New technologies to assess genotype-phenotype relationships

A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters

Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal "growth" rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.

Within-host microevolution of Pseudomonas aeruginosa in Italian cystic fibrosis patients

BACKGROUND

Chronic infection with Pseudomonas aeruginosa is a major cause of morbidity and mortality in cystic fibrosis (CF) patients, and a more complete understanding of P. aeruginosa within-host genomic evolution, transmission, and population genomics may provide a basis for improving intervention strategies. Here, we report the first genomic analysis of P. aeruginosa isolates sampled from Italian CF patients.

RESULTS

By genome sequencing of 26 isolates sampled over 19 years from four patients, we elucidated the within-host evolution of clonal lineages in each individual patient. Many of the identified mutations were located in pathoadaptive genes previously associated with host adaptation, and we correlated mutations with changes in CF-relevant phenotypes such as antibiotic resistance. In addition, the genomic analysis revealed that three patients shared the same clone. Furthermore, we compared the genomes of the Italian CF isolates to a panel of genome sequenced strains of P. aeruginosa from other countries. Isolates from two of the Italian lineages belonged to clonal complexes of P. aeruginosa that have previously been identified in Danish CF patients, and our genomic comparison showed that clonal isolates from the same country may be more distantly related than clonal isolates from different countries.

CONCLUSIONS

This is the first whole-genome analysis of P. aeruginosa isolated from Italian CF patients, and together with both phenotypic and clinical information this dataset facilitates a more detailed understanding of P. aeruginosa within-host genomic evolution, transmission, and population genomics. We conclude that the evolution of the Italian lineages resembles what has been found in other countries.

Biotransformation of β-hexachlorocyclohexane by the saprotrophic soil fungus Penicillium griseofulvum

β-Hexachlorocyclohexane (β-HCH) is a persistent organic pollutant (POP) of global concern with potentially toxic effects on humans and ecosystems. Fungal tolerance and biotransformation of toxic substances hold considerable promise in environmental remediation technologies as many fungi can tolerate extreme environmental conditions and possess efficient extracellular degradative enzymes with relatively non-specific activities. In this research, we have investigated the potential of a saprotrophic soil fungus, Penicillium griseofulvum Dierckx, isolated from soils with high concentrations of isomers of hexachlorocyclohexane, to biotransform β-HCH, the most recalcitrant isomer to microbial activity. The growth kinetics of the fungus were characterized after growth in stirred liquid Czapek-Dox medium. It was found that P. griseofulvum was able to grow in the presence of 1 mg L(-1) β-HCH and in stressful nutritional conditions at different concentrations of sucrose in the medium (0 and 5 g L(-1)). The effects of β-HCH and the toluene, used as a solvent for β-HCH addition, on P. griseofulvum were investigated by means of a Phenotype MicroArray™ technique, which suggested the activation of certain metabolic pathways as a response to oxidative stress due to the presence of the xenobiotics. Gas chromatographic analysis of β-HCH concentration confirmed biodegradation of the isomer with a minimum value of β-HCH residual concentration of 18.6%. The formation of benzoic acid derivatives as dead-end products of β-HCH biotransformation was observed and this could arise from a possible biodegradation pathway for β-HCH with important connections to fungal secondary metabolism.

Genome and Phenotype Microarray Analyses of Rhodococcus sp. BCP1 and Rhodococcus opacus R7: Genetic Determinants and Metabolic Abilities with Environmental Relevance

In this paper comparative genome and phenotype microarray analyses of Rhodococcus sp. BCP1 and Rhodococcus opacus R7 were performed. Rhodococcus sp. BCP1 was selected for its ability to grow on short-chain n-alkanes and R. opacus R7 was isolated for its ability to grow on naphthalene and on o-xylene. Results of genome comparison, including BCP1, R7, along with other Rhodococcus reference strains, showed that at least 30% of the genome of each strain presented unique sequences and only 50% of the predicted proteome was shared. To associate genomic features with metabolic capabilities of BCP1 and R7 strains, hundreds of different growth conditions were tested through Phenotype Microarray, by using Biolog plates and plates manually prepared with additional xenobiotic compounds. Around one-third of the surveyed carbon sources was utilized by both strains although R7 generally showed higher metabolic activity values compared to BCP1. Moreover, R7 showed broader range of nitrogen and sulphur sources. Phenotype Microarray data were combined with genomic analysis to genetically support the metabolic features of the two strains. The genome analysis allowed to identify some gene clusters involved in the metabolism of the main tested xenobiotic compounds. Results show that R7 contains multiple genes for the degradation of a large set of aromatic and PAHs compounds, while a lower variability in terms of genes predicted to be involved in aromatic degradation was found in BCP1. This genetic feature can be related to the strong genetic pressure exerted by the two different environment from which the two strains were isolated. According to this, in the BCP1 genome the smo gene cluster involved in the short-chain n-alkanes degradation, is included in one of the unique regions and it is not conserved in the Rhodococcus strains compared in this work. Data obtained underline the great potential of these two Rhodococcus spp. strains for biodegradation and environmental decontamination processes.

Phenotype MicroArrays as a complementary tool to next generation sequencing for characterization of tree endophytes

There is an increasing need to calibrate microbial community profiles obtained through next generation sequencing (NGS) with relevant taxonomic identities of the microbes, and to further associate these identities with phenotypic attributes. Phenotype MicroArray (PM) techniques provide a semi-high throughput assay for characterization and monitoring the microbial cellular phenotypes. Here, we present detailed descriptions of two different PM protocols used in our recent studies on fungal endophytes of forest trees, and highlight the benefits and limitations of this technique. We found that the PM approach enables effective screening of substrate utilization by endophytes. However, the technical limitations are multifaceted and the interpretation of the PM data challenging. For the best result, we recommend that the growth conditions for the fungi are carefully standardized. In addition, rigorous replication and control strategies should be employed whether using pre-configured, commercial microwell-plates or in-house designed PM plates for targeted substrate analyses. With these precautions, the PM technique is a valuable tool to characterize the metabolic capabilities of individual endophyte isolates, or successional endophyte communities identified by NGS, allowing a functional interpretation of the taxonomic data. Thus, PM approaches can provide valuable complementary information for NGS studies of fungal endophytes in forest trees.

Comparative phenomics and targeted use of genomics reveals variation in carbon and nitrogen assimilation among different Brettanomyces bruxellensis strains

Recent studies have suggested a correlation between genotype groups of Brettanomyces bruxellensis and their source of isolation. To further explore this relationship, the objective of this study was to assess metabolic differences in carbon and nitrogen assimilation between different B. bruxellensis strains from three beverages, including beer, wine, and soft drink, using Biolog Phenotype Microarrays. While some similarities of physiology were noted, many traits were variable among strains. Interestingly, some phenotypes were found that could be linked to strain origin, especially for the assimilation of particular α- and β-glycosides as well as α- and β-substituted monosaccharides. Based upon gene presence or absence, an α-glucosidase and β-glucosidase were found explaining the observed phenotypes. Further, using a PCR screen on a large number of isolates, we have been able to specifically link a genomic deletion to the beer strains, suggesting that this region may have a fitness cost for B. bruxellensis in certain fermentation systems such as brewing. More specifically, none of the beer strains were found to contain a β-glucosidase, which may have direct impacts on the ability for these strains to compete with other microbes or on flavor production.

Phenotypic profiling of Scedosporium aurantiacum, an opportunistic pathogen colonizing human lungs

Genotyping studies of Australian Scedosporium isolates have revealed the strong prevalence of a recently described species: Scedosporium aurantiacum. In addition to occurring in the environment, this fungus is also known to colonise the respiratory tracts of cystic fibrosis (CF) patients. A high throughput Phenotype Microarray (PM) analysis using 94 assorted substrates (sugars, amino acids, hexose-acids and carboxylic acids) was carried out for four isolates exhibiting different levels of virulence, determined using a Galleria mellonella infection model. A significant difference was observed in the substrate utilisation patterns of strains displaying differential virulence. For example, certain sugars such as sucrose (saccharose) were utilised only by low virulence strains whereas some sugar derivatives such as D-turanose promoted respiration only in the more virulent strains. Strains with a higher level of virulence also displayed flexibility and metabolic adaptability at two different temperature conditions tested (28 and 37°C). Phenotype microarray data were integrated with the whole-genome sequence data of S. aurantiacum to reconstruct a pathway map for the metabolism of selected substrates to further elucidate differences between the strains.

Novel R pipeline for analyzing Biolog Phenotypic MicroArray data

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells’ respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.

Signatures of adaptation in human invasive Salmonella Typhimurium ST313 populations from sub-Saharan Africa

Two lineages of Salmonella enterica serovar Typhimurium (S. Typhimurium) of multi-locus sequence type ST313 have been linked with the emergence of invasive Salmonella disease across sub-Saharan Africa. The expansion of these lineages has a temporal association with the HIV pandemic and antibiotic usage. We analysed the whole genome sequence of 129 ST313 isolates representative of the two lineages and found evidence of lineage-specific genome degradation, with some similarities to that observed in S. Typhi. Individual ST313 S. Typhimurium isolates exhibit a distinct metabolic signature and modified enteropathogenesis in both a murine and cattle model of colitis, compared to S. Typhimurium outside of the ST313 lineages. These data define phenotypes that distinguish ST313 isolates from other S. Typhimurium and may represent adaptation to a distinct pathogenesis and lifestyle linked to an-immuno-compromised human population.

Novel functions of (p)ppGpp and Cyclic di-GMP in mycobacterial physiology revealed by phenotype microarray analysis of wild-type and isogenic strains of Mycobacterium smegmatis

The bacterial second messengers (p)ppGpp and bis-(3'-5')-cyclic dimeric GMP (c-di-GMP) regulate important functions, such as transcription, virulence, biofilm formation, and quorum sensing. In mycobacteria, they regulate long-term survival during starvation, pathogenicity, and dormancy. Recently, a Pseudomonas aeruginosa strain lacking (p)ppGpp was shown to be sensitive to multiple classes of antibiotics and defective in biofilm formation. We were interested to find out whether Mycobacterium smegmatis strains lacking the gene for either (p)ppGpp synthesis (ΔrelMsm) or c-di-GMP synthesis (ΔdcpA) would display similar phenotypes. We used phenotype microarray technology to compare the growth of the wild-type and the knockout strains in the presence of several antibiotics. Surprisingly, the ΔrelMsm and ΔdcpA strains showed enhanced survival in the presence of many antibiotics, but they were defective in biofilm formation. These strains also displayed altered surface properties, like impaired sliding motility, rough colony morphology, and increased aggregation in liquid cultures. Biofilm formation and surface properties are associated with the presence of glycopeptidolipids (GPLs) in the cell walls of M. smegmatis. Thin-layer chromatography analysis of various cell wall fractions revealed that the levels of GPLs and polar lipids were reduced in the knockout strains. As a result, the cell walls of the knockout strains were significantly more hydrophobic than those of the wild type and the complemented strains. We hypothesize that reduced levels of GPLs and polar lipids may contribute to the antibiotic resistance shown by the knockout strains. Altogether, our data suggest that (p)ppGpp and c-di-GMP may be involved in the metabolism of glycopeptidolipids and polar lipids in M. smegmatis.

Fast and robust group-wise eQTL mapping using sparse graphical models

Background

Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to hidden biological pathways.

Results

We introduce a new approach to identify novel group-wise associations between sets of SNPs and sets of genes. Such associations are captured by hidden variables connecting SNPs and genes. Our model is a linear-Gaussian model and uses two types of hidden variables. One captures the set associations between SNPs and genes, and the other captures confounders. We develop an efficient optimization procedure which makes this approach suitable for large scale studies. Extensive experimental evaluations on both simulated and real datasets demonstrate that the proposed methods can effectively capture both individual and group-wise signals that cannot be identified by the state-of-the-art eQTL mapping methods.

Conclusions

Considering group-wise associations significantly improves the accuracy of eQTL mapping, and the successful multi-layer regression model opens a new approach to understand how multiple SNPs interact with each other to jointly affect the expression level of a group of genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0421-z) contains supplementary material, which is available to authorized users.

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

GenoBase: comprehensive resource database of Escherichia coli K-12

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources.

Nonmucoid conversion of mucoid Pseudomonas aeruginosa induced by sulfate-stimulated growth

Alginate-overproducing mucoid Pseudomonas aeruginosa, responsible for chronic airway infections in cystic fibrosis (CF) patients, is resistant to antibiotic treatments and host immune clearance. In this study, we performed a phenotype microarray screen and identified sulfate ion as a molecule that can suppress alginate production. When a mucoid P. aeruginosa strain CM21 and additional mucoid isolates were grown with 5% sodium sulfate, significantly decreased levels of alginate were produced. Suppression of alginate production was also induced by other sulfate salts. Expression of a reporter gene fused to the algD promoter was considerably decreased when grown with sulfate. Furthermore, bacterial cell shape was abnormally altered in CM21, but not in PAO1, a prototype nonmucoid strain, suggesting that sulfate-stimulated cell shape change is associated with transcriptional suppression of the alginate operon. Finally, a CM21 lpxC mutant defective in lipid A biosynthesis continued to produce alginate and maintained the correct cell shape when grown with sulfate. These results suggest a potential involvement of lipoploysaccharide biosynthesis in the sulfate-induced reversion to nonmucoid phenotype. This study proposes a novel strategy that can be potentially applied to treat persistent infection by recalcitrant mucoid P. aeruginosa.

Development of a high throughput assay for indirectly measuring phage growth using the OmniLog(TM) system

The conventional and most accepted method of measuring the lytic activity of a phage against its bacterial host is the plaque assay. This method is laborious, time consuming and expensive, especially in high throughput analyses where multiple phage-bacterial interactions are required to be monitored simultaneously. It can also vary considerably with the experimenter and by the growth and plating conditions. Alternatively, the lytic activity can be measured indirectly by following the decrease in optical density of the bacterial cultures owing to lysis. Here we describe an automated, high throughput, indirect liquid lysis assay to evaluate phage growth using the OmniLog^TM system. The OmniLog^TM system uses redox chemistry, employing cell respiration as a universal reporter. During active growth of bacteria, cellular respiration reduces a tetrazolium dye and produces a color change that is measured in an automated fashion. On the other hand, successful phage infection and subsequent growth of the phage in its host bacterium results in reduced bacterial growth and respiration and a concomitant reduction in color. Here we show that microtiter plate wells inoculated with Bacillus anthracis and phage show decreased or no growth, compared with the wells containing bacteria only or phage resistant bacteria plus phage. Also, we show differences in the kinetics of bacterial growth and the timing of appearance of phage resistant bacteria in the presence of individual phages or a cocktail of B. anthracis specific phages. The results of these experiments indicate that the OmniLog^TM system could be used reliably for indirectly measuring phage growth in high throughput host range and phage and antibiotics combination studies.

Coexistence and within-host evolution of diversified lineages of hypermutable Pseudomonas aeruginosa in long-term cystic fibrosis infections

The advent of high-throughput sequencing techniques has made it possible to follow the genomic evolution of pathogenic bacteria by comparing longitudinally collected bacteria sampled from human hosts. Such studies in the context of chronic airway infections by Pseudomonas aeruginosa in cystic fibrosis (CF) patients have indicated high bacterial population diversity. Such diversity may be driven by hypermutability resulting from DNA mismatch repair system (MRS) deficiency, a common trait evolved by P. aeruginosa strains in CF infections. No studies to date have utilized whole-genome sequencing to investigate within-host population diversity or long-term evolution of mutators in CF airways. We sequenced the genomes of 13 and 14 isolates of P. aeruginosa mutator populations from an Argentinian and a Danish CF patient, respectively. Our collection of isolates spanned 6 and 20 years of patient infection history, respectively. We sequenced 11 isolates from a single sample from each patient to allow in-depth analysis of population diversity. Each patient was infected by clonal populations of bacteria that were dominated by mutators. The in vivo mutation rate of the populations was ∼100 SNPs/year–∼40-fold higher than rates in normo-mutable populations. Comparison of the genomes of 11 isolates from the same sample showed extensive within-patient genomic diversification; the populations were composed of different sub-lineages that had coexisted for many years since the initial colonization of the patient. Analysis of the mutations identified genes that underwent convergent evolution across lineages and sub-lineages, suggesting that the genes were targeted by mutation to optimize pathogenic fitness. Parallel evolution was observed in reduction of overall catabolic capacity of the populations. These findings are useful for understanding the evolution of pathogen populations and identifying new targets for control of chronic infections.

Patients with cystic fibrosis (CF) are often colonized by a single clone of the common, widespread bacterium Pseudomonas aeruginosa, resulting in chronic airway infections. Long-term persistence of the bacteria involves the emergence and selection of multiple phenotypic variants. Among these are “mutator” variants characterized by increased mutation rates resulting from the inactivation of DNA repair systems. The genetic evolution of mutators during the course of chronic infection is poorly understood, and the effects of hypermutability on bacterial population structure have not been studied using genomic approaches. We evaluated the genomic changes undergone by mutator populations of P. aeruginosa obtained from single sputum samples from two chronically infected CF patients, and found that mutators completely dominated the infecting population in both patients. These populations displayed high genomic diversity based on vast accumulation of stochastic mutations. Our results are in contrast to the concept of a homogeneous population consisting of a single dominant clone; rather, they support a model of populations structured by diverse subpopulations that coexist within the patient. Certain genes involved in adaptation were highly and convergently mutated in both lineages, suggesting that these genes were beneficial and potentially responsible for the co-selection of mutator alleles.

Graph-regularized dual Lasso for robust eQTL mapping

MOTIVATION

As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and pathway information are not integrated.

RESULTS

To address the limitations of the existing methods, we propose Graph-regularized Dual Lasso (GDL), a robust approach for eQTL mapping. GDL integrates the correlation structures among genetic markers and traits simultaneously. It also takes into account the incompleteness of the networks and is robust to the noise. GDL utilizes graph-based regularizers to model the prior networks and does not require an explicit clustering step. Moreover, it enables further refinement of the partial and noisy networks. We further generalize GDL to incorporate the location of genetic makers and gene-pathway information. We perform extensive experimental evaluations using both simulated and real datasets. Experimental results demonstrate that the proposed methods can effectively integrate various available priori knowledge and significantly outperform the state-of-the-art eQTL mapping methods.

AVAILABILITY

Software for both C++ version and Matlab version is available at http://www.cs.unc.edu/∼weicheng/.

Computational approaches for microalgal biofuel optimization: a review

The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Environmental heterogeneity drives within-host diversification and evolution of Pseudomonas aeruginosa

Microbial population polymorphisms are commonly observed in natural environments, including long-term infected hosts. However, the underlying processes promoting and stabilizing diversity are difficult to unravel and are not well understood. Here, we use chronic infection of cystic fibrosis airways by the opportunistic pathogen Pseudomonas aeruginosa as a system for investigating bacterial diversification processes during the course of infection. We analyze clonal bacterial isolates sampled during a 32-year period and map temporal and spatial variations in population diversity to different infection sites within the infected host. We show that the ancestral infecting strain diverged into distinct sublineages, each with their own functional and genomic signatures and rates of adaptation, immediately after initial colonization. The sublineages coexisted in the host for decades, suggesting rapid evolution of stable population polymorphisms. Critically, the observed generation and maintenance of population diversity was the result of partitioning of the sublineages into physically separated niches in the CF airway. The results reveal a complex within-host population structure not previously realized and provide evidence that the heterogeneity of the highly structured and complex host environment promotes the evolution and long-term stability of pathogen population diversity during infection.

Within-host pathogen evolution and diversification during the course of chronic infections is of importance in relation to therapeutic intervention strategies, yet our understanding of these processes is limited. Here, we investigate intraclonal population diversity in P. aeruginosa during chronic airway infections in cystic fibrosis patients. We show the evolution of a diverse population structure immediately after initial colonization, with divergence into multiple distinct sublineages that coexisted for decades and occupied distinct niches. Our results suggest that the spatial heterogeneity in CF airways plays a major role in relation to the generation and maintenance of population diversity and emphasize that a single isolate in sputum may not represent the entire pathogen population in the infected individual. A more complete understanding of the evolution of distinct clonal variants and their distribution in different niches could have positive implications for efficient therapy.

Multiple phenotypic changes associated with large-scale horizontal gene transfer

Horizontal gene transfer often leads to phenotypic changes within recipient organisms independent of any immediate evolutionary benefits. While secondary phenotypic effects of horizontal transfer (i.e., changes in growth rates) have been demonstrated and studied across a variety of systems using relatively small plasmids and phage, little is known about the magnitude or number of such costs after the transfer of larger regions. Here we describe numerous phenotypic changes that occur after a large-scale horizontal transfer event (∼1 Mb megaplasmid) within Pseudomonas stutzeri including sensitization to various stresses as well as changes in bacterial behavior. These results highlight the power of horizontal transfer to shift pleiotropic relationships and cellular networks within bacterial genomes. They also provide an important context for how secondary effects of transfer can bias evolutionary trajectories and interactions between species. Lastly, these results and system provide a foundation to investigate evolutionary consequences in real time as newly acquired regions are ameliorated and integrated into new genomic contexts.

Development of a phenotypic assay for characterisation of ethanologenic yeast strain sensitivity to inhibitors released from lignocellulosic feedstocks

Inhibitors released by the breakdown of plant cell walls prevent efficient conversion of sugar into ethanol. The aim of this study was to develop a fast and reliable inhibitor sensitivity assay for ethanologenic yeast strains. The assay comprised bespoke 96-well plates containing inhibitors in isolation or combination in a format that was compatible with the Phenotypic Microarray Omnilog reader (Biolog, hayward, CA, USA). A redox reporter within the assay permits analysis of inhibitor sensitivity in aerobic and/or anaerobic conditions. Results from the assay were verified using growth on spot plates and tolerance assays in which maintenance of viability was assessed. The assay allows for individual and synergistic effects of inhibitors to be determined. It was observed that the presence of both acetic and formic acid significantly inhibited the yeast strains assessed, although this impact could be partially mitigated by buffering to neutral pH. Scheffersomyces stipitis, Candida spp., and Pichia guilliermondii demonstrated increased sensitivity to short chain weak acids at concentrations typically present in lignocellulosic hydrolysates. S. cerevisiae exhibited robustness to short chain weak acids at these concentrations. However, S. stipitis, Candida spp., and P. guilliermondii displayed increased tolerance to HMF when compared to that observed for S. cerevisiae. The results demonstrate that the phenotypic microarray assay developed in the current study is a valuable tool that can be used to identify yeast strains with desirable resistance to inhibitory compounds found in lignocellulosic hydrolysates.

Molecular applications for identifying microbial pathogens in the post-9/11 era

Rapid advances in molecular and optical technologies over the past 10 years have dramatically impacted the way biologic research is conducted today. Examples include microarrays, capillary sequencing, optical mapping and real-time sequencing (Pyrosequencing). These technologies are capable of rapidly delivering massive amounts of genetic information and are becoming routine mainstays of many laboratories. Fortunately, advances in scientific computing have provided the enormous computing power necessary to analyze these enormous data sets. The application of molecular technologies should prove useful to the burgeoning field of microbial forensics. In the post-9/11 era, when securing America's food supply is a major endeavor, the need for rapid identification of microbes that accidentally or intentionally find their way into foods is apparent. The principle that distinguishes a microbial forensic investigation from a molecular epidemiology study is that a biocrime has been committed. If proper attribution is to be attained, a link must be made between a particular microbe in the food and the perpetrator who placed it there. Therefore, the techniques used must be able to discriminate individual isolates of a particular microbe. A battery of techniques in development for distinguishing individual isolates of particular foodborne pathogens is discussed.

Biolog Phenotype Microarrays for phenotypic characterization of microbial cells

Biolog Phenotype MicroArrays for microorganisms provide a high-throughput method for the global analysis of microbial growth phenotypes. Using a colorimetric reaction that is indicative of respiration, these microplate assays measure the response of an individual strain or microbial community to a large and diverse range of nutrients and chemicals. Phenotype MicroArrays have been used to study gene function and to improve genome annotation in single microorganisms and for physiological profiling of bacterial communities. The microplate system can be used to obtain a comprehensive overview of metabolic capability, or it can be tailored, through the use of subsets of plates, to address specific research needs.

Transcriptomic and biochemical analyses identify a family of chlorhexidine efflux proteins

Chlorhexidine is widely used as an antiseptic or disinfectant in both hospital and community settings. A number of bacterial species display resistance to this membrane-active biocide. We examined the transcriptomic response of a representative nosocomial human pathogen, Acinetobacter baumannii, to chlorhexidine to identify the primary chlorhexidine resistance elements. The most highly up-regulated genes encoded components of a major multidrug efflux system, AdeAB. The next most highly overexpressed gene under chlorhexidine stress was annotated as encoding a hypothetical protein, named here as AceI. Orthologs of the aceI gene are conserved within the genomes of a broad range of proteobacterial species. Expression of aceI or its orthologs from several other γ- or β-proteobacterial species in Escherichia coli resulted in significant increases in resistance to chlorhexidine. Additionally, disruption of the aceI ortholog in Acinetobacter baylyi rendered it more susceptible to chlorhexidine. The AceI protein was localized to the membrane after overexpression in E. coli. This protein was purified, and binding assays demonstrated direct and specific interactions between AceI and chlorhexidine. Transport assays using [(14)C]-chlorhexidine determined that AceI was able to mediate the energy-dependent efflux of chlorhexidine. An E15Q AceI mutant with a mutation in a conserved acidic residue, although unable to mediate chlorhexidine resistance and transport, was still able to bind chlorhexidine. Taken together, these data are consistent with AceI being an active chlorhexidine efflux protein and the founding member of a family of bacterial drug efflux transporters.

The Pseudomonas aeruginosa periplasmic protease CtpA can affect systems that impact its ability to mount both acute and chronic infections

Proteases play important roles in the virulence of Pseudomonas aeruginosa. Some are exported to act on host targets and facilitate tissue destruction and bacterial dissemination. Others work within the bacterial cell to process virulence factors and regulate virulence gene expression. Relatively little is known about the role of one class of bacterial serine proteases known as the carboxyl-terminal processing proteases (CTPs). The P. aeruginosa genome encodes two CTPs annotated as PA3257/Prc and PA5134/CtpA in strain PAO1. Prc degrades mutant forms of the anti-sigma factor MucA to promote mucoidy in some cystic fibrosis lung isolates. However, nothing is known about the role or importance of CtpA. We have now found that endogenous CtpA is a soluble periplasmic protein and that a ctpA null mutant has specific phenotypes consistent with an altered cell envelope. Although a ctpA null mutation has no major effect on bacterial growth in the laboratory, CtpA is essential for the normal function of the type 3 secretion system (T3SS), for cytotoxicity toward host cells, and for virulence in a mouse model of acute pneumonia. Conversely, increasing the amount of CtpA above its endogenous level induces an uncharacterized extracytoplasmic function sigma factor regulon, an event that has been reported to attenuate P. aeruginosa in a rat model of chronic lung infection. Therefore, a normal level of CtpA activity is critical for T3SS function and acute virulence, whereas too much activity can trigger an apparent stress response that is detrimental to chronic virulence.

Efficiently identifying significant associations in genome-wide association studies

Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75.

Global insights into energetic and metabolic networks in Rhodobacter sphaeroides

Background

Improving our understanding of processes at the core of cellular lifestyles can be aided by combining information from genetic analyses, high-throughput experiments and computational predictions.

Results

We combined data and predictions derived from phenotypic, physiological, genetic and computational analyses to dissect the metabolic and energetic networks of the facultative photosynthetic bacterium Rhodobacter sphaeroides. We focused our analysis on pathways crucial to the production and recycling of pyridine nucleotides during aerobic respiratory and anaerobic photosynthetic growth in the presence of an organic electron donor. In particular, we assessed the requirement for NADH/NADPH transhydrogenase enzyme, PntAB during respiratory and photosynthetic growth. Using high-throughput phenotype microarrays (PMs), we found that PntAB is essential for photosynthetic growth in the presence of many organic electron donors, particularly those predicted to require its activity to produce NADPH. Utilizing the genome-scale metabolic model iRsp1095, we predicted alternative routes of NADPH synthesis and used gene expression analyses to show that transcripts from a subset of the corresponding genes were conditionally increased in a ΔpntAB mutant. We then used a combination of metabolic flux predictions and mutational analysis to identify flux redistribution patterns utilized in the ΔpntAB mutant to compensate for the loss of this enzyme. Data generated from metabolic and phenotypic analyses of wild type and mutant cells were used to develop iRsp1140, an expanded genome-scale metabolic reconstruction for R. sphaeroides with improved ability to analyze and predict pathways associated with photosynthesis and other metabolic processes.

Conclusions

These analyses increased our understanding of key aspects of the photosynthetic lifestyle, highlighting the added importance of NADPH production under these conditions. It also led to a significant improvement in the predictive capabilities of a metabolic model for the different energetic lifestyles of a facultative organism.

DNA sequencing methods in human genetics and disease research

DNA sequencing has revolutionized biological and medical research, and is poised to have a similar impact in medicine. This tool is just one of a number of developments in our capability to identify, quantitate and functionally characterize the components of the biological networks keeping us healthy or making us sick, but in many respects it has played the leading role in this process. The new technologies do, however, also provide a bridge between genotype and phenotype, both in man and model (as well as all other) organisms, revolutionize the identification of elements involved in a multitude of human diseases or other phenotypes, and generate a wealth of medically relevant information on every single person, as the basis of a truly personalized medicine of the future.

Development of a carbohydrate-supplemented semidefined medium for the semiselective cultivation of Lactobacillus spp

The macronutrient and micronutrient compositions of traditional media used to cultivate Lactic Acid Bacteria (LAB) are largely undefined, which precludes their use in many metabolic bioassays. In order to address this deficiency, we developed MS: a carbohydrate-supplemented semidefined medium with low-background coloration. MS was designed to support the semiselective cultivation of a wide range of fastidious species belonging to the Lactobacillus clade of the LAB. When supplemented with 100 mM D-glucose, the MS medium stimulated the proliferation of 21 strains of LAB, including Pediococcus spp. and Lactobacillus spp. The MS medium supported biomass accumulation comparable with MRS, an undefined medium routinely used for the cultivation of lactobacilli. Interestingly, however, the novel MS medium exhibited greater semiselectivity against non-LAB than MRS. Together, these results suggest that MS is an acceptable alternative to MRS for use in metabolic and phenotypic bioassays that use a colorimetric reporter system or would benefit from a semidefined nutrient composition.

Charting microbial phenotypes in multiplex nanoliter batch bioreactors

High-throughput growth phenotyping is receiving great attention for establishing the genotype-phenotype map of sequenced organisms owing to the ready availability of complete genome sequences. To date, microbial growth phenotypes have been investigated mostly by the conventional method of batch cultivation using test tubes, Erlenmeyer flasks, or the recently available microwell plates. However, the current batch cultivation methods are time- and labor-intensive and often fail to consider sophisticated environmental changes. The implementation of batch cultures at the nanoliter scale has been difficult because of the quick evaporation of the culture medium inside the reactors. Here, we report a microfluidic system that allows independent cell cultures in evaporation-free multiplex nanoliter reactors under different culture conditions to assess the behavior of cells. The design allows three experimental replicates for each of eight culture environments in a single run. We demonstrate the versatility of the device by performing growth curve experiments with Escherichia coli and microbiological assays of antibiotics against the opportunistic pathogen Pseudomonas aeruginosa. Our study highlights that the microfluidic system can effectively replace the traditional batch culture methods with nanoliter volumes of bacterial cultivations, and it may be therefore promising for high-throughput growth phenotyping as well as for single-cell analyses.

Nutritional physiology of a rock-inhabiting, model microcolonial fungus from an ancestral lineage of the Chaetothyriales (Ascomycetes)

Rock-inhabiting black fungi [also microcolonial or meristematic fungi (MCF)] are a phylogenetically diverse group of melanised ascomycetes with distinctive morphological features that confer extensive stress tolerance and permit survival in hostile environments. The MCF strain A95 Knufia petricola (syn. Sarcinomyces petricola) belongs to an ancestral lineage of the order Chaetothyriales (class Eurotiomycetes). K. petricola strain A95 is a rock-inhabiting MCF and its growth requirements were studied using the 96-well plate-based Biolog System under ∼1070 different conditions (osmotic stress, pH growth optima, growth factor requirements and nutrient catabolism). A95 is an osmotolerant, oligotrophic MCF that grows best around pH 5. Remarkably, A95 shows metabolic activity in the absence of added nitrogen, phosphorus or sulphur. Correlations could be drawn between the known nutrient requirements of A95 and what probably is available in sub-aerial systems (rock and other material surfaces). Detailed knowledge of A95's metabolic requirements allowed formulation of a synthetic medium that supports strong fungal growth.

Using phenotype microarrays to determine culture conditions that induce or repress toxin production by Clostridium difficile and other microorganisms

Toxin production is a central issue in the pathogenesis of Clostridium difficile and many other pathogenic microorganisms. Toxin synthesis is influenced by a variety of known and unknown factors of genetics, physiology, and environment. To facilitate the study of toxin production by C. difficile, we have developed a new, reliable, quantitative, and robust cell-based cytotoxicity assay. Then we combined this new assay with Phenotype MicroArrays (PM) technology which provides high throughput testing of culture conditions. This allowed us to quantitatively measure toxin production by C. difficile type strain ATCC 9689 under 768 culture conditions. The culture conditions include different carbon, nitrogen, phosphorus, and sulfur sources. Among these, 89 conditions produced strong toxin induction and 31 produced strong toxin repression. Strong toxin inducers included adenine, guanosine, arginine dipeptides, γ-D-Glu-Gly, methylamine, and others. Some leucine dipeptides and the triple-leucine tripeptide were among the strongest toxin repressors. While some results are consistent with previous observations, others are new observations that provide insights into toxin regulation and pathogenesis of C. difficile. Additionally, we have demonstrated that this combined assay technology can be applied broadly to a wide range of toxin producing microorganisms. This study is the first demonstration of simultaneous assessment of a large number of culture conditions influencing bacterial toxin production. The new functional cytotoxin quantitation method developed provides a valuable tool for studying toxigenic microorganisms and may also find applications in clinical and epidemiological research.

Crp induces switching of the CsrB and CsrC RNAs in Yersinia pseudotuberculosis and links nutritional status to virulence

Colonization of the intestinal tract and dissemination into deeper tissues by the enteric pathogen Yersinia pseudotuberculosis demands expression of a special set of virulence factors important for the initiation and the persistence of the infection. In this study we demonstrate that many virulence-associated functions are coregulated with the carbohydrate metabolism. This link is mediated by the carbon storage regulator (Csr) system, including the regulatory RNAs CsrB and CsrC, and the cAMP receptor protein (Crp), which both control virulence gene expression in response to the nutrient composition of the medium. Here, we show that Crp regulates the synthesis of both Csr RNAs in an opposite manner. A loss of the crp gene resulted in a strong upregulation of CsrB synthesis, whereas CsrC levels were strongly reduced leading to downregulation of the virulence regulator RovA. Switching of the Csr RNA involves Crp-mediated repression of the response regulator UvrY which activates csrB transcription. To elucidate the regulatory links between virulence and carbon metabolism, we performed comparative metabolome, transcriptome, and phenotypic microarray analyses and found that Crp promotes oxidative catabolism of many different carbon sources, whereas fermentative patterns of metabolism are favored when crp is deleted. Mouse infection experiments further demonstrated that Crp is pivotal for a successful Y. pseudotuberculosis infection. In summary, placement of the Csr system and important virulence factors under control of Crp enables this pathogen to link its nutritional status to virulence in order to optimize biological fitness and infection efficiency through the infectious life cycle.

Optofluidic detection for cellular phenotyping

Quantitative analysis of the output of processes and molecular interactions within a single cell is highly critical to the advancement of accurate disease screening and personalized medicine. Optical detection is one of the most broadly adapted measurement methods in biological and clinical assays and serves cellular phenotyping. Recently, microfluidics has obtained increasing attention due to several advantages, such as small sample and reagent volumes, very high throughput, and accurate flow control in the spatial and temporal domains. Optofluidics, which is the attempt to integrate optics with microfluidics, shows great promise to enable on-chip phenotypic measurements with high precision, sensitivity, specificity, and simplicity. This paper reviews the most recent developments of optofluidic technologies for cellular phenotyping optical detection.

Extracytoplasmic function σ factors of the widely distributed group ECF41 contain a fused regulatory domain

Bacteria need signal transducing systems to respond to environmental changes. Next to one- and two-component systems, alternative σ factors of the extra-cytoplasmic function (ECF) protein family represent the third fundamental mechanism of bacterial signal transduction. A comprehensive classification of these proteins identified more than 40 phylogenetically distinct groups, most of which are not experimentally investigated. Here, we present the characterization of such a group with unique features, termed ECF41. Among analyzed bacterial genomes, ECF41 σ factors are widely distributed with about 400 proteins from 10 different phyla. They lack obvious anti-σ factors that typically control activity of other ECF σ factors, but their structural genes are often predicted to be cotranscribed with carboxymuconolactone decarboxylases, oxidoreductases, or epimerases based on genomic context conservation. We demonstrate for Bacillus licheniformis and Rhodobacter sphaeroides that the corresponding genes are preceded by a highly conserved promoter motif and are the only detectable targets of ECF41-dependent gene regulation. In contrast to other ECF σ factors, proteins of group ECF41 contain a large C-terminal extension, which is crucial for σ factor activity. Our data demonstrate that ECF41 σ factors are regulated by a novel mechanism based on the presence of a fused regulatory domain.

Coordinated phenotype switching with large-scale chromosome flip-flop inversion observed in bacteria

Genome inversions are ubiquitous in organisms ranging from prokaryotes to eukaryotes. Typical examples can be identified by comparing the genomes of two or more closely related organisms, where genome inversion footprints are clearly visible. Although the evolutionary implications of this phenomenon are huge, little is known about the function and biological meaning of this process. Here, we report our findings on a bacterium that generates a reversible, large-scale inversion of its chromosome (about half of its total genome) at high frequencies of up to once every four generations. This inversion switches on or off bacterial phenotypes, including colony morphology, antibiotic susceptibility, hemolytic activity, and expression of dozens of genes. Quantitative measurements and mathematical analyses indicate that this reversible switching is stochastic but self-organized so as to maintain two forms of stable cell populations (i.e., small colony variant, normal colony variant) as a bet-hedging strategy. Thus, this heritable and reversible genome fluctuation seems to govern the bacterial life cycle; it has a profound impact on the course and outcomes of bacterial infections.

Comparative multi-omics systems analysis of Escherichia coli strains B and K-12

BACKGROUND

Elucidation of a genotype-phenotype relationship is critical to understand an organism at the whole-system level. Here, we demonstrate that comparative analyses of multi-omics data combined with a computational modeling approach provide a framework for elucidating the phenotypic characteristics of organisms whose genomes are sequenced.

RESULTS

We present a comprehensive analysis of genome-wide measurements incorporating multifaceted holistic data - genome, transcriptome, proteome, and phenome - to determine the differences between Escherichia coli B and K-12 strains. A genome-scale metabolic network of E. coli B was reconstructed and used to identify genetic bases of the phenotypes unique to B compared with K-12 through in silico complementation testing. This systems analysis revealed that E. coli B is well-suited for production of recombinant proteins due to a greater capacity for amino acid biosynthesis, fewer proteases, and lack of flagella. Furthermore, E. coli B has an additional type II secretion system and a different cell wall and outer membrane composition predicted to be more favorable for protein secretion. In contrast, E. coli K-12 showed a higher expression of heat shock genes and was less susceptible to certain stress conditions.

CONCLUSIONS

This integrative systems approach provides a high-resolution system-wide view and insights into why two closely related strains of E. coli, B and K-12, manifest distinct phenotypes. Therefore, systematic understanding of cellular physiology and metabolism of the strains is essential not only to determine culture conditions but also to design recombinant hosts.

Global transcriptome response to ionic liquid by a tropical rain forest soil bacterium, Enterobacter lignolyticus

To process plant-based renewable biofuels, pretreatment of plant feedstock with ionic liquids has significant advantages over current methods for deconstruction of lignocellulosic feedstocks. However, ionic liquids are often toxic to the microorganisms used subsequently for biomass saccharification and fermentation. We previously isolated Enterobacter lignolyticus strain SCF1, a lignocellulolytic bacterium from tropical rain forest soil, and report here that it can grow in the presence of 0.5 M 1-ethyl-3-methylimidazolium chloride, a commonly used ionic liquid. We investigated molecular mechanisms of SCF1 ionic liquid tolerance using a combination of phenotypic growth assays, phospholipid fatty acid analysis, and RNA sequencing technologies. Potential modes of resistance to 1-ethyl-3-methylimidazolium chloride include an increase in cyclopropane fatty acids in the cell membrane, scavenging of compatible solutes, up-regulation of osmoprotectant transporters and drug efflux pumps, and down-regulation of membrane porins. These findings represent an important first step in understanding mechanisms of ionic liquid resistance in bacteria and provide a basis for engineering microbial tolerance.

Genotype-phenotype associations in a nonmodel prokaryote

To help define the biological functions of nonessential genes of Francisella novicida, we measured the growth of arrayed members of a comprehensive transposon mutant library under a variety of nutrition and stress conditions. Mutant phenotypes were identified for 37% of the genes, corresponding to ten carbon source utilization pathways, nine amino acid- and nucleotide-biosynthetic pathways, ten intrinsic antibiotic resistance traits, and six other stress resistance traits. The greatest surprise of the analysis was the large number of genotype-phenotype relationships that were not predictable from studies of Escherichia coli and other model species. The study identified candidate genes for a missing glycolysis function (phosphofructokinase), an unusual proline-biosynthetic pathway, parallel outer membrane lipid asymmetry maintenance systems, and novel antibiotic resistance functions. The analysis provides an evaluation of annotation predictions, identifies cases in which fundamental processes differ from those in model species, and helps create an empirical foundation for understanding virulence and other complex processes.

The value of genome sequences as foundations for analyzing complex traits in nonmodel organisms is limited by the need to rely almost exclusively on sequence similarities to predict gene functions in annotations. Many genes cannot be assigned functions, and some predictions are incorrect or incomplete. Due to these limitations, genome-scale experimental approaches that test and extend bioinformatics-based predictions are sorely needed. In this study, we describe such an approach based on phenotypic analysis of a comprehensive, sequence-defined transposon mutant library.

Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study

Genome-wide expression quantitative trait loci (eQTL) studies have emerged as a powerful tool to understand the genetic basis of gene expression and complex traits. In a typical eQTL study, the huge number of genetic markers and expression traits and their complicated correlations present a challenging multiple-testing correction problem. The resampling-based test using permutation or bootstrap procedures is a standard approach to address the multiple-testing problem in eQTL studies. A brute force application of the resampling-based test to large-scale eQTL data sets is often computationally infeasible. Several computationally efficient methods have been proposed to calculate approximate resampling-based P-values. However, these methods rely on certain assumptions about the correlation structure of the genetic markers, which may not be valid for certain studies. We propose a novel algorithm, rapid and exact multiple testing correction by resampling (REM), to address this challenge. REM calculates the exact resampling-based P-values in a computationally efficient manner. The computational advantage of REM lies in its strategy of pruning the search space by skipping genetic markers whose upper bounds on test statistics are small. REM does not rely on any assumption about the correlation structure of the genetic markers. It can be applied to a variety of resampling-based multiple-testing correction methods including permutation and bootstrap methods. We evaluate REM on three eQTL data sets (yeast, inbred mouse, and human rare variants) and show that it achieves accurate resampling-based P-value estimation with much less computational cost than existing methods. The software is available at http://csbio.unc.edu/eQTL.

Application of phenotypic microarrays to environmental microbiology

Environmental organisms are extremely diverse and only a small fraction has been successfully cultured in the laboratory. Culture in micro wells provides a method for rapid screening of a wide variety of growth conditions and commercially available plates contain a large number of substrates, nutrient sources, and inhibitors, which can provide an assessment of the phenotype of an organism. This review describes applications of phenotype arrays to anaerobic and thermophilic microorganisms, use of the plates in stress response studies, in development of culture media for newly discovered strains, and for assessment of phenotype of environmental communities. Also discussed are considerations and challenges in data interpretation and visualization, including data normalization, statistics, and curve fitting.

Large-scale comparative phenotypic and genomic analyses reveal ecological preferences of shewanella species and identify metabolic pathways conserved at the genus level

The use of comparative genomics for the study of different microbiological species has increased substantially as sequence technologies become more affordable. However, efforts to fully link a genotype to its phenotype remain limited to the development of one mutant at a time. In this study, we provided a high-throughput alternative to this limiting step by coupling comparative genomics to the use of phenotype arrays for five sequenced Shewanella strains. Positive phenotypes were obtained for 441 nutrients (C, N, P, and S sources), with N-based compounds being the most utilized for all strains. Many genes and pathways predicted by genome analyses were confirmed with the comparative phenotype assay, and three degradation pathways believed to be missing in Shewanella were confirmed as missing. A number of previously unknown gene products were predicted to be parts of pathways or to have a function, expanding the number of gene targets for future genetic analyses. Ecologically, the comparative high-throughput phenotype analysis provided insights into niche specialization among the five different strains. For example, Shewanella amazonensis strain SB2B, isolated from the Amazon River delta, was capable of utilizing 60 C compounds, whereas Shewanella sp. strain W3-18-1, isolated from deep marine sediment, utilized only 25 of them. In spite of the large number of nutrient sources yielding positive results, our study indicated that except for the N sources, they were not sufficiently informative to predict growth phenotypes from increasing evolutionary distances. Our results indicate the importance of phenotypic evaluation for confirming genome predictions. This strategy will accelerate the functional discovery of genes and provide an ecological framework for microbial genome sequencing projects.

Comparative genomics of 28 Salmonella enterica isolates: evidence for CRISPR-mediated adaptive sublineage evolution

Despite extensive surveillance, food-borne Salmonella enterica infections continue to be a significant burden on public health systems worldwide. As the S. enterica species comprises sublineages that differ greatly in antigenic representation, virulence, and antimicrobial resistance phenotypes, a better understanding of the species' evolution is critical for the prediction and prevention of future outbreaks. The roles that virulence and resistance phenotype acquisition, exchange, and loss play in the evolution of S. enterica sublineages, which to a certain extent are represented by serotypes, remains mostly uncharacterized. Here, we compare 17 newly sequenced and phenotypically characterized nontyphoidal S. enterica strains to 11 previously sequenced S. enterica genomes to carry out the most comprehensive comparative analysis of this species so far. These phenotypic and genotypic data comparisons in the phylogenetic species context suggest that the evolution of known S. enterica sublineages is mediated mostly by two mechanisms, (i) the loss of coding sequences with known metabolic functions, which leads to functional reduction, and (ii) the acquisition of horizontally transferred phage and plasmid DNA, which provides virulence and resistance functions and leads to increasing specialization. Matches between S. enterica clustered regularly interspaced short palindromic repeats (CRISPR), part of a defense mechanism against invading plasmid and phage DNA, and plasmid and prophage regions suggest that CRISPR-mediated immunity could control short-term phenotype changes and mediate long-term sublineage evolution. CRISPR analysis could therefore be critical in assessing the evolutionary potential of S. enterica sublineages and aid in the prediction and prevention of future S. enterica outbreaks.

Comparative genome-wide analysis of small RNAs of major Gram-positive pathogens: from identification to application

In the recent years, the number of drug- and multi-drug-resistant microbial strains has increased rapidly. Therefore, the need to identify innovative approaches for development of novel anti-infectives and new therapeutic targets is of high priority in global health care. The detection of small RNAs (sRNAs) in bacteria has attracted considerable attention as an emerging class of new gene expression regulators. Several experimental technologies to predict sRNA have been established for the Gram-negative model organism Escherichia coli. In many respects, sRNA screens in this model system have set a blueprint for the global and functional identification of sRNAs for Gram-positive microbes, but the functional role of sRNAs in colonization and pathogenicity for Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, Enterococcus faecalis and Clostridium difficile is almost completely unknown. Here, we report the current knowledge about the sRNAs of these socioeconomically relevant Gram-positive pathogens, overview the state-of-the-art high-throughput sRNA screening methods and summarize bioinformatics approaches for genome-wide sRNA identification and target prediction. Finally, we discuss the use of modified peptide nucleic acids (PNAs) as a novel tool to inactivate potential sRNA and their applications in rapid and specific detection of pathogenic bacteria.

Assay of the multiple energy-producing pathways of mammalian cells

BACKGROUND

To elucidate metabolic changes that occur in diabetes, obesity, and cancer, it is important to understand cellular energy metabolism pathways and their alterations in various cells.

METHODOLOGY AND PRINCIPAL FINDINGS

Here we describe a technology for simultaneous assessment of cellular energy metabolism pathways. The technology employs a redox dye chemistry specifically coupled to catabolic energy-producing pathways. Using this colorimetric assay, we show that human cancer cell lines from different organ tissues produce distinct profiles of metabolic activity. Further, we show that murine white and brown adipocyte cell lines produce profiles that are distinct from each other as well as from precursor cells undergoing differentiation.

CONCLUSIONS

This technology can be employed as a fundamental tool in genotype-phenotype studies to determine changes in cells from shared lineages due to differentiation or mutation.

Measuring growth rate in high-throughput growth phenotyping

Growth rate is an important variable and parameter in biology with a central role in evolutionary, functional genomics, and systems biology studies. In this review the pros and cons of the different technologies presently available for high-throughput measurements of growth rate are discussed. Growth rate can be measured in liquid microcultivation of individual strains, in competition between strains, as growing colonies on agar, as division of individual cells, and estimated from molecular reporters. Irrespective of methodology, statistical issues such as spatial biases and batch effects are crucial to investigate and correct for to ensure low false discovery rates. The rather low correlations between studies indicate that cross-laboratory comparison and standardization are pressing issue to assure high-quality and comparable growth-rate data.

The sensitivity of Bacillus subtilis to diverse antimicrobial compounds is influenced by Abh

Abh is a transition state regulator of Bacillus subtilis that controls biofilm formation and the production of several diverse antimicrobial compounds. Using a high-throughput non-biased technique, we show for the first time that Abh influences the sensitivity of B. subtilis to diverse antimicrobial compounds. Following up on these findings with a combination of classical genetics and antibiotic susceptibility assays, we demonstrate that Abh influences cellular processes such as the remodelling of the cell wall. We present data demonstrating that the extracytoplasmic function sigma factor σ(X) controls resistance to β-lactam antibiotics by activating abh transcription. Downstream from Abh, activation of slrR expression by Abh is responsible for controlling the sensitivity of B. subtilis to such antibiotics due to the role that SlrR plays in regulating autolysin biosynthesis. The abh mutant additionally exhibits increased resistance to aminoglycoside antimicrobials. We confirm that aminoglycoside killing of B. subtilis is likely to be caused by oxidative damage but rule out the possibility that the increased resistance of the abh mutant to aminoglycosides is due to a general increase in resistance to oxidative stress.

Regulatory exaptation of the catabolite repression protein (Crp)-cAMP system in Pseudomonas putida

The genome of the soil bacterium Pseudomonas putida KT2440 encodes singular orthologues of genes crp (encoding the catabolite repression protein, Crp) and cyaA (adenylate cyclase) of Escherichia coli. The levels of cAMP formed by P. putida cells were below detection with a Dictyostelium biosensor in vivo. The cyaA(P. putida) gene was transcribed in vivo but failed to complement the lack of maltose consumption of a cyaA mutant of E. coli, thereby indicating that cyaA(P. putida) was poorly translated or rendered non-functional in the heterologous host. Yet, generation of cAMP by CyaA(P. putida) could be verified by expressing the cyaA(P. putida) gene in a hypersensitive E. coli strain. On the other hand, the crp(P. putida) gene restored the metabolic capacities of an equivalent crp mutant of E. coli, but not in a double crp/cyaA strain, suggesting that the ability to regulate such functions required cAMP. In order to clarify the breadth of the Crp/cAMP system in P. putida, crp and cyaA mutants were generated and passed through a battery of phenotypic tests for recognition of gross metabolic properties and stress-endurance abilities. These assays revealed that the loss of each gene led in most (but not all) cases to the same phenotypic behaviour, indicating a concerted functionality. Unexpectedly, none of the mutations affected the panel of carbon compounds that can be used by P. putida as growth substrates, the mutants being impaired only in the use of various dipeptides as N sources. Furthermore, the lack of crp or cyaA had little influence on the gross growth fingerprinting of the cells. The poor physiological profile of the Crp-cAMP system of P. putida when compared with E. coli exposes a case of regulatory exaptation, i.e. the process through which a property evolved for a particular function is co-opted for a new use.

Global genotype-phenotype correlations in Pseudomonas aeruginosa

Once the genome sequence of an organism is obtained, attention turns from identifying genes to understanding their function, their organization and control of metabolic pathways and networks that determine its physiology. Recent technical advances in acquiring genome-wide data have led to substantial progress in identifying gene functions. However, we still do not know the function of a large number of genes and, even when a gene product has been assigned to a functional class, we cannot normally predict its contribution to the phenotypic behaviour of the cell or organism--the phenome. In this study, we assessed bacterial growth parameters of 4030 non-redundant PA14 transposon mutants in the pathogenic bacterium Pseudomonas aeruginosa. The genome-wide simultaneous analysis of 119 distinct growth-related phenotypes uncovered a comprehensive phenome and provided evidence that most genotypes are not phenotypically isolated but rather define specific complex phenotypic clusters of genotypes. Since phenotypic overlap was demonstrated to reflect the relatedness of genotypes on a global scale, knowledge of an organism's phenome might significantly contribute to the advancement of functional genomics.

Statistical methods for comparative phenomics using high-throughput phenotype microarrays

We propose statistical methods for comparing phenomics data generated by the Biolog Phenotype Microarray (PM) platform for high-throughput phenotyping. Instead of the routinely used visual inspection of data with no sound inferential basis, we develop two approaches. The first approach is based on quantifying the distance between mean or median curves from two treatments and then applying a permutation test; we also consider a permutation test applied to areas under mean curves. The second approach employs functional principal component analysis. Properties of the proposed methods are investigated on both simulated data and data sets from the PM platform.