Figure 1

SLST allows high-resolution discrimination of different bacterial phylotypes, (sub)species and strains. (A) Overview of current sequencing-based and alternative methods for microbiota identification and classification, including their (dis)advantages. Table legend as follows: estimation and indication of costs, return time and data analysis complexity; methods that allow for accurate phenotyping; amount of information retrieved with regard to genomic (functional) potential, taxonomic composition and resolution. The symbols represent open versus closed pie charts, meaning: low vs. high, fast vs. slow, simple vs. complex, etc. (B) Hypothetical dataset with 16S sequencing microbiota data of “healthy” and “diseased” subjects who suffer from an exemplary skin disease. The colored bars represent skin microbiota composition, and is clustered on microbiota profiles. In this hypothetical example, the bacterium Pseudomonas associates with skin disease state of the volunteers. (C) Pseudomonas genomes are collected and analyzed with the bioinformatics tool TaxPhlAn in order to search for candidate SLST targets. The goal of TaxPhlAn is to find genetic regions that allow for as perfect as possible discrimination of Pseudomonas biodiversity. Shown is a hypothetical SLST region (aligned DNA sequences) that allows for discriminating different Pseudomonas (sub)species. (D) Hypothetical example of how the SLST target as identified in (C) could allow for high-resolution Pseudomonas typing, and thereby to determine the (sub)species or strain-level bacteria that associate with disease. Panel represents an heatmap with data of identified Pseudomonas biodiversity, and their relative abundance as measured for each sample. Available (clinical) isolates of these bacteria can be subsequent candidates for follow-up studies.