Bats harbor a myriad of viruses and some of these viruses may have spilled over to other species including humans. Spillover events are rare and several factors must align to create the “perfect storm” that would ultimately lead to a spillover. One of these factors is the increased shedding of virus by bats. Several studies have indicated that bats have unique defense mechanisms that allow them to be persistently or latently infected with viruses. Factors leading to an increase in the viral load of persistently infected bats would facilitate shedding of virus. This article reviews the unique nature of bat immune defenses that regulate virus replication and the various molecular mechanisms that play a role in altering the balanced bat–virus relationship.

As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.

Bats are important reservoir hosts for emerging viruses, including coronaviruses that cause diseases in people. Although there have been several studies on the pathogenesis of coronaviruses in humans and surrogate animals, there is little information on the interactions of these viruses with their natural bat hosts. We detected a coronavirus in the intestines of 53/174 hibernating little brown bats (Myotis lucifugus), as well as in the lungs of some of these individuals. Interestingly, the presence of the virus was not accompanied by overt inflammation. Viral RNA amplified from little brown bats in this study appeared to be from two distinct clades. The sequences in clade 1 were very similar to the archived sequence derived from little brown bats and the sequences from clade 2 were more closely related to the archived sequence from big brown bats. This suggests that two closely related coronaviruses may circulate in little brown bats. Sequence variation among coronavirus detected from individual bats suggested that infection occurred prior to hibernation, and that the virus persisted for up to 4 months of hibernation in the laboratory. Based on the sequence of its genome, the coronavirus was placed in the Alphacoronavirus genus, along with some human coronaviruses, bat viruses and the porcine epidemic diarrhoea virus. The detection and identification of an apparently persistent coronavirus in a local bat species creates opportunities to understand the dynamics of coronavirus circulation in bat populations.

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Spillover of viruses from bats to other animals may be associated with increased contact between them, as well as increased shedding of viruses by bats. Here, we tested the prediction that little brown bats (Myotis lucifugus) co-infected with the M. lucifugus coronavirus (Myl-CoV) and with Pseudogymnoascus destructans (Pd), the fungus that causes bat white-nose syndrome (WNS), exhibit different disease severity, viral shedding and molecular responses than bats infected with only Myl-CoV or only P. destructans. We took advantage of the natural persistence of Myl-CoV in bats that were experimentally inoculated with P. destructans in a previous study. Here, we show that the intestines of virus-infected bats that were also infected with fungus contained on average 60-fold more viral RNA than bats with virus alone. Increased viral RNA in the intestines correlated with the severity of fungus-related pathology. Additionally, the intestines of bats infected with fungus exhibited different expression of mitogen-activated protein kinase pathway and cytokine related transcripts, irrespective of viral presence. Levels of coronavirus antibodies were also higher in fungal-infected bats. Our results suggest that the systemic effects of WNS may down-regulate anti-viral responses in bats persistently infected with M. lucifugus coronavirus and increase the potential of virus shedding.

T cell exhaustion is associated with failure to clear chronic infections and malignant cells. Defining the molecular mechanisms of T cell exhaustion and reinvigoration is essential to improving immunotherapeutic modalities. Here we confirmed pervasive phenotypic, functional, and transcriptional differences between memory and exhausted antigen-specific CD8+ T cells in human hepatitis C virus (HCV) infection before and after treatment. After viral cure, phenotypic changes in clonally stable exhausted T cell populations suggested differentiation towards a memory-like profile. However, functionally, the cells showed little improvement and critical transcriptional regulators remained in the exhaustion state. Notably, T cells from chronic HCV infection that were exposed to antigen for less time because of viral escape mutations were functionally and transcriptionally more similar to memory T cells from spontaneously resolved HCV infection. Thus, T cell stimulation duration impacts exhaustion recovery, with antigen removal after long-term exhaustion being insufficient for development of functional T cell memory.

As predicting the trajectory of COVID-19 disease is challenging, machine learning models could assist physicians determine high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) healthcare database, we developed and internally validated models using patients presenting to Emergency Department (ED) between March-April 2020 (n = 1144) and externally validated them using those individuals who encountered ED between May-August 2020 (n = 334). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and procalcitonin levels were important for ICU admission models whereas eGFR <60 ml/min/1.73m2, ventilator use, and potassium levels were the most important variables for predicting mortality. Implementing such models would help in clinical decision-making for future COVID-19 and other infectious disease outbreaks.

coronaviruses that cause severe acute respiratory syndrome (SARS) and Middle east respiratory syndrome (MeRS) are speculated to have originated in bats. the mechanisms by which these viruses are maintained in individuals or populations of reservoir bats remain an enigma. Mathematical models have predicted long-term persistent infection with low levels of periodic shedding as a likely route for virus maintenance and spillover from bats. In this study, we tested the hypothesis that bat cells and MERS coronavirus (coV) can co-exist in vitro. To test our hypothesis, we established a long-term coronavirus infection model of bat cells that are persistently infected with MeRS-coV. We infected cells from Eptesicus fuscus with MERS-CoV and maintained them in culture for at least 126 days. We characterized the persistently infected cells by detecting virus particles, protein and transcripts. Basal levels of type I interferon in the long-term infected bat cells were higher, relative to uninfected cells, and disrupting the interferon response in persistently infected bat cells increased virus replication. By sequencing the whole genome of MERS-CoV from persistently infected bat cells, we identified that bat cells repeatedly selected for viral variants that contained mutations in the viral open reading frame 5 (ORF5) protein. Furthermore, bat cells that were persistently infected with ΔORF5 MERS-CoV were resistant to superinfection by wildtype virus, likely due to reduced levels of the virus receptor, dipeptidyl peptidase 4 (DPP4) and higher basal levels of interferon in these cells. In summary, our study provides evidence for a model of coronavirus persistence in bats, along with the establishment of a unique persistently infected cell culture model to study MeRS-coV-bat interactions.On rare occasions, viruses spill over from reservoir species to other animals, including humans 1 . Establishment of infection in the new host requires viruses to adapt to the efficient use of entry receptors and circumvent innate antiviral defense mechanisms that are unique to each host species 2 . The elaborate mechanisms underlying such changes that govern new virus-host dynamics are not well known. Bats are speculated to be reservoirs of several emerging viruses, including coronaviruses (CoVs) that cause severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) in humans, and porcine epidemic diarrhea (PED) and swine acute diarrhoea syndrome (SADS) in pigs 3-6 . Although bats harbor SARS-and MERS-like coronaviruses, overt signs of disease in bats that are naturally or experimentally infected are often undetectable 7 . In contrast, infections in spillover species, such as humans and pigs lead to diseases with high morbidity and mortality 7-13 .MERS-CoV is an on-going concern as it causes periodic outbreaks in the Middle East with a mortality rate of about thirty-five percent 14,15 . Human-to-human transmission of the virus occurs through aerosol or close contact. Camels are the known reservoirs of MERS-CoV 16,17 and...