Natural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach

BACKGROUND

Plant-produced specialised metabolites are a powerful part of a plant's first line of defence against herbivorous insects, bacteria and fungi. Wild ancestors of present-day cultivated tomato produce a plethora of acylsugars in their type-I/IV trichomes and volatiles in their type-VI trichomes that have a potential role in plant resistance against insects. However, metabolic profiles are often complex mixtures making identification of the functionally interesting metabolites challenging. Here, we aimed to identify specialised metabolites from a wide range of wild tomato genotypes that could explain resistance to vector insects whitefly (Bemisia tabaci) and Western flower thrips (Frankliniella occidentalis). We evaluated plant resistance, determined trichome density and obtained metabolite profiles of the glandular trichomes by LC-MS (acylsugars) and GC-MS (volatiles). Using a customised Random Forest learning algorithm, we determined the contribution of specific specialised metabolites to the resistance phenotypes observed.

RESULTS

The selected wild tomato accessions showed different levels of resistance to both whiteflies and thrips. Accessions resistant to one insect can be susceptible to another. Glandular trichome density is not necessarily a good predictor for plant resistance although the density of type-I/IV trichomes, related to the production of acylsugars, appears to correlate with whitefly resistance. For type VI-trichomes, however, it seems resistance is determined by the specific content of the glands. There is a strong qualitative and quantitative variation in the metabolite profiles between different accessions, even when they are from the same species. Out of 76 acylsugars found, the random forest algorithm linked two acylsugars (S3:15 and S3:21) to whitefly resistance, but none to thrips resistance. Out of 86 volatiles detected, the sesquiterpene α-humulene was linked to whitefly susceptible accessions instead. The algorithm did not link any specific metabolite to resistance against thrips, but monoterpenes α-phellandrene, α-terpinene and β-phellandrene/D-limonene were significantly associated with susceptible tomato accessions.

CONCLUSIONS

Whiteflies and thrips are distinctly targeted by certain specialised metabolites found in wild tomatoes. The machine learning approach presented helped to identify features with efficacy toward the insect species studied. These acylsugar metabolites can be targets for breeding efforts towards the selection of insect-resistant cultivars.

The Contribution of Genetic Variation of Streptococcus pneumoniae to the Clinical Manifestation of Invasive Pneumococcal Disease

Background

Different clinical manifestations of invasive pneumococcal disease (IPD) have thus far mainly been explained by patient characteristics. Here we studied the contribution of pneumococcal genetic variation to IPD phenotype.

Methods

The index cohort consisted of 349 patients admitted to 2 Dutch hospitals between 2000-2011 with pneumococcal bacteremia. We performed genome-wide association studies to identify pneumococcal lineages, genes, and allelic variants associated with 23 clinical IPD phenotypes. The identified associations were validated in a nationwide (n = 482) and a post-pneumococcal vaccination cohort (n = 121). The contribution of confirmed pneumococcal genotypes to the clinical IPD phenotype, relative to known clinical predictors, was tested by regression analysis.

Results

Among IPD patients, the presence of pneumococcal gene slaA was a nationwide confirmed independent predictor of meningitis (odds ratio [OR], 10.5; P = .001), as was sequence cluster 9 (serotype 7F: OR, 3.68; P = .057). A set of 4 pneumococcal genes co-located on a prophage was a confirmed independent predictor of 30-day mortality (OR, 3.4; P = .003). We could detect the pneumococcal variants of concern in these patients' blood samples.

Conclusions

In this study, knowledge of pneumococcal genotypic variants improved the clinical risk assessment for detrimental manifestations of IPD. This provides us with novel opportunities to target, anticipate, or avert the pathogenic effects related to particular pneumococcal variants, and indicates that information on pneumococcal genotype is important for the diagnostic and treatment strategy in IPD. Ongoing surveillance is warranted to monitor the clinical value of information on pneumococcal variants in dynamic microbial and susceptible host populations.

Correction: Supplementation of diet with non-digestible oligosaccharides alters the intestinal microbiota, but not arthritis development, in IL-1 receptor antagonist deficient mice

[This corrects the article DOI: 10.1371/journal.pone.0219366.].

Supplementation of diet with non-digestible oligosaccharides alters the intestinal microbiota, but not arthritis development, in IL-1 receptor antagonist deficient mice

The intestinal microbiome is perturbed in patients with new-onset and chronic autoimmune inflammatory arthritis. Recent studies in mouse models suggest that development and progression of autoimmune arthritis is highly affected by the intestinal microbiome. This makes modulation of the intestinal microbiota an interesting novel approach to suppress inflammatory arthritis. Prebiotics, defined as non-digestible carbohydrates that selectively stimulate the growth and activity of beneficial microorganisms, provide a relatively non-invasive approach to modulate the intestinal microbiota. The aim of this study was to assess the therapeutic potential of dietary supplementation with a prebiotic mixture of 90% short-chain galacto-oligosaccharides and 10% long-chain fructo-oligosaccharides (scGOS/lcFOS) in experimental arthritis in mice. We here show that dietary supplementation with scGOS/lcFOS has a pronounced effect on the composition of the fecal microbiota. Interestingly, the genera Enterococcus and Clostridium were markedly decreased by scGOS/lcFOS dietary supplementation. In contrast, the family Lachnospiraceae and the genus Lactobacillus, both associated with healthy microbiota, increased in mice receiving scGOS/lcFOS diet. However, the scGOS/lcFOS induced alterations of the intestinal microbiota did not induce significant effects on the intestinal and systemic T helper cell subsets and were not sufficient to reproducibly suppress arthritis in mice. As expected, we did observe a significant increase in the bone mineral density in mice upon dietary supplementation with scGOS/lcFOS for 8 weeks. Altogether, this study suggests that dietary scGOS/lcFOS supplementation is able to promote presumably healthy gut microbiota and improve bone mineral density, but not inflammation, in arthritis-prone mice.

Reconstruction and inference of the Lactococcus lactis MG1363 gene co-expression network

Lactic acid bacteria are Gram-positive bacteria used throughout the world in many industrial applications for their acidification, flavor and texture formation attributes. One of the species, Lactococcus lactis, is employed for the production of fermented milk products like cheese, buttermilk and quark. It ferments lactose to lactic acid and, thus, helps improve the shelf life of the products. Many physiological and transcriptome studies have been performed in L. lactis in order to comprehend and improve its biotechnological assets. Using large amounts of transcriptome data to understand and predict the behavior of biological processes in bacterial or other cell types is a complex task. Gene networks enable predicting gene behavior and function in the context of transcriptionally linked processes. We reconstruct and present the gene co-expression network (GCN) for the most widely studied L. lactis strain, MG1363, using publicly available transcriptome data. Several methods exist to generate and judge the quality of GCNs. Different reconstruction methods lead to networks with varying structural properties, consequently altering gene clusters. We compared the structural properties of the MG1363 GCNs generated by five methods, namely Pearson correlation, Spearman correlation, GeneNet, Weighted Gene Co-expression Network Analysis (WGCNA), and Sparse PArtial Correlation Estimation (SPACE). Using SPACE, we generated an L. lactis MG1363 GCN and assessed its quality using modularity and structural and biological criteria. The L. lactis MG1363 GCN has structural properties similar to those of the gold-standard networks of Escherichia coli K-12 and Bacillus subtilis 168. We showcase that the network can be used to mine for genes with similar expression profiles that are also generally linked to the same biological process.

TLR-Induced IL-12 and CCL2 Production by Myeloid Cells Is Dependent on Adenosine A3 Receptor-Mediated Signaling

TLR-induced signaling potently activates cells of the innate immune system and is subject to regulation at different levels. Inflammatory conditions are associated with increased levels of extracellular adenosine, which can modulate TLR-induced production of cytokines through adenosine receptor-mediated signaling. There are four adenosine receptor subtypes that induce different signaling cascades. In this study, we demonstrate a pivotal contribution of adenosine A₃ receptor (A₃R)-mediated signaling to the TLR4-induced expression of IL-12 in different types of human myeloid APC. In dendritic cells, IL-12 and CCL2 responses as evoked by TLR2, 3, 4, 5, and 8, as well as IL-12 responses evoked by whole pathogens, were all reduced when A₃R-mediated signaling was blocked. As a result, concomitant production of IFN-γ and IL-17 by T cells was significantly inhibited. We further show that selective inhibition of A₃R-mediated signaling reduced TLR-induced phosphorylation of the transcription factor STAT1 at tyrosine 701. Next-generation sequencing revealed that A₃R-mediated signaling controls the expression of metallothioneins, known inhibitors of STAT1 phosphorylation. Together our results reveal a novel regulatory layer of innate immune responses, with a central role for metallothioneins and autocrine/paracrine signaling via A₃Rs.

The bacterial and fungal microbiome of the skin of healthy dogs and dogs with atopic dermatitis and the impact of topical antimicrobial therapy, an exploratory study

Canine atopic dermatitis is a genetically predisposed inflammatory and pruritic allergic skin disease that is often complicated by (secondary) bacterial and fungal (yeast) infections. High-throughput DNA sequencing was used to characterize the composition of the microbiome (bacteria and fungi) inhabiting specific sites of skin in healthy dogs and dogs with atopic dermatitis (AD) before and after topical antimicrobial treatment. Skin microbiome samples were collected from six healthy control dogs and three dogs spontaneously affected by AD by swabbing at (non-) predilection sites before, during and after treatment. Bacteria and fungi were profiled by Illumina sequencing of the 16S ribosomal RNA gene of bacteria (16S) and the internally transcribed spacer of the ribosomal gene cassette in fungi (ITS). The total cohort of dogs showed a high diversity of microbes on skin with a strong individual variability of both 16S and ITS profiles. The genera of Staphylococcus and Porphyromonas were dominantly present both on atopic and healthy skin and across all skin sites studied. In addition, bacterial and fungal alpha diversity were similar at the different skin sites. The topical antimicrobial treatment increased the diversity of bacterial and fungal compositions in course of time on both AD and healthy skin.

Transcriptome Analysis of a Spray Drying-Resistant Subpopulation Reveals a Zinc-Dependent Mechanism for Robustness in L. lactis SK11

The viability of starter cultures is essential for an adequate contribution to the fermentation process and end-product. Therefore, robustness during processing and storage is an important characteristic of starter culture strains. For instance, during spray drying cells are exposed to heat and oxidative stress, generally resulting in loss of viability. In this study, we exposed the industrially relevant but stress-sensitive Lactococcus lactis strain SK11 to two cycles of heat stress, with intermediate recovery and cultivation at moderate temperatures. After these two cycles of heat exposure, the abundance of robust derivatives was increased as compared with the original culture, which enabled isolation of heat-resistant subpopulations displaying up to 1,000-fold enhanced heat stress survival. Moreover, this heat-resistant subpopulation demonstrated an increased survival during spray drying. Derivatives from two independent lineages displayed different transcriptome changes as compared with the wild type strain, indicating that the increased robustness within these lineages was established by different adaptive strategies. Nevertheless, an overlap in differential gene expression in all five derivatives tested in both lineages included three genes in an operon involved in zinc transport. The link between zinc homeostasis and heat stress survival in L. lactis was experimentally established by culturing of the wild type strain SK11 in medium with various levels of zinc ions, which resulted in alterations in heat stress survival phenotypes. This study demonstrates that robust derivatives of a relatively sensitive L. lactis strain can be isolated by repeated exposure to heat stress. Moreover, this work demonstrates that transcriptome analysis of these robust derivatives can provide clues for improvement of the robustness of the original strain. This could boost the industrial application of strains with specific desirable traits but inadequate robustness characteristics.

Haemophilus is overrepresented in the nasopharynx of infants hospitalized with RSV infection and associated with increased viral load and enhanced mucosal CXCL8 responses

BACKGROUND

While almost all infants are infected with respiratory syncytial virus (RSV) before the age of 2 years, only a small percentage develops severe disease. Previous studies suggest that the nasopharyngeal microbiome affects disease development. We therefore studied the effect of the nasopharyngeal microbiome on viral load and mucosal cytokine responses, two important factors influencing the pathophysiology of RSV disease. To determine the relation between (i) the microbiome of the upper respiratory tract, (ii) viral load, and (iii) host mucosal inflammation during an RSV infection, nasopharyngeal microbiota profiles of RSV infected infants (< 6 months) with different levels of disease severity and age-matched healthy controls were determined by 16S rRNA marker gene sequencing. The viral load was measured using qPCR. Nasopharyngeal CCL5, CXCL10, MMP9, IL6, and CXCL8 levels were determined with ELISA.

RESULTS

Viral load in nasopharyngeal aspirates of patients associates significantly to total nasopharyngeal microbiota composition. Healthy infants (n = 21) and RSV patients (n = 54) display very distinct microbial patterns, primarily characterized by a loss in commensals like Veillonella and overrepresentation of opportunistic organisms like Haemophilus and Achromobacter in RSV-infected individuals. Furthermore, nasopharyngeal microbiota profiles are significantly different based on CXCL8 levels. CXCL8 is a chemokine that was previously found to be indicative for disease severity and for which we find Haemophilus abundance as the strongest predictor for CXCL8 levels.

CONCLUSIONS

The nasopharyngeal microbiota in young infants with RSV infection is marked by an overrepresentation of the genus Haemophilus. We present that this bacterium is associated with viral load and mucosal CXCL8 responses, both which are involved in RSV disease pathogenesis.

Advances and perspectives in computational prediction of microbial gene essentiality

The minimal subset of genes required for cellular growth, survival and viability of an organism are classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. This might lead to more efficient antimicrobial drug discovery, to elucidation of the correlations between genotype and phenotype, and a better understanding of the minimal requirements for a (synthetic) cell. Traditionally, constructing a catalog of essential genes for a given microbe involved costly and time-consuming laboratory experiments. While experimental methods have produced abundant gene essentiality data for model organisms like Escherichia coli and Bacillus subtilis, the knowledge generated cannot automatically be extrapolated to predict essential genes in all bacteria. In addition, essential genes identified in the laboratory are by definition 'conditionally essential', as they are essential under the specified experimental conditions: these might not resemble conditions in the microorganisms' natural habitat(s). Also, large-scale experimental assaying for essential genes is not always feasible because of the time investment required to setup these assays. The ability to rapidly and precisely identify essential genes in silico is therefore important and has great potential for applications in medicine, biotechnology and basic biological research. Here, we review the advances made in the use of computational methods to predict microbial gene essentiality, perspectives for the future of these techniques and the possible practical applications of essential genes.

Whole-Genome Sequencing of Bacterial Pathogens: the Future of Nosocomial Outbreak Analysis

Outbreaks of multidrug-resistant bacteria present a frequent threat to vulnerable patient populations in hospitals around the world. Intensive care unit (ICU) patients are particularly susceptible to nosocomial infections due to indwelling devices such as intravascular catheters, drains, and intratracheal tubes for mechanical ventilation. The increased vulnerability of infected ICU patients demonstrates the importance of effective outbreak management protocols to be in place. Understanding the transmission of pathogens via genotyping methods is an important tool for outbreak management. Recently, whole-genome sequencing (WGS) of pathogens has become more accessible and affordable as a tool for genotyping. Analysis of the entire pathogen genome via WGS could provide unprecedented resolution in discriminating even highly related lineages of bacteria and revolutionize outbreak analysis in hospitals. Nevertheless, clinicians have long been hesitant to implement WGS in outbreak analyses due to the expensive and cumbersome nature of early sequencing platforms. Recent improvements in sequencing technologies and analysis tools have rapidly increased the output and analysis speed as well as reduced the overall costs of WGS. In this review, we assess the feasibility of WGS technologies and bioinformatics analysis tools for nosocomial outbreak analyses and provide a comparison to conventional outbreak analysis workflows. Moreover, we review advantages and limitations of sequencing technologies and analysis tools and present a real-world example of the implementation of WGS for antimicrobial resistance analysis. We aimed to provide health care professionals with a guide to WGS outbreak analysis that highlights its benefits for hospitals and assists in the transition from conventional to WGS-based outbreak analysis.

Gut microbiome in ADHD and its relation to neural reward anticipation

BACKGROUND

Microorganisms in the human intestine (i.e. the gut microbiome) have an increasingly recognized impact on human health, including brain functioning. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with abnormalities in dopamine neurotransmission and deficits in reward processing and its underlying neuro-circuitry including the ventral striatum. The microbiome might contribute to ADHD etiology via the gut-brain axis. In this pilot study, we investigated potential differences in the microbiome between ADHD cases and undiagnosed controls, as well as its relation to neural reward processing.

METHODS

We used 16S rRNA marker gene sequencing (16S) to identify bacterial taxa and their predicted gene functions in 19 ADHD and 77 control participants. Using functional magnetic resonance imaging (fMRI), we interrogated the effect of observed microbiome differences in neural reward responses in a subset of 28 participants, independent of diagnosis.

RESULTS

For the first time, we describe gut microbial makeup of adolescents and adults diagnosed with ADHD. We found that the relative abundance of several bacterial taxa differed between cases and controls, albeit marginally significant. A nominal increase in the Bifidobacterium genus was observed in ADHD cases. In a hypothesis-driven approach, we found that the observed increase was linked to significantly enhanced 16S-based predicted bacterial gene functionality encoding cyclohexadienyl dehydratase in cases relative to controls. This enzyme is involved in the synthesis of phenylalanine, a precursor of dopamine. Increased relative abundance of this functionality was significantly associated with decreased ventral striatal fMRI responses during reward anticipation, independent of ADHD diagnosis and age.

CONCLUSIONS

Our results show increases in gut microbiome predicted function of dopamine precursor synthesis between ADHD cases and controls. This increase in microbiome function relates to decreased neural responses to reward anticipation. Decreased neural reward anticipation constitutes one of the hallmarks of ADHD.

Transmissible Mycobacterium tuberculosis Strains Share Genetic Markers and Immune Phenotypes

RATIONALE

Successful transmission of tuberculosis depends on the interplay of human behavior, host immune responses, and Mycobacterium tuberculosis virulence factors. Previous studies have been focused on identifying host risk factors associated with increased transmission, but the contribution of specific genetic variations in mycobacterial strains themselves are still unknown.

OBJECTIVES

To identify mycobacterial genetic markers associated with increased transmissibility and to examine whether these markers lead to altered in vitro immune responses.

METHODS

Using a comprehensive tuberculosis registry (n = 10,389) and strain collection in the Netherlands, we identified a set of 100 M. tuberculosis strains either least or most likely to be transmitted after controlling for host factors. We subjected these strains to whole-genome sequencing and evolutionary convergence analysis, and we repeated this analysis in an independent validation cohort. We then performed immunological experiments to measure in vitro cytokine production and neutrophil responses to a subset of the original strains with or without the identified mutations associated with increased transmissibility.

MEASUREMENTS AND MAIN RESULTS

We identified the loci espE, PE-PGRS56, Rv0197, Rv2813-2814c, and Rv2815-2816c as targets of convergent evolution among transmissible strains. We validated four of these regions in an independent set of strains, and we demonstrated that mutations in these targets affected in vitro monocyte and T-cell cytokine production, neutrophil reactive oxygen species release, and apoptosis.

CONCLUSIONS

In this study, we identified genetic markers in convergent evolution of M. tuberculosis toward enhanced transmissibility in vivo that are associated with altered immune responses in vitro.

Aberrant intestinal microbiota due to IL-1 receptor antagonist deficiency promotes IL-17- and TLR4-dependent arthritis

BACKGROUND

Perturbation of commensal intestinal microbiota has been associated with several autoimmune diseases. Mice deficient in interleukin-1 receptor antagonist (Il1rn ^-/- mice) spontaneously develop autoimmune arthritis and are susceptible to other autoimmune diseases such as psoriasis, diabetes, and encephalomyelitis; however, the mechanisms of increased susceptibility to these autoimmune phenotypes are poorly understood. We investigated the role of interleukin-1 receptor antagonist (IL-1Ra) in regulation of commensal intestinal microbiota, and assessed the involvement of microbiota subsets and innate and adaptive mucosal immune responses that underlie the development of spontaneous arthritis in Il1rn ^-/- mice.

RESULTS

Using high-throughput 16S rRNA gene sequencing, we show that IL-1Ra critically maintains the diversity and regulates the composition of intestinal microbiota in mice. IL-1Ra deficiency reduced the intestinal microbial diversity and richness, and caused specific taxonomic alterations characterized by overrepresented Helicobacter and underrepresented Ruminococcus and Prevotella. Notably, the aberrant intestinal microbiota in IL1rn ^-/- mice specifically potentiated IL-17 production by intestinal lamina propria (LP) lymphocytes and skewed the LP T cell balance in favor of T helper 17 (Th17) cells, an effect transferable to WT mice by fecal microbiota. Importantly, LP Th17 cell expansion and the development of spontaneous autoimmune arthritis in IL1rn ^-/- mice were attenuated under germ-free condition. Selective antibiotic treatment revealed that tobramycin-induced alterations of commensal intestinal microbiota, i.e., reduced Helicobacter, Flexispira, Clostridium, and Dehalobacterium, suppressed arthritis in IL1rn ^-/- mice. The arthritis phenotype in IL1rn ^-/- mice was previously shown to depend on Toll-like receptor 4 (TLR4). Using the ablation of both IL-1Ra and TLR4, we here show that the aberrations in the IL1rn ^-/- microbiota are partly TLR4-dependent. We further identify a role for TLR4 activation in the intestinal lamina propria production of IL-17 and cytokines involved in Th17 differentiation preceding the onset of arthritis.

CONCLUSIONS

These findings identify a critical role for IL1Ra in maintaining the natural diversity and composition of intestinal microbiota, and suggest a role for TLR4 in mucosal Th17 cell induction associated with the development of autoimmune disease in mice.

Strain-Dependent Transcriptome Signatures for Robustness in Lactococcus lactis

Recently, we demonstrated that fermentation conditions have a strong impact on subsequent survival of Lactococcus lactis strain MG1363 during heat and oxidative stress, two important parameters during spray drying. Moreover, employment of a transcriptome-phenotype matching approach revealed groups of genes associated with robustness towards heat and/or oxidative stress. To investigate if other strains have similar or distinct transcriptome signatures for robustness, we applied an identical transcriptome-robustness phenotype matching approach on the L. lactis strains IL1403, KF147 and SK11, which have previously been demonstrated to display highly diverse robustness phenotypes. These strains were subjected to an identical fermentation regime as was performed earlier for strain MG1363 and consisted of twelve conditions, varying in the level of salt and/or oxygen, as well as fermentation temperature and pH. In the exponential phase of growth, cells were harvested for transcriptome analysis and assessment of heat and oxidative stress survival phenotypes. The variation in fermentation conditions resulted in differences in heat and oxidative stress survival of up to five 10-log units. Effects of the fermentation conditions on stress survival of the L. lactis strains were typically strain-dependent, although the fermentation conditions had mainly similar effects on the growth characteristics of the different strains. By association of the transcriptomes and robustness phenotypes highly strain-specific transcriptome signatures for robustness towards heat and oxidative stress were identified, indicating that multiple mechanisms exist to increase robustness and, as a consequence, robustness of each strain requires individual optimization. However, a relatively small overlap in the transcriptome responses of the strains was also identified and this generic transcriptome signature included genes previously associated with stress (ctsR and lplL) and novel genes, including nanE and genes encoding transport proteins. The transcript levels of these genes can function as indicators of robustness and could aid in selection of fermentation parameters, potentially resulting in more optimal robustness during spray drying.

An In vitro Model for Bacterial Growth on Human Stratum Corneum

The diversity and dynamics of the skin microbiome in health and disease have been studied recently, but adequate model systems to study skin microbiotas in vitro are largely lacking. We developed an in vitro system that mimics human stratum corneum, using human callus as substrate and nutrient source for bacterial growth. The growth of several commensal and pathogenic bacterial strains was measured for up to one week by counting colony-forming units or by quantitative PCR with strain-specific primers. Human skin pathogens were found to survive amidst a minimal microbiome consisting of 2 major skin commensals: Staphylococcus epidermidis and Propionibacterium acnes. In addition, complete microbiomes, taken from the backs of healthy volunteers, were inoculated and maintained using this system. This model may enable the modulation of skin microbiomes in vitro and allow testing of pathogens, biological agents and antibiotics in a medium-throughput format.

Complete genome sequence of thermophilic Bacillus smithii type strain DSM 4216(T)

Bacillus smithii is a facultatively anaerobic, thermophilic bacterium able to use a variety of sugars that can be derived from lignocellulosic feedstocks. Being genetically accessible, it is a potential new host for biotechnological production of green chemicals from renewable resources. We determined the complete genomic sequence of the B. smithii type strain DSM 4216^T, which consists of a 3,368,778 bp chromosome (GenBank accession number CP012024.1) and a 12,514 bp plasmid (GenBank accession number CP012025.1), together encoding 3880 genes. Genome annotation via RAST was complemented by a protein domain analysis. Some unique features of B. smithii central metabolism in comparison to related organisms included the lack of a standard acetate production pathway with no apparent pyruvate formate lyase, phosphotransacetylase, and acetate kinase genes, while acetate was the second fermentation product.

Akkermansia muciniphila and Helicobacter typhlonius modulate intestinal tumor development in mice

Gastrointestinal tumor growth is thought to be promoted by gastrointestinal bacteria and their inflammatory products. We observed that intestine-specific conditional Apc mutant mice (FabplCre;Apc (15lox/+)) developed many more colorectal tumors under conventional than under pathogen-low housing conditions. Shotgun metagenomic sequencing plus quantitative PCR analysis of feces DNA revealed the presence of two bacterial species in conventional mice, absent from pathogen-low mice. One, Helicobacter typhlonius, has not been associated with cancer in man, nor in immune-competent mice. The other species, mucin-degrading Akkermansia muciniphila, is abundantly present in healthy humans, but reduced in patients with inflammatory gastrointestinal diseases and in obese and type 2 diabetic mice. Eradication of H.typhlonius in young conventional mice by antibiotics decreased the number of intestinal tumors. Additional presence of A.muciniphila prior to the antibiotic treatment reduced the tumor number even further. Colonization of pathogen-low FabplCre;Apc (15lox/+) mice with H.typhlonius or A.muciniphila increased the number of intestinal tumors, the thickness of the intestinal mucus layer and A.muciniphila colonization without H.typhlonius increased the density of mucin-producing goblet cells. However, dual colonization with H.typhlonius and A.muciniphila significantly reduced the number of intestinal tumors, the mucus layer thickness and goblet cell density to that of control mice. By global microbiota composition analysis, we found a positive association of A.muciniphila, and of H.typhlonius, and a negative association of unclassified Clostridiales with increased tumor burden. We conclude that A.muciniphila and H.typhlonius can modulate gut microbiota composition and intestinal tumor development in mice.

Microbial bioinformatics for food safety and production

In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput 'omics' technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety.

Genome-Wide Transcriptional Profiling of Clostridium perfringens SM101 during Sporulation Extends the Core of Putative Sporulation Genes and Genes Determining Spore Properties and Germination Characteristics

The formation of bacterial spores is a highly regulated process and the ultimate properties of the spores are determined during sporulation and subsequent maturation. A wide variety of genes that are expressed during sporulation determine spore properties such as resistance to heat and other adverse environmental conditions, dormancy and germination responses. In this study we characterized the sporulation phases of C. perfringens enterotoxic strain SM101 based on morphological characteristics, biomass accumulation (OD600), the total viable counts of cells plus spores, the viable count of heat resistant spores alone, the pH of the supernatant, enterotoxin production and dipicolinic acid accumulation. Subsequently, whole-genome expression profiling during key phases of the sporulation process was performed using DNA microarrays, and genes were clustered based on their time-course expression profiles during sporulation. The majority of previously characterized C. perfringens germination genes showed upregulated expression profiles in time during sporulation and belonged to two main clusters of genes. These clusters with up-regulated genes contained a large number of C. perfringens genes which are homologs of Bacillus genes with roles in sporulation and germination; this study therefore suggests that those homologs are functional in C. perfringens. A comprehensive homology search revealed that approximately half of the upregulated genes in the two clusters are conserved within a broad range of sporeforming Firmicutes. Another 30% of upregulated genes in the two clusters were found only in Clostridium species, while the remaining 20% appeared to be specific for C. perfringens. These newly identified genes may add to the repertoire of genes with roles in sporulation and determining spore properties including germination behavior. Their exact roles remain to be elucidated in future studies.

Diversity of acid stress resistant variants of Listeria monocytogenes and the potential role of ribosomal protein S21 encoded by rpsU

The dynamic response of microorganisms to environmental conditions depends on the behavior of individual cells within the population. Adverse environments can select for stable stress resistant subpopulations. In this study, we aimed to get more insight in the diversity within Listeria monocytogenes LO28 populations, and the genetic basis for the increased resistance of stable resistant fractions isolated after acid exposure. Phenotypic cluster analysis of 23 variants resulted in three clusters and four individual variants and revealed multiple-stress resistance, with both unique and overlapping features related to stress resistance, growth, motility, biofilm formation, and virulence indicators. A higher glutamate decarboxylase activity correlated with increased acid resistance. Whole genome sequencing revealed mutations in rpsU, encoding ribosomal protein S21 in the largest phenotypic cluster, while mutations in ctsR, which were previously shown to be responsible for increased resistance of heat and high hydrostatic pressure resistant variants, were not found in the acid resistant variants. This underlined that large population diversity exists within one L. monocytogenes strain and that different adverse conditions drive selection for different variants. The finding that acid stress selects for rpsU variants provides potential insights in the mechanisms underlying population diversity of L. monocytogenes.

CiVi: circular genome visualization with unique features to analyze sequence elements

We have developed CiVi, a user-friendly web-based tool to create custom circular maps to aid the analysis of microbial genomes and sequence elements. Sequence related data such as gene-name, COG class, PFAM domain, GC%, and subcellular location can be comprehensively viewed. Quantitative gene-related data (e.g. expression ratios or read counts) as well as predicted sequence elements (e.g. regulatory sequences) can be uploaded and visualized. CiVi accommodates the analysis of genomic elements by allowing a visual interpretation in the context of: (i) their genome-wide distribution, (ii) provided experimental data and (iii) the local orientation and location with respect to neighboring genes. CiVi thus enables both experts and non-experts to conveniently integrate public genome data with the results of genome analyses in circular genome maps suitable for publication.

CONTACT

L.Overmars@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AVAILABILITY AND IMPLEMENTATION

CiVi is freely available at http://civi.cmbi.ru.nl.

Transcriptomic signatures of peroxisome proliferator-activated receptor α (PPARα) in different mouse liver models identify novel aspects of its biology

Background

The peroxisome proliferator-activated receptor alpha (PPARα) is a ligand-activated transcription factor that regulates lipid catabolism and inflammation and is hepatocarcinogenic in rodents. It is presumed that the functions of PPARα in liver depend on cross-talk between parenchymal (hepatocytes) and non-parenchymal (Kupffer and endothelial cells) fractions as well as inter-organ interactions. In order to determine how cellular composition and inter-organ interactions influence gene expression upon pharmacological activation of PPARα, we performed a meta-analysis of transcriptomics data obtained from mouse hepatocytes (containing only the parenchymal fraction), mouse liver slices (containing both fractions), and mouse livers exposed to a PPARα agonist. The aim was to obtain a comprehensive view of common and model-specific PPARα-dependent genes and biological processes to understand the impact of cross-talk between parenchymal and non-parenchymal fractions as well as the effect of inter-organ interactions on the hepatic PPARα transcriptome.

To this end we analyzed microarray data of experiments performed in mouse primary hepatocytes treated with the PPARα agonist Wy14643 for 6 or 24 h (in vitro), mouse precision cut liver slices treated with Wy14643 for 24 h (ex vivo), and livers of wild type and Ppara knockout mice treated with Wy14643 for 6 h or 5 days (in vivo).

Results

In all models, activation of PPARα significantly altered processes related to various aspects of lipid metabolism. In ex vivo and in vivo models, PPARα activation significantly regulated processes involved in inflammation; these processes were unaffected in hepatocytes. Only in vivo models showed significant regulation of genes involved in coagulation, carcinogenesis, as well as vesicular trafficking and extracellular matrix.

Conclusions

PPARα-dependent regulation of genes/processes involved in lipid metabolism is mostly independent of the presence of non-parenchymal cells or systemic factors, as it was observed in all liver models. PPARα-dependent regulation of inflammatory genes requires the presence of non-parenchymal cells, as it was observed only ex vivo and in vivo. However, the full spectrum of PPARα biology at the level of lipid metabolism, immunity, carcinogenesis, as well as novel aspects of PPARα signaling such as coagulation, vesicular trafficking and the extracellular matrix, seems to require systemic factors, as it was observed exclusively in vivo.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1106) contains supplementary material, which is available to authorized users.

From microbial gene essentiality to novel antimicrobial drug targets

Background

Bacterial respiratory tract infections, mainly caused by Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are among the leading causes of global mortality and morbidity. Increased resistance of these pathogens to existing antibiotics necessitates the search for novel targets to develop potent antimicrobials.

Result

Here, we report a proof of concept study for the reliable identification of potential drug targets in these human respiratory pathogens by combining high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics. Approximately 20% of all genes in these three species were essential for growth and viability, including 128 essential and conserved genes, part of 47 metabolic pathways. By comparing these essential genes to the human genome, and a database of genes from commensal human gut microbiota, we identified and excluded potential drug targets in respiratory tract pathogens that will have off-target effects in the host, or disrupt the natural host microbiota. We propose 249 potential drug targets, 67 of which are targets for 75 FDA-approved antimicrobials and 35 other researched small molecule inhibitors. Two out of four selected novel targets were experimentally validated, proofing the concept.

Conclusion

Here we have pioneered an attempt in systematically combining the power of high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics to discover potential drug targets at genome-scale. By circumventing the time-consuming and expensive laboratory screens traditionally used to select potential drug targets, our approach provides an attractive alternative that could accelerate the much needed discovery of novel antimicrobials.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-958) contains supplementary material, which is available to authorized users.

Fermentation-induced variation in heat and oxidative stress phenotypes of Lactococcus lactis MG1363 reveals transcriptome signatures for robustness

BACKGROUND

Lactococcus lactis is industrially employed to manufacture various fermented dairy products. The most cost-effective method for the preservation of L. lactis starter cultures is spray drying, but during this process cultures encounter heat and oxidative stress, typically resulting in low survival rates. However, viability of starter cultures is essential for their adequate contribution to milk fermentation, supporting the ambition to better understand and improve their robustness phenotypes.

RESULTS

This study describes a transcriptome-phenotype matching approach in which the starter L. lactis MG1363 was fermented under a variety of conditions that differed in the levels of oxygen and/or salt, as well as the fermentation pH and temperature. Samples derived from these fermentations in the exponential phase of bacterial growth were analyzed by full-genome transcriptomics and the assessment of heat and oxidative stress phenotypes. Variations in the fermentation conditions resulted in up to 1000-fold differences in survival during heat and oxidative stress. More specifically, aeration during fermentation induced protection against heat stress, whereas a relatively high fermentation temperature resulted in enhanced robustness towards oxidative stress. Concomitantly, oxygen levels and fermentation temperature induced differential expression of markedly more genes when compared with the other fermentation parameters. Correlation analysis of robustness phenotypes and gene expression levels revealed transcriptome signatures for oxidative and/or heat stress survival, including the metC-cysK operon involved in methionine and cysteine metabolism. To validate this transcriptome-phenotype association we grew L. lactis MG1363 in the absence of cysteine which led to enhanced robustness towards oxidative stress.

CONCLUSIONS

Overall, we demonstrated the importance of careful selection of fermentation parameters prior to industrial processing of starter cultures. Furthermore, established stress genes as well as novel genes were associated with robustness towards heat and/or oxidative stress. Assessment of the expression levels of this group of genes could function as an indicator for enhanced selection of fermentation parameters resulting in improved robustness during spray drying. The increased robustness after growth without cysteine appeared to confirm the role of expression of the metC-cysK operon as an indicator of robustness and suggests that sulfur amino acid metabolism plays a pivotal role in oxidative stress survival.

Degenerate target sites mediate rapid primed CRISPR adaptation

Prokaryotes encode adaptive immune systems, called CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated), to provide resistance against mobile invaders, such as viruses and plasmids. Host immunity is based on incorporation of invader DNA sequences in a memory locus (CRISPR), the formation of guide RNAs from this locus, and the degradation of cognate invader DNA (protospacer). Invaders can escape type I-E CRISPR-Cas immunity in Escherichia coli K12 by making point mutations in the seed region of the protospacer or its adjacent motif (PAM), but hosts quickly restore immunity by integrating new spacers in a positive-feedback process termed "priming." Here, by using a randomized protospacer and PAM library and high-throughput plasmid loss assays, we provide a systematic analysis of the constraints of both direct interference and subsequent priming in E. coli. We have defined a high-resolution genetic map of direct interference by Cascade and Cas3, which includes five positions of the protospacer at 6-nt intervals that readily tolerate mutations. Importantly, we show that priming is an extremely robust process capable of using degenerate target regions, with up to 13 mutations throughout the PAM and protospacer region. Priming is influenced by the number of mismatches, their position, and is nucleotide dependent. Our findings imply that even outdated spacers containing many mismatches can induce a rapid primed CRISPR response against diversified or related invaders, giving microbes an advantage in the coevolutionary arms race with their invaders.

Effect of growth rate and selection pressure on rates of transfer of an antibiotic resistance plasmid between E. coli strains

Antibiotic resistance increases costs for health care and causes therapy failure. An important mechanism for spreading resistance is transfer of plasmids containing resistance genes and subsequent selection. Yet the factors that influence the rate of transfer are poorly known. Rates of plasmid transfer were measured in co-cultures in chemostats of a donor and a acceptor strain under various selective pressures. To document whether specific mutations in either plasmid or acceptor genome are associated with the plasmid transfer, whole genome sequencing was performed. The DM0133 TetR tetracycline resistance plasmid was transferred between Escherichia coli K-12 strains during co-culture at frequencies that seemed higher at increased growth rate. Modeling of the take-over of the culture by the transformed strain suggests that in reality more transfer events occurred at low growth rates. At moderate selection pressure due to an antibiotic concentration that still allowed growth, a maximum transfer frequency was determined of once per 10(11) cell divisions. In the absence of tetracycline or in the presence of high concentrations the frequency of transfer was sometimes zero, but otherwise reduced by at least a factor of 5. Whole genome sequencing showed that the plasmid was transferred without mutations, but two functional mutations in the genome of the recipient strain accompanied this transfer. Exposure to concentrations of antibiotics that fall within the mutant selection window stimulated transfer of the resistance plasmid most.

Bacillus pumilus reveals a remarkably high resistance to hydrogen peroxide provoked oxidative stress

Bacillus pumilus is characterized by a higher oxidative stress resistance than other comparable industrially relevant Bacilli such as B. subtilis or B. licheniformis. In this study the response of B. pumilus to oxidative stress was investigated during a treatment with high concentrations of hydrogen peroxide at the proteome, transcriptome and metabolome level. Genes/proteins belonging to regulons, which are known to have important functions in the oxidative stress response of other organisms, were found to be upregulated, such as the Fur, Spx, SOS or CtsR regulon. Strikingly, parts of the fundamental PerR regulon responding to peroxide stress in B. subtilis are not encoded in the B. pumilus genome. Thus, B. pumilus misses the catalase KatA, the DNA-protection protein MrgA or the alkyl hydroperoxide reductase AhpCF. Data of this study suggests that the catalase KatX2 takes over the function of the missing KatA in the oxidative stress response of B. pumilus. The genome-wide expression analysis revealed an induction of bacillithiol (Cys-GlcN-malate, BSH) relevant genes. An analysis of the intracellular metabolites detected high intracellular levels of this protective metabolite, which indicates the importance of bacillithiol in the peroxide stress resistance of B. pumilus.

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.

Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction

Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35–52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.

Explaining microbial phenotypes on a genomic scale: GWAS for microbes

There is an increasing availability of complete or draft genome sequences for microbial organisms. These data form a potentially valuable resource for genotype-phenotype association and gene function prediction, provided that phenotypes are consistently annotated for all the sequenced strains. In this review, we address the requirements for successful gene-trait matching. We outline a basic protocol for microbial functional genomics, including genome assembly, annotation of genotypes (including single nucleotide polymorphisms, orthologous groups and prophages), data pre-processing, genotype-phenotype association, visualization and interpretation of results. The methodologies for association described herein can be applied to other data types, opening up possibilities to analyze transcriptome-phenotype associations, and correlate microbial population structure or activity, as measured by metagenomics, to environmental parameters.

Genome sequence of Lactobacillus pentosus KCA1: vaginal isolate from a healthy premenopausal woman

The vaginal microbiota, in particular Lactobacillus species, play an important role in female health through modulation of immunity, countering pathogens and maintaining a pH below 4.7. We report the isolation and genome sequence of Lactobacillus pentosus strain KCA1 (formally known as L. plantarum) from the vagina of a healthy Nigerian woman. The genome was sequenced using Illumina GA II technology. The resulting 16,920,226 paired-end reads were assembled with the Velvet tool. Contigs were annotated using the RAST server, and manually curated. A comparative analysis with the available genomes of L. pentosus IG1 and L. plantarum WCFS1 showed that over 15% of the predicted functional activities are found only in this strain. The strain has a chromosome sequence of 3,418,159 bp with a G+C content of 46.4%, and is devoid of plasmids. Novel gene clusters or variants of known genes relative to the reference genomes were found. In particular, the strain has loci encoding additional putative mannose phosphotransferase systems. Clusters of genes include those for utilization of hydantoin, isopropylmalate, malonate, rhamnosides, and genes for assimilation of polyglycans, suggesting the metabolic versatility of L. pentosus KCA1. Loci encoding putative phage defense systems were also found including clustered regularly interspaced short palindromic repeats (CRISPRs), abortive infection (Abi) systems and toxin-antitoxin systems (TA). A putative cluster of genes for biosynthesis of a cyclic bacteriocin precursor, here designated as pentocin KCA1 (penA) were identified. These findings add crucial information for understanding the genomic and geographic diversity of vaginal lactobacilli.

Inferring patient to patient transmission of Mycobacterium tuberculosis from whole genome sequencing data

BACKGROUND

Mycobacterium tuberculosis is characterised by limited genomic diversity, which makes the application of whole genome sequencing particularly attractive for clinical and epidemiological investigation. However, in order to confidently infer transmission events, an accurate knowledge of the rate of change in the genome over relevant timescales is required.

METHODS

We attempted to estimate a molecular clock by sequencing 199 isolates from epidemiologically linked tuberculosis cases, collected in the Netherlands spanning almost 16 years.

RESULTS

Multiple analyses support an average mutation rate of ~0.3 SNPs per genome per year. However, all analyses revealed a very high degree of variation around this mean, making the confirmation of links proposed by epidemiology, and inference of novel links, difficult. Despite this, in some cases, the phylogenetic context of other strains provided evidence supporting the confident exclusion of previously inferred epidemiological links.

CONCLUSIONS

This in-depth analysis of the molecular clock revealed that it is slow and variable over short time scales, which limits its usefulness in transmission studies. However, the superior resolution of whole genome sequencing can provide the phylogenetic context to allow the confident exclusion of possible transmission events previously inferred via traditional DNA fingerprinting techniques and epidemiological cluster investigation. Despite the slow generation of variation even at the whole genome level we conclude that the investigation of tuberculosis transmission will benefit greatly from routine whole genome sequencing.

Screening metatranscriptomes for toxin genes as functional drivers of human colorectal cancer

The colonic mucosa is in constant physical interaction with a dense and complex bacterial community that comprises health-promoting and pathogenic microbes. Here, we highlight important clinical studies and experimental models that have linked the intestinal microbiota to the development of colorectal cancer (CRC). Moreover, we use recently published metatranscriptome sequencing data to test whether potentially carcinogenic toxin genes exhibit higher expression levels in human CRC tissue compared to adjacent non-malignant mucosa. Our analyses show a large variation in expression of toxin(-related) genes from different species. Surprisingly, Enterobacterial toxins were among the highest expressed, while Enterobacteria were not among the most abundant species in these samples. Although we can differentiate on- and off-tumour sites based on toxin reads, the read depth profiles are quite similar and show only limited coverage of the toxin genes. Thus, extended metagenomic studies are needed to obtain a high-resolution picture of host-pathogen interactions during human CRC.

Differences in sequencing technologies improve the retrieval of anammox bacterial genome from metagenomes

Background

Sequencing technologies have different biases, in single-genome sequencing and metagenomic sequencing; these can significantly affect ORFs recovery and the population distribution of a metagenome. In this paper we investigate how well different technologies represent information related to a considered organism of interest in a metagenome, and whether it is beneficial to combine information obtained using different technologies. We analyze comparatively three metagenomic datasets acquired from a sample containing the anammox bacterium Candidatus ’Brocadia fulgida’ (B. fulgida). These datasets were obtained using Roche 454 FLX and Sanger sequencing with two different libraries (shotgun and fosmid).

Results

In each dataset, the abundance of the reads annotated to B. fulgida was much lower than the abundance expected from available cell count information. This was due to the overrepresentation of GC-richer organisms, as shown by GC-content distribution of the reads. Nevertheless, by considering the union of B. fulgida reads over the three datasets, the number of B. fulgida ORFs recovered for at least 80% of their length was twice the amount recovered by the best technology. Indeed, while taxonomic distributions of reads in the three datasets were similar, the respective sets of B. fulgida ORFs recovered for a large part of their length were highly different, and depth of coverage patterns of 454 and Sanger were dissimilar.

Conclusions

Precautions should be sought in order to prevent the overrepresentation of GC-rich microbes in the datasets. This overrepresentation and the consistency of the taxonomic distributions of reads obtained with different sequencing technologies suggests that, in general, abundance biases might be mainly due to other steps of the sequencing protocols. Results show that biases against organisms of interest could be compensated combining different sequencing technologies, due to the differences of their genome-level sequencing biases even if the species was present in not very different abundances in the metagenomes.

Genome-wide prediction and validation of sigma70 promoters in Lactobacillus plantarum WCFS1

BACKGROUND

In prokaryotes, sigma factors are essential for directing the transcription machinery towards promoters. Various sigma factors have been described that recognize, and bind to specific DNA sequence motifs in promoter sequences. The canonical sigma factor σ(70) is commonly involved in transcription of the cell's housekeeping genes, which is mediated by the conserved σ(70) promoter sequence motifs. In this study the σ(70)-promoter sequences in Lactobacillus plantarum WCFS1 were predicted using a genome-wide analysis. The accuracy of the transcriptionally-active part of this promoter prediction was subsequently evaluated by correlating locations of predicted promoters with transcription start sites inferred from the 5'-ends of transcripts detected by high-resolution tiling array transcriptome datasets.

RESULTS

To identify σ(70)-related promoter sequences, we performed a genome-wide sequence motif scan of the L. plantarum WCFS1 genome focussing on the regions upstream of protein-encoding genes. We obtained several highly conserved motifs including those resembling the conserved σ(70)-promoter consensus. Position weight matrices-based models of the recovered σ(70)-promoter sequence motif were employed to identify 3874 motifs with significant similarity (p-value<10(-4)) to the model-motif in the L. plantarum genome. Genome-wide transcript information deduced from whole genome tiling-array transcriptome datasets, was used to infer transcription start sites (TSSs) from the 5'-end of transcripts. By this procedure, 1167 putative TSSs were identified that were used to corroborate the transcriptionally active fraction of these predicted promoters. In total, 568 predicted promoters were found in proximity (≤ 40 nucleotides) of the putative TSSs, showing a highly significant co-occurrence of predicted promoter and TSS (p-value<10(-263)).

CONCLUSIONS

High-resolution tiling arrays provide a suitable source to infer TSSs at a genome-wide level, and allow experimental verification of in silico predicted promoter sequence motifs.

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.

PhenoLink--a web-tool for linking phenotype to ~omics data for bacteria: application to gene-trait matching for Lactobacillus plantarum strains

BACKGROUND

Linking phenotypes to high-throughput molecular biology information generated by ~omics technologies allows revealing cellular mechanisms underlying an organism's phenotype. ~Omics datasets are often very large and noisy with many features (e.g., genes, metabolite abundances). Thus, associating phenotypes to ~omics data requires an approach that is robust to noise and can handle large and diverse data sets.

RESULTS

We developed a web-tool PhenoLink (http://bamics2.cmbi.ru.nl/websoftware/phenolink/) that links phenotype to ~omics data sets using well-established as well new techniques. PhenoLink imputes missing values and preprocesses input data (i) to decrease inherent noise in the data and (ii) to counterbalance pitfalls of the Random Forest algorithm, on which feature (e.g., gene) selection is based. Preprocessed data is used in feature (e.g., gene) selection to identify relations to phenotypes. We applied PhenoLink to identify gene-phenotype relations based on the presence/absence of 2847 genes in 42 Lactobacillus plantarum strains and phenotypic measurements of these strains in several experimental conditions, including growth on sugars and nitrogen-dioxide production. Genes were ranked based on their importance (predictive value) to correctly predict the phenotype of a given strain. In addition to known gene to phenotype relations we also found novel relations.

CONCLUSIONS

PhenoLink is an easily accessible web-tool to facilitate identifying relations from large and often noisy phenotype and ~omics datasets. Visualization of links to phenotypes offered in PhenoLink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data. PhenoLink can be used to uncover phenotype links to a multitude of ~omics data, e.g., gene presence/absence (determined by e.g.: CGH or next-generation sequencing), gene expression (determined by e.g.: microarrays or RNA-seq), or metabolite abundance (determined by e.g.: GC-MS).

Defining the structure of the general stress regulon of Bacillus subtilis using targeted microarray analysis and random forest classification

The structure of the SigB-dependent general stress regulon of Bacillus subtilis has previously been characterized by proteomics approaches as well as DNA array-based expression studies. However, comparing the SigB targets published in three previous major transcriptional profiling studies it is obvious that although each of them identified well above 100 target genes, only 67 were identified in all three studies. These substantial differences can likely be attributed to the different strains, growth conditions, microarray platforms and experimental setups used in the studies. In order to gain a better understanding of the structure of this important regulon, a targeted DNA microarray analysis covering most of the known SigB-inducing conditions was performed, and the changes in expression kinetics of 252 potential members of the SigB regulon and appropriate control genes were recorded. Transcriptional data for the B. subtilis wild-type strain 168 and its isogenic sigB mutant BSM29 were analysed using random forest, a machine learning algorithm, by incorporating the knowledge from previous studies. This analysis revealed a strictly SigB-dependent expression pattern for 166 genes following ethanol, butanol, osmotic and oxidative stress, low-temperature growth and heat shock, as well as limitation of oxygen or glucose. Kinetic analysis of the data for the wild-type strain identified 30 additional members of the SigB regulon, which were also subject to control by additional transcriptional regulators, thus displaying atypical SigB-independent induction patterns in the mutant strain under some of the conditions tested. For 19 of these 30 SigB regulon members, published reports support control by secondary regulators along with SigB. Thus, this microarray-based study assigns a total of 196 genes to the SigB-dependent general stress regulon of B. subtilis.

FACIL: Fast and Accurate Genetic Code Inference and Logo

Motivation: The intensification of DNA sequencing will increasingly unveil uncharacterized species with potential alternative genetic codes. A total of 0.65% of the DNA sequences currently in Genbank encode their proteins with a variant genetic code, and these exceptions occur in many unrelated taxa.

Results: We introduce FACIL (Fast and Accurate genetic Code Inference and Logo), a fast and reliable tool to evaluate nucleic acid sequences for their genetic code that detects alternative codes even in species distantly related to known organisms. To illustrate this, we apply FACIL to a set of mitochondrial genomic contigs of Globobulimina pseudospinescens. This foraminifer does not have any sequenced close relative in the databases, yet we infer its alternative genetic code with high confidence values. Results are intuitively visualized in a Genetic Code Logo.

Availability and implementation: FACIL is available as a web-based service at http://www.cmbi.ru.nl/FACIL/ and as a stand-alone program.

Contact:dutilh@cmbi.ru.nl.

Supplementary information:Supplementary data are available at Bioinformatics online.

Genome-scale diversity and niche adaptation analysis of Lactococcus lactis by comparative genome hybridization using multi-strain arrays

Lactococcus lactis produces lactic acid and is widely used in the manufacturing of various fermented dairy products. However, the species is also frequently isolated from non-dairy niches, such as fermented plant material. Recently, these non-dairy strains have gained increasing interest, as they have been described to possess flavour-forming activities that are rarely found in dairy isolates and have diverse metabolic properties. We performed an extensive whole-genome diversity analysis on 39 L. lactis strains, isolated from dairy and plant sources. Comparative genome hybridization analysis with multi-strain microarrays was used to assess presence or absence of genes and gene clusters in these strains, relative to all L. lactis sequences in public databases, whereby chromosomal and plasmid-encoded genes were computationally analysed separately. Nearly 3900 chromosomal orthologous groups (chrOGs) were defined on basis of four sequenced chromosomes of L. lactis strains (IL1403, KF147, SK11, MG1363). Of these, 1268 chrOGs are present in at least 35 strains and represent the presently known core genome of L. lactis, and 72 chrOGs appear to be unique for L. lactis. Nearly 600 and 400 chrOGs were found to be specific for either the subspecies lactis or subspecies cremoris respectively. Strain variability was found in presence or absence of gene clusters related to growth on plant substrates, such as genes involved in the consumption of arabinose, xylan, α-galactosides and galacturonate. Further niche-specific differences were found in gene clusters for exopolysaccharides biosynthesis, stress response (iron transport, osmotolerance) and bacterial defence mechanisms (nisin biosynthesis). Strain variability of functions encoded on known plasmids included proteolysis, lactose fermentation, citrate uptake, metal ion resistance and exopolysaccharides biosynthesis. The present study supports the view of L. lactis as a species with a very flexible genome.

Molecular description and industrial potential of Tn6098 conjugative transfer conferring alpha-galactoside metabolism in Lactococcus lactis

A novel 51-kb conjugative transposon of Lactococcus lactis, designated Tn6098, encoding the capacity to utilize α-galactosides such as raffinose and stachyose, was identified and characterized. Alpha-galactosides are a dominant carbon source in many plant-derived foods. Most dairy lactococcus strains are unable to use α-galactosides as a growth substrate, yet many of these strains are known to have beneficial industrial traits. Conjugal transfer of Tn6098 was demonstrated from the plant-derived donor strain L. lactis KF147 to the recipient L. lactis NZ4501, a derivative of the dairy model strain L. lactis MG1363. The integration of Tn6098 into the genome of the recipient strain was confirmed by Illumina sequencing of the transconjugant L. lactis NIZO3921. The molecular structure of the integration site was confirmed by a PCR product spanning the insertion site. A 15-bp direct repeat sequence (TTATACCATAATTAC) is present on either side of Tn6098 in the chromosome of L. lactis KF147. One copy of this sequence is also present in the L. lactis MG1363 chromosome and represents the sole integration site. Phenotypic characterization of all strains showed that the transconjugant has not only acquired the ability to grow well in soy milk, a substrate rich in α-galactosides, but also has retained the flavor-forming capabilities of the recipient strain L. lactis MG1363. This study demonstrates how (induced) conjugation can be used to exploit the beneficial industrial traits of industrial dairy lactic acid bacteria in fermentation of plant-derived substrates.

SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks

Summary: SpotXplore is a plugin for Cytoscape for extraction and visualization of differentially expressed subnetworks (hotspots) from gene networks. The hotspot-based visualization approach enables interactive exploration of regulatory interactions in differentially expressed gene sets, and it allows a researcher to explore gene expression in direct relation to the affected cellular gene network. The hotspots provide a view beyond the commonly used metabolic pathways and gene ontologies.

Availability:http://www.win.tue.nl/∼mwestenb/spotxplore/

Contact:m.a.westenberg@tue.nl

Supplementary information: Example data and user manual are available at SpotXplore's web site.

A predicted physicochemically distinct sub-proteome associated with the intracellular organelle of the anammox bacterium Kuenenia stuttgartiensis

BACKGROUND

Anaerobic ammonium-oxidizing (anammox) bacteria perform a key step in global nitrogen cycling. These bacteria make use of an organelle to oxidize ammonia anaerobically to nitrogen (N2) and so contribute approximately 50% of the nitrogen in the atmosphere. It is currently unknown which proteins constitute the organellar proteome and how anammox bacteria are able to specifically target organellar and cell-envelope proteins to their correct final destinations. Experimental approaches are complicated by the absence of pure cultures and genetic accessibility. However, the genome of the anammox bacterium Candidatus "Kuenenia stuttgartiensis" has recently been sequenced. Here, we make use of these genome data to predict the organellar sub-proteome and address the molecular basis of protein sorting in anammox bacteria.

RESULTS

Two training sets representing organellar (30 proteins) and cell envelope (59 proteins) proteins were constructed based on previous experimental evidence and comparative genomics. Random forest (RF) classifiers trained on these two sets could differentiate between organellar and cell envelope proteins with ~89% accuracy using 400 features consisting of frequencies of two adjacent amino acid combinations. A physicochemically distinct organellar sub-proteome containing 562 proteins was predicted with the best RF classifier. This set included almost all catabolic and respiratory factors encoded in the genome. Apparently, the cytoplasmic membrane performs no catabolic functions. We predict that the Tat-translocation system is located exclusively in the organellar membrane, whereas the Sec-translocation system is located on both the organellar and cytoplasmic membranes. Canonical signal peptides were predicted and validated experimentally, but a specific (N- or C-terminal) signal that could be used for protein targeting to the organelle remained elusive.

CONCLUSIONS

A physicochemically distinct organellar sub-proteome was predicted from the genome of the anammox bacterium K. stuttgartiensis. This result provides strong in silico support for the existing experimental evidence for the existence of an organelle in this bacterium, and is an important step forward in unravelling a geochemically relevant case of cytoplasmic differentiation in bacteria. The predicted dual location of the Sec-translocation system and the apparent absence of a specific N- or C-terminal signal in the organellar proteins suggests that additional chaperones may be necessary that act on an as-yet unknown property of the targeted proteins.

PanCGHweb: a web tool for genotype calling in pangenome CGH data

Summary: A pangenome is the total of genes present in strains of the same species. Pangenome microarrays allow determining the genomic content of bacterial strains more accurately than conventional comparative genome hybridization microarrays. PanCGHweb is the first tool that effectively calls genotype based on pangenome microarray data.

Availability: PanCGHweb, the web tool is accessible from: http://bamics2.cmbi.ru.nl/websoftware/pancgh/

Contact:sacha.vanhijum@nizo.nl

Mechanisms and evolution of control logic in prokaryotic transcriptional regulation

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.

MOTIFATOR: detection and characterization of regulatory motifs using prokaryote transcriptome data

SUMMARY

Unraveling regulatory mechanisms (e.g. identification of motifs in cis-regulatory regions) remains a major challenge in the analysis of transcriptome experiments. Existing applications identify putative motifs from gene lists obtained at rather arbitrary cutoff and require additional manual processing steps. Our standalone application MOTIFATOR identifies the most optimal parameters for motif discovery and creates an interactive visualization of the results. Discovered putative motifs are functionally characterized, thereby providing valuable insight in the biological processes that could be controlled by the motif.

AVAILABILITY

MOTIFATOR is freely available at http://www.motifator.nl.