Early Career Research Award Lecture. Structure, evolution and dynamics of transcriptional regulatory networks

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.

Full-Length Transcriptome of Thalassiosira weissflogii as a Reference Resource and Mining of Chitin-Related Genes

β-Chitin produced by diatoms is expected to have significant economic and ecological value due to its structure, which consists of parallel chains of chitin, its properties and the high abundance of diatoms. Nevertheless, few studies have functionally characterised chitin-related genes in diatoms owing to the lack of omics-based information. In this study, we first compared the chitin content of three representative Thalassiosira species. Cell wall glycosidic linkage analysis and chitin/chitosan staining assays showed that Thalassiosira weissflogii was an appropriate candidate chitin producer. A full-length (FL) transcriptome of T. weissflogii was obtained via PacBio sequencing. In total, the FL transcriptome comprised 23,362 annotated unigenes, 710 long non-coding RNAs (lncRNAs), 363 transcription factors (TFs), 3113 alternative splicing (AS) events and 3295 simple sequence repeats (SSRs). More specifically, 234 genes related to chitin metabolism were identified and the complete biosynthetic pathways of chitin and chitosan were explored. The information presented here will facilitate T. weissflogii molecular research and the exploitation of β-chitin-derived high-value enzymes and products.

Exploratory adaptation in large random networks

The capacity of cells and organisms to respond to challenging conditions in a repeatable manner is limited by a finite repertoire of pre-evolved adaptive responses. Beyond this capacity, cells can use exploratory dynamics to cope with a much broader array of conditions. However, the process of adaptation by exploratory dynamics within the lifetime of a cell is not well understood. Here we demonstrate the feasibility of exploratory adaptation in a high-dimensional network model of gene regulation. Exploration is initiated by failure to comply with a constraint and is implemented by random sampling of network configurations. It ceases if and when the network reaches a stable state satisfying the constraint. We find that successful convergence (adaptation) in high dimensions requires outgoing network hubs and is enhanced by their auto-regulation. The ability of these empirically validated features of gene regulatory networks to support exploratory adaptation without fine-tuning, makes it plausible for biological implementation.

Recent works suggest that cellular networks may respond to novel challenges on the time-scale of cellular lifetimes through large-scale perturbation of gene expression and convergence to a new state. Here, the authors demonstrate the theoretical feasibility of exploratory adaptation in cellular networks by showing that convergence to new states depends on known features of these networks.

Characterization of transcription factor response kinetics in parallel

BACKGROUND

Transcription factors (TFs) are effectors of cell signaling pathways that regulate gene expression. TF networks are highly interconnected; one signal can lead to changes in many TF levels, and one TF level can be changed by many different signals. TF regulation is central to normal cell function, with altered TF function being implicated in many disease conditions. Thus, measuring TF levels in parallel, and over time, is crucial for understanding the impact of stimuli on regulatory networks and on diseases.

RESULTS

Here, we report the parallel analysis of temporal TF level changes due to multiple stimuli in distinct cell types. We have analyzed short-term dynamic changes in the levels of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB), signal transducer and activator of transcription 3 (Stat3), cAMP response element-binding protein (CREB), glucocorticoid receptor (GR), and TATA binding protein (TBP), in breast and liver cancer cells after tumor necrosis factor-alpha (TNF-α) and palmitic acid (PA) exposure. In response to both stimuli, NF-kB and CREB levels were increased, Stat3 decreased, and TBP was constant. GR levels were unchanged in response to TNF-α stimulation and increased in response to PA treatment.

CONCLUSIONS

Our results show significant overlap in signaling initiated by TNF-α and by PA, with the exception that the events leading to PA-mediated cytotoxicity likely also include induction of GR signaling. These results further illuminate the dynamics of TF responses to cytokine and fatty acid exposure, while concomitantly demonstrating the utility of parallel TF measurement approaches in the analysis of biological phenomena.

Transcription Factors Exhibit Differential Conservation in Bacteria with Reduced Genomes

The description of transcriptional regulatory networks has been pivotal in the understanding of operating principles under which organisms respond and adapt to varying conditions. While the study of the topology and dynamics of these networks has been the subject of considerable work, the investigation of the evolution of their topology, as a result of the adaptation of organisms to different environmental conditions, has received little attention. In this work, we study the evolution of transcriptional regulatory networks in bacteria from a genome reduction perspective, which manifests itself as the loss of genes at different degrees. We used the transcriptional regulatory network of Escherichia coli as a reference to compare 113 smaller, phylogenetically-related γ-proteobacteria, including 19 genomes of symbionts. We found that the type of regulatory action exerted by transcription factors, as genomes get progressively smaller, correlates well with their degree of conservation, with dual regulators being more conserved than repressors and activators in conditions of extreme reduction. In addition, we found that the preponderant conservation of dual regulators might be due to their role as both global regulators and nucleoid-associated proteins. We summarize our results in a conceptual model of how each TF type is gradually lost as genomes become smaller and give a rationale for the order in which this phenomenon occurs.

Analysis of the hierarchical structure of the B. subtilis transcriptional regulatory network

Hierarchical decomposition of transcriptional regulators into Top, Middle and Bottom levels in theB. subtilistranscriptional regulatory network.

Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and "swarm intelligence"

Our knowledge of the biology of solid cancer has greatly progressed during the last few years, and many excellent reviews dealing with the various aspects of this biology have appeared. In the present review, we attempt to bring together these subjects in a general systems biology narrative. It starts from the roles of what we term entropy of signaling and noise in the initial oncogenic events, to the first major transition of tumorigenesis: the independence of the tumor cell and the switch in its physiology, i.e., from subservience to the organism to its own independent Darwinian evolution. The development after independence involves a constant dynamic reprogramming of the cells and the emergence of a sort of collective intelligence leading to invasion and metastasis and seldom to the ultimate acquisition of immortality through inter-individual infection. At each step, the probability of success is minimal to infinitesimal, but the number of cells possibly involved and the time scale account for the relatively high occurrence of tumorigenesis and metastasis in multicellular organisms.

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.

Network function shapes network structure: the case of the Arabidopsis flower organ specification genetic network

The reconstruction of many biological networks has allowed detailed studies of their structural properties. Several features exhibited by these networks have been interpreted to be the result of evolutionary dynamics. For instance the degree distributions may follow from a preferential attachment of new genes to older ones during evolution. Here we argue that even in the absence of any evolutionary dynamics, the presence of atypical features may follow from the fact that the network implements certain functions. To examine this network function shapes network structure scenario, we focus on the Arabidopsis genetic network controlling early flower organogenesis in which gene expression dynamics has been modelled using a Boolean framework. Specifically, for a system with 15 master genes, the phenotype consists of 10 experimentally determined steady-state expression patterns, considered here as the functional constraints on the network. The space of genetic networks satisfying these constraints is sometimes referred to as the neutral or genotype network. We sample this space using Markov Chain Monte Carlo which allows us to demonstrate how the functional (phenotypic) constraints shape the gene network structure. We find that this shaping is strongest for the edge (interaction) usage, with effects that are functionally interpretable. In contrast, higher order features such as degree assortativity and network motifs are hardly shaped by the phenotypic constraints.

Quantitative, solution-phase profiling of multiple transcription factors in parallel

Transcription factors are regulatory proteins that bind to specific sites of chromosomal DNA to enact responses to intracellular and extracellular stimuli. Transcription factor signalling networks are branched and interconnected so that any single transcription factor can activate many different genes and one gene can be activated by a combination of different transcription factors. Thus, trying to characterize a cellular response to a stimulus by measuring the level of only one transcription factor potentially ignores important simultaneous events that contribute to the response. Hence, parallel measurements of transcription factors are necessary to capture the breadth of valuable information about cellular responses that would not be obtained by measuring only a single transcription factor. We have sought to develop a new, scalable, flexible, and sensitive approach to analysis of transcription factor levels that complements existing parallel approaches. Here, we describe proof-of-principle analyses of purified human transcription factors and breast cancer nuclear extracts. Our assay can successfully quantify transcription factors in parallel with ~10-fold better sensitivity than current techniques. Sensitivity of the assay can be further increased by 200-fold through the use of PCR for signal amplification.

Global analysis of viral infection in an archaeal model system

The origin and evolutionary relationship of viruses is poorly understood. This makes archaeal virus-host systems of particular interest because the hosts generally root near the base of phylogenetic trees, while some of the viruses have clear structural similarities to those that infect prokaryotic and eukaryotic cells. Despite the advantageous position for use in evolutionary studies, little is known about archaeal viruses or how they interact with their hosts, compared to viruses of bacteria and eukaryotes. In addition, many archaeal viruses have been isolated from extreme environments and present a unique opportunity for elucidating factors that are important for existence at the extremes. In this article we focus on virus-host interactions using a proteomics approach to study Sulfolobus Turreted Icosahedral Virus (STIV) infection of Sulfolobus solfataricus P2. Using cultures grown from the ATCC cell stock, a single cycle of STIV infection was sampled six times over a 72 h period. More than 700 proteins were identified throughout the course of the experiments. Seventy one host proteins were found to change their concentration by nearly twofold (p < 0.05) with 40 becoming more abundant and 31 less abundant. The modulated proteins represent 30 different cell pathways and 14 clusters of orthologous groups. 2D gel analysis showed that changes in post-translational modifications were a common feature of the affected proteins. The results from these studies showed that the prokaryotic antiviral adaptive immune system CRISPR-associated proteins (CAS proteins) were regulated in response to the virus infection. It was found that regulated proteins come from mRNAs with a shorter than average half-life. In addition, activity-based protein profiling (ABPP) profiling on 2D-gels showed caspase, hydrolase, and tyrosine phosphatase enzyme activity labeling at the protein isoform level. Together, this data provides a more detailed global view of archaeal cellular responses to viral infection, demonstrates the power of quantitative two-dimensional differential gel electrophoresis and ABPP using 2D gel compatible fluorescent dyes.

A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence

Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a ‘birds-eye’ view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies.

Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human

The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.

Dissecting ensemble networks in ES cell populations reveals micro-heterogeneity underlying pluripotency

Analysis of transcription at the level of single cells in prokaryotes and eukaryotes has revealed the existence of heterogeneities in the expression of individual genes within genetically homogeneous populations. This variation is an emerging hallmark of populations of Embryonic Stem (ES) cells and has been ascribed to the stochasticity associated with the biochemical events that mediate gene expression. It has been suggested that these heterogeneities play a role in the maintenance of pluripotency. However, for the most part, studies have focused on individual genes in large cell populations. Here we use an existing dataset on the expression of eight genes involved in pluripotency in eighty-three ES cells to create Gene Regulatory Networks (GRNs) at the single cell level. We observe widespread heterogeneities in the expression of the eight genes, but analysis of correlations within individual cells reveals three distinct classes centered on the expression of Nanog, a marker of pluripotency, and Fgf5, a gene associated with differentiation: high levels of Nanog and low levels of Fgf5, low levels of Nanog and high levels of Fgf5, and low levels of both. Each of these classes is associated with a collection of active sub-networks, with differing degrees of connectivity between their elements, which define a cellular state: self-renewal, primed for differentiation or transition between the two. Though every cell should be governed by the same network, the active sub-networks may emerge due to considerations such as variation in (i) the expression level of active transcription factors (e.g. through post-translational modification or ligand/co-factor availability) or (ii) access to the target gene locus (e.g. via changes in chromatin status or epigenetic modifications). We conclude that heterogeneities in gene expression should not be interpreted as representing different states of a single unique network, but as a reflection of the activity of different sub-networks in sub-populations of cells.

Interplay between gene expression noise and regulatory network architecture

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.

Benchmarks for flexible and rigid transcription factor-DNA docking

BACKGROUND

Structural insight from transcription factor-DNA (TF-DNA) complexes is of paramount importance to our understanding of the affinity and specificity of TF-DNA interaction, and to the development of structure-based prediction of TF binding sites. Yet the majority of the TF-DNA complexes remain unsolved despite the considerable experimental efforts being made. Computational docking represents a promising alternative to bridge the gap. To facilitate the study of TF-DNA docking, carefully designed benchmarks are needed for performance evaluation and identification of the strengths and weaknesses of docking algorithms.

RESULTS

We constructed two benchmarks for flexible and rigid TF-DNA docking respectively using a unified non-redundant set of 38 test cases. The test cases encompass diverse fold families and are classified into easy and hard groups with respect to the degrees of difficulty in TF-DNA docking. The major parameters used to classify expected docking difficulty in flexible docking are the conformational differences between bound and unbound TFs and the interaction strength between TFs and DNA. For rigid docking in which the starting structure is a bound TF conformation, only interaction strength is considered.

CONCLUSIONS

We believe these benchmarks are important for the development of better interaction potentials and TF-DNA docking algorithms, which bears important implications to structure-based prediction of transcription factor binding sites and drug design.

Exploring the symbiotic pangenome of the nitrogen-fixing bacterium Sinorhizobium meliloti

Background

Sinorhizobium meliloti is a model system for the studies of symbiotic nitrogen fixation. An extensive polymorphism at the genetic and phenotypic level is present in natural populations of this species, especially in relation with symbiotic promotion of plant growth. AK83 and BL225C are two nodule-isolated strains with diverse symbiotic phenotypes; BL225C is more efficient in promoting growth of the Medicago sativa plants than strain AK83. In order to investigate the genetic determinants of the phenotypic diversification of S. meliloti strains AK83 and BL225C, we sequenced the complete genomes for these two strains.

Results

With sizes of 7.14 Mbp and 6.97 Mbp, respectively, the genomes of AK83 and BL225C are larger than the laboratory strain Rm1021. The core genome of Rm1021, AK83, BL225C strains included 5124 orthologous groups, while the accessory genome was composed by 2700 orthologous groups. While Rm1021 and BL225C have only three replicons (Chromosome, pSymA and pSymB), AK83 has also two plasmids, 260 and 70 Kbp long. We found 65 interesting orthologous groups of genes that were present only in the accessory genome, consequently responsible for phenotypic diversity and putatively involved in plant-bacterium interaction. Notably, the symbiosis inefficient AK83 lacked several genes required for microaerophilic growth inside nodules, while several genes for accessory functions related to competition, plant invasion and bacteroid tropism were identified only in AK83 and BL225C strains. Presence and extent of polymorphism in regulons of transcription factors involved in symbiotic interaction were also analyzed. Our results indicate that regulons are flexible, with a large number of accessory genes, suggesting that regulons polymorphism could also be a key determinant in the variability of symbiotic performances among the analyzed strains.

Conclusions

In conclusions, the extended comparative genomics approach revealed a variable subset of genes and regulons that may contribute to the symbiotic diversity.