Baseline Gait and Motor Function Predict Long-Term Severity of Neurological Outcomes of Viral Infection

Author:

Karmakar Moumita1,Pérez Gómez Aracely A.2ORCID,Carroll Raymond J.1,Lawley Koedi S.2,Amstalden Katia A. Z.2,Welsh C. Jane2ORCID,Threadgill David W.3,Brinkmeyer-Langford Candice2ORCID

Affiliation:

1. Department of Statistics, College of Science, Texas A & M University, College Station, TX 77843, USA

2. Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A & M University, College Station, TX 77843, USA

3. Department of Molecular and Cellular Medicine, Texas A & M Health Science Center, Texas A & M University, College Station, TX 77843, USA

Abstract

Neurological dysfunction following viral infection varies among individuals, largely due to differences in their genetic backgrounds. Gait patterns, which can be evaluated using measures of coordination, balance, posture, muscle function, step-to-step variability, and other factors, are also influenced by genetic background. Accordingly, to some extent gait can be characteristic of an individual, even prior to changes in neurological function. Because neuromuscular aspects of gait are under a certain degree of genetic control, the hypothesis tested was that gait parameters could be predictive of neuromuscular dysfunction following viral infection. The Collaborative Cross (CC) mouse resource was utilized to model genetically diverse populations and the DigiGait treadmill system used to provide quantitative and objective measurements of 131 gait parameters in 142 mice from 23 CC and SJL/J strains. DigiGait measurements were taken prior to infection with the neurotropic virus Theiler’s Murine Encephalomyelitis Virus (TMEV). Neurological phenotypes were recorded over 90 days post-infection (d.p.i.), and the cumulative frequency of the observation of these phenotypes was statistically associated with discrete baseline DigiGait measurements. These associations represented spatial and postural aspects of gait influenced by the 90 d.p.i. phenotype score. Furthermore, associations were found between these gait parameters with sex and outcomes considered to show resistance, resilience, or susceptibility to severe neurological symptoms after long-term infection. For example, higher pre-infection measurement values for the Paw Drag parameter corresponded with greater disease severity at 90 d.p.i. Quantitative trait loci significantly associated with these DigiGait parameters revealed potential relationships between 28 differentially expressed genes (DEGs) and different aspects of gait influenced by viral infection. Thus, these potential candidate genes and genetic variations may be predictive of long-term neurological dysfunction. Overall, these findings demonstrate the predictive/prognostic value of quantitative and objective pre-infection DigiGait measurements for viral-induced neuromuscular dysfunction.

Funder

NIH National Institute of Neurological Disorders and Stroke

National Science Foundation Graduate Research Fellowship

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

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