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

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.

Automated summary

HPLC methods are effective for predicting phytase activity, simplifying candidate selection, and revealing diversity of phytases. Combining with 16S sequencing and bioinformatics can uncover the contribution of MINPP activity to soil phytase activity.