Hydrocarbon phenotyping of algal species using pyrolysis-gas chromatography mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Biofuels derived from algae biomass and algae lipids might reduce dependence on fossil fuels. Existing analytical techniques need to facilitate rapid characterization of algal species by phenotyping hydrocarbon-related constituents.</p> <p>Results</p> <p>In this study, we compared the hydrocarbon rich algae <it>Botryococcus braunii </it>against the photoautotrophic model algae <it>Chlamydomonas reinhardtii </it>using pyrolysis-gas chromatography quadrupole mass spectrometry (pyGC-MS). Sequences of up to 48 dried samples can be analyzed using pyGC-MS in an automated manner without any sample preparation. Chromatograms of 30-min run times are sufficient to profile pyrolysis products from C8 to C40 carbon chain length. The freely available software tools AMDIS and SpectConnect enables straightforward data processing. In <it>Botryococcus </it>samples, we identified fatty acids, vitamins, sterols and fatty acid esters and several long chain hydrocarbons. The algae species <it>C. reinhardtii, B. braunii </it>race A and <it>B. braunii </it>race B were readily discriminated using their hydrocarbon phenotypes. Substructure annotation and spectral clustering yielded network graphs of similar components for visual overviews of abundant and minor constituents.</p> <p>Conclusion</p> <p>Pyrolysis-GC-MS facilitates large scale screening of hydrocarbon phenotypes for comparisons of strain differences in algae or impact of altered growth and nutrient conditions.</p

The Post-COVID-19 Era: Interdisciplinary Demands of Contagion Surveillance Mass Spectrometry for Future Pandemics

Mass spectrometry (MS) can become a potentially useful instrument type for aerosol, droplet and fomite (ADF) contagion surveillance in pandemic outbreaks, such as the ongoing SARS-CoV-2 pandemic. However, this will require development of detection protocols and purposing of instrumentation for in situ environmental contagion surveillance. These approaches include: (1) enhancing biomarker detection by pattern recognition and machine learning; (2) the need for investigating viral degradation induced by environmental factors; (3) representing viral molecular data with multidimensional data transforms, such as van Krevelen diagrams, that can be repurposed to detect viable viruses in environmental samples; and (4) absorbing engineering attributes for developing contagion surveillance MS from those used for astrobiology and chemical, biological, radiological, nuclear (CBRN) monitoring applications. Widespread deployment of such an MS-based contagion surveillance could help identify hot zones, create containment perimeters around them and assist in preventing the endemic-to-pandemic progression of contagious diseases

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Novel methods for the analysis of small molecule fragmentation mass spectra

The identification of small molecules, such as metabolites, in a high throughput manner plays an important in many research areas. Mass spectrometry (MS) is one of the predominant analysis technologies and is much more sensitive than nuclear magnetic resonance spectroscopy. Fragmentation of the molecules is used to obtain information beyond its mass. Gas chromatography-MS is one of the oldest and most widespread techniques for the analysis of small molecules. Commonly, the molecule is fragmented using electron ionization (EI). Using this technique, the molecular ion peak is often barely visible in the mass spectrum or even absent. We present a method to calculate fragmentation trees from high mass accuracy EI spectra, which annotate the peaks in the mass spectrum with molecular formulas of fragments and explain relevant fragmentation pathways. Fragmentation trees enable the identification of the molecular ion and its molecular formula if the molecular ion is present in the spectrum. The method works even if the molecular ion is of very low abundance. MS experts confirm that the calculated trees correspond very well to known fragmentation mechanisms.Using pairwise local alignments of fragmentation trees, structural and chemical similarities to already-known molecules can be determined. In order to compare a fragmentation tree of an unknown metabolite to a huge database of fragmentation trees, fast algorithms for solving the tree alignment problem are required. Unfortunately the alignment of unordered trees, such as fragmentation trees, is NP-hard. We present three exact algorithms for the problem. Evaluation of our methods showed that thousands of alignments can be computed in a matter of minutes. Both the computation and the comparison of fragmentation trees are rule-free approaches that require no chemical knowledge about the unknown molecule and thus will be very helpful in the automated analysis of metabolites that are not included in common libraries

Bayesian methods for small molecule identification

Confident identification of small molecules remains a major challenge in untargeted metabolomics, natural product research and related fields. Liquid chromatography-tandem mass spectrometry is a predominant technique for the high-throughput analysis of small molecules and can detect thousands of different compounds in a biological sample. The automated interpretation of the resulting tandem mass spectra is highly non-trivial and many studies are limited to re-discovering known compounds by searching mass spectra in spectral reference libraries. But these libraries are vastly incomplete and a large portion of measured compounds remains unidentified. This constitutes a major bottleneck in the comprehensive, high-throughput analysis of metabolomics data. In this thesis, we present two computational methods that address different steps in the identification process of small molecules from tandem mass spectra. ZODIAC is a novel method for de novo that is, database-independent molecular formula annotation in complete datasets. It exploits similarities of compounds co-occurring in a sample to find the most likely molecular formula for each individual compound. ZODIAC improves on the currently best-performing method SIRIUS; on one dataset by 16.5 fold. We show that de novo molecular formula annotation is not just a theoretical advantage: We discover multiple novel molecular formulas absent from PubChem, one of the biggest structure databases. Furthermore, we introduce a novel scoring for CSI:FingerID, a state-of-the-art method for searching tandem mass spectra in a structure database. This scoring models dependencies between different molecular properties in a predicted molecular fingerprint via Bayesian networks. This problem has the unusual property, that the marginal probabilities differ for each predicted query fingerprint. Thus, we need to apply Bayesian networks in a novel, non-standard fashion. Modeling dependencies improves on the currently best scoring