Biology subjects

Conway, J. M.

Publications and source records attributed to Conway, J. M..

22 records · Page 2Linked to original sources

bioRxiv2023

Investigating Alternative Models of Acute HIV infection

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the dynamics to acute HIV infection may provide insights into the mechanisms of early viral control with potential implications for vaccine design. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV. For example, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models--1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)--to investigate acute infection dynamics in 43 people tested very early after HIV exposure. We find that all models explain the data, but different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious model, viral peaks are better explained by a model allowing for cellular MOI. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Thus depending on the aspect of interest, a corresponding model should be employed.

microbiology↗

bioRxiv2020

Models of SIV rebound after treatment interruption that involve multiple reactivation events

In order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate, and a 11% decrease of the viral growth rate. We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event. Author SummaryHIV-1 persists during suppressive antiretroviral therapy (ART) due to a reservoir of latently infected cells. When ART is stopped, HIV generally rebounds within a few weeks. However, there is a small fraction of patients that do not rebound over a period of months or years. A variety of treatments are being tested for their ability to reduce the size of the latent reservoir, to induce effective immune responses against the virus, or to prevent or prolong the time to viral rebound after ART interruption. These novel treatments are typically first tested in SIV infected macaques, and the efficacy of the treatment assessed by interrupting ART and measuring the time to viral rebound. Here, we develop and test a mathematical and statistical model that describes the process of viral rebound. The model can be used for statistical inference of the efficacy of newly developed treatments. Importantly, the model takes into account that multiple recrudescence events can precede rebound. We test the model using data from early treated SIV infected macaques.

immunology↗

bioRxiv2019

Root development is maintained by specific bacteria-bacteria interactions within a complex microbiome

Plants grow within a complex web of species interacting with each other and with the plant. Many of these interactions are governed by a wide repertoire of chemical signals, and the resulting chemical landscape of the rhizosphere can strongly affect root health and development. To understand how microbe-microbe interactions influence root development in Arabidopsis, we established a model system for plant-microbe-microbe-environment interactions. We inoculated seedlings with a 185-member bacterial synthetic community (SynCom), manipulated the abiotic environment, and measured bacterial colonization of the plant. This enabled classification of the SynCom into four modules of co-occurring strains. We deconstructed the SynCom based on these modules, identifying microbe-microbe interactions that determine root phenotypes. These interactions primarily involve a single bacterial genus, Variovorax, which completely reverts severe root growth inhibition (RGI) induced by a wide diversity of bacterial strains as well as by the entire 185-member community. We demonstrate that Variovorax manipulate plant hormone levels to balance this ecologically realistic root communitys effects on root development. We identify a novel auxin degradation operon in the Variovorax genome that is necessary and sufficient for RGI reversion. Therefore, metabolic signal interference shapes bacteria-plant communication networks and is essential for maintaining the roots developmental program. Optimizing the feedbacks that shape chemical interaction networks in the rhizosphere provides a promising new ecological strategy towards the development of more resilient and productive crops.

microbiology↗

bioRxiv2018

Modeling the impact of ex-nido transmitted parasites on ant colony dynamics

Infectious disease outbreaks are a common constraint of group living organisms. Ants (Hymenoptera: Formicidae) live in large, dense colonies and are host to a diverse range of parasites and pathogens, facilitating the possibility of epidemic-induced collapse. However, the majority of parasites infecting ants require a period of development outside of the nest before they can transmit to their next ant host ( ex-nido transmission) and the impact of these parasites on colony dynamics is unknown. Here we develop a mathematical model to assess ant colony dynamics in the presence of such parasites. We find that under field-realistic model conditions, such parasites are unlikely to cause the epidemic collapse of mature ant colonies, unless colony birth rate drops below 0.2328 ants/day. The preponderance of ex-nido transmitting parasites infecting ants and their limited epidemiological impact on colony dynamics may partly explain why collapsed ant colonies are rarely, if ever, observed in natural populations.

ecology↗