Biology subjects

Chiba-Falek, O.

Publications and source records attributed to Chiba-Falek, O..

14 recordsLinked to original sources

bioRxiv2026

TOMM40 '523' genotype induces sex- and tissue- specific differences in cholesterol and triglyceride levels in an APOE-TOMM40 humanized mouse model

IntroductionGenetic variants within the APOE-TOMM40 locus are associated with Alzheimers disease (AD). A specific role for TOMM40 is indicated by the finding that 523 poly-T variants are associated with AD risk, but the mechanism for this effect has not been established. Our studies have shown that suppression of Tomm40 in mice increased brain cholesterol content, an AD risk factor, and thus the present study sought to assess whether major 523 poly-T variants (Short [S] and Very Long [VL]) are associated with altered lipid content of brain and other tissues. MethodsWe utilized a mouse model containing the entire human APOE3-TOMM40 locus to quantify cholesterol and triglyceride levels in brain, liver, and white adipose tissue (WAT), as well as brain content of the AD biomarkers A{beta} 42 and tau, in mice carrying two homozygous TOMM40 523 poly-T genotypes (S/S and VL/VL). ResultsMale mice carrying the 523-S/S genotype, but not females, showed higher brain cholesterol and triglyceride levels than VL/VL carriers, together with greater brain A{beta} 42 content. WAT showed similar lipid differences as in the brain, while hepatic lipid content was broadly similar between 523-S/S and -VL/VL genotypes, though there was a trend for higher triglycerides in VL/VL mice in a sex- and age-dependent manner. DiscussionThese results demonstrate that TOMM40 523 poly-T variants drive tissue-specific, sex-, and age-dependent lipid differences in humanized APOE3-TOMM40 mice, with the S/S genotype linked to elevated brain cholesterol and A{beta} 42 levels, effects that link this locus to AD pathogenesis.

genetics↗

bioRxiv2026

A diagnostic plasma omics-biomarker for Alzheimer's disease informed by microglial single-cell transcriptomics: A pilot study

BackgroundThe current biomarker framework for the diagnosis and staging of Alzheimers disease (AD) relies mainly on neuropathological features; thus, its performance for diagnosis is limited prior to the initiation of neurodegeneration. Here, we leveraged transcriptomic data to develop a new framework for omic-informed blood-based diagnostic biomarkers for AD from early-stage. MethodsMicroglial gene expression from single-nucleus (sn)RNA-seq data was analyzed via 6 statistical methods to identify candidate panels of genes predictive of AD. A total of 78 gene panels, 30-2000 genes in size, were selected and evaluated for their ability to distinguish AD patients from controls. Three top-ranked panels of 300, 50 and 30 genes were transferred to blood (monocyte) transcriptomic data obtained from living subjects via a graph-based mapping approach based on optimal transport statistics. ResultsThe 300-panel method resulted in an AUC of 0.7 and moderate accuracy (75%) in classifying AD; however, the accuracy in predicting cognitively normal patients was lower (53%). While the 300 genes provided high accuracy, inspection of the distribution of p values for the gene set revealed that the panel could be greatly reduced in size to capture the most significant differences between AD patients and cognitively normal individuals. The accuracy and specificity of the 50 and 30 panels demonstrated similar AUC values but improved the balance between the prediction of AD patients and normal controls. Specifically, the 50-gene panel resulted in an AUC of 0.7, with 65% AD accuracy and 71% normal accuracy. ConclusionsIntegrating multiomics datasets into the AD biomarker discovery pipeline offers a powerful modality to increase precision and comprehensiveness in AD research and clinical applications.

genomics↗

bioRxiv2025

Single cell transcriptomics reveals enrichment of aggregation-prone alpha-synuclein isoforms across synucleinopathies

Alpha-synuclein (-Syn) is the primary component of Lewy bodies, the pathological hallmark of neurodegenerative synucleinopathies, including Parkinsons disease (PD) and dementia with Lewy bodies (DLB). Dysregulated expression of its encoding gene, SNCA, has been identified in association with both PD and DLB in short-read sequencing studies. However, such studies do not capture variation in transcript isoforms expressed. Here we combine for the first time SNCA-targeted long-read multiplexed arrays isoform sequencing (MAS-Iso-seq) with unbiased short-read single nucleus (sn) RNA-seq for simultaneous characterization of the SNCA transcript isoform landscape and mapping of isoform expression to specific cell types and subtypes in PD, DLB, and control sample cortical tissues. This approach enabled discovery of numerous SNCA transcript isoforms displaying novel splicing patterns and incorporating novel exons. We further identified an abundant class of transcript isoforms encoding a previously unreported -Syn protein variant (-Syn-115) and displaying increased proportional detection in excitatory neurons of PD and DLB tissues in comparison to controls. The proportion of these isoforms was found to be especially high within several specific glutamatergic neuron subtypes. In-depth characterization of the predicted structural and biochemical properties of -Syn-115 using an in silico modeling approach revealed a greater aggregative affinity compared with canonical -Syn-140, suggesting the potential for increased cytosolic -Syn-115 abundance to induce aggregation between heterogeneous -Syn isoforms, potentially driving fibril formation and disease progression. Together, our findings provide new insights into the molecular mechanisms underlying -Syn involvement in multiple synucleinopathies and have translational implications for the development of new precision medicine strategies to combat these diseases, indicating the potential for treatments targeting both specific transcript and protein isoforms, as well as disease-driving cell subtypes.

neuroscience↗

bioRxiv2025

SNCA-targeted epigenome therapy for Parkinsons disease alleviates pathological and behavioral perturbations in a mouse model

Alpha-synuclein (SNCA) overexpression is implicated in Parkinsons disease (PD) pathogenesis, making SNCA downregulation a promising therapeutic strategy. We developed a SNCA-targeted epigenome therapy using an all-in-one lentiviral vector (LV) carrying deactivated CRISPR/(d)Cas9, gRNA targeted at SNCA-intron1, and either the catalytic domain of DNA-methyltransferase3A (DNMT3A), or a synthetic repressor molecule of Kruppel-associated box (KRAB)/ methyl CpG binding protein 2 transcription repression domain (MeCp2-TRD). Therapeutic efficacy was evaluated in a new PD mouse model, generated with an adeno-associated viral vector carrying an engineered minigene comprised of the human (h)A53T-SNCA expressed via the human native regulatory region. Both therapeutic vectors reduced expression of -synuclein in the substantia nigra (SN), with LV/dSaCas9-KRAB-MeCP2(TRD) demonstrating greater repression. LV/dSaCas9-KRAB-MeCP2(TRD) also significantly reduced pathological -synuclein aggregation and phosphorylation (Ser129), and preserved tyrosine hydroxylase expression in the SN and the striatum. Behavioral analysis following LV/dSaCas9-KRAB-MeCP2(TRD) injection, showed significant improvement in motor deficits characteristic of our PD-mouse model. Safety assessments found normal blood counts, serum chemistry, and weights. Collectively, we provide in vivo proof-of-concept for our SNCA-targeted epigenome therapy in a PD-mouse model. Our results support the systems therapeutic potential for PD and related synucleinopathies and establish the foundation for further preclinical studies toward investigational new drug enablement.

neuroscience↗

bioRxiv2024

Comparative mapping of single-cell transcriptomic landscapes in neurodegenerative diseases

INTRODUCTIONAlzheimers disease (AD), Dementia with Lewy bodies (DLB), and Parkinsons disease (PD) represent a spectrum of neurodegenerative disorders (NDDs). Here, we performed the first direct comparison of their transcriptomic landscapes. METHODSWe profiled the whole transcriptomes of NDD cortical tissue by snRNA-seq. We used computational analyses to identify common and distinct differentially expressed genes (DEGs), biological pathways, vulnerable and disease-driver cell subtypes, and alteration in cell-to-cell interactions. RESULTSThe same vulnerable inhibitory neuron subtype was depleted in both AD and DLB. Potentially disease-driving neuronal cell subtypes were present in both PD and DLB. Cell-cell communication was predicted to be increased in AD but decreased in DLB and PD. DEGs were most commonly shared across NDDs within inhibitory neuron subtypes. Overall, we observed the greatest transcriptomic divergence between AD and PD, while DLB exhibited an intermediate transcriptomic signature. DISCUSSIONThese results help explain the clinicopathological spectrum of this group of NDDs and provide unique insights into the shared and distinct molecular mechanisms underlying the pathogenesis of NDDs.

neuroscience↗

bioRxiv2024

Microglia States are Susceptible to Senescence and Cholesterol Dysregulation in Alzheimer's Disease

Cellular senescence is a major contributor to aging-related degenerative diseases, including Alzheimers disease (AD) but much less is known on the key cell types and pathways driving mechanisms of senescence in the brain. We hypothesized that dysregulated cholesterol metabolism is central to cellular senescence in AD. We analyzed whole transcriptomic data and utilized single-cell RNA seq integration techniques to unveil the convoluted cell-type-specific and sub-cell-type-state-specific senescence pathologies in AD using both ROSMAP and Sea-AD datasets. We identified that microglia are central components to AD associated senescence phenotypes in ROSMAP snRNA-seq data (982,384 nuclei from postmortem prefrontal cortex of 239 AD and 188 non-AD) among non-neuron cell types. We identified that homeostatic, inflammatory, phagocytic, lipid processing and neuronal surveillance microglia states were associated with AD associated senescence in ROSMAP (152,459 microglia nuclei from six regions of brain tissue of 138 early AD, 79 late AD and 226 control subject) and in Sea-AD (82,486 microglia nuclei of 42 dementia, 42 no dementia and 5 reference subjects) via integrative analysis, which preserves the meaningful biological information of microglia cell states across the datasets. We assessed top senescence associated bioprocesses including mitochondrial, apoptosis, oxidative stress, ER stress, endosomes, and lysosomes systems. Specifically, we found that senescent microglia have altered cholesterol related bioprocesses and dysregulated cholesterol. We discovered three gene co-expression modules, which represent the specific cholesterol related senescence transcriptomic signatures in postmortem brains. To validate these findings, the activation of specific cholesterol associated senescence transcriptomic signatures was assessed using integrative analysis of snRNA-seq data from iMGs (microglia induced from iPSCs) exposed to myelin, Abeta, and synaptosomes (56,454 microglia across two replicates of untreated and four treated groups). In vivo cholesterol associated senescence transcriptomic signatures were preserved and altered after treatment with AD pathological substrates in iMGs. This study provides the first evidence that dysregulation of cholesterol metabolism in microglia is a major driver of senescence pathologies in AD. Targeting cholesterol pathways in senescent microglia is an attractive strategy to slow down AD progression.

neuroscience↗

bioRxiv2023

Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimers disease brains

BackgroundThe genetic underpinnings of late-onset Alzheimers disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD. MethodsHere, we present the largest parallel single-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co-accessibility networks (CCANs). ResultsIntegrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which were LOAD-DEG, for example, ELK1 in excitatory neurons (Exc1) and KLF13 and JUN, found in multiple cell subtypes. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs including, APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes. ConclusionsTo our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings revealed crosstalk between epigenetic, genomic, and transcriptomic determinates of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specific cis-trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.

neuroscience↗

bioRxiv2023

All-in-one AAV-delivered epigenome-editing platform: proof-of-concept and therapeutic implications for neurodegenerative disorders

Safely and efficiently controlling gene expression is a long-standing goal of biomedical research, and the recently discovered bacterial CRISPR/Cas system can be harnessed to create powerful tools for epigenetic editing. Current state-of-the-art systems consist of a deactivated-Cas9 nuclease (dCas9) fused to one of several epigenetic effector motifs/domains, along with a guide RNA (gRNA) which defines the genomic target. Such systems have been used to safely and effectively silence or activate a specific gene target under a variety of circumstances. Adeno-associated vectors (AAVs) are the therapeutic platform of choice for the delivery of genetic cargo; however, their small packaging capacity is not suitable for delivery of large constructs, which includes most CRISPR/dCas9-effector systems. To circumvent this, many AAV-based CRISPR/Cas tools are delivered in two pieces, from two separate viral cassettes. However, this approach requires higher viral payloads and usually is less efficient. Here we develop a compact dCas9-based repressor system packaged within a single, optimized AAV vector. The system uses a smaller dCas9 variant derived from Staphylococcus aureus (Sa). A novel repressor was engineered by fusing the small transcription repression domain (TRD) from MeCP2 with the KRAB repression domain. The final dSaCas9-KRAB-MeCP2(TRD) construct can be efficiently packaged, along with its associated gRNA, into AAV particles. Using reporter assays, we demonstrate that the platform is capable of robustly and sustainably repressing the expression of multiple genes-of-interest, both in vitro and in vivo. Moreover, we successfully reduced the expression of ApoE, the stronger genetic risk factor for late onset Alzheimers disease (LOAD). This new platform will broaden the CRISPR/dCas9 toolset available for transcriptional manipulation of gene expression in research and therapeutic settings.

bioengineering↗

bioRxiv2021

Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer's disease brains

In the post-GWAS era, there is an unmet need to decode the underpinning genetic etiologies of late-onset Alzheimers disease (LOAD) and translate the associations to causation. Toward that goal, we conducted ATAC-seq profiling using neuronal nuclear protein (NeuN) sorted-nuclei from 40 frozen brain tissues to determine LOAD-specific changes in chromatin accessibility landscape in a cell-type specific manner. We identified 211 LOAD-specific differential chromatin accessibility sites in neuronal-nuclei, four of which overlapped with LOAD-GWAS regions ({+/-}100kb of SNP). While the non-neuronal nuclei did not show LOAD-specific differences, stratification by sex identified 842 LOAD-specific chromatin accessibility sites in females. Seven of these sex-dependent sites in the non-neuronal samples overlapped LOAD-GWAS regions including APOE. LOAD loci were functionally validated using single-nuclei RNA-seq datasets. In conclusion, using brain sorted-nuclei enabled the identification of sex-dependent cell type-specific LOAD alterations in chromatin structure. These findings enhance the interpretation of LOAD-GWAS discoveries, provide potential pathomechanisms, and suggest novel LOAD-loci. Furthermore, our results convey mechanistic insights into sex differences in LOAD risk and clinicopathology. GRAPHICAL ABSTRACT O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

neuroscience↗

bioRxiv2020

Identifying Nootropic Drug Targets via Large-Scale Cognitive GWAS and Transcriptomics

BackgroundCognitive traits demonstrate significant genetic correlations with many psychiatric disorders and other health-related traits. Many neuropsychiatric and neurodegenerative disorders are marked by cognitive deficits. Therefore, genome-wide association studies (GWAS) of general cognitive ability might suggest potential targets for nootropic drug repurposing. Our previous effort to identify "druggable genes" (i.e., GWAS-identified genes that produce proteins targeted by known small molecules) was modestly powered due to the small cognitive GWAS sample available at the time. Since then, two large cognitive GWAS meta-analyses have reported 148 and 205 genome-wide significant loci, respectively. Additionally, large-scale gene expression databases, derived from post-mortem human brain, have recently been made available for GWAS annotation. Here, we 1) reconcile results from these two cognitive GWAS meta-analyses to further enhance power for locus discovery; 2) employ several complementary transcriptomic methods to identify genes in these loci with variants that are credibly associated with cognition; and 3) further annotate the resulting genes to identify "druggable" targets. MethodsGWAS summary statistics were harmonized and jointly analysed using Multi-Trait Analysis of GWAS [MTAG], which is optimized for handling sample overlaps. Downstream gene identification was carried out using MAGMA, S-PrediXcan/S-TissueXcan Transcriptomic Wide Analysis, and eQTL mapping, as well as more recently developed methods that integrate GWAS and eQTL data via Summary-statistics Mendelian Randomization [SMR] and linkage methods [HEIDI], Available brain-specific eQTL databases included GTEXv7, BrainEAC, CommonMind, ROSMAP, and PsychENCODE. Intersecting credible genes were then annotated against multiple chemoinformatic databases [DGIdb, KI, and a published review on "druggability"]. ResultsUsing our meta-analytic data set (N = 373,617) we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. 26 genes were associated with general cognitive ability via SMR, 16 genes via STissueXcan/S-PrediXcan, 47 genes via eQTL mapping, and 68 genes via MAGMA pathway analysis. The use of the HEIDI test permitted the exclusion of candidate genes that may have been artifactually associated to cognition due to linkage, rather than direct causal or indirect pleiotropic effects. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging on our various transcriptome and pathway analyses, as well as available chemoinformatic databases, we identified 16 putative genes that may suggest drug targets with nootropic properties. DiscussionResults converged on several categories of significant drug targets, including serotonergic and glutamatergic genes, voltage-gated ion channel genes, carbonic anhydrase genes, and phosphodiesterase genes. The current results represent the first efforts to apply a multi-method approach to integrate gene expression and SNP level data to identify credible actionable genes for general cognitive ability.

genomics↗

bioRxiv2019

Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways

Liability to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published GWAS in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected (\"Concordant\") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants demonstrating the counterintuitive (\"Discordant\") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education and/or schizophrenia at p<5x10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs, and many of these have been validated by larger, more recent single-phenotype GWAS. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms: early neurodevelopmental pathways that characterize concordant allelic variation, and adulthood synaptic pruning pathways that were linked to the paradoxical positive genetic association between education and schizophrenia. Further, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia, but also to the broader biological dimensions that are implicated in both general health outcomes and psychiatric illness.

genetics↗

bioRxiv2019

The landscape of SNCA transcripts across synucleinopathies: New insights from long reads sequencing analysis

Dysregulation of alpha-synuclein expression has been implicated in the pathogenesis of synucleinopathies, in particular Parkinsons Disease (PD) and Dementia with Lewy bodies (DLB). Previous studies have shown that the alternatively spliced isoforms of the SNCA gene are differentially expressed in different parts of the brain for PD and DLB patients. Similarly, SNCA isoforms with skipped exons can have a functional impact on the protein domains. The large intronic region of the SNCA gene was also shown to harbor structural variants that affect transcriptional levels. Here we apply the first study of using long read sequencing with targeted capture of both the gDNA and cDNA of the SNCA gene in brain tissues of PD, DLB, and control samples using the PacBio Sequel system. The targeted full-length cDNA (Iso-Seq) data confirmed complex usage of known alternative start sites and variable 3 UTR lengths, as well as novel 5 starts and 3 ends not previously described. The targeted gDNA data allowed phasing of up to 81% of the ~114kb SNCA region, with the longest phased block excedding 54 kb. We demonstrate that long gDNA and cDNA reads have the potential to reveal long-range information not previously accessible using traditional sequencing methods. This approach has a potential impact in studying disease risk genes such as SNCA, providing new insights into the genetic etiologies, including perturbations to the landscape the gene transcripts, of human complex diseases such as synucleinopathies.

genetics↗

bioRxiv2017

GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimers dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

genetics↗

bioRxiv2017

Large-Scale cognitive GWAS Meta-analysis Reveals Tissue-Specific Neural Expression and Potential Nootopic Drug Targets

Neurocognitive ability is a fundamental readout of brain function, and cognitive deficits are a critical component of neuropsychiatric disorders, yet neurocognition is poorly understood at the molecular level. In the present report, we present the largest genome-wide association studies (GWAS) of cognitive ability to date (N=107,207), and further enhance signal by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with cognitive ability, 34 of which were novel. A total of 350 genes were implicated, and this list showed significant enrichment for genes associated with Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis of gene results implicated the biological process of neurogenesis, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker; and LY97241, a potassium channel inhibitor. Transcriptome-wide analysis revealed that the implicated genes were strongly expressed in neurons, but not astrocytes or oligodendrocytes, and were more strongly associated with fetal brain expression than adult brain expression. Several tissue-specific gene expression relationships to cognitive ability were observed (for example, DAG1 levels in the hippocampus). Finally, we report novel genetic correlations between cognitive ability and disparate phenotypes such as maternal age at first birth and number of children, as well as several autoimmune disorders.

genomics↗