Spinocerebellar ataxia type 27B (SCA27B) in India: insights from a large cohort study suggest ancient origin.

BACKGROUND: The ethnic diversity of India provides a unique opportunity to study the history of the origin of mutations of genetic disorders. Spinocerebellar ataxia type 27B (SCA27B), a recently identified dominantly inherited cerebellar disorder is caused by GAA-repeat expansions in intron 1 of Fibroblast Growth Factor 14 (FGF14). Predominantly reported in the European population, we aimed to screen this mutation and study the founder haplotype of SCA27B in Indian ataxia patients. METHODS: We have undertaken screening of GAA repeats in a large Indian cohort of ~ 1400 uncharacterised ataxia patients and kindreds and long-read sequencing-based GAA repeat length assessment. High throughput genotyping-based haplotype analysis was also performed. We utilized ~ 1000 Indian genomes to study the GAA at-risk expansion alleles. FINDINGS: We report a high frequency of 1.83% (n = 23) of SCA27B in the uncharacterized Indian ataxia cohort. We observed several biallelic GAA expansion mutations (n = 5) with younger disease onset. We observed a risk haplotype (AATCCGTGG) flanking the FGF14-GAA locus over a 74 kb region in linkage disequilibrium. We further studied the frequency of this risk haplotype across diverse geographical population groups. The highest prevalence of the risk haplotype was observed in the European population (29.9%) followed by Indians (21.5%). The observed risk haplotype has existed through ~ 1100 generations (~ 22,000 years), assuming a correlated genealogy. INTERPRETATION: This study provides valuable insights into SCA27B and its Upper Paleolithic origin in the Indian subcontinent. The high occurrence of biallelic expansion is probably relevant to the endogamous nature of the Indian population.

Correlation Between Optical Coherence Tomography Angiography Findings at 3 to 6 Weeks and Functional Outcome at 3 Months Following Acute Ischaemic Stroke Due to Extracranial Carotid Artery Atherosclerotic Disease.

We wanted to evaluate if optical coherence tomography angiography OCTA findings could predict the functional outcome in extracranial carotid artery atherosclerotic disease (ECAD) associated stroke. This exploratory study was performed on adults with acute ischaemic stroke due to ECAD at 3-6 weeks following stroke onset with risk factor matched controls without carotid artery stenosis. Twenty-three stroke patients (cases) and 23 controls were enrolled. There was significant difference between cases and controls in deep vessel density at the macula (p = .0007) and in radial peripapillary capillary perfusion density (RPCPD) at the optic nerve head (ONH) (p = .0007). Statistically significant difference was noted in the total superficial vessel density (SVD) at the macula (SVD within 1 standard deviation [SD] versus SVD beyond 1 SD of control data) in the ipsilateral eye and functional outcome at 3 months (poor versus very good outcome, modified Rankin scale [mRS] 0-1 versus mRS 2-6, respectively; p = .0361). There was statistically insignificant correlation between the RPCPD at the ONH and the National Institutes of Health Stroke Scale score at admission, mRS at discharge, and mRS at 3 months following stroke onset (r = .33, r = .35, r = .39; p = .11, p = .09, p = .06, respectively). The findings of this exploratory study suggested that OCTA findings may predict 3 month outcomes in cases of ECAD-related stroke and could be useful in decision making in future intervention studies as to whether intervene or not in patients having critical or non-critical ECAD for preventing stroke.

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.

Gray Matter Atrophy in a 6-OHDA-induced Model of Parkinson's Disease.

INTRODUCTION: Magnetic resonance imaging (MRI) based brain morphometric changes in unilateral 6-hydroxydopamine (6-OHDA) induced Parkinson's disease (PD) model can be elucidated using voxel-based morphometry (VBM), study of alterations in gray matter volume and Machine Learning (ML) based analyses. METHODS: We investigated gray matter atrophy in 6-OHDA induced PD model as compared to sham control using statistical and ML based analysis. VBM and atlas-based volumetric analysis was carried out at regional level. Support vector machine (SVM)-based algorithms wherein features (volume) extracted from (a) each of the 150 brain regions (b) statistically significant features (only) and (c) volumes of each cluster identified after application of VBM (VBM_Vol) were used for training the decision model. The lesion of the 6-OHDA model was validated by estimating the net contralateral rotational behaviour by the injection of apomorphine drug and motor impairment was assessed by rotarod and open field test. RESULTS AND DISCUSSION: In PD, gray matter volume (GMV) atrophy was noted in bilateral cortical and subcortical brain regions, especially in the internal capsule, substantia nigra, midbrain, primary motor cortex and basal ganglia-thalamocortical circuits in comparison with sham control. Behavioural results revealed an impairment in motor performance. SVM analysis showed 100% classification accuracy, sensitivity and specificity at both 3 and 7 weeks using VBM_Vol. CONCLUSION: Unilateral 6-OHDA induced GMV changes in both hemispheres at 7th week may be associated with progression of the disease in the PD model. SVM based approaches provide an increased classification accuracy to elucidate GMV atrophy.

Prevalence and Risk Factors of Cerebral Microbleeds in Community-Dwelling Adults in Urban Delhi.

BACKGROUND: Several observational studies have reported the prevalence of cerebral microbleeds (CMBs) and their risk factors in an elderly population. Any information in this regard is currently lacking from India. Aim of this study was to estimate the prevalence, risk factors of CMBs, and association with cognition in an Indian urban population aged 50 years and above. METHODS: Household surveys were conducted as part of ongoing Longitudinal Cognition and Aging Research on Population of the National Capital Region (LoCARPoN) study in areas of urban Delhi. Magnetic resonance imaging of the brain was performed in 2599 participants. Using standard neuropsychological battery, mean Z-scores for each domain (memory, executive, information) were derived. Binary and stepwise logistic regression models were used to determine associated risk factors for the presence of CMB and its association with cognitive domains. RESULTS: The prevalence of CMBs was 14.42% (95% confidence interval [CI]: 13.06-15.73). Of these, 203 (7.81%) participants had single CMBs and 172 (6.61%) had multiple microbleeds (≥2). Higher prevalence was observed in older age (60-70 years: odds ratio [OR]: 1.25 [95% CI: 0.93-1.67]; 70-80 years: OR: 2.05 [95% CI: 1.48-2.84]; ≥80 years: OR: 3.27 [95% CI: 1.97-5.44]) compared to individuals in the age group 50-60 years. History of stroke (OR: 2.97 [95% CI: 1.56-5.66]), hypertension (OR: 1.36 [95% CI: 1.05-1.75]), and smoking (OR: 1.43 [95% CI: 1.11-1.85]) was associated with at least one CMB. Multiple CMBs were associated with worse scores in memory and executive domains. CONCLUSION: Older age, hypertension, history of stroke, and history of smoking emerged as important risk factors for the presence of multiple CMBs. Follow-up study is required to determine implications of CMBs.

Identifying unstable CNG repeat loci in the human genome: a heuristic approach and implications for neurological disorders.

Tandem nucleotide repeat (TNR) expansions, particularly the CNG nucleotide configuration, are associated with a variety of neurodegenerative disorders. In this study, we aimed to identify novel unstable CNG repeat loci associated with the neurogenetic disorder spinocerebellar ataxia (SCA). Using a computational approach, 15,069 CNG repeat loci in the coding and noncoding regions of the human genome were identified. Based on the feature selection criteria (repeat length >10 and functional location of repeats), we selected 52 repeats for further analysis and evaluated the repeat length variability in 100 control subjects. A subset of 19 CNG loci observed to be highly variable in control subjects was selected for subsequent analysis in 100 individuals with SCA. The genes with these highly variable repeats also exhibited higher gene expression levels in the brain according to the tissue expression dataset (GTEx). No pathogenic expansion events were identified in patient samples, which is a limitation given the size of the patient group examined; however, these loci contain potential risk alleles for expandability. Recent studies have implicated GLS, RAI1, GIPC1, MED15, EP400, MEF2A, and CNKSR2 in neurological diseases, with GLS, GIPC1, MED15, RAI1, and MEF2A sharing the same repeat loci reported in this study. This finding validates the approach of evaluating repeat loci in different populations and their possible implications for human pathologies.

Expanding the phenotypic and genotypic spectrum of DYT-TUBB4A with seven patients from India.

BACKGROUND: Variants in the TUBB4A gene are associated with dystonia (DYT-TUBB4A), Hypomyelination with Atrophy of the Basal Ganglia and Cerebellum (H-ABC) and spastic paraplegia. Phenotypes intermediate to these three broad phenotypes are also observed. These are rare disorders, and data from diverse populations remains limited. We report seven Indian cases with dystonia phenotype related to TUBB4A mutation. CASES: Among these seven patients, age at onset ranged from 5 to 48 years. Five patients had cranio-cervical onset of dystonia. One patient had prominent parkinsonism with dystonia. Patients responded well to botulinum toxin injected for laryngeal, cervical and jaw dystonia. The patient with parkinsonism responded well to levodopa, albeit with development of dyskinesias. Apart from the common p.Arg2Gly variant in three patients with DYT-TUBB4A, other variants included p.Arg262Pro, p.Arg39Cys and p.Asp245Asn. CONCLUSIONS: We report the first collection of cases with TUBB4A mutation from India. We expand the phenotype to include levodopa-responsive parkinsonism. Indian patients, consistent with global literature, harbor prominent adductor dysphonia, cervical and jaw dystonia, which responds well to botulinum treatment.

Molecular clues unveiling spinocerebellar ataxia type-12 pathogenesis.

Spinocerebellar Ataxia type-12 (SCA12) is a neurodegenerative disease caused by tandem CAG repeat expansion in the 5'-UTR/non-coding region of PPP2R2B. Molecular pathology of SCA12 has not been studied in the context of CAG repeats, and no appropriate models exist. We found in human SCA12-iPSC-derived neuronal lineage that expanded CAG in PPP2R2B transcript forms nuclear RNA foci and were found to sequester variety of proteins. Further, the ectopic expression of transcript containing varying length of CAG repeats exhibits non-canonical repeat-associated non-AUG (RAN) translation in multiple frames in HEK293T cells, which was further validated in patient-derived neural stem cells using specific antibodies. mRNA sequencing of the SCA12 and control neurons have shown a network of crucial transcription factors affecting neural fate, in addition to alteration of various signaling pathways involved in neurodevelopment. Altogether, this study identifies the molecular signatures of SCA12 disorder using patient-derived neuronal cell lines.

Acute bilateral sensorineural hearing loss as presentation of leptomeningeal metastases.

We describe a rare occurrence of bilateral acute severe sensorineural hearing loss in a middle-aged man that heralded the diagnosis of metastatic gastric cancer.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.