Pinto, R. M. | bioRxiv Science

bioRxiv2018

Pleotropic effects of mutations in the effector domain of influenza A virus NS1 protein

ObjectiveThe multifunctional NS1 protein of influenza A virus has roles in antagonising cellular innate immune responses and promoting viral gene expression. To better understand the interplay between these functions, we tested the effects of NS1 effector domain mutations known to affect homo-dimerisation or interactions with cellular PI3 kinase or Trim25 on NS1 ability to promote nuclear export of viral mRNAs.\n\nResultsThe NS1 dimerisation mutant W187R retained the functions of binding cellular NXF1 as well as stabilising NXF1 interaction with viral segment 7 mRNAs and promoting their nuclear export. Two PI3K-binding mutants, NS1 Y89F and Y89A still bound NXF1 but no longer promoted NXF1 interactions with segment 7 mRNA or its nuclear export. The Trim25-binding mutant NS1 E96A/E97A bound NXF1 and supported NXF1 interactions with segment 7 mRNA but no longer supported mRNA nuclear export. Analysis of WT and mutant NS1 interaction partners identified hsp70 as specifically binding to NS1 E96A/E97A. Whilst these data suggest the possibility of functional links between NS1s effects on intracellular signalling and its role in viral mRNA nuclear export, they also indicate potential pleiotropic effects of the NS1 mutations; in the case of E96A/E97A possibly via disrupted protein folding leading to chaperone recruitment.

microbiology↗

bioRxiv2018

Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models

BackgroundPhenotypic networks describing putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effects in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps. A structural equation model (SEM) can be used for such purposes.\n\nMethodsWe applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among body weight (BW), breast meat (BM), hen-house production (HHP), and SNPs. We assessed the performance of SEM-GWAS by comparing the model results with those obtained from traditional multi-trait association analyses (MTM-GWAS).\n\nResultsThree different putative causal path diagrams were inferred from highest posterior density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation algorithm. A positive path coefficient was estimated for BM[->]BW, and negative values were obtained for BM[->]HHP and BW[->]HHP in all implemented scenarios. Further, the application of SEM-GWAS enabled the decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on a given trait. In contrast, MTM-GWAS only captured overall genetic effects on traits, which is equivalent to combining the direct and indirect SNP effects from SEMGWAS.\n\nConclusionsAlthough MTM-GWAS and SEM-GWAS use the same probabilistic models, we provide evidence that SEM-GWAS captures complex relationships and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results showed that SEM-GWAS provides important insight regarding the mechanism by which identified SNPs control traits by partitioning them into direct, indirect, and total SNP effects.

genetics↗