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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., Author Summary Phenotypes are determined by the expression of genes, the environment, and the interaction of gene expression and the environment. However, a complete understanding of the inheritance of and genome-wide regulation of gene expression is lacking. One approach, called expression quantitative trait locus (eQTL) mapping provides the opportunity to examine the genome-wide inheritance and regulation of gene expression. In this paper, we conducted high-throughput sequencing of gene transcripts to examine gene expression in the shoot apex of a maize biparental mapping population. We quantified expression levels from 28,603 genes in the population and showed that the vast majority of genes exhibited the expected pattern of Mendelian inheritance. We genetically mapped the expression patterns and identified genomic regions associated with gene expression. Notably, we detected gene expression patterns that exhibited non-Mendelian inheritance. These included 145 genes that exhibited expression patterns in the progeny that were similar to only one of the parents and 210 genes with unexpected presence/absence expression patterns. The findings of non-Mendelian inheritance underscore the complexity of gene expression and provide a framework for understanding these complexities.

Advances in alfalfa [Medicago sativa (L.) subsp. sativa] breeding, molecular genetics, and genomics have been slow because this crop is an allogamous autotetraploid (2n = 4x = 32) with complex polysomic inheritance and few genomic resources. Increasing cellulose and decreasing lignin in alfalfa stem cell walls would improve this crop as a cellulosic ethanol feedstock. We conducted genome-wide analysis of single-feature polymorphisms (SFPs) of two alfalfa genotypes (252, 1283) that differ in stem cell wall lignin and cellulose concentrations. SFP analysis was conducted using the Medicago GeneChip (Affymetrix, Santa Clara, CA) as a crossspecies platform. Analysis of GeneChip expression data fi les of alfalfa stem internodes of genotypes 252 and 1283 at two growth stages (elongating, post-elongation) revealed 10,890 SFPs in 8230 probe sets. Validation analysis by polymerase chain reaction (PCR)-sequencing of a random sample of SFPs indicated a 17% false discovery rate. Functional classifi cation and over-representation analysis showed that genes involved in photosynthesis, stress response and cell wall biosynthesis were highly enriched among SFP-harboring genes. The Medicago GeneChip is a suitable cross-species platform for detecting SFPs in tetraploid alfalfa.

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.

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.

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.

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.

Background The GeneChip® 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® 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.