Transcriptional regulators are commonly modified at the post-transcriptional level, and consequently their biological activities do not correlate significantly with expression levels. Previous work infer TFAs from expression levels of genes regulated by single factors or in combination [2
]. In general, the major difference between previous work and that of NCA is that the former require an explicit quantification of the control strengths (the A matrix in NCA) a priori
. Bussemaker et al. [7
] defined the control strength as the motif copy number in corresponding promoters and found that there was no statistical benefit to model expression with more than single factors and thus deduced single TFAs. Wang et al. [6
] considered an expression-weighted motif to find potential target genes of single transcription factors. Gao et al. [18
] used ChIP-chip log occupancy ratios as a surrogate for transcription factor binding affinity. In contrast, NCA explicitly models combinatorial regulation of gene expression, and allows both the control strengths and the TFAs to be deduced simultaneously with given network connectivity. In this approach, the lack of connectivity in specific pairs of transcription factor and promoter is used to provide constraints for data decomposition in order to obtain unique solutions when specific criteria are satisfied [3
]. gNCA expands these capabilities by allowing incorporation of constraints onto the deduced TFAs, such as transcription factor knockout experiments, which offer a rich source of data and biochemical information. Development of these methodologies significantly expands the capabilities of transcriptional regulation analysis. With gNCA, we analyzed the combined wild-type and fkh1 fkh2
mutant data set and showed that gNCA can be used to identify TFAs which are consistent with cell physiology, transcription factors with potential cell cycle dependent roles, as well as interactions between transcription factors.
On the basis that transcription factors exhibiting similar activity patterns function together, we identified 11 transcription factors that clustered closely with the known cell cycle regulators. We performed a periodicity test to determine the TFA profiles that exhibit periodic behavior, and by combining the sets of transcription factors collected by these two methods, we identified 5 putative cell cycle-related regulators: Dal81, Hap2, Hir2, Mss11, and Rlm1. These transcription factors may participate in functions driven by cell cycles, or may regulate cell cycle directly or indirectly.
Our comparison between the wild-type TFAs and the mutant TFAs confirmed that the forkhead transcription factors interact with Ace2, Ndd1, and Swi5. This result is consistent with previous reports [16
]. Using this approach, we identified 4 additional transcription factors that may functionally interact with Fkh1 Fkh2 directly or indirectly: Hap2, Rts2, Cha4, and Fhl1. Most of these transcription factors are not known to be related to cell cycle, suggesting that cell cycle regulation interacts with other physiological functions.
It is worth noting that our analysis can be sensitive to errors in the connectivity graph. Through a sensitivity analysis we determined that all the known cell cycle regulators and all the known forkhead interaction partners have TFAs that exhibit low sensitivity to the connectivity network when using 0.5 as a correlation coefficient threshold. These results suggest that our analysis is robust to errors in connectivity. With the same sensitivity criterion, 2 (Dal81 and Rlm1) of the 5 putative cell cycle-related regulators and 1 (Fhl1) of the 4 putative forkhead interaction partners were determined to be robust to connectivity errors. The lack of sensitivity to error increases confidence in these predictions.
A total of 1529 (out of 6200) genes and 74 (out of 104) transcription factors were analyzed from 69 microarray experiments. Both limited connectivity information from ChIP-chip and missing data points in DNA microarray expression attribute to the limited number of genes and TFs that can be studied in the current investigation. On the other hand, the results suggest that our analysis on this limited set of genes and TFs appears to be sufficient: our analysis strategy recovers nearly 90% of the known cell cycle regulators. On the basis of the TFA profiles, the time series of the key cell cycle regulators are summarized in Figure . A dominant feature in this map is the overlapping TFAs among known cell cycle regulators. This is common among some transcription factor complexes, including SBF (Swi4/Swi6) and MBF (Mbp1/Swi6), as well as transcription factors known to regulate the same phase in the cell cycle including Ace2, Swi5 and Mcm1 (Figure ). In addition, overlapping TFAs were also observed among different phases of cell cycle: transcription factors during one stage regulate transcription factors that function in the next stage. It is well known that serial regulation among transcription factors forms a connected regulatory network [17
]. From the TFA profiles, we also observe the intrinsic property of the connected regulatory network among cell cycle factors in cell cycle regulation (Figure ).
Phase diagrams of TFAs. (A) 11 known Transcription factors and (B) 5 deduced cell cycle-dependent factors.
Among the putative cell cycle-related transcription factors (Figure ), Dal81 is phosphorylated by Cdk1 [19
] which is considered to be involved in G2/M transition [20
]. The activity of Dal81 peaked over the G2/M phase is also in agreement with its regulatory role (Figure ). Hir2 functions as a transcriptional repressor of histone gene expression during the cell cycle [15
]. Histone synthesis is triggered at the beginning of the S phase. So Hir2 is expected to have the lowest TFA around S phase. As such, TFA of Hir2 from NCA showed that it indeed has the lowest TFA around S phase (Figure ). Most well characterized transcription factors showed biologically relevant activity profiles at specific cell cycle phases, suggesting that TFAs deduced from gNCA are biologically meaningful and a good predictor of transcription factor function and interaction.
Hierarchical clustering of TFA profiles of the 11 known cell cycle factors (blue) and the 5 putative cell cycle-dependent factors (black). TFs denoted in blue are those TFs known to be involved in cell cycle.