Mining Functional Elements in Messenger RNAs: Overview, Challenges, and Perspectives

Eukaryotic messenger RNA (mRNA) contains not only protein-coding regions but also a plethora of functional cis-elements that influence or coordinate a number of regulatory aspects of gene expression, such as mRNA stability, splicing forms, and translation rates. Understanding the rules that apply to each of these element types (e.g., whether the element is defined by primary or higher-order structure) allows for the discovery of novel mechanisms of gene expression as well as the design of transcripts with controlled expression. Bioinformatics plays a major role in creating databases and finding non-evident patterns governing each type of eukaryotic functional element. Much of what we currently know about mRNA regulatory elements in eukaryotes is derived from microorganism and animal systems, with the particularities of plant systems lagging behind. In this review, we provide a general introduction to the most well-known eukaryotic mRNA regulatory motifs (splicing regulatory elements, internal ribosome entry sites, iron-responsive elements, AU-rich elements, zipcodes, and polyadenylation signals) and describe available bioinformatics resources (databases and analysis tools) to analyze eukaryotic transcripts in search of functional elements, focusing on recent trends in bioinformatics methods and tool development. We also discuss future directions in the development of better computational tools based upon current knowledge of these functional elements. Improved computational tools would advance our understanding of the processes underlying gene regulations. We encourage plant bioinformaticians to turn their attention to this subject to help identify novel mechanisms of gene expression regulation using RNA motifs that have potentially evolved or diverged in plant species

TransportTP: A two-phase classification approach for membrane transporter prediction and characterization

<p>Abstract</p> <p>Background</p> <p>Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides.</p> <p>Results</p> <p>In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation.</p> <p>Conclusions</p> <p>TransportTP is the most effective tool for eukaryotic transporter characterization up to date.</p

Apple Pomace Consumption Favorably Alters Hepatic Lipid Metabolism in Young Female Sprague-Dawley Rats Fed a Western Diet

Apple pomace, which is a waste byproduct of processing, is rich in several nutrients, particularly dietary fiber, indicating potential benefits for diseases that are attributed to poor diets, such as non-alcoholic fatty liver disease (NAFLD). NAFLD affects over 25% of United States population and is increasing in children. Increasing fruit consumption can influence NAFLD. The study objective was to replace calories in standard or Western diets with apple pomace to determine the effects on genes regulating hepatic lipid metabolism and on risk of NAFLD. Female Sprague-Dawley rats were randomly assigned (n = 8 rats/group) to isocaloric diets of AIN-93G and AIN-93G/10% w/w apple pomace (AIN/AP) or isocaloric diets of Western (45% fat, 33% sucrose) and Western/10% w/w apple pomace (Western/AP) diets for eight weeks. There were no significant effects on hepatic lipid metabolism in rats fed AIN/AP. Western/AP diet containing fiber-rich apple pomace attenuated fat vacuole infiltration, elevated monounsaturated fatty acid content, and triglyceride storage in the liver due to higher circulating bile and upregulated hepatic DGAT2 gene expression induced by feeding a Western diet. The study results showed the replacement of calories in Western diet with apple pomace attenuated NAFLD risk. Therefore, apple pomace has the potential to be developed into a sustainable functional food for human consumption

LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org

Response of root explants to in vitro cultivation of marketable garlic cultivars

Garlic cultivars are sexually sterile under standard growth conditions, with direct implications for commercial production costs as well as breeding programs. Garlic is propagated commercially via bulblets, which facilitates disease transmission and virus load accumulation over vegetative generations. Tissue culture produces virus-free clones that are more productive, while keeping the desired traits of the cultivar. Consequently, this technique allows studies of garlic genetics as well as guarantees genetic conservation of varieties. We aimed at analyzing the in vitroregeneration of eight marketable cultivars of garlic using root segments as explants. For each genotype, bulblet-derived explants were isolated and introduced into MS medium supplemented with 2,4-D and 2-iP. Calli were transferred to MS medium supplemented with 8.8 mM BAP and 0.1 mM NAA (regeneration medium A), or with 4.6 mM kinetin alone (regeneration medium B). The calli were then evaluated for regeneration frequency after sixty days of in vitro cultivation. The noble cultivar \u27Jonas\u27 presented the highest rates of plant regeneration among the cultivars tested. The medium A, which contained auxin and cytokinin, induced the highest regeneration rates of all cultivars. The process described herein is simple, reproducible and can potentially be used as a tool in molecular breeding strategies for other marketable cultivars and genotypes of garlic

Convergence of developmental mutants into a single tomato model system: 'Micro-Tom' as an effective toolkit for plant development research

<p>Abstract</p> <p>Background</p> <p>The tomato (<it>Solanum lycopersicum </it>L.) plant is both an economically important food crop and an ideal dicot model to investigate various physiological phenomena not possible in <it>Arabidopsis thaliana</it>. Due to the great diversity of tomato cultivars used by the research community, it is often difficult to reliably compare phenotypes. The lack of tomato developmental mutants in a single genetic background prevents the stacking of mutations to facilitate analysis of double and multiple mutants, often required for elucidating developmental pathways.</p> <p>Results</p> <p>We took advantage of the small size and rapid life cycle of the tomato cultivar Micro-Tom (MT) to create near-isogenic lines (NILs) by introgressing a suite of hormonal and photomorphogenetic mutations (altered sensitivity or endogenous levels of auxin, ethylene, abscisic acid, gibberellin, brassinosteroid, and light response) into this genetic background. To demonstrate the usefulness of this collection, we compared developmental traits between the produced NILs. All expected mutant phenotypes were expressed in the NILs. We also created NILs harboring the wild type alleles for <it>dwarf</it>, <it>self-pruning </it>and <it>uniform fruit</it>, which are mutations characteristic of MT. This amplified both the applications of the mutant collection presented here and of MT as a genetic model system.</p> <p>Conclusions</p> <p>The community resource presented here is a useful toolkit for plant research, particularly for future studies in plant development, which will require the simultaneous observation of the effect of various hormones, signaling pathways and crosstalk.</p

LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.Oklahoma Center for The Advancement of Science and Technology: (OCAST Grant No. PSB11-031)

Response of root explants to in vitro cultivation of marketable garlic cultivars

Garlic cultivars are sexually sterile under standard growth conditions, with direct implications for commercial production costs as well as breeding programs. Garlic is propagated commercially via bulblets, which facilitates disease transmission and virus load accumulation over vegetative generations. Tissue culture produces virus-free clones that are more productive, while keeping the desired traits of the cultivar. Consequently, this technique allows studies of garlic genetics as well as guarantees genetic conservation of varieties. We aimed at analyzing the in vitro regeneration of eight marketable cultivars of garlic using root segments as explants. For each genotype, bulblet-derived explants were isolated and introduced into MS medium supplemented with 2,4-D and 2-iP. Calli were transferred to MS medium supplemented with 8.8 mM BAP and 0.1 mM NAA (regeneration medium A), or with 4.6 mM kinetin alone (regeneration medium B). The calli were then evaluated for regeneration frequency after sixty days of in vitro cultivation. The noble cultivar 'Jonas' presented the highest rates of plant regeneration among the cultivars tested. The medium A, which contained auxin and cytokinin, induced the highest regeneration rates of all cultivars. The process described herein is simple, reproducible and can potentially be used as a tool in molecular breeding strategies for other marketable cultivars and genotypes of garlic