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Data fusion has gained much attention in the field of life sciences, and this is because analysis of biological samples may require the use of data coming from multiple complementary sources to express the samples fully. Data fusion lies in the idea that different data platforms detect different biological entities. Therefore, if these different biological compounds are then combined, they can provide comprehensive profiling and understanding of the research question in hand. Data fusion can be performed in three different traditional ways: low-level, mid-level, and high-level data fusion. However, the increasing complexity and amount of generated data require the development of more sophisticated fusion approaches. In that regard, the current study presents an advanced data fusion approach (i.e. proximities stacking) based on random forest proximities coupled with the pseudo-sample principle. Four different data platforms of 130 samples each (faecal microbiome, blood, blood headspace, and exhaled breath samples of patients who have Crohn's disease) were used to demonstrate the classification performance of this new approach. More specifically, 104 samples were used to train and validate the models, whereas the remaining 26 samples were used to validate the models externally. Mid-level, high-level, as well as individual platform classification predictions, were made and compared against the proximities stacking approach. The performance of each approach was assessed by calculating the sensitivity and specificity of each model for the external test set, and visualized by performing principal component analysis on the proximity matrices of the training samples to then, subsequently, project the test samples onto that space. The implementation of pseudo-samples allowed for the identification of the most important variables per platform, finding relations among variables of the different data platforms, and the ex-amination of how variables behave in the samples. The proximities stacking approach outperforms both mid-level and high-level fusion approaches, as well as all individual platform predictions. Concurrently, it tackles significant bottlenecks of the traditional ways of fusion and of another advanced fusion way discussed in the paper, and finally, it contradicts the general belief that the more data, the merrier the result, and therefore, considerations have to be taken into account before any data fusion analysis is conducted. (c) 2021 Published by Elsevier B.V.

Gas chromatography (GC) coupled with electron ionization (EI) mass spectrometry (MS) is a well-established technique for the analysis of volatile and semi-volatile compounds. The main advantage is the highly repeatable fragmentation of the compounds into the ion source, generating intense and diagnostic fragmentation when the ionization is performed at 70 eV; this is considered the standard ionization condition and has been used for creating many established databases, which are of great support in the analyte identification process. However, such an intense fragmentation often causes the loss of the molecular ion or more diagnostic ions, which can be detrimental for the identification of homologous series or isomers, as for instance fatty acids. To obtain this information chemical or soft ionization can be used, but dedicated ion sources and conditions are required. In this work, we explored different ionization voltages in GC-EI-MS to preserve the intensity of the molecular ion using a conventional quadrupole MS. Twenty, 30, 50, and 70 eV were tested using a mixture of fatty acid methyl esters standards. Intensity and repeatability of the most informative ions were compared. Twenty and 70 eV were then used to analyze the fatty acid composition of six different strains of mycobacteria. Two approaches were used for elaborating the data: (1) a single average spectrum of the entire chromatogram was derived, which can be considered (in terms of concept) as a direct EI-MS analysis; (2) the actual chromatographic separation of the compounds was considered after automatic alignment. The results obtained are discussed herein. Graphical abstract ᅟ.

In this study, the volatile molecule profile of Streptococcus pneumoniae serotypes was evaluated using solid phase microextraction (SPME) and two dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). Here, seven serotypes (6B, 14, 15, 18C, 19F, 9V, and 23F) were analyzed in an isogenic background. We identified 13 core molecules associated with all seven serotypes, and seven molecules that were differentially produced between serotypes. Serotype 14 was found to have the most distinct volatile profile, and could be discriminated from the other six serotypes in aggregate with an area under the curve (AUC) of 89%. This study suggests that molecules from S. pneumoniae culture headspace show potential for rapid serotype identification.

Cluster resolution feature selection (CR-FS) is a hybrid feature selection algorithm which involves the evaluation of ranked variables via sequential backward elimination (SBE) and sequential forward selection (SFS). The implementation of CR-FS requires two main inputs, namely, start and stop number. The start number is the number of the highly ranked variables for the SBE while the stop number is the point at which the search for additional features during the SFS stage is halted. The setting of these critical parameters has always relied on trial and error which introduced subjectivity in the results obtained. The start and stop numbers are known to vary with each dataset. Drawing inspiration from overlapping coefficients, a method for comparing two probability density functions, empirical equations toward the estimation of start and stop number for a dataset were developed. All of the parameters in the empirical equations are obtained from the comparisons of the two probability density functions except the constant termed d. The equations were optimized using three real-world datasets. The optimum range of d was determined to be 0.48 to 0.57. An implementation of CR-FS using two new datasets demonstrated the validity of this approach. Partial least squares discriminant analysis (PLS-DA) model prediction accuracies increased from 90 and 96 to 100% for both datasets using start and stop numbers calculated with this approach. Additionally, there was a twofold increase in the explained variance captured in the first two principal components. Graphical abstract Here, we describe how to determine the start and stop numbers for an automated feature selection routine, ensuring that you get the best model you can for your data with minimal effort.

The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa. To determine a discriminatory core of molecules in the “breath-print” of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific “breath-print” signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20–25, 2014. https://doi.org/10.1111/2049-632X.12107 ). Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model. Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups. To the best of our knowledge, this is the first study to report on a ‘core’ murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.

The analysis of bacterial volatile organic compounds has gained attraction as a non-invasive way to identify disease-causing organisms, given that bacteria have unique metabolisms and volatile metabolic byproducts. In the present research, different adsorbent materials (Carbopack Y, X, B, Carboxen 1000 and Tenax TA), packed singularly or in combination, were compared in terms of sampling performance (sensitivity, repeatability and selectivity) for the extraction of standards and bacterial volatile metabolites in vitro (from Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). After extraction, bacterial volatile organic compounds were desorbed and analyzed in a comprehensive two-dimensional gas chromatography system coupled to a time-of-flight mass spectrometer (GC × GC-ToF MS). The results show that Tenax has the greater ability to extract the standard mix as well as volatile organic compounds with better repeatability (4–26 RSD%), higher sensitivity (on average ∼24 fold) compared to Carbopack Y, X and Carboxen 1000 tube, which followed in terms of performance. In addition, Tenax confirmed the best sensitivity and discriminatory power with no misclassification in the untargeted and unsupervised analysis for the differentiation of the bacterial species.

Previous studies have demonstrated that Pseudomonas aeruginosa PAO1 is chemotactic towards proteinogenic amino acids, however, the chemotaxis response of this strain towards non-proteinogenic amino acids and the specific chemoreceptors involved in this response are essentially unknown. In this study, we analysed the chemotactic response of PAO1 towards two degradation products of elastin, the lysine-rich, non-proteinogenic amino acids, desmosine and isodesmosine. We observed that isodesmosine, a potential biomarker for different diseases, served as a chemoattractant for PAO1. A screen of 251methyl-accepting chemotaxis proteins mutants of PAO1 identified PctA as the chemoreceptor for isodesmosine. We also showed that the positive chemotactic response to isodesmosine is potentially common by demonstrating chemoattraction in 12 of 15 diverse (in terms of source of isolation) clinical isolates, suggesting that the chemotactic response to this non-proteinogenic amino acid might be a conserved feature of acute infection isolates and thus could influence the colonization of potential infection sites.

The potential of high-resolution analytical technologies like GC × GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC × GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC × GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC × GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.

Untargeted metabolomics study of volatile organic compounds produced by different cell cultures is a field that has gained increasing attention over the years. Solid-phase microextraction has been the sampling technique of choice for most of the applications mainly due to its simplicity to implement. However, a careful optimization of the analytical conditions is necessary to obtain the best performances, which are highly matrix-dependent. In this work, five different solid-phase microextraction fibers were compared for the analysis of the volatiles produced by cell culture infected with the human respiratory syncytial virus. A central composite design was applied to determine the best time-temperature combination to maximize the extraction efficiency and the salting-out effect was evaluated as well. The linearity of the optimized method, along with limits of detection and quantification and repeatability was assessed. Finally, the effect of i) different normalization techniques (i.e. z-score and probabilistic quotient normalization), ii) data transformation (i.e. in logarithmic scale), and iii) different feature selection algorithms (i.e. Fisher ratio and random forest) on the capability of discriminating between infected and not-infected cell culture was evaluated.

Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection. Human breath has been evaluated in the setting of active tuberculosis using thermal desorption-comprehensive two-dimensional gas chromatography–time of flight mass spectrometry methodology. From the entire spectrum of volatile metabolites in breath, three random forest machine learning models were applied leading to the generation of a panel of 46 breath features. The twenty-two common features within each random forest model used were selected as a set that could distinguish subjects with confirmed pulmonary M. tuberculosis infection and people with other pathologies than TB.

Microorganisms catabolize carbon-containing compounds in their environment during growth, releasing a subset of metabolic byproducts as volatile compounds. However, the relationship between growth media and the production of volatile compounds has been largely unexplored to-date. To assess the core and media-specific components of the Klebsiella pneumoniae volatile metabolome via growth in four in vitro culture media. Headspace volatiles produced by cultures of K. pneumoniae after growth to stationary phase in four rich media (brain heart infusion broth, lysogeny broth, Mueller-Hinton broth, and tryptic soy broth) were analyzed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). Differences in the composition of headspace volatiles as a function of growth media were assessed using hierarchical clustering analysis (HCA) and principal component analysis (PCA). A total of 365 volatile compounds were associated with the growth of K. pneumoniae across all media, of which 36 (10%) were common to all growth media, and 148 (41%) were specific to a single medium. In addition, utilizing all K. pneumoniae-associated volatile compounds, strains clustered as a function of growth media, demonstrating the importance of media in determining the metabolic profile of this organism. K. pneumoniae produces a core suite of volatile compounds across all growth media studied, although the volatile metabolic signature of this organism is fundamentally media-dependent.

The broad topic of biomarker research has an often-overlooked component: the documentation and interpretation of the surrounding chemical environment and other meta-data, especially from visualization, analytical and statistical perspectives. A second concern is how the environment interacts with human systems biology, what the variability is in “normal” subjects, and how such biological observations might be reconstructed to infer external stressors. In this article, we report on recent research presentations from a symposium at the 248th American Chemical Society meeting held in San Francisco, 10–14 August 2014, that focused on providing some insight into these important issues.

Clostridium difficile is a bacterial pathogen capable of causing life-threatening infections of the gastrointestinal tract characterized by severe diarrhea. Exposure to certain classes of antibiotics, advanced age, and prolonged hospitalizations are known risk factors for infection by this organism. Anecdotally, healthcare providers have reported that they can smell C. difficile infections in their patients, and several studies have suggested that there may indeed be an olfactory signal associated with C. difficile-associated diarrhea. In this study, we sought to characterize the volatile molecules produced by an epidemic strain of C. difficile (R20291) using headspace solid-phase microextraction (HS-SPME) followed by two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). We report on a set of 77 volatile compounds, of which 59 have not previously been associated with C. difficile growth in vitro. Amongst these reported compounds, we detect both straight-chain and branched-chain carboxylic acids, as well as p-cresol, which have been the primary foci of C. difficile volatile metabolomic studies to-date. We additionally report on novel sulfur-containing and carbonyl-containing molecules that have not previously been reported for C. difficile. With the identification of these novel C. difficile-associated volatile compounds, we demonstrate the superior resolution and sensitivity of GC×GC-TOFMS relative to traditional GC-MS.

Improved plant access to native soil phosphorus (P) species such as phytate (metal ion derivatives of myo-inositol hexakisphosphate (IHP)) could minimize agricultural dependence on nonrenewable mineral phosphates and reduce surface water pollution. Nicotiana tabacum plant lines with unique organic anion (OA) and phytase production patterns were used to investigate the effect of OA-driven solubilization on the bioavailability of precipitated and sorbed IHP. Organic anions released IHP sorbed to goethite (Gt) by chelation or reductive dissolution mechanisms in the order: ascorbate > citrate > oxalate > pyruvate > acetate. Transgenic tobacco overexpressing Peniophora lycii phytase (PHY) and a MATE-type citrate transporter (CIT) exuded 2.2- to 2.6-fold higher OA compared with that of wild-type plants. The PHY plants had 33-fold higher exudate phytase activity (6.0 × 10−2 nkat plant−1 day−1) compared with those of wild-type and CIT plants, produced the largest zone of iron-IHP hydrolysis in agar media, and incorporated the most shoot P (2.3 μg) when grown on Gt-IHP. Plants grown on IHP at 2× Gt saturation (0.26 mmol/L) were 10-fold higher in shoot P compared with the 1× Gt condition (0.13 mmol/L IHP) with PHY plants approaching excess P status (1 % shoot P). The addition of Gt diminished shoot P in plants grown without P (−70 to −80 %) and with phosphate (−50 %) or IHP (−100 %); the exception was the high citrate–exuding plant line (21 nmol citrate plant−1 day−1), for which phosphate uptake was only 20 % reduced. Plant OA production mitigates P inhibition by Gt when weakly sorbing phosphate species are supplied or when high phytase production by plants can maximize the hydrolysis of IHP.

Allergies are caused by the binding of IgE antibodies onto specific sites on allergens. However, in the assessment of exposure to airborne allergens, current techniques such as whole spore counts fail to account for the presence of these allergenic epitopes that trigger allergic reactions. The objective of the research is to develop a DNA aptamer for the Asp f 1 allergen of the pathogenic fungus Aspergillus fumigatus, using an IgE-binding epitope of the allergen as the target for aptamer selection. Through in vitro SELEX, an aptamer has been produced that binds with nanomolar affinity to the Asp f 1 IgE-epitope. The aptamer is also able to recognize the native Asp f 1 allergen, and does not bind to allergenic proteins from non-target mold species such as Alternaria alternata. Production of this aptamer provides proof-of-principle that allergen measurement methods can be developed to indicate the potent fraction, or allergenicity, of allergens.

Organic phosphorus (P) is abundant in most soils but is largely unavailable to plants. Pseudomonas spp. can improve the availability of P to plants through the production of phytases and organic anions. Gluconate is a major component of Pseudomonas organic anion production and may therefore play an important role in the mineralization of insoluble organic P forms such as calcium–phytate (CaIHP). Organic anion and phytase production was characterized in 2 Pseudomonas spp. soil isolates (CCAR59, Ha200) and an isogenic mutant of strain Ha200, which lacked a functional glucose dehydrogenase (Gcd) gene (strain Ha200 gcd::Tn5B8). Wild-type and mutant strains of Pseudomonas spp. were evaluated for their ability to solubilize and hydrolyze CaIHP and to promote the growth and assimilation of P by tobacco plants. Gluconate, 2-keto-gluconate, pyruvate, ascorbate, acetate, and formate were detected in Pseudomonas spp. supernatants. Wild-type pseudomonads containing a functional gcd could produce gluconate and mineralize CaIHP, whereas the isogenic mutant could not. Inoculation with Pseudomonas improved the bioavailability of CaIHP to tobacco plants, but there was no difference in plant growth response due to Gcd function. Gcd function is required for the mineralization of CaIHP in vitro; however, further studies will be needed to quantify the relative contribution of specific organic anions such as gluconate to plant growth promotion by soil pseudomonads.

Bacteria produce unique volatile mixtures that could be used to identify infectious agents to the species, and possibly the strain level. However, due to the immense variety of human pathogens, and the close relatedness of some of these bacteria, the robust identification of the bacterium based on its volatile metabolome is likely to require a large number of volatile compounds for each species. We applied comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) to the identification of the headspace volatiles of Pseudomonas aeruginosa PA14 grown for 24 h in lysogeny broth. This is the first reported use of GC × GC–TOFMS for the characterization of bacterial headspace volatiles. The analytical purity that is afforded by this chromatographic method facilitated the identification of 28 new P. aeruginosa-derived volatiles, nearly doubling the list of volatiles for this species.

Many types of organic phosphorus (P) molecules exist in environmental samples1. Traditional P measurements do not detect these organic P compounds since they do not react with colorimetric reagents2,3. Enzymatic hydrolysis (EH) is an emerging method for accurately characterizing organic P forms in environmental samples4,5. This method is only trumped in accuracy by Phosphorus-31 Nuclear Magnetic Resonance Spectroscopy (31P-NMR) -a method that is expensive and requires specialized technical training6. We have adapted an enzymatic hydrolysis method capable of measuring three classes of phosphorus (monoester P, diester P and inorganic P) to a microplate reader system7. This method provides researchers with a fast, accurate, affordable and user-friendly means to measure P species in soils, sediments, manures and, if concentrated, aquatic samples. This is the only high-throughput method for measuring the forms and enzyme-lability of organic P that can be performed in a standard laboratory. The resulting data provides insight to scientists studying system nutrient content and eutrophication potential.

(1-->3)-Beta-D-Glucans are structural cell wall components of fungi, plants, and some bacteria and have been linked with human respiratory symptoms following aerosol exposure. A clear interpretation of the health impact of (1-->3)-beta-D-glucans is limited by the high cost and uncertainties associated with current glucan quantitation methods. The objective of this research is to develop DNA aptamers for the measurement of (1-->3)-beta-D-glucans. Aptamers are synthetic DNA functional binding molecules that fold into unique conformations, allowing them to bind specifically to their target. Through the in vitro selection process SELEX, we have produced aptamers that are able to bind with sub-micromolar affinity to curdlan, a linear unbranched form of (1-->3)-beta-D-glucans. These aptamers display high selectivity to curdlan and do not bind to non-(1-->3)-beta-D-polysaccharides, suggesting specificity for the beta-(1-->3)-glycosidic linkage. The aptamers produced here will enable the production of more cost-effective, less ambiguous assays for the environmental measurement of (1-->3)-beta-D-glucans.

The influence of bacterial growth stage and the evolution of surface macromolecules on cell adhesion have been examined by using a mutant of Escherichia coli K-12. To better understand the adhesion kinetics of bacteria in the mid-exponential and stationary growth phases under flow conditions, deposition experiments were conducted in a well-controlled radial stagnation point flow (RSPF) system. Complementary cell characterization techniques were conducted in combination with the RSPF experiments to evaluate the hydrophobicity, electrophoretic mobility, size, and titratable surface charge of the cells in the two growth phases considered. It was observed that cells in stationary phase were notably more adhesive than those in mid-exponential phase. This behavior is attributed to the high degree of local charge heterogeneity on the outer membranes of stationary-phase cells, which results in decreased electrostatic repulsion between the cells and a quartz surface. The mid-exponential-phase cells, on the other hand, have a more uniform charge distribution on the outer membrane, resulting in greater electrostatic repulsion and, subsequently, less adhesion. Our results suggest that the macromolecules responsible for this phenomenon are outer membrane-bound proteins and lipopolysaccharide-associated functional groups.