AsianScientist (Jan. 28, 2016) – An international team of researchers from the Duke-NUS Medical School (Duke-NUS), the University of Bristol, Monash University and RIKEN have developed an algorithm that can predict the transcription factors required to convert one human cell type to another without going through a pluripotent state.
These findings, published in the journal Nature Genetics, could have significant implications for regenerative medicine, and paves the way for further research into cell reprogramming.
It is known that cell types are not fixed and that one cell type can be reprogrammed, or converted, to become another cell type by the addition of transcription factors, proteins involved in the process of converting, or transcribing, DNA into RNA. This approach was brought to the fore by Shinya Yamanaka, whose Nobel prize-winning work involved the reprogramming of fibroblast cells from the skin to induced pluripotent stem cells (iPSCs).
In theory, iPS could then be directly reprogrammed to become, for instance, retinal cells that could help treat macular degeneration, an eye condition which leads to vision loss. In practice, however, there are technical and safety concerns with this approach of cell conversion, due to the accumulation of cancerous mutations in the reprogrammed cells.
In addition, determining the unique set of transcription factors that require manipulation for each cell conversion is a long and costly process. As a result, this first step of identifying the key set of transcription factors for cell conversion is the major problem researchers and doctors face in the field of cell reprogramming.
In order to overcome this obstacle, Senior Research Fellow from the Systems Genetics of Complex Disease Laboratory at Duke-NUS, Dr. Owen Rackham, worked for five years to develop a computational algorithm to predict the transcription factors for cell conversions. The algorithm, called Mogrify, is able to predict the optimal set of transcription factors required for any given cell conversion.
During tests, Mogrify was able to accurately predict the set of transcription factors required for previously published cell conversions. To further confirm Mogrify’s predictive ability, the team conducted two cell conversions in the laboratory using human cells, and these were successful in both attempts—solely using the predictions of Mogrify.
“Mogrify acts like a ‘world atlas’ for the cell and allows us to map out new territories in cell conversions in humans,” explained Dr. Rackham.
“One of the first clinical applications that we hope to achieve with this innovative approach would be to reprogram ‘defective’ cells from patients into ‘functioning’ healthy cells, without the intermediate iPS step. These then can be re-implanted into patients, and should, in practice, effectively enable new regenerative medicine techniques.”
Co-author Associate Professor Enrico Petretto highlighted that since Mogrify is completely data-driven, its robustness and accuracy will only continue to improve as more comprehensive data are collected and fed into the framework.
“Mogrify is a game-changing method that leverages big-data and systems-biology; this will inspire new translational applications as the result of the work and expertise here at Duke-NUS,” said Petretto.
Mogrify has been made available online for other researchers and scientists. The team at Duke-NUS now plan to focus on Mogrify’s application in translational medicine. Collaborative efforts between research groups within Duke-NUS are already in place to apply the algorithm to help develop treatments for specific diseases, such as cancer.
The article can be found at: Rackham et al. (2016) A Predictive Computational Framework for Direct Reprogramming Between Human Cell Types.
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Source: Duke-NUS Graduate Medical School Singapore.
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