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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., (© 2021 by The American Society of Hematology.)

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, G 2 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., Competing Interests: Declaration of interests The authors declare no conflicts of interest., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (© 2020 by The American Society of Hematology.)

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., (© 2017 Wang et al.; Published by Cold Spring Harbor Laboratory Press.)

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.

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.

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 Author 2016. Published by Oxford University Press.)

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.

Background: Stem cell differentiation is a complex biological process. Cellular heterogeneity, such as the co-existence of different cell subpopulations within a population, partly hampers our understanding of this process. The modern single-cell gene expression technologies, such as single-cell RT-PCR and RNA-seq, have enabled us to elucidate such heterogeneous cell subpopulations. However, the identification of a transcriptional regulatory network (TRN) for each cell subpopulation within a population and genes determining specific cell fates (lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational differential network analysis approach for predicting lineage specifiers in binary-fate differentiation events., Methods: The proposed method is based on a model that considers each stem cell subpopulation being in a stable state maintained by its specific TRN stability core, and cell differentiation involves changes in these stability cores between parental and daughter cell subpopulations. The method first reconstructs topologically different cell-subpopulation specific TRNs from single-cell gene expression data, literature knowledge and transcription factor (TF)-DNA binding-site prediction. Then, it systematically predicts lineage specifiers by identifying genes in the TRN stability cores in both parental and daughter cell subpopulations., Results: Application of this method to different stem cell differentiation systems was able to predict known and putative novel lineage specifiers. These examples include the differentiation of inner cell mass into either primitive endoderm or epiblast, different progenitor cells in the hematopoietic system, and the lung alveolar bipotential progenitor into either alveolar type 1 or alveolar type 2., Conclusions: The method is generally applicable to any binary-fate differentiation system, for which single-cell gene expression data are available. Therefore, it should aid in understanding stem cell lineage specification, and in the development of experimental strategies for regenerative medicine., Competing Interests: The authors declare no conflict of interest.

The low-density lipoprotein receptor (LDLR) captures circulating lipoproteins and delivers them in the endosome for degradation. Its function is essential for cholesterol homeostasis, and mutations in the LDLR are the major cause of familiar hypercholesterolemia. The release of LDL is usually attributed to endosome acidification. As the pH drops, the affinity of the LDLR/LDL complex is reduced, whereas the strength of a self-complex formed between two domains of the receptor (i.e. the LDL binding domain and the β-propeller domain) increases. However, an alternative model states that, as a consequence of a drop in both pH and Ca(2+) concentration, the LDLR binding domain is destabilized in the endosome, which weakens the LDLR/LDL complex, thus liberating the LDL particles. In the present study, we test a key underlying assumption of the second model, namely that the lipoprotein binding repeats of the receptor (specifically repeats 4 and 5, LR4 and LR5) rapidly sense endosomal changes in Ca(2+) concentration. Our kinetic and thermodynamic analysis of Ca(2+) and Mg(2+) binding to LR4 and LR5, as well as to the tandem of the two (LR4-5), shows that both repeats spontaneously release Ca(2+) in a time scale much shorter than endosomal delivery of LDL, thus acting as Ca(2+) sensors that become unfolded under endosomal conditions. Our analysis additionally explains the lower Ca(2+) affinity of repeat LR4, compared to LR5, as arising from a very slow Ca(2+) binding reaction in the former, most likely related to the lower conformational stability of apolipoprotein LR4, compared to apolipoprotein LR5, as determined from thermal unfolding experiments and molecular dynamics simulations., (© 2014 FEBS.)

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.

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.

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., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

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., ((c) 2009 Wiley-Liss, Inc.)

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.

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.

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.

Nitrogen signalling in cyanobacteria involves a complex network in which the availability of iron plays an important role. In the nitrogen-fixing cyanobacterium Anabaena sp. PCC 7120, iron uptake is controlled by FurA, while NtcA is the master regulator of nitrogen metabolism and shows a mutual dependence with HetR in the first steps of heterocyst development. Expression of FurA is modulated by NtcA and it is enhanced in a hetR(-) background. Iron starvation in cells grown in the presence of combined nitrogen causes a moderate increase in the transcription of glnA that is more evident in a ntcA(-) background. Those results evidence a tight link between the reserves of iron and nitrogen metabolism that leads us to search for target genes potentially co-regulated by FurA and NtcA. Using a bioinformatic approach we have found a significant number of NtcA-regulated genes exhibiting iron boxes in their upstream regions. Our computational predictions have been validated using electrophoretic mobility shift assay (EMSA) analysis. These candidates for dual regulation are involved in different functions such as photosynthesis (i.e. psaL, petH, rbcL, isiA), heterocyst differentiation (i.e. xisA, hanA, prpJ, nifH), transcriptional regulation (several alternative sigma factors) or redox balance (i.e. trxA, ftrC, gor). The identification of common elements overlapping the NtcA and FurA regulons allows us to establish a previously unrecognized transcriptional regulatory connection between iron homeostasis, redox control and nitrogen metabolism.

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br and http://www.ccg.unam.mx/tractorDB. 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.

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.