Antecedent presentation of neurological phenotypes in the Collaborative Cross reveals four classes with complex sex-dependencies

Abstract

Antecedent viral infection may contribute to increased susceptibility to several neurological diseases, such as multiple sclerosis and Parkinson’s disease. Variation in clinical presentations of these diseases is often associated with gender, genetic background, or a combination of these and other factors. The complicated etiologies of these virally influenced diseases are difficult to study in conventional laboratory mouse models, which display a very limited number of phenotypes. We have used the genetically and phenotypically diverse Collaborative Cross mouse panel to examine complex neurological phenotypes after viral infection. Female and male mice from 18 CC strains were evaluated using a multifaceted phenotyping pipeline to define their unique disease profiles following infection with Theiler’s Murine Encephalomyelitis Virus, a neurotropic virus. We identified 4 distinct disease progression profiles based on limb-specific paresis and paralysis, tremors and seizures, and other clinical signs, along with separate gait profiles. We found that mice of the same strain had more similar profiles compared to those of different strains, and also identified strains and phenotypic parameters in which sex played a significant role in profile differences. These results demonstrate the value of using CC mice for studying complex disease subtypes influenced by sex and genetic background. Our findings will be useful for developing novel mouse models of virally induced neurological diseases with heterogenous presentation, an important step for designing personalized, precise treatments.

Viral infections and their relationship to neurological disorders

“…Although neurological disorders have a multifactorial etiology, most of them have a strong genetic and environmental association [4]. Recently, some studies have also associated NDs such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Alzheimer's disease (AD), Guillain Barré syndrome (GBS), and epilepsy with viral infections [5][6][7][8][9]. Viral diseases are widely distributed, easily transmitted, and challenging to control.…”

Host genetic diversity drives variable central nervous system lesion distribution in chronic phase of Theiler’s Murine Encephalomyelitis Virus (TMEV) infection

“…All mice were assessed using established methods for evaluating TMEV-induced neurological disease phenotypes and sickness [15]. Mice were assessed twice daily for clinical signs from 0-14dpi (acute phase).…”

“…The CC mouse resource has been used to model variation in pathogenic phenotypes after fungal [10], bacterial [11,12], and viral infections [11,13,14], and is well suited to elucidate the role of genetic background on infectious disease pathophysiology. We have previously used the CC to model the myriad of subtle differences in neurological outcomes to TMEV infection in addition to demyelination [15,16].…”

“…For the current work, we have hypothesized that clinical outcomes to TMEV infectionwhich often resemble human neurological responses to viral infections [15]-are associated with the relative locations in the central nervous system. To test our hypothesis, we have precisely localized lesions in six CC mouse strains selected to represent phenotypic extremes of TMEV outcomes, including three previously described [15] and three novel CC strains. We also evaluated TMEV-induced phenotypes and lesions in two inbred strains commonly used to study TMEV infection (SJL/J and C57BL/6J), to place our findings in context.…”

“…have identified statistical correlations between clinical symptoms (phenotypes) and lesion frequency and location. For this, we evaluated relationships between lesion location and the observational frequencies of neurological disease phenotypes including righting reflex, paresis, paralysis, seizures, limb clasping, ruffling, and encephalitis [15], all defined at the same chronic phase time point. We identified statistical correlations, overall and on a strain-by-strain basis, between these phenotypes and lesion burden at specific locations.…”