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Recipes and mechanisms of cellular reprogramming: a case study on budding yeast Saccharomyces cerevisiae.

Ding S, Wang W - BMC Syst Biol (2011)

Bottom Line: A key challenge is to find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cells function in the same way as the natural cells.We present here a systems biology approach that allows systematic search for effective reprogramming recipes and monitoring the reprogramming progress to uncover the underlying mechanisms.As the heterogeneity of natural cells is important in many biological processes, we find that the extent of this heterogeneity restored by the reprogrammed cells varies significantly upon reprogramming recipes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA.

ABSTRACT

Background: Generation of induced pluripotent stem cells (iPSCs) and converting one cell type to another (transdifferentiation) by manipulating the expression of a small number of genes highlight the progress of cellular reprogramming, which holds great promise for regenerative medicine. A key challenge is to find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cells function in the same way as the natural cells.

Results: We present here a systems biology approach that allows systematic search for effective reprogramming recipes and monitoring the reprogramming progress to uncover the underlying mechanisms. Using budding yeast as a model system, we have curated a genetic network regulating cell cycle and sporulation. Phenotypic consequences of perturbations can be predicted from the network without any prior knowledge, which makes it possible to computationally reprogram cell fate. As the heterogeneity of natural cells is important in many biological processes, we find that the extent of this heterogeneity restored by the reprogrammed cells varies significantly upon reprogramming recipes. The heterogeneity difference between the reprogrammed and natural cells may have functional consequences.

Conclusions: Our study reveals that cellular reprogramming can be achieved by many different perturbations and the reprogrammability of a cell depends on the heterogeneity of the original cell state. We provide a general framework that can help discover new recipes for cellular reprogramming in human.

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Monitoring the reprogramming process. Green and red nodes/links represent wild type and reprogramming paths, respectively. States in the cell cycle biological path are colored in blue. States on the reprogramming path from the wild type attractor to the reprogrammed attractor are colored in pink. Cell cycle, sporulation and other attractors are colored in blue, red and black respectively. The width of the links is proportional to the transition flux. For the clarity of illustration, we only (A) plot 320 transitions between 254 states selected from 9213 transitions between 7191 states in the reprogramming from cell cycle to sporulation under the growth condition by the recipe of GCN5OE RDP3KD SUM1KD TUP1KD, (B) plot 350 transitions between 255 states selected from 14157 transitions between 11089 states in the reprogramming from sporulation to cell cycle under the sporulation condition by the recipe of IME1KDMIG1OEMSN4KDTUP1OE. (C) Example of reprogramming paths from the cell cycle attractors to the sporulation attractors under the growth condition. C1 and C2 are two different cell cycle attractors and S is a sporulation attractor. Each node represents a state on the reprogramming paths and the height reflects the potential in the landscape. See also Additional file 5, Table S5.
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Figure 6: Monitoring the reprogramming process. Green and red nodes/links represent wild type and reprogramming paths, respectively. States in the cell cycle biological path are colored in blue. States on the reprogramming path from the wild type attractor to the reprogrammed attractor are colored in pink. Cell cycle, sporulation and other attractors are colored in blue, red and black respectively. The width of the links is proportional to the transition flux. For the clarity of illustration, we only (A) plot 320 transitions between 254 states selected from 9213 transitions between 7191 states in the reprogramming from cell cycle to sporulation under the growth condition by the recipe of GCN5OE RDP3KD SUM1KD TUP1KD, (B) plot 350 transitions between 255 states selected from 14157 transitions between 11089 states in the reprogramming from sporulation to cell cycle under the sporulation condition by the recipe of IME1KDMIG1OEMSN4KDTUP1OE. (C) Example of reprogramming paths from the cell cycle attractors to the sporulation attractors under the growth condition. C1 and C2 are two different cell cycle attractors and S is a sporulation attractor. Each node represents a state on the reprogramming paths and the height reflects the potential in the landscape. See also Additional file 5, Table S5.

Mentions: To illustrate how the reprogramming proceeds, we monitored the cell state transition for the wild-type and reprogrammed cells. First, under, for example, growth condition we randomly sampled states from the basin of a given attractor state (the root node). Second, we evolved all the states in the previous step under the reprogramming perturbations and added all new states on the evolving paths to this state-transition graph. Figure 6A and 6B show two examples of the state-transitions for both the wild-type and reprogrammed cells. It is obvious that the state-evolving paths are significantly altered by reprogramming perturbations and some nodes act as converging transition states (see below). Exactly as the landscape concept suggests, there are many transition routes between the cell cycle and sporulation attractors.


Recipes and mechanisms of cellular reprogramming: a case study on budding yeast Saccharomyces cerevisiae.

Ding S, Wang W - BMC Syst Biol (2011)

Monitoring the reprogramming process. Green and red nodes/links represent wild type and reprogramming paths, respectively. States in the cell cycle biological path are colored in blue. States on the reprogramming path from the wild type attractor to the reprogrammed attractor are colored in pink. Cell cycle, sporulation and other attractors are colored in blue, red and black respectively. The width of the links is proportional to the transition flux. For the clarity of illustration, we only (A) plot 320 transitions between 254 states selected from 9213 transitions between 7191 states in the reprogramming from cell cycle to sporulation under the growth condition by the recipe of GCN5OE RDP3KD SUM1KD TUP1KD, (B) plot 350 transitions between 255 states selected from 14157 transitions between 11089 states in the reprogramming from sporulation to cell cycle under the sporulation condition by the recipe of IME1KDMIG1OEMSN4KDTUP1OE. (C) Example of reprogramming paths from the cell cycle attractors to the sporulation attractors under the growth condition. C1 and C2 are two different cell cycle attractors and S is a sporulation attractor. Each node represents a state on the reprogramming paths and the height reflects the potential in the landscape. See also Additional file 5, Table S5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094211&req=5

Figure 6: Monitoring the reprogramming process. Green and red nodes/links represent wild type and reprogramming paths, respectively. States in the cell cycle biological path are colored in blue. States on the reprogramming path from the wild type attractor to the reprogrammed attractor are colored in pink. Cell cycle, sporulation and other attractors are colored in blue, red and black respectively. The width of the links is proportional to the transition flux. For the clarity of illustration, we only (A) plot 320 transitions between 254 states selected from 9213 transitions between 7191 states in the reprogramming from cell cycle to sporulation under the growth condition by the recipe of GCN5OE RDP3KD SUM1KD TUP1KD, (B) plot 350 transitions between 255 states selected from 14157 transitions between 11089 states in the reprogramming from sporulation to cell cycle under the sporulation condition by the recipe of IME1KDMIG1OEMSN4KDTUP1OE. (C) Example of reprogramming paths from the cell cycle attractors to the sporulation attractors under the growth condition. C1 and C2 are two different cell cycle attractors and S is a sporulation attractor. Each node represents a state on the reprogramming paths and the height reflects the potential in the landscape. See also Additional file 5, Table S5.
Mentions: To illustrate how the reprogramming proceeds, we monitored the cell state transition for the wild-type and reprogrammed cells. First, under, for example, growth condition we randomly sampled states from the basin of a given attractor state (the root node). Second, we evolved all the states in the previous step under the reprogramming perturbations and added all new states on the evolving paths to this state-transition graph. Figure 6A and 6B show two examples of the state-transitions for both the wild-type and reprogrammed cells. It is obvious that the state-evolving paths are significantly altered by reprogramming perturbations and some nodes act as converging transition states (see below). Exactly as the landscape concept suggests, there are many transition routes between the cell cycle and sporulation attractors.

Bottom Line: A key challenge is to find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cells function in the same way as the natural cells.We present here a systems biology approach that allows systematic search for effective reprogramming recipes and monitoring the reprogramming progress to uncover the underlying mechanisms.As the heterogeneity of natural cells is important in many biological processes, we find that the extent of this heterogeneity restored by the reprogrammed cells varies significantly upon reprogramming recipes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA.

ABSTRACT

Background: Generation of induced pluripotent stem cells (iPSCs) and converting one cell type to another (transdifferentiation) by manipulating the expression of a small number of genes highlight the progress of cellular reprogramming, which holds great promise for regenerative medicine. A key challenge is to find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cells function in the same way as the natural cells.

Results: We present here a systems biology approach that allows systematic search for effective reprogramming recipes and monitoring the reprogramming progress to uncover the underlying mechanisms. Using budding yeast as a model system, we have curated a genetic network regulating cell cycle and sporulation. Phenotypic consequences of perturbations can be predicted from the network without any prior knowledge, which makes it possible to computationally reprogram cell fate. As the heterogeneity of natural cells is important in many biological processes, we find that the extent of this heterogeneity restored by the reprogrammed cells varies significantly upon reprogramming recipes. The heterogeneity difference between the reprogrammed and natural cells may have functional consequences.

Conclusions: Our study reveals that cellular reprogramming can be achieved by many different perturbations and the reprogrammability of a cell depends on the heterogeneity of the original cell state. We provide a general framework that can help discover new recipes for cellular reprogramming in human.

Show MeSH
Related in: MedlinePlus