We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.Given its ease of use and low operational cost, GC-MS has applications with broad societal effect, such as detection of metabolic disease in newborns, toxicology, doping, forensics, food science and clinical testing. The predominant ionization technique in GC-MS is electron ionization (EI), in which all compounds are ionized by high-energy (70-eV) electrons. Because fragmentation occurs with ionization, EI GC-MS data are subjected to spectral deconvolution, a process that separates fragmentation ion patterns for each eluting molecule into a composite mass spectrum.The 70 eV for ionizing electrons in GC-MS has been the standard, making it possible to use decades-old EI reference spectra for annotation 1 . There are ~1.2 million reference spectra that have been accumulated and curated over a period of more than 50 years 2 . Many tools and repositories for GC-MS data have been introduced [3][4][5][6][7][8][9][10][11][12][13][14][15] ; however, much of GC-MS data processing is restricted to vendor-specific formats and software 8 . Currently, deconvolution requires setting multiple parameters manually [3][4][5] or posessing computational skills to run the software 7 . Also, the lack of data sharing in a uniform format precludes data comparison between laboratories and prevents taking advantage of repository-scale information and community knowledge, resulting in infrequent reuse of GC-MS data 8,[11][12][13][14][15] .Although batch modes exist, deconvolution quality is currently not enhanced by using information from all other files. To leverage across-file information, improve scalability of spectral deconvolution and eliminate the need for manually setting the deconvolution parameters (m/z error correction of the ions and peak shapeslopes of raising and trailing edges, peak RT shifts and noise/intensity thresholds), we developed an algorithmic learning strategy for auto-deconvolution (Fig. 1a-f). We deployed this functionality within GNPS/MassIVE (https://gnps.ucsd.edu) 16 (Fig. 1f-i). To promote analysis reproducibility, all GNPS jobs performed are retained in the 'My User' space and can be shared as hyperlinks.This user-independent 'automatic' parameter optimization is accomplished via fast Fourier transform (FFT), multiplication and inverse Fourier transform for each ion across an entire data set, followed by an unsupervised non-negative matrix factorization (NMF) (one-layer neural network). Then, the compositional consistency of spectral patterns for each spec...

Predation risk is allegedly reduced in Batesian and Müllerian mimics, because their coloration resembles the conspicuous coloration of unpalatable prey. The efficacy of mimicry is thought to be affected by variation in the unpalatability of prey, the conspicuousness of the signals, and the visual system of predators that see them. Many frog species exhibit small colorful patches contrasting against an otherwise dark body. By measuring toxicity and color reflectance in a geographically variable frog species and the syntopic toxic species, we tested whether unpalatability was correlated with between-species color resemblance and whether resemblance was highest for the most conspicuous components of coloration pattern. Heterospecific resemblance in colorful patches was highest between species at the same locality, but unrelated to concomitant variation in toxicity. Surprisingly, resemblance was lower for the conspicuous femoral patches compared to the inconspicuous dorsum. By building visual models, we further tested whether resemblance was affected by the visual system of model predators. As predicted, mimic-model resemblance was higher under the visual system of simulated predators compared to no visual system at all. Our results indicate that femoral patches are aposematic signals and support a role of mimicry in driving phenotypic divergence or mimetic radiation between localities.

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BackgroundPrevious studies have demonstrated the role of volatile organic compounds (VOCs) produced by skin microbiota in the attraction of mosquitoes to humans. Recently, behavioral experiments confirmed the importance of VOCs released by skin microbiota in the attraction of Rhodnius prolixus (Hemiptera: Triatominae), a vector of Chagas disease.Methods/FindingsIn this study, we screened for VOCs released in vitro by bacteria isolated from human facial skin that were able to elicit behavioral responses in R. prolixus. The VOCs released in vitro by eight bacterial species during two growth phases were tested with adult Rhodnius prolixus insects using a dual-choice “T”-shaped olfactometer. In addition, the VOCs released by the bacteria were analyzed with headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). The VOCs produced by Staphylococcus capitis 11C, Staphylococcus warneri and Staphylococcus epidermidis 1 were attractive to R. prolixus, while the VOCs released by Citrobacter koseri 6P, Brevibacterium epidermidis and Micrococcus luteus 23 were non-attractive.ConclusionsThe results shown here indicate that VOCs released by bacteria isolated from human facial skin have a potential for biotechnological uses as a strategy to prevent the vectorial transmission of Chagas disease mediated by Rhodnius prolixus.

Malassezia furfur is part of the human skin microbiota. Its volatile organic compounds (VOCs) possibly contribute to the characteristic odour in humans, as well as to microbiota interaction. The aim of this study was to investigate how the lipid composition of the liquid medium influences the production of VOCs. Growth was performed in four media: (1) mDixon, (2) oleic acid (OA), (3) oleic acid + palmitic acid (OA+PA), and (4) palmitic acid (PA). The profiles of the VOCs were characterized by HS-SPME/GC-MS in the exponential and stationary phases. A total number of 61 VOCs was found in M. furfur, among which alkanes, alcohols, ketones, and furanic compounds were the most abundant. Some compounds previously reported for Malassezia (γ-dodecalactone, 3-methylbutan-1-ol, and hexan-1-ol) were also found. Through our experiments, using univariate and multivariate unsupervised (Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA)) and supervised (Projection to Latent Structures Discriminant Analysis (PLS-DA)) statistical techniques, we have proven that each tested growth medium stimulates the production of a different volatiles profile in M. furfur. Carbon dioxide, hexan-1-ol, pentyl acetate, isomer5 of methyldecane, dimethyl sulphide, undec-5-ene, isomer2 of methylundecane, isomer1 of methyldecane, and 2-methyltetrahydrofuran were established as differentiating compounds among treatments by all the techniques. The significance of our findings deserves future research to investigate if certain volatile profiles could be related to the beneficial or pathogenic role of this yeast.