Department of Statistics, College of Science, Texas A & M University, College Station, TX 77843, USA.
Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A & M University, College Station, TX 77843, USA.
Int J Mol Sci. 2023 Feb 2;24(3):2843. doi: 10.3390/ijms24032843.
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.
病毒感染后的神经功能障碍在个体之间存在差异，这主要归因于遗传背景的不同。步态模式也受到遗传背景的影响，步态模式可以通过协调性、平衡性、姿势、肌肉功能、步幅变化等因素来评估。因此，在一定程度上，步态可以是个体的特征，甚至在神经功能发生变化之前。由于步态的神经肌肉方面受到一定程度的遗传控制，因此我们假设步态参数可以预测病毒感染后的神经肌肉功能障碍。我们利用了 CC 小鼠资源来模拟遗传多样性群体，并使用 DigiGait 跑步机系统来提供 23 个 CC 和 SJL/J 品系的 142 只小鼠的 131 个步态参数的定量和客观测量。在感染嗜神经病毒 Theiler's Murine Encephalomyelitis Virus (TMEV) 之前，我们进行了 DigiGait 测量。在感染后 90 天（d.p.i.）记录神经表型，并对这些表型的观测的累积频率与离散的基线 DigiGait 测量值进行了统计学关联。这些关联代表了步态的空间和姿势方面，受 90 d.p.i. 表型评分的影响。此外，我们还发现了这些步态参数与性别之间的关联，以及与长期感染后表现出抵抗、弹性或易患严重神经症状的结果之间的关联。例如，在感染前 Paw Drag 参数的测量值较高，与 90 d.p.i. 时的疾病严重程度较高相关。与这些 DigiGait 参数显著相关的数量性状基因座揭示了 28 个差异表达基因（DEGs）与病毒感染影响的不同步态方面之间的潜在关系。因此，这些潜在的候选基因和遗传变异可能是长期神经功能障碍的预测指标。总的来说，这些发现证明了定量和客观的感染前 DigiGait 测量值对病毒诱导的神经肌肉功能障碍具有预测/预后价值。
