Biology subjects

Sachdev, P.

Publications and source records attributed to Sachdev, P..

5 recordsLinked to original sources

bioRxiv2022

Deep proteome analysis of plasma reveals novel biomarkers of mild cognitive impairment and Alzheimer's disease: A longitudinal study

Ageing is the primary risk factor for AD; however, there is a poor understanding of the biological mechanisms by which the ageing process contributes to the development of AD in some individuals, while others progress to advanced age with relatively little AD neuropathology. To halt the progression of AD, the preclinical stage of neurodegeneration (before the onset of clinical symptoms) is anticipated to be the more effective time point for applying potentially disease-modifying interventions in AD. The main objective of this study was to understand the age and disease related proteomic changes are detectable in plasma, based on retrospective analysis of longitudinal data and cross-sectional analyses of clinically diagnosed cases. We conducted an in-depth plasma proteomics analysis using intensive depletion of high-abundant plasma proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 14 (Hu14) followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) technique. In this study, we have begun to address the following questions; (1) differences in plasma proteomic profiles between normal ageing, vs ageing with progress to cognitive decline (MCI) or disease (dementia, probable AD), (2) cross-sectional analysis of baseline data, when all subjects are clinically identified as cognitively normal, provides insight into the preclinical changes which precede subsequent progression to AD and potentially provide early biomarkers, and (3) comparison of plasma at the point of progression to clinically diagnosed onset of cognitive decline or AD, can provide potential plasma biomarkers to facilitate clinical diagnosis. Furthermore, our findings also identified some proteins previously discovered in AD CSF and brain proteomics signatures that could provide clinically meaningful information. We identified differentially expressed proteins which were associated with several biological and molecular processes that may serve as therapeutic targets and fluid biomarkers for the disease.

neuroscience↗

bioRxiv2022

Impact of APOE e3 and e4 genotypes on plasma proteome signatures in Alzheimer's disease

The {varepsilon}4 allele of the apolipoprotein E (APOE) gene is a high-risk factor for Alzheimers disease (AD). However, approximately 25%-40% of patients with AD do not carry the APOE{varepsilon}4 allele, and the pathophysiological mechanisms underlying AD are less evident in these individuals. The main objective of this study was to understand better the changes in plasma that may contribute to disease pathogenesis in AD and how APOE{varepsilon}3 and APOE{varepsilon}4 contribute to biomarker profiles in AD. We conducted an in-depth plasma proteomics analysis using intensive depletion of high-abundant plasma proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 14 (Hu14) followed by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS PAGE) technique. In this study, we identified a high number of protein expression alterations in plasma which were found uniquely in APOE{varepsilon}3 and APOE{varepsilon}4 carriers. These differentially expressed proteins (DEPs) were associated with several molecular functions, including complement cascade, glycolysis, metabolism, plasma lipoprotein assembly, remodelling, and clearance. In addition to unique changes in both APOE genotypes, many proteins were also dysregulated in the presence of both APOE{varepsilon}3 and APOE{varepsilon}4 genotypes depicting the involvement of these proteins in the pathogenesis of AD regardless of the APOE genotypes. We also compared the plasma proteomes of{varepsilon} 4 and{varepsilon} 3 carriers in normal controls, which provided insight into factors that may provide protection from progression to AD despite the presence of the{varepsilon} 4 allele. Furthermore, our findings also identified some proteins previously discovered in AD CSF and brain proteomics signatures that could provide clinically meaningful information.

neuroscience↗

bioRxiv2021

Age- and sex-related topological organisation of human brain functional networks and their relationship to cognition

BACKGROUNDAge and sex associated with changes in functional brain network topology and cognition in large population of older adults have been poorly understood. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 UK Biobank participants (mean=62.83{+/-}7.41 years). METHODSBrain connectivity toolbox was used to derive the graph theory measures that assessed network integration, segregation, and strength. Multiple linear regression was performed the relationship between age, sex, cognition, and network measures. Subsequently, multivariate analysis was done to further examine the joint effect of the network measures on cognitive functions. RESULTSAge was associated with an overall decrease in the effectiveness of network communication (i.e. integration) and loss of functional specialisation (i.e. segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks which were less segregated than in men (FDR adjusted p<.05). Age-related changes were also more apparent in men than women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition (FDR adjusted p[≤].002). This may imply that individual measures may be inadequate to capture much of the variance in neural activity or its output and need further refinement. CONCLUSIONThe complexity of the functional brain organisation may be shaped by an individuals age and sex, which ultimately may influence cognitive performance of older adults. Age and sex stratification may be used to inform clinical neuroscience research to identify older adults at risk of cognitive dysfunction.

neuroscience↗

bioRxiv2020

Novel genetic variants associated with brain functional networks in 18,445 adults from the UK Biobank

This is the first study investigating the genetics of weighted functional brain network graph theory measures from 18,445 participants of the UK Biobank (44-80 years). The eighteen measures studied showed low heritability (mean h2SNP =0.12) and were highly genetically correlated. Genome-wide association studies for these measures observed 14 significant variants associated with strength of somatomotor and limbic networks. These intergenic variants were located near the PAX8 gene on chromosome 2. Gene-based analyses identified five significantly associated genes for five of the network measures, which have been implicated in sleep duration, neuronal differentiation/development, cancer, and susceptibility to neurodegenerative diseases. Genetic correlations with other traits were examined and significant correlations were observed with sleep measures and psychiatric symptoms. Further analysis found that somatomotor network strength was phenotypically associated with sleep duration and insomnia. Single nucleotide polymorphism (SNP) and gene level associations with functional network measures were identified, which may help uncover novel biological pathways relevant to human brain functional network integrity and diseases that affect it.

neuroscience↗

bioRxiv2018

Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models

Alzheimers disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, \"The Virtual Brain (TVB)\", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioral spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

neuroscience↗