Growth rate optimization of malaria identifies species
Growth rate optimization of malaria identifies species- and stage-specific differences in nutrient essentiality and drug targeting Alyaa M. Abdel-Haleem1, Hooman Hefzi2, Katsuhiko Mineta1, Xin Gao1, Takashi Gojobori1, Bernhard O. Palsson2, Nathan E. Lewis2 & Neema Jamshidi2 1Computational
bioscience research center (CRBC), King Abdullah University of Science and Technology (KAUST), 2Systems Biology Research Group (SBRG), University of California, San Diego
1 Abstract
4 Stage-dependent redirection of central carbon flux in Malaria
Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite1. Malaria infections are difficult to treat particularly due to the ability of the parasite to remain latent in hosts as well as its complex life cycle. Genome-scale metabolic models (GeMMs) enable hierarchical integration of disparate data types into a framework amenable to computational simulations enabling deeper mechanistic insights from high-throughput data measurements. In this study, GeMMs of multiple malaria species were used to study metabolic similarities and differences across the malaria genus. Data-driven biological constraints, and objectives were formulated mathematically and, computational optimization was used to determine optimal steady-state flux solutions for the stages and species GeMMs. In silico geneknock out simulations across species and stages GeMMs uncovered functional metabolic differences between human- and rodent-infecting species as well as across the parasite’s lifecycle stages. These findings may help identify drug regimens that are more effective in targeting human-infecting species across multiple stages of the parasite.
Generic P. falciparum model (iAM-Pf480)
Characterize stage-specific metabolic features Contextualization4 of high throughput data
Identify multi-stage drug targets Life stage-specific RNA-Seq3 expression data
2 Introduction: Constraint-based metabolic modeling
Asexual stages
Among potential drug targets, metabolic genes are of particular interest, since many anabolic and catabolic processes are critical for cellular growth and survival. Furthermore, methods have been developed to identify vulnerabilities in human pathogens by accurately predicting essential metabolic genes in genome-scale metabolic network reconstructions.
Sexual stages
Oxidative PPP
Upper glycolysis
Inositol metabolism
Non-oxidative PPP
Upper and lower glycolysis
Lower glycolysis
Fructose & mannose metabolism
Oxidative PPP
Upper glycolysis
Inositol metabolism
Growth related
Lower glycolysis
Upper and lower glycolysis
Nicotinate & nicotinamide metabolism
Fatty acid metabolism
Redox metabolism
Hemoglobin degradation
Non-oxidative PPP & Inositol GPI-anchor metabolism biosynthesis
Phospholipid biosynthesis
Growth related
Purine salvage
Non-oxidative PPP
Non-oxidative PPP
Folate biosynthesis
Inositol Phospholipid biosynthesis metabolism
Lysine degradation
Nucleotide metabolism
Lysine Isoprenoid degradation biosynthesis
Fig. 3| Stage-specific central metabolic flux patterns in malaria. Correlated reaction sets for iAM-Pf480 were used to define stage and model specific pathways, which were analyzed and compared across different stages. Glycolysis was split into upper and lower branches in all stages except GV where the non-oxidative PPP branch was correlated with inositol metabolism. The direction of fluxes reflect the importance of inositol metabolism in the sexual stages relative to generation of glycolytic intermediates (cf. asexual stages)
5 Pan-metabolic profiling of Malaria Through optimization of an objective function, Flux Balance Analysis (FBA) can identify a single optimal flux distribution Multiple species metabolic reconstructions
Comparative genomics pnto-R_e
Fig. 1| Constraint-based modeling. Orth, J.D. et al., What is flux balance analysis ? Nature Biotechnology 28 (2010)
pnto-R_e
ACP_h
3 Reconstructing and validating the metabolic accoa_h
ACOATAc
ACCOACc
malACP_h
co2_h
malcoa_h
ACP_h
accoa_h
MCOATAc
KAS14c
PNTK
atp_c
HMPK1
pi_h
coa_h actACP_h
adp_c
Literature
pnto-R_c
malACP_h malcoa_h
DPCOAKc
ACP_h
PNTK
MCOATAc
adp_c
4ppan_c
malcoa_h
ctp_c cys-L_c
4ampm_c adp_c
atp_c ACP_h
DPCOAKc
4ppan_c
malcoa_h
ctp_c
atp_h
cys-L_c
dpcoa_h
PPNCL2
HMPK1 PMPK TMPPP THMP TMDPK TDP THMDP TMPKr
GPDDA1
PMPK
atp_h
adp_c
dpcoa_h
DPCOAtc
4ppcys_c
2mahmp_c ppi_c
PTPATi
PPCDC
dpcoa_c
THMP
atp_c
ppi_c
iAM-Pf480 essential 7.5 ●
5.0
●
essential nonessential
2.5
nonessential
●
●
1
iAM-Pf480 essential essential
●
7
42 16
nonessential 0 13 nonessential
pan4p_c
0.0
Pearson correla+on coefficient = 0.77 y = 0.933 + 24.9x, r2 = 0.594
● ●
0.00
0.05
0.10
0.15
predicted_fluxes Predicted sampled flux rates (nmol/10^8cells/min)
MCC
TMPKr
95% (6.88e-05)
0.9
TDP pi_c
Accuracy
71%
Fig. 2| Performance evaluation of iAM-Pf480 |Comparison of iAM-Pf480 gene essentiality predictions (simulating standard in vitro growth conditions) showed 95% and 71% accuracy when compared to single gene deletion and drug inhibition experiments, respectively. The correlation plot reflects the Validation of iAM-Pf480 predicted glycolytic flux rates against experimentally measured fluxomic data2. 0.20
atp_c
thmpp_c
Accuracy (p value)
LPASE
lpchol
pan4p_c atp_c
adp_c
● ●
co2_c
THMDP
HMPK1 adp_c
CHOLtu
ethamp_c
adp_c ethamp_c
adp_c atp_c
4ampm_c
glyc3p_c chol_c
ETHAMPtg
cmp_c
ETHAMPtg
atp_c
GPDDA1
CHOLK
adp_c
4mpetz_c
ethamp_g 3
amet_g
ctp_c 2mahmp_c P. falciparum P. berghei ETHAK --- PETHMTg ppi_c thm_c CHOLPtg ETHAMPtg --pi_c -TMPPP 3 dag PETHMTg -- THMP ✕ cholp_g SMPD3g --- ppi_c atp_c crm CEPTC CEPTC 100% 100% thmmp_c TMDPK pi_c CHLPCTD 100% 100% SMPD3pfg 2 CHOLPtg -- amp_c -atp_c pchol THMDP TMPKr CHOLK 3% 100% adp_c LPASE -sphymyln -thmpp_c GPDDA1 -- TDP -CHOLtu --pi_c
cdpchol_c
PPCDC ppi_c
pi_c amp_c
ETHAK
adp_c
CHLPCTD
hdca_c
thmmp_c
TMDPK
2
1 -
PTPATi ppi_c
co2_c
atp_c
Experimental Predictions
Experimentally measured flux rates (nmol/10^8cells/min) measured_fluxes
iAM-Pf480 accurately predicts gene essentiality and internal flux rates pfal_WT P. falciparum wild-type
TMPPP
pi_c
chol_e
ETHAK
PMPK
4ppcys_c
thm_c
4ahmmp_c
cholp_c
DPCOAtc
dpcoa_c
atp_c
CHOLK
g3pc_c
4mpetz_c cmp_c
ppi_c
etha_c
atp_c
PPNCL2
cmp_c
Add new knowledge
P. falciparum P. berghei chol_e -✕ -✕ -✕ CHOLtu ---100% glyc3p_c --chol_c ---- atp_c --
4ahmmp_c
atp_c
ACCOACc
co2_h
Genome Manual curation
hco3_h
adp_h
ACP_h
Choline metabolism
etha_c
atp_c
atp_h
acACP_h
pnto-R_c
pi_h
coa_h
actACP_h
PNTOt6 Thiamine metabolism
ACOATAc
atp_c
Step 2: Model formulation and validation adp_h
Step 1: Build species-specific reconstruction
ACP_h
hco3_h
network of P. falciparum, iAM-Pf480 KAS14c
Variations in thiamine and choline metabolism in rodent versus non-rodent infecting species
PNTOt6
atp_h
acACP_h
Identify metabolic differences and similarities across species
adp_c
g3pc_c
ethamp_g
cholp_c
CHLPCTD
hdca_c
ctp_c
cdpchol_c ppi_c
LPASE
amet_g
3
PETHMTg
CHOLPtg
ahcys_g
3
dag cholp_g
ahcys_g
crm
CEPTC lpchol
SMPD3pfg cmp_c pchol
sphymyln
6 Conclusions and Future Directions • Our results suggest new therapeutic drug targets for malaria that could have dual efficacy as antimalarial and blocking transmission to the mosquito vector. • In addition, our models provide insight into the relevance of data derived from experimental animal models. • The availability of the life cycle stages’ and species-specific models also provide a resource for analysis and interpretation of various levels of data in the context of prior knowledge paving the way for dissecting the metabolic interactions between the parasite and human host cells. References [1[Organization, W.H. in World Malaria Report Vol. 2015 (2015). [2] Cobbold, S.A. et al. Kinetic flux profiling elucidates two independent acetyl-CoA biosynthetic pathways in Plasmodium falciparum. J Biol Chem 288, 36338-36350 (2013). [3]López-Barragán MJ et al., Directional gene expression and antisense transcripts in sexual and asexual stages of Plasmodium falciparum. BMC Genomics (2011). [4]Becker S, Palsson B .Context-specific metabolic networks are consistent with experiments. PLoS Comput Biology (2008)
bioscience research center (CRBC), King Abdullah University of Science and Technology (KAUST), 2Systems Biology Research Group (SBRG), University of California, San Diego
1 Abstract
4 Stage-dependent redirection of central carbon flux in Malaria
Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite1. Malaria infections are difficult to treat particularly due to the ability of the parasite to remain latent in hosts as well as its complex life cycle. Genome-scale metabolic models (GeMMs) enable hierarchical integration of disparate data types into a framework amenable to computational simulations enabling deeper mechanistic insights from high-throughput data measurements. In this study, GeMMs of multiple malaria species were used to study metabolic similarities and differences across the malaria genus. Data-driven biological constraints, and objectives were formulated mathematically and, computational optimization was used to determine optimal steady-state flux solutions for the stages and species GeMMs. In silico geneknock out simulations across species and stages GeMMs uncovered functional metabolic differences between human- and rodent-infecting species as well as across the parasite’s lifecycle stages. These findings may help identify drug regimens that are more effective in targeting human-infecting species across multiple stages of the parasite.
Generic P. falciparum model (iAM-Pf480)
Characterize stage-specific metabolic features Contextualization4 of high throughput data
Identify multi-stage drug targets Life stage-specific RNA-Seq3 expression data
2 Introduction: Constraint-based metabolic modeling
Asexual stages
Among potential drug targets, metabolic genes are of particular interest, since many anabolic and catabolic processes are critical for cellular growth and survival. Furthermore, methods have been developed to identify vulnerabilities in human pathogens by accurately predicting essential metabolic genes in genome-scale metabolic network reconstructions.
Sexual stages
Oxidative PPP
Upper glycolysis
Inositol metabolism
Non-oxidative PPP
Upper and lower glycolysis
Lower glycolysis
Fructose & mannose metabolism
Oxidative PPP
Upper glycolysis
Inositol metabolism
Growth related
Lower glycolysis
Upper and lower glycolysis
Nicotinate & nicotinamide metabolism
Fatty acid metabolism
Redox metabolism
Hemoglobin degradation
Non-oxidative PPP & Inositol GPI-anchor metabolism biosynthesis
Phospholipid biosynthesis
Growth related
Purine salvage
Non-oxidative PPP
Non-oxidative PPP
Folate biosynthesis
Inositol Phospholipid biosynthesis metabolism
Lysine degradation
Nucleotide metabolism
Lysine Isoprenoid degradation biosynthesis
Fig. 3| Stage-specific central metabolic flux patterns in malaria. Correlated reaction sets for iAM-Pf480 were used to define stage and model specific pathways, which were analyzed and compared across different stages. Glycolysis was split into upper and lower branches in all stages except GV where the non-oxidative PPP branch was correlated with inositol metabolism. The direction of fluxes reflect the importance of inositol metabolism in the sexual stages relative to generation of glycolytic intermediates (cf. asexual stages)
5 Pan-metabolic profiling of Malaria Through optimization of an objective function, Flux Balance Analysis (FBA) can identify a single optimal flux distribution Multiple species metabolic reconstructions
Comparative genomics pnto-R_e
Fig. 1| Constraint-based modeling. Orth, J.D. et al., What is flux balance analysis ? Nature Biotechnology 28 (2010)
pnto-R_e
ACP_h
3 Reconstructing and validating the metabolic accoa_h
ACOATAc
ACCOACc
malACP_h
co2_h
malcoa_h
ACP_h
accoa_h
MCOATAc
KAS14c
PNTK
atp_c
HMPK1
pi_h
coa_h actACP_h
adp_c
Literature
pnto-R_c
malACP_h malcoa_h
DPCOAKc
ACP_h
PNTK
MCOATAc
adp_c
4ppan_c
malcoa_h
ctp_c cys-L_c
4ampm_c adp_c
atp_c ACP_h
DPCOAKc
4ppan_c
malcoa_h
ctp_c
atp_h
cys-L_c
dpcoa_h
PPNCL2
HMPK1 PMPK TMPPP THMP TMDPK TDP THMDP TMPKr
GPDDA1
PMPK
atp_h
adp_c
dpcoa_h
DPCOAtc
4ppcys_c
2mahmp_c ppi_c
PTPATi
PPCDC
dpcoa_c
THMP
atp_c
ppi_c
iAM-Pf480 essential 7.5 ●
5.0
●
essential nonessential
2.5
nonessential
●
●
1
iAM-Pf480 essential essential
●
7
42 16
nonessential 0 13 nonessential
pan4p_c
0.0
Pearson correla+on coefficient = 0.77 y = 0.933 + 24.9x, r2 = 0.594
● ●
0.00
0.05
0.10
0.15
predicted_fluxes Predicted sampled flux rates (nmol/10^8cells/min)
MCC
TMPKr
95% (6.88e-05)
0.9
TDP pi_c
Accuracy
71%
Fig. 2| Performance evaluation of iAM-Pf480 |Comparison of iAM-Pf480 gene essentiality predictions (simulating standard in vitro growth conditions) showed 95% and 71% accuracy when compared to single gene deletion and drug inhibition experiments, respectively. The correlation plot reflects the Validation of iAM-Pf480 predicted glycolytic flux rates against experimentally measured fluxomic data2. 0.20
atp_c
thmpp_c
Accuracy (p value)
LPASE
lpchol
pan4p_c atp_c
adp_c
● ●
co2_c
THMDP
HMPK1 adp_c
CHOLtu
ethamp_c
adp_c ethamp_c
adp_c atp_c
4ampm_c
glyc3p_c chol_c
ETHAMPtg
cmp_c
ETHAMPtg
atp_c
GPDDA1
CHOLK
adp_c
4mpetz_c
ethamp_g 3
amet_g
ctp_c 2mahmp_c P. falciparum P. berghei ETHAK --- PETHMTg ppi_c thm_c CHOLPtg ETHAMPtg --pi_c -TMPPP 3 dag PETHMTg -- THMP ✕ cholp_g SMPD3g --- ppi_c atp_c crm CEPTC CEPTC 100% 100% thmmp_c TMDPK pi_c CHLPCTD 100% 100% SMPD3pfg 2 CHOLPtg -- amp_c -atp_c pchol THMDP TMPKr CHOLK 3% 100% adp_c LPASE -sphymyln -thmpp_c GPDDA1 -- TDP -CHOLtu --pi_c
cdpchol_c
PPCDC ppi_c
pi_c amp_c
ETHAK
adp_c
CHLPCTD
hdca_c
thmmp_c
TMDPK
2
1 -
PTPATi ppi_c
co2_c
atp_c
Experimental Predictions
Experimentally measured flux rates (nmol/10^8cells/min) measured_fluxes
iAM-Pf480 accurately predicts gene essentiality and internal flux rates pfal_WT P. falciparum wild-type
TMPPP
pi_c
chol_e
ETHAK
PMPK
4ppcys_c
thm_c
4ahmmp_c
cholp_c
DPCOAtc
dpcoa_c
atp_c
CHOLK
g3pc_c
4mpetz_c cmp_c
ppi_c
etha_c
atp_c
PPNCL2
cmp_c
Add new knowledge
P. falciparum P. berghei chol_e -✕ -✕ -✕ CHOLtu ---100% glyc3p_c --chol_c ---- atp_c --
4ahmmp_c
atp_c
ACCOACc
co2_h
Genome Manual curation
hco3_h
adp_h
ACP_h
Choline metabolism
etha_c
atp_c
atp_h
acACP_h
pnto-R_c
pi_h
coa_h
actACP_h
PNTOt6 Thiamine metabolism
ACOATAc
atp_c
Step 2: Model formulation and validation adp_h
Step 1: Build species-specific reconstruction
ACP_h
hco3_h
network of P. falciparum, iAM-Pf480 KAS14c
Variations in thiamine and choline metabolism in rodent versus non-rodent infecting species
PNTOt6
atp_h
acACP_h
Identify metabolic differences and similarities across species
adp_c
g3pc_c
ethamp_g
cholp_c
CHLPCTD
hdca_c
ctp_c
cdpchol_c ppi_c
LPASE
amet_g
3
PETHMTg
CHOLPtg
ahcys_g
3
dag cholp_g
ahcys_g
crm
CEPTC lpchol
SMPD3pfg cmp_c pchol
sphymyln
6 Conclusions and Future Directions • Our results suggest new therapeutic drug targets for malaria that could have dual efficacy as antimalarial and blocking transmission to the mosquito vector. • In addition, our models provide insight into the relevance of data derived from experimental animal models. • The availability of the life cycle stages’ and species-specific models also provide a resource for analysis and interpretation of various levels of data in the context of prior knowledge paving the way for dissecting the metabolic interactions between the parasite and human host cells. References [1[Organization, W.H. in World Malaria Report Vol. 2015 (2015). [2] Cobbold, S.A. et al. Kinetic flux profiling elucidates two independent acetyl-CoA biosynthetic pathways in Plasmodium falciparum. J Biol Chem 288, 36338-36350 (2013). [3]López-Barragán MJ et al., Directional gene expression and antisense transcripts in sexual and asexual stages of Plasmodium falciparum. BMC Genomics (2011). [4]Becker S, Palsson B .Context-specific metabolic networks are consistent with experiments. PLoS Comput Biology (2008)
