From the sequence to cell modeling: comprehensive functional genomics in Escherichia coli

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.

Predictive modelling of complex agronomic and biological systems

Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead.

After genomics, what proteomics tools could help us understand the antimicrobial resistance of Escherichia coli?

Proteomic approaches have been considerably improved during the past decade and have been used to investigate the differences in protein expression profiles of cells grown under a broad spectrum of growth conditions and with different stress factors including antibiotics. In Europe, the most significant disease threat remains the presence of microorganisms that have become resistant to antimicrobials and so it is important that different scientific tools are combined to achieve the largest amount of knowledge in this area of expertise. The emergence and spread of the antibiotic-resistant Gram-negative pathogens, such as Escherichia coli, can lead to serious problem public health in humans. E. coli, a very well described prokaryote, has served as a model organism for several biological and biotechnological studies increasingly so since the completion of the E. coli genome-sequencing project. The purpose of this review is to present an overview of the different proteomic approaches to antimicrobial-resistant E. coli that will be helpful to obtain a better knowledge of the antibiotic-resistant mechanism(s). This can also aid to understand the molecular determinants involved with pathogenesis, which is essential for the development of effective strategies to combat infection and to reveal new therapeutic targets. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Towards a dynamical network view of brain ischemia and reperfusion. Part IV: additional considerations

The general failure of neuroprotectants in clinical trials of ischemic stroke points to the possibility of a fundamental blind spot in the current conception of ischemic brain injury, the "ischemic cascade". This is the fourth in a series of four papers whose purpose is to work towards a revision of the concept of brain ischemia by applying network concepts to develop a bistable model of brain ischemia. Here we consider additional issues to round out and close out this initial presentation of the bistable network view of brain ischemia. Initial considerations of the network architecture underlying the post-ischemic state space are discussed. Network and differential equation models of brain ischemia are compared. We offer a first look at applying the bistable model to focal cerebral ischemia. The limitations of the present formulation of the bistable model are discussed. This work concludes with a series of questions by which to direct future efforts.

The Modular Organization of Protein Interactions in Escherichia coli

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a 'systems' view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins ( approximately 45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks.

Systems-level approaches for identifying and analyzing genetic interaction networks in Escherichia coli and extensions to other prokaryotes

Molecular interactions define the functional organization of the cell. Epistatic (genetic, or gene-gene) interactions, one of the most informative and commonly encountered forms of functional relationships, are increasingly being used to map process architecture in model eukaryotic organisms. In particular, 'systems-level' screens in yeast and worm aimed at elucidating genetic interaction networks have led to the generation of models describing the global modular organization of gene products and protein complexes within a cell. However, comparable data for prokaryotic organisms have not been available. Given its ease of growth and genetic manipulation, the Gram-negative bacterium Escherichia coli appears to be an ideal model system for performing comprehensive genome-scale examinations of genetic redundancy in bacteria. In this review, we highlight emerging experimental and computational techniques that have been developed recently to examine functional relationships and redundancy in E. coli at a systems-level, and their potential application to prokaryotes in general. Additionally, we have scanned PubMed abstracts and full-text published articles to manually curate a list of approximately 200 previously reported synthetic sick or lethal genetic interactions in E. coli derived from small-scale experimental studies.

Towards a systems biology approach to study type II/IV secretion systems

Many gram-negative bacteria produce thin protein filaments, named pili, which extend beyond the confines of the outer membrane. The importance of these pili is illustrated by the fact that highly complex, multi-protein pilus-assembly machines have evolved, not once, but several times. Their many functions include motility, adhesion, secretion, and DNA transfer, all of which can contribute to the virulence of bacterial pathogens or to the spread of virulence factors by horizontal gene transfer. The medical importance has stimulated extensive biochemical and genetic studies but the assembly and function of pili remains an enigma. It is clear that progress in this field requires a more holistic approach where the entire molecular apparatus that forms the pilus is studied as a system. In recent years systems biology approaches have started to complement classical studies of pili and their assembly. Moreover, continued progress in structural biology is building a picture of the components that make up the assembly machine. However, the complexity and multiple-membrane spanning nature of these secretion systems pose formidable technical challenges, and it will require a concerted effort before we can create comprehensive and predictive models of these remarkable molecular machines.

Development of a microfluidic biochip for online monitoring of fungal biofilm dynamics

Microfabricated biochips are developed to continuously monitor cell population dynamics in a non-invasive manner. In the presented work we describe the novel combination of contact-less dielectric microsensors and microfluidics to promote biofilm formation for quantitative cell analysis. The cell chip consists of a polymeric fluidic (PDMS) system bonded to a glass wafer containing the electrodes while temperature and fluid flow are controlled by external heating and pumping stations. The high-density interdigitated capacitors (microIDES) are isolated by a 550 nm multi-passivation layer of defined dielectric property and provide stable, robust and non-drifting measurement conditions. The performance of this detector is evaluated using various bacterial and yeast strains. The high sensitivity of the developed dielectric microsensors allows direct identification of microbial strains based on morphological differences and biological composition. The novel biofilm analysis platform is used to continuously monitor the dynamic responses of C. albicans and P. pastoris biofilms to increased shear stress and antimicrobial agent concentration. While the presence of shear stress triggers significant changes in yeast growth profiles, the addition of 0.5 microg mL(-1) amphotericin B revealed two distinct dynamic behaviors of the C. albicans biofilm. Initially, impedance spectra increased linearly at 30 Omega h(-1) for two hours followed by 10 Omega h(-1) (at 50 kHz) over 10 hours while cell viability remained above 95% during fungicide administration. These results demonstrate the ability to directly monitor dielectric changes of sub-cellular components within a living cell population.

CoryneRegNet 4.0 - A reference database for corynebacterial gene regulatory networks

Background

Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum.

Results

Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB.

Conclusion

The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at .

Laboratory strains of Escherichia coli: model citizens or deceitful delinquents growing old disgracefully?

Escherichia coli stands unchallenged as biology's premier model organism. However, we propose, equipped with insights from the post-genomic era, a contrary view: that microbiology's chief idol has feet of clay. E. coli laboratory strains, particularly E. coli K-12, are far from model citizens, but instead degenerate and deceitful delinquents growing old disgracefully in our scientific institutions. E. coli K-12 is neither archetype nor ancestor. In addition, it has a far from optimal provenance for a model organism, with strong grounds for believing that current versions of the strain are quite distinct from any original wild-type free-living ancestor. In addition, it is usually studied under conditions far removed from its natural habitats and in ignorance of the selective pressures that have shaped its evolution. Fortunately, a flood of information from high-throughput genome sequencing, together with a new 'eco-evo' view of this model organism, promises to help put K-12 better into context.

CoryneRegNet 3.0—An interactive systems biology platform for the analysis of gene regulatory networks in corynebacteria and Escherichia coli

CoryneRegNet is an ontology-based data warehouse for the reconstruction and visualization of transcriptional regulatory interactions in prokaryotes. To extend the biological content of CoryneRegNet, we added comprehensive data on transcriptional regulations in the model organism Escherichia coli K-12, originally deposited in the international reference database RegulonDB. The enhanced web interface of CoryneRegNet offers several types of search options. The results of a search are displayed in a table-based style and include a visualization of the genetic organization of the respective gene region. Information on DNA binding sites of transcriptional regulators is depicted by sequence logos. The results can also be displayed by several layouters implemented in the graphical user interface GraphVis, allowing, for instance, the visualization of genome-wide network reconstructions and the homology-based inter-species comparison of reconstructed gene regulatory networks. In an application example, we compare the composition of the gene regulatory networks involved in the SOS response of E. coli and Corynebacterium glutamicum. CoryneRegNet is available at the following URL: http://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/.

Characterization of Escherichia coli MG1655 grown in a low-shear modeled microgravity environment

Background

Extra-cellular shear force is an important environmental parameter that is significant both medically and in the space environment. Escherichia coli cells grown in a low-shear modeled microgravity (LSMMG) environment produced in a high aspect rotating vessel (HARV) were subjected to transcriptional and physiological analysis.

Results

Aerobic LSMMG cultures were grown in rich (LB) and minimal (MOPS + glucose) medium with a normal gravity vector HARV control. Reproducible changes in transcription were seen, but no specific LSMMG responsive genes were identified. Instead, absence of shear and a randomized gravity vector appears to cause local extra-cellular environmental changes, which elicit reproducible cellular responses. In minimal media, the majority of the significantly up- or down-regulated genes of known function were associated with the cell envelope. In rich medium, most LSMMG down-regulated genes were involved in translation. No observable changes in post-culture stress responses and antibiotic sensitivity were seen in cells immediately after exposure to LSMMG. Comparison with earlier studies of Salmonella enterica serovar Typhimurium conducted under similar growth conditions, revealed essentially no similarity in the genes that were significantly up- or down-regulated.

Conclusion

Comparison of these results to previous studies suggests that different organisms may dramatically differ in their responses to medically significant low-shear and space environments. Depending on their specific response, some organisms, such as Salmonella, may become preadapted in a manner that predisposes them to increased virulence.

Whole-cell modeling framework in which biochemical dynamics impact aspects of cellular geometry

A mathematical framework for modeling biological cells from a physicochemical perspective is described. Cells modeled within this framework consist of at least two regions, including a cytosolic volume encapsulated by a membrane surface. The cytosol is viewed as a well-stirred chemical reactor capable of changing volume while the membrane is assumed to be an oriented 2-D surface capable of changing surface area. Two physical properties of the cell, namely volume and surface area, are determined by (and determine) the reaction dynamics generated from a set of chemical reactions designed to be occurring in the cell. This framework allows the modeling of complex cellular behaviors, including self-replication. This capability is illustrated by constructing two self-replicating prototypical whole-cell models. One protocell was designed to be of minimal complexity; the other to incorporate a previously reported well-known mechanism of the eukaryotic cell cycle. In both cases, self-replicative behavior was achieved by seeking stable physically possible oscillations in concentrations and surface-to-volume ratio, and by synchronizing the period of such oscillations to the doubling of cytosolic volume and membrane surface area. Rather than being enforced externally or artificially, growth and division occur naturally as a consequence of the assumed chemical mechanism operating within the framework.

Integration of omics data: how well does it work for bacteria?

In the current omics era, innovative high-throughput technologies allow measuring temporal and conditional changes at various cellular levels. Although individual analysis of each of these omics data undoubtedly results into interesting findings, it is only by integrating them that gaining a global insight into cellular behaviour can be aimed at. A systems approach thus is predicated on data integration. However, because of the complexity of biological systems and the specificities of the data-generating technologies (noisiness, heterogeneity, etc.), integrating omics data in an attempt to reconstruct signalling networks is not trivial. Developing its methodologies constitutes a major research challenge. Besides for their intrinsic value towards health care, environment and industry, prokaryotes are ideal model systems to further develop these methods because of their lower regulatory complexity compared with eukaryotes, and the ease with which they can be manipulated. Several successful examples outlined in this review already show the potential of the systems approach for both fundamental and industrial applications, which would be time-consuming or impossible to develop solely through traditional reductionist approaches.

Highly accurate genome sequences of Escherichia coli K-12 strains MG1655 and W3110

With the goal of solving the whole-cell problem with Escherichia coli K-12 as a model cell, highly accurate genomes were determined for two closely related K-12 strains, MG1655 and W3110. Completion of the W3110 genome and comparison with the MG1655 genome revealed differences at 267 sites, including 251 sites with short, mostly single-nucleotide, insertions or deletions (indels) or base substitutions (totaling 358 nucleotides), in addition to 13 sites with an insertion sequence element or defective prophage in only one strain and two sites for the W3110 inversion. Direct DNA sequencing of PCR products for the 251 regions with short indel and base disparities revealed that only eight sites are true differences. The other 243 discrepancies were due to errors in the original MG1655 sequence, including 79 frameshifts, one amino-acid residue deletion, five amino-acid residue insertions, 73 missense, and 17 silent changes within coding regions. Errors in the original MG1655 sequence (<1 per 13 000 bases) were mostly within portions sequenced with out-dated technology based on radioactive chemistry.

Functional analysis of 1440 Escherichia coli genes using the combination of knock-out library and phenotype microarrays

Escherichia coli is one of the best elucidated organisms. However, about 40% of E. coli genes have not been assigned to their function yet. We analyzed 1440 single gene knock-out mutants using the GN2-MicroPlate, which permits assay of 95 carbon-source utilizations simultaneously. In the knock-out library there are 1044 of so called y-genes with no apparent function. The raw dataset was analyzed and genes were interrelated by the clustering method of the GeneSpring software. In the resulted dendrogram of genes, a group of genes with known and related function tended to be assembled into a cluster. Our clustering method would be useful for functional assignment of so called y-genes with no apparent function, since the resulted dendrogram could connect y-genes to phenotype and function of well-studied genes.

Prediction of beta-barrel membrane proteins by searching for restricted domains

BACKGROUND

The identification of beta-barrel membrane proteins out of a genomic/proteomic background is one of the rapidly developing fields in bioinformatics. Our main goal is the prediction of such proteins in genome/proteome wide analyses.

RESULTS

For the prediction of beta-barrel membrane proteins within prokaryotic proteomes a set of parameters was developed. We have focused on a procedure with a low false positive rate beside a procedure with lowest false prediction rate to obtain a high certainty for the predicted sequences. We demonstrate that the discrimination between beta-barrel membrane proteins and other proteins is improved by analyzing a length limited region. The developed set of parameters is applied to the proteome of E. coli and the results are compared to four other described procedures.

CONCLUSION

Analyzing the beta-barrel membrane proteins revealed the presence of a defined membrane inserted beta-barrel region. This information can now be used to refine other prediction programs as well. So far, all tested programs fail to predict outer membrane proteins in the proteome of the prokaryote E. coli with high reliability. However, the reliability of the prediction is improved significantly by a combinatory approach of several programs. The consequences and usability of the developed scores are discussed.

Functional coupling between vanillate-O-demethylase and formaldehyde detoxification pathway

Pseudomonas putida vanillate-O-demethylase consisting of VanA and VanB was expressed in Escherichia coli strain K-12. Recombinant E. coli strain K-12 cells expressing VanAB efficiently converted vanillate into protocatechuate with glucose consumption. Mutant lacking either pgi or zwf showed higher or lower converting activity than the parental strain, respectively. Formaldehyde, which is the by-product of the demethylation, was converted into formate in the cellular reaction. Formate accumulation was blocked by gene disruption of the E. coli frmA that coded glutathione-dependent formaldehyde dehydrogenase.

Effect of an artificial RNA marker on gene expression in Escherichia coli

Transcriptional analysis was used to examine the effect of a genomically encoded artificial RNA on Escherichia coli in rich and minimal media. Only the expression of a single gene, deoC, was unequivocally affected under both conditions. E. coli marker strains of this type may be useful in monitoring the fate and transport of bacteria in various applications.

All systems go: launching cell simulation fueled by integrated experimental biology data

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.

A framework for whole-cell mathematical modeling

The default framework for modeling biochemical processes is that of a constant-volume reactor operating under steady-state conditions. This is satisfactory for many applications, but not for modeling growth and division of cells. In this study, a whole-cell modeling framework is developed that assumes expanding volumes and a cell-division cycle. A spherical newborn cell is designed to grow in volume during the growth phase of the cycle. After 80% of the cycle period, the cell begins to divide by constricting about its equator, ultimately affording two spherical cells with total volume equal to twice that of the original. The cell is partitioned into two regions or volumes, namely the cytoplasm (Vcyt) and membrane (Vmem), with molecular components present in each. Both volumes change during the cell cycle; Vcyt changes in response to osmotic pressure changes as nutrients enter the cell from the environment, while Vmem changes in response to this osmotic pressure effect such that membrane thickness remains invariant. The two volumes change at different rates; in most cases, this imposes periodic or oscillatory behavior on all components within the cell. Since the framework itself rather than a particular set of reactions and components is responsible for this behavior, it should be possible to model various biochemical processes within it, affording stable periodic solutions without requiring that the biochemical process itself generates oscillations as an inherent feature. Given that these processes naturally occur in growing and dividing cells, it is reasonable to conclude that the dynamics of component concentrations will be more realistic than when modeled within constant-volume and/or steady-state frameworks. This approach is illustrated using a symbolic whole cell model.

Chemical genomics in yeast

Four recent 'chemical genomic' studies, using genome-scale collections of yeast gene deletions, have presented complementary approaches to identifying gene-drug and pathway-drug interactions.

Many drugs have unknown, controversial or multiple mechanisms of action. Four recent 'chemical genomic' studies, using genome-scale collections of yeast gene deletions that were either arrayed or barcoded, have presented complementary approaches to identifying gene-drug and pathway-drug interactions.