Pmp27 Promotes Peroxisomal Proliferation

Peroxisomes perform many essential functions in eukaryotic cells. The weight of evidence indicates that these organelles divide by budding from preexisting peroxisomes. This process is not understood at the molecular level. Peroxisomal proliferation can be induced in Saccharomyces cerevisiae by oleate. This growth substrate is metabolized by peroxisomal enzymes. We have identified a protein, Pmp27, that promotes peroxisomal proliferation. This protein, previously termed Pmp24, was purified from peroxisomal membranes, and the corresponding gene, PMP27, was isolated and sequenced. Prop27 shares sequence similarity with the Pmp30 family in Candida boidinii. Pmp27 is a hydrophobic peroxisomal membrane protein but it can be extracted by high pH, suggesting that it does not fully span the bilayer. Its expression is regulated by oleate. The function of Pmp27 was probed by observing the phenotype of strains in which the protein was eliminated by gene disruption or overproduced by expression from a multicopy plasmid. The strain containing the disruption (3B) was able to grow on all carbon sources tested, including oleate, although growth on oleate, glycerol, and acetate was slower than wild type. Strain 3B contained peroxisomes with all of the enzymes of β-oxidation. However, in addition to the presence of a few modestly sized peroxisomes seen in a typical thin section of a cell growing on oleate-containing medium, cells of strain 3B also contained one or two very large peroxisomes. In contrast, cells in a strain in which Pmp27 was overexpressed contained an increased number of normal-sized peroxisomes. We suggest that Pmp27 promotes peroxisomal proliferation by participating in peroxisomal elongation or fission.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even data sets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper we re-examine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (6 CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1357 individuals in 124 pedigrees) takes less than 2 minutes and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 minutes and 1.5 GB of memory

Structural Analysis and Methionine Ehancement of the Bean Seed Storage Protein Phaseolin.

Common beans are widely utilized as a food source, yet are limited as a complete source of protein due to low levels of methionine, an essential amino acid for humans. A protein engineering strategy was developed to increase the methionine content of phaseolin, the primary seed storage protein in common beans. The engineering strategy consists of three major parts. In the first part, a set of biophysical probes was developed to characterize the stability of wild-type and modified phaseolin proteins. I used absorbance, fluorescence emission, circular dichroism, and fluorescence polarization anisotropy to monitor phaseolin denaturation induced by urea, guanidinium chloride, pH, and temperature. The protein denatured irreversibly at 65\sp\circC when dissolved in 6.0 M guanidinium chloride, indicating that phaseolin has exceptional structural stability. In the second part, the complete three-dimensional structure of phaseolin was generated from $\alpha$-carbon coordinates. This structure was used as a template to simulate modifications aimed at increasing the methionine content of phaseolin. Three types of modifications were tested: replacement of 10 variant hydrophobic residues with methionine in each of the $\beta$-barrel structures, insertion of short methionine-rich sequences at surface exposed regions of the protein, and insertion of a 9 kd methionine-rich domain into the N-terminal hypervariable region of phaseolin. In the last part, 24 mutant phaseolin cDNAs were constructed and expressed in E. coli to determine the effects of the mutations on the protein structure in vivo. Methionine enhancement ranged from 5 to 45 residues. Thermal denaturation of purified proteins demonstrated that significant modifications for methionine enhancement in the $\beta$-barrels did not alter the structural stability of the protein. In addition, protein denaturation was a reversible event, which allowed a thermodynamic analysis of protein stability. The utilization of these strategies permits a thorough investigation of the effects of mutagenesis on phaseolin stability. Since mutant proteins with properties most similar to wild-type are likely to survive the process of accumulation in seeds, this knowledge is crucial for the identification of optimal candidates for plant transformation

The Quantitative-MFG Test: A linear mixed effect model to detect maternal-offspring gene interactions

Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the Quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT’s alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With GWAS data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered

Two-divisibility of the coefficients of certain weakly holomorphic modular forms

We study a canonical basis for spaces of weakly holomorphic modular forms of weights 12, 16, 18, 20, 22, and 26 on the full modular group. We prove a relation between the Fourier coefficients of modular forms in this canonical basis and a generalized Ramanujan tau-function, and use this to prove that these Fourier coefficients are often highly divisible by 2.Comment: Corrected typos. To appear in the Ramanujan Journa

Linkage disequilibrium across two different single-nucleotide polymorphism genome scans

Linkage disequilibrium (LD) content was calculated for the Genetic Analysis Workshop 14 Affymetrix and Illumina single-nucleotide polymorphism (SNP) genome scans of the Collaborative Study on the Genetics of Alcoholism samples. Pair-wise LD was measured as both D' and r(2 )on 505 pedigree founder individuals. The r(2 )estimates were then used to correct the multipoint identity by descent matrix (MIBD) calculation to account for LD and LOD scores on chromosomes 3 and 18 were calculated for COGA's ttdt3 electrophysiological trait using those MIBDs. Extensive LD was observed throughout both marker sets, and it was higher in Affymetrix's more dense SNP map. However, SNP density did not solely account for Affymetrix's higher LD. MIBD estimation procedures assume linkage equilibrium to construct genotypes of non-genotyped pedigree founder individuals, and dense SNP genotyping maps are likely to contain moderate to high LD between markers. LOD score plots calculated after correction for LD followed the same general pattern as uncorrected ones. Since in our study almost half of the pedigree founders were genotyped, it is possible that LD had a minor impact on the LOD scores. Caution should probably be taken when using high density SNP maps when many non-genotyped founders are present in the study pedigrees