Myeloid lncRNA LOUP mediates opposing regulatory effects of RUNX1 and RUNX1-ETO in t(8;21) AML

The mechanism underlying cell type-specific gene induction conferred by ubiquitous transcription factors as well as disruptions caused by their chimeric derivatives in leukemia is not well understood. Here, we investigate whether RNAs coordinate with transcription factors to drive myeloid gene transcription. In an integrated genome-wide approach surveying for gene loci exhibiting concurrent RNA and DNA interactions with the broadly expressed Runt-related transcription factor 1 (RUNX1), we identified the long noncoding RNA (lncRNA) originating from the upstream regulatory element of PU.1 (LOUP). This myeloid-specific and polyadenylated lncRNA induces myeloid differentiation and inhibits cell growth, acting as a transcriptional inducer of the myeloid master regulator PU.1. Mechanistically, LOUP recruits RUNX1 to both the PU.1 enhancer and the promoter, leading to the formation of an active chromatin loop. In t(8;21) acute myeloid leukemia (AML), wherein RUNX1 is fused to ETO, the resulting oncogenic fusion protein, RUNX1-ETO, limits chromatin accessibility at the LOUP locus, causing inhibition of LOUP and PU.1 expression. These findings highlight the important role of the interplay between cell-type-specific RNAs and transcription factors, as well as their oncogenic derivatives in modulating lineage-gene activation and raise the possibility that RNA regulators of transcription factors represent alternative targets for therapeutic development.

Proneural genes define ground-state rules to regulate neurogenic patterning and cortical folding

Asymmetric neuronal expansion is thought to drive evolutionary transitions between lissencephalic and gyrencephalic cerebral cortices. We report that Neurog2 and Ascl1 proneural genes together sustain neurogenic continuity and lissencephaly in rodent cortices. Using transgenic reporter mice and human cerebral organoids, we found that Neurog2 and Ascl1 expression defines a continuum of four lineage-biased neural progenitor cell (NPC) pools. Double⁺ NPCs, at the hierarchical apex, are least lineage restricted due to Neurog2-Ascl1 cross-repression and display unique features of multipotency (more open chromatin, complex gene regulatory network, G₂ pausing). Strikingly, selectively eliminating double⁺ NPCs by crossing Neurog2-Ascl1 split-Cre mice with diphtheria toxin-dependent "deleter" strains locally disrupts Notch signaling, perturbs neurogenic symmetry, and triggers cortical folding. In support of our discovery that double⁺ NPCs are Notch-ligand-expressing "niche" cells that control neurogenic periodicity and cortical folding, NEUROG2, ASCL1, and HES1 transcript distribution is modular (adjacent high/low zones) in gyrencephalic macaque cortices, prefiguring future folds.

Lysine acetyltransferase Tip60 is required for hematopoietic stem cell maintenance

Hematopoietic stem cells (HSCs) have the potential to replenish the blood system for the lifetime of the organism. Their 2 defining properties, self-renewal and differentiation, are tightly regulated by the epigenetic machineries. Using conditional gene-knockout models, we demonstrated a critical requirement of lysine acetyltransferase 5 (Kat5, also known as Tip60) for murine HSC maintenance in both the embryonic and adult stages, which depends on its acetyltransferase activity. Genome-wide chromatin and transcriptome profiling in murine hematopoietic stem and progenitor cells revealed that Tip60 colocalizes with c-Myc and that Tip60 deletion suppress the expression of Myc target genes, which are associated with critical biological processes for HSC maintenance, cell cycling, and DNA repair. Notably, acetylated H2A.Z (acH2A.Z) was enriched at the Tip60-bound active chromatin, and Tip60 deletion induced a robust reduction in the acH2A.Z/H2A.Z ratio. These results uncover a critical epigenetic regulatory layer for HSC maintenance, at least in part through Tip60-dependent H2A.Z acetylation to activate Myc target genes.

Repurposing Dihydropyridines for Treatment of Helicobacter pylori Infection

Antibiotic resistance is a major cause of the increasing failures in the current eradication therapies against Helicobacter pylori. In this scenario, repurposing drugs could be a valuable strategy to fast-track novel antimicrobial agents. In the present study, we analyzed the inhibitory capability of 1,4-dihydropyridine (DHP) antihypertensive drugs on the essential function of the H. pylori response regulator HsrA and investigated both the in vitro antimicrobial activities and the in vivo efficacy of DHP treatments against H. pylori. Six different commercially available and highly prescribed DHP drugs-namely, Nifedipine, Nicardipine, Nisoldipine, Nimodipine, Nitrendipine, and Lercanidipine-noticeably inhibited the DNA binding activity of HsrA and exhibited potent bactericidal activities against both metronidazole- and clarithromycin-resistant strains of H. pylori, with minimal inhibitory concentration (MIC) values in the range of 4 to 32 mg/L. The dynamics of the decline in the bacterial counts at 2 × MIC appeared to be correlated with the lipophilicity of the drugs, suggesting different translocation efficiencies of DHPs across the bacterial membrane. Oral treatments with 100 mg/kg/day of marketed formulations of Nimodipine or Nitrendipine in combination with omeprazole significantly reduced the H. pylori gastric colonization in mice. The results presented here support a novel therapeutic solution for treatment of antibiotic-resistant H. pylori infections.

Identifying potential novel drugs against Helicobacter pylori by targeting the essential response regulator HsrA

The increasing antibiotic resistance evolved by Helicobacter pylori has alarmingly reduced the eradication rates of first-line therapies. To overcome the current circulating resistome, we selected a novel potential therapeutic target in order to identify new candidate drugs for treating H. pylori infection. We screened 1120 FDA-approved drugs for molecules that bind to the essential response regulator HsrA and potentially inhibit its biological function. Seven natural flavonoids were identified as HsrA binders. All of these compounds noticeably inhibited the in vitro DNA binding activity of HsrA, but only four of them, apigenin, chrysin, kaempferol and hesperetin, exhibited high bactericidal activities against H. pylori. Chrysin showed the most potent bactericidal activity and the most synergistic effect in combination with clarithromycin or metronidazole. Flavonoid binding to HsrA occurs preferably at its C-terminal effector domain, interacting with amino acid residues specifically involved in forming the helix-turn-helix DNA binding motif. Our results validate the use of HsrA as a novel and effective therapeutic target in H. pylori infection and provide molecular evidence of a novel antibacterial mechanism of some natural flavonoids against H. pylori. The results further support the valuable potential of natural flavonoids as candidate drugs for novel antibacterial strategies.

The RNA Polymerase II Factor RPAP1 Is Critical for Mediator-Driven Transcription and Cell Identity

The RNA polymerase II-associated protein 1 (RPAP1) is conserved across metazoa and required for stem cell differentiation in plants; however, very little is known about its mechanism of action or its role in mammalian cells. Here, we report that RPAP1 is essential for the expression of cell identity genes and for cell viability. Depletion of RPAP1 triggers cell de-differentiation, facilitates reprogramming toward pluripotency, and impairs differentiation. Mechanistically, we show that RPAP1 is essential for the interaction between RNA polymerase II (RNA Pol II) and Mediator, as well as for the recruitment of important regulators, such as the Mediator-specific RNA Pol II factor Gdown1 and the C-terminal domain (CTD) phosphatase RPAP2. In agreement, depletion of RPAP1 diminishes the loading of total and Ser5-phosphorylated RNA Pol II on many genes, with super-enhancer-driven genes among the most significantly downregulated. We conclude that Mediator/RPAP1/RNA Pol II is an ancient module, conserved from plants to mammals, critical for establishing and maintaining cell identity.

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RPAP1 is an RNA Pol II regulator, conserved from plants to mammals

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RPAP1 depletion erases cell identity gene expression, triggering de-differentiation

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Mechanistically, RPAP1 is critical for the Mediator-RNA Pol II interaction

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RPAP1 preferentially contributes to enhancer-driven gene transcription

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

The stochastic dynamics and regulatory mechanisms that govern differentiation of individual human neural precursor cells (NPC) into mature neurons are currently not fully understood. Here, we used single-cell RNA-sequencing (scRNA-seq) of developing neurons to dissect/identify NPC subtypes and critical developmental stages of alternative lineage specifications. This study comprises an unsupervised, high-resolution strategy for identifying cell developmental bifurcations, tracking the stochastic transcript kinetics of the subpopulations, elucidating regulatory networks, and finding key regulators. Our data revealed the bifurcation and developmental tracks of the two NPC subpopulations, and we captured an early (24 h) transition phase that leads to alternative neuronal specifications. The consequent up-regulation and down-regulation of stage- and subpopulation-specific gene groups during the course of maturation revealed biological insights with regard to key regulatory transcription factors and lincRNAs that control cellular programs in the identified neuronal subpopulations.

Bioinformatics Tools for Genome-Wide Epigenetic Research

Epigenetics play a central role in the regulation of many important cellular processes, and dysregulations at the epigenetic level could be the source of serious pathologies, such as neurological disorders affecting brain development, neurodegeneration, and intellectual disability. Despite significant technological advances for epigenetic profiling, there is still a need for a systematic understanding of how epigenetics shapes cellular circuitry, and disease pathogenesis. The development of accurate computational approaches for analyzing complex epigenetic profiles is essential for disentangling the mechanisms underlying cellular development, and the intricate interaction networks determining and sensing chromatin modifications and DNA methylation to control gene expression. In this chapter, we review the recent advances in the field of "computational epigenetics," including computational methods for processing different types of epigenetic data, prediction of chromatin states, and study of protein dynamics. We also discuss how "computational epigenetics" has complemented the fast growth in the generation of epigenetic data for uncovering the main differences and similarities at the epigenetic level between individuals and the mechanisms underlying disease onset and progression.

Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data

The epigenetics landscape of cells plays a key role in the establishment of cell-type specific gene expression programs characteristic of different cellular phenotypes. Different experimental procedures have been developed to obtain insights into the accessible chromatin landscape including DNase-seq, FAIRE-seq and ATAC-seq. However, current downstream computational tools fail to reliably determine regulatory region accessibility from the analysis of these experimental data. In particular, currently available peak calling algorithms are very sensitive to their parameter settings and show highly heterogeneous results, which hampers a trustworthy identification of accessible chromatin regions. Here, we present a novel method that predicts accessible and, more importantly, inaccessible gene-regulatory chromatin regions solely relying on transcriptomics data, which complements and improves the results of currently available computational methods for chromatin accessibility assays. We trained a hierarchical classification tree model on publicly available transcriptomics and DNase-seq data and assessed the predictive power of the model in six gold standard datasets. Our method increases precision and recall compared to traditional peak calling algorithms, while its usage is not limited to the prediction of accessible and inaccessible gene-regulatory chromatin regions, but constitutes a helpful tool for optimizing the parameter settings of peak calling methods in a cell type specific manner.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

Prediction of disease-gene-drug relationships following a differential network analysis

Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.

Exploring the complete mutational space of the LDL receptor LA5 domain using molecular dynamics: linking SNPs with disease phenotypes in familial hypercholesterolemia

Familial hypercholesterolemia (FH), a genetic disorder with a prevalence of 0.2%, represents a high-risk factor to develop cardiovascular and cerebrovascular diseases. The majority and most severe FH cases are associated to mutations in the receptor for low-density lipoproteins receptor (LDL-r), but the molecular basis explaining the connection between mutation and phenotype is often unknown, which hinders early diagnosis and treatment of the disease. We have used atomistic simulations to explore the complete SNP mutational space (227 mutants) of the LA5 repeat, the key domain for interacting with LDL that is coded in the exon concentrating the highest number of mutations. Four clusters of mutants of different stability have been identified. The majority of the 50 FH known mutations (33) appear distributed in the unstable clusters, i.e. loss of conformational stability explains two-third of FH phenotypes. However, one-third of FH phenotypes (17 mutations) do not destabilize the LR5 repeat. Combining our simulations with available structural data from different laboratories, we have defined a consensus-binding site for the interaction of the LA5 repeat with LDL-r partner proteins and have found that most (16) of the 17 stable FH mutations occur at binding site residues. Thus, LA5-associated FH arises from mutations that cause either the loss of stability or a decrease in domain's-binding affinity. Based on this finding, we propose the likely phenotype of each possible SNP in the LA5 repeat and outline a procedure to make a full computational diagnosis for FH.

The FurA regulon in Anabaena sp. PCC 7120: in silico prediction and experimental validation of novel target genes

In the filamentous cyanobacterium Anabaena sp. PCC 7120, the ferric uptake regulator FurA functions as a global transcriptional regulator. Despite several analyses have focused on elucidating the FurA-regulatory network, the number of target genes described for this essential transcription factor is limited to a handful of examples. In this article, we combine an in silico genome-wide predictive approach with experimental determinations to better define the FurA regulon. Predicted FurA-binding sites were identified upstream of 215 genes belonging to diverse functional categories including iron homeostasis, photosynthesis and respiration, heterocyst differentiation, oxidative stress defence and light-dependent signal transduction mechanisms, among others. The probabilistic model proved to be effective at discerning FurA boxes from non-cognate sequences, while subsequent electrophoretic mobility shift assay experiments confirmed the in vitro specific binding of FurA to at least 20 selected predicted targets. Gene-expression analyses further supported the dual role of FurA as transcriptional modulator that can act both as repressor and as activator. In either role, the in vitro affinity of the protein to its target sequences is strongly dependent on metal co-regulator and reducing conditions, suggesting that FurA couples in vivo iron homeostasis and the response to oxidative stress to major physiological processes in cyanobacteria.

PrionScan: an online database of predicted prion domains in complete proteomes

Background

Prions are a particular type of amyloids related to a large variety of important processes in cells, but also responsible for serious diseases in mammals and humans. The number of experimentally characterized prions is still low and corresponds to a handful of examples in microorganisms and mammals. Prion aggregation is mediated by specific protein domains with a remarkable compositional bias towards glutamine/asparagine and against charged residues and prolines. These compositional features have been used to predict new prion proteins in the genomes of different organisms. Despite these efforts, there are only a few available data sources containing prion predictions at a genomic scale.

Description

Here we present PrionScan, a new database of predicted prion-like domains in complete proteomes. We have previously developed a predictive methodology to identify and score prionogenic stretches in protein sequences. In the present work, we exploit this approach to scan all the protein sequences in public databases and compile a repository containing relevant information of proteins bearing prion-like domains. The database is updated regularly alongside UniprotKB and in its present version contains approximately 28000 predictions in proteins from different functional categories in more than 3200 organisms from all the taxonomic subdivisions. PrionScan can be used in two different ways: database query and analysis of protein sequences submitted by the users. In the first mode, simple queries allow to retrieve a detailed description of the properties of a defined protein. Queries can also be combined to generate more complex and specific searching patterns. In the second mode, users can submit and analyze their own sequences.

Conclusions

It is expected that this database would provide relevant insights on prion functions and regulation from a genome-wide perspective, allowing researches performing cross-species prion biology studies. Our database might also be useful for guiding experimentalists in the identification of new candidates for further experimental characterization.

Discovering putative prion sequences in complete proteomes using probabilistic representations of Q/N-rich domains

Background

Prion proteins conform a special class among amyloids due to their ability to transmit aggregative folds. Prions are known to act as infectious agents in neurodegenerative diseases in animals, or as key elements in transcription and translation processes in yeast. It has been suggested that prions contain specific sequential domains with distinctive amino acid composition and physicochemical properties that allow them to control the switch between soluble and β-sheet aggregated states. Those prion-forming domains are low complexity segments enriched in glutamine/asparagine and depleted in charged residues and prolines. Different predictive methods have been developed to discover novel prions by either assessing the compositional bias of these stretches or estimating the propensity of protein sequences to form amyloid aggregates. However, the available algorithms hitherto lack a thorough statistical calibration against large sequence databases, which makes them unable to accurately predict prions without retrieving a large number of false positives.

Results

Here we present a computational strategy to predict putative prion-forming proteins in complete proteomes using probabilistic representations of prionogenic glutamine/asparagine rich regions. After benchmarking our predictive model against large sets of non-prionic sequences, we were able to filter out known prions with high precision and accuracy, generating prediction sets with few false positives. The algorithm was used to scan all the proteomes annotated in public databases for the presence of putative prion proteins. We analyzed the presence of putative prion proteins in all taxa, from viruses and archaea to plants and higher eukaryotes, and found that most organisms encode evolutionarily unrelated proteins with susceptibility to behave as prions.

Conclusions

To our knowledge, this is the first wide-ranging study aiming to predict prion domains in complete proteomes. Approaches of this kind could be of great importance to identify potential targets for further experimental testing and to try to reach a deeper understanding of prions’ functional and regulatory mechanisms.

Structure of RdxA--an oxygen-insensitive nitroreductase essential for metronidazole activation in Helicobacter pylori

The RdxA oxygen-insensitive nitroreductase of the human gastric pathogen Helicobacter pylori is responsible for the susceptibility of this organism to the redox active prodrug metronidazole [2-(2-methyl-5-nitro-1H-imidazol-1-yl)ethanol]. Loss-of-function mutations in rdxA are primarily responsible for resistance to this therapeutic. RdxA exhibits potent NADPH oxidase activity under aerobic conditions and metronidazole reductase activity under strictly anaerobic conditions. In the present study, we report the crystal structure of RdxA, which is a homodimer exhibiting domain swapping and containing two molecules of FMN bound at the dimer interface. We have found a gap between the side chain of Tyr47 and the isoalloxazine ring of FMN that appears to be appropriate for substrate binding. The structure does not include residues 97-128, which correspond to a locally unstable part of the NTR from Escherichia coli, and might be involved in cofactor binding. Comparison of H. pylori RdxA with other oxidoreductases of known structure suggests that RdxA may belong to a new subgroup of oxidoreductases in which a cysteine side chain close to the FMN cofactor could be involved in the reductive activity. In this respect, the mutation of C159 to A or S (C159A/S) has resulted in a loss of metronidazole reductase activity but not NADPH oxidase activity. The RdxA structure enables the interpretation of the many loss-of-function mutations described previously, including those affecting C159, a residue whose interaction with FMN is required for the nitroreduction of metronidazole. The present studies provide unique insights into the redox behaviour of the flavin in this key enzyme for metronidazole activation, including a potential use in gene therapy.

DATABASE

Structural data have been deposited in the Protein Data Bank under accession number 3QDL.

Protein dynamics governed by interfaces of high polarity and low packing density

The folding pathway, three-dimensional structure and intrinsic dynamics of proteins are governed by their amino acid sequences. Internal protein surfaces with physicochemical properties appropriate to modulate conformational fluctuations could play important roles in folding and dynamics. We show here that proteins contain buried interfaces of high polarity and low packing density, coined as LIPs: Light Interfaces of high Polarity, whose physicochemical properties make them unstable. The structures of well-characterized equilibrium and kinetic folding intermediates indicate that the LIPs of the corresponding native proteins fold late and are involved in local unfolding events. Importantly, LIPs can be identified using very fast and uncomplicated computational analysis of protein three-dimensional structures, which provides an easy way to delineate the protein segments involved in dynamics. Since LIPs can be retained while the sequences of the interacting segments diverge significantly, proteins could in principle evolve new functional features reusing pre-existing encoded dynamics. Large-scale identification of LIPS may contribute to understanding evolutionary constraints of proteins and the way protein intrinsic dynamics are encoded.

Design and structure of an equilibrium protein folding intermediate: a hint into dynamical regions of proteins

Partly unfolded protein conformations close to the native state may play important roles in protein function and in protein misfolding. Structural analyses of such conformations which are essential for their fully physicochemical understanding are complicated by their characteristic low populations at equilibrium. We stabilize here with a single mutation the equilibrium intermediate of apoflavodoxin thermal unfolding and determine its solution structure by NMR. It consists of a large native region identical with that observed in the X-ray structure of the wild-type protein plus an unfolded region. Small-angle X-ray scattering analysis indicates that the calculated ensemble of structures is consistent with the actual degree of expansion of the intermediate. The unfolded region encompasses discontinuous sequence segments that cluster in the 3D structure of the native protein forming the FMN cofactor binding loops and the binding site of a variety of partner proteins. Analysis of the apoflavodoxin inner interfaces reveals that those becoming destabilized in the intermediate are more polar than other inner interfaces of the protein. Natively folded proteins contain hydrophobic cores formed by the packing of hydrophobic surfaces, while natively unfolded proteins are rich in polar residues. The structure of the apoflavodoxin thermal intermediate suggests that the regions of natively folded proteins that are easily responsive to thermal activation may contain cores of intermediate hydrophobicity.

Comparison of DNA binding across protein superfamilies

Specific protein-DNA interactions are central to a wide group of processes in the cell and have been studied both experimentally and computationally over the years. Despite the increasing collection of protein-DNA complexes, so far only a few studies have aimed at dissecting the structural characteristics of DNA binding among evolutionarily related proteins. Some questions that remain to be answered are: (a) what is the contribution of the different readout mechanisms in members of a given structural superfamily, (b) what is the degree of interface similarity among superfamily members and how this affects binding specificity, (c) how DNA-binding protein superfamilies distribute across taxa, and (d) is there a general or family-specific code for the recognition of DNA. We have recently developed a straightforward method to dissect the interface of protein-DNA complexes at the atomic level and here we apply it to study 175 proteins belonging to nine representative superfamilies. Our results indicate that evolutionarily unrelated DNA-binding domains broadly conserve specificity statistics, such as the ratio of indirect/direct readout and the frequency of atomic interactions, therefore supporting the existence of a set of recognition rules. It is also found that interface conservation follows trends that are superfamily-specific. Finally, this article identifies tendencies in the phylogenetic distribution of transcription factors, which might be related to the evolution of regulatory networks, and postulates that the modular nature of zinc finger proteins can explain its role in large genomes, as it allows for larger binding interfaces in a single protein molecule.

From sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters

Background

The fine tuning of two features of the bacterial regulatory machinery have been known to contribute to the diversity of gene expression within the same regulon: the sequence of Transcription Factor (TF) binding sites, and their location with respect to promoters. While variations of binding sequences modulate the strength of the interaction between the TF and its binding sites, the distance between binding sites and promoters alter the interaction between the TF and the RNA polymerase (RNAP).

Results

In this paper we estimated the dissociation constants (K_d) of several E. coli TFs in their interaction with variants of their binding sequences from the scores resulting from aligning them to Positional Weight Matrices. A correlation coefficient of 0.78 was obtained when pooling together sites for different TFs. The theoretically estimated K_d values were then used, together with the dissociation constants of the RNAP-promoter interaction to analyze activated and repressed promoters. The strength of repressor sites -- i.e., the strength of the interaction between TFs and their binding sites -- is slightly higher than that of activated sites. We explored how different factors such as the variation of binding sequences, the occurrence of more than one binding site, or different RNAP concentrations may influence the promoters' response to the variations of TF concentrations. We found that the occurrence of several regulatory sites bound by the same TF close to a promoter -- if they are bound by the TF in an independent manner -- changes the effect of TF concentrations on promoter occupancy, with respect to individual sites. We also found that the occupancy of a promoter will never be more than half if the RNAP concentration-to-K_p ratio is 1 and the promoter is subject to repression; or less than half if the promoter is subject to activation. If the ratio falls to 0.1, the upper limit of occupancy probability for repressed drops below 10%; a descent of the limits occurs also for activated promoters.

Conclusion

The number of regulatory sites may thus act as a versatility-producing device, in addition to serving as a source of robustness of the transcription machinery. Furthermore, our results show that the effects of TF concentration fluctuations on promoter occupancy are constrained by RNAP concentrations.

Prediction of TF target sites based on atomistic models of protein-DNA complexes

Background

The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence.

Results

Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models.

Conclusion

Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition.

The role of DNA-binding specificity in the evolution of bacterial regulatory networks

Understanding the mechanisms by which transcriptional regulatory networks (TRNs) change through evolution is a fundamental problem.Here, we analyze this question using data from Escherichia coli and Bacillus subtilis, and find that paralogy relationships are insufficient to explain the global or local role observed for transcription factors (TFs) within regulatory networks. Our results provide a picture in which DNA-binding specificity, a molecular property that can be measured in different ways, is a predictor of the role of transcription factors. In particular, we observe that global regulators consistently display low levels of binding specificity, while displaying comparatively higher expression values in microarray experiments. In addition, we find a strong negative correlation between binding specificity and the number of co-regulators that help coordinate genetic expression on a genomic scale. A close look at several orthologous TFs,including FNR, a regulator found to be global in E. coli and local in B.subtilis, confirms the diagnostic value of specificity in order to understand their regulatory function, and highlights the importance of evaluating the metabolic and ecological relevance of effectors as another variable in the evolutionary equation of regulatory networks. Finally, a general model is presented that integrates some evolutionary forces and molecular properties,aiming to explain how regulons grow and shrink, as bacteria tune their regulation to increase adaptation.

Impact of Transcription Units rearrangement on the evolution of the regulatory network of gamma-proteobacteria

BACKGROUND

In the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that focus on a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms, such as E. coli among the prokaryotes.

RESULTS

In this paper we used the information annotated in Tractor_DB (a database of regulatory networks in gamma-proteobacteria) to calculate a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory link across thirty genomes of this subclass. Then we used this SOS to assess how regulatory connections have evolved in this group, and how the variation of basic regulatory connection is reflected on the structure of the chromosome. We found that individual regulatory interactions shift between different organisms, a process that may be described as rewiring the network. At this evolutionary scale (the gamma-proteobacteria subclass) this rewiring process may be an important source of variation of regulatory incoming interactions for individual networks. We also noticed that the regulatory links that form feed forward motifs are conserved in a better correlated manner than triads of random regulatory interactions or pairs of co-regulated genes. Furthermore, the rewiring process that takes place at the most basic level of the regulatory network may be linked to rearrangements of genetic material within bacterial chromosomes, which change the structure of Transcription Units and therefore the regulatory connections between Transcription Factors and structural genes.

CONCLUSION

The rearrangements that occur in bacterial chromosomes-mostly inversion or horizontal gene transfer events - are important sources of variation of gene regulation at this evolutionary scale.

Tractor_DB (version 2.0): a database of regulatory interactions in gamma-proteobacterial genomes

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: , and . This database contains a collection of computationally predicted Transcription Factors' binding sites in gamma-proteobacterial genomes. These data should aid researchers in the design of microarray experiments and the interpretation of their results. They should also facilitate studies of Comparative Genomics of the regulatory networks of this group of organisms. In this paper we describe the main improvements incorporated to the database in the past year and a half which include incorporating information on the regulatory networks of 13—increasing to 30—new gamma-proteobacteria and developing a new computational strategy to complement the putative sites identified by the original weight matrix-based approach. We have also added dynamically generated navigation tabs to the navigation interfaces. Moreover, we developed a new interface that allows users to directly retrieve information on the conservation of regulatory interactions in the 30 genomes included in the database by navigating a map that represents a core of the known Escherichia coli regulatory network.

Complementing computationally predicted regulatory sites in Tractor_DB using a pattern matching approach

Prokaryotic genomes annotation has focused on genes location and function. The lack of regulatory information has limited the knowledge on cellular transcriptional regulatory networks. However, as more phylogenetically close genomes are sequenced and annotated, the implementation of phylogenetic footprinting strategies for the recognition of regulators and their regulons becomes more important. In this paper we describe a comparative genomics approach to the prediction of new gamma-proteobacterial regulon members. We take advantage of the phylogenetic proximity of Escherichia coli and other 16 organisms of this subdivision and the intensive search of the space sequence provided by a pattern-matching strategy. Using this approach we complement predictions of regulatory sites made using statistical models currently stored in Tractor_DB, and increase the number of transcriptional regulators with predicted binding sites up to 86. All these computational predictions may be reached at Tractor_DB (www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br, www.ccg.unam.mx/Computational_Genomics/tractorDB/). We also take a first step in this paper towards the assessment of the conservation of the architecture of the regulatory network in the gamma-proteobacteria through evaluating the conservation of the overall connectivity of the network.