A Python Analytical Pipeline to Identify Prohormone Precursors and Predict Prohormone Cleavage Sites

Neuropeptides and hormones are signaling molecules that support cell–cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides

Characterization of the prohormone complement in cattle using genomic libraries and cleavage prediction approaches

<p>Abstract</p> <p>Background</p> <p>Neuropeptides are cell to cell signalling molecules that regulate many critical biological processes including development, growth and reproduction. These peptides result from the complex processing of prohormone proteins, making their characterization both challenging and resource demanding. In fact, only 42 neuropeptide genes have been empirically confirmed in cattle. Neuropeptide research using high-throughput technologies such as microarray and mass spectrometry require accurate annotation of prohormone genes and products. However, the annotation and associated prediction efforts, when based solely on sequence homology to species with known neuropeptides, can be problematic.</p> <p>Results</p> <p>Complementary bioinformatic resources were integrated in the first survey of the cattle neuropeptide complement. Functional neuropeptide characterization was based on gene expression profiles from microarray experiments. Once a gene is identified, knowledge of the enzymatic processing allows determination of the final products. Prohormone cleavage sites were predicted using several complementary cleavage prediction models and validated against known cleavage sites in cattle and other species. Our bioinformatics approach identified 92 cattle prohormone genes, with 84 of these supported by expressed sequence tags. Notable findings included an absence of evidence for a cattle relaxin 1 gene and evidence for a cattle galanin-like peptide pseudogene. The prohormone processing predictions are likely accurate as the mammalian proprotein convertase enzymes, except for proprotein convertase subtilisin/kexin type 9, were also identified. Microarray analysis revealed the differential expression of 21 prohormone genes in the liver associated with nutritional status and 8 prohormone genes in the placentome of embryos generated using different reproductive techniques. The neuropeptide cleavage prediction models had an exceptional performance, correctly predicting cleavage in more than 86% of the prohormone sequence positions.</p> <p>Conclusion</p> <p>A substantial increase in the number of cattle prohormone genes identified and insights into the expression profiles of neuropeptide genes were obtained from the integration of bioinformatics tools and database resources and gene expression information. Approximately 20 prohormones with no empirical evidence were detected and the prohormone cleavage sites were predicted with high accuracy. Most prohormones were supported by expressed sequence tag data and many were differentially expressed across nutritional and reproductive conditions. The complete set of cattle prohormone sequences identified and the cleavage prediction approaches are available at <url>http://neuroproteomics.scs.uiuc.edu/neuropred.html</url>.</p

Kato square root problem with unbounded leading coefficients

We prove the Kato conjecture for elliptic operators, $L=-\nabla\cdot\left((\mathbf A+\mathbf D)\nabla\ \right)$, with $\mathbf A$ a complex measurable bounded coercive matrix and $\mathbf D$ a measurable real-valued skew-symmetric matrix in $\mathbb{R}^n$ with entries in $BMO(\mathbb{R}^n)$;\, i.e., the domain of $\sqrt{L}\,$ is the Sobolev space $\dot H^1(\mathbb{R}^n)$ in any dimension, with the estimate $\|\sqrt{L}\, f\|_2\lesssim \| \nabla f\|_2$

Meta-analysis of genome-wide expression patterns associated with behavioral maturation in honey bees

<p>Abstract</p> <p>Background</p> <p>The information from multiple microarray experiments can be integrated in an objective manner <it>via </it>meta-analysis. However, multiple meta-analysis approaches are available and their relative strengths have not been directly compared using experimental data in the context of different gene expression scenarios and studies with different degrees of relationship. This study investigates the complementary advantages of meta-analysis approaches to integrate information across studies, and further mine the transcriptome for genes that are associated with complex processes such as behavioral maturation in honey bees. Behavioral maturation and division of labor in honey bees are related to changes in the expression of hundreds of genes in the brain. The information from various microarray studies comparing the expression of genes at different maturation stages in honey bee brains was integrated using complementary meta-analysis approaches.</p> <p>Results</p> <p>Comparison of lists of genes with significant differential expression across studies failed to identify genes with consistent patterns of expression that were below the selected significance threshold, or identified genes with significant yet inconsistent patterns. The meta-analytical framework supported the identification of genes with consistent overall expression patterns and eliminated genes that exhibited contradictory expression patterns across studies. Sample-level meta-analysis of normalized gene-expression can detect more differentially expressed genes than the study-level meta-analysis of estimates for genes that were well described by similar model parameter estimates across studies and had small variation across studies. Furthermore, study-level meta-analysis was well suited for genes that exhibit consistent patterns across studies, genes that had substantial variation across studies, and genes that did not conform to the assumptions of the sample-level meta-analysis. Meta-analyses confirmed previously reported genes and helped identify genes (e.g. <it>Tomosyn</it>, <it>Chitinase 5, Adar, Innexin 2, Transferrin 1</it>, <it>Sick</it>, <it>Oatp26F</it>) and Gene Ontology categories (e.g. purine nucleotide binding) not previously associated with maturation in honey bees.</p> <p>Conclusion</p> <p>This study demonstrated that a combination of meta-analytical approaches best addresses the highly dimensional nature of genome-wide microarray studies. As expected, the integration of gene expression information from microarray studies using meta-analysis enhanced the characterization of the transcriptome of complex biological processes.</p

Fine-mapping of a QTL influencing pork tenderness on porcine chromosome 2

<p>Abstract</p> <p>Background</p> <p>In a previous study, a quantitative trait locus (QTL) exhibiting large effects on both Instron shear force and taste panel tenderness was detected within the Illinois Meat Quality Pedigree (IMQP). This QTL mapped to the q arm of porcine chromosome 2 (SSC2q). Comparative analysis of SSC2q indicates that it is orthologous to a segment of human chromosome 5 (HSA5) containing a strong positional candidate gene, calpastatin (<it>CAST</it>). <it>CAST </it>polymorphisms have recently been shown to be associated with meat quality characteristics; however, the possible involvement of other genes and/or molecular variation in this region cannot be excluded, thus requiring fine-mapping of the QTL.</p> <p>Results</p> <p>Recent advances in porcine genome resources, including high-resolution radiation hybrid and bacterial artificial chromosome (BAC) physical maps, were utilized for development of novel informative markers. Marker density in the ~30-Mb region surrounding the most likely QTL position was increased by addition of eighteen new microsatellite markers, including nine publicly-available and nine novel markers. Two newly-developed markers were derived from a porcine BAC clone containing the <it>CAST </it>gene. Refinement of the QTL position was achieved through linkage and haplotype analyses. Within-family linkage analyses revealed at least two families segregating for a highly-significant QTL in strong positional agreement with <it>CAST </it>markers. A combined analysis of these two families yielded QTL intervals of 36 cM and 7 cM for Instron shear force and taste panel tenderness, respectively, while haplotype analyses suggested further refinement to a 1.8 cM interval containing <it>CAST </it>markers. The presence of additional tenderness QTL on SSC2q was also suggested.</p> <p>Conclusion</p> <p>These results reinforce <it>CAST </it>as a strong positional candidate. Further analysis of <it>CAST </it>molecular variation within the IMQP F₁boars should enhance understanding of the molecular basis of pork tenderness, and thus allow for genetic improvement of pork products. Furthermore, additional resources have been generated for the targeted investigation of other putative QTL on SSC2q, which may lead to further advancements in pork quality.</p

Semiparametric approach to characterize unique gene expression trajectories across time

BACKGROUND: A semiparametric approach was used to identify groups of cDNAs and genes with distinct expression profiles across time and overcome the limitations of clustering to identify groups. The semiparametric approach allows the generalization of mixtures of distributions while making no specific parametric assumptions about the distribution of the hidden heterogeneity of the cDNAs. The semiparametric approach was applied to study gene expression in the brains of Apis mellifera ligustica honey bees raised in two colonies (A. m. mellifera and ligustica) with consistent patterns across five maturation ages. RESULTS: The semiparametric approach provided unambiguous criteria to detect groups of genes, trajectories and probability of gene membership to groups. The semiparametric results were cross-validated in both colony data sets. Gene Ontology analysis enhanced by genome annotation helped to confirm the semiparametric results and revealed that most genes with similar or related neurobiological function were assigned to the same group or groups with similar trajectories. Ten groups of genes were identified and nine groups had highly similar trajectories in both data sets. Differences in the trajectory of the reminder group were consistent with reports of accelerated maturation in ligustica colonies compared to mellifera colonies. CONCLUSION: The combination of microarray technology, genomic information and semiparametric analysis provided insights into the genomic plasticity and gene networks linked to behavioral maturation in the honey bee

Cerebellum Transcriptome of Mice Bred for High Voluntary Activity Offers Insights into Locomotor Control and Reward-Dependent Behaviors.

The role of the cerebellum in motivation and addictive behaviors is less understood than that in control and coordination of movements. High running can be a self-rewarding behavior exhibiting addictive properties. Changes in the cerebellum transcriptional networks of mice from a line selectively bred for High voluntary running (H) were profiled relative to an unselected Control (C) line. The environmental modulation of these changes was assessed both in activity environments corresponding to 7 days of Free (F) access to running wheel and to Blocked (B) access on day 7. Overall, 457 genes exhibited a significant (FDR-adjusted P-value &lt; 0.05) genotype-by-environment interaction effect, indicating that activity genotype differences in gene expression depend on environmental access to running. Among these genes, network analysis highlighted 6 genes (Nrgn, Drd2, Rxrg, Gda, Adora2a, and Rab40b) connected by their products that displayed opposite expression patterns in the activity genotype contrast within the B and F environments. The comparison of network expression topologies suggests that selection for high voluntary running is linked to a predominant dysregulation of hub genes in the F environment that enables running whereas a dysregulation of ancillary genes is favored in the B environment that blocks running. Genes associated with locomotor regulation, signaling pathways, reward-processing, goal-focused, and reward-dependent behaviors exhibited significant genotype-by-environment interaction (e.g. Pak6, Adora2a, Drd2, and Arhgap8). Neuropeptide genes including Adcyap1, Cck, Sst, Vgf, Npy, Nts, Penk, and Tac2 and related receptor genes also exhibited significant genotype-by-environment interaction. The majority of the 183 differentially expressed genes between activity genotypes (e.g. Drd1) were under-expressed in C relative to H genotypes and were also under-expressed in B relative to F environments. Our findings indicate that the high voluntary running mouse line studied is a helpful model for understanding the molecular mechanisms in the cerebellum that influence locomotor control and reward-dependent behaviors

A Novel Dynamic Impact Approach (DIA) for Functional Analysis of Time-Course Omics Studies: Validation Using the Bovine Mammary Transcriptome

The overrepresented approach (ORA) is the most widely-accepted method for functional analysis of microarray datasets. The ORA is computationally-efficient and robust; however, it suffers from the inability of comparing results from multiple gene lists particularly with time-course experiments or those involving multiple treatments. To overcome such limitation a novel method termed Dynamic Impact Approach (DIA) is proposed. The DIA provides an estimate of the biological impact of the experimental conditions and the direction of the impact. The impact is obtained by combining the proportion of differentially expressed genes (DEG) with the log2 mean fold change and mean –log P-value of genes associated with the biological term. The direction of the impact is calculated as the difference of the impact of up-regulated DEG and down-regulated DEG associated with the biological term. The DIA was validated using microarray data from a time-course experiment of bovine mammary gland across the lactation cycle. Several annotation databases were analyzed with DIA and compared to the same analysis performed by the ORA. The DIA highlighted that during lactation both BTA6 and BTA14 were the most impacted chromosomes; among Uniprot tissues those related with lactating mammary gland were the most positively-impacted; within KEGG pathways ‘Galactose metabolism’ and several metabolism categories related to lipid synthesis were among the most impacted and induced; within Gene Ontology “lactose biosynthesis” among Biological processes and “Lactose synthase activity” and “Stearoyl-CoA 9-desaturase activity” among Molecular processes were the most impacted and induced. With the exception of the terms ‘Milk’, ‘Milk protein’ and ‘Mammary gland’ among Uniprot tissues and SP_PIR_Keyword, the use of ORA failed to capture as significantly-enriched (i.e., biologically relevant) any term known to be associated with lactating mammary gland. Results indicate the DIA is a biologically-sound approach for analysis of time-course experiments. This tool represents an alternative to ORA for functional analysis