Miguel Cervantes-Cervantes

Okadaic acid, a protein phosphatase inhibitor, blocks calcium changes, gene expression, and cell death induced by gibberellin in wheat aleurone cells.

The cereal aleurone functions during germination by secreting hydrolases, mainly alpha-amylase, into the starchy endosperm. Multiple signal transduction pathways exist in cereal aleurone cells that enable them to modulate hydrolase production in response to both hormonal and environmental stimuli. Gibberellic acid (GA) promotes hydrolase production, whereas abscisic acid (ABA), hypoxia, and osmotic stress reduce amylase production. In an effort to identify the components of transduction pathways in aleurone cells, we have investigated the effect of okadaic acid (OA), a protein phosphatase inhibitor, on stimulus-response coupling for GA, ABA, and hypoxia. We found that OA (100 nM) completely inhibited all the GA responses that we measured, from rapid changes in cytosolic Ca2+ through changes in gene expression and accelerated cell death. OA (100 nM) partially inhibited ABA responses, as measured by changes in the level of PHAV1, a cDNA for an ABA-induced mRNA in barley. In contrast, OA had no effect on the response to hypoxia, as measured by changes in cytosolic Ca2+ and by changes in enzyme activity and RNA levels of alcohol dehydrogenase. Our data indicate that OA-sensitive protein phosphatases act early in the transduction pathway of GA but are not involved in the response to hypoxia. These data provide a basis for a model of multiple transduction pathways in which the level of cytosolic Ca2+ is a key point of convergence controlling changes in stimulus-response coupling.

Maize cDNAs Expressed in Endosperm Encode Functional Farnesyl Diphosphate Synthase with Geranylgeranyl Diphosphate Synthase Activity

Isoprenoids are the most diverse and abundant group of natural products. In plants, farnesyl diphosphate (FPP) and geranylgeranyl diphosphate (GGPP) are precursors to many isoprenoids having essential functions. Terpenoids and sterols are derived from FPP, whereas gibberellins, carotenoids, casbenes, taxenes, and others originate from GGPP. The corresponding synthases (FPP synthase [FPPS] and GGPP synthase [GGPPS]) catalyze, respectively, the addition of two and three isopentenyl diphosphate molecules to dimethylallyl diphosphate. Maize (Zea mays L. cv B73) endosperm cDNAs encoding isoprenoid synthases were isolated by functional complementation of Escherichia coli cells carrying a bacterial gene cluster encoding all pathway enzymes needed for carotenoid biosynthesis, except for GGPPS. This approach indicated that the maize gene products were functional GGPPS enzymes. Yet, the predicted enzyme sequences revealed FPPS motifs and homology with FPPS enzymes. In vitro assays demonstrated that indeed these maize enzymes produced both FPP and GGPP and that the N-terminal sequence affected the ratio of FPP to GGPP. Their functionality in E. coli demonstrated that these maize enzymes can be coupled with a metabolon to provide isoprenoid substrates for pathway use, and suggests that enzyme bifunctionality can be harnessed. The maize cDNAs are encoded by a small gene family whose transcripts are prevalent in endosperm beginning mid development. These maize cDNAs will be valuable tools for assessing the critical structural properties determining prenyl transferase specificity and in metabolic engineering of isoprenoid pathways, especially in cereal crops.

Surrogate biochemistry: use of Escherichia coli to identify plant cDNAs that impact metabolic engineering of carotenoid accumulation

Abstract. Carotenoids synthesized in plants but not animals are essential for human nutrition. Therefore, ongoing efforts to metabolically engineer plants for improved carotenoid content benefit from the identification of genes that affect carotenoid accumulation, possibly highlighting potential challenges when pyramiding traits represented by multiple biosynthetic pathways. We employed a heterologous bacterial system to screen for maize cDNAs encoding products that alter carotenoid accumulation either positively or negatively. Genes encoding carotenoid biosynthetic enzymes from the bacterium Erwinia uredovora were introduced into Escherichia coli cells that were subsequently transfected with a maize endosperm cDNA expression library; and these doubly transformed cells were then screened for altered carotenoid accumulation. DNA sequencing and characterization of one cDNA class conferring increased carotenoid content led to the identification of maize cDNAs encoding isopentenyl diphosphate isomerase. A cDNA that caused a reduced carotenoid content in E. coli was also identified. Based on DNA sequence analysis, DNA hybridization, and further functional testing, this latter cDNA was found to encode the small subunit of ADP-glucose pyrophosphorylase, a rate-controlling enzyme in starch biosynthesis that has been of interest for enhancing plant starch content.

In silico prediction of noncoding RNAs using supervised learning and feature ranking methods

We propose here a new approach for ncRNA prediction. Our approach selects features derived from RNA folding programs and ranks these features using a class separation method that measures the ability of the features to differentiate between positive and negative classes. The target feature set comprising top-ranked features is then used to construct several classifiers with different supervised learning algorithms. These classifiers are compared to the same supervised learning algorithms with the baseline feature set employed in a state-of-the-art method. Experimental results based on ncRNA families taken from the Rfam database demonstrate the good performance of the proposed approach.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Research Article: Novel features for identifying A-minors in three-dimensional RNA molecules

RNA tertiary interactions or tertiary motifs are conserved structural patterns formed by pairwise interactions between nucleotides. They include base-pairing, base-stacking, and base-phosphate interactions. A-minor motifs are the most common tertiary interactions in the large ribosomal subunit. The A-minor motif is a nucleotide triple in which minor groove edges of an adenine base are inserted into the minor groove of neighboring helices, leading to interaction with a stabilizing base pair. We propose here novel features for identifying and predicting A-minor motifs in a given three-dimensional RNA molecule. By utilizing the features together with machine learning algorithms including random forests and support vector machines, we show experimentally that our approach is capable of predicting A-minor motifs in the given RNA molecule effectively, demonstrating the usefulness of the proposed approach. The techniques developed from this work will be useful for molecular biologists and biochemists to analyze RNA tertiary motifs, specifically A-minor interactions.