Microorganisms bacterial identification

Rossi TM and Warner IM (1985) Bacterial identification using fluorescence spectroscopy. In Nelson WH (ed.) Rapid Detection and Identification of Microorganisms, pp. 1-50. New York Pergamon. 

The refractory nature of some pollutants, notably, persistent polyhalogenated compounds, has raised problems of bioremediation of contaminated sites (e.g., sediments and dumping sites). There has been interest in the identification, or the production by genetic manipulation, of strains of microorganisms that can metabolically degrade recalcitrant molecules. For example, there are bacterial strains that can reductively dechlorinate PCBs under anaerobic conditions. 

Multiply charged proteins can also be partially sequenced, and microsequences of proteins isolated from several microorganisms have been reported, accomplished with electrospray ionization and FTMS.23,90 Nonadjacent fragment ions may be used to identify bacterial proteins in these top-down strategies.91 In all cases these sequences could be related by bioinformatics to the parent species. An obvious extension would be to characterize proteins from intact microorganisms in this way. In at least one instance a microsequence has been obtained from a protein released from a contaminated intact bacteriophage sample (MS2) to provide a chemotaxonomic identification.77 This work was carried out in an ion trap mass spectrometer. 

For PyMS to be used for (1) routine identification of microorganisms and (2) in combination with ANNs for quantitative microbiological applications, new spectra must be comparable with those previously collected and held in a data base.127 Recent work within our laboratory has demonstrated that this problem may be overcome by the use of ANNs to correct for instrumental drift. By calibrating with standards common to both data sets, ANN models created using previously collected data gave accurate estimates of determi-nand concentrations, or bacterial identities, from newly acquired spectra.127 In this approach calibration samples were included in each of the two runs, and ANNs were set up in which the inputs were the 150 new calibration masses while the outputs were the 150 old calibration masses. These associative nets could then by used to transform data acquired on that one day to data acquired at an earlier data. For the first time PyMS was used to acquire spectra that were comparable with those previously collected and held in a database. In a further study this neural network transformation procedure was extended to allow comparison between spectra, previously collected on one machine, with spectra later collected on a different machine 129 thus calibration transfer by ANNs was affected. Wilkes and colleagues130 have also used this strategy to compensate for differences in culture conditions to construct robust microbial mass spectral databases. 

Few pyrolysis studies were performed on particular components of bacterial lipopolysaccharides. Py-MS results on KDO were reported [3]. The Py-MS studies on individual lipopolysaccharides may encounter problems caused by the difficulty of obtaining pure compounds from this class. As previously indicated, a significant amount of work has been done on direct Py-MS of a variety of whole microorganisms with the purpose of their rapid identification and classification [3]. 

Bacterial sulfate reduction has been invoked as a factor in the formation of ancient mineral deposits and it is relevant to enquire how long sulfate-reducing bacteria have functioned on earth. Because of the structural delicacy of microorganisms, they seldom leave recognisable traces of their existence, and positive identification of fossilized bacteria is extremely difficult. 

Bacterial culture, identification of the causative pathogenic microorganism, susceptibility testing, definitive diagnosis, selection of the antimicrobial agent of choice, and... 

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