Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks

Free fatty acids link metabolism and regulation of the insulin-sensitizing fibroblast growth factor-21

OBJECTIVE—Fibroblast growth factor (FGF)-21 improves insulin sensitivity and lipid metabolism in obese or diabetic animal models, while human studies revealed increased FGF-21 levels in obesity and type 2 diabetes. Given that FGF-21 has been suggested to be a peroxisome proliferator–activator receptor (PPAR) –dependent regulator of fasting metabolism, we hypothesized that free fatty acids (FFAs), natural agonists of PPAR, might modify FGF-21 levels. RESEARCH DESIGN AND METHODS—The effect of fatty acids on FGF-21 was investigated in vitro in HepG2 cells. Within a randomized controlled trial, the effects of elevated FFAs were studied in 21 healthy subjects (13 women and 8 men). Within a clinical trial including 17 individuals, the effect of insulin was analyzed using an hyperinsulinemic-euglycemic clamp and the effect of PPAR activation was studied subsequently in a rosiglitazone treatment trial over 8 weeks. RESULTS—Oleate and linoleate increased FGF-21 expression and secretion in a PPAR-dependent fashion, as demonstrated by small-interfering RNA–induced PPAR knockdown, while palmitate had no effect. In vivo, lipid infusion induced an increase of circulating FGF-21 in humans, and a strong correlation between the change in FGF-21 levels and the change in FFAs was observed. An artificial hyperinsulinemia, which was induced to delineate the potential interaction between elevated FFAs and hyperinsulinemia, revealed that hyperinsulinemia also increased FGF-21 levels in vivo, while rosiglitazone treatment had no effect. CONCLUSIONS—The results presented here offer a mechanism explaining the induction of the metabolic regulator FGF-21 in the fasting situation but also in type 2 diabetes and obesity

Changes in bacterial and fungal communities across compost recipes, preparation methods, and composting times. PLoS One

Abstract Compost production is a critical component of organic waste handling, and compost applications to soil are increasingly important to crop production. However, we know surprisingly little about the microbial communities involved in the composting process and the factors shaping compost microbial dynamics. Here, we used high-throughput sequencing approaches to assess the diversity and composition of both bacterial and fungal communities in compost produced at a commercial-scale. Bacterial and fungal communities responded to both compost recipe and composting method. Specifically, bacterial communities in manure and hay recipes contained greater relative abundances of Firmicutes than hardwood recipes with hay recipes containing relatively more Actinobacteria and Gemmatimonadetes. In contrast, hardwood recipes contained a large relative abundance of Acidobacteria and Chloroflexi. Fungal communities of compost from a mixture of dairy manure and silage-based bedding were distinguished by a greater relative abundance of Pezizomycetes and Microascales. Hay recipes uniquely contained abundant Epicoccum, Thermomyces, Eurotium, Arthrobotrys, and Myriococcum. Hardwood recipes contained relatively abundant Sordariomycetes. Holding recipe constant, there were significantly different bacterial and fungal communities when the composting process was managed by windrow, aerated static pile, or vermicompost. Temporal dynamics of the composting process followed known patterns of degradative succession in herbivore manure. The initial community was dominated by Phycomycetes, followed by Ascomycota and finally Basidiomycota. Zygomycota were associated more with manure-silage and hay than hardwood composts. Most commercial composters focus on the thermophilic phase as an economic means to insure sanitation of compost from pathogens. However, the community succeeding the thermophilic phase begs further investigation to determine how the microbial dynamics observed here can be best managed to generate compost with the desired properties

PaVESy: Pathway visualization and editing system

A data managing system for editing and visualization of biological pathways is presented. The main component of PaVESy (Pathway Visualization and Editing System) is a relational SQL database system. The database design allows storage of biological objects, such as metabolites, proteins, genes and respective relations, which are required to assemble metabolic and regulatory biological interactions. The database model accommodates highly flexible annotation of biological objects by user-defined attributes. In addition, specific roles of objects are derived from these attributes in the context of user-defined interactions, e.g. in the course of pathway generation or during editing of the database content. Furthermore, the user may organize and arrange the database content within a folder structure and is free to group and annotate database objects of interest within customizable subsets. Thus, we allow an individualized view on the database content and facilitate user customization. A JAVA-based class library was developed, which serves as the database programming interface to PaVESy. This API provides classes, which implement the concepts of object persistence in SQL databases, such as entries, interactions, annotations, folders and subsets. We created editing and visualization tools for navigation in and visualization of the database content. User approved pathway assemblies are stored and may be retrieved for continued modification, annotation and export. Data export is interfaced with a range of network visualization programs, such as Pajek or other software allowing import of SBML or GML data format

Abstract PaVESy: Combining profiling data with pathway knowledge

Since a couple of years high-throughput profiling methods are entering life sciences. For instance, a gene expression profile contains information about the transcriptional response of a complete genome to different experimental conditions. For scientists this abundance of data is often difficult to interpret. Several statistical methods are developed, and progress in data processing and management makes it more and more feasible to connect the results of a concrete experiment to the confirmed but seemly unmanageable knowledge already known. The pathway database software PaVES