Characterization of the Clostridium difficile volatile metabolome using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

The Impact of Artificial Intelligence on Microbial Diagnosis

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

C. difficile biomarkers, pathogenicity and detection

BACKGROUND

Clostridioides difficile infection (CDI) is the main etiologic agent of antibiotic-associated diarrhea. CDI contributes to gut inflammation and can lead to disruption of the intestinal epithelial barrier. Recently, the rate of CDI cases has been increased. Thus, early diagnosis of C. difficile is critical for controlling the infection and guiding efficacious therapy.

APPROACH

A search strategy was set up using the terms C. difficile biomarkers and diagnosis. The found references were classified into two general categories; conventional and advanced methods.

RESULTS

The pathogenicity and biomarkers of C. difficile, and the collection manners for CDI-suspected specimens were briefly explained. Then, the conventional CDI diagnostic methods were subtly compared in terms of duration, level of difficulty, sensitivity, advantages, and disadvantages. Thereafter, an extensive review of the various newly proposed techniques available for CDI detection was conducted including nucleic acid isothermal amplification-based methods, biosensors, and gene/single-molecule microarrays. Also, the detection mechanisms, pros and cons of these methods were highlighted and compared with each other. In addition, approximately complete information on FDA-approved platforms for CDI diagnosis was collected.

CONCLUSION

To overcome the deficiencies of conventional methods, the potential of advanced methods for C. difficile diagnosis, their direction, perspective, and challenges ahead were discussed.

Detection ofClostridioides difficileinfection by assessment of exhaled breath volatile organic compounds

Clostridioides difficileinfection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity andClostridioides difficilepolymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negativeC.difficiletesting (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

Volatilomes of human infection

The human volatilome comprises a vast mixture of volatile emissions produced by the human body and its microbiomes. Following infection, the human volatilome undergoes significant shifts, and presents a unique medium for non-invasive biomarker discovery. In this review, we examine how the onset of infection impacts the production of volatile metabolites that reflects dysbiosis by pathogenic microbes. We describe key analytical workflows applied across both microbial and clinical volatilomics and emphasize the value in linking microbial studies to clinical investigations to robustly elucidate the metabolic species and pathways leading to the observed volatile signatures. We review the current state of the art across microbial and clinical volatilomics, outlining common objectives and successes of microbial-clinical volatilomic workflows. Finally, we propose key challenges, as well as our perspectives on emerging opportunities for developing clinically useful and targeted workflows that could significantly enhance and expedite current practices in infection diagnosis and monitoring.

Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases

Gastrointestinal (GI) diseases have a high prevalence throughout the United States. Screening and diagnostic modalities are often expensive and invasive, and therefore, people do not utilize them effectively. Lack of proper screening and diagnostic assessment may lead to delays in diagnosis, more advanced disease at the time of diagnosis, and higher morbidity and mortality rates. Research on the intestinal microbiome has demonstrated that dysbiosis, or unfavorable alteration of organismal composition, precedes the onset of clinical symptoms for various GI diseases. GI disease diagnostic research has led to a shift towards non-invasive methods for GI screening, including chemical-detection tests that measure changes in volatile organic compounds (VOCs), which are the byproducts of bacterial metabolism that result in the distinct smell of stool. Many of these tools are expensive, immobile benchtop instruments that require highly trained individuals to interpret the results. These attributes make them difficult to implement in clinical settings. Alternatively, electronic noses (E-noses) are relatively cheaper, handheld devices that utilize multi-sensor arrays and pattern recognition technology to analyze VOCs. The purpose of this review is to (1) highlight how dysbiosis impacts intestinal diseases and how VOC metabolites can be utilized to detect alterations in the microbiome, (2) summarize the available VOC analytical platforms that can be used to detect aberrancies in intestinal health, (3) define the current technological advancements and limitations of E-nose technology, and finally, (4) review the literature surrounding several intestinal diseases in which headspace VOCs can be used to detect or predict disease.

Volatiles of the Apicomplexan Alga Chromera velia and Associated Bacteria

Volatiles released by the apicomplexan alga Chromera velia CCAP1602/1 and their associated bacteria have been investigated. A metagenome analysis allowed the identification of the most abundant heterotrophic bacteria of the phycosphere, but the isolation of additional strains showed that metagenomics underestimated the complexity of the algal microbiome, However, a culture-independent approach revealed the presence of a planctomycete that likely represents a novel bacterial family. We analysed algal and bacterial volatiles by open-system-stripping analysis (OSSA) on Tenax TA desorption tubes, followed by thermodesorption, cryofocusing and GC-MS-analysis. The analyses of the alga and the abundant bacterial strains Sphingopyxis litoris A01A-101, Algihabitans albus A01A-324, "Coraliitalea coralii" A01A-333 and Litoreibacter sp. A01A-347 revealed sulfur- and nitrogen-containing compounds, ketones, alcohols, aldehydes, aromatic compounds, amides and one lactone, as well as the typical algal products, apocarotenoids. The compounds were identified by gas chromatographic retention indices, comparison of mass spectra and syntheses of reference compounds. A major algal metabolite was 3,4,4-trimethylcyclopent-2-en-1-one, an apocarotenoid indicating the presence of carotenoids related to capsanthin, not reported from algae so far. A low overlap in volatiles bouquets between C. velia and the bacteria was found, and the xenic algal culture almost exclusively released algal components.

Thiol Metabolism and Volatile Metabolome of Clostridioides difficile

Clostridioides difficile (previously Clostridium difficile) causes life-threatening gut infections. The central metabolism of the bacterium is strongly influencing toxin production and consequently the infection progress. In this context, the composition and potential origin of the volatile metabolome was investigated, showing a large number of sulfur-containing volatile metabolites. Gas chromatography/mass spectrometry (GC/MS)-based headspace analyses of growing C. difficile 630Δerm cultures identified 105 mainly sulfur-containing compounds responsible of the typical C. difficile odor. Major components were identified to be 2-methyl-1-propanol, 2-methyl-1-propanethiol, 2-methyl-1-butanethiol, 4-methyl-1-pentanethiol, and as well as their disulfides. Structurally identified were 64 sulfur containing volatiles. In order to determine their biosynthetic origin, the concentrations of the sulfur-containing amino acids methionine and cysteine were varied in the growth medium. The changes observed in the volatile metabolome profile indicated that cysteine plays an essential role in the formation of the sulfur-containing volatiles. We propose that disulfides are derived from cysteine via formation of cystathionine analogs, which lead to corresponding thiols. These thiols may then be oxidized to disulfides. Moreover, methionine may contribute to the formation of short-chain disulfides through integration of methanethiol into the disulfide biosynthesis. In summary, the causative agents of the typical C. difficile odor were identified and first hypotheses for their biosynthesis were proposed.

Clostridioides difficile Infection Dysregulates Brain Dopamine Metabolism

Gastrointestinal illnesses and dysbiosis are among the most common comorbidities reported in patients with neurodevelopmental disorders. The manuscript reports that C. difficile infection (CDI), predisposed by antibiotic-induced gut dysbiosis, causes significant alterations in dopamine metabolism in major dopaminergic brain regions in mice (P < 0.05). In addition, C. difficile infected mice exhibited significantly reduced dopamine beta-hydroxylase (DBH) activity compared to controls (P < 0.01). Moreover, a significantly increased serum concentration of p-cresol, a DBH inhibiting gut metabolite produced by C. difficile, was also observed in C. difficile infected mice (P < 0.05). Therefore, this study suggests a potential mechanistic link between CDI and alterations in the brain dopaminergic axis. Such alterations may plausibly influence the precipitation and aggravation of dopamine dysmetabolism-associated neurologic diseases in infected patients.

IMPORTANCE The gut-brain axis is thought to play a significant role in the development and manifestation of neurologic diseases. This study reports significant alterations in the brain dopamine metabolism in mice infected with C. difficile, an important pathogen that overgrows in the gut after prolonged antibiotic therapy. Such alterations in specific brain regions may have an effect on the precipitation or manifestation of neurodevelopmental disorders in humans.

Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool: A cross-sectional proof-of-concept study

Clostridioides difficile infection (CDI) is an important infectious cause of antibiotic-associated diarrhea, with significant morbidity and mortality. Current diagnostic algorithms are based on identifying toxin by enzyme immunoassay (EIA) and toxin gene by real-time polymerase chain reaction (PCR) in patients with diarrhea. EIA’s sensitivity is poor, and PCR, although highly sensitive and specific, cannot differentiate infection from colonization. An ideal test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection, and assess prognosis and recurrence. The study of volatile organic compounds (VOCs) has the potential to be an ideal diagnostic test. The presence of VOCs accounts for the characteristic odor of stool in CDI but their presence in breath and plasma has not been studied yet. A cross-sectional proof-of-concept study analyzing VOCs using selected ion flow tube mass spectrometry (SIFT-MS) was done on breath, stool, and plasma of patients with clinical features and positive PCR for CDI (cases) and compared with patients with clinical features but a negative PCR (control). Our results showed that VOC patterns in breath, stool, and plasma, had good accuracy [area under the receiver operating characteristic curve (ROC) 93%, 86%, and 91%, respectively] for identifying patients with CDI.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

MALDI-TOF MS: An alternative approach for ribotyping Clostridioides difficile isolates in Brazil

Clostridioides difficile is an important organism causing healthcare-associated infections. It has been documented that specific strains caused multiple outbreaks globally, and patients infected with those strains are more likely to develop severe C. difficile infection (CDI). With the appearance of a variant strain, BI/NAP1 ribotype 027, responsible for several outbreaks and high mortality rates worldwide, the epidemiology of the CDI changed drastically in the United States, Europe, and some Latin American countries. Although the epidemic strain 027 was not yet detected in Brazil, there are ribotypes exclusively found in the country, such as, 131, 132, 133, 135, 142 and 143, which are responsible for outbreaks in Brazilian hospitals and nursing homes. Although PCR-ribotyping is the most used method in epidemiology studies of C. difficile, it is not available in Brazil. This study aimed to develop and validate an in-house database for detecting C. difficile ribotypes, usually involved in CDI in Brazilian hospitals, by using MALDI-TOF MS. A database with 19 different ribotypes, 13 with worldwide circulation and 6 Brazilian-restricted, was created based on 27 spectra readings of each ribotype. After BioNumerics analysis, neighbor-joining trees revealed that spectra were distributed in clusters according to ribotypes, showing that MALDI-TOF MS could discriminate all 19 ribotypes. Moreover, each ribotype showed a different profile with 42 biomarkers detected in total. Based on their intensity and occurrence, 13 biomarkers were chosen to compose ribotype-specific profiles, and in silico analysis showed that most of these biomarkers were uncharacterized proteins or well-conserved peptides, such as ribosomal proteins. A double-blind assessment using the 13 biomarkers correctly assigned the ribotype in 73% of the spectra analyzed, with 94%-100% of correct hits for 027 and for Brazilian ribotypes. Although further analyses are required, our results show that MALDI-TOF MS might be a reliable, fast and feasible alternative for epidemiological surveillance of C. difficile in Brazil.

Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition

In vitro cultivation of staphylococci is fundamental to both clinical and research microbiology, but few studies, to-date, have investigated how the differences in rich media can influence the volatilome of cultivated bacteria. The objective of this study was to determine the influence of rich media composition on the chemical characteristics of the volatilomes of Staphylococcus aureus and Staphylococcus epidermidis. S. aureus (ATCC 12600) and S. epidermidis (ATCC 12228) were cultured in triplicate in four rich complex media (brain heart infusion (BHI), lysogeny broth (LB), Mueller Hinton broth (MHB), and tryptic soy broth (TSB)), and the volatile metabolites produced by each culture were analyzed using headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography—time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). When comparing the chemical compositions of the staph volatilomes by the presence versus absence of volatiles produced in each medium, we observed few differences. However, when the relative abundances of volatiles were included in the analyses, we observed that culturing staph in media containing free glucose (BHI and TSB) resulted in volatilomes dominated by acids and esters (67%). The low-glucose media (LB and MHB) produced ketones in greatest relative abundances, but the volatilome compositions in these two media were highly dissimilar. We conclude that the staphylococcal volatilome is strongly influenced by the nutritional composition of the growth medium, especially the availability of free glucose, which is much more evident when the relative abundances of the volatiles are analyzed, compared to the presence versus absence.

On-line profiling of volatile compounds produced in vitro by pathogenic oral bacteria

Infections by oral pathogens are one of the most common health problems worldwide. Due to the intimate connection between exhaled breath and the oral cavity, breath analysis could potentially be used to diagnose these infections. However, little is known about the volatile emissions of important oral pathogens that are connected with gingivitis and periodontitis. In this study, we have performed in vitro headspace measurements on four important oral pathogens (P. gingivalis, T. forsythia, P. intermedia and P. nigrescens) using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). Some of the most abundant compounds produced by the bacteria include hydrogen sulphide, methanethiol, acetone, dimethylsulphide, isoprene, cyclopentanone and indole as tentatively assigned from the mass spectra. Several other abundant mass signals were recorded but the assignment of these is less certain. Some of the bacterial species can be separated from each other by the emitted volatile fingerprints. The results of this study can be used in potential development of a diagnostic breath test for oral infections. In addition, as several of the measured compounds are known to be toxic, the results point to an intriguing possibility of studying the connection between the bacterial virulence and the emitted volatile compounds.

Solid-phase microextraction: An appealing alternative for the determination of endogenous substances - A review

The determination of endogenous substances is of great significance for obtaining important biotic information such as biological components, metabolic pathways and disease biomarkers in different living organisms (e.g. plants, insects, animals and humans). However, due to the complex matrix and the trace concentrations of target analytes, the sample preparation procedure is an essential step before the analytes of interest are introduced into a detection instrument. Solid-phase microextraction (SPME), an emerging sample preparation technique that integrates sampling, extraction, concentration, and sample introduction into one step, has gained wide acceptance in various research fields, including in the determination of endogenous compounds. In this review, recent developments and applications of SPME for the determination of endogenous substances over the past five years are summarized. Several aspects, including the design of SPME devices (sampling configuration and coating), applications (in vitro and in vivo sampling), and coupling with emerging instruments (comprehensive two-dimensional gas chromatography (GC × GC), ambient mass spectrometry (AMS) and surface enhanced Raman scattering (SERS)) are involved. Finally, the challenges and opportunities of SPME methods in endogenous substances analysis are also discussed.

Metabolism the Difficile Way: The Key to the Success of the Pathogen Clostridioides difficile

Strains of Clostridioides difficile cause detrimental diarrheas with thousands of deaths worldwide. The infection process by the Gram-positive, strictly anaerobic gut bacterium is directly related to its unique metabolism, using multiple Stickland-type amino acid fermentation reactions coupled to Rnf complex-mediated sodium/proton gradient formation for ATP generation. Major pathways utilize phenylalanine, leucine, glycine and proline with the formation of 3-phenylproprionate, isocaproate, butyrate, 5-methylcaproate, valerate and 5-aminovalerate. In parallel a versatile sugar catabolism including pyruvate formate-lyase as a central enzyme and an incomplete tricarboxylic acid cycle to prevent unnecessary NADH formation completes the picture. However, a complex gene regulatory network that carefully mediates the continuous adaptation of this metabolism to changing environmental conditions is only partially elucidated. It involves the pleiotropic regulators CodY and SigH, the known carbon metabolism regulator CcpA, the proline regulator PrdR, the iron regulator Fur, the small regulatory RNA CsrA and potentially the NADH-responsive regulator Rex. Here, we describe the current knowledge of the metabolic principles of energy generation by C. difficile and the underlying gene regulatory scenarios.

Opportunities for green microextractions in comprehensive two-dimensional gas chromatography / mass spectrometry-based metabolomics - A review

Microextractions have become an attractive class of techniques for metabolomics. The most popular technique is solid-phase microextraction that revolutionized the field of modern sample preparation in the early nineties. Ever since this milestone, microextractions have taken on many principles and formats comprising droplets, fibers, membranes, needles, and blades. Sampling devices may be customized to impart exhaustive or equilibrium-based characteristics to the extraction method. Equilibrium-based approaches may rely on additional methods for calibration, such as diffusion-based or on-fiber kinetic calibration to improve bioanalysis. In addition, microextraction-based methods may enable minimally invasive sampling protocols and measure the average free concentration of analytes in heterogeneous multiphasic biological systems. On-fiber derivatization has evidenced new opportunities for targeted and untargeted analysis in metabolomics. All these advantages have highlighted the potential of microextraction techniques for in vivo and on-site sampling and sample preparation, while many opportunities are still available for laboratory protocols. In this review, we outline and discuss some of the most recent applications using microextractions techniques for comprehensive two-dimensional gas chromatography-based metabolomics, including potential research opportunities.

Mass spectrometry-based metabolomics for tuberculosis meningitis

Tuberculosis meningitis (TBM) is a prevalent form of extra-pulmonary tuberculosis that causes substantial morbidity and mortality. Diagnosis of TBM is difficult because of the limited sensitivity of existing laboratory techniques. A metabolomics approach can be used to investigate the sets of metabolites of both bacteria and host, and has been used to clarify the mechanisms underlying disease development, and identify metabolic changes, leadings to improved methods for diagnosis, treatment, and prognostication. Mass spectrometry (MS) is a major analysis platform used in metabolomics, and MS-based metabolomics provides wide metabolite coverage, because of its high sensitivity, and is useful for the investigation of Mycobacterium tuberculosis (Mtb) and related diseases. It has been used to investigate TBM diagnosis; however, the processes involved in the MS-based metabolomics approach are complex and flexible, and often consist of several steps, and small changes in the methods used can have a huge impact on the final results. Here, the process of MS-based metabolomics is summarized and its applications in Mtb and Mtb-related diseases discussed. Moreover, the current status of TBM metabolomics is described.

Machine learning for the meta-analyses of microbial pathogens' volatile signatures

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

Comprehensive volatile metabolic fingerprinting of bacterial and fungal pathogen groups

The identification of pathogen-specific volatile metabolic 'fingerprints' could lead to the rapid identification of disease-causing organisms either directly from ex vivo patient bio-specimens or from in vitro cultures. In the present study, we have evaluated the volatile metabolites produced by 100 clinical isolates belonging to ten distinct pathogen groups that, in aggregate, account for 90% of bloodstream infections, 90% of urinary tract infections, and 80% of infections encountered in the intensive care unit setting. Headspace volatile metabolites produced in vitro were concentrated using headspace solid-phase microextraction and analyzed via two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). A total of 811 volatile metabolites were detected across all samples, of which 203 were: (1) detected in 9 or 10 (of 10) isolates belonging to one or more pathogen groups, and (2) significantly more abundant in cultures relative to sterile media. Network analysis revealed a distinct metabolic fingerprint associated with each pathogen group, and analysis via Random Forest using leave-one-out cross-validation resulted in a 95% accuracy for the differentiation between groups. The present findings support the results of prior studies that have reported on the differential production of volatile metabolites across pathogenic bacteria and fungi, and provide additional insight through the inclusion of pathogen groups that have seldom been studied previously, including Acinetobacter spp., coagulase-negative Staphylococcus, and Proteus mirabilis, as well as the utilization of HS-SPME-GC×GC-TOFMS for improved sensitivity and resolution relative to traditional gas chromatography-based techniques.

Nontarget screening using passive air and water sampling with a level II fugacity model to identify unregulated environmental contaminants

It is thought that there are many unregulated anthropogenic chemicals in the environment. For risk assessment of chemicals, it is essential to estimate the predicted environmental concentrations. As an effort of identifying residual organic contaminants in air and water in Korea, nontarget screening using two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) was conducted at 10 sites using polyurethane foam passive air sampler and at 6 sites using polydimethyl siloxane (PDMS) passive water sampler in three different seasons in 2014. More than 600 chemical peaks were identified satisfying the identification criteria in air and water samples, respectively, providing a list for further investigation. Chemical substances with reported national emission rates in 2014 (n=149) were also screened for potential existence in the environment using a level II fugacity model. Most of chemical substances classified as not detectable were not identified with detection frequency greater than 20% by nontarget screening, indicating that a simple equilibrium model has a strong potential to be used to exclude chemicals that are not likely to remain in the environment after emissions from targeted monitoring.

Sniffing out the hypoxia volatile metabolic signature of Aspergillus fumigatus

Invasive aspergillosis (IA) is a life-threatening infectious disease caused by fungi from the genus Aspergillus, with an associated mortality as high as 90% in certain populations. IA-associated pulmonary lesions are characteristically depleted in oxygen relative to normal lung tissue, and it has been shown that the most common causal agent of IA, Aspergillus fumigatus, must respond to low-oxygen environments for pathogenesis and disease progression. Previous studies have demonstrated marked alterations to the Aspergillus fumigatus transcriptome in response to low-oxygen environments that induce a 'hypoxia response'. Consequently, we hypothesized that these transcriptomic changes would alter the volatile metabolome and generate a volatile hypoxia signature. In the present study, we analyzed the volatile molecules produced by A. fumigatus in both oxygen replete (normoxia) and depleted (hypoxia) environments via headspace solid-phase micro-extraction coupled to two-dimensional gas chromatography-time-of-flight mass spectrometry. Using the machine learning algorithm random forest, we identified 19 volatile molecules that were discriminatory between the four growth conditions assessed in this study (i.e., early hypoxia (1 h), late hypoxia (8 h), early normoxia (1 h), and late normoxia (8 h)), as well as a set of 19 that were discriminatory between late hypoxia cultures and all other growth conditions in aggregate. Nine molecules were common to both comparisons, while the remaining 20 were specific to only one of two. We assigned putative identifications to 13 molecules, of which six were most highly abundant in late hypoxia cultures. Previously acquired transcriptomic data identified putative biochemical pathways induced in hypoxia conditions that plausibly account for the production of a subset of these molecules, including 2,3-butanedione and 3-hydroxy-2-butanone. These two molecules may represent a novel hypoxia fitness pathway in A. fumigatus, and could be useful in the detection of hypoxia-associated A. fumigatus lesions that develop in established IA infections.

High metabolic versatility of different toxigenic and non-toxigenic Clostridioides difficile isolates

Clostridioides difficile (formerly Clostridium difficile) is a major nosocomial pathogen with an increasing number of community-acquired infections causing symptoms from mild diarrhea to life-threatening colitis. The pathogenicity of C. difficile is considered to be mainly associated with the production of genome-encoded toxins A and B. In addition, some strains also encode and express the binary toxin CDT. However; a large number of non-toxigenic C. difficile strains have been isolated from the human gut and the environment. In this study, we characterized the growth behavior, motility and fermentation product formation of 17 different C. difficile isolates comprising five different major genomic clades and five different toxin inventories in relation to the C. difficile model strains 630Δerm and R20291. Within 33 determined fermentation products, we identified two yet undescribed products (5-methylhexanoate and 4-(methylthio)-butanoate) of C. difficile. Our data revealed major differences in the fermentation products obtained after growth in a medium containing casamino acids and glucose as carbon and energy source. While the metabolism of branched chain amino acids remained comparable in all isolates, the aromatic amino acid uptake and metabolism and the central carbon metabolism-associated fermentation pathways varied strongly between the isolates. The patterns obtained followed neither the classification of the clades nor the ribotyping patterns nor the toxin distribution. As the toxin formation is strongly connected to the metabolism, our data allow an improved differentiation of C. difficile strains. The observed metabolic flexibility provides the optimal basis for the adaption in the course of infection and to changing conditions in different environments including the human gut.