Infection and inflammation: New perspectives on Alzheimer's disease

Whitson, Heather E.; Colton, Carol A.; Khoury, Joseph El; Gate, David; Goate, Alison; Heneka, Michael T.; Kaddurah‐Daouk, Rima; Klein, Robyn S.; Sisodia, Sangram S.; Spudich, Serena; Stevens, Beth; Tanzi, Rudolph E.; Ting, Jenny P.‐Y.; Garden, Gwenn A.; Aiello, Alison E.; Chiba‐Falek, Ornit; Heitman, Joseph; Johnson, Kurt E.; Luftig, Micah A.; Moseman, Ashley; Rawls, Jonathan; Shinohara, Mari L.; Swanstrom, Ronald; Terrando, Niccolò

doi:10.1016/j.bbih.2022.100462

Cited by 20 publications

(14 citation statements)

References 68 publications

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“…On a biological level, the obtained results are in accordance with what is expected from these models and with previous published literature [ 39 , 47 , 85 – 87 ], confirming the reliability and sensitivity of the tool in the three species. Recent clinical and genetical studies have enfolded the role of microglial cells as an essential contributor in late onset Alzheimer’s disease [ 88 , 89 ]. Hence, the need for reproducible, translational, and effective analysis of microglial images is warranted in the microglia research community.…”

Section: Discussionmentioning

confidence: 99%

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

et al. 2023

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Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson’s disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.

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Section: Discussionmentioning

confidence: 99%

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

et al. 2023

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“…Neuronal function is thereby impaired in these patients affected with AD because their neurons are structurally unstable due to these intracellular tangles [ 9 , 10 ]. While genetics and aging are acknowledged and accepted contributors to the development of AD, emerging evidence suggests that infectious agents, specifically viruses, could influence or modulate the balance of molecular events leading to AD pathology [ 11 , 12 ]. There are studies that have highlighted the complexity in triggering or exacerbating AD including but not limited to genetic susceptibility, environmental influences, and the potential role of infections.…”

Section: Overview Of Ad Pathologymentioning

confidence: 99%

Viral Infections, Are They a Trigger and Risk Factor of Alzheimer’s Disease?

Rippee-Brooks,

Wu,

Dong

et al. 2024

Pathogens

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Alzheimer’s Disease (AD), a progressive and debilitating condition, is reported to be the most common type of dementia, with at least 55 million people believed to be currently affected. Many causation hypotheses of AD exist, yet the intriguing link between viral infection and its possible contribution to the known etiology of AD has become an attractive focal point of research for the field and a challenging study task. In this review, we will explore the historical perspective and milestones that led the field to investigate the viral connection to AD. Specifically, several viruses such as Herpes Simplex Virus 1 (HSV-1), Zika virus (ZIKV), and severe cute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with several others mentioned, include the various viruses presently considered within the field. We delve into the strong evidence implicating these viruses in the development of AD such as the lytic replication and axonal transport of HSV-1, the various mechanisms of ZIKV neurotropism through the human protein Musashi-1 (MSI1), and the spread of SARS-CoV-2 through the transfer of the virus through the BBB endothelial cells to glial cells and then to neurons via transsynaptic transfer. We will also explore beyond these mere associations by carefully analyzing the potential mechanisms by which these viruses may contribute to AD pathology. This includes but is not limited to direct neuronal infections, the dysregulation of immune responses, and the impact on protein processing (Aβ42 and hyperphosphorylated tau). Controversies and challenges of the virus–AD relationship emerge as we tease out these potential mechanisms. Looking forward, we emphasize future directions, such as distinct questions and proposed experimentations to explore, that the field should take to tackle the remaining unanswered questions and the glaring research gaps that persist. Overall, this review aims to provide a comprehensive survey of the past, present, and future of the potential link between viral infections and their association with AD development while encouraging further discussion.

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“…To elucidate the role of microglia and other immune cells in neuronal damage, different genomic and transcriptomic assays have been performed in in vitro and in vivo models of AD [ 119 ]. For example, in brains of people with AD, T cells were associated with cerebral amyloid angiopathy blood vessels, and CD8+ T cells were specifically linked with microglia and amyloid plaque deposits [ 120 ].…”

Section: Hypoxiamentioning

confidence: 99%

Relationship between Hypoxic and Immune Pathways Activation in the Progression of Neuroinflammation: Role of HIF-1α and Th17 Cells

Arias

Sepúlveda

Castillo

et al. 2023

IJMS

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Neuroinflammation is a common event in degenerative diseases of the central and peripheral nervous system, triggered by alterations in the immune system or inflammatory cascade. The pathophysiology of these disorders is multifactorial, whereby the therapy available has low clinical efficacy. This review propounds the relationship between the deregulation of T helper cells and hypoxia, mainly Th17 and HIF-1α molecular pathways, events that are involved in the occurrence of the neuroinflammation. The clinical expression of neuroinflammation is included in prevalent pathologies such as multiple sclerosis, Guillain–Barré syndrome, and Alzheimer’s disease, among others. In addition, therapeutic targets are analyzed in relation to the pathways that induced neuroinflammation.

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Infection and inflammation: New perspectives on Alzheimer's disease

Cited by 20 publications

References 68 publications

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

Viral Infections, Are They a Trigger and Risk Factor of Alzheimer’s Disease?

Relationship between Hypoxic and Immune Pathways Activation in the Progression of Neuroinflammation: Role of HIF-1α and Th17 Cells

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