Discovery, Expression, and In Silico Safety Evaluation of Honey Truffle Sweetener, a Sweet Protein Derived from Mattirolomyces terfezioides and Produced by Heterologous Expression in Komagataella phaffii.

Honey truffle sweetener (HTS), a 121 amino acid protein is identified as a high-intensity sweetener found naturally occurring in the Hungarian Sweet Truffle Mattirolomyces terfezioides, an edible mushroom used in regional diets. The protein is intensely sweet, but the truffle is difficult to cultivate; therefore, the protein was systematically characterized, and the gene coding for the protein was expressed in a commonly used host yeast Komagataella phaffii. The heterologously expressed protein maintained the structural characteristics and sweet taste of the truffle. Preliminary safety evaluations for use as a food ingredient were performed on the protein including digestibility and in silico approaches for predicting the allergenicity and toxicity of the protein. HTS is predicted to be nonallergenic, nontoxic, and readily digestible. This protein is readily produced by precision fermentation of the host yeast, making it a potential replacement for both added sugars and small molecule high-intensity sweeteners in food.

Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights.

The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g. CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA damage response and repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell, and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients who died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.