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Enrichment of oral microbiota in early cystic precursors to invasive pancreatic cancer

Objectives Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cysts that can progress to invasive pancreatic cancer. Associations between oncogenesis and oral microbiome alterations have been reported. This study aims to investigate a potential intracystic pancreatic microbiome in a pancreatic cystic neoplasm (PCN) surgery patient cohort. Design Paired cyst fluid and plasma were collected at pancreatic surgery from patients with suspected PCN (n=105). Quantitative and qualitative assessment of bacterial DNA by qPCR, PacBio sequencing (n=35), and interleukin (IL)-1β quantification was performed. The data were correlated to diagnosis, lesion severity and clinical and laboratory profile, including proton-pump inhibitor (PPI) usage and history of invasive endoscopy procedures. Results Intracystic bacterial 16S DNA copy number and IL-1β protein quantity were significantly higher in IPMN with high-grade dysplasia and IPMN with cancer compared with non-IPMN PCNs. Despite high interpersonal variation of intracystic microbiota composition, bacterial network and linear discriminant analysis effect size analyses demonstrated co-occurrence and enrichment of oral bacterial taxa including Fusobacterium nucleatum and Granulicatella adiacens in cyst fluid from IPMN with high-grade dysplasia. The elevated intracystic bacterial DNA is associated with, but not limited to, prior exposure to invasive endoscopic procedures, and is independent from use of PPI and antibiotics. Conclusions Collectively, these findings warrant further investigation into the role of oral bacteria in cystic precursors to pancreatic cancer and have added values on the aetiopathology as well as the management of pancreatic cysts.


Bacterial DNA isolation and absolute quantification of 16S rRNA gene by real-time PCR
To detect and quantify bacterial DNA in pancreatic cyst fluid, plasma and FFPE tissue, a realtime quantitative PCR assay targeting a highly conserved region of the bacterial 16S rRNA gene was used. Microbial DNA was purified from 200 µL pancreatic cyst fluid or plasma using the ZymoBIOMICS™ DNA Miniprep kit (Zymo Research, Irvine, USA) according to manufacturer's instructions for protein-rich biofluids, under sterile conditions in a biological flow cabinet and using 10% bleach-cleaned and UV-irradiated equipment. DNA was eluted in 100 µL nuclease-free H2O. From FFPE tissue, DNA was purified using the AllPrep DNA/RNA FFPE Kit (Qiagen), which was eluted in 100 µL nuclease-free H2O. DNA purity and concentration were assessed by spectrometric analysis using a NanoDrop ND-1000 (NanoDrop Technologies, Wilmington, DE, USA) and fluorescence-based using the QuantiFluor® dsDNA System (Promega, Madison, USA) and a Qubit 3.0 (Thermo Fischer Scientific). Extraction blanks and nuclease-free H2O were processed in parallel as controls to set the assay cut-off.
Standard curves for the 16S rRNA gene were generated using 10-fold serial dilutions from purified genomic DNA of E. coli ATCC 25922 to allow 16S copy number quantification.

PacBio full length 16S rRNA gene amplicon sequencing and microbiota analysis
Library preparation, quality control, sequencing, and raw data filtering were performed at GATC Biotech (Konstanz, Germany). Briefly, the V1-V8 region (1381 bp) of the 16S rRNA gene was amplified by PCR using primers 27F (5'-AGAGTTTGATCCTGGCTCAG-3') and BS- analysis. Chimera sequences were filtered with UCHIME [3] using a full length, good quality, and non-chimeric 16S rRNA gene reference database. One FASTA file per sample was delivered by GATC. To filter out low quality sequences and decrease the amount of noise, all sequence files were concatenated, de-replicated and clustered to 99% identity, excluding singletons in this step. This generated 4277 centroid sequences, which were submitted to the online RDP classifier, v. 16 [4]. 589 sequences that were not classified as Bacteria with 100% bootstrap support were excluded. No centroids were classified as Archaea with >90% bootstrap support. Non-bacteria sequences (Chloroplast), which came overwhelmingly from a single cancer sample with low DNA yield, were excluded. The remaining sequences were kept and their taxonomic annotation was truncated to the phylum rank with >70% bootstrap support.
To increase the granularity of the taxonomic assignment, sequences were also mapped to the HOMD 15.1 database with minimum 90% identity and maximum 10 best hits kept. This mapping was then parsed so that species-level assignments were accepted if they were in agreement for all hits with >97% identity, and genus-level assignments if they were in agreement for all hits with >95% identity. If no such hits were found, the RDP classification was kept. In total, 659 centroids could be mapped to HOMD. Finally, the FASTA files for each sample were mapped back to the centroids and assigned to the best hit with >97% identity >97% of the query length, to quantify the relative abundance of centroids in each sample.

Statistical analyses
Statistically significant differences of 16S DNA copy number and IL-1b concentration between diagnose groups were assessed using Kruskal-Wallis test with Dunn's multiple comparison test. Differences between proportions of DNA-positive samples between groups were tested using the chi-square test. Log10-transformed 16S DNA copy numbers were used as input for one-way ANOVA followed by a post test for linear trend by diagnose group. Spearman correlation was calculated between 16S DNA copy numbers and IL-1b concentrations per sample. All calculations were performed in GraphPad Prism 8.0.
Richness (Chao1), diversity (Shannon's entropy) and Inverted Simpson's were calculated at each taxonomic level using R packages Fossil 0.3.7 [5] and Vegan 2.4-5 [6], group-wise statistical comparisons were done using Welch's t-test with Benjamini-Hochberg FDR correction. Between-sample diversity was estimated using Bray-Curtis dissimilarity and plotted with package Pheatmap 1.0.8 [7]. PCoA calculation and plotting was performed with package Ape 5.2 [8]. ANOSIM was calculated using the Vegan package, based on Bray-Curtis distances and using standard parameters. These calculations were performed in R v. 3.4.3 [9]. Linear Discriminant Analysis Effect Size (LEfSe) [10] was used to find differentially abundant taxa between three diagnosis groups (classes), with per-sample normalization to 1 million, an alpha cut-off value of 0.05 for the Kruskall-Wallis factorial test, the "one-against-all" multi-class analysis strategy, and a threshold for discriminative features at a logarithmic LDA score >3.
Multivariate Analysis by Linear Models (MaAsLin, http://huttenhower.sph.harvard.edu/maaslin) was used to find correlations between clinical metadata and bacterial abundance data, with a q-value threshold of > 0.05, minimum feature abundance of 0.001, and minimum feature prevalence filter at 1%. Microbial co-occurrence analysis was done using only OTUs identified up to species level using the CoNet plugin [11] with the following five methods; Pearson, top (positive correlation) and bottom (negative correlation) edges were calculated. The permutations output to compute p-values included randomisation, shuffling of rows, and renormalisation were stored in a file prior to method-and edge-specific permutation (100 iterations). This file was used to obtain bootstrap score (100 iterations) distributions to compute final p-values, merging all five method-specific p-values of each edge into one single p-value.
The resulting network of statistically significant interactions with p < 0.01 and supported by >3 methods was visualized in Cytoscape 3.6.1. Generation of other graphs and statistical test were performed in GraphPad Prism 8.0.

Ethical considerations
This study follows the Helsinki convention and good clinical practice. This study was conducted at Karolinska University Hospital under permission of the Ethical Review Board Stockholm and Karolinska Biobank Board (Dnr 2015/1580-31/1). Written informed consent was obtained from all patients.