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Ghoshal, N.

Publications and source records attributed to Ghoshal, N..

3 recordsLinked to original sources

Distribution of big tau isoforms in the human central and peripheral nervous system

ObjectiveTau is widely studied in the field of neurodegenerative disease research, yet most work has focused on canonical brain tau isoforms. A longer isoform, "big tau," produced by inclusion of exon 4a, is expressed in the peripheral nervous system (PNS) and select central nervous system (CNS) regions. We sought to characterize big tau molecular composition, anatomical distribution, and relevance to neurodegenerative disease. MethodsMass spectrometry was used to sequence big tau and map its distribution across the human nervous system. Postmortem samples included brain tissue from Alzheimers disease (AD), amyotrophic lateral sclerosis (ALS), and controls; spinal cord from ALS and controls; and peripheral nerves. Big and canonical ("small") tau isoforms were also quantified in cerebrospinal fluid (CSF) from young controls and participants stratified by amyloid status and cognitive impairment. ResultsHuman big tau results from insertion of either 355 or 251 amino acids encoded by exon 4a-long and exon 4a-short, respectively. Alternative splicing of exons 2, 3, and 10 generates multiple big tau isoforms. Total tau levels were [~]1000-fold higher in brain than in the PNS; however, the relative abundance of big tau increased from the CNS to the PNS, comprising 50 % of the total tau in the periphery and exhibiting considerable regional heterogeneity in the brain ([~] 1 % of total tau). In CSF, big tau levels were unchanged by amyloid abnormalities or cognitive impairment, whereas canonical tau increased with AD-related pathology. InterpretationBig tau represents a distinct tau population enriched in the PNS and largely uncoupled from disease-associated changes in brain-derived tau, suggesting that distinguishing big tau from canonical tau may improve interpretation of tau biomarkers and help differentiate CNS neurodegeneration from peripheral nerve pathology.

neuroscience↗

Sex differences in the clinical manifestation of autosomal dominant frontotemporal dementia

INTRODUCTIONSex differences are apparent in neurodegenerative diseases, but have not been comprehensively characterized in frontotemporal dementia (FTD). METHODSParticipants included 337 adults with autosomal dominant FTD enrolled in the ALLFTD Consortium. Clinical assessments and plasma were collected annually for up to six years. Linear mixed-effects models investigated how sex and disease stage associated with longitudinal trajectories of cognition, function, and neurofilament light chain (NfL). RESULTSWhile sex differences were not apparent at asymptomatic stages, females showed more rapid declines across all outcomes in symptomatic stages compared to males. In asymptomatic participants, the association between baseline NfL and clinical trajectories was weaker in females versus males, a difference that attenuated in symptomatic participants. DISCUSSIONIn genetic FTD, females show cognitive resilience in early disease stages followed by steeper clinical declines later in disease. Baseline NfL may be a less sensitive prognostic tool for clinical progression in females with FTD-causing mutations.

neuroscience↗

Revealing heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementias underlying heterogeneity, compared to analyzing the entire cohort as a single homogeneous group. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.

neuroscience↗