MR Elastography Analysis of Glioma Stiffness and IDH1-Mutation Status.

BACKGROUND AND PURPOSE: Our aim was to noninvasively evaluate gliomas with MR elastography to characterize the relationship of tumor stiffness with tumor grade and mutations in the isocitrate dehydrogenase 1 (IDH1) gene. MATERIALS AND METHODS: Tumor stiffness properties were prospectively quantified in 18 patients (mean age, 42 years; 6 women) with histologically proved gliomas using MR elastography from 2014 to 2016. Images were acquired on a 3T MR imaging unit with a vibration frequency of 60 Hz. Tumor stiffness was compared with unaffected contralateral white matter, across tumor grade, and by IDH1-mutation status. The performance of the use of tumor stiffness to predict tumor grade and IDH1 mutation was evaluated with the Wilcoxon rank sum, 1-way ANOVA, and Tukey-Kramer tests. RESULTS: Gliomas were softer than healthy brain parenchyma, 2.2 kPa compared with 3.3 kPa (P < .001), with grade IV tumors softer than grade II. Tumors with an IDH1 mutation were significantly stiffer than those with wild type IDH1, 2.5 kPa versus 1.6 kPa, respectively (P = .007). CONCLUSIONS: MR elastography demonstrated that not only were gliomas softer than normal brain but the degree of softening was directly correlated with tumor grade and IDH1-mutation status. Noninvasive determination of tumor grade and IDH1 mutation may result in improved stratification of patients for different treatment options and the evaluation of novel therapeutics. This work reports on the emerging field of "mechanogenomics": the identification of genetic features such as IDH1 mutation using intrinsic biomechanical information.

Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America.

Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g., those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: (1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, (2) understanding how the structure of different poultry sectors impacts within-flock transmission, (3) determining mechanisms and rates of between-farm spread, and (4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.

Effects of influenza antivirals on individual and population immunity over many epidemic waves.

Antivirals are an important defence against novel strains of influenza. However, the impact of widespread drug usage on strain circulation across multiple epidemic waves - via their impact on host immunity - is unknown despite antivirals having the likelihood of extensive use during a pandemic. To explore how drug usage by individuals affects population strain dynamics, we embedded a two-strain model of within-host dynamics within an epidemic model. We found that when 40% of hosts took drugs early during the infectious period, transmission was reduced by 30% and average levels of immunity by 2·9-fold (comparable to antibody concentrations), relative to 14% and 1·5-fold reductions when drugs were taken late. The novel strain was more successful relative to the resident strain when drugs were not taken, and an intermediate level of drug coverage minimized incidence in subsequent waves. We discuss how drug regimens, coverage and R 0 could impact pandemic preparedness.

Quantification of heterosubtypic immunity between avian influenza subtypes H3N8 and H4N6 in multiple avian host species.

Low-pathogenicity avian influenza virus (LPAIV) can lead to epizootics that cause economic losses in poultry or the emergence of human-infectious strains. LPAIVs experience a complex immunity landscape as they are endemic in numerous host species, and many antigenically distinct strains co-circulate. Prevention and control of emergence of detrimental strains requires an understanding of infection/transmission characteristics of the various subtypes in different hosts, including interactions between subtypes. In order to develop analytical frameworks for examining control efficacy, quantification of heterosubtypic immunity interactions is fundamental. However, these data are scarce, especially for wild avian subtypes in natural hosts. Consequently, in this study, three host species (mallards, quail and pheasants) were infected with two LPAIV subtypes isolated from wild birds: H3N8 and H4N6. The recovered hosts were also reinfected with the alternate subtype to measure the effects of heterosubtypic immunity. Oropharyngeal and cloacal swabs were collected and viral RNA load was quantified by real-time RT-PCR. For secondary infections in recovered hosts, peak viral load was up to four orders of magnitude lower and shedding length was up to 4 days shorter. However, both the magnitude and presence of heterosubtypic immunity varied across specific host species/subtype combinations. Using a mathematical model of virus replication, the variation in virus replication dynamics due to host individuals was quantified. It was found that accounting for individual heterogeneity is important for drawing accurate conclusions about treatment effects. These results are relevant for developing epidemiological models to inform control practices and for analysing virus replication data.

Phenotypic differences in viral immune escape explained by linking within-host dynamics to host-population immunity.

Viruses that do not cause life-long immunity persist by evolving rapidly in response to prevailing host immunity. The immune-escape mutants emerge frequently, displacing or co-circulating with native strains even though mutations conferring immune evasion are often detrimental to viral replication. The epidemiological dynamics of immune-escape in acute-infection viruses with high transmissibility have been interpreted mainly through immunity dynamics at the host population level, despite the fact that immune-escape evolution involves dynamical processes that feedback across the within- and between-host scales. To address this gap, we use a nested model of within- and between-host infection dynamics to examine how the interaction of viral replication rate and cross-immunity imprint host population immunity, which in turn determines viral immune escape. Our explicit consideration of direct and immune-mediated competitive interactions between strains within-hosts revealed three insights pertaining to risk and control of viral immune-escape: (1) replication rate and immune-stimulation deficiencies (i.e., original antigenic sin) act synergistically to increase immune escape, (2) immune-escape mutants with replication deficiencies relative to their wildtype progenitor are most successful under moderate cross-immunity and frequent re-infections, and (3) the immunity profile along short host-transmission chains (local host-network structure) is a key determinant of immune escape.

Dos and don'ts of testing the geographic mosaic theory of coevolution.

The geographic mosaic theory of coevolution is stimulating much new research on interspecific interactions. We provide a guide to the fundamental components of the theory, its processes and main predictions. Our primary objectives are to clarify misconceptions regarding the geographic mosaic theory of coevolution and to describe how empiricists can test the theory rigorously. In particular, we explain why confirming the three main predicted empirical patterns (spatial variation in traits mediating interactions among species, trait mismatching among interacting species and few species-level coevolved traits) does not provide unequivocal support for the theory. We suggest that strong empirical tests of the geographic mosaic theory of coevolution should focus on its underlying processes: coevolutionary hot and cold spots, selection mosaics and trait remixing. We describe these processes and discuss potential ways each can be tested.