Biology subjects

Adewale, Q.

Publications and source records attributed to Adewale, Q..

5 recordsLinked to original sources

bioRxiv2026

MicroRNA correlates of resilience to Alzheimer's disease identify candidate therapeutic targets for neuroprotection

Many aged individuals accumulate advanced Alzheimers disease (AD) neuropathology without cognitive decline, signifying potent endogenous resilience mechanisms. Mimicking resilience to AD may offer new therapeutic opportunities by emulating endogenous neuroprotection, but the molecular pathways that underpin resilience remain largely unknown. MicroRNAs (miRNAs)--small noncoding RNA molecules that post-transcriptionally regulate gene expression--influence neuronal and glial processes associated with development, aging, and AD neurodegeneration but their role in resilience has not been characterized. We investigated resilience-associated miRNAs and their targeted programs in the dorsolateral prefrontal cortex of post-mortem human brains. We analyzed matched miRNA and messenger RNA (mRNA) expression across resilient, AD, and control subjects from the Religious Order Study and Memory and Aging Project (ROSMAP) cohort. Nine miRNAs were differentially expressed in resilience compared with AD, including previously unreported miR-362-3p and miR-433-3p and new resilience-associated roles for known AD-related miRNAs, including neuronal miR-132-3p and miR-129-5p. By analyzing miRNA activity across AD progression, we found additional cognitive-associated miRNAs (e.g., miR-335-5p and miR-19b) and one plaque-restricted miRNA (miR-199a-5p). Sex-specific miRNA dysregulation was observed: miR-7-5p showed male-specific upregulation in AD versus resilience and suggested sex-specific differences in AD patients. Integrated co-expression and target-enrichment analyses linked resilience-associated miRNAs to pathways that were associated with cognitive decline, including transforming growth factor {beta}, Rho guanosine triphosphatases, and neurotransmitter receptor regulation. We also report restricted co-activity in AD subjects for miR-362-3p with inflammatory pathways, including tumor necrosis factor and Toll-like receptor signaling. These results demonstrate systematic involvement of miRNAs across neuronal and glial programs of AD resilience, cognitive decline, and sex-specific regulation. Our study provides an important resource for discovery of actionable regulatory programs that could lead to new therapies, based on endogenous molecules, that emulate natural resilience to AD. Key pointsO_LINine distinct cortical microRNA (miRNA) signatures correlate with resilience to Alzheimers disease (AD)-related cognitive decline, pointing to endogenous regulatory programs that may help preserve cognitive function. C_LIO_LIAt the miRNA level, resilient brains show minimal or no detectable differences to healthy individuals, despite the profound pathological differences, supporting the idea that resilience reflects preserved molecular homeostasis rather than a separate disease state. C_LIO_LISome miRNA signatures are specific to a certain pathology (e.g., one linked to amyloid-{beta} plaques independently of tau pathology and cognitive decline), helping to disentangle the regulation of these processes. C_LIO_LISex-specific miRNA dysregulation in AD suggests that resilience-linked pathways may be regulated differently in males and females, reinforcing the value of sex-stratified analyses and the potential for differential therapeutic strategies. C_LIO_LIIntegrative miRNA-pathway analysis highlights candidate regulatory networks involved in inflammation, matrisome, and cellular stress responses, providing a framework for resilience-linked therapeutic targets. C_LI

neuroscience↗

bioRxiv2023

Transcriptomic signatures of Abeta- and tau-induced neuronal dysfunction reveal inflammatory processes at the core of Alzheimer's disease pathophysiology

Molecular mechanisms enabling pathology-induced neuronal dysfunction in Alzheimers disease (AD) remain elusive. Here, we use mechanistic computational models to infer the combined influence of PET-measured A{beta} and tau burdens on fMRI-derived neuronal activity and to subsequently identify the transcriptomic spatial correlates of AD pathophysiology. Our results reveal overrepresented genes and biological processes that participate in synaptic degeneration and interact with A{beta} and tau deposits. Furthermore, we confirmed the central role of the immune system and neuroinflammatory pathways within AD pathogenesis; microglia were significantly enriched in the gene set associated with A{beta} and tau synergistic influences on neuronal activity. Lastly, our computational approach unveiled drug candidates with the potential to halt or reduce the observed pathological effects on neuronal activity, including existing medication for cancer, immune disorders, and cardiovascular diseases, many currently under clinical evaluation in AD. Overall, these findings support the notion that the AD brain experiences functional changes intricately associated with a diverse spectrum of molecular processes.

neuroscience↗

bioRxiv2023

Distinctive Whole-brain Cell-Types Strongly Predict Tissue Damage Patterns in Eleven Neurodegenerative Disorders

For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in thirteen neurodegenerative conditions, including early-and late-onset Alzheimers disease, Parkinsons disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and three clinical variants of frontotemporal lobar degeneration (behavioural variant, semantic and non-fluent primary progressive aphasia) along with associated 3-repeat and 4-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorders pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.

neuroscience↗

bioRxiv2019

Mental Workload Estimation Using Wireless EEG Signals

Previous studies have shown that electroencephalogram (EEG) can be used in estimating mental workload. However, developing fast and reliable models for cross-task, cross-subject and cross-session classifications of workload remains a challenge. In this study, a wireless Emotiv EPOC headset was used to evaluate workload in two different mental tasks: n-back task and mental arithmetic task. 0-back task and 2-back task were employed as low and high workload in the n-back task while 1-digit and 3-digit addition were used as the two different workload levels in the arithmetic task. Using power spectral density as features, a fast signal processing and feature extraction framework was developed to facilitate real-time estimation of workload. Within-session accuracies of 98.5% and 95.5% were achieved in the n-back and arithmetic tasks respectively. Adaptive subspace feature matching (ASFM) was applied for cross-session, cross-task and cross-subject classifications. The feature adaptation provided average cross-session accuracies of 80.5% and 74.4% in the n-back and the arithmetic tasks respectively. An average cross-task accuracy of 68.6% was achieved while cross-subject accuracies were 74.4% and 64.1% in the n-back and arithmetic tasks respectively. The framework generalised well across subjects and tasks, and it provided a promising approach towards developing subject and task-independent models. This study also shows that a consumer-level wireless EEG headset can be applied in cognitive monitoring for real-time estimation of workload in practice.

neuroscience↗

bioRxiv2019

Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration

Neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological aging processes. For all neurodegenerative conditions, we lack longitudinal gene expression (GE) data covering their large temporal evolution, which hinders the fully understanding of the underlying dynamic molecular mechanisms. Here, we aimed to overcome this limitation by introducing a novel GE contrastive trajectory inference (GE-cTI) method that reveals enriched temporal patterns in a diseased population. Evaluated on 1969 subjects in the spectrum of late-onset Alzheimers and Huntingtons diseases (from ROSMAP, HBTRC and ADNI studies), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, Amyloid and Vonsattel stages). Furthermore, when applied to in-vivo blood samples (ADNI), it predicts 97% of the variance in memory deterioration and its future declining rate, supporting the identification of a powerful and minimally invasive (blood-based) tool for early clinical screening and disease prevention. This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty percent of the most predictive molecular pathways identified in the brain were also top predictors in the blood. The GE-cTI is a promising tool for revealing complex neuropathological mechanisms, with direct implications for implementing personalized dynamic treatments in neurology. HIGHLIGHTS- Unsupervised learning detects enriched gene expression (GE) trajectories in disease - These plasma and brain GE trajectories predict neuropathology and future cognitive impairment - Most predictive molecular functions/pathways in the brain are also top predictors in the plasma - By identifying plasma GE trajectories, patients can be easily screened and follow dynamic treatments

neuroscience↗