Evaluation of chromatographic methods for the detection of bacterial contamination in biotechnical processes

Elmroth, Ingrid; Sundin, Peter; Valeur, Anders; Larsson, Lennart; Odham, Göran

doi:10.1016/0167-7012(92)90042-3

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…Certain metabolites have been considered indicative of the presence of particular microorganisms. These include muramic acid for all bacteria, myristic acid for Gram-negative bacteria, and glucosamine for fungi (4)(5)(6). However, these metabolic markers are not completely specific for a group of microorganisms, and it is known that the quantities of these metabolites vary from species to species, making it difficult to ascertain consistently the level of contamination based on metabolite levels.…”

Section: Discussionmentioning

confidence: 99%

“…Very few studies have examined the entire metabolite profile of contaminated cultures. Elmroth et al (5,6) previously compared the metabolite profiles of axenic cultures against those of contaminated cultures. Those authors detected changes in the metabolite profiles caused by the presence of microbial contaminants.…”

Section: Discussionmentioning

confidence: 99%

“…The targeted analysis of signature metabolites is an approach that has been used in many studies for the detection of microorganisms in complex samples (4)(5)(6)16). Certain metabolites have been considered indicative of the presence of particular microorganisms.…”

Section: Discussionmentioning

confidence: 99%

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An Exometabolomics Approach to Monitoring Microbial Contamination in Microalgal Fermentation Processes by Using Metabolic Footprint Analysis

Sue

Obolonkin

Griffiths³

et al. 2011

Appl Environ Microbiol

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The early detection of microbial contamination is crucial to avoid process failure and costly delays in fermentation industries. However, traditional detection methods such as plate counting and microscopy are labor-intensive, insensitive, and time-consuming. Modern techniques that can detect microbial contamination rapidly and cost-effectively are therefore sought. In the present study, we propose gas chromatography-mass spectrometry (GC-MS)-based metabolic footprint analysis as a rapid and reliable method for the detection of microbial contamination in fermentation processes. Our metabolic footprint analysis detected statistically significant differences in metabolite profiles of axenic and contaminated batch cultures of microalgae as early as 3 h after contamination was introduced, while classical detection methods could detect contamination only after 24 h. The data were analyzed by discriminant function analysis and were validated by leave-one-out cross-validation. We obtained a 97% success rate in correctly classifying samples coming from contaminated or axenic cultures. Therefore, metabolic footprint analysis combined with discriminant function analysis presents a rapid and cost-effective approach to monitor microbial contamination in industrial fermentation processes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An Exometabolomics Approach to Monitoring Microbial Contamination in Microalgal Fermentation Processes by Using Metabolic Footprint Analysis

Sue

Obolonkin

Griffiths³

et al. 2011

Appl Environ Microbiol

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“…They applied the protocol set up for the study of a yeast mutant that did not show any apparent change in phenotypic characteristics and observed clear differences in the metabolic profiles in comparison with the wild type. The use of metabolic profiling approaches for the authentication of strains [13,31,79] and the detection of microbial cross-contamination [18,104] has been reported before. Also, van Dam et al [96] reported preliminary metabolome analysis results with S. cerevisiae.…”

Section: Microbial Metabolomics: State-of-the-artmentioning

confidence: 99%

“…Few examples of microbial metabolomics studies aimed at the non-biased comprehensive study of metabolites have been reported. One of the first papers of what could be described as metabolomics is a study applying three different GC-MS methods for the analysis of fatty acids, amino acids, and carbohydrates in combination with the MVDA tool SIMCA, in order to monitor microbial contamination during the fermentative production of dextran by Leuconostoc mesenteroides [18]. The metabolome of E. coli has been determined using two-dimensional TLC by Ferenci and co-workers [58,92,93].…”

Section: Microbial Metabolomics: State-of-the-artmentioning

confidence: 99%

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets

Werf

Jellema

Hankemeier

2005

J IND MICROBIOL BIOTECHNOL

126

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Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on 'educated guesses' and 'gut feeling'. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues.

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Detection of gram-negativeErwinia herbicola outdoor aerosols with pyrolysis-gas chromatography/ion-mobility spectrometry

Snyder

Maswadeh

Tripathi

et al. 2000

Field Analyt. Chem. Technol.

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Evaluation of chromatographic methods for the detection of bacterial contamination in biotechnical processes

Cited by 10 publications

References 16 publications

An Exometabolomics Approach to Monitoring Microbial Contamination in Microalgal Fermentation Processes by Using Metabolic Footprint Analysis

An Exometabolomics Approach to Monitoring Microbial Contamination in Microalgal Fermentation Processes by Using Metabolic Footprint Analysis

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets

Detection of gram-negativeErwinia herbicola outdoor aerosols with pyrolysis-gas chromatography/ion-mobility spectrometry

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