| Literature DB >> 33854418 |
Amrita A Iyer1,2, Andrew K Groves1,2,3.
Abstract
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.Entities:
Keywords: hair cell; inner ear; pioneer factor; regeneration; reprogramming; transcription factors
Year: 2021 PMID: 33854418 PMCID: PMC8039129 DOI: 10.3389/fncel.2021.660748
Source DB: PubMed Journal: Front Cell Neurosci ISSN: 1662-5102 Impact factor: 5.505
Computational approaches developed to predict transcription factor/s (TF) suitable for reprogramming one somatic cell type to another.
| No. | Model type | Approach incorporated | Validation status | Reference |
|---|---|---|---|---|
| 1. | Expression reversal based | Data-driven approach. Representation and analysis of gene expression data as gene pairs. Identification of each gene’s strength in cell type reversal based on calculated normalizations. | No new experimental validation available | Heinäniemi et al. ( |
| 2. | Polycomb repression TF model | A data-driven approach using ChIP seq and RNA seq data. The model predicts that all those TFs strongly polycomb repressed in the source cell and highly expressed in target cells are reprogramming factors for that cell pair. | No new experimental validation available | Davis and Eddy ( |
| 3. | TF Cross repression model | The model predicts the reprogramming effect of unique gene set perturbations based on their influence on the stability of cell fate-specific gene networks. No prior knowledge of candidate genes/pathways was considered. | No new experimental validation available | Crespo and del Sol ( |
| 4. | Epigenetic landscape mathematical model | Employing 63 cell fates and 1337 TFs from mouse microarray gene expression data, a predictive epigenetic model was built to identify hybrid cell fates, known reprogramming factors, new factors that could reprogram specific cell types. | No new experimental validation available. | Lang et al. ( |
| 5. | CellNet | Gene regulatory network-based approach to compare engineered cells to target cells. New reprogramming factors were identified to uncover transitionary cellular programs and enhance the quality of engineered cells to mimic target cells. | CellNet results were tested on the conversion of B cells into macrophages. A new intestinal program was identified and fine-tuned in mouse fibroblasts reprogrammed to hepatic cells. | Morris et al. ( |
| 6. | Candidate core TF atlas | An entropy-based method used to identify and build an atlas of candidate core TFs across a range of human cell types. | Results obtained from this model were tested on the conversion of human fibroblasts into induced retinal pigment epithelial-like cells. | D’Alessio et al. ( |
| 7. | Mogrify | Integration of gene expression data and regulatory network information to predict reprogramming factors. A method applicable to diverse sets of TFs and cell types. | Results tested on the induction of keratinocytes from dermal fibroblasts, induction of microvascular endothelial cells from keratinocytes. | Rackham et al. ( |
| 8. | Stem cell differentiation model | Exclusive stem cell differentiation factor prediction model based on gene regulatory networks. | Results tested on neural stem cells. Overexpression of RUNX2 and ESR1 reprogrammed neural stem cells to neuronal and astrocyte cell fate, respectively. | Okawa et al. ( |
Figure 1Schematic cross-sectional view of the postnatal mammalian organ of Corti, denoting some structural features and a variety of cell types of interest for in vivo hair cell reprogramming.
Figure 2A summary of some current in vitro (A) and in vivo (B) reprogramming studies employing overexpression of different transcription factor combinations.