SARS-CoV-2 envelope protein-derived extracellular vesicles act as potential media for viral spillover.

Extracellular vesicles (EVs) are shown to be a novel viral transmission model capable of increasing a virus's tropism. According to our earlier research, cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or transfected with envelope protein plasmids generate a novel type of EVs that are micrometer-sized and able to encase virus particles. Here, we showed the capacity of these EVs to invade various animals both in vitro and in vivo independent of the angiotensin-converting enzyme 2 receptor. First, via macropinocytosis, intact EVs produced from Vero E6 (monkey) cells were able to enter cells from a variety of animals, including cats, dogs, bats, hamsters, and minks, and vice versa. Second, when given to zebrafish with cutaneous wounds, the EVs showed favorable stability in aqueous environments and entered the fish. Moreover, infection of wild-type (WT) mice with heterogeneous EVs carrying SARS-CoV-2 particles led to a strong cytokine response and a notable amount of lung damage. Conversely, free viral particles did not infect WT mice. These results highlight the variety of processes behind viral transmission and cross-species evolution by indicating that EVs may be possible vehicles for SARS-CoV-2 spillover and raising risk concerns over EVs' potential for viral gene transfer.

Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.

Backgrounds: Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet. Methods: The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs. Results: We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity. Conclusions: Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.

Uncovering hub genes and immunological characteristics for heart failure utilizing RRA, WGCNA and Machine learning.

Background: Heart failure (HF) is a major public health issue with high mortality and morbidity. This study aimed to find potential diagnostic markers for HF by the combination of bioinformatics analysis and machine learning, as well as analyze the role of immune infiltration in the pathological process of HF. Methods: The gene expression profiles of 124 HF patients and 135 nonfailing donors (NFDs) were obtained from six datasets in the NCBI Gene Expression Omnibus (GEO) public database. We applied robust rank aggregation (RRA) and weighted gene co-expression network analysis (WGCNA) method to identify critical genes in HF. To discover novel diagnostic markers in HF, three machine learning methods were employed, including best subset regression, regularization technique, and support vector machine-recursive feature elimination (SVM-RFE). Besides, immune infiltration was investigated in HF by single-sample gene set enrichment analysis (ssGSEA). Results: Combining RRA with WGCNA method, we recognized 39 critical genes associated with HF. Through integrating three machine learning methods, FCN3 and SMOC2 were determined as novel diagnostic markers in HF. Differences in immune infiltration signature were also found between HF patients and NFDs. Moreover, we explored the potential associations between two diagnostic markers and immune response in the pathogenesis of HF. Conclusions: In summary, FCN3 and SMOC2 can be used as diagnostic markers of HF, and immune infiltration plays an important role in the initiation and progression of HF.