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.

Gas Chromatography Coupled to High Resolution Time-of-Flight Mass Spectrometry as a High-Throughput Tool for Characterizing Geochemical Biomarkers in Sediments.

The performance of gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC-HRTofMS) for characterizing geochemical biomarkers from sediment samples was evaluated. Two approaches to obtain the geochemical biomarkers were tested: (1) extraction with organic solvent and subsequent derivatization and (2) in-situ derivatization thermal desorption. Results demonstrated that both approaches can be conveniently applied for simultaneous characterization of many geochemical biomarkers (alkanes, alkanols, sterols, and fatty acids), avoiding conventional time-consuming purification procedures. GC-HRTofMS reduces both sample preparation time and the number of chromatographic runs compared to traditional methodologies used in organic geochemistry. Particularly, the approach based on in-situ derivatization thermal desorption represents a very simple method that can be performed in-line employing few milligrams of sediment, eliminating the need for any sample preparation and solvent use. The high resolving power (m/Δm 50% 25,000) and high mass accuracy (error ≤ 1 ppm) offered by the "zig-zag" time-of-flight analyzer were indispensable to resolve the complexity of the total ion chromatograms, representing a high-throughput tool. Extracted ion chromatograms using exact m/z were useful to eliminate many isobaric interferences and to increase significantly the signal to noise ratio. Characteristic fragment ions allowed the identification of homologous series, such as alkanes, alkanols, fatty acids, and sterols. Polycyclic aromatic hydrocarbons were also identified in the samples by their molecular ions. The characterization of geochemical biomarkers along a sedimentary core collected in the area of Valo Grande Channel (Cananéia-Iguape Estuarine-Lagunar System (São Paulo, Brazil)) provided evidences of environmental changes. Sediments deposited before opening of channel showed dominance of biomarkers from mangrove vegetation, whereas sediments of the pos-opening period showed an increase of biomarkers from aquatic macrophyte (an invasive vegetation).

Data dependent peak model based spectrum deconvolution for analysis of high resolution LC-MS data.

A data dependent peak model (DDPM) based spectrum deconvolution method was developed for analysis of high resolution LC-MS data. To construct the selected ion chromatogram (XIC), a clustering method, the density based spatial clustering of applications with noise (DBSCAN), is applied to all m/z values of an LC-MS data set to group the m/z values into each XIC. The DBSCAN constructs XICs without the need for a user defined m/z variation window. After the XIC construction, the peaks of molecular ions in each XIC are detected using both the first and the second derivative tests, followed by an optimized chromatographic peak model selection method for peak deconvolution. A total of six chromatographic peak models are considered, including Gaussian, log-normal, Poisson, gamma, exponentially modified Gaussian, and hybrid of exponential and Gaussian models. The abundant nonoverlapping peaks are chosen to find the optimal peak models that are both data- and retention-time-dependent. Analysis of 18 spiked-in LC-MS data demonstrates that the proposed DDPM spectrum deconvolution method outperforms the traditional method. On average, the DDPM approach not only detected 58 more chromatographic peaks from each of the testing LC-MS data but also improved the retention time and peak area 3% and 6%, respectively.

Data preprocessing method for liquid chromatography-mass spectrometry based metabolomics.

A set of data preprocessing algorithms for peak detection and peak list alignment are reported for analysis of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics data. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the z-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC-MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers demonstrates that the developed data preprocessing method performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS(2), for peak picking, peak list alignment, and quantification.