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Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite.

Siwo GH, Tan A, Button-Simons KA, Samarakoon U, Checkley LA, Pinapati RS, Ferdig MT - BMC Genomics (2015)

Bottom Line: Resulting networks provide insights into pfcrt's biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds.We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping.This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA. siwomolbio@gmail.com.

ABSTRACT

Background: The paradigm of resistance evolution to chemotherapeutic agents is that a key coding mutation in a specific gene drives resistance to a particular drug. In the case of resistance to the anti-malarial drug chloroquine (CQ), a specific mutation in the transporter pfcrt is associated with resistance. Here, we apply a series of analytical steps to gene expression data from our lab and leverage 3 independent datasets to identify pfcrt-interacting genes. Resulting networks provide insights into pfcrt's biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds.

Results: To identify pfcrt-interacting genes, we analyze pfcrt co-expression networks in 2 phenotypic states - CQ-resistant (CQR) and CQ-sensitive (CQS) recombinant progeny clones - using a computational approach that prioritizes gene interactions into functional and regulatory relationships. For both phenotypic states, pfcrt co-expressed gene sets are associated with hemoglobin metabolism, consistent with CQ's expected mode of action. To predict the drivers of co-expression divergence, we integrate topological relationships in the co-expression networks with available high confidence protein-protein interaction data. This analysis identifies 3 transcriptional regulators from the ApiAP2 family and histone acetylation as potential mediators of these divergences. We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping.

Conclusions: This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies. The approaches outlined here can be readily generalized to other parasite populations and drug resistances.

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Prediction and validation of regulatory mechanisms underlying diverging co-expression networks. (A) Potential regulators of the pfcrt co-expression networks by interrogation of the topological relationships between pfcrt partners using the transitivity, t, score. Top scoring candidate regulators- the AP2 transcription factor PF3D7_1007700 (AP2-3) has the highest score in CQS while in CQR the AP2 regulator PF3D7_0420300 (AP2-2) has 3rd highest score considering all genes correlated to pfcrt (FDR ≤ 0.20). The case of t =0 denotes functional (direct) pfcrt partners which also includes another AP2 transcription factor, PF3D7_0802100 (AP2-1). (B) Top scoring regulators are all part of a previously published high confidence protein-protein interaction sub-network [44] and interact with the histone acetyltransferase (Gcn5). Other transcriptional regulators physically interacting with Gcn5 include CAF1- a component of the CCR4-NOT mRNA deadenylase complex- and adenosine deaminase ADA2, leading to the hypothesis that the Gcn5 protein interaction network could be involved in integration of transcriptional regulation and mRNA stability [44]. (C) Validation of dysregulated histone acetylation as a potential regulatory mechanism using drug response assays. QTL mapping of quantitative dose responses to the HDACi apicidin in progeny of the Dd2 × HB3 genetic cross found significant association to genetic loci on chromosome 5, 57.3 cM (LOD = 5.4) and 8, 77.5 cM (LOD 2.3). The chromosome 5 locus includes a gene encoding CCR4 while the chromosome 8 locus contains CAF1, which physically interacts with Gcn5. (D) Validation of dysregulated histone acetylation using data from previous studies [53]. Promoters of the top 100 genes that are not correlated to pfcrt in CQS but show positive correlation in CQR (gain of positive correlation) carry vastly higher levels of H3K9ac compared to the average levels in all genes (Wilcoxon test P < 2.2 x 10−16). In contrast, H3K9ac levels of the top 100 genes that gain negative correlation are significantly lower compared to the genome-wide promoter baseline (Wilcoxon test P = 3.4 x 10−16).
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Fig3: Prediction and validation of regulatory mechanisms underlying diverging co-expression networks. (A) Potential regulators of the pfcrt co-expression networks by interrogation of the topological relationships between pfcrt partners using the transitivity, t, score. Top scoring candidate regulators- the AP2 transcription factor PF3D7_1007700 (AP2-3) has the highest score in CQS while in CQR the AP2 regulator PF3D7_0420300 (AP2-2) has 3rd highest score considering all genes correlated to pfcrt (FDR ≤ 0.20). The case of t =0 denotes functional (direct) pfcrt partners which also includes another AP2 transcription factor, PF3D7_0802100 (AP2-1). (B) Top scoring regulators are all part of a previously published high confidence protein-protein interaction sub-network [44] and interact with the histone acetyltransferase (Gcn5). Other transcriptional regulators physically interacting with Gcn5 include CAF1- a component of the CCR4-NOT mRNA deadenylase complex- and adenosine deaminase ADA2, leading to the hypothesis that the Gcn5 protein interaction network could be involved in integration of transcriptional regulation and mRNA stability [44]. (C) Validation of dysregulated histone acetylation as a potential regulatory mechanism using drug response assays. QTL mapping of quantitative dose responses to the HDACi apicidin in progeny of the Dd2 × HB3 genetic cross found significant association to genetic loci on chromosome 5, 57.3 cM (LOD = 5.4) and 8, 77.5 cM (LOD 2.3). The chromosome 5 locus includes a gene encoding CCR4 while the chromosome 8 locus contains CAF1, which physically interacts with Gcn5. (D) Validation of dysregulated histone acetylation using data from previous studies [53]. Promoters of the top 100 genes that are not correlated to pfcrt in CQS but show positive correlation in CQR (gain of positive correlation) carry vastly higher levels of H3K9ac compared to the average levels in all genes (Wilcoxon test P < 2.2 x 10−16). In contrast, H3K9ac levels of the top 100 genes that gain negative correlation are significantly lower compared to the genome-wide promoter baseline (Wilcoxon test P = 3.4 x 10−16).

Mentions: The divergent co-expression of 3 AP2 transcription factor genes in the pfcrt co-expression networks led us to investigate their functional relationships (Figure 3 A and B). While limited information is available about these regulators (PF3D7_0802100, PF3D7_0420300 and PF3D7_1007700), [41-43], we were intrigued to note that LaCount et al. [44] identified physical interactions among them as part of a high confidence protein-protein interaction subnetwork centered on the histone acetyltransferase, Gcn5, and containing additional chromatin-modifying proteins (Figure 3 B) [44]. The interactions among these transcription factors and Gcn5-containing complexes suggest that the AP2s interface with histone acetylation in their regulatory roles. These interactions also have been observed in Toxoplasma [45] and Arabidopsis [46]. That these associations may extend to regulatory relationships is implied by the up-regulation of the AP2 genes following perturbations by apicidin, a histone deacetylase inhibitor (HDACi) [47]. Consistent with these observations, the predicted regulon of the AP2 in the CQS pfcrt network (PF3D7_1007700) includes Gcn5, CCR4 and two hypothetical proteins (PF3D7_1366900 and PF3D7_0817300) that are also members of the Gcn5 protein-protein interaction sub-network [44]. Moreover, the Gcn5 regulon contains ADA2 and CCR4 associated factor 1 (CAF1) which are integral components of the CCR4-NOT complex, a regulator of mRNA stability and transcriptional regulation [48-51].Figure 3


Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite.

Siwo GH, Tan A, Button-Simons KA, Samarakoon U, Checkley LA, Pinapati RS, Ferdig MT - BMC Genomics (2015)

Prediction and validation of regulatory mechanisms underlying diverging co-expression networks. (A) Potential regulators of the pfcrt co-expression networks by interrogation of the topological relationships between pfcrt partners using the transitivity, t, score. Top scoring candidate regulators- the AP2 transcription factor PF3D7_1007700 (AP2-3) has the highest score in CQS while in CQR the AP2 regulator PF3D7_0420300 (AP2-2) has 3rd highest score considering all genes correlated to pfcrt (FDR ≤ 0.20). The case of t =0 denotes functional (direct) pfcrt partners which also includes another AP2 transcription factor, PF3D7_0802100 (AP2-1). (B) Top scoring regulators are all part of a previously published high confidence protein-protein interaction sub-network [44] and interact with the histone acetyltransferase (Gcn5). Other transcriptional regulators physically interacting with Gcn5 include CAF1- a component of the CCR4-NOT mRNA deadenylase complex- and adenosine deaminase ADA2, leading to the hypothesis that the Gcn5 protein interaction network could be involved in integration of transcriptional regulation and mRNA stability [44]. (C) Validation of dysregulated histone acetylation as a potential regulatory mechanism using drug response assays. QTL mapping of quantitative dose responses to the HDACi apicidin in progeny of the Dd2 × HB3 genetic cross found significant association to genetic loci on chromosome 5, 57.3 cM (LOD = 5.4) and 8, 77.5 cM (LOD 2.3). The chromosome 5 locus includes a gene encoding CCR4 while the chromosome 8 locus contains CAF1, which physically interacts with Gcn5. (D) Validation of dysregulated histone acetylation using data from previous studies [53]. Promoters of the top 100 genes that are not correlated to pfcrt in CQS but show positive correlation in CQR (gain of positive correlation) carry vastly higher levels of H3K9ac compared to the average levels in all genes (Wilcoxon test P < 2.2 x 10−16). In contrast, H3K9ac levels of the top 100 genes that gain negative correlation are significantly lower compared to the genome-wide promoter baseline (Wilcoxon test P = 3.4 x 10−16).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
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Fig3: Prediction and validation of regulatory mechanisms underlying diverging co-expression networks. (A) Potential regulators of the pfcrt co-expression networks by interrogation of the topological relationships between pfcrt partners using the transitivity, t, score. Top scoring candidate regulators- the AP2 transcription factor PF3D7_1007700 (AP2-3) has the highest score in CQS while in CQR the AP2 regulator PF3D7_0420300 (AP2-2) has 3rd highest score considering all genes correlated to pfcrt (FDR ≤ 0.20). The case of t =0 denotes functional (direct) pfcrt partners which also includes another AP2 transcription factor, PF3D7_0802100 (AP2-1). (B) Top scoring regulators are all part of a previously published high confidence protein-protein interaction sub-network [44] and interact with the histone acetyltransferase (Gcn5). Other transcriptional regulators physically interacting with Gcn5 include CAF1- a component of the CCR4-NOT mRNA deadenylase complex- and adenosine deaminase ADA2, leading to the hypothesis that the Gcn5 protein interaction network could be involved in integration of transcriptional regulation and mRNA stability [44]. (C) Validation of dysregulated histone acetylation as a potential regulatory mechanism using drug response assays. QTL mapping of quantitative dose responses to the HDACi apicidin in progeny of the Dd2 × HB3 genetic cross found significant association to genetic loci on chromosome 5, 57.3 cM (LOD = 5.4) and 8, 77.5 cM (LOD 2.3). The chromosome 5 locus includes a gene encoding CCR4 while the chromosome 8 locus contains CAF1, which physically interacts with Gcn5. (D) Validation of dysregulated histone acetylation using data from previous studies [53]. Promoters of the top 100 genes that are not correlated to pfcrt in CQS but show positive correlation in CQR (gain of positive correlation) carry vastly higher levels of H3K9ac compared to the average levels in all genes (Wilcoxon test P < 2.2 x 10−16). In contrast, H3K9ac levels of the top 100 genes that gain negative correlation are significantly lower compared to the genome-wide promoter baseline (Wilcoxon test P = 3.4 x 10−16).
Mentions: The divergent co-expression of 3 AP2 transcription factor genes in the pfcrt co-expression networks led us to investigate their functional relationships (Figure 3 A and B). While limited information is available about these regulators (PF3D7_0802100, PF3D7_0420300 and PF3D7_1007700), [41-43], we were intrigued to note that LaCount et al. [44] identified physical interactions among them as part of a high confidence protein-protein interaction subnetwork centered on the histone acetyltransferase, Gcn5, and containing additional chromatin-modifying proteins (Figure 3 B) [44]. The interactions among these transcription factors and Gcn5-containing complexes suggest that the AP2s interface with histone acetylation in their regulatory roles. These interactions also have been observed in Toxoplasma [45] and Arabidopsis [46]. That these associations may extend to regulatory relationships is implied by the up-regulation of the AP2 genes following perturbations by apicidin, a histone deacetylase inhibitor (HDACi) [47]. Consistent with these observations, the predicted regulon of the AP2 in the CQS pfcrt network (PF3D7_1007700) includes Gcn5, CCR4 and two hypothetical proteins (PF3D7_1366900 and PF3D7_0817300) that are also members of the Gcn5 protein-protein interaction sub-network [44]. Moreover, the Gcn5 regulon contains ADA2 and CCR4 associated factor 1 (CAF1) which are integral components of the CCR4-NOT complex, a regulator of mRNA stability and transcriptional regulation [48-51].Figure 3

Bottom Line: Resulting networks provide insights into pfcrt's biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds.We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping.This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA. siwomolbio@gmail.com.

ABSTRACT

Background: The paradigm of resistance evolution to chemotherapeutic agents is that a key coding mutation in a specific gene drives resistance to a particular drug. In the case of resistance to the anti-malarial drug chloroquine (CQ), a specific mutation in the transporter pfcrt is associated with resistance. Here, we apply a series of analytical steps to gene expression data from our lab and leverage 3 independent datasets to identify pfcrt-interacting genes. Resulting networks provide insights into pfcrt's biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds.

Results: To identify pfcrt-interacting genes, we analyze pfcrt co-expression networks in 2 phenotypic states - CQ-resistant (CQR) and CQ-sensitive (CQS) recombinant progeny clones - using a computational approach that prioritizes gene interactions into functional and regulatory relationships. For both phenotypic states, pfcrt co-expressed gene sets are associated with hemoglobin metabolism, consistent with CQ's expected mode of action. To predict the drivers of co-expression divergence, we integrate topological relationships in the co-expression networks with available high confidence protein-protein interaction data. This analysis identifies 3 transcriptional regulators from the ApiAP2 family and histone acetylation as potential mediators of these divergences. We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping.

Conclusions: This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies. The approaches outlined here can be readily generalized to other parasite populations and drug resistances.

Show MeSH