Cole Brokamp

Probenecid: novel use as a non-injurious positive inotrope acting via cardiac TRPV2 stimulation.

Probenecid is a highly lipid soluble benzoic acid derivative originally used to increase serum antibiotic concentrations. It was later discovered to have uricosuric effects and was FDA approved for gout therapy. It has recently been found to be a potent agonist of transient receptor potential vanilloid 2 (TRPV2). We have shown that this receptor is in the cardiomyocyte and report a positive inotropic effect of the drug. Using echocardiography, Langendorff and isolated myocytes, we measured the change in contractility and, using TRPV2(-/-) mice, proved that the effect was mediated by TRPV2 channels in the cardiomyocytes. Analysis of the expression of Ca(2+) handling and β-adrenergic signaling pathway proteins showed that the contractility was not increased through activation of the β-ADR. We propose that the response to probenecid is due to activation of TRPV2 channels secondary to SR release of Ca(2+).

Coordinated post-transcriptional regulation of Hsp70.3 gene expression by microRNA and alternative polyadenylation.

Heat shock protein 70 (Hsp70) is well documented to possess general cytoprotective properties in protecting the cell against stressful and noxious stimuli. We have recently shown that expression of the stress-inducible Hsp70.3 gene in the myocardium in response to ischemic preconditioning is NF-κB-dependent and necessary for the resulting late phase cardioprotection against a subsequent ischemia/reperfusion injury. Here we show that the Hsp70.3 gene product is subject to post-transcriptional regulation through parallel regulatory processes involving microRNAs and alternative polyadenylation of the mRNA transcript. First, we show that cardiac ischemic preconditioning of the in vivo mouse heart results in decreased levels of two Hsp70.3-targeting microRNAs: miR-378* and miR-711. Furthermore, an ischemic or heat shock stimulus induces alternative polyadenylation of the expressed Hsp70.3 transcript that results in the accumulation of transcripts with a shortened 3′-UTR. This shortening of the 3′-UTR results in the loss of the binding site for the suppressive miR-378* and thus renders the alternatively polyadenylated transcript insusceptible to miR-378*-mediated suppression. Results also suggest that the alternative polyadenylation-mediated shortening of the Hsp70.3 3′-UTR relieves translational suppression observed in the long 3′-UTR variant, allowing for a more robust increase in protein expression. These results demonstrate alternative polyadenylation of Hsp70.3 in parallel with ischemic or heat shock-induced up-regulation of mRNA levels and implicate the importance of this process in post-transcriptional control of Hsp70.3 expression.

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

Air pollution, epigenetics, and asthma.

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma.

A field application of a personal sensor for ultrafine particle exposure in children

BACKGROUND Ultrafine particles (UFPs) have been associated with adverse health outcomes in children, but studies are often limited by surrogate estimates of exposure. Accurately characterizing childrens personal exposure to UFP is difficult due to the high spatiotemporal variability of UFP and childrens time-activity patterns. OBJECTIVE The objectives of this study were to conduct a field test of a personal sensor for UFP (PUFP) by measuring UFP exposure among children and assess the sensors capabilities and limitations. METHODS Children wore the sensor at school, during transit periods between school and home, and in their home for 2-4h on 2 consecutive days and provided feedback regarding their experience with the sensor. The PUFP sensor recorded UFP number concentration at one second intervals and recorded GPS location allowing for comparisons of UFP exposure at homes, schools, and during transit. A mixed-effects linear model was used to compare the effect of microenvironment on personal UFP measurements. RESULTS The overall total median personal exposure to UFP was 12,900 particles/cm(3) (p/cm(3)). Median UFP exposure at homes, schools and during transit was 17,800, 11,900, and 13,600 p/cm(3), respectively. Results of the mixed-effects model found that riding in a car and walking were significantly associated with 1.36 (95% CI 1.33-1.39) and 2.51 (95% CI 2.44-2.57) times higher UFP concentrations compared to the home. CONCLUSIONS The PUFP sensor can measure near real-time exposure to UFP with high spatiotemporal resolution. Childrens exposure to UFP varies by location, with increased exposure during transit to and from school.

Indoor air quality in green-renovated vs. non-green low-income homes of children living in a temperate region of US (Ohio).

Green eco-friendly housing includes approaches to reduce indoor air pollutant sources and to increase energy efficiency. Although sealing/tightening buildings can save energy and reduce the penetration of outdoor pollutants, an adverse outcome can be increased buildup of pollutants with indoor sources. The objective of this study was to determine the differences in the indoor air quality (IAQ) between green and non-green homes in low-income housing complexes. In one housing complex, apartments were renovated using green principles (n=28). Home visits were conducted immediately after the renovation, and subsequently at 6 months and at 12 months following the renovation. Of these homes, eight homes had pre-renovation home visits; this allowed pre- and post-renovation comparisons within the same homes. Parallel visits were conducted in non-green (control) apartments (n=14) in a nearby low-income housing complex. The IAQ assessments included PM2.5, black carbon, ultrafine particles, sulfur, total volatile organic compounds (VOCs), formaldehyde, and air exchange rate. Data were analyzed using linear mixed-effects models. None of the indoor pollutant concentrations were significantly different between green and non-green homes. However, we found differences when comparing the concentrations before and after renovation. Measured immediately after renovation, indoor black carbon concentrations were significantly lower averaging 682 ng/m(3) in post-renovation vs. 2364 ng/m(3) in pre-renovation home visits (p=0.01). In contrast, formaldehyde concentrations were significantly higher in post-renovated (0.03 ppm) than in pre-renovated homes (0.01 ppm) (p=0.004). Questionnaire data showed that opening of windows occurred less frequently in homes immediately post-renovation compared to pre-renovation; this factor likely affected the levels of indoor black carbon (from outdoor sources) and formaldehyde (from indoor sources) more than the renovation status itself. To reduce IAQ problems and potentially improve health, careful selection of indoor building materials and ensuring sufficient ventilation are important for green building designs.

Electrophysiology of single and aggregate Cx43 hemichannels.

Connexin43 (Cx43) is the most ubiquitous gap junction protein in the human body and is essential for cell-to-cell communication in a variety of organs and organ systems. As a result, Cx43 is responsible for mediating both electrical and chemical signals, passing dissolved solutes and small signaling molecules between cells in a coordinated fashion. Here, we explore the electrophysiological properties of hemichannels formed from Cx43 and Cx43 fused to eGFP (Cx43eGFP) and their interactions in a planar lipid membrane (BLM). Unlike in vivo patch clamp experiments, Cx43 was purified and isolated from other membrane constituents allowing elucidation of individual protein responses to various electrical and chemical stimuli. Using this system, we examined hemichannel electrophysiology and the roles of several well-known gap junction blockers, namely: lanthanum, heptanol, carbenoxalone and lindane. We also observed a critical number of hemichannels required for an accelerated conductance increase, an emergent electrical signature indicative of plaque formation.

Phenotypes of Rapid Cystic Fibrosis Lung Disease Progression during Adolescence and Young Adulthood

Rationale: Individuals with cystic fibrosis are at risk for prolonged drops in lung function, clinically termed rapid decline, during discreet periods of the disease. Objectives: To identify phenotypes of rapid pulmonary decline and determine how these phenotypes are related to patient characteristics. Methods: A longitudinal cohort study of patients with cystic fibrosis aged 6‐21 years was conducted using the Cystic Fibrosis Foundation Patient Registry. A statistical approach for clustering longitudinal profiles, sparse functional principal components analysis, was used to classify patients into distinct phenotypes by evaluating trajectories of FEV1 decline. Phenotypes were compared with respect to baseline and mortality characteristics. Measurements and Main Results: Three distinct phenotypes of rapid decline were identified, corresponding to early, middle, and late timing of maximal FEV1 loss, in the overall cohort (n = 18,387). The majority of variation (first functional principal component, 94%) among patient profiles was characterized by differences in mean longitudinal FEV1 trajectories. Average degree of rapid decline was similar among phenotypes (roughly −3% predicted/yr); however, average timing differed, with early, middle, and late phenotypes experiencing rapid decline at 12.9, 16.3, and 18.5 years of age, respectively. Individuals with the late phenotype had the highest initial FEV1 but experienced the greatest loss of lung function. The early phenotype was more likely to have respiratory infections and acute exacerbations at baseline or to develop them subsequently, compared with other phenotypes. Conclusions: By identifying phenotypes and associated risk factors, timing of interventions may be more precisely targeted for subgroups at highest risk of lung function loss.

Residential mobility impacts exposure assessment and community socioeconomic characteristics in longitudinal epidemiology studies

Epidemiologic studies commonly use residential locations to estimate environmental exposures or community-level characteristics. The impact of residential mobility on these characteristics, however, is rarely considered. The objective of this analysis was to examine the effect of residential mobility on estimates of traffic-related air pollution (TRAP), greenspace, and community-level characteristics. All residential addresses were reported from birth through age seven for children enrolled in the Cincinnati Childhood Allergy and Air Pollution Study. Exposure to TRAP at each address was estimated using a land use model. Greenspace was estimated using satellite imagery. Indices of neighborhood deprivation and race were created based on socioeconomic-census tract measures. Exposure estimates using the birth record address, the last known address, and the annual address history were used to determine exposure estimation error and bias in the association with asthma at age seven. Overall, 54% of the cohort moved at least once prior to age seven. Each move was separated by a median of 4 miles and associated with a median decrease of 4.4% in TRAP exposure, a 5.3% increase in greenspace, an improved deprivation index, and no change in the race index. Using the birth record address or the last known address instead of the annual address history resulted in exposure misclassification leading to a bias toward the null when associating the exposures with asthma. Using a single address to estimate environmental exposures and community-level characteristics over a time period may result in differential assessment error.

Decentralized and reproducible geocoding and characterization of community and environmental exposures for multisite studies

Abstract Objective Geocoding and characterizing geographic, community, and environmental characteristics of study participants is frequently done in epidemiological studies. However, participant addresses are identifiable protected health information (PHI) and geocoding must be conducted in a Health Insurance Portability and Accountability Act–compliant manner. Our objective was to create a software application for this process that addresses limitations in current approaches. Materials and Methods We used a containerization platform to create DeGAUSS (Decentralized Geomarker Assessment for Multi-Site Studies), a software application that facilitates reproducible geocoding and geomarker assessment while maintaining the confidentiality of PHI. To validate the software, 215 350 addresses in Hamilton County, Ohio, were geocoded using DeGAUSS, ArcGIS, Google, and SAS and compared to a gold-standard approach. We distributed the DeGAUSS software to sites in an ongoing multisite study (Electronic Medical Records and Genomics, or eMERGE), and individual sites independently geocoded and assigned median census tract–level income and distance to nearest major roadway to their participants’ addresses, removed associated PHI, and returned deidentified data. Results Within a multisite study, 52 244 study participants’ addresses across 5 sites were geocoded with a median distance to roadway of 10 022m and a median census tract income of