MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.

Examining the effect of wound cleansing on the microbiome of venous stasis ulcers.

Common treatment for venous leg wounds includes topical wound dressings with compression. At each dressing change, wounds are debrided and washed; however, the effect of the washing procedure on the wound microbiome has not been studied. We hypothesized that wound washing may alter the wound microbiome. To characterize microbiome changes with respect to wound washing, swabs from 11 patients with chronic wounds were sampled before and after washing, and patient microbiomes were characterized using 16S rRNA sequencing and culturing. Microbiomes across patient samples prior to washing were typically polymicrobial but varied in the number and type of bacterial genera present. Proteus and Pseudomonas were the dominant genera in the study. We found that washing does not consistently change microbiome diversity but does cause consistent changes in microbiome composition. Specifically, washing caused a decrease in the relative abundance of the most highly represented genera in each patient cluster. The finding that venous leg ulcer wound washing, a standard of care therapy, can induce changes in the wound microbiome is novel and could be potentially informative for future guided therapy strategies.

Genomic diversity of SARS-CoV-2 during early introduction into the Baltimore-Washington metropolitan area.

The early COVID-19 pandemic was characterized by rapid global spread. In Maryland and Washington, DC, United States, more than 2500 cases were reported within 3 weeks of the first COVID-19 detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2 - the virus that causes COVID-19 - in the region. We analyzed 620 samples collected from the Johns Hopkins Health System during March 11-31, 2020, comprising 28.6% of the total cases in Maryland and Washington, DC. From these samples, we generated 114 complete viral genomes. Analysis of these genomes alongside a subsampling of over 1000 previously published sequences showed that the diversity in this region rivaled global SARS-CoV-2 genetic diversity at that time and that the sequences belong to all of the major globally circulating lineages, suggesting multiple introductions into the region. We also analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and found that clinically severe cases had viral genomes belonging to all major viral lineages. We conclude that efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and the interconnectedness of the region as a whole.

Genomic Diversity of SARS-CoV-2 During Early Introduction into the United States National Capital Region.

BACKGROUND: The early COVID-19 pandemic has been characterized by rapid global spread. In the United States National Capital Region, over 2,000 cases were reported within three weeks of its first detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2, the virus that causes COVID-19, in the region. By correlating genetic information to disease phenotype, we also aimed to gain insight into any correlation between viral genotype and case severity or transmissibility. METHODS: We performed whole genome sequencing of clinical SARS-CoV-2 samples collected in March 2020 by the Johns Hopkins Health System. We analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and the global phylogeny to understand early establishment of the virus within the region. RESULTS: We analyzed 620 samples from the Johns Hopkins Health System collected between March 11-31, 2020, comprising 37.3% of the total cases in Maryland during this period. We selected 143 of these samples for sequencing, generating 114 complete viral genomes. These genomes belong to all five major Nextstrain-defined clades, suggesting multiple introductions into the region and underscoring the diversity of the regional epidemic. We also found that clinically severe cases had genomes belonging to all of these clades. CONCLUSIONS: We established a pipeline for SARS-CoV-2 sequencing within the Johns Hopkins Health system, which enabled us to capture the significant viral diversity present in the region as early as March 2020. Efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and interconnectedness of the region as a whole.

Complete Genome Sequence of an Influenza D Virus Strain Identified in a Pig with Subclinical Infection in the United States.

Influenza D virus was first described in 2011 from a pig with respiratory disease; however, recent evidence indicates that cattle are the major viral reservoir. Here, we describe the genome sequence of the eighth complete swine-origin influenza D virus deposited into GenBank, D/swine/Kentucky/17TOSU1262/2017, which was collected at a 2017 swine exhibition.