Development of a real-time PCR method for the genoserotyping of Salmonella Paratyphi B variant Java

Discriminating between D-tartrate fermenting and non-fermenting strains of Salmonella enterica subsp. enterica serotype Paratyphi B is of major importance as these two variants have different pathogenic profiles. While D-tartrate non-fermenting S. Paratyphi B isolates are the causative agent of typhoid-like fever, D-tartrate fermenting isolates (also called variant Java) of the same serotype trigger the less dangerous gastroenteritis. The determination of S. Paratyphi B variants requires a time-consuming process and complex biochemical tests. Therefore, a quadruplex real-time PCR method, based on the allelic discrimination of molecular markers selected from the scientific literature and from whole genome sequencing data produced in-house, was developed in this study, to be applied to Salmonella isolates. This method was validated with the analysis of 178 S. Paratyphi B (D-tartrate fermenting and non-fermenting) and other serotypes reaching an accuracy, compared with the classical methods, of 98% for serotyping by slide agglutination and 100% for replacement of the biochemical test. The developed real-time PCR permits to save time and to obtain an accurate identification of a S. Paratyphi B serotype and its D-tartrate fermenting profile, which is needed in routine laboratories for fast and efficient diagnostics.

Organellar carbon metabolism is coordinated with distinct developmental phases of secondary xylem

Subcellular compartmentation of plant biosynthetic pathways in the mitochondria and plastids requires coordinated regulation of nuclear encoded genes, and the role of these genes has been largely ignored by wood researchers. In this study, we constructed a targeted systems genetics coexpression network of xylogenesis in Eucalyptus using plastid and mitochondrial carbon metabolic genes and compared the resulting clusters to the aspen xylem developmental series. The constructed network clusters reveal the organization of transcriptional modules regulating subcellular metabolic functions in plastids and mitochondria. Overlapping genes between the plastid and mitochondrial networks implicate the common transcriptional regulation of carbon metabolism during xylem secondary growth. We show that the central processes of organellar carbon metabolism are distinctly coordinated across the developmental stages of wood formation and are specifically associated with primary growth and secondary cell wall deposition. We also demonstrate that, during xylogenesis, plastid-targeted carbon metabolism is partially regulated by the central clock for carbon allocation towards primary and secondary xylem growth, and we discuss these networks in the context of previously established associations with wood-related complex traits. This study provides a new resolution into the integration and transcriptional regulation of plastid- and mitochondrial-localized carbon metabolism during xylogenesis.

Associating expression and genomic data using co-occurrence measures

Abstract

Recent technological evolutions have led to an exponential increase in data in all the omics fields. It is expected that integration of these different data sources, will drastically enhance our knowledge of the biological mechanisms behind genomic diseases such as cancer. However, the integration of different omics data still remains a challenge. In this work we propose an intuitive workflow for the integrative analysis of expression, mutation and copy number data taken from the METABRIC study on breast cancer. First, we present evidence that the expression profile of many important breast cancer genes consists of two modes or ‘regimes’, which contain important clinical information. Then, we show how the co-occurrence of these expression regimes can be used as an association measure between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes.

Reviewers

This article was reviewed by Dirk Walther, Francisco Garcia and Isabel Nepomuceno.

Electronic supplementary material

The online version of this article (10.1186/s13062-019-0240-2) contains supplementary material, which is available to authorized users.

The Crabtree Effect Shapes the Saccharomyces cerevisiae Lag Phase during the Switch between Different Carbon Sources

When faced with environmental changes, microbes often enter a temporary growth arrest during which they reprogram the expression of specific genes to adapt to the new conditions. A prime example of such a lag phase occurs when microbes need to switch from glucose to other, less-preferred carbon sources. Despite its industrial relevance, the genetic network that determines the duration of the lag phase has not been studied in much detail. Here, we performed a genome-wide Bar-Seq screen to identify genetic determinants of the Saccharomyces cerevisiae glucose-to-galactose lag phase. The results show that genes involved in respiration, and specifically those encoding complexes III and IV of the electron transport chain, are needed for efficient growth resumption after the lag phase. Anaerobic growth experiments confirmed the importance of respiratory energy conversion in determining the lag phase duration. Moreover, overexpression of the central regulator of respiration, HAP4, leads to significantly shorter lag phases. Together, these results suggest that the glucose-induced repression of respiration, known as the Crabtree effect, is a major determinant of microbial fitness in fluctuating carbon environments.IMPORTANCE The lag phase is arguably one of the prime characteristics of microbial growth. Longer lag phases result in lower competitive fitness in variable environments, and the duration of the lag phase is also important in many industrial processes where long lag phases lead to sluggish, less efficient fermentations. Despite the immense importance of the lag phase, surprisingly little is known about the exact molecular processes that determine its duration. Our study uses the molecular toolbox of S. cerevisiae combined with detailed growth experiments to reveal how the transition from fermentative to respirative metabolism is a key bottleneck for cells to overcome the lag phase. Together, our findings not only yield insight into the key molecular processes and genes that influence lag duration but also open routes to increase the efficiency of industrial fermentations and offer an experimental framework to study other types of lag behavior.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well.

1-((2,4-Dichlorophenethyl)Amino)-3-Phenoxypropan-2-ol Kills Pseudomonas aeruginosa through Extensive Membrane Damage

The ever increasing multidrug-resistance of clinically important pathogens and the lack of novel antibiotics have resulted in a true antibiotic crisis where many antibiotics are no longer effective. Further complicating the treatment of bacterial infections are antibiotic-tolerant persister cells. Besides being responsible for the recalcitrant nature of chronic infections, persister cells greatly contribute to the observed antibiotic tolerance in biofilms and even facilitate the emergence of antibiotic resistance. Evidently, eradication of these persister cells could greatly improve patient outcomes and targeting persistence may provide an alternative approach in combatting chronic infections. We recently characterized 1-((2,4-dichlorophenethyl)amino)-3-phenoxypropan-2-ol (SPI009), a novel anti-persister molecule capable of directly killing persisters from both Gram-negative and Gram-positive pathogens. SPI009 potentiates antibiotic activity in several in vitro and in vivo infection models and possesses promising anti-biofilm activity. Strikingly, SPI009 restores antibiotic sensitivity even in resistant strains. In this study, we investigated the mode of action of this novel compound using several parallel approaches. Genetic analyses and a macromolecular synthesis assays suggest that SPI009 acts by causing extensive membrane damage. This hypothesis was confirmed by liposome leakage assay and membrane permeability studies, demonstrating that SPI009 rapidly impairs the bacterial outer and inner membranes. Evaluation of SPI009-resistant mutants, which only could be generated under severe selection pressure, suggested a possible role for the MexCD-OprJ efflux pump. Overall, our results demonstrate the extensive membrane-damaging activity of SPI009 and confirm its clinical potential in the development of novel anti-persister therapies.

Comparison of SNP-based subtyping workflows for bacterial isolates using WGS data, applied to Salmonella enterica serotype Typhimurium and serotype 1,4,[5],12:i:

Whole genome sequencing represents a promising new technology for subtyping of bacterial pathogens. Besides the technological advances which have pushed the approach forward, the last years have been marked by considerable evolution of the whole genome sequencing data analysis methods. Prior to application of the technology as a routine epidemiological typing tool, however, reliable and efficient data analysis strategies need to be identified among the wide variety of the emerged methodologies. In this work, we have compared three existing SNP-based subtyping workflows using a benchmark dataset of 32 Salmonella enterica subsp. enterica serovar Typhimurium and serovar 1,4,[5],12:i:- isolates including five isolates from a confirmed outbreak and three isolates obtained from the same patient at different time points. The analysis was carried out using the original (high-coverage) and a down-sampled (low-coverage) datasets and two different reference genomes. All three tested workflows, namely CSI Phylogeny-based workflow, CFSAN-based workflow and PHEnix-based workflow, were able to correctly group the confirmed outbreak isolates and isolates from the same patient with all combinations of reference genomes and datasets. However, the workflows differed strongly with respect to the SNP distances between isolates and sensitivity towards sequencing coverage, which could be linked to the specific data analysis strategies used therein. To demonstrate the effect of particular data analysis steps, several modifications of the existing workflows were also tested. This allowed us to propose data analysis schemes most suitable for routine SNP-based subtyping applied to S. Typhimurium and S. 1,4,[5],12:i:-. Results presented in this study illustrate the importance of using correct data analysis strategies and to define benchmark and fine-tune parameters applied within routine data analysis pipelines to obtain optimal results.

Molecular processes underlying synergistic linuron mineralization in a triple-species bacterial consortium biofilm revealed by differential transcriptomics

The proteobacteria Variovorax sp. WDL1, Comamonas testosteroni WDL7, and Hyphomicrobium sulfonivorans WDL6 compose a triple‐species consortium that synergistically degrades and grows on the phenylurea herbicide linuron. To acquire a better insight into the interactions between the consortium members and the underlying molecular mechanisms, we compared the transcriptomes of the key biodegrading strains WDL7 and WDL1 grown as biofilms in either isolation or consortium conditions by differential RNAseq analysis. Differentially expressed pathways and cellular systems were inferred using the network‐based algorithm PheNetic. Coculturing affected mainly metabolism in WDL1. Significantly enhanced expression of hylA encoding linuron hydrolase was observed. Moreover, differential expression of several pathways involved in carbohydrate, amino acid, nitrogen, and sulfur metabolism was observed indicating that WDL1 gains carbon and energy from linuron indirectly by consuming excretion products from WDL7 and/or WDL6. Moreover, in consortium conditions, WDL1 showed a pronounced stress response and overexpression of cell to cell interaction systems such as quorum sensing, contact‐dependent inhibition, and Type VI secretion. Since the latter two systems can mediate interference competition, it prompts the question if synergistic linuron degradation is the result of true adaptive cooperation or rather a facultative interaction between bacteria that coincidentally occupy complementary metabolic niches.

Simultaneous discovery of cancer subtypes and subtype features by molecular data integration

MOTIVATION

Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates` mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering

RESULTS

We introduce a novel integrative framework to identify subtypes by combining mutational and expression features. The incomparable measurement data is integrated by transformation into ranked data and subtypes are defined as multi-view bi-clusters We formalize the model using rank matrix factorization, resulting in the SRF algorithm. Experiments on simulated data and the TCGA breast cancer data demonstrate that SRF is able to capture subtle differences that existing methods may miss.

AVAILABILITY AND IMPLEMENTATION

The implementation is available at: https://github.com/rankmatrixfactorisation/SRF CONTACT: kathleen.marchal@intec.ugent.be, siegfried.nijssen@cs.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Effect of genomic distance on coexpression of coregulated genes in E. coli

In prokaryotes, genomic distance is a feature that in addition to coregulation affects coexpression. Several observations, such as genomic clustering of highly coexpressed small regulons, support the idea that coexpression behavior of coregulated genes is affected by the distance between the coregulated genes. However, the specific contribution of distance in addition to coregulation in determining the degree of coexpression has not yet been studied systematically. In this work, we exploit the rich information in RegulonDB to study how the genomic distance between coregulated genes affects their degree of coexpression, measured by pairwise similarity of expression profiles obtained under a large number of conditions. We observed that, in general, coregulated genes display higher degrees of coexpression as they are more closely located on the genome. This contribution of genomic distance in determining the degree of coexpression was relatively small compared to the degree of coexpression that was determined by the tightness of the coregulation (degree of overlap of regulatory programs) but was shown to be evolutionary constrained. In addition, the distance effect was sufficient to guarantee coexpression of coregulated genes that are located at very short distances, irrespective of their tightness of coregulation. This is partly but definitely not always because the close distance is also the cause of the coregulation. In cases where it is not, we hypothesize that the effect of the distance on coexpression could be caused by the fact that coregulated genes closely located to each other are also relatively more equidistantly located from their common TF and therefore subject to more similar levels of TF molecules. The absolute genomic distance of the coregulated genes to their common TF-coding gene tends to be less important in determining the degree of coexpression. Our results pinpoint the importance of taking into account the combined effect of distance and coregulation when studying prokaryotic coexpression and transcriptional regulation.

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis

AIMS/HYPOTHESIS

Type 1 diabetes is an endocrine disease where a long preclinical phase, characterised by immune cell infiltration in the islets of Langerhans, precedes elevated blood glucose levels and disease onset. Although several studies have investigated the role of the immune system in this process of insulitis, the importance of the beta cells themselves in the initiation of type 1 diabetes is less well understood. The aim of this study was to investigate intrinsic differences present in the islets from diabetes-prone NOD mice before the onset of insulitis.

METHODS

The islet transcriptome and proteome of 2-3-week-old mice was investigated by microarray and 2-dimensional difference gel electrophoresis (2D-DIGE), respectively. Subsequent analyses using sophisticated pathway analysis and ranking of differentially expressed genes and proteins based on their relevance in type 1 diabetes were performed.

RESULTS

In the preinsulitic period, alterations in general pathways related to metabolism and cell communication were already present. Additionally, our analyses pointed to an important role for post-translational modifications (PTMs), especially citrullination by PAD2 and protein misfolding due to low expression levels of protein disulphide isomerases (PDIA3, 4 and 6), as causative mechanisms that induce beta cell stress and potential auto-antigen generation.

CONCLUSIONS/INTERPRETATION

We conclude that the pancreatic islets, irrespective of immune differences, may contribute to the initiation of the autoimmune process.

DATA AVAILABILITY

All microarray data are available in the ArrayExpress database ( www.ebi.ac.uk/arrayexpress ) under accession number E-MTAB-5264.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Fermentation assays reveal differences in sugar and (off-) flavor metabolism across different Brettanomyces bruxellensis strains

Brettanomyces (Dekkera) bruxellensis is an ascomycetous yeast of major importance in the food, beverage and biofuel industry. It has been isolated from various man-made ecological niches that are typically characterized by harsh environmental conditions such as wine, beer, soft drink, etc. Recent comparative genomics studies revealed an immense intraspecific diversity, but it is still unclear whether this genetic diversity also leads to systematic differences in fermentation performance and (off-)flavor production, and to what extent strains have evolved to match their ecological niche. Here, we present an evaluation of the fermentation properties of eight genetically diverse B. bruxellensis strains originating from beer, wine and soft drinks. We show that sugar consumption and aroma production during fermentation are determined by both the yeast strain and composition of the medium. Furthermore, our results indicate a strong niche adaptation of B. bruxellensis, most clearly for wine strains. For example, only strains originally isolated from wine were able to thrive well and produce the typical Brettanomyces-related phenolic off-flavors 4-ethylguaiacol and 4-ethylphenol when inoculated in red wine. Sulfite tolerance was found as a key factor explaining the observed differences in fermentation performance and off-flavor production. Sequence analysis of genes related to phenolic off-flavor production, however, revealed only marginal differences between the isolates tested, especially at the amino acid level. Altogether, our study provides novel insights in the Brettanomyces metabolism of flavor production, and is highly relevant for both the wine and beer industry.

SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the-art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information.

Elucidation of the Mode of Action of a New Antibacterial Compound Active against Staphylococcus aureus and Pseudomonas aeruginosa

Nosocomial and community-acquired infections caused by multidrug resistant bacteria represent a major human health problem. Thus, there is an urgent need for the development of antibiotics with new modes of action. In this study, we investigated the antibacterial characteristics and mode of action of a new antimicrobial compound, SPI031 (N-alkylated 3, 6-dihalogenocarbazol 1-(sec-butylamino)-3-(3,6-dichloro-9H-carbazol-9-yl)propan-2-ol), which was previously identified in our group. This compound exhibits broad-spectrum antibacterial activity, including activity against the human pathogens Staphylococcus aureus and Pseudomonas aeruginosa. We found that SPI031 has rapid bactericidal activity (7-log reduction within 30 min at 4x MIC) and that the frequency of resistance development against SPI031 is low. To elucidate the mode of action of SPI031, we performed a macromolecular synthesis assay, which showed that SPI031 causes non-specific inhibition of macromolecular biosynthesis pathways. Liposome leakage and membrane permeability studies revealed that SPI031 rapidly exerts membrane damage, which is likely the primary cause of its antibacterial activity. These findings were supported by a mutational analysis of SPI031-resistant mutants, a transcriptome analysis and the identification of transposon mutants with altered sensitivity to the compound. In conclusion, our results show that SPI031 exerts its antimicrobial activity by causing membrane damage, making it an interesting starting point for the development of new antibacterial therapies.

EXPLoRA-web: linkage analysis of quantitative trait loci using bulk segregant analysis

Identification of genomic regions associated with a phenotype of interest is a fundamental step toward solving questions in biology and improving industrial research. Bulk segregant analysis (BSA) combined with high-throughput sequencing is a technique to efficiently identify these genomic regions associated with a trait of interest. However, distinguishing true from spuriously linked genomic regions and accurately delineating the genomic positions of these truly linked regions requires the use of complex statistical models currently implemented in software tools that are generally difficult to operate for non-expert users. To facilitate the exploration and analysis of data generated by bulked segregant analysis, we present EXPLoRA-web, a web service wrapped around our previously published algorithm EXPLoRA, which exploits linkage disequilibrium to increase the power and accuracy of quantitative trait loci identification in BSA analysis. EXPLoRA-web provides a user friendly interface that enables easy data upload and parallel processing of different parameter configurations. Results are provided graphically and as BED file and/or text file and the input is expected in widely used formats, enabling straightforward BSA data analysis. The web server is available at http://bioinformatics.intec.ugent.be/explora-web/.

Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html

Optimized MOL-PCR for Characterization of Microbial Pathogens

Characterization of microbial pathogens is necessary for surveillance, outbreak detection, and tracing of outbreak sources. This unit describes a multiplex oligonucleotide ligation-PCR (MOL-PCR) optimized for characterization of microbial pathogens. With MOL-PCR, different types of markers, like unique sequences, single-nucleotide polymorphisms (SNPs) and indels, can be simultaneously analyzed in one assay. This assay consists of a multiplex ligation for detection of the markers, a singleplex PCR for signal amplification, and hybridization to MagPlex-TAG beads for readout on a Luminex platform after fluorescent staining. The current protocol describes the MOL-PCR, as well as methods for DNA isolation, probe design, and data interpretation and it is based on an optimized MOL-PCR assay for subtyping of Salmonella Typhimurium.

Adaptation to High Ethanol Reveals Complex Evolutionary Pathways

Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts.

Organisms can evolve resistance to specific stress factors, which allows them to thrive in environments where non-adapted organisms fail to grow. However, the molecular mechanisms that underlie adaptation to complex stress factors that interfere with basic cellular processes are poorly understood. In this study, we reveal how yeast populations adapt to high ethanol concentrations, an ecologically and industrially relevant stress that is still poorly understood. We exposed six independent populations of genetically identical yeast cells to gradually increasing ethanol levels, and we monitored the changes in their DNA sequence over a two-year period. Together with novel computational analyses, we could identify the mutational dynamics and molecular mechanisms underlying increased ethanol resistance. Our results show how adaptation to high ethanol is complex and can be reached through different mutational pathways. Together, our study offers a detailed picture of how populations adapt to a complex continuous stress and identifies several mutations that increase ethanol resistance, which opens new routes to obtain superior biofuel yeast strains.

Whole Genome Sequence Analysis of Salmonella Enteritidis PT4 Outbreaks from a National Reference Laboratory's Viewpoint

INTRODUCTION

In April and May 2014, two suspected egg-related outbreaks of Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis) were investigated by the Belgian National Reference Laboratory of Foodborne Outbreaks. Both the suspected food and human isolates being available, and this for both outbreaks, made these the ideal case study for a retrospective whole genome sequencing (WGS) analysis with the goal to investigate the feasibility of this technology for outbreak investigation by a National Reference Laboratory or National Reference Centre without thorough bioinformatics expertise.

METHODS

The two outbreaks were originally investigated epidemiologically with a standard questionnaire and with serotyping, phage typing, multiple-locus variable-number of tandem repeats analysis (MLVA) and antimicrobial susceptibility testing as classical microbiological methods. Retrospectively, WGS of six outbreak isolates was done on an Illumina HiSeq. Analysis of the WGS data was performed with currently available, user-friendly software and tools, namely CLC Genomics Workbench, the tools available on the server of the Center for Genomic Epidemiology and BLAST Ring Image Generator (BRIG).

RESULTS

To all collected human and food outbreak isolates, classical microbiological investigation assigned phage type PT4 (variant phage type PT4a for one human isolate) and MLVA profile 3-10-5-4-1, both of which are common for human isolates in Belgium. The WGS analysis confirmed the link between food and human isolates for each of the outbreaks and clearly discriminated between the two outbreaks occurring in a same time period, thereby suggesting a non-common source of contamination. Also, an additional plasmid carrying an antibiotic resistance gene was discovered in the human isolate with the variant phage type PT4a.

DISCUSSION

For the two investigated outbreaks occurring at geographically separated locations, the gold standard classical microbiological subtyping methods were not sufficiently discriminative to distinguish between or assign a common origin of contamination for the two outbreaks, while WGS analysis could do so. This case study demonstrated the added value of WGS for outbreak investigations by confirming and/or discriminating food and human isolates between and within outbreaks. It also proved the feasibility of WGS as complementary or even future replacing (sub)typing method for the average routine laboratory.

Ultraviolet-B radiation stimulates downward leaf curling in Arabidopsis thaliana

Plants are very well adapted to growth in ultraviolet-B (UV-B) containing light. In Arabidopsis thaliana, many of these adaptations are mediated by the UV-B receptor UV resistance locus 8 (UVR8). Using small amounts of supplementary UV-B light, we observed changes in the shape of rosette leaf blades. Wild type plants show more pronounced epinasty of the blade edges, while this is not the case in uvr8 mutant plants. The UVR8 effect thus mimics the effect of phytochrome (phy) B in red light. In addition, a meta-analysis of transcriptome data indicates that the UVR8 and phyB signaling pathways have over 70% of gene regulation in common. Moreover, in low levels of supplementary UV-B light, mutant analysis revealed that phyB signaling is necessary for epinasty of the blade edges. Analysis of auxin levels and the auxin signal reporter DR5::GUS suggest that the epinasty relies on altered auxin distribution, keeping auxin at the leaf blade edges in the presence of UV-B. Together, our results suggest a co-action of phyB and UVR8 signaling, with auxin as a downstream factor.

Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration

The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival.

A multiplex oligonucleotide ligation-PCR as a complementary tool for subtyping of Salmonella Typhimurium

Subtyping below the serovar level is essential for surveillance and outbreak detection and investigation of Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) and its monophasic variant 1,4,[5],12:i:- (S. 1,4,[5],12:i:-), frequent causes of foodborne infections. In an attempt to overcome the intrinsic shortcomings of currently used subtyping techniques, a multiplex oligonucleotide ligation-PCR (MOL-PCR) assay was developed which combines different types of molecular markers in a high-throughput microsphere suspension array. The 52 molecular markers include prophage genes, amplified fragment length polymorphism (AFLP) elements, Salmonella genomic island 1 (SGI1), allantoinase gene allB, MLVA locus STTR10, antibiotic resistance genes, single nucleotide polymorphisms (SNPs) and phase 2 flagellar gene fljB. The in vitro stability of these markers was confirmed in a serial passage experiment. The validation of the MOL-PCR assay for subtyping of S. Typhimurium and S. 1,4,[5],12:i:- on 519 isolates shows that the method is rapid, reproducible, flexible, accessible, easy to use and relatively inexpensive. Additionally, a 100 % typeability and a discriminatory power equivalent to that of phage typing were observed, and epidemiological concordance was assessed on isolates of 2 different outbreaks. Furthermore, a data analysis method is provided so that the MOL-PCR assay allows for objective, computerised data analysis and data interpretation of which the results can be easily exchanged between different laboratories in an international surveillance network.