Robertsdanielsen3502

ociated with Bayesian methods by parallel computing will lead to greater use of these methods.BACKGROUND The mammary gland is a unique organ for milk synthesis, secretion and storage, and it undergoes cyclical processes of development, differentiation, lactation and degeneration. At different developmental periods, the biological processes governing mammary gland physiology and internal environmental homeostasis depend on a complex network of genes and regulatory factors. Emerging evidence indicates that lncRNAs have arbitrarily critical functions in regulating gene expression in many organisms; however, the systematic characteristics, expression, and regulatory roles of lncRNAs in the mammary gland tissues of dairy goats have not been determined. RESULT In the present study, we profiled long noncoding RNA (lncRNA) expression in the mammary gland tissues of Laoshan dairy goats (Capra hircus) from different lactation periods at the whole-genome level, to identify, characterize and explore the regulatory functions of lncRNAs. A total of 37,249 transcripts were obtained, of which 2381 lncRNAs and 37,249 mRNAs were identified, 22,488 transcripts, including 800 noncoding transcripts and 21,688 coding transcripts, differed significantly (p ≤ 0.01) among the different lactation stages. The results of lncRNA-RNA interaction analysis showed that six known lncRNAs belonging to four families were identified as the precursors of 67 known microRNAs; 1478 and 573 mRNAs were predicted as hypothetical cis-regulation elements and antisense mRNAs, respectively. GO annotation and KEGG analysis indicated that the coexpressed mRNAs were largely enriched in biological processes related to such activities as metabolism, immune activation, and stress,., and most genes were involved in pathways related to such phenomena as inflammation, cancer, signal transduction, and metabolism. CONCLUSIONS Our results clearly indicated that lncRNAs involved in responses to stimuli, multiorganism processes, development, reproductive processes and growth, are closely related to mammary gland development and lactation.BACKGROUND Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. RESULTS ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. CONCLUSIONS ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.BACKGROUND dADD1 and dXNP proteins are the orthologs in Drosophila melanogaster of the ADD and SNF2 domains, respectively, of the ATRX vertebrate's chromatin remodeler, they suppress position effect variegation phenotypes and participate in heterochromatin maintenance. RESULTS We performed a search in human cancer databases and found that ATRX protein levels were elevated in more than 4.4% of the samples analyzed. Using the Drosophila model, we addressed the effects of over and under-expression of dADD1 proteins in polytene cells. Elevated levels of dADD1 in fly tissues caused different phenotypes, such as chromocenter disruption and loss of banding pattern at the chromosome arms. Analyses of the heterochromatin maintenance protein HP1a, the dXNP ATPase and the histone post-translational modification H3K9me3 revealed changes in their chromatin localization accompanied by mild transcriptional defects of genes embedded in heterochromatic regions. Furthermore, the expression of heterochromatin embedded genes in null dadd1 organisms is lower than in the wild-type conditions. CONCLUSION These data indicate that dADD1 overexpression induces chromatin changes, probably affecting the stoichiometry of HP1a containing complexes that lead to transcriptional and architectural changes. Our results place dADD1 proteins as important players in the maintenance of chromatin architecture and heterochromatic gene expression.BACKGROUND An important process for plant survival is the immune system. The induced systemic resistance (ISR) triggered by beneficial microbes is an important cost-effective defense mechanism by which plants are primed to an eventual pathogen attack. Defense mechanisms such as ISR depend on an accurate and context-specific regulation of gene expression. Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRNs). Here, we explore the regulatory mechanism of the ISR defense response triggered by the beneficial bacterium Paraburkholderia phytofirmans PsJN in Arabidopsis thaliana plants infected with Pseudomonas syringae DC3000. To achieve this, a GRN underlying the ISR response was inferred using gene expression time-series data of certain defense-related genes, differential evolution, and threshold Boolean networks. https://www.selleckchem.com/products/glpg0187.html RESULTS One thousand threshold Boolean networks were inferred that met the restriction of the desired dynamics. From these networks, a consensus network was obtained that helped to find plausible interactions between the genes.