Online RITMS Research Tools
Hydrofracking-associated Hospitalization Dataset
Over the past ten years, unconventional gas and oil drilling (UGOD) has markedly expanded in the United States. Despite substantial increases in well drilling, the health consequences of UGOD toxicant exposure remain unclear. This study examines an association between wells and healthcare use by zip code from 2007 to 2011 in Pennsylvania. Inpatient discharge databases from the Pennsylvania Healthcare Cost Containment Council were correlated with active wells by zip code in three counties in Pennsylvania. For overall inpatient prevalence rates and 25 specific medical categories, the association of inpatient prevalence rates with number of wells per zip code and, separately, with wells per km2 (separated into quantiles and defined as well density) were estimated using fixed-effects Poisson models. To account for multiple comparisons, a Bonferroni correction with associations of p<0.00096 was considered statistically significant. Cardiology inpatient prevalence rates were significantly associated with number of wells per zip code (p<0.00096) and wells per km2 (p<0.00096) while neurology inpatient prevalence rates were significantly associated with wells per km2 (p<0.00096). Furthermore, evidence also supported an association between well density and inpatient prevalence rates for the medical categories of dermatology, neurology, oncology, and urology. These data suggest that UGOD wells, which dramatically increased in the past decade, were associated with increased inpatient prevalence rates within specific medical categories in Pennsylvania. Further studies are necessary to address healthcare costs of UGOD and determine whether specific toxicants or combinations are associated with organ-specific responses.
Jemielita T, Gerton GL, Neidell M, Chillrud S, Yan B, Stute M, Howarth M, Saberi P, Fausti N, Penning TM, Roy J, Propert KJ, Panettieri RA Jr. PLoS One. 2015 Jul 15;10(7):e0131093. doi: 10.1371/journal.pone.0131093. eCollection 2015.
RNAseq Dataset: Response to Different Asthma Medications
Asthma is a chronic inflammatory respiratory disease that affects over 300 million people worldwide. Glucocorticoids are a mainstay therapy for asthma because they exert anti-inflammatory effects in multiple lung tissues, including the airway smooth muscle (ASM). However, the mechanism by which glucocorticoids suppress inflammation in ASM remains poorly understood. Using RNA-Seq, a high-throughput sequencing method, we characterized transcriptomic changes in four primary human ASM cell lines that were treated with dexamethasone—a potent synthetic glucocorticoid (1 µM for 18 hours). Based on a Benjamini-Hochberg corrected p-value <0.05, we identified 316 differentially expressed genes, including both well known (DUSP1, KLF15, PER1, TSC22D3) and less investigated (C7, CCDC69, CRISPLD2) glucocorticoid-responsive genes. CRISPLD2, which encodes a secreted protein previously implicated in lung development and endotoxin regulation, was found to have SNPs that were moderately associated with inhaled corticosteroid resistance and bronchodilator response among asthma patients in two previously conducted genome-wide association studies. Quantitative RT-PCR and Western blotting showed that dexamethasone treatment significantly increased CRISPLD2 mRNA and protein expression in ASM cells. CRISPLD2 expression was also induced by the inflammatory cytokine IL1β, and small interfering RNA-mediated knockdown of CRISPLD2 further increased IL1β-induced expression of IL6 and IL8. Our findings offer a comprehensive view of the effect of a glucocorticoid on the ASM transcriptome and identify CRISPLD2 as an asthma pharmacogenetics candidate gene that regulates anti-inflammatory effects of glucocorticoids in the ASM.
Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri RA Jr, Tantisira KG, Weiss ST, Lu Q.
PLoS One. 2014 Jun 13;9(6):e99625. doi: 10.1371/journal.pone.0099625. eCollection 2014.
The RNA-Seq data is available at the Gene Expression Omnibus Web site (http://www.ncbi.nlm.nih.gov/geo/) under accession GSE52778.
RNAseq Dataset: Response to Vitamin D Treatment
Globally, asthma is a chronic inflammatory respiratory disease affecting over 300 million people. Some asthma patients remain poorly controlled by conventional therapies and experience more life-threatening exacerbations. Vitamin D, as an adjunct therapy, may improve disease control in severe asthma patients since vitamin D enhances glucocorticoid responsiveness and mitigates airway smooth muscle (ASM) hyperplasia. We sought to characterize differences in transcriptome responsiveness to vitamin D between fatal asthma- and non-asthma-derived ASM by using RNA-Seq to measure ASM transcript expression in five donors with fatal asthma and ten non-asthma-derived donors at baseline and with vitamin D treatment. Based on a Benjamini-Hochberg corrected p-value <0.05, 838 genes were differentially expressed in fatal asthma vs. non-asthma-derived ASM at baseline, and vitamin D treatment compared to baseline conditions induced differential expression of 711 and 867 genes in fatal asthma- and non-asthma-derived ASM, respectively. Functional gene categories that were highly represented in all groups included extracellular matrix, and responses to steroid hormone stimuli and wounding. Genes differentially expressed by vitamin D also included cytokine and chemokine activity categories. Follow-up qPCR and individual analyte ELISA experiments were conducted for four cytokines (i.e. CCL2, CCL13, CXCL12, IL8) to measure TNFα-induced changes by asthma status and vitamin D treatment. Vitamin D inhibited TNFα-induced IL8 protein secretion levels to a comparable degree in fatal asthma- and non-asthma-derived ASM even though IL8 had significantly higher baseline levels in fatal asthma-derived ASM. Our findings identify vitamin D-specific gene targets and provide transcriptomic data to explore differences in the ASM of fatal asthma- and non-asthma-derived donors.
Himes BE, Koziol-White C, Johnson M, Nikolos C, Jester W, Klanderman B, Litonjua AA, Tantisira KG, Truskowski K, MacDonald K, Panettieri RA Jr, Weiss ST.
PLoS One. 2015 Jul 24;10(7):e0134057. doi: 10.1371/journal.pone.0134057. eCollection 2015.
The RNA-Seq data is available at the Gene Expression Omnibus Web site (http://www.ncbi.nlm.nih.gov/geo/).