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Inferring a transcriptional regulatory network of the cytokinesis-related genes by network component analysis.

Chen SF, Juang YL, Chou WK, Lai JM, Huang CY, Kao CY, Wang FS - BMC Syst Biol (2009)

Bottom Line: No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset.In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes.Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan. n810011@yahoo.com.tw

ABSTRACT

Background: Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody) and/or temporal synchronicity (cytokinesis) to avoid full-scale computation from genome-wide databases.

Results: NCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5).

Conclusion: In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes. The regulated action for four selected cytokinesis-related genes (BUD4, CHS2, IQG1, and CDC5) belongs to the M-phase or M/G1 phase, consistent with the empirical observations that in S. cerevisiae, the Mcm1-Ndd1-Fkh2 transcription factor complex can regulate expression of the cytokinesis-related genes BUD4, CHS2, IQG1, and CDC5. Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

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Relations between genes and PCA. Relations between genes and the first, second, and third principal components, as well as the genes regulated by TF.
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Figure 4: Relations between genes and PCA. Relations between genes and the first, second, and third principal components, as well as the genes regulated by TF.

Mentions: We cannot establish a full scale NCA for the midbody due to the lack of genome-wide cytokinesis-related information. In Case I, we use 16 of the 60 cytokinesis-related genes to select the 592 × 18 data matrix and 592 × 15 initial control strength matrix, and then to infer the corresponding control strength and TFAs. The unique solution could be obtained through NCA as discussed above. We are concerned with whether the solution is scale-free for the selected data, since we lack of full information on the cytokinesis-related genes. We found the inferred dynamics of TFAs through NCA to be independent of the scale of the data matrix. To investigate this fact, we select various genes, as shown in Figure 1, from the 60 cytokinesis-related genes to construct the data matrix and initial control strength, to infer the control strength matrix and the TFA matrix. In Case II, following the similar procedures in Case I, we use 12 of the 60 cytokinesis-related genes to select the 510 × 18 data matrix and 510 × 10 initial control strength matrix, and then to infer the corresponding control strength and TFAs. The genes of Cases II, III, and IV are a complete subset of Case I. However, Case III is not a complete subset of Case II, although they intersect. The genes in Cases III and IV are selected through PCA from the 60 cytokinesis-related genes. Table 1 lists the absolute loading values for the first, second, and third principal components, which consist of 9 genes because both the first and second principal components include the gene CHS2. The regulated strength is inferred from NCA as shown in Table 2 and Additional File 1: Table S4, i.e. Ndd1 regulated on gene CHS2 has a control strength of 3.1035, and Fkh2 regulated on CHS2 has -1.9908. Figure 4 shows the relations between genes and the first, second, and third principal components, as well as the genes regulated by TF. The gene CHS2 is regulated by Fkh2 and Ndd1.


Inferring a transcriptional regulatory network of the cytokinesis-related genes by network component analysis.

Chen SF, Juang YL, Chou WK, Lai JM, Huang CY, Kao CY, Wang FS - BMC Syst Biol (2009)

Relations between genes and PCA. Relations between genes and the first, second, and third principal components, as well as the genes regulated by TF.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2800846&req=5

Figure 4: Relations between genes and PCA. Relations between genes and the first, second, and third principal components, as well as the genes regulated by TF.
Mentions: We cannot establish a full scale NCA for the midbody due to the lack of genome-wide cytokinesis-related information. In Case I, we use 16 of the 60 cytokinesis-related genes to select the 592 × 18 data matrix and 592 × 15 initial control strength matrix, and then to infer the corresponding control strength and TFAs. The unique solution could be obtained through NCA as discussed above. We are concerned with whether the solution is scale-free for the selected data, since we lack of full information on the cytokinesis-related genes. We found the inferred dynamics of TFAs through NCA to be independent of the scale of the data matrix. To investigate this fact, we select various genes, as shown in Figure 1, from the 60 cytokinesis-related genes to construct the data matrix and initial control strength, to infer the control strength matrix and the TFA matrix. In Case II, following the similar procedures in Case I, we use 12 of the 60 cytokinesis-related genes to select the 510 × 18 data matrix and 510 × 10 initial control strength matrix, and then to infer the corresponding control strength and TFAs. The genes of Cases II, III, and IV are a complete subset of Case I. However, Case III is not a complete subset of Case II, although they intersect. The genes in Cases III and IV are selected through PCA from the 60 cytokinesis-related genes. Table 1 lists the absolute loading values for the first, second, and third principal components, which consist of 9 genes because both the first and second principal components include the gene CHS2. The regulated strength is inferred from NCA as shown in Table 2 and Additional File 1: Table S4, i.e. Ndd1 regulated on gene CHS2 has a control strength of 3.1035, and Fkh2 regulated on CHS2 has -1.9908. Figure 4 shows the relations between genes and the first, second, and third principal components, as well as the genes regulated by TF. The gene CHS2 is regulated by Fkh2 and Ndd1.

Bottom Line: No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset.In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes.Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan. n810011@yahoo.com.tw

ABSTRACT

Background: Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody) and/or temporal synchronicity (cytokinesis) to avoid full-scale computation from genome-wide databases.

Results: NCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5).

Conclusion: In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes. The regulated action for four selected cytokinesis-related genes (BUD4, CHS2, IQG1, and CDC5) belongs to the M-phase or M/G1 phase, consistent with the empirical observations that in S. cerevisiae, the Mcm1-Ndd1-Fkh2 transcription factor complex can regulate expression of the cytokinesis-related genes BUD4, CHS2, IQG1, and CDC5. Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

Show MeSH