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

Ecological studies often require studying the common structure of a pair of data tables. Co‐inertia analysis is a multivariate method for coupling two tables. It is often neglected by ecologists who prefer the widely used methods of redundancy analysis and canonical correspondence analysis. We present the co‐inertia criterion for measuring the adequacy between two data sets. Co‐inertia analysis is based on this criterion as are canonical correspondence analysis or canonical correlation analysis, but the latter two have additional constraints. Co‐inertia analysis is very flexible and allows many possibilities for coupling. Co‐inertia analysis is suitable for quantitative and/or qualitative or fuzzy environmental variables. Moreover, various weighting of sites and various transformations and/or centering of species data are available for this method. Hence, more ecological considerations can be taken into account in the statistical procedures. Moreover, the principle of this method is very general and can be easily extended to the case of distance matrices or to the case of more than two tables. Simulated ecological data are used to compare the co‐inertia approach with other available methods.

MADE4, microarray ade4, is a software package that facilitates multivariate analysis of microarray gene-expression data. MADE4 accepts a wide variety of gene-expression data formats. MADE4 takes advantage of the extensive multivariate statistical and graphical functions in the R package ade4, extending these for application to microarray data. In addition, MADE4 provides new graphical and visualization tools that aid in interpretation of multivariate analysis of microarray data.

Automated rRNA intergenic spacer analysis (ARISA) was used to characterise bacterial (B-ARISA) and fungal (F-ARISA) communities from different soil types. The 16S-23S intergenic spacer region from the bacterial rRNA operon was amplified from total soil community DNA for B-ARISA. Similarly, the two internal transcribed spacers and the 5.8S rRNA gene (ITS1-5.8S-ITS2) from the fungal rRNA operon were amplified from total soil community DNA for F-ARISA. Universal fluorescence-labeled primers were used for the PCRs, and fragments of between 200 and 1,200 bp were resolved on denaturing polyacrylamide gels by use of an automated sequencer with laser detection. Methodological (DNA extraction and PCR amplification) and biological (interand intrasite) variations were evaluated by comparing the number and intensity of peaks (bands) between electrophoregrams (profiles) and by multivariate analysis. Our results showed that ARISA is a high-resolution, highly reproducible technique and is a robust method for discriminating between microbial communities. To evaluate the potential biases in community description provided by ARISA, we also examined databases on length distribution of ribosomal intergenic spacers among bacteria (L. Ranjard, E. Brothier, and S. Nazaret, Appl. Environ. Microbiol. 66:5334-5339, 2000) and fungi.

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Spatial scaling and determinism of the wide-scale distribution of macroorganism diversity has been largely demonstrated over a century. For microorganisms, and especially for soil bacteria, this fundamental question requires more thorough investigation, as little information has been reported to date. Here by applying the taxa-area relationship to the largest spatially explicit soil sampling available in France (2,085 soils, area covered B5.3 Â 10 5 km 2 ) and developing an innovative evaluation of the habitat-area relationship, we show that the turnover rate of bacterial diversity in soils on a wide scale is highly significant and strongly correlated with the turnover rate of soil habitat. As the diversity of micro-and macroorganisms appears to be driven by similar processes (dispersal and selection), maintaining diverse and spatially structured habitats is essential for soil biological patrimony and the resulting ecosystem services.

Aim The spatial organization of soil microbial communities on large scales and the identification of environmental factors structuring their distribution have been little investigated. The overall objective of this study was to determine the spatial patterning of microbial biomass in soils over a wide extent and to rank the environmental filters most influencing this distribution.Location French territory using the French Soil Quality Monitoring Network. This network covers the entire French territory and soils were sampled at 2150 sites along a systematic grid. MethodsThe soil DNA extracted from all these soils was expressed in terms of soil molecular microbial biomass and related to other soil and land-use data over French territory. ResultsThis study provides the first extensive map of microbial biomass and reveals the heterogeneous and spatially structured distribution of this biomass on the scale of France. The main factors driving biomass distribution are the physicochemical properties of the soil (texture, pH and total organic carbon) as well as land use. Soils from land used for intensive agriculture, especially monoculture and vineyards, exhibited the smallest biomass pools. Interestingly, factors known to influence the large-scale distribution of macroorganisms, such as climatic factors, were not identified as important drivers for microbial communities.Main conclusions Microbial abundance is spatially structured and dependent on local filters such as soil characteristics and land use but is relatively independent of global filters such as climatic factors or the presence of natural barriers. Our study confirms that the biogeography of microorganisms differs fundamentally from the biogeography of 'macroorganisms' and that soil management can have significant large-scale effects.