Learning about microbial language: possible interactions mediated by microbial volatile organic compounds (VOCs) and relevance to understanding Malassezia spp. metabolism

Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study

BACKGROUND

Globally, chronic respiratory diseases have become the third leading cause of death, including chronic obstructive pulmonary disease (COPD) and asthma, and have been threatening human life for a long time. To alleviate the disease burden, it is crucial to develop rapid and convenient screening methods for COPD, preserved ratio impaired spirometry (PRISm), and asthma. Volatile organic compounds (VOCs) in breath can reflect the pathophysiological processes of disease, thereby having the potential to serve as a promising approach for diagnosing respiratory diseases. Can we identify VOC markers in breath with the potential to serve as classification indicators, and further establish learning models for the early detection of COPD, asthma, or PRISm patients?

METHODS

This is a cross-sectional study in which exhaled breath samples were collected from 184 patients with COPD, 66 patients with asthma, 72 PRISm individuals, and 45 healthy individuals. From August 2023 to June 2024, the breath samples were analyzed using portable micro gas chromatography (CXBA-Alpha, ChromX Health Co., Ltd.). Potential VOC markers for classification were identified by univariate and multivariate analyses. Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. The sensitivity, specificity, and accuracy of these models were calculated to assess their overall discriminatory performance.

RESULTS

A total of 367 patients were enrolled in our study. We identified nine VOCs distinguishing COPD patients from healthy controls, nine VOCs differentiating the PRISm population from healthy controls, five VOCs separating asthma patients from healthy controls, five VOCs distinguishing COPD patients from asthma patients, and seven VOCs differentiating the PRISm population from asthma patients based on breathomics feature selection. We utilized five algorithms to establish diagnostic models and selected the optimal one among them. The random forest model best distinguished COPD from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.01. The support vector classifier (SVC) model was most effective in separating PRISm from healthy controls, achieving an AUC of 0.78 ± 0.01. Logistic regression performed well in discriminating asthma from PRISm (AUC, 0.74 ± 0.02) and COPD (AUC, 0.92 ± 0.01), in contrast, the random forest model differentiated asthma from healthy controls with an AUC of 0.81 ± 0.02.

CONCLUSION

VOC panel-based classification models have the potential to be a novel strategy for the discrimination of chronic respiratory diseases. Using the portal micro gas chromatography enables swift detection of chronic respiratory disease and, most importantly, facilitates the rapid identification of PRISm individuals within the population.

Volatile Organic Compounds of Scheffersomyces spartinae W9 Have Antifungal Effect against Botrytis cinerea on Strawberry Fruit

This study aims to evaluate the antifungal effects of volatile organic compounds (VOCs) produced by a marine biocontrol yeast, Scheffersomyces spartinae W9. The results showed that the VOCs from the yeast inhibited the growth of Botrytis cinerea mycelium and spore germination by 77.8% and 58.3%, respectively. Additionally, it reduced the disease incidence and lesion diameter of gray mold on the strawberry fruit surface by 20.7% and 67.4%, respectively. Electronic micrographs showed that VOCs caused damage to the morphology and ultrastructure of the hyphae. Based on headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME/GC-MS), S. spartinae W9 emitted 18 main VOCs, and the pure substance of VOCs, such as 3-methyl-1-butanol, 2-methyl-1-butanol, 2-phenylethanol, and isoamyl acetate, showed antifungal effects against B. cinerea mycelium growth. Among them, 2-phenylethanol exhibited the strongest antifungal activity. It has been concluded that VOCs are the key antifungal mechanism of S. spartinae W9, and a promising strategy for controlling gray mold on strawberry fruit.

Why Do These Yeasts Smell So Good? Volatile Organic Compounds (VOCs) Produced by Malassezia Species in the Exponential and Stationary Growth Phases

Malassezia synthesizes and releases volatile organic compounds (VOCs), small molecules that allow them to carry out interaction processes. These lipid-dependent yeasts belong to the human skin mycobiota and are related to dermatological diseases. However, knowledge about VOC production and its function is lacking. This study aimed to determine the volatile profiles of Malassezia globosa, Malassezia restricta, and Malassezia sympodialis in the exponential and stationary growth phases. The compounds were separated and characterized in each growth phase through headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). We found a total of 54 compounds, 40 annotated. Most of the compounds identified belong to alcohols and polyols, fatty alcohols, alkanes, and unsaturated aliphatic hydrocarbons. Unsupervised and supervised statistical multivariate analyses demonstrated that the volatile profiles of Malassezia differed between species and growth phases, with M. globosa being the species with the highest quantity of VOCs. Some Malassezia volatiles, such as butan-1-ol, 2-methylbutan-1-ol, 3-methylbutan-1-ol, and 2-methylpropan-1-ol, associated with biological interactions were also detected. All three species show at least one unique compound, suggesting a unique metabolism. The ecological functions of the compounds detected in each species and growth phase remain to be studied. They could interact with other microorganisms or be an important clue in understanding the pathogenic role of these yeasts.

Many ways, one microorganism: Several approaches to study Malassezia in interactions with model hosts

Malassezia, a lipophilic and lipid-dependent yeast, is a microorganism of current interest to mycobiologists because of its role as a commensal or pathogen in health conditions such as dermatological diseases, fungemia, and, as discovered recently, cancer and certain neurological disorders. Various novel approaches in the study of Malassezia have led to increased knowledge of the cellular and molecular mechanisms of this yeast. However, additional efforts are needed for more comprehensive understanding of the behavior of Malassezia in interactions with the host. This article reviews advances useful in the experimental field for Malassezia.

Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs)

The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors.