1. Mother of Green Phylogenomics of the P
Mother of Green Phylogenomics of the P. falciparum Apicoplast Indiana Center for Insect Genomics An International Center of Excellence University of Notre Dame Purdue University Indiana University

2. Mother of Green Malaria causes 1.5 - 2.7 million deaths every year
3,000 children under age five die of malaria every day
Plasmodium falciparum causes human malaria
Drug resistance a world-wide problem
Targeted drug design through phylogenomics
P. falciparum

3. Mother of Green P. falciparum has three genomes
Nuclear, mitochondrial, plastid
Animals and insects have only two
Target the third genome
No harm to animals
New antimalarial drug
High risk, high tech, high payoff
J. Romero-Severson
Department of Biological Sciences
Greg Madey
Department of Computer Science

4. Mother of Green Plastids are the third genome Intracellular organelles
Terrestrial plants, algae, apicomplexans
Functions in plants and algae
Photosynthesis
Oxidation of water
Reduction of NADP
Synthesis of ATP
Fatty acid biosynthesis
Aromatic amino acid biosynthesis
Functions in apicomplexans ?
Chloroplast in plant cell
plastid
Apicoplast in P. falciparum
Plastid in Toxoplasma sp.

5. Mother of Green The apicoplast appears to code for <30 proteins.
Repair, replication and transcription proteins
Why is the apicoplast essential?

6. Mother of Green Phylogenomics Find the ancestors of the apicoplast
Identify genes in the ancestors
Determine gene function
Look for these genes in the P. falciparum nucleus
Then study regulatory mechanisms in candidate genes

7. Phylogenomics of plastids
Very old lineage (> 2.5 billion years)
Cyanobacterial ancestor
Three main plastid lineages
Glaucophytes
Group of freshwater algae
Chloroplast resembles intact cyanobacteria
Chlorophytes
Green plant lineage
Chloroplast genome reduced
Many chloroplast genes now in nuclear genome
Rhodophytes
Red algal lineage
Chloroplast genome bigger than in green plants
Oomycetes
Apicomplexans

8. Phylogenomics of plastids
One plastid origin
Phylogenomics of plastids
One cyanobacterial ancestor ?
Many?
Lineages are not linear
Multiple plastid origins

9. The process of endosymbiosis.
Nucleus
The process of endosymbiosis.
Horizontal Gene Transfer (arrows) from the plastid to the nucleus.
The nucleomorph is a remnant of the original endosymbiont nucleus.
Cyanobacteria
Primitive eukaryote
Endosymbiont plastid
Nucleus
Second eukaryote
Nucleomorph
Secondary
endosymbionts
Plastid disappears
Secondary
nonphotosynthetic
endosymbiont

10. Tertiary endosymbiosis. Horizontal Gene Transfer
Secondary
endosymbiont
Third eukaryote
Tertiary
endosymbionts
Plastid disappears
Tertiary
nonphotosynthetic endosymbiont
P. falciparum

11. The information gathering problem
Rapid accumulation of raw sequence information
~100 sequenced chloroplast genomes
~55 sequenced cyanobacterial genomes
Rate of accumulation is increasing
Information accumulates faster than analyses finish
Information in forms not readily accessible
Solution
Semi-automated web-services
“Smart” web-services

12. The computational problem
Phylogenetic trees
NP-hard
Poisoned by information conflict
Phylogenies based on individual genes
Maximum likelihood models exist
Processes are parallelizable
Access to compute farms inadequate
RAW number-crunching power
Greedy
Similar genealogies may be merged
Convergence not possible for all
Makes computational problem more daunting

13. Candidate genes for deep phylogeny
The synthesis of ATP

14. The wheel that powers life
Light-dependent ATP synthesis (photophosphorylation)
Hypothesis:
Evolution of ATP synthase severely constrained
Candidate for ascertainment of deep phylogeny
1st: Individual subunit genealogy
2nd: Merge the data, reanalyze
ATP synthase
The wheel that powers life

15. Phylogenomics of the P. falciparum Apicoplast
Extract data from public and private databases
Web services
Choose a metric for sequence comparison
Megablast and others
Choose a method to infer genealogy
Maximum Likelihood (ML)
Develop a strategy to use ML that is feasible
fastDNAml and others
Create a computational infrastructure
Compute farms
Dedicated chunks of compute farms
Deal with management issues
Solve band width problems
Convince someone to fund this!

16. Indiana Center for Insect Genomics
Mission
Create genomics tools for high impact arthropods lacking such tools
Develop integrated bioinformatics programs for arthropod genomics
Develop specific projects with potential practical application
Foster high risk ideas with mini-grants
Jeanne Romero-Severson, Director
Frank Collins, Co-PI at University of Notre Dame
Peter Cherbas, Co-PI at Indiana University
Jeff Stuart, Co-PI at Purdue University