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 ß-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.

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.

Integrating glycomics and genomics uncovers SLC10A7 as essential factor for bone mineralization by regulating post-Golgi protein transport and glycosylation.

Genomics methodologies have significantly improved elucidation of Mendelian disorders. The combination with high-throughput functional-omics technologies potentiates the identification and confirmation of causative genetic variants, especially in singleton families of recessive inheritance. In a cohort of 99 individuals with abnormal Golgi glycosylation, 47 of which being unsolved, glycomics profiling was performed of total plasma glycoproteins. Combination with whole-exome sequencing in 31 cases revealed a known genetic defect in 15 individuals. To identify additional genetic factors, hierarchical clustering of the plasma glycomics data was done, which indicated a subgroup of four patients that shared a unique glycomics signature of hybrid type N-glycans. In two siblings, compound heterozygous mutations were found in SLC10A7, a gene of unknown function in human. These included a missense mutation that disrupted transmembrane domain 4 and a mutation in a splice acceptor site resulting in skipping of exon 9. The two other individuals showed a complete loss of SLC10A7 mRNA. The patients' phenotype consisted of amelogenesis imperfecta, skeletal dysplasia, and decreased bone mineral density compatible with osteoporosis. The patients' phenotype was mirrored in SLC10A7 deficient zebrafish. Furthermore, alizarin red staining of calcium deposits in zebrafish morphants showed a strong reduction in bone mineralization. Cell biology studies in fibroblasts of affected individuals showed intracellular mislocalization of glycoproteins and a defect in post-Golgi transport of glycoproteins to the cell membrane. In contrast to yeast, human SLC10A7 localized to the Golgi. Our combined data indicate an important role for SLC10A7 in bone mineralization and transport of glycoproteins to the extracellular matrix.

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.

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.

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.

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.

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.

Nomadic lifestyle of Lactobacillus plantarum revealed by comparative genomics of 54 strains isolated from different habitats.

The ability of bacteria to adapt to diverse environmental conditions is well-known. The process of bacterial adaptation to a niche has been linked to large changes in the genome content, showing that many bacterial genomes reflect the constraints imposed by their habitat. However, some highly versatile bacteria are found in diverse habitats that almost share nothing in common. Lactobacillus plantarum is a lactic acid bacterium that is found in a large variety of habitat. With the aim of unravelling the link between evolution and ecological versatility of L. plantarum, we analysed the genomes of 54 L. plantarum strains isolated from different environments. Comparative genome analysis identified a high level of genomic diversity and plasticity among the strains analysed. Phylogenomic and functional divergence studies coupled with gene-trait matching analyses revealed a mixed distribution of the strains, which was uncoupled from their environmental origin. Our findings revealed the absence of specific genomic signatures marking adaptations of L. plantarum towards the diverse habitats it is associated with. This suggests fundamentally similar trends of genome evolution in L. plantarum, which occur in a manner that is apparently uncoupled from ecological constraint and reflects the nomadic lifestyle of this species.

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

UNLABELLED: 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.

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.

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.

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.

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.

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.

Diversity in robustness of Lactococcus lactis strains during heat stress, oxidative stress, and spray drying stress.

In this study we tested 39 Lactococcus lactis strains isolated from diverse habitats for their robustness under heat and oxidative stress, demonstrating high diversity in survival (up to 4 log units). Strains with an L. lactis subsp. lactis phenotype generally displayed more-robust phenotypes than strains with an L. lactis subsp. cremoris phenotype, whereas the habitat from which the strains had been isolated did not appear to influence stress survival. Comparison of the stress survival phenotypes with already available comparative genomic data sets revealed that the absence or presence of specific genes, including genes encoding a GntR family transcriptional regulator, a manganese ABC transporter permease, a cellobiose phosphotransferase system (PTS) component, the FtsY protein, and hypothetical proteins, was associated with heat or oxidative stress survival. Finally, 14 selected strains also displayed diversity in survival after spray drying, ranging from 20% survival for the most robust strains, which appears acceptable for industrial application, to 0.1% survival for the least-tolerant strains. The high and low levels of survival upon spray drying correlated clearly with the combined robustness under heat and oxidative stress. These results demonstrate the relevance of screening culture collections for robustness under heat and oxidative stress on top of the typical screening for acidifying and flavor-forming properties.

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.

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.

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.