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Mosquito-borne pathogens plague much of the world, yet rapid and simple diagnosis is not available for many affected patients. Using a custom stereolithography 3D printer, we created microfluidic devices with affinity monoliths that could retain, noncovalently attach a fluorescent tag, and detect oligonucleotide and viral RNA. We optimized the fluorescent binding and sample load times using an oligonucleotide sequence from chikungunya virus (CHIKV). We also tested the specificity of CHIKV capture relative to genetically similar Sindbis virus. Moreover, viral RNA from both viruses was flowed through capture columns to study the efficiency and specificity of the column for viral CHIKV. We detected ~107 loaded viral genome copies, which was similar to levels in clinical samples during acute infection. These results show considerable promise for development of this platform into a rapid mosquito-borne viral pathogen detection system. [ABSTRACT FROM AUTHOR]

The novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells. Well-known immunoregulatory genes including CSF2, IL32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were upregulated. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) and eukaryotic initiation factor 4 (eIF4b). We also identified a viral sequence variant with a statistically significant skew associated with age of infection, that may contribute to intracellular host–pathogen interactions. These findings can help identify host mechanisms that can be targeted by prophylactics and/or therapeutics to reduce the severity of COVID-19. Ferrarini & Lal et al. developed a novel bioinformatic pipeline to explore how SARS-CoV-2 interacts with human respiratory cells using public available host gene expression and viral genome sequence data. Several human genes and proteins were predicted to play a role in the viral life cycle and the host response to SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Infection with influenza can be aggravated by bacterial co-infections, which often results in disease exacerbation. The effects of influenza infection on the upper respiratory tract (URT) microbiome are largely unknown. Here, we report a longitudinal study to assess the temporal dynamics of the URT microbiomes of uninfected and influenza virus-infected humans and ferrets. Uninfected human patients and ferret URT microbiomes have stable healthy ecostate communities both within and between individuals. In contrast, infected patients and ferrets exhibit large changes in bacterial community composition over time and between individuals. The unhealthy ecostates of infected individuals progress towards the healthy ecostate, coinciding with viral clearance and recovery. Pseudomonadales associate statistically with the disturbed microbiomes of infected individuals. The dynamic and resilient microbiome during influenza virus infection in multiple hosts provides a compelling rationale for the maintenance of the microbiome homeostasis as a potential therapeutic target to prevent IAV associated bacterial co-infections. Influenza A virus (IAV) infection can be exacerbated by bacterial co-infections but the effect of IAV on the upper respiratory tract (URT) microbiome remains unclear. Here, the authors compare the dynamics of the UTR microbiome in IAV-infected ferrets and humans, finding similar trends at the ecosystem and individual taxon level in both hosts. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

The recent emergence of Zika virus (ZIKV) has been concentrated in the Caribbean, Southeastern United States, and South- and Central America; resulting in travel-based cases being reported around the globe. As multi-disciplinary collaborations are combatting the ZIKV outbreak, the need to validate the sequence of existing strains has become apparent. Here, we report high-quality sequence data for multiple ZIKV strains made publicly available through the National Institutes of Health- (NIH) funded biorepository, BEI Resources (www.beiresources.org). Next-generation sequencing, 3′ rapid amplification of cDNA ends (RACE), and viral genome annotation pipelines generated GenBank sequence records for 16 BEI Resources strains. Minor variants, consensus mutations, and consensus insertions/deletions were identified within the viral stocks using next-generation sequencing (NGS) and consensus changes were confirmed with Sanger sequencing. Bioinformatics analyses of the sequencing results confirm that the virus stocks available to the scientific research community through BEI Resources adequately represent the viral population diversity of ZIKV. [ABSTRACT FROM AUTHOR]