Molecular and genetic characterization of rf2, a mitochondrial aldehyde dehydrogenase gene required for male fertility in maize (Zea mays L)

T-cytoplasm induced male sterility (cms-T) is a maternally inherited inability to produce viable pollen in maize. The causal factor, the URF13 protein, is encoded by the mitochondrial genome and accumulates in the mitochondrial inner membrane. Fertility restoration of cms-T is mediated by the complementary action of two nuclear genes, rf1 and rf2. The rf2 gene was cloned via transposon tagging. Sequence analysis revealed that it has high sequence similarity to mammalian mitochondrial aldehyde dehydrogenases (mtALDHs). Sequence, mRNA, and protein analyses of the spontaneous mutant allele rf2-R213 demonstrated that the ALDH activity is necessary for rf2\u27s function as a cms-T restorer. Detailed genetic characterizations of rf2 mutants revealed that loss of rf2 function not only causes male sterility in T-cytoplasm plants, but also causes anther arrest in the lower florets of maize plants that carry the normal (N) cytoplasm. To identify the biochemical pathway through which the rf2 gene functions in both N and T cytoplasms, the fermentation pathway was tested as a candidate. Mutations in three pdc genes, which encode the rate-limiting pyruvate decarboxylase (PDC) enzyme, were isolated via a reverse genetic approach. Detailed characterizations revealed that pdc3 mutants dramatically reduced anaerobic stress resistance; however, pdc3 mutants did not affect male fertility in either T- or N-cytoplasm plants.;Analyses of rf2 Mu-insertion mutant alleles revealed that some of these alleles could lose their capacity to condition a mutant phenotype without excisional loss of the associated Mu transposons, which is a phenomenon known as Mu suppression. These analyses have revealed that Mu suppression can occur not only at alleles caused by Mu insertions in 5\u27 UTRs via the recruitment of alternative transcription initiation sites as reported previously, but also at alleles caused by Mu insertions in 3\u27 UTRs. Suppression of this new class of Mu -suppressible alleles occurs via the recruitment of alternative polyadenylation sites within the TIRs of the inserted Mu transposon. In addition, these studies establish that insertions of two additional classes of Mu transposons can generate suppressible alleles

Statistical tests for differential expression in cDNA microarray experiments

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation

Genetic Regulation of Myofiber Hypertrophy?

Introduction. Progressive, resistance exercise training (RT) induces skeletal muscle hypertrophy, increases strength, power, and quality of muscle, and is potentially the most promising method to regenerate and re-grow muscle in populations suffering from involuntary atrophy. However, we have previously shown that there is a large degree of intersubject variability for myofiber hypertrophy in response to RT with adults having no response [-16μm2 (mean myofiber growth), Non], a modest response (1111μm2, Mod), or an extreme hypertrophic response (2475μm2, Xtr). Underlying mechanisms for this differential growth response are largely unknown. Therefore, the purpose of this study was to determine whether differences in the skeletal muscle transcriptome exist among the three response clusters, prior to 16 weeks of RT. Methods. mRNA was isolated from muscle biopsies taken from the vastus lateralis of 44 previously clustered men and women (aged 19-75y). Agilent 4X44K single color genechips were used to determine differences in skeletal muscle gene expression among the three response clusters. Ingenuity Pathways Analysis (IPA) and available Gene Ontology were used for functional annotation of differentially expressed genes and identification of informative genes that may instigate the observed myofiber growth phenotypes. Results. After removing genes with low signal intensities and normalizing the data, we identified substantial differences in the transcript profile among the response clusters with the most notable differences between the Xtr- and Non-responders. 8026 differentially expressed genes were identified between Xtr vs. Non, 2463 between Xtr vs. Mod, and 1294 between Mod vs. Non. There were 1632 genes with expression specific to the Xtr (i.e. differences existed between Xtr vs. Non and Mod, but not between the Non vs. Mod) and 617 genes with expression specific to the Non. Functional classification, with IPA, identified Skeletal Muscle System Development and Function (SMSDF) as a top functional category containing a significant number of differentially expressed genes (p\u3c0.05) in all three comparisons. SMSDF was also a top five functional category for the genes specific to both Xtr and Non (p\u3c0.05). Within the broad SMSDF category, IPA defined sub-categories of functional annotation, which allowed us to further interpret the differentially expressed genes. We have highlighted several genes that primarily had expression specific to the Xtr or had increased expression from Non to Mod to Xtr. Highlighted genes are involved with satellite cell activation and function (SOX8, HGF, PAX7), differentiation (MYOD1, MYOG, APOE, TRIO, MSTN), skeletal muscle growth (DGKZ, ESR1, OXT, OXTR, UCN2, GREB1), modulation of inflammation and fuel utilization (PYY), and improved function (TFAM, UCN2, CRHR1, CRHR2). Additionally, there was a decrease in expression (Xtr vs. Non) for several genes involved with modulation of inflammation and fuel utilization (AEBP1, NFKB1, CD36, AIF1). Discussion. These results indicate that differences in gene expression do exist among the response clusters prior to mechanically induced hypertrophy and that the Xtr-responders were “primed” to respond. We identified several genes and signaling pathways that may promote or inhibit muscle growth and thus, initiate the three observed hypertrophic response phenotypes. Results from this study enabled us to identify distinctive molecular pathways, particularly between the Xtr- and Non-responders, for development of targeted interventions. Further research is necessary to determine which of these genes or networks of genes truly distinguish load mediated hypertrophy potential

Single nucleotide polymorphisms affect both cis- and trans-eQTLs

AbstractSingle nucleotide polymorphisms (SNPs) between microarray probes and RNA targets can affect the performance of expression array by weakening the hybridization. In this paper, we examined the effect of the SNPs on Affymetrix GeneChip probe set summaries and the expression quantitative trait loci (eQTL) mapping results in two eQTL datasets, one from mouse and one from human. We showed that removing SNP-containing probes significantly changed the probe set summaries and the more SNP-containing probes we removed the greater the change. Comparison of the eQTL mapping results between with and without SNP-containing probes showed that less than 70% of the significant eQTL peaks were concordant regardless of the significance threshold. These results indicate that SNPs do affect both probe set summaries and eQTLs (both cis and trans), thus SNP-containing probes should be filtered out to improve the performance of eQTL mapping

Predicting gene expression using DNA methylation in three human populations

Background DNA methylation, an important epigenetic mark, is well known for its regulatory role in gene expression, especially the negative correlation in the promoter region. However, its correlation with gene expression across genome at human population level has not been well studied. In particular, it is unclear if genome-wide DNA methylation profile of an individual can predict her/his gene expression profile. Previous studies were mostly limited to association analyses between single CpG site methylation and gene expression. It is not known whether DNA methylation of a gene has enough prediction power to serve as a surrogate for gene expression in existing human study cohorts with DNA samples other than RNA samples. Results We examined DNA methylation in the gene region for predicting gene expression across individuals in non-cancer tissues of three human population datasets, adipose tissue of the Multiple Tissue Human Expression Resource Projects (MuTHER), peripheral blood mononuclear cell (PBMC) from Asthma and normal control study participates, and lymphoblastoid cell lines (LCL) from healthy individuals. Three prediction models were investigated, single linear regression, multiple linear regression, and least absolute shrinkage and selection operator (LASSO) penalized regression. Our results showed that LASSO regression has superior performance among these methods. However, the prediction power is generally low and varies across datasets. Only 30 and 42 genes were found to have cross-validation R2 greater than 0.3 in the PBMC and Adipose datasets, respectively. A substantially larger number of genes (258) were identified in the LCL dataset, which was generated from a more homogeneous cell line sample source. We also demonstrated that it gives better prediction power not to exclude any CpG probe due to cross hybridization or SNP effect. Conclusion In our three population analyses DNA methylation of CpG sites at gene region have limited prediction power for gene expression across individuals with linear regression models. The prediction power potentially varies depending on tissue, cell type, and data sources. In our analyses, the combination of LASSO regression and all probes not excluding any probe on the methylation array provides the best prediction for gene expression

Negative binomial mixed models for analyzing microbiome count data

Background: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. Results: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. Conclusions: We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data