- Methodology article
- Open Access
Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data
© Yang and Wu.; licensee BioMed Central Ltd. 2012
Received: 22 May 2012
Accepted: 2 August 2012
Published: 16 August 2012
Transcription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using statistical methods for detecting differentially expressed genes between the gene expression microarray data of the mutant and wild type strains, the TF knockout targets of the knocked-out TF can be identified. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches.
To solve these two problems, we developed a method to filter out the false positives in the original TF knockout targets (identified by statistical approaches) so that the biologically interpretable TF knockout targets can be extracted. Our method can further generate experimentally testable hypotheses of the molecular mechanisms of how a TF regulates its biologically interpretable TF knockout targets. The details of our method are as follows. First, a TF binding network was constructed using the ChIP-chip data deposited in the YEASTRACT database. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the knocked-out TF to this target could be identified by our path search algorithm. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly through intermediate TFs. After checking all the original TF knockout targets, the biologically interpretable ones could be extracted and the false positives could be filtered out. We validated the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures.
By jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets by identifying paths (in the TF binding network) from the knocked-out TF to these targets. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our methods have been experimentally validated in the literature. Our work demonstrates that integrating different data sources is a powerful approach to study complex biological systems.
A living cell responds to physiological and environmental changes mainly by reorganization of transcriptional programs, which are regulated by transcription factors (TFs) [1–5]. TFs control the expressions of their targets in two ways. TFs either directly regulate their targets by binding to the promoters or indirectly regulate their targets by the transcriptional regulatory chains through intermediate TFs [6, 7]. Thus, identifying the direct and indirect targets of TFs is very crucial for understanding the transcriptional rewiring in response to various stimuli.
A powerful high-throughput experimental technology, called the transcription factor knockout microarray (TFKM) , is widely used to investigate the regulatory relationships between TFs and genes. First, the genome-wide gene expression profiles between a TF knockout strain and a wild type strain are measured using microarrays. Then the differentially expressed genes between these two strains can be identified by using various statistical methods [9, 10]. These genes are called the TF knockout targets because their expressions change significantly due to the knockout of the TF-encoding gene under study. In yeast, experimental data of a compendium of 269 TFKMs performed by Hu et al.  were released in 2007. Covering almost all known TFs in yeast, these data are the most comprehensive TF knockout experiments available for any organism and provide rich information for studying gene regulation . Hu et al.  used an error model for identifying differentially expressed genes in their TFKMs. Later, Reimand et al.  applied a more sophisticated statistical method, called the moderated eBayes t-test , to Hu et al.’s TFKMs and found nine times the total TF knockout targets reported by Hu et al. They also showed that their result was more biologically meaningful than that of Hu et al. However, due to the experimental noises inherent in the high-throughput microarray technology, the TF knockout targets inferred solely from the noisy TFKMs may contain a certain amount of false positives. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches. Therefore, further justifications of the identified TF knockout targets are needed before they can be used as a high quality source for gene regulation study.
Unlike Reimand et al.  who attacked the problem from the statistical perspective, we solved this problem from the biological perspective. It is known that TFs regulate their direct targets by binding to the targets’ promoters and regulate their indirect targets by transcriptional regulatory chains through intermediate TFs [6, 7]. In this paper, we proposed a method that uses this knowledge as a biological filter for extracting biologically interpretable TF knockout targets from the original TF knockout targets identified by Reimand et al. .
On average 90% in the original TF knockout targets are biologically interpretable
We claim that our refined TF knockout targets are more biologically meaningful than the original ones identified by Reimand et al. . To justify our claim, the following three analyses were performed.
The refined dataset displays greater functional enrichment
Since TF knockout targets represent the genes that are co-regulated by the same TF, they should be associated with common molecular functions or biological processes. For each of the 112 TFs, the Generic GO Term Finder  web server was used to identify enriched GO terms  (with the chosen ontology aspect and FDR cutoff) in the refined and original TF knockout targets, respectively. We used all three ontology aspects (molecular function, biological process, and cellular component) and 0.05 as the FDR cutoff. Then for each TF, an enrichment score (proposed by Reimand et al. ) was used to measure the enrichment of functional annotations in the refined/original dataset by summing the absolute logarithms of the p-values of the enriched GO terms found in the refined/original dataset. Finally, an aggregate enrichment score (also proposed by Reimand et al. ) of the whole refined/original datasets for all 112 TFs was computed as the sum of the enrichment score for each TF.
Comparing individual TFs, the refined dataset has an equal or higher enrichment score than the original dataset in 84% (94/112) of the cases. If we compare all 112 TFs as a whole, the refined datasets also have a higher aggregate enrichment score (47859 vs. 44069) than the original datasets (see Additional file 3: Table S2 for more details). In summary, the refined dataset displays greater functional enrichment than the original dataset.
The refined dataset has better expression coherence
Since TF knockout targets represent the genes that are co-regulated by the same TF, their expression patterns are expected to be correlated. This motivates us to test which dataset (refined or original) has higher expression coherence. The expression data were downloaded from Ihmels et al.’s study  which collected 1011 published genome-wide expression profiles. The testing procedure is as follows. First, two distributions were formed by computing the absolute value of the Pearson correlation coefficient between the expression data of any two genes in the refined and original dataset, respectively. Then one dataset is said to have higher expression coherence than the other if its distribution is stochastically greater than the other. The statistical significance was computed using Wilcoxon rank sum test . The above procedure was applied for each of the 112 TFs under study. Finally, the p-values were corrected for multiple hypotheses testing to ensure FDR < 0.05.
Among 112 TFs, 55% (62/112) show significantly higher expression coherence in the refined dataset. In contrast, only 4% (4/112) show significantly higher expression coherence in the original dataset (see Additional file 4: Table S3 for more details). In summary, the refined dataset has better expression coherence than the original dataset.
The refined dataset shows higher tendency to have physical protein-protein interactions
It has been reported that TFs tend to regulate genes that interact with each other . Reimand et al.  proposed a measure to test this tendency by calculating the statistical significance of the TF knockout targets for being in the same protein-protein interaction module. According to Reimand et al.’s definition, a protein-protein interaction module consists of core genes and neighborhood genes. Core genes are those genes which are in the dataset and have physical protein-protein interactions with at least one gene in the dataset. Neighborhood genes are those genes which are not in the dataset but have physical protein-protein interactions with at least one of the core genes. The physical protein-protein interaction data were downloaded from BioGRID database . For each of the 112 TFs, we tested whether a dataset (refined or original) is enriched in the same protein-protein interaction module using Reimand et al.’s measure. The statistical significance was computed using hypergeometric distribution  (see Methods for more details). Finally, the p-values were corrected for multiple hypotheses testing to ensure FDR < 0.05.
Of the 112 TFs, 82% (92/112) are enriched for membership to a protein-protein interaction module in the refined dataset, compared with only 71% (80/112) for the original dataset (see Additional file 5: Table S4 for more details). The refined dataset has 11% performance improvement over the original dataset on this test. In summary, the refined dataset shows higher tendency to have physical protein-protein interactions.
Our method can generate experimentally testable hypotheses of how a TF may regulate its knockout targets
In our method, an original TF knockout target (identified by Reimand et al. ) is said to be biologically interpretable if a path from the knocked-out TF to this target could be identified in the TF binding network. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its TF knockout targets, providing possible insights for biologists to do more detailed investigations.
Another example is a hypothesis of how Stp2 regulates ETR1. ETR1 encodes a member of the medium chain dehydrogenase/reductase family with 2-enoyl thioester reductase activity and has a probable role in fatty acid synthesis . Stp2 is a TF which activates transcription of amino acid permease genes . Since the ETR1 promoter has no Stp2 binding sites, it is hard to imagine how Stp2 regulate ETR1. Our method identified a path Stp2→ADR1→ETR1, suggesting Stp2 regulates ETR1 through the intermediate TF Adr1, a TF that regulates expression of genes involved in the fatty acid utilization . The identified path has been experimentally proven to exist in the yeast cells. Several studies [6, 7, 23, 27–29] showed that Stp2 can directly regulate ADR1 by binding to the Stp2 binding site (GYGCCGYR) in the ADR1 promoter and Adr1 can directly regulate ETR1 by binding to the UAS1 (type 1 upstream activation sequence) in the ETR1 promoter (see Figure 4b). In summary, the identified path explains how a TF, which activates transcription of amino acid permease genes, can regulate a protein involved in the fatty acid synthesis, indicating a close linkage between the extracellular amino acid uptake and fatty acid synthesis. Another 690 examples which also have been experimentally validated in the literature are listed in Additional file 6: Table S5.
Our method can separate signals from noises in the original dataset
The signals are more biologically meaningful than the noises
Test (with FDR = 0.05)
Our result has an equal or higher enrichment score than the noises in 94% (105/112) of the cases.
Of the 112 TFs, 57% (64/112) show significantly higher expression coherence in the signals, compared with only 6% (7/112) in the noises.
The prevalence of protein-protein interactions
Of the 112 TFs, 82% (92/112) are enriched for membership to a protein-protein interaction module in the signals, compared with only 19% (21/112) in the noises
Our result is better than the random results
Our method performs better than two other existing methods
Our result is more biologically meaningful than Hu et al.’s and Jiang et al.’s results
Test (with FDR = 0.05)
Our result vs. Hu et al.’s result
Our result vs. Jiang et al.’s result
Our result has an equal or higher enrichment score than Hu et al.’s result in 83% (93/112) of the cases.
Our result has an equal or higher enrichment score than Jiang et al.’s result in 83% (29/35) of the cases.
Of the 112 TFs, 86% (96/112) show significantly higher expression coherence in our result, compared with only 2% (2/112) in Hu et al.’s result.
Of the 35 TFs, 49% (17/35) show significantly higher expression coherence in our result, compared with only 31% (11/35) in Jiang et al.’s result.
The prevalence of protein-protein interactions
Of the 112 TFs, 82% (92/112) are enriched for membership to a protein-protein interaction module in our result, compared with only 38% (43/112) in Hu et al.’s result.
Of the 35 TFs, 91% (32/35) are enriched for membership to a protein-protein interaction module in our result, compared with only 31% (11/35) in Jiang et al.’s result.
Two issues related to our method are discussed
Two issues related to our method are worthy of discussion. First, there is tradeoff between coverage and precision for using different underlying network to search the possible paths from a knocked-out TF to its knockout targets. We tested two underlying networks. The first one was the TF binding network whose edges are supported only by TF binding evidence deposited in the YEASTRACT database . The other one was the TF regulatory network whose edges are supported by both TF binding and TF regulation evidence deposited in the YEASTRACT database. Our analyses showed that using the TF binding network as the underlying network, the coverage (i.e. the percentage of the biologically interpretable knockout targets) is 90% but the precision (i.e. the average confidence score of an identified path) is only 18%. The confidence score of a path is defined as the ratio of the TF-gene pairs (along the direction of the identified path) that has literature evidence of TF regulation (see Additional file 2: Table S1 for more details). On the contrary, using the TF regulatory network as the underlying network, the coverage reduces to 23% but the precision increases to 73%.
The low precision (18%) resulting from using the TF binding network is not surprising since the overlap between the TF binding data and TF knockout data is very low. Several possible reasons have been proposed in the literature [8, 32] to explain this low overlap. First, only a subset of bound TFs may affect a target gene’s expression, depending on the location and orientation of binding sites and the presence of other cofactors . Second, different TFs occupying the same promoter could compensate for each other’s loss, masking the deletion effect [8, 32]. Third, a TF could bind a promoter under normal growth conditions but function under other specific stressful conditions . On the other hand, the low coverage (23%) resulting from using the TF regulatory network is also understandable. The reason is that TF regulation information with experimental evidence in the literature now is not rich enough to construct a biologically meaningful TF regulatory network. That is, there are too many missing edges (i.e., false negatives) in the constructed TF regulatory network. We believe that this problem will be solved in the near future since the high-throughput experimental technology for systems biology study evolves rapidly.
The other issue is about the predicted false positives. Our method regards an original TF knockout target as a false positive if no path (in the TF binding network) from the knocked-out TF to this target could be found. However, the knocked-out TF may regulate some of its knockout targets through TF-TF interactions at the protein level but not through transcriptional regulatory chains. In that case, our method would incorrectly regard a real TF knockout target as a false positive. We investigated the severity of this problem in details. Among the false positives defined by our method, only 4% (153/3492) has independent literature evidence of TF regulation other than Reimand et al.’s study  (see Additional file 10: Table S9 for more details). Therefore, we believe that most of the predicted false positives indeed represent the noises in the original TF knockout targets.
In this paper, we developed a method that can extract biologically interpretable TF knockout targets from the original TF knockout targets inferred solely from the noisy TFKMs. An original TF knockout target is said to be biologically interpretable if a path could be identified from the knocked-out TF to this target in the TF binding network. Our refined TF knockout targets outperform the original TF knockout targets across all measures: the functional enrichment, the expression coherence, and the prevalence of protein-protein interactions. Moreover, the identified paths from the knocked-out TF to its knockout targets in the TF binding network form experimentally testable hypotheses of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our method have been experimentally validated in the literature. We believe that the other hypotheses provide valuable information for biologists to design traditional gene-specific experiments for studying the molecular mechanisms of gene regulation.
Two data sources were used in this study. First, the original TF knockout targets of 112 TFs under study were downloaded from Reimand et al.’s study . The knockout targets of each TF (in Reimand et al.’s study) were those differentially expressed genes identified by applying the moderated eBayes t-test  to Hu et al.’s TFKMs . Second, the ChIP-chip data used to construct the TF binding network were downloaded from the YEASTRACT database . This is the most comprehensive ChIP-chip dataset since almost all the ChIP-chip data available in the public domain are collected in the YEASTRACT database.
Finding a shortest path from the knocked-out TF to its knockout targets in the TF binding network
In our method, an original TF knockout target (inferred solely from the noisy TFKMs) is said to be biologically interpretable if a path from the knocked-out TF to this target could be identified in the TF binding network. A famous graph search algorithm, called the breadth-first search (BFS) algorithm , in the graph theory was modified to search paths in a network. Our modified version can handle loops in the graph which cannot be done in the original BFS algorithm. For each original TF knockout target, our modified BFS (mBFS) algorithm was applied to find a shortest path from the knocked-out TF to this target in the TF binding network. The pseudocode of our mBFS algorithm is as follows.
mBFS (Directed graph = TF binding network, Start node = Knocked-out TF, Destination node = TF knockout target being tested):
Set Visited_list and Waiting_list be two empty sets.
Add Start node into Waiting_list.
Set Path[Start node] = Start node.
Set Path[i] be an empty set for each node i (except for Start node) in Directed graph.
while (Waiting_list is not empty)
Remove the first node v in Waiting_list.
Add v to the end of Visited_list.
for (each direct successor u of v in Direct graph)
if(u is the Destination node)
Add u to the end of Visited_list.
Append u to path[v] and set it path[u].
TERMINATE while loop.
else if (u is not in Visited_list)
Add u to the end of Waiting_list.
Append u to path[v] and set it path[u].
if (path[Destination node] is empty)
return “No Path Exists!”
return path[Destination node]
Calculating statistical significance using the hypergeometric distribution
where |G| means the number of genes in set G.
This study was supported by the Taiwan National Science Council NSC 99-2628-B-006-015-MY3.
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