ハンチントン病

Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Traditionally, studies relied on age-CAG models to estimate disease onset and progression, based on the well-established relationship between CAG repeat length and age at onset. However, additional genetic, environmental, and clinical factors can cause substantial variability. Recent machine learning approaches integrate clinical, imaging, and molecular data for more precise prediction of disease progression. Following PRISMA guidelines, we systematically reviewed studies on HD onset and progression. Using Web of Science, PubMed, and IEEE Xplore, 20 studies published between 2003 and 2024 met the inclusion criteria. We analyzed the machine learning approaches and input features used, assessed methodological quality, and evaluated risk of bias using the PROBAST tool. Overall, machine learning models, particularly support vector machines and ensemble approaches, consistently outperformed traditional age-CAG models. Several studies predicted conversion from premanifest to manifest HD within 5-10 years with high accuracy (88-98%). Beyond predicting onset, machine learning models have also been used to model dis-ease progression using clinical scores assessing motor, cognitive, and functional impairment. Performance was higher in studies incorporating structural and functional MRI biomarkers, and improved further with longitudinal clinical integration, enabling pre-diction of decline years before symptoms onset. Overall, machine learning shows strong potential to improve prognostic modeling in HD, especially through multimodal and longitudinal data. However, common methodological weaknesses and bias highlight the need for larger, externally validated studies using objective biomarkers.

Neurodegeneration results from the convergence of several molecular processes, including inflammation in the brain (i.e., neuroinflammation), elevated levels of free radicals that damage cells, mitochondrial dysfunction, and the inability to remove damaged proteins from the brain. Even though many agents provide neuroprotection in research models, their clinical use is limited because they cannot effectively cross the blood-brain barrier to reach the areas of the brain where they are needed. Limitations include the inability to cross the blood-brain barrier, poor bioavailability, rapid metabolism and clearance, non-specific targeting, efflux by transport proteins, toxicity, and low solubility and stability. The classification of micronutrients (e.g., vitamins, polyphenols, minerals), which are naturally present antioxidants and anti-inflammatory substances, plays a role in modulating the most important signaling pathways in the body, including those mediating the inflammatory response (i.e., NF-κB and NLRP3) and the process that causes glial cell death (i.e., JAK/STAT). Micronutrients have a significant drawback for therapeutic use because they are rapidly metabolized and cannot cross the blood-brain barrier. Developments in synthetic biomaterials and nanotechnology offer a potential avenue for addressing the challenges of delivering micronutrients to the brain by targeting them to specific areas and releasing them over a sustained period. This study presents current information on the mechanisms by which micronutrients modulate molecular pathways and their potential application in emerging biomaterials to develop a new class of neuroprotective therapeutic agents that may ultimately be used to treat patients with degenerative diseases (e.g., Alzheimer's, Parkinson's, and Huntington's). Additionally, clinical challenges are addressed to translate these products from the laboratory to the clinic. The idea presented in this review connects molecular neuromodulation via micronutrients and bioactive nutraceuticals with a new strategy for pharmacological delivery using biomaterials. Instead of considering nutrition and those biomaterials as separate therapeutic areas, an integrated mechanistic model is presented that shows how micronutrients can act as endogenous pathway regulators and how biomaterials can enhance pharmacokinetics and targeting.

Neurodegenerative diseases are progressive disorders that involve the loss and dysfunction of neurons. Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis, Huntington's disease, Frontotemporal dementia are examples of diseases. While different clinically, these disorders have a common genetic, molecular and cellular basis. This review examines the common genetic pathways, along with the interactions between genes of major neurodegenerative diseases, with a focus on the key genes, such as APOE, SNCA, MAPT, TARDBP, LRRK2 and HTT. The common pathogenic mechanisms considered to play a major role in disease progression include protein misfolding and aggregation, mitochondrial dysfunction, oxidative stress, neuroinflammation, diminished autophagy, and impaired lysosomal function, as well as synaptic degeneration. The review also emphasizes the role of systems biology strategies, such as genome-wide association studies, transcriptomics, proteomics, metabolomics, interactome analysis, and multi-omics integration, to unveiling complex molecular networks in neurodegeneration. Furthermore, the emerging biomarker strategies and therapeutic strategies targeting convergence signaling pathways including NF-κB, PI3K-Akt-mTOR, MAPK and Wnt/β-catenin are summarized. The common genetic basis and the cross-connecting molecular mechanisms of the various neurodegenerative diseases could help in the discovery of new biomarkers and pan-therapeutic targets. Further advances in molecular genetics, computational biology and precision medicine are needed to enhance early detection and the creation of effective disease-modifying treatments.

Huntington's disease (HD) is characterized by mutant huntingtin (mHTT) aggregation and impaired proteostasis; however, upstream regulators of ubiquitin system imbalance remain incompletely understood. This study identified the deubiquitinase USP28 as a potential modulator of ubiquitin-dependent proteostasis in HD. Bulk RNA sequencing of R6/2 mouse brain tissues showed reduced USP28 expression compared with wild-type controls. Consistently, USP28 expression decreased in STHdh striatal cells expressing expanded polyQ huntingtin (Q111). HD cells (STHdh-Q111) exhibited increased accumulation of ubiquitinated proteins and altered ubiquitin turnover, consistent with impaired proteostasis. USP28 overexpression attenuated ubiquitinated protein accumulation and reduced mHTT aggregation, whereas a catalytically inactive USP28 mutant showed limited rescue effects. In addition, USP28 depletion was associated with reduced UBR5 levels, while USP28 restoration partially recovered UBR5 expression in a catalytic activity-dependent manner. Modulation of HECT E3 ligase activity further altered ubiquitination dynamics and mHTT aggregation, suggesting that HECT E3 ligase-related pathways may contribute to proteostasis regulation in HD cells. Collectively, these findings identify USP28 as a proteostasis-associated deubiquitinase reduced in HD models and suggest that USP28 deficiency contributes to ubiquitin burden and mHTT aggregation. Changes in UBR5 expression further point to a potential involvement of HECT E3 ligase-linked ubiquitin regulation, although the direct mechanistic relationship between USP28 and UBR5 remains.

Huntington's disease (HD) is a progressive, autosomal dominant neurodegenerative disorder characterized by motor dysfunction, cognitive decline, and psychiatric disturbances, for which no disease-modifying therapies are currently available. Emerging evidence implicates metabolic impairment, mitochondrial dysfunction, oxidative stress, and neuroinflammation as central contributors to HD pathogenesis, thereby highlighting novel avenues for therapeutic intervention beyond conventional strategies. In this context, drug repurposing has gained considerable attention as an efficient approach to accelerate therapeutic development. Alogliptin has multiple complementary mechanisms of action that exert neuroprotective effects through inhibition of DPP-4 to boost endogenous incretin signaling (especially GLP-1), inhibition of inflammatory pathways, inhibition of oxidative stress, preservation of mitochondrial function, and modulation of neuronal survival signaling. The review summarizes existing data on the contribution of the incretin signaling to neuroprotection and critically analyzes the mechanism through which alogliptin might regulate important pathological events in HD, such as apoptosis, oxidative stress, and neuroinflammation. Additionally, preclinical results and pharmacological properties in favor of its translational potential are presented, as well as the reflection on its clinical usage and additional research perspectives. Even though direct evidence in HD is not extensive, the overlap of metabolic and neurodegenerative pathways offers a strong argument to study. This review identifies alogliptin as a potent repurposable agent and the necessity to conduct specific experimental and clinical research to determine its effectiveness in refining symptoms and changing the disease course in HD. This narrative review critically evaluates the available experimental evidence supporting the repurposing potential of Alogliptin for HD.