The Microbiome of Cervical Cancer: Exploring the Link Between Bacteria and Tumor

The microbiome of cervical cancer: exploring the link between bacteria and tumor

Khiem Lam BioResource Research Oregon State University, Corvallis, OR

Andrey Morgun, MD, PhD College of Pharmacy Oregon State University, Corvallis, OR What is cervical cancer?

• A malignant tumor of the cervix • Fourth common cancer in Cervical Cancer women worldwide • Caused by human papillomavirus (HPV)

http://www.cdc.gov/cancer/hpv/statistics/cases.htm Most HPV infections do not lead to cervical cancer

HPV infection is necessary but not sufficient in causing cervical cancer.

Shulzhenko, et al., 2014 Role of bacteria in driving human cancer

http://www.pyroenergen.com/articles13/images/gastric-ulcer-pylori.jpg Previous models of cervical cancer show specific host gene expression patterns and role of HPV, but has not explored role of other microbes (bacteria)

What is the role of microbiota in cervical cancer?

Host Genes Microbiota ? Cell Cycle Antiviral Response

Epithelial Differentiation

Mine, K., et al. Nature Comm. 2013.

HPV The use of network analysis to analyze host- microbe interactions

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation Methodology

Cervical Cancer DNA Bacterial DNA Tumor Samples Extraction Detection using qPCR

16s rRNA Sequencing & QIIME Analysis

Relative Abundance Gene Expression Data of Microbes

Analysis of Microbes and In vitro Experiments Host Gene Expression The use of network analysis to analyze host- microbe interactions

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation Why sequencing?

Only small proportion of microbiota can be cultivated High-throughput, cheaper, less labor intensive, single sample analysis Three main sequencing methods

Shotgun Sequencing 16S rRNA gene RNA-Seq of DNA sequencing

Schluenzen F, Tocilj, et al. 2000 Cell. http://rnaseq.uoregon.edu/ http://www.discoveryandinnovation.com/BIOL202/notes/lecture24.html 16S rRNA Sequencing

• Most common bacterial sequencing • Uses MiSeq sequencing platform • Well-described 16S rRNA gene

Sample 1 Sample 2 Sample 3 16S 16S adapter adapter primer primer ….. barcodes ……….….n

http://www.alimetrics.net/en/index.php/dna-sequence-analysis Caporaso et al., 2012 ISME J Low bacterial DNA content in tissue samples

• Bacterial DNA amounts checked using qPCR

Soil

Intestine Stool Lung Tumor Kidney

High Low Bacterial DNA Bacterial DNA Increased bacteria content with cancer FIGO stage progession

* 1 6 S S e q N o 1 6 S *

1 0 0 0 * 1 .0

t o

t 1 0 0

0 n

1 1 0

e u

A 0 .5

q N

1 e

c a

b 0 .1

g p 0 .0 1 0 .0 I II III IV I II III IV

F IG O S ta g e F IG O S ta g e

ANOVA P = 0.0119 Cochran-Armitage test for trend: Tukey IV-I (0.020) IV-II (0.011) IV-III (p=0.012) P = 0.0309 Data Processing (QIIME)

OTU1 OTU2 OTU3

OTU OTU Abundance Bin Similar Sequences Sample into Operational Identify OTU in Sample and Sequencing Data Taxonomic Units (OTUs) Relative Frequencies

Compare OTUs Remove Primers to Databases and Quality Filter Bacterial abundance compared to healthy body sites

100% other 90% g__ (o_Bacteroidales) 80% g__Campylobacter g__Faecalibacterium 70% g__Corynebacterium 60% g__Peptoniphilus g__Porphyromonas 50% g__Staphylococcus 40% g__Anaerococcus

Relative Abundance Relative g__Alistipes 30% g__Fusobacterium 20% g__Propionibacterium g__Bacteroides 10% g__Lactobacillus 0% g__Prevotella cervical cancer vagina stool skin Differentially expressed genes of cervical cancer

Host Genes

Cell Cycle

Antiviral Response Epithelial Differentiation Gene expression and microbiome data in cervical cancer patients

Cervical Cancer Tumors

cDNA Microarray 16S rRNA Sequencing

Host Genes Microbiota Cell Cycle ? 14.9% Prevotella Antiviral Response 9.6% Fusobacterium

Epithelial Differentiation 7.3% Porphyromonas

Mine, K., et al. Nature Comm. 2013. The use of network analysis to analyze host- microbe interactions

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation Correlation analysis to infer causal relationships

https://utw10426.utweb.utexas.edu/Topics/Correlation/Text_files/image002.jpg Differentially expressed genes of cervical cancer

Host Genes

Cell Cycle

Antiviral Response Epithelial Differentiation Network reconstruction of bacterial groups reveal a model of interactions

Relative Family Assignment Abundance (%, mean) Planococcaceae 6.71 Halomonadaceae 3.34 Bacillaceae 1.87 Halobacteriaceae 1.11 Moraxellaceae 0.95 Pseudomonadaceae 0.90 Corynebacteriaceae 0.68 Prevotellaceae 0.41 Node: Operational Taxonomic Unit (OTU) Blue edge: positive correlation Red edge: negative correlation Transkingdom network shows relationship between regulatory cancer genes and abundant bacteria

Differentially Expressed Cancer Gene Network

Transkingdom Network Microbial Network Transkingdom network shows relationship between regulatory cancer genes and abundant bacteria The use of network analysis to analyze host- microbe interactions

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation Bipartite Betweenness Centrality reveals top bacterial candidate in cervical cancer

Bipartite Betweenness Centrality number of shortest paths from all orange nodes to all blue that pass through that node

Low BC High BC

Microbes Genes

Dong, X., et al. Bioinformatics and Biology Insights. 2015. Selection of Bacterial Candidates Selection of Bacterial Candidates

Transkingdom Network Bacterial Candidates

Prevotella bivia Prevotella buccalis Prevotella disiens Prevotella oris Bi-Partite Betweenness Centrality Alignment to SILVA (16S) Literature Search Lactobacillus crispatus Availability (control)

Prevotella spp. are listed as anaerobic Lactobacillus crispatus is listed as facultative anaerobe The use of network analysis to analyze host- microbe interactions

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation Co-Culture Experiments

-24 0 6/24 Hour

HeLa

RNeasy qScript Kit Mini Kit

5% CO2 Anaerobic

Bacterial RNA Extraction Cells plated at Reverse qPCR treatment On-Column 75,000 cells/well Transcription (10 ng/well cDNA) MOI 1/10 DNase Treatment Gene targets for RT-qPCR

Bi-partite betweenness centrality results between DEGs and microbes

epithelial cell Node name subnet all degs antiviral cell cycle differentiation regulation IFI44L antiviral 358 38 380 188 UP LAMP3 antiviral 69 10 67 95 UP CEP70 cell cycle 13 108 15 189 UP S100PBP cell cycle 18 88 22 94 UP TPX2 cell cycle 72 169 41 270 UP NEK2 cell cycle 237 280 189 325 UP RFC4 cell cycle 277 378 196 254 UP

Listed rank out of 738 genes in network

Rank less than 50 highlighted Initial gene expression results Selection of Bacterial Candidates

Transkingdom Network Bacterial Candidates

Prevotella bivia Prevotella buccalis Prevotella disiens Prevotella oris Bi-Partite Betweenness Centrality Alignment to SILVA (16S) Literature Search Lactobacillus crispatus Availability (control)

Prevotella spp. are listed as anaerobic Lactobacillus crispatus is listed as facultative anaerobe Selection of Bacterial Candidates

Transkingdom Network Bacterial Candidates

Prevotella bivia Prevotella disiens

Bi-Partite Betweenness Centrality Alignment to SILVA (16S) Lactobacillus crispatus Literature Search (control) Availability

Prevotella spp. are listed as anaerobic Lactobacillus crispatus is listed as facultative anaerobe Co-Culture Experiments

-24 0 24 Hour

HeLa

RNeasy qScript Kit Mini Kit

5% CO2 Anaerobic

Bacterial RNA Extraction Cells plated at Reverse qPCR treatment On-Column 75,000 cells/well Transcription (10 ng/well cDNA) MOI 10 DNase Treatment The use of network analysis to analyze host- microbe interactions

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation LAMP3 shows up-regulation in Prevotella bivia treatment

*p-value < 0.05, one-sided paired t-test 2^-dCT between treatment and no bacteria control CEP70 and S100PBP shows up-regulation in Prevotella disiens treatment

*p-value < 0.05, one-sided paired t-test 2^-dCT between treatment and no bacteria control Summary

Transkingdom Network Selection of Bacterial Candidates

Prevotella bivia Prevotella disiens L. crispatus (control)

In vitro Bacterial Co-Culture Bi-Partite Betweenness Centrality

Prevotella sp.

Betweenness Centrality Betweenness Bacterial Groups What’s next?

Data

In vitro/vivo Network Experiment Reconstruction

Network Interrogation • RNA sequencing for global gene expression changes • Revision of model based on co-culture experiments • Determine host genes regulated by bacteria • siRNA co-culture experiment to test revised network Acknowledgements

Support from: Dariia Vyshenska Dr. Richard Rodrigues Dr. Xiaoxi Dong Dr. Jialu Hu Dr. Andrey Morgun Dr. Natalia Shulzhenko Dr. Heidi Lyng Lab Group Funding: OSU College of Pharmacy E.R. Jackman Internship Support USDA Multicultural Scholars Program Undergraduate Research, Scholarship, and the Arts Undergraduate Research, Innovation, Scholarship & Creativity The microbiome of cervical cancer: exploring the link between bacteria and tumor

Data

In vitro/vivo Network Experiment Reconstruction Cervical Cancer Microbiome

Network Interrogation