Towards high-throughput screening (HTS) of polyhydroxyalkanoate (PHA) production via Fourier transform infrared (FTIR) spectroscopy of Halomonas sp. R5-57 and Pseudomonas sp. MR4-99

High-throughput screening (HTS) methods for characterization of microbial production of polyhydroxyalkanoates (PHA) are currently under investigated, despite the advent of such systems in related fields. In this study, phenotypic microarray by Biolog PM1 screening of Halomonas sp. R5-57 and Pseudomonas sp. MR4-99 identified 49 and 54 carbon substrates to be metabolized by these bacteria, respectively. Growth on 15 (Halomonas sp. R5-57) and 14 (Pseudomonas sp. MR4-99) carbon substrates was subsequently characterized in 96-well plates using medium with low nitrogen concentration. Bacterial cells were then harvested and analyzed for putative PHA production using two different Fourier transform infrared spectroscopy (FTIR) systems. The FTIR spectra obtained from both strains contained carbonyl-ester peaks indicative of PHA production. Strain specific differences in the carbonyl-ester peak wavenumber indicated that the PHA side chain configuration differed between the two strains. Confirmation of short chain length PHA (scl-PHA) accumulation in Halomonas sp. R5-57 and medium chain length PHA (mcl-PHA) in Pseudomonas sp. MR4-99 was done using Gas Chromatography-Flame Ionization Detector (GC-FID) analysis after upscaling to 50 mL cultures supplemented with glycerol and gluconate. The strain specific PHA side chain configurations were also found in FTIR spectra of the 50 mL cultures. This supports the hypothesis that PHA was also produced in the cells cultivated in 96-well plates, and that the HTS approach is suitable for analysis of PHA production in bacteria. However, the carbonyl-ester peaks detected by FTIR are only indicative of PHA production in the small-scale cultures, and appropriate calibration and prediction models based on combining FTIR and GC-FID data needs to be developed and optimized by performing more extensive screenings and multivariate analyses