Digital Reprogramming Decodes Epigenetic Barriers of Cell Fate Changes Article Swipe
Ana Janeva
,
Christopher A. Penfold
,
Sara Llorente-Armijo
,
Huiwen Li
,
Tomáš Zikmund
,
Marco Stock
,
Jérôme Jullien
,
Tobias Straub
,
Ignasi Forné
,
Axel Imhof
,
Juan M. Vaquerizas
,
J. B. Gurdon
,
Eva Hörmanseder
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.01.22.634227
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.01.22.634227
Fates of differentiated cells in our body can be induced to change by nuclear reprogramming. In this way, cells valuable for therapeutic purposes and disease modeling can be produced. However, the efficiency of this process is low, partly due to the properties of somatic donor nuclei which stabilize their differentiated fate but also act as barriers reprogramming-associated cell fate changes. The identity of these reprogramming barriers is not fully understood. Here, we developed an artificial intelligence-based approach to model nuclear reprogramming and used it to identify the chromatin modification H3K27ac as a novel epigenetic barrier to reprogramming-induced cell fate changes. Using reprogramming by nuclear transfer (NT) to eggs of Xenopus laevis as a model system, we profiled chromatin modifications in different cell types alongside gene expression patterns before and after reprogramming. Our model integrated the data to accurately predict reprogramming outcomes on a transcriptome level. By leveraging model predictions, we find that genes resisting inactivation during reprogramming display specific chromatin modification barcodes, including the known reprogramming barrier H3K4me3 alongside a novel candidate barrier, H3K27ac. Accordingly, reducing H3K27ac levels using p300/CBP inhibitors before reprogramming led to an improved downregulation of genes linked to H3K27ac-modified enhancers after reprogramming. Importantly, these effects were accompanied by improved embryonic development of the resulting nuclear transfer embryos. In summary, our study developed Digital Reprogramming, an artificial intelligence approach capable of predicting resistance to cell-fate reprogramming, which led to the identification of H3K27ac as a critical barrier to reprogramming-associated cell-fate changes. Keywords: machine learning, predictive modeling, SCNT, nuclear reprogramming, H3K27ac, p300/CBP, epigenetic memory, cell fate stability.
Related Topics
Concepts
Reprogramming
Decodes
Epigenetics
Cell biology
Cell fate determination
Cell
Biology
Computer science
Genetics
Decoding methods
Telecommunications
Transcription factor
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.01.22.634227
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406787255
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406787255Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.01.22.634227Digital Object Identifier
- Title
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Digital Reprogramming Decodes Epigenetic Barriers of Cell Fate ChangesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-24Full publication date if available
- Authors
-
Ana Janeva, Christopher A. Penfold, Sara Llorente-Armijo, Huiwen Li, Tomáš Zikmund, Marco Stock, Jérôme Jullien, Tobias Straub, Ignasi Forné, Axel Imhof, Juan M. Vaquerizas, J. B. Gurdon, Eva HörmansederList of authors in order
- Landing page
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https://doi.org/10.1101/2025.01.22.634227Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://doi.org/10.1101/2025.01.22.634227Direct OA link when available
- Concepts
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Reprogramming, Decodes, Epigenetics, Cell biology, Cell fate determination, Cell, Biology, Computer science, Genetics, Decoding methods, Telecommunications, Transcription factor, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effects | 198 |
| abstract_inverted_index.induced | 9 |
| abstract_inverted_index.machine | 245 |
| abstract_inverted_index.memory, | 255 |
| abstract_inverted_index.nuclear | 13, 79, 103, 208, 250 |
| abstract_inverted_index.predict | 138 |
| abstract_inverted_index.process | 34 |
| abstract_inverted_index.somatic | 43 |
| abstract_inverted_index.system, | 114 |
| abstract_inverted_index.H3K27ac, | 252 |
| abstract_inverted_index.H3K27ac. | 173 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.approach | 76, 221 |
| abstract_inverted_index.barrier, | 172 |
| abstract_inverted_index.barriers | 55, 65 |
| abstract_inverted_index.changes. | 59, 99, 243 |
| abstract_inverted_index.critical | 238 |
| abstract_inverted_index.embryos. | 210 |
| abstract_inverted_index.identify | 85 |
| abstract_inverted_index.identity | 61 |
| abstract_inverted_index.improved | 186, 202 |
| abstract_inverted_index.modeling | 25 |
| abstract_inverted_index.outcomes | 140 |
| abstract_inverted_index.p300/CBP | 179 |
| abstract_inverted_index.patterns | 126 |
| abstract_inverted_index.profiled | 116 |
| abstract_inverted_index.purposes | 22 |
| abstract_inverted_index.reducing | 175 |
| abstract_inverted_index.specific | 158 |
| abstract_inverted_index.summary, | 212 |
| abstract_inverted_index.transfer | 104, 209 |
| abstract_inverted_index.valuable | 19 |
| abstract_inverted_index.Keywords: | 244 |
| abstract_inverted_index.alongside | 123, 168 |
| abstract_inverted_index.barcodes, | 161 |
| abstract_inverted_index.candidate | 171 |
| abstract_inverted_index.cell-fate | 227, 242 |
| abstract_inverted_index.chromatin | 87, 117, 159 |
| abstract_inverted_index.developed | 72, 215 |
| abstract_inverted_index.different | 120 |
| abstract_inverted_index.embryonic | 203 |
| abstract_inverted_index.enhancers | 193 |
| abstract_inverted_index.including | 162 |
| abstract_inverted_index.learning, | 246 |
| abstract_inverted_index.modeling, | 248 |
| abstract_inverted_index.p300/CBP, | 253 |
| abstract_inverted_index.produced. | 28 |
| abstract_inverted_index.resisting | 153 |
| abstract_inverted_index.resulting | 207 |
| abstract_inverted_index.stabilize | 47 |
| abstract_inverted_index.accurately | 137 |
| abstract_inverted_index.artificial | 74, 219 |
| abstract_inverted_index.efficiency | 31 |
| abstract_inverted_index.epigenetic | 93, 254 |
| abstract_inverted_index.expression | 125 |
| abstract_inverted_index.inhibitors | 180 |
| abstract_inverted_index.integrated | 133 |
| abstract_inverted_index.leveraging | 146 |
| abstract_inverted_index.predicting | 224 |
| abstract_inverted_index.predictive | 247 |
| abstract_inverted_index.properties | 41 |
| abstract_inverted_index.resistance | 225 |
| abstract_inverted_index.stability. | 258 |
| abstract_inverted_index.accompanied | 200 |
| abstract_inverted_index.development | 204 |
| abstract_inverted_index.therapeutic | 21 |
| abstract_inverted_index.understood. | 69 |
| abstract_inverted_index.Accordingly, | 174 |
| abstract_inverted_index.Importantly, | 196 |
| abstract_inverted_index.inactivation | 154 |
| abstract_inverted_index.intelligence | 220 |
| abstract_inverted_index.modification | 88, 160 |
| abstract_inverted_index.predictions, | 148 |
| abstract_inverted_index.modifications | 118 |
| abstract_inverted_index.reprogramming | 64, 80, 101, 139, 156, 165, 182 |
| abstract_inverted_index.transcriptome | 143 |
| abstract_inverted_index.Reprogramming, | 217 |
| abstract_inverted_index.differentiated | 2, 49 |
| abstract_inverted_index.downregulation | 187 |
| abstract_inverted_index.identification | 233 |
| abstract_inverted_index.reprogramming, | 228, 251 |
| abstract_inverted_index.reprogramming. | 14, 130, 195 |
| abstract_inverted_index.H3K27ac-modified | 192 |
| abstract_inverted_index.intelligence-based | 75 |
| abstract_inverted_index.reprogramming-induced | 96 |
| abstract_inverted_index.reprogramming-associated | 56, 241 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile.value | 0.02032043 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |