Integrated Metagenomics Analysis Identifies Loss of Diversity in - PowerPoint PPT Presentation

Integrated Metagenomics Analysis Identifies Loss of Diversity in Periodontitis Li Charlie Xia Dongmei Ai Medical Oncology / Stanford University Applied Mathematics / University of Science & Technology Beijing GIW 2016

OVERVIEW

Oral microbiome contributes to Periodontitis (PD) Periodontitis involves progressive loss of alveolar bone around teeth and leads to loss of teeth. Periodontitis affects more than half of world's adult population. Its etiology is unknown, however, oral microbiome (dental plaque) is believed contributor to the disease.

Microbiome is best studied by Metagenomics Before metagenomics: • Bacterial isolation and culture • Biased, tedious and low throughput Shotgun metagenomics: • No isolation and culture needed • High-throughput, low cost • Less biased, full species spectrum Integrate metagenomic datasets: • Effectively increase sample size • Higher power for statistical analysis

ANALYSIS

Integrate Published Oral Metagenomics Datasets 13 samples Debates on Periodontitis: 30 samples (Duran- (Yost et al) • One or multiple etiology path? Pinedo et al) • One or multiple pathogens? • Stable or progressing? Published Periodontitis Metagenomics Datasets: • 13 subgingival plaque samples (Duran- 43 samples: Pinedo et al ISME J 2014) 6 healthy controls • 30 subgingival plaque samples (Yost et al 14 stable periodontitis Genome Medicine 2015 ) 16 progressing periodontitis 7 unknown periodontitis

Bioinformatics and Statistical Analysis 1. Input Integrated Metagenomics Datasets to the Pipeline 1 2. Quality Control and Preprocessing: 2 § TagCleaner, PRINSEQ, DeconSeq and FLASH 3. Expanded Phylogenetic Analysis: § MetaPhylan 4. Refined Phylogenetic Analysis 3 § BWA-MEM § GRAMMy 5. Differential Abundance Analysis 4 6. Logistic Regression Analysis 7. Bi-clustering Analysis 5 8. Network Analysis § ELSA 6 7 8

GRAMMy: Accurately Finds Microbiome Abundance Accurately estimates relative abundance using probabilistic mixture modeling. Allows ambiguous assignments in read mapping. Uses Expectation- Maximization Algorithm to find the MLE of mixing parameter/relative Xia et al . PLOS ONE 2011 abundance. https://bitbucket.org/charade/grammy/overview

ELSA: Efficient Co-occurrence Network Analysis Xia et al . BMC Systems Biology 2011 Xia et al . Bioinformatics 2013 https://bitbucket.org/charade/elsa

RESULTS

Periodontitis Oral Microbiome healthy (green) Bar plots of top 20 abundant species stratified by sample disease states. Error bars shows considerable stable (yellow) variation among samples even with the same disease state. Many species are found to be top abundant in all states: healthy, stable and progress • S. gondornii, S. Sanguinis, F. Alocis ... progressing (red) Some species are found to be top abundant in only diseased states: • L. gasseri, S. Epidermidis, ...

Loss of Microbiome Diversity in Periodontitis Periodontitis is associated with Alpha-diversity (*P<0.011): ! !" ! ! ! = − 4 . 343 ! + 10 . 212 , d: alpha-diversity p: probability of Periodontitis Alpha-diversity accurately predicts Periodontitis: • Shannon Index of 2 seems to be a significant diversity threshold distinguishing diseased and healthy samples. • Our logistic model shows d=2 gives p>82%. • Our Naïve (yes if p>50%) logistic classifier achieves accuracy 94.4% when applied to new samples not used in training.

Marker Species in Periodontitis Mcirobiome A table of plots of single molecule statistics Species loss of abundance in Periodontitis samples: G. Morbillorum, V. Parvula, H. parainfluenzae, C. Matruchotii, N. Flavescens L. gasseri gain abundance in progressing Periodontitis samples:

Keynote and Marker Species of Periodontitis 1. Marker Species (Blue): Significantly different abundance between disease states 2. New Keystone Species (Pink): H. haemolyticus, P. Prevotella, C. ochracea, who share the same composition profile with known keystone species: Porphyromonas gingivalis.

Microbiome Correlation Networks of Periodontitis 1. Significant reduction of edges in diseased sample network. 2. Loss of all negative correlation in diseased samples. 3. Potential loss of check-and-balance through negative feedbacks.

Keystone Species Mediated Loss of Diversity Model

SUMMARY

Summary We integrated two published Periodontitis metagenomics datasets. We analyzed the integrated datasets with standardized bioinformatics pipelines. We found loss of oral microbiota diversity Is strongly associated with Periodontitis. Alpha diversity accurately predicts Periodontitis disease states. Loss of diversity has been associated with other conditions including obesity. Our finding suggest diversity could be useful measure for early Periodontitis diagnosis and therapeutic intervention.

Bioinformatics Tools for Meta- / Human Genomics Phylogenetic Abundance Analysis: GRAMMy: https://bitbucket.org/charade/grammy (Xia et al Plos One 2011 ) Correlation/Network Analysis: ELSA: https://bitbucket.org/charade/elsa (Xia et al Bioinformatics 2013 , benchmarked in Weiss et al ISME J 2016 ) Structural Variant Analysis: SWAN: https://bitbucket.org/charade/swan (Xia et al NAR 2016 )

Acknowledgement University of Science and Technology Beijing • Ruocheng Huang • Chao Li • Jiangping Zhu Shanghai Jiao Tong University Funding Source • Jin Wen NIH/NHGRI: 2R01HG006137 CNSF: 61370131 University of Southern California • Fengzhu Sun • Jed Fuhrman Stanford University University of Pennsylvania • Nancy Zhang • Hanlee Ji • Gerard Schellenberg • David Siegmund • Li-san Wang