Cucumber chloroplast trnL(CAA) gene: nucleotide sequence and in vivo expression analysis in etiolated cucumber seedlings treated with benzyladenine and light.

The nucleotide sequence of a 714 bp BamHI-EcoRI fragment of cucumber chloroplast DNA was determined. The fragment contained a gene for tRNA(Leu) together with its flanking regions. The trnL(CAA) gene sequence is about 99% in similarity to broad bean, cauliflower, maize, spinach and tobacco corresponding genes. The relative expression level of the gene was determined by Northern (tRNA) gel blot and Northern (total cellular RNA) slot-blot analyses using the trnL gene probe in 6-day old etiolated cucumber seedlings and the seedlings that had been kept in the dark (dark-grown), treated with benzyladenine (BA) and kept in the dark (BA-treated dark-grown), illuminated (light-grown), and treated with BA and illuminated (BA-treated light-grown), for additional 4, 8 or 12 hr. The trnL transcripts and tRNA(Leu) levels in BA-treated dark-grown seedlings were 5 and 3 times higher, respectively after 4 hr BA treatment, while in the BA treated light-grown seedlings the level of trnL transcripts was only 3 times higher and had no detectable effect on mature tRNA(Leu) when compared to the time-4 hr dark-grown seedlings. However, the level of mature tRNA(Leu) did not show marked changes in the light-grown seedlings, whereas the level of trnL transcripts increases 3 times after 8 hr illumination of dark-grown seedlings. These data indicate that both light and cytokinin can signal changes in plastid tRNA gene expression. The possible regulatory mechanisms for such changes are discussed.

Cucumber chloroplast trnL(CAA) gene: nucleotide sequence and in vivo expression analysis in etiolated cucumber seedlings treated with benzyladenine and light

The nucleotide sequence of a 714 bp BamHI-EcoRI fragment of cucumber chloroplast DNA was determined. The fragment contained a gene for tRNA(Leu) together with its flanking regions. The trnL(CAA) gene sequence is about 99% in similarity to broad bean, cauliflower, maize, spinach and tobacco corresponding genes. The relative expression level of the gene was determined by Northern (tRNA) gel blot and Northern (total cellular RNA) slot-blot analyses using the trnL gene probe in 6-day old etiolated cucumber seedlings and the seedlings that had been kept in the dark (dark-grown), treated with benzyladenine (BA) and kept in the dark (BA-treated dark-grown), illuminated (light-grown), and treated with BA and illuminated (BA- treated light-grown), for additional 4, 8 or 12 hr. The trnL transcripts and tRNA(Leu) levels in BA-treated dark-grown seedlings were 5 and 3 times higher, respectively after 4 hr BA treatment, while in the BA treated light-grown seedlings the level of trnL transcripts was only 3 times higher and had not detectable effect on mature tRNA(Leu) when compared to the time-4 hr dark-grown seedlings. However, the level of mature tRNA(Leu) did not show marked changes in the light-grown seedlings, whereas the level of trnL transcripts increases 3 times after 8 hr illumination of dark-grown seedlings. These date indicate that both light and cytokinin can signal changes in plastid tRNA gene expression. The possible regulatory mechanisms for such changes are discussed.

NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

Background: Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. Results: We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. Conclusions: NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems. The source code for NETGEM is available from https://github.com/vjethava/NETGEM

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of similar to 58% and similar to 40% for localization and functions respectively of proteins were determined at a threshold of similar to 30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k nearest neighbor classifier confirmed that our results compared favorably. Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.

Role of DNA sequence based structural features of promoters in transcription initiation and gene expression

D Regulatory information for transcription initiation is present in a stretch of genomic DNA, called the promoter region that is located upstream of the transcription start site (TSS) of the gene. The promoter region interacts with different transcription factors and RNA polymerase to initiate transcription and contains short stretches of transcription factor binding sites (TFBSs), as well as structurally unique elements. Recent experimental and computational analyses of promoter sequences show that they often have non-B-DNA structural motifs, as well as some conserved structural properties, such as stability, bendability, nucleosome positioning preference and curvature, across a class of organisms. Here, we briefly describe these structural features, the differences observed in various organisms and their possible role in regulation of gene expression.

Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

A novel gene THSD7A is associated with obesity

Body mass index (BMI) is a non-invasive measurement of obesity. It is commonly used for assessing adiposity and obesity-related risk prediction. Genetic differences between ethnic groups are important factors, which contribute to the variation in phenotypic effects. India inhabited by the first out-of-Africa human population and the contemporary Indian populations are admixture of two ancestral populations; ancestral north Indians (ANI) and ancestral south Indians (ASI). Although ANI are related to Europeans, ASI are not related to any group outside Indian-subcontinent. Hence, we expect novel genetic loci associated with BMI. In association analysis, we found eight genic SNPs in extreme of distribution (P <= 3.75 x 10(-5)), of which WWOX has already been reported to be associated with obesity-related traits hence excluded from further study. Interestingly, we observed rs1526538, an intronic SNP of THSD7A; a novel gene significantly associated with obesity (P = 2.88 x 10(-5), 8.922 x 10(-6) and 2.504 x 10(-9) in discovery, replication and combined stages, respectively). THSD7A is neural N-glycoprotein, which promotes angiogenesis and it is well known that angiogenesis modulates obesity, adipose metabolism and insulin sensitivity, hence our result find a correlation. This information can be used for drug target, early diagnosis of obesity and treatment.