Transcription factors that convert adult cell identity are differentially polycomb repressed

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.

Transcription Factor Reprogramming in the Inner Ear: Turning on Cell Fate Switches to Regenerate Sensory Hair Cells

Non-mammalian vertebrates can restore their auditory and vestibular hair cells naturally by triggering the regeneration of adjacent supporting cells. The transcription factor ATOH1 is a key regulator of hair cell development and regeneration in the inner ear. Following the death of hair cells, supporting cells upregulate ATOH1 and give rise to new hair cells. However, in the mature mammalian cochlea, such natural regeneration of hair cells is largely absent. Transcription factor reprogramming has been used in many tissues to convert one cell type into another, with the long-term hope of achieving tissue regeneration. Reprogramming transcription factors work by altering the transcriptomic and epigenetic landscapes in a target cell, resulting in a fate change to the desired cell type. Several studies have shown that ATOH1 is capable of reprogramming cochlear non-sensory tissue into cells resembling hair cells in young animals. However, the reprogramming ability of ATOH1 is lost with age, implying that the potency of individual hair cell-specific transcription factors may be reduced or lost over time by mechanisms that are still not clear. To circumvent this, combinations of key hair cell transcription factors have been used to promote hair cell regeneration in older animals. In this review, we summarize recent findings that have identified and studied these reprogramming factor combinations for hair cell regeneration. Finally, we discuss the important questions that emerge from these findings, particularly the feasibility of therapeutic strategies using reprogramming factors to restore human hearing in the future.

A computer-guided design tool to increase the efficiency of cellular conversions

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.

H3K27me3 Depletion during Differentiation Promotes Myogenic Transcription in Porcine Satellite Cells

BACKGROUND

Porcine skeletal muscle satellite cells play important roles in myogenesis and muscle regeneration. Integrated analysis of transcriptome and histone modifications would reveal epigenomic roles in promoting myogenic differentiation in swine.

METHODS

Porcine satellite cells (PSCs) were isolated and in-vitro cultured from newborn piglets. RNA Sequencing (RNA-Seq) and Chromatin Immunoprecipitation Sequencing (ChIP-Seq) experiments were performed using proliferating cells and terminal myotubes in order to interrogate the transcriptomic profiles, as well as the distribution of histone markers-H3K4me3, H3K27me3, and H3K27ac-and RNA polymerase II.

RESULTS

The study identified 917 differentially expressed genes during cell differentiation. The landscape of epigenetic marks was displayed on a genome-wide scale, which had globally shrunken. H3K27me3 reinforcement participated in obstructing the transcription of proliferation-related genes, while its depletion was closely related to the up-regulation of myogenic genes. Furthermore, the degree of H3K27me3 modification was dramatically reduced by 50%, and 139 myogenic genes were upregulated to promote cell differentiation.

CONCLUSIONS

The depletion of H3K27me3 was shown to promote porcine satellite cell differentiation through upregulating the transcription level of myogenic genes. Our findings in this study provide new insights of the epigenomic mechanisms occurring during myogenic differentiation, and shed light on chromatin states and the dynamics underlying myogenesis.

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.

Computational methods for direct cell conversion

Directed cell conversion (or transdifferentiation) of one somatic cell-type to another can be achieved by ectopic expression of a set of transcription factors. Since the experimental identification of transcription factors for transdifferentiation is extremely time-consuming and expensive, there are still relatively few transdifferentiations achieved in comparison to the number of human cell-types. However, the growing volume of transcriptional data available and the recent introduction of data-driven algorithmic approaches that predict factors for transdifferentiation holds great promise for accelerating this field. Here we review those computational methods whose in-silico predictions have been experimentally validated, highlighting differences and similarities. Our analysis reveals that the factors predicted by each method tend to be different due to varying source cells used, gene expression quantification and algorithmic steps. We show these differences have an impact on the regulatory influences downstream, with some methods favoring transcription factors regulating developmental progression and others favoring factors regulating mature cell processes. These computational approaches offer a starting point to predict and test novel factors for transdifferentiation. We argue that collecting high-quality gene expression data from single-cells or pure cell-populations across a broader set of cell-types would be necessary to improve the quality and consistency of the in-silico predictions.

Computational Tools for Stem Cell Biology

For over half a century, the field of developmental biology has leveraged computation to explore mechanisms of developmental processes. More recently, computational approaches have been critical in the translation of high throughput data into knowledge of both developmental and stem cell biology. In the past several years, a new subdiscipline of computational stem cell biology has emerged that synthesizes the modeling of systems-level aspects of stem cells with high-throughput molecular data. In this review, we provide an overview of this new field and pay particular attention to the impact that single cell transcriptomics is expected to have on our understanding of development and our ability to engineer cell fate.

A Systematic Approach to Identify Candidate Transcription Factors that Control Cell Identity

Hundreds of transcription factors (TFs) are expressed in each cell type, but cell identity can be induced through the activity of just a small number of core TFs. Systematic identification of these core TFs for a wide variety of cell types is currently lacking and would establish a foundation for understanding the transcriptional control of cell identity in development, disease, and cell-based therapy. Here, we describe a computational approach that generates an atlas of candidate core TFs for a broad spectrum of human cells. The potential impact of the atlas was demonstrated via cellular reprogramming efforts where candidate core TFs proved capable of converting human fibroblasts to retinal pigment epithelial-like cells. These results suggest that candidate core TFs from the atlas will prove a useful starting point for studying transcriptional control of cell identity and reprogramming in many human cell types.

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Core transcription factors (TFs) are predicted for >200 cell types/tissues

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Predicted TFs for retinal pigment epithelial (RPE) cells can reprogram fibroblasts

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These reprogrammed RPE-like cells are functionally similar to primary RPE

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The sets of predicted factors may facilitate studies of control of cell identity

Biological computational approaches: new hopes to improve (re)programming robustness, regenerative medicine and cancer therapeutics

Hundreds of transcription factors (TFs) are expressed and work in each cell type, but the identity of the cells is defined and maintained through the activity of a small number of core TFs. Existing reprogramming strategies predominantly focus on the ectopic expression of core TFs of an intended fate in a given cell type regardless of the state of native/somatic gene regulatory networks (GRNs) of the starting cells. Interestingly, an important point is that how much products of the reprogramming, transdifferentiation and differentiation (programming) are identical to their in vivo counterparts. There is evidence that shows that direct fate conversions of somatic cells are not complete, with target cell identity not fully achieved. Manipulation of core TFs provides a powerful tool for engineering cell fate in terms of extinguishment of native GRNs, the establishment of a new GRN, and preventing installation of aberrant GRNs. Conventionally, core TFs are selected to convert one cell type into another mostly based on literature and the experimental identification of genes that are differentially expressed in one cell type compared to the specific cell types. Currently, there is not a universal standard strategy for identifying candidate core TFs. Remarkably, several biological computational platforms are developed, which are capable of evaluating the fidelity of reprogramming methods and refining existing protocols. The current review discusses some deficiencies of reprogramming technologies in the production of a pure population of authentic target cells. Furthermore, it reviews the role of computational approaches (e.g. CellNet, KeyGenes, Mogrify, etc.) in improving (re)programming methods and consequently in regenerative medicine and cancer therapeutics.

A predictive computational framework for direct reprogramming between human cell types

Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion. We have applied Mogrify to 173 human cell types and 134 tissues, defining an atlas of cellular reprogramming. Mogrify correctly predicts the transcription factors used in known transdifferentiations. Furthermore, we validated two new transdifferentiations predicted by Mogrify. We provide a practical and efficient mechanism for systematically implementing novel cell conversions, facilitating the generalization of reprogramming of human cells. Predictions are made available to help rapidly further the field of cell conversion.

CEBPA exerts a specific and biologically important proapoptotic role in pancreatic β cells through its downstream network targets

Transcription factor CEBPA has been widely studied for its involvement in hematopoietic cell differentiation and causal role in hematological malignancies. It is shown for the first time that CEBPA also has a causal role in cytokine-induced apoptosis of pancreas β cells.

Transcription factor CEBPA has been widely studied for its involvement in hematopoietic cell differentiation and causal role in hematological malignancies. We demonstrate here that it also performs a causal role in cytokine-induced apoptosis of pancreas β cells. Treatment of two mouse pancreatic α and β cell lines (αTC1-6 and βTC1) with proinflammatory cytokines IL-1β, IFN-γ, and TNF-α at doses that specifically induce apoptosis of βTC1 significantly increased the amount of mRNA and protein encoded by Cebpa and its proapoptotic targets, Arl6ip5 and Tnfrsf10b, in βTC1 but not in αTC1-6. Cebpa knockdown in βTC1 significantly decreased cytokine-induced apoptosis, together with the amount of Arl6ip5 and Tnfrsf10b. Analysis of the network comprising CEBPA, its targets, their first interactants, and proteins encoded by genes known to regulate cytokine-induced apoptosis in pancreatic β cells (genes from the apoptotic machinery and from MAPK and NFkB pathways) revealed that CEBPA, ARL6IP5, TNFRSF10B, TRAF2, and UBC are the top five central nodes. In silico analysis further suggests TRAF2 as trait d'union node between CEBPA and the NFkB pathway. Our results strongly suggest that Cebpa is a key regulator within the apoptotic network activated in pancreatic β cells during insulitis, and Arl6ip5, Tnfrsf10b, Traf2, and Ubc are key executioners of this program.