Genomics and Cell Biology of the Apicomplexa

Transcript of Part 3: Designing and Mining Pathogen Genome Databases: From Genes to Drugs and Vaccines I

00:00:05.04	Hello, my name is David Roos,
00:00:07.06	and I'm a professor at the
00:00:09.14	University of Pennsylvania in Philadelphia.
00:00:11.25	And welcome to the third in this series
00:00:15.11	of iBio's lectures on my five favorite parasites,
00:00:20.14	organisms in the phylum Apicomplexa,
00:00:22.29	which include a wide range of pathogens
00:00:26.19	responsible for diseases such as malaria and toxoplasmosis.
00:00:31.13	And as you may have heard in the last couple of talks,
00:00:35.10	we've discussed various aspects of the cell biology
00:00:38.20	of these parasites
00:00:40.27	and how they serve...
00:00:43.14	provide a fantastic window
00:00:46.03	into a wide range of cell biological
00:00:48.07	and molecular genetic studies
00:00:50.12	on these organisms,
00:00:52.24	and a window into eukaryotic pathogens in general.
00:00:56.25	In this talk, I'd like to turn our attention
00:00:59.05	to another aspect of technologically driven science,
00:01:05.00	in fact an area that's really changing the way
00:01:09.14	we think about biology as a whole,
00:01:11.10	something that I'm sure has been touched on in many of the iBio seminars.
00:01:13.19	In, particular that's the area of genomics,
00:01:15.25	an area which is characterized by vast amounts of data --
00:01:22.16	complete data sets for all of the genes in the genome,
00:01:24.24	all of the proteins that are expressed
00:01:27.01	and the interactions between them.
00:01:30.24	And with the genome projects for...
00:01:34.19	the genome projects for humans
00:01:36.23	and for many other organisms,
00:01:38.19	such as the mosquitoes
00:01:41.13	that carry malaria parasites
00:01:43.28	and from the malaria parasite itself -- Plasmodium falciparum --
00:01:46.27	with these large scale data sets available,
00:01:49.21	we can start to look at many interesting biological questions
00:01:54.09	and explore those in interesting ways.
00:02:01.01	So, by way of background, or to provide some sort of a context,
00:02:04.14	let me remind you that in the last couple of lectures
00:02:07.21	we've discussed
00:02:11.11	a remarkable cell biological system present in malaria parasites,
00:02:15.22	and I've told you how malaria parasites and their relatives
00:02:21.28	harbor a fascinating organelle,
00:02:23.28	which they stole from an ancestral plant or alga
00:02:27.07	when an ancient parasite -- symbolized here by this...
00:02:30.17	by this cartoon diagram indicated in pink --
00:02:34.12	ate a eukaryotic alga
00:02:37.05	and retained the algal chloroplast
00:02:40.14	as a distinctive organelle
00:02:44.05	that is essential for parasite survival,
00:02:47.07	although it is no longer photosynthetic.
00:02:50.26	Here's a picture of that organelle,
00:02:53.04	colored in green in the electron micrograph
00:02:55.22	of a malaria parasite,
00:02:57.24	quite distinct from the parasite nucleus
00:02:59.17	or the Golgi apparatus
00:03:02.07	and specialized secretory organelles involved in invasion,
00:03:05.04	the inner membrane complex that we talked about
00:03:08.15	in the first of the... of these... of this iBio lecture series
00:03:13.02	that's critical for the assembly of parasites in a remarkable process
00:03:17.24	-- and the apicoplast --
00:03:21.19	the apicomplexan plastid surrounded by multiple membranes,
00:03:23.13	distinct from a primary endosymbiotic organelle,
00:03:26.19	the mitochondria.
00:03:28.26	And I told you how this organelle
00:03:32.03	is essential for parasite survival.
00:03:33.19	And we now have, effectively,
00:03:35.25	a complete metabolic pathway map,
00:03:37.22	including many enzymes and pathways
00:03:42.02	which are the subject of intense interest
00:03:45.00	as possible targets for new
00:03:48.07	anti-malarial development.
00:03:50.09	What I also told you in the last talk
00:03:52.23	is that that metabolic pathway map
00:03:57.00	was described using a variety of traditional biological approaches
00:04:02.25	-- biochemistry, cell biological purification,
00:04:05.20	more modern approaches of proteomics --
00:04:08.23	but that the most successful strategy
00:04:10.25	for identifying pathways and processes
00:04:13.25	associated with the apicoplast
00:04:15.22	was in fact a bioinformatics approach,
00:04:18.25	taking advantage of the availability of genome sequence data
00:04:22.27	-- some of it complete, some not --
00:04:24.25	developing a range of computational
00:04:29.08	and experimental screens
00:04:31.03	to identify proteins that had one or another features
00:04:36.04	that we suspected might be associated
00:04:39.12	with the apicoplast,
00:04:41.11	giving rise to a candidate series of genes
00:04:43.20	which could then be tested experimentally
00:04:46.03	at the laboratory bench.
00:04:49.05	So, this is, I think,
00:04:51.00	a dramatic illustration of the success of
00:04:56.00	computational biological approaches
00:04:57.22	for practical application in expediting traditional biological research.
00:05:05.13	And it rapidly became clear
00:05:09.28	that the same sorts of approaches
00:05:11.28	that were so successful in asking questions like
00:05:14.02	"find me plant chloroplast genes
00:05:16.27	in malaria parasites"
00:05:18.25	might readily be used to address
00:05:21.09	a variety of other questions.
00:05:23.12	We might want to ask, for example,
00:05:25.29	can we find proteins
00:05:30.00	on the left-hand end of chromosome 10
00:05:33.05	in malaria parasites
00:05:36.03	which have multiple transmembrane domains
00:05:38.12	and might therefore be involved in importing nutrients,
00:05:41.29	or exporting drugs,
00:05:43.29	and play an important role in drug sensitivity
00:05:46.16	or drug resistance?
00:05:49.27	And so, the desire to be able to address questions like that
00:05:52.05	gave rise to the Plasmodium genome database,
00:05:54.25	which we will talk about further,
00:05:56.11	as part of the Plasmodium genome project.
00:06:01.03	PlasmoDB, the Plasmodium genome database,
00:06:03.12	is accessible at PlasmoDB.org.
00:06:06.03	And this project has itself been so successful
00:06:10.15	that it's given rise to a series of other pathogen databases.
00:06:17.06	Let's take just a moment to talk about
00:06:21.19	the philosophy behind PlasmoDB.
00:06:24.21	Our goal in this project is
00:06:30.19	to take information that emerges from genome sequencing projects...
00:06:33.19	ideally, curated information...
00:06:35.22	but to integrate that with automated analyses,
00:06:38.16	recognizing that the rapid pace with which data emerges
00:06:41.17	from these large-scale projects
00:06:43.28	precludes the ability to look at
00:06:46.26	each and every bit of information manually.
00:06:50.15	We want to incorporate data that comes
00:06:53.01	not only from genome sequencing projects
00:06:56.10	and cDNA sequencing projects,
00:06:58.22	but also information that comes from population genetics studies,
00:07:00.20	from functional genomic studies on RNAs and proteins,
00:07:05.05	from chromatin modifications,
00:07:07.04	protein-protein interactome studies,
00:07:08.25	clinical outcomes data,
00:07:10.19	and many other sorts of projects.
00:07:13.23	To provide that data in a rapid way
00:07:17.17	that most importantly of all enables laboratory researchers like you,
00:07:22.15	like myself, like individuals in my laboratory
00:07:25.24	to ask their own questions.
00:07:28.00	And what I mean by this is that
00:07:30.26	our goal is not simply to provide a catalog,
00:07:32.27	an encyclopedia of malaria,
00:07:34.16	in which one could look up the answer
00:07:38.21	to any question that might arise.
00:07:40.18	And the reason for that is best manifested
00:07:43.23	by the apicoplast project.
00:07:47.06	So, for example,
00:07:50.06	imagine that we had a catalog
00:07:52.28	of all of the information associated
00:07:55.13	with malaria parasites, circa a decade ago,
00:07:58.02	and we wanted to look up the answer to the question of,
00:08:01.19	find plant genes in malaria parasites?
00:08:04.25	That's not an entry that we would
00:08:07.16	have found in that encyclopedia.
00:08:10.23	And yet we're able to identify those genes
00:08:12.25	in what seemed like a nonsensical question
00:08:14.29	by taking advantage of generic tools
00:08:16.29	that allow us to look for proteins
00:08:19.02	that have particular attributes
00:08:21.11	associated with targeting to this organelle
00:08:23.21	or its evolutionary history,
00:08:25.21	as we talked about in the last lecture of this series.
00:08:28.07	And this project as a whole
00:08:30.11	has been quite successful,
00:08:32.22	with worldwide access, many, many thousands of hits per day
00:08:37.22	from dozens and dozens of countries,
00:08:40.26	over 100 countries around the world.
00:08:44.21	Our motivating goal is that there is
00:08:47.11	no such thing as a stupid question.
00:08:49.21	So, let's see what we would look at...
00:08:52.02	observe if we were to look at
00:08:55.15	the Plasmodium genome database,
00:08:58.02	and I encourage all of you to do so yourselves.
00:09:01.12	So, let's turn to look at
00:09:05.19	what we would see if we look at the Plasmodium genome database,
00:09:07.29	and I encourage each of you to do so...
00:09:10.08	do so yourselves,
00:09:11.23	and we'll be spending a bit of time on this database.
00:09:14.13	If you were to look at that...
00:09:16.17	we can look... we can enter the database
00:09:18.26	from the standpoint of an individual gene,
00:09:20.28	this particular gene,
00:09:22.29	a gene with the uninformative name PF10_0407
00:09:27.14	corresponds to a gene that is annotated
00:09:30.13	as a dihydrolipoamide acetyl transferase,
00:09:33.00	at least putatively.
00:09:35.23	Now, this gene may be of interest to you;
00:09:37.28	it's of no particular interest to me.
00:09:41.04	We can see that it is on chromosome 10
00:09:43.11	located at about 840,000+ nucleotides
00:09:47.00	from one end,
00:09:48.29	that it's the subject of an ongoing annotation effort.
00:09:55.16	Including a modification of the initial gene model,
00:09:57.27	it appears that a new exon has been...
00:09:59.14	has been added into this predicted gene.
00:10:01.13	And I've excised a vast wealth of additional information
00:10:03.14	that comes from expression profiling studies
00:10:05.17	and computational analyses
00:10:08.05	of predicted motifs
00:10:09.29	and knockout studies that are indicated
00:10:12.23	down at the bottom of this gene.
00:10:14.13	And if you know more about this gene,
00:10:16.13	I'd encourage you to click on the link
00:10:18.14	for user comments, popping up a window,
00:10:21.04	adding additional information
00:10:23.14	that can serve as a global online laboratory notebook
00:10:27.00	about this particular gene
00:10:29.05	so that the information is available to others.
00:10:31.24	Now, of course, you might not want to explore
00:10:34.18	the genome from the standpoint of individual genes one at a time.
00:10:37.29	You might want to look at the genome
00:10:39.28	from the standpoint of the genome sequence itself.
00:10:44.20	And so, we can take a look at this from a chromosomal point of view.
00:10:48.08	In this case, we're now looking at chromosome 11,
00:10:52.07	at the entire 2 million nucleotides of chromosome 11
00:10:54.21	spanning this range above,
00:10:56.26	and there are dozens or hundreds of genes on this,
00:11:00.10	too many packed in together too tightly
00:11:02.24	to be able to explore.
00:11:05.26	And we can look at additional information,
00:11:08.02	in this case displaying information
00:11:10.18	related to polymorphic data
00:11:12.26	coming from the emerging genome sequences
00:11:15.03	of dozens and soon to be hundreds
00:11:18.15	of malaria parasites,
00:11:20.20	in this case color-coded
00:11:23.04	to represent those that are coding versus non-coding
00:11:25.20	or synonymous versus non-synonymous polymorphisms.
00:11:29.28	We can zoom in on that further
00:11:32.18	and display additional information,
00:11:34.10	in this case from a comparative genomic standpoint
00:11:36.16	looking at chromosome 4
00:11:39.12	from Plasmodium falciparum
00:11:42.00	and comparing that genome sequence
00:11:45.27	with comparable regions,
00:11:48.00	syntenic regions,
00:11:50.01	regions which can be aligned from Plasmodium vivax
00:11:52.10	-- another human malaria parasite --
00:11:54.13	or Plasmodium yoelii
00:11:56.12	-- a rodent malaria parasite.
00:11:58.27	And by aligning these,
00:12:01.29	we can see that virtually all of the genes
00:12:03.28	across this long span of DNA
00:12:06.25	-- hundreds of thousands of nucleotides --
00:12:09.01	can be matched up one-to-one with genes
00:12:12.15	in the Plasmodium vivax genome,
00:12:14.05	with a few exceptions.
00:12:15.27	There are some regions that don't line up very well.
00:12:17.26	We seem to be unable to find
00:12:20.00	a corresponding portion to the telomeric ends
00:12:22.04	-- the left or the right telomeric ends of this chromosome --
00:12:26.13	and that's perhaps not surprising
00:12:28.26	given that this region is rich in polymorphic genes
00:12:31.29	specific to Plasmodium falciparum.
00:12:34.29	Similarly, we have other gaps.
00:12:36.19	This gap here,
00:12:39.13	in which more polymorphic antigens
00:12:42.01	from Plasmodium falciparum correspond
00:12:44.18	to a break between two contiguous sequences
00:12:47.00	of Plasmodium vivax,
00:12:49.16	and another region down here in which an insertion
00:12:51.26	of several of these polymorphic genes
00:12:54.05	absent from Plasmodium vivax
00:12:56.00	corresponds to no obvious genes in Plasmodium vivax,
00:13:00.07	and here, conversely,
00:13:03.01	a region... a family of genes
00:13:05.02	-- vir genes in Plasmodium vivax --
00:13:07.09	which seem to have no match in Plasmodium falciparum.
00:13:11.09	Interestingly, we can see that
00:13:14.09	while the Plasmodium yoelii genome
00:13:16.03	has not been assembled to completion...
00:13:18.08	there are scores of individual contigs aligned here...
00:13:24.11	we can nevertheless find a one-to-one mapping
00:13:28.20	between most of these genes and the genes in Plasmodium vivax
00:13:30.25	and Plasmodium falciparum.
00:13:32.16	Thus, while the genome sequence for Plasmodium yoelii
00:13:36.02	is not complete,
00:13:38.17	it's sufficiently complete
00:13:40.22	that we can find most of the genes that may be of interest.
00:13:44.19	Now, I tried to make the point that
00:13:47.26	these tools are not simply resources to look up the answer to these questions
00:13:53.15	-- how many genes are there on chromosome 4
00:13:56.05	in Plasmodium falciparum? --
00:13:58.08	but can be used as research tools as well.
00:14:01.09	And I'd like to give you an illustration of that
00:14:03.19	from an interesting study that came
00:14:06.08	as a result of a workshop
00:14:09.16	for East African students in Tanzania
00:14:12.22	who were excited by the recent release
00:14:16.13	of Plasmodium vivax data
00:14:18.10	and wanted to explore that a little bit differently...
00:14:21.13	a little bit further.
00:14:23.11	And in the course of looking,
00:14:25.19	they noticed there are certain regions of the chromosome...
00:14:27.02	and here, a small portion of the Plasmodium falciparum chromosome...
00:14:30.12	chromosome 4,
00:14:33.26	zooming in on that portion of the larger chromosome
00:14:36.07	that we looked at earlier...
00:14:38.02	they noticed a particular region
00:14:39.29	with no annotated genes
00:14:42.03	that was extraordinarily rich in A and T nucleotides,
00:14:46.03	that the percent GC composition is virtually nil,
00:14:50.05	even in the context of Plasmodium falciparum,
00:14:52.17	the most AT-rich eukaryotic genome known.
00:14:56.08	And so, they asked the question,
00:14:57.29	whether this region,
00:14:59.19	which is known to correspond to the centromeric region
00:15:01.25	of the chromosome in Plasmodium falciparum,
00:15:03.28	might also correspond
00:15:07.07	to centromeric regions in Plasmodium vivax
00:15:09.04	or Plasmodium yoelii.
00:15:11.01	And so, they pulled up individual tracks
00:15:14.04	to display the genomes of Plasmodium yoelii
00:15:17.01	and found that in this case
00:15:20.02	the incompletely assembled Plasmodium yoelii genome
00:15:23.09	contained no sequence which was known to map to this region.
00:15:25.29	But in Plasmodium vivax,
00:15:27.26	we can see a single contiguous piece of DNA
00:15:30.05	that spans this region
00:15:32.20	that is also poor in annotated genes.
00:15:35.06	And sure enough, when these students turned on a display
00:15:38.20	to visualize the A and T nucleotides,
00:15:41.02	they see an AT-rich region of the Plasmodium vivax genome,
00:15:45.12	which undoubtedly corresponds to the...
00:15:48.07	the centromere...
00:15:49.21	these genes as well.
00:15:51.26	So, just to be clear, a group of students
00:15:53.27	with no previous exposure to these approaches,
00:15:56.24	sitting in Tanzania,
00:15:58.25	was able, for the first time anywhere in the world,
00:16:01.26	to carry out exploratory research,
00:16:04.28	computationally identifying highly probable
00:16:09.27	centromeric regions of the Plasmodium vivax genome.
00:16:14.03	Now, this is certainly a question
00:16:16.21	of great intellectual and academic interest.
00:16:20.02	But when we're dealing with important human pathogens
00:16:23.10	such as malaria parasites,
00:16:25.02	we obviously have many other concerns as well
00:16:29.02	of a practical nature.
00:16:30.24	We might want to ask, for example,
00:16:32.11	what are appropriate targets for drug or vaccine development?
00:16:36.19	And in addressing those kinds of questions,
00:16:40.04	it's worth taking a little bit of time
00:16:42.14	to think about what the questions are
00:16:46.14	that you'd like to ask
00:16:48.12	and how to formulate those in a computationally accessible form.
00:16:51.05	Just as we asked for the apicoplast,
00:16:53.26	find all genes that have one or another aspect
00:16:56.26	of the targeting signals associated with proteins
00:17:00.15	destined for the... that organelle,
00:17:02.28	and one or another of the evolutionary signatures
00:17:05.19	that suggests a plant or algal plastid ancestry.
00:17:09.06	We might want to ask,
00:17:11.16	if we were looking for targets for drug development,
00:17:13.10	for parasite homologues of genes
00:17:17.06	that are known to be targets for drug development...
00:17:19.14	dihydrofolate reductase, for example,
00:17:21.22	a prominent target for chemotherapeutic drugs
00:17:24.02	and indeed for drugs effective against malaria,
00:17:26.29	or proteases, a more recently explored class of targets
00:17:33.29	for a... for a variety of in infectious diseases,
00:17:37.18	genes that might be associated with the cytoskeleton,
00:17:40.21	that might highlight those remarkable aspects
00:17:43.13	of parasite division
00:17:45.27	that distinguish them from the way our cells divide
00:17:48.15	that we talked about in the first iBio lecture in this series.
00:17:51.14	We might want to look for proteins associated with the apicoplast
00:17:55.02	or that are expressed at appropriate stages of the parasite life cycle,
00:17:58.29	or in the appropriate place,
00:18:01.15	or for which we have candidate inhibitors.
00:18:04.12	All of these are questions
00:18:07.12	that might be relevant to drug target identification,
00:18:10.28	and we'll come back to look at some of those questions
00:18:14.01	a little bit later on.
00:18:15.17	But as maybe a more challenging exercise,
00:18:18.16	I'd like to think instead about vaccine candidates.
00:18:22.25	And this is a... I would submit,
00:18:24.19	a more challenging exercise
00:18:26.25	for at least two reasons,
00:18:28.11	first of all because we know perhaps less
00:18:32.07	about what constitutes an effective vaccine target,
00:18:35.01	and secondly because I for one
00:18:39.07	claim no expert knowledge
00:18:41.11	about vaccinology.
00:18:43.01	But even as a naive immunologist,
00:18:44.26	I might imagine that we might want to look for genes, for example,
00:18:49.19	that are immunodominant or expressed on the surface of the...
00:18:52.21	of the parasite.
00:18:55.07	Now, I'd be happy to take you through
00:18:58.10	a series of graphic illustrations
00:19:01.12	of how one might go about identifying candidate drug targets
00:19:04.16	or vaccine targets,
00:19:08.00	but I would urge all of you,
00:19:10.07	when confronted with resources
00:19:14.18	-- online resources that are designed to be able to explore these projects --
00:19:19.20	to challenge yourself and challenge whoever is introducing you to those resources
00:19:24.18	to let you explore it on your own.
00:19:27.10	And so, rather than providing a series
00:19:32.06	of pre-prepared graphic illustrations,
00:19:34.05	I'd like to run a live demonstration.
00:19:37.15	We're going to take a break for just a moment,
00:19:39.16	and I'm going to set up my laptop here
00:19:43.03	to be able to run a live experiment --
00:19:47.03	a series of questions that's designed
00:19:49.27	to identify candidate vaccine targets.
00:19:52.17	And I hope you'll find this of interest.
00:19:54.11	It's something you may want to follow along on your own computer,
00:19:58.12	either now or at some other time.
00:19:59.29	So, let's take just a break and get this set up.