Biology subjects

Harris, J. D.

Publications and source records attributed to Harris, J. D..

3 recordsLinked to original sources

bioRxiv2025

Genetic compensation in podocalyxin-like mutants during zebrafish liver development

Hepatic stellate cells (HSCs) are critical for normal liver development and regeneration. Podocalyxin-like (podxl) is highly expressed in zebrafish HSCs, but its role in liver development is not known. Here we report that podxl knockdown using CRISPR/Cas9 (CRISPants) significantly decreased HSC number in zebrafish larvae at different time points and in two independent HSC reporter lines, supporting a role for podxl in HSC development. We generated five podxl mutants, including two mutants lacking the predicted podxl promoter region, and found that none of the mutants recapitulated the knockdown phenotype. Podxl CRISPR/Cas9 injection in mutants lacking the podxl guide RNA cut site did not affect HSC number, supporting the hypothesis that the CRISPant phenotype was specific, requiring intact podxl. Podxl mRNA levels in three podxl mutants were similar to those of wildtype controls. RNA sequencing of podxl mutants and controls showed no significant change in transcript levels of genes with sequence similarity to podxl, but it revealed upregulation of a network of extracellular matrix genes in podxl mutants. These results support a role for podxl in zebrafish liver development and suggest that upregulation of a group of functionally related genes represents the main mechanism of compensation for podxl genomic loss.

developmental biology↗

bioRxiv2024

Accounting for Cellular-Level Variation in Lysis: Implications for Virus-Host Dynamics

Viral impacts on microbial populations depend on interaction phenotypes - including viral traits spanning adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of latent period via one-step growth curves is systematically biased - generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations. ImportanceQuantifying viral traits - including the adsorption rate, burst size, and latent period - is critical to characterize viral infection dynamics and to develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation - the one-step growth curve - to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically under-estimate the mean latent period and, in turn, over-estimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

ecology↗

bioRxiv2019

Cellular co-infection increases the speed and efficiency of influenza A virus production and shapes the interferon response to infection

During viral infection, the numbers of virions infecting individual cells can vary significantly over time and space. The functional consequences of this variation in cellular multiplicity of infection (MOI) remain poorly understood. Here, we rigorously quantify the phenotypic consequences of cellular MOI during influenza A virus (IAV) infection over a single round of replication in terms of cell death rates, viral output kinetics, interferon and antiviral effector gene transcription, and superinfection potential. By statistically fitting mathematical models to our data, we precisely define specific functional forms that quantitatively describe the modulation of these phenotypes by MOI at the single cell level. To determine the generality of these functional forms, we compare two distinct cell lines (MDCK cells and A549 cells), both infected with the H1N1 strain A/Puerto Rico/8/1934 (PR8). We find that a model assuming that infected cell death rates are independent of cellular MOI best fits the experimental data in both cell lines. We further observe that a model in which the rate and efficiency of virus production increase with cellular co-infection best fits our observations in MDCK cells, but not in A549 cells. In A549 cells, we also find that induction of type III interferon, but not type I interferon, is highly dependent on cellular MOI, especially at early timepoints. This finding identifies a role for cellular co-infection in shaping the innate immune response to IAV infection. Finally, we show that higher cellular MOI is associated with more potent superinfection exclusion, thus limiting the total number of virions capable of infecting a cell. Overall, this study suggests that the extent of cellular co-infection by influenza viruses may be a critical determinant of both viral production kinetics and cellular infection outcomes in a host cell type-dependent manner. AUTHOR SUMMARYDuring influenza A virus (IAV) infection, the number of virions to enter individual cells can be highly variable. Cellular co-infection appears to be common and plays an essential role in facilitating reassortment for IAV, yet little is known about how cellular co-infection influences infection outcomes at the cellular level. Here, we combine quantitative in vitro infection experiments with statistical model fitting to precisely define the phenotypic consequences of cellular co-infection in two cell lines. We reveal that cellular co-infection can increase and accelerate the efficiency of IAV production in a cell line-dependent fashion, identifying it as a potential determinant of viral replication kinetics. We also show that induction of type III, but not type I, interferon is highly dependent upon the number of virions that infect a given cell, implicating cellular co-infection as an important determinant of the host innate immune response to infection. Altogether, our findings show that cellular co-infection plays a crucial role in determining infection outcome. The integration of experimental and statistical modeling approaches detailed here represents a significant advance in the quantitative study of influenza virus infection and should aid ongoing efforts focused on the construction of mathematical models of IAV infection.

microbiology↗