Identification and characterization of a novel stay-green QTL that increases yield in maize.

Functional stay-green is a valuable trait that extends the photosynthetic period, increases source capacity and biomass and ultimately translates to higher grain yield. Selection for higher yields has increased stay-green in modern maize hybrids. Here, we report a novel QTL controlling functional stay-green that was discovered in a mapping population derived from the Illinois High Protein 1 (IHP1) and Illinois Low Protein 1 (ILP1) lines, which show very different rates of leaf senescence. This QTL was mapped to a single gene containing a NAC-domain transcription factor that we named nac7. Transgenic maize lines where nac7 was down-regulated by RNAi showed delayed senescence and increased both biomass and nitrogen accumulation in vegetative tissues, demonstrating NAC7 functions as a negative regulator of the stay-green trait. More importantly, crosses between nac7 RNAi parents and two different elite inbred testers produced hybrids with prolonged stay-green and increased grain yield by an average 0.29 megagram/hectare (4.6 bushel/acre), in 2 years of multi-environment field trials. Subsequent RNAseq experiments, one employing nac7 RNAi leaves and the other using leaf protoplasts overexpressing Nac7, revealed an important role for NAC7 in regulating genes in photosynthesis, chlorophyll degradation and protein turnover pathways that each contribute to the functional stay-green phenotype. We further determined the putative target of NAC7 and provided a logical extension for the role of NAC7 in regulating resource allocation from vegetative source to reproductive sink tissues. Collectively, our findings make a compelling case for NAC7 as a target for improving functional stay-green and yields in maize and other crops.

Discriminating response groups in metabolic and regulatory pathway networks.

MOTIVATION: Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. RESULTS: The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. AVAILABILITY: We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. CONTACT: julied@iastate.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PathwayAccess: CellDesigner plugins for pathway databases.

UNLABELLED: CellDesigner provides a user-friendly interface for graphical biochemical pathway description. Many pathway databases are not directly exportable to CellDesigner models. PathwayAccess is an extensible suite of CellDesigner plugins, which connect CellDesigner directly to pathway databases using respective Java application programming interfaces. The process is streamlined for creating new PathwayAccess plugins for specific pathway databases. Three PathwayAccess plugins, MetNetAccess, BioCycAccess and ReactomeAccess, directly connect CellDesigner to the pathway databases MetNetDB, BioCyc and Reactome. PathwayAccess plugins enable CellDesigner users to expose pathway data to analytical CellDesigner functions, curate their pathway databases and visually integrate pathway data from different databases using standard Systems Biology Markup Language and Systems Biology Graphical Notation. AVAILABILITY: Implemented in Java, PathwayAccess plugins run with CellDesigner version 4.0.1 and were tested on Ubuntu Linux, Windows XP and 7, and MacOSX. Source code, binaries, documentation and video walkthroughs are freely available at http://vrac.iastate.edu/~jlv.

A garter snake transcriptome: pyrosequencing, de novo assembly, and sex-specific differences.

BACKGROUND: The reptiles, characterized by both diversity and unique evolutionary adaptations, provide a comprehensive system for comparative studies of metabolism, physiology, and development. However, molecular resources for ectothermic reptiles are severely limited, hampering our ability to study the genetic basis for many evolutionarily important traits such as metabolic plasticity, extreme longevity, limblessness, venom, and freeze tolerance. Here we use massively parallel sequencing (454 GS-FLX Titanium) to generate a transcriptome of the western terrestrial garter snake (Thamnophis elegans) with two goals in mind. First, we develop a molecular resource for an ectothermic reptile; and second, we use these sex-specific transcriptomes to identify differences in the presence of expressed transcripts and potential genes of evolutionary interest. RESULTS: Using sex-specific pools of RNA (one pool for females, one pool for males) representing 7 tissue types and 35 diverse individuals, we produced 1.24 million sequence reads, which averaged 366 bp in length after cleaning. Assembly of the cleaned reads from both sexes with NEWBLER and MIRA resulted in 96,379 contigs containing 87% of the cleaned reads. Over 34% of these contigs and 13% of the singletons were annotated based on homology to previously identified proteins. From these homology assignments, additional clustering, and ORF predictions, we estimate that this transcriptome contains ~13,000 unique genes that were previously identified in other species and over 66,000 transcripts from unidentified protein-coding genes. Furthermore, we use a graph-clustering method to identify contigs linked by NEWBLER-split reads that represent divergent alleles, gene duplications, and alternatively spliced transcripts. Beyond gene identification, we identified 95,295 SNPs and 31,651 INDELs. From these sex-specific transcriptomes, we identified 190 genes that were only present in the mRNA sequenced from one of the sexes (84 female-specific, 106 male-specific), and many highly variable genes of evolutionary interest. CONCLUSIONS: This is the first large-scale, multi-organ transcriptome for an ectothermic reptile. This resource provides the most comprehensive set of EST sequences available for an individual ectothermic reptile species, increasing the number of snake ESTs 50-fold. We have identified genes that appear to be under evolutionary selection and those that are sex-specific. This resource will assist studies on gene expression and comparative genomics, and will facilitate the study of evolutionarily important traits at the molecular level.

Arabidopsis gene co-expression network and its functional modules.

BACKGROUND: Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. RESULTS: We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000) of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM) based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. CONCLUSION: This study reveals new insight into the topological properties of biological networks. The preferential hub-hub connections might be necessary for the formation of modular structure in gene co-expression networks. The study also reveals new insight into the organization of gene functional modules.

Identification and comparison of key RNA interference machinery from western corn rootworm, fall armyworm, and southern green stink bug.

RNA interference (RNAi)-based technology shows great potential for use in agriculture, particularly for management of costly insect pests. In the decade since the insecticidal effects of environmentally-introduced RNA were first reported, this treatment has been applied to several types of insect pests. Through the course of those efforts, it has become apparent that different insects exhibit a range of sensitivity to environmentally-introduced RNAs. The variation in responses across insect is not well-understood, with differences in the underlying RNAi mechanisms being one explanation. This study evaluates eight proteins among three agricultural pests whose responses to environmental RNAi are known to differ: western corn rootworm (Diabrotica virgifera virgifera), fall armyworm (Spodoptera frugiperda), and southern green stink bug (Nezara viridula). These proteins have been identified in various organisms as centrally involved in facilitating the microRNA- and small interfering-RNA-mediated interference responses. Various bioinformatics tools, as well as gene expression profiling, were used to identify and evaluate putative homologues for characteristics that may contribute to the differing responses of these insects, such as the absence of critical functional domains within expressed sequences, the absence of entire gene sequences, or unusually low or undetectable expression of critical genes. Though many similarities were observed, the number of isoforms and expression levels of double-stranded RNA-binding and argonaute proteins varied across insect. Differences among key RNAi machinery genes of these three pests may impact the function of their RNAi pathways, and therefore, their respective responses to exogenous RNAs.

Monte Carlo randomization tests for large-scale abundance datasets on the GPU.

Statistical tests are often performed to discover which experimental variables are reacting to specific treatments. Time-series statistical models usually require the researcher to make assumptions with respect to the distribution of measured responses which may not hold. Randomization tests can be applied to data in order to generate null distributions non-parametrically. However, large numbers of randomizations are required for the precise p-values needed to control false discovery rates. When testing tens of thousands of variables (genes, chemical compounds, or otherwise), significant q-value cutoffs can be extremely small (on the order of 10(-5) to 10(-8)). This requires high-precision p-values, which in turn require large numbers of randomizations. The NVIDIA(®) Compute Unified Device Architecture(®) (CUDA(®)) platform for General Programming on the Graphics Processing Unit (GPGPU) was used to implement an application which performs high-precision randomization tests via Monte Carlo sampling for quickly screening custom test statistics for experiments with large numbers of variables, such as microarrays, Next-Generation sequencing read counts, chromatographical signals, or other abundance measurements. The software has been shown to achieve up to more than 12 fold speedup on a Graphics Processing Unit (GPU) when compared to a powerful Central Processing Unit (CPU). The main limitation is concurrent random access of shared memory on the GPU. The software is available from the authors.