A census of human transcription factors: function, expression and evolution

A feedback loop between GATA2-AS1 and GATA2 promotes colorectal cancer cell proliferation, invasion, epithelial-mesenchymal transition and stemness via recruiting DDX3X

BACKGROUND

Colorectal cancer (CRC) is a common malignant tumor with a high risk of metastasis. Long non-coding RNAs (lncRNAs) have been reported to be implicated in cancer progression via regulating its nearby gene. Herein, we investigated the function of GATA binding protein 2 (GATA2) and lncRNA GATA2 antisense RNA 1 (GATA2-AS1) in CRC and the mechanism underlying their interaction.

METHODS

Colony formation assay, flow cytometry analysis and transwell assay were implemented to detect cell proliferation, apoptosis and invasion. Western blot analysis and sphere formation assay were conducted to assess epithelial-mesenchymal transition (EMT) and cancer stemness of CRC cells. RNA pull down, RNA-binding protein immunoprecipitation (RIP), chromatin immunoprecipitation (ChIP) and luciferase reporter assays were implemented to investigate the regulatory mechanism between GATA2-AS1 and GATA2.

RESULTS

GATA2-AS1 and GATA2 were highly expressed in CRC cells. Knockdown of GATA2-AS1 and GATA2 impeded CRC cell proliferation, invasion, EMT and cancer stemness, and induced cell apoptosis. GATA2-AS1 expression was positively correlated with GATA2. GATA2-AS1 recruited DEAD-box helicase 3 X-linked (DDX3X) to stabilize GATA2 mRNA. GATA2 combined with GATA2-AS1 promoter to enhance GATA2-AS1 expression.

CONCLUSION

Our study confirmed that a feedback loop between GATA2-AS1 and GATA2 promotes CRC progression, which might offer novel targets for CRC treatment.

Endothelial cell heterogeneity and microglia regulons revealed by a pig cell landscape at single-cell level

Pigs are valuable large animal models for biomedical and genetic research, but insights into the tissue- and cell-type-specific transcriptome and heterogeneity remain limited. By leveraging single-cell RNA sequencing, we generate a multiple-organ single-cell transcriptomic map containing over 200,000 pig cells from 20 tissues/organs. We comprehensively characterize the heterogeneity of cells in tissues and identify 234 cell clusters, representing 58 major cell types. In-depth integrative analysis of endothelial cells reveals a high degree of heterogeneity. We identify several functionally distinct endothelial cell phenotypes, including an endothelial to mesenchymal transition subtype in adipose tissues. Intercellular communication analysis predicts tissue- and cell type-specific crosstalk between endothelial cells and other cell types through the VEGF, PDGF, TGF-β, and BMP pathways. Regulon analysis of single-cell transcriptome of microglia in pig and 12 other species further identifies MEF2C as an evolutionally conserved regulon in the microglia. Our work describes the landscape of single-cell transcriptomes within diverse pig organs and identifies the heterogeneity of endothelial cells and evolutionally conserved regulon in microglia.

A bioinformatics analysis of zinc finger protein family reveals potential oncogenic biomarkers in breast cancer

BACKGROUND

Zinc finger protein family is the largest transcription factor family in the human genome. Studies have shown that the aberrant expression of zinc finger protein (ZNF) had a potential role in tumorigenesis. However, due to the high complexity of the ZNF family genes, the role of the ZNF family genes in breast cancer (BRCA) is still lacking in systematic understanding.

AIM

In the study, we aim to understand the expression profile, prognostic value, immune invasion pattern, tumor microenvironment, epigenetic and pathway relationships, and drug sensitivity of ZNFs using multi-omics data from public databases.

RESULTS

We focused on six members of ZNFs, which were upregulated in a variety of cancers. Notably, ZNF750 and ZNF224 were lower expressed in BRCA, and their expressions were significantly associated with BRCA prognosis. We confirmed the observations obtained by analyzing the clinic-pathological data. Otherwise, the expressions of ZNFs were significantly related to stromal and immune scores, and was significantly different among different immune subtypes in BRCA. Here, we found down-regulated methylation of ZNF217 and ZNF750. The relationship between methylation and survival showed the survival was worse for hypo-methylation of ZNF750 in BRCA, which is consistent with the correlation of high expression of ZNF750 in BRCA with worse survival.

CONCLUSIONS

Collectively, our results provide clues for a better understanding of the characterization of ZNF family genes in BRCA from a multi-omics perspective and show their potential for use as new tumor markers and therapeutic targets.

Metabolic consequences of PGC-1α dysregulation in adult zebrafish muscle

The peroxisome proliferator activated receptor gamma co-activator 1 alpha (PGC-1α) is central to the regulation of cellular and mitochondrial energy homeostasis in mammals, but its role in other vertebrates remains unclear. Indeed, previous work suggests extensive structural and functional divergence of PGC-1α in teleosts but this remains to be directly tested. Here, we describe the initial characterization of heterozygous PGC-1α mutant zebrafish lines created by CRISPR-Cas9 disruptions of an evolutionarily conserved regulatory region of the PGC-1α proximal promoter. Using qPCR, we confirmed the disruption of PGC-1α gene expression in striated muscle, leading to a simultaneous 4-fold increase in mixed skeletal muscle PGC-1α mRNA levels and an opposite 4-fold downregulation in cardiac muscle. In mixed skeletal muscle, most downstream effector genes were largely unaffected yet two mitochondrial lipid transporters, carnitine palmitoyltransferase 1 and 2, were strongly induced. Conversely, PGC-1α depression in cardiac muscle reduced the expression of several transcriptional regulators (estrogen related receptor alpha, nuclear respiratory factor 1 and PGC-1β) without altering metabolic gene expression. Using high resolution respirometry, we determined that white muscle exhibited increased lipid oxidative capacity with little difference in markers of mitochondrial abundance. Finally, using whole animal intermittent respirometry, we show that mutant fish exhibit a 2-fold higher basal metabolism than their wildtype counterparts. Altogether, this new model confirms a central but complex role for PGC-1α in mediating energy utilization in zebrafish and we propose its use as a valuable tool to explore the intricate regulatory pathways of energy homeostasis in a popular biomedical model.

nMOWChIP-seq: low-input genome-wide mapping of non-histone targets

Genome-wide profiling of interactions between genome and various functional proteins is critical for understanding regulatory processes involved in development and diseases. Conventional assays require a large number of cells and high-quality data on tissue samples are scarce. Here we optimized a low-input chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology for profiling RNA polymerase II (Pol II), transcription factor (TF), and enzyme binding at the genome scale. The new approach produces high-quality binding profiles using 1,000-50,000 cells. We used the approach to examine the binding of Pol II and two TFs (EGR1 and MEF2C) in cerebellum and prefrontal cortex of mouse brain and found that their binding profiles are highly reflective of the functional differences between the two brain regions. Our analysis reveals the potential for linking genome-wide TF or Pol II profiles with neuroanatomical origins of brain cells.

ZNF384: A Potential Therapeutic Target for Psoriasis and Alzheimer’s Disease Through Inflammation and Metabolism

Background

Psoriasis is an immune-related skin disease notable for its chronic inflammation of the entire system. Alzheimer’s disease (AD) is more prevalent in psoriasis than in the general population. Immune-mediated pathophysiologic processes may link these two diseases, but the mechanism is still unclear. This article aimed to explore potential molecular mechanisms in psoriasis and AD.

Methods

Gene expression profiling data of psoriasis and AD were acquired in the Gene Expression Omnibus (GEO) database. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were first applied in two datasets. Differentially expressed genes (DEGs) of two diseases were identified, and common DEGs were selected. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to explore common biological pathways. Signature transcription factors (STFs) were identified and their diagnostic values was calculated by receiver operating characteristic (ROC) curve analysis in the exploration cohort and verified in the validation cohort. The expression levels of STFs were further investigated in the validation cohort and the GTEx Portal Database. Additionally, four kinds of interaction analysis were performed: correlation analysis among STFs, gene-gene, chemical-protein, and protein-ligand interaction analyses. In the end, we predicted the transcription factor that potentially regulates STFs.

Results

Biosynthesis and metabolic pathways were enriched in GSEA analysis. In ssGSEA analysis, most immunoreaction gene lists exhibited differential enrichment in psoriasis cases, whereas three receptor-related gene lists did in AD. The KEGG analysis of common DEGs redetermined inflammatory and metabolic pathways essential in both diseases. 5 STFs (PPARG, ZFPM2, ZNF415, HLX, and ANHX) were screened from common DEGs. The ROC analysis indicated that all STFs have diagnostic values in two diseases, especially ZFPM2. The correlation analysis, gene-gene, chemical-protein, and protein-ligand interaction analyses suggested that STFs interplay and involve inflammation and aberrant metabolism. Eventually, ZNF384 was the predicted transcription factor regulating PPARG, ZNF415, HLX, and ANHX.

Conclusions

The STFs (PPARG, ZFPM2, ZNF415, HLX, and ANHX) may increase the morbidity rate of AD in psoriasis by initiating a positive feedback loop of excessive inflammation and metabolic disorders. ZNF384 is a potential therapeutic target for psoriasis and AD by regulating PPARG, ZNF415, HLX, and ANHX.

Function moves biomolecular condensates in phase space

Phase separation underlies the formation of biomolecular condensates. We hypothesize the cellular processes that occur within condensates shape their structural features. We use the example of transcription to discuss structure-function relationships in condensates. Various types of transcriptional condensates have been reported across the evolutionary spectrum in the cell nucleus as well as in mitochondrial and bacterial nucleoids. In vitro and in vivo observations suggest that transcriptional activity of condensates influences their supramolecular structure, which in turn affects their function. Condensate organization thus becomes driven by differences in miscibility among the DNA and proteins of the transcription machinery and the RNA transcripts they generate. These considerations are in line with the notion that cellular processes shape the structural properties of condensates, leading to a dynamic, mutual interplay between structure and function in the cell.

Introduction to Genomic Network Reconstruction for Cancer Research

High-throughput genomic technologies have revolutionized the study of cancer. Current research in oncology is now limited more for the capacity of analyzing and interpreting data, rather than the availability of data itself. Integrative approaches to obtain functional information from data are at the core of the disciplines gathered under the systems biology banner. In this context, network models have been used to study cancer, from the identification of key molecules involved in the disease to the discovery of functional alterations associated with specific manifestations of the disease.In this chapter, we describe the state of the art of network reconstruction from genomic data, with an emphasis in gene expression experiments. We explore the strengths and limitations of correlation, Bayesian, and information theoretic approaches to network reconstruction. We present tools that leverage the flexibility of network science to gain a deeper understanding of cancer biology.

Neural network modeling of differential binding between wild-type and mutant CTCF reveals putative binding preferences for zinc fingers 1-2

BACKGROUND

Many transcription factors (TFs), such as multi zinc-finger (ZF) TFs, have multiple DNA binding domains (DBDs), and deciphering the DNA binding motifs of individual DBDs is a major challenge. One example of such a TF is CCCTC-binding factor (CTCF), a TF with eleven ZFs that plays a variety of roles in transcriptional regulation, most notably anchoring DNA loops. Previous studies found that CTCF ZFs 3-7 bind CTCF's core motif and ZFs 9-11 bind a specific upstream motif, but the motifs of ZFs 1-2 have yet to be identified.

RESULTS

We developed a new approach to identifying the binding motifs of individual DBDs of a TF through analyzing chromatin immunoprecipitation sequencing (ChIP-seq) experiments in which a single DBD is mutated: we train a deep convolutional neural network to predict whether wild-type TF binding sites are preserved in the mutant TF dataset and interpret the model. We applied this approach to mouse CTCF ChIP-seq data and identified the known binding preferences of CTCF ZFs 3-11 as well as a putative GAG binding motif for ZF 1. We analyzed other CTCF datasets to provide additional evidence that ZF 1 is associated with binding at the motif we identified, and we found that the presence of the motif for ZF 1 is associated with CTCF ChIP-seq peak strength.

CONCLUSIONS

Our approach can be applied to any TF for which in vivo binding data from both the wild-type and mutated versions of the TF are available, and our findings provide new potential insights binding preferences of CTCF's DBDs.

Evolution of binding preferences among whole-genome duplicated transcription factors

Throughout evolution, new transcription factors (TFs) emerge by gene duplication, promoting growth and rewiring of transcriptional networks. How TF duplicates diverge was studied in a few cases only. To provide a genome-scale view, we considered the set of budding yeast TFs classified as whole-genome duplication (WGD)-retained paralogs (~35% of all specific TFs). Using high-resolution profiling, we find that ~60% of paralogs evolved differential binding preferences. We show that this divergence results primarily from variations outside the DNA-binding domains (DBDs), while DBD preferences remain largely conserved. Analysis of non-WGD orthologs revealed uneven splitting of ancestral preferences between duplicates, and the preferential acquiring of new targets by the least conserved paralog (biased neo/sub-functionalization). Interactions between paralogs were rare, and, when present, occurred through weak competition for DNA-binding or dependency between dimer-forming paralogs. We discuss the implications of our findings for the evolutionary design of transcriptional networks.

CRISPRs in the human genome are differentially expressed between malignant and normal adjacent to tumor tissue

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) have been identified in bacteria, archaea and mitochondria of plants, but not in eukaryotes. Here, we report the discovery of 12,572 putative CRISPRs randomly distributed across the human chromosomes, which we termed hCRISPRs. By using available transcriptome datasets, we demonstrate that hCRISPRs are distinctively expressed as small non-coding RNAs (sncRNAs) in cell lines and human tissues. Moreover, expression patterns thereof enabled us to distinguish normal from malignant tissues. In prostate cancer, we confirmed the differential hCRISPR expression between normal adjacent and malignant primary prostate tissue by RT-qPCR and demonstrate that the SHERLOCK and DETECTR dipstick tools are suitable to detect these sncRNAs. We anticipate that the discovery of CRISPRs in the human genome can be further exploited for diagnostic purposes in cancer and other medical conditions, which certainly will lead to the development of point-of-care tests based on the differential expression of the hCRISPRs.

A histidine cluster determines YY1-compartmentalized coactivators and chromatin elements in phase-separated enhancer clusters

As an oncogenic transcription factor, Yin Yang 1 (YY1) regulates enhancer and promoter connection. However, gaps still exist in understanding how YY1 coordinates coactivators and chromatin enhancer elements to assemble enhancers and super-enhancers. Here, we demonstrate that a histidine cluster in YY1’s transactivation domain is essential for its formation of phase separation condensates, which can be extended to additional proteins. The histidine cluster is also required for YY1-promoted cell proliferation, migration, clonogenicity and tumor growth. YY1-rich nuclear puncta contain coactivators EP300, BRD4, MED1 and active RNA polymerase II, and colocalize with histone markers of gene activation, but not that of repression. Furthermore, YY1 binds to the consensus motifs in the FOXM1 promoter to activate its expression. Wild-type YY1, but not its phase separation defective mutant, connects multiple enhancer elements and the FOXM1 promoter to form an enhancer cluster. Consistently, fluorescent in situ hybridization (FISH) assays reveal the colocalization of YY1 puncta with both the FOXM1 gene locus and its nascent RNA transcript. Overall, this study demonstrates that YY1 activates target gene expression through forming liquid-liquid phase separation condensates to compartmentalize both coactivators and enhancer elements, and the histidine cluster of YY1 plays a determinant role in this regulatory mechanism.

Exploring genetic variation and population structure in a threatened species, Noturus placidus, with whole-genome sequence data

The Neosho madtom (Noturus placidus) is a small catfish, generally less than 3 inches in length, unique to the Neosho-Spring River system within the Arkansas River Basin. It was federally listed as threatened in 1990, largely due to habitat loss. For conservation efforts, we generated whole-genome sequence data from 10 Neosho madtom individuals originating from 3 geographically separated populations to evaluate genetic diversity and population structure. A Neosho madtom genome was de novo assembled, and genome size and content were assessed. Single nucleotide polymorphisms were assessed from de Bruijn graphs, and via reference alignment with both the channel catfish (Ictalurus punctatus) reference genome and Neosho madtom reference genome. Principal component analysis and structure analysis indicated weak population structure, suggesting fish from the 3 locations represent a single population. Using a novel method, genome-wide conservation and divergence between the Neosho madtom, channel catfish, and zebrafish (Danio rerio) was assessed by pairwise contig alignment, which demonstrated that genes important to embryonic development frequently had conserved sequences. This research in a threatened species with no previously published genomic resources provides novel genetic information to guide current and future conservation efforts and demonstrates that using whole-genome sequencing provides detailed information of population structure and demography using only a limited number of rare and valuable samples.

DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network

BACKGROUND

Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using networking techniques and the concept of influence maximization, some models have been proposed to identify these genes.

OBJECTIVES

We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a network science approach without the use of mutation and genomic data.

MATERIALS AND METHODS

In this study, we will employ the social influence network theory to identify CDGs in the human gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs.

RESULTS

The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method can identify many CDGs that are overlooked by all previously published methods.

CONCLUSIONS

Our study demonstrated that the Google's PageRank algorithm can be utilized and modified as a network-based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.

Novel ZNF414 activity characterized by integrative analysis of ChIP-exo, ATAC-seq and RNA-seq data

Transcription factor binding to DNA is a central mechanism regulating gene expression. Thus, thorough characterization of this process is essential for understanding cellular biology in both health and disease. We combined data from three sequencing-based methods to unravel the DNA binding function of the novel ZNF414 protein in cells representing two tumor types. ChIP-exo served to map protein binding sites, ATAC-seq allowed identification of open chromatin, and RNA-seq examined the transcriptome. We show that ZNF414 is a DNA-binding protein that both induces and represses gene expression. This transcriptional response has an impact on cellular processes related to proliferation and other malignancy-associated functions, such as cell migration and DNA repair. Approximately 20% of the differentially expressed genes harbored ZNF414 binding sites in their promoters in accessible chromatin, likely representing direct targets of ZNF414. De novo motif discovery revealed several putative ZNF414 binding sequences, one of which was validated using EMSA. In conclusion, this study illustrates a highly efficient integrative approach for the characterization of the DNA binding and transcriptional activity of transcription factors.

m6A-mediated modulation coupled with transcriptional regulation shapes long noncoding RNA repertoire of the cGAS-STING signaling

The cGAS-STING signaling plays pivotal roles not only in host antiviral defense but also in various noninfectious contexts. Compared with protein-coding genes, much less was known about long noncoding RNAs involved in this pathway. Here, we performed an integrative study to elucidate the lncRNA repertoire and the mechanisms modulating lncRNA’s expression following cGAS-STING signaling activation. We uncovered a reliable set of 672 lncRNAs closely linked to cGAS-STING signaling activation (cs-lncRNA), which might be associated with type-I interferon response and infection-related phenotypes. The ChIP-seq analysis demonstrated that cs-lncRNA was strongly regulated at the transcriptional level. We further found N6-methyladenosine (m⁶A) regulatory machinery was indispensable for establishing cs-lncRNA repertoire via modulating m⁶A modification on cs-lncRNA transcripts and promoting the expression of signaling transduction key components, including IFNAR1. Loss of IFNAR1 led to the dysregulation of cs-lncRNAs resembled that of loss of an essential subunit of m⁶A writer METTL14. We also found m⁶A system affected transcriptional machinery to modulate cs-lncRNAs by targeting multiple crucial transcription factors. Inhibiting an m⁶A modification regulated transcription factor, EZH2, markedly enhanced the expression pattern of cs-lncRNAs. Taken together, our results uncovered the composition of the cs-lncRNAs and revealed m⁶A-mediated modulation coupled with transcriptional regulation significantly shaped cs-lncRNA repertoire.

Vitamin D and Its Target Genes

The vitamin D metabolite 1α,25-dihydroxyvitamin D₃ is the natural, high-affinity ligand of the transcription factor vitamin D receptor (VDR). In many tissues and cell types, VDR binds in a ligand-dependent fashion to thousands of genomic loci and modulates, via local chromatin changes, the expression of hundreds of primary target genes. Thus, the epigenome and transcriptome of VDR-expressing cells is directly affected by vitamin D. Vitamin D target genes encode for proteins with a large variety of physiological functions, ranging from the control of calcium homeostasis, innate and adaptive immunity, to cellular differentiation. This review will discuss VDR’s binding to genomic DNA, as well as its genome-wide locations and interaction with partner proteins, in the context of chromatin. This information will be integrated into a model of vitamin D signaling, explaining the regulation of vitamin D target genes.

Identify a DNA Damage Repair Gene Signature for Predicting Prognosis and Immunotherapy Response in Cervical Squamous Cell Carcinoma

The DNA damage repair (DDR) genes are increasingly gaining attention as potential therapeutic targets in cancers. In this study, we identified the DDR genes associated with the tumor mutation burden (TMB) and prognosis of cervical squamous cell carcinoma (CESC) based on The Cancer Genome Atlas (TCGA) database. Through LASSO Cox regression, the prognostic signature involving five DDR genes (ACTR2, TEX12, UBE2V1, HSF1, and FBXO6) was established, and the risk score was identified as an independent risk factor for CESC. The nomogram consisting of the five genes accurately predicted the overall survival (OS) and the immunotherapeutic response of CESC patients. Finally, the loss of the copies of the transcription factor (TF) SP140 in CESC patients may decrease the expression of FBXO6, improve DNA repair function, and reduce the diversity of neoantigens, thereby lowering the response to immunotherapies. Therefore, the DDR gene signature is a novel prognostic model and a biomarker for immunotherapies in CESC patients.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

PCR-based profiling of transcription factor activity in vivo by a virus-based reporter battery

Understanding the molecular mechanisms of gene regulation is pivotal for understanding how cells establish and modify their identities and functions. Multiple transcription factors (TFs) coordinate to alter gene expression in cells; however, a method to quantitatively analyze the activity of each TF is lacking, particularly in vivo. Here, we introduce a viral-vector-based TF reporter battery that can be used to simultaneously analyze the activity of multiple TFs, visualized as the TF activity profile (TFAP) obtained by qPCR. We show that the cells possess distinct TFAPs that dynamically change according to experimental manipulation or physiological activity. We report a practical method to obtain the TFAP of a defined cell population and their experience-dependent changes in the mouse brain in vivo. The TFAP obtained by our method will help bridge the information gap between the genome and transcriptome and aid the multi-omics view of understanding the gene regulation system.

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A virus-based reporter battery for obtaining the TF activity profile of cells

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TFAP analysis reveals the dynamic change of TF activity upon stimulation

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Experience-dependent change of TFAP of the mouse brain in vivo

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Neural and glial change of TF activity revealed by the cell-type-specific TFAP

Addressing the mean-correlation relationship in co-expression analysis

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.

Transcription factors evolve faster than their structural gene targets in the flavonoid pigment pathway

Dissecting the relationship between gene function and substitution rates is key to understanding genome-wide patterns of molecular evolution. Biochemical pathways provide powerful systems for investigating this relationship because the functional role of each gene is often well characterized. Here, we investigate the evolution of the flavonoid pigment pathway in the colorful Petunieae clade of the tomato family (Solanaceae). This pathway is broadly conserved in plants, both in terms of its structural elements and its MYB, basic helix–loop–helix, and WD40 transcriptional regulators, and its function has been extensively studied, particularly in model species of petunia. We built a phylotranscriptomic data set for 69 species of Petunieae to infer patterns of molecular evolution across pathway genes and across lineages. We found that transcription factors exhibit faster rates of molecular evolution (dN/dS) than their targets, with the highly specialized MYB genes evolving fastest. Using the largest comparative data set to date, we recovered little support for the hypothesis that upstream enzymes evolve slower than those occupying more downstream positions, although expression levels do predict molecular evolutionary rates. Although shifts in floral pigmentation were only weakly related to changes affecting coding regions, we found a strong relationship with the presence/absence patterns of MYB transcripts. Intensely pigmented species express all three main MYB anthocyanin activators in petals, whereas pale or white species express few or none. Our findings reinforce the notion that pathway regulators have a dynamic history, involving higher rates of molecular evolution than structural components, along with frequent changes in expression during color transitions.

Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 11.3% to 48.8% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor https://bioconductor.org/packages/release/bioc/html/ADImpute.html) that automatically determines the best imputation method for each gene in a dataset. Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.

Identification and Functional Evaluation of a Novel TBX4 Mutation Underlies Small Patella Syndrome

Small patella syndrome (SPS) is a rare autosomal dominant disorder caused by mutations in TBX4 gene which encodes a transcription factor of FGF10. However, how TBX4 mutations result in SPS is poorly understood. Here, a novel TBX4 mutation c.1241C>T (p.P414L) was identified in a SPS family and series of studies were performed to evaluate the influences of TBX4 mutations (including c.1241C>T and two known mutations c.256G>C and c.743G>T). Results showed that mesenchymal stem cells (MSCs) with stable overexpression of either TBX4 wild-type (TBX4^wt) or mutants (TBX4^mt) were successfully generated. Immunofluorescence study revealed that both the overexpressed TBX4 wild-type and mutants were evenly expressed in the nucleus suggesting that these mutations do not alter the translocation of TBX4 into the nucleus. Interestingly, MSCs overexpression of TBX4^mt exhibited reduced differentiation activities and decreased FGF10 expression. Chromatin immunoprecipitation (ChIP) study demonstrated that TBX4 mutants still could bind to the promoter of FGF10. However, dual luciferase reporter assay clarified that the binding efficiencies of TBX4 mutants to FGF10 promoter were reduced. Taken together, MSCs were firstly used to study the function of TBX4 mutations in this study and the results indicate that the reduced binding efficiencies of TBX4 mutants (TBX4^mt) to the promoter of FGF10 result in the abnormal biological processes which provide important information for the pathogenesis of SPS.

Human transcription factor protein interaction networks

Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here, we identify 6703 and 1536 protein–protein interactions for 109 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis identifies more high-confidence interactions, highlighting the transient and dynamic nature of many of the TF interactions. By performing clustering and correlation analyses, we identify subgroups of TFs associated with specific biological functions, such as RNA splicing or chromatin remodeling. We also observe 202 TF-TF interactions, of which 118 are interactions with nuclear factor 1 (NFI) family members, indicating uncharacterized cross-talk between NFI signaling and other TF signaling pathways. Moreover, TF interactions with basal transcription machinery are mainly observed through TFIID and SAGA complexes. This study provides a rich resource of human TF interactions and also act as a starting point for future studies aimed at understanding TF-mediated transcription.

Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here the authors identify 6703 and 1536 protein–protein interactions for 109 different human TFs through BioID and AP-MS analyses, respectively.

Compendium of human transcription factor effector domains

Transcription factors (TFs) regulate gene expression by binding to DNA sequences and modulating transcriptional activity through their effector domains. Despite the central role of effector domains in TF function, there is a current lack of a comprehensive resource and characterization of effector domains. Here, we provide a catalog of 924 effector domains across 594 human TFs. Using this catalog, we characterized the amino acid composition of effector domains, their conservation across species and across the human population, and their roles in human diseases. Furthermore, we provide a classification system for effector domains that constitutes a valuable resource and a blueprint for future experimental studies of TF effector domain function.

The KRAB Domain of ZNF10 Guides the Identification of Specific Amino Acids That Transform the Ancestral KRAB-A-Related Domain Present in Human PRDM9 into a Canonical Modern KRAB-A Domain

Krüppel-associated box (KRAB) zinc finger proteins are a large class of tetrapod transcription factors that usually exert transcriptional repression through recruitment of TRIM28/KAP1. The evolutionary root of modern KRAB domains (mKRAB) can be traced back to an ancestral motif (aKRAB) that occurs even in invertebrates. Here, we first stratified three subgroups of aKRAB sequences from the animal kingdom (PRDM9, SSX and coelacanth KZNF families) and defined ancestral subdomains for KRAB-A and KRAB-B. Using human ZNF10 mKRAB-AB as blueprints for function, we then identified the necessary amino acid changes that transform the inactive aKRAB-A of human PRDM9 into an mKRAB domain capable of mediating silencing and complexing TRIM28/KAP1 in human cells when employed as a hybrid with ZNF10-B. Full gain of function required replacement of residues KR by the conserved motif MLE (positionsA32-A34), which inserted an additional residue, and exchange of A9/S for F, A20/M for L, and A27/R for V. AlphaFold2 modelling documented an evolutionary conserved L-shaped body of two α-helices in all KRAB domains. It is transformed into a characteristic spatial arrangement typical for mKRAB-AB upon the amino acid replacements and in conjunction with a third helix supplied by mKRAB-B. Side-chains pointing outward from the core KRAB 3D structure may reveal a protein-protein interaction code enabling graded binding of TRIM28 to different KRAB domains. Our data provide basic insights into structure-function relationships and emulate transitions of KRAB during evolution.

Multivariate competing endogenous RNA network characterization for cancer MicroRNA biomarker discovery: a novel bioinformatics model with application to prostate cancer metastasis

Background

MicroRNAs (miRNAs) are post-transcriptional regulators with potential as biomarkers for cancer management. Data-driven competing endogenous RNA (ceRNA) network modeling is an effective way to decipher the complex interplay between miRNAs and spongers. However, there are currently no general rules for ceRNA network-based biomarker prioritization.

Methods and results

In this study, a novel bioinformatics model was developed by integrating gene expression with multivariate miRNA-target data for ceRNA network-based biomarker discovery. Compared with traditional methods, the structural vulnerability in the human long non-coding RNA (lncRNA)–miRNA–messenger RNAs (mRNA) network was comprehensively analyzed, and the single-line regulatory or competing mode among miRNAs, lncRNAs, and mRNAs was characterized and quantified as statistical evidence for miRNA biomarker identification. The application of this model to prostate cancer (PCa) metastasis identified a total of 12 miRNAs as putative biomarkers from the metastatic PCa-specific lncRNA–miRNA–mRNA network and nine of them have been previously reported as biomarkers for PCa metastasis. The receiver operating characteristic curve and cell line qRT-PCR experiments demonstrated the power of miR-26b-5p, miR-130a-3p, and miR-363-3p as novel candidates for predicting PCa metastasis. Moreover, PCa-associated pathways such as prostate cancer signaling, ERK/MAPK signaling, and TGF-β signaling were significantly enriched by targets of identified miRNAs, indicating the underlying mechanisms of miRNAs in PCa carcinogenesis.

Conclusions

A novel ceRNA-based bioinformatics model was proposed and applied to screen candidate miRNA biomarkers for PCa metastasis. Functional validations using human samples and clinical data will be performed for future translational studies on the identified miRNAs.

Identification and External Validation of a Transcription Factor-Related Prognostic Signature in Pediatric Neuroblastoma

Background

Neuroblastoma is a common solid tumor originating from the sympathetic nervous system, commonly found in children, and it is one of the leading causes of tumor-related deaths in children. In addition to pathological features, molecular-level features, such as how much gene expression is present and the mutational profile, may provide useful information for the precise treatment of neuroblastoma. Transcription factors (TFs) play an important regulatory role in all aspects of cellular life activities. But there are currently no studies on transcription factor-based biomarkers of neuroblastoma prognosis, and this study is much needed.

Methods

We downloaded RNA transcriptome data and clinical data from the TARGET database to construct a prognostic model. The prognostic model was constructed by using univariate Cox analysis, LASSO, and multivariate Cox regression. We divided the patients into low-risk and high-risk groups using the median value of the risk score as the cut-off. Then, we validated the prognostic model with the dataset GSE49710.

Results

We constructed a prognostic model consisting of eight genes (SATB1, ZNF564, SOX14, EN1, IKZF2, SLC2A4RG, FOXJ2, and ZNF521). Patients in the high-risk group had a lower survival rate than those in the low-risk group. The area under the 3-year ROC curve of the model reached 0.825, suggesting a good predictive efficacy. We performed target gene prediction for the eight transcription factors in the model using six online databases and found that TUT1 may be a target gene for transcription factor EN1 and is associated with immune infiltration.

Conclusion

This prognostic model consisting of eight transcription factor-associated genes demonstrated reliable predictive efficacy. This prediction model may provide new potential targets for the treatment of neuroblastoma and personalized monitoring of neuroblastoma patients with high and low risk.

TOX4, an insulin receptor-independent regulator of hepatic glucose production, is activated in diabetic liver

Increased hepatic glucose production (HGP) contributes to hyperglycemia in type 2 diabetes. Hormonal regulation of this process is primarily, but not exclusively, mediated by the AKT-FoxO1 pathway. Here, we show that cAMP and dexamethasone regulate the high-mobility group superfamily member TOX4 to mediate HGP, independent of the insulin receptor/FoxO1 pathway. TOX4 inhibition decreases glucose production in primary hepatocytes and liver and increases glucose tolerance. Combined genetic ablation of TOX4 and FoxO1 in liver has additive effects on glucose tolerance and gluconeogenesis. Moreover, TOX4 ablation fails to reverse the metabolic derangement brought by insulin receptor knockout. TOX4 expression is increased in livers of patients with steatosis and diabetes and in diet-induced obese and db/db mice. In the latter two murine models, knockdown Tox4 decreases glycemia and improves glucose tolerance. We conclude that TOX4 is an insulin receptor-independent regulator of HGP and a candidate contributor to the pathophysiology of diabetes.

The Non-continuum Nature of Eukaryotic Transcriptional Regulation

Eukaryotic transcription factors are versatile mediators of specificity in gene regulation. This versatility is achieved through mutual specification by context-specific DNA binding on the one hand, and identity-specific protein-protein partnerships on the other. This interactivity, known as combinatorial control, enables a repertoire of complex transcriptional outputs that are qualitatively disjoint, or non-continuum, with respect to binding affinity. This feature contrasts starkly with prokaryotic gene regulators, whose activities in general vary quantitatively in step with binding affinity. Biophysical studies on prokaryotic model systems and more recent investigations on transcription factors highlight an important role for folded state dynamics and molecular hydration in protein/DNA recognition. Analysis of molecular models of combinatorial control and recent literature in low-affinity gene regulation suggest that transcription factors harbor unique conformational dynamics that are inaccessible or unused by prokaryotic DNA-binding proteins. Thus, understanding the intrinsic dynamics involved in DNA binding and co-regulator recruitment appears to be a key to understanding how transcription factors mediate non-continuum outcomes in eukaryotic gene expression, and how such capability might have evolved from ancient, structurally conserved counterparts.

Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research

The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.

Single-nucleus chromatin accessibility profiling highlights distinct astrocyte signatures in progressive supranuclear palsy and corticobasal degeneration

Tauopathies such as progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) exhibit characteristic neuronal and glial inclusions of hyperphosphorylated Tau (pTau). Although the astrocytic pTau phenotype upon neuropathological examination is the most guiding feature in distinguishing both diseases, regulatory mechanisms controlling their transitions into disease-specific states are poorly understood to date. Here, we provide accessible chromatin data of more than 45,000 single nuclei isolated from the frontal cortex of PSP, CBD, and control individuals. We found a strong association of disease-relevant molecular changes with astrocytes and demonstrate that tauopathy-relevant genetic risk variants are tightly linked to astrocytic chromatin accessibility profiles in the brains of PSP and CBD patients. Unlike the established pathogenesis in the secondary tauopathy Alzheimer disease, microglial alterations were relatively sparse. Transcription factor (TF) motif enrichments in pseudotime as well as modeling of the astrocytic TF interplay suggested a common pTau signature for CBD and PSP that is reminiscent of an inflammatory immediate-early response. Nonetheless, machine learning models also predicted discriminatory features, and we observed marked differences in molecular entities related to protein homeostasis between both diseases. Predicted TF involvement was supported by immunofluorescence analyses in postmortem brain tissue for their highly correlated target genes. Collectively, our data expand the current knowledge on risk gene involvement (e.g., MAPT, MAPK8, and NFE2L2) and molecular pathways leading to the phenotypic changes associated with CBD and PSP.

Estrogen differentially regulates transcriptional landscapes of preoptic and arcuate kisspeptin neuron populations

Kisspeptin neurons residing in the rostral periventricular area of the third ventricle (KP^RP3V) and the arcuate nucleus (KP^ARC) mediate positive and negative estrogen feedback, respectively. Here, we aim to compare transcriptional responses of KP^RP3V and KP^ARC neurons to estrogen. Transgenic mice were ovariectomized and supplemented with either 17β-estradiol (E2) or vehicle. Fluorescently tagged KP^RP3V neurons collected by laser-capture microdissection were subjected to RNA-seq. Bioinformatics identified 222 E2-dependent genes. Four genes encoding neuropeptide precursors (Nmb, Kiss1, Nts, Penk) were robustly, and Cartpt was subsignificantly upregulated, suggesting putative contribution of multiple neuropeptides to estrogen feedback mechanisms. Using overrepresentation analysis, the most affected KEGG pathways were neuroactive ligand-receptor interaction and dopaminergic synapse. Next, we re-analyzed our previously obtained KP^ARC neuron RNA-seq data from the same animals using identical bioinformatic criteria. The identified 1583 E2-induced changes included suppression of many neuropeptide precursors, granins, protein processing enzymes, and other genes related to the secretory pathway. In addition to distinct regulatory responses, KP^RP3V and KP^ARC neurons exhibited sixty-two common changes in genes encoding three hormone receptors (Ghsr, Pgr, Npr2), GAD-65 (Gad2), calmodulin and its regulator (Calm1, Pcp4), among others. Thirty-four oppositely regulated genes (Kiss1, Vgf, Chrna7, Tmem35a) were also identified. The strikingly different transcriptional responses in the two neuron populations prompted us to explore the transcriptional mechanism further. We identified ten E2-dependent transcription factors in KP^RP3V and seventy in KP^ARC neurons. While none of the ten transcription factors interacted with estrogen receptor-α, eight of the seventy did. We propose that an intricate, multi-layered transcriptional mechanism exists in KP^ARC neurons and a less complex one in KP^RP3V neurons. These results shed new light on the complexity of estrogen-dependent regulatory mechanisms acting in the two functionally distinct kisspeptin neuron populations and implicate additional neuropeptides and mechanisms in estrogen feedback.

Transcriptional and epigenetic regulation in thymic epithelial cells

The thymus is required for the development of both adaptive and innate-like T cell subsets. There is keen interest in manipulating thymic function for therapeutic purposes in circumstances of autoimmunity, immunodeficiency, and for purposes of immunotherapy. Within the thymus, thymic epithelial cells play essential roles in directing T cell development. Several transcription factors are known to be essential for thymic epithelial cell development and function, and a few transcription factors have been studied in considerable detail. However, the role of many other transcription factors is less well understood. Further, it is likely that roles exist for other transcription factors not yet known to be important in thymic epithelial cells. Recent progress in understanding of thymic epithelial cell heterogeneity has provided some new insight into transcriptional requirements in subtypes of thymic epithelial cells. However, it is unknown whether progenitors of thymic epithelial cells exist in the adult thymus, and consequently, developmental relationships linking putative precursors with differentiated cell types are poorly understood. While we do not presently possess a clear understanding of stage-specific requirements for transcription factors in thymic epithelial cells, new single-cell transcriptomic and epigenomic technologies should enable rapid progress in this field. Here, we review our current knowledge of transcription factors involved in the development, maintenance, and function of thymic epithelial cells, and the mechanisms by which they act.

Hierarchical regulation of the resting and activated T cell epigenome by major transcription factor families

T cell activation, a key early event in the adaptive immune response, is subject to elaborate transcriptional control. Here, we examined how the activities of eight major transcription factor (TF) families are integrated to shape the epigenome of naïve and activated CD4 and CD8 T cells. By leveraging extensive polymorphisms in evolutionarily divergent mice, we identified the “heavy lifters” positively influencing chromatin accessibility. Members of Ets, Runx, and TCF/Lef TF families occupied the vast majority of accessible chromatin regions, acting as “housekeepers”, “universal amplifiers”, and “placeholders”, respectively, at sites that maintained or gained accessibility upon T cell activation. Additionally, a small subset of strongly induced immune response genes displayed a non-canonical TF recruitment pattern. Our study provides a key resource and foundation for the understanding of transcriptional and epigenetic regulation in T cells and offers a new perspective on the hierarchical interactions between critical TFs.

Single-cell transcriptomic analysis of zebrafish cranial neural crest reveals spatiotemporal regulation of lineage decisions during development

Neural crest (NC) cells migrate throughout vertebrate embryos to give rise to a huge variety of cell types, but when and where lineages emerge and their regulation remain unclear. We have performed single-cell RNA sequencing (RNA-seq) of cranial NC cells from the first pharyngeal arch in zebrafish over several stages during migration. Computational analysis combining pseudotime and real-time data reveals that these NC cells first adopt a transitional state, becoming specified mid-migration, with the first lineage decisions being skeletal and pigment, followed by neural and glial progenitors. In addition, by computationally integrating these data with RNA-seq data from a transgenic Wnt reporter line, we identify gene cohorts with similar temporal responses to Wnts during migration and show that one, Atp6ap2, is required for melanocyte differentiation. Together, our results show that cranial NC cell lineages arise progressively and uncover a series of spatially restricted cell interactions likely to regulate such cell-fate decisions.

Tatarakis et al. provide a single-cell transcriptomic timeline of cranial neural crest (NC) development in zebrafish and address long-standing questions surrounding the integration of NC cell migration and lineage specification. They find that lineages are specified mid-migration. These fate decisions correspond to shifts in Wnt signaling, and lineages rapidly segregate.

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

Androgen receptor and MYC equilibration centralizes on developmental super-enhancer

Androgen receptor (AR) in prostate cancer (PCa) can drive transcriptional repression of multiple genes including MYC, and supraphysiological androgen is effective in some patients. Here, we show that this repression is independent of AR chromatin binding and driven by coactivator redistribution, and through chromatin conformation capture methods show disruption of the interaction between the MYC super-enhancer within the PCAT1 gene and the MYC promoter. Conversely, androgen deprivation in vitro and in vivo increases MYC expression. In parallel, global AR activity is suppressed by MYC overexpression, consistent with coactivator redistribution. These suppressive effects of AR and MYC are mitigated at shared AR/MYC binding sites, which also have markedly higher levels of H3K27 acetylation, indicating enrichment for functional enhancers. These findings demonstrate an intricate balance between AR and MYC, and indicate that increased MYC in response to androgen deprivation contributes to castration-resistant PCa, while decreased MYC may contribute to responses to supraphysiological androgen therapy.

Androgen receptor in prostate cancer (PCa) transcriptionally represses multiple genes including MYC. Here, the authors suggest that increased MYC in response to androgen deprivation contributes to castration-resistant PCa, while decreased MYC may contribute to responses to supraphysiological androgen therapy.

Transcription Factor SP2 Regulates Ski-mediated Astrocyte Proliferation In Vitro

Transcription factors bind specific sequences upstream of the 5' end of their target genes to ensure proper spatiotemporal expression of the target gene. This study aims to demonstrate that the transcription factor SP2 regulates expression of the Ski gene, which has specific binding sites for SP2, and thus enables Ski to regulate astrocyte proliferation. The upstream regulation mechanism of astrocyte proliferation was explored to further regulate the formation of glial scar in specific time and space after spinal cord injury. JASPAR and UCSC databases were used to predict transcription factor binding and the threshold was gradually reduced to screen transcription factors upstream of Ski, leading to the identification of SP2. Next, we analyzed the correlation between the expression of SP2 and Ski in normal astrocytes and reactive astrocytes, as well as the changes in astrocyte proliferation. To confirm that SP2 regulates Ski during astrocyte proliferation, astrocytes were transfected siRNA targeting SP2 and then astrocyte proliferation were analyzed. Finally, a dual luciferase reporter assay and Chromatin immunoprecipitation (ChIP) assay confirmed that the promoter region of Ski contained a specific SP2 binding site. This is the first that SP2 has been identified and confirmed to play an important role in astrocyte proliferation by regulating Ski expression. These results may help identify novel targets for the treatment of spinal cord injury.

BACH2 inhibition reverses β cell failure in type 2 diabetes models

Type 2 diabetes (T2D) is associated with defective insulin secretion and reduced β cell mass. Available treatments provide a temporary reprieve, but secondary failure rates are high, making insulin supplementation necessary. Reversibility of β cell failure is a key translational question. Here, we reverse engineered and interrogated pancreatic islet-specific regulatory networks to discover T2D-specific subpopulations characterized by metabolic inflexibility and endocrine progenitor/stem cell features. Single-cell gain- and loss-of-function and glucose-induced Ca2+ flux analyses of top candidate master regulatory (MR) proteins in islet cells validated transcription factor BACH2 and associated epigenetic effectors as key drivers of T2D cell states. BACH2 knockout in T2D islets reversed cellular features of the disease, restoring a nondiabetic phenotype. BACH2-immunoreactive islet cells increased approximately 4-fold in diabetic patients, confirming the algorithmic prediction of clinically relevant subpopulations. Treatment with a BACH inhibitor lowered glycemia and increased plasma insulin levels in diabetic mice, and restored insulin secretion in diabetic mice and human islets. The findings suggest that T2D-specific populations of failing β cells can be reversed and indicate pathways for pharmacological intervention, including via BACH2 inhibition.

A Co-Association Network Analysis Reveals Putative Regulators for Health-Related Traits in Pigs

In recent years, the increase in awareness of antimicrobial resistance together with the societal demand of healthier meat products have driven attention to health-related traits in livestock production. Previous studies have reported medium to high heritabilities for these traits and described genomic regions associated with them. Despite its genetic component, health- and immunity-related traits are complex and its study by association analysis with genomic markers may be missing some information. To analyse multiple phenotypes and gene-by-gene interactions, systems biology approaches, such as the association weight matrix (AWM), allows combining genome wide association study results with network inference algorithms. The present study aimed to identify gene networks, key regulators and candidate genes associated to immunocompetence in pigs by integrating multiple health-related traits, enriched for innate immune phenotypes, using the AWM approach. The co-association network analysis unveiled a network comprised of 3,636 nodes (genes) and 451,407 edges (interactions), including a total of 246 regulators. From these, five genes (ARNT2, BRMS1L, MED12L, SUPT3H and TRIM25) were selected as key regulators as they were associated with the maximum number of genes with the minimum overlapping (1,827 genes in total). The five regulators were involved in pathways related to immunity such as lymphocyte differentiation and activation, platelet activation and degranulation, megakaryocyte differentiation, FcγR-mediated phagocytosis and response to nitric oxide, among others, but also in immunometabolism. Furthermore, we identified genes co-associated with the key regulators previously reported as candidate genes (e.g., ANGPT1, CD4, CD36, DOCK1, PDE4B, PRKCE, PTPRC and SH2B3) for immunity traits in humans and pigs, but also new candidate ones (e.g., ACSL3, CXADR, HBB, MMP12, PTPN6, WLS) that were not previously described. The co-association analysis revealed new regulators associated with health-related traits in pigs. This approach also identified gene-by-gene interactions and candidate genes involved in pathways related to cell fate and metabolic and immune functions. Our results shed new light in the regulatory mechanisms involved in pig immunity and reinforce the use of the pig as biomedical model.

Structure-function relationships explain CTCF zinc finger mutation phenotypes in cancer

CCCTC-binding factor (CTCF) plays fundamental roles in transcriptional regulation and chromatin architecture maintenance. CTCF is also a tumour suppressor frequently mutated in cancer, however, the structural and functional impact of mutations have not been examined. We performed molecular and structural characterisation of five cancer-specific CTCF missense zinc finger (ZF) mutations occurring within key intra- and inter-ZF residues. Functional characterisation of CTCF ZF mutations revealed a complete (L309P, R339W, R377H) or intermediate (R339Q) abrogation as well as an enhancement (G420D) of the anti-proliferative effects of CTCF. DNA binding at select sites was disrupted and transcriptional regulatory activities abrogated. Molecular docking and molecular dynamics confirmed that mutations in residues specifically contacting DNA bases or backbone exhibited loss of DNA binding. However, R339Q and G420D were stabilised by the formation of new primary DNA bonds, contributing to gain-of-function. Our data confirm that a spectrum of loss-, change- and gain-of-function impacts on CTCF zinc fingers are observed in cell growth regulation and gene regulatory activities. Hence, diverse cellular phenotypes of mutant CTCF are clearly explained by examining structure-function relationships.

Homodimeric and Heterodimeric Interactions among Vertebrate Basic Helix-Loop-Helix Transcription Factors

The basic helix–loop–helix transcription factor (bHLH TF) family is involved in tissue development, cell differentiation, and disease. These factors have transcriptionally positive, negative, and inactive functions by combining dimeric interactions among family members. The best known bHLH TFs are the E-protein homodimers and heterodimers with the tissue-specific TFs or ID proteins. These cooperative and dynamic interactions result in a complex transcriptional network that helps define the cell’s fate. Here, the reported dimeric interactions of 67 vertebrate bHLH TFs with other family members are summarized in tables, including specifications of the experimental techniques that defined the dimers. The compilation of these extensive data underscores homodimers of tissue-specific bHLH TFs as a central part of the bHLH regulatory network, with relevant positive and negative transcriptional regulatory roles. Furthermore, some sequence-specific TFs can also form transcriptionally inactive heterodimers with each other. The function, classification, and developmental role for all vertebrate bHLH TFs in four major classes are detailed.

Targeting Oncogenic Transcriptional Networks in Neuroblastoma: From N-Myc to Epigenetic Drugs

Neuroblastoma (NB) is one of the most frequently occurring neurogenic extracranial solid cancers in childhood and infancy. Over the years, many pieces of evidence suggested that NB development is controlled by gene expression dysregulation. These unleashed programs that outline NB cancer cells make them highly dependent on specific tuning of gene expression, which can act co-operatively to define the differentiation state, cell identity, and specialized functions. The peculiar regulation is mainly caused by genetic and epigenetic alterations, resulting in the dependency on a small set of key master transcriptional regulators as the convergence point of multiple signalling pathways. In this review, we provide a comprehensive blueprint of transcriptional regulation bearing NB initiation and progression, unveiling the complexity of novel oncogenic and tumour suppressive regulatory networks of this pathology. Furthermore, we underline the significance of multi-target therapies against these hallmarks, showing how novel approaches, together with chemotherapy, surgery, or radiotherapy, can have substantial antineoplastic effects, disrupting a wide variety of tumorigenic pathways through combinations of different treatments.

Underestimation of Heritability across the Molecular Layers of the Gene Expression Process

We investigated the extent of the heritability underestimation for molecules from an infinitesimal model in mixed model analysis. To this end, we estimated the heritability of transcription, ribosome occupancy, and translation in lymphoblastoid cell lines from Yoruba individuals. Upon considering all genome-wide nucleotide variants, a considerable underestimation in heritability was observed for mRNA transcription (−0.52), ribosome occupancy (−0.48), and protein abundance (−0.47). We employed a mixed model with an optimal number of nucleotide variants, which maximized heritability, and identified two novel expression quantitative trait loci (eQTLs; p < 1.0 × 10−5): rs11016815 on chromosome 10 that influences the transcription of SCP2, a trans-eGene on chromosome 1—whose expression increases in response to MGMT downregulation-induced apoptosis, the cis-eGene of rs11016815—and rs1041872 on chromosome 11 that influences the ribosome occupancy of CCDC25 on chromosome 8 and whose cis-eGene encodes ZNF215, a transcription factor that potentially regulates the translation speed of CCDC25. Our results suggest that an optimal number of nucleotide variants should be used in a mixed model analysis to accurately estimate heritability and identify eQTLs. Moreover, a heterogeneous covariance structure based on gene identity and the molecular layers of the gene expression process should be constructed to better explain polygenic effects and reduce errors in identifying eQTLs.

Establishment of the TBX-code reveals aberrantly activated T-box gene TBX3 in Hodgkin lymphoma

T-box genes encode transcription factors which control basic processes in development of several tissues including cell differentiation in the hematopoietic system. Here, we analyzed the physiological activities of all 17 human T-box genes in early hematopoiesis and in lymphopoiesis including developing and mature B-cells, T-cells, natural killer (NK)-cells and innate lymphoid cells. The resultant expression pattern comprised six genes, namely EOMES, MGA, TBX1, TBX10, TBX19 and TBX21. We termed this gene signature TBX-code which enables discrimination of normal and aberrant activities of T-box genes in lymphoid malignancies. Accordingly, expression analysis of T-box genes in Hodgkin lymphoma (HL) patients using a public profiling dataset revealed overexpression of EOMES, TBX1, TBX2, TBX3, TBX10, TBX19, TBX21 and TBXT while MGA showed aberrant downregulation. Analysis of T-cell acute lymphoid leukemia patients indicated aberrant overexpression of six T-box genes while no deregulated T-box genes were detected in anaplastic large cell lymphoma patients. As a paradigm we focused on TBX3 which was ectopically activated in about 6% of HL patients analyzed. Normally, TBX3 is expressed in tissues like lung, adrenal gland and retina but not in hematopoiesis. HL cell line KM-H2 expressed enhanced TBX3 levels and was used as an in vitro model to identify upstream regulators and downstream targets in this malignancy. Genomic studies of this cell line showed focal amplification of the TBX3 locus at 12q24 which may underlie its aberrant expression. In addition, promoter analysis and comparative expression profiling of HL cell lines followed by knockdown experiments revealed overexpressed transcription factors E2F4 and FOXC1 and chromatin modulator KDM2B as functional activators. Furthermore, we identified repressed target genes of TBX3 in HL including CDKN2A, NFKBIB and CD19, indicating its respective oncogenic function in proliferation, NFkB-signaling and B-cell differentiation. Taken together, we have revealed a lymphoid TBX-code and used it to identify an aberrant network around deregulated T-box gene TBX3 in HL which promotes hallmark aberrations of this disease. These findings provide a framework for future studies to evaluate deregulated T-box genes in lymphoid malignancies.

Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.