An investigation of rhinovirus infection on cellular uptake of poly (glycerol-adipate) nanoparticles.

Viral infections represent 44% of newly emerging infections, and as is shown by the COVID-19 outbreak constitute a major risk to human health and wellbeing. Although there are many efficient antiviral agents, they still have drawbacks such as development of virus resistance and accumulation within off-target organs. Encapsulation of antiviral agents into nanoparticles (NPs) has been shown to improve bioavailability, control release, and reduce side effects. However, there is little quantitative understanding of how the uptake of NPs into virally infected cells compares to uninfected cells. In this work, the uptake of fluorescently labeled polymer NPs was investigated in several models of rhinovirus (RV) infected cells. Different multiplicities of RV infections (MOI) and timings of NPs uptake were also investigated. In some cases, RV infection resulted in a significant increase of NPs uptake, but this was not universally noted. For HeLa cells, RV-A16 and RV-A01 infection elevated NPs uptake upon increasing the incubation time, whereas at later timepoints (6 h) a reduced uptake was noted with RV-A01 infection (owing to decreased cell viability). Beas-2B cells exhibited more complex trends: decreases in NPs uptake (cf. uninfected cells) were observed at short incubation times following RV-A01 and RV-A16 infection. At later incubation times (4 h), we found a marked decrease of NPs uptake for RV-A01 infected cells but an increase in uptake with RV-A16 infected cells. Where increases in NPs uptake were found, they were very modest compared to results previously reported for a hepatitis C/ Huh7.5 cell line model. An increase in RV dose (MOI) was not associated with any notable change of NPs uptake. We argue that the diverse endocytic pathways among the different cell lines, together with changes in virus nature, size, and entry mechanism are responsible for these differences. These findings suggest that NPs entry into virally infected cells is a complex process, and further work is required to unravel the different factors which govern this. Undertaking this additional research will be crucial to develop potent nanomedicines for the delivery of antiviral agents.

Breath analysis for detection of viral infection, the current position of the field.

The COVID-19 pandemic has highlighted the importance of rapid, cost effective, accurate, and non-invasive testing for viral infections. Volatile compounds (VCs) have been suggested for several decades as fulfilling these criteria. However currently very little work has been done in trying to diagnose viral infections using VCs. Much of the work carried out to date involves the differentiation of bacterial and viral sources of infection and often the detection of bacterial and viral co-infection. However, this has usually been done in vitro and very little work has involved the use of human participants. Viruses hijack the host cell metabolism and do not produce their own metabolites so identifying virus specific VCs is at best a challenging task. However, there are proteins and lipids that are potential candidates as markers of viral infection. The current understanding is that host cell glycolysis is upregulated under viral infection to increase the available energy for viral replication. There is some evidence that viral infection leads to the increase of production of fatty acids, alkanes, and alkanes related products. For instance, 2,3-butandione, aldehydes, 2,8-dimethyl-undecane and n-propyl acetate have all been correlated with viral infection. Currently, the literature points to markers of oxidative stress (e.g. nitric oxide, aldehydes etc) being the most useful in the determination of viral infection. The issue, however, is that there are also many other conditions that can lead to oxidative stress markers being produced. In this review a range of (mainly mass spectrometric) methods are discussed for viral detection in breath, including breath condensate. Currently MALDI-ToF-MS is likely to be the preferred method for the identification of viral strains and variants of those strains, however it is limited by its need for the viral strains to have been sequenced and logged in a database.