The exact probability distribution of the rank product statistics for replicated experiments

The rank product method is a widely accepted technique for detecting differentially regulated genes in replicated microarray experiments. To approximate the sampling distribution of the rank product statistic, the original publication proposed a permutation approach, whereas recently an alternative approximation based on the continuous gamma distribution was suggested. However, both approximations are imperfect for estimating small tail probabilities. In this paper we relate the rank product statistic to number theory and provide a derivation of its exact probability distribution and the true tail probabilities.

LC-MS metabolomics from study design to data-analysis - using a versatile pathogen as a test case

Thanks to significant improvements in LC-MS technology, metabolomics is increasingly used as a tool to discriminate the responses of organisms to various stimuli or drugs. In this minireview we discuss all aspects of the LC-MS metabolomics pipeline, using a complex and versatile model organism, Leishmania donovani, as an illustrative example. The benefits of a hyphenated mass spectrometry platform and a detailed overview of the entire experimental pipeline from sampling, sample storage and sample list set-up to LC-MS measurements and the generation of meaningful results with state-of-the-art data-analysis software will be thoroughly discussed. Finally, we also highlight important pitfalls in the processing of LC-MS data and comment on the benefits of implementing metabolomics in a systems biology approach.

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.

mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data

MOTIVATION

Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography-mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch-ISO, a new extension to the metabolomics analysis pipeline mzMatch.R.

RESULTS

Targeted and untargeted isotope profiling using mzMatch-ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch-ISO by analysing a (13)C-labelled metabolome dataset from trypanosomal parasites.

AVAILABILITY

mzMatch.R and mzMatch-ISO are available free of charge from http://mzmatch.sourceforge.net and can be used on Linux and Windows platforms running the latest version of R.

CONTACT

rainer.breitling@manchester.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation

The combination of high-resolution LC–MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly ¹³C-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.

Human neuroblastoma cells with acquired resistance to the p53 activator RITA retain functional p53 and sensitivity to other p53 activating agents

Adaptation of wild-type p53 expressing UKF-NB-3 cancer cells to the murine double minute 2 inhibitor nutlin-3 causes de novo p53 mutations at high frequency (13/20) and multi-drug resistance. Here, we show that the same cells respond very differently when adapted to RITA, a drug that, like nutlin-3, also disrupts the p53/Mdm2 interaction. All of the 11 UKF-NB-3 sub-lines adapted to RITA that we established retained functional wild-type p53 although RITA induced a substantial p53 response. Moreover, all RITA-adapted cell lines remained sensitive to nutlin-3, whereas only five out of 10 nutlin-3-adapted cell lines retained their sensitivity to RITA. In addition, repeated adaptation of the RITA-adapted sub-line UKF-NB-3^rRITA^10 μM to nutlin-3 resulted in p53 mutations. The RITA-adapted UKF-NB-3 sub-lines displayed no or less pronounced resistance to vincristine, cisplatin, and irradiation than nutlin-3-adapted UKF-NB-3 sub-lines. Furthermore, adaptation to RITA was associated with fewer changes at the expression level of antiapoptotic factors than observed with adaptation to nutlin-3. Transcriptomic analyses indicated the RITA-adapted sub-lines to be more similar at the gene expression level to the parental UKF-NB-3 cells than nutlin-3-adapted UKF-NB-3 sub-lines, which correlates with the observed chemotherapy and irradiation sensitivity phenotypes. In conclusion, RITA-adapted cells retain functional p53, remain sensitive to nutlin-3, and display a less pronounced resistance phenotype than nutlin-3-adapted cells.

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or "awakening" of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology.

A turning point for natural product discovery--ESF-EMBO research conference: synthetic biology of antibiotic production

Synthetic Biology is in a critical phase of its development: it has finally reached the point where it can move from proof-of-principle studies to real-world applications. Secondary metabolite biosynthesis, especially the discovery and production of antibiotics, is a particularly relevant target area for such applications of synthetic biology. The first international conference to explore this subject was held in Spain in October 2011. In four sessions on General Synthetic Biology, Filamentous Fungal Systems, Actinomyces Systems, and Tools and Host Structures, scientists presented the most recent technological and scientific advances, and a final-day Forward Look Plenary Discussion identified future trends in the field.

msCompare: a framework for quantitative analysis of label-free LC-MS data for comparative candidate biomarker studies

Data processing forms an integral part of biomarker discovery and contributes significantly to the ultimate result. To compare and evaluate various publicly available open source label-free data processing workflows, we developed msCompare, a modular framework that allows the arbitrary combination of different feature detection/quantification and alignment/matching algorithms in conjunction with a novel scoring method to evaluate their overall performance. We used msCompare to assess the performance of workflows built from modules of publicly available data processing packages such as SuperHirn, OpenMS, and MZmine and our in-house developed modules on peptide-spiked urine and trypsin-digested cerebrospinal fluid (CSF) samples. We found that the quality of results varied greatly among workflows, and interestingly, heterogeneous combinations of algorithms often performed better than the homogenous workflows. Our scoring method showed that the union of feature matrices of different workflows outperformed the original homogenous workflows in some cases. msCompare is open source software (https://trac.nbic.nl/mscompare), and we provide a web-based data processing service for our framework by integration into the Galaxy server of the Netherlands Bioinformatics Center (http://galaxy.nbic.nl/galaxy) to allow scientists to determine which combination of modules provides the most accurate processing for their particular LC-MS data sets.

Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

Metabolomic analysis of a synthetic metabolic switch in Streptomyces coelicolor A3(2)

The global analysis of metabolism by liquid chromatography coupled to mass spectrometry is often hampered by a large amount of biological and technical variability. Here, we introduce an experimental and analytical strategy that can produce robust metabolome profiles in the face of this challenge. By applying a new computational approach based on concordance analysis to an extremely large number of analytical replicates, we are able to show that the overexpression of an antisense non-coding RNA targeting glutamine synthetase I results in a major reorganization of the metabolism of Streptomyces coelicolor, the model species of antibiotic-producing bacteria. We identified 97 metabolites with statistically significant reproducible dynamic behavior across the time series. The observed metabolic changes are very rapid, specific and widespread across metabolism, but focus on the nitrogen assimilation pathways. Our results demonstrate the power of highly replicated experimental designs for the robust characterization of metabolite dynamics. The identified global rearrangement of metabolism suggests the usefulness of RNA interference as an efficient strategy to manipulate the physiology of bacteria with wider biotechnological applicability in microorganisms.

Physiological adaptation of the bacterium Lactococcus lactis in response to the production of human CFTR

Biochemical and biophysical characterization of CFTR (the cystic fibrosis transmembrane conductance regulator) is thwarted by difficulties to obtain sufficient quantities of correctly folded and functional protein. Here we have produced human CFTR in the prokaryotic expression host Lactococcus lactis. The full-length protein was detected in the membrane of the bacterium, but the yields were too low (< 0.1% of membrane proteins) for in vitro functional and structural characterization, and induction of the expression of CFTR resulted in growth arrest. We used isobaric tagging for relative and absolute quantitation based quantitative proteomics to find out why production of CFTR in L. lactis was problematic. Protein abundances in membrane and soluble fractions were monitored as a function of induction time, both in CFTR expression cells and in control cells that did not express CFTR. Eight hundred and forty six proteins were identified and quantified (35% of the predicted proteome), including 163 integral membrane proteins. Expression of CFTR resulted in an increase in abundance of stress-related proteins (e.g. heat-shock and cell envelope stress), indicating the presence of misfolded proteins in the membrane. In contrast to the reported consequences of membrane protein overexpression in Escherichia coli, there were no indications that the membrane protein insertion machinery (Sec) became overloaded upon CFTR production in L. lactis. Nutrients and ATP became limiting in the control cells as the culture entered the late exponential and stationary growth phases but this did not happen in the CFTR expressing cells, which had stopped growing upon induction. The different stress responses elicited in E. coli and L. lactis upon membrane protein production indicate that different strategies are needed to overcome low expression yields and toxicity.

Prioritizing orphan proteins for further study using phylogenomics and gene expression profiles in Streptomyces coelicolor

Background

Streptomyces coelicolor, a model organism of antibiotic producing bacteria, has one of the largest genomes of the bacterial kingdom, including 7825 predicted protein coding genes. A large number of these genes, nearly 34%, are functionally orphan (hypothetical proteins with unknown function). However, in gene expression time course data, many of these functionally orphan genes show interesting expression patterns.

Results

In this paper, we analyzed all functionally orphan genes of Streptomyces coelicolor and identified a list of "high priority" orphans by combining gene expression analysis and additional phylogenetic information (i.e. the level of evolutionary conservation of each protein).

Conclusions

The prioritized orphan genes are promising candidates to be examined experimentally in the lab for further characterization of their function.

antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences

Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org.

Deletion of the signalling molecule synthase ScbA has pleiotropic effects on secondary metabolite biosynthesis, morphological differentiation and primary metabolism in Streptomyces coelicolor A3(2)

Streptomycetes have high biotechnological relevance as producers of diverse metabolites widely used in medical and agricultural applications. The biosynthesis of these metabolites is controlled by signalling molecules, γ-butyrolactones, that act as bacterial hormones. In Streptomyces coelicolor, a group of signalling molecules called SCBs (S. coelicolorbutanolides) regulates production of the pigmented antibiotics coelicolor polyketide (CPK), actinorhodin and undecylprodigiosin. The γ-butyrolactone synthase ScbA is responsible for the biosynthesis of SCBs. Here we show the results of a genome-wide transcriptome analysis of a scbA deletion mutant prior to and during the transition to antibiotic production. We report a strong perturbation in the expression of three pigmented antibiotic clusters in the mutant throughout the growth curve, thus providing a molecular explanation for the antibiotic phenotype observed previously. Our study also revealed, for the first time, that the secondary metabolite cluster responsible for synthesis of the siderophore desferrioxamine is under the control of SCB signalling. Moreover, expression of the genes encoding enzymes for primary metabolism pathways, which supply antibiotic precursors and genes for morphological differentiation, was found shifted earlier in time in the mutant. In conclusion, our time series analysis demonstrates new details of the regulatory effects of the γ-butyrolactone system in Streptomyces.

Synthetic biology in Streptomyces bacteria

Actinomycete bacteria of the genus Streptomyces are major producers of bioactive compounds for the biotechnology industry. They are the source of most clinically used antibiotics, as well as of several widely used drugs against common diseases, including cancer . Genome sequencing has revealed that the potential of Streptomyces species for the production of valuable secondary metabolites is even larger than previously realized. Accessing this rich genomic resource to discover new compounds by activating "cryptic" pathways is an interesting challenge for synthetic biology. This approach is facilitated by the inherent natural modularity of secondary metabolite biosynthetic pathways, at the level of individual enzymes (such as modular polyketide synthases), but also of gene cassettes/operons and entire biosynthetic gene clusters. It also benefits from a long tradition of molecular biology in Streptomyces, which provides a number of specific tools, ranging from cloning vectors to inducible promoters and translational control elements. In this chapter, we first provide an overview of the synthetic biology challenges in Streptomyces and then present the existing toolbox of molecular methods that can be employed in this organism.

Genome-wide gene expression changes in an industrial clavulanic acid overproduction strain of Streptomyces clavuligerus

To increase production of the important pharmaceutical compound clavulanic acid, a β-lactamase inhibitor, both random mutagenesis approaches and rational engineering of Streptomyces clavuligerus strains have been extensively applied. Here, for the first time, we compared genome-wide gene expression of an industrial S. clavuligerus strain, obtained through iterative mutagenesis, with that of the wild-type strain. Intriguingly, we found that the majority of the changes contributed not to a complex rewiring of primary metabolism but consisted of a simple upregulation of various antibiotic biosynthesis gene clusters. A few additional transcriptional changes in primary metabolism at key points seem to divert metabolic fluxes to the biosynthetic precursors for clavulanic acid. In general, the observed changes largely coincide with genes that have been targeted by rational engineering in recent years, yet the presence of a number of previously unexplored genes clearly demonstrates that functional genomic analysis can provide new leads for strain improvement in biotechnology.

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

BACKGROUND

Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.

RESULTS

We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset.

CONCLUSIONS

DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.