Involvement of Burkholderiaceae and sulfurous volatiles in disease-suppressive soils.

Disease-suppressive soils are ecosystems in which plants suffer less from root infections due to the activities of specific microbial consortia. The characteristics of soils suppressive to specific fungal root pathogens are comparable to those of adaptive immunity in animals, as reported by Raaijmakers and Mazzola (Science 352:1392-3, 2016), but the mechanisms and microbial species involved in the soil suppressiveness are largely unknown. Previous taxonomic and metatranscriptome analyses of a soil suppressive to the fungal root pathogen Rhizoctonia solani revealed that members of the Burkholderiaceae family were more abundant and more active in suppressive than in non-suppressive soils. Here, isolation, phylogeny, and soil bioassays revealed a significant disease-suppressive activity for representative isolates of Burkholderia pyrrocinia, Paraburkholderia caledonica, P. graminis, P. hospita, and P. terricola. In vitro antifungal activity was only observed for P. graminis. Comparative genomics and metabolite profiling further showed that the antifungal activity of P. graminis PHS1 was associated with the production of sulfurous volatile compounds encoded by genes not found in the other four genera. Site-directed mutagenesis of two of these genes, encoding a dimethyl sulfoxide reductase and a cysteine desulfurase, resulted in a loss of antifungal activity both in vitro and in situ. These results indicate that specific members of the Burkholderiaceae family contribute to soil suppressiveness via the production of sulfurous volatile compounds.

Exploring bacterial interspecific interactions for discovery of novel antimicrobial compounds.

Recent studies indicated that the production of secondary metabolites by soil bacteria can be triggered by interspecific interactions. However, little is known to date about interspecific interactions between Gram-positive and Gram-negative bacteria. In this study, we aimed to understand how the interspecific interaction between the Gram-positive Paenibacillus sp. AD87 and the Gram-negative Burkholderia sp. AD24 affects the fitness, gene expression and the production of soluble and volatile secondary metabolites of both bacteria. To obtain better insight into this interaction, transcriptome and metabolome analyses were performed. Our results revealed that the interaction between the two bacteria affected their fitness, gene expression and the production of secondary metabolites. During interaction, the growth of Paenibacillus was not affected, whereas the growth of Burkholderia was inhibited at 48 and 72 h. Transcriptome analysis revealed that the interaction between Burkholderia and Paenibacillus caused significant transcriptional changes in both bacteria as compared to the monocultures. The metabolomic analysis revealed that the interaction increased the production of specific volatile and soluble antimicrobial compounds such as 2,5-bis(1-methylethyl)-pyrazine and an unknown Pederin-like compound. The pyrazine volatile compound produced by Paenibacillus was subjected to bioassays and showed strong inhibitory activity against Burkholderia and a range of plant and human pathogens. Moreover, strong additive antimicrobial effects were observed when soluble extracts from the interacting bacteria were combined with the pure 2,5-bis(1-methylethyl)-pyrazine. The results obtained in this study highlight the importance to explore bacterial interspecific interactions to discover novel secondary metabolites and to perform simultaneously metabolomics of both, soluble and volatile compounds.

Spatial identification of critical nutrient loads of large shallow lakes: Implications for Lake Taihu (China).

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.

Use of propidium monoazide for selective profiling of viable microbial cells during Gouda cheese ripening.

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.

Finished Genome Sequence of Collimonas arenae Cal35.

We announce the finished genome sequence of soil forest isolate Collimonas arenae Cal35, which comprises a 5.6-Mbp chromosome and 41-kb plasmid. The Cal35 genome is the second one published for the bacterial genus Collimonas and represents the first opportunity for high-resolution comparison of genome content and synteny among collimonads.

Multifactorial diversity sustains microbial community stability.

Maintenance of a high degree of biodiversity in homogeneous environments is poorly understood. A complex cheese starter culture with a long history of use was characterized as a model system to study simple microbial communities. Eight distinct genetic lineages were identified, encompassing two species: Lactococcus lactis and Leuconostoc mesenteroides. The genetic lineages were found to be collections of strains with variable plasmid content and phage sensitivities. Kill-the-winner hypothesis explaining the suppression of the fittest strains by density-dependent phage predation was operational at the strain level. This prevents the eradication of entire genetic lineages from the community during propagation regimes (back-slopping), stabilizing the genetic heterogeneity in the starter culture against environmental uncertainty.

The molecular signature of oxidative metabolism and the mode of macrophage activation determine the shift from acute to chronic disease in experimental arthritis: critical role of interleukin-12p40.

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.

TF Target Mapper: a BLAST search tool for the identification of Transcription Factor target genes.

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 (Transcription Factor 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. alpha-globin and zeta-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.