Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

The Role of MicroRNA in the Airway Surface Liquid Homeostasis.

Mucociliary clearance, mediated by a coordinated function of cilia bathing in the airway surface liquid (ASL) on the surface of airway epithelium, protects the host from inhaled pathogens and is an essential component of the innate immunity. ASL is composed of the superficial mucus layer and the deeper periciliary liquid. Ion channels, transporters, and pumps coordinate the transcellular and paracellular movement of ions and water to maintain the ASL volume and mucus hydration. microRNA (miRNA) is a class of non-coding, short single-stranded RNA regulating gene expression by post-transcriptional mechanisms. miRNAs have been increasingly recognized as essential regulators of ion channels and transporters responsible for ASL homeostasis. miRNAs also influence the airway host defense. We summarize the most up-to-date information on the role of miRNAs in ASL homeostasis and host-pathogen interactions in the airway and discuss concepts for miRNA-directed therapy.