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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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Co-expression of all genes withpfcrtgene in CQR and CQS parasites. (A) Correlation between the levels of each transcript in the genome to that of pfcrt, determined separately for CQS (x-axis) and CQR (y-axis) parasites. Grey region indicates genes whose correlation to pfcrt passed the threshold of FDR ≤ 0.20. (B) Average correlation between pfcrt and each transcript in 100 pairs of randomly sampled subsets of CQS (x-axis) and CQR (y-axis) parasites. Each subset of CQR or CQS parasites consists of transcriptional data from 8 parasite clones. (C) Average correlation between the transcript level of each gene to that of pfcrt in 100 pairs of randomly sampled subsets of CQS parasites. (D) Comparison of average pfcrt correlations in 100 pairs of randomly sampled subsets of CQR parasites. Like in (B), each randomly sampled subset of parasites consists of 8 parasites.
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Fig2: Co-expression of all genes withpfcrtgene in CQR and CQS parasites. (A) Correlation between the levels of each transcript in the genome to that of pfcrt, determined separately for CQS (x-axis) and CQR (y-axis) parasites. Grey region indicates genes whose correlation to pfcrt passed the threshold of FDR ≤ 0.20. (B) Average correlation between pfcrt and each transcript in 100 pairs of randomly sampled subsets of CQS (x-axis) and CQR (y-axis) parasites. Each subset of CQR or CQS parasites consists of transcriptional data from 8 parasite clones. (C) Average correlation between the transcript level of each gene to that of pfcrt in 100 pairs of randomly sampled subsets of CQS parasites. (D) Comparison of average pfcrt correlations in 100 pairs of randomly sampled subsets of CQR parasites. Like in (B), each randomly sampled subset of parasites consists of 8 parasites.

Mentions: To determine the co-expression relationship between pfcrt and other genes, we reanalyzed microarray data from our lab that profiled transcripts at 18 hr post-erythrocye invasion of 19 CQS and 17 CQR recombinant progeny of a cross between the CQR parent Dd2 and the CQS clone HB3 (GSE12515) [12]. Each gene was considered as co-expressed with pfcrt if the absolute Spearman correlation coefficient threshold, /r/, between the transcript levels of the two genes exceeded a threshold of 0.5 (/r/ ≥ 0.5, FDR ≤ 0.20) across all CQR or CQS parasites. Of the 5150 genes in the P. falciparum genome for which transcript level data were available, transcripts for 581 (11%) genes were co-expressed with pfcrt in CQR progeny and 638 (12%) in CQS parasites (Figure 2 A and Additional file 2: Table S1). Of the genes that were co-expressed with pfcrt, 206 (30%) genes that were co-expressed with pfcrt in CQS parasites also were co-expressed with the gene in CQR; 70 (12%) would be expected by chance (hypergeometric test P = 1.25 x 10−57). We reasoned that if these observations are biologically meaningful, then gene pairs under high evolutionary constraint would show limited co-expression divergence in the two parasite groups. Synthetic lethal interactions are known to be under such a constraint [23,24]: in such cases, deletion of either single gene partner is compatible with growth because the second gene can buffer the loss of the first. However, the simultaneous deletion of the interacting gene leads to death. The ability of such genes to buffer mutations in their counterpart is influenced by the negative regulatory relationships that exist between them [23,25,26]. Of 14 synthetic lethal gene pairs determined by flux balance analysis [27], 2 are significantly co-expressed (Spearman correlation, /r/ ≥ 0.5) in CQS parasites, and one of these 2 pairs also is co-expressed in CQR parasites (Additional file 1 section B and Additional file 3: Table S2). That is, when considering co-expression between synthetic lethal pairs, half of the observable co-expression relationships are conserved between the 2 networks compared to 30% in 1000 random gene pairs; however, given only two co-expressed synthetic lethal pairs, this observation is of limited value. To follow this point further, we observed that co-expressed synthetic lethal pairs in both CQS and CQR parasites are negatively correlated as expected for synthetic lethal pairs (Additional file 1: section B). No such skew towards negative correlation is observed in randomly selected gene pairs (Wilcoxon test, P = 0.05, Additional file 1: section B). This led us to hypothesize that, if pfcrt genotype constrains pfcrt co-expression, then the divergence of pfcrt co-expression networks should be much lower within subsets of CQS or CQR progeny than between the two parasite groups. The divergence between CQR and CQS progeny (Figure 2 A and B) compared to within each parasite group (Figure 2 C and D) is much higher: While only 30% of pfcrt co-expressed genes are similarly co-expressed between CQR and CQS progeny, this percentage rises to 57% when comparing co-expression between randomly sampled subsets of CQR or CQS (60%). The divergence within each group is not statistically significant (divergence within CQS subsets Wilcoxon test, P = 0.35- Figure 2 C; within CQR subsets Wilcoxon test, P = 0.43- Figure 2 D), while the divergence between the groups is highly significant (Wilcoxon test P = 6.61 × 10−5, Figure 2 A and B). In addition, a very strong correlation is observed between the correlations of pfcrt and other genes within CQR or CQS subsets (r = 0.99 within CQS or CQR subsets, Figure 2 C and D) compared to between CQR and CQS subsets (r = 0.49) (Figure 2 A and B). Together these observations indicate that different pfcrt genotypes are associated with functionally relevant differential co-expression.Figure 2


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)

Co-expression of all genes withpfcrtgene in CQR and CQS parasites. (A) Correlation between the levels of each transcript in the genome to that of pfcrt, determined separately for CQS (x-axis) and CQR (y-axis) parasites. Grey region indicates genes whose correlation to pfcrt passed the threshold of FDR ≤ 0.20. (B) Average correlation between pfcrt and each transcript in 100 pairs of randomly sampled subsets of CQS (x-axis) and CQR (y-axis) parasites. Each subset of CQR or CQS parasites consists of transcriptional data from 8 parasite clones. (C) Average correlation between the transcript level of each gene to that of pfcrt in 100 pairs of randomly sampled subsets of CQS parasites. (D) Comparison of average pfcrt correlations in 100 pairs of randomly sampled subsets of CQR parasites. Like in (B), each randomly sampled subset of parasites consists of 8 parasites.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Fig2: Co-expression of all genes withpfcrtgene in CQR and CQS parasites. (A) Correlation between the levels of each transcript in the genome to that of pfcrt, determined separately for CQS (x-axis) and CQR (y-axis) parasites. Grey region indicates genes whose correlation to pfcrt passed the threshold of FDR ≤ 0.20. (B) Average correlation between pfcrt and each transcript in 100 pairs of randomly sampled subsets of CQS (x-axis) and CQR (y-axis) parasites. Each subset of CQR or CQS parasites consists of transcriptional data from 8 parasite clones. (C) Average correlation between the transcript level of each gene to that of pfcrt in 100 pairs of randomly sampled subsets of CQS parasites. (D) Comparison of average pfcrt correlations in 100 pairs of randomly sampled subsets of CQR parasites. Like in (B), each randomly sampled subset of parasites consists of 8 parasites.
Mentions: To determine the co-expression relationship between pfcrt and other genes, we reanalyzed microarray data from our lab that profiled transcripts at 18 hr post-erythrocye invasion of 19 CQS and 17 CQR recombinant progeny of a cross between the CQR parent Dd2 and the CQS clone HB3 (GSE12515) [12]. Each gene was considered as co-expressed with pfcrt if the absolute Spearman correlation coefficient threshold, /r/, between the transcript levels of the two genes exceeded a threshold of 0.5 (/r/ ≥ 0.5, FDR ≤ 0.20) across all CQR or CQS parasites. Of the 5150 genes in the P. falciparum genome for which transcript level data were available, transcripts for 581 (11%) genes were co-expressed with pfcrt in CQR progeny and 638 (12%) in CQS parasites (Figure 2 A and Additional file 2: Table S1). Of the genes that were co-expressed with pfcrt, 206 (30%) genes that were co-expressed with pfcrt in CQS parasites also were co-expressed with the gene in CQR; 70 (12%) would be expected by chance (hypergeometric test P = 1.25 x 10−57). We reasoned that if these observations are biologically meaningful, then gene pairs under high evolutionary constraint would show limited co-expression divergence in the two parasite groups. Synthetic lethal interactions are known to be under such a constraint [23,24]: in such cases, deletion of either single gene partner is compatible with growth because the second gene can buffer the loss of the first. However, the simultaneous deletion of the interacting gene leads to death. The ability of such genes to buffer mutations in their counterpart is influenced by the negative regulatory relationships that exist between them [23,25,26]. Of 14 synthetic lethal gene pairs determined by flux balance analysis [27], 2 are significantly co-expressed (Spearman correlation, /r/ ≥ 0.5) in CQS parasites, and one of these 2 pairs also is co-expressed in CQR parasites (Additional file 1 section B and Additional file 3: Table S2). That is, when considering co-expression between synthetic lethal pairs, half of the observable co-expression relationships are conserved between the 2 networks compared to 30% in 1000 random gene pairs; however, given only two co-expressed synthetic lethal pairs, this observation is of limited value. To follow this point further, we observed that co-expressed synthetic lethal pairs in both CQS and CQR parasites are negatively correlated as expected for synthetic lethal pairs (Additional file 1: section B). No such skew towards negative correlation is observed in randomly selected gene pairs (Wilcoxon test, P = 0.05, Additional file 1: section B). This led us to hypothesize that, if pfcrt genotype constrains pfcrt co-expression, then the divergence of pfcrt co-expression networks should be much lower within subsets of CQS or CQR progeny than between the two parasite groups. The divergence between CQR and CQS progeny (Figure 2 A and B) compared to within each parasite group (Figure 2 C and D) is much higher: While only 30% of pfcrt co-expressed genes are similarly co-expressed between CQR and CQS progeny, this percentage rises to 57% when comparing co-expression between randomly sampled subsets of CQR or CQS (60%). The divergence within each group is not statistically significant (divergence within CQS subsets Wilcoxon test, P = 0.35- Figure 2 C; within CQR subsets Wilcoxon test, P = 0.43- Figure 2 D), while the divergence between the groups is highly significant (Wilcoxon test P = 6.61 × 10−5, Figure 2 A and B). In addition, a very strong correlation is observed between the correlations of pfcrt and other genes within CQR or CQS subsets (r = 0.99 within CQS or CQR subsets, Figure 2 C and D) compared to between CQR and CQS subsets (r = 0.49) (Figure 2 A and B). Together these observations indicate that different pfcrt genotypes are associated with functionally relevant differential co-expression.Figure 2

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