A Novel Huntington's Disease Assessment Platform to Support Future Drug Discovery and Development

Sex-Dependent Efficacy of Sphingosine-1-Phosphate Receptor Agonist FTY720 in Mitigating Huntington's Disease

Huntington's disease (HD) is a debilitating neurodegenerative disorder characterized by severe motor deficits, cognitive decline and psychiatric disturbances. An early and significant morphological hallmark of HD is the activation of astrocytes triggered by mutant huntingtin, leading to the release of inflammatory mediators. Fingolimod (FTY), an FDA-approved sphingosine-1-phosphate (S1P) receptor agonist is used to treat multiple sclerosis (MS), a neuroinflammatory disease, and has shown therapeutic promise in other neurological conditions. Our study aimed to investigate the therapeutic potential of FTY for treating HD by utilizing a well-characterized mouse model of HD (zQ175dn) and wild-type littermates. The study design included a crossover, long-term oral treatment with 1mg/kg to 2mg/kg FTY from the age of 15 to 46 weeks (n = 128). Different motor behavior and physiological parameters were assessed throughout the study. The findings revealed that FTY rescued disease-related body weight loss in a sex-dependent manner, indicating its potential to regulate metabolic disturbances and to counteract neurodegenerative processes in HD. FTY intervention also rescued testicular atrophy, restored testis tissue structure in male mice suggesting a broader impact on peripheral tissues affected by huntingtin pathology. Histological analyses of the brain revealed delayed accumulation of activated astrocytes contributing to the preservation of the neural microenvironment by reducing neuroinflammation. The extent of FTY-related disease improvement was sex-dependent. Motor functions and body weight improved mostly in female mice with sustained estrogen levels, whereas males had to compensate for the ongoing, disease-related testis atrophy and the loss of androgen production. Our study underscores the beneficial therapeutic effects of FTY on HD involving endogenous steroid hormones and their important anabolic effects. It positions FTY as a candidate for therapeutic interventions targeting various aspects of HD pathology. Further studies are needed to fully evaluate its therapeutic potential in patients.

Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers

BACKGROUND

Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain.

NEW METHOD

We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice.

RESULTS

We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated.

COMPARISON WITH EXISTING METHODS

This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone.

CONCLUSIONS

The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.

Enhanced Hippocampal Spare Capacity in Q175DN Mice Despite Elevated mHTT Aggregation

Background

Huntington's disease (HD) is a neurodegenerative disease resulting in devastating motor, cognitive, and psychiatric deficits. The striatum is a brain region that controls movement and some forms of cognition and is most significantly impacted in HD. However, despite well-documented deficits in learning and memory in HD, knowledge of the potential implication of other brain regions such as the hippocampus remains limited.

Objective

Here, we study the comparative impact of enhanced mHTT aggregation and neuropathology in the striatum and hippocampus of two HD mouse models.

Methods

We utilized the zQ175 as a control HD mouse model and the Q175DN mice lacking the PGK-Neomycin cassette generated in house. We performed a comparative characterization of the neuropathology between zQ175 and Q175DN mice in the striatum and the hippocampus by assessing HTT aggregation, neuronal and glial pathology, chaperone expression, and synaptic density.

Results

We showed that Q175DN mice presented enhanced mHTT aggregation in both striatum and hippocampus compared to zQ175. Striatal neurons showed a greater susceptibility to enhanced accumulation of mHTT than hippocampal neurons in Q175DN despite high levels of mHTT in both regions. Contrary to the pathology seen in the striatum, Q175DN hippocampus presented enhanced spare capacity showing increased synaptic density, decreased Iba1+ microglia density and enhanced HSF1 levels in specific subregions of the hippocampus compared to zQ175.

Conclusions

Q175DN mice are a valuable tool to understand the fundamental susceptibility differences to mHTT toxicity between striatal neurons and other neuronal subtypes. Furthermore, our findings also suggest that cognitive deficits observed in HD animals might arise from either striatum dysfunction or other regions involved in cognitive processes but not from hippocampal degeneration.

New insights into the mechanism of resistance to lenvatinib and strategies for lenvatinib sensitization in hepatocellular carcinoma

Lenvatinib is a multikinase inhibitor that suppresses vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), platelet-derived growth factor receptor α (PDGFRα), as well as the proto-oncogenes RET and KIT. Lenvatinib has been approved by the US Food and Drug Administration (FDA) for the first-line treatment of hepatocellular carcinoma (HCC) due to its superior efficacy when compared to sorafenib. Unfortunately, the development of drug resistance to lenvatinib is becoming increasingly common. Thus, there is an urgent need to identify the factors that lead to drug resistance and ways to mitigate it. We summarize the molecular mechanisms that lead to lenvatinib resistance (LR) in HCC, which involve programmed cell death (PCD), translocation processes, and changes in the tumor microenvironment (TME), and provide strategies to reverse resistance.

Medicinal polypharmacology-a scientific glossary of terminology and concepts

Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.

Deep behavioural phenotyping of the Q175 Huntington disease mouse model: effects of age, sex, and weight

BACKGROUND

Huntington disease (HD) is a neurodegenerative disorder with complex motor and behavioural manifestations. The Q175 knock-in mouse model of HD has gained recent popularity as a genetically accurate model of the human disease. However, behavioural phenotypes are often subtle and progress slowly in this model. Here, we have implemented machine-learning algorithms to investigate behaviour in the Q175 model and compare differences between sexes and disease stages. We explore distinct behavioural patterns and motor functions in open field, rotarod, water T-maze, and home cage lever-pulling tasks.

RESULTS

In the open field, we observed habituation deficits in two versions of the Q175 model (zQ175dn and Q175FDN, on two different background strains), and using B-SOiD, an advanced machine learning approach, we found altered performance of rearing in male manifest zQ175dn mice. Notably, we found that weight had a considerable effect on performance of accelerating rotarod and water T-maze tasks and controlled for this by normalizing for weight. Manifest zQ175dn mice displayed a deficit in accelerating rotarod (after weight normalization), as well as changes to paw kinematics specific to males. Our water T-maze experiments revealed response learning deficits in manifest zQ175dn mice and reversal learning deficits in premanifest male zQ175dn mice; further analysis using PyMouseTracks software allowed us to characterize new behavioural features in this task, including time at decision point and number of accelerations. In a home cage-based lever-pulling assessment, we found significant learning deficits in male manifest zQ175dn mice. A subset of mice also underwent electrophysiology slice experiments, revealing a reduced spontaneous excitatory event frequency in male manifest zQ175dn mice.

CONCLUSIONS

Our study uncovered several behavioural changes in Q175 mice that differed by sex, age, and strain. Our results highlight the impact of weight and experimental protocol on behavioural results, and the utility of machine learning tools to examine behaviour in more detailed ways than was previously possible. Specifically, this work provides the field with an updated overview of behavioural impairments in this model of HD, as well as novel techniques for dissecting behaviour in the open field, accelerating rotarod, and T-maze tasks.

Emerging Role of ABC Transporters in Glia Cells in Health and Diseases of the Central Nervous System

ATP-binding cassette (ABC) transporters play a crucial role for the efflux of a wide range of substrates across different cellular membranes. In the central nervous system (CNS), ABC transporters have recently gathered significant attention due to their pivotal involvement in brain physiology and neurodegenerative disorders, such as Alzheimer's disease (AD). Glial cells are fundamental for normal CNS function and engage with several ABC transporters in different ways. Here, we specifically highlight ABC transporters involved in the maintenance of brain homeostasis and their implications in its metabolic regulation. We also show new aspects related to ABC transporter function found in less recognized diseases, such as Huntington's disease (HD) and experimental autoimmune encephalomyelitis (EAE), as a model for multiple sclerosis (MS). Understanding both their impact on the physiological regulation of the CNS and their roles in brain diseases holds promise for uncovering new therapeutic options. Further investigations and preclinical studies are warranted to elucidate the complex interplay between glial ABC transporters and physiological brain functions, potentially leading to effective therapeutic interventions also for rare CNS disorders.

Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington's disease

Huntington's disease (HD) is a progressive neurodegenerative disease affecting motor and cognitive abilities. Multiple studies have found white matter anomalies in HD-affected humans and animal models of HD. The identification of sensitive white-matter-based biomarkers in HD animal models will be important in understanding disease mechanisms and testing the efficacy of therapeutic interventions. Here we investigated the progression of white matter deficits in the knock-in zQ175DN heterozygous (HET) mouse model of HD at 3, 6 and 11 months of age (M), reflecting different states of phenotypic progression. We compared findings from traditional diffusion tensor imaging (DTI) and advanced fixel-based analysis (FBA) diffusion metrics for their sensitivity in detecting white matter anomalies in the striatum, motor cortex, and segments of the corpus callosum. FBA metrics revealed progressive and widespread reductions of fiber cross-section and fiber density in myelinated bundles of HET mice. The corpus callosum genu was the most affected structure in HET mice at 6 and 11 M based on the DTI and FBA metrics, while the striatum showed the earliest progressive differences starting at 3 M based on the FBA metrics. Overall, FBA metrics detected earlier and more prominent alterations in myelinated fiber bundles compared to the DTI metrics. Luxol fast blue staining showed no loss in myelin density, indicating that diffusion anomalies could not be explained by myelin reduction but diffusion anomalies in HET mice were accompanied by increased levels of neurofilament light chain protein at 11 M. Altogether, our findings reveal progressive alterations in myelinated fiber bundles that can be measured using diffusion MRI, representing a candidate noninvasive imaging biomarker to study phenotype progression and the efficacy of therapeutic interventions in zQ175DN mice. Moreover, our study exposed higher sensitivity of FBA than DTI metrics, suggesting a potential benefit of adopting these advanced metrics in other contexts, including biomarker development in humans.

The contribution of preclinical magnetic resonance imaging and spectroscopy to Huntington's disease

Huntington's disease is an inherited disorder characterized by psychiatric, cognitive, and motor symptoms due to degeneration of medium spiny neurons in the striatum. A prodromal phase precedes the onset, lasting decades. Current biomarkers include clinical score and striatal atrophy using Magnetic Resonance Imaging (MRI). These markers lack sensitivity for subtle cellular changes during the prodromal phase. MRI and MR spectroscopy offer different contrasts for assessing metabolic, microstructural, functional, or vascular alterations in the disease. They have been used in patients and mouse models. Mouse models can be of great interest to study a specific mechanism of the degenerative process, allow better understanding of the pathogenesis from the prodromal to the symptomatic phase, and to evaluate therapeutic efficacy. Mouse models can be divided into three different constructions: transgenic mice expressing exon-1 of human huntingtin (HTT), mice with an artificial chromosome expressing full-length human HTT, and knock-in mouse models with CAG expansion inserted in the murine htt gene. Several studies have used MRI/S to characterized these models. However, the multiplicity of modalities and mouse models available complicates the understanding of this rich corpus. The present review aims at giving an overview of results obtained using MRI/S for each mouse model of HD, to provide a useful resource for the conception of neuroimaging studies using mouse models of HD. Finally, despite difficulties in translating preclinical protocols to clinical applications, many biomarkers identified in preclinical models have already been evaluated in patients. This review also aims to cover this aspect to demonstrate the importance of MRI/S for studying HD.

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

HD_BPMDS: a curated binary pattern multitarget dataset of Huntington's disease-targeting agents

The discovery of both distinctive lead molecules and novel drug targets is a great challenge in drug discovery, which particularly accounts for orphan diseases. Huntington's disease (HD) is an orphan, neurodegenerative disease of which the pathology is well-described. However, its pathophysiological background and molecular mechanisms are poorly understood. To date, only 2 drugs have been approved on the US and European markets, both of which address symptomatic aspects of this disease only. Although several hundreds of agents were described with efficacy against the HD phenotype in in vitro and/or in vivo models, a successful translation into clinical use is rarely achieved. Two major impediments are, first, the lack of awareness and understanding of the interactome-the sum of key proteins, cascades, and mediators-that contributes to HD initiation and progression; and second, the translation of the little gained knowledge into useful model systems. To counteract this lack of data awareness, we manually compiled and curated the entire modulator landscape of successfully evaluated pre-clinical small-molecule HD-targeting agents which are annotated with substructural molecular patterns, physicochemical properties, as well as drug targets, and which were linked to benchmark databases such as PubChem, ChEMBL, or UniProt. Particularly, the annotation with substructural molecular patterns expressed as binary code allowed for the generation of target-specific and -unspecific fingerprints which could be used to determine the (poly)pharmacological profile of molecular-structurally distinct molecules.

Enrichment analysis of phenotypic data for drug repurposing in rare diseases

Drug-induced Behavioral Signature Analysis (DBSA), is a machine learning (ML) method for in silico screening of compounds, inspired by analytical methods quantifying gene enrichment in genomic analyses. When applied to behavioral data it can identify drugs that can potentially reverse in vivo behavioral symptoms in animal models of human disease and suggest new hypotheses for drug discovery and repurposing. We present a proof-of-concept study aiming to assess Drug-induced Behavioral Signature Analysis (DBSA) as a systematic approach for drug discovery for rare disorders. We applied Drug-induced Behavioral Signature Analysis to high-content behavioral data obtained with SmartCube®, an automated in vivo phenotyping platform. The therapeutic potential of several dozen approved drugs was assessed for phenotypic reversal of the behavioral profile of a Huntington's Disease (HD) murine model, the Q175 heterozygous knock-in mice. The in silico Drug-induced Behavioral Signature Analysis predictions were enriched for drugs known to be effective in the symptomatic treatment of Huntington's Disease, including bupropion, modafinil, methylphenidate, and several SSRIs, as well as the atypical antidepressant tianeptine. To validate the method, we tested acute and chronic effects of tianeptine (20 mg/kg, i. p.) in vivo, using Q175 mice and wild type controls. In both experiments, tianeptine significantly rescued the behavioral phenotype assessed with the SmartCube® platform. Our target-agnostic method thus showed promise for identification of symptomatic relief treatments for rare disorders, providing an alternative method for hypothesis generation and drug discovery for disorders with huge disease burden and unmet medical needs.

Time- and Sex-Dependent Effects of Fingolimod Treatment in a Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia. Fingolimod has previously shown beneficial effects in different animal models of AD. However, it has shown contradictory effects when it has been applied at early disease stages. Our objective was to evaluate fingolimod in two different treatment paradigms. To address this aim, we treated male and female APP-transgenic mice for 50 days, starting either before plaque deposition at 50 days of age (early) or at 125 days of age (late). To evaluate the effects, we investigated the neuroinflammatory and glial markers, the Aβ load, and the concentration of the brain-derived neurotrophic factor (BDNF). We found a reduced Aβ load only in male animals in the late treatment paradigm. These animals also showed reduced microglia activation and reduced IL-1β. No other treatment group showed any difference in comparison to the controls. On the other hand, we detected a linear correlation between BDNF and the brain Aβ concentrations. The fingolimod treatment has shown beneficial effects in AD models, but the outcome depends on the neuroinflammatory state at the start of the treatment. Thus, according to our data, a fingolimod treatment would be effective after the onset of the first AD symptoms, mainly affecting the neuroinflammatory reaction to the ongoing Aβ deposition.