Title: Genome Evolution in the Irish Potato Famine Pathogen Lineage

We face a crisis in food production and food prices.  (New York Times article from Feb 4th) If you can’t eat, you don’t worry about cancer.

World population is expected to peak at 9billion, and we’re not producing enough food.  One aspect of that is crop diseases, which could allow millions more to be fed if we could overcome this problem.

One of the most important is oomycete Phytophthora – (latin: plant destroyer), kills dicots – 10’s of Billions of dollars worth annually.  World’s biggest potato producer is China.

P.infestans suppresses or triggers plant immunity.  Able to invade cells, and forms stuctures between cells. (hyphae)

Some plants carry resistance and have an apoptosis like response.  Resistant plants also suppress the immunity supressors.  [strange phrasing is all mine.]

Genome sequence of Phytophthora infestans is complete.  Published last year. (Cover of nature – a rotten potato!)

Compare P infestans genome to others of the family – very large expansion. Number of genes is about the same, but 240Mbp vs. 65-95Mbp.  Much of it is repeat driven.

Effectors (immunity suppressors) typically occur in expanded repeat-rich and gene-poor loci. (Examples :RXLR, AVR4)

Most of the genes in the genome are all clustered with 1kb of each other, except for a spattering that occur in the repeat-rich regions.  This is an unusual distribution.

Core othologs are all in the clustered regular regions, effector genes all seem to be in the repeat-rich, again, unusual distribution.

Some discussion of how the parasite evolved along with “host jumping”.  Resequenced several isolates of 4 related strains. (all of which have the large genome expansion), and compared them.

4-fold number of genes missing in repeat region vs non-repeat regions. Repeat regions are more plastic. Look at dn/ds, and there is also different selection pressures between the two.

Repeat regions are also highly enriched in genes induced during colonization of tomato and potato (Raffaele et al, Science 2010)

Summary

• Core genome- high density region/low repeat content
• “plastic” region – low gene density/high repeat content
• high rates of gene turnover and positive selection in the plastic genome
• “niches” in the genome for rapid effector evolution.
• Unexpected rapidly-evolving “plastic” genome familes – cell wall hydrolases, histone and rRNA methyltransferases. [wasn’t discussed in the talk, as far as I can tell, bt interesting nonetheless.]

Applications:

Using Genomics to improve isease resistance.  Emergence of P infestans “blue 13” clone which is dominating UK isolates, but was barely present 10  years ago.

Core effectors as targets for resistance.

Synthetic R genes with expanded effector recognition.  (modify potato genes to improve resistance.)  Expansion: An R3a mutatnt that recognizes both AVR3a(ki and em form), it is expected to be effective against all P.infestans isolates.  Did create this in the lab… and some success. some clones were able to trigger cell death response.

Summary:

Single resitue mutations expand effector recognition.

Non-Gm solution through genome editing?

The knowledge of pathogen effectors and comparative genomes is essential.

[Again, a neat talk on a topic I knew nothing about.  Well delivered and very clearly explained.]

Title: Utilization of Next Generation Sequencing for Creation and Exploitation of the Tomato Genome.

[He’s creating tomato genomes?  Odd phrasing, but we’ll see how this works out.]

Think of this as a side dish to the genome technology you’ve been hearing. [nice…]

Tomato has a lot uses: eating, ketchup, de-skunking.  Most important source of vitamin A and C, simply because the amount of it that we eat.

Looking for a reference genome – a great biological system for studying fruit ripening.  Synteny of tomato and potato is high.  So, high quality reference for one of these will be a big help.  Related to pepper & eggplant as well.

Also a wide variety of tomato species, in a wide variety of environments.  (Picture shown of the wild progenitor of the tomato, but it’s really hard to see, unfortunately) Originated in south america, but domestication happened in Europe after Cortez brought it back. Modern breeding of tomatoes has all descended from european stock, so there is a LOT of diversity in the americas that has not yet been tapped.

International consortium working on this. Sequencing efforts started in 2004.  Originally started with a BAC approach.  1500 bacs were sequenced by the consortium.  Things have changed fast, and there is now 454 (31x), Sanger (3.6x), Illumina (82x) and Solid (141x) reads.

Assembler strategy covered – (sequencing, filtering and assembly – using all available information).

Very pretty genetic map and FISH slides. Also a summary of metrics from version 1.0 to 2.3, which is currently frozen for publication. Validation summary as well. [I’m sure all of this will be in the paper, so I’m not copying out details.]

An automated annotation pipeline, run by collaborators in Belgium, also frozen for publication.

Sequence is available: http://solgenomics.net

Still working on the sequence – setting a high target.  apx 1/3 of gaps can be closed by in silico means. (using Celera CABOG assembly).  using 100 bacs that spanned gaps, most of the sequences match the gap. [I may have missed something]

IMAGE2 used for closure and finishing – Iterative mapping and assembly for gap elimination.  Closed 11 of 12 gaps, and was able to reduce size of 12th gap.  Very resource intensive, tho.

Insights:

• duplications common to plant genomes are found here.
• triplication event for dicot clade
• etc.

New carotenoid genes with novel tissue-specificities.

Neat explanation/slide of the genetic regulation of the development and maturation of fleshy fruit. Chlorophyll degradation is inversely related to non-photosynthetic pigments.

Decoding the fruit transcriptome using large-scale strand-specific transcriptome sequencing.  Looking at both strands has caused them to revise how they believe expression occurs in the fruit. (skipped over examples in the interest of time.) About 5% of genes needed to be revised when strand-specific was taken into account.

Also doing ChIP-Seq, for tomato epigenome, both TF and histone data. (plant specific transcription factors)

Only fleshy fruits ripen, histone methylation seems to be correlated with regulation of genes involved – processes are tied together. [again, I missed part of the explanation] (Manning et al, 2006, Nat Gen 38)

Conclusions:

• high quality assembly of tomato genome
• continuing to refine it
• 97% of assembly is in 91 scaffolds, linked to 12 tomato chromosomes
• annotation of 35,000 genes.
• evidence of whole duplication events.
• epigenetic and RNA-Seq providing novel insights into control of fruit ripening.

[Interesting talk… I knew nothing about the tomato, and little about fruit ripening beyond what you learn in undergrad… There seem to be good papers on this, which would make a nice blog entry one day.]

Title: Spatially and Temporally Explicit Studies of the Human Microbiome

Sequencing is getting dramatically searching, as we all know.  What we can do now is dramatically different than what we could do a decade ago.

We know, since the invention of the microscope, (van Leeuwenhoek in 1683), we know that the human body is covered in bacteria.

Why should you care about your microbes? They can have interesting effects, eg, determine whether tylenol is toxic to your liver. (PNAS).  If you’re a fruit fly, it can determine your partner preferences (PNAS), steal genes from your food to help you digest it.

There are as many E. coli in your gut as there are people on earth.  It’s not the dominant member in the cut, though – it’s just best at growing on a petri dish.

Any two people you pick have 99.9% the same genome, but E. coli genomes can differ up to 40%.  Humans may not be unique like snow flakes, but our symbionts are!

Can start asking intelligent questions about our microbial selves.

How human are we?  in terms of cells, we’re 10% of the cells in our body.  only one percent of the DNA [if I got that right]

Most of the world is made of bacteria – animals and plants are a very small number of organisms.  and 99% aren’t culturable.

How do we look at them, then?  Get samples and extract DNA -> PCR amplify (usually SSU rRNA gene) -> sequence -> blast against genebank (but this is less and less useful. You now get a lot of hits on uncultured stuff. so skip this.) -> align and build trees to figure out what you can.

Problem: big trees are hard to understand and analyze.

Issue: need to interpret vast amounts of sequence/tree data.  Interpretation isn’t trivial as trees become massive.

Experiment: microbial biogeography on the keyboard?  (Are keys deserts for bacteria, different from fingertips?)  Result: we have distinct cultures, each of us, but our keyboards mirror our fingertip bacteria. (PNAS) – it was on CSI: Miami, so you know it’s true.

Darwin’s “Origin” has the first phylogenitic tree. [I did not know that]

Calculating a community distance metric. If trees are identical, distance =0.  If complete separation right from root, then distance = 1. [Very visually informative slides – discussing how we perceive the data in the metric.]

(Lozupone & Knight 2007, PNAS) [hope I got the name right – it’s jammed into the corner of a slide.]  Experiment looking for related-ness among a large number of samples.  They did see a significant divide between saline/non-saline.

Interesting: Extreme environments are not outliers.  However, there are outliers: they’re in the vertibrate gut!

QIIME: integrating analysis of hundreds of samples using barcodes.  Use 454 mostly, but also illumina. Use sequences to build phylogenetic trees.

[Joke about why we still call it “454”…. because that’s the temperature your money burns at when you do these experiments…. ]

[Joke section on sequencing technologies to watch out for… I can’t do it justice.]

but i digress…..

Different body habitats are very different from each other.  (2009 Science)  [I recall seeing this last year at AGBT, I think.] When on antibiotics, your communities change dramatically, and getting a picture of overall human microbiome variability.

You don’t need a lot of sequences per samples to see the patterns.  same pattern in 10 seq/sample as 1500seq/sample.

Have done these studies over time – over 3 months , visualized in a live 3D graph. [worth seeing, actually, very cool.]

Picture of Rodrigo Salvadore Dali painting. (It’s a pretty picture, but doesn’t tell you the whole story)

Detailed biogography of the human face.

[Nifty visualizations for the visualization of distribution of bacteria on the face.  Obviously can’t blog that.]

Where do the bacteria come from?  (Which raises privacy issues.)  Babies who come out vaginally  all have vaginal communities,  those that are born by c-section have a very different population.

Diversity of babies’ bacterial communities increases by day, and by the end of 3 years, they resemble their mother’s bacterial communities.

Do differences in the microbiome matter?  Fat mouse experiment says yes. (Two examples – Leptin and TLR5)  With TLR5 knockout mouse, the bacteria are different and seem to make the mouse hungrier – you can “rescue” the mouse by changing the bacteria. Same applies to Burmese pythons.

Fat vs thin are Bacteriodes vs Firmicules [missed which one is which, tho, and not sure about the spelling.]

Future directions: personalized medicine in developing nations. Pilot studies in “humanized mice” measuring input microbes, diet change and BMI.  Can you develop test from gut microbes to predict effects of diet/obesity/etc?

Much of the work is in developing systems for measuring and recording environmental conditions, etc.

Earth Microbiome project coming…

Conclusion: we all have a microbiome, and anyone can do this type of work now that sequencing is so cheap  – much of the cost of experiment is now in DNA extraction.

[A neat talk, summarizing a lot of published work.  Unfortunately, I couldn’t read most of the citations.  Talk was memorable for it’s good visualization tools and the excellent speaker.]

Title: Hybrid selection for sequencing pathogen genomes from clinical samples

Sample bottleneck for pathogen sequencing applications.  Host DNA monopolizes any sample, but culturing pathogens takes 6 weeks and can introduce bias.  Instead, use Hybrid selection.

Example: Plasmodium falciparium Malaria, but generalizes to any pathogen. (Some background presented.)

• Seq first in 2003,
• AT Rich: 81%!
• hard to sequence

“Agilent’s Sure select system” is new name of Hybrid Selection.

• Use bait, wash away non target DNA, then wash way rest, unhybridize.

40-fold enrichment when done on parasite DNA.  But, starting from blood spots, you don’t always have a lot.  However, no penalty for Whole Genome Amplification. (Same results as for straight blood.)

Technology helps remove human contamination, and gives much better data on regions covered by baits.

New approach: Whole Genome Baits (Rogov & Melnikov).

Whole Genome -> adaptors, (T7 tail) -> bait.

Whole genome bait results are comparable to synthetic baits, and again, no penalty for simulated clinical samples.

Coverage Similar to pure parasite DNA.  There are peaks and troughs, but comparable to pure DNA.  Dips correspond to AT rich regions, which are hard to sequence anyhow, and can’t be improved much over what you get with regular sequencing.

Tested on real clinical sample – blood spot, one year old.  Amplified, then whole genome selection.  Used HiSeq, and got 5.9% plasmodium DNA.

Figure of “Accurate SNP calling from Hyb-Sel data.”  Not just picking up parts that look like ref plasmodium, but also finding other stuff, which influences SNP calling – but still reasonable.

Other examples, with 50x enrichment.  Did other examples from 12 clinical samples, again saw good enrichment.

Summary: This is a good method for enriching pathogens, which helps reduce the amount of sequencing needed – and that means cost is also reduced.  Good for everyone.

Used on malaria for a model, but could work well on other pathogens.  Will enable sequencing of clinical samples collected in drug & vaccine trials.