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Though the bacterial transcription regulation apparatus is distinct in terms of several structural and functional features from its eukaryotic counterpart, the gross structure of the transcription regulatory network (TRN) is believed to be similar in both superkingdoms. Here, we explore the fine structure of the bacterial TRN and the underlying "co-regulatory network" (CRN) to show that despite the superficial similarities to the TRN of the eukaryotic model organism yeast, the bacterial networks display entirely different organizational principles. In particular unlike in eukaryotes, hubs of the bacterial networks are both global regulators and integrators of diverse disparate transcriptional responses. These and other organizational differences might correlate with the fundamental differences in gene and promoter organization in the two superkingdoms, especially the presence of operons and regulons in bacteria. Further we explored to find the interplay, if any, between network structures, mode of regulatory interactions and signal sensing of transcription factors (TFs) in shaping up the bacterial transcriptional regulatory responses. For this purpose, we first classified TFs according to their regulatory mode (activator, repressor or dual regulator) and sensory mechanism (one-component systems responding to internal or external signals, TFs from two-component systems and chromosomal structure modifying TFs) in the bacterial model organism Escherichia coli and then we studied the overall evolutionary optimization of network structures. The incorporation of TFs in different hierarchical elements of the TRN appears to involve on a multi-dimensional selection process depending on regulatory and sensory modes of TFs in motifs, co-regulatory associations between TFs of different functional classes and transcript half-lives. As a result it appears to have generated circuits that allow intricately regulated physiological state changes. We identified the biological significance of most of these optimizations, which can be further used as the basis to explore similar controls in other bacteria. We also show that, though on the larger evolutionary scale, unrelated TFs have evolved to become hubs, within lineages like gamma-proteobacteria there is strong tendency to retain hubs, as well as certain higher-order network modules that have emerged through lineage specific paralog duplications.

The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia coli to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments.

The serendipitous observation of a C–H···O, hydrogen bond mediated polypeptide chain reversal in synthetic peptide helices has led to a search for the occurrence of a similar motif in protein structures. From a dataset of 634 proteins, 1304 helices terminating in a Schellman motif have been examined. The C–H···O interaction between the T−4 $C^\alpha H$ and T+1 C=O group $(C...O \leq3.5 \AA)$ becomes possible only when the T+1 residue adopts an extended $\beta$ conformation (T is defined as the helix terminating residue adopting an $\alpha_L $ conformation). In all, 111 examples of this chain reversal motif have been identified and the compositional and conformational preferences at positions T−4, T, and T+1 determined. A marked preference for residues like Ser, Glu and Gln is observed at T−4 position with the motif being further stabilized by the formation of a side-chain–backbone Ocdots, three dots, centeredH–N hydrogen bond involving the side-chain of residue T−4 and the N–H group of residue T+3. In as many as 57 examples, the segment following the helix was extended with three to four successive residues in $\beta$ conformation. In a majority of these cases, the succeeding $\beta$ strand lies approximately antiparallel with the helix, suggesting that the backbone C–H...O interactions may provide a means of registering helices and strands in an antiparallel orientation. Two examples were identified in which extended registry was detected with two sets of C–H...O hydrogen bonds between (T−4) $C^\alpha H...C=O$ (T+1) and (T−8) $C^\alpha H...C=O (T+3)$.

Numerous studies in both prokaryotes and eukaryotes have shown that, under standard growth conditions, less than 20% of the protein-coding genes are essential for survival. This suggests that biological systems have evolved to have a high degree of robustness to mutational disruptions that can affect the majority of their genes. This mutational robustness could arise either due to redundancy, i.e. direct backup, or due to distributed architecture, i.e. indirect backup where multiple genes contribute to the functioning of a process in the system. Despite clear evidence for direct backup, the prevalence of indirect backup is poorly understood. In this study, we reveal the existence of a hidden distributed architecture behind the scale-free transcriptional regulatory network of yeast by applying a unique network transformation procedure and show that the network is tolerant even to mutations that disrupt regulatory hubs. Contrary to what is generally accepted, our observation that hubs can be lost or replaced in evolution suggests that this hidden distributed architecture behind scale-free networks protects the overall transcriptional program of the organism from mutations affecting major regulatory hubs. We show that the distributed architecture has been provided by an unexpectedly large number of coordinating partners for any regulatory protein. On the basis of these findings, we propose that the existence of such architecture can allow organisms to explore the adaptive landscape in changing environments by providing the plasticity required to reprogram levels of expression of specific genes that may enhance survival. Thus, an "over-engineered" backup system in the form of distributed architecture is likely to be a major determinant of the "evolvability" of the gene expression in organisms faced with environmental diversity.

Studies on various model systems have shown that a relatively small number of transcription factors can set up strikingly complex spatial and temporal patterns of gene expression. This is achieved mainly by means of combinatorial or differential gene regulation, i.e. regulation of a gene by two or more transcription factors simultaneously or under different conditions. While a number of specific molecular details of the mechanisms of combinatorial regulation have emerged, our understanding of the general principles of combinatorial regulation on a genomic scale is still limited. In this work, we approach this problem by using the largest assembled transcriptional regulatory network for yeast. A specific network transformation procedure was used to obtain the co-regulatory network describing the set of all significant associations among transcription factors in regulating common target genes. Analysis of the global properties of the co-regulatory network suggested the presence of two classes of regulatory hubs: (i) those that make many co-regulatory associations, thus serving as integrators of disparate cellular processes; and (ii) those that make few co-regulatory associations, and thereby specifically regulate one or a few major cellular processes. Investigation of the local structure of the co-regulatory network revealed a significantly higher than expected modular organization, which might have emerged as a result of selection by functional constraints. These constraints probably emerge from the need for extensive modular backup and the requirement to integrate transcriptional inputs of multiple distinct functional systems. We then explored the transcriptional control of three major regulatory systems (ubiquitin signaling, protein kinase and transcriptional regulation systems) to understand specific aspects of their upstream control. As a result, we observed that ubiquitin E3 ligases are regulated primarily by unique transcription factors, whereas E1 and E2 enzymes share common transcription factors to a much greater extent. This suggested that the deployment of E3s unique to specific functional contexts may be mediated significantly at the transcriptional level. Likewise, we were able to uncover evidence for much higher upstream transcription control of transcription factors themselves, in comparison to components of other regulatory systems. We believe that the results presented here might provide a framework for testing the role of co-regulatory associations in eukaryotic transcriptional control.

The serendipitous observation of a C-H cdots, three dots, centered O hydrogen bond mediated polypeptide chain reversal in synthetic peptide helices has led to a search for the occurrence of a similar motif in protein structures. From a dataset of 634 proteins, 1304 helices terminating in a Schellman motif have been examined. The C-H triplebond O interaction between the T-4 C(alpha)H and T+1 Cz doublebond O group (C triplebond Oor =3.5A) becomes possible only when the T+1 residue adopts an extended beta conformation (T is defined as the helix terminating residue adopting an alpha(L) conformation). In all, 111 examples of this chain reversal motif have been identified and the compositional and conformational preferences at positions T-4, T, and T+1 determined. A marked preference for residues like Ser, Glu and Gln is observed at T-4 position with the motif being further stabilized by the formation of a side-chain-backbone O triplebond H-N hydrogen bond involving the side-chain of residue T-4 and the N-H group of residue T+3. In as many as 57 examples, the segment following the helix was extended with three to four successive residues in beta conformation. In a majority of these cases, the succeeding beta strand lies approximately antiparallel with the helix, suggesting that the backbone C-H triplebond O interactions may provide a means of registering helices and strands in an antiparallel orientation. Two examples were identified in which extended registry was detected with two sets of C-H cdots, three dots, centered O hydrogen bonds between (T-4) C(alpha)H triplebond O (T+1) and (T-8) C(alpha)H triplebondC doublebond O (T+3).