ensemble of receptor conformations generated from a molecular dynamics simulation for molecular docking. Two molecular dynamics simulations were conducted to generate snapshots for protein kinase A: one with the ligand bound, the other without. The ligand, balanol, was then docked to conformations of the receptors presented by these trajectories. The Lamarckian genetic algorithm in Autodock [Goodsell et al. J Mol Recognit 1996;9(1):1-5; Morris et al. J Comput Chem 1998;19(14):1639-1662] was used in the docking. Three ligand models were used: rigid, flexible, and flexible with torsional potentials. When the snapshots were taken from the molecular dynamics simulation of the protein-ligand complex, the correct docking structure could be recovered easily by the docking algorithm in all cases. This was an easier case for challenging the docking algorithm because, by using the structure of the protein in a protein-ligand complex, one essentially assumed that the protein already had a pocket to which the ligand can fit well. However, when the snapshots were taken from the ligand-free protein simulation, which is more useful for a practical application when the structure of the protein-ligand complex is not known, several clusters of structures were found. Of the 10 docking runs for each snapshot, at least one structure was close to the correctly docked structure when the flexible-ligand models were used. We found that a useful way to identify the correctly docked structure was to locate the structure that appeared most frequently as the lowest energy structure in the docking experiments to different snapshots.