Evolution of substrate-specific gene expression and RNA editing in brown rot wood-decaying fungi.

Fungi that decay wood have characteristic associations with certain tree species, but the mechanistic bases for these associations are poorly understood. We studied substrate-specific gene expression and RNA editing in six species of wood-decaying fungi from the 'Antrodia clade' (Polyporales, Agaricomycetes) on three different wood substrates (pine, spruce, and aspen) in submerged cultures. We identified dozens to hundreds of substrate-biased genes (i.e., genes that are significantly upregulated in one substrate relative to the other two substrates) in each species, and these biased genes are correlated with their host ranges. Evolution of substrate-biased genes is associated with gene family expansion, gain and loss of genes, and variation in cis- and trans- regulatory elements, rather than changes in protein coding sequences. We also demonstrated widespread RNA editing events in the Antrodia clade, which differ from those observed in the Ascomycota in their distribution, substitution types, and the genomic environment. Moreover, we found that substrates could affect editing positions and frequency, including editing events occurring in mRNA transcribed from wood-decay-related genes. This work shows the extent to which gene expression and RNA editing differ among species and substrates, and provides clues into mechanisms by which wood-decaying fungi may adapt to different hosts

Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants

<p>Abstract</p> <p>Background</p> <p>One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness).</p> <p>Results</p> <p>Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in <it>Arabidopsis </it>according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration.</p> <p>Conclusion</p> <p>Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change.</p

Comparative analyses of dynamic transcriptome profiles highlight key response genes and dominant isoforms for muscle development and growth in chicken

Abstract Background Modern breeding strategies have resulted in significant differences in muscle mass between indigenous chicken and specialized broiler. However, the molecular regulatory mechanisms that underlie these differences remain elusive. The aim of this study was to identify key genes and regulatory mechanisms underlying differences in breast muscle development between indigenous chicken and specialized broiler. Results Two time-series RNA-sequencing profiles of breast muscles were generated from commercial Arbor Acres (AA) broiler (fast-growing) and Chinese indigenous Lushi blue-shelled-egg (LS) chicken (slow-growing) at embryonic days 10, 14, and 18, and post-hatching day 1 and weeks 1, 3, and 5. Principal component analysis of the transcriptome profiles showed that the top four principal components accounted for more than 80% of the total variance in each breed. The developmental axes between the AA and LS chicken overlapped at the embryonic stages but gradually separated at the adult stages. Integrative investigation of differentially-expressed transcripts contained in the top four principal components identified 44 genes that formed a molecular network associated with differences in breast muscle mass between the two breeds. In addition, alternative splicing analysis revealed that genes with multiple isoforms always had one dominant transcript that exhibited a significantly higher expression level than the others. Among the 44 genes, the TNFRSF6B gene, a mediator of signal transduction pathways and cell proliferation, harbored two alternative splicing isoforms, TNFRSF6B-X1 and TNFRSF6B-X2. TNFRSF6B-X1 was the dominant isoform in both breeds before the age of one week. A switching event of the dominant isoform occurred at one week of age, resulting in TNFRSF6B-X2 being the dominant isoform in AA broiler, whereas TNFRSF6B-X1 remained the dominant isoform in LS chicken. Gain-of-function assays demonstrated that both isoforms promoted the proliferation of chicken primary myoblasts, but only TNFRSF6B-X2 augmented the differentiation and intracellular protein content of chicken primary myoblasts. Conclusions For the first time, we identified several key genes and dominant isoforms that may be responsible for differences in muscle mass between slow-growing indigenous chicken and fast-growing commercial broiler. These findings provide new insights into the regulatory mechanisms underlying breast muscle development in chicken

Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition

Abstract Background New advances in high-throughput technologies have allowed for the massive analysis of genomic data, providing new opportunities for the characterization of the transcriptome architectures. Recent studies in pigs have employed RNA-Seq to explore the transcriptome of different tissues in a reduced number of animals. The main goal of this study was the identification of differentially-expressed genes in the liver of Iberian x Landrace crossbred pigs showing extreme phenotypes for intramuscular fatty acid composition using RNA-Seq. Results The liver transcriptomes of two female groups (H and L) with phenotypically extreme intramuscular fatty acid composition were sequenced using RNA-Seq. A total of 146 and 180 unannotated protein-coding genes were identified in intergenic regions for the L and H groups, respectively. In addition, a range of 5.8 to 7.3% of repetitive elements was found, with SINEs being the most abundant elements. The expression in liver of 186 (L) and 270 (H) lncRNAs was also detected. The higher reproducibility of the RNA-Seq data was validated by RT-qPCR and porcine expression microarrays, therefore showing a strong correlation between RT-qPCR and RNA-Seq data (ranking from 0.79 to 0.96), as well as between microarrays and RNA-Seq (r=0.72). A differential expression analysis between H and L animals identified 55 genes differentially-expressed between groups. Pathways analysis revealed that these genes belong to biological functions, canonical pathways and three gene networks related to lipid and fatty acid metabolism. In concordance with the phenotypic classification, the pathways analysis inferred that linolenic and arachidonic acids metabolism was altered between extreme individuals. In addition, a connection was observed among the top three networks, hence suggesting that these genes are interconnected and play an important role in lipid and fatty acid metabolism. Conclusions In the present study RNA-Seq was used as a tool to explore the liver transcriptome of pigs with extreme phenotypes for intramuscular fatty acid composition. The differential gene expression analysis showed potential gene networks which affect lipid and fatty acid metabolism. These results may help in the design of selection strategies to improve the sensorial and nutritional quality of pork meat.This work was funded by MICINN projects AGL2008-04818-C03/GAN and AGL2011-29821-C02 (Ministerio de Economía y Competitividad), and by the Innovation Consolider-Ingenio 2010 Program (CSD2007-00036, Centre for Research in Agrigenomics). Y. Ramayo-Caldas was funded by a FPU PhD grant from the Spanish Ministerio de Educación (AP2008-01450), J. Corominas was funded by a FPI PhD grant from the Spanish Ministerio de Educación (BES-2009-018223), A. Esteve-Codina is recipient of a FPI PhD fellowship from the Ministerio de Educación (BES-2008-005772), Spain.Peer Reviewe

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

<p>Abstract</p> <p>Background</p> <p>Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry.</p> <p>Results</p> <p>We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four.</p> <p>Conclusions</p> <p>By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at <url>http://jimcooperlab.mcdb.ucsb.edu/autosome</url>.</p