| Literature DB >> 32703080 |
Christoph Ammer-Herrmenau1, Nina Pfisterer1, Mark Fj Weingarten1, Albrecht Neesse1.
Abstract
The human microbiota exerts multiple physiological functions such as the regulation of metabolic and inflammatory processes. High-throughput sequencing techniques such as next-generation sequencing have become widely available in preclinical and clinical settings and have exponentially increased our knowledge about the microbiome and its interaction with host cells and organisms. There is now emerging evidence that microorganisms also contribute to inflammatory and neoplastic diseases of the pancreas. This review summarizes current clinical and translational microbiome studies in acute and chronic pancreatitis as well as pancreatic cancer and provides evidence that the microbiome has a high potential for biomarker discovery. Furthermore, the intestinal and pancreas-specific microbiome may also become an integrative part of diagnostic and therapeutic approaches of pancreatic diseases in the near future.Entities:
Keywords: 16S rRNA sequencing; Pancreatic cancer; metagenomic sequencing; microbiome; pancreatitis
Mesh:
Substances:
Year: 2020 PMID: 32703080 PMCID: PMC7707879 DOI: 10.1177/2050640620944720
Source DB: PubMed Journal: United European Gastroenterol J ISSN: 2050-6406 Impact factor: 4.623
Figure 1.Key findings and current perceptions of the orointestinal microbiome in pancreatic ductal adenocarcinoma (PDAC) and acute and chronic pancreatitis.
Summary of current studies regarding the oral microbiome as non-invasive biomarker for pancreatic cancer.
| Year of publication | Authors | Study design | Method | Number of patients | Change in bacterial composition | References |
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| 2012 | Farrell et al. | Case control | Real-time quantitative PCR of saliva | 38 PDAC, 38 HC, 27 CP |
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| 2013 | Michaud et al. | Prospective population-based | Blood samples, immunoblot array | 405 PDAC, 416 HC |
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| 2013 | Lin et al. | Pilot study | 16S RNA sequencing of oral wash samples | 13 PDAC, three CP, 12 HC |
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| 2015 | Torres et al. | Cross-sectional | 16S RNA sequencing and quantitative PCR of saliva | Eight PDAC, 22 HC |
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| 2016 | Fan et al. | Case control | 16S RNA sequencing of saliva | 361 PDAC, 371 HC |
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| 2017 | Olson et al. | Case control | 16S RNA sequencing of saliva | 40 PDAC, 39 IPMN, 58 HC |
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| 2019 | Lu et al. | Case control | 16S RNA sequencing of tongue swabs | 30 PDAC, 25 HC |
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| 2020 | Vogtmann et al. | Case control | 16S RNA sequencing of saliva | 273 PDAC, 285 HC |
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CP: chronic pancreatitis; HC: healthy controls; PCR: polymerase chain reaction; PDAC: pancreatic ductal adenocarcinoma; IPMN: intraductal papillary mucinous neoplasm.
Comparison of 16S rRNA and metagenomic sequencing.
| Advantages | Disadvantages | |
|---|---|---|
| Marker gene sequencing (e.g. 16S rRNA sequencing for bacteria) | Fast, less complicated, cheaper library preparation and analysis | PCR introduces amplification bias, thus interferes with abundance analysis |
| Suitable for highly host-contaminated samples and samples with low biomass | Choice of primers and variable regions have a huge influence of taxonomic | |
| Well-established bioinformatic tools | No possibility of de novo assembly | |
| Limited information at species level, best resolution at genus level | ||
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| Metagenomic (whole genome sequencing) | Reliable abundances analysis | More expensive and labour-intensive library preparation and analysis |
| Resolution to species and strain level | Deep sequencing is required due to host DNA contamination | |
| Possibility of de novo assembly | For example, viruses are not well annotated by widely used analysis workflows | |
| No PCR bias | ||
| Provides information of all sequenced and characterized microbes (bacteria, fungi, viruses, archaea) | ||
| Better resolution for functional profiling | ||
PCR: polymerase chain reaction; rRNA: ribosomal ribonucleic acid.