#ScienceSaturday posts share exciting scientific developments and educational resources with the KAND community. Each week, Dr. Dominique Lessard and Dr. Dylan Verden of KIF1A.ORG summarize newly published KIF1A-related research and highlight progress in rare disease research and therapeutic development.
KIF1A-Related Research
Huntingtin-KIF1A-mediated axonal transport of synaptic vesicle precursors influences synaptic transmission and motor skill learning in mice
Glossary:
- Synapse: The location where one neuron sends signals to another. Synapses grow or shrink during learning, which requires transport of synaptic vesicles, a known cargo of KIF1A.
- Phosphorylation: The addition of a phosphate group to a protein, which can activate or deactivate certain functions.
In KAND, KIF1A dysfunction is related to mutations that change the protein structure directly, but KAND isn’t the only disease impacted by altered KIF1A transport. In this week’s pre-print* article, researchers investigated the relationship between Huntington’s Disease and KIF1A.
Huntington’s Disease is another genetic neurodegenerative disorder, caused by a mutation in the huntingtin (HTT) gene, which is expressed in neurons. This mutation creates a long repeated sequence that causes the protein to break up into small sticky segments that form clumps within neuronal axons, which prevents proper function and eventually damages the cells.
Because healthy and toxic HTT both travel along axons, it is important to understand how its transport affects neuronal health and behavior in mice. HTT can recruit transporters when activated by a process called phosphorylation. By creating a mutant version of HTT that is always phosphorylated, the researchers were able to probe what happens when HTT transport is overactive. This HTT mutation is not the same as what causes Huntington’s disease, but can provide insight into how HTT is moved around in cells.
The mutant HTT associated with KIF1A and increased its movement of synaptic cargo to the ends of neuronal axons. As a result, neurons with overactive HTT transport were also more electrically active. This might sound like a good thing, but it really amounts to the circuits being “noisier”. When these neurons were purposely stimulated, they were less likely to strengthen connections than neurons with normal HTT. Mice carrying this mutation weren’t able to learn or perform new motor skills as well as healthy mice.
Because permanently phosphorylated HTT seemed to be transported by KIF1A, the authors then knocked down KIF1A protein by 83% in mice with mutant HTT. They found that this decreased the amount of synaptic vesicle transport, and restored some of the motor learning deficits in mice. Notably, knocking down KIF1A in mice with normal HTT caused many synaptic vesicles to move in reverse, which may cause other types of neuronal dysfunction.
This study highlights some very important points for KAND. Firstly, it reinforces the relationship between KIF1A transport and behavior. Secondly, it shows that increasing or decreasing KIF1A activity might not be a simple therapeutic answer: Underactive and overactive KIF1A mutations both need to be brought back toward a middle ground.
*What’s a preprint? Check out this #ScienceSaturday post to learn more!
Rare Roundup
Peptide Delivered by Nasal Spray Can Reduce Seizure Activity and Protect Neurons in Alzheimer’s and Epilepsy
Epilepsy describes a devastating collection of disorders that cause seizures, events of excessive electrical activity that interferes with normal function. Treating nervous system disorders like epilepsy is particularly challenging for many reasons:
- Neuronal proteins may be expressed in many other parts of the body, causing side effects.
- Drugs must be able to bypass the blood brain barrier.
- Directly suppressing neuronal activity can have drastic impact on many aspects of health.
Researchers from Augusta University have developed a new treatment that attempts to address these challenges. The peptide, a customized protein segment called A1R-CT, acts by freeing up adenosine-1 receptor (A1R), a protein that dampens activity in overexcited neurons.
A1R is expressed throughout the body and targeting it could result in side effects; but the authors found a neuron-enriched protein called neurabin that inactivates A1R and increases neuronal excitability. The newly developed peptide binds to neurabin, preventing it from acting on A1R and decreasing seizure-like activity.
Researchers treated mouse models of Alzheimer’s and epilepsy, both of which are seizure prone, with A1R-CT. To get the peptide to the brain, they used intranasal administration, which bypasses the blood brain barrier at lower concentrations than other methods. Intranasal A1R-CT was able to suppress seizure length and severity in both mouse models.
The development of an anti-seizure medication tailored for the central nervous system is very encouraging, but more research needs to be done. A1R-CT is still suppressing neuronal activity, and inhibiting neurabin too much could make someone fall asleep. Future preclinical studies will assess how A1R-CT acts at different doses.
Study trains AI to predict optimal anti-seizure meds for new epilepsy patients
As we consider potential treatments, it’s important to acknowledge that there probably is no silver bullet for epilepsy; seizures can manifest differently because there are many causes and circuits that could be involved. What this means for families is an uphill battle of finding the correct anti-seizure medications that relieve symptoms without side effects. Shortening the journey to the right treatment would have huge impacts on quality of life for many families.
In an effort to accelerate this process, a team led by researchers at Monash University developed a machine learning model to predict which of seven anti-seizure medications would work best for different patients. The model pooled clinical data from 1798 adult patients from five cohorts; when this algorithm was reapplied to the cohorts, it predicted the most successful medication with 65% accuracy.
This is a modest success rate, and the model has clear limitations: It was only applied to newly diagnosed adult patients, included only seven medications, and did not consider informative clinical data like EEG or brain imaging. However, it represents an important step toward leveraging data to tailor anti-seizure medication to new patients. The researchers hope to expand their efforts with additional cohorts and clinical data moving forward.