Effects of common atopy-associated amino acid substitutions in the IL-4 receptor alpha chain on IL-4 induced phenotypes

The human IL-4 receptor alpha chain gene (IL4R) is highly polymorphic and controversial reports have been published with respect to the association of different single nucleotide polymorphisms (SNPs) with atopy markers. Here we analyzed the functional and associational relevance of common IL4R coding SNPs. Transfection of B cell lines expressing the IL-4R variant V75+R576 did not result in enhanced IL-4 induced CD23 expression compared to cell lines expressing the wild type IL-4R alpha chain. Transfection of the IL-4R variant P503 into a murine T cell line did not influence IL-4 induced T-cell proliferation compared to wild type constructs. Analysis of six IL4R coding SNPs (I75V, E400A, C431R, S436L, S503P, Q576R) and common haplotypes (frequency 0.05%) in blood donors (n=300) did not indicate a significant association with elevated serum IgE level. Moreover, the most informative IL4R coding SNPs (I75V, C431R, Q576R) and related two- and three-point haplotypes (frequency 0.05%) were analyzed in a second, extended group of blood donors (n=689). Again, no significant association with elevated serum IgE was detectable. We conclude that common coding SNPs in the IL4R gene are unlikely to contribute significantly to increased IgE levels and variations outside the coding region may influence atopy susceptibility.     

Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

Background

In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased.

Methods

In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects.

Results and conclusions

We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.     

Chloroplast biogenesis 92: In situ screening for divinyl chlorophyll(ide) a reductase mutants by spectrofluorometry

Chlorophyll biosynthetic heterogeneity is rooted mainly in parallel divinyl (DV) and monovinyl (MV) biosynthetic routes interconnected by 4-vinyl reductases (4VRs) that convert DV tetrapyrroles to MV tetrapyrroles by conversion of the vinyl group at position 4 of the macrocycle to ethyl. What is not clear at this stage is whether the various 4VR activities are catalyzed by one enzyme of broad specificity or by a family of enzymes encoded by one gene or multiple genes with each enzyme having narrow specificity. Additional research is needed to identify the various regulatory components of 4-vinyl reduction. In this undertaking, Arabidopsis mutants that accumulate DV chlorophyllide a and/or DV chlorophyll [Chl(ide)] a are likely to provide an appropriate resource. Because the Arabidopsis genome has been completely sequenced, the best strategy for identifying 4VR and/or putative regulatory 4VR genes is to screen Arabidopsis Chl mutants for DV Chl(ide) a accumulation. In wild-type Arabidopsis, a DV plant species, only MV chlorophyllide (Chlide) a is detectable. However in Chl mutants lacking 4VR activity, DV Chl(ide) a may accumulate in addition to MV Chl(ide) a. In the current work, an in situ assay of DV Chl(ide) a accumulation, suitable for screening a large number of mutants lacking 4-vinyl Chlide a reductase activity with minimal experimental handling, is described. The assay involves homogenization of the tissues in Tris-HCl:glycerol buffer and the recording of Soret excitation spectra at 77K. DV Chlide a formation is detected by a Soret excitation shoulder at 459 nm over a wide range of DV Chlide a/MV Chl a ratios. The DV Chlide a shoulder became undetectable at DV Chlide a/MV Chl a ratios less than 0.049, that is, at a DV Chlide a content of less than 5%.     

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Background  

Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data.