Discovery of novel Mamastroviruses in Bactrian camels and dromedaries reveals complex recombination history

Leveraging machine learning for taxonomic classification of emerging astroviruses

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.

Diversity of classic and novel human astrovirus in outpatient children with acute gastroenteritis in Shanghai, China

Introduction

Human astrovirus (HAstV) is an important pathogen of acute gastroenteritis (AGE) in children. This study was aimed at investigating the diversity and epidemiology of classic and novel HAstV in outpatient children aged 0-16 years old with AGE in Shanghai.

Methods

From May 2020 to December 2022, a total of 1,482 stool samples were collected from children diagnosed as AGE from the Children's Hospital of Fudan University. HAstV was identified using pan-astrovirus consensus primers by Reverse transcription PCR.

Results

During the study period, 3.3% (49/1,482) of specimens were identified as HAstV, with a detection rate of 2.5% (37/1,482) for classic HAstV and 0.8% (12/1,482) for novel HAstV. Among the 12 novel HAstV strains, 11 (91.7%) belonged to the HAstV-MLB and 1 (8.3%) was HAstV-VA. Genotyping revealed six circulating genotypes. Strain HAstV-1 was predominant in the study population with a detection rate of 1.8% (26/1,482) followed by HAstV-MLB1 (0.7%, 10/1,482) and HAstV-4 (0.6%, 9/1,482). Of note, all the HAstV-4 strains detected in this study were close to one astrovirus strain isolated from Bactrian camels with 99.0-100.0% amino acid sequences identity. In this study, HAstV was detected in all age groups with the highest detection rate of HAstV-positive specimens observed in children older than 73 months (5.7%, 12/209).

Discussion

This study provided useful information and contributed to the molecular epidemiology of both classic and novel HAstV, which were simultaneously characterized and reported for the first time in Shanghai.