Browsing by Author "Delalle, Ivana"

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    miRNA, transcriptional regulation and cognitive decline
    (Wiley, 2025-01-03) Fischer, Andre; Sananbenesi, Farahnaz; Nho, Kwangsik; Manfred Krüger, Dennis; Shaw, Leslie M.; Saykin, Andrew J.; Delalle, Ivana; Radiology and Imaging Sciences, School of Medicine
    Background: Despite significant advancements in the development of blood biomarkers for AD, challenges persist due to the complex interplay of genetic and environmental risk factors in AD pathogenesis. Epigenetic processes, including non‐coding RNAs and especially microRNAs (miRs), have emerged as important players in the molecular mechanisms underlying neurodegenerative diseases. MiRs have the ability to fine‐tune gene expression and proteostasis, and microRNAome profiling in liquid biopsies is gaining increasing interest since changes in miR levels can indicate the presence of multiple pathologies. We have profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE and 847 individuals of the ANDI cohort. Methods: We profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE (German Longitudinal Cognitive Impairment and Dementia Study) and 847 individuals of the ANDI (Aging and Dementia in the Community) cohort, consisting of individuals diagnosed with SCD, MCI, AD, or control. Results: By applying differential expression, WGCNA, as well as linear and non‐linear machine learning approaches, we identify microRNA signatures that can help identify patients at distinct stages of disease progression, as well as signatures that can predict the course of the disease. These data are compared with phenotyping data, such as cognitive function and ATN biomarkers. We will also discuss the role of other non‐coding RNAs besides microRNAs and provide a framework for developing RNA‐based point‐of‐care assays.
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    Plasma miRNAs across the Alzheimer's disease continuum: Relationship to central biomarkers
    (Wiley, 2024) Liu, Shiwei; Park, Tamina; Krüger, Dennis M.; Pena-Centeno, Tonatiuh; Burkhardt, Susanne; Schutz, Anna-Lena; Huang, Yen-Ning; Rosewood, Thea; Chaudhuri, Soumilee; Cho, MinYoung; Risacher, Shannon L.; Wan, Yang; Shaw, Leslie M.; Sananbenesi, Farahnaz; Brodsky, Alexander S.; Lin, Honghuang; Krunic, Andre; Krzysztof Blusztajn, Jan; Saykin, Andrew J.; Delalle, Ivana; Fischer, Andre; Nho, Kwangsik; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Introduction: MicroRNAs (miRNAs) play important roles in gene expression regulation and Alzheimer's disease (AD) pathogenesis. Methods: We investigated the association between baseline plasma miRNAs and central AD biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 803): amyloid, tau, and neurodegeneration (A/T/N). Differentially expressed miRNAs and their targets were identified, followed by pathway enrichment analysis. Machine learning approaches were applied to investigate the role of miRNAs as blood biomarkers. Results: We identified nine, two, and eight miRNAs significantly associated with A/T/N positivity, respectively. We identified 271 genes targeted by amyloid-related miRNAs with estrogen signaling receptor-mediated signaling among the enriched pathways. Additionally, 220 genes targeted by neurodegeneration-related miRNAs showed enrichment in pathways including the insulin growth factor 1 pathway. The classification performance of demographic information for A/T/N positivity was increased up to 9% with the inclusion of miRNAs. Discussion: Plasma miRNAs were associated with central A/T/N biomarkers, highlighting their potential as blood biomarkers. Highlights: We performed association analysis of microRNAs (miRNAs) with amyloid/tau/neurodegeneration (A/T/N) biomarker positivity. We identified dysregulated miRNAs for A/T/N biomarker positivity. We identified Alzheimer's disease biomarker-specific/common pathways related to miRNAs. miRNAs improved the classification for A/T/N positivity by up to 9%. Our study highlights the potential of miRNAs as blood biomarkers.
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    Response to the Letter, "Circulating small RNAs shed light on dementia risk," by Anthony S. Zannas
    (Wiley, 2025) Fischer, Andre; Nho, Kwangsik; Saykin, Andrew J.; Delalle, Ivana; Radiology and Imaging Sciences, School of Medicine
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    The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals
    (Wiley, 2024) Krüger, Dennis M.; Pena-Centeno, Tonatiuh; Liu, Shiwei; Park, Tamina; Kaurani, Lalit; Pradhan, Ranjit; Huang, Yen-Ning; Risacher, Shannon L.; Burkhardt, Susanne; Schütz, Anna-Lena; Wan, Yang; Shaw, Leslie M.; Brodsky, Alexander S.; DeStefano, Anita L.; Lin, Honghuang; Schroeder, Robert; Krunic, Andre; Hempel, Nina; Sananbenesi, Farahnaz; Krzysztof Blusztajn, Jan; Saykin, Andrew J.; Delalle, Ivana; Nho, Kwangsik; Fischer, Andre; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Introduction: MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). Methods: We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Results: We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. Discussion: Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. Highlights: We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.