Retinal Aging in 3× Tg-AD Mice Model of Alzheimer's Disease

YAP in development and disease: Navigating the regulatory landscape from retina to brain

The distinctive role of Yes-associated protein (YAP) in the nervous system has attracted widespread attention. This comprehensive review strategically uses the retina as a vantage point, embarking on an extensive exploration of YAP's multifaceted impact from the retina to the brain in development and pathology. Initially, we explore the crucial roles of YAP in embryonic and cerebral development. Our focus then shifts to retinal development, examining in detail YAP's regulatory influence on the development of retinal pigment epithelium (RPE) and retinal progenitor cells (RPCs), and its significant effects on the hierarchical structure and functionality of the retina. We also investigate the essential contributions of YAP in maintaining retinal homeostasis, highlighting its precise regulation of retinal cell proliferation and survival. In terms of retinal-related diseases, we explore the epigenetic connections and pathophysiological regulation of YAP in diabetic retinopathy (DR), glaucoma, and proliferative vitreoretinopathy (PVR). Lastly, we broaden our exploration from the retina to the brain, emphasizing the research paradigm of "retina: a window to the brain." Special focus is given to the emerging studies on YAP in brain disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD), underlining its potential therapeutic value in neurodegenerative disorders and neuroinflammation.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Normative mice retinal thickness: 16-month longitudinal characterization of wild-type mice and changes in a model of Alzheimer's disease

Animal models of disease are paramount to understand retinal development, the pathophysiology of eye diseases, and to study neurodegeneration using optical coherence tomography (OCT) data. In this study, we present a comprehensive normative database of retinal thickness in C57BL6/129S mice using spectral-domain OCT data. The database covers a longitudinal period of 16 months, from 1 to 16 months of age, and provides valuable insights into retinal development and changes over time. Our findings reveal that total retinal thickness decreases with age, while the thickness of individual retinal layers and layer aggregates changes in different ways. For example, the outer plexiform layer (OPL), photoreceptor inner segments (ILS), and retinal pigment epithelium (RPE) thickened over time, whereas other retinal layers and layer aggregates became thinner. Additionally, we compare the retinal thickness of wild-type (WT) mice with an animal model of Alzheimer's disease (3 × Tg-AD) and show that the transgenic mice exhibit a decrease in total retinal thickness compared to age-matched WT mice, with statistically significant differences observed at all evaluated ages. This normative database of retinal thickness in mice will serve as a reference for future studies on retinal changes in neurodegenerative and eye diseases and will further our understanding of the pathophysiology of these conditions.

Retinal thickness changes in different subfields reflect the volume change of cerebral white matter hyperintensity

Purpose

To investigate the relationship between the retinal thickness in different subfields and the volume of white matter hyperintensity (WMH), with the hope to provide new evidence for the potential association between the retina and the brain.

Methods

A total of 185 participants aged over 40 years were included in our study. Magnetic resonance imaging (MRI) was used to image the WMH, and WMH volume was quantitatively measured by a specific toolbox. The thickness of the total retina, the retinal nerve fiber layer (RNFL), and the ganglion cell and inner plexiform layer (GCIP) was measured by optical coherence tomography (OCT) in nine subfields. The association between retinal thickness and WMH volume was demonstrated using binary logistic regression and Pearson correlation analysis.

Results

Participants were divided into two groups by the WMH volume (‰, standardized WMH volume) median. In the quartile-stratified binary logistic regression analysis, we found that the risk of higher WMH volume showed a positive linear trend correlation with the thickness of total retina (95% CI: 0.848 to 7.034; P for trend = 0.044)/ GCIP (95% CI: 1.263 to 10.549; P for trend = 0.038) at the central fovea, and a negative linear trend correlation with the thickness of nasal inner RNFL (95% CI: 0.086 to 0.787; P for trend = 0.012), nasal outer RNFL (95% CI: 0.058 to 0.561; P for trend = 0.004), and inferior outer RNFL (95% CI: 0.081 to 0.667; P for trend = 0.004), after adjusting for possible confounders. Correlation analysis results showed that WMH volume had a significant negative correlation with superior outer RNFL thickness (r = −0.171, P = 0.02) and nasal outer RNFL thickness (r = −0.208, P = 0.004).

Conclusion

It is suggested that central fovea and outer retina thickness are respectively associated with WMH volume. OCT may be a biological marker for early detection and longitudinal monitoring of WMH.