The Coordinating Research on Emerging Arboviral Threats Encompassing the Neotropics (CREATE-NEO)

Exploration of the Pathogenicity and Inflammatory Mechanism of Wuxiang Virus: A Newly Identified Phlebovirus

WUXV is phlebovirus transmitted by sandflies, which can cause death in suckling mice and infect humans and chickens. However, little is known about the pathogenicity and pathogenic mechanisms of animals. Therefore, BALB/c WT mice and BALB/c nude mice were infected with WUXV. No fatal diseases occurred except for weight loss among the BALB/c WT mice, while BALB/c nude mice observed significant neurological symptoms and death. The virus nucleic acid was detected in the organs and blood of both mice. The blood routine showed a significant increase in RDW, and IgM, IgG, and neutralizing antibodies were detected in the serum of the BALB/c WT mice, which were significantly higher than those of BALB/c nude mice. The inflammatory factors in tissues, and pathological section results, indicate that WUXV caused severe inflammatory reactions. In addition, we found that WUXV activates the inflammatory pathway by activating TLR5 and TLR9. Inhibition of TLR5 and TLR9 can eliminate the activation of NF-κb and downstream inflammatory factors. In summary, we systematically studied the pathogenicity and pathogenesis of WUXV infection in mice with different immune states, and found that TLR5 in mammalian cells can serve as RNA sensor, expanding our understanding of the virus.

A cell atlas of the adult female Aedes aegypti midgut revealed by single-cell RNA sequencing

Aedes aegypti is a primary vector for transmitting various arboviruses, including Yellow fever, dengue and Zika virus. The mosquito midgut is the principal organ for blood meal digestion, nutrient absorption and the initial site of arbovirus infection. Although a previous study delineated midgut's transcriptome of Ae. aegypti at the single-nucleus resolution, there still lacks an established protocol for isolating and RNA sequencing of single cells of Ae. aegypti midgut, which is required for investigating arbovirus-midgut interaction at the single-cell level. Here, we established an atlas of the midgut cells for Ae. aegypti by single-cell RNA sequencing. We annotated the cell clusters including intestinal stem cells/enteroblasts (ISC/EB), cardia cells (Cardia), enterocytes (EC, EC-like), enteroendocrine cells (EE), visceral muscle (VM), fat body cells (FBC) and hemocyte cells (HC). This study will provide a foundation for further studies of arbovirus infection in mosquito midgut at the single-cell level.

A review of emerging health threats from zoonotic New World mammarenaviruses

Despite repeated spillover transmission and their potential to cause significant morbidity and mortality in human hosts, the New World mammarenaviruses remain largely understudied. These viruses are endemic to South America, with animal reservoir hosts covering large geographic areas and whose transmission ecology and spillover potential are driven in part by land use change and agriculture that put humans in regular contact with zoonotic hosts.We compiled published studies about Guanarito virus, Junin virus, Machupo virus, Chapare virus, Sabia virus, and Lymphocytic Choriomeningitis virus to review the state of knowledge about the viral hemorrhagic fevers caused by New World mammarenaviruses. We summarize what is known about rodent reservoirs, the conditions of spillover transmission for each of these pathogens, and the characteristics of human populations at greatest risk for hemorrhagic fever diseases. We also review the implications of repeated outbreaks and biosecurity concerns where these diseases are endemic, and steps that countries can take to strengthen surveillance and increase capacity of local healthcare systems. While there are unique risks posed by each of these six viruses, their ecological and epidemiological similarities suggest common steps to mitigate spillover transmission and better contain future outbreaks.

HostNet: improved sequence representation in deep neural networks for virus-host prediction

BACKGROUND

The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand.

RESULTS

To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability.

CONCLUSION

HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.