A revised phylogeny of Boletaceae using whole genome sequences

The porcini mushroom family Boletaceae is a diverse, widespread group of ectomycorrhizal (ECM) mushroom-forming fungi that so far has eluded intrafamilial phylogenetic resolution based on morphology and multilocus data sets. In this study, we present a genome-wide molecular data set of 1764 single-copy gene families from a global sampling of 418 Boletaceae specimens. The resulting phylogenetic analysis has strong statistical support for most branches of the tree, including the first statistically robust backbone. The enigmatic Phylloboletellus chloephorus from non-ECM Argentinian subtropical forests was recovered as a new subfamily sister to the core Boletaceae. Time-calibrated branch lengths estimate that the family first arose in the early to mid-Cretaceous and underwent a rapid radiation in the Eocene, possibly when the ECM nutritional mode arose with the emergence and diversification of ECM angiosperms. Biogeographic reconstructions reveal a complex history of vicariance and episodic long-distance dispersal correlated with historical geologic events, including Gondwanan origins and inferred vicariance associated with its disarticulation. Together, this study represents the most comprehensively sampled, data-rich molecular phylogeny of the Boletaceae to date, establishing a foundation for future robust inferences of biogeography in the group.

Boletus edulis Extract-A New Modulator of Dysbiotic Microbiota

The regular administration of antibiotics is a public concern due to the prejudices of large population groups and the high frequency with which antimicrobial products are prescribed. The current study aimed to evaluate the in vitro effect of a new extract from Boletus edulis (BEE) on the human microbiota. One of the disadvantages of this extensive use is the disruption of the human microbiota, leading to potential negative health consequences. The in vitro evaluation of BEE consisted in determining its cytotoxicity, influence on the concentration of four types of cytokines (IL-6, IL-10, IL-1β, TNFα), and capacity to modulate the human microbiota after administering antibiotics. The latter was assessed by microbiome analysis and the evaluation of short-chain fatty acid synthesis (SCFAs). Simultaneously, the content of total polyphenols, the antioxidant capacity, and the compositional analysis of the extract (individual polyphenols composition) were determined. The results showed that BEE modulates the microbial pattern and reduces inflammatory progression. The data demonstrated antioxidant properties correlated with the increase in synthesizing some biomarkers, such as SCFAs, which mitigated antibiotic-induced dysbiosis without using probiotic products.

Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA

BACKGROUND

How to quickly identify poisonous mushrooms is a worldwide problem, because poisonous mushrooms and edible mushrooms have very similar appearances. Even some edible mushrooms must be processed further before they can be eaten. In addition, mushrooms from different geographical origins contain different levels of heavy metals. Eating frequent mushrooms with excessive heavy metal content can also cause food poisoning. This information is very important and needs to be informed to consumers in advance. Through the demand for the safety of porcini mushrooms in the Yunnan area we propose a hierarchical identification system based on Fourier-transform near-infrared (FT-NIR) spectroscopy to evaluate the edible safety of porcini species.

RESULTS

We found that deep learning is the most effective means to identify the edible safety of porcini, and the recognition accuracy was 100%, by comparing two pattern recognition tools, deep learning and partial least square discriminant analysis (PLS-DA). Although the accuracy of the PLS-DA test set is 96.10%, the poisonous porcini is not allowed to be wrongly judged. In addition, the cadmium (Cd) content of Leccinum rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%.

CONCLUSION

The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of being eaten. This has useful application prospects in food safety. © 2021 Society of Chemical Industry.