Genetic Regulation ofZfp30, CXCL1, and Neutrophilic Inflammation in Murine Lung

Abstract

Allergic asthma is a complex disease characterized in part by granulocytic inflammation of the airways. In addition to eosinophils, neutrophils (PMN) are also present, particularly in cases of severe asthma. We sought to identify the genetic determinants of neutrophilic inflammation in a mouse model of house dust mite (HDM)-induced asthma. We applied an HDM model of allergic asthma to the eight founder strains of the Collaborative Cross (CC) and 151 incipient lines of the CC (preCC). Lung lavage fluid was analyzed for PMN count and the concentration of CXCL1, a hallmark PMN chemokine. PMN and CXCL1 were strongly correlated in preCC mice. We used quantitative trait locus (QTL) mapping to identify three variants affecting PMN, one of which colocalized with a QTL for CXCL1 on chromosome (Chr) 7. We used lung eQTL data to implicate a variant in the gene Zfp30 in the CXCL1/PMN response. This genetic variant regulates both CXCL1 and PMN by altering Zfp30 expression, and we model the relationships between the QTL and these three endophenotypes. We show that Zfp30 is expressed in airway epithelia in the normal mouse lung and that altering Zfp30 expression in vitro affects CXCL1 responses to an immune stimulus. Our results provide strong evidence that Zfp30 is a novel regulator of neutrophilic airway inflammation.

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

“…However, considerable breeding difficulties were encountered (Philip et al 2011); and as of January 1, 2015, only 69 strains are listed as available on the CC website (Table 1) with a current goal of about 100 strains. Despite these difficulties, investigators have used these 69 completed strains and other incipient lines to study the genetics of infection, body weight, food intake, cancer, albuminuria, and other traits Durrant et al 2011;Foulds Mathes et al 2011;Philip et al 2011;Kelada et al 2012Thaisz et al 2012;Ferris et al 2013; Phillippi et al 2014;Rogala et al 2014;Rutledge et al 2014;Vered et al 2014).…”

A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations

“…Except for the FIXED-A and RANDOM-A methods, all other methods detected a marker on chromosome 2 (Table 5). This marker also was detected by Rutledge et al (2014) in the original study. They found a candidate gene (Dpn1) near this marker.…”

“…The data were obtained from Rutledge et al (2014) and consist of 151 individuals. This data set includes 27,039 SNPs evenly distributed among the 20 chromosomes (including the X chromosome).…”

“…We used a CC mouse population (Collaborative Cross Consortium 2012) to perform simulations to examine the properties of the new methods (there are no phenotypic values available for the CC mouse population). The Arabidopsis MAGIC population of Kover et al (2009) and the pre-CC mouse population of Rutledge et al (2014) were reanalyzed using the new methods to demonstrate the differences between the new and existing methods. …”

Susceptibility to quantum dot induced lung inflammation differs widely among the Collaborative Cross founder mouse strains

“…Inter-class correlation, r 1 , was calculated as (MSB − MSW) / (MSB + (n − 1)MSW), where MSB is the mean square between strains and MSW is the mean square within strains obtained from ANOVA analysis, and n is the number of mice. The coefficient of genetic determination, g 2 was calculated as (MSB − MSW) / (MSB + (2n − 1)MSW) (Billat et al, 2005; Rutledge et al, 2014). The sensitivity analysis performed on percent neutrophils in BALF entailed removing potential outliers using Tukey's interquartile range (IQR) method which defines outliers as either less than Q1 minus 1.5 times, or Q3 plus 1.5 times the IQR within each strain and treatment group, and then repeating the two-way ANOVA and t-tests (Tukey, 1977).…”