Correction: Mendelian and Non-Mendelian Regulation of Gene Expression in Maize.

[This corrects the article DOI: 10.1371/journal.pgen.1003202.].

Mendelian and non-Mendelian regulation of gene expression in maize.

Transcriptome variation plays an important role in affecting the phenotype of an organism. However, an understanding of the underlying mechanisms regulating transcriptome variation in segregating populations is still largely unknown. We sought to assess and map variation in transcript abundance in maize shoot apices in the intermated B73 × Mo17 recombinant inbred line population. RNA-based sequencing (RNA-seq) allowed for the detection and quantification of the transcript abundance derived from 28,603 genes. For a majority of these genes, the population mean, coefficient of variation, and segregation patterns could be predicted by the parental expression levels. Expression quantitative trait loci (eQTL) mapping identified 30,774 eQTL including 96 trans-eQTL "hotspots," each of which regulates the expression of a large number of genes. Interestingly, genes regulated by a trans-eQTL hotspot tend to be enriched for a specific function or act in the same genetic pathway. Also, genomic structural variation appeared to contribute to cis-regulation of gene expression. Besides genes showing Mendelian inheritance in the RIL population, we also found genes whose expression level and variation in the progeny could not be predicted based on parental difference, indicating that non-Mendelian factors also contribute to expression variation. Specifically, we found 145 genes that show patterns of expression reminiscent of paramutation such that all the progeny had expression levels similar to one of the two parents. Furthermore, we identified another 210 genes that exhibited unexpected patterns of transcript presence/absence. Many of these genes are likely to be gene fragments resulting from transposition, and the presence/absence of their transcripts could influence expression levels of their ancestral syntenic genes. Overall, our results contribute to the identification of novel expression patterns and broaden the understanding of transcriptional variation in plants.

Identification of quantitative trait loci controlling gene expression during the innate immunity response of soybean.

Microbe-associated molecular pattern-triggered immunity (MTI) is an important component of the plant innate immunity response to invading pathogens. However, most of our knowledge of MTI comes from studies of model systems with relatively little work done with crop plants. In this work, we report on variation in both the microbe-associated molecular pattern-triggered oxidative burst and gene expression across four soybean (Glycine max) genotypes. Variation in MTI correlated with the level of pathogen resistance for each genotype. A quantitative trait locus analysis on these traits identified four loci that appeared to regulate gene expression during MTI in soybean. Likewise, we observed that both MTI variation and pathogen resistance were quantitatively inherited. The approach utilized in this study may have utility for identifying key resistance loci useful for developing improved soybean cultivars.

Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems.

BACKGROUND: Alfalfa, [Medicago sativa (L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling. RESULTS: Cell walls in stems of alfalfa genotype 708 have higher cellulose and lower lignin concentrations compared to cell walls in stems of genotype 773. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. In addition, 341,984 ESTs were generated from ES and PES internodes of genotype 773 using the GS FLX Titanium platform. The first alfalfa (Medicago sativa) gene index (MSGI 1.0) was assembled using the Sanger ESTs available from GenBank, the GS FLX Titanium EST sequences, and the de novo assembled Illumina sequences. MSGI 1.0 contains 124,025 unique sequences including 22,729 tentative consensus sequences (TCs), 22,315 singletons and 78,981 pseudo-singletons. We identified a total of 1,294 simple sequence repeats (SSR) among the sequences in MSGI 1.0. In addition, a total of 10,826 single nucleotide polymorphisms (SNPs) were predicted between the two genotypes. Out of 55 SNPs randomly selected for experimental validation, 47 (85%) were polymorphic between the two genotypes. We also identified numerous allelic variations within each genotype. Digital gene expression analysis identified numerous candidate genes that may play a role in stem development as well as candidate genes that may contribute to the differences in cell wall composition in stems of the two genotypes. CONCLUSIONS: Our results demonstrate that RNA-Seq can be successfully used for gene identification, polymorphism detection and transcript profiling in alfalfa, a non-model, allogamous, autotetraploid species. The alfalfa gene index assembled in this study, and the SNPs, SSRs and candidate genes identified can be used to improve alfalfa as a forage crop and cellulosic feedstock.

Parallel multiplicity and error discovery rate (EDR) in microarray experiments.

BACKGROUND: In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array. RESULTS: This study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small. CONCLUSIONS: Microarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.

Transcript profiling of two alfalfa genotypes with contrasting cell wall composition in stems using a cross-species platform: optimizing analysis by masking biased probes.

BACKGROUND: The GeneChip(R) Medicago Genome Array, developed for Medicago truncatula, is a suitable platform for transcript profiling in tetraploid alfalfa [Medicago sativa (L.) subsp. sativa]. However, previous research involving cross-species hybridization (CSH) has shown that sequence variation between two species can bias transcript profiling by decreasing sensitivity (number of expressed genes detected) and the accuracy of measuring fold-differences in gene expression. RESULTS: Transcript profiling using the Medicago GeneChip(R) was conducted with elongating stem (ES) and post-elongation stem (PES) internodes from alfalfa genotypes 252 and 1283 that differ in stem cell wall concentrations of cellulose and lignin. A protocol was developed that masked probes targeting inter-species variable (ISV) regions of alfalfa transcripts. A probe signal intensity threshold was selected that optimized both sensitivity and accuracy. After masking for both ISV regions and previously identified single-feature polymorphisms (SFPs), the number of differentially expressed genes between the two genotypes in both ES and PES internodes was approximately 2-fold greater than the number detected prior to masking. Regulatory genes, including transcription factor and receptor kinase genes that may play a role in development of secondary xylem, were significantly over-represented among genes up-regulated in 252 PES internodes compared to 1283 PES internodes. Several cell wall-related genes were also up-regulated in genotype 252 PES internodes. Real-time quantitative RT-PCR of differentially expressed regulatory and cell wall-related genes demonstrated increased sensitivity and accuracy after masking for both ISV regions and SFPs. Over 1,000 genes that were differentially expressed in ES and PES internodes of genotypes 252 and 1283 were mapped onto putative orthologous loci on M. truncatula chromosomes. Clustering simulation analysis of the differentially expressed genes suggested co-expression of some neighbouring genes on Medicago chromosomes. CONCLUSIONS: The problems associated with transcript profiling in alfalfa stems using the Medicago GeneChip as a CSH platform were mitigated by masking probes targeting ISV regions and SFPs. Using this masking protocol resulted in the identification of numerous candidate genes that may contribute to differences in cell wall concentration and composition of stems of two alfalfa genotypes.

Single-feature polymorphism discovery by computing probe affinity shape powers.

BACKGROUND: Single-feature polymorphism (SFP) discovery is a rapid and cost-effective approach to identify DNA polymorphisms. However, high false positive rates and/or low sensitivity are prevalent in previously described SFP detection methods. This work presents a new computing method for SFP discovery. RESULTS: The probe affinity differences and affinity shape powers formed by the neighboring probes in each probe set were computed into SFP weight scores. This method was validated by known sequence information and was comprehensively compared with previously-reported methods using the same datasets. A web application using this algorithm has been implemented for SFP detection. Using this method, we identified 364 SFPs in a barley near-isogenic line pair carrying either the wild type or the mutant uniculm2 (cul2) allele. Most of the SFP polymorphisms were identified on chromosome 6H in the vicinity of the Cul2 locus. CONCLUSION: This SFP discovery method exhibits better performance in specificity and sensitivity over previously-reported methods. It can be used for other organisms for which GeneChip technology is available. The web-based tool will facilitate SFP discovery. The 364 SFPs discovered in a barley near-isogenic line pair provide a set of genetic markers for fine mapping and future map-based cloning of the Cul2 locus.

Magnetic Levitation of MC3T3 Osteoblast Cells as a Ground-Based Simulation of Microgravity.

Diamagnetic samples placed in a strong magnetic field and a magnetic field gradient experience a magnetic force. Stable magnetic levitation occurs when the magnetic force exactly counter balances the gravitational force. Under this condition, a diamagnetic sample is in a simulated microgravity environment. The purpose of this study is to explore if MC3T3-E1 osteoblastic cells can be grown in magnetically simulated hypo-g and hyper-g environments and determine if gene expression is differentially expressed under these conditions. The murine calvarial osteoblastic cell line, MC3T3-E1, grown on Cytodex-3 beads, were subjected to a net gravitational force of 0, 1 and 2 g in a 17 T superconducting magnet for 2 days. Microarray analysis of these cells indicated that gravitational stress leads to up and down regulation of hundreds of genes. The methodology of sustaining long-term magnetic levitation of biological systems are discussed.