Biology subjects

Atri, A.

Publications and source records attributed to Atri, A..

3 recordsLinked to original sources

bioRxiv2023

Guggulsterone Enhances NKX3.1 Expression and Induces Apoptosis in Prostate Cancer Cells: Implications for Chemoprevention

AimProstate cancer is a leading cause of cancer-related mortality, necessitating novel therapeutic and preventive strategies. This study aimed to investigate the anti-cancer properties of Guggulsterone (GS) on prostate cancer cells, specifically focusing on its effects on homeobox protein NKX3.1, apoptosis, and mitochondrial function. MethodThe study utilized the LNCaP prostate cancer cell line and involved cell culture, dose-response analysis, flow cytometry for apoptosis quantification, siRNA-mediated knockdown of NKX3.1, mitochondrial membrane potential assessment using TMRM staining, qRT-PCR analysis, and Western blotting. The stability of NKX3.1 mRNA and protein in response to GS was also evaluated. ResultGS treatment induced apoptosis in LNCaP cells in a dose and time-dependent manner, with an impact on early and late-stage apoptosis. This effect was mediated, at least in part, through the upregulation of NKX3.1. GS enhanced NKX3.1 expression at both the protein and mRNA levels, even in the absence of androgens. Furthermore, GS treatment extended the half-life of NKX3.1 mRNA and protein, suggesting an effect on their stability. ConclusionThis study provides valuable insights into the anti-cancer mechanisms of GS in prostate cancer cells. The upregulation of NKX3.1 by GS and its role in mediating apoptosis offers NKX3.1 as a potential prevention target for PCa. The findings open new research avenues for the development of targeted therapies that stabilize NKX3.1 expression and protect mitochondrial function. Further investigations are needed to understand the intricate molecular mechanisms underlying GSs effects fully, potentially improving prostate cancer management and outcomes.

cancer biology↗

bioRxiv2023

Disease progression modelling reveals heterogeneity in trajectories of Lewy-type alpha-synuclein pathology

Lewy body (LB) disorders, characterized by the aggregation of misfolded -synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. By applying data-driven disease progression modelling to regional neuropathological LB density scores from 814 brain donors, we describe three inferred trajectories of LB pathology that were characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) showed earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) exhibited the first abnormalities in brainstem regions. Early limbic pathology was associated with Alzheimers disease-associated characteristics. Meanwhile, brainstem-first pathology was associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in LBDs.

pathology↗

bioRxiv2023

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons With and Without Significant Amyloid Plaques

BackgroundAmyloid-{beta} (A{beta}) plaques play a pivotal role in Alzheimers disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing A{beta} plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of A{beta}-plaques. ObjectiveTo develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma A{beta}42/40 ratio. MethodsWe extracted hippocampal multivariate morphometry statistics (MMS) from MR images and together with plasma A{beta}42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimers Institute (BAI), including prediction accuracy, precision, recall rate, F1 score and AUC score. ResultsResults from ADNI (mean age 72.6, A{beta}+ rate 49.5%) and BAI (mean age 66.2, A{beta}+ rate 36.9%) datasets revealed the integrated multimodal (IMM) models superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma A{beta}42/40 (0.73 and 0.81) predictors. ConclusionOur IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimers disease diagnostics, leveraging diverse pathologic features related to A{beta} plaques and structural MRI.

neuroscience↗