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PLoS Comput Biol. 2008 Nov;4(11):e1000224. doi: 10.1371/journal.pcbi.1000224. Epub 2008 Nov 14.

A predictive model of the oxygen and heme regulatory network in yeast.

Kundaje A, Xin X, Lan C, Lianoglou S, Zhou M, Zhang L, Leslie C.

Source

Department of Computer Science, Columbia University, New York, New York, United States of America.

Abstract

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.

PMID:: 19008939; [PubMed - indexed for MEDLINE]
PMCID:: PMC2573020

Free PMC Article

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Figure 2

A schematic flow chart showing the algorithmic steps for learning the oxygen regulatory program with MEDUSA.

(A) The mRNA expression data is discretized into three states, up (over-expressed), down (under-expressed), and baseline (not significantly differentially expressed), and genes are partitioned into potential regulators (transcription factors and signal transducers) and targets. The regulators are also included in the list of target genes so that their transcriptional regulation can be modeled. (B) The MEDUSA learning algorithm is presented with the promoter sequences of target genes, the discretized expression profiles of the regulators across multiple conditions, and the differentially expressed (up and down) target gene examples from these experiments. Baseline examples are not used to train MEDUSA. In the first stage of training, MEDUSA considers rules based on promoter sequence data and regulator expression states. MEDUSA uses a boosting strategy to avoid overfitting over many rounds of the algorithm. At each iteration i, a motif/regulator rule is chosen based on the current weights on the training examples; this rule predict that targets whose promoters contain the motif will go up (or down) in experiments where the regulator is over- (or under-) expressed. Before the next iteration, the examples are reweighted to emphasize the ones that are difficult to predict. (C) To learn the sequence motif, the algorithm agglomerates predictive k-mer sequences to produce candidate PSSMs, and it optimizes both the choice of PSSM and the probabilistic threshold used to determine where the hits of the motif occur. (D) At the end of each round of training, motif /regulator rules are placed into an alternating decision tree, building a global regulatory program. This regulatory program can be used to predict target gene up/down regulation for gene-experiment examples that were not seen in training. In order to produce a more stable decision tree, we perform a second pass of the tree-learning algorithm using a stabilized variant of boosting that gives more consistent models over different subsets of the training data. At this stage, both the motifs learned previously by MEDUSA and TF occupancies from ChIP-chip experiments are used as sequence features for the final regulatory program.

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

PLoS Comput Biol. 2008 November;4(11):e1000224.

Figure 8

Comparison of significance and abundance of motifs learned by MEDUSA and AlignACE for the 16 expression signatures identified in the dataset.

Each row in the table represents a motif found by MEDUSA only (top section), by both MEDUSA and AlignACE (middle section), or by AlignACE only (bottom section). The first column describes the motif by the name of the corresponding transcription factor followed by the consensus motif sequence. Some transcription factor names are followed by ‘ChIP’, indicating that these are significant ChIP-chip occupancy features identified by MEDUSA. Motif descriptions highlighted in red indicate transcription factors that are specifically known to have an important function in hypoxia. The remainder of the table shows MEDUSA (left section) and AlignACE (right section) results for each signature (S1 to S16), represented by a pair of columns scoring motifs by statistical significance (left column in each pair) and abundance within the set of genes making up the signature (right column in each pair). For statistical significance scores, columns labeled ‘S’ represent the margin scores (in shades of blue) assigned by MEDUSA, and columns labeled ‘M’ represent the maximum a posteriori (MAP) scores (in shades of green) assigned by AlignACE. In both cases, dark shades indicate higher statistical significance. The columns labeled ‘A’ show the percentage abundance scores of the motifs in each of the signatures. For AlignACE, the abundance score of a motif simply reflects the ratio of the number of genes in each cluster whose promoters contain the motif, to the cluster size. For MEDUSA, it refers to the ratio of the number of genes in each cluster for which the motif contributes positively to the margin score, to the size of the cluster. A motif could be present in the promoter of a gene but not identified as significant by MEDUSA. In such cases, the motif does not contribute to the MEDUSA abundance score. Dark shades of pink indicate strong abundance scores.

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

PLoS Comput Biol. 2008 November;4(11):e1000224.

Figure 3

Simplified example showing how the regulatory program learned by MEDUSA predicts context-specific up/down gene expression.

MEDUSA learns a global regulatory program described by an alternating decision tree. A simple regulatory program is shown in part A of the figure, along with the prediction it makes in two contexts, indicated as context B (top right) and context C (bottom right). The interaction between a regulator and a motif and the effect on targets is described by a decision node, which contains a logical condition to be tested, e.g., “Is regulator i up in the experiment and is motif i present in the promoter?”, and by the contribution that this motif/regulator pair makes to the up/down prediction of target gene expression if the logical condition is true, which is indicated by a colored bar. Contributions to upregulation of targets are shown in red and downregulation of targets in green. Combinatorial regulation is encoded by the tree structure: we obtain a prediction score for the up/down regulation of a target gene in a given experimental condition by starting at the root and recursively working downwards in the tree, seeing which prediction nodes are reachable by answering “yes” to logical conditions and summing all score contributions for the nodes visited. (Context B) In the first context, both Reg 2, a transcriptional activator, and Reg 1, a repressor, are expressed, and the promoter of gene A contains the motifs associated by the regulatory program to both these regulators. The regulatory program computes the prediction score by summing the larger contribution of the repressor (green bar) with the smaller contribution of the activator (red bar) to obtain a negative prediction score (indicated by the dashed line on the far right), i.e., gene A is predicted to be downregulated. (Context C) In the second context, both the activator Reg 2 and a co-factor, Reg 3, are expressed and can bind to the promoter of gene B based on the presence of the associated motifs in the regulatory program. The logic of the tree requires that the condition involving Reg 2 must hold before the contribution of the node containing Reg 3, at the next level of the tree, can be counted. Here, both conditions hold, and the regulatory program adds two positive contributions to obtain a confident prediction that gene B will be upregulated.

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

PLoS Comput Biol. 2008 November;4(11):e1000224.