Sacha A. F. T. van Hijum

The raw starch binding domain of cyclodextrin glycosyltransferase from Bacillus circulans strain 251

The E-domain of cyclodextrin glycosyltransferase (CGTase) (EC 2.4.1.19) from Bacillus circulans strain 251 is a putative raw starch binding domain. Analysis of the maltose-dependent CGTase crystal structure revealed that each enzyme molecule contained three maltose molecules, situated at contact points between protein molecules. Two of these maltoses were bound to specific sites in the E-domain, the third maltose was bound at the C-domain. To delineate the roles in raw starch binding and cyclization reaction kinetics of the two maltose binding sites in the E-domain, we replaced Trp-616 and Trp-662 of maltose binding site 1 and Tyr-633 of maltose binding site 2 by alanines using site-directed mutagenesis. Purified mutant CGTases were characterized with respect to raw starch binding and cyclization reaction kinetics on both soluble and raw starch. The results show that maltose binding site 1 is most important for raw starch binding, whereas maltose binding site 2 is involved in guiding linear starch chains into the active site. β-Cyclodextrin causes product inhibition by interfering with catalysis in the active site and the function of maltose binding site 2 in the E-domain. CGTase mutants in the E-domain maltose binding site 1 could no longer be crystallized as maltose-dependent monomers. Instead, the W616A mutant CGTase protein was successfully crystallized as a carbohydrate-independent dimer; its structure has been refined to 2.2 Å resolution. The three-dimensional structure shows that, within the error limits, neither the absence of carbohydrates nor the W616A mutation caused significant further conformational changes. The modified starch binding and cyclization kinetic properties observed with the mutant CGTase proteins thus can be directly related to the amino acid replacements.

The Lactococcus lactis CodY regulon - Identification of a conserved cis-regulatory element

CodY of Lactococcus lactis MG1363 is a transcriptional regulator that represses the expression of several genes encoding proteins of the proteolytic system. DNA microarray analysis, comparing the expression profiles of L. lactis MG1363 and an isogenic strain in which codY was mutated, was used to determine the CodY regulon. In peptide-rich medium and exponentially growing cells, where CodY exerts strong repressing activity, the expression of over 30 genes was significantly increased upon removal of codY. The differentially expressed genes included those predominantly involved in amino acid transport and metabolism. In addition, several genes belonging to other functional categories were derepressed, stressing the pleiotropic role of CodY. Scrutinizing the transcriptome data with bioinformatics tools revealed the presence of a novel over-represented motif in the upstream regions of several of the genes derepressed in L. lactis MG1363ΔcodY. Evidence is presented that this 15-bp cis-sequence, AATTTTCWGAAAATT, serves as a high affinity binding site for CodY, as shown by electrophoretic mobility shift assays and DNase I footprinting analyses. The presence of this CodY-box is sufficient to evoke CodY-mediated regulation in vivo. A copy of this motif is also present in the upstream region of codY itself. It is shown that CodY regulates its own synthesis and requires the CodY-box and branched-chain amino acids to interact with its promoter.

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

In the Life Sciences ‘omics’ data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.

BAGEL: a web-based bacteriocin genome mining tool

A common problem in the annotation of open reading frames (ORFs) is the identification of genes that are functionally similar but have limited or no sequence homology. This is particularly the case for bacteriocins, a very diverse group of antimicrobial peptides produced by bacteria and usually encoded by small, poorly conserved ORFs. ORFs surrounding bacteriocin genes are often biosynthetic genes. This information can be used to locate putative structural bacteriocin genes. Here, we describe BAGEL, a web server that identifies putative bacteriocin ORFs in a DNA sequence using novel, knowledge-based bacteriocin databases and motif databases. Many bacteriocins are encoded by small genes that are often omitted in the annotation process of bacterial genomes. Thus, we have implemented ORF detection using a number of published ORF prediction tools. In addition, BAGEL takes into account the genomic context, i.e. for each potential bacteriocin-encoding ORF, the sequence of the surrounding region on the genome is analyzed for genes that might encode proteins involved in biosynthesis, transport, regulation and/or immunity. These innovations make BAGEL unique in its ability to detect putative bacteriocin gene clusters in (new) bacterial genomes. BAGEL is freely accessible at: .

A generally applicable validation scheme for the assessment of factors involved in reproducibility and quality of DNA-microarray data

BackgroundIn research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.ResultsA number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance.ConclusionEven in cases where 90 % of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25 %. Clustering experiments showed that trends can be reliably detected also from genes with very low expression levels. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality.

The relative value of operon predictions

For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation, (iv) sequence elements and (v) experimental evidence. The performance estimates of operon predictions reported in literature cannot directly be compared due to differences in methods and data used in these studies. Here, we survey the current status of operon prediction methods. Based on a comparison of the performance of operon predictions on Escherichia coli and Bacillus subtilis we conclude that there is still room for improvement. We expect that existing and newly generated genomics and transcriptomics data will further improve accuracy of operon prediction methods.

Regulation of Glutamine and Glutamate Metabolism by GlnR and GlnA in Streptococcus pneumoniae

Several genes involved in nitrogen metabolism are known to contribute to the virulence of pathogenic bacteria. Here, we studied the function of the nitrogen regulatory protein GlnR in the Gram-positive human pathogen Streptococcus pneumoniae. We demonstrate that GlnR mediates transcriptional repression of genes involved in glutamine synthesis and uptake (glnA and glnPQ), glutamate synthesis (gdhA), and the gene encoding the pentose phosphate pathway enzyme Zwf, which forms an operon with glnPQ. Moreover, the expression of gdhA is also repressed by the pleiotropic regulator CodY. The GlnR-dependent regulation occurs through a conserved operator sequence and is responsive to the concentration of glutamate, glutamine, and ammonium in the growth medium. By means of in vitro binding studies and transcriptional analyses, we show that the regulatory function of GlnR is dependent on GlnA. Mutants of glnA and glnP displayed significantly reduced adhesion to Detroit 562 human pharyngeal epithelial cells, suggesting a role for these genes in the colonization of the host by S. pneumoniae. Thus, our results provide a thorough insight into the regulation of glutamine and glutamate metabolism of S. pneumoniae mediated by both GlnR and GlnA.

Mixed-Culture Transcriptome Analysis Reveals the Molecular Basis of Mixed-Culture Growth in Streptococcus thermophilus and Lactobacillus bulgaricus

ABSTRACT Many food fermentations are performed using mixed cultures of lactic acid bacteria. Interactions between strains are of key importance for the performance of these fermentations. Yogurt fermentation by Streptococcus thermophilus and Lactobacillus bulgaricus (basonym, Lactobacillus delbrueckii subsp. bulgaricus) is one of the best-described mixed-culture fermentations. These species are believed to stimulate each others growth by the exchange of metabolites such as folic acid and carbon dioxide. Recently, postgenomic studies revealed that an upregulation of biosynthesis pathways for nucleotides and sulfur-containing amino acids is part of the global physiological response to mixed-culture growth in S. thermophilus, but an in-depth molecular analysis of mixed-culture growth of both strains remains to be established. We report here the application of mixed-culture transcriptome profiling and a systematic analysis of the effect of interaction-related compounds on growth, which allowed us to unravel the molecular responses associated with batch mixed-culture growth in milk of S. thermophilus CNRZ1066 and L. bulgaricus ATCC BAA-365. The results indicate that interactions between these bacteria are primarily related to purine, amino acid, and long-chain fatty acid metabolism. The results support a model in which formic acid, folic acid, and fatty acids are provided by S. thermophilus. Proteolysis by L. bulgaricus supplies both strains with amino acids but is insufficient to meet the biosynthetic demands for sulfur and branched-chain amino acids, as becomes clear from the upregulation of genes associated with these amino acids in mixed culture. Moreover, genes involved in iron uptake in S. thermophilus are affected by mixed-culture growth, and genes coding for exopolysaccharide production were upregulated in both organisms in mixed culture compared to monocultures. The confirmation of previously identified responses in S. thermophilus using a different strain combination demonstrates their generic value. In addition, the postgenomic analysis of the responses of L. bulgaricus to mixed-culture growth allows a deeper understanding of the ecology and interactions of this important industrial food fermentation process.

Microbiome dynamics of human epidermis following skin barrier disruption

BackgroundRecent advances in sequencing technologies have enabled metagenomic analyses of many human body sites. Several studies have catalogued the composition of bacterial communities of the surface of human skin, mostly under static conditions in healthy volunteers. Skin injury will disturb the cutaneous homeostasis of the host tissue and its commensal microbiota, but the dynamics of this process have not been studied before. Here we analyzed the microbiota of the surface layer and the deeper layers of the stratum corneum of normal skin, and we investigated the dynamics of recolonization of skin microbiota following skin barrier disruption by tape stripping as a model of superficial injury.ResultsWe observed gender differences in microbiota composition and showed that bacteria are not uniformly distributed in the stratum corneum. Phylogenetic distance analysis was employed to follow microbiota development during recolonization of injured skin. Surprisingly, the developing neo-microbiome at day 14 was more similar to that of the deeper stratum corneum layers than to the initial surface microbiome. In addition, we also observed variation in the host response towards superficial injury as assessed by the induction of antimicrobial protein expression in epidermal keratinocytes.ConclusionsWe suggest that the microbiome of the deeper layers, rather than that of the superficial skin layer, may be regarded as the host indigenous microbiome. Characterization of the skin microbiome under dynamic conditions, and the ensuing response of the microbial community and host tissue, will shed further light on the complex interaction between resident bacteria and epidermis.

Mechanisms and evolution of control logic in prokaryotic transcriptional regulation.

SUMMARY A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.