Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Predicting Transcription Factor Binding Sites with Deep Learning

Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with different approaches, Machine Learning being one of the most successful. Nevertheless, we note that many such approaches fail to propose a robust and meaningful method to embed the genetic data under analysis. We try to overcome this problem by proposing a bidirectional transformer-based encoder, empowered by bidirectional long-short term memory layers and with a capsule layer responsible for the final prediction. To evaluate the efficiency of the proposed approach, we use benchmark ChIP-seq datasets of five cell lines available in the ENCODE repository (A549, GM12878, Hep-G2, H1-hESC, and Hela). The results show that the proposed method can predict TFBS within the five different cell lines very well; moreover, cross-cell predictions provide satisfactory results as well. Experiments conducted across cell lines are reinforced by the analysis of five additional lines used only to test the model trained using the others. The results confirm that prediction across cell lines remains very high, allowing an extensive cross-transcription factor analysis to be performed from which several indications of interest for molecular biology may be drawn.

Individual transcription factors modulate both the micromovement of chromatin and its long-range structure

The control of eukaryotic gene expression is intimately connected to highly dynamic chromatin structures. Gene regulation relies on activator and repressor transcription factors (TFs) that induce local chromatin opening and closing. However, it is unclear how nucleus-wide chromatin organization responds dynamically to the activity of specific TFs. Here, we examined how two TFs with opposite effects on local chromatin accessibility modulate chromatin dynamics nucleus-wide. We combine high-resolution diffusion mapping and dense flow reconstruction and correlation in living cells to obtain an imaging-based, nanometer-scale analysis of local diffusion processes and long-range coordinated movements of both chromatin and TFs. We show that the expression of either an individual transcriptional activator (CDX2) or repressor (SIX6) with large numbers of binding sites increases chromatin mobility nucleus-wide, yet they induce opposite coherent chromatin motions at the micron scale. Hi-C analysis of higher-order chromatin structures shows that induction of the pioneer factor CDX2 leads both to changes in local chromatin interactions and the distribution of A and B compartments, thus relating the micromovement of chromatin with changes in compartmental structures. Given that inhibition of transcription initiation and elongation by RNA Pol II has a partial impact on the global chromatin dynamics induced by CDX2, we suggest that CDX2 overexpression alters chromatin structure dynamics both dependently and independently of transcription. Our biophysical analysis shows that sequence-specific TFs can influence chromatin structure on multiple architectural levels, arguing that local chromatin changes brought by TFs alter long-range chromatin mobility and its organization.

Transcriptomic landscape of human induced pluripotent stem cell-derived osteogenic differentiation identifies a regulatory role of KLF16

Osteogenic differentiation is essential for bone development and metabolism, but the underlying gene regulatory networks have not been well investigated. We differentiated mesenchymal stem cells, derived from 20 human induced pluripotent stem cell lines, into preosteoblasts and osteoblasts, and performed systematic RNA-seq analyses of 60 samples for differential gene expression. We noted a highly significant correlation in expression patterns and genomic proximity among transcription factor (TF) and long noncoding RNA (lncRNA) genes. We identified TF-TF regulatory networks, regulatory roles of lncRNAs on their neighboring coding genes for TFs and splicing factors, and differential splicing of TF, lncRNA, and splicing factor genes. TF-TF regulatory and gene co-expression network analyses suggested an inhibitory role of TF KLF16 in osteogenic differentiation. We demonstrate that in vitro overexpression of human KLF16 inhibits osteogenic differentiation and mineralization, and in vivo Klf16+/- mice exhibit increased bone mineral density, trabecular number, and cortical bone area. Thus, our model system highlights the regulatory complexity of osteogenic differentiation and identifies novel osteogenic genes.

Identification and characterization of Hand2 upstream genomic enhancers active in developing stomach and limbs

BACKGROUND

The bHLH transcription factor HAND2 plays important roles in the development of the embryonic heart, face, limbs, and sympathetic and enteric nervous systems. To define how and when HAND2 regulates these developmental systems, requires understanding the transcriptional regulation of Hand2.

RESULTS

Remarkably, Hand2 is flanked by an extensive upstream gene desert containing a potentially diverse enhancer landscape. Here, we screened the regulatory interval 200 kb proximal to Hand2 for putative enhancers using evolutionary conservation and histone marks in Hand2-expressing tissues. H3K27ac signatures across embryonic tissues pointed to only two putative enhancer regions showing deep sequence conservation. Assessment of the transcriptional enhancer potential of these elements using transgenic reporter lines uncovered distinct in vivo enhancer activities in embryonic stomach and limb mesenchyme, respectively. Activity of the identified stomach enhancer was restricted to the developing antrum and showed expression within the smooth muscle and enteric neurons. Surprisingly, the activity pattern of the limb enhancer did not overlap Hand2 mRNA but consistently yielded a defined subectodermal anterior expression pattern within multiple transgenic lines.

CONCLUSIONS

Together, these results start to uncover the diverse regulatory potential inherent to the Hand2 upstream regulatory interval.

Transcriptional regulation of the HIV-1 inhibitory factor human mannose receptor 1 by the myeloid-specific transcription factor PU.1

HIV-1 infection of human macrophages leads to the downmodulation of human mannose receptor 1 (hMRC1), a cell-surface glycoprotein that is involved in the host innate immune response. We previously reported that downmodulation of hMRC1 involves the transactivator of transcription (Tat)-dependent transcriptional silencing of the hMRC1 promoter. However, the inhibitory effect of Tat on hMRC1 transcription was indirect and involved inhibition of the transcriptional activator PU.1, which normally upregulates hMRC1 expression in macrophages and other myeloid cells. We cloned a 284-bp fragment of the hMRC1 promoter, and within it, we identified four PU.1 box elements. We assessed the relative contribution of each of the four PU.1 boxes to PU.1-dependent transcriptional regulation and, surprisingly, found that only one of the four PU.1 boxes [PU.1(b)] was critically required for PU.1-mediated upregulation of luciferase expression. Transfer of this PU.1 box to a heterologous promoter conferred PU.1 responsiveness to an otherwise PU.1 insensitive promoter. Electrophoretic mobility shift assays identified this PU.1 box as a direct binding site for PU.1 both in the context of the hMRC1 promoter and the heterologous promoter. Furthermore, mutational analysis of the PU.1 protein identified the C-terminal DNA-binding domain in PU.1 as the region responsible for interaction with the PU.1 box. Recombinant HIV-1 Tat protein did not bind to the hMRC1 promoter element but efficiently interfered with the binding of PU.1 protein to the hMRC1 promoter. Thus, Tat is likely to inhibit the formation of active PU.1 transcription complexes, presumably by binding to and depleting common transcriptional cofactors.IMPORTANCEHIV-1 infection of cells results in the modulation of cellular gene expression by virus-encoded proteins in a manner that benefits the virus. We reported that HIV-1 transactivator of transcription (Tat) dysregulates the expression of the human mannose receptor 1 (hMRC1). hMRC1 is involved in the innate immune response of macrophages to foreign pathogens. Tat does not act directly on the hMRC1 promoter but instead inhibits PU.1, a cellular transcription factor regulating hMRC1 gene expression. Here, we characterize the PU.1-dependent regulation of hMRC1 expression. We identified four potential PU.1 binding sites in the hMRC1 promoter region but found that only one, PU.1(b), functioned as a true binding site for PU.1. Transfer of the PU.1(b) box to a heterologous promoter did not activate this promoter per se but rendered it responsive to PU.1. Our results support the view that PU.1 acts as a transcriptional co-factor whose activity can be regulated by HIV-1 Tat.

Transcription factors in the development and treatment of immune disorders

Immune function is highly controlled at the transcriptional level by the binding of transcription factors (TFs) to promoter and enhancer elements. Several TF families play major roles in immune gene expression, including NF-κB, STAT, IRF, AP-1, NRs, and NFAT, which trigger anti-pathogen responses, promote cell differentiation, and maintain immune system homeostasis. Aberrant expression, activation, or sequence of isoforms and variants of these TFs can result in autoimmune and inflammatory diseases as well as hematological and solid tumor cancers. For this reason, TFs have become attractive drug targets, even though most were previously deemed "undruggable" due to their lack of small molecule binding pockets and the presence of intrinsically disordered regions. However, several aspects of TF structure and function can be targeted for therapeutic intervention, such as ligand-binding domains, protein-protein interactions between TFs and with cofactors, TF-DNA binding, TF stability, upstream signaling pathways, and TF expression. In this review, we provide an overview of each of the important TF families, how they function in immunity, and some related diseases they are involved in. Additionally, we discuss the ways of targeting TFs with drugs along with recent research developments in these areas and their clinical applications, followed by the advantages and disadvantages of targeting TFs for the treatment of immune disorders.

A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

Epigenetic regulator RNF20 underlies temporal hierarchy of gene expression to regulate postnatal cardiomyocyte polarization

Differentiated cardiomyocytes (CMs) must undergo diverse morphological and functional changes during postnatal development. However, the mechanisms underlying initiation and coordination of these changes remain unclear. Here, we delineate an integrated, time-ordered transcriptional network that begins with expression of genes for cell-cell connections and leads to a sequence of structural, cell-cycle, functional, and metabolic transitions in mouse postnatal hearts. Depletion of histone H2B ubiquitin ligase RNF20 disrupts this gene network and impairs CM polarization. Subsequently, assay for transposase-accessible chromatin using sequencing (ATAC-seq) analysis confirmed that RNF20 contributes to chromatin accessibility in this context. As such, RNF20 is likely to facilitate binding of transcription factors at the promoters of genes involved in cell-cell connections and actin organization, which are crucial for CM polarization and functional integration. These results suggest that CM polarization is one of the earliest events during postnatal heart development and provide insights into how RNF20 regulates CM polarity and the postnatal gene program.

Understanding blood development and leukemia using sequencing-based technologies and human cell systems

Our current understanding of human hematopoiesis has undergone significant transformation throughout the years, challenging conventional views. The evolution of high-throughput technologies has enabled the accumulation of diverse data types, offering new avenues for investigating key regulatory processes in blood cell production and disease. In this review, we will explore the opportunities presented by these advancements for unraveling the molecular mechanisms underlying normal and abnormal hematopoiesis. Specifically, we will focus on the importance of enhancer-associated regulatory networks and highlight the crucial role of enhancer-derived transcription regulation. Additionally, we will discuss the unprecedented power of single-cell methods and the progression in using in vitro human blood differentiation system, in particular induced pluripotent stem cell models, in dissecting hematopoietic processes. Furthermore, we will explore the potential of ever more nuanced patient profiling to allow precision medicine approaches. Ultimately, we advocate for a multiparameter, regulatory network-based approach for providing a more holistic understanding of normal hematopoiesis and blood disorders.

An extended transcription factor regulatory network controls hepatocyte identity

Cell identity is specified by a core transcriptional regulatory circuitry (CoRC), typically limited to a small set of interconnected cell-specific transcription factors (TFs). By mining global hepatic TF regulons, we reveal a more complex organization of the transcriptional regulatory network controlling hepatocyte identity. We show that tight functional interconnections controlling hepatocyte identity extend to non-cell-specific TFs beyond the CoRC, which we call hepatocyte identity (Hep-ID)CONNECT TFs. Besides controlling identity effector genes, Hep-IDCONNECT TFs also engage in reciprocal transcriptional regulation with TFs of the CoRC. In homeostatic basal conditions, this translates into Hep-IDCONNECT TFs being involved in fine tuning CoRC TF expression including their rhythmic expression patterns. Moreover, a role for Hep-IDCONNECT TFs in the control of hepatocyte identity is revealed in dedifferentiated hepatocytes where Hep-IDCONNECT TFs are able to reset CoRC TF expression. This is observed upon activation of NR1H3 or THRB in hepatocarcinoma or in hepatocytes subjected to inflammation-induced loss of identity. Our study establishes that hepatocyte identity is controlled by an extended array of TFs beyond the CoRC.

Transcriptomic Effects of Low-Dose Inorganic Arsenic Exposure on Murine Bone Marrow-Derived Macrophages

Both tissue-resident macrophages and monocytes recruited from the bone marrow that transform into tissue-resident cells play critical roles in mediating homeostasis as well as in the pathology of inflammatory diseases. Inorganic arsenic (iAs) is the most common drinking water contaminant worldwide and represents a major public health concern. Several diseases that macrophages have implicated involvement in are caused by iAs exposure, including cardiovascular disease, cancer, and increased risk of infectious disease. Therefore, understanding the effects of iAs exposure on macrophages can help us better grasp the full range of arsenic immunotoxicity and better design therapeutic targets for iAs-induced diseases particularly in exposed populations. In this study, we analyzed the transcriptome of low dose iAs-exposed male and female murine bone marrow-derived macrophages (BMDMs) with either M0, M1, or M2 stimulation. We identified differentially expressed genes by iAs in a sex- and stimulation-dependent manner and used bioinformatics tools to predict protein-protein interactions, transcriptional regulatory networks, and associated biological processes. Overall, our data suggest that M1-stimulated, especially female-derived, BMDMs are most susceptible to iAs exposure. Most notably, we observed significant downregulation of major proinflammatory transcription factors, like IRF8, and its downstream targets, as well as genes encoding proteins involved in pattern recognition and antigen presentation, such as TLR7, TLR8, and H2-D1, potentially providing causal insight regarding arsenic's role in perturbing immune responses to infectious diseases. We also observed significant downregulation of genes involved in processes crucial to coordinating a proinflammatory response including leukocyte migration, differentiation, and cytokine and chemokine production and response. Finally, we discovered that 24 X-linked genes were dysregulated in iAs-exposed female stimulation groups compared to only 3 across the iAs-exposed male stimulation groups. These findings elucidate the potential mechanisms underlying the sex-differential iAs-associated immune-related disease risk.

Transcription factors and splice factors - interconnected regulators of stem cell differentiation

Purpose of review

The underlying molecular mechanisms that direct stem cell differentiation into fully functional, mature cells remain an area of ongoing investigation. Cell state is the product of the combinatorial effect of individual factors operating within a coordinated regulatory network. Here, we discuss the contribution of both gene regulatory and splicing regulatory networks in defining stem cell fate during differentiation and the critical role of protein isoforms in this process.

Recent findings

We review recent experimental and computational approaches that characterize gene regulatory networks, splice regulatory networks, and the resulting transcriptome and proteome they mediate during differentiation. Such approaches include long-read RNA sequencing, which has demonstrated high-resolution profiling of mRNA isoforms, and Cas13-based CRISPR, which could make possible high-throughput isoform screening. Collectively, these developments enable systems-level profiling of factors contributing to cell state.

Summary

Overall, gene and splice regulatory networks are important in defining cell state. The emerging high-throughput systems-level approaches will characterize the gene regulatory network components necessary in driving stem cell differentiation.

Escape from oncogene-induced senescence is controlled by POU2F2 and memorized by chromatin scars

Although oncogene-induced senescence (OIS) is a potent tumor-suppressor mechanism, recent studies revealed that cells could escape from OIS with features of transformed cells. However, the mechanisms that promote OIS escape remain unclear, and evidence of post-senescent cells in human cancers is missing. Here, we unravel the regulatory mechanisms underlying OIS escape using dynamic multidimensional profiling. We demonstrate a critical role for AP1 and POU2F2 transcription factors in escape from OIS and identify senescence-associated chromatin scars (SACSs) as an epigenetic memory of OIS detectable during colorectal cancer progression. POU2F2 levels are already elevated in precancerous lesions and as cells escape from OIS, and its expression and binding activity to cis-regulatory elements are associated with decreased patient survival. Our results support a model in which POU2F2 exploits a precoded enhancer landscape necessary for senescence escape and reveal POU2F2 and SACS gene signatures as valuable biomarkers with diagnostic and prognostic potential.

Microfat exerts an anti-fibrotic effect on human hypertrophic scar via fetuin-A/ETV4 axis

BACKGROUND

Hypertrophic scar is a fibrotic disease following wound healing and is characterized by excessive extracellular matrix deposition. Autologous microfat grafting proves an effective strategy for the treatment thereof as it could improve the texture of scars and relieve relevant symptoms. This study aims to explore the potential mechanisms underlying the anti-fibrotic effect of microfat on hypertrophic scars.

METHODS

In this study, we injected microfat into transplanted hypertrophic scars in mouse models and investigated the subsequent histological changes and differential expression of mRNAs therein. As for in vitro studies, we co-cultured microfat and hypertrophic scar fibroblasts (HSFs) and analyzed molecular profile changes in HSFs co-cultured with microfat by RNA sequencing. Moreover, to identify the key transcription factors (TFs) which might be responsible for the anti-fibrotic function of microfat, we screened the differentially expressed TFs and transfected HSFs with lentivirus to overexpress or knockdown certain differentially expressed TFs. Furthermore, comparative secretome analyses were conducted to investigate the proteins secreted by co-cultured microfat; changes in gene expression of HSFs were examined after the administration of the potential anti-fibrotic protein. Finally, the relationship between the key TF in HSFs and the microfat-secreted anti-fibrotic adipokine was analyzed.

RESULTS

The anti-fibrotic effect of microfat was confirmed by in vivo transplanted hypertrophic scar models, as the number of α-SMA-positive myofibroblasts was decreased and the expression of fibrosis-related genes downregulated. Co-cultured microfat suppressed the extracellular matrix production of HSFs in in vitro experiment, and the transcription factor ETV4 was primarily differentially expressed in HSFs when compared with normal skin fibroblasts. Overexpression of ETV4 significantly decreased the expression of fibrosis-related genes in HSFs at both mRNA and protein levels. Fetuin-A secreted by microfat could also downregulate the expression of fibrosis-related genes in HSFs, partially through upregulating ETV4 expression.

CONCLUSIONS

Our results demonstrated that transcription factor ETV4 is essential for the anti-fibrotic effect of microfat on hypertrophic scars, and that fetuin-A secreted by microfat could suppress the fibrotic characteristic of HSFs through upregulating ETV4 expression. Microfat wields an alleviative influence over hypertrophic scars via fetuin-A/ETV4 axis.

Leukemia core transcriptional circuitry is a sparsely interconnected hierarchy stabilized by incoherent feed-forward loops

Lineage-defining transcription factors form densely interconnected circuits in chromatin occupancy assays, but the functional significance of these networks remains underexplored. We reconstructed the functional topology of a leukemia cell transcription network from the direct gene-regulatory programs of eight core transcriptional regulators established in pre-steady state assays coupling targeted protein degradation with nascent transcriptomics. The core regulators displayed narrow, largely non-overlapping direct transcriptional programs, forming a sparsely interconnected functional hierarchy stabilized by incoherent feed-forward loops. BET bromodomain and CDK7 inhibitors disrupted the core regulators' direct programs, acting as mixed agonists/antagonists. The network is predictive of dynamic gene expression behaviors in time-resolved assays and clinically relevant pathway activity in patient populations.

TRANSPARENT: a Python tool for designing transcription factor regulatory networks

Transcription factors are proteins able to selectively bind DNA short traits, namely transcription factors binding sites, in order to regulate gene expression in terms of both repression and activation. Despite plenty of studies focusing on transcription factors and on the role they play in specific biological tasks or diseases, is available in the literature, to our knowledge there is no tool able to automatically provide a list of transcription factors involved in this task and the associated interaction network through a solid computational analysis. TRANScriPtion fActor REgulatory NeTwork (TRANSPARENT) is a user-friendly Python tool designed to help researchers in studying given biological tasks or given diseases in human, by identifying transcription factors controlling and regulating the expression of genes associated with that task or disease. The tool takes in input a list of gene IDs and provides (1) a set of transcription factors that are significantly associated with the input genes, (2) the correspondent P values (i.e., the probability that this observed association was driven by chance) and (3) a transcription factor network that can be directly visualized through STRING database. The effectiveness and reliability of the tool were assessed by applying it to two different test cases: schizophrenia and autism disorders. The obtained results clearly show that identified TFs, for both datasets, are significantly associated with those disorders, in terms of both gene enrichment and coherence with the literature. The proposed tool TRANSPARENT can be a useful instrument to investigate transcription factor networks and unveil the role that TFs play in given biological tasks and diseases.

A synthetic transcription platform for programmable gene expression in mammalian cells

Precise, scalable, and sustainable control of genetic and cellular activities in mammalian cells is key to developing precision therapeutics and smart biomanufacturing. Here we create a highly tunable, modular, versatile CRISPR-based synthetic transcription system for the programmable control of gene expression and cellular phenotypes in mammalian cells. Genetic circuits consisting of well-characterized libraries of guide RNAs, binding motifs of synthetic operators, transcriptional activators, and additional genetic regulatory elements express mammalian genes in a highly predictable and tunable manner. We demonstrate the programmable control of reporter genes episomally and chromosomally, with up to 25-fold more activity than seen with the EF1α promoter, in multiple cell types. We use these circuits to program the secretion of human monoclonal antibodies and to control T-cell effector function marked by interferon-γ production. Antibody titers and interferon-γ concentrations significantly correlate with synthetic promoter strengths, providing a platform for programming gene expression and cellular function in diverse applications.

Regulation of cardiac ion channels by transcription factors: Looking for new opportunities of druggable targets for the treatment of arrhythmias

Cardiac electrical activity is governed by different ion channels that generate action potentials. Acquired or inherited abnormalities in the expression and/or function of ion channels usually result in electrophysiological changes that can cause cardiac arrhythmias. Transcription factors (TFs) control gene transcription by binding to specific DNA sequences adjacent to target genes. Linkage analysis, candidate-gene screening within families, and genome-wide association studies have linked rare and common genetic variants in the genes encoding TFs with genetically-determined cardiac arrhythmias. Besides its critical role in cardiac development, recent data demonstrated that they control cardiac electrical activity through the direct regulation of the expression and function of cardiac ion channels in adult hearts. This narrative review summarizes some studies showing functional data on regulation of the main human atrial and ventricular Na⁺, Ca²⁺, and K⁺ channels by cardiac TFs such as Pitx2c, Tbx20, Tbx5, Zfhx3, among others. The results have improved our understanding of the mechanisms regulating cardiac electrical activity and may open new avenues for therapeutic interventions in cardiac acquired or inherited arrhythmias through the identification of TFs as potential drug targets. Even though TFs have for a long time been considered as 'undruggable' targets, advances in structural biology have led to the identification of unique pockets in TFs amenable to be targeted with small-molecule drugs or peptides that are emerging as novel therapeutic drugs.

Molecular and cellular mechanisms that regulate human erythropoiesis

To enable effective oxygen transport, ∼200 billion red blood cells (RBCs) need to be produced every day in the bone marrow through the fine-tuned process of erythropoiesis. Erythropoiesis is regulated at multiple levels to ensure that defective RBC maturation or overproduction can be avoided. Here, we provide an overview of different layers of this control, ranging from cytokine signaling mechanisms that enable extrinsic regulation of RBC production to intrinsic transcriptional pathways necessary for effective erythropoiesis. Recent studies have also elucidated the importance of posttranscriptional regulation and highlighted additional gatekeeping mechanisms necessary for effective erythropoiesis. We additionally discuss the insights gained by studying human genetic variation affecting erythropoiesis and highlight the discovery of BCL11A as a regulator of hemoglobin switching through genetic studies. Finally, we provide an outlook of how our ability to measure multiple facets of this process at single-cell resolution, while accounting for the impact of human variation, will continue to refine our knowledge of erythropoiesis and how this process is perturbed in disease. As we learn more about this intricate and important process, additional opportunities to modulate erythropoiesis for therapeutic purposes will undoubtedly emerge.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

Common genetic aspects between COVID-19 and sarcoidosis: A network-based approach using gene expression data

The pandemic situation of novel coronavirus disease 2019 (COVID-19) is a global threat on our current planet, with its rapid spread and high mortality rate. Sarcoidosis patients are at high risk to COVID-19 severity for having lung injuries as well as treating with immunosuppressive agents. So, physicians are in dilemma whether they should use immunosuppressive agents or not for the patients with sarcoidosis history and COVID-19 infection. Therefore, common factors should be identified to provide effective treatment. For determining the common genes between COVID-19 and sarcoidosis, GSE164805 and GSE18781 were retrieved from the Gene Expression Omnibus (GEO) database. Common upregulated genes were identified by using R language to investigate their involved pathways and gene ontologies (GO). With the aid of the STRING Cytoscape plugin tool, protein-protein interactions (PPIs) network was constructed. From the PPIs network, Hub genes and essential modules were detected by using Cytohubba, and MCODE respectively. For hub genes, TFs, TFs-miRNA, and drug, interaction networks were built through the NetworkAnalyst web platform. A total of 34 common upregulated genes were identified and among them, five hub genes, including TET2, MUC5AC, VDR, NFE2L2, and BCL6 were determined. In addition, a cluster having VDR and NFE2L2 was detected from the PPIs network. Moreover, 32 transcription factors and 9 miRNA were recognized for hub genes. Furthermore, vitamin D and some of its analogous compounds were obtained from the drug interaction network. In conclusion, hub genes identified in this study might have potential roles in modulating COVID-19 infection and sarcoidosis. However, further studies are required to corroborate this study.

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Sarcoidosis patients are at high risk to COVID-19 severity.

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This study aimed to find out common genetic factors for COVID-19 and Sarcoidosis.

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34 common upregulated genes were identified from GSE164805 and GSE18781 datasets.

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From common upregulated genes, five hub genes, VDR, NFE2L2, BCL6, TET2, and MUC5AC, were recognized.

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Hub genes associated with miRNA, TFs, and drug molecules were also identified.

Disruption of a GATA2-TAL1-ERG regulatory circuit promotes erythroid transition in healthy and leukemic stem cells

Changes in gene regulation and expression govern orderly transitions from hematopoietic stem cells to terminally differentiated blood cell types. These transitions are disrupted during leukemic transformation, but knowledge of the gene regulatory changes underpinning this process is elusive. We hypothesized that identifying core gene regulatory networks in healthy hematopoietic and leukemic cells could provide insights into network alterations that perturb cell state transitions. A heptad of transcription factors (LYL1, TAL1, LMO2, FLI1, ERG, GATA2, and RUNX1) bind key hematopoietic genes in human CD34+ hematopoietic stem and progenitor cells (HSPCs) and have prognostic significance in acute myeloid leukemia (AML). These factors also form a densely interconnected circuit by binding combinatorially at their own, and each other's, regulatory elements. However, their mutual regulation during normal hematopoiesis and in AML cells, and how perturbation of their expression levels influences cell fate decisions remains unclear. In this study, we integrated bulk and single-cell data and found that the fully connected heptad circuit identified in healthy HSPCs persists, with only minor alterations in AML, and that chromatin accessibility at key heptad regulatory elements was predictive of cell identity in both healthy progenitors and leukemic cells. The heptad factors GATA2, TAL1, and ERG formed an integrated subcircuit that regulates stem cell-to-erythroid transition in both healthy and leukemic cells. Components of this triad could be manipulated to facilitate erythroid transition providing a proof of concept that such regulatory circuits can be harnessed to promote specific cell-type transitions and overcome dysregulated hematopoiesis.

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

Advances in promoter engineering: novel applications and predefined transcriptional control

Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.

A RUNX-CBFβ-driven enhancer directs the Irf8 dose-dependent lineage choice between DCs and monocytes

The transcription factor IRF8 is essential for the development of monocytes and dendritic cells (DCs), whereas it inhibits neutrophilic differentiation. It is unclear how Irf8 expression is regulated and how this single transcription factor supports the generation of both monocytes and DCs. Here, we identified a RUNX-CBFβ-driven enhancer 56 kb downstream of the Irf8 transcription start site. Deletion of this enhancer in vivo significantly decreased Irf8 expression throughout the myeloid lineage from the progenitor stages, thus resulting in loss of common DC progenitors and overproduction of Ly6C+ monocytes. We demonstrated that high, low or null expression of IRF8 in hematopoietic progenitor cells promotes differentiation toward type 1 conventional DCs, Ly6C+ monocytes or neutrophils, respectively, via epigenetic regulation of distinct sets of enhancers in cooperation with other transcription factors. Our results illustrate the mechanism through which IRF8 controls the lineage choice in a dose-dependent manner within the myeloid cell system.

Post-transcriptional control of cellular differentiation by the RNA exosome complex

Given the complexity of intracellular RNA ensembles and vast phenotypic remodeling intrinsic to cellular differentiation, it is instructive to consider the role of RNA regulatory machinery in controlling differentiation. Dynamic post-transcriptional regulation of protein-coding and non-coding transcripts is vital for establishing and maintaining proteomes that enable or oppose differentiation. By contrast to extensively studied transcriptional mechanisms governing differentiation, many questions remain unanswered regarding the involvement of post-transcriptional mechanisms. Through its catalytic activity to selectively process or degrade RNAs, the RNA exosome complex dictates the levels of RNAs comprising multiple RNA classes, thereby regulating chromatin structure, gene expression and differentiation. Although the RNA exosome would be expected to control diverse biological processes, studies to elucidate its biological functions and how it integrates into, or functions in parallel with, cell type-specific transcriptional mechanisms are in their infancy. Mechanistic analyses have demonstrated that the RNA exosome confers expression of a differentiation regulatory receptor tyrosine kinase, downregulates the telomerase RNA component TERC, confers genomic stability and promotes DNA repair, which have considerable physiological and pathological implications. In this review, we address how a broadly operational RNA regulatory complex interfaces with cell type-specific machinery to control cellular differentiation.

Epigenetic cell fate in Candida albicans is controlled by transcription factor condensates acting at super-enhancer-like elements

Cell identity in eukaryotes is controlled by transcriptional regulatory networks (TRNs) that define cell type-specific gene expression. In the opportunistic fungal pathogen Candida albicans, TRNs regulate epigenetic switching between two alternative cell states, ‘white’ and ‘opaque’, that exhibit distinct host interactions. Here, we reveal that the transcription factors (TFs) regulating cell identity contain prion-like domains (PrLDs) that enable liquid-liquid demixing and the formation of phase-separated condensates. Multiple white-opaque TFs can co-assemble into complex condensates as observed on single DNA molecules. Moreover, heterotypic interactions between PrLDs supports the assembly of multifactorial condensates at a synthetic locus within live eukaryotic cells. Mutation of the Wor1 PrLD revealed that substitution of acidic residues abolished its ability to phase separate and to co-recruit other TFs in live cells, as well as its function in C. albicans cell fate determination. Together, these studies reveal that PrLDs support the assembly of TF complexes that control fungal cell identity and highlight parallels with the ‘super-enhancers’ that regulate mammalian cell fate.

A new view of sexual differentiation of mammalian brain

Establishment of enduring sex differences in brain and behavior occurs during pre- or perinatal development, depending on species. For over 50 years the focus has been on gonadal steroid production by male fetuses and the impact on developing brain. An increasing awareness of the importance of sex chromosome complement has broadened the focus but identifying specific roles in development has yet to be achieved. Recent emphasis on transcriptomics has revealed myriad and unexpected differences in gene expression in specific regions of male and female brains which may produce sex differences, serve a compensatory role or provide latent sex differences revealed only in response to challenge. More surprising, however, has been the consistent observation of a central role for inflammatory signaling molecules and immune cells in masculinization of brain and behavior. The signal transduction pathways and specific immune cells vary by brain region, as does the neuroanatomical substrate subject to differentiation, reflecting substantial complexity emerging from what may be a common origin, the maternal immune system. A working hypothesis integrating these various ideas is proposed.

Microarray Analysis of Differential Gene Expression in Alzheimer's Disease Identifies Potential Biomarkers with Diagnostic Value

BACKGROUND Alzheimer disease (AD) is a common and fatal subtype of dementia that remains a challenge to diagnose and treat. This study aimed to identify potential biomarkers that influence the prognosis of AD. MATERIAL AND METHODS A total of 6 gene expression profiles from the Gene Expression Omnibus (GEO) database were assessed for their potential as AD biomarkers. We identified differentially expressed genes (DEGs) using the prediction analysis for microarray (PAM) algorithm and obtained hub genes through the analysis of the protein-protein interaction (PPI) network and module analysis. RESULTS We identified 6 gene expression profiles from the GEO database and assessed their potential as AD biomarkers. Shared gene sets were extracted and integrated into large expression profile matrices. We identified 2514 DEGs including 68 upregulated- and 2446 downregulated genes through analysis of the limma package. We screened 379 significant DEGs including 68 upregulated and 307 downregulated genes for their ability to distinguish AD from control samples using PAM algorithm. Functional enrichment of the 379 target genes was produced from Database for Annotation, Visualization and Integrated Discovery.(DAVID) and included histone function, beta receptor signaling, cell growth, and angiogenesis. The downregulated genes were significantly enriched in MAPK signaling, synaptic signaling, neuronal apoptosis and AD associated pathways. Upon analysis of the PPI network, 32 hub genes including ENO2, CCT2, CALM2, ACACB, ATP5B, MDH1, and PP2CA were screened. Of these hub genes, NFKBIA and ACACB were upregulated and 29 genes were downregulated in AD patients. CONCLUSIONS We screened 379 significant DEGs as potential biomarkers of AD using PAM and obtained 32 hub genes through PPI network and module analysis. These findings reveal new potential AD biomarkers with prognostic and therapeutic value.

Adaptive Landscape Shaped by Core Endogenous Network Coordinates Complex Early Progenitor Fate Commitments in Embryonic Pancreas

The classical development hierarchy of pancreatic cell fate commitments describes that multipotent progenitors (MPs) first bifurcate into tip cells and trunk cells, and then these cells give rise to acinar cells and endocrine/ductal cells separately. However, lineage tracings reveal that pancreatic progenitors are highly heterogeneous in tip and trunk domains in embryonic pancreas. The progenitor fate commitments from multipotency to unipotency during early pancreas development is insufficiently characterized. In pursuing a mechanistic understanding of the complexity in progenitor fate commitments, we construct a core endogenous network for pancreatic lineage decisions based on genetic regulations and quantified its intrinsic dynamic properties using dynamic modeling. The dynamics reveal a developmental landscape with high complexity that has not been clarified. Not only well-characterized pancreatic cells are reproduced, but also previously unrecognized progenitors-tip progenitor (TiP), trunk progenitor (TrP), later endocrine progenitor (LEP), and acinar progenitors (AciP/AciP2) are predicted. Further analyses show that TrP and LEP mediate endocrine lineage maturation, while TiP, AciP, AciP2 and TrP mediate acinar and ductal lineage maturation. The predicted cell fate commitments are validated by analyzing single-cell RNA sequencing (scRNA-seq) data. Significantly, this is the first time that a redefined hierarchy with detailed early pancreatic progenitor fate commitment is obtained.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Transcriptional networks in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex disease characterized by a diverse range of recurrent molecular aberrations that occur in many different combinations. Components of transcriptional networks are a common target of these aberrations, leading to network-wide changes and deployment of novel or developmentally inappropriate transcriptional programs. Genome-wide techniques are beginning to reveal the full complexity of normal hematopoietic stem cell transcriptional networks and the extent to which they are deregulated in AML, and new understandings of the mechanisms by which AML cells maintain self-renewal and block differentiation are starting to emerge. The hope is that increased understanding of the network architecture in AML will lead to identification of key oncogenic dependencies that are downstream of multiple network aberrations, and that this knowledge will be translated into new therapies that target these dependencies. Here, we review the current state of knowledge of network perturbation in AML with a focus on major mechanisms of transcription factor dysregulation, including mutation, translocation, and transcriptional dysregulation, and discuss how these perturbations propagate across transcriptional networks. We will also review emerging mechanisms of network disruption, and briefly discuss how increased knowledge of network disruption is already being used to develop new therapies.

HNF4 factors control chromatin accessibility and are redundantly required for maturation of the fetal intestine

As embryos mature, cells undergo remarkable transitions that are accompanied by shifts in transcription factor regulatory networks. Mechanisms driving developmental transitions are incompletely understood. The embryonic intestine transitions from a rapidly proliferating tube with pseudostratified epithelium prior to murine embryonic day (E) 14.5 to an exquisitely folded columnar epithelium in fetal stages. We sought to identify factors driving mouse fetal intestinal maturation by mining chromatin accessibility data for transcription factor motifs. ATAC-seq accessible regions shift during tissue maturation, with CDX2 transcription factor motifs abundant at chromatin-accessible regions of the embryo. Hepatocyte nuclear factor 4 (HNF4) transcription factor motifs are the most abundant in the fetal stages (>E16.5). Genetic inactivation of Hnf4a and its paralog Hnf4g revealed that HNF4 factors are redundantly required for fetal maturation. CDX2 binds to and activates Hnf4 gene loci to elevate HNF4 expression at fetal stages. HNF4 and CDX2 transcription factors then occupy shared genomic regulatory sites to promote chromatin accessibility and gene expression in the maturing intestine. Thus, HNF4 paralogs are key components of an intestinal transcription factor network shift during the embryonic to fetal transition.

Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

BACKGROUND & AIMS

Alterations of individual genes variably affect the development of hepatocellular carcinoma (HCC). Thus, we aimed to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRNs).

METHODS

Using data from The Cancer Genome Atlas, from the LIRI-JP (Liver Cancer - RIKEN, JP project), and from our transcriptomic, transfection and mouse transgenic experiments, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of β-catenin (CTNNB1). We further generated and validated a quantitative mathematical model of the GRN using human cell lines and in vivo expression data.

RESULTS

We found that LIN28B and CTNNB1 form a GRN with SMARCA4, Let-7b (MIRLET7B), SOX9, TP53 and MYC. GRN functionality is detected in HCC and gastrointestinal cancers, but not in other cancer types. GRN status negatively correlates with HCC prognosis, and positively correlates with hyperproliferation, dedifferentiation and HGF/MET pathway activation, suggesting that it contributes to a transcriptomic profile typical of the proliferative class of HCC. The mathematical model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal conditions, and irreversibly in HCC. The mathematical model is available via a web-based tool which can evaluate the GRN status of HCC samples and predict the impact of therapeutic agents on the GRN.

CONCLUSIONS

We conclude that identification and modelling of the GRN provide insights into the prognosis of HCC and the mechanisms by which tumor-promoting genes impact on HCC development.

LAY SUMMARY

Hepatocellular carcinoma (HCC) is a heterogeneous disease driven by the concomitant deregulation of several genes functionally organized as networks. Here, we identified a gene regulatory network involved in a subset of HCCs. This subset is characterized by increased proliferation and poor prognosis. We developed a mathematical model which uncovers the dynamics of the network and allows us to predict the impact of a therapeutic agent, not only on its specific target but on all the genes belonging to the network.

Expanded potential stem cell media as a tool to study human developmental hematopoiesis in vitro

Pluripotent stem cell (PSC) differentiation in vitro represents a powerful and tractable model to study mammalian development and an unlimited source of cells for regenerative medicine. Within hematology, in vitro PSC hematopoiesis affords novel insights into blood formation and represents an exciting potential approach to generate hematopoietic and immune cell types for transplantation and transfusion. Most studies to date have focused on in vitro hematopoiesis from mouse PSCs and human PSCs. However, differences in mouse and human PSC culture protocols have complicated the translation of discoveries between these systems. We recently developed a novel chemical media formulation, expanded potential stem cell medium (EPSCM), that maintains mouse PSCs in a unique cellular state and extraembryonic differentiation capacity. Herein, we describe how EPSCM can be directly used to stably maintain human PSCs. We further demonstrate that human PSCs maintained in EPSCM can spontaneously form embryoid bodies and undergo in vitro hematopoiesis using a simple differentiation protocol, similar to mouse PSC differentiation. EPSCM-maintained human PSCs generated at least two hematopoietic cell populations, which displayed distinct transcriptional profiles by RNA-sequencing (RNA-seq) analysis. EPSCM also supports gene targeting using homologous recombination, affording generation of an SPI1 (PU.1) reporter PSC line to study and track in vitro hematopoiesis. EPSCM therefore provides a useful tool not only to study pluripotency but also hematopoietic cell specification and developmental-lineage commitment.

A periodic table of cell types

Single cell biology is currently revolutionizing developmental and evolutionary biology, revealing new cell types and states in an impressive range of biological systems. With the accumulation of data, however, the field is grappling with a central unanswered question: what exactly is a cell type? This question is further complicated by the inherently dynamic nature of developmental processes. In this Hypothesis article, we propose that a 'periodic table of cell types' can be used as a framework for distinguishing cell types from cell states, in which the periods and groups correspond to developmental trajectories and stages along differentiation, respectively. The different states of the same cell type are further analogous to 'isotopes'. We also highlight how the concept of a periodic table of cell types could be useful for predicting new cell types and states, and for recognizing relationships between cell types throughout development and evolution.

An ERG Enhancer-Based Reporter Identifies Leukemia Cells with Elevated Leukemogenic Potential Driven by ERG-USP9X Feed-Forward Regulation

Acute leukemia is a rapidly progressing blood cancer with low survival rates. Unfavorable prognosis is attributed to insufficiently characterized subpopulations of leukemia stem cells (LSC) that drive chemoresistance and leukemia relapse. Here we utilized a genetic reporter that assesses stemness to enrich and functionally characterize LSCs. We observed heterogeneous activity of the ERG+85 enhancer-based fluorescent reporter in human leukemias. Cells with high reporter activity (tagBFP^High) exhibited elevated expression of stemness and chemoresistance genes and demonstrated increased clonogenicity and resistance to chemo- and radiotherapy as compared with their tagBFP^Neg counterparts. The tagBFP^High fraction was capable of regenerating the original cellular heterogeneity and demonstrated increased invasive ability. Moreover, the tagBFP^High fraction was enriched for leukemia-initiating cells in a xenograft assay. We identified the ubiquitin hydrolase USP9X as a novel ERG transcriptional target that sustains ERG+85-positive cells by controlling ERG ubiquitination. Therapeutic targeting of USP9X led to preferential inhibition of the ERG-dependent leukemias. Collectively, these results characterize human leukemia cell functional heterogeneity and suggest that targeting ERG via USP9X inhibition may be a potential treatment strategy in patients with leukemia. SIGNIFICANCE: This study couples a novel experimental tool with state-of-the-art approaches to delineate molecular mechanisms underlying stem cell-related characteristics in leukemia cells.

Identification of key transcription factors in endometrial cancer by systems bioinformatics analysis

Endometrial cancer (EC) is one of the most common malignant diseases worldwide. Although many studies have been performed on EC, a systems analysis between transcription factors (TFs) and EC relationship remains poorly characterized. Here, we present a systems bioinformatics analysis of TFs in EC patient samples to identify key TFs in EC. First, dysregulated and survival-related TFs were identified in EC using data from The Cancer Genome Atlas database and Gene Expression Omnibus. Second, we investigated the mechanisms of dysregulated TFs and tested whether their expression is correlated with prognosis of EC. Finally, we addressed new perspectives in EC biomarker research, including comprehensive knowledge of previously suggested candidate biomarkers in conjunction with novel mass spectrometry-based proteomic technologies with enhanced sensitivity and specificity not yet applied to EC studies, enabling a directed clinical perspective of the study design. Our study identified three promising TFs, E2F1, HMGA1, and PGR, which closely correlate with EC. Although treatments targeting TFs are not always efficient, these TFs may be useful as biomarkers for the diagnosis and prognosis of EC. Furthermore, we found that these dysregulated TFs and their target genes are primarily involved in the cell cycle and may promote endometrial carcinoma occurrence and development. Using integrated bioinformatic analysis, we identified candidate genes and pathways in EC, which could improve our understanding of the etiology and underlying molecular events of EC. Furthermore, these candidate genes and pathways could be therapeutic targets for EC.

A time-resolved proteomic analysis of transcription factors regulating adipogenesis of human adipose derived stem cells

Adipogenesis is one of the key processes during obesity development. Better understanding of this process could advance our knowledge on obesity and its treatment. Transcription factors (TFs) are master regulators during adipogenesis, however, a system-wide analysis of TFs dynamic proteome during adipogenesis is lacking. Here, we profiled 472 TFs and systematically elucidated their roles during the first 7 days of adipogenesis of human adipose-derived stem cells (hADSCs) on proteome scale. We identified two main and four sub-phases during adipogenesis. The commitment phase (0 h-8 h) mainly mediated stem cell proliferation, differentiation and chromatin remodeling, in which proteins of SWI/SNF family are the key centroid nodes. The determination phase (1D-7D) predominately regulated fat cell differentiation and response to lipid and oxygen, which could be associated with terminal differentiation of adipocyte and responsible for maturation. PPARγ, CREB1 and MYC are the centroid nodes of this phase. Remarkably, we identified and verified three TFs (BATF3, MAFF and MXD4) as novel regulators of adipogenesis, whose over-expression could inhibit adipogenesis of hADSCs in vitro. Overall, our study provided a valuable TFs resource to understand the complex process of adipogenesis.

Investigation of transcriptome mechanism associated with osteoporosis explored by microarray analysis

Microarray data of osteoporosis (OP) were analyzed based on prediction of transcription factors (TFs) or their targets as well as influences of TFs or TF network to uncover key TFs in OP. The microarray data E-GEOD-35956 was downloaded from the GPL570 platform. Differentially expressed genes (DEGs) with logarithm of fold change (|logFC|) >2 and P-value <0.05 were identified between OP samples and normal controls. TF genes were screened from the DEGs based on ITFP, Marbach 2016, TRRUST databases. TF targets were enriched from DEGs using Fisher's exact test. TF targets were selected based on their impact factors. TF targets were chosen from TF network analysis. Finally, key TFs were identified by based on TFs coverage. A total of 300 DEGs were obtained. There were no TF genes screened from the DEGs. In total 165, 87 and 178 TF targets were screened from DEGs respectively based on Fisher's exact test, influence of TFs or TF network analysis. According to the optimal TF set with TFs having maximum coverage of DEGs, 178 TF targets was the most. Thus, the optimal sets of TFs were FOXO1, KLF16, RXRA, RARA, HNF4A, CEBPB, ESR1, SOX8, ZNF219, and SP1. Altogether, these results suggested identified crucial TFs in OP might play a significant role in OP development, showing these key TFs probably would aid in unveiling the underlying molecular mechanisms and may be therapeutic targets, diagnostic or prognostic biomarkers for OP.

Transcription factor mutations as a cause of familial myeloid neoplasms

The initiation and evolution of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are driven by genomic events that disrupt multiple genes controlling hematopoiesis. Human genetic studies have discovered germline mutations in single genes that instigate familial MDS/AML. The best understood of these genes encode transcription factors, such as GATA-2, RUNX1, ETV6, and C/EBPα, which establish and maintain genetic networks governing the genesis and function of blood stem and progenitor cells. Many questions remain unanswered regarding how genes and circuits within these networks function in physiology and disease and whether network integrity is exquisitely sensitive to or efficiently buffered from perturbations. In familial MDS/AML, mutations change the coding sequence of a gene to generate a mutant protein with altered activity or introduce frameshifts or stop codons or disrupt regulatory elements to alter protein expression. Each mutation has the potential to exert quantitatively and qualitatively distinct influences on networks. Consistent with this mechanistic diversity, disease onset is unpredictable and phenotypic variability can be considerable. Efforts to elucidate mechanisms and forge prognostic and therapeutic strategies must therefore contend with a spectrum of patient-specific leukemogenic scenarios. Here we illustrate mechanistic advances in our understanding of familial MDS/AML syndromes caused by germline mutations of hematopoietic transcription factors.

A novel method for detecting the cellular stemness state in normal and leukemic human hematopoietic cells can predict disease outcome and drug sensitivity

Acute leukemia is an aggressive blood malignancy with low survival rates. A high expression of stem-like programs in leukemias predicts poor prognosis and is assumed to act in an aberrant fashion in the phenotypically heterogeneous leukemia stem cell (LSC) population. A lack of suitable genome engineering tools that can isolate LSCs based on their stemness precludes their comprehensive examination and full characterization. We hypothesized that tagging endogenous stemness-regulatory regions could generate a genome reporter for the putative leukemia stemness-state. Our analysis revealed that the ERG  + 85 enhancer region can serve as a marker for stemness-state and a fluorescent lentiviral reporter was developed that can accurately recapitulate the endogenous activity. Using our novel reporter, we revealed cellular heterogeneity in several leukemia cell lines and patient-derived samples. Alterations in reporter activity were associated with transcriptomic and functional changes that were closely related to the hematopoietic stem cell (HSC) identity. Notably, the differentiation potential was skewed towards the erythro-megakaryocytic lineage. Moreover, an ERG  + 85^High fraction of AML cells could regenerate the original cellular heterogeneity and was enriched for LSCs. RNA-seq analysis coupled with in silico drug-screen analysis identified 4HPR as an effective inhibitor of ERG  + 85^High leukemia growth. We propose that further utilization of our novel molecular tool will identify crucial determinants of LSCs, thus providing a rationale for their therapeutic targeting.

The hematopoietic stem cell diet

Hematopoietic stem cells (HSCs) are responsible for sustaining life-long blood formation or hematopoiesis and are also used clinically in a form of bone marrow transplantation, a curative cellular therapy for a range of hematological diseases. HSCs are maintained throughout adult life by a complex biological niche or microenvironment, which is thought to be composed of a range of cellular, molecular, and metabolic components. The metabolic components of the HSC niche have become of increasing interest over the past few years. It is now well-recognized that metabolic activity is intimately linked to HSC function, and dysregulation of these metabolic pathways result in hematological pathologies such as leukemia. Here, we review the recent progress in this field including our current understanding of the "dietary" requirements of HSCs and how nutrition influences HSC activity. These recent findings have suggested promising new metabolic approaches to improve clinical HSC transplantation and leukemia therapies.