A study for identifying how the bacteria present in the lungs of smokers and non-smokers differ.

1. MISAEL FERNANDEZ
MENTOR: GIRI NARASIMHAN
A Study of the Lung Microbiome in
Chronic Obstructive Pulmonary
Disease (COPD) Using Metagenomics

2. .
Microbial Communities
2

3. Metagenomics Is Like Solving a Puzzle
3

4. A Modular Analytical Workflow
4
Data
Preprocessing
• Screen for
Quality
• Contamination
Removal
Classification
• Assign
Taxonomies
• Group
Sequences
Single-Sample
Analysis
• Estimate
Richness
• Estimate
Diversity
Multiple-Sample
Analysis
• Compare
Samples
• Additional
Statistics

5. Richness vs. Diversity
5
 Low Diversity  High Diversity
Equal Richness

6. Classification Accuracy
6
0% Substitution 5% Substitution 10% Substitution 15% Substitution 20% Substitution 25% Substitution
Mean St. Dev. Mean St. Dev. Mean St. Dev. Mean St. Dev. Mean St. Dev. Mean St. Dev.
Kingdom
400 bp 100.00% 0.00% 99.99% 0.01% 99.57% 0.09% 95.09% 0.28% 75.07% 4.64% 43.68% 12.88%
300 bp 100.00% 0.00% 99.97% 0.01% 99.16% 0.13% 91.06% 4.66% 66.47% 17.82% 39.57% 26.71%
200 bp 100.00% 0.00% 99.84% 0.11% 96.51% 3.07% 80.63% 17.16% 55.46% 34.64% 37.50% 39.21%
100 bp 99.91% 0.10% 96.96% 3.71% 81.04% 22.63% 59.84% 43.62% 46.06% 51.46% 38.65% 50.81%
Genus
400 bp 92.65% 19.55% 81.99% 28.57% 49.03% 38.96% 15.99% 23.13% 2.05% 6.25% 0.08% 0.74%
300 bp 88.84% 22.60% 74.29% 30.31% 36.45% 32.66% 8.62% 14.84% 0.94% 3.50% 0.04% 0.53%
200 bp 82.06% 26.21% 56.87% 30.29% 19.91% 21.47% 3.65% 7.11% 0.30% 1.40% 0.01% 0.21%
100 bp 56.21% 29.54% 20.82% 16.77% 4.24% 5.83% 0.51% 1.53% 0.06% 0.50% 0.00% 0.03%

7. Chao Richness Estimate - Genus
7
0
20
40
60
80
100
120
EstimatedNumberofGenera
Datasets
Chao Estimate
Actual Genera

8. COPD Is a Leading Cause of Death
8

9. A Highly Interdisciplinary Study
9

10. Study Participants Came from Three Groups
10

11. A Large Amount of Data Was Analyzed
11
3%
3%
13%
27%
53%
Low Quality
Chimeras
Contaminants
Unclassified Genera
Classified

12. Richness & Diversity Distributions
12
0
2
4
6
8
10
12
14
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
More
Frequency
Estimated Genera
Richness Distribution
0
1
2
3
4
5
6
7
8
9
Frequency Inverse Simpson Diversity Index
Diversity Distribution

13. Richness & Diversity Estimates
13
0
5
10
15
20
25
30
0
50
100
150
200
250
300
350
07_MJ
43_MJ
44_MJ
11_MJ
64_MJ
19_MJ
22_MJ
47_MJ
67_MJ
30_MJ
39_MJ
21_MJ
62_MJ
49_MJ
33_MJ
58_MJ
10_MJ
12_MJ
16_MJ
65_MJ
37_MJ
18_MJ
66_MJ
20_MJ
23_MJ
34_MJ
03_MJ
28_MJ
50_MJ
32_MJ
40_MJ
36_MJ
31_MJ
06_MJ
57_MJ
13_MJ
42_MJ
24_MJ
26_MJ
45_MJ
09_MJ
55_MJ
54_MJ
29_MJ
63_MJ
56_MJ
59_MJ
53_MJ
14_MJ
25_MJ
17_MJ
52_MJ
27_MJ
05_MJ
15_MJ
DiversityIndex
EstimatedNumberofGenera
Richness
Diversity

14. Differences in Richness and Diversity Exist
14
0
5
10
15
20
25
30
0
50
100
150
200
250
300
350
07_MJ
43_MJ
44_MJ
11_MJ
64_MJ
19_MJ
22_MJ
47_MJ
67_MJ
30_MJ
39_MJ
21_MJ
62_MJ
49_MJ
33_MJ
58_MJ
10_MJ
12_MJ
16_MJ
65_MJ
37_MJ
18_MJ
66_MJ
20_MJ
23_MJ
34_MJ
03_MJ
28_MJ
50_MJ
32_MJ
40_MJ
36_MJ
31_MJ
06_MJ
57_MJ
13_MJ
42_MJ
24_MJ
26_MJ
45_MJ
09_MJ
55_MJ
54_MJ
29_MJ
63_MJ
56_MJ
59_MJ
53_MJ
14_MJ
25_MJ
17_MJ
52_MJ
27_MJ
05_MJ
15_MJ
DiversityIndex
EstimatedNumberofGenera
COPD
Smoker
Never Smoker

15. Most Abundant Genera
15
0
5,000
10,000
15,000
20,000
59_MJ
17_MJ
28_MJ
10_MJ
45_MJ
07_MJ
20_MJ
67_MJ
33_MJ
58_MJ
22_MJ
19_MJ
55_MJ
25_MJ
32_MJ
64_MJ
63_MJ
66_MJ
62_MJ
36_MJ
65_MJ
16_MJ
57_MJ
42_MJ
21_MJ
53_MJ
24_MJ
54_MJ
30_MJ
31_MJ
NumberofReads
Oribacterium
Campylobacter
unclassified14
unclassified13
unclassified12
unclassified11
Granulicatella
unclassified10
Gemella
Parvimonas
unclassified09
Stenotrophomonas
unclassified08
Staphylococcus
unclassified07
Gp2
Burkholderia
Corynebacterium
Actinomyces
unclassified06
Porphyromonas
unclassified05
Neisseria
Veillonella
Fusobacterium
Delftia
unclassified04
unclassified03
Propionibacterium
Streptococcus
Ralstonia
Prevotella
unclassified02
unclassified01
Halomonas

16. Differences in Genera - COPD vs. Never Smokers
16
More Abundant in COPD More Abundant in Never Smokers
Propionibacterium unclassified14 Streptococcus unclassified63
unclassified04 Azospira Rothia Solirubrobacter
unclassified22 Escherichia_Shigella Phocoenobacter unclassified99
unclassified30 Brevundimonas Paludibacter Caulobacter
Sulfuricurvum Brevibacterium Simkania unclassified81
unclassified28 Simonsiella unclassified78 unclassified90
Serpens Parvibaculum Iamia Pediococcus
Tropheryma Hyphomonas Thermomonas Chelativorans
Massilia unclassified106 Cedecea

17. R O N A L D E . M C N A I R S C H O L A R S P R O G R A M
M B R S - R I S E
( N I H G R A N T # R 5 G M 0 6 1 3 4 7 )
F L O R I D A D E P T . O F H E A L T H
DR. DEETTA KAY MILLS
DR. WALTER GOLDBERG
Thank You

18. DR. KALAI MATHEE
L I S A S C H N E P E R , J O N A T H A N S E G A L ,
E U G E N I A S I L V A - H E R Z O G
MICHAEL CAMPOS , J O E L F I S H M A N , M A T H I A S S A L A T H E ,
A D A M W A N N E R , J U A N I N F A N T E
MELITA JARIC
DR. GIRI NARASIMHAN
Thank You

19. References and Credits
19
 "Chronic Obstructive Pulmonary Disease (COPD)." Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, 01 Mar. 2012. Web. 23 Aug. 2012. <http://www.cdc.gov/copd/data.htm>.
 "Chronic Obstructive Pulmonary Disease (COPD)." WHO. N.p., n.d. Web. 03 Sept. 2012. <http://www.who.int/mediacentre/factsheets/fs315/en/index.html>.
 "Schloss SOP." - Mothur. N.p., n.d. Web. 23 Aug. 2012. <http://www.mothur.org/wiki/Schloss_SOP>. Blankenberg, D., A. Gordon, G. Von Kuster, N. Coraor, J. Taylor, and A. Nekrutenko. "Manipulation of FASTQ Data with Galaxy." Bioinformatics 26.14
(2010): 1783-785.
 Bunge, John, Linda Woodard, Dankmar Böhning, James A. Foster, Sean Connolly, and Heather K. Allen. "Estimating Population Diversity with CatchAll." Bioinformatics 28.17 (2012): n. pag.
 Cole, J. R., Q. Wang, E. Cardenas, J. Fish, B. Chai, R. J. Farris, A. S. Kulam-Syed-Mohideen, D. M. McGarrell, T. Marsh, G. M. Garrity, and J. M. Tiedje. "The Ribosomal Database Project: Improved Alignments and New Tools for RRNA Analysis." Nucleic Acids
Research 37.Database (2009): D141-145.
 Costello, E. K., C. L. Lauber, M. Hamady, N. Fierer, J. I. Gordon, and R. Knight. "Bacterial Community Variation in Human Body Habitats Across Space and Time." Science 326.5960 (2009): 1694-697.
 Edgar, R. C., B. J. Haas, J. C. Clemente, C. Quince, and R. Knight. "UCHIME Improves Sensitivity and Speed of Chimera Detection." Bioinformatics 27.16 (2011): 2194-200
 Erb-Downward JR, Thompson DL, Han MK, Freeman CM, McCloskey L, Schmidt LA, Young VB, Toews GB, Curtis JL, Sundaram B, Martinez FJ, Huffnagle GB (2010). Analysis of the lung microbiome in the "healthy" smoker and in COPD. PLoS One. 2011,
6(2):e16384.
 Fonseca, V. G., B. Nichols, D. Lallias, C. Quince, G. R. Carvalho, D. M. Power, and S. Creer. "Sample Richness and Genetic Diversity as Drivers of Chimera Formation in NSSU Metagenetic Analyses." Nucleic Acids Research 40.11 (2012): n. pag
 Generalized Draft Form of HMP Data Generation Working Group 16S 454 Default Protocol Version 4.2- Pilot Study P.1. N.p.: n.p., n.d.
 Hankinson JL, Odencrantz JR, Fedan KB (1999) Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med 159:179–187.Jones, William J. "High-Throughput Sequencing and Metagenomics." Estuaries and Coasts 33
(2010): 944-52.
 Li, H, Durbin, R (2010). Fast and accurate long-read alignment with Burrows-Wheeler Transform. Bioinformatics, Epub. [PMID: 20080505]
 Liesack, W., H. Weyland, and E. Stackebrandt. "Potential Risks of Gene Amplification by PCR as Determined by 16S RDNA Analysis of a Mixed-culture of Strict Barophilic Bacteria." Microbial Ecology 21.1 (1991): 191-98.
 Martinez, FJ, Han, MK, Flaherty, K, Curtis, J (2006). “Role of infection and antimicrobial therapy in acute exacerbations of chronic obstructive pulmonary disease.” Expert Rev Anti Infect Ther 4: 101–124.Petrosino, J. F., S. Highlander, R. A. Luna, R. A. Gibbs,
and J. Versalovic. "Metagenomic Pyrosequencing and Microbial Identification." Clinical Chemistry 55.5 (2009): 856-66.
 Pond, SK, Wadhawan, S, Chiaromonte, F, Ananda, G, Chung, W, Taylor, J, Nekrutenko, A, The Galaxy Team (2009). Windshield splatter analysis with the Galaxy metagenomic pipeline. Genome Research, 2009, 19: 2144-2153
 Qiu, X., L. Wu, H. Huang, P. E. McDonel, A. V. Palumbo, J. M. Tiedje, and J. Zhou. "Evaluation of PCR-Generated Chimeras, Mutations, and Heteroduplexes with 16S RRNA Gene-Based Cloning." Applied and Environmental Microbiology 67.2 (2001): 880-87.
 Richter, Daniel C., Felix Ott, Alexander F. Auch, Ramona Schmid, and Daniel H. Huson. "MetaSim—A Sequencing Simulator for Genomics and Metagenomics." Ed. Dawn Field. PLoS ONE 3.10 (2008): E3373.
 Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009). Introducing mothur: open-source, platform-independent,
community-supported software for describing and comparing microbial communities. Applied Environmental Ecology. 2009, 75(23):7537-41.
 Schmeider, R, Edwards, R (2011). Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6(3):e17288. doi:10.1371/journal.pone.0017288
 Smyth, R.p., T.e. Schlub, A. Grimm, V. Venturi, A. Chopra, S. Mallal, M.p. Davenport, and J. Mak. "Reducing Chimera Formation during PCR Amplification to Ensure Accurate Genotyping." Gene 469.1-2 (2010): 45-51.
 Stevens, David A., John R. Hamilton, Nancy Johnson, Kwang Kyu Kim, and Jung-Sook Lee. "Halomonas, a Newly Recognized Human Pathogen Causing Infections and Contamination in a Dialysis Center." Medicine 88.4 (2009): 244-49. T. Huber, G. Faulkner
and P. Hugenholtz. “Bellerophon; a program to detect chimeric sequences in multiple sequence alignments.” Bioinformatics 20 (2004): 2317-2319.
 Wang, G. C. Y., and Y. Wang. "The Frequency of Chimeric Molecules as a Consequence of PCR Co-amplification of 16S RRNA Genes from Different Bacterial Species." Microbiology 142.5 (1996): 1107-114.
 Wang, Q, Garrity, GM, Tiedje, JM, Cole, JR (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied Environmental Microbiology. 2007 Aug;73(16):5261-7. Epub 2007 Jun 22.
 Wintzingerode, V., Friedrich, Ulf B. Gobel, and Erko Stackebrandt. "Determination of Microbial Diversity in Environmental Samples: Pitfalls of PCR-based RRNA Analysis." FEMS Microbiology Reviews 21.3 (1997): 213-29.
 Wooley, John C., Adam Godzick, and Iddo Friedberg. "A Primer on Metagenomics." PLoS Computational Biology 6.10 (2010): n. pag.
 IMAGES
 http://eco-restorellc.com/wp-content/uploads/2011/10/green-bacteria.jpg
 http://www.rikenresearch.riken.jp
 http://www.seaveg.com/
 http://mytechbyme.files.wordpress.com/
 http://www.jgi.doe.gov/
 http://www.nhlbi.nih.gov/
 http://www.bioquell.com/technology/microbiology/multidrug-resistant-pseudomonas-aeruginosa/
 http://fc00.deviantart.net/

20. 0
500
1,000
1,500
2,000
2,500
3,000
3,500
NumberofReads
Streptococcus
Rothia
Phocoenobacter
Paludibacter
Simkania
unclassified78
Iamia
Thermomonas
unclassified106
unclassified63
Solirubrobacter
unclassified99
Caulobacter
unclassified81
unclassified90
Pediococcus
Chelativorans
Cedecea
Massilia
Hyphomonas
Parvibaculum
Simonsiella
Brevibacterium
Brevundimonas
Escherichia_Shigella
Azospira
unclassified14
Tropheryma
Serpens
unclassified28
Sulfuricurvum
unclassified30
unclassified22
unclassified04
Propionibacterium
Differentially Significant Genera
20

21. PCA and Clustering
21
Cluster 1
COPD Smoker NS
COPD Smoker NS
COPD Smoker NS
COPD Smoker NS
COPD Smoker Smoker
COPD COPD Smoker
COPD COPD
Cluster 2
COPD Smoker NS
COPD Smoker NS
COPD Smoker Smoker
COPD Smoker Smoker
COPD Smoker Smoker
COPD Smoker Smoker
COPD COPD COPD
COPD COPD COPD
Cluster 3
Smoker NS Smoker
Smoker NS Smoker
Smoker NS Smoker
Smoker Smoker
Summary NS Smoker COPD
Group 1 4 7 9
Group 2 2 10 12
Group 3 3 8 0

22. Differentially Significant Genera
22
Name
Mean
(COPD) Var. (COPD)
Mean (Never
Smoker)
Var. (Never
Smoker) p-value
Mean
Difference Name
Mean
(COPD) Var. (COPD)
Mean (Never
Smoker)
Var. (Never
Smoker) p-value
Mean
Difference
Propionibacterium 8.2754% 2.69E-03 4.8787% 7.29E-04 0.03996 3.3967% Cedecea 0.0000% 0.00E+00 0.0013% 1.59E-09 0.04600 -0.0013%
unclassified04 0.7459% 1.23E-05 0.5007% 7.76E-06 0.04895 0.2452% Chelativorans 0.0000% 0.00E+00 0.0013% 1.59E-09 0.04600 -0.0013%
unclassified22 0.4113% 1.82E-06 0.2491% 1.58E-06 0.00500 0.1622% Pediococcus 0.0007% 8.98E-10 0.0020% 3.57E-09 0.03312 -0.0013%
unclassified30 0.1755% 2.77E-06 0.0624% 2.28E-07 0.00599 0.1131% unclassified90 0.0003% 2.01E-10 0.0020% 3.57E-09 0.03312 -0.0017%
Sulfuricurvum 0.1203% 3.79E-06 0.0225% 9.85E-08 0.01598 0.0978% unclassified81 0.0014% 9.60E-10 0.0033% 9.93E-09 0.02623 -0.0019%
unclassified28 0.1749% 1.73E-06 0.0785% 4.08E-07 0.01499 0.0964% Caulobacter 0.0020% 3.91E-09 0.0047% 1.95E-08 0.00135 -0.0027%
Serpens 0.1660% 9.75E-07 0.0806% 5.16E-07 0.02098 0.0853% unclassified99 0.0002% 7.46E-11 0.0033% 9.93E-09 0.00224 -0.0031%
Tropheryma 0.0738% 7.19E-06 0.0000% 0.00E+00 0.00100 0.0738% Solirubrobacter 0.0015% 3.24E-09 0.0053% 2.54E-08 0.00013 -0.0038%
unclassified14 0.1499% 9.39E-07 0.0905% 1.94E-07 0.04795 0.0594% unclassified63 0.0016% 3.56E-09 0.0071% 2.37E-08 0.02623 -0.0055%
Azospira 0.0357% 7.34E-07 0.0000% 0.00E+00 0.00500 0.0357% unclassified106 0.0004% 3.40E-10 0.0068% 2.26E-08 0.00877 -0.0064%
Escherichia_Shigella 0.0406% 2.51E-07 0.0135% 3.88E-08 0.04595 0.0271% Thermomonas 0.0000% 0.00E+00 0.0069% 4.32E-08 0.00212 -0.0069%
Brevundimonas 0.0253% 1.40E-07 0.0016% 2.26E-09 0.00899 0.0238% Iamia 0.0023% 4.16E-09 0.0104% 9.72E-08 0.02703 -0.0081%
Brevibacterium 0.0094% 4.27E-08 0.0000% 0.00E+00 0.01245 0.0094% unclassified78 0.0004% 4.18E-10 0.0103% 5.57E-08 0.03312 -0.0098%
Simonsiella 0.0085% 5.60E-08 0.0000% 0.00E+00 0.01989 0.0085% Simkania 0.0000% 0.00E+00 0.0120% 1.31E-07 0.00045 -0.0120%
Parvibaculum 0.0079% 6.97E-08 0.0000% 0.00E+00 0.03317 0.0079% Paludibacter 0.0039% 2.04E-08 0.0206% 2.23E-07 0.00446 -0.0166%
Hyphomonas 0.0062% 7.25E-08 0.0000% 0.00E+00 0.03184 0.0062% Phocoenobacter 0.0031% 7.05E-09 0.0442% 1.25E-06 0.01704 -0.0411%
Massilia 0.0061% 3.78E-08 0.0040% 1.43E-08 0.00446 0.0021% Rothia 0.1105% 1.19E-06 0.5286% 2.54E-05 0.02298 -0.4181%
Streptococcus 1.8356% 7.99E-05 4.8217% 1.70E-03 0.04795 -2.9861%