Jos Boekhorst

Complete genome sequence of Lactobacillus plantarum WCFS1

The 3,308,274-bp sequence of the chromosome of Lactobacillus plantarum strain WCFS1, a single colony isolate of strain NCIMB8826 that was originally isolated from human saliva, has been determined, and contains 3,052 predicted protein-encoding genes. Putative biological functions could be assigned to 2,120 (70%) of the predicted proteins. Consistent with the classification of L. plantarum as a facultative heterofermentative lactic acid bacterium, the genome encodes all enzymes required for the glycolysis and phosphoketolase pathways, all of which appear to belong to the class of potentially highly expressed genes in this organism, as was evident from the codon-adaptation index of individual genes. Moreover, L. plantarum encodes a large pyruvate-dissipating potential, leading to various end-products of fermentation. L. plantarum is a species that is encountered in many different environmental niches, and this flexible and adaptive behavior is reflected by the relatively large number of regulatory and transport functions, including 25 complete PTS sugar transport systems. Moreover, the chromosome encodes >200 extracellular proteins, many of which are predicted to be bound to the cell envelope. A large proportion of the genes encoding sugar transport and utilization, as well as genes encoding extracellular functions, appear to be clustered in a 600-kb region near the origin of replication. Many of these genes display deviation of nucleotide composition, consistent with a foreign origin. These findings suggest that these genes, which provide an important part of the interaction of L. plantarum with its environment, form a lifestyle adaptation region in the chromosome.

Iron fortification adversely affects the gut microbiome, increases pathogen abundance and induces intestinal inflammation in Kenyan infants

Background In-home iron fortification for infants in developing countries is recommended for control of anaemia, but low absorption typically results in >80% of the iron passing into the colon. Iron is essential for growth and virulence of many pathogenic enterobacteria. We determined the effect of high and low dose in-home iron fortification on the infant gut microbiome and intestinal inflammation. Methods We performed two double-blind randomised controlled trials in 6-month-old Kenyan infants (n=115) consuming home-fortified maize porridge daily for 4 months. In the first, infants received a micronutrient powder (MNP) containing 2.5 mg iron as NaFeEDTA or the MNP without iron. In the second, they received a different MNP containing 12.5 mg iron as ferrous fumarate or the MNP without the iron. The primary outcome was gut microbiome composition analysed by 16S pyrosequencing and targeted real-time PCR (qPCR). Secondary outcomes included faecal calprotectin (marker of intestinal inflammation) and incidence of diarrhoea. We analysed the trials separately and combined. Results At baseline, 63% of the total microbial 16S rRNA could be assigned to Bifidobacteriaceae but there were high prevalences of pathogens, including Salmonella Clostridium difficile, Clostridium perfringens, and pathogenic Escherichia coli. Using pyrosequencing, +FeMNPs increased enterobacteria, particularly Escherichia/Shigella (p=0.048), the enterobacteria/bifidobacteria ratio (p=0.020), and Clostridium (p=0.030). Most of these effects were confirmed using qPCR; for example, +FeMNPs increased pathogenic E. coli strains (p=0.029). +FeMNPs also increased faecal calprotectin (p=0.002). During the trial, 27.3% of infants in +12.5 mgFeMNP required treatment for diarrhoea versus 8.3% in −12.5 mgFeMNP (p=0.092). There were no study-related serious adverse events in either group. Conclusions In this setting, provision of iron-containing MNPs to weaning infants adversely affects the gut microbiome, increasing pathogen abundance and causing intestinal inflammation. Trial registration number NCT01111864.

Pyrosequencing-based comparative genome analysis of the nosocomial pathogen Enterococcus faecium and identification of a large transferable pathogenicity island

BackgroundThe Gram-positive bacterium Enterococcus faecium is an important cause of nosocomial infections in immunocompromized patients.ResultsWe present a pyrosequencing-based comparative genome analysis of seven E. faecium strains that were isolated from various sources. In the genomes of clinical isolates several antibiotic resistance genes were identified, including the vanA transposon that confers resistance to vancomycin in two strains. A functional comparison between E. faecium and the related opportunistic pathogen E. faecalis based on differences in the presence of protein families, revealed divergence in plant carbohydrate metabolic pathways and oxidative stress defense mechanisms. The E. faecium pan-genome was estimated to be essentially unlimited in size, indicating that E. faecium can efficiently acquire and incorporate exogenous DNA in its gene pool. One of the most prominent sources of genomic diversity consists of bacteriophages that have integrated in the genome. The CRISPR-Cas system, which contributes to immunity against bacteriophage infection in prokaryotes, is not present in the sequenced strains. Three sequenced isolates carry the esp gene, which is involved in urinary tract infections and biofilm formation. The esp gene is located on a large pathogenicity island (PAI), which is between 64 and 104 kb in size. Conjugation experiments showed that the entire esp PAI can be transferred horizontally and inserts in a site-specific manner.ConclusionsGenes involved in environmental persistence, colonization and virulence can easily be aquired by E. faecium. This will make the development of successful treatment strategies targeted against this organism a challenge for years to come.

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.

Flavour formation from amino acids by lactic acid bacteria: predictions from genome sequence analysis

Flavour development in dairy fermentations is the result of a series of chemical and biochemical processes during ripening. Starter lactic acid bacteria provide the enzymes involved in the formation of specific flavours. Amino acids, and in particular methionine, the aromatic and the branched-chain amino acids, are major precursors for volatile aroma compounds. The recent sequencing of complete genomes of several lactic acid bacteria (i.e. Lactococcus lactis, Lactobacillus plantarum, Streptococcus thermophilus) is beginning to provide insight into the full complement of proteins that may be involved in flavour-forming reactions, and hence the potential for formation of specific flavour compounds. Examples are given how bioinformatics tools can be used to search in genomes for essential components, such as proteinases, peptidases, aminotransferases, enzymes for biosynthesis of amino acids, and transport systems for peptides and amino acids.

In-depth Qualitative and Quantitative Profiling of Tyrosine Phosphorylation Using a Combination of Phosphopeptide Immunoaffinity Purification and Stable Isotope Dimethyl Labeling

Several mass spectrometry-based assays have emerged for the quantitative profiling of cellular tyrosine phosphorylation. Ideally, these methods should reveal the exact sites of tyrosine phosphorylation, be quantitative, and not be cost-prohibitive. The latter is often an issue as typically several milligrams of (stable isotope-labeled) starting protein material are required to enable the detection of low abundance phosphotyrosine peptides. Here, we adopted and refined a peptidecentric immunoaffinity purification approach for the quantitative analysis of tyrosine phosphorylation by combining it with a cost-effective stable isotope dimethyl labeling method. We were able to identify by mass spectrometry, using just two LC-MS/MS runs, more than 1100 unique non-redundant phosphopeptides in HeLa cells from about 4 mg of starting material without requiring any further affinity enrichment as close to 80% of the identified peptides were tyrosine phosphorylated peptides. Stable isotope dimethyl labeling could be incorporated prior to the immunoaffinity purification, even for the large quantities (mg) of peptide material used, enabling the quantification of differences in tyrosine phosphorylation upon pervanadate treatment or epidermal growth factor stimulation. Analysis of the epidermal growth factor-stimulated HeLa cells, a frequently used model system for tyrosine phosphorylation, resulted in the quantification of 73 regulated unique phosphotyrosine peptides. The quantitative data were found to be exceptionally consistent with the literature, evidencing that such a targeted quantitative phosphoproteomics approach can provide reproducible results. In general, the combination of immunoaffinity purification of tyrosine phosphorylated peptides with large scale stable isotope dimethyl labeling provides a cost-effective approach that can alleviate variation in sample preparation and analysis as samples can be combined early on. Using this approach, a rather complete qualitative and quantitative picture of tyrosine phosphorylation signaling events can be generated.

Genome-Wide Detection and Analysis of Cell Wall-Bound Proteins with LPxTG-Like Sorting Motifs

Surface proteins of gram-positive bacteria often play a role in adherence of the bacteria to host tissue and are frequently required for virulence. A specific subgroup of extracellular proteins contains the cell wall-sorting motif LPxTG, which is the target for cleavage and covalent coupling to the peptidoglycan by enzymes called sortases. A comprehensive set of putative sortase substrates was identified by in silico analysis of 199 completely sequenced prokaryote genomes. A combination of detection methods was used, including secondary structure prediction, pattern recognition, sequence homology, and genome context information. With the hframe algorithm, putative substrates were identified that could not be detected by other methods due to errors in open reading frame calling, frameshifts, or sequencing errors. In total, 732 putative sortase substrates encoded in 49 prokaryote genomes were identified. We found striking species-specific variation for the LPxTG motif. A hidden Markov model (HMM) based on putative sortase substrates was created, which was subsequently used for the automatic detection of sortase substrates in recently completed genomes. A database was constructed, LPxTG-DB (http://bamics3.cmbi.kun.nl/sortase_substrates), containing for each genome a list of putative sortase substrates, sequence information of these substrates, the organism-specific HMMs based on the consensus sequence of the sortase recognition motif, and a graphic representation of this consensus.

LocateP: Genome-scale subcellular-location predictor for bacterial proteins

BackgroundIn the past decades, various protein subcellular-location (SCL) predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase) cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP.ResultsLocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms current tools especially where the N-terminally anchored and the SPIase-cleaved secreted proteins are concerned. Overall, the accuracy of LocateP was always higher than 90%. LocateP was then used to predict the SCLs of all proteins encoded by completed Gram-positive bacterial genomes. The results are stored in the database LocateP-DB http://www.cmbi.ru.nl/locatep-db[1].ConclusionLocateP is by far the most accurate and detailed protein SCL predictor for Gram-positive bacteria currently available.

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.

Lactobacillus plantarum gene clusters encoding putative cell-surface protein complexes for carbohydrate utilization are conserved in specific gram-positive bacteria.

BackgroundGenomes of gram-positive bacteria encode many putative cell-surface proteins, of which the majority has no known function. From the rapidly increasing number of available genome sequences it has become apparent that many cell-surface proteins are conserved, and frequently encoded in gene clusters or operons, suggesting common functions, and interactions of multiple components.ResultsA novel gene cluster encoding exclusively cell-surface proteins was identified, which is conserved in a subgroup of gram-positive bacteria. Each gene cluster generally has one copy of four new gene families called cscA, cscB, cscC and cscD. Clusters encoding these cell-surface proteins were found only in complete genomes of Lactobacillus plantarum, Lactobacillus sakei, Enterococcus faecalis, Listeria innocua, Listeria monocytogenes, Lactococcus lactis ssp lactis and Bacillus cereus and in incomplete genomes of L. lactis ssp cremoris, Lactobacillus casei, Enterococcus faecium, Pediococcus pentosaceus, Lactobacillius brevis, Oenococcus oeni, Leuconostoc mesenteroides, and Bacillus thuringiensis. These genes are neither present in the genomes of streptococci, staphylococci and clostridia, nor in the Lactobacillus acidophilus group, suggesting a niche-specific distribution, possibly relating to association with plants. All encoded proteins have a signal peptide for secretion by the Sec-dependent pathway, while some have cell-surface anchors, novel WxL domains, and putative domains for sugar binding and degradation. Transcriptome analysis in L. plantarum shows that the cscA-D genes are co-expressed, supporting their operon organization. Many gene clusters are significantly up-regulated in a glucose-grown, ccpA- mutant derivative of L. plantarum, suggesting catabolite control. This is supported by the presence of predicted CRE-sites upstream or inside the up-regulated cscA-D gene clusters.ConclusionWe propose that the CscA, CscB, CscC and CscD proteins form cell-surface protein complexes and play a role in carbon source acquisition. Primary occurrence in plant-associated gram-positive bacteria suggests a possible role in degradation and utilization of plant oligo- or poly-saccharides.