Published April 28, 2024 | Version v1

Datasets for paper "Global biochemical profiling of fast-growing Antarctic bacteria isolated from meltwater ponds by high-throughput FTIR spectroscopy"

  • 1. ROR icon Norwegian University of Life Sciences
  • 1. ROR icon Norwegian University of Life Sciences
  • 2. ROR icon Belarusian State University
  • 3. ROR icon National Academy of Sciences of Belarus

Description

Fourier transform infrared (FTIR) spectroscopy is a biophysical technique used for non-destructive biochemical profiling of biological samples. It can provide comprehensive information about the total cellular biochemical profile of microbial cells. In this study, FTIR spectroscopy was used to perform biochemical characterization of twenty-nine bacterial strains isolated from the Antarctic meltwater ponds. The bacteria were grown on two forms of brain heart infusion (BHI) medium: agar at six different temperatures (4, 10, 18, 25, 30, and 37 °C) and on broth at 18 °C. Multivariate data analysis approaches such as principal component analysis (PCA) and correlation analysis were used to study the difference in biochemical profiles induced by the cultivation conditions. The observed results indicated a strong correlation between FTIR spectra and the phylogenetic relationships among the studied bacteria. The most accurate taxonomy-aligned clustering was achieved with bacteria cultivated on agar. Cultivation on two forms of BHI medium provided biochemically different bacterial biomass. The impact of temperature on the total cellular biochemical profile of the studied bacteria was species-specific, however, similarly for all bacteria, lipid spectral region was the least affected while polysaccharide region was the most affected by different temperatures. The biggest temperature-triggered changes of the cell chemistry were detected for bacteria with a wide temperature tolerance such Pseudomonas lundensis strains and Acinetobacter lwoffii BIM B-1558.

Notes (English)

This research was supported by the project “Belanoda Multidisciplinary graduate and post-graduate education in big data analysis for life sciences” (CPEA-LT-2016/10126), funded by the Eurasia program, Norwegian Agency for International Cooperation and Quality Enhancement in Higher Education (Diku), and the Belanoda Digital learning platform for boosting multidisciplinary education in data analysis for life sciences in the Eurasia region (CPEA-STA-2019/10025). Additional support was covered by BYPROVALUE (NFR-MATFONDAVTALE- 301834/E50), SAFE (NFR-BIONÆR 327114), SFI-IB (NFR-SFI 309558) and OIL4FEED (NFR-HAVBRUK2, 302543/E40) projects.

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List of strains and collection numbers .pdf

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