Data mining driven LC-MS/MS detection of bacterial siderophores


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tilmann.harder [ at ] awi.de

Abstract

Iron is an essential micronutrient for almost all microorganisms, plants and animals. Despite the abundance of iron in the earth crust, its bioavailability is very low due to the low solubility of iron(III). Microorganisms have developed different strategies to scavenge iron. One of them is the production of siderophores, small molecules with high affinity to iron. With this strategy, microorganisms can survive even in environments with very low iron concentration like seawater. Detection of siderophores in natural samples is challenging. Natural samples are very complex and siderophore concentrations low. Liquid chromatography-mass spectrometry (LC-MS) in combination with data mining techniques is able to detect siderophores in very low concentrations in complex samples. In this work, a siderophore database with 469 entries was created. With this, different methods for the detection of siderophores in complex samples were tested. An inhouse R-Script with the use of Pearson correlation was established as optimal method. The minimal correlation between extracted ion chromatograms was estimated to 0.85. The established method was then used for the detection of siderophores in the supernatant of five different bacterial cultures.



Item Type
Thesis (Master)
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Not peer-reviewed
Publication Status
Published
Eprint ID
53712
Cite as
Mier, L. (2021): Data mining driven LC-MS/MS detection of bacterial siderophores , Master thesis, University of Bremen.


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