30 research outputs found
Mining for genotype-phenotype relations in Saccharomyces using partial least squares
<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p
Characterization of the cork oak transcriptome dynamics during acorn development
Background: Cork oak (Quercus suber L.) has a natural distribution across western Mediterranean regions and is a keystone forest tree species in these ecosystems. The fruiting phase is especially critical for its regeneration but the molecular mechanisms underlying the biochemical and physiological changes during cork oak acorn development are poorly understood. In this study, the transcriptome of the cork oak acorn, including the seed, was characterized in five stages of development, from early development to acorn maturation, to identify the dominant processes in each stage and reveal transcripts with important functions in gene expression regulation and response to water.
Results: A total of 80,357 expressed sequence tags (ESTs) were de novo assembled from RNA-Seq libraries representative of the several acorn developmental stages. Approximately 7.6Â % of the total number of transcripts present in Q. suber transcriptome was identified as acorn specific. The analysis of expression profiles during development returned 2,285 differentially expressed (DE) transcripts, which were clustered into six groups. The stage of development corresponding to the mature acorn exhibited an expression profile markedly different from other stages. Approximately 22Â % of the DE transcripts putatively code for transcription factors (TF) or transcriptional regulators, and were found almost equally distributed among the several expression profile clusters, highlighting their major roles in controlling the whole developmental process. On the other hand, carbohydrate metabolism, the biological pathway most represented during acorn development, was especially prevalent in mid to late stages as evidenced by enrichment analysis. We further show that genes related to response to water, water deprivation and transport were mostly represented during the early (S2) and the last stage (S8) of acorn development, when tolerance to water desiccation is possibly critical for acorn viability.
Conclusions: To our knowledge this work represents the first report of acorn development transcriptomics in oaks. The obtained results provide novel insights into the developmental biology of cork oak acorns, highlighting transcripts putatively involved in the regulation of the gene expression program and in specific processes likely essential for adaptation. It is expected that this knowledge can be transferred to other oak species of great ecological value.Fundação para a Ciência e a Tecnologi
Growth Kinetics of Bp8 Mouse Ascites Sarcoma after Single Doses of Whole Body Irradiation: II. Analysis of the Progression of Cells Through the Cell Cycle
Regulation of Viral and Cellular Gene Expression by Kaposi's Sarcoma-Associated Herpesvirus Polyadenylated Nuclear RNA
Kaposi's sarcoma-associated herpesvirus (KSHV) is the cause of Kaposi's sarcoma and body cavity lymphoma. In cell culture, KSHV results in a latent infection, and lytic reactivation is usually induced with the expression of K-Rta or by treatment with phorbol 12-myristate 13-acetate (TPA) and/or n-butyrate. Lytic infection is marked by the activation of the entire viral genomic transcription cascade and the production of infectious virus. KSHV-infected cells express a highly abundant, long, noncoding transcript referred to as polyadenylated nuclear RNA (PAN RNA). PAN RNA interacts with specific demethylases and physically binds to the KSHV genome to mediate activation of viral gene expression. A recombinant BACmid lacking the PAN RNA locus fails to express K-Rta and does not produce virus. We now show that the lack of PAN RNA expression results in the failure of the initiation of the entire KSHV transcription program. In addition to previous findings of an interaction with demethylases, we show that PAN RNA binds to protein components of Polycomb repression complex 2 (PRC2). RNA-Seq analysis using cell lines that express PAN RNA shows that transcription involving the expression of proteins involved in cell cycle, immune response, and inflammation is dysregulated. Expression of PAN RNA in various cell types results in an enhanced growth phenotype, higher cell densities, and increased survival compared to control cells. Also, PAN RNA expression mediates a decrease in the production of inflammatory cytokines. These data support a role for PAN RNA as a major global regulator of viral and cellular gene expression
Structural analysis of an HIV-1 protease I47A mutant resistant to the protease inhibitor lopinavir
We have identified a rare HIV-1 protease (PR) mutation, I47A, associated with a high level of resistance to the protease inhibitor lopinavir (LPV) and with hypersusceptibility to the protease inhibitor saquinavir (SQV). The I47A mutation was found in 99 of 112,198 clinical specimens genotyped after LPV became available in late 2000, but in none of 24,426 clinical samples genotyped from 1998 to October 2000. Phenotypic data obtained for five I47A mutants showed unexpected resistance to LPV (86- to >110-fold) and hypersusceptibility to SQV (0.1- to 0.7-fold). Molecular modeling and energy calculations for these mutants using our structural phenotyping methodology showed an increase in the binding energy of LPV by 1.9–3.1 kcal/mol with respect to the wild type complex, corresponding to a 20- to >100-fold decrease in binding affinity, consistent with the observed high levels of LPV resistance. In the WT PR–LPV complex, the Ile 47 side chain is positioned close to the phenoxyacetyl moiety of LPV and its van der Waals interactions contribute significantly to the ligand binding. These interactions are lost for the smaller Ala 47 residue. Calculated binding energy changes for SQV ranged from −0.4 to −1.2 kcal/mol. In the mutant I47A PR–SQV complexes, the PR flaps are packed more tightly around SQV than in the WT complex, resulting in the formation of additional hydrogen bonds that increase binding affinity of SQV consistent with phenotypic hypersusceptibility. The emergence of mutations at PR residue 47 strongly correlates with increasing prescriptions of LPV (Spearman correlation rs=0.96, P<.0001)