Biology subjects

Elahi, F.

Publications and source records attributed to Elahi, F..

7 recordsLinked to original sources

bioRxiv2026

Resilience to neuronal hyperactivity and restoration of the neuroimmune interactome by blocking fibrin-induced microglia activation in Alzheimers disease

Cerebrovascular pathology and neuronal network dysfunction are early features of Alzheimers disease (AD) associated with neuroinflammation and cognitive decline, but the vascular and immune triggers of neuronal hyperactivity remain largely unknown. Here, we show that the blood coagulation protein fibrin disrupts microglia-neuron interactions, promoting neuronal hyperactivity in an AD mouse model. Genetic elimination of the fibrin inflammatory domain reduced neuronal hyperactivity, restored dynamic microglial interactions with active neurons and protected from high-risk decision making in 5XFAD mice. Leveraging the transcriptional signatures of microglia and inhibitory and excitatory neurons, a ligand-receptor atlas revealed fibrin-dependent disruption of innate immune and glutamatergic signaling between microglia and neurons in AD mice. Patients with AD also showed a correlation of cerebrospinal fluid (CSF) fibrinogen levels with biomarkers of inflammation, vascular and synaptic dysfunction. Thus, resilience to neuronal hyperactivity and restoration of the neuroimmune interactome by targeting fibrin may have therapeutic implications for Alzheimers disease and related conditions. There is a companion manuscript submitted to bioRxiv (Lauderdale et al., 2026) HighlightsO_LIVascular-microglia axis drives neuronal hyperactivity C_LIO_LIFibrin inflammatory activity disrupts the microglia-neuron interactome C_LIO_LIMicroglia activation by fibrin impairs decision-making in AD mice C_LIO_LISynaptic dysfunction and immune biomarkers correlate with CSF fibrinogen in AD patients C_LI

neuroscience↗

bioRxiv2026

Acid sphingomyelinase inhibition restores RPE homeostasis and photoreceptor function in preclinical Stargardt macular degeneration models

Stargardt disease, which destroys central high-resolution vision in over 2 million people globally, lacks effective therapies. The primary site of damage in Stargardt disease is the retinal pigment epithelium (RPE), which safeguards photoreceptor health and function. Progressive loss of RPE integrity precedes visual deficits, yet insight into mechanisms driving RPE dysfunction and how this influences disease pathogenesis remains elusive. Here, we addressed this in cell-based and pigmented Abca4-/- Stargardt mice models using super-resolution imaging, bioinformatics, and biochemical approaches. We show that ceramide accumulation induced by bisretinoid-mediated overactivation of acid sphingomyelinase (ASM) in Abca4-/- RPE selectively disrupts Rab GTPases and ESCRT machinery involved in apical membrane trafficking and small extracellular vesicle (EV) biogenesis. Consequently, connexin 43 (Cx43) is misrouted from cell-cell junctions into EVs that are released apically by the RPE. This compromises RPE integrity and promotes subretinal immune cell recruitment, leading to photoreceptor dysfunction. Pharmacological ASM inhibition normalizes EV biogenesis and restores Cx43 localization. Decreasing RPE ceramide safeguards RPE structural integrity, limits subretinal microglia, and improves visual function in Abca4-/- mice. This study underscores the importance of the RPE as a communication hub in the retina and identifies ASM as a potential therapeutic target to prevent progressive vision loss.

neuroscience↗

bioRxiv2025

MACHINE LEARNING FOR MISSING DATA IMPUTATION IN ALZHEIMER'S RESEARCH: PREDICTING MEDIAL TEMPORAL LOBE FLEXIBILITY

BACKGROUNDAlzheimers disease (AD) begins years before symptoms appear, making early detection essential. The medial temporal lobe (MTL) is one of the earliest regions affected, and its network flexibility, a dynamic measure of brain connectivity, may serve as a sensitive biomarker of early decline. Cognitive (acquisition, generalization), genetic (APOE, ABCA7), and biochemical (P-tau217) markers may predict MTL dynamic flexibility. Given the high rate of missing data in AD research, this study uses machine learning with advanced imputation methods to predict MTL dynamic flexibility from multimodal predictors in an aging cohort. METHODSIn an ongoing study at Rutgerss Aging and Brain Health Alliance, data from 656 participants are utilized, including cognitive assessments, genetic and blood-derived biomarkers, and demographics. Due to MRI-related constraints, only 34.15% of participants had measurable MTL dynamic flexibility from resting-state fMRI. To estimate MTL dynamic flexibility from available data, we evaluated four missing data handling methods (case deletion, MICE, MissForest, and GAIN), and trained five regression models: Ridge, k-NN, SVR, regression trees (bagging, random forest, boosting), and ANN. Hyperparameters were optimized via grid search with 3-fold cross-validation. Model performance was assessed using mean absolute error (MAE), root mean squared error (RMSE), and runtime through 5-fold cross-validation repeated 25 times to ensure robustness in clinical data settings. RESULTSA total of 1,866 missing values (25.86%) were identified in the dataset, with only 42 complete cases (6.40%) remaining after listwise deletion, highlighting the need for effective imputation. In the initial analysis using only complete cases, support vector regression (SVR) achieved the lowest mean absolute error (MAE = 0.184), though overall performance was limited due to small sample size. In the second phase, three imputation techniques were applied, significantly improving model accuracy. MissForest combined with Random Forest produced the best results (MAE = 0.083), representing a 54.7% improvement over case deletion. Statistical analysis confirmed significant differences in performance across imputation methods (p < 0.001), with MissForest outperforming GAIN and MICE. GAIN was the fastest imputation method. DISCUSSIONThe findings underscore the importance of using robust imputation strategies to maximize data utility and model reliability in studies with high missingness. Further research is needed, particularly incorporating additional neuroimaging measures, to localize the brain regions most affected by biomarker-driven changes and to refine predictive models for clinical applications.

neuroscience↗

bioRxiv2024

Targeting complement C3a receptor resolves mitochondrial hyperfusion and subretinal microglial activation in progranulin-deficient frontotemporal dementia

Mutations in progranulin (GRN) cause frontotemporal dementia (GRN-FTD) due to deficiency of the pleiotropic protein progranulin. GRN-FTD exhibits diverse pathologies including lysosome dysfunction, lipofuscinosis, microgliosis, and neuroinflammation. Yet, how progranulin loss causes disease remains unresolved. Here, we report that non-invasive retinal imaging of GRN-FTD patients revealed deficits in photoreceptors and the retinal pigment epithelium (RPE) that correlate with cognitive decline. Likewise, Grn-/- mice exhibit early RPE dysfunction, microglial activation, and subsequent photoreceptor loss. Super-resolution live imaging and transcriptomic analyses identified RPE mitochondria as an early driver of retinal dysfunction. Loss of mitochondrial fission protein 1 (MTFP1) in Grn-/- RPE causes mitochondrial hyperfusion and bioenergetic defects, leading to NF-kB-mediated activation of complement C3a-C3a receptor signaling, which drives further mitochondrial hyperfusion and retinal inflammation. C3aR antagonism restores RPE mitochondrial integrity and limits subretinal microglial activation. Our study identifies a previously unrecognized mechanism by which progranulin modulates mitochondrial integrity and complement-mediated neuroinflammation.

neuroscience↗

bioRxiv2024

Proteomic profiling of intracranial atherosclerotic plaque in the human brain

BackgroundIntracranial atherosclerotic disease (ICAD) is one of the major causes of ischemic stroke and associated with high risk of stroke recurrence. There are no reliable and specific fluid biomarkers for ICAD, and little is known about the proteomic profiling of ICAD. In this study we aimed to explore the feasibility of applying proteomics technology to profile intracranial atherosclerotic plaques extracted from postmortem human brain arteries. MethodsEighteen segments (5-10mm in length) of major arteries from 10 postmortem brains were collected from the Mount Sinai Neuropathology Brain Bank. Among these segments, 5 had no evidence of atherosclerotic disease, and 13 had wall thickening or visible plaques with various degree of stenosis. Proteins were extracted from the vessel segments, quantified, and digested into peptides. Subsequently, the peptides underwent tandem mass tag (TMT) labeling, pooling, and analysis using two-dimensional liquid chromatography-tandem mass spectrometry (LC/LC-MS/MS). Protein identification and quantification were performed using the JUMP software. Differentially expressed proteins (DEPs) were defined as proteins with p.adj < 0.05 and absolute log2 (fold change) > log2 (1.2). ResultsA total of 7,492 unique proteins were detected, and 6,726 quantifiable proteins were retained for further analysis. Among these, 265 DEPs, spanning on 252 unique gene, were found to be associated with ICAD by comparing the arterial segments with vs those without atherosclerotic disease. The top 4 most significant DEPs include LONP1, RPS19, MRPL12 and SNU13. Among the top 50 DEPs, FADD, AIFM1 and PGK1 were associated with atherosclerotic disease or cardiovascular events in previous studies. Moreover, the previously reported proteins associated with atherosclerosis such as APCS, MMP12, CTSD were elevated in arterial segments with atherosclerotic changes. Furthermore, the up-regulation of APOE and LPL, the ICAD GWAS risk genes, was shown to be associated with the plaque severity. Finally, gene set enrichment analysis revealed the DEP signature is enriched for biological pathways such as chromatin structure, plasma lipoprotein, nucleosome, and protein-DNA complex, peroxide catabolic and metabolic processes, critical in ICAD pathology. ConclusionsDirect proteomic profiling of fresh-frozen intracranial artery samples by MS-based proteomic technology is a feasible approach to identify ICAD-associated proteins, which can be potential biomarker candidates for ICAD. Further plaque proteomic study in a larger sample size is warranted to uncover mechanistic insights into ICAD and discover novel biomarkers that may help to improve diagnosis and risk stratification in ICAD.

systems biology↗

bioRxiv2019

A/T/N polygenic risk score for cognitive decline in old age

INTRODUCTIONWe developed a novel polygenic risk score (PRS) based on the A/T/N (amyloid plaques (A), phosphorylated tau tangles (T), and neurodegeneration (N)) framework and compared a PRS based on clinical AD diagnosis to assess which was a better predictor of cognitive decline. METHODSWe used summary statistics from genome wide association studies of cerebrospinal fluid amyloid-{beta} (A{beta}42) and phosphorylated-tau (ptau181), left hippocampal volume (LHIPV), and late-onset AD dementia to calculate PRS for 1181 participants in the Alzheimers Disease Neuroimaging Initiative (ADNI). Individual PRS were averaged to generate a composite A/T/N PRS. We assessed the association of PRS with baseline and longitudinal cognitive composites of executive function and memory. RESULTSThe A/T/N PRS showed superior predictive performance on AD biomarkers and executive function decline compared to the clinical AD PRS. DISCUSSIONResults suggest that integration of genetic risk across AD biomarkers may improve prediction of disease progression. Research in ContextO_ST_ABSSystematic ReviewC_ST_ABSAuthors reviewed relevant literature using PubMed and Google Scholar. Key studies that generated and validated polygenic risk scores (PRS) for clinical and pathologic AD were cited. PRS scores have been increasingly used in the literature but clinical utility continues to be questioned. InterpretationIn the current research landscape concerning PRS clinical utility in the AD space, there is room for model improvement and our hypothesis was that a PRS with integrated risk for AD biomarkers could yield a better model for cognitive decline. Future DirectionsThis study serves as proof-of-concept that encourages future study of integrated PRS across disease markers and utility in taking an A/T/N (amyloidosis, tauopathy and neurodegeneration) focused approach to genetic risk for cognitive decline and AD.

genomics↗

bioRxiv2018

Lipid associated polygenic enrichment in Alzheimer’s disease

Cardiovascular (CV) and lifestyle associated risk factors (RFs) are increasingly recognized as important for Alzheimers disease (AD) pathogenesis. Beyond the [isin]4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV associated genes also increase risk for AD (genetic pleiotropy). Using large genome-wide association studies (GWASs) (total n > 500,000 cases and controls) and validated tools to quantify genetic pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 57 SNPs on 19 different chromosomes that were jointly associated with AD and CV outcomes including APOA4, ABCA1, ABCG5, LIPG, and MTCH2/SPI1. We found that common genetic variants influencing AD are associated with multiple CV RFs, at times with a different directionality of effect. Expression of these AD/CV pleiotropic genes was enriched for lipid metabolism processes, over-represented within astrocytes and vascular structures, highly co-expressed, and differentially altered within AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid associated RFs. Rather than a single causal link between genetic loci, RF and the outcome, we found that common genetic variants influencing AD are associated with multiple CV RFs. Our collective findings suggest that a network of genes involved in lipid biology also influence Alzheimers risk.

genetics↗