Biology subjects

Funk, C. C.

Publications and source records attributed to Funk, C. C..

15 recordsLinked to original sources

bioRxiv2026

Mitochondrial Complex I Modulator Restores Network Resilience in Advanced Alzheimer's Disease Through Metabolic Reprogramming

Mitochondrial dysfunction and lipid dysregulation are among the earliest abnormalities in Alzheimers disease (AD), yet their mechanistic interplay and therapeutic potential remain poorly understood. Here, we investigated whether restoration of mitochondrial function can reverse metabolic dysfunction and promote resilience in advanced-stage AD. Female APP/PS1 mice were treated with the brain-penetrant mitochondrial complex I (mtCI) modulator CP2 beginning at 19 months of age, when pathology and cognitive deficits were well established. To define the metabolic mechanisms underlying therapeutic response, we developed iMiceBrain, the first brain-specific genome-scale metabolic model of the mouse brain, and integrated transcriptomics, targeted metabolomics, lipidomics, and metabolic network analyses. CP2 treatment broadly reprogrammed AD-associated molecular signatures and restored pathways involved in mitochondrial function, glucose utilization, lipid metabolism, synaptic activity, and cellular stress responses. Metabolic modeling identified enhanced mitochondrial substrate flexibility, activation of fatty acid utilization, restoration of pyruvate dehydrogenase flux, and normalization of cholesterol metabolism as key features of the therapeutic response. Lipidomic analyses further demonstrated correction of disease-associated alterations in cholesteryl esters, phospholipids, and sphingolipids. Together, these findings demonstrate that mild mtCI modulation restores metabolic resilience by coordinating mitochondrial and lipid metabolism, establishing it as a disease-modifying therapeutic strategy for AD.

neuroscience↗

bioRxiv2024

APOE Genotype and Biological Age Impact Inter-Omic Associations Related to Bioenergetics

Apolipoprotein E (APOE) modifies human aging; specifically, the {varepsilon}2 and {varepsilon}4 alleles are among the strongest genetic predictors of longevity and Alzheimers disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. Herein, we analyzed inter-omic, context-dependent association patterns across APOE genotypes, sex, and health axes in 2,229 community-dwelling individuals to test APOE genotypes for variation in metabolites and metabolite-associations tied to a previously-validated metric of biological aging (BA) based on blood biomarkers. Our analysis, supported by validation in an independent cohort, identified top APOE-associated plasma metabolites as diacylglycerols, which were increased in {varepsilon}2-carriers and trended higher in {varepsilon}4-carriers compared to {varepsilon}3-homozygotes, despite the known opposing aging effects of the allele variants. Omics association patterns of {varepsilon}2-carriers and increased biological age were also counter-intuitively similar, displaying increased associations between insulin resistance markers and energy-generating pathway metabolites. These results provide an atlas of APOE-related omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.

systems biology↗

bioRxiv2024

Multi-omic ADNI CSF and plasma data integration identifies distinct metabolic transitions in disease progression in Alzheimer's Disease

Age and APOE genotype are the strongest known risk factors for late-onset Alzheimers disease (AD), but the mechanisms linking them to neuronal loss remain incompletely defined. Using multiomic data from the Alzheimers Disease Neuroimaging Initiative (ADNI), we propose a unified hypothesis in which two interdependent failure modes--saturation of microglial lipid flux capacity and disruption of the astrocyte-neuron lactate shuttle (ANLS) due to excess astrocytic membrane cholesterol--drive disease progression upstream of amyloid and tau pathology. Stratifying participants by cognitive score quartiles, we find consistent associations linking impaired lipid clearance, metabolic stress, and genetic variants regulating cholesterol handling. These processes appear to reinforce each other, resulting in accelerating neurodegeneration. Our hypothesis reframes AD as a systems-level collapse in metabolic coordination, rather than a purely linear pathological cascade. These insights emerged during the development of digital twin models for personalized interventions, highlighting the power of systems approaches to reveal hidden drivers of neurodegeneration.

neuroscience↗

bioRxiv2024

Fokker-Planck diffusion maps of multiple single cell microglial transcriptomes reveals radial differentiation into substates associated with Alzheimer's pathology

The identification of microglia subtypes is important for understanding the role of innate immunity in neu-rodegenerative diseases. Current methods of unsupervised cell type identification assume a small noise-to-signal ratio of transcriptome measurements that would produce well-separated cell clusters. However, identification of subtypes is obscured by gene expression noise, diminishing the distances in transcriptome space between distinct cell types and blurring boundaries. Here we use Fokker-Planck (FP) diffusion maps to model cellular differentiation as a stochastic process whereby cells settle into local minima, corresponding to cell subtypes, in a potential landscape constructed from transcriptome data using a nearest neighbor graph approach. By applying critical transition fields, we identify individual cells on the verge of transitioning between subtypes, revealing microglial cells in inactivated, homeostatic state before radially transitioning into various specialized subtypes. Specifically, we show that cells from Alzheimers disease patients are enriched in a microglia subtype associated to antigen presentation and T-cell recruitment.

systems biology↗

bioRxiv2024

Ndufs4 knockout induces transcriptomic signatures of Alzheimer's Diseases that are partially reversed by mitochondrial complex I inhibitor

Mitochondrial dysfunction is well documented in Alzheimers Disease (AD). However, whether it instigates the onset of AD remains unclear. We demonstrate that a reduction of complex I activity in wild type (WT) mice caused by a global knockout of Ndufs4, an accessory mitochondrial complex I subunit, was sufficient to induce transcriptomic changes in the brain reminiscent of those observed in AD patients and familial mouse models of AD. Reduced complex I activity affected expression of genes in the networks related to mitochondrial homeostasis, neuronal and synaptic function. Transcriptomic signatures in male and female Ndufs4-/-mice reflected a different severity of AD phenotype. Unexpectedly, these changes were partially rescued by a neuroprotective small molecule mild complex I inhibitor CP2. Consistent with studies in AD mice, CP2 treatment in Ndufs4-/- mice augmented the expression of genes associated with mitochondrial biogenesis and turnover, synaptic activity, autophagy, redox balance, and reduced expression of genes related to inflammation. Female Ndufs4-/- mice demonstrated a greater reversal of gene expression toward WT mice. These studies provide further support for mitochondria as a causative factor of AD pathophysiology and complex I as a putative therapeutic target.

neuroscience↗

bioRxiv2023

Aβ Amyloid Scaffolds the Accumulation of Matrisome and Additional Proteins in Alzheimer's Disease

We report a highly significant correlation between human Alzheimers disease (AD) brain proteome changes and those in CRND8 APP695NL/F transgenic mice. Comparing protein changes observed in the CRND8 mice with co-expression networks derived from human Alzheimers disease (AD), reveals both conserved and divergent module changes. Many proteins in the most highly conserved module (M42, matrisome) accumulate in plaques, cerebrovascular amyloid (CAA), dystrophic neuronal processes, or a combination thereof. Overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), in CRND8 mice brains leads to increased accumulation of A{beta} in plaques and in blood vessels; further, recombinant MDK and PTN enhance A{beta} aggregation into amyloid structures. Multiple M42 proteins bind to fibrillar A{beta}42 and a non-human amyloid fibril in vitro. Supporting this binding data, MDK and PTN co-accumulate with transthyretin (TTR) amyloid in the heart. Notably, our findings establish that proteomic changes in modules observed in human AD brains define an A{beta} amyloid "responsome" that is well conserved from mouse models to humans. Further, distinct amyloid structures appear to serve as scaffolds, facilitating the co-accumulation of proteins with signaling functions, and this co-accumulation may contribute to downstream pathological sequalae. Overall, this contextualized understanding of proteomic changes and their interplay with amyloid deposition provides valuable insights into the complexity of AD pathogenesis and potential biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2022

A Genome Scale Transcriptional Regulatory Model of the Human Placenta

Gene regulation is essential to placental function and fetal development. We report a genome-scale transcriptional regulatory network (TRN) of the human placenta built using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out of sample R2 value greater than 0.25 for 74% of target genes. We performed siRNA knockdowns of 4 TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for 3 of the 4 TFs. Our final model contained 113,158 interactions across 391 TFs and 7,712 target genes and is publicly available. We identified six TFs which were significantly enriched as regulators for genes previously associated with preterm birth.

systems biology↗

bioRxiv2021

Manifestations of genetic risk for Alzheimer's Disease in the blood: a cross-sectional multi-omic analysis in healthy adults aged 18-90+

Deeply phenotyped cohort data can elucidate differences associated with genetic risk for common complex diseases across an age spectrum. Previous work has identified genetic variants associated with Alzheimers disease (AD) risk from large-scale genome-wide association study meta-analyses. To explore effects of known AD-risk variants, we performed a phenome-wide association study on ~2000 clinical, proteomic, and metabolic blood-based analytes obtained from 2,831 cognitively normal adult clients of a consumer-based scientific wellness company. Results uncovered statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE). These effects were detectable from early adulthood. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. Sex-stratified GWAS results from an independent AD case-control meta-analysis supported sexspecific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. These analyses support evidence from previous functional genomics studies in the identification of a causal variant within the PILRA gene. Taken together, this study highlights clues to the earliest effects of AD genetic risk variants in individuals where disease symptoms have not (yet) arisen.

genetics↗

bioRxiv2021

Microglia show differential transcriptomic response to Aβ peptide aggregates ex vivo and in vivo

Aggregation and accumulation of amyloid-{beta} (A{beta}) is a defining feature of Alzheimers disease (AD) pathology. To study microglial responses to A{beta}, we applied exogenous A{beta} peptide, in either oligomeric or fibrillar conformation, to primary mouse microglial cultures and evaluated system level transcriptional changes and then compared these to transcriptomic changes in the brains of CRND8 APP mice. We find that primary microglial cultures have rapid and massive transcriptional change to in response to A{beta}. Transcriptomic responses to oligomeric or fibrillar A{beta} in primary microglia, though partially overlapping, are distinct and are not recapitulated in vivo where A{beta} progressively accumulates. Furthermore, though classic immune mediators show massive transcriptional changes in the primary microglial cultures, these changes are not observed in the mouse model. Together, these data extend previous studies which demonstrate that microglia responses ex vivo are poor proxies for in vivo responses. Finally, these data demonstrate the potential utility of using microglia as biosensors of different aggregate conformation, as the transcriptional responses to oligomeric and fibrillar A{beta} can be distinguished.

neuroscience↗

bioRxiv2020

Partial inhibition of mitochondrial complex I attenuates neurodegeneration and restores energy homeostasis and synaptic function in a symptomatic Alzheimers mouse model

We demonstrate that mitochondrial respiratory chain complex I is an important small molecule druggable target in Alzheimers Disease (AD). Partial inhibition of complex I triggers the AMP-activated protein kinase-dependent signaling network leading to neuroprotection in symptomatic APP/PS1 mice, a translational model of AD. Treatment of APP/PS1 mice with complex I inhibitor after the onset of AD-like neuropathology improved energy homeostasis, synaptic activity, long-term potentiation, dendritic spine maturation, cognitive function and proteostasis, and reduced oxidative stress and inflammation in brain and periphery, ultimately blocking the ongoing neurodegeneration. Therapeutic efficacy in vivo was monitored using translational biomarkers FDG-PET, 31P NMR, and metabolomics. Cross-validation of the mouse and the human AMP-AD transcriptomic data demonstrated that pathways improved by the treatment in APP/PS1 mice, including the immune system response and neurotransmission, represent mechanisms essential for therapeutic efficacy in AD patients.

neuroscience↗

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↗

bioRxiv2018

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

bioinformatics↗

bioRxiv2017

Genome-scale transcriptional regulatory network models of psychiatric and neurodegenerative disorders

Genetic and genomic studies suggest an important role for transcriptional regulatory changes in brain diseases, but roles for specific transcription factors (TFs) remain poorly understood. We integrated human brain-specific DNase I footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain, predicting the brain-specific binding sites and target genes for 741 TFs. We used this model to predict core TFs involved in psychiatric and neurodegenerative diseases. Our results suggest that disease-related transcriptomic and genetic changes converge on small sets of disease-specific regulators, with distinct networks underlying neurodegenerative vs. psychiatric diseases. Core TFs were frequently implicated in a disease through multiple mechanisms, including differential expression of their target genes, disruption of their binding sites by disease-associated SNPs, and associations of the genetic loci encoding these TFs with disease risk. We validated our models predictions through systematic comparison to publicly available ChIP-seq and TF perturbation studies and through experimental studies in primary human neural stem cells. Combined genetic and transcriptional evidence supports roles for neuronal and microglia-enriched, MEF2C-regulated networks in Alzheimers disease; an oligodendrocyte-enriched, SREBF1-regulated network in schizophrenia; and a neural stem cell and astrocyte-enriched, POU3F2-regulated network in bipolar disorder. We provide our models of brain-specific TF binding sites and target genes as a resource for network analysis of brain diseases.

genetics↗

bioRxiv2017

iREAD: A Tool For Intron Retention Detection From RNA-seq Data

SummaryDetecting intron retention (IR) events is emerging as a specialized need for RNA-seq data analysis. Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input an existing BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR vents by analyzing features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. The output can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions.\n\nAvailabilitywww.libpls.net/iread\n\nContactNathan.Price@systemsbiology.org

bioinformatics↗

bioRxiv2016

Motivational, proteostatic and transcriptional deficits precede synapse loss, gliosis and neurodegeneration in the B6.HttQ111/+ model of Huntington’s disease

We investigated the appearance and progression of disease-relevant signs in the B6.HttQ111/+ mouse, a genetically precise model of the mutation that causes Huntingtons disease (HD). We find that B6.HttQ111/+ mice are healthy, show no overt signs of central or peripheral inflammation, and no gross motor impairment as late as 12 months of age. Behaviorally, we find that 4-9 month old B6.HttQ111/+ mice have normal activity levels and show no clear signs of anxiety or depression, but do show clear signs of reduced motivation. The neuronal density, neuronal size, synaptic density and number of glia is normal in B6.HttQ111/+ striatum, the most vulnerable brain region in HD, up to 12 months of age. Despite this preservation of the synaptic and cellular composition of the striatum, we observe clear progressive, striatal-specific, transcriptional dysregulation and accumulation of neuronal intranuclear inclusions (NIIs). Simulation studies suggest these molecular endpoints are sufficiently robust for future preclinical studies, and that B6.HttQ111/+ mice are a useful tool for modeling disease-modifying or neuroprotective strategies for disease processes before the onset of overt phenotypes.

neuroscience↗