More robust detection of motifs in coexpressed genes by using phylogenetic information

BACKGROUND: Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of genes containing the motif versus the number lacking the motif might not be sufficiently high to allow detection by classical motif detection tools. To still recover motifs which are not significantly enriched but still present, we developed a procedure in which we use phylogenetic footprinting to first delineate all potential motifs in each gene. Then we mutually compare all detected motifs and identify the ones that are shared by at least a few genes in the data set as potential candidates. RESULTS: We applied our methodology to a compiled test data set containing known regulatory motifs and to two biological data sets derived from genome wide expression studies. By executing four consecutive steps of 1) identifying conserved regions in orthologous intergenic regions, 2) aligning these conserved regions, 3) clustering the conserved regions containing similar regulatory regions followed by extraction of the regulatory motifs and 4) screening the input intergenic sequences with detected regulatory motif models, our methodology proves to be a powerful tool for detecting regulatory motifs when a low signal to noise ratio is present in the input data set. Comparing our results with two other motif detection algorithms points out the robustness of our algorithm. CONCLUSION: We developed an approach that can reliably identify multiple regulatory motifs lacking a high degree of overrepresentation in a set of coexpressed genes (motifs belonging to sparsely connected hubs in the regulatory network) by exploiting the advantages of using both coexpression and phylogenetic information

LC-MS analysis to determine the biodistribution of a polymer coated ilomastat ocular implant

Ilomastat is a matrix metalloproteinase inhibitor (MMPi) that has shown the potential to inhibit scarring (fibrosis) by mediating healing after injury or surgery. A long lasting ocular implantable pharmaceutical formulation of ilomastat is being developed to mediate the healing process to prevent scarring after glaucoma filtration surgery. The ilomastat implant was coated with water permeable and biocompatible phosphoryl choline polymer (PC1059) displayed extended slow release of ilomastat in vitro and in vivo. The ocular distribution of ilomastat from the implant in rabbits at day 30 post surgery was determined by the extraction of ilomastat and its internal standard marimastat from the ocular tissues, plasma, aqueous humour and vitreous fluid followed by capillary-flow liquid chromatography (cap-LC), the column effluent was directed into a triple quadrupole mass spectrometer operating in product scan mode. The lower limits of quantification (LLOQs) were 0.3 pg/μL for ocular fluids and plasma, and 3 pg/mg for ocular tissues. The extraction recoveries were 90-95% for ilomastat and its internal standard from ocular tissues. Ilomastat was found in ocular fluids and tissues at day 30 after surgery. The level of ilomastat was 18 times higher in the aqueous humour than vitreous humour. The concentration ranking of ilomastat in the ocular tissues was sclera > bleb conjunctiva > conjunctiva (rest of the eye) > cornea. Mass spectrometry analysis to confirm the presence of ilomastat in the ocular tissues and fluids at day 30 post-surgery establishes the extended release of ilomastat can be achieved in vivo, which is crucial information for optimisation of the ilomastat coated implant

Depletion of Trypanosome CTR9 Leads to Gene Expression Defects

The Paf complex of Opisthokonts and plants contains at least five subunits: Paf1, Cdc73, Rtf1, Ctr9, and Leo1. Mutations in, or loss of Paf complex subunits have been shown to cause defects in histone modification, mRNA polyadenylation, and transcription by RNA polymerase I and RNA polymerase II. We here investigated trypanosome CTR9, which is essential for trypanosome survival. The results of tandem affinity purification suggested that trypanosome CTR9 associates with homologues of Leo1 and Cdc73; genes encoding homologues of Rtf1 and Paf1 were not found. RNAi targeting CTR9 resulted in at least ten-fold decreases in 131 essential mRNAs: they included several that are required for gene expression and its control, such as those encoding subunits of RNA polymerases, exoribonucleases that target mRNA, RNA helicases and RNA-binding proteins. Simultaneously, some genes from regions subject to chromatin silencing were derepressed, possibly as a secondary effect of the loss of two proteins that are required for silencing, ISWI and NLP1

Motif detection towards the inference of the transcriptional network

status: publishe

Circadian succession of molecular processes in living tissues

Abstract Background Oscillations of different origin, period and amplitude play an important role in the regulation of cellular processes. Most widely studied is the circadian or approximately daily variation in gene expression activity. Timing of gene expression is controlled by internal molecular clock keeping steady periodic expression. In this study, we shift attention towards a broad range of periodically expressed genes involved in multiple cellular functions which may or may not be under direct control of the intrinsic circadian clock. Are all molecular functions represented in expressed genes at all times? Alternatively, are different molecular functions performed at different times? Is there a pattern of succession for molecular processes and functions throughout their daily activity period? Results To answer these questions, we re-analyzed a number of mouse circadian gene expression data available from public sources. These data represent the normal function of metabolically active peripheral tissues (white adipose tissue, brown adipose tissue, liver). We applied novel methods for the estimation of confidence in phase assignment to identify groups of synchronous genes peaking at the same time regardless of the amplitude or the absolute intensity of expression. Each synchronous group has been annotated to identify Gene Ontology (GO) terms and molecular pathways. Our analysis identified molecular functions specific to a particular time of the day in different tissues. Conclusion Improved methodology for datamining allowed for the discovery of functions and biological pathways in groups of genes with synchronized peak expression time. In particular, such functions as oxidative phase of energy metabolism, DNA repair, mRNA processing, lipid biosynthesis and others are separated in time. This timewise compartmentalization is important for understanding the cellular circuitry and can be used to optimize the time of intervention with drug or genome medication

Confidence in Phase Definition for Periodicity in Genes Expression Time Series

<div><p>Circadian oscillation in baseline gene expression plays an important role in the regulation of multiple cellular processes. Most of the knowledge of circadian gene expression is based on studies measuring gene expression over time. Our ability to dissect molecular events in time is determined by the sampling frequency of such experiments. However, the real peaks of gene activity can be at any time on or between the time points at which samples are collected. Thus, some genes with a peak activity near the observation point have their phase of oscillation detected with better precision then those which peak between observation time points. Separating genes for which we can confidently identify peak activity from ambiguous genes can improve the analysis of time series gene expression. In this study we propose a new statistical method to quantify the phase confidence of circadian genes. The numerical performance of the proposed method has been tested using three real gene expression data sets.</p></div

Additional file 2: of Circadian succession of molecular processes in living tissues

Gene Ontology Charts. (XLSX 224 kb

IWAT, BAT and Liver data sets: number of circadian genes identified using Fisher’s <i>g</i>-test, Permutation test and JTK-CYCLE respectively.

<p>IWAT, BAT and Liver data sets: number of circadian genes identified using Fisher’s <i>g</i>-test, Permutation test and JTK-CYCLE respectively.</p

Graph of the moving block bootstrap principle.

<p>Graph showing the principal of moving block bootstrap. The moving block bootstrap randomly selects blocks of the original data (top) and concatenate them together (center) to form a resample (bottom).</p