Transcriptomic Profiling Reveals Intense Host-Pathogen Dispute Compromising Homeostasis during Acute Rift Valley Fever Virus Infection

Rift Valley fever virus (RVFV) (family Phenuiviridae) can cause severe disease, and outbreaks of this mosquito-borne pathogen pose a significant threat to public and animal health. Yet many molecular aspects of RVFV pathogenesis remain incompletely understood. Natural RVFV infections are acute, characterized by a rapid onset of peak viremia during the first days post-infection, followed by a rapid decline. Although in vitro studies identified a major role of interferon (IFN) responses in counteracting the infection, a comprehensive overview of the specific host factors that play a role in RVFV pathogenesis in vivo is still lacking. Here, the host in vivo transcriptional profiles in the liver and spleen tissues of lambs exposed to RVFV are studied using RNA sequencing (RNA-seq) technology. We validate that IFN-mediated pathways are robustly activated in response to infection. We also link the observed hepatocellular necrosis with severely compromised organ function, which is reflected as a marked downregulation of multiple metabolic enzymes essential for homeostasis. Furthermore, we associate the elevated basal expression of LRP1 in the liver with RVFV tissue tropism. Collectively, the results of this study deepen the knowledge of the in vivo host response during RVFV infection and reveal new insights into the gene regulation networks underlying pathogenesis in a natural host. IMPORTANCE Rift Valley fever virus (RVFV) is a mosquito-transmitted pathogen capable of causing severe disease in animals and humans. Outbreaks of RVFV pose a significant threat to public health and can result in substantial economic losses. Little is known about the molecular basis of RVFV pathogenesis in vivo, particularly in its natural hosts. We employed RNA-seq technology to investigate genome-wide host responses in the liver and spleen of lambs during acute RVFV infection. We show that RVFV infection drastically decreases the expression of metabolic enzymes, which impairs normal liver function. Moreover, we highlight that basal expression levels of the host factor LRP1 may be a determinant of RVFV tissue tropism. This study links the typical pathological phenotype induced by RVFV infection with tissue-specific gene expression profiles, thereby improving our understanding of RVFV pathogenesis.

COVID-19 underreporting and its impact on vaccination strategies

BACKGROUND

Underreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mortality rate. Such estimates can be provided for instance by testing the presence of the virus. However, during an ongoing epidemic, such tests' implementation is a daunting task. This work addresses this issue by presenting a methodology to estimate underreported infections based on approximations of the stable rates of hospitalization and death.

METHODS

We present a novel methodology for the stable rate estimation of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from various distinct communities. These rates are then used to estimate underreported infections on the corresponding areas by making use of reported daily hospitalizations and deaths. The impact of underreporting infections on vaccination strategies is estimated under different disease-transmission scenarios using a Susceptible-Exposed-Infective-Removed-like (SEIR) epidemiological model.

RESULTS

For the considered locations, during the period of study, the estimations suggest that the number of infected individuals could reach 30% of the population of these places, representing, in some cases, more than six times the observed numbers. These results are in close agreement with estimates from independent seroprevalence studies, thus providing a strong validation of the proposed methodology. Moreover, the presence of large numbers of underreported infections can reduce the perceived impact of vaccination strategies in reducing rates of mortality and hospitalization.

CONCLUSIONS

pBy using the proposed methodology and employing a judiciously chosen data analysis implementation, we estimate COVID-19 underreporting from publicly available data. This leads to a powerful way of quantifying underreporting impact on the efficacy of vaccination strategies. As a byproduct, we evaluate the impact of underreporting in the designing of vaccination strategies.