Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency.

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context.

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.

Target (MexB)- and Efflux-Based Mechanisms Decreasing the Effectiveness of the Efflux Pump Inhibitor D13-9001 in Pseudomonas aeruginosa PAO1: Uncovering a New Role for MexMN-OprM in Efflux of ß-Lactams and a Novel Regulatory Circuit (MmnRS) Controlling MexMN Expression.

Efflux pumps contribute to antibiotic resistance in Gram-negative pathogens. Correspondingly, efflux pump inhibitors (EPIs) may reverse this resistance. D13-9001 specifically inhibits MexAB-OprM in Pseudomonas aeruginosa Mutants with decreased susceptibility to MexAB-OprM inhibition by D13-9001 were identified, and these fell into two categories: those with alterations in the target MexB (F628L and ΔV177) and those with an alteration in a putative sensor kinase of unknown function, PA1438 (L172P). The alterations in MexB were consistent with reported structural studies of the D13-9001 interaction with MexB. The PA1438L172P alteration mediated a >150-fold upregulation of MexMN pump gene expression and a >50-fold upregulation of PA1438 and the neighboring response regulator gene, PA1437. We propose that these be renamed mmnR and mmnS for MexMN regulator and MexMN sensor, respectively. MexMN was shown to partner with the outer membrane channel protein OprM and to pump several ß-lactams, monobactams, and tazobactam. Upregulated MexMN functionally replaced MexAB-OprM to efflux these compounds but was insusceptible to inhibition by D13-9001. MmnSL172P also mediated a decrease in susceptibility to imipenem and biapenem that was independent of MexMN-OprM. Expression of oprD, encoding the uptake channel for these compounds, was downregulated, suggesting that this channel is also part of the MmnSR regulon. Transcriptome sequencing (RNA-seq) of cells encoding MmnSL172P revealed, among other things, an interrelationship between the regulation of mexMN and genes involved in heavy metal resistance.