Benildo G. de los Reyes

Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress.

BackgroundThe transcriptional regulatory network involved in low temperature response leading to acclimation has been established in Arabidopsis. In japonica rice, which can only withstand transient exposure to milder cold stress (10°C), an oxidative-mediated network has been proposed to play a key role in configuring early responses and short-term defenses. The components, hierarchical organization and physiological consequences of this network were further dissected by a systems-level approach.ResultsRegulatory clusters responding directly to oxidative signals were prominent during the initial 6 to 12 hours at 10°C. Early events mirrored a typical oxidative response based on striking similarities of the transcriptome to disease, elicitor and wounding induced processes. Targets of oxidative-mediated mechanisms are likely regulated by several classes of bZIP factors acting on as1/ocs/TGA-like element enriched clusters, ERF factors acting on GCC-box/JAre-like element enriched clusters and R2R3-MYB factors acting on MYB2-like element enriched clusters.Temporal induction of several H2O2-induced bZIP, ERF and MYB genes coincided with the transient H2O2 spikes within the initial 6 to 12 hours. Oxidative-independent responses involve DREB/CBF, RAP2 and RAV1 factors acting on DRE/CRT/rav1-like enriched clusters and bZIP factors acting on ABRE-like enriched clusters. Oxidative-mediated clusters were activated earlier than ABA-mediated clusters.ConclusionGenome-wide, physiological and whole-plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress and developmental responses leads to modulated growth and vigor maintenance contributing to a delay of plastic injuries.

Supra-optimal expression of the cold-regulated OsMyb4 transcription factor in transgenic rice changes the complexity of transcriptional network with major effects on stress tolerance and panicle development

The R2R3-type OsMyb4 transcription factor of rice has been shown to play a role in the regulation of osmotic adjustment in heterologous overexpression studies. However, the exact composition and organization of its underlying transcriptional network has not been established to be a robust tool for stress tolerance enhancement by regulon engineering. OsMyb4 network was dissected based on commonalities between the global chilling stress transcriptome and the transcriptome configured by OsMyb4 overexpression. OsMyb4 controls a hierarchical network comprised of several regulatory sub-clusters associated with cellular defense and rescue, metabolism and development. It regulates target genes either directly or indirectly through intermediary MYB, ERF, bZIP, NAC, ARF and CCAAT-HAP transcription factors. Regulatory sub-clusters have different combinations of MYB-like, GCC-box-like, ERD1-box-like, ABRE-like, G-box-like, as1/ocs/TGA-like, AuxRE-like, gibberellic acid response element (GARE)-like and JAre-like cis-elements. Cold-dependent network activity enhanced cellular antioxidant capacity through radical scavenging mechanisms and increased activities of phenylpropanoid and isoprenoid metabolic processes involving various abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene and reactive oxygen species (ROS) responsive genes. OsMyb4 network is independent of drought response element binding protein/C-repeat binding factor (DREB/CBF) and its sub-regulons operate with possible co-regulators including nuclear factor-Y. Because of its upstream position in the network hierarchy, OsMyb4 functions quantitatively and pleiotrophically. Supra-optimal expression causes misexpression of alternative targets with costly trade-offs to panicle development.

Application of cranberry concentrate (Vaccinium macrocarpon) to control Escherichia coli O157:H7 in ground beef and its antimicrobial mechanism related to the downregulated slp, hdeA and cfa

The possible use of cranberry concentrate (CC) as a natural food preservative was studied by examining its antimicrobial effect on the growth of Escherichia coli O157:H7 inoculated in ground beef, its organoleptical effect on beef patties, and its antimicrobial mechanism on the gene regulation level. Inoculated ground beef was added with CC and stored at 4 degrees C for 5 days. Bacteria were detected on day 0, 1, 3, and 5. Cranberry concentrate (2.5%, 5%, and 7.5% w/w) reduced total aerobic bacteria 1.5 log, 2.1 log, and 2.7 log CFU/g and E. coli O157:H7 0.4 log, 0.7 log, and 2.4 log CFU/g, respectively, when compared to the control on day 5. Fifty panelists evaluated the burgers supplemented with CC. No differences in appearance, flavor, and taste were found among burgers with 0%, 2.5%, and 5% CC. The expression of E. coli O157:H7 cyclopropane fatty acyl phospholipid synthase (cfa), hypothetical protein (hdeA), outer membrane porin protein C (ompC), hyperosmotically inducible periplasmic protein (osmY), and outer membrane protein induced after carbon starvation (slp) genes with or without CC (2.5% v/v) treatment was investigated by quantitative real-time PCR. Compared to the control, slp, hdeA, and cfa were markedly downregulated, ompC was slightly downregulated, while osmY was slightly affected.

Reverse engineering module networks by PSO-RNN hybrid modeling

BackgroundInferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.ResultsThis study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information.ConclusionThe reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e.g. normalization and missing value imputation) to reduce the data noise; (2) clustering genes based on gene expression data and gene functional category information to identify biologically meaningful modules, thereby reducing the dimensionality of the data; (3) modeling GRNs with the PSO-RNN method between the modules to capture their nonlinear and dynamic relationships. The method is shown to lead to biologically meaningful modules and networks among the modules.

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

BackgroundIntegrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.ResultsThe proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.ConclusionThe major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.

Construction of a sugar beet BAC library from a hybrid with diverse traits

A bacterial artificial chromosome (BAC) library of the 750-Mbp sugar beet genome represented in hybrid US H20 was constructed fromHind III-digested DNA, with an average insert size of 120 kbp. US H20 is a variety grown in the eastern United States. It exhibits heterosis for emergence and yield, presumably because of its hybridity between eastern and western US germplasm sources. Filter arrays were used to assess the abundance and distribution of particular nucleotide sequences. An rRNA gene probe found that 1.2% of the library carried sequences similar to these highly repetitive and conserved sequences. A simple sequence repeat element (CA)8 thought to be predominantly distributed throughout centromere regions of all chromosomes was present in 1.7% of clones. For more than half of the 28 randomly chosen expressed sequence tags (ESTs) used as probes, a higher-than-expected number of single-copy hybridization signals was observed. Assuming 6× genome coverage, this suggests that many duplicate genes exist in the beet genome.

Transcriptome profiling characterizes phosphate deficiency effects on carbohydrate metabolism in rice leaves

Phosphorus (P) is a structural component of nucleic acids and phospholipids and plays important roles in plant growth and development. P accumulation was significantly reduced (about 35%) in rice leaves from plants grown under low (32 μM) P compared to 320 μM P grown plants. Genome response to low P was examined using the rice 60K oligonucleotide DNA microarrays. At the threshold significance of |log₂| fold>2.0, 21,033 genes (about 33.7% of all genes on the microarray) were affected by P deficiency. Among all genes on the microarray, 4271 genes were sorted into 51 metabolic pathways. Low P affected 1494 (35.0%) genes and the largest category of genes was related to sucrose degradation to ethanol and lactate pathway. To survey the role of P in rice, 25 pathways were selected based on number of affected genes. Among these pathways, cytosolic glycolysis contained the least number of upregulated but most down-regulated genes. Low P decreased glucose, pyruvate and chlorophyll, and genes related to carbon metabolism and chlorophyllide a biosynthesis. However, sucrose and starch levels increased. These results indicate that P nutrition affects diverse metabolic pathways mostly related to glucose, pyruvate, sucrose, starch, and chlorophyll a.

Identification of candidate network hubs involved in metabolic adjustments of rice under drought stress by integrating transcriptome data and genome-scale metabolic network

In this study, we have integrated a rice genome-scale metabolic network and the transcriptome of a drought-tolerant rice line, DK151, to identify the major transcriptional regulators involved in metabolic adjustments necessary for adaptation to drought. This was achieved by examining the differential expressions of transcription factors and metabolic genes in leaf, root and young panicle of rice plants subjected to drought stress during tillering, booting and panicle elongation stages. Critical transcription factors such as AP2/ERF, bZIP, MYB and NAC that control the important nodes in the gene regulatory pathway were identified through correlative analysis of the patterns of spatio-temporal expression and cis-element enrichment. We showed that many of the candidate transcription factors involved in metabolic adjustments were previously linked to phenotypic variation for drought tolerance. This approach represents the first attempt to integrate models of transcriptional regulation and metabolic pathways for the identification of candidate regulatory genes for targeted selection in rice breeding.

Reconstruction of gene regulatory modules in cancer cell cycle by multi-source data integration.

Background Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells. Results and Principal Findings We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results. Conclusions We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.

Comparative analysis of putative pathogenesis-related gene expression in two Rhizoctonia solani pathosystems.

Rhizoctonia solani, teleomorph Thanatephorus cucumeris, is a polyphagous necrotrophic plant pathogen of the Basidiomycete order that is split into 14 different anastomosis groups (AGs) based on hyphal interactions and host range. In this investigation, quantitative real-time PCR (qRT-PCR) techniques were used to determine potential pathogenicity factors of R. solani in the AG1-IA/rice and AG3/potato pathosystems. These factors were identified by mining for sequences of pathogen origin in a library of rice tissue infected with R. solani AG1-IA and comparing these sequences against the recently released R. solani AG3 genome. Ten genes common to both AGs and two specific to AG1-IA were selected for expression analysis by qRT-PCR. Results indicate that a number of genes are similarly expressed by AG1 and AG3 during the early stages of pathogenesis. Grouping of these pathogenicity factors based on relatedness of expression profiles suggests three key events are involved in R. solani pathogenesis: early host contact and infiltration, adjustment to the host environment, and pathogen proliferation through necrotic tissue. Further studies of the pathogenesis-associated genes identified in this project will enable more precise elucidation of the molecular mechanisms that allow for the widespread success of R. solani as a phytopathogen and allow for more targeted, effective methods of management.