NAIP-dependent sensing of cytosolic microbial patterns is LRR-dependent, and is currently known to respond to Legionella and Salmonella typhimurium flagellin [26].
We especially highlight recent advances in the molecular understanding of how microbial patterns are detected and discriminated from endogenous compounds by inflammasome sensors.
Recently, it has become apparent that the term 'pathogen-associated microbial pattern' is a misnomer.
If the actual microbial pattern was uncorrelated with phylogenetic similarity, the community detected by symmetric NMF may be unreliable.
Next, from the clustering results, we infer how microbial pattern was influenced by body habitats and host genders.
Since skin microbial pattern was closely associated with external environment [ 34] and oral cavity was an open system where microbiome from external environment was imported by breathing, eating food and drinking water [ 35], oral cavity and skin would respond to outside environmental conditions, and gradually evolve similar microbiomes.
In Additional file 2: Figure S2, gut and oral cavity microbial community patterns were only fit with one clustering criterion, gut consistent with K-means and oral cavity with hierarchical clustering.
In fact, it is not microbial patterns that are recognized but rather specific molecules, that are integral constituents of microorganisms that are recognized, suggesting this system is highly discriminatory [ 35, 36].
Relationships between microbial beta diversity patterns were examined by using Bray-Curtis dissimilarities and Principal Coordinate Analyses.
Finally, none of the microbial beta diversity patterns were due to geographic distances, neither at the landscape scale (Fig. 4, Fig. S1), nor for different classes of spatial distances (Fig. S2).
