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The red fox is a highly adaptable mammal that has established itself world-wide in many different environments. Contributing to its success is a social structure based on chemical signalling between individuals. Urine scent marking behaviour has long been known in foxes, but there has not been a recent study of the chemical composition of fox urine. We have used solid-phase microextraction and gas chromatography-mass spectrometry to analyze the urinary volatiles in 15 free-ranging wild foxes (2 female) living in farmlands and bush in Victoria, Australia. Foxes here are routinely culled as feral pests, and the urine was collected by bladder puncture soon after death. Compounds were identified from their mass spectra and Kovats retention indices. There were 53 possible endogenous scent compounds, 10 plant-derived compounds and 5 anthropogenic xenobiotics. Among the plant chemicals were several aromatic apocarotenoids previously found in greater abundance in the fox tail gland. They reflect the dietary consumption of carotenoids, essential for optimal health. One third of all the endogenous volatiles were sulfur compounds, a highly odiferous group which included thiols, methylsulfides and polysulfides. Five of the sulfur compounds (3-isopentenyl thiol, 1- and 2-phenylethyl methyl sulfide, octanethiol and benzyl methyl sulfide) have only been found in foxes, and four others (isopentyl methyl sulfide, 3-isopentenyl methyl sulfide, and 1- and 2-phenylethane thiol) only in some canid, mink and skunk species. This indicates that they are not normal mammalian metabolites and have evolved to serve a specific role. This role is for defence in musteloids and most likely for chemical communication in canids. The total production of sulfur compounds varied greatly between foxes (median 1.2, range 0.4-32.3 μg 'acetophenone equivalents'/mg creatinine) as did the relative abundance of different chemical types. The urinary scent chemistry may represent a highly evolved system of semiochemicals for communication between foxes., Competing Interests: The authors have declared that no competing interests exist.

Prostate cancer is the second leading cause of cancer death in men in the developed world. A more sensitive and specific detection strategy for lethal prostate cancer beyond serum prostate specific antigen (PSA) population screening is urgently needed. Diagnosis by canine olfaction, using dogs trained to detect cancer by smell, has been shown to be both specific and sensitive. While dogs themselves are impractical as scalable diagnostic sensors, machine olfaction for cancer detection is testable. However, studies bridging the divide between clinical diagnostic techniques, artificial intelligence, and molecular analysis remains difficult due to the significant divide between these disciplines. We tested the clinical feasibility of a cross-disciplinary, integrative approach to early prostate cancer biosensing in urine using trained canine olfaction, volatile organic compound (VOC) analysis by gas chromatography-mass spectroscopy (GC-MS) artificial neural network (ANN)-assisted examination, and microbial profiling in a double-blinded pilot study. Two dogs were trained to detect Gleason 9 prostate cancer in urine collected from biopsy-confirmed patients. Biopsy-negative controls were used to assess canine specificity as prostate cancer biodetectors. Urine samples were simultaneously analyzed for their VOC content in headspace via GC-MS and urinary microbiota content via 16S rDNA Illumina sequencing. In addition, the dogs' diagnoses were used to train an ANN to detect significant peaks in the GC-MS data. The canine olfaction system was 71% sensitive and between 70-76% specific at detecting Gleason 9 prostate cancer. We have also confirmed VOC differences by GC-MS and microbiota differences by 16S rDNA sequencing between cancer positive and biopsy-negative controls. Furthermore, the trained ANN identified regions of interest in the GC-MS data, informed by the canine diagnoses. Methodology and feasibility are established to inform larger-scale studies using canine olfaction, urinary VOCs, and urinary microbiota profiling to develop machine olfaction diagnostic tools. Scalable multi-disciplinary tools may then be compared to PSA screening for earlier, non-invasive, more specific and sensitive detection of clinically aggressive prostate cancers in urine samples., Competing Interests: The authors have read the journal’s policy and have the following competing interests: ST is a paid employee of Imagination Engines, Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

There has been rapidly accelerating interest in the utilization of volatile organic compounds (VOCs) as non-invasive methods for rapid point-of-care medical diagnostics. There is widespread variation in analytical methods and protocols, with little understanding of the effects of sample storage on VOC profiles. This study aimed to determine the effects on VOC profiles of different storage times, at room temperature, prior to freezing, of sealed urine samples from healthy individuals. Analysis using Field Asymmetric Ion Motility Spectrometry (FAIMS) determined the alterations in VOC and total ion count profiles as a result of increasing room temperature storage times. Results indicated that increasing exposure time to room temperature prior to freezing had a threefold effect. Firstly, increased urinary VOC profile variability, with a plateau phase between 12 and 48 hours, before further degradation. Secondly, an increase in total ion count with time exposed to room temperature. Finally, a deterioration in VOCs with each sample run during the analysis process. This provides new insight into the effect of storage of urine samples for VOC analysis using FAIMS technology. Results of this study provide a recommendation for a 12-hour maximum duration at room temperature prior to storage., Competing Interests: The authors have declared that no competing interests exist.

Psychiatric disorders including depression and anxiety comprise a broad range of conditions with different symptoms. We have developed a mouse model of depression/anxiety in mice deficient in the St3gal4 gene. In this study, we performed a comparative analysis of urinary volatile organic compounds (VOCs) in St3gal4-deficient (St3gal4-KO) and wild-type mice using gas chromatography-mass spectrometry, and we screened 18 putative VOCs. Principal component analysis (PCA) based on these VOCs identified a major group of 11 VOCs, from which two groups were clarified by hierarchical clustering analysis. One group including six VOCs (pentanoic acid, 4-methyl-, ethyl ester; 3-heptanone, 6-methyl; benzaldehyde; 5,9-undecadien-2-ol, 6,10-dimethyl; and unknown compounds RI1291 and RI1237) was correlated with the startle response (r = 0.620), which is related to an unconscious defensive response. The other group including two VOCs (beta-farnesene and alpha-farnesene) comprised pheromones which increased in KO mice. Next, male mice underwent a social behavior test with female mice in the estrus stage, showing reduced access of KO male mice to female mice. Comparative analysis of urinary VOCs before and after encounters revealed that the six VOCs were not changed by these encounters. However, in WT mice, the two farnesenes increased after the encounters, reaching the level observed in KO mice, which was not altered following the encounter. Taken together, these results indicated that St3gal4 was involved in modulating urinary VOCs. Moreover, VOC clusters discovered by comparison of St3gal4-KO mice with WT mice were correlated with differential emotional behaviors., Competing Interests: The authors have declared that no competing interests exist.

Chemical cues are thought to play an important role in mate identification in the solitary giant panda (Ailuropoda melanoleuca). The goal of this study was to detect and identify volatile compounds present in the enclosure air of captive giant pandas. We hypothesized that a subset of compounds produced from breeding animals would be detected in environmental samples because highly volatile chemicals are likely to facilitate mate detection. Samples were collected from the enclosures of 8 giant pandas (n = 4 male, n = 4 female) during the Mar-June breeding season and the Aug-Jan non-breeding period from 2012-2015. Volatile compounds were captured by securing a solid phase micro extraction fiber approximately 3 meters above the ground within a panda enclosure for 6-12 hours. Compounds adsorbed onto the SPME fibers were analyzed by gas chromatography mass spectrometry. Thirty-three compounds were detected in at least 10% of all samples within individual and season and across all subjects within each season. Aromatic compounds made up 27.3% of the enclosure volatile profile, while 21.2% was made of cyclic aliphatic compounds and 51.5% of the enclosure profile was comprised of acyclic aliphatic compounds. Three compounds were likely to be present in male enclosures regardless of season, while Undecane, 4-methyl had a significant (p<0.05) predicted probability of being present in female enclosures. 3,3'-(1,1-Ethanediyl)bis(1H-indole) had a significant (p<0.05) probability of occurrence in male enclosures during the breeding season. Given the prevalence of these compounds, we suspect that these chemicals are important in giant panda communication. This novel sampling technique can detect volatile compounds produced by captive species and also may be a useful tool for detecting pheromones in free-ranging individuals., Competing Interests: The authors have declared that no competing interests exist.

Motivation: The measurement of disease biomarkers in easily-obtained bodily fluids has opened the door to a new type of non-invasive medical diagnostics. New technologies are being developed and fine-tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person's metabolism and can in principle be used for disease diagnostic purposes. Key to the effective use of such data are well-developed data processing pipelines, which are necessary to extract the most useful data from the complex underlying biological structure., Results: In this study, we present a new data analysis pipeline for FAIMS data, and demonstrate a number of improvements over previously used methods. We evaluate the effect of a series of candidate operational steps during data processing, such as the use of wavelet transforms, principal component analysis (PCA), and classifier ensembles. We also demonstrate the use of FAIMS data in our pipeline to diagnose diabetes on the basis of a simple urine sample using machine learning classifiers. We present results for data generated from a case-control study of 115 urine samples, collected from 72 type II diabetic patients, with 43 healthy volunteers as negative controls. The resulting pipeline combines the steps that resulted in the best classification model performance. These include the use of a two-dimensional discrete wavelet transform, and the Wilcoxon rank-sum test for feature selection. We are able to achieve a best ROC curve AUC of 0.825 (0.747-0.9, 95% CI) for classification of diabetes vs control. We also note that this result is robust to changes in the data pipeline and different analysis runs, with AUC > 0.80 achieved in a range of cases. This is a substantial improvement in performance over previously used data processing methods in this area. Our ability to make strong statements about FAIMS ability to diagnose diabetes is sadly limited, as we found confounding effects from the demographics when including these data in the pipeline. The demographics alone produced a best AUC of 0.87 (0.795-0.94, 95% CI). While the combination of the demographics and FAIMS data resulted in an improvement on the AUC (0.907; 0.848-0.97, 95% CI), it did not prove to be a significant difference. Nevertheless, the pipeline itself shows a significant improvement in performance over more basic methods which have been used with FAIMS data in the past., Competing Interests: The authors have declared that no competing interests exist.

The aim of this work was to investigate volatile organic compounds (VOCs) emanating from urine samples to determine whether they can be used to classify samples into those from prostate cancer and non-cancer groups. Participants were men referred for a trans-rectal ultrasound-guided prostate biopsy because of an elevated prostate specific antigen (PSA) level or abnormal findings on digital rectal examination. Urine samples were collected from patients with prostate cancer (n = 59) and cancer-free controls (n = 43), on the day of their biopsy, prior to their procedure. VOCs from the headspace of basified urine samples were extracted using solid-phase micro-extraction and analysed by gas chromatography/mass spectrometry. Classifiers were developed using Random Forest (RF) and Linear Discriminant Analysis (LDA) classification techniques. PSA alone had an accuracy of 62-64% in these samples. A model based on 4 VOCs, 2,6-dimethyl-7-octen-2-ol, pentanal, 3-octanone, and 2-octanone, was marginally more accurate 63-65%. When combined, PSA level and these four VOCs had mean accuracies of 74% and 65%, using RF and LDA, respectively. With repeated double cross-validation, the mean accuracies fell to 71% and 65%, using RF and LDA, respectively. Results from VOC profiling of urine headspace are encouraging and suggest that there are other metabolomic avenues worth exploring which could help improve the stratification of men at risk of prostate cancer. This study also adds to our knowledge on the profile of compounds found in basified urine, from controls and cancer patients, which is useful information for future studies comparing the urine from patients with other disease states.

Coeliac disease (CD), a T-cell-mediated gluten sensitive enteropathy, affects ∼ 1% of the UK population and can present with wide ranging clinical features, often being mistaken for Irritable Bowel Syndrome (IBS). Heightened clinical awareness and serological screening identifies those with potential coeliac disease; the diagnosis is confirmed with duodenal biopsies, and symptom improvement with a gluten-free diet. Limitations to diagnosis are false negative serology and reluctance to undergo biopsy. The gut microbiome is altered in several gastrointestinal disorders, causing altered gut fermentation patterns recognisable by volatile organic compounds (VOC) analysis in urine, breath and faeces. We aimed to determine if CD alters the urinary VOC pattern, distinguishing it from IBS. 47 patients were recruited, 27 with established CD, on gluten free diets, and 20 with diarrhoea-predominant IBS (D-IBS). Collected urine was stored frozen in 10 ml aliquots. For assay, the specimens were heated to 40 ± 0.1°C and the headspace analysed by Field Asymmetric Ion Mobility Spectrometry (FAIMS). Machine learning algorithms were used for statistical evaluation. Samples were also analysed using Gas chromatography and mass spectroscopy (GC-MS). Sparse logistic regression showed that FAIMS distinguishes VOCs in CD vs D-IBS with ROC curve AUC of 0.91 (0.83-0.99), sensitivity and specificity of 85% respectively. GCMS showed a unique peak at 4'67 found only in CD, not D-IBS, which correlated with the compound 1,3,5,7 cyclooctatetraene. This study suggests that FAIMS offers a novel, non-invasive approach to identify those with possible CD, and distinguishes from D-IBS. It offers the potential for monitoring compliance with a gluten-free diet at home. The presence of cyclooctatetraene in CD specimens will need further validation.

Colorectal cancer (CRC) is a leading cause of cancer related death in Europe and the USA. There is no universally accepted effective non-invasive screening test for CRC. Guaiac based faecal occult blood (gFOB) testing has largely been superseded by Faecal Immunochemical testing (FIT), but sensitivity still remains poor. The uptake of population based FOBt testing in the UK is also low at around 50%. The detection of volatile organic compounds (VOCs) signature(s) for many cancer subtypes is receiving increasing interest using a variety of gas phase analytical instruments. One such example is FAIMS (Field Asymmetric Ion Mobility Spectrometer). FAIMS is able to identify Inflammatory Bowel disease (IBD) patients by analysing shifts in VOCs patterns in both urine and faeces. This study extends this concept to determine whether CRC patients can be identified through non-invasive analysis of urine, using FAIMS. 133 patients were recruited; 83 CRC patients and 50 healthy controls. Urine was collected at the time of CRC diagnosis and headspace analysis undertaken using a FAIMS instrument (Owlstone, Lonestar, UK). Data was processed using Fisher Discriminant Analysis (FDA) after feature extraction from the raw data. FAIMS analyses demonstrated that the VOC profiles of CRC patients were tightly clustered and could be distinguished from healthy controls. Sensitivity and specificity for CRC detection with FAIMS were 88% and 60% respectively. This study suggests that VOC signatures emanating from urine can be detected in patients with CRC using ion mobility spectroscopy technology (FAIMS) with potential as a novel screening tool.