Impact of preoperative antibiotics and other variables on integrated microbiome-host transcriptomic data generated from colorectal cancer resections.

Malik, Sarah A, Zhu, Chencan, Li, Jinyu, LaComb, Joseph F, Denoya, Paula I, Kravets, Igor, Miller, Joshua D, Yang, Jie, Kramer, Melissa, McCombie, W Richard, Robertson, Charles E, Frank, Daniel N, Li, Ellen (April 2021) Impact of preoperative antibiotics and other variables on integrated microbiome-host transcriptomic data generated from colorectal cancer resections. World Journal of Gastroenterology, 27 (14). pp. 1465-1482. ISSN 1007-9327

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URL: https://www.ncbi.nlm.nih.gov/pubmed/33911468
DOI: 10.3748/wjg.v27.i14.1465

Abstract

BACKGROUND: Integrative multi-omic approaches have been increasingly applied to discovery and functional studies of complex human diseases. Short-term preoperative antibiotics have been adopted to reduce site infections in colorectal cancer (CRC) resections. We hypothesize that the antibiotics will impact analysis of multi-omic datasets generated from resection samples to investigate biological CRC risk factors. AIM: To assess the impact of preoperative antibiotics and other variables on integrated microbiome and human transcriptomic data generated from archived CRC resection samples. METHODS: Genomic DNA (gDNA) and RNA were extracted from prospectively collected 51 pairs of frozen sporadic CRC tumor and adjacent non-tumor mucosal samples from 50 CRC patients archived at a single medical center from 2010-2020. The 16S rRNA gene sequencing (V3V4 region, paired end, 300 bp) and confirmatory quantitative polymerase chain reaction (qPCR) assays were conducted on gDNA. RNA sequencing (IPE, 125 bp) was performed on parallel tumor and non-tumor RNA samples with RNA Integrity Numbers scores ≥ 6. RESULTS: PERMANOVA detected significant effects of tumor vs nontumor histology (P = 0.002) and antibiotics (P = 0.001) on microbial β-diversity, but CRC tumor location (left vs right), diabetes mellitus vs not diabetic and Black/African Ancestry (AA) vs not Black/AA, did not reach significance. Linear mixed models detected significant tumor vs nontumor histology*antibiotics interaction terms for 14 genus level taxa. QPCR confirmed increased Fusobacterium abundance in tumor vs nontumor groups, and detected significantly reduced bacterial load in the (+)antibiotics group. Principal coordinate analysis of the transcriptomic data showed a clear separation between tumor and nontumor samples. Differentially expressed genes obtained from separate analyses of tumor and nontumor samples, are presented for the antibiotics, CRC location, diabetes and Black/AA race groups. CONCLUSION: Recent adoption of additional preoperative antibiotics as standard of care, has a measurable impact on -omics analysis of resected specimens. This study still confirmed increased Fusobacterium nucleatum in tumor.

Item Type: Paper
Subjects: diseases & disorders > cancer > cancer types > colorectal
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes
CSHL Authors:
Communities: CSHL labs > McCombie lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 14 April 2021
Date Deposited: 10 May 2021 18:06
Last Modified: 10 May 2021 18:06
PMCID: PMC8047535
URI: https://repository.cshl.edu/id/eprint/40073

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