Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example

Graph Exploration With Embedding-Guided Layouts

Node-link diagrams are widely used to visualize graphs. Most graph layout algorithms only use graph topology for aesthetic goals (e.g., minimize node occlusions and edge crossings) or use node attributes for exploration goals (e.g., preserve visible communities). Existing hybrid methods that bind the two perspectives still suffer from various generation restrictions (e.g., limited input types and required manual adjustments and prior knowledge of graphs) and the imbalance between aesthetic and exploration goals. In this article, we propose a flexible embedding-based graph exploration pipeline to enjoy the best of both graph topology and node attributes. First, we leverage embedding algorithms for attributed graphs to encode the two perspectives into latent space. Then, we present an embedding-driven graph layout algorithm, GEGraph, which can achieve aesthetic layouts with better community preservation to support an easy interpretation of the graph structure. Next, graph explorations are extended based on the generated graph layout and insights extracted from the embedding vectors. Illustrated with examples, we build a layout-preserving aggregation method with Focus+Context interaction and a related nodes searching approach with multiple proximity strategies. Finally, we conduct quantitative and qualitative evaluations, a user study, and two case studies to validate our approach.

Metagenomic and metabolic analyses of poly-extreme microbiome from an active crater volcano lake

El Chichón volcano is one of the most active volcanoes in Mexico. Previous studies have described its poly-extreme conditions and its bacterial composition, although the functional features of the complete microbiome have not been characterized yet. By using metabarcoding analysis, metagenomics, metabolomics and enzymology techniques, the microbiome of the crater lake was characterized in this study. New information is provided on the taxonomic and functional diversity of the representative Archaea phyla, Crenarchaeota and Euryarchaeota, as well as those that are representative of Bacteria, Thermotogales and Aquificae. With culture of microbial consortia and with the genetic information collected from the natural environment sampling, metabolic interactions were identified between prokaryotes, which can withstand multiple extreme conditions. The existence of a close relationship between the biogeochemical cycles of carbon and sulfur in an active volcano has been proposed, while the relationship in the energy metabolism of thermoacidophilic bacteria and archaea in this multi-extreme environment was biochemically revealed for the first time. These findings contribute towards understanding microbial metabolism under extreme conditions, and provide potential knowledge pertaining to "microbial dark matter", which can be applied to biotechnological processes and evolutionary studies.

Plants Metabolome Study: Emerging Tools and Techniques

Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

When Transcriptomics and Metabolomics Work Hand in Hand: A Case Study Characterizing Plant CDF Transcription Factors

Over the last three decades, novel “omics” platform technologies for the sequencing of DNA and complementary DNA (cDNA) (RNA-Seq), as well as for the analysis of proteins and metabolites by mass spectrometry, have become more and more available and increasingly found their way into general laboratory life. With this, the ability to generate highly multivariate datasets on the biological systems of choice has increased tremendously. However, the processing and, perhaps even more importantly, the integration of “omics” datasets still remains a bottleneck, although considerable computational and algorithmic advances have been made in recent years. In this mini-review, we use a number of recent “multi-omics” approaches realized in our laboratories as a common theme to discuss possible pitfalls of applying “omics” approaches and to highlight some useful tools for data integration and visualization in the form of an exemplified case study. In the selected example, we used a combination of transcriptomics and metabolomics alongside phenotypic analyses to functionally characterize a small number of Cycling Dof Transcription Factors (CDFs). It has to be remarked that, even though this approach is broadly used, the given workflow is only one of plenty possible ways to characterize target proteins.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCoreTM, MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

Integration of omics: more than the sum of its parts

Genome scale data on biological systems has increasingly become available by sequencing of DNA and RNA, and by mass spectrometric quantification of proteins and metabolites. The cellular components from which these -omics regimes are derived act as one integrated system in vivo; thus, there is a natural instinct to integrate -omics data types. Statistical analyses, the use of previous knowledge in the form of networks, and the use of time-resolved measurements are three key design elements for life scientists to consider in planning integrated -omics studies. These design elements are reviewed in the context of multiple recent systems biology studies that leverage data from different types of -omics analyses. While most of these studies rely on well-established model organisms, the concepts for integrating -omics data that were developed in these studies can help to enable systems research in the field of cancer biology.

Defining bacterial regulons using ChIP-seq

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is a powerful method that identifies protein-DNA binding sites in vivo. Recent studies have illustrated the value of ChIP-seq in studying transcription factor binding in various bacterial species under a variety of growth conditions. These results show that in addition to identifying binding sites, correlation of ChIP-seq data with expression data can reveal important information about bacterial regulons and regulatory networks. In this chapter, we provide an overview of the current state of knowledge about ChIP-seq methodology in bacteria, from sample preparation to raw data analysis. We also describe visualization and various bioinformatic analyses of processed ChIP-seq data.

PathVisio 3: an extendable pathway analysis toolbox

PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways. Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application. PathVisio can be downloaded from http://www.pathvisio.org and in 2014 PathVisio 3 has been downloaded over 5,500 times. There are already more than 15 plugins available in the central plugin repository. PathVisio is a freely available, open-source tool published under the Apache 2.0 license (http://www.apache.org/licenses/LICENSE-2.0). It is implemented in Java and thus runs on all major operating systems. The code repository is available at http://svn.bigcat.unimaas.nl/pathvisio. The support mailing list for users is available on https://groups.google.com/forum/#!forum/wikipathways-discuss and for developers on https://groups.google.com/forum/#!forum/wikipathways-devel.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

Interactions between the jasmonic and salicylic acid pathway modulate the plant metabolome and affect herbivores of different feeding types

The phytohormones jasmonic acid (JA) and salicylic acid (SA) mediate induced plant defences and the corresponding pathways interact in a complex manner as has been shown on the transcript and proteine level. Downstream, metabolic changes are important for plant-herbivore interactions. This study investigated metabolic changes in leaf tissue and phloem exudates of Plantago lanceolata after single and combined JA and SA applications as well as consequences on chewing-biting (Heliothis virescens) and piercing-sucking (Myzus persicae) herbivores. Targeted metabolite profiling and untargeted metabolic fingerprinting uncovered different categories of plant metabolites, which were influenced in a specific manner, indicating points of divergence, convergence, positive crosstalk and pronounced mutual antagonism between the signaling pathways. Phytohormone-specific decreases of primary metabolite pool sizes in the phloem exudates may indicate shifts in sink-source relations, resource allocation, nutrient uptake or photosynthesis. Survival of both herbivore species was significantly reduced by JA and SA treatments. However, the combined application of JA and SA attenuated the negative effects at least against H. virescens suggesting that mutual antagonism between the JA and SA pathway may be responsible. Pathway interactions provide a great regulatory potential for the plant that allows triggering of appropriate defences when attacked by different antagonist species.

Data visualization in environmental proteomics

From raw data to gene expression profiles, from single cultures to complex microbial communities, environmental proteomics works with data of different complexity levels that need to be interpreted in detail or in its entirety. Although data visualization is closely connected with data analysis approaches, this work will solely focus on data visualization. Complementing traditional tools such as bar charts or line graphs, scientists and visualization professionals have been provided sophisticated visualization tools. Many rules and concerns regarding the display of single but also complex data will be reviewed and discussed. Visual approaches such as microcharts, heat maps, stream graphs, and tree maps will be brought to the reader's attention and demonstrated by utilizing real data sets.

Protein S-mycothiolation functions as redox-switch and thiol protection mechanism in Corynebacterium glutamicum under hypochlorite stress

AIMS

Protein S-bacillithiolation was recently discovered as important thiol protection and redox-switch mechanism in response to hypochlorite stress in Firmicutes bacteria. Here we used transcriptomics to analyze the NaOCl stress response in the mycothiol (MSH)-producing Corynebacterium glutamicum. We further applied thiol-redox proteomics and mass spectrometry (MS) to identify protein S-mycothiolation.

RESULTS

Transcriptomics revealed the strong upregulation of the disulfide stress σ(H) regulon by NaOCl stress in C. glutamicum, including genes for the anti sigma factor (rshA), the thioredoxin and MSH pathways (trxB1, trxC, cg1375, trxB, mshC, mca, mtr) that maintain the redox balance. We identified 25 S-mycothiolated proteins in NaOCl-treated cells by liquid chromatography-tandem mass spectrometry (LC-MS/MS), including 16 proteins that are reversibly oxidized by NaOCl in the thiol-redox proteome. The S-mycothiolome includes the methionine synthase (MetE), the maltodextrin phosphorylase (MalP), the myoinositol-1-phosphate synthase (Ino1), enzymes for the biosynthesis of nucleotides (GuaB1, GuaB2, PurL, NadC), and thiamine (ThiD), translation proteins (TufA, PheT, RpsF, RplM, RpsM, RpsC), and antioxidant enzymes (Tpx, Gpx, MsrA). We further show that S-mycothiolation of the thiol peroxidase (Tpx) affects its peroxiredoxin activity in vitro that can be restored by mycoredoxin1. LC-MS/MS analysis further identified 8 proteins with S-cysteinylations in the mshC mutant suggesting that cysteine can be used for S-thiolations in the absence of MSH.

INNOVATION AND CONCLUSION

We identified widespread protein S-mycothiolations in the MSH-producing C. glutamicum and demonstrate that S-mycothiolation reversibly affects the peroxidase activity of Tpx. Interestingly, many targets are conserved S-thiolated across bacillithiol- and MSH-producing bacteria, which could become future drug targets in related pathogenic Gram-positives.

Metabolic flux pattern of glucose utilization by Xanthomonas campestris pv. campestris: prevalent role of the Entner–Doudoroff pathway and minor fluxes through the pentose phosphate pathway and glycolysis

Complex metabolic flux pattern ofX. campestris.

The bacterial response regulator ArcA uses a diverse binding site architecture to regulate carbon oxidation globally

Despite the importance of maintaining redox homeostasis for cellular viability, how cells control redox balance globally is poorly understood. Here we provide new mechanistic insight into how the balance between reduced and oxidized electron carriers is regulated at the level of gene expression by mapping the regulon of the response regulator ArcA from Escherichia coli, which responds to the quinone/quinol redox couple via its membrane-bound sensor kinase, ArcB. Our genome-wide analysis reveals that ArcA reprograms metabolism under anaerobic conditions such that carbon oxidation pathways that recycle redox carriers via respiration are transcriptionally repressed by ArcA. We propose that this strategy favors use of catabolic pathways that recycle redox carriers via fermentation akin to lactate production in mammalian cells. Unexpectedly, bioinformatic analysis of the sequences bound by ArcA in ChIP-seq revealed that most ArcA binding sites contain additional direct repeat elements beyond the two required for binding an ArcA dimer. DNase I footprinting assays suggest that non-canonical arrangements of cis-regulatory modules dictate both the length and concentration-sensitive occupancy of DNA sites. We propose that this plasticity in ArcA binding site architecture provides both an efficient means of encoding binding sites for ArcA, σ(70)-RNAP and perhaps other transcription factors within the same narrow sequence space and an effective mechanism for global control of carbon metabolism to maintain redox homeostasis.

MeltDB 2.0-advances of the metabolomics software system

Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization.

Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions.

Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de. A login is required but freely available.

Contact:nkessler@cebitec.uni-bielefeld.de

3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data

Background

Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment.

Results

3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics’ coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics’ phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature.

Conclusions

3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.

Genome-scale analysis of escherichia coli FNR reveals complex features of transcription factor binding

FNR is a well-studied global regulator of anaerobiosis, which is widely conserved across bacteria. Despite the importance of FNR and anaerobiosis in microbial lifestyles, the factors that influence its function on a genome-wide scale are poorly understood. Here, we report a functional genomic analysis of FNR action. We find that FNR occupancy at many target sites is strongly influenced by nucleoid-associated proteins (NAPs) that restrict access to many FNR binding sites. At a genome-wide level, only a subset of predicted FNR binding sites were bound under anaerobic fermentative conditions and many appeared to be masked by the NAPs H-NS, IHF and Fis. Similar assays in cells lacking H-NS and its paralog StpA showed increased FNR occupancy at sites bound by H-NS in WT strains, indicating that large regions of the genome are not readily accessible for FNR binding. Genome accessibility may also explain our finding that genome-wide FNR occupancy did not correlate with the match to consensus at binding sites, suggesting that significant variation in ChIP signal was attributable to cross-linking or immunoprecipitation efficiency rather than differences in binding affinities for FNR sites. Correlation of FNR ChIP-seq peaks with transcriptomic data showed that less than half of the FNR-regulated operons could be attributed to direct FNR binding. Conversely, FNR bound some promoters without regulating expression presumably requiring changes in activity of condition-specific transcription factors. Such combinatorial regulation may allow Escherichia coli to respond rapidly to environmental changes and confer an ecological advantage in the anaerobic but nutrient-fluctuating environment of the mammalian gut.

Regulation of gene expression by transcription factors (TFs) is key to adaptation to environmental changes. Our comprehensive, genome-scale analysis of a prototypical global TF, the anaerobic regulator FNR from Escherichia coli, leads to several novel and unanticipated insights into the influences on FNR binding genome-wide and the complex structure of bacterial regulons. We found that binding of NAPs restricts FNR binding at a subset of sites, suggesting that the bacterial genome is not freely accessible for FNR binding. Our finding that less than half of the predicted FNR binding sites were occupied in vivo further challenges the utility of using bioinformatic searches alone to predict regulon structure, reinforcing the need for experimental determination of TF binding. By correlating the occupancy data with transcriptomic data, we confirm that FNR serves as a global signal of anaerobiosis but expression of some operons in the FNR regulon require other regulators sensitive to alternative environmental stimuli. Thus, FNR binding and regulation appear to depend on both the nucleoprotein structure of the chromosome and on combinatorial binding of FNR with other regulators. Both of these phenomena are typical of TF binding in eukaryotes; our results establish that they are also features of bacterial TF binding.

Computational tools for the secondary analysis of metabolomics experiments

Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.

Proteomics approaches for the analysis of enriched microbial subpopulations and visualization of complex functional information

Advances in the separation of microbial subpopulations and in proteomics technologies have paved the way for the global molecular characterization of microbial cells that share common functional characteristics. Quantitative characterization of the dynamics of microbial proteomes enables an unprecedented view of the adaptive responses of microbes to environmental stimuli or during interaction with other species or host cells. However, the intrinsic complexity of such data requires sophisticated visualization methods for the display, mining, interpretation and efficient exploitation of these data resources. In this review, we discuss how new approaches in data visualization such as streamgraphs, network graphs or Voronoi treemaps are being used in the field to provide new insights into the functional complexity of microbial cells, populations and multispecies consortia.

EasyLCMS: an asynchronous web application for the automated quantification of LC-MS data

Background

Downstream applications in metabolomics, as well as mathematical modelling, require data in a quantitative format, which may also necessitate the automated and simultaneous quantification of numerous metabolites. Although numerous applications have been previously developed for metabolomics data handling, automated calibration and calculation of the concentrations in terms of μmol have not been carried out. Moreover, most of the metabolomics applications are designed for GC-MS, and would not be suitable for LC-MS, since in LC, the deviation in the retention time is not linear, which is not taken into account in these applications. Moreover, only a few are web-based applications, which could improve stand-alone software in terms of compatibility, sharing capabilities and hardware requirements, even though a strong bandwidth is required. Furthermore, none of these incorporate asynchronous communication to allow real-time interaction with pre-processed results.

Findings

Here, we present EasyLCMS (http://www.easylcms.es/), a new application for automated quantification which was validated using more than 1000 concentration comparisons in real samples with manual operation. The results showed that only 1% of the quantifications presented a relative error higher than 15%. Using clustering analysis, the metabolites with the highest relative error distributions were identified and studied to solve recurrent mistakes.

Conclusions

EasyLCMS is a new web application designed to quantify numerous metabolites, simultaneously integrating LC distortions and asynchronous web technology to present a visual interface with dynamic interaction which allows checking and correction of LC-MS raw data pre-processing results. Moreover, quantified data obtained with EasyLCMS are fully compatible with numerous downstream applications, as well as for mathematical modelling in the systems biology field.

Intuitive visualization and analysis of multi-omics data and application to Escherichia coli carbon metabolism

Combinations of ‘omics’ investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work.

Visualizing multi-omics data in metabolic networks with the software Omix: a case study

Systems Biology is a multi-disciplinary research field with the aim of understanding the function of complex processes in living organisms. These intracellular processes are described by biochemical networks. Experimental studies in alliance with computer simulation lead to a continually increasing amount of data in liaison with different layers of biochemical networks. Thus, visualization is very important for getting an overview of data in association with the network components. Omix is a software for the visualization of any data in biochemical networks. The unique feature of Omix is: the software is programmable by a scripting language called Omix Visualization Language (OVL). In Omix, the visualization of data coming from experiment or simulation is completely performed by the software user realized in concise OVL scripts. By this, visualization becomes most flexible and adaptable to the requirements of the user and can be adapted to new application fields. We present four case studies of visualizing data of diverse kind in biochemical networks on metabolic level by using Omix and the OVL scripting language. These worked examples demonstrate the power of OVL in conjunction with pleasing visualization, an important requirement for successful interdisciplinary communication in the interface between more experimental and more theoretical researchers.

Pathogenomics of Xanthomonas: understanding bacterium-plant interactions

Xanthomonas is a large genus of Gram-negative bacteria that cause disease in hundreds of plant hosts, including many economically important crops. Pathogenic species and pathovars within species show a high degree of host plant specificity and many exhibit tissue specificity, invading either the vascular system or the mesophyll tissue of the host. In this Review, we discuss the insights that functional and comparative genomic studies are providing into the adaptation of this group of bacteria to exploit the extraordinary diversity of plant hosts and different host tissues.

Diversity of metabolic shift in response to oxygen deprivation in Corynebacterium glutamicum and its close relatives

Oxygen-deprived Corynebacterium glutamicum R cells remain metabolically active, producing considerable amounts of organic acids even when not actively growing. We compared the proficiencies of C. glutamicum and close relatives grown under aerobic conditions to metabolize glucose when deprived of oxygen. Eight strains that readily consumed glucose without cell growth subsequently produced organic acids. Among these, the glucose consumption rates of the two C. glutamicum strains (>40 mM/h) and Corynebacterium efficiens (>12 mM/h) were an order of magnitude higher than those of the other five strains. The resultant organic acid yields of these three strains (>86%) consequently exceeded those of the other five (<60%). This difference is probably rooted in the comparatively inferior activities of glyceraldehyde-3-phosphate dehydrogenase, lactate dehydrogenase, and malate dehydrogenase observed in the five strains. Moreover, under oxygen deprivation, phosphoenolpyruvate carboxylase (PEPC) activity of C. efficiens was elevated tenfold, but its lack of fumarase activity meant that no succinic acid could be produced. The metabolic shift occasioned by addition of the PEPC substrate sodium bicarbonate resulted in a doubling of the glucose consumption rate of the two C. glutamicum strains but not that of the other six close relatives.

Gene expression profiling of Corynebacterium glutamicum during Anaerobic nitrate respiration: induction of the SOS response for cell survival

The gene expression profile of Corynebacterium glutamicum under anaerobic nitrate respiration revealed marked differences in the expression levels of a number of genes involved in a variety of cellular functions, including carbon metabolism and respiratory electron transport chain, compared to the profile under aerobic conditions using DNA microarrays. Many SOS genes were upregulated by the shift from aerobic to anaerobic nitrate respiration. An elongated cell morphology, similar to that induced by the DivS-mediated suppression of cell division upon cell exposure to the DNA-damaging reagent mitomycin C, was observed in cells subjected to anaerobic nitrate respiration. None of these transcriptional and morphological differences were observed in a recA mutant strain lacking a functional RecA regulator of the SOS response. The recA mutant cells additionally showed significantly reduced viability compared to wild-type cells similarly grown under anaerobic nitrate respiration. These results suggest a role for the RecA-mediated SOS response in the ability of cells to survive any DNA damage that may result from anaerobic nitrate respiration in C. glutamicum.

Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data

Motivation: The development of the omics technologies such as transcriptomics, proteomics and metabolomics has made possible the realization of systems biology studies where biological systems are interrogated at different levels of biochemical activity (gene expression, protein activity and/or metabolite concentration). An effective approach to the analysis of these complex datasets is the joined visualization of the disparate biomolecular data on the framework of known biological pathways.

Results: We have developed the Paintomics web server as an easy-to-use bioinformatics resource that facilitates the integrated visual analysis of experiments where transcriptomics and metabolomics data have been measured on different conditions for the same samples. Basically, Paintomics takes complete transcriptomics and metabolomics datasets, together with lists of significant gene or metabolite changes, and paints this information on KEGG pathway maps.

Availability: Paintomics is freely available at http://www.paintomics.org.

Contact:aconesa@cipf.es

Size exclusion chromatography: an improved method to harvest Corynebacterium glutamicum cells for the analysis of cytosolic metabolites

The efficient separation of Corynebacterium glutamicum cells from culture medium by size exclusion chromatography (SEC) is presented. Residue analysis demonstrated that this method effectively depletes extracellular compounds. For evaluation, SEC was compared with the common methods cold methanol treatment, fast centrifugation and fast filtration. For this purpose, samples of C. glutamicum cells from fermenter cultures were harvested and subjected to a metabolome analysis. In particular, the wild type strain C. glutamicum ATCC13032 and the lysine production strain C. glutamicum DM1730 were grown in a minimal or in a complex medium. Comparison of metabolite pool sizes after harvesting C. glutamicum cells by the methods mentioned above by gas chromatography coupled to mass spectrometry (GC-MS) revealed that SEC is the most suitable method when intracellular metabolite pools are to be measured during growth in complex media or in the presence of significant amounts of secreted metabolites. In contrast to the other methods tested, the SEC method turned out to be fast and able to remove extracellular compounds almost completely.