Breathomics for the clinician: the use of volatile organic compounds in respiratory diseases.

Exhaled breath analysis has the potential to provide valuable insight on the status of various metabolic pathways taking place in the lungs locally and other vital organs, via systemic circulation. For years, volatile organic compounds (VOCs) have been proposed as feasible alternative diagnostic and prognostic biomarkers for different respiratory pathologies.We reviewed the currently published literature on the discovery of exhaled breath VOCs and their utilisation in various respiratory diseasesKey barriers in the development of clinical breath tests include the lack of unified consensus for breath collection and analysis and the complexity of understanding the relationship between the exhaled VOCs and the underlying metabolic pathways. We present a comprehensive overview, in light of published literature and our experience from coordinating a national breathomics centre, of the progress made to date and some of the key challenges in the field and ways to overcome them. We particularly focus on the relevance of breathomics to clinicians and the valuable insights it adds to diagnostics and disease monitoring.Breathomics holds great promise and our findings merit further large-scale multicentre diagnostic studies using standardised protocols to help position this novel technology at the centre of respiratory disease diagnostics.

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software.

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high-complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1tR and 2tR was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.

Structural identification of petroleum acids by conversion to hydrocarbons and multidimensional gas chromatography-mass spectrometry.

Identification of individual petroleum acids ("naphthenic" acids, NA) has proved challenging for decades, due to the extreme complexity of many petroleum acid mixtures. This has hindered detailed understanding of the role of NA in petroleum generation and oil production processes, refinery corrosion, as wood preservatives, and as environmental toxicants. Some recent advances have been made due to improved chromatographic separation of esters of the acids by multidimensional gas chromatography-mass spectrometry (GC × GC-MS), but relatively few reference spectra of esters are available for comparison. Here we report a complementary method based on a combination of a modified historical approach of converting NA to the corresponding hydrocarbons, followed by analysis by GC × GC-MS. Many published spectra exist for reference hydrocarbons making comparisons of reference spectra with those of the unknowns, much more feasible. As an example, we report identification of over 30 individual bicyclic naphthenic acids as the bicyclane hydrocarbons. These include both fused and bridged acids possessing methyl, dimethyl, and ethyl alkyl substituents as well as some terpenoid-derived acids. The study provides the most comprehensive analysis of one of the major classes of NA (the bicyclic acids) to date. There is now clear potential for this method to be used for the structural elucidation of other unknown acids (e.g., oil sands acids) and functionalized biomarkers in complex mixtures.

Can two-dimensional gas chromatography/mass spectrometric identification of bicyclic aromatic acids in petroleum fractions help to reveal further details of aromatic hydrocarbon biotransformation pathways?

RATIONALE: The identification of key acid metabolites ('signature' metabolites) has allowed significant improvements to be made in our understanding of the biodegradation of petroleum hydrocarbons, in reservoir and in contaminated natural systems, such as aquifers and seawater. On this basis, anaerobic oxidation is now more widely accepted as one viable mechanism, for instance. However, identification of metabolites in the complex acid mixtures from petroleum degradation is challenging and would benefit from use of more highly resolving analytical methods. METHODS: Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GCxGC/TOFMS) with both nominal mass and accurate mass measurement was used to study the complex mixtures of aromatic acids (as methyl esters) in petroleum fractions. RESULTS: Numerous mono- and di-aromatic acid isomers were identified in a commercial naphthenic acids fraction from petroleum and in an acids fraction from a biodegraded petroleum. In many instances, compounds were identified by comparison of mass spectral and retention time data with those of authentic compounds. CONCLUSIONS: The identification of a variety of alkyl naphthalene carboxylic and alkanoic and alkyl tetralin carboxylic and alkanoic acids, plus identifications of a range of alkyl indane acids, provides further evidence for 'signature' metabolites of biodegradation of aromatic petroleum hydrocarbons. Identifications such as these now offer the prospect of better differentiation of metabolites of bacterial processes (e.g. aerobic, methanogenic, sulphate-reducing) in polar petroleum fractions.

If speed is of the essence: rapid analysis of ambergris by APCI compact mass spectrometry.

The use of atmospheric pressure chemical ionisation (APCI) compact mass spectrometry (CMS) was investigated for the analysis of jetsam and museum-archived ambergris and of ambergris components in perfumes. The data were compared with those from existing methods. Authentic samples of some individual ambergris constituents (ambrein, coprostanol, epicoprostanol and coprostanone), were also examined. Rapid APCI CMS was achieved using either a solids probe or a probe with solutions held in capillary melting point tubes. Interpretation is made of the spectra of the principal natural product components, the relative ion responses were measured and the elemental composition of key ions in the spectra confirmed using high resolution accurate mass APCI MS. Rapid analysis of ambergris by APCI CMS may prove to be a further convenient method of identifying ambrein, of measuring the relative ratios of ambrein and steroids in ambergris and even of quantifying the latter, with minimal sample preparation.

Chemical analysis of flotsam ambergris.

The natural product ambergris is only found rarely on beaches, as jetsam. Even more scarce, or even absent, are accounts of flotsam ambergris. Here, we report the chemical analysis of a rare, large piece (>100kg) of flotsam found in the Atlantic in 2019. About 95% of subsamples from the outside of the coprolith was soluble in dichloromethane. Of this, FTIR spectroscopy, APCI-MS and GC-MS indicated the presence of ambrein. Radiocarbon dating indicated that the sample was post 1950s in age. The 13C/12C isotope ratio (-22.5 ‰) was typical of those reported to date for whale 'body' ambergris. Metals of ambergris have hardly been reported previously. The distribution found here for the flotsam, was dominated by copper and zinc, which is similar to that of several squid species. This is also consistent with the presence of squid beaks in the coprolith. Squid are a major prey species of sperm whales.

Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry.

BACKGROUND: The global COVID-19 pandemic has led to extensive development in many fields, including the diagnosis of COVID-19 infection by mass spectrometry. The aim of this systematic review and meta-analysis was to assess the accuracy of mass spectrometry diagnostic tests developed so far, across a wide range of biological matrices, and additionally to assess risks of bias and applicability in studies published to date. METHOD: 23 retrospective observational cohort studies were included in the systematic review using the PRISMA-DTA framework, with a total of 2858 COVID-19 positive participants and 2544 controls. Risks of bias and applicability were assessed via a QUADAS-2 questionnaire. A meta-analysis was also performed focusing on sensitivity, specificity, diagnostic accuracy and Youden's Index, in addition to assessing heterogeneity. FINDINGS: Sensitivity averaged 0.87 in the studies reviewed herein (interquartile range 0.81-0.96) and specificity 0.88 (interquartile range 0.82-0.98), with an area under the receiver operating characteristic summary curve of 0.93. By subgroup, the best diagnostic results were achieved by viral proteomic analyses of nasopharyngeal swabs and metabolomic analyses of plasma and serum. The performance of other sampling matrices (breath, sebum, saliva) was less good, indicating that these protocols are currently insufficiently mature for clinical application. CONCLUSIONS: This systematic review and meta-analysis demonstrates the potential for mass spectrometry and 'omics in achieving accurate test results for COVID-19 diagnosis, but also highlights the need for further work to optimize and harmonize practice across laboratories before these methods can be translated to clinical applications.

Association of gut-related metabolites with respiratory symptoms in COVID-19: A proof-of-concept study.

Gut-related metabolites have been linked with respiratory disease. The crosstalk between the gut and lungs suggests that gut health may be compromised in COVID-19. The aims of the present study were to analyze a panel of gut-related metabolites (acetyl-L-carnitine, betaine, choline, L-carnitine, trimethylamine, and trimethylamine N-oxide) in patients with COVID-19, matched with healthy individuals and patients with non-COVID-19 respiratory symptoms. As results, metabolites from this panel were impaired in patients with COVID-19 and were associated with the symptoms of breathlessness and temperature, and it was possible to differentiate between COVID-19 and asthma. Preliminary results showed that lower levels of betaine appeared to be associated with poor outcomes in patients with COVID-19, suggesting betaine as a marker of gut microbiome health.

An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics.

The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.

Visualization of exhaled breath metabolites reveals distinct diagnostic signatures for acute cardiorespiratory breathlessness.

Acute cardiorespiratory breathlessness accounts for one in eight of all emergency hospitalizations. Early, noninvasive diagnostic testing is a clinical priority that allows rapid triage and treatment. Here, we sought to find and replicate diagnostic breath volatile organic compound (VOC) biomarkers of acute cardiorespiratory disease and understand breath metabolite network enrichment in acute disease, with a view to gaining mechanistic insight of breath biochemical derangements. We collected and analyzed exhaled breath samples from 277 participants presenting acute cardiorespiratory exacerbations and aged-matched healthy volunteers. Topological data analysis phenotypes differentiated acute disease from health and acute cardiorespiratory exacerbation subtypes (acute heart failure, acute asthma, acute chronic obstructive pulmonary disease, and community-acquired pneumonia). A multibiomarker score (101 breath biomarkers) demonstrated good diagnostic sensitivity and specificity (≥80%) in both discovery and replication sets and was associated with all-cause mortality at 2 years. In addition, VOC biomarker scores differentiated metabolic subgroups of cardiorespiratory exacerbation. Louvain clustering of VOCs coupled with metabolite enrichment and similarity assessment revealed highly specific enrichment patterns in all acute disease subgroups, for example, selective enrichment of correlated C5-7 hydrocarbons and C3-5 carbonyls in heart failure and selective depletion of correlated aldehydes in acute asthma. This study identified breath VOCs that differentiate acute cardiorespiratory exacerbations and associated subtypes and metabolic clusters of disease-associated VOCs.

Diagnosis of COVID-19 by exhaled breath analysis using gas chromatography-mass spectrometry.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has claimed over two and a half million lives worldwide so far. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is perceived to be seasonally recurrent, and a rapid noninvasive biomarker to accurately diagnose patients early on in their disease course will be necessary to meet the operational demands for COVID-19 control in the coming years. OBJECTIVE: The aim of this study was to evaluate the role of exhaled breath volatile biomarkers in identifying patients with suspected or confirmed COVID-19 infection, based on their underlying PCR status and clinical probability. METHODS: A prospective, real-world, observational study was carried out, recruiting adult patients with suspected or confirmed COVID-19 infection. Breath samples were collected using a standard breath collection bag, modified with appropriate filters to comply with local infection control recommendations, and samples were analysed using gas chromatography-mass spectrometry (TD-GC-MS). RESULTS: 81 patients were recruited between April 29 and July 10, 2020, of whom 52 out of 81 (64%) tested positive for COVID-19 by reverse transcription-polymerase chain reaction (RT-PCR). A regression analysis identified a set of seven exhaled breath features (benzaldehyde, 1-propanol, 3,6-methylundecane, camphene, beta-cubebene, iodobenzene and an unidentified compound) that separated PCR-positive patients with an area under the curve (AUC): 0.836, sensitivity: 68%, specificity: 85%. CONCLUSIONS: GC-MS-detected exhaled breath biomarkers were able to identify PCR-positive COVID-19 patients. External replication of these compounds is warranted to validate these results.

Volatile organic compounds in a headspace sampling system and asthmatics sputum samples.

The headspace of a biological sample contains exogenous volatile organic compounds (VOCs) present within the sampling environment which represent the background signal. This study aimed to characterise the background signal generated from a headspace sampling system in a clinical site, to evaluate intra- and inter-day variation of background VOC and to understand the impact of a sample itself upon commonly reported background VOC using sputum headspace samples from severe asthmatics. The headspace, in absence of a biological sample, was collected hourly from 11am to 3pm within a day (time of clinical samples acquisition), and from Monday to Friday in a week, and analysed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Chemometric analysis identified 1120 features, 37 of which were present in at least the 80% of all the samples. The analyses of intra- and inter-day background variations were performed on 13 of the most abundant features, ubiquitously present in headspace samples. The concentration ratios relative to background were reported for the selected abundant VOC in 36 asthmatic sputum samples, acquired from 36 stable severe asthma patients recruited at Glenfield Hospital, Leicester, UK. The results identified no significant intra- or inter-day variations in compounds levels and no systematic bias ofz-scores, with the exclusion of benzothiazole, whose abundance increased linearly between 11am and 3pm with a maximal intra-day fold change of 2.13. Many of the identified background features are reported in literature as components of headspace of biological samples and are considered potential biomarkers for several diseases. The selected background features were identified in headspace of all severe asthma sputum samples, albeit with varying levels of enrichment relative to background. Our observations support the need to consider the background signal derived from the headspace sampling system when developing and validating headspace biomarker signatures using clinical samples.

Volatile and semi-volatile components of jetsam ambergris.

Volatile and semi-volatile compounds account for the odors, long valued in the perfumery industry, of the natural product, ambergris. Here we demonstrate application of solid phase micro extraction (SPME) and gas chromatography-mass spectrometry (GC-MS) to headspace analysis of the volatiles and semi-volatiles of jetsam ambergris. The samples collected in 2017/2018, ranged from a black, sticky material from New Zealand, likely recently ejected from a sperm whale, to a white solid found on a beach in Chile and radiocarbon-dated previously to be about 1000 years old. The traces of volatile/semi-volatile compounds extracted included, odorous γ-dihydroionone and odor-free pristane (2,6,10,14-tetramethylpentadecane), as the major constituents. The ratios of these to one another and to many other minor constituents, varied, depending on sample color and age.

Breath analysis by two-dimensional gas chromatography with dual flame ionisation and mass spectrometric detection - Method optimisation and integration within a large-scale clinical study.

Precision medicine has spurred new innovations in molecular pathology leading to recent advances in the analysis of exhaled breath as a non-invasive diagnostic tool. Volatile organic compounds (VOCs) detected in exhaled breath have the potential to reveal a wealth of chemical and metabolomic information. This study describes the development of a method for the analysis of breath, based on automated thermal desorption (TD) combined with flow modulated comprehensive two-dimensional gas chromatography (GC×GC) with dual flame ionisation and quadrupole mass spectrometric detection (FID and qMS). The constrained optimisation and analytical protocol was designed to meet the practical demands of a large-scale multi-site clinical study, while maintaining analytical rigour to produce high fidelity data. The results demonstrate a comprehensive method optimisation for the collection and analysis of breath VOCs by GC×GC, integral to the standardisation and integration of breath analysis within large clinical studies.

Bicyclic naphthenic acids in oil sands process water: identification by comprehensive multidimensional gas chromatography-mass spectrometry.

Although bicyclic acids have been reported to be the major naphthenic acids in oil sands process-affected water (OSPW) and a well-accepted screening assay indicated that some bicyclics were the most acutely toxic acids tested, none have yet been identified. Here we show by comprehensive multidimensional gas chromatography-mass spectrometry (GC×GC-MS), that >100 C8-15 bicyclic acids are typically present in OSPW. Synthesis or purchase allowed us to establish the GC×GC retention times of methyl esters of numerous of these and the mass spectra and published spectra of some additional types, allowed us to identify bicyclo[2.2.1]heptane, bicyclo[3.2.1]octane, bicyclo[4.3.0]nonane, bicyclo[3.3.1]nonane and bicyclo[4.4.0]decane acids in OSPW and a bicyclo[2.2.2]octane acid in a commercial acid mixture. The retention positions of authentic bicyclo[3.3.0]octane and bicyclo[4.2.0]octane carboxylic acid methyl esters and published retention indices, showed these were also possibilities, as were bicyclo[3.1.1]heptane acids. Bicyclo[5.3.0]decane and cyclopentylcyclopentane carboxylic acids were ruled out in the samples analysed, on the basis that the corresponding alkanes eluted well after bicyclo[4.4.0]decane (latest eluting acids). Bicyclo[4.2.1]nonane, bicyclo[3.2.2]nonane, bicyclo[3.3.2]decane, bicyclo[4.2.2]decane and spiro[4.5]decane carboxylic acids could not be ruled out or in, as no authentic compounds or literature data were available. Mass spectra of the methyl esters of the higher bicyclic C12-15 acids suggested that many were simply analogues of the acids identified above, with longer alkanoate chains and/or alkyl substituents. Our hypothesis is that these acids represent the biotransformation products of the initially somewhat more bio-resistant bicyclanes of petroleum. Although remediation studies suggest that many bicyclic acids can be relatively quickly removed from suitably treated OSPW, examination by GC×GC-MS may show which isomers are affected most. Knowledge of the structures will allow the toxicity of any residual isomers to be calculated and measured.

Discovering RNA-protein interactome by using chemical context profiling of the RNA-protein interface.

RNA-protein (RNP) interactions generally are required for RNA function. At least 5% of human genes code for RNA-binding proteins. Whereas many approaches can identify the RNA partners for a specific protein, finding the protein partners for a specific RNA is difficult. We present a machine-learning method that scores a protein's binding potential for an RNA structure by utilizing the chemical context profiles of the interface from known RNP structures. Our approach is applicable even when only a single RNP structure is available. We examined 801 mammalian proteins and find that 37 (4.6%) potentially bind transfer RNA (tRNA). Most are enzymes involved in cellular processes unrelated to translation and were not known to interact with RNA. We experimentally tested six positive and three negative predictions for tRNA binding in vivo, and all nine predictions were correct. Our computational approach provides a powerful complement to experiments in discovering new RNPs.

Studies of long chain lipids in insects by high temperature gas chromatography and high temperature gas chromatography-mass spectrometry.

The organic compounds occurring naturally on the cuticles (surfaces) of insects are important for insect communication, help to act as protective water barriers and are useful in chemical taxonomy. Typically the cuticular lipids are only studied by gas chromatography-mass spectrometry (GC-MS) of hexane or pentane extracts, so the normal limitations of GC-MS makes it perhaps unsurprising that compounds with more than about 35 carbon atoms have only rarely been reported. Here we show by high temperature (HT) GC and HTGC-MS of extracts of eleven species of insects from nine genera, that longer chain compounds are actually common. Wax esters and triacylglycerides are virtually ubiquitous in such extracts, but long chain (>C35) hydrocarbons also sometimes occur. Whilst the latter have occasionally been reported previously from mass spectrometry studies, the use of the HTGC combination with MS allowed even some isobaric isomers to be separated and thus more complete lipid distributions to be monitored. Since the physical properties of cuticular compounds depend on this composition of the mixtures, such differences may influence the water loss rates of the insects, amongst other effects. In addition, the high molecular weight compound profiles may allow species to be more easily differentiated, one from another. It would be interesting to apply these methods to examination of the cuticular lipids of insects on a more routine basis, ideally in combination with MALDI-TOF-MS and imaging methods.

Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing.

The increasingly complex research questions addressed by neuroimaging research impose substantial demands on computational infrastructures. These infrastructures need to support management of massive amounts of data in a way that affords rapid and precise data analysis, to allow collaborative research, and to achieve these aims securely and with minimum management overhead. Here we present an approach that overcomes many current limitations in data analysis and data sharing. This approach is based on open source database management systems that support complex data queries as an integral part of data analysis, flexible data sharing, and parallel and distributed data processing using cluster computing and Grid computing resources. We assess the strengths of these approaches as compared to current frameworks based on storage of binary or text files. We then describe in detail the implementation of such a system and provide a concrete description of how it was used to enable a complex analysis of fMRI time series data.