An adaptable in silico ensemble model of the arachidonic acid cascade.

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

Bioproduction of methylated phenylpropenes and isoeugenol in Escherichia coli.

Phenylpropenes are a class of natural products that are synthesised by a vast range of plant species and hold considerable promise in the flavour and fragrance industries. Many in vitro studies have been carried out to elucidate and characterise the enzymes responsible for the production of these volatile compounds. However, there is a scarcity of studies demonstrating the in vivo production of phenylpropenes in microbial cell factories. In this study, we engineered Escherichia coli to produce methylchavicol, methyleugenol and isoeugenol from their respective phenylacrylic acid precursors. We achieved this by extending and modifying a previously optimised heterologous pathway for the biosynthesis of chavicol and eugenol. We explored the potential of six S-adenosyl l-methionine (SAM)-dependent O-methyltransferases to produce methylchavicol and methyleugenol from chavicol and eugenol, respectively. Additionally, we examined two isoeugenol synthases for the production of isoeugenol from coniferyl acetate. The best-performing strains in this study were able to achieve titres of 13 mg L-1 methylchavicol, 59 mg L-1 methyleugenol and 361 mg L-1 isoeugenol after feeding with their appropriate phenylacrylic acid substrates. We were able to further increase the methyleugenol titre to 117 mg L-1 by supplementation with methionine to facilitate SAM recycling. Moreover, we report the biosynthesis of methylchavicol and methyleugenol from l-tyrosine through pathways involving six and eight enzymatic steps, respectively.

Data integration and mechanistic modelling for breast cancer biology: Current state and future directions.

Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of 'big data' and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.

Investigation of the effects of actinorhodin biosynthetic gene cluster expression and a rpoB point mutation on the metabolome of Streptomyces coelicolor M1146.

The previously reported Streptomyces coelicolor M1146 is commonly used as a host strain for engineering of secondary metabolite production. In this study, absolute quantification of intracellular and extracellular metabolites of M1146 was performed in mid-log phase and stationary phase to observe major metabolites and the changes that occurred during growth. Decreased levels of central carbon metabolites (glycolysis, TCA cycle, and pentose phosphate pathway) and increased levels of amino acids were observed in stationary phase compared to mid-log phase. Furthermore, comparative metabolome analyses of M1146 upon expression of the actinorhodin biosynthetic gene cluster (M1146+ACT), a point mutation on the rpoB gene encoding RNA polymerase beta-subunit (M1152), and both expression of actinorhodin biosynthetic gene cluster and a rpoB point mutation (M1152+ACT) were performed. M1146+ACT showed higher levels of important cofactors, such as ATP, NADPH, and FMN while M1152 led to higher levels of intracellular S-adenosyl-methionine, acyl-CoAs, and extracellular nucleosides compared to M1146. M1152+ACT exhibited the highest levels of actinorhodin with elevated bases, nucleosides, and nucleotides, such as intracellular PRPP (phosphoribosyl phosphate), ATP, along with extracellular inosine, uridine, and guanine compared to the other three strains, which were considered to be combined effects of actinorhodin gene cluster expression and a rpoB point mutation. Metabolites analysis by means of absolute quantification demonstrated changes in precursors of secondary metabolites before and after phosphate depletion in M1146. Comparative metabolome analysis provided further insights into the effects of actinorhodin gene cluster expression along with a rpoB point mutation on the metabolome of S. coelicolor.

Multi-Omics Analysis of the Effect of cAMP on Actinorhodin Production in Streptomyces coelicolor.

Cyclic adenosine monophosphate (cAMP) has been known to play an important role in regulating morphological development and antibiotic production in Streptomyces coelicolor. However, the functional connection between cAMP levels and antibiotic production and the mechanism by which cAMP regulates antibiotic production remain unclear. In this study, metabolomics- and transcriptomics-based multi-omics analysis was applied to S. coelicolor strains that either produce the secondary metabolite actinorhodin (Act) or lack most secondary metabolite biosynthesis pathways including Act. Comparative multi-omics analysis of the two strains revealed that intracellular and extracellular cAMP abundance was strongly correlated with actinorhodin production. Notably, supplementation of cAMP improved cell growth and antibiotic production. Further multi-omics analysis of cAMP-supplemented S. coelicolor cultures showed an increase of guanine and the expression level of purine metabolism genes. Based on this phenomenon, supplementation with 7-methylguanine, a competitive inhibitor of reactions utilizing guanine, with or without additional cAMP supplementation, was performed. This experiment revealed that the reactions inhibited by 7-methylguanine are mediating the positive effect on growth and antibiotic production, which may occur downstream of cAMP supplementation.

Engineering Escherichia coli towards de novo production of gatekeeper (2S)-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol.

Natural plant-based flavonoids have drawn significant attention as dietary supplements due to their potential health benefits, including anti-cancer, anti-oxidant and anti-asthmatic activities. Naringenin, pinocembrin, eriodictyol and homoeriodictyol are classified as (2S)-flavanones, an important sub-group of naturally occurring flavonoids, with wide-reaching applications in human health and nutrition. These four compounds occupy a central position as branch point intermediates towards a broad spectrum of naturally occurring flavonoids. Here, we report the development of Escherichia coli production chassis for each of these key gatekeeper flavonoids. Selection of key enzymes, genetic construct design and the optimization of process conditions resulted in the highest reported titers for naringenin (484 mg/l), improved production of pinocembrin (198 mg/l) and eriodictyol (55 mg/l from caffeic acid), and provided the first example of in vivo production of homoeriodictyol directly from glycerol (17 mg/l). This work provides a springboard for future production of diverse downstream natural and non-natural flavonoid targets.

Rational cell culture optimization enhances experimental reproducibility in cancer cells.

Optimization of experimental conditions is critical in ensuring robust experimental reproducibility. Through detailed metabolomic analysis we found that cell culture conditions significantly impacted on glutaminase (GLS1) sensitivity resulting in variable sensitivity and irreproducibility in data. Baseline metabolite profiling highlighted that untreated cells underwent significant changes in metabolic status. Both the extracellular levels of glutamine and lactate and the intracellular levels of multiple metabolites changed drastically during the assay. We show that these changes compromise the robustness of the assay and make it difficult to reproduce. We discuss the implications of the cells' metabolic environment when studying the effects of perturbations to cell function by any type of inhibitor. We then devised 'metabolically rationalized standard' assay conditions, in which glutaminase-1 inhibition reduced glutamine metabolism differently in both cell lines assayed, and decreased the proliferation of one of them. The adoption of optimized conditions such as the ones described here should lead to an improvement in reproducibility and help eliminate false negatives as well as false positives in these assays.

antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification.

Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

Pep2Path: automated mass spectrometry-guided genome mining of peptidic natural products.

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.

MetAssign: probabilistic annotation of metabolites from LC-MS data using a Bayesian clustering approach.

MOTIVATION: The use of liquid chromatography coupled to mass spectrometry has enabled the high-throughput profiling of the metabolite composition of biological samples. However, the large amount of data obtained can be difficult to analyse and often requires computational processing to understand which metabolites are present in a sample. This article looks at the dual problem of annotating peaks in a sample with a metabolite, together with putatively annotating whether a metabolite is present in the sample. The starting point of the approach is a Bayesian clustering of peaks into groups, each corresponding to putative adducts and isotopes of a single metabolite. RESULTS: The Bayesian modelling introduced here combines information from the mass-to-charge ratio, retention time and intensity of each peak, together with a model of the inter-peak dependency structure, to increase the accuracy of peak annotation. The results inherently contain a quantitative estimate of confidence in the peak annotations and allow an accurate trade-off between precision and recall. Extensive validation experiments using authentic chemical standards show that this system is able to produce more accurate putative identifications than other state-of-the-art systems, while at the same time giving a probabilistic measure of confidence in the annotations. AVAILABILITY AND IMPLEMENTATION: The software has been implemented as part of the mzMatch metabolomics analysis pipeline, which is available for download at http://mzmatch.sourceforge.net/.

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.

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.

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.

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.

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.

Anti-cancer effects of artesunate in a panel of chemoresistant neuroblastoma cell lines.

Artemisinin derivatives are well-tolerated anti-malaria drugs that also exert anti-cancer activity. Here, we investigated artemisinin and its derivatives dihydroartemisinin and artesunate in a panel of chemosensitive and chemoresistant human neuroblastoma cells as well as in primary neuroblastoma cultures. Only dihydroartemisinin and artesunate affected neuroblastoma cell viability with artesunate being more active. Artesunate-induced apoptosis and reactive oxygen species in neuroblastoma cells. Of 16 cell lines and two primary cultures, only UKF-NB-3(r)CDDP(1000) showed low sensitivity to artesunate. Characteristic gene expression signatures based on a previous analysis of artesunate resistance in the NCI60 cell line panel clearly separated UKF-NB-3(r)CDDP(1000) from the other cell lines. l-Buthionine-S,R-sulfoximine, an inhibitor of GCL (glutamate-cysteine ligase), resensitised in part UKF-NB-3(r)CDDP(1000) cells to artesunate. This finding together with bioinformatic analysis of expression of genes involved in glutathione metabolism showed that this pathway is involved in artesunate resistance. These data indicate that neuroblastoma represents an artesunate-sensitive cancer entity and that artesunate is also effective in chemoresistant neuroblastoma cells.

Chemoresistance acquisition induces a global shift of expression of aniogenesis-associated genes and increased pro-angogenic activity in neuroblastoma cells.

BACKGROUND: Chemoresistance acquisition may influence cancer cell biology. Here, bioinformatics analysis of gene expression data was used to identify chemoresistance-associated changes in neuroblastoma biology. RESULTS: Bioinformatics analysis of gene expression data revealed that expression of angiogenesis-associated genes significantly differs between chemosensitive and chemoresistant neuroblastoma cells. A subsequent systematic analysis of a panel of 14 chemosensitive and chemoresistant neuroblastoma cell lines in vitro and in animal experiments indicated a consistent shift to a more pro-angiogenic phenotype in chemoresistant neuroblastoma cells. The molecular mechanisms underlying increased pro-angiogenic activity of neuroblastoma cells are individual and differ between the investigated chemoresistant cell lines. Treatment of animals carrying doxorubicin-resistant neuroblastoma xenografts with doxorubicin, a cytotoxic drug known to exert anti-angiogenic activity, resulted in decreased tumour vessel formation and growth indicating chemoresistance-associated enhanced pro-angiogenic activity to be relevant for tumour progression and to represent a potential therapeutic target. CONCLUSION: A bioinformatics approach allowed to identify a relevant chemoresistance-associated shift in neuroblastoma cell biology. The chemoresistance-associated enhanced pro-angiogenic activity observed in neuroblastoma cells is relevant for tumour progression and represents a potential therapeutic target.

Expression quantitative trait loci are highly sensitive to cellular differentiation state.

Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of "static" eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of "dynamic" eQTLs showing cell-type-dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.

Analyses of intricate kinetics of the serum proteome during and after colon surgery by protein expression time series.

Monitoring changes in serum protein expression in response to acute events such as trauma, infection or drug intervention may reveal key proteins of great value in predicting recovery or treatment response. Concerted actions of many proteins are expected. Proteins sharing similar expression changes may function in the same physiological process. As a model we analyzed expression changes in serum of colon cancer patients, before, during, and after laparoscopic colon resection. Eight samples were taken from each of four patients before, during, and up to 5 days after surgery. Total serum and a low molecular weight fraction were analyzed by SELDI-TOF-MS. In total 146 masses were detected. A principal components analysis (PCA) illustrates the temporal variation in the postsurgery proteome. Time series for each mass could be clustered into four distinct groups based on similarity in expression pattern. Two masses of 11.4 and 11.6 kDa, part of a slow response cluster, were identified as forms of the acute phase protein serum amyloid A (SAA). Fourteen more proteins belong to this cluster and may also function in acute phase response. We present an approach to analyze temporal variation in the proteome. This approach may be useful to evaluate surgical, nutritional, and pharmacological interventions.

Regulation of ubiquitin-binding proteins by monoubiquitination.

Proteins containing ubiquitin-binding domains (UBDs) interact with ubiquitinated targets and regulate diverse biological processes, including endocytosis, signal transduction, transcription and DNA repair. Many of the UBD-containing proteins are also themselves monoubiquitinated, but the functional role and the mechanisms that underlie this modification are less well understood. Here, we demonstrate that monoubiquitination of the endocytic proteins Sts1, Sts2, Eps15 and Hrs results in intramolecular interactions between ubiquitin and their UBDs, thereby preventing them from binding in trans to ubiquitinated targets. Permanent monoubiquitination of these proteins, mimicked by the fusion of ubiquitin to their carboxyl termini, impairs their ability to regulate trafficking of ubiquitinated receptors. Moreover, we mapped the in vivo monoubiquitination site in Sts2 and demonstrated that its mutation enhances the Sts2-mediated effects of epidermal-growth-factor-receptor downregulation. We propose that monoubiquitination of ubiquitin-binding proteins inhibits their capacity to bind to and control the functions of ubiquitinated targets in vivo.