An ORFeome-based analysis of human transcription factor genes and the construction of a microarray to interrogate their expression

Genome-scale identification and comparative analysis of transcription factors in thermophilic cyanobacteria

BACKGROUND

The transcription factors (TFs) in thermophilic cyanobacteria might represent a uniquely evolved gene repertoire in light of the strong selective pressure caused by hostile habitats. Understanding the molecular composition of the TF genes in thermophilic cyanobacteria will facilitate further studies regarding verifying their exact biochemical functions and genetic engineering. However, limited information is available on the TFs of thermophilic cyanobacteria. Herein, a thorough investigation and comparative analysis were performed to gain insights into the molecular composition of the TFs in 22 thermophilic cyanobacteria.

RESULTS

The results suggested a fascinating diversity of the TFs among these thermophiles. The abundance and type of TF genes were diversified in these genomes. The identified TFs are speculated to play various roles in biological regulations. Further comparative and evolutionary genomic analyses revealed that HGT may be associated with the genomic plasticity of TF genes in Thermostichus and Thermosynechococcus strains. Comparative analyses also indicated different pattern of TF composition between thermophiles and corresponding mesophilic reference cyanobacteria. Moreover, the identified unique TFs of thermophiles are putatively involved in various biological regulations, mainly as responses to ambient changes, may facilitating the thermophiles to survive in hot springs.

CONCLUSION

The findings herein shed light on the TFs of thermophilic cyanobacteria and fundamental knowledge for further research regarding thermophilic cyanobacteria with a broad potential for transcription regulations in responses to environmental fluctuations.

TFCheckpoint database update, a cross-referencing system for transcription factors from human, mouse and rat

Prior knowledge about DNA-binding transcription factors (dbTFs), transcription co-regulators (coTFs) and general transcriptional factors (GTFs) is crucial for the study and understanding of the regulation of transcription. This is reflected by the many publications and database resources describing knowledge about TFs. We previously launched the TFCheckpoint database, an integrated resource focused on human, mouse and rat dbTFs, providing users access to a comprehensive overview of these proteins. Here, we describe TFCheckpoint 2.0 (https://www.tfcheckpoint.org/index.php), comprising 13 collections of dbTFs, coTFs and GTFs. TFCheckpoint 2.0 provides an easy and versatile cross-referencing system for users to view and download collections that may otherwise be cumbersome to find, compare and retrieve.

Structural Evaluation and Conformational Dynamics of ZNF141T474I Mutation Provoking Postaxial Polydactyly Type A

Postaxial Polydactyly (PAP) is a congenital disorder of limb abnormalities characterized by posterior extra digits. Mutations in the N-terminal region of the Zinc finger protein 141 (ZNF141) gene were recently linked with PAP type A. Zinc finger proteins exhibit similarity at their N-terminal regions due to C2-H2 type Zinc finger domains, but their functional preferences vary significantly by the binding patterns of DNA. Methods: This study delineates the pathogenic association, miss-fold aggregation, and conformational paradigm of a missense variant (c.1420C > T; p.T474I) in ZNF141 gene segregating PAP through a molecular dynamics simulations approach. Results: In ZNF141 protein, helices play a crucial role by attaching three specific target DNA base pairs. In ZNF141T474I protein, H1, H3, and H6 helices attain more flexibility by acquiring loop conformation. The outward disposition of the proximal portion of H9-helix in mutant protein occurs due to the loss of prior beta-hairpins at the C terminal region of the C2-H2 domain. The loss of hydrogen bonds and exposure of hydrophobic residues to solvent and helices turning to loops cause dysfunction of ZNF141 protein. These significant changes in the stability and conformation of the mutant protein were validated using essential dynamics and cross-correlation maps, which revealed that upon point mutation, the overall motion of the proteins and the correlation between them were completely different, resulting in Postaxial polydactyly type A. Conclusions: This study provides molecular insights into the structural association of ZNF141 protein with PAP type A. Identification of active site residues and legends offers new therapeutic targets for ZNF141 protein. Further, it reiterates the functional importance of the last residue of a protein.

Cerebrospinal fluid of progressive multiple sclerosis patients reduces differentiation and immune functions of oligodendrocyte progenitor cells

Oligodendrocyte progenitor cells (OPCs) are responsible for remyelination in the central nervous system (CNS) in health and disease. For patients with multiple sclerosis (MS), remyelination is not always successful, and the mechanisms differentiating successful from failed remyelination are not well‐known. Growing evidence suggests an immune role for OPCs, in addition to their regenerative role; however, it is not clear if this helps or hinders the regenerative process. We studied the effect of cerebrospinal fluid (CSF) from relapsing MS (rMS) and progressive MS (pMS) patients on primary OPC differentiation and immune gene expression and function. We observed that CSF from either rMS or pMS patients has a differential effect on the ability of mice OPCs to differentiate into mature oligodendrocytes and to express immune functions. CSF of pMS patients impaired differentiation into mature oligodendrocytes. In addition, it led to decreased major histocompatibility complex class (MHC)‐II expression, tumor necrosis factor (TNF)‐α secretion, nuclear factor kappa‐B (NFκB) activation, and less activation and proliferation of T cells. Our findings suggest that OPCs are not only responsible for remyelination, but they may also play an active role as innate immune cells in the CNS.

Integration of transcription coregulator complexes with sequence-specific DNA-binding factor interactomes

The domain of transcription regulation has been notoriously difficult to annotate in the Gene Ontology, partly because of the intricacies of gene regulation which involve molecular interactions with DNA as well as amongst protein complexes. The molecular function 'transcription coregulator activity' is a part of the biological process 'regulation of transcription, DNA-templated' that occurs in the cellular component 'chromatin'. It can mechanistically link sequence-specific DNA-binding transcription factor (dbTF) regulatory DNA target sites to coactivator and corepressor target sites through the molecular function 'cis-regulatory region sequence-specific DNA binding'. Many questions arise about transcription coregulators (coTF). Here, we asked how many unannotated, putative coregulators can be identified in protein complexes? Therefore, we mined the CORUM and hu.MAP protein complex databases with known and strongly presumed human transcription coregulators. In addition, we trawled the BioGRID and IntAct molecular interaction databases for interactors of the known 1457 human dbTFs annotated by the GREEKC and GO consortia. This yielded 1093 putative transcription factor coregulator complex subunits, of which 954 interact directly with a dbTF. This substantially expands the set of coTFs that could be annotated to 'transcription coregulator activity' and sets the stage for renewed annotation and wet-lab research efforts. To this end, we devised a prioritisation score based on existing GO annotations of already curated transcription coregulators as well as interactome representation. Since all the proteins that we mined are parts of protein complexes, we propose to concomitantly engage in annotation of the putative transcription coregulator-containing complexes in the Complex Portal database.

ZNF354C is a transcriptional repressor that inhibits endothelial angiogenic sprouting

Zinc finger proteins (ZNF) are a large group of transcription factors with diverse functions. We recently discovered that endothelial cells harbour a specific mechanism to limit the action of ZNF354C, whose function in endothelial cells is unknown. Given that ZNF354C has so far only been studied in bone and tumour, its function was determined in endothelial cells. ZNF354C is expressed in vascular cells and localises to the nucleus and cytoplasm. Overexpression of ZNF354C in human endothelial cells results in a marked inhibition of endothelial sprouting. RNA-sequencing of human microvascular endothelial cells with and without overexpression of ZNF354C revealed that the protein is a potent transcriptional repressor. ZNF354C contains an active KRAB domain which mediates this suppression as shown by mutagenesis analysis. ZNF354C interacts with dsDNA, TRIM28 and histones, as observed by proximity ligation and immunoprecipitation. Moreover, chromatin immunoprecipitation revealed that the ZNF binds to specific endothelial-relevant target-gene promoters. ZNF354C suppresses these genes as shown by CRISPR/Cas knockout and RNAi. Inhibition of endothelial sprouting by ZNF354C is dependent on the amino acids DV and MLE of the KRAB domain. These results demonstrate that ZNF354C is a repressive transcription factor which acts through a KRAB domain to inhibit endothelial angiogenic sprouting.

Chromatin Environment and Cellular Context Specify Compensatory Activity of Paralogous MEF2 Transcription Factors

Compensation among paralogous transcription factors (TFs) confers genetic robustness of cellular processes, but how TFs dynamically respond to paralog depletion on a genome-wide scale in vivo remains incompletely understood. Using single and double conditional knockout of myocyte enhancer factor 2 (MEF2) family TFs in granule neurons of the mouse cerebellum, we find that MEF2A and MEF2D play functionally redundant roles in cerebellar-dependent motor learning. Although both TFs are highly expressed in granule neurons, transcriptomic analyses show MEF2D is the predominant genomic regulator of gene expression in vivo. Strikingly, genome-wide occupancy analyses reveal upon depletion of MEF2D, MEF2A occupancy robustly increases at a subset of sites normally bound to MEF2D. Importantly, sites experiencing compensatory MEF2A occupancy are concentrated within open chromatin and undergo functional compensation for genomic activation and gene expression. Finally, motor activity induces a switch from non-compensatory to compensatory MEF2-dependent gene regulation. These studies uncover genome-wide functional interdependency between paralogous TFs in the brain.

Majidi et al. study how transcription factors respond to paralog depletion by conditionally depleting MEF2A and MEF2D in mouse cerebellum. Depletion of MEF2D induces functionally compensatory genomic occupancy by MEF2A. Compensation occurs within accessible chromatin in a context-dependent manner. This study explores the interdependency between paralogous transcription factors.

An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells

Electrophilic compounds originating from nature or chemical synthesis have profound effects on immune cells. These compounds are thought to act by cysteine modification to alter the functions of immune-relevant proteins; however, our understanding of electrophile-sensitive cysteines in the human immune proteome remains limited. Here, we present a global map of cysteines in primary human T cells that are susceptible to covalent modification by electrophilic small molecules. More than 3,000 covalently liganded cysteines were found on functionally and structurally diverse proteins, including many that play fundamental roles in immunology. We further show that electrophilic compounds can impair T cell activation by distinct mechanisms involving the direct functional perturbation and/or degradation of proteins. Our findings reveal a rich content of ligandable cysteines in human T cells and point to electrophilic small molecules as a fertile source for chemical probes and ultimately therapeutics that modulate immunological processes and their associated disorders.

Twist-1 is upregulated by NSD2 and contributes to tumour dissemination and an epithelial-mesenchymal transition-like gene expression signature in t(4;14)-positive multiple myeloma

Approximately 15% of patients with multiple myeloma (MM) harbour the t(4;14) chromosomal translocation, leading to the overexpression of the histone methyltransferase NSD2. Patients with this translocation display increased tumour dissemination, accelerated disease progression and rapid relapse. Using publicly available gene expression profile data from NSD2^high (n = 135) and NSD2^low (n = 878) MM patients, we identified 39 epithelial-mesenchymal transition (EMT)-associated genes which are overexpressed in NSD2^high MM plasma cells. In addition, our analyses identified Twist-1 as a key transcription factor upregulated in NSD2^high MM patients and t(4;14)-positive cell lines. Overexpression and knockdown studies confirmed that Twist-1 is involved in driving the expression of EMT-associated genes in the human MM cell line KMS11 and promoted the migration of myeloma cell lines in vitro. Notably, Twist-1 overexpression in the mouse MM cell line 5TGM1 significantly increased tumour dissemination in an intratibial tumour model. These findings demonstrate that Twist-1, downstream of NSD2, contributes to the induction of an EMT-like signature in t(4;14)-positive MM and enhances the dissemination of MM plasma cells in vivo, which may, in part, explain the aggressive disease features associated with t(4;14)-positive MM.

Characterisation of transcription factor profiles in polycystic kidney disease (PKD): identification and validation of STAT3 and RUNX1 in the injury/repair response and PKD progression

Abstract

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic renal disease, caused in the majority of the cases by a mutation in either the PKD1 or the PKD2 gene. ADPKD is characterised by a progressive increase in the number and size of cysts, together with fibrosis and distortion of the renal architecture, over the years. This is accompanied by alterations in a complex network of signalling pathways. However, the underlying molecular mechanisms are not well characterised. Previously, we defined the PKD Signature, a set of genes typically dysregulated in PKD across different disease models from a meta-analysis of expression profiles. Given the importance of transcription factors (TFs) in modulating disease, we focused in this paper on characterising TFs from the PKD Signature. Our results revealed that out of the 1515 genes in the PKD Signature, 92 were TFs with altered expression in PKD, and 32 of those were also implicated in tissue injury/repair mechanisms. Validating the dysregulation of these TFs by qPCR in independent PKD and injury models largely confirmed these findings. STAT3 and RUNX1 displayed the strongest activation in cystic kidneys, as demonstrated by chromatin immunoprecipitation (ChIP) followed by qPCR. Using immunohistochemistry, we showed a dramatic increase of expression after renal injury in mice and cystic renal tissue of mice and humans. Our results suggest a role for STAT3 and RUNX1 and their downstream targets in the aetiology of ADPKD and indicate that the meta-analysis approach is a viable strategy for new target discovery in PKD.

Key messages

We identified a list of transcription factors (TFs) commonly dysregulated in ADPKD.

Out of the 92 TFs identified in the PKD Signature, 35% are also involved in injury/repair processes.

STAT3 and RUNX1 are the most significantly dysregulated TFs after injury and during PKD progression.

STAT3 and RUNX1 activity is increased in cystic compared to non-cystic mouse kidneys.

Increased expression of STAT3 and RUNX1 is observed in the nuclei of renal epithelial cells, also in human ADPKD samples.

Electronic supplementary material

The online version of this article (10.1007/s00109-019-01852-3) contains supplementary material, which is available to authorized users.

Genetic Variation in Human Gene Regulatory Factors Uncovers Regulatory Roles in Local Adaptation and Disease

Differences in gene regulation have been suggested to play essential roles in the evolution of phenotypic changes. Although DNA changes in cis-regulatory elements affect only the regulation of its corresponding gene, variations in gene regulatory factors (trans) can have a broader effect, because the expression of many target genes might be affected. Aiming to better understand how natural selection may have shaped the diversity of gene regulatory factors in human, we assembled a catalog of all proteins involved in controlling gene expression. We found that at least five DNA-binding transcription factor classes are enriched among genes located in candidate regions for selection, suggesting that they might be relevant for understanding regulatory mechanisms involved in human local adaptation. The class of KRAB-ZNFs, zinc-finger (ZNF) genes with a Krüppel-associated box, stands out by first, having the most genes located on candidate regions for positive selection. Second, displaying most nonsynonymous single nucleotide polymorphisms (SNPs) with high genetic differentiation between populations within these regions. Third, having 27 KRAB-ZNF gene clusters with high extended haplotype homozygosity. Our further characterization of nonsynonymous SNPs in ZNF genes located within candidate regions for selection, suggests regulatory modifications that might influence the expression of target genes at population level. Our detailed investigation of three candidate regions revealed possible explanations for how SNPs may influence the prevalence of schizophrenia, eye development, and fertility in humans, among other phenotypes. The genetic variation we characterized here may be responsible for subtle to rough regulatory changes that could be important for understanding human adaptation.

Structural Features of Transcription Factors Associating with Nucleosome Binding

Fate-changing transcription factors (TFs) scan chromatin to initiate new genetic programs during cell differentiation and reprogramming. Yet the protein structure domains that allow TFs to target nucleosomal DNA remain unexplored. We screened diverse TFs for binding to nucleosomes containing motif-enriched sequences targeted by pioneer factors in vivo. FOXA1, OCT4, ASCL1/E12α, PU1, CEBPα, and ZELDA display a range of nucleosome binding affinities that correlate with their cell reprogramming potential. We further screened 593 full-length human TFs on protein microarrays against different nucleosome sequences, followed by confirmation in solution, to distinguish among factors that bound nucleosomes, such as the neuronal AP-2α/β/γ, versus factors that only bound free DNA. Structural comparisons of DNA binding domains revealed that efficient nucleosome binders use short anchoring α helices to bind DNA, whereas weak nucleosome binders use unstructured regions and/or β sheets. Thus, specific modes of DNA interaction allow nucleosome scanning that confers pioneer activity to transcription factors.

wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool

BACKGROUND

Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.

RESULTS

Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.

CONCLUSION

In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).

Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis. One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions or execute final biological functions in pathways, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.

Gene co-expression networks provide insights into the mechanism of cellular activity and gene regulation. Condition-specific mechanisms may be identified by constructing and comparing co-expression networks of multiple conditions. We propose a novel statistical method to jointly construct co-expression networks for gene expression profiles from multiple conditions. By using a data-driven approach to capture condition-specific co-expression patterns, this method is effective in identifying both co-expression patterns that are specific to a condition and that are common across conditions. The application of this method to real datasets reveals interesting biological insights.

Mechanisms and Measurement of Changes in Gene Expression

Research on gene expression (GE) provides insights into the physiology of a cell or group of cells at a given point in time. Studies of changes in GE can be used to identify patients at higher risk for various medical conditions, a higher symptom burden, and/or the adverse consequences associated with various treatments. The aims of this article are as follows: (1) to describe the different types of RNA transcripts, (2) to describe the processes involved in GE (i.e., RNA transcription, epigenetics, and posttranscriptional modifications), (3) to describe common sources of variation in GE, (4) to describe the most common methods used to measure GE, and (5) to discuss factors to consider when choosing tissue for a GE study. This article begins with an overview of the mechanisms involved in GE. Then, the factors that can influence the findings from GE experiments (e.g., tissue specificity, host age, host gender, and time of sample collection) are described and potential solutions are presented. This article concludes with a discussion of how the types of tissue used in GE studies can affect study findings. Given that the costs associated with the measurement of changes in GE are decreasing and the methods to analyze GE data are becoming easier to use, nurse scientists need to understand the basic principles that underlie any GE study.

Construction of Arabidopsis Transcription Factor ORFeome Collections and Identification of Protein-DNA Interactions by High-Throughput Yeast One-Hybrid Screens

Identification of transcription factor (TF)-promoter interactions is key to understanding the basic molecular underpinnings of gene regulation. The complexity of gene regulation, however, is driven by the combined function of several TFs recruited to the promoter region, which often confounds the discovery of transcriptional regulatory mechanisms. Genome sequencing enabled the construction of TF-specific ORFeome clone collections that can be used to study TF function with unprecedented coverage. Among the recently developed methods, gene-centered yeast one-hybrid (Y1H) screens performed with these ORFeome collections provide a simple and reliable strategy to identify TF-promoter interactions. Here, we describe high-throughput cloning protocols used to generate a gold standard TF ORFeome collection for the model organism Arabidopsis thaliana. Furthermore, we outline the protocol to build a daughter clone collection suitable for the Y1H assay and a high-throughput Y1H screening procedure that enables rapid assessment of thousands TF-promoter interactions using a robotic platform. These protocols can be universally adopted to build ORFeome libraries and thus expand the usage of gene-centered Y1H screens or other alternative strategies for discovery and characterization of TF functions.

Bioinformatics and genomic databases

High-throughput, low-cost sequencing technologies have begun to yield new insights into biology and medicine. New data enable the interrogation of the molecular biology of disease from DNA to RNA to protein, charting the central dogma. This chapter reviews some of the key advances and resources in the application of bioinformatics to understanding, and ultimately diagnosing and treating, diseases of the nervous system. Array genotyping, exome sequencing, and whole-genome sequencing, in both disease and healthy populations, have enabled the interpretation of new genetic data. Profiling of epigenetic markers, such as histone modifications, has added to our understanding of the regulatory machinery of the genome. Downstream, mRNA, and protein expression data from published experiments and high-throughput studies enable complex analyses of gene function across many experimental conditions and tissues. Further delineation of molecular mechanism arises from the concept of genes working together in pathways or networks, reflecting direct protein interactions and regulatory relationships. The rapidly moving field of bioinformatics has made significant contributions to neurology in these early days; continued advances promise to transform medicine from basic science to clinical practice, as more genomics data are generated, combined, and analyzed in the future.

A convex optimization approach for identification of human tissue-specific interactomes

Motivation: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.

Results: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.

Availability and implementation:http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

Contact:mohammadi@purdue.edu

New factors in mammalian DNA repair-the chromatin connection

In response to DNA damage mammalian cells activate a complex network of stress response pathways collectively termed DNA damage response (DDR). DDR involves a temporary arrest of the cell cycle to allow for the repair of the damage. DDR also attenuates gene expression by silencing global transcription and translation. Main function of DDR is, however, to prevent the fixation of debilitating changes to DNA by activation of various DNA repair pathways. Proper execution of DDR requires careful coordination between these interdependent cellular responses. Deregulation of some aspects of DDR orchestration is potentially pathological and could lead to various undesired outcomes such as DNA translocations, cellular transformation or acute cell death. It is thus critical to understand the regulation of DDR in cells especially in the light of a strong linkage between the DDR impairment and the occurrence of common human diseases such as cancer. In this review we focus on recent advances in understanding of mammalian DNA repair regulation and a on the function of PAXX/c9orf142 and ZNF281 proteins that recently had been discovered to play a role in that process. We focus on regulation of double-strand DNA break (DSB) repair via the non-homologous end joining pathway, as unrepaired DSBs are the primary cause of pathological cellular states after DNA damage. Interestingly these new factors operate at the level of chromatin, which reinforces a notion of a central role of chromatin structure in the regulation of cellular DDR regulation.

Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state-space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical "external stimulus-induced information loss" model of biological systems.

ESE-1 in Early Development: Approaches for the Future

E26 transformation-specific (Ets) family of transcription factors are characterized by the presence of Ets-DNA binding domain and have been found to be highly involved in hematopoiesis and various tissue differentiation. ESE-1, or Elf3 in mice, is a member of epithelium-specific Ets sub-family which is most prominently expressed in epithelial tissues such as the gut, mammary gland, and lung. The role of ESE-1 during embryogenesis had long been alluded from 30% fetal lethality in homozygous knockout mice and its high expression in preimplantation mouse embryos, but there has been no in-depth of analysis of ESE-1 function in early development. With improved proteomics, gene editing tools and increasing knowledge of ESE-1 function in adult tissues, we hereby propose future research directions for the study of ESE-1 in embryogenesis, including studying its regulation at the protein level and at the protein family level, as well as better defining the developmental phase under investigation. Understanding the role of ESE-1 in early development will provide new insights into its involvement in tissue regeneration and cancer, as well as how it functions with other Ets factors as a protein family.

Every transcription factor deserves its map: Scaling up epitope tagging of proteins to bypass antibody problems

Genome-wide identification of transcription factor binding sites with the ChIP-seq method is an extremely important scientific endeavor - one that should ideally be performed for every transcription factor in as many cell types as possible. A major hurdle on the way to this goal is the necessity for a specific, ChIP-grade antibody for each transcription factor of interest, which is often not available. Here, we describe CETCh-seq, a recently published method utilizing genome engineering with the CRISPR/Cas9 system to circumvent the need for a specific antibody. Using the CETCh-seq method, targeted genomic editing results in an epitope-tagged transcription factor, which is recognized by a well-characterized, standard antibody, efficacious for ChIP-seq. We have used CETCh-seq in human cancer cell lines as well as mouse embryonic stem cells. We find that roughly 60% of transcription factors tagged using CETCh-seq produce a high quality ChIP-seq map, a significant improvement over traditional antibody-based methods.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

Transcription factors in microalgae: genome-wide prediction and comparative analysis

Background

Studying transcription factors, which are some of the key players in gene expression, is of outstanding interest for the investigation of the evolutionary history of organisms through lineage-specific features. In this study we performed the first genome-wide TF identification and comparison between haptophytes and other algal lineages.

Results

For TF identification and classification, we created a comprehensive pipeline using a combination of BLAST, HMMER and InterProScan software. The accuracy evaluation of the pipeline shows its applicability for every alga, plant and cyanobacterium, with very good PPV and sensitivity. This pipeline allowed us to identify and classified the transcription factor complement of the three haptophytes Tisochrysis lutea, Emiliania huxleyi and Pavlova sp.; the two stramenopiles Phaeodactylum tricornutum and Nannochloropsis gaditana; the chlorophyte Chlamydomonas reinhardtii and the rhodophyte Porphyridium purpureum. By using T. lutea and Porphyridium purpureum, this work extends the variety of species included in such comparative studies, allowing the detection and detailed study of lineage-specific features, such as the presence of TF families specific to the green lineage in Porphyridium purpureum, haptophytes and stramenopiles. Our comprehensive pipeline also allowed us to identify fungal and cyanobacterial TF families in the algal nuclear genomes.

Conclusions

This study provides examples illustrating the complex evolutionary history of algae, some of which support the involvement of a green alga in haptophyte and stramenopile evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2610-9) contains supplementary material, which is available to authorized users.

Identification of allelic expression imbalance genes in human hepatocellular carcinoma through massively parallel DNA and RNA sequencing

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. The prognosis and treatment of this disease have changed little in recent decades because the mechanisms underlying most events of this disease remain obscure. Allelic variation of gene expression is associated with many important biological processes, which provide a new perspective to understand HCC pathogenesis at the molecular level. To identify allelic expression imbalance (AEI) genes in HCCs, we developed a computational method that considered accurate mapping and vigorous AEI detection using paired DNA-seq and RNA-seq data. We analyzed the DNA-seq and RNA-seq data derived from two HCC samples and two cell lines. By applying a strict criterion, a total of 203 tumor-specific AEI genes were identified with high confidence, and several genes have been reported to be associated with the migration or proliferation of cancer cells, such as the genes RELN and DHRS3. In addition, we also found some novel AEI genes in HCCs, such as HNRNPR and PTAFR. Our study provides new insight into AEI events that may contribute to understanding gene expression regulation, cell proliferation and migration, and tumorigenesis.

Transcription Factors Expressed in Mouse Cochlear Inner and Outer Hair Cells

Regulation of gene expression is essential to determining the functional complexity and morphological diversity seen among different cells. Transcriptional regulation is a crucial step in gene expression regulation because the genetic information is directly read from DNA by sequence-specific transcription factors (TFs). Although several mouse TF databases created from genome sequences and transcriptomes are available, a cell type-specific TF database from any normal cell populations is still lacking. We identify cell type-specific TF genes expressed in cochlear inner hair cells (IHCs) and outer hair cells (OHCs) using hair cell-specific transcriptomes from adult mice. IHCs and OHCs are the two types of sensory receptor cells in the mammalian cochlea. We show that 1,563 and 1,616 TF genes are respectively expressed in IHCs and OHCs among 2,230 putative mouse TF genes. While 1,536 are commonly expressed in both populations, 73 genes are differentially expressed (with at least a twofold difference) in IHCs and 13 are differentially expressed in OHCs. Our datasets represent the first cell type-specific TF databases for two populations of sensory receptor cells and are key informational resources for understanding the molecular mechanism underlying the biological properties and phenotypical differences of these cells.

A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.

Large Maf Transcription Factors: Cousins of AP-1 Proteins and Important Regulators of Cellular Differentiation

A large number of mammalian transcription factors possess the evolutionary conserved basic and leucine zipper domain (bZIP). The basic domain interacts with DNA while the leucine zipper facilitates homo- and hetero-dimerization. These factors can be grouped into at least seven families: AP-1, ATF/CREB, CNC, C/EBP, Maf, PAR, and virus-encoded bZIPs. Here, we focus on a group of four large Maf proteins: MafA, MafB, c-Maf, and NRL. They act as key regulators of terminal differentiation in many tissues such as bone, brain, kidney, lens, pancreas, and retina, as well as in blood. The DNA-binding mechanism of large Mafs involves cooperation between the basic domain and an adjacent ancillary DNA-binding domain. Many genes regulated by Mafs during cellular differentiation use functional interactions between the Pax/Maf, Sox/Maf, and Ets/Maf promoter and enhancer modules. The prime examples are crystallin genes in lens and glucagon and insulin in pancreas. Novel roles for large Mafs emerged from studying generations of MafA and MafB knockouts and analysis of combined phenotypes in double or triple null mice. In addition, studies of this group of factors in invertebrates revealed the evolutionarily conserved function of these genes in the development of multicellular organisms.

Epigenetic regulation of puberty via Zinc finger protein-mediated transcriptional repression

In primates, puberty is unleashed by increased GnRH release from the hypothalamus following an interval of juvenile quiescence. GWAS implicates Zinc finger (ZNF) genes in timing human puberty. Here we show that hypothalamic expression of several ZNFs decreased in agonadal male monkeys in association with the pubertal reactivation of gonadotropin secretion. Expression of two of these ZNFs, GATAD1 and ZNF573, also decreases in peripubertal female monkeys. However, only GATAD1 abundance increases when gonadotropin secretion is suppressed during late infancy. Targeted delivery of GATAD1 or ZNF573 to the rat hypothalamus delays puberty by impairing the transition of a transcriptional network from an immature repressive epigenetic configuration to one of activation. GATAD1 represses transcription of two key puberty-related genes, KISS1 and TAC3, directly, and reduces the activating histone mark H3K4me2 at each promoter via recruitment of histone demethylase KDM1A. We conclude that GATAD1 epitomizes a subset of ZNFs involved in epigenetic repression of primate puberty.

Nanopore sensing of individual transcription factors bound to DNA

Transcription factor (TF)-DNA interactions are the primary control point in regulation of gene expression. Characterization of these interactions is essential for understanding genetic regulation of biological systems and developing novel therapies to treat cellular malfunctions. Solid-state nanopores are a highly versatile class of single-molecule sensors that can provide rich information about local properties of long charged biopolymers using the current blockage patterns generated during analyte translocation, and provide a novel platform for characterization of TF-DNA interactions. The DNA-binding domain of the TF Early Growth Response Protein 1 (EGR1), a prototypical zinc finger protein known as zif268, is used as a model system for this study. zif268 adopts two distinct bound conformations corresponding to specific and nonspecific binding, according to the local DNA sequence. Here we implement a solid-state nanopore platform for direct, label- and tether-free single-molecule detection of zif268 bound to DNA. We demonstrate detection of single zif268 TFs bound to DNA according to current blockage sublevels and duration of translocation through the nanopore. We further show that the nanopore can detect and discriminate both specific and nonspecific binding conformations of zif268 on DNA via the distinct current blockage patterns corresponding to each of these two known binding modes.

Sharing and Specificity of Co-expression Networks across 35 Human Tissues

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

Comparison of MAPK specificity across the ETS transcription factor family identifies a high-affinity ERK interaction required for ERG function in prostate cells

Background

The RAS/MAPK signaling pathway can regulate gene expression by phosphorylating and altering the function of some, but not all, ETS transcription factors. ETS family transcription factors bind similar DNA sequences and can compete for genomic binding sites. However, MAPK regulation varies across the ETS family. Therefore, changing the ETS factor bound to a cis-regulatory element can alter MAPK regulation of gene expression. To understand RAS/MAPK regulated gene expression programs, comprehensive knowledge of the ETS family members that are MAPK targets and relative MAPK targeting efficiency across the family is needed.

Results

An in vitro kinase assay was used to rank-order 27 human ETS family transcription factors based on phosphorylation by ERK2, JNK1, and p38α. Many novel MAPK targets and specificities were identified within the ETS family, including the identification of the prostate cancer oncoprotein ERG as a specific target of ERK2. ERK2 phosphorylation of ERG S215 required a DEF docking domain and was necessary for ERG to activate transcription of cell migration genes and promote prostate cell migration. The ability of ERK2 to bind ERG with higher affinity than ETS1 provided a potential molecular explanation for why ERG overexpression drives migration of prostate cells with low levels of RAS/ERK signaling, while ETS1 has a similar function only when RAS/ERK signaling is high.

Conclusions

The rank ordering of ETS transcription factors as MAPK targets provides an important resource for understanding ETS proteins as mediators of MAPK signaling. This is emphasized by the difference in rank order of ERG and ETS1, which allows these factors to have distinct roles based on the level of RAS/ERK signaling present in the cell.

Electronic supplementary material

The online version of this article (doi:10.1186/s12964-015-0089-7) contains supplementary material, which is available to authorized users.

System-wide analysis of the transcriptional network of human myelomonocytic leukemia cells predicts attractor structure and phorbol-ester-induced differentiation and dedifferentiation transitions

We present a system-wide transcriptional network structure that controls cell types in the context of expression pattern transitions that correspond to cell type transitions. Co-expression based analyses uncovered a system-wide, ladder-like transcription factor cluster structure composed of nearly 1,600 transcription factors in a human transcriptional network. Computer simulations based on a transcriptional regulatory model deduced from the system-wide, ladder-like transcription factor cluster structure reproduced expression pattern transitions when human THP-1 myelomonocytic leukaemia cells cease proliferation and differentiate under phorbol myristate acetate stimulation. The behaviour of MYC, a reprogramming Yamanaka factor that was suggested to be essential for induced pluripotent stem cells during dedifferentiation, could be interpreted based on the transcriptional regulation predicted by the system-wide, ladder-like transcription factor cluster structure. This study introduces a novel system-wide structure to transcriptional networks that provides new insights into network topology.

MEF2 transcription factors regulate distinct gene programs in mammalian skeletal muscle differentiation

Skeletal muscle differentiation requires precisely coordinated transcriptional regulation of diverse gene programs that ultimately give rise to the specialized properties of this cell type. In Drosophila, this process is controlled, in part, by MEF2, the sole member of an evolutionarily conserved transcription factor family. By contrast, vertebrate MEF2 is encoded by four distinct genes, Mef2a, -b, -c, and -d, making it far more challenging to link this transcription factor to the regulation of specific muscle gene programs. Here, we have taken the first step in molecularly dissecting vertebrate MEF2 transcriptional function in skeletal muscle differentiation by depleting individual MEF2 proteins in myoblasts. Whereas MEF2A is absolutely required for proper myoblast differentiation, MEF2B, -C, and -D were found to be dispensable for this process. Furthermore, despite the extensive redundancy, we show that mammalian MEF2 proteins regulate a significant subset of nonoverlapping gene programs. These results suggest that individual MEF2 family members are able to recognize specific targets among the entire cohort of MEF2-regulated genes in the muscle genome. These findings provide opportunities to modulate the activity of MEF2 isoforms and their respective gene programs in skeletal muscle homeostasis and disease.

Myocardial ischemic preconditioning upregulated protein 1(Mipu1):zinc finger protein 667 - a multifunctional KRAB/C2H2 zinc finger protein

Myocardial ischemic preconditioning upregulated protein 1 (Mipu1) is a newly discovered upregulated gene produced in rats during the myocardial ischemic preconditioning process. Mipu1 cDNA contains a 1824-base pair open reading frame and encodes a 608 amino acid protein with an N-terminal Krüppel-associated box (KRAB) domain and classical zinc finger C2H2 motifs in the C-terminus. Mipu1 protein is located in the cell nucleus. Recent studies found that Mipu1 has a protective effect on the ischemia-reperfusion injury of heart, brain, and other organs. As a nuclear factor, Mipu1 may perform its protective function through directly transcribing and repressing the expression of proapoptotic genes to repress cell apoptosis. In addition, Mipu1 also plays an important role in regulating the gene expression of downstream inflammatory mediators by inhibiting the activation of activator protein-1 and serum response element.

Extracellular signal-regulated kinase signaling regulates the opposing roles of JUN family transcription factors at ETS/AP-1 sites and in cell migration

JUN transcription factors bind DNA as part of the AP-1 complex, regulate many cellular processes, and play a key role in oncogenesis. The three JUN proteins (c-JUN, JUNB, and JUND) can have both redundant and unique functions depending on the biological phenotype and cell type assayed. Mechanisms that allow this dynamic switching between overlapping and distinct functions are unclear. Here we demonstrate that JUND has a role in prostate cell migration that is the opposite of c-JUN's and JUNB's. RNA sequencing reveals that opposing regulation by c-JUN and JUND defines a subset of AP-1 target genes with cell migration roles. cis-regulatory elements for only this subset of targets were enriched for ETS factor binding, indicating a specificity mechanism. Interestingly, the function of c-JUN and JUND in prostate cell migration switched when we compared cells with an inactive versus an active RAS/extracellular signal-regulated kinase (ERK) signaling pathway. We show that this switch is due to phosphorylation and activation of JUND by ERK. Thus, the ETS/AP-1 sequence defines a unique gene expression program regulated by the relative levels of JUN proteins and RAS/ERK signaling. This work provides a rationale for how transcription factors can have distinct roles depending on the signaling status and the biological function in question.

The transcriptome of utricle hair cell regeneration in the avian inner ear

Sensory hair cell loss is the major cause of hearing and balance disorders. Mammals are incapable of sustained hair cell regeneration, but lower vertebrates can regenerate these mechano-electrical transducers. We present the first comprehensive transcriptome (by mRNA-Seq) of hair cell regeneration in the chick utricle. We provide pathway and pattern annotations and correlate these with the phenotypic events that occur during regeneration. These patterns are surprisingly synchronous and highly punctuated. We show how these patterns are a new resource for identifying components of the hair cell transcriptome and identify 494 new putative hair-cell-specific genes and validate three of these (of three tested) by immunohistochemical staining. We describe many surprising new components and dynamic expression patterns, particularly within NOTCH signaling. For example, we show that HES7 is specifically expressed during utricle hair cell regeneration and closely parallels the expression of HES5. Likewise, the expression of ATOH1 is closely correlated with HEYL and the HLH inhibitory transcription factors ID1, ID2, and ID4. We investigate the correlation between fibroblast growth factor signaling and supporting cell proliferation and show that FGF20 inhibits supporting cell proliferation. We also present an analysis of 212 differentially expressed transcription factor genes in the regenerative time course that fall into nine distinct gene expression patterns, many of which correlate with phenotypic events during regeneration and represent attractive candidates for future analysis and manipulation of the regenerative program in sensory epithelia and other vertebrate neuroepithelia.

An improved SELEX-Seq strategy for characterizing DNA-binding specificity of transcription factor: NF-κB as an example

SELEX-Seq is now the optimal high-throughput technique for characterizing DNA-binding specificities of transcription factors. In this study, we introduced an improved EMSA-based SELEX-Seq strategy with several advantages. The improvements of this strategy included: (1) using a FAM-labeled probe to track protein-DNA complex in polyacrylamide gel for rapidly recovering the protein-bound dsDNA without relying on traditional radioactive labeling or ethidium bromide staining; (2) monitoring the specificity of SELEX selection by detecting a positive and negative sequence doped into the input DNAs used in each round with PCR amplification; (3) using nested PCR to ensure the specificity of PCR amplification of the selected DNAs after each round; (4) using the nucleotides added at the 5′ end of the nested PCR primers as the split barcode to code DNAs from various rounds for multiplexing sequencing samples. The split barcode minimized selection times and thus greatly simplified the current SELEX-Seq procedure. The reliability of the strategy was demonstrated by performing a successful SELEX-Seq of a well-known transcription factor, NF-κB. Therefore, this study provided a useful SELEX-Seq strategy for characterizing DNA-binding specificities of transcription factors.

Expression, purification and molecular analysis of the human ZNF706 protein

Background

The ZNF706 gene encodes a protein that belongs to the zinc finger family of proteins and was found to be highly expressed in laryngeal cancer, making the structure and function of ZNF706 worthy of investigation. In this study, we expressed and purified recombinant human ZNF706 that was suitable for structural analysis in Escherichia coli BL21(DH3).

Findings

ZNF706 mRNA was extracted from a larynx tissue sample, and cDNA was ligated into a cloning vector using the TOPO method. ZNF706 protein was expressed according to the E. coli expression system procedures and was purified using a nickel-affinity column. The structural qualities of recombinant ZNF706 and quantification alpha, beta sheet, and other structures were obtained by spectroscopy of circular dichroism. ZNF706's structural modeling showed that it is composed of α-helices (28.3%), β-strands (19.4%), and turns (20.9%), in agreement with the spectral data from the dichroism analysis.

Conclusions

We used circular dichroism and molecular modeling to examine the structure of ZNF706. The results suggest that human recombinant ZNF706 keeps its secondary structures and is appropriate for functional and structural studies. The method of expressing ZNF706 protein used in this study can be used to direct various functional and structural studies that will contribute to the understanding of its function as well as its relationship with other biological molecules and its putative role in carcinogenesis.

Purification and characterization of transcription factors

Transcription factors (TFs) are essential for the expression of all proteins, including those involved in human health and disease. However, TFs are resistant to proteomic characterization because they are frequently masked by more abundant proteins due to the limited dynamic range of capillary liquid chromatography-tandem mass spectrometry and protein database searching. Purification methods, particularly strategies that exploit the high affinity of TFs for DNA response elements (REs) on gene promoters, can enrich TFs prior to proteomic analysis to improve dynamic range and penetrance of the TF proteome. For example, trapping of TF complexes specific for particular REs has been achieved by recovering the element DNA-protein complex on solid supports. Additional methods for improving dynamic range include two- and three-dimensional gel electrophoresis incorporating electrophoretic mobility shift assays and Southwestern blotting for detection. Here we review methods for TF purification and characterization. We fully expect that future investigations will apply these and other methods to illuminate this important but challenging proteome.

TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors

SUMMARY

Gene regulatory network assembly and analysis requires high-quality knowledge sources that cover functional aspects of the various components of the gene regulatory machinery. A multiplicity of resources exists with information about mammalian transcription factors (TFs); yet, only few of these provide sufficiently accurate classifications of the functional roles of individual TFs, or standardized evidence that would justify the information on which these functional classifications are based. We compiled the list of all putative TFs from nine different resources, ignored factors such as general TFs, mediator complexes and chromatin modifiers, and for the remaining factors checked the available literature for references that support their function as a true sequence-specific DNA-binding RNA polymerase II TF (DbTF). The results are available in the TFcheckpoint database, an exhaustive collection of TFs annotated according to experimental and other evidence on their function as true DbTFs. TFcheckpoint.org provides a high-quality and comprehensive knowledge source for genome-scale regulatory network studies.

AVAILABILITY

The TFcheckpoint database is freely available at www.tfcheckpoint.org

Cys2His2 zinc finger protein family: classification, functions, and major members

Cys2His2 (C2H2)-type zinc fingers are widespread DNA binding motifs in eukaryotic transcription factors. Zinc fingers are short protein motifs composed of two or three β-layers and one α-helix. Two cysteine and two histidine residues located in certain positions bind zinc to stabilize the structure. Four other amino acid residues localized in specific positions in the N-terminal region of the α-helix participate in DNA binding by interacting with hydrogen donors and acceptors exposed in the DNA major groove. The number of zinc fingers in a single protein can vary over a wide range, thus enabling variability of target DNA sequences. Besides DNA binding, zinc fingers can also provide protein-protein and RNA-protein interactions. For the most part, proteins containing the C2H2-type zinc fingers are trans regulators of gene expression that play an important role in cellular processes such as development, differentiation, and suppression of malignant cell transformation (oncosuppression).

[The databases of transcription factors.]

The control of gene transcription is a critical level of gene expression regulation. The interactions between transcription factors (TF) and their DNA binding sites (TFBS) play a key role at this level. In order to decipher the molecular mechanism of the interactions of TFs with TFBSs and construct transcription regulatory network, it is necessary to systematically collect, save, and analyze the information of discovered TFs and their TFBSs. In recent years, multiple TF and TFBS-related databases have been established. These databeses significantly promoted the TF-related studies in the fields of molecular biology, bioinformatics, and system biology. This paper summarized the contents, characteristics, access, and advances of main TFs and TFBSs-related databases, including TRANSFAC, JASPAR, TFDB, TRRD, TRED, PAZAR, MAPPER and others.

The emergence of protein complexes: quaternary structure, dynamics and allostery. Colworth Medal Lecture

All proteins require physical interactions with other proteins in order to perform their functions. Most of them oligomerize into homomers, and a vast majority of these homomers interact with other proteins, at least part of the time, forming transient or obligate heteromers. In the present paper, we review the structural, biophysical and evolutionary aspects of these protein interactions. We discuss how protein function and stability benefit from oligomerization, as well as evolutionary pathways by which oligomers emerge, mostly from the perspective of homomers. Finally, we emphasize the specificities of heteromeric complexes and their structure and evolution. We also discuss two analytical approaches increasingly being used to study protein structures as well as their interactions. First, we review the use of the biological networks and graph theory for analysis of protein interactions and structure. Secondly, we discuss recent advances in techniques for detecting correlated mutations, with the emphasis on their role in identifying pathways of allosteric communication.

[DNA-binding profiles of mammalian transcription factors]

The differential gene expression is the molecular base of development and responses to stimuli of organisms. Transcription factors (TFs) play important regulatory roles in this kind of differential gene expression. Therefore, to elucidate how these TFs regulate the complex differential gene expression, it is necessary to identify all target genes of them and construct the gene transcription regulatory network controlled by them. DNA binding is a key step for TFs regulating gene transcription. Therefore, in order to identify their target genes, it is indispensable to identify all possible DNA sequences that can be recognized and bound by TFs at the molecular level of their interactions with DNA, i.e., construction of the DNA-binding profiles of TFs. In recent years, along with the development of DNA microarray and high-throughput DNA sequencing techniques, there appeared some revolutionary new techniques for constructing DNA-binding profiles of TFs, which greatly promotes studies in this field. These techniques include ChIP-chip and ChIP-Seq for constructing in vivo DNA-binding profiles of TFs, dsDNA microarray, SELEX-SAGE, Bind-n-Seq, MMP-SELEX, EMSA-Seq, and HiTS-FLIP for constructing in vitro DNA-binding profiles of TFs. This paper reviewed these techniques.

Learning transcriptional regulatory relationships using sparse graphical models

Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The expression level of a gene is modeled as a linear combination of the expression levels of known transcription factors and hidden factors. Using gene expression data covering 39,296 oligonucleotide probes from 1109 human liver samples, we demonstrate that our model better predicts out-of-sample data than a model with no hidden variables. We also show that some of the gene sets associated with hidden variables are strongly correlated with Gene Ontology categories. The software including source code is available at http://grnl1.codeplex.com.

p53-Dependent transcriptional responses to interleukin-3 signaling

p53 is critical in the normal response to a variety of cellular stresses including DNA damage and loss of p53 function is a common feature of many cancers. In hematological malignancies, p53 deletion is less common than in solid malignancies but is associated with poor prognosis and resistance to chemotherapy. Compared to their wild-type (WT) counterparts, hematopoietic progenitor cells lacking p53 have a greater propensity to survive cytokine loss, in part, due to the failure to transcribe Puma, a proapoptotic Bcl-2 family member. Using expression arrays, we have further characterized the differences that distinguish p53^−/− cells from WT myeloid cells in the presence of Interleukin-3 (IL-3) to determine if such differences contribute to the increased clonogenicity and survival responses observed in p53^−/− cells. We show that p53^−/− cells have a deregulated intracellular signaling environment and display a more rapid and sustained response to IL-3. This was accompanied by an increase in active ERK1/2 and a dependence on an intact MAP kinase signaling pathway. Contrastingly, we find that p53^−/− cells are independent on AKT for their survival. Thus, loss of p53 in myeloid cells results in an altered transcriptional and kinase signaling environment that favors enhanced cytokine signaling.

How do you find transcription factors? Computational approaches to compile and annotate repertoires of regulators for any genome

Transcription factors (TFs) play an important role in regulating gene expression. The availability of complete genome sequences and associated functional genomic data offer excellent opportunities to understand the transcriptional regulatory system of an entire organism. To do so, however, it is essential to compile a reliable dataset of regulatory components. Here, we review computational methods and publicly accessible resources that help identify TF-coding genes in prokaryotic and eukaryotic genomes. Since the regulatory functions of most TFs remain unknown, we also discuss approaches for combining diverse genomic datasets that will help elucidate their chromosomal organisation, expression, and evolutionary conservation. These analysis methods provide a solid foundation for further investigations of the transcriptional regulatory system.

A directed protein interaction network for investigating intracellular signal transduction

Cellular signal transduction is a complex process involving protein-protein interactions (PPIs) that transmit information. For example, signals from the plasma membrane may be transduced to transcription factors to regulate gene expression. To obtain a global view of cellular signaling and to predict potential signal modulators, we searched for protein interaction partners of more than 450 signaling-related proteins by means of automated yeast two-hybrid interaction mating. The resulting PPI network connected 1126 proteins through 2626 PPIs. After expansion of this interaction map with publicly available PPI data, we generated a directed network resembling the signal transduction flow between proteins with a naïve Bayesian classifier. We exploited information on the shortest PPI paths from membrane receptors to transcription factors to predict input and output relationships between interacting proteins. Integration of directed PPI with time-resolved protein phosphorylation data revealed network structures that dynamically conveyed information from the activated epidermal growth factor and extracellular signal-regulated kinase (EGF/ERK) signaling cascade to directly associated proteins and more distant proteins in the network. From the model network, we predicted 18 previously unknown modulators of EGF/ERK signaling, which we validated in mammalian cell-based assays. This generic experimental and computational approach provides a framework for elucidating causal connections between signaling proteins and facilitates the identification of proteins that modulate the flow of information in signaling networks.