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Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.

Dowell KG, Simons AK, Wang ZZ, Yun K, Hibbs MA - PLoS ONE (2013)

Bottom Line: We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination.Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant.Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies.

View Article: PubMed Central - PubMed

Affiliation: The Jackson Laboratory, Bar Harbor, Maine, USA.

ABSTRACT
Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.

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Data Visualization for Mining mESC Self-Renewal Gene Predictions.A. Views of Tdh-centric networks created using our StemSight Scout visualization tool, available at StemSight.org. Adjusting Scout network views to display only edges with inference scores of 0.5 and 0.9997 show that the novel gene Tdh is tightly connected to many well-known self-renewal genes in our training gold standard, including Pou5f1, Sox2, Nanog, Nr0B1, and Phc1. B. Supporting edge info for the Tdh – Pou5f1 edge. Supporting edge information shows that this edge is supported by several protein-DNA interaction (PDI) assays as well as gene expression datasets from a study investigating mESC cell differentiation in different mESC cell lines. For supporting edge detail between Tdh and other gold standard genes, see Figure S5 or explore the Tdh interactome online at StemSight.org/scout. C. SPELL for StemSight Search Results. From a supporting edge information window, you can drill down to the individual gene expression levels in microarray datasets. This view shows how expression data reveals rank-ordered correlations observed between Tdh and gold standard genes Gbf3, Fbxo15, Nr0b1, Phc1, Pou5f1, and Sox2.
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pone-0056810-g004: Data Visualization for Mining mESC Self-Renewal Gene Predictions.A. Views of Tdh-centric networks created using our StemSight Scout visualization tool, available at StemSight.org. Adjusting Scout network views to display only edges with inference scores of 0.5 and 0.9997 show that the novel gene Tdh is tightly connected to many well-known self-renewal genes in our training gold standard, including Pou5f1, Sox2, Nanog, Nr0B1, and Phc1. B. Supporting edge info for the Tdh – Pou5f1 edge. Supporting edge information shows that this edge is supported by several protein-DNA interaction (PDI) assays as well as gene expression datasets from a study investigating mESC cell differentiation in different mESC cell lines. For supporting edge detail between Tdh and other gold standard genes, see Figure S5 or explore the Tdh interactome online at StemSight.org/scout. C. SPELL for StemSight Search Results. From a supporting edge information window, you can drill down to the individual gene expression levels in microarray datasets. This view shows how expression data reveals rank-ordered correlations observed between Tdh and gold standard genes Gbf3, Fbxo15, Nr0b1, Phc1, Pou5f1, and Sox2.

Mentions: L-threonine dehydrogenase (Tdh; SRC: 0.9058) is one of the less well-studied genes in our list of high-confidence novel gene candidates for experimental validation that was strongly predicted to be involved in self-renewal, pluripotency and cell fate, and tightly linked to many of our “golden” gold standard genes, including Pou5f1, Sox2, Nanog, Nr0b1, and Rif1, (Figure 4A, Figure S5). Tdh catabolizes threonine into glycine and acetyl-CoA, which is used by the TCA cycle to generate ATP. While there were no GO annotations for this gene based on experimental data at the time we developed our training set, nor articles about the role of Tdh in mESCs at the time we created our gold standard, recently published articles confirmed that mESCs are dependent on threonine catabolism to support accelerated cell cycle kinetics [42], [43]. To learn more about the underlying datasets that support functional linkages between Tdh and key self-renewal genes, such as Pou5f1, we evaluated Bayes net statistics for edge weight and top supporting datasets (Figure 4B,C). These statistics showed that the functional relationship between Tdh and Pou5f1 was supported by ChIP-Chip binding data from five different studies investigating the regulatory circuitry of mESCs and microarray data from a study analyzing mESC differentiation. Tdh connections to other golden gold standard genes were largely supported by the same type of ChIP-Chip data (Figure S5). By drilling down to the most reliable datasets, as determined by our machine learning evaluations, we were able to quickly identify Tdh as a potential target of the core regulatory circuitry of mESC self-renewal and pluripotency [3], [44] to manage cell-cycle controls during the rapid growth phase of early embryonic development.


Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.

Dowell KG, Simons AK, Wang ZZ, Yun K, Hibbs MA - PLoS ONE (2013)

Data Visualization for Mining mESC Self-Renewal Gene Predictions.A. Views of Tdh-centric networks created using our StemSight Scout visualization tool, available at StemSight.org. Adjusting Scout network views to display only edges with inference scores of 0.5 and 0.9997 show that the novel gene Tdh is tightly connected to many well-known self-renewal genes in our training gold standard, including Pou5f1, Sox2, Nanog, Nr0B1, and Phc1. B. Supporting edge info for the Tdh – Pou5f1 edge. Supporting edge information shows that this edge is supported by several protein-DNA interaction (PDI) assays as well as gene expression datasets from a study investigating mESC cell differentiation in different mESC cell lines. For supporting edge detail between Tdh and other gold standard genes, see Figure S5 or explore the Tdh interactome online at StemSight.org/scout. C. SPELL for StemSight Search Results. From a supporting edge information window, you can drill down to the individual gene expression levels in microarray datasets. This view shows how expression data reveals rank-ordered correlations observed between Tdh and gold standard genes Gbf3, Fbxo15, Nr0b1, Phc1, Pou5f1, and Sox2.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585227&req=5

pone-0056810-g004: Data Visualization for Mining mESC Self-Renewal Gene Predictions.A. Views of Tdh-centric networks created using our StemSight Scout visualization tool, available at StemSight.org. Adjusting Scout network views to display only edges with inference scores of 0.5 and 0.9997 show that the novel gene Tdh is tightly connected to many well-known self-renewal genes in our training gold standard, including Pou5f1, Sox2, Nanog, Nr0B1, and Phc1. B. Supporting edge info for the Tdh – Pou5f1 edge. Supporting edge information shows that this edge is supported by several protein-DNA interaction (PDI) assays as well as gene expression datasets from a study investigating mESC cell differentiation in different mESC cell lines. For supporting edge detail between Tdh and other gold standard genes, see Figure S5 or explore the Tdh interactome online at StemSight.org/scout. C. SPELL for StemSight Search Results. From a supporting edge information window, you can drill down to the individual gene expression levels in microarray datasets. This view shows how expression data reveals rank-ordered correlations observed between Tdh and gold standard genes Gbf3, Fbxo15, Nr0b1, Phc1, Pou5f1, and Sox2.
Mentions: L-threonine dehydrogenase (Tdh; SRC: 0.9058) is one of the less well-studied genes in our list of high-confidence novel gene candidates for experimental validation that was strongly predicted to be involved in self-renewal, pluripotency and cell fate, and tightly linked to many of our “golden” gold standard genes, including Pou5f1, Sox2, Nanog, Nr0b1, and Rif1, (Figure 4A, Figure S5). Tdh catabolizes threonine into glycine and acetyl-CoA, which is used by the TCA cycle to generate ATP. While there were no GO annotations for this gene based on experimental data at the time we developed our training set, nor articles about the role of Tdh in mESCs at the time we created our gold standard, recently published articles confirmed that mESCs are dependent on threonine catabolism to support accelerated cell cycle kinetics [42], [43]. To learn more about the underlying datasets that support functional linkages between Tdh and key self-renewal genes, such as Pou5f1, we evaluated Bayes net statistics for edge weight and top supporting datasets (Figure 4B,C). These statistics showed that the functional relationship between Tdh and Pou5f1 was supported by ChIP-Chip binding data from five different studies investigating the regulatory circuitry of mESCs and microarray data from a study analyzing mESC differentiation. Tdh connections to other golden gold standard genes were largely supported by the same type of ChIP-Chip data (Figure S5). By drilling down to the most reliable datasets, as determined by our machine learning evaluations, we were able to quickly identify Tdh as a potential target of the core regulatory circuitry of mESC self-renewal and pluripotency [3], [44] to manage cell-cycle controls during the rapid growth phase of early embryonic development.

Bottom Line: We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination.Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant.Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies.

View Article: PubMed Central - PubMed

Affiliation: The Jackson Laboratory, Bar Harbor, Maine, USA.

ABSTRACT
Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.

Show MeSH
Related in: MedlinePlus