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Esteban Gonzalez Burchard and colleagues explore how making medical research more diverse would aid not only social justice but scientific quality and clinical effectiveness, too.Esteban Gonzalez Burchard and colleagues explore how making medical research more diverse would aid not only social justice but scientific quality and clinical effectiveness, too.

Air pollution is a known asthma trigger and has been associated with short-term asthma symptoms, airway inflammation, decreased lung function, and reduced response to asthma rescue medications. To assess a causal relationship between air pollution and childhood asthma using data that address temporality by estimating air pollution exposures before the development of asthma and to establish the generalizability of the association by studying diverse racial/ethnic populations in different geographic regions. This study included Latino (n = 3,343) and African American (n = 977) participants with and without asthma from five urban regions in the mainland United States and Puerto Rico. Residential history and data from local ambient air monitoring stations were used to estimate average annual exposure to five air pollutants: ozone, nitrogen dioxide (NO₂), sulfur dioxide, particulate matter not greater than 10 μm in diameter, and particulate matter not greater than 2.5 μm in diameter. Within each region, we performed logistic regression to determine the relationship between early-life exposure to air pollutants and subsequent asthma diagnosis. A random-effects model was used to combine the region-specific effects and generate summary odds ratios for each pollutant. After adjustment for confounders, a 5-ppb increase in average NO₂ during the first year of life was associated with an odds ratio of 1.17 for physician-diagnosed asthma (95% confidence interval, 1.04-1.31). Early-life NO₂ exposure is associated with childhood asthma in Latinos and African Americans. These results add to a growing body of evidence that traffic-related pollutants may be causally related to childhood asthma.

Populations are often divided categorically into distinct racial/ethnic groups based on social rather than biological constructs. Genetic ancestry has been suggested as an alternative to this categorization. Herein, we typed over 450,000 CpG sites in whole blood of 573 individuals of diverse Hispanic origin who also had high-density genotype data. We found that both self-identified ethnicity and genetically determined ancestry were each significantly associated with methylation levels at 916 and 194 CpGs, respectively, and that shared genomic ancestry accounted for a median of 75.7% (IQR 45.8% to 92%) of the variance in methylation associated with ethnicity. There was a significant enrichment (p=4.2×10 -64 ) of ethnicity-associated sites amongst loci previously associated environmental exposures, particularly maternal smoking during pregnancy. We conclude that differential methylation between ethnic groups is partially explained by the shared genetic ancestry but that environmental factors not captured by ancestry significantly contribute to variation in methylation. Whether a person develops a particular disease can depend on both genetic and environmental factors. Many studies have found that people of different races and ethnicities have different likelihoods of acquiring certain diseases. Race and ethnicity are social constructs; that is, they are not necessarily defined biologically. However, shared ancestry will produce genetic links between members of a group. In addition, members of an ethnic group often share a culture or environment that may influence their risk of disease. For example, the ‘Mediterranean diet’ inspired by the dietary habits of Southern Italians has been shown to reduce the risk of heart disease, diabetes and cancer. The addition of chemical groups – such as methyl groups – to DNA strands can affect the activity of nearby genes. Methylation is controlled by both genetic and environmental factors, and altered patterns of DNA methylation are seen in some diseases. It is therefore an ideal biological process to study to determine how race/ethnicity and ancestry contribute to a person’s susceptibility to disease. Galanter et al. have now studied the patterns of methylation found in the blood of 573 people from diverse Latino ethnic sub-groups. The different groups displayed significantly different patterns of methylation at hundreds of locations across the genome. Genetic ancestry explained approximately 75% of the variation in methylation between the sub-groups. In addition, the methylation patterns at DNA locations known to be affected by environmental exposures – for example, by exposure to tobacco while in the womb – were disproportionately likely to be methylated differently in different sub-groups. Now that more is known about the relative effects of race/ethnicity and genetic ancestry on methylation, the next step is to apply this knowledge to disease processes. This will help us to better understand the source of health disparities across different groups of people.

Air pollution particulate matter <2.5 μm (PM2.5) exposure is associated with poor respiratory outcomes. Mechanisms underlying PM2.5-induced lung pathobiology are poorly understood but likely involve cellular and molecular changes to the airway epithelium. We extracted and chemically characterized the organic and water-soluble components of air pollution PM2.5 samples, then determined the whole transcriptome response of human nasal mucociliary airway epithelial cultures to a dose series of PM2.5 extracts. We found that PM2.5 organic extract (OE), but not water-soluble extract, elicited a potent, dose-dependent transcriptomic response from the mucociliary epithelium. Exposure to a moderate OE dose modified the expression of 424 genes, including activation of aryl hydrocarbon receptor signaling and an IL-1 inflammatory program. We generated an OE-response gene network defined by eight functional enrichment groups, which exhibited high connectivity through CYP1A1, IL1A, and IL1B. This OE exposure also robustly activated a mucus secretory expression program (>100 genes), which included transcriptional drivers of mucus metaplasia (SPDEF and FOXA3). Exposure to a higher OE dose modified the expression of 1,240 genes and further exacerbated expression responses observed at the moderate dose, including the mucus secretory program. Moreover, the higher OE dose significantly increased the MUC5AC/MUC5B gel-forming mucin expression ratio and strongly downregulated ciliated cell expression programs, including key ciliating cell transcription factors (e.g., FOXJ1 and MCIDAS). Chronic OE stimulation induced mucus metaplasia–like remodeling characterized by increases in MUC5AC+ secretory cells and MUC5AC mucus secretions. This epithelial remodeling may underlie poor respiratory outcomes associated with high PM2.5 exposure.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

Background Respiratory illness caused by viral infection is associated with the development and exacerbation of childhood asthma. Little is known about the effects of respiratory viral infections in the absence of illness. Using quantitative PCR (qPCR) for common respiratory viruses and for two genes known to be highly upregulated in viral infections ( CCL8 / CXCL11 ), we screened 92 asthmatic and 69 healthy children without illness for respiratory virus infections. Results We found 21 viral qPCR-positive and 2 suspected virus-infected subjects with high expression of CCL8/CXCL11 . We applied a dual RNA-seq workflow to these subjects, together with 25 viral qPCR-negative subjects, to compare qPCR with sequencing-based virus detection and to generate the airway transcriptome for analysis. RNA-seq virus detection achieved 86% sensitivity when compared to qPCR-based screening. We detected additional respiratory viruses in the two CCL8/CXCL11 -high subjects and in two of the qPCR-negative subjects. Viral read counts varied widely and were used to stratify subjects into Virus-High and Virus-Low groups. Examination of the host airway transcriptome found that the Virus-High group was characterized by immune cell airway infiltration, downregulation of cilia genes, and dampening of type 2 inflammation. Even the Virus-Low group was differentiated from the No-Virus group by 100 genes, some involved in eIF2 signaling. Conclusions Respiratory virus infection without illness is not innocuous but may determine the airway function of these subjects by driving immune cell airway infiltration, cellular remodeling, and alteration of asthmogenic gene expression.

Severe early-life respiratory illnesses, particularly those caused by respiratory syncytial virus (RSV) and human rhinovirus (HRV), are strongly associated with the development of asthma in children. Puerto Rican children in particular have a strikingly high asthma burden. However, prior studies of the potential associations between early-life respiratory illnesses and asthma in Puerto Rican and other minority populations have been limited. We sought to determine whether early-life respiratory illness was associated with asthma in Puerto Rican, Mexican American, and African American children. Using a logistic regression analysis, we examined the association between early-life respiratory illnesses (report of upper respiratory infection (URI), pneumonia, bronchitis, and bronchiolitis/RSV) within the first two years of life and physician-diagnosed asthma after the age of two in a large cohort of Puerto Rican, Mexican American, and African American children. While early-life respiratory illnesses were associated with greater asthma odds in Puerto Ricans, Mexican Americans, and African Americans, these associations were stronger among Puerto Rican children. Specifically, in Puerto Ricans, the odds was 6.15 (95% CI: 4.21-9.05) if the child reported at least one of the following respiratory illness: URI, pneumonia, bronchitis or bronchiolitis. The odds were also higher in Puerto Ricans when considering these conditions separately. We observed population-specific associations between early-life respiratory illnesses and asthma, which were especially significant and stronger in Puerto Ricans. Taken together with the known high burden of RSV in Puerto Rico, our results may help explain the high burden of asthma in Puerto Ricans.

For the 2016 end-of-the-year editorial, the PLOS Medicine editors asked 7 global health leaders to discuss developments relevant to the equitable provision of medical care to all populations. The result is a collection of expert views on ethical trial design, research during outbreaks, high-burden infectious diseases, diversity in research and protection of migrants.

Obesity is associated with increased asthma morbidity, lower drug responsiveness to inhaled corticosteroids, and worse asthma control. However, most prior investigations on obesity and asthma control have not focused on pediatric populations, considered environmental exposures, or included minority children. To examine the association between body mass index categories and asthma control among boys and girls; and whether these associations are modified by age and race/ethnicity. Children and adolescents ages 8-19 years (n = 2,174) with asthma were recruited from the Genes-environments and Admixture in Latino Americans (GALA II) Study and the Study of African Americans, Asthma, Genes, and Environments (SAGE II). Ordinal logistic regression was used to estimate odds ratios (OR) and their confidence intervals (95% CI) for worse asthma control. In adjusted analyses, boys who were obese had a 33% greater chance of having worse asthma control than their normal-weight counterparts (OR, 1.33; 95% CI, 1.04-1.71). However, for girls this association varied with race and ethnicity (P interaction = 0.008). When compared with their normal-weight counterparts, obese African American girls (OR, 0.65; 95% CI, 0.41-1.05) were more likely to have better controlled asthma, whereas Mexican American girls had a 1.91 (95% CI, 1.12-3.28) greater odds of worse asthma control. Worse asthma control is uniformly associated with increased body mass index in boys. Among girls, the direction of this association varied with race/ethnicity.

Background Global gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context. Methods We used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments & Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age. Results The SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p  = 6 × 10 − 9 ), suggesting worsening asthma control with increasing age. This result replicated in GALA II ( p  = 3.8 × 10 − 8 ). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells. Conclusion Focusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids.