4 resultados para FINGERPRINTS

em Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer


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An accurate amplified fragment length polymorphism (AFLP) method, including three primer sets for the selective amplification step, was developed to display the phylogenetic position of Photobacterium isolates collected from salmon products. This method was efficient for discriminating the three species Photobacterium phosphoreum, Photobacterium iliopiscarium and Photobacterium kishitanii, until now indistinctly gathered in the Photobacterium phosphoreum species group known to be strongly responsible for seafood spoilage. The AFLP fingerprints enabled the isolates to be separated into two main clusters that, according to the type strains, were assigned to the two species P. phosphoreum and P. iliopiscarium. P. kishitanii was not found in the collection. The accuracy of the method was validated by using gyrB-gene sequencing and luxA-gene PCR amplification, which confirmed the species delineation. Most of the isolates of each species were clonally distinct and even those that were isolated from the same source showed some diversity. Moreover, this AFLP method may be an excellent tool for genotyping isolates in bacterial communities and for clarifying our knowledge of the role of the different members of the Photobacterium species group in seafood spoilage.

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The marine diatom Haslea ostrearia produces a water-soluble blue-pigment named marennine of economic interest (e.g. in aquaculture for the greening of oysters). Up to date the studies devoted to ecological conditions under which this microalga develops never took into account the bacterial-H. ostrearia relationships. In this study the bacterial community was analysed by PCR-TTGE before and after H. ostrearia isolation cells recovered from 4 localities, to distinguish the relative part of the biotope and the biocenose and eventually to describe the temporal dynamic of the structure of the bacterial community. The bacterial structure of the phycosphere differed strongly from that of the bulk sediment. The similarity between bacteria recovered from the biofilm and the suspended bacteria did not exceed 10% (vs. > 90% amongst biofilms). The differences in genetic fingerprints, more especially high between two H. ostrearia isolates showed also the highest differences in the bacterial structure as the result of specific metabolomics profiles. The non-targeted metabolomic investigation showed that these profiles were more distinct in case of bacteria-alga associations than for the H. ostrearia monoculture. At the scale of a culture cycle in laboratory conditions, the bacterial community was specific to the growth stage. When H. ostrearia was subcultured for 9 months, a shift in the bacterial structure was shown from 3-months subculturing and the bacterial structure stabilized afterwards (70-86% similarities). A first insight of the relationships between H. ostrearia and its surrounding bacteria was shown for a better understanding of the ecological feature of this diatom.

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The marine diatom Haslea ostrearia [1] produces a water-soluble blue-pigment named marennine [2] of economic interest. But the lack of knowledge of the ecological conditions, under which this microalga develops in its natural ecosystem, more especially bacteria H. ostrearia interactions, prevents any optimization of its culture in well-controlled conditions. The structure of the bacterial community was analyzed by PCR-TTGE before and after the isolation of H. ostrearia cells recovered from 4 localities, to distinguish the relative part of the biotope and the biocenose and eventually to describe the temporal dynamic of the structure of the bacterial community at two time-scales. The differences in genetic fingerprints, more especially high between two H. ostrearia isolates (HO-R and HO-BM) showed also the highest differences in the bacterial structure [3] as the result of specific metabolomics profiles. The non-targeted metabolomic investigation showed that these profiles were more distinct in case of bacteria-alga associations than for the H. ostrearia monoculture Here we present a Q-TOF LC/MS metabolomic fingerprinting approach [3]: - to investigate differential metabolites of axenic versus non axenic H. ostrearia cultures. - to focus on the specific metabolites of a bacterial surrounding associated with the activation or inhibition of the microalga growing. The Agilent suite of data processing software makes feature finding, statistical analysis, and identification easier. This enables rapid transformation of complex raw data into biologically relevant metabolite information.

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The marine diatom Haslea ostrearia [1] produces a water-soluble blue-pigment named marennine [2] of economic interest. But the lack of knowledge of the ecological conditions, under which this microalga develops in its natural ecosystem, more especially bacteria H. ostrearia interactions, prevents any optimization of its culture in well-controlled conditions. The structure of the bacterial community was analyzed by PCR-TTGE before and after the isolation of H. ostrearia cells recovered from 4 localities, to distinguish the relative part of the biotope and the biocenose and eventually to describe the temporal dynamic of the structure of the bacterial community at two time-scales. The differences in genetic fingerprints, more especially high between two H. ostrearia isolates (HO-R and HO-BM) showed also the highest differences in the bacterial structure [3] as the result of specific metabolomics profiles. The non-targeted metabolomic investigation showed that these profiles were more distinct in case of bacteria-alga associations than for the H. ostrearia monoculture Here we present a Q-TOF LC/MS metabolomic fingerprinting approach [3]: - to investigate differential metabolites of axenic versus non axenic H. ostrearia cultures. - to focus on the specific metabolites of a bacterial surrounding associated with the activation or inhibition of the microalga growing. The Agilent suite of data processing software makes feature finding, statistical analysis, and identification easier. This enables rapid transformation of complex raw data into biologically relevant metabolite information.