Oral and gastric microbiome in relation to gastric intestinal metaplasia

Abstract

Evidence suggests that Helicobacter pylori plays a role in gastric cancer (GC) initiation.However, epidemiologic studies on the specific role of other bacteria in the development of GC are lacking. We conducted a case-control study of 89 cases with gastric intestinal metaplasia (IM) and 89 matched controls who underwent upper gastrointestinal endoscopy at three sites affiliated with NYU Langone Health. We performed shotgun metagenomic sequencing using oral wash samples from 89 case-control pairs and antral mucosal brushing samples from 55 case-control pairs. We examined the associations of relative abundances of bacterial taxa and functional pathways with IM using conditional logistic regression with and without elastic-net penalty. Compared with controls, oral species Peptostreptococcus stomatis, Johnsonella ignava, Neisseria elongata and Neisseria flavescens were enriched in cases (odds ratios [ORs] = 1.29-1.50, P = .004-.01) while Lactobacillus gasseri, Streptococcus mutans, S parasanguinis and S sanguinis were under-represented (ORs = 0.66-0.76, P = .006-.042) in cases. Species J ignava and Filifactor alocis in the gastric microbiota were enriched (ORs = 3.27 and 1.43, P = .005 and .035, respectively), while S mutans, S parasanguinis and S sanguinis were under-represented (ORs = 0.61-0.75, P = .024-.046), in cases compared with controls. The lipopolysaccharide and ubiquinol biosynthesis pathways were more abundant in IM, while the

Porphyromonas gingivalis-Helicobacter pylori co-incubation enhances Porphyromonas gingivalis virulence and increases migration of infected human oral keratinocytes

“…Both P. gingivalis [ 6–9 ] and H. pylori [ 16 , 17 , 62 ] reside in the subgingival biofilm, and both possess membrane proteins that allow them to interact with and bind to other bacteria in the same community [ 12–14 , 27–29 ]. Therefore, it was to be suspected that interactions between both bacteria may occur, especially after the studies showing not only that H. pylori is present in saliva and dental plaques [ 18–20 ], but also is part of the oral microbiome and subgingival biofilm, where interactions via adhesins with other members of the community, such as F. nucleatum , have been observed [ 63 ]. Furthermore, studies have linked the presence of H. pylori to poorer periodontal health and an increase in the relative abundance of other bacteria, such as P. gingivalis [ 34 ].…”

“…In this context relevant are observations indicating that Helicobacter pylori ( H. pylori ), a microaerophilic Gram-negative bacterium that infects the stomachs of over 50% of the world population and is held responsible for a variety of gastric disorders, also colonizes the subgingival and supragingival space and is present in human saliva [ 15–17 ]. The relevance of these findings is underscored by molecular methods and microbiome analysis identifying H. pylori as a member of the oral microbiome [ 18–21 ]. Furthermore, different studies have suggested that the main extragastric reservoir of H. pylori is indeed the oral cavity [ 22–24 ], which represents the main route of infection for many pathogens, including H. pylori .…”

An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data

“…We retrieved oral and gastric microbiome data from a case-control study including 89 cases with gastric intestinal metaplasia (IM) and 55 matched controls in New York City, USA (Wu et al 2021). Oral wash and antral mucosal brushing samples from the same subject were treated as repeated measures in this analysis.…”

“…To validate the performance of our approach, we designed extensive simulations to compare the results of LongStrain with three broadly used tools: StrainPhlAn3, MIDAS, and ConStrains. Additionally, we tested LongStrain on a subset of The Environmental Determinants of Diabetes in the Young (TEDDY) study (Vatanen et al 2018) and on a study of oral and gastric microbiome in relation to gastric intestinal metaplasia (Wu et al 2021).…”

An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data

“…To validate the performance of our approach, we designed extensive simulations to compare the results of LongStrain with three broadly used reference genome-based tools: StrainPhlAn3, MIDAS, and ConStrains. Additionally, we tested LongStrain on a subset of The Environmental Determinants of Diabetes in the Young (TEDDY) study [31] and on a study of oral and gastric microbiome in relation to gastric intestinal metaplasia [33].…”