Biology subjects

Nho, K.

Publications and source records attributed to Nho, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression

Aging is the greatest risk factor for Alzheimer's disease (AD), yet how and when AD-related pathological progression diverges from aging remains poorly understood. This distinction is particularly difficult at early stages, when clinically and biomarker-defined populations contain individuals following fundamentally different trajectories. Here, we model AD progression as a deviation from aging using longitudinal amyloid PET and a self-supervised trajectory-learning framework. In ADNI, the learned trajectory revealed a shared early path that bifurcated into an aging branch and an AD-related branch. The two branches showed distinct profiles in amyloid burden, cognitive decline, risk of progression to AD dementia, and genetic risk. Unseen participants from the independent NACC cohort were projected onto the trajectory without retraining, further reproducing key branch-specific biological and genetic patterns. Together, the bifurcating trajectory provides a biologically grounded framework for resolving early-stage heterogeneity and enabling risk stratification beyond binary amyloid status.

neuroscience↗

bioRxiv2026

Cross-Regional Trajectory Analysis Reveals Distinct Temporal Progression of Shared Proteomic Programs in Alzheimer's Disease

Alzheimer's pathology progresses through anatomically distinct brain regions, yet how molecular programs are temporally organized across vulnerable regions remains poorly understood. Here, we reconstructed continuous proteomic trajectories from matched dorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) proteomes to investigate cross-regional molecular progression in AD. The inferred trajectories closely recapitulated established neuropathological staging while remaining independent of age, sex, and race. Across regions, a mitochondrial bioenergetic protein subset declined substantially earlier in the STG than in the DLPFC, revealing a previously unrecognized regional temporal offset in metabolic dysfunction. In parallel, a broadly shared proteomic program progressed in both regions but more rapidly in the STG, indicating that shared molecular responses differ in their temporal progression despite overall coordination. Together, these findings demonstrate that regional vulnerability in AD is reflected not only by the molecular programs involved but also by their temporal organization during disease progression.

neuroscience↗

bioRxiv2026

MicroRNA-181 influences Alzheimer's risk by regulating neprilysin and microtubule-associated tau pathways, offering a novel target

Alzheimers disease (AD) is characterized by amyloid-{beta} (A{beta}) peptide plaques and neurofibrillary tangles from hyperphosphorylated tau, though factors linking amyloid and tau pathology remain unclear. We investigated whether microRNA-181d-5p (miR-181d) associates with AD-related brain changes and regulates neprilysin and tau. Modeling miR-181d across individuals with no cognitive impairment, mild cognitive impairment, and AD revealed region- and sex-specific associations. Higher miR-181d levels associated with greater AD probability in the temporal lobe and cerebellum, and lower probability in the posterior cingulate cortex of males; miR-181c attenuated these probabilities. SNPs near MIR181 associated with altered entorhinal cortical thickness. In cellular models, miR-181 reduced neprilysin 3'-UTR activity, mRNA, protein, and enzymatic activity, while increasing tau mRNA and protein. Neprilysin diminution impairs A{beta} clearance and elevates tau, contributing to AD. RNA sequencing identified miR-181d-responsive neurodegenerative pathways. These findings identify miR-181 as a regulator of AD-relevant amyloid and tau pathways, providing novel targets. TeaserMiRNA-181 is a key regulator of Alzheimers risk through its effects on neprilysin and tau proteins, a novel potential target.

neuroscience↗

bioRxiv2024

Cell Type Differentiation Using Network Clustering Algorithms

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

bioinformatics↗

bioRxiv2024

Transcriptional Regulation of human NMNAT2: Insights from 3D Genome Sequencing and Bioinformatics

AbstractNicotinamide mononucleotide adenylyl transferases 2 (NMNAT2) is a crucial nicotinamide adenine dinucleotide (NAD)-synthesizing enzyme essential for neuronal health. In the Religious Orders Study/Memory and Aging Project (ROSMAP), human brain levels of NMNAT2 mRNA positively correlated with cognitive capabilities in older adults. NMNAT2 mRNA abundance is significantly reduced following various insults or proteinopathies. To elucidate the transcriptional regulation of NMNAT2, we employed circular chromosome conformation capture followed by high-throughput sequencing (4C-seq) to identify potential NMNAT2 enhancer and silencer regions by determining genomic regions interacting with the NMNAT2 promoter in human SH-SY5Y cells. We discovered distinct NMNAT2 promoter interactomes in undifferentiated versus neuron-like SH-SY5Y cells. Utilizing bioinformatics analyses, we identified putative transcriptional factors and NMNAT2-associated genes. Notably, the mRNA levels of many of these genes showed a significant correlation with NMNAT2 mRNA levels in [~]400 single-nuclei RNA-seq datasets from ROSMAP. Additionally, using CRISPR-Cas9 strategies, we confirmed the requirement of two specific genomic regions within the interactomes and four transcription factors in regulating NMNAT2 transcription. In summary, our study identifies genomic loci containing NMNAT2 regulatory elements and predicts associated genes and transcription factors through computational analyses.

neuroscience↗

bioRxiv2024

Human microRNA-153-3p targets specific neuronal genes and is associated with the risk of Alzheimer's disease.

Alzheimers disease (AD) is a progressive degenerative disease characterized by a significant loss of neurons and synapses in cognitive brain regions and is the leading cause of dementia worldwide. AD pathology comprises extracellular amyloid plaques and intracellular neurofibrillary tangles. However, the triggers of this pathology are still poorly understood. Repressor element 1-silencing transcription/neuron-restrictive silencer factor (REST/NRSF), a transcription repressor of neuronal genes, is dysregulated during AD pathogenesis. How REST is dysregulated is still poorly understood, especially at the post-transcriptional level. MicroRNAs (miRNAs), a group of short non-coding RNAs, typically regulate protein expression by interacting with target mRNA transcript 3-untranslated region (UTR) and play essential roles in AD pathogenesis. Herein, we demonstrate that miR-153-3p reduces REST 3-UTR activities, mRNA, and protein levels in human cell lines, along with downregulating amyloid-{beta} precursor protein (APP) and -synuclein (SNCA). We determine by mutational analyses that miR-153-3p interacts with specific targets via the seed sequence present within the respective mRNA 3UTR. We show that miR-153-3p treatment alters the expression of these specific proteins in human neuronally differentiated cell lines and human induced pluripotent stem cells and that miR-153-3p is itself dysregulated in AD. We further find that single nucleotide polymorphisms (SNPs) within 5kb of the MIR153-1 and MIR153-2 genes are associated with AD-related endophenotypes. Elevation of miR-153-3p is associated with a reduced probability of AD, while elevated REST may associate with a greater probability of AD. Our work suggests that a supplement of miR-153-3p would reduce levels of toxic protein aggregates by reduced expression of APP, SNCA and REST expression, all pointing towards a therapeutic and biomarker potential of miR-153-3p in AD and related dementias.

neuroscience↗

bioRxiv2023

Amyloid pathology reduces ELP3 expression and tRNA modifications leading to impaired proteostasis in Alzheimer's disease models

Alzheimers Disease (AD) is a progressive and irreversible neurodegenerative disorder, characterized by the accumulation of abeta-amyloid aggregates, which triggers tau hyperphosphorylation and neuronal loss. While the precise mechanisms underlying neurodegeneration in AD are not entirely understood, it is known that loss of proteostasis is implicated in this process. Maintaining neuronal proteostasis requires proper transfer RNA (tRNA) modifications, which are crucial for optimal translation. However, research into tRNA epitranscriptome in AD is limited, and it is not yet clear how alterations in tRNA modifying enzymes and tRNA modifications might contribute to disease progression. Here, we report that expression of the tRNA modifying enzyme ELP3 is reduced in the brain of AD patients and amyloid AD mouse models, suggesting ELP3 is implicated in proteostasis dysregulation observed in AD. To investigate the role of ELP3 specifically in neuronal proteostasis impairments in the context of amyloid pathology, we analyzed SH-SY5Y neuronal cells carrying the amyloidogenic Swedish familial AD mutation in the APP gene (SH-SWE) or the wild-type gene (SH-WT). Similarly to the amyloid mouse models, SH-SWE exhibited reduced levels of ELP3 which was associated with tRNA hypomodifications and reduced abundance, as well as proteostasis impairments. Furthermore, the knock-down of ELP3 in SH-WT recapitulated the proteostasis impairments observed in SH-SWE cells. Importantly, the correction of tRNA deficits due to ELP3 reduction rescued and reverted proteostasis impairments of SH-SWE and SH-WT knock-down for ELP3, respectively. Additionally, SH-WT exposed to the secretome of SH-SWE or synthetic amyloid aggregates recapitulate the SH-SWE phenotype, characterized by reduced ELP3 expression, tRNA hypomodification and increased protein aggregation. Taken together, our data suggest that amyloid pathology dysregulates neuronal proteostasis through the reduction of ELP3 and tRNA modifications. This study highlights the modulation of tRNA modifications as a potential therapeutic avenue to restore neuronal proteostasis in AD and preserve neuronal function. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/538928v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@ed5cf4org.highwire.dtl.DTLVardef@171b14aorg.highwire.dtl.DTLVardef@12eb19dorg.highwire.dtl.DTLVardef@8ed4ab_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

Deep multi-omic network fusion for marker discovery of Alzheimer's Disease

Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored to interpret findings from genome wide association studies of Alzheimers disease (AD) and to gain more insight of the disease mechanism. However, each -omics data type is usually examined individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore give rise to challenges in interpretation. To address this problem, we propose a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set. It aims to identify a subnetwork of functional interactions predictive of AD evidenced by multi-omic measures. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using multi-omic data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded multi-omic sub-networks related to innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. Around 50% of these findings were replicated in another independent cohort. Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron-neuron or neuron-glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage.

systems biology↗

bioRxiv2021

Single Nuclei Transcriptome Reveals Perturbed Brain Vascular Molecules in Alzheimer's Disease

Blood-brain barrier (BBB) dysfunction is well-known in Alzheimers disease (AD), but the precise molecular changes contributing to its pathophysiology are unclear. To understand the transcriptional changes in brain vascular cells, we performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in 24 AD and control brains resulting in 79,751 nuclei, 4,604 of which formed three distinct vascular clusters characterized as activated pericytes, endothelia and resting pericytes. We identified differentially expressed genes (DEGs) and their enriched pathways in these clusters and detected the most transcriptional changes within activated pericytes. Using our data and a knowledge-based predictive algorithm, we discovered and prioritized molecular interactions between vascular and astrocyte clusters, the main cell types of the gliovascular unit (GVU) of the BBB. Vascular targets predicted to interact with astrocytic ligands have biological functions in signalling, angiogenesis, amyloid {beta} metabolism and cytoskeletal structure. Top astrocytic and vascular interacting molecules include both novel and known AD risk genes such as APOE, APP and ECE1. Our findings provide information on transcriptional changes in predicted vascular-astrocytic partners at the GVU, bringing insights to the molecular mechanisms of BBB breakdown in AD. Graphical AbstractPericytes (yellow), endothelia (salmon) and astrocytes (purple) that form the gliovascular unit (GVU) at the blood brain barrier (BBB) were interrogated for their differentially expressed genes (DEG) and vascular cell (pericyte or endothelia) to astrocyte interactions using single nucleus RNA sequencing (RNAseq) transcriptome obtained from brains of Alzheimers disease (AD) patients and controls. We identified many upregulated (red) or downregulated (blue) DEGs in AD brains in these cell types. These genes have known biological functions in amyloid {beta} (A{beta}) clearance, immune modulation, astrogliosis and neuronal death. Novel predicted interactions were identified between vascular cells and astrocytic DEGs. Collectively, our findings highlight the vast transcriptome changes that occur at the GVU and provide mechanistic insights into BBB dysfunction in AD. This figure was created with Biorender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/474255v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@f48748org.highwire.dtl.DTLVardef@1ddc3f4org.highwire.dtl.DTLVardef@6bfa5eorg.highwire.dtl.DTLVardef@1024506_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Serum Metabolites Associate with Brain Amyloid Beta Deposition, Cognitive Dysfunction, and Alzheimers Disease Progression

RATIONALEMetabolomics in the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort provides a powerful tool for mapping biochemical changes in AD, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-{beta} deposition in AD. OBJECTIVESWe examined 140 serum metabolites and their associations with brain amyloid-{beta} deposition, cognition, and conversion from mild cognitive impairment (MCI) to AD. FINDINGSSerum-based targeted metabolite levels were measured in 1,531 ADNI participants. We performed association analysis of metabolites with brain amyloid-{beta} deposition measured from [18F] Florbetapir PET scans. We identified nine metabolites as significantly associated with amyloid-{beta} deposition after FDR-based multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-{beta} accumulation. However, higher levels of seven phosphatidylcholines (PC) were associated with increased amyloid deposition. In addition, PC ae C44:4 was significantly associated with cognition and conversion from MCI to AD dementia. CONCLUSIONPerturbations in PC and acylcarnitine metabolism may play a role in features intrinsic to AD including amyloid-{beta} deposition and cognitive performance.

neuroscience↗

bioRxiv2020

INPP5D expression is associated with risk for Alzheimer's disease and induced by plaque-associated microglia

BackgroundAlzheimers disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, robust microgliosis, neuroinflammation, and neuronal loss. Genome-wide association studies recently highlighted a prominent role for microglia in late-onset AD (LOAD). Specifically, inositol polyphosphate-5-phosphatase (INPP5D), also known as SHIP1, is selectively expressed in brain microglia and has been reported to be associated with LOAD. Although INPP5D is likely a crucial player in AD pathophysiology, its role in disease onset and progression remains unclear. MethodsWe performed differential gene expression analysis to investigate INPP5D expression in LOAD and its association with plaque density and microglial markers using transcriptomic (RNA-Seq) data from the Accelerating Medicines Partnership for Alzheimers Disease (AMP-AD) cohort. We also performed quantitative real-time PCR, immunoblotting, and immunofluorescence assays to assess INPP5D expression in the 5xFAD amyloid mouse model. ResultsDifferential gene expression analysis found that INPP5D expression was upregulated in LOAD and positively correlated with amyloid plaque density. In addition, in 5xFAD mice, Inpp5d expression increased as the disease progressed, and selectively in plaque-associated microglia. Increased Inpp5d expression levels in 5xFAD mice were abolished entirely by depleting microglia with the colony-stimulating factor receptor-1 antagonist PLX5622. ConclusionsOur findings show that INPP5D expression increases as AD progresses, predominantly in plaque-associated microglia. Importantly, we provide the first evidence that increased INPP5D expression might be a risk factor in AD, highlighting INPP5D as a potential therapeutic target. Moreover, we have shown that the 5xFAD mouse model is appropriate for studying INPP5D in AD.

neuroscience↗

bioRxiv2020

Deep learning detection of informative features in tau PET for Alzheimer' s disease classification

BackgroundAlzheimers disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. MethodsWe analysed [18F]flortaucipir PET image data from the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort. We first developed an image classifier to distinguish AD from cognitively normal (CN) older adults by training a 3D convolutional neural network (CNN)-based deep learning model on tau PET images (N=132; 66 CN and 66 AD), then applied the classifier to images from individuals with mild cognitive impairment (MCI; N=168). In addition, we applied a layer-wise relevance propagation (LRP)-based model to identify informative features and to visualize classification results. We compared these results with those from whole brain voxel-wise between-group analysis using conventional Statistical Parametric Mapping (SPM12). ResultsThe 3D CNN-based classification model of AD from CN yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The LRP results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r=0.43 for early MCI and r=0.49 for late MCI). ConclusionA deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

neuroscience↗

bioRxiv2020

PLCG2 as a Risk Factor for Alzheimer's Disease

Alzheimers disease (AD) is characterized by robust microgliosis and phenotypic changes that accompany disease pathogenesis. Indeed, genetic variants in microglial genes are linked to risk for AD. Phospholipase C{gamma} 2 (PLCG2) participates in the transduction of signals emanating from immune cell-surface receptors that regulate the inflammatory response and is selectively expressed by microglia in the brain. A rare variant in PLCG2 (P522R) was previously found to be protective against AD, indicating that PLCG2 may play a role in AD pathophysiology. Here, we report that a rare missense variant in PLCG2 confers increased AD risk (p=0.047; OR=1.164 [95% CI=1.002-1.351]). Additionally, we observed that PLCG2 expression levels are increased in several brain regions of AD patients, correlating with brain amyloid deposition. This provides further evidence that PLCG2 may play an important role in AD pathophysiology. Together, our findings indicate that PLCG2 is a potential new therapeutic target for AD.

neuroscience↗

bioRxiv2020

Elevated plasma cortisol associated with larger ventricles and smaller hippocampal volumes, a study in 2 independent elderly cohorts

Cortisol is considered the most fundamental stress hormone and is elevated in stress and multiple neuropsychiatric conditions. Prior studies have shown associations of plasma cortisol levels with total cerebral and hippocampal volumes and less consistently with the amygdala. Here, we extend our hypothesis to test associations of plasma cortisol with 1) ventricular 2) hippocampal and 3) amygdalar volumes, in two independent elderly cohorts across a broad cognitive spectrum ranging from normal cognition to Alzheimers disease. We demonstrate elevated cortisol to be associated with larger lateral ventricular volumes and smaller hippocampal volumes, predominantly in the right cerebral hemisphere, regardless of age, sex or cognitive status. We noted a non-significant trend of smaller amygdalar volumes with elevated cortisol. Our findings support smaller brain parenchyma volumes seen with elevated cortisol and may encourage effective strategies reducing cortisol and stress. They may also serve as imaging biomarkers for assessing therapeutic benefits of stress and cortisol lowering interventions aiming to halt or reverse the brain volume alterations and theoretically improve cognition and quality of life. HighlightsO_LIElevated cortisol associated with larger ventricular volumes and smaller hippocampal volumes C_LIO_LIAssociations are predominantly noted in the right cerebral hemisphere. C_LIO_LISimilar non-significant trends noted in amygdalar volumes C_LIO_LICortisol and stress reducing strategies may halt brain changes and improve quality of life C_LIO_LIImaging biomarkers may help assess efficacy of cortisol-lowering therapeutic interventions C_LI

neuroscience↗

bioRxiv2020

Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer's Disease

Approximately 30% of older adults exhibit the neuropathologic features of Alzheimers disease (AD) without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively normal in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modeling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5,108 participants from a clinical trial of AD and three longitudinal cohort studies of cognitive aging. All analyses were run across all participants and repeated restricting the sample to individuals with normal cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (p-values<2.5x10-20), and we observed novel correlations with neuropsychiatric conditions (p-values<7.9x10-4). Notably, neither resilience metric was genetically correlated with clinical AD (p-values>0.42) nor associated with APOE (p-values>0.13). In single variant analyses, we observed a genome-wide significant locus among participants with normal cognition on chromosome 18 upstream of ATP8B1 (index SNP rs2571244, MAF=0.08, p=2.3x10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; p=2x10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical AD, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

genetics↗

bioRxiv2020

Functional biological paths altered in Alzheimer's disease: from genes to bile acids

Large-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimers disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.

bioinformatics↗

bioRxiv2019

Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

Alzheimers disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

systems biology↗

bioRxiv2019

Causal relationship of cerebrospinal fluid biomarkers with the risk of Alzheimer’s disease: A two-sample Mendelian randomization study

Whether the epidemiological association of amyloid beta (A{beta}) and tau pathology with Alzheimers disease (AD) is causal remains unclear. The recent failures to demonstrate the efficacy of several amyloid beta-modifying drugs may indicate the possibility that the observed association is not causal. These failures also led to efforts to develop tau-directed treatments whose efficacy is still tentative. Herein, we conducted a two-sample Mendelian randomization analysis to determine whether the relationship between the cerebrospinal fluid (CSF) biomarkers for amyloid and tau pathology and the risk of AD is causal. We used the summary statistics of a genome-wide association study (GWAS) for CSF biomarkers (A{beta}1-42, phosphorylated tau 181 [p-tau], and total tau [t-tau]) in 3,146 individuals and for late-onset AD (LOAD) in 21,982 LOAD cases and 41,944 cognitively normal controls. We tested the association between the change in the genetically predicted CSF biomarkers and LOAD risk. We found a modest decrease in the LOAD risk per one standard deviation (SD) increase in the genetically predicted CSF A{beta} (odds ratio [OR], 0.63 for AD; 95% confidence interval [CI], 0.38-0.87; P = 0.02). In contrast, we observed a significant increase in the LOAD risk per one SD increase in the genetically predicted CSF p-tau (OR, 2.37; 95% CI, 1.46-3.28; P = 1.09x10-5). However, no causal association was observed of the CSF t-tau with the LOAD risk (OR, 1.15; 95% CI, 0.85-1.45; P = 0.29). Our findings need to be validated in future studies with more genetic variants identified in larger GWASs for CSF biomarkers.

genomics↗