Horizontal transfer of multiresistance plasmids in the environment contributes to the growing problem of drug-resistant pathogens. Even though the plasmid host cell is the primary environment in which the plasmid functions, possible effects of the plasmid donor on the range of bacteria to which plasmids spread in microbial communities have not been investigated. In this study we show that the host range of a broad-host-range plasmid within an activated-sludge microbial community was influenced by the donor strain and that various mating conditions and isolation strategies increased the diversity of transconjugants detected. To detect transconjugants, the plasmid pB10 was marked with lacp-rfp, while rfp expression was repressed in the donors by chromosomal lacI q . The phylogeny of 306 transconjugants obtained was determined by analysis of partial 16S rRNA gene sequences. The transconjugants belonged to 15 genera of the ␣-and ␥-Proteobacteria. The phylogenetic diversity of transconjugants obtained in separate matings with donors Pseudomonas putida SM1443, Ralstonia eutropha JMP228, and Sinorhizobium meliloti RM1021 was significantly different. For example, the transconjugants obtained after matings in sludge with S. meliloti RM1021 included eight genera that were not represented among the transconjugants obtained with the other two donors. Our results indicate that the spectrum of hosts to which a promiscuous plasmid transfers in a microbial community can be strongly influenced by the donor from which it transfers.

We present a novel application of a stochastic ecological model to the study and analysis of microbial growth dynamics as influenced by environmental conditions in an extensive experimental data set. The model proved to be useful in bridging the gap between theoretical ideas in ecology and an applied problem in microbiology. The data consisted of recorded growth curves of Escherichia coli grown in triplicate in a base medium with all 32 possible combinations of five supplements: glucose, NH 4 Cl, HCl, EDTA, and NaCl. The potential complexity of 2 5 experimental treatments and their effects was reduced to 2 2 as just the metal chelator EDTA, the presumed osmotic pressure imposed by NaCl, and the interaction between these two factors were enough to explain the variability seen in the data. The statistical analysis showed that the positive and negative effects of the five chemical supplements and their combinations were directly translated into an increase or decrease in time required to attain stationary phase and the population size at which the stationary phase started. The stochastic ecological model proved to be useful, as it effectively explained and summarized the uncertainty seen in the recorded growth curves. Our findings have broad implications for both basic and applied research and illustrate how stochastic mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding basic processes in microbial ecology.Mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding the interaction of organisms with their physical and biological environment (8,47,48,50,52,56). Studies in the field of predictive microbiology have shown that successful modeling requires both adequate models and thorough data sets (57; for extensive reviews, see references 10, 42, and 55). In predictive microbiology a two-step modeling approach is used (reference 55 and citations therein). Primary models describe the basic rules of how microbial numbers change over time (52). Next, these simple models are used to derive secondary models that account for the effect of a set of factors in microbial growth.The forces of the environment and the events of reproduction and growth are themselves stochastic in nature (3,21,24,32,36,39,44,45,51,53), yet simple ecological models, such as the Verhulst logistic equation, result in deterministic predictions. However, smooth convergence to asymptotic results is not what is usually seen, even in rigorous experimental settings (17, 31). Hence, a more realistic alternative to population growth modeling is to confront stochastic equations with the data at hand (references 5, 14, and 17 and citations therein and references 41 and 47).In this paper, we describe a novel application of a stochastic population model to analyze how environmental conditions influence microbial growth dynamics using an extensive experimental data set. The primary model used was the stochastic Ricker (SR) equation (27, 51). Our secondary model...

We have identified two types of siderophores produced by Pseudomonas, one of which has never before been found in the genus. Twelve strains of Pseudomonas stutzeri belonging to genomovars 1, 2, 3, 4, 5, and 9 produced proferrioxamines, the hydroxamate-type siderophores. Pseudomonas stutzeri JM 300 (genomovar 7) and DSM 50238 (genomovar 8) and Pseudomonas balearica DSM 6082 produced amonabactins, catecholate-type siderophores. The major proferrioxamines detected were the cyclic proferrioxamines E and D2. Pseudomonas stutzeri KC also produced cyclic (X1 and X2) and linear (G1 and G2a-c) proferrioxamines. Our data indicate that the catecholate-type siderophores belong to amonabactins P 750, P 693, T 789, and T 732. A mutant of P. stutzeri KC (strain CTN1) that no longer produced the secondary siderophore pyridine-2,6-dithiocarboxylic acid continued to produce all other siderophores in its normal spectrum. Siderophore profiles suggest that strain KC (genomovar 9) belongs to the proferrioxamine-producing P. stuzeri. Moreover, a putative ferrioxamine outer membrane receptor gene foxA was identified in strain KC, and colony hybridization showed the presence of homologous receptor genes in all P. stutzeri and P. balearica strains tested.