High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a highresolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence

Enhancing Data Integration with Text Analysis to Find Proteins Implicated in Plant Stress Response

High throughput genomic studies can identify large numbers of potential candidate genes, which must be interpreted and filtered by investigators to select the best ones for further analysis. Prioritization is generally based on evidence that supports the role of a gene product in the biological process being investigated. The two most important bodies of information providing such evidence are bioinformatics databases and the scientific literature. In this paper we present an extension to the Ondex data integration framework that uses text mining techniques over Medline abstracts as a method for accessing both these bodies of evidence in a consistent way. In an example use case, we apply our method to create a knowledge base of Arabidopsis proteins implicated in plant stress response and use various scoring metrics to identify key protein-stress associations. In conclusion, we show that the additional text mining features are able to highlight proteins using the scientific literature that would not have been seen using data integration alone. Ondex is an open-source software project and can be downloaded, together with the text mining features described here, from www.ondex.org

Enhancing data integration with text analysis to find proteins implicated in plant stress response

High throughput genomic studies can identify large numbers of potential candidate genes, which must be interpreted and filtered by investigators to select the best ones for further analysis. Prioritization is generally based on evidence that supports the role of a gene product in the biological process being investigated. The two most important bodies of information providing such evidence are bioinformatics databases and the scientific literature. In this paper we present an extension to the Ondex data integration framework that uses text mining techniques over Medline abstracts as a method for accessing both these bodies of evidence in a consistent way. In an example use case, we apply our method to create a knowledge base of Arabidopsis proteins implicated in plant stress response and use various scoring metrics to identify key protein-stress associations. In conclusion, we show that the additional text mining features are able to highlight proteins using the scientific literature that would not have been seen using data integration alone. Ondex is an open-source software project and can be downloaded, together with the text mining features described here, from www.ondex.org

Hyperdiploid tumor cells increase phenotypic heterogeneity within Glioblastoma tumors

Here we report the identification of a proliferative, viable, and hyperdiploid tumor cell subpopulation present within Glioblastoma (GB) patient tumors. Using xenograft tumor models, we demonstrate that hyperdiploid cell populations are maintained in xenograft tumors and that clonally expanded hyperdiploid cells support tumor formation and progression in vivo. In some patient tumorsphere lines, hyperdiploidy is maintained during long-term culture and in vivo within xenograft tumor models, suggesting that hyperdiploidy can be a stable cell state. In other patient lines hyperdiploid cells display genetic drift in vitro and in vivo, suggesting that in these patients hyperdiploidy is a transient cell state that generates novel phenotypes, potentially facilitating rapid tumor evolution. We show that the hyperdiploid cells are resistant to conventional therapy, in part due to infrequent cell division due to a delay in the G0/G1 phase of the cell cycle. Hyperdiploid tumor cells are significantly larger and more metabolically active than euploid cancer cells, and this correlates to an increased sensitivity to the effects of glycolysis inhibition. Together these data identify GB hyperdiploid tumor cells as a potentially important subpopulation of cells that are well positioned to contribute to tumor evolution and disease recurrence in adult brain cancer patients, and suggest tumor metabolism as a promising point of therapeutic intervention against this subpopulation

De novo transcriptome assemblies for the spiny mouse (Acomys cahirinus)

Transcriptome assemblies generated per https://dx.doi.org/10.1101/076067 (preprint) / https://dx.doi.org/10.17504/protocols.io.ghebt3e (protocol). Manuscript available at Scientific Reports

The dynamic architecture of the metabolic switch in Streptomyces coelicolor

Background: During the lifetime of a fermenter culture, the soil bacterium S. coelicolor undergoes a major metabolic switch from exponential growth to antibiotic production. We have studied gene expression patterns during this switch, using a specifically designed Affymetrix genechip and a high-resolution time-series of fermenter-grown samples. Results: Surprisingly, we find that the metabolic switch actually consists of multiple finely orchestrated switching events. Strongly coherent clusters of genes show drastic changes in gene expression already many hours before the classically defined transition phase where the switch from primary to secondary metabolism was expected. The main switch in gene expression takes only 2 hours, and changes in antibiotic biosynthesis genes are delayed relative to the metabolic rearrangements. Furthermore, global variation in morphogenesis genes indicates an involvement of cell differentiation pathways in the decision phase leading up to the commitment to antibiotic biosynthesis. Conclusions: Our study provides the first detailed insights into the complex sequence of early regulatory events during and preceding the major metabolic switch in S. coelicolor, which will form the starting point for future attempts at engineering antibiotic production in a biotechnological setting

Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture

Bacteria in the genus Streptomyces are soil-dwelling oligotrophs and important producers of secondary metabolites. Previously, we showed that global messenger RNA expression was subject to a series of metabolic and regulatory switches during the lifetime of a fermentor batch culture of Streptomyces coelicolor M145. Here we analyze the proteome from eight time points from the same fermentor culture and, because phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolor mutant, INB201 (ΔphoP), defective in the control of phosphate utilization. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index, which also revealed the importance of stress pathway proteins in both cultures. As expected, the ΔphoP mutant was deficient in expression of PhoP-dependent genes, and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that coordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures

Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis

Transcriptional reprogramming forms a major part of a plant’s response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea