Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models

Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High throughput annotation and metabolic modeling of these genomes is now a reality. The high throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represents the next frontier in microbial bioinformatics. The fruition of this next frontier will depend upon the integration of numerous data sources relating to mechanisms, components, and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data, and binding site locations, and we explore how these data are being used for the reconstruction of new regulatory networks. From template network based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. Finally, we explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.J.P.F. acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. The work was supported in part by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), National Funds through the FCT within projects [FCOMP-01-0124-FEDER015079] (ToMEGIM—Computational Tools for Metabolic Engineering using Genome-scale Integrated Models) and FCOMP-01-0124-FEDER009707 (HeliSysBio—molecular Systems Biology in Helicobacter pylori), the U.S. Department of Energy under contract [DE-ACO2-06CH11357] and the National Science Foundation under [0850546]

Metabolomic profiles associated with physiological resistance to Sclerotinia sclerotiorum (Lib.) de Bary in common bean

Includes bibliographical references.2015 Summer.Common bean (Phaseolus vulgaris L.) is an important global food crop with a recently sequenced and annotated genome. Plant metabolic and hormone processes are being increasingly recognized as central to disease resistance. For common bean, the molecular and metabolic processes that mediate resistance to white mold disease (caused by Sclerotinia sclerotiorum, (Lib.) de Bary) are largely unknown. Identifying metabolites associated with Sclerotinia infection may provide novel targets to breed for enhanced resistance. The metabolic changes that occur during S. sclerotiorum infection of a detached leaf were characterized using a non-targeted metabolomics workflow spanning primary and secondary metabolism, and a targeted panel of 13 hormones. Partial resistant (A195, beige seed coat color) and susceptible (Sacramento, light red kidney market class) Andean bean lines were inoculated with isolate S20 for non-targeted metabolite profiling at 16, 24, and 48 hours post inoculation (hpi) and at 8 and 16 hpi for hormones. Metabolites from healthy tissue adjacent to the necrotic lesion were extracted with the solvent methanol:water (80:20) and detected using non-targeted UPLC-TOF-MS and GC-MS workflows, and hormones were profiled using UPLC-MS/MS. The analysis detected 140 metabolites that varied between A195 and Sacramento, with the greatest metabolite variation occurring at 16 hpi. The metabolites that varied included amines, amino acids, saccharides, organic acids, phytoalexins, hormones, ureides, and molecules involved in cell wall and membrane composition. The diversity in observed metabolic changes points towards a multi-faceted response associated with plant resistance to S. sclerotiorum in common bean. The integration of metabolomics and genomic data discover functional markers of metabolic resistance to white mold

Evaluating plant immunity using mass spectrometry-based metabolomics workflows

Metabolic processes in plants are key components of physiological and biochemical disease resistance. Metabolomics, the analysis of a broad range of small molecule compounds in a biological system, has been used to provide a systems-wide overview of plant metabolism associated with defense responses. Plant immunity has been examined using multiple metabolomics workflows that vary in methods of detection, annotation, and interpretation, and the choice of workflow can significantly impact the conclusions inferred from a metabolomics investigation. The broad range of metabolites involved in plant defense often supports the need for multiple chemical detection platforms and implementation of a non-targeted approach. A review of the current literature reveals a wide range of workflows that are currently used in plant metabolomics, and new methods for analyzing and reporting mass spectrometry data can improve the ability to translate investigative findings among different plant-pathogen systems

Proteome Characterization of Leaves in Common Bean

Dry edible bean (Phaseolus vulgaris L.) is a globally relevant food crop. The bean genome was recently sequenced and annotated allowing for proteomics investigations aimed at characterization of leaf phenotypes important to agriculture. The objective of this study was to utilize a shotgun proteomics approach to characterize the leaf proteome and to identify protein abundance differences between two bean lines with known variation in their physiological resistance to biotic stresses. Overall, 640 proteins were confidently identified. Among these are proteins known to be involved in a variety of molecular functions including oxidoreductase activity, binding peroxidase activity, and hydrolase activity. Twenty nine proteins were found to significantly vary in abundance (p-value < 0.05) between the two bean lines, including proteins associated with biotic stress. To our knowledge, this work represents the first large scale shotgun proteomic analysis of beans and our results lay the groundwork for future studies designed to investigate the molecular mechanisms involved in pathogen resistance

Arabidopsis thaliana VOZ (Vascular plant One-Zinc finger) transcription factors are required for proper regulation of flowering time

Includes bibliographical references (page 431).Transition to flowering in plants is tightly controlled by environmental cues, which regulate the photoperiod and vernalization pathways, and endogenous signals, which mediate the autonomous and gibberellin pathways. In this work, we investigated the role of two Zn2+-finger transcription factors, the paralogues AtVOZ1 and AtVOZ2, in Arabidopsis thaliana flowering. Single atvoz1-1 and atvoz2-1 mutants showed no significant phenotypes as compared to wild type. However, atvoz1-1 atvoz2-1 double mutant plants exhibited several phenotypes characteristic of flowering-time mutants. The double mutant displayed a severe delay in flowering, together with additional pleiotropic phenotypes. Late flowering correlated with elevated expression of FLOWERING LOCUS C (FLC), which encodes a potent floral repressor, and decreased expression of its target, the floral promoter FD. Vernalization rescued delayed flowering of atvoz1-1 atvoz2-1 and reversed elevated FLC levels. Accumulation of FLC transcripts in atvoz1-1 atvoz2-1 correlated with increased expression of several FLC activators, including components of the PAF1 and SWR1 chromatin-modifying complexes. Additionally, AtVOZs were shown to bind the promoter of MOS3/SAR3 and directly regulate expression of this nuclear pore protein, which is known to participate in the regulation of flowering time, suggesting that AtVOZs exert at least some of their flowering regulation by influencing the nuclear pore function. Complementation of atvoz1-1 atvoz2-1 with AtVOZ2 reversed all double mutant phenotypes, confirming that the observed morphological and molecular changes arise from the absence of functional AtVOZ proteins, and validating the functional redundancy between AtVOZ1 and AtVOZ2.Published with support from the Colorado State University Libraries Open Access Research and Scholarship Fund

H3K36 dimethylation shapes the epigenetic interaction landscape by directing repressive chromatin modifications in embryonic stem cells

Epigenetic modifications on the chromatin do not occur in isolation. Chromatin-associated proteins and their modification products form a highly interconnected network, and disturbing one component may rearrange the entire system. We see this increasingly clearly in epigenetically dysregulated cancers. It is important to understand the rules governing epigenetic interactions. Here, we use the mouse embryonic stem cell (mESC) model to describe in detail the relationships within the H3K27-H3K36-DNA methylation subnetwork. In particular, we focus on the major epigenetic reorganization caused by deletion of the histone 3 lysine 36 methyltransferase NSD1, which in mESCs deposits nearly all of the intergenic H3K36me2. Although disturbing the H3K27 and DNA methylation (DNAme) components also affects this network to a certain extent, the removal of H3K36me2 has the most drastic effect on the epigenetic landscape, resulting in full intergenic spread of H3K27me3 and a substantial decrease in DNAme. By profiling DNMT3A and CHH methylation (mCHH), we show that H3K36me2 loss upo

Longitudinal Transcriptomic, Proteomic, and Metabolomic Analysis of Citrus limon Response to Graft Inoculation by Candidatus Liberibacter asiaticus

Presymptomatic detection of citrus trees infected with Candidatus Liberibacter asiaticus (CLas), the bacterial pathogen associated with Huanglongbing (HLB; citrus greening disease), is critical to controlling the spread of the disease. To test whether infected citrus trees produce systemic signals that may be used for indirect disease detection, lemon (Citrus limon) plants were graft-inoculated with either CLas-infected or control (CLas-) budwood, and leaf samples were longitudinally collected over 46 weeks and analyzed for plant changes associated with CLas infection. RNA, protein, and metabolite samples extracted from leaves were analyzed using RNA-Seq, mass spectrometry, and 1H NMR spectroscopy, respectively. Significant differences in specific transcripts, proteins, and metabolites were observed between CLas-infected and control plants as early as 2 weeks post graft (wpg). The most dramatic differences between the transcriptome and proteome of CLas-infected and control plants were observed at 10 wpg, including coordinated increases in transcripts and proteins of citrus orthologs of known plant defense genes. This integrated approach to quantifying plant molecular changes in leaves of CLas-infected plants supports the development of diagnostic technology for presymptomatic or early disease detection as part of efforts to control the spread of HLB into uninfected citrus groves