This guest blog post is written by Alden Little, a Marketing Intern at Promega.
Alzheimer’s disease (AD) is one of the most devastating neurodegenerative disorders, affecting millions worldwide. While much attention has been given to amyloid plaques and tau tangles, emerging research suggests that metabolic dysfunction in the brain plays a crucial role in the disease’s progression. A recent study published in Acta Neuropathologica by Schröder et al. sheds new light on how astrocytes—the brain’s metabolic support cells—are affected in AD, and how their dysfunction impacts neurons.
Astrocytes in Brain Metabolism
Astrocytes are vital for maintaining neuronal function, supporting synaptic activity, and regulating energy metabolism. In a healthy brain, astrocytes facilitate neurotransmitter recycling, particularly glutamate uptake, and provide neurons with lactate, an essential energy source (Chen, 2023). However, in Alzheimer’s, astrocytes become metabolically dysfunctional, contributing to synaptic loss and cognitive decline (Acosta, 2017).
For decades, neurodegeneration research has primarily focused on the coding regions of the genome—genes that are translated into proteins. However, protein-coding genes account for only about 1.5% of the human genome, while most of the transcriptome consists of long non-coding RNAs (lncRNAs) (Mu, 2011). These RNA molecules do not encode proteins and were initially dismissed as transcriptional noise. However, recent studies have revealed that lncRNAs play essential roles in regulating biological processes by interacting with transcription factors and epigenetic regulators to influence gene expression (Mattick, 2023). Notably, approximately 40% of human lncRNAs are reported to be brain-specific, yet our understanding of their roles in the brain remains limited—particularly in astrocytes (Derrien, 2012). Here we outline how Schröder et al. identified PRDM16-DT, a lncRNA, as a key regulator of astrocytic function with implications for AD progression (Schröder, 2024).
Identifying Brain Metabolic Disruptions in AD Patients
Using single-nucleus RNA sequencing (snucRNA-seq) of postmortem human brain samples, Schröder et al. identified PRDM16-DT as highly expressed in astrocytes but significantly downregulated in samples from AD brains. This pattern suggested a potential role in astrocyte function and neurodegenerative disease progression, as lower PRDM16-DT levels in AD brains correlated with increased neuronal degeneration.
To investigate PRDM16-DT’s function and its role in AD, they examined its murine homologue, Prdm16os. By knocking down Prdm16os in primary mouse astrocytes, they examined its impact on cellular metabolism and neuronal support functions. In primary cultures, Prdm16os knockdown resulted in reduced expression of monocarboxylate transporter 4 (MCT4), a critical protein responsible for lactate secretion. Using Promega’s Lactate-Glo™ Assay, the researchers determined that decreased MCT4 expression led to a significant reduction in lactate release from astrocytes underscoring the role of Prdm16os in providing neuronal energy support.
Further metabolic disruption was observed in astrocytes through impaired glutamate uptake, as assessed by Promega’s Glutamate-Glo™ Assay. Astrocytes play a crucial role in clearing excess glutamate from the extracellular space, a process essential for terminating synaptic transmission and preventing neurotoxicity. Schröder et al. found that reduced lactate secretion coincided with impaired glutamate uptake, which was linked to the downregulation of GLT-1 and GLAST—two key glutamate transporters. This reduction in transporter expression significantly compromised astrocytic glutamate clearance, leading to synaptic dysfunction.
These metabolic impairments—marked by disrupted lactate secretion and glutamate uptake—were further associated with reduced neuronal viability and decreased synaptic spine density, emphasizing the essential role of PRDM16-DT in maintaining neuronal function. Given the importance of astrocyte-derived lactate in neuronal energy metabolism and the necessity of efficient glutamate clearance, these findings suggest that PRDM16-DT may represent a promising therapeutic target for neurodegenerative diseases such as Alzheimer’s disease.
A Step Toward Metabolic Therapeutics in Alzheimer’s
With tools like Promega’s Metabolic Activity Assays, researchers can continue to unravel the complex metabolic disruptions in AD. Understanding how astrocytes lose their ability to regulate neuronal energy supply could open new doors for treatments focused on restoring brain metabolism. This work highlights the importance of innovative research tools in advancing our fight against Alzheimer’s and points toward a promising future where metabolic interventions may complement traditional approaches targeting amyloid and tau pathology. By targeting astrocytic metabolism—potentially through RNA-based therapies that restore PRDM16-DT expression—scientists hope to develop interventions that can slow or even halt neurodegeneration.
By addressing Alzheimer’s through the lens of metabolic dysfunction, we move closer to understanding how to sustain brain health and ultimately, to developing more effective treatments for this devastating disease.
To read the full research article click here.
References
Acosta, C., et al (2017). Astrocyte dysfunction in Alzheimer disease. Journal of neuroscience research, 95(12), 2430-2447.
Chen, Z., et al (2023). Brain Energy Metabolism: Astrocytes in Neurodegenerative Diseases. CNS neuroscience & therapeutics, 29(1), 24–36. https://doi.org/10.1111/cns.13982
Derrien, T., et al (2012). The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome research, 22(9), 1775–1789. https://doi.org/10.1101/gr.132159.111
Schröder, S., et al (2024). PRDM16-DT is a novel lncRNA that regulates astrocyte function in Alzheimer’s disease. Acta neuropathologica, 148(1), 32. https://doi.org/10.1007/s00401-024-02787-x
Mattick, J.S., etal(2023). Long non-coding RNAs: definitions, functions, challenges and recommendations. Nat Rev Mol Cell Biol 24, 430–447. https://doi.org/10.1038/s41580-022-00566-8
Mu, X. J., et al (2011). Analysis of genomic variation in non-coding elements using population-scale sequencing data from the 1000 Genomes Project. Nucleic acids research, 39(16), 7058–7076. https://doi.org/10.1093/nar/gkr342
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