Expression dynamics of pregnane X receptor-controlled genes in 3D primary human hepatocyte spheroids.

The pregnane X receptor (PXR) is a ligand-activated nuclear receptor controlling hepatocyte expression of numerous genes. Although expression changes in xenobiotic-metabolizing, lipogenic, gluconeogenic and bile acid synthetic genes have been described after PXR activation, the temporal dynamics of their expression is largely unknown. Recently, 3D spheroids of primary human hepatocytes (PHHs) have been characterized as the most phenotypically relevant hepatocyte model. We used 3D PHHs to assess time-dependent expression profiles of 12 prototypic PXR-controlled genes in the time course of 168 h of rifampicin treatment (1 or 10 µM). We observed a similar bell-shaped time-induction pattern for xenobiotic-handling genes (CYP3A4, CYP2C9, CYP2B6, and MDR1). However, we observed either biphasic profiles for genes involved in endogenous metabolism (FASN, GLUT2, G6PC, PCK1, and CYP7A1), a decrease for SHP or oscillation for PDK4 and PXR. The rifampicin concentration determined the expression profiles for some genes. Moreover, we calculated half-lives of CYP3A4 and CYP2C9 mRNA under induced or basal conditions and we used a mathematical model to describe PXR-mediated regulation of CYP3A4 expression employing 3D PHHs. The study shows the importance of long-term time-expression profiling of PXR target genes in phenotypically stable 3D PHHs and provides insight into PXR function in liver beyond our knowledge from conventional 2D in vitro models.

Mathematical modelling of SARS-CoV-2 infection of human and animal host cells reveals differences in the infection rates and delays in viral particle production by infected cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV -2), a causative agent of COVID-19 disease, poses a significant threat to public health. Since its outbreak in December 2019, Wuhan, China, extensive collection of diverse data from cell culture and animal infections as well as population level data from an ongoing pandemic, has been vital in assessing strategies to battle its spread. Mathematical modelling plays a key role in quantifying determinants that drive virus infection dynamics, especially those relevant for epidemiological investigations and predictions as well as for proposing efficient mitigation strategies. We utilized a simple mathematical model to describe and explain experimental results on viral replication cycle kinetics during SARS-CoV-2 infection of animal and human derived cell lines, green monkey kidney cells, Vero-E6, and human lung epithelium cells, A549-ACE2, respectively. We conducted cell infections using two distinct initial viral concentrations and quantified viral loads over time. We then fitted the model to our experimental data and quantified the viral parameters. We showed that such cellular tropism generates significant differences in the infection rates and incubation times of SARS-CoV-2, that is, the times to the first release of newly synthesised viral progeny by SARS-CoV-2-infected cells. Specifically, the rate at which A549-ACE2 cells were infected by SARS-CoV-2 was 15 times lower than that in the case of Vero-E6 cell infection and the duration of latent phase of A549-ACE2 cells was 1.6 times longer than that of Vero-E6 cells. On the other hand, we found no statistically significant differences in other viral parameters, such as viral production rate or infected cell death rate. Since in vitro infection assays represent the first stage in the development of antiviral treatments against SARS-CoV-2, discrepancies in the viral parameter values across different cell hosts have to be identified and quantified to better target vaccine and antiviral research.

Defective viral genomes as therapeutic interfering particles against flavivirus infection in mammalian and mosquito hosts.

Arthropod-borne viruses pose a major threat to global public health. Thus, innovative strategies for their control and prevention are urgently needed. Here, we exploit the natural capacity of viruses to generate defective viral genomes (DVGs) to their detriment. While DVGs have been described for most viruses, identifying which, if any, can be used as therapeutic agents remains a challenge. We present a combined experimental evolution and computational approach to triage DVG sequence space and pinpoint the fittest deletions, using Zika virus as an arbovirus model. This approach identifies fit DVGs that optimally interfere with wild-type virus infection. We show that the most fit DVGs conserve the open reading frame to maintain the translation of the remaining non-structural proteins, a characteristic that is fundamental across the flavivirus genus. Finally, we demonstrate that the high fitness DVG is antiviral in vivo both in the mammalian host and the mosquito vector, reducing transmission in the latter by up to 90%. Our approach establishes the method to interrogate the DVG fitness landscape, and enables the systematic identification of DVGs that show promise as human therapeutics and vector control strategies to mitigate arbovirus transmission and disease.

Defective viral genomes from chikungunya virus are broad-spectrum antivirals and prevent virus dissemination in mosquitoes.

Defective viral genomes (DVGs) are truncated and/or rearranged viral genomes produced during virus replication. Described in many RNA virus families, some of them have interfering activity on their parental virus and/or strong immunostimulatory potential, and are being considered in antiviral approaches. Chikungunya virus (CHIKV) is an alphavirus transmitted by Aedes spp. that infected millions of humans in the last 15 years. Here, we describe the DVGs arising during CHIKV infection in vitro in mammalian and mosquito cells, and in vivo in experimentally infected Aedes aegypti mosquitoes. We combined experimental and computational approaches to select DVG candidates most likely to have inhibitory activity and showed that, indeed, they strongly interfere with CHIKV replication both in mammalian and mosquito cells. We further demonstrated that some DVGs present broad-spectrum activity, inhibiting several CHIKV strains and other alphaviruses. Finally, we showed that pre-treating Aedes aegypti with DVGs prevented viral dissemination in vivo.

Modelling Degradation and Replication Kinetics of the Zika Virus In Vitro Infection.

Mathematical models of in vitro viral kinetics help us understand and quantify the main determinants underlying the virus-host cell interactions. We aimed to provide a numerical characterization of the Zika virus (ZIKV) in vitro infection kinetics, an arthropod-borne emerging virus that has gained public recognition due to its association with microcephaly in newborns. The mathematical model of in vitro viral infection typically assumes that degradation of extracellular infectious virus proceeds in an exponential manner, that is, each viral particle has the same probability of losing infectivity at any given time. We incubated ZIKV stock in the cell culture media and sampled with high frequency for quantification over the course of 96 h. The data showed a delay in the virus degradation in the first 24 h followed by a decline, which could not be captured by the model with exponentially distributed decay time of infectious virus. Thus, we proposed a model, in which inactivation of infectious ZIKV is gamma distributed and fit the model to the temporal measurements of infectious virus remaining in the media. The model was able to reproduce the data well and yielded the decay time of infectious ZIKV to be 40 h. We studied the in vitro ZIKV infection kinetics by conducting cell infection at two distinct multiplicity of infection and measuring viral loads over time. We fit the mathematical model of in vitro viral infection with gamma distributed degradation time of infectious virus to the viral growth data and identified the timespans and rates involved within the ZIKV-host cell interplay. Our mathematical analysis combined with the data provides a well-described example of non-exponential viral decay dynamics and presents numerical characterization of in vitro infection with ZIKV.

Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay.

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4-5 log10 decline in virus within 1 day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.

Density-dependent selection on mate search and evolution of Allee effects.

Sexually reproducing organisms require males and females to find each other. Increased difficulty of females finding mates as male density declines is the most frequently reported mechanism of Allee effects in animals. Evolving more effective mate search may alleviate Allee effects, but may depend on density regimes a population experiences. In particular, high-density populations may evolve mechanisms that induce Allee effects which become detrimental when populations are reduced and maintained at a low density. We develop an individual-based, eco-genetic model to study how mating systems and fitness trade-offs interact with changes in population density to drive evolution of the rate at which males or females search for mates. Finite mate search rate triggers Allee effects in our model and we explore how these Allee effects respond to such evolution. We allow a population to adapt to several population density regimes and examine whether high-density populations are likely to reverse adaptations attained at low densities. We find density-dependent selection in most of scenarios, leading to search rates that result in lower Allee thresholds in populations kept at lower densities. This mainly occurs when fecundity costs are imposed on mate search, and provides an explanation for why Allee effects are often observed in anthropogenically rare species. Optimizing selection, where the attained trait value minimizes the Allee threshold independent of population density, depended on the trade-off between search and survival, combined with monogamy when females were searching. Other scenarios led to runaway selection on the mate search rate, including evolutionary suicide. Trade-offs involved in mate search may thus be crucial to determining how density influences the evolution of Allee effects. Previous studies did not examine evolution of a trait related to the strength of Allee effects under density variation. We emphasize the crucial role that mating systems, fitness trade-offs and the evolving sex have in determining the density threshold for population persistence, in particular since evolution need not always take the Allee threshold to its minimum value.

Evolution of mate-finding Allee effect in prey.

The search for mates is often accompanied with conspicuous behaviour or morphology that can be exploited by predators. Here we explore the evolutionary consequences of a trade-off that arises naturally between mate acquisition and risk of predation and study evolution of the rate at which male prey search for mates in a population subject to a mate-finding Allee effect and exposed to either generalist or specialist predators. Since we show that the mate search rate determines the strength of the mate-finding Allee effect, we can alternatively view this as evolution of the mate-finding Allee effect in prey. We contrast two different life histories and find that, predominantly, male prey either evolve towards the maximal mate search rate yielding the weakest possible mate-finding Allee effect (thus showing no adaptive response in mating behaviour to predation risk) or evolutionary bi-stability occurs. In the latter case, males evolve a relatively low mate search rate (hence a relatively strong mate-finding Allee effect, interpreted as an adaptive response of male prey to predation) when initially slow or the maximal mate search rate when initially fast. Disruptive selection does not occur in populations exposed to generalist predators but is possible when predators are specialists. The dimorphic phase, in which fast and conspicuous male prey coexist with slow and cryptic ones, is however but a transient in evolutionary dynamics as one branch goes extinct while the other evolves towards the maximal mate search rate.

Evolution of early male-killing in horizontally transmitted parasites.

Early male-killing (MK) bacteria are vertically transmitted reproductive parasites which kill male offspring that inherit them. Whereas their incidence is well documented, characteristics allowing originally non-MK bacteria to gradually evolve MK ability remain unclear. We show that horizontal transmission is a mechanism enabling vertically transmitted bacteria to evolve fully efficient MK under a wide range of host and parasite characteristics, especially when the efficacy of vertical transmission is high. We also show that an almost 100% vertically transmitted and 100% effective male-killer may evolve from a purely horizontally transmitted non-MK ancestor, and that a 100% efficient male-killer can form a stable coexistence only with a non-MK bacterial strain. Our findings are in line with the empirical evidence on current MK bacteria, explain their high efficacy in killing infected male embryos and their variability within and across insect taxa, and suggest that they may have evolved independently in phylogenetically distinct species.