1. Alyssa Kent 6/1/2013 C-MORE Student Symposium
Geographical differentiation of the genome content of the marine cyanobacterium Prochlorococcus
Alyssa Kent
6/1/2013
C-MORE Student Symposium

2. Phylogeography
Relationships between gene genealogies--phylogenetics--and geography
Geographically structured genetic signals within and among species
Emerson K J et al. PNAS 2010;107:
depth and temperature were the strongest factors for prochlorococcus whereas nutrients were stronger factors for synechococcus—this was accomplished by looking at dot blot hybridizations along several cruises on transects and at depth.

3. Phylogeographic importance
Understanding genetic structure in situ
important for modeling
helps determine biological and physical factors that govern who is there (ecotypes or genes)
a richer ecological framework will help inform understanding of microbial evolution
Courtesy:

4. Prochlorococcus as a model organism
Genetically diverse with several clades
Different ecotypes, e.g. eMIT9312, eMED4, and eMIT9313
Fall along different environmental gradients: light, temperature, nutrients
Widely distributed
40°S to 40°N
Tropical and subtropical waters
Up to 200 meters deep
Numerically abundant
Up to 105 cells per ml in oligotrophic waters
Heavily studied and sequenced
Johnson et al. 2006
Image courtesy Bob Andersen and David Patterson

5. Microbial Phylogeography
Dot-blot hybridizations, flow cytometry
Horizontal and vertical partitioning of ecotypes occurs globally
Prochlorococcus more influenced by temperature & depth than nutrients
Can we look at whole genome content to reveal geographic patterns?

6. Genome content signal Can gene patterns define biogeography?
Core versus noncore genes
Noncore-genome = selectome?
niche exploitation
phage recognition
polyclonal populations
which indicates,
Kettler & Martiny et al., 2007

7. Clustering Pipeline Map reads to orthologous groups
Threshold sample sizes
Rarefy
Normalize to gene length
Hierarchical Clustering
Extract reads mapping to Prochlorococcus from files
Bray Curtis Similarity
Collect metagenomes from
previous projects
Add into MG-RAST pipeline, if not already present
Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of rarefaction curves. This curve is a plot of the number of species as a function of the number of samples. On the left, the steep slope indicates that a large fraction of the species diversity remains to be discovered. If the curve becomes flatter to the right, a reasonable number of individual samples have been taken: more intensive sampling is likely to yield only few additional species.
Retrieve best hits with
protein annotations
Compare to geography

8. Starting Samples

9. Threshold

10. Rarefaction Curve
Sample sites
Orthologs

11. CORE rarefied to min(noncore, core) Bray-Curtis used as distance
hierarchical clustering (complete)
* note that there are samples with different depths

12. NONCORE rarefied to min(noncore, core) Bray-Curtis used as distance
hierarchical clustering (complete)
* note that there are samples with different depths

13. Permanova (Adonis in R)
NON-CORE
CORE
Source df MS F
P(Perm)
Regions 10
0.125
1.498
0.036*
0.039
1.199
0.172
Depth 1
0.158
1.898
0.040*
0.041
1.285
0.097
Regions X Depth 4
0.083
0.840
0.706
0.031
0.947
0.494
Permanova-Analysis of variance using distance matrices — for partitioning distance matrices among sources of variation and fitting linear models (e.g., factors, polynomial regression) to distance matrices; uses a permutation test with pseudo-F ratios

14. Conclusions
Gene content based similarity clustering in the non-core rather than the core genes is driven by both region and depth.
Does not seem to be any interactive effect observed in statistic between region and depth
Supports Zwirglmaier et al., 2008, however not all of the variation is accounted for in these factors

15. Future Directions Is there phylogenetic signal as well?
Explore ecotype differentiation using a marker gene found in metagenomes
Look at genetic drivers of gene content clustering (efforts have not yielded any obvious patterns yet)
Is there any dependence on diversity of other organisms captured in metagenomes
Correlate with more environmental factors to assess the ecological drivers

16. Acknowledgments Dr. Adam Martiny Dr. Renaud Berlemont Cecilia Batmalle
This investigation was supported by the National Institute of Biomedical Imaging and Bioengineering, National Research Service Award EB from the University of California, Irvine, Center for Complex Biological Systems
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship.
Center for Complex Biological Systems, UC Irvine
Dr. Adam Martiny
Dr. Renaud Berlemont
Cecilia Batmalle
Stephen Hatosy
Celine Mouginot
Dr. Agathe Talarmin