Improving gene regulatory network inference and assessment: The importance of using network structure.

Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.

Non-synonymous to synonymous substitutions suggest that orthologs tend to keep their functions, while paralogs are a source of functional novelty.

Orthologs separate after lineages split from each other and paralogs after gene duplications. Thus, orthologs are expected to remain more functionally coherent across lineages, while paralogs have been proposed as a source of new functions. Because protein functional divergence follows from non-synonymous substitutions, we performed an analysis based on the ratio of non-synonymous to synonymous substitutions (dN/dS), as proxy for functional divergence. We used five working definitions of orthology, including reciprocal best hits (RBH), among other definitions based on network analyses and clustering. The results showed that orthologs, by all definitions tested, had values of dN/dS noticeably lower than those of paralogs, suggesting that orthologs generally tend to be more functionally stable than paralogs. The differences in dN/dS ratios remained suggesting the functional stability of orthologs after eliminating gene comparisons with potential problems, such as genes with high codon usage biases, low coverage of either of the aligned sequences, or sequences with very high similarities. Separation by percent identity of the encoded proteins showed that the differences between the dN/dS ratios of orthologs and paralogs were more evident at high sequence identity, less so as identity dropped. The last results suggest that the differences between dN/dS ratios were partially related to differences in protein identity. However, they also suggested that paralogs undergo functional divergence relatively early after duplication. Our analyses indicate that choosing orthologs as probably functionally coherent remains the right approach in comparative genomics.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks.

Synthetic biology aims to apply engineering principles for the rational, systematical design and construction of biological systems displaying functions that do not exist in nature or even building a cell from scratch. Understanding how molecular entities interconnect, work, and evolve in an organism is pivotal to this aim. Here, we summarize and discuss some historical organizing principles identified in bacterial gene regulatory networks. We propose a new layer, the concilion, which is the group of structural genes and their local regulators responsible for a single function that, organized hierarchically, coordinate a response in a way reminiscent of the deliberation and negotiation that take place in a council. We then highlight the importance that the network structure has, and discuss that the natural decomposition approach has unveiled the system-level elements shaping a common functional architecture governing bacterial regulatory networks. We discuss the incompleteness of gene regulatory networks and the need for network inference and benchmarking standardization. We point out the importance that using the network structural properties showed to improve network inference. We discuss the advances and controversies regarding the consistency between reconstructions of regulatory networks and expression data. We then discuss some perspectives on the necessity of studying regulatory networks, considering the interactions' strength distribution, the challenges to studying these interactions' strength, and the corresponding effects on network structure and dynamics. Finally, we explore the ability of evolutionary systems biology studies to provide insights into how evolution shapes functional architecture despite the high evolutionary plasticity of regulatory networks.

Corynebacterium glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small RNA and Protein-Protein Interactions.

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by nondirected experiments, and protein-protein interactions with a direct effect on gene transcription; sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNA-mediated regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C.glutamicum regulatory network characterization.

Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of meta-curated, historical, bacterial regulatory networks, their completeness and system-level characterization.

Some organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species (Mycobacterium tuberculosis, Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pyogenes, Streptococcus pneumoniae, and Streptomyces coelicolor), containing 8459 regulons and 4335 modules. Database URL: https://abasy.ccg.unam.mx/.

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions.

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random and therefore attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.

Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage.

Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as l-glutamic acid and l-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage=73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage=71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria.

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy ( A: cross- BA: cteria SY: stems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.Database URL: http://abasy.ccg.unam.mx.

Mathematical modeling of the apo and holo transcriptional regulation in Escherichia coli.

Transcription factors (TFs) modulate gene expression as a consequence of internal or exogenous changes in cell signaling. TFs can bind to DNA either with their effector bound (holo conformation), or as free proteins (apo conformation). With the aim of contributing to the understanding of the evolutionary fitness and organizational principles behind the different TF conformations, we inquire into the origins of these conformational differences by analyzing these two TF conformations from the perspective of Savageau's demand theory. For the control of a gene whose function is in high demand, we found that evolutionary constraints are responsible for activator TFs binding to DNA mainly in holo conformation whereas apo activation is under-represented. The mathematically controlled comparison of the apo and holo conformations reveals formal and evolutionary arguments in favor of this TF control asymmetry, which suggests that evolution favors holo activation under environmental conditions commonly found by E. coli in the human digestive tract. Specifically, the sensibility analysis performed for the holo conformation, in the positive mode of regulation, shows that the wild-type is more robust for situations where realizable changes in the model's parameters favored a better performance under non-stressful environmental conditions commonly found by E. coli in the human digestive tract. By contrast, the positive apo conformation is better adapted to adverse situations. On the other hand, the sensibility analysis performed for the negative mode of regulation showing none of the TF active conformations presents an advantage.

Lessons from the modular organization of the transcriptional regulatory network of Bacillus subtilis.

BACKGROUND: The regulation of gene expression at the transcriptional level is a fundamental process in prokaryotes. Among the different kind of mechanisms modulating gene transcription, the one based on DNA binding transcription factors, is the most extensively studied and the results, for a great number of model organisms, have been compiled making it possible the in silico construction of their corresponding transcriptional regulatory networks and the analysis of the biological relationships of the components of these intricate networks, that allows to elucidate the significant aspects of their organization and evolution. RESULTS: We present a thorough review of each regulatory element that constitutes the transcriptional regulatory network of Bacillus subtilis. For facilitating the discussion, we organized the network in topological modules. Our study highlight the importance of σ factors, some of them acting as master regulators which characterize modules by inter- or intra-connecting them and play a key role in the cascades that define relevant cellular processes in this organism. We discussed that some particular functions were distributed in more than one module and that some modules contained more than one related function. We confirm that the presence of paralogous proteins confers advantages to B. subtilis to adapt and select strategies to successfully face the extreme and changing environmental conditions in which it lives. CONCLUSIONS: The intricate organization is the product of a non-random network evolution that primarily follows a hierarchical organization based on the presence of transcription and σ factor, which is reflected in the connections that exist within and between modules.

Prokaryotic regulatory systems biology: Common principles governing the functional architectures of Bacillus subtilis and Escherichia coli unveiled by the natural decomposition approach.

Escherichia coli and Bacillus subtilis are two of the best-studied prokaryotic model organisms. Previous analyses of their transcriptional regulatory networks have shown that they exhibit high plasticity during evolution and suggested that both converge to scale-free-like structures. Nevertheless, beyond this suggestion, no analyses have been carried out to identify the common systems-level components and principles governing these organisms. Here we show that these two phylogenetically distant organisms follow a set of common novel biologically consistent systems principles revealed by the mathematically and biologically founded natural decomposition approach. The discovered common functional architecture is a diamond-shaped, matryoshka-like, three-layer (coordination, processing, and integration) hierarchy exhibiting feedback, which is shaped by four systems-level components: global transcription factors (global TFs), locally autonomous modules, basal machinery and intermodular genes. The first mathematical criterion to identify global TFs, the κ-value, was reassessed on B. subtilis and confirmed its high predictive power by identifying all the previously reported, plus three potential, master regulators and eight sigma factors. The functionally conserved cores of modules, basal cell machinery, and a set of non-orthologous common physiological global responses were identified via both orthologous genes and non-orthologous conserved functions. This study reveals novel common systems principles maintained between two phylogenetically distant organisms and provides a comparison of their lifestyle adaptations. Our results shed new light on the systems-level principles and the fundamental functions required by bacteria to sustain life.

Molecular characterization of chloranilic acid degradation in Pseudomonas putida TQ07.

Pentachlorophenol is the most toxic and recalcitrant chlorophenol because both aspects are directly proportional to the halogenation degree. Biological and abiotic pentachlorophenol degradation generates p-chloranil, which in neutral to lightly alkaline environmental conditions is hydrolyzed to chloranilic acid that present a violet-reddish coloration in aqueous solution. Several genes of the degradation pathway, cadR-cadCDX, as well as other uncharacterized genes (ORF5 and 6), were isolated from a chloranilic acid degrading bacterium, Pseudomonas putida strain TQ07. The disruption by random mutagenesis of the cadR and cadC genes in TQ07 resulted in a growth deficiency in the presence of chloranilic acid, indicating that these genes are essential for TQ07 growth with chloranilic acid as the sole carbon source. Complementation assays demonstrated that a transposon insertion in mutant CAD82 (cadC) had a polar effect on other genes contained in cosmid pLG3562. These results suggest that at least one of these genes, cadD and cadX, also takes part in chloranilic acid degradation. Based on molecular modeling and function prediction, we strongly suggest that CadC is a pyrone dicarboxylic acid hydrolase and CadD is an aldolase enzyme like dihydrodipicolinate synthase. The results of this study allowed us to propose a novel pathway that offers hypotheses on chloranilic acid degradation (an abiotic by-product of pentachlorophenol) by means of a very clear phenotype that is narrowly related to the capability of Pseudomonas putida strain TQ07 to degrade this benzoquinone.

Identification of network topological units coordinating the global expression response to glucose in Bacillus subtilis and its comparison to Escherichia coli.

BACKGROUND: Glucose is the preferred carbon and energy source for Bacillus subtilis and Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzymatic activities, in response to the presence of this sugar. We present a comparison of the cellular response to glucose in these two model organisms, using an approach combining global transcriptome and regulatory network analyses. RESULTS: Transcriptome data from strains grown in Luria-Bertani medium (LB) or LB+glucose (LB+G) were analyzed, in order to identify differentially transcribed genes in B. subtilis. We detected 503 genes in B. subtilis that change their relative transcript levels in the presence of glucose. A similar previous study identified 380 genes in E. coli, which respond to glucose. Catabolic repression was detected in the case of transport and metabolic interconversion activities for both bacteria in LB+G. We detected an increased capacity for de novo synthesis of nucleotides, amino acids and proteins. A comparison between orthologous genes revealed that global regulatory functions such as transcription, translation, replication and genes relating to the central carbon metabolism, presented similar changes in their levels of expression. An analysis of the regulatory network of a subset of genes in both organisms revealed that the set of regulatory proteins responsible for similar physiological responses observed in the transcriptome analysis are not orthologous. An example of this observation is that of transcription factors mediating catabolic repression for most of the genes that displayed reduced transcript levels in the case of both organisms. In terms of topological functional units in both these bacteria, we found interconnected modules that cluster together genes relating to heat shock, respiratory functions, carbon and peroxide metabolism. Interestingly, B. subtilis functions not found in E. coli, such as sporulation and competence were shown to be interconnected, forming modules subject to catabolic repression at the level of transcription. CONCLUSION: Our results demonstrate that the response to glucose is partially conserved in model organisms E. coli and B. subtilis, including genes encoding basic functions such as transcription, translation, replication and genes involved in the central carbon metabolism.

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

BACKGROUND: Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. RESULTS: We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. CONCLUSIONS: This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.

BACKGROUND: Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses. RESULTS: Transcriptome data from isogenic wild type and crp- strains grown in Luria-Bertani medium (LB) or LB + 4 g/L glucose (LB+G) were analyzed to identify differentially transcribed genes. We detected 180 and 200 genes displaying increased and reduced relative transcript levels in the presence of glucose, respectively. The observed expression pattern in LB was consistent with a gluconeogenic metabolic state including active transport and interconversion of small molecules and macromolecules, induction of protease-encoding genes and a partial heat shock response. In LB+G, catabolic repression was detected for transport and metabolic interconversion activities. We also detected an increased capacity for de novo synthesis of nucleotides, amino acids and proteins. Cluster analysis of a subset of genes revealed that CRP mediates catabolite repression for most of the genes displaying reduced transcript levels in LB+G, whereas Fis participates in the upregulation of genes under this condition. An analysis of the regulatory network, in terms of topological functional units, revealed 8 interconnected modules which again exposed the importance of Fis and CRP as directly responsible for the coordinated response of the cell. This effect was also seen with other not extensively connected transcription factors such as FruR and PdhR, which showed a consistent response considering media composition. CONCLUSION: This work allowed the identification of eight interconnected regulatory network modules that includes CRP, Fis and other transcriptional factors that respond directly or indirectly to the presence of glucose. In most cases, each of these modules includes genes encoding physiologically related functions, thus indicating a connection between regulatory network topology and related cellular functions involved in nutrient sensing and metabolism.

Modular analysis of the transcriptional regulatory network of E. coli.

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.