Improved sensitivity, accuracy and prediction provided by a high-performance liquid chromatography screen for the isolation of phytase-harbouring organisms from environmental samples

Rix, G. D., Todd, J. D., Neal, AndyORCID logo and Brearley, C. A. (2020) Improved sensitivity, accuracy and prediction provided by a high-performance liquid chromatography screen for the isolation of phytase-harbouring organisms from environmental samples. Microbial Biotechnology, 14 (4). pp. 1409-1421. 10.1111/1751-7915.13733
Copy

HPLC methods are shown to be of predictive value for classification of phytase activity of aggregate microbial communities and pure cultures. Applied in initial screens, they obviate the problems of ‘false‐positive’ detection arising from impurity of substrate and imprecision of methodologies that rely on phytate‐specific media. In doing so, they simplify selection of candidates for biotechnological applications. Combined with 16S sequencing and simple bioinformatics, they reveal diversity of the histidine phosphatase class of phytases most commonly exploited for biotechnological use. They reveal contribution of multiple inositol‐polyphosphate phosphatase (MINPP) activity to aggregate soil phytase activity, and they identity Acinetobacter spp. as harbouring this prevalent soil phytase activity. Previously, among bacteria MINPP was described exclusively as an activity of gut commensals. HPLC methods have also identified, in a facile manner, a known commercially successful histidine (acid) phosphatase enzyme. The methods described afford opportunity for isolation of phytases for biotechnological use from other environments. They reveal the position of attack on phytate by diverse histidine phosphatases, something that other methods lack.


picture_as_pdf
1751-7915.13733.pdf
subject
Published Version
Available under Creative Commons: Attribution 4.0

View Download

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads