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Ongoing eutrophication frequently causes toxic phytoplankton blooms. This induces huge worldwide challenges for drinking water quality, food security and public health. Of crucial importance in avoiding and reducing blooms is to determine the maximum nutrient load ecosystems can absorb, while remaining in a good ecological state. These so called critical nutrient loads for lakes depend on the shape of the load-response curve. Due to spatial variation within lakes, load-response curves and therefore critical nutrient loads could vary throughout the lake. In this study we determine spatial patterns in critical nutrient loads for Lake Taihu (China) with a novel modelling approach called Spatial Ecosystem Bifurcation Analysis (SEBA). SEBA evaluates the impact of the lake's total external nutrient load on the local lake dynamics, resulting in a map of critical nutrient loads for different locations throughout the lake. Our analysis shows that the largest part of Lake Taihu follows a nonlinear load-response curve without hysteresis. The corresponding critical nutrient loads vary within the lake and depend on management goals, i.e. the maximum allowable chlorophyll concentration. According to our model, total nutrient loads need to be more than halved to reach chlorophyll-a concentrations of 30–40 μg L−1 in most sections of the lake. To prevent phytoplankton blooms with 20 μg L−1 chlorophyll-a throughout Lake Taihu, both phosphorus and nitrogen loads need a nearly 90% reduction. We conclude that our approach is of great value to determine critical nutrient loads of lake ecosystems such as Taihu and likely of spatially heterogeneous ecosystems in general.

Item does not contain fulltext DNA based microbial community profiling of food samples is confounded by the presence of DNA derived from membrane compromised (dead or injured) cells. Selective amplification of DNA from viable (intact) fraction of the community by propidium monoazide (PMA) treatment could circumvent this problem. Gouda cheese manufacturing is a proper model to evaluate the use of PMA for selective detection of intact cells since large fraction of membrane compromised cells emerges as a background in the cheese matrix during ripening. In this study, the effect of PMA on cheese community profiles was evaluated throughout manufacturing and ripening using quantitative PCR (qPCR). PMA effectively inhibited the amplification of DNA derived from membrane compromised cells and enhanced the analysis of the intact fraction residing in the cheese samples. Furthermore, a two-step protocol, which involves whole genome amplification (WGA) to enrich the DNA not modified with PMA and subsequent sequencing, was developed for the selective metagenome sequencing of viable fraction in the Gouda cheese microbial community. The metagenome profile of PMA treated cheese sample reflected the viable community profile at that time point in the cheese manufacturing.

Microbial genomics and related transcriptomics methods rely on culturing techniques to obtain enough DNA suitable for high‐throughput sequencing without resorting to DNA amplification techniques. A few microgram of DNA is needed for most common next‐generation sequencing methods. For transcriptome analysis, sufficient cDNA is needed to measure low abundance mRNA copies in the cell. However, the large majority of microbes on earth resist cultivation, hampering research into their relevant gene pool, ecological niche or industrial relevance. For example, many environmental or gut‐related species cannot be grown outside their natural habitat. Even if we isolate the metagenome or the metatranscriptome from these environments, this reveals only a fragmented sequence landscape that is difficult to assign to individual species. Although enrichment techniques or metatransciptome analysis of previously unculturable species have been shown to assist in directed culturing, e.g. of a Rikenella‐like bacterium (Bomar et al., 2011), the unravelling of a complex metagenome into its individual genomes and their organization is impossible using current technologies. A major challenge is the analysis of bacteria and other organisms living inside a complex matrix, like biofilms. Metagenome or transcriptome analysis of microorganisms has been described for biofilms consisting of a single species by scraping of the biofilm to obtain enough material (Holmes et al., 2006), but for multi‐species biofilms this method results in a metagenome or metatranscriptome dataset. The solution to these challenges may be the isolation and genomic analysis of unculturable single cells isolated from such environments. Here we describe in brief the state‐of‐the‐art in single‐cell microbial genomics.

Background Lysobacter species are Gram-negative bacteria widely distributed in soil, plant and freshwater habitats. Lysobacter owes its name to the lytic effects on other microorganisms. To better understand their ecology and interactions with other (micro)organisms, five Lysobacter strains representing the four species L. enzymogenes, L. capsici, L. gummosus and L. antibioticus were subjected to genomics and metabolomics analyses. Results Comparative genomics revealed a diverse genome content among the Lysobacter species with a core genome of 2,891 and a pangenome of 10,028 coding sequences. Genes encoding type I, II, III, IV, V secretion systems and type IV pili were highly conserved in all five genomes, whereas type VI secretion systems were only found in L. enzymogenes and L. gummosus. Genes encoding components of the flagellar apparatus were absent in the two sequenced L. antibioticus strains. The genomes contained a large number of genes encoding extracellular enzymes including chitinases, glucanases and peptidases. Various nonribosomal peptide synthase (NRPS) and polyketide synthase (PKS) gene clusters encoding putative bioactive metabolites were identified but only few of these clusters were shared between the different species. Metabolic profiling by imaging mass spectrometry complemented, in part, the in silico genome analyses and allowed visualisation of the spatial distribution patterns of several secondary metabolites produced by or induced in Lysobacter species during interactions with the soil-borne fungus Rhizoctonia solani. Conclusions Our work shows that mining the genomes of Lysobacter species in combination with metabolic profiling provides novel insights into the genomic and metabolic potential of this widely distributed but understudied and versatile bacterial genus. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2191-z) contains supplementary material, which is available to authorized users.

Here, we report the complete genome of Lactococcus lactis subsp. cremoris UC509.9, an Irish dairy starter. The circular chromosome of L. lactis UC509.9 represents the smallest among those of the sequenced lactococcal strains, while its large complement of eight plasmids appears to be a reflection of its adaptation to the dairy environment.

Contains fulltext : 97549.pdf (Publisher’s version ) (Open Access) Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org. 01 juli 2011

Contains fulltext : 70716.pdf (Publisher’s version ) (Closed access) OBJECTIVE: Repeated injection of streptococcal cell wall (SCW) fragments results in chronic arthritis in mice. The objective of this study was to identify genes and pathways that determine disease progression based on gene expression profiling in this model. METHODS: Chronic arthritis was induced in mice by 4 injections of SCW fragments. RNA samples were isolated from synovial tissue obtained at various time points and were analyzed using mouse genome array and quantitative reverse transcription-polymerase chain reaction techniques. The functional role of potential key genes was evaluated in mice with specific gene deletions. RESULTS: Gene expression analyses revealed a shift in molecular signature. In contrast to an up-regulation of the inflammatory response pathway, the pathways involved in oxidative metabolism were significantly down-regulated during the chronic phase of arthritis. Since oxidative metabolism determines the mode of macrophage activation, we investigated phenotype switching in macrophages. Markers of alternatively activated macrophages, such as arginase 1, were at maximal levels during acute inflammation. In contrast, induction of markers of classically activated macrophages (M1), such as interleukin-1beta (IL-1beta) and inducible nitric oxide synthase (iNOS), was relatively low during the acute phase of disease, but highly increased toward the chronic phase. M1 polarization during the chronic phase was accompanied by a Th1 signature, characterized by IL-12p40, IL-12p35, and interferon-gamma. However, the absence of IL-12p40, but not IL-12p35, significantly inhibited the chronic phase of arthritis and was marked by a reduction in IL-17 and iNOS levels, as well as restored expression of oxidative metabolism genes. CONCLUSION: M1 polarization accompanied by a decline in oxidative metabolism determine the chronic phase of arthritis. IL-12p40, most likely acting through the IL-23/IL-17 axis, plays a critical role in this process.

Pectinolytic Pectobacterium spp. and Dickeya spp. are necrotrophic bacterial pathogens of many important crops, including potato, worldwide. This study reports on the isolation and characterization of broad host lytic bacteriophages able to infect the dominant Pectobacterium spp. and Dickeya spp. affecting potato in Europe viz. Pectobacterium carotovorum subsp. carotovorum (Pcc), P. wasabiae (Pwa) and Dickeya solani (Dso) with the objective to assess their potential as biological disease control agents. Two lytic bacteriophages infecting stains of Pcc, Pwa and Dso were isolated from potato samples collected from two potato fields in central Poland. The ΦPD10.3 and ΦPD23.1 phages have morphology similar to other members of the Myoviridae family and the Caudovirales order, with a head diameter of 85 and 86 nm and length of tails of 117 and 121 nm, respectively. They were characterized for optimal multiplicity of infection, the rate of adsorption to the Pcc, Pwa and Dso cells, the latent period and the burst size. The phages were genotypically characterized with RAPD-PCR and RFLP techniques. The structural proteomes of both phages were obtained by fractionation of phage proteins by SDS-PAGE. Phage protein identification was performed by liquid chromatography-mass spectrometry (LC-MS) analysis. Pulsed-field gel electrophoresis (PFGE), genome sequencing and comparative genome analysis were used to gain knowledge of the length, organization and function of the ΦPD10.3 and ΦPD23.1 genomes. The potential use of ΦPD10.3 and ΦPD23.1 phages for the biocontrol of Pectobacterium spp. and Dickeya spp. infections in potato is discussed.

Background In the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it. Results In order to facilitate this analysis we generated TF Target Mapper (T ranscription F actor Target Mapper). TF Target Mapper is a BLAST search tool allowing rapid extraction of annotated information on genes around each hit. It combines sequence cleaning/filtering, pattern searching and BLAST searches with extraction of information on genes located around each BLAST hit and comparisons of the output list of genes or gene ontology IDs with user-implemented lists. We successfully applied and tested TF Target Mapper to analyse sequences bound in vivo by the transcription factor GATA-1. We show that TF Target Mapper efficiently extracted information on genes around ChIPed sequences, thus identifying known (e.g. α-globin and ζ-globin) and potentially novel GATA-1 gene targets. Conclusion TF Target Mapper is a very efficient BLAST search tool that allows the rapid extraction of annotated information on the genes around each hit. It can contribute to the comprehensive bioinformatic transcriptome/regulome analysis, by providing insight into the mechanisms of action of specific transcription factors, thus helping to elucidate the pathways these factors regulate.

Background SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. Results We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. Conclusion SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.