Most neurodegenerative diseases are characterized by the accumulation of protein aggregates, some of which are toxic to cells. Mounting evidence demonstrates that in several diseases, protein aggregates can pass from neuron to neuron along connected networks, although the role of this spreading phenomenon in disease pathogenesis is not completely understood. Here we briefly review the molecular and histopathological features of protein aggregation in neurodegenerative disease, we summarize the evidence for release of proteins from donor cells into the extracellular space, and we highlight some other mechanisms by which protein aggregates might be transmitted to recipient cells. We also discuss the evidence that supports a role for spreading of protein aggregates in neurodegenerative disease pathogenesis and some limitations of this model. Finally, we consider potential therapeutic strategies to target spreading of protein aggregates in the treatment of neurodegenerative diseases.
Significant advances have been made in recent years in identifying the genetic components of Wallerian degeneration, the process that brings the progressive destruction and removal of injured axons. It has now been accepted that Wallerian degeneration is an active and dynamic cellular process that is well regulated at molecular and cellular levels. In this review, we describe our current understanding of Wallerian degeneration, focusing on the molecular players and mechanisms that mediate the injury response, activate the degenerative program, transduce the death signal, execute the destruction order, and finally, clear away the debris. By highlighting the starring roles and sketching out the molecular script of Wallerian degeneration, we hope to provide a useful framework to understand Wallerian and Wallerian-like degeneration and to lay a foundation for developing new therapeutic strategies to treat axon degeneration in neural injury as well as in neurodegenerative disease. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
In neurodegenerative diseases, neurodegeneration has been related to several mitochondrial dynamics imbalances such as excessive fragmentation of mitochondria, impaired mitophagy, and blocked mitochondria mitochondrial transport in axons. Mitochondria are dynamic organelles, and essential for energy conversion, neuron survival, and cell death. As mitochondrial dynamics have a significant influence on homeostasis, in this review, we mainly discuss the role of mitochondrial dynamics in several neurodegenerative diseases. There is evidence that several mitochondrial dynamics-associated proteins, as well as related pathways, have roles in the pathological process of neurodegenerative diseases with an impact on mitochondrial functions and metabolism. However, specific pathological mechanisms need to be better understood in order to propose new therapeutic strategies targeting mitochondrial dynamics that have shown promise in recent studies.
This paper presents four domains of markers that have been found to predict later cognitive impairment and neurodegenerative disease. These four domains are (1) data patterns of memory performance, (2) cardiovascular factors, (3) genetic markers, and (4) brain activity. The critical features of each domain are illustrated with data from the longitudinal Betula Study on memory, aging, and health ( Nilsson et al., 1997 ; Nilsson et al., 2004 ). Up to now, early signs regarding these domains have been examined one by one and it has been found that they are associated with later cognitive impairment and neurodegenerative disease. However, it was also found that each marker accounts for only a very small part of the total variance, implying that single markers should not be used as predictors for cognitive decline or neurodegenerative disease. It is discussed whether modeling and simulations should be used as tools to combine markers at different levels to increase the amount of explained variance.