Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function

Decoding the ubiquitin language: Orchestrating transcription initiation and gene expression through chromatin remodelers and histones

Eukaryotic transcription is a finely orchestrated process and it is controlled by transcription factors as well as epigenetic regulators. Transcription factors and epigenetic regulators undergo different types of posttranslational modifications including ubiquitination to control transcription process. Ubiquitination, traditionally associated with protein degradation, has emerged as a crucial contributor to the regulation of chromatin structure through ubiquitination of histone and chromatin remodelers. Ubiquitination introduces new layers of intricacy to the regulation of transcription initiation through controlling the equilibrium between euchromatin and heterochromatin states. Nucleosome, the fundamental units of chromatin, spacing in euchromatin and heterochromatin states are regulated by histone modification and chromatin remodeling complexes. Chromatin remodeling complexes actively sculpt the chromatin architecture and thereby influence the transcriptional states of genes. Therefore, understanding the dynamic behavior of nucleosome spacing is critical as it impacts various cellular functions through controlling gene expression profiles. In this comprehensive review, we discussed the intricate interplay between ubiquitination and transcription initiation, and illuminated the underlying molecular mechanisms that occur in a variety of biological contexts. This exploration sheds light on the complex regulatory networks that govern eukaryotic transcription, providing important insights into the fine orchestration of gene expression and chromatin dynamics.

Pioneer factors in viral infection

Pioneer factors are transcription factors sharing the fascinating ability to bind to compact chromatin and thereby alter its transcriptional fate. Most pioneer factors are known for their importance during embryonic development, for instance, in inducing zygotic genome activation or cell fate decision. Some pioneer factors are actively induced or downregulated by viral infection. With this, viruses are capable to modulate different signaling pathways resulting for example in MHC-receptor up/downregulation which contributes to viral immune evasion. In this article, we review the current state of research on how different viruses (Herpesviruses, Papillomaviruses and Hepatitis B virus) use pioneer factors for their viral replication and persistence in the host, as well as for the development of viral cancer.

Sequence-Specific Structural Features and Solvation Properties of Transcription Factor Binding DNA Motifs: Insights from Molecular Dynamics Simulation

Sequence-specific recognition of transcription factor (TF) binding motifs in the target site of DNA over the vast amount of non-target DNA is of primary importance for the transcriptional regulation of gene expression by the TFs. Binding of TFs to the target site of DNA relies not only on the direct contact formation but also on the structural and conformational features of DNA. Recognition of DNA structural features or shape readout by proteins is an important factor in the context of TF-DNA interaction. Based on the atomistic molecular simulation, here we report the sequence-dependent unique structural features, solvation, and ion-binding properties of biologically relevant AT- and GC-rich human TF binding motifs in DNA. Counterion and water distribution around the motif is found to be sensitive to the motif sequence, which is accompanied with the DNA shape features. The motif sequence affects the electrostatic potential along the grooves, and cytosine methylation alters the DNA shape features. Characteristic solvation properties of TF binding motif DNA fragments infer that an ionic environment and hydration influences are essential to describe TF-DNA interactions.

Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan-Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.

Emerging role of pioneer transcription factors in targeted ERα positive breast cancer

Transcription factors (TFs) are modular protein groups that preferably bind to DNA sequences and guide genomic expression through transcription. Among these key regulators, “pioneer factors” are an emerging class of TFs that specifically interact with nucleosomal DNA and facilitate accessible genomic binding sites for the additional TFs. There is growing evidence of these specialized modulators in particular malignancies, as highlighted by agents’ clinical efficacy, specifically targeting nuclear hormone receptors. They have been implicated in multiple cancers more recently, with a high proportion inculpating on hormone influential cancers. Moreover, extended crosstalk and cooperation between ERα pioneering factors in estrogen-dependent breast cancer (BC) remain elucidated. This review discusses on the recent advances in our understanding of pioneer TFs in cancer, especially highlighting its potentiality to modulate chromatin condensation to permit ERα recruitment in BC cells. Through the study it was concluded that the highly prospected pioneer TFs in BC, including FOXA1, TLE1, PBX1, and GATA3, possess the potential therapeutic significance and further innovations in the field could yield targeted therapy in cancer treatment.

Interactions of Cognitive and Physical Functions Associated with Toilet Independence in Stroke Patients

OBJECTIVES

The purpose of this study was to clarify the interaction among cognitive and physical functions associated with toilet independence in stroke patients.

MATERIALS AND METHODS

We retrospectively examined 125 stroke patients. We performed decision tree analysis to detect the interaction associated with toilet independent using assessment of motor function on the affected side, muscle strength on unaffected side, trunk function, neglect, motivation, and cognitive function. The interactions detected via decision tree confirmed the existence and influence using logistic regression.

RESULTS

The verticality test of the Stroke Impairment Assessment Set (3 or ≤2 points) was selected at the first level, and the Revised Hasegawa's dementia scale (≥19 or ≤18 points) and age (≥70 or ≤69 y) were selected at the second level of decision tree. Interaction terms created by these factors were significantly associated with toilet independence after adjusting for the independent influence of each factor using logistic regression.

CONCLUSIONS

Our results show an interaction of trunk and cognitive functions or trunk function and age associated with toilet independence. The probability of toilet independence dramatically changes if two factors of each interaction were satisfied in stroke patients.

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Benchmark and integration of resources for the estimation of human transcription factor activities

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

Promoter-Intrinsic and Local Chromatin Features Determine Gene Repression in LADs

It is largely unclear whether genes that are naturally embedded in lamina-associated domains (LADs) are inactive due to their chromatin environment or whether LADs are merely secondary to the lack of transcription. We show that hundreds of human promoters become active when moved from their native LAD position to a neutral context in the same cells, indicating that LADs form a repressive environment. Another set of promoters inside LADs is able to "escape" repression, although their transcription elongation is attenuated. By inserting reporters into thousands of genomic locations, we demonstrate that escaper promoters are intrinsically less sensitive to LAD repression. This is not simply explained by promoter strength but by the interplay between promoter sequence and local chromatin features that vary strongly across LADs. Enhancers also differ in their sensitivity to LAD chromatin. This work provides a general framework for the systematic understanding of gene regulation by repressive chromatin.

The biological characteristics of transcription factors AP-2α and AP-2γ and their importance in various types of cancers

The Activator Protein 2 (AP-2) transcription factor (TF) family is vital for the regulation of gene expression during early development as well as carcinogenesis process. The review focusses on the AP-2α and AP-2γ proteins and their dualistic regulation of gene expression in the process of carcinogenesis. Both AP-2α and AP-2γ influence a wide range of physiological or pathological processes by regulating different pathways and interacting with diverse molecules, i.e. other proteins, long non-coding RNAs (lncRNA) or miRNAs. This review summarizes the newest information about the biology of two, AP-2α and AP-2γ, TFs in the carcinogenesis process. We emphasize that these two proteins could have either oncogenic or suppressive characteristics depending on the type of cancer tissue or their interaction with specific molecules. They have also been found to contribute to resistance and sensitivity to chemotherapy in oncological patients. A better understanding of molecular network of AP-2 factors and other molecules may clarify the atypical molecular mechanisms occurring during carcinogenesis, and may assist in the recognition of new diagnostic biomarkers.

Thirty biologically interpretable clusters of transcription factors distinguish cancer type

Background

Transcription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of genes. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types.

Results

We recently developed a new algorithm, Thresher, that combines principal component analysis, outlier filtering, and von Mises-Fisher mixture models to cluster genes (in this case, transcription factors) based on expression, determining the optimal number of clusters in the process. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA). We found that 30 clusters of transcription factors from a 29-dimensional principal component space were able to distinguish between most cancer types, and could separate tumor samples from normal controls. Moreover, each cluster of transcription factors could be either (i) linked to a tissue-specific expression pattern or (ii) associated with a fundamental biological process such as cell cycle, angiogenesis, apoptosis, or cytoskeleton. Clusters of the second type were more likely also to be associated with embryonically lethal mouse phenotypes.

Conclusions

Using our approach, we have shown that the mRNA expression patterns of transcription factors contain most of the information needed to distinguish different cancer types. The Thresher method is capable of discovering biologically interpretable clusters of genes. It can potentially be applied to other gene sets, such as signaling pathways, to decompose them into simpler, yet biologically meaningful, components.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5093-z) contains supplementary material, which is available to authorized users.

The multisystemic functions of FOXD1 in development and disease

Transcription factors (TFs) participate in a wide range of cellular processes due to their inherent function as essential regulatory proteins. Their dysfunction has been linked to numerous human diseases. The forkhead box (FOX) family of TFs belongs to the "winged helix" superfamily, consisting of proteins sharing a related winged helix-turn-helix DNA-binding motif. FOX genes have been extensively present during vertebrates and invertebrates' evolution, participating in numerous molecular cascades and biological functions, such as embryonic development and organogenesis, cell cycle regulation, metabolism control, stem cell niche maintenance, signal transduction, and many others. FOXD1, a forkhead TF, has been related to different key biological processes such as kidney and retina development and embryo implantation. FOXD1 dysfunction has been linked to different pathologies, thereby constituting a diagnostic biomarker and a promising target for future therapies. This paper aims to present, for the first time, a comprehensive review of FOXD1's role in mouse development and human disease. Molecular, structural, and functional aspects of FOXD1 are presented in light of physiological and pathogenic conditions, including its role in human disease aetiology, such as cancer and recurrent pregnancy loss. Taken together, the information given here should enable a better understanding of FOXD1 function for basic science researchers and clinicians.