Literature DB >> 17612173

Metabolic footprinting: a new approach to identify physiological changes in complex microbial communities upon exposure to toxic chemicals.

Inês D S Henriques1, Diana S Aga, Pedro Mendes, Seamus K O'Connor, Nancy G Love.   

Abstract

Metabolic footprinting coupled with statistical analysis was applied to multiple, chemically stressed activated sludge cultures to identify probable biomarkers that indicate community stress. The impact of cadmium (Cd), 2,4-dinitrophenol (DNP), and N-ethyl-maleimide (NEM) shock loads on the composition of the soluble fraction of activated sludge cultures was analyzed by gross biomolecular analyses and liquid chromatography-mass spectrometry (LC-MS). Fresh mixed liquor from four distinct treatment plants was each divided in four different batches and was subjected to no chemical addition (control) and spike additions of the stressors Cd, DNP, or NEM. The results indicate that chemical stress caused a significant release of proteins, carbohydrates, and humic acids from the floc structure into the bulk liquid. Using discriminant function analysis (DFA) with genetic algorithm variable selection (GA-DFA), the samples subjected to the different stress conditions plus control could be differentiated, thereby indicating that the footprints of the soluble phase generated by LC-MS were different for the four conditions tested and, therefore, were toxin-specific but community-independent. These footprints, thus, contain information about specific biomolecular differences between the stressed samples, and we found that only a limited number of m/z (mass to charge) ratios from the mass spectra were needed to differentiate between the control and each stressed sample. Since the experiments were conducted with mixed liquor from four distinct wastewater treatment plants, the discriminant m/z ratios may potentially be used as universal stress biomarkers in activated sludge systems.

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Year:  2007        PMID: 17612173     DOI: 10.1021/es062796t

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  Dynamic substrate preferences predict metabolic properties of a simple microbial consortium.

Authors:  Onur Erbilgin; Benjamin P Bowen; Suzanne M Kosina; Stefan Jenkins; Rebecca K Lau; Trent R Northen
Journal:  BMC Bioinformatics       Date:  2017-01-23       Impact factor: 3.169

2.  Determining the metabolic footprints of hydrocarbon degradation using multivariate analysis.

Authors:  Renee J Smith; Thomas C Jeffries; Eric M Adetutu; Peter G Fairweather; James G Mitchell
Journal:  PLoS One       Date:  2013-11-25       Impact factor: 3.240

  2 in total

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