Background About a half of the world's population is infected with Helicobacter pylori (H. pylori), but only 1%–3% of them develop gastric cancer. As a primary risk factor for gastric cancer, the relationship between H. pylori infection and gastric microbiome has been a focus in recent years. Materials and methods We reanalyze 11 human gastric microbiome datasets with or without H. pylori, covering the healthy control (HC) and four disease stages (chronic gastritis (CG), atrophic gastritis (AG), intestinal metaplasia (IM), and gastric cancer (GC)) of gastric cancer development to quantitatively compare the influences of the H. pylori infection and disease stages on the diversity, heterogeneity, and composition of gastric microbiome. Four medical ecology approaches including (i) diversity analysis with Hill numbers, (ii) heterogeneity analysis with Taylor's power law extensions (TPLE), (iii) diversity scaling analysis with diversity–area relationship (DAR) model, and (iv) shared species analysis were applied to fulfill the data reanalysis. Results (i) The influences of H. pylori infection on the species diversity, spatial heterogeneity, and potential diversity of gastric microbiome seem to be more prevalent than the influences of disease stages during gastric cancer development. (ii) The influences of H. pyloriinfection on diversity, heterogeneity, and composition of gastric microbiomes in HC, CG, IM, and GC stages appear more prevalent than those in AG stage. Conclusion Our study confirmed the impact of H. pylori infection on human gastric microbiomes: The influences of H. pylori infection on the diversity, heterogeneity, and composition of gastric microbiomes appear to be disease‐stage dependent.

The human virome is a critical component of the human microbiome, and it is believed to hold the richest diversity within human microbiomes. Yet, the inter-individual scaling (changes) of the human virome has not been formally investigated to the best of our knowledge. Here we fill the gap by applying diversity-area relationship (DAR) modeling (a recent extension to the classic species-area law in biodiversity and biogeography research) for analyzing four large datasets of the human virome with three DAR profiles: DAR scaling (z)—measuring the inter-individual heterogeneity in virome diversity, MAD (maximal accrual diversity: Dmax) and LGD ratio (ratio of local diversity to global diversity)—measuring the percentage of individual to population level diversity. Our analyses suggest: (i) The diversity scaling parameter (z) is rather resilient against the diseases as indicated by the lack of significant differences between the healthy and diseased treatments. (ii) The potential maximal accrual diversity (Dmax) is less resilient and may vary between the healthy and diseased groups or between different body sites. (iii) The LGD ratio of bacterial communities is much smaller than for viral communities, and relates to the comparatively greater heterogeneity between local vs. global diversity levels found for bacterial-biomes.