Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus

Abstract

A B S T R A C TAssessing the risk of infection from emerging viruses or of existing viruses jumping the species barrier into novel hosts is limited by the lack of dose response data. The initial stages of the infection of a host by a virus involve a series of specific contact interactions between molecules in the host and on the virus surface. The strength of the interaction is quantified in the literature by the dissociation constant (K d ) which is determined experimentally and is specific for a given virus molecule/host molecule combination. Here, two stages of the initial infection process of host intestinal cells are modelled, namely escape of the virus in the oral challenge dose from the innate host defenses (e.g. mucin proteins in mucus) and the subsequent binding of any surviving virus to receptor molecules on the surface of the host epithelial cells. The strength of virus binding to host cells and to mucins may be quantified by the association constants, K a and K mucin , respectively. Here, a mechanistic dose-response model for the probability of infection of a host by a given virus dose is constructed using K a and K mucin which may be derived from published K d values taking into account the number of specific molecular interactions. It is shown that the effectiveness of the mucus barrier is determined not only by the amount of mucin but also by the magnitude of K mucin . At very high K mucin values, slight excesses of mucin over virus are sufficient to remove all the virus according to the model. At lower K mucin values, high numbers of virus may escape even with large excesses of mucin. The output from the mechanistic model is the probability (p 1 ) of infection by a single virion which is the parameter used in conventional dose-response models to predict the risk of infection of the host from the ingested dose. It is shown here how differences in K a (due to molecular differences in an emerging virus strain or new host) affect p 1 , and how these differences in K a may be quantified in terms of two thermodynamic parameters, namely enthalpy and entropy. This provides the theoretical link between sequencing data and risk of infection. Lack of data on entropy is a limitation at present and may also affect our interpretation of K d in terms of infectivity. It is concluded that thermodynamic approaches have a major contribution to make in developing dose-response models for emerging viruses.

Towards a thermodynamic mechanistic model for the effect of temperature on arthropod vector competence for transmission of arboviruses

“…The magnitude of ΔG a_receptor is related to the changes in enthalpy (ΔH a_receptor ) and entropy (ΔS a_receptor ) in the GP and Cr proteins on molecular interaction during binding according to Equation 9 (Gale, 2017;. The ΔS a_receptor term may be broken down into a number of components which act additively (Bostrom et al, 2011;Mammen et al, 1998;Caro et al, 2017;Gale, 2018) according to Equation 10. These components are the change in entropy of the water solvent molecules on the GP and Cr surfaces (ΔS solvent ) and the changes in the internal conformational freedoms within the GP and Cr proteins as represented by S conf GP and S conf Cr respectively.…”

“…Furthermore changing these parameters in the model e.g. on the basis of molecular structure data (Gale, 2018), could aid the interpretation and understanding of experimental vector competence data (Wittmann et al, 2002) in terms of molecular mechanisms, highlighting the direction of further experiments at the molecular level. For example, it is shown here that the thermodynamic nature of virus binding could determine whether significant transmission occurs at low temperatures or whether instead there is greatly diminished transmission.…”

“…This is important for assessing the seasonal risks of BTV transmission in northern Europe, for example. The model here could also be used to develop a dose-response for infection of an arthropod at constant temperature although the probability of getting through the pathogen-recognition receptors of the innate immune system in the arthropod midgut would need to be modelled, perhaps using the thermodynamic approach developed for virus binding to mucins in the (Gale, 2018). Although not discussed here, this could provide useful information for arbovirus transmission in an arthropod at low challenge doses and hence separate "dead end" vertebrate host species from potential reservoir vertebrate host species on the basis of measured viral titres in their blood and hence in the blood-meal ingested by an individual arthropod.…”

“…The model could be used to model transmission efficiency in terms of two independent variables, namely arbovirus dose and vector temperature. A unique advantage of the model developed here is that the predicted differences in virus infectivity and vector competence are due to differences in the input parameters ΔH a_virus , ΔS a_virus , E A and p complete283 which in the arbovirus/arthropod infection process will be related to the gene sequences of specific viral and host proteins (see Gale, 2018). Thus this thermodynamic treatment may provide the link to relate genetic sequence data to virus infectivity and vector competence which is a major goal of current research.…”

“…In entropy-assisted binding a small positive ΔS a_virus term adds to a small negative ΔH a_virus term in maximizing K a ( Table 2). The magnitude of K a together with the virus dose (∼V free ) in the midgut dictate the number (C.V T ) of arthropod midgut cells with bound virus at a given temperature (Gale, 2018) according to Equation 5 and Equation 6. In addition to the strength of the binding of GP to Cr as represented by the thermodynamic dissociation constant, K d_receptor (Xiong et al, 2013), two other factors affect K a , namely the number, n, of GP/Cr contacts made on binding of the virus to a cell and the unknown entropy term ΔS a_immob (see above).…”