Microbial volatile compounds in health and disease conditions

Investigating the action model of the resistance enhancement induced by bacterial volatile organic compounds against Botrytis cinerea in tomato fruit

Introduction

Inducing natural resistance against pathogen infection in postharvest tomatoes is a sustainable strategy for reducing postharvest losses. The action model underlying the resistance enhancement of tomatoes induced by bacterial volatile organic compounds (VOCs) against Botrytis cinerea, however, have not been explored.

Methods

In this study, RNA-seq, metabolomics and physiological analysis were used to evaluate global change of defense response induced by VOCs in tomatoes.

Results

The application of VOCs inhibited the damage to tomatoes caused by B. cinerea. VOCs treatment had remarkable beneficial effects on the activities of the main defence-related enzymes, including chitinases, glucanases, peroxidases, ascorbate peroxidases, polyphenol oxidases, and phenylalanine ammonia-lyases. The expression of response genes involved in salicylic acid and jasmonic acid biosynthesis and signalling pathways was enhanced upon VOCs treatment. Metabolomics data demonstrated that VOC treatment triggered the accumulation of phenolic acids, including substrates in phenolic acid biosynthesis pathways, hydroxycinnamic acid, hydroxybenzoic acid, and their derivatives. Transcriptomics analysis and qRT-PCR verification revealed that VOCs treatment significantly upregulates the expression of core genes related to phenolic acid biosynthesis, specifically in shikimate pathway (SlDAHPS, SlSDH, SlCS, and SlADT3) and phenylalanine metabolic pathway (SlPAL, Sl4CL, SlBAHD1, SlCYP98A2 and SlCAP84A1).

Discussion

Results confirmed that VOCs enhanced tomatoes postharvest resistance against B. cinerea by regulating defence enzyme activity, SA/JA signalling, and phenolic acid biosynthesis pathway. This study provides new insights into the mechanisms by which VOCs fumigation manages postharvest grey mould in tomatoes.

Analysis of the Urine Volatilome of COVID-19 Patients and the Possible Metabolic Alterations Produced by the Disease

Alterations in metabolism caused by SARS-CoV-2 infection have been highlighted in various investigations and have been used to search for biomarkers in different biological matrices. However, the selected biomarkers vary greatly across studies. Our objective is to provide a robust selection of biomarkers, including results from different sample treatments in the analysis of volatile organic compounds (VOCs) present in urine samples from patients with COVID-19. Between September 2021 and May 2022, urine samples were collected from 35 hospitalized COVID-19 patients and 32 healthy controls. The samples were analyzed by headspace (HS) solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Analyses were conducted on untreated urine samples and on samples that underwent specific pretreatments: lyophilization and treatment with sulfuric acid. Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Subwindow Permutation Analysis (SPA) models were established to distinguish patterns between COVID-19 patients and healthy controls. The results identify compounds that are present in different proportions in urine samples from COVID-19 patients compared to those from healthy individuals. Analysis of urine samples using HS-SPME-GC-MS reveals differences between COVID-19 patients and healthy individuals. These differences are more pronounced when methods that enhance VOC formation are used. However, these pretreatments can cause reactions between sample components, creating additional products or removing compounds, so biomarker selection could be altered. Therefore, using a combination of methods may be more informative when evaluating metabolic alterations caused by viral infections and would allow for a better selection of biomarkers.

Volatile organic compounds (VOCs) in terrestrial extreme environments: implications for life detection beyond Earth

Covering: 1961 to 2024Discovering and identifying unique natural products/biosignatures (signatures that can be used as evidence for past or present life) that are abundant, and complex enough that they indicate robust evidence of life is a multifaceted process. One distinct category of biosignatures being explored is organic compounds. A subdivision of these compounds not yet readily investigated are volatile organic compound (VOCs). When assessing these VOCs as a group (volatilome) a fingerprint of all VOCs within an environment allows the complex patterns in metabolic data to be unravelled. As a technique already successfully applied to many biological and ecological fields, this paper explores how analysis of volatilomes in terrestrial extreme environments could be used to enhance processes (such as metabolomics and metagenomics) already utilised in life detection beyond Earth. By overcoming some of the complexities of collecting VOCs in remote field sites, a variety of lab based analytical equipment and techniques can then be utilised. Researching volatilomics in astrobiology requires time to characterise the patterns of VOCs. They must then be differentiated from abiotic (non-living) signals within extreme environments similar to those found on other planetary bodies (analogue sites) or in lab-based simulated environments or microcosms. Such an effort is critical for understanding data returned from past or upcoming missions, but it requires a step change in approach which explores the volatilome as a vital additional tool to current 'Omics techniques.

Evaluation of urinary volatile organic compounds as a novel metabolomic biomarker to assess chronic kidney disease progression

BACKGROUND

There is a need to develop accurate and reliable non-invasive methods to evaluate chronic kidney disease (CKD) status and assess disease progression. Given it is recognized that dysregulation in metabolic pathways occur from early CKD, there is a basis in utilizing metabolomic biomarkers to monitor CKD progression. Volatile Organic Compounds (VOCs), a form of metabolomic biomarker, are gaseous products of metabolic processes in organisms which are typically released with greater abundance in disease conditions when there is dysregulation in metabolism. How urinary VOCs reflect the abnormal metabolic profile of patients with CKD status is unknown. Our study aimed to explore this.

METHODS

Individuals aged 18-75 years undergoing kidney biopsy were included. Pre-biopsy urine samples were collected. All biopsy samples had an interstitial fibrosis and tubular atrophy (IFTA) grade scored by standardized assessment. Urine supernatant was extracted from residue and sampled for stir bar sorptive extraction followed by Gas chromatography-mass spectrometry (GC-MS) analysis. Post-processing of GC-MS data separated complex mixtures of VOCs based on their volatility and polarity. Mass-to-charge ratios and fragment patterns were measured for individual VOCs identification and quantification. Linear discriminant analysis (LDA) was performed to assess the ability of urinary VOCs in discriminating between IFTA 0 ('no or minimal IFTA' i.e. <10%, IFTA), IFTA 1 ('mild IFTA' i.e. 10-25% IFTA) and IFTA ≥ 2 ('moderate or severe IFTA' i.e. >25% IFTA). Linear regression analysis adjusting for age, sex, estimated glomerular filtration rate, diabetes mellitus (DM) status, and albuminuria was conducted to determine significantly regulated urinary VOCs amongst the groups.

RESULTS

64 study participants (22 individuals IFTA 0, 15 individuals IFTA 1, 27 individuals IFTA ≥ 2) were included. There were 34 VOCs identified from GC-MS which were statistically associated with correct classification between the IFTA groups, and LDA demonstrated individuals with IFTA 0, IFTA 1 and IFTA ≥ 2 could be significantly separated by their urinary VOCs profile (p < 0.001). Multivariate linear regression analysis reported 4 VOCs significantly upregulated in the IFTA 1 compared to the IFTA 0 group, and 2 VOCs significantly upregulated in the IFTA ≥ 2 compared to the IFTA 1 group (p < 0.05). Significantly upregulated urinary VOCs belonged to one of four functional groups - aldehydes, ketones, hydrocarbons, or alcohols.

CONCLUSIONS

We report novel links between urinary VOCs and tubulointerstitial histopathology. Our findings suggest the application of urinary VOCs as a metabolomic biomarker may have a useful clinical role to non-invasively assess CKD status during disease progression.

Potential of volatile organic compounds in the management of insect pests and diseases of food legumes: a comprehensive review

Cool season legumes (Faba bean, chickpea, lentil, pea, and grass pea) are important protein harvests for food and nutrition security in many countries. They play key roles in sustainable cereal production through their ecological benefits. However, diseases and pests attack continue to have a substantial impact on crop yield and quality. Although growers used different control options to manage these biotic stresses such as pesticide application, cultural practices, and resistant varieties, there is a pressing need for the development of new, more cost-effective and environmentally friendly solution to help farmers in facing the existing environmental issues. Recently, there is a growing interest among researchers in exploiting Volatile Organic Compounds (VOCs) for the elaboration of disease and pest control strategies in food legumes and other crops. These compounds have important functions in ecological relationships occurring between plants and their surrounding environment, as well as plants and others species, such as pests and pathogens. Due to their unique properties, VOCs can be employed in improving management alternatives for food legume diseases and pests. In this assessment, we investigated the role of VOCs in plant-pest and plant-pathogen interactions and their present applications in pest and diseases control strategies. We emphasized the ecological importance of employing plant VOCs in legume farming and crop breeding. Additionally, we highlighted the potential of microbial VOCs in facilitating microbe-microbe, microbe-plant and microbe-plant-pest interactions, along with their role in food legume protection.

Volatile organic compounds in exhaled breath: a promising approach for accurate differentiation of lung adenocarcinoma and squamous cell carcinoma

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

The Potential of Fecal Volatile Organic Compound Analysis for the Early Diagnosis of Late-Onset Sepsis in Preterm Infants: A Narrative Review

Early diagnosis and treatment of late-onset sepsis (LOS) is crucial for survival, but challenging. Intestinal microbiota and metabolome alterations precede the clinical onset of LOS, and the preterm gut is considered an important source of bacterial pathogens. Fecal volatile organic compounds (VOCs), formed by physiologic and pathophysiologic metabolic processes in the preterm gut, reflect a complex interplay between the human host, the environment, and microbiota. Disease-associated fecal VOCs can be detected with an array of devices with various potential for the development of a point-of-care test (POCT) for preclinical LOS detection. While characteristic VOCs for common LOS pathogens have been described, their VOC profiles often overlap with other pathogens due to similarities in metabolic pathways, hampering the construction of species-specific profiles. Clinical studies have, however, successfully discriminated LOS patients from healthy individuals using fecal VOC analysis with the highest predictive value for Gram-negative pathogens. This review discusses the current advancements in the development of a non-invasive fecal VOC-based POCT for early diagnosis of LOS, which may potentially provide opportunities for early intervention and targeted treatment and could improve clinical neonatal outcomes. Identification of confounding variables impacting VOC synthesis, selection of an optimal detection device, and development of standardized sampling protocols will allow for the development of a novel POCT in the near future.

Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity

INTRODUCTION

Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media.

METHODS

Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation.

RESULTS

The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature.

CONCLUSION

This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

Exhaled volatile fatty acids, ruminal methane emission, and their diurnal patterns in lactating dairy cows

To date, the commonly used methods to assess rumen fermentation are invasive. Exhaled breath contains hundreds of volatile organic compounds (VOC) that can reflect animal physiological processes. In the present study, for the first time, we aimed to use a noninvasive metabolomics approach based on high-resolution mass spectrometry to identify rumen fermentation parameters in dairy cows. Enteric methane (CH4) production from 7 lactating cows was measured 8 times over 3 consecutive days using the GreenFeed system (C-Lock Technology Inc.). Simultaneously, exhalome samples were collected in Tedlar gas sampling bags and analyzed offline using a secondary electrospray ionization high-resolution mass spectrometry system. In total, 1,298 features were detected, among them targeted exhaled volatile fatty acids (eVFA; i.e., acetate, propionate, butyrate), which were putatively annotated using their exact mass-to-charge ratio. The intensity of eVFA, in particular acetate, increased immediately after feeding and followed a similar pattern to that observed for ruminal CH4 production. The average total eVFA concentration was 35.5 count per second (CPS), and among the individual eVFA, acetate had the greatest concentration, averaging 21.3 CPS, followed by propionate at 11.5 CPS, and butyrate at 2.67 CPS. Further, exhaled acetate was on average the most abundant of the individual eVFA at around 59.3%, followed by 32.5 and 7.9% of the total eVFA for propionate and butyrate, respectively. This corresponds well with the previously reported proportions of these VFA in the rumen. The diurnal patterns of ruminal CH4 emission and individual eVFA were characterized using a linear mixed model with cosine function fit. The model characterized similar diurnal patterns for eVFA and ruminal CH4 and H2 production. Regarding the diurnal patterns of eVFA, the phase (time of peak) of butyrate occurred first, followed by that of acetate and propionate. Importantly, the phase of total eVFA occurred around 1 h before that of ruminal CH4. This corresponds well with existing data on the relationship between rumen VFA production and CH4 formation. Results from the present study revealed a great potential to assess the rumen fermentation of dairy cows using exhaled metabolites as a noninvasive proxy for rumen VFA. Further validation, with comparisons to rumen fluid, and establishment of the proposed method are required.

Oral microbiome as a co-mediator of halitosis and periodontitis: a narrative review

Objective

Halitosis or oral malodor is an unpleasant odor from the oral cavity. However, although patients with periodontitis often complain of halitosis, their relationship has not been fully elucidated. We reviewed previous literature based on the hypothesis that the relationship between halitosis and periodontitis is mediated by the oral microbiome.

Materials and methods

This narrative review sought to provide insight into the causative role of the oral microbiome in influencing halitosis and periodontitis. In addition, we tried to deepen knowledge related to the relationship between halitosis and periodontitis generated by the oral microbiome accumulated over the past 40 years.

Results

From 1984 to 2023, a total of 106 papers that carefully and scientifically dealt with halitosis and periodontitis were included in this narrative review. Based on previous results, halitosis and periodontitis were closely related. For decades, researchers have taken an intriguing approach to the question of whether there is a relationship between halitosis and periodontitis. Central factors in the relationship between halitosis and periodontitis include volatile sulfur compounds (VSCs), the oral microbiota that produce VSCs, and the inflammatory response.

Conclusions

Taken together, the more severe periodontitis, the higher the level of VSC in halitosis, which may be mediated by oral microbiome. However, the relationship between the occurrence, maintenance, and exacerbation of periodontitis and halitosis is not a necessary and sufficient condition for each other because they are complex interplay even in one individual.

Lactobacillus paracasei ET-22 and derived postbiotics reduce halitosis and modulate oral microbiome dysregulation - a randomized, double-blind placebo-controlled clinical trial

Oral microbial dysbiosis is the primary etiologic factor for halitosis and may be the critical preventive target for halitosis. This study included randomized controlled trials (RCTs) assessing the effects of Lactobacillus paracasei ET-22 live and heat-killed bacteria on halitosis and the related oral microbiome. 68 halitosis subjects were divided into placebo, ET-22 live (ET-22.L) and ET-22 heat-killed (ET-22.HK) groups. Subjects took different lozenges three times a day for 4 weeks and underwent saliva collection and assessment of breath volatile sulfur compound (VSC) levels at the beginning and end of the intervention. Salivary volatile organic compounds were measured using HS-SPME-GC/MS, and the microbiome profile was determined by 16S rRNA gene amplicon sequencing. A positive decrease in breath volatile sulfur compound (VSC) levels was observed in the means of both ET-22.L and ET-22.HK groups after 4 weeks of intervention, being more marked in the ET-22.L group (p = 0.0148). Moreover, ET-22.L and ET-22.HK intervention remarkably changed the composition of total salivary volatile organic compounds (VOCs) and aroma-active VOCs. Key undesirable VOCs, such as indole, pyridine, nonanoic acid, benzothiazole, and valeric acid, were significantly reduced. Meanwhile, ET-22.L or ET-22.HK also altered the taxonomic composition of the salivary microbiome. The halitosis pathogens Rothia and Streptococcus were significantly reduced in the ET-22.HK group and the pathogenic Solobacterium and Peptostreptococcus were significantly inhibited in the ET-22.L group. Collectively, our study suggests that both ET-22.L and ET-22.HK can significantly inhibit the production of undesirable odor compounds in subjects with halitosis, which may be related to the changes of the oral microbiome.

Microbial volatile compounds (MVCs): an eco-friendly tool to manage abiotic stress in plants

Microbial volatile compounds (MVCs) are produced during the metabolism of microorganisms, are widely distributed in nature, and have significant applications in various fields. To date, several MVCs have been identified. Microbial groups such as bacteria and fungi release many organic and inorganic volatile compounds. They are typically small odorous compounds with low molecular masses, low boiling points, and lipophilic moieties with high vapor pressures. The physicochemical properties of MVCs help them to diffuse more readily in nature and allow dispersal to a more profound distance than other microbial non-volatile metabolites. In natural environments, plants communicate with several microorganisms and respond differently to MVCs. Here, we review the following points: (1) MVCs produced by various microbes including bacteria, fungi, viruses, yeasts, and algae; (2) How MVCs are effective, simple, efficient, and can modulate plant growth and developmental processes; and (3) how MVCs improve photosynthesis and increase plant resistance to various abiotic stressors.

Alterations in the gut microbiome implicate key taxa and metabolic pathways across inflammatory arthritis phenotypes

Musculoskeletal diseases affect up to 20% of adults worldwide. The gut microbiome has been implicated in inflammatory conditions, but large-scale metagenomic evaluations have not yet traced the routes by which immunity in the gut affects inflammatory arthritis. To characterize the community structure and associated functional processes driving gut microbial involvement in arthritis, the Inflammatory Arthritis Microbiome Consortium investigated 440 stool shotgun metagenomes comprising 221 adults diagnosed with rheumatoid arthritis, ankylosing spondylitis, or psoriatic arthritis and 219 healthy controls and individuals with joint pain without an underlying inflammatory cause. Diagnosis explained about 2% of gut taxonomic variability, which is comparable in magnitude to inflammatory bowel disease. We identified several candidate microbes with differential carriage patterns in patients with elevated blood markers for inflammation. Our results confirm and extend previous findings of increased carriage of typically oral and inflammatory taxa and decreased abundance and prevalence of typical gut clades, indicating that distal inflammatory conditions, as well as local conditions, correspond to alterations to the gut microbial composition. We identified several differentially encoded pathways in the gut microbiome of patients with inflammatory arthritis, including changes in vitamin B salvage and biosynthesis and enrichment of iron sequestration. Although several of these changes characteristic of inflammation could have causal roles, we hypothesize that they are mainly positive feedback responses to changes in host physiology and immune homeostasis. By connecting taxonomic alternations to functional alterations, this work expands our understanding of the shifts in the gut ecosystem that occur in response to systemic inflammation during arthritis.

Evaluation of Linkers' Influence on Peptide-Based Piezoelectric Biosensors' Sensitivity to Aldehydes in the Gas Phase

Recent findings qualified aldehydes as potential biomarkers for disease diagnosis. One of the possibilities is to use electrochemical biosensors in point-of-care (PoC), but these need further development to overcome some limitations. Currently, the primary goal is to enhance their metrological parameters in terms of sensitivity and selectivity. Previous findings indicate that peptide OBPP4 (KLLFDSLTDLKKKMSEC-NH₂) is a promising candidate for further development of aldehyde-sensitive biosensors. To increase the affinity of a receptor layer to long-chain aldehydes, a structure stabilization of the peptide active site via the incorporation of different linkers was studied. Indeed, the incorporation of linkers improved sensitivity to and binding of aldehydes in comparison to that of the original peptide-based biosensor. The tendency to adopt disordered structures was diminished owing to the implementation of suitable linkers. Therefore, to improve the metrological characteristics of peptide-based piezoelectric biosensors, linkers were added at the C-terminus of OBPP4 peptide (KLLFDSLTDLKKKMSE-linker-C-NH₂). Those linkers consist of proteinogenic amino acids from group one: glycine, L-proline, L-serine, and non proteinogenic amino acids from group two: β-alanine, 4-aminobutyric acid, and 6-aminohexanoic acid. Linkers were evaluated with in silico studies, followed by experimental verification. All studied linkers enhanced the detection of aldehydes in the gas phase. The highest difference in frequency (60 Hz, nonanal) was observed between original peptide-based biosensors and ones based on peptides modified with the GSGSGS linker. It allowed evaluation of the limit of detection for nonanal at the level of 2 ppm, which is nine times lower than that of the original peptide. The highest sensitivity values were also obtained for the GSGSGS linker: 0.3312, 0.4281, and 0.4676 Hz/ppm for pentanal, octanal, and nonanal, respectively. An order of magnitude increase in sensitivity was observed for the six linkers used. Generally, the linker's rigidity and the number of amino acid residues are much more essential for biosensors' metrological characteristics than the amino acid sequence itself. It was found that the longer the linkers, the better the effect on docking efficiency.

Human Urinary Volatilome Analysis in Renal Cancer by Electronic Nose

Currently, in clinical practice there are still no useful markers available that are able to diagnose renal cancer in the early stages in the context of population screening. This translates into very high costs for healthcare systems around the world. Analysing urine using an electronic nose (EN) provides volatile organic compounds that can be easily used in the diagnosis of urological diseases. Although no convincing results have been published, some previous studies suggest that dogs trained to sniff urine can recognize different types of tumours (bladder, lung, breast cancer) with different success rates. We therefore hypothesized that urinary volatilome profiling may be able to distinguish patients with renal cancer from healthy controls. A total of 252 individuals, 110 renal patients and 142 healthy controls, were enrolled in this pilot monocentric study. For each participant, we collected, stabilized (at 37 °C) and analysed urine samples using a commercially available electronic nose (Cyranose 320^®). Principal component (PCA) analyses, discriminant analysis (CDA) and ROC curves were performed to provide a complete statistical analysis of the sensor responses. The best discriminating principal component groups were identified with univariable ANOVA analysis. The study correctly identified 79/110 patients and 127/142 healthy controls, respectively (specificity 89.4%, sensitivity 71.8%, positive predictive value 84.04%, negative predictive value 80.37%). In order to test the study efficacy, the Cross Validated Accuracy was calculated (CVA 81.7%, p < 0.001). At ROC analysis, the area under the curve was 0.85. The results suggest that urine volatilome profiling by e-Nose seems a promising, accurate and non-invasive diagnostic tool in discriminating patients from controls. The low costs and ease of execution make this test useful in clinical practice.

The tongue biofilm metatranscriptome identifies metabolic pathways associated with the presence or absence of halitosis

Intra-oral halitosis usually results from the production of volatile sulfur compounds, such as methyl mercaptan and hydrogen sulfide, by the tongue microbiota. There are currently no reports on the microbial gene-expression profiles of the tongue microbiota in halitosis. In this study, we performed RNAseq of tongue coating samples from individuals with and without halitosis. The activity of Streptococcus (including S. parasanguinis), Veillonella (including V. dispar) and Rothia (including R. mucilaginosa) was associated with halitosis-free individuals while Prevotella (including P. shahi), Fusobacterium (including F. nucleatum) and Leptotrichia were associated with halitosis. Interestingly, the metatranscriptome of patients that only had halitosis levels of methyl mercaptan was similar to that of halitosis-free individuals. Finally, gene expression profiles showed a significant over-expression of genes involved in L-cysteine and L-homocysteine synthesis, as well as nitrate reduction genes, in halitosis-free individuals and an over-expression of genes responsible for cysteine degradation into hydrogen sulfide in halitosis patients.

Volatile organic compounds shape belowground plant-fungi interactions

Volatile organic compounds (VOCs), a bouquet of chemical compounds released by all life forms, play essential roles in trophic interactions. VOCs can facilitate a large number of interactions with different organisms belowground. VOCs-regulated plant-plant or plant-insect interaction both below and aboveground has been reported extensively. Nevertheless, there is little information about the role of VOCs derived from soilborne pathogenic fungi and beneficial fungi, particularly mycorrhizae, in influencing plant performance. In this review, we show how plant VOCs regulate plant-soilborne pathogenic fungi and beneficial fungi (mycorrhizae) interactions. How fungal VOCs mediate plant-soilborne pathogenic and beneficial fungi interactions are presented and the most common methods to collect and analyze belowground volatiles are evaluated. Furthermore, we suggest a promising method for future research on belowground VOCs.

The Microbiota-Gut-Brain Axis in Psychiatric Disorders

Modulating the gut microbiome and its influence on human health is the subject of intense research. The gut microbiota could be associated not only with gastroenterological diseases but also with psychiatric disorders. The importance of factors such as stress, mode of delivery, the role of probiotics, circadian clock system, diet, and occupational and environmental exposure in the relationship between the gut microbiota and brain function through bidirectional communication, described as "the microbiome-gut-brain axis", is especially underlined. In this review, we discuss the link between the intestinal microbiome and the brain and host response involving different pathways between the intestinal microbiota and the nervous system (e.g., neurotransmitters, endocrine system, immunological mechanisms, or bacterial metabolites). We review the microbiota alterations and their results in the development of psychiatric disorders, including major depressive disorder (MDD), schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), and attention-deficit hyperactivity disorder (ADHD).

Volatilome Analysis in Prostate Cancer by Electronic Nose: A Pilot Monocentric Study

Urine analysis via an electronic nose provides volatile organic compounds easily usable in the diagnosis of urological diseases. Although challenging and highly expensive for health systems worldwide, no useful markers are available in clinical practice that aim to anticipate prostate cancer (PCa) diagnosis in the early stages in the context of wide population screening. Some previous works suggested that dogs trained to smell urine could recognize several types of cancers with various success rates. We hypothesized that urinary volatilome profiling may distinguish PCa patients from healthy controls. In this study, 272 individuals, 133 patients, and 139 healthy controls participated. Urine samples were collected, stabilized at 37 °C, and analyzed using a commercially available electronic nose (Cyranose C320). Statistical analysis of the sensor responses was performed off-line using principal component (PCA) analyses, discriminant analysis (CDA), and ROC curves. Principal components best discriminating groups were identified with univariable ANOVA analysis. groups were identified with univariable ANOVA analysis. Here, 110/133 and 123/139 cases were correctly identified in the PCa and healthy control cohorts, respectively (sensitivity 82.7%, specificity 88.5%; positive predictive value 87.3%, negative predictive value 84.2%). The Cross Validated Accuracy (CVA 85.3%, p < 0.001) was calculated. Using ROC analysis, the area under the curve was 0.9. Urine volatilome profiling via an electronic nose seems a promising non-invasive diagnostic tool.

A Preliminary Study on the Use of Rice Husk-Based Smoke Powder for Meatball Preservatives

This study examines the use of smoke powder derived from rice husks as a preservative for chicken meatballs. Rice husks were pyrolyzed in a slow pyrolysis reactor at the temperatures of 300°C (T1), 350°C (T2), and 400°C (T3) to produce liquid smoke. Each of the liquid smoke was distilled at 190°C and then converted into smoke powder by spray drying method. The smoke powder’s feasibility as a meatball preservative was examined by total plate count (TPC), total volatile base (TVB), most probable number (MPN) test of E. coli, and organoleptic (aroma, texture, and color) tests. The results showed that the TPC and TVB increased with storage time. At the storage time of 76 hours, the meatballs were no longer suitable for consumption as the TPC had exceeded the minimum limit. In meatballs with T3 smoke powder at 72-hour storage, the number of colonies was 6.87 × 104 CFU/g, indicating the TPC value has not exceeded the threshold yet. The TVB test showed that up to 72 hours of storage, the meatballs remained fresh with a TVB value of less than 0.20 mgN/g. The result of the organoleptic test also showed that meatballs could last for 72 hours. The MPN test, on the other hand, revealed that the E. coli was still permissible after 68 hours of preservation.

Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.

A Novel Approach for Monitoring the Volatile Metabolome in Biological Samples from Ruminants through Miniaturized Liquid-Liquid Extraction and Multiclass Gas Chromatography Analysis

A straightforward and versatile methodology for the extraction of volatile metabolites in biological samples from ruminants for gas chromatography analysis is proposed. The methodology was applied in the determination of multiclass metabolites (short-chain fatty acids, aldehydes, alcohols, ketones, esters, phenols, and sulfides) in different analytical matrices (rumen fluid, urine, and feces) collected from Holstein cows. The 24 multiclass volatile metabolites reported in the different biological samples and their respective concentrations were critically discussed in the context of digestive physiology. Most detected compounds are derived from the rumen and lower gut fermentation of carbohydrates, proteins, and lipids or their metabolism, being consistent with the prior state of the art. The proposed method also takes advantage of the already existing tools in animal nutrition laboratories, providing a novel methodological ground that can generate relevant bioanalytical information with a significant impact on ruminant's nutritional studies.

Real-time detection of volatile metabolites enabling species-level discrimination of bacterial biofilms associated with wound infection

AIMS

The main aim of this study was to investigate the real-time detection of volatile metabolites for the species-level discrimination of pathogens associated with clinically relevant wound infection, when grown in a collagen wound biofilm model.

METHODS AND RESULTS

This work shows that Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pyogenes produce a multitude of volatile compounds when grown as biofilms in a collagen-based biofilm model. The real-time detection of these complex volatile profiles using selected ion flow tube mass spectrometry and the use of multivariate statistical analysis on the resulting data can be used to successfully differentiate between the pathogens studied.

CONCLUSIONS

The range of bacterial volatile compounds detected between the species studied vary and are distinct. Discrimination between bacterial species using real-time detection of volatile metabolites and multivariate statistical analysis was successfully demonstrated.

SIGNIFICANCE AND IMPACT OF THE STUDY

Development of rapid point-of-care diagnostics for wound infection would improve diagnosis and patient care. Such technological approaches would also facilitate the appropriate use of antimicrobials, minimizing the emergence of antimicrobial resistance. This study further develops the use of volatile metabolite detection as a new diagnostic approach for wound infection.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogeneous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD.

METHODS

Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis.

RESULTS

A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, p_FDR=0.0006), 3-methylpyridine (p = 0.0002, p_FDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, p_FDR = 0.0174), valeric acid (p = 0.005, p_FDR = 0.0162) and isoprene (p = 0.038, p_FDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96).

CONCLUSION

According to the results of this study, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.

Differentiation of Cystic Fibrosis-Related Pathogens by Volatile Organic Compound Analysis with Secondary Electrospray Ionization Mass Spectrometry

Identifying and differentiating bacteria based on their emitted volatile organic compounds (VOCs) opens vast opportunities for rapid diagnostics. Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an ideal technique for VOC-biomarker discovery because of its speed, sensitivity towards polar molecules and compound characterization possibilities. Here, an in vitro SESI-HRMS workflow to find biomarkers for cystic fibrosis (CF)-related pathogens P. aeruginosa, S. pneumoniae, S. aureus, H. influenzae, E. coli and S. maltophilia is described. From 180 headspace samples, the six pathogens are distinguishable in the first three principal components and predictive analysis with a support vector machine algorithm using leave-one-out cross-validation exhibited perfect accuracy scores for the differentiation between the groups. Additionally, 94 distinctive features were found by recursive feature elimination and further characterized by SESI-MS/MS, which yielded 33 putatively identified biomarkers. In conclusion, the six pathogens can be distinguished in vitro based on their VOC profiles as well as the herein reported putative biomarkers. In the future, these putative biomarkers might be helpful for pathogen detection in vivo based on breath samples from patients with CF.

Volatile organic compounds: from figurants to leading actors in fungal symbiosis

Symbiosis involving two (or more) prokaryotic and/or eukaryotic partners is extremely widespread in nature, and it has performed, and is still performing, a key role in the evolution of several biological systems. The interaction between symbiotic partners is based on the emission and perception of a plethora of molecules, including volatile organic compounds (VOCs), synthesized by both prokaryotic and eukaryotic (micro)organisms. VOCs acquire increasing importance since they spread above and below ground and act as infochemicals regulating a very complex network. In this work we review what is known about the VOCs synthesized by fungi prior to and during the interaction(s) with their partners (either prokaryotic or eukaryotic) and their possible role(s) in establishing and maintaining the symbiosis. Lastly, we also describe the potential applications of fungal VOCs from different biotechnological perspectives, including medicinal, pharmaceutical and agronomical.

DNA methylome signatures as epigenetic biomarkers of hexanal associated with lung toxicity

Background

Numerous studies have investigated the relationship of environmental exposure, epigenetic effects, and human diseases. These linkages may contribute to the potential toxicity mechanisms of environmental chemicals. Here, we investigated the epigenetic pulmonary response of hexanal, a major indoor irritant, following inhalation exposure in F-344 rats.

Methods

Based on DNA methylation profiling in gene promoter regions, we identified hexanal-characterized methylated sites and target genes using an unpaired t-test with a fold-change cutoff of ≥ 3.0 and a p-value < 0.05. We also conducted an integrated analysis of DNA methylation and mRNA expression data to identify core anti-correlated target genes of hexanal exposure. To further investigate the potential key biological processes and pathways of core DNA methylated target genes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed.

Results

Thirty-six dose-dependent methylated genes and anti-correlated target genes of DNA methylation and mRNA in lung tissue of hexanal exposed F-344 rats were identified. These genes were involved in diverse biological processes such as neuroactive ligand-receptor interaction, protein kinase cascade, and intracellular signaling cascade associated with pulmonary toxicity. These results suggest that novel DNA methylation-based epigenetic biomarkers of exposure to hexanal and elucidate the potential pulmonary toxicological mechanisms of action of hexanal.

Towards the Identification of Antibiotic-Resistant Bacteria Causing Urinary Tract Infections Using Volatile Organic Compounds Analysis-A Pilot Study

Antibiotic resistance is an unprecedented threat to modern medicine. The analysis of volatile organic compounds (VOCs) from bacteria potentially offers a rapid way to determine antibiotic susceptibility in bacteria. This study aimed to find the optimal conditions to obtain the maximum number of VOCs detected which next allowed the assessment of differences in VOC profiles between susceptible and resistant isolates of Escherichia coli causing urinary tract infections. The analysis of VOCs in the headspace above the bacterial cultures allowed the distinguishing of resistant and susceptible bacteria based on the abundance of six VOCs with 85.7% overall accuracy. The results of this preliminary study are promising, and with development could lead to a practical, faster diagnostic method for use in routine microbiology.

Multi-strain volatile profiling of pathogenic and commensal cutaneous bacteria

The detection of volatile organic compounds (VOC) emitted by pathogenic bacteria has been proposed as a potential non-invasive approach for characterising various infectious diseases as well as wound infections. Studying microbial VOC profiles in vitro allows the mechanisms governing VOC production and the cellular origin of VOCs to be deduced. However, inter-study comparisons of microbial VOC data remains a challenge due to the variation in instrumental and growth parameters across studies. In this work, multiple strains of pathogenic and commensal cutaneous bacteria were analysed using headspace solid phase micro-extraction coupled with gas chromatography-mass spectrometry. A kinetic study was also carried out to assess the relationship between bacterial VOC profiles and the growth phase of cells. Comprehensive bacterial VOC profiles were successfully discriminated at the species-level, while strain-level variation was only observed in specific species and to a small degree. Temporal emission kinetics showed that the emission of particular compound groups were proportional to the respective growth phase for individual S. aureus and P. aeruginosa samples. Standardised experimental workflows are needed to improve comparability across studies and ultimately elevate the field of microbial VOC profiling. Our results build on and support previous literature and demonstrate that comprehensive discriminative results can be achieved using simple experimental and data analysis workflows.

Impact of oral cleansing strategies on exhaled volatile organic compound levels

RATIONALE

The analysis of volatile organic compounds (VOCs) within exhaled breath potentially offers a non-invasive method for the detection and surveillance of human disease. Oral contamination of exhaled breath may influence the detection of systemic VOCs relevant to human disease. This study aims to assess the impact of oral cleansing strategies on exhaled VOC levels in order to standardise practice for breath sampling.

METHODS

Ten healthy volunteers consumed a nutrient challenge followed by four oral cleansing methods: (a) water, (b) saltwater, (c) toothbrushing, and (d) alcohol-free mouthwash. Direct breath sampling was performed using selected ion flow tube mass spectrometry after each intervention.

RESULTS

Proposed reactions suggest that volatile fatty acid and alcohol levels (butanoic, pentanoic acid, ethanol) declined with oral cleansing interventions, predominantly after an initial oral rinse with water. Concentrations of aldehydes and phenols (acetaldehyde, menthone, p-cresol) declined with oral water rinse; however, they increased after toothbrushing and mouthwash use, secondary to flavoured ingredients within these products. No significant reductions were observed with sulphur compounds.

CONCLUSIONS

Findings suggest that oral rinsing with water prior to breath sampling may reduce oral contamination of VOC levels, and further interventions for oral decontamination with flavoured products may compromise results. This intervention may serve as a simple and inexpensive method of standardisation within breath research.

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Background

Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique.

Methods

We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy.

Results

A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement.

Conclusions

There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

Red and processed meat consumption within two different dietary patterns: Effect on the colon microbial community and volatile metabolites in pigs

Pigs were fed either red and processed meat or chicken meat within either a prudent or a Western dietary pattern for four weeks (2 × 2 full factorial design). The colon microbial community and volatile organic compounds were assessed (either quantified or based on their presence). Results show that Lactobacilli were characteristic for the chicken × prudent dietary pattern treatment and Paraprevotella for the red and processed meat × prudent dietary pattern treatment. Enterobacteriaceae and Desulfovibrio were characteristic for the chicken × Western dietary pattern treatment and Butyrivibrio for the red and processed meat × Western dietary pattern treatment. Campylobacter was characteristic for chicken consumption and Clostridium XIVa for red and processed meat, irrespective of the dietary pattern. Ethyl valerate and 1-methylthio-propane were observed more frequently in pigs fed red and processed meat compared to chicken meat. The prevalence of 3-methylbutanal was >80% for pigs receiving a Western dietary pattern, whereas for pigs fed a prudent dietary pattern the prevalence was <35%. The concentration of butanoic acid was significantly higher when the prudent dietary pattern was given, compared to the Western dietary pattern, but no differences for other short chain fatty acids or protein fermentation products were observed.

Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.

Antagonistic and plant-growth promoting novel Bacillus species from long-term organic farming soils from Sikkim, India

Three bacteria namely Bacillus luciferensis K2, Bacillus amyloliquefaciens K12 and Bacillus subtilis BioCWB possessing plant growth promotion and biocontrol potential against phytopathogens and rice leaf folder were identified from organic soils of Sikkim, India. The results revealed significant higher production of phytohormones IAA (97.1 μg mL^-1) and GA₃ (10.6 μg mL^-1) was found in K2, whereas BioCWB had higher phosphate solubilization (570.0 μg mL^-1) efficacy and also possessed nitrogen fixation ability (5.34 log copy number mL^-1 culture). All these bacteria had higher antagonistic activities against phytopathogens viz. Rhizoctonia solani, Fusarium proliferatum, Athelia rolfsii and Colletotrichum gloeosporioides and also had higher larvicidal activity against rice leaf folder Cnaphalocrocis medinalis (Guenne) under in vitro conditions. Molecular insights into the antagonistic mechanisms of Bacillus strains deciphered the presence of several antimicrobial peptides (ericin, subtilin, surfactin, iturin, bacilysin, subtilosin, fengycin and bacillomycin), volatiles (dimethyl disulphide, methyl-Furan, acetic acid, Z-1,3-pentadiene and 3-hexyn-2-ol) and soluble metabolites (9-octadecenamide, E-15-heptadecenal, E-3-eicosene and 5-octadecene). Furthermore, liquid microbial inoculum prepared using the bacterial strains (K2, K12 and BioCWB) were evaluated under glass house (rice) and field condition (capsicum), which significantly enhanced plant growth in rice and yield in capsicum compared to control. The present study revealed the combination of Bacillus spp. (K2, K12 and BioCWB) can be used as bio-inoculants for improving agricultural production in Sikkim. Moreover, for the first time, we demonstrated plant growth promoting (PGP) traits, antifungal and insecticidal properties of B. luciferensis.

Sugar Beet Pectin Supplementation Did Not Alter Profiles of Fecal Microbiota and Exhaled Breath in Healthy Young Adults and Healthy Elderly

Aging is accompanied with increased frailty and comorbidities, which is potentially associated with microbiome perturbations. Dietary fibers could contribute to healthy aging by beneficially impacting gut microbiota and metabolite profiles. We aimed to compare young adults with elderly and investigate the effect of pectin supplementation on fecal microbiota composition, short chain fatty acids (SCFAs), and exhaled volatile organic compounds (VOCs) while using a randomized, double-blind, placebo-controlled parallel design. Fifty-two young adults and 48 elderly consumed 15 g/day sugar beet pectin or maltodextrin for four weeks. Fecal and exhaled breath samples were collected before and after the intervention period. Fecal samples were used for microbiota profiling by 16S rRNA gene amplicon sequencing, and for analysis of SCFAs by gas chromatography (GC). Breath was used for VOC analysis by GC-tof-MS. Young adults and elderly showed similar fecal SCFA and exhaled VOC profiles. Additionally, fecal microbiota profiles were similar, with five genera significantly different in relative abundance. Pectin supplementation did not significantly alter fecal microbiota, SCFA or exhaled VOC profiles in elderly or young adults. In conclusion, aside from some minor differences in microbial composition, healthy elderly and young adults showed comparable fecal microbiota composition and activity, which were not altered by pectin supplementation.

An Optimistic Vision of Future: Diagnosis of Bacterial Infections by Sensing Their Associated Volatile Organic Compounds

Simple tests using sniff analysis that have the ability of diagnosing and rapidly distinguishing between infections due to different bacteria are urgently required by medical community worldwide. Professionals interested in this topic wish for these tests to be simultaneously cheap, fast, easily applicable, non-invasive, robust, reliable, and sensitive. Current analytical instrumentation has already the ability for performing real time (minutes or a few dozens of minutes) analysis of volatile bacterial biomarkers (the VOCs emitted by bacteria). Although many articles are available, a review displaying an objective evaluation of the current status in the field is still needed. This review tries to present an overview regarding the bacterial biomarkers released from in vitro cultivation of various bacterial strains and also from different biological matrices investigated, over the last 10 years. We have described results of relevant studies, which used modern analytical techniques to evaluate specific biomarker profiles associated with bacterial infections. Our purpose was to present a comprehensive view of available possibilities for detection of emitted bacterial VOCs from different matrices. We intend that this review to be of general interest for both medical doctors and for all researchers preoccupied with bacterial infectious diseases and their rapid diagnosis using analytical instrumentation.

A Barcoded Polymer-Based Cross-Reactive Spectroscopic Sensor Array for Organic Volatiles

The development of cross-reactive sensor arrays for volatile organics (electronic noses, e-noses) is an active area of research. In this manuscript, we present a new format for barcoded polymer sensor arrays based on porous polymer beads. An array of nine self-encoded polymers was analyzed by Raman spectroscopy before and after exposure to a series of volatile organic compounds, and the changes in the vibrational fingerprints of their polymers was recorded before and after exposure. Our results show that the spectroscopic changes experienced by the porous spectroscopically encoded beads after exposure to an analyte can be used to identify and classify the target analytes. To expedite this analysis, analyte-specific changes induced in the sensor arrays were transformed into a response pattern using multivariate data analysis. These studies established the barcoded bead array format as a potentially effective sensing element in e-nose devices. Devices such as these have the potential to advance personalized medicine, providing a platform for non-invasive, real-time volatile metabolite detection.

Screening of Microbial Volatile Organic Compounds for Detection of Disease in Cattle: Development of Lab-scale Method

The primary hurdle for diagnosis of some diseases is the long incubation required to culture and confirm the presence of bacteria. The concept of using microbial VOCs as "signature markers" could provide a faster and noninvasive diagnosis. Finding biomarkers is challenging due to the specificity required in complex matrices. The objectives of this study were to (1) build/test a lab-scale platform for screening of microbial VOCs and (2) apply it to Mycobacterium avium paratuberculosis; the vaccine strain of M. bovis Bacillus Calmette-Guérin; and M. kansasii to demonstrate detection times greater those typically required for culture. SPME-GC-MS was used for sampling, sample preparation, and analyses. For objective (1), a testing platform was built for headspace sampling of bacterial cultures grown in standard culture flasks via a biosecure closed-loop circulating airflow system. For (2), results show that the suites of VOCs produced by Mycobacteria ssp. change over time and that individual strains produce different VOCs. The developed method was successful in discriminating between strains using a pooled multi-group analysis, and in timepoint-specific multi- and pair-wise comparisons. The developed testing platform can be useful for minimally invasive and biosecure collection of biomarkers associated with human, wildlife and livestock diseases for development of diagnostic point-of-care and field surveillance.

Effect of medium compositions on microbially mediated volatile organic compounds release profile

AIMS

To monitor temporal changes in the volatile organic compounds' (VOCs) profile generated by the metabolic activities of Pseudomonads in real time.

METHODS AND RESULTS

Three Pseudomonas strains were cultivated in Vogel's broth, supplemented with glucose (0·5 or 1%) and/or protein (egg white powder at 0 or 2%) at 25°C. Glucose or egg white protein contents influenced the VOCs' release profile for alcohols, carbonyls and sulphur derivatives. Increasing glucose content resulted in higher alcohol and ketone contents. Glucose showed a lower effect on the VOCs' release profile, mainly impacting on individual compounds, such as m/z 89 (3-methyl-1-butanol). In contrast, egg white protein enhanced production of VOCs such as m/z 75 (2-methyl-1-propanol) and m/z 63 (dimethyl sulphide) regardless of glucose level present in the medium. At the end of bacteria growth phase (54, 60 and 72 h), the fingerprint of VOCs was different from the early growth phase. Cells near to the end of their growth phase (54, 60 and 72 h) produced a distinctly different array of compounds compared to those produced in early growth phase, for example, cyclic compounds were detected in early growth phase, whereas sulphur derivatives were more common in late growth phase.

CONCLUSIONS

Pseudomonads-mediated VOCs' fingerprint as a response to varying growth conditions can be identified as latent biomarkers.

SIGNIFICANCE AND IMPACT OF THE STUDY

Understanding how microbially mediated VOCs' release profile responds to varying growth conditions can potentially be used as a rapid method for detecting microbial activities in controlled conditions such as food quality systems.

Identification of Pseudomonas aeruginosa and airway bacterial colonization by an electronic nose in bronchiectasis

RATIONALE

Airway colonization by Potentially Pathogenic Microorganisms (PPM) in bronchiectasis is associated with worse clinical outcomes. The electronic nose is a non-invasive technology capable of distinguishing volatile organic compounds (VOC) in exhaled breath. We aim to explore if an electronic nose can reliably discriminate airway bacterial colonization in patients with bronchiectasis.

METHODS

Seventy-three clinically stable bronchiectasis patients were included. PPM presence was determined using sputum culture. Exhaled breath was collected in Tedlar bags and VOC breath-prints were detected by the electronic nose Cyranose 320^®. Raw data was reduced to three factors with principal component analysis. Univariate ANOVA followed by post-hoc least significant difference test was performed with these factors. Patients were then classified using linear canonical discriminant analysis. Cross-validation accuracy values were defined by the percentage of correctly classified patients.

RESULTS

Forty-one (56%) patients were colonized with PPM. Pseudomonas aeruginosa (n = 27, 66%) and Haemophilus influenzae (n = 7, 17%) were the most common PPM. VOC breath-prints from colonized and non-colonized patients were significantly different (accuracy of 72%, AUROC 0.75, p < 0.001). VOC breath-prints from Pseudomonas aeruginosa colonized patients were significantly different from those of patients colonized with other PPM (accuracy of 89%, AUROC 0.97, p < 0.001) and non-colonized patients (accuracy 73%, AUROC 0.83, p = 0.007).

CONCLUSIONS

An electronic nose can accurately identify VOC breath-prints of clinically stable bronchiectasis patients with airway bacterial colonization, especially in those with Pseudomonas aeruginosa.

Cutting Edge Methods for Non-Invasive Disease Diagnosis Using E-Tongue and E-Nose Devices

Biomimetic cross-reactive sensor arrays (B-CRSAs) have been used to detect and diagnose a wide variety of diseases including metabolic disorders, mental health diseases, and cancer by analyzing both vapor and liquid patient samples. Technological advancements over the past decade have made these systems selective, sensitive, and affordable. To date, devices for non-invasive and accurate disease diagnosis have seen rapid improvement, suggesting a feasible alternative to current standards for medical diagnostics. This review provides an overview of the most recent B-CRSAs for diagnostics (also referred to electronic noses and tongues in the literature) and an outlook for future technological development.

In vitro discrimination of wound-associated bacteria by volatile compound profiling using selected ion flow tube-mass spectrometry

AIMS

To determine if bacterial species responsible for clinically relevant wound infection produce specific volatile profiles that would allow their speciation.

METHODS AND RESULTS

Selected ion flow tube-mass spectrometry (SIFT-MS) in full mass scan mode was used to analyse headspace gases produced by wound-associated bacteria grown in vitro, so as to enable identification of bacterial volatile product ion profiles in the resulting mass spectra. Applying multivariate statistical analysis (hierarchical clustering and principal component analysis) to the resultant mass spectra enabled clear speciation. Moreover, bacterial volatile product ions could be detected from artificially contaminated wound dressing material, although the pattern of product ions detected was influenced by culture conditions.

CONCLUSIONS

Using selected product ions from the SIFT-MS mass spectra it is possible to discriminate wound-associated bacterial species grown under specific in vitro culture conditions.

SIGNIFICANCE AND IMPACT OF THE STUDY

The results of this study have shown that wound-associated bacteria can be discriminated using volatile analysis in vitro and that bacterial volatiles can be detected from wound dressing material. This indicates that volatile analysis of wounds or dressing material to identify infecting microbes has potential and warrants further study.