More than efficacy revealed by single-cell analysis of antiviral therapeutics

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.

Differential inhibition of intra- and inter-molecular protease cleavages by antiviral compounds

IMPORTANCE

Most protease-targeted antiviral development evaluates the ability of small molecules to inhibit the cleavage of artificial substrates. However, before they can cleave any other substrates, viral proteases need to cleave themselves out of the viral polyprotein in which they have been translated. This can occur either intra- or inter-molecularly. Whether this process occurs intra- or inter-molecularly has implications for the potential for precursors to accumulate and for the effectiveness of antiviral drugs. We argue that evaluating candidate antivirals for their ability to block these cleavages is vital to drug development because the buildup of uncleaved precursors can be inhibitory to the virus and potentially suppress the selection of drug-resistant variants.

Advanced and Smart Textiles during and after the COVID-19 Pandemic: Issues, Challenges, and Innovations

The COVID-19 pandemic has hugely affected the textile and apparel industry. Besides the negative impact due to supply chain disruptions, drop in demand, liquidity problems, and overstocking, this pandemic was found to be a window of opportunity since it accelerated the ongoing digitalization trends and the use of functional materials in the textile industry. This review paper covers the development of smart and advanced textiles that emerged as a response to the outbreak of SARS-CoV-2. We extensively cover the advancements in developing smart textiles that enable monitoring and sensing through electrospun nanofibers and nanogenerators. Additionally, we focus on improving medical textiles mainly through enhanced antiviral capabilities, which play a crucial role in pandemic prevention, protection, and control. We summarize the challenges that arise from personal protective equipment (PPE) disposal and finally give an overview of new smart textile-based products that emerged in the markets related to the control and spread reduction of SARS-CoV-2.

Viro-fluidics: Real-time analysis of virus production kinetics at the single-cell level

Real-time visualization and quantification of viruses released by a cell are crucial to further decipher infection processes. Kinetics studies at the single-cell level will circumvent the limitations of bulk assays with asynchronous virus replication. We have implemented a "viro-fluidic" method, which combines microfluidics and virology at single-cell and single-virus resolutions. As an experimental model, we used standard cell lines producing fluorescent HIV-like particles (VLPs). First, to scale the strategy to the single-cell level, we validated a sensitive flow virometry system to detect VLPs in low concentration samples (≥10⁴ VLPs/mL). Then, this system was coupled to a single-cell trapping device to monitor in real-time the VLPs released, one at a time, from single cells under cell culture conditions. Our results revealed an average production rate of 50 VLPs/h/cell similar to the rate estimated for the same cells grown in population. Thus, the virus-producing capacities of the trapped cells were preserved and its real-time monitoring was accurate. Moreover, single-cell analysis revealed a release of VLPs with stochastic bursts with typical time intervals of few minutes, revealing the existence of limiting step(s) in the virus biogenesis process. Our tools can be applied to other pathogens or to extracellular vesicles to elucidate the dissemination mechanisms of these biological nanoparticles.

Dengue virus is sensitive to inhibition prior to productive replication

Uncovering vulnerable steps in the life cycle of viruses supports the rational design of antiviral treatments. However, information on viral replication dynamics obtained from traditional bulk assays with host cell populations is inherently limited as the data represent averages over a multitude of unsynchronized replication cycles. Here, we use time-lapse imaging of virus replication in thousands of single cells, combined with computational inference, to identify rate-limiting steps for dengue virus (DENV), a widespread human pathogen. Comparing wild-type DENV with a vaccine candidate mutant, we show that the viral spread in the mutant is greatly attenuated by delayed onset of productive replication, whereas wild-type and mutant virus have identical replication rates. Single-cell analysis done after applying the broad-spectrum antiviral drug, ribavirin, at clinically relevant concentrations revealed the same mechanism of attenuating viral spread. We conclude that the initial steps of infection, rather than the rate of established replication, are quantitatively limiting DENV spread.

Single-cell analysis for the study of viral inhibitors

Stochastic outcomes of viral infections are attributed in large part to multiple layers of intrinsic and extrinsic heterogeneity that exist within a population of cells and viruses. Traditional methods in virology often lack the ability to demonstrate cell-to-cell variability in response to the invasion of viruses, and to decipher the sources of heterogeneities that are reflected in the variable infection dynamics. To overcome this challenge, the field of single-cell virology emerged less than a decade ago, enabling researchers to reveal the behavior of single, isolated, infected cells that has been masked in population-based assays. The use of microfluidics in single-cell virology, in particular, has resulted in the development of high-throughput devices that are capable of capturing, isolating, and monitoring single infected cells over the duration of an infection. Results from the studies of viral infection dynamics presented in this chapter indicate how single-cell data provide a more accurate prediction of the start time, replication rate, duration, and yield of infection when compared to population-based data. Additionally, single-cell analysis reveals striking differences between genetically distinct viruses that are almost indistinguishable in population methods. Importantly, both the efficacy and distinct mechanisms of action of antiviral compounds can be elucidated by using single-cell analysis.

Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data

Viruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to replicate. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to time-lapse imaging data from high-throughput single-cell poliovirus infection experiments. The model's mechanistic parameters provide estimates of several aspects associated with the virus's intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.

Single-virus genomics and beyond

Viruses are extremely diverse and modulate important biological and ecological processes globally. However, much of viral diversity remains uncultured and yet to be discovered. Several powerful culture-independent tools, in particular metagenomics, have substantially advanced virus discovery. Among those tools is single-virus genomics, which yields sequenced reference genomes from individual sorted virus particles without the need for cultivation. This new method complements virus culturing and metagenomic approaches and its advantages include targeted investigation of specific virus groups and investigation of genomic microdiversity within viral populations. In this Review, we provide a brief history of single-virus genomics, outline how this emergent method has facilitated advances in virus ecology and discuss its current limitations and future potential. Finally, we address how this method may synergistically intersect with other single-virus and single-cell approaches.

Microfluidics in Single-Cell Virology: Technologies and Applications

Microfluidics has proven to be a powerful tool for probing biology at the single-cell level. However, it is only in the past 5 years that single-cell microfluidics has been used in the field of virology. An array of strategies based on microwells, microvalves, and droplets is now available for tracking viral infection dynamics, identifying cell subpopulations with particular phenotypes, as well as high-throughput screening. The insights into the virus-host interactions gained at the single-cell level are unprecedented and usually inaccessible by population-based experiments. Therefore, single-cell microfluidics, which opens new avenues for mechanism elucidation and development of antiviral therapeutics, would be a valuable tool for the study of viral pathogenesis.