Solving the mystery of the Chukotka stinky gray whales.

Gray whales (Eschrichtius robustus) constitute an important part of the diet of Chukotka Native population, reaching 30% of consumed food for the inland Chukchas. Over one hundred licenses for whale hunting are issued on an annual basis. After the USSR collapse natives had to hunt whales near the shore from the small boats. The problem of "stinky" whales arose immediately, as the meat of some harvested species possessed a strong medicinal/chemical odour. The hypotheses explaining the phenomenon ranged from biotoxins, to oil spills. To understand the problem, various tissues of normal and stinky Gray whales were collected in 2020-2021 and analyzed using headspace solid phase microextraction with Gas Chromatography - Mass Spectrometry. Here, we show that dozens of smelly organic compounds were identified among over 500 compounds detected in the samples. The most interesting analytes related to the off odour are bromophenols. The most probable suspect is 2,6-dibromophenol with strong iodoformic odour, perfectly matching that of the "stinky" whales. Quantitative results demonstrated its levels were up to 500-fold higher in the "stinky" whales' tissues. The source of 2,6-dibromophenol is likely polychaetes, producing 2,6-dibromophenol and colonising near shore waters where whales feed. Therefore, the mystery of the stinky whales may be considered resolved.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Gas chromatography-mass spectrometry with chemometric analysis for determining ¹²C and ¹³C labeled contributions in metabolomics and ¹³C flux analysis.

A novel method for the analysis of nearly co-eluting ¹²C and ¹³C isotopically labeled metabolites has been developed and evaluated for gas chromatography coupled to mass spectrometry (GC-MS) data. The method utilizes parallel factor analysis (PARAFAC) with two-dimensional GC-MS data when sample replicates are aligned and stacked in series to create a three-dimensional data cube for mathematical peak deconvolution and ¹²C and ¹³C contribution isolation, with the intent of increasing the accuracy and precision of quantitative metabolomics and ¹³C flux analysis. The platform is demonstrated with ¹³C-labeled metabolite extracts, generated via biosynthesis, added as an internal standard to unlabeled ¹²C metabolites extracted from the methanol-utilizing bacterium Methylobacterium extorquens AM1. Eleven representative metabolites that are common targets for flux analysis were chosen for validation. Good quantitative accuracy and precision were acquired for a 5.00 µM known metabolite concentration (for the 11 metabolites), with an average predicted concentration of 5.07 µM, and a RSD range of 1.2-13.0%. This study demonstrates the ability to reliably deconvolute ¹²C-unlabeled and ¹³C-labeled contributions for a given metabolite. Additionally, using this chemical analysis platform, a dynamic flux experiment is presented in which the incorporation of ¹³C-labeled cell extract can be detected in the methane-utilizing bacterium Methylosinus trichosporium OB3b and measured temporally.