Scholarly Work - Center for Biofilm Engineering
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/9335
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Item Seven genome sequences of bacterial, environmental isolates from Pony Lake, Antarctica(American Society for Microbiology, 2023-12) Foreman, Christine M.; Smith, Heidi J.; Dieser, MarkusDissolved organic matter (DOM) in Antarctic inland waters is unique in that its precursor molecules are microbially derived and lack the chemical signature of higher plants. Here, we report the genomic sequences of seven environmental, bacterial isolates from Pony Lake, Antarctica, to explore the genetic potential linked to DOM processing.Item Investigation of Raman Spectroscopic Signatures with Multivariate Statistics: An Approach for Cataloguing Microbial Biosignatures(Mary Ann Liebert Inc, 2021-09) Messmer, Mitch W.; Dieser, Markus; Smith, Heidi J.; Parker, Albert E.; Foreman, Christine M.Spectroscopic instruments are increasingly being implemented in the search for extraterrestrial life. However, microstructural spectral analyses of alien environments could prove difficult without knowledge on the molecular identification of individual spectral signatures. To bridge this gap, we introduce unsupervised K-means clustering as a statistical approach to discern spectral patterns of biosignatures without prior knowledge of spectral regions of biomolecules. Spectral profiles of bacterial isolates from analogous polar ice sheets were measured with Raman spectroscopy. Raman analysis identified carotenoid and violacein pigments, and key cellular features including saturated and unsaturated fats, triacylglycerols, and proteins. Principal component analysis and targeted spectra integration biplot analysis revealed that the clustering of bacterial isolates was attributed to spectral biosignatures influenced by carotenoid pigments and ratio of unsaturated/saturated fat peaks. Unsupervised K-means clustering highlighted the prevalence of the corresponding spectral peaks, while subsequent supervised permutational multivariate analysis of variance provided statistical validation for spectral differences associated with the identified cellular features. Establishing a validated catalog of spectral signatures of analogous biotic and abiotic materials, in combination with targeted supervised tools, could prove effective at identifying extant biosignatures.