Integrating metabolomics and metatranscriptomics to explore the formation pathway of aroma-active volatile phenolics and metabolic profile during industrial radish paocai fermentation.

The aroma profile of industrial Sichuan paocai is formed and regulated by complex physiological and biochemical reactions and microbial metabolism, but little is known so far. In this study, we comprehensively analyzed the changes of metabolic profile and gene expression profile, mainly explored the formation pathways of two skeleton aroma-active compounds, 4-ethylphenol and 4-ethylguaiacol, and verified the pathways at multiple levels. The results showed that a total of 136 volatile metabolites and 560 non-volatile metabolites were identified in the whole fermentation process. The types and concentrations of metabolites in paocai were higher than those in brine, and gradually converged with fermentation. Differential analysis of metabolism and transcription levels were both enriched in three pathways: amino acid metabolism, phenylpropanoid metabolism and lipid metabolism. Among them, 4-ethylphenol and 4-ethylguaiacol, the products of the phenylpropanoid metabolism, were converted from p-coumaric acid and ferulic acid in plant cell walls, respectively. Under the action of decarboxylase produced by yeast (such as Debaryomyces Hansenii) and lactic acid bacteria (such as Lactobacillus versmoldensis), intermediate metabolites vinylphenols were produced, and the intermediate metabolites further produce the final products under the action of vinylphenol reductase. The key gene copy number, enzyme activity, and metabolite concentration in the pathways were detected to provide stronger evidence for the formation pathways. This study provided meaningful new insights for the development of aroma-producing enzymes and further guidance for the flavor improvement of industrial paocai.

The faecal metabolome and mycobiome in Parkinson's disease.

BACKGROUND: Gut fungal composition and its metabolites have not been assessed simultaneously in Parkinson's disease (PD) despite their potential pathogenic contribution. OBJECTIVE: To evaluate the faecal metabolome and mycobiome in PD by assessing volatile organic compounds (VOCs) and fungal rRNA. METHODS: Faecal VOCs from 35 PD patients and two control groups (n = 35; n = 15) were assessed using gas chromatography and mass spectrometry. DNA was extracted from 44 samples: 18S rRNA gene amplicons were prepared and sequenced. Metabolomics, mycobiome and integrated analyses were performed. RESULTS: Several VOCs were more abundant and short chain fatty acids were less abundant in PD. Hanseniaspora, Kazachstania, uncultured Tremellaceae and Penicillium genera were more abundant, and Saccharomyces less abundant in PD (FDR<0.0007). Torulaspora was associated with PD and two VOCs. CONCLUSION: PD patients had a distinct metabolome and mycobiome suggesting that fungal dysbiosis may contribute to PD pathogenesis.

Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning.

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography-quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

Correlates of immune exacerbations in leprosy.

Leprosy is still a considerable health threat in pockets of several low and middle income countries worldwide where intense transmission is witnessed, and often results in irreversible disabilities and deformities due to delayed- or misdiagnosis. Early detection of leprosy represents a substantial hurdle in present-day leprosy health care. The dearth of timely diagnosis has, however, particularly severe consequences in the case of inflammatory episodes, designated leprosy reactions, which represent the major cause of leprosy-associated irreversible neuropathy. There is currently no accurate, routine diagnostic test to reliably detect leprosy reactions, or to predict which patients will develop these immunological exacerbations. Identification of host biomarkers for leprosy reactions, particularly if correlating with early onset prior to development of clinical symptoms, will allow timely interventions that contribute to decreased morbidity. Development of a point-of-care (POC) test based on such correlates would be a definite game changer in leprosy health care. In this review, proteomic-, transcriptomic and metabolomic research strategies aiming at identification of host biomarker-based correlates of leprosy reactions are discussed, next to external factors associated with occurrence of these episodes. The vast diversity in research strategies combined with the variability in patient- and control cohorts argues for harmonisation of biomarker discovery studies with geographically overarching study sites. This will improve identification of specific correlates associated with risk of these damaging inflammatory episodes in leprosy and subsequent application to rapid field tests.

Untargeted GC-MS Metabolomics Reveals Changes in the Metabolite Dynamics of Industrial Scale Batch Fermentations of Streptoccoccus thermophilus Broth.

An industrial scale biomass production using batch or fed-batch fermentations usually optimized by selection of bacterial strains, tuning fermentation media, feeding strategy, and temperature. However, in-depth investigation of the biomass metabolome during the production may reveal new knowledge for better optimization. In this study, for the first time, the authors investigated seven fermentation batches performed on five Streptoccoccus thermophilus strains during the biomass production at Chr. Hansen (Denmark) in a real life large scale fermentation process. The study is designed to investigate effects of batch fermentation, fermentation time, production line, and yeast extract brands on the biomass metabolome using untargeted GC-MS metabolomics. Processing of the raw GC-MS data using PARAFAC2 revealed a total of 90 metabolites out of which 64 are identified. Partitioning of the data variance according to the experimental design was performed using ASCA and revealed that batch and fermentation time effects and their interaction term were the most significant effects. The yeast extract brand had a smaller impact on the biomass metabolome, while the production line showed no effect. This study shows that in-depth metabolic analysis of fermentation broth provides a new tool for advanced optimization of high-volume-low-cost biomass production by lowering the cost, increase the yield, and augment the product quality.

Metabonomics reveals drastic changes in anti-inflammatory/pro-resolving polyunsaturated fatty acids-derived lipid mediators in leprosy disease.

Despite considerable efforts over the last decades, our understanding of leprosy pathogenesis remains limited. The complex interplay between pathogens and hosts has profound effects on host metabolism. To explore the metabolic perturbations associated with leprosy, we analyzed the serum metabolome of leprosy patients. Samples collected from lepromatous and tuberculoid patients before and immediately after the conclusion of multidrug therapy (MDT) were subjected to high-throughput metabolic profiling. Our results show marked metabolic alterations during leprosy that subside at the conclusion of MDT. Pathways showing the highest modulation were related to polyunsaturated fatty acid (PUFA) metabolism, with emphasis on anti-inflammatory, pro-resolving omega-3 fatty acids. These results were confirmed by eicosanoid measurements through enzyme-linked immunoassays. Corroborating the repertoire of metabolites altered in sera, metabonomic analysis of skin specimens revealed alterations in the levels of lipids derived from lipase activity, including PUFAs, suggesting a high lipid turnover in highly-infected lesions. Our data suggest that omega-6 and omega-3, PUFA-derived, pro-resolving lipid mediators contribute to reduced tissue damage irrespectively of pathogen burden during leprosy disease. Our results demonstrate the utility of a comprehensive metabonomic approach for identifying potential contributors to disease pathology that may facilitate the development of more targeted treatments for leprosy and other inflammatory diseases.

Serum metabolomics reveals higher levels of polyunsaturated fatty acids in lepromatous leprosy: potential markers for susceptibility and pathogenesis.

BACKGROUND: Leprosy is a disease of the skin and peripheral nervous system caused by the obligate intracellular bacterium Mycobacterium leprae. The clinical presentations of leprosy are spectral, with the severity of disease determined by the balance between the cellular and humoral immune response of the host. The exact mechanisms that facilitate disease susceptibility, onset and progression to certain clinical phenotypes are presently unclear. Various studies have examined lipid metabolism in leprosy, but there has been limited work using whole metabolite profiles to distinguish the clinical forms of leprosy. METHODOLOGY AND PRINCIPAL FINDINGS: In this study we adopted a metabolomics approach using high mass accuracy ultrahigh pressure liquid chromatography mass spectrometry (UPLC-MS) to investigate the circulatory biomarkers in newly diagnosed untreated leprosy patients. Sera from patients having bacterial indices (BI) below 1 or above 4 were selected, subjected to UPLC-MS, and then analyzed for biomarkers which distinguish the polar presentations of leprosy. We found significant increases in the abundance of certain polyunsaturated fatty acids (PUFAs) and phospholipids in the high-BI patients, when contrasted with the levels in the low-BI patients. In particular, the median values of arachidonic acid (2-fold increase), eicosapentaenoic acid (2.6-fold increase) and docosahexaenoic acid (1.6-fold increase) were found to be greater in the high-BI patients. SIGNIFICANCE: Eicosapentaenoic acid and docosahexaenoic acid are known to exert anti-inflammatory properties, while arachidonic acid has been reported to have both pro- and anti-inflammatory activities. The observed increase in the levels of several lipids in high-BI patients may provide novel clues regarding the biological pathways involved in the immunomodulation of leprosy. Furthermore, these results may lead to the discovery of biomarkers that can be used to investigate susceptibility to infection, facilitate early diagnosis and monitor the progression of disease.