The expression of most Staphylococcus aureus virulence factors is controlled by the agr locus, which encodes a two-component signaling pathway whose activating ligand is an agr-encoded autoinducing peptide (AIP). A polymorphism in the amino acid sequence of the AIP and of its corresponding receptor divides S. aureus strains into four major groups. Within a given group, each strain produces a peptide that can activate the agr response in the other member strains, whereas the AIPs belonging to different groups are usually mutually inhibitory. We investigated a possible relationship between agr groups and human S. aureus disease by studying 198 S. aureus strains isolated from 14 asymptomatic carriers, 66 patients with suppurative infection, and 114 patients with acute toxemia. The agr group and the distribution of 24 toxin genes were analyzed by PCR, and the genetic background was determined by means of amplified fragment length polymorphism (AFLP) analysis. The isolates were relatively evenly distributed among the four agr groups, with 61 strains belonging to agr group I, 49 belonging to group II, 43 belonging to group III, and 45 belonging to group IV. Principal coordinate analysis performed on the AFLP distance matrix divided the 198 strains into three main phylogenetic groups, AF1 corresponding to strains of agr group IV, AF2 corresponding to strains of agr groups I and II, and AF3 corresponding to strains of agr group III. This indicated that the agr type was linked to the genetic background. A relationship between genetic background, agr group, and disease type was observed for several toxinmediated diseases: for instance, agr group IV strains were associated with generalized exfoliative syndromes, and phylogenetic group AF1 strains with bullous impetigo. Among the suppurative infections, endocarditis strains mainly belonged to phylogenetic group AF2 and agr groups I and II. While these results do not show a direct role of the agr type in the type of human disease caused by S. aureus, the agr group may reflect an ancient evolutionary division of S. aureus in terms of this species' fundamental biology.

HOW TO CITE TSPACE ITEMSAlways cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the TSpace version (original manuscript or accepted manuscript) because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page. Abstract. Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes. REVIEWS