HDAC11: A novel inflammatory biomarker in Huntington's disease.
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ABSTRACT:
SUBMITTER: Kumar V
PROVIDER: S-EPMC9149973 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
HDAC11: A novel inflammatory biomarker in Huntington's disease.
Kumar Vishal V Kaur Simranjit S Kapil Lakshay L Singh Charan C Singh Arti A
EXCLI journal 20220324
Similar Datasets
Project description:In several neurodegenerative diseases, like Huntington's disease (HD), treatments are still lacking. To determine whether a treatment is effective, sensitive disease progression biomarkers are especially needed for the premanifest phase, since this allows the evaluation of neuroprotective treatments preventing, or delaying disease manifestation. On the basis of a longitudinal study we present a biomarker that was derived by integrating behavioural and neurophysiological data reflecting cognitive processes of action control. The measure identified is sensitive enough to track disease progression over a period of only 6 month. Changes tracked were predictive for a number of clinically relevant parameters and the sensitivity of the measure was higher than that of currently used parameters to track prodromal disease progression. The study provides a biomarker, which could change practice of progression diagnostics in a major basal ganglia disease and which may help to evaluate potential neuroprotective treatments in future clinical trials.
| S-EPMC3647202 | biostudies-literature
Project description:Huntington's disease (HD) is genetically determined but with variability in symptom onset, leading to uncertainty as to when pharmacological intervention should be initiated. Here we take a computational approach based on neurocognitive phenotyping, computational modeling, and classification, in an effort to provide quantitative predictors of HD before symptom onset. A large sample of subjects-consisting of both pre-manifest individuals carrying the HD mutation (pre-HD), and early symptomatic-as well as healthy controls performed the antisaccade conflict task, which requires executive control and response inhibition. While symptomatic HD subjects differed substantially from controls in behavioral measures [reaction time (RT) and error rates], there was no such clear behavioral differences in pre-HD. RT distributions and error rates were fit with an accumulator-based model which summarizes the computational processes involved and which are related to identified mechanisms in more detailed neural models of prefrontal cortex and basal ganglia. Classification based on fitted model parameters revealed a key parameter related to executive control differentiated pre-HD from controls, whereas the response inhibition parameter declined only after symptom onset. These findings demonstrate the utility of computational approaches for classification and prediction of brain disorders, and provide clues as to the underlying neural mechanisms.
| S-EPMC4752511 | biostudies-literature
Project description:BackgroundImpaired gait plays an important role for quality of life in patients with Huntington's disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments.ObjectiveTo objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington's Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC).MethodsPatients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age- and gender-matched controls performed a defined gait test (4 × 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV).ResultsStride length (- 15%) and gait velocity (- 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416 ≤ rSp ≤ 0.690). Objective gait variability parameters correlated with disease stage based upon TFC.ConclusionsSensor-based gait variability parameters were identified as clinically most relevant digital biomarker for gait impairment in HD. Altered gait variability represents characteristic irregularity of gait in HD and reflects disease severity.
| S-EPMC7293689 | biostudies-literature
Project description:BackgroundThe evaluation of effective disease-modifying therapies for neurodegenerative disorders relies on objective and accurate measures of progression in at-risk individuals. Here we used a computational approach to identify a functional brain network associated with the progression of preclinical Huntington's disease (HD).MethodsTwelve premanifest HD mutation carriers were scanned with [18F]-fluorodeoxyglucose PET to measure cerebral metabolic activity at baseline and again at 1.5, 4, and 7 years. At each time point, the subjects were also scanned with [11C]-raclopride PET and structural MRI to measure concurrent declines in caudate/putamen D2 neuroreceptor binding and tissue volume. The rate of metabolic network progression in this cohort was compared with the corresponding estimate obtained in a separate group of 21 premanifest HD carriers who were scanned twice over a 2-year period.ResultsIn the original premanifest cohort, network analysis disclosed a significant spatial covariance pattern characterized by progressive changes in striato-thalamic and cortical metabolic activity. In these subjects, network activity increased linearly over 7 years and was not influenced by intercurrent phenoconversion. The rate of network progression was nearly identical when measured in the validation sample. Network activity progressed at approximately twice the rate of single region measurements from the same subjects.ConclusionMetabolic network measurements provide a sensitive means of quantitatively evaluating disease progression in premanifest individuals. This approach may be incorporated into clinical trials to assess disease-modifying agents.Trial registrationRegistration is not required for observational studies.FundingNIH (National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering) and CHDI Foundation Inc.
| S-EPMC3754266 | biostudies-literature
Project description:In this study we investigated the suitability of blood to identify HD transcriptomic biomarkers, validated the outcome in an independent cohort and derived a first empiric panel of biomarkers capable of predicting HD motor scores. Finally we examined whether patient gene expression profiles could provide information about HD affected biological pathways. Examination of differentially expressed genes in peripheral whole blood using linear modelling of gene expression data (3 DGE) against UHDRS total motor scores of Huntington's Disease mutation carriers and controls.
2015-02-02 | E-GEOD-51799 | biostudies-arrayexpress
Project description:IntroductionAlzheimer's disease (AD) is characterized by amyloid pathology and neuroinflammation, leading to cognitive decline. Targeting histone deacetylase-11 (HDAC11) offers a novel therapeutic strategy due to its role in immune regulation.MethodsWe conducted neuropathological analyses on human AD post mortem brain tissues and 5xFAD transgenic mice. We developed PB94, a brain-permeable HDAC11-selective inhibitor, and assessed its effects using live-animal imaging and behavioral studies.ResultsHDAC11 was significantly upregulated in AD brains, correlating with amyloid pathology and neuroinflammatory markers. PB94 treatment reduced amyloid burden and neuroinflammation, improving cognitive function in 5xFAD mice.DiscussionOur findings highlight HDAC11 as a promising drug target for AD. PB94's ability to reduce amyloid pathology and neuroinflammation suggests its potential as an effective therapeutic. This study supports further exploration of HDAC11 inhibition as a treatment strategy for AD.HighlightsHistone deacetylase-11 (HDAC11) is significantly upregulated in Alzheimer's disease (AD) brains and colocalizes with amyloid pathology and neuroinflammatory markers. Novel brain-permeable HDAC11-selective inhibitor PB94 demonstrates promising therapeutic potential for AD treatment. PB94 treatment reduces amyloid burden and neuroinflammation in AD mouse models, confirmed by live imaging studies. HDAC11 inhibition enhances microglial phagocytosis of amyloid beta proteins and modulates inflammatory cytokine levels. PB94 treatment improves cognitive function in AD mouse models while showing favorable brain penetration and selectivity.
| S-EPMC11923381 | biostudies-literature
Project description:IntroductionImmune system activation is involved in Huntington's disease (HD) pathogenesis and biomarkers for this process could be relevant to study the disease and characterise the therapeutic response to specific interventions. We aimed to study inflammatory cytokines and microglial markers in the CSF of HD patients.MethodsCSF TNF-α, IL-1β, IL-6, IL-8, YKL-40, chitotriosidase, total tau and neurofilament light chain (NFL) from 23 mutation carriers and 14 healthy controls were assayed.ResultsCSF TNF-α and IL-1β were below the limit of detection. Mutation carriers had higher YKL-40 (p = 0.003), chitotriosidase (p = 0.015) and IL-6 (p = 0.041) than controls. YKL-40 significantly correlated with disease stage (p = 0.007), UHDRS total functional capacity score (r = -0.46, p = 0.016), and UHDRS total motor score (r = 0.59, p = 4.5*10-4) after adjustment for age.ConclusionYKL-40 levels in CSF may, after further study, come to have a role as biomarkers for some aspects of HD. Further investigation is needed to support our exploratory findings.
| S-EPMC5033331 | biostudies-literature
Project description:With several therapeutic approaches in development for Huntington's disease, there is a need for easily accessible biomarkers to monitor disease progression and therapy response. We performed next-generation sequencing-based transcriptome analysis of total RNA from peripheral blood of 91 mutation carriers (27 presymptomatic and, 64 symptomatic) and 33 controls. Transcriptome analysis by DeepSAGE identified 167 genes significantly associated with clinical total motor score in Huntington's disease patients. Relative to previous studies, this yielded novel genes and confirmed previously identified genes, such as H2AFY, an overlap in results that has proven difficult in the past. Pathway analysis showed enrichment of genes of the immune system and target genes of miRNAs, which are downregulated in Huntington's disease models. Using a highly parallelized microfluidics array chip (Fluidigm), we validated 12 of the top 20 significant genes in our discovery cohort and 7 in a second independent cohort. The five genes (PROK2, ZNF238, AQP9, CYSTM1 and ANXA3) that were validated independently in both cohorts present a candidate biomarker panel for stage determination and therapeutic readout in Huntington's disease. Finally we suggest a first empiric formula predicting total motor score from the expression levels of our biomarker panel. Our data support the view that peripheral blood is a useful source to identify biomarkers for Huntington's disease and monitor disease progression in future clinical trials.
| S-EPMC4592077 | biostudies-literature
Project description:ObjectiveDetermining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine-grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.MethodsWe employ a probabilistic event-based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track-HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.ResultsThe model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross-validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow-up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.InterpretationWe used a data-driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event-based model, to provide new insight into Huntington's disease progression and to support fine-grained patient stratification for future precision medicine in Huntington's disease.
| S-EPMC5945962 | biostudies-literature
Project description: Not available
| S-EPMC6585568 | biostudies-literature