Biology subjects

Zhao, J.

Publications and source records attributed to Zhao, J..

At least 217 records · Page 12Linked to original sources

bioRxiv2024

A Comprehensive Assessment of Methylation-Based Age Prediction Methods

DNA methylation (DNAm) clock is widely used to measure biological age, helping to identify key biomarkers associated with aging, infer the progression of aging, and have promise for elucidating, delaying, or even reversing aging. During the past decade, a large number of epigenetic clocks have been developed. However, they are decentralized, with applicable scopes overlapping. We benchmark 15 of these methods on 142 Illumina DNAm array datasets in five criteria and analyze the biological significance of CPGs about aging and overlapping. There are many exciting commons in models performance. We found the optimal model closely related to the numbers and characteristics of the training data. We provided a comprehensive assessment process to guide DNAm clock research at (https://dnamclock.com), the corresponding data and evaluation pipeline are freely available (https://github.comyNENUBioCompute/MethylationEvaluation), this study will aid in the development of improved tools designed to analyze increasingly large DNAm datasets.

bioinformatics↗

bioRxiv2024

Deaminase-mediated chromatin accessibility profiling with single-allele resolution

Binding of transcription factors (TFs) at gene regulatory elements controls cellular epigenetic state and gene expression. Current genome-wide chromatin profiling approaches have inherently limited resolution, complicating assessment of TF occupancy and co-occupancy, especially at individual alleles. In this work, we introduce Accessible Chromatin by Cytosine Editing Site Sequencing with ATAC-seq (ACCESS-ATAC), which harnesses a double-stranded DNA cytosine deaminase (Ddd) enzyme to stencil TF binding locations within accessible chromatin regions. We optimize bulk and single-cell ACCESS-ATAC protocols and develop computational methods to show that the increased resolution compared with ATAC-seq improves the accuracy of TF binding site prediction. We use ACCESS-ATAC to perform genome-wide allelic occupancy and co-occupancy imputation for 64 TFs each in HepG2 and K562, revealing that the propensity of a majority of TFs to co-occupy nearby motifs oscillates with a period approximating the helical turn of DNA. Altogether, ACCESS-ATAC expands the resolution and capabilities of bulk and single-cell epigenomic profiling.

genomics↗

bioRxiv2024

ldentify potential feature genes and immune cell infiltration of HIRI based on branched-chain amino acid-related genes by machine learning and experimental validation

BackgroundBranched-chain amino acid metabolism is involved in the pathogenesis of various liver diseases. In this study, we investigate the potential role of branched-chain amino acid metabolism-related genes in the pathogenesis of hepatic ischemia reperfusion (HIRI). MethodsThe gene Expression profiles of HIRI were obtained from the Gene Expression Omnibu database. To determine the differential expression of branched-chain amino acid metabolism-related genes between HIRI and normal tissues. Then, the GO and KEGG analyses were performed, and the protein-protein interaction network was constructed. Next, the random forest and LASSO algorithms were used to screen hub genes, and machine learning techniques were used to build diagnostic models. immunoinfiltration was analyzed in both HIRI patients and controls and the ceRNA network was established. Finally, quantitative real-time PCR was used to verify the expression of hub gene. ResultsBased on data set GSE23649, three central DEGs (SLC7A5, SLC1A5, SLC43A2) were determined by the intersection of three machine learning algorithms and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.733-0.922). In the external GSE15480 dataset, AUC value for three key genes is as high as 1.000. Further analysis of nomogram, decision curve and calibration curve also confirme the predictive efficacy of diagnosis. GSEA and GSVA suggest that these three marker genes were involved in multiple pathways associated with HIRI progression. Immunoinfiltration analysis suggest that the proportion of macrophages, neutrophils, aDCs, Treg, and Th1 cells in HIRI group is higher than that in control group, with statistical significance(P<0.05). The ceRNA network demonstrates the complex regulatory relationships among the three hub genes and these mRNA levels were further confirmed in mouse HIRI liver samples. ConclusionsOur study have provided a comprehensive understanding of the association between branched-chain amino acid and HIRI, may provide potential target for HIRI treatment and diagnosis. And provide new insights into the mechanisms of HIRI. Graphical Abstract

bioinformatics↗

bioRxiv2024

Nonuple atg8 mutant provides genetic evidence for functional specialization of ATG8 isoforms in Arabidopsis thaliana

Autophagy sustains cellular health by recycling damaged or excess components through autophagosomes. It is mediated by conserved ATG proteins, which coordinate autophagosome biogenesis and selective cargo degradation. Among these, the ubiquitin-like ATG8 protein plays a central role by linking cargo to the growing autophagosomes through interacting with selective autophagy receptors. Unlike most ATG proteins, the ATG8 gene family is significantly expanded in vascular plants, but its functional specialization remains poorly understood. Using transcriptional and translational reporters in Arabidopsis thaliana, we revealed that ATG8 isoforms are differentially expressed across tissues and form distinct autophagosomes within the same cell. To explore ATG8 specialization, we generated the nonuple{Delta} atg8 mutant lacking all nine ATG8 isoforms. The mutant displayed hypersensitivity to carbon and nitrogen starvation, coupled with defects in bulk and selective autophagy as shown by biochemical and ultrastructural analyses. Complementation experiments demonstrated that ATG8A could rescue both carbon and nitrogen starvation phenotypes, whereas ATG8H could only complement carbon starvation. Proximity labeling proteomics further identified isoform-specific interactors under nitrogen starvation, underscoring their functional divergence. These findings provide genetic evidence for functional specialization of ATG8 isoforms in plants and lay the foundation for investigating their roles in diverse cell types and stress conditions.

plant biology↗

bioRxiv2024

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

Understanding cellular responses to genetic perturbations is essential for understanding gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional responses to perturbations at the single-cell level, there remains a pressing need for computational models that can decode the mechanisms driving these responses and accurately predict outcomes to prioritize target genes for experimental design. Here, we present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations. By leveraging gene embeddings derived from large language models, scLAMBDA effectively integrates prior biological knowledge and disentangles basal cell states from perturbation-specific salient representations. Through comprehensive evaluations on multiple single-cell CRISPR Perturb-seq datasets, scLAMBDA consistently outperformed state-of-the-art methods in predicting perturbation outcomes, achieving higher prediction accuracy. Notably, scLAMBDA demonstrated robust generalization to unseen target genes and perturbations, and its predictions captured both average expression changes and the heterogeneity of single-cell responses. Furthermore, its predictions enable diverse downstream analyses, including the identification of differentially expressed genes and the exploration of genetic interactions, demonstrating its utility and versatility.

bioinformatics↗

bioRxiv2024

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data Analysis

Single-cell Multi-modal Data Integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline, and evaluate the contributions of data integration. In this manuscript, we revisit the definition and contributions of multi-modal data integration, and propose a novel and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration. Our proposed method is capable of integrating different types of omic and sensing data. It offers an approach to discovering important relationships among biological features or cell states. We demonstrate that our method outperforms other baseline models in preserving both local and global structures and perform a comprehensive analysis for mining structural relationships in complex biological systems, including inference of gene regulatory networks, extraction of significant biological linkages, and analysis of differentially regulatory relationships.

bioinformatics↗

bioRxiv2024

Ultra-small Particle Size Nanobubbles Water Drinking Exerts Antifatigue Effects in Chronic Exercise Fatigue Rats via Improving Antioxidative Activities

Chronic exercise fatigue is a prevalent issue that can lead to a decline in physical performance, metabolic dysregulation, and oxidative damage, posing significant health risks. This study aims to investigate the effects of Ultra-small Particle Size Nanobubbles Water (UsNBsW) which was Koishio technology produced water on physiological and biochemical parameters in a rat model of chronic exercise fatigue. We established a chronic fatigue model, and evaluated various physiological and biochemical indicators. Our key findings revealed that Koishio water significantly improved rats swimming performance, reduced blood lactate levels, and enhanced antioxidant capacity, as evidenced by increased hemoglobin content, decreased lactate dehydrogenase and creatine kinase activities (p<0.01). Additionally, liver glycogen levels were notably elevated (p<0.05). Futhermore, the antioxidant markers, including superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities were significantly higher (p<0.01), while malondialdehyde (MDA) and 8-hydroxydeoxyguanosine (8-OH-dG) levels were markedly reduced (p<0.01). Our results provide new clues for the promising role of Koishio water in mitigating chronic exercise fatigue and supply a reliable reference for future research into nutritional interventions and recovery strategies in exercise physiology.

physiology↗

bioRxiv2024

Astrocyte-specific secretome profiling reveals its correlation with neurological disorders

Secreted proteins mediate intercellular communication throughout the lives of multicellular organisms. However, due to the lack of new technology for secreted protein capturing, the progress of "secretomics lags behind. Here, we report a two-step secretome enrichment method (tsSEM) combining unnatural amino acid labeling and click chemistry-based biorthogonal reaction, which enables in vitro secretome profiling in the presence of serum. Using this novel method, we systematically investigated the secretome of human iPSCs-derived astrocytes (iAst) in different disease models and identified a panel of astrocyte-secreted proteins that are responsible for its non-cell autonomous toxicity under disease conditions. Furthermore, we validated two astrocytes-derived novel neurotrophic proteins, FAM3C and KITLG, which we identified from disease models, and found that they could boost neurite outgrowth, protect neurons, and promote neural progenitor proliferation. Our study highlights the utility of secretome profiling of iAst and demonstrates its applications in disease study and target identification and validation in drug development.

neuroscience↗

bioRxiv2024

Generating snoRNA-guided Programmable 2'O-methylation

Small nucleolar RNAs (snoRNAs) are critical in guiding post-transcriptional modifications like 2-O-methylation (Nm), which play crucial roles in downstream processes such as splicing and translation. This study tests a novel method for Nm validation, addressing a significant gap in modern Nm research, and offers insight into the intricacies of snoRNA-guided Nm. While mapping of Nm modifications has seen significant improvement within the past decade, no major techniques have been able to validate these potential sites. Additionally, many mapping techniques lack consensus among proposed Nm sites, especially on mRNAs. Without a proper validation technique, Nm research lags compared to its peer post-transcriptional modifications. The RNase H-based Nm-VAQ assay used here quantifies 2-O-methylation at single nucleotide resolution across various RNA species including rRNA, snRNA, and mRNA. Its optimization for mRNA allows for an unprecedented way to study the effects of Nm modifications in low abundance transcripts. Utilizing this, the study also explores the potential of creating synthetic snoRNAs to guide Nm modifications. Exogenous snoRNAs are shown to rescue Nm in genetic knockout models and can be mutated to guide Nm at any location along the target RNA transcript. Preliminary work indicates that synthetic snoRNAs demonstrate the ability to modify luciferase, impacting translation efficiency. Targeting an exon increases mRNA abundance but decreases protein expression, consistent with previous findings on Pxdn mRNA. These findings set the scene for novel understanding of the relationship between snoRNA abundance, 2-O-methylation efficiency, and Nms impact on gene expression.

molecular biology↗

bioRxiv2024

Decoding the Neural Dynamics of Headed Syntactic Structure Building

The brain builds hierarchical phrases during language comprehension; however, the representational details and dynamics of the phrase-building process remain underspecified. This study directly probes whether the neural code of a phrase involves reactivating the syntactic property of a key subcomponent (the "phrasal head"). To this end, we train a part-of-speech sliding-window neural decoder (verb vs. adverb) on EEG signals recorded while participants (N = 30) read sentences in a controlled experiment. The decoder reaches above-chance performance that is spatiotemporally consistent and generalizes to unseen data across sentence positions. Appling the decoder to held-out data yields predicted activation levels for the verbal "head" of a verb phrase at a distant non-head word (adverb); the critical adverb appeared either at the end of a verb phrase or at a sequentially and lexically matched position with no verb phrase boundary. There is stronger verb activation beginning at [~]600 milliseconds at the critical adverb when it appears at a verb phrase boundary; this effect is not modulated by the strength of conceptual association between the two subcomponents in the verb phrase nor does it reflect word predictability. Time-locked analyses additionally reveal a negativity waveform component and increased beta-delta inter-trial phase coherence, both previously linked to linguistic composition, in a similar time window. With a novel application of neural decoding, our findings delineate the temporal dynamics by which the brain encodes phrasal representations by, in part, reactivating the representation of key subcomponents. We thus establish a link between cognitive accounts of phrase structure representations and electrophysiological dynamics.

neuroscience↗

bioRxiv2024

Target-specific design of drug-like PPI inhibitors via hot-spot-guided generative deep learning

Protein-protein interactions (PPIs) are vital therapeutic targets. However, the large and flat PPI interfaces pose challenges for the development of small-molecule inhibitors. Traditional computer-aided drug design approaches typically rely on pre-existing libraries or expert knowledge, limiting the exploration of novel chemical spaces needed for effective PPI inhibition. To overcome these limitations, we introduce Hot2Mol, a deep learning framework for the de novo design of drug-like, target-specific PPI inhibitors. Hot2Mol generates small molecules by mimicking the pharmacophoric features of hot-spot residues, enabling precise targeting of PPI interfaces without the need for bioactive ligands. The framework integrates three key components: a conditional transformer for pharmacophore-guided, drug-likeness-constrained molecular generation; an E(n)-equivariant graph neural network for accurate alignment with PPI hot-spot pharmacophores; and a variational autoencoder for generating novel and diverse molecular structures. Experimental evaluations demonstrate that Hot2Mol outperforms baseline models across multiple metrics, including docking affinities, drug-likenesses, synthetic accessibility, validity, uniqueness, and novelty. Furthermore, molecular dynamics simulations confirm the good binding stability of the generated molecules. Case studies underscore Hot2Mols ability to design high-affinity and selective PPI inhibitors, demonstrating its potential to accelerate rational PPI drug discovery.

bioinformatics↗

bioRxiv2024

Development of an ELISA assay for EV quantification to assist the large-scale EV manufacture

Accurate EV quantitation is essential for ensuring quality, consistency, and safety during large-scale extracellular vesicle (EV) manufacturing. In the upstream phase, EV quantitation allows for the monitoring of cell culture conditions that impact EV yield and quality, while in the downstream phase, it helps to control the efficiency and purity of EV purification. Nanoparticle Tracking Analysis is the most commonly used method for EV quantification, while it faces significant limitations due to interference from nanosize contaminants like protein aggregates, especially in crude samples (e.g. cell culture media). To address this issue, we developed a highly specific and accurate ELISA assay that quantifies EVs even in crude samples. With ultra-pure EV standard samples, this assay showed reliable quantitative result of EV detection to support method development as well as in-process control of large-scale EV manufacture. The detection range of this assay is from 4.1E7 to 3E10 EVs/mL, with an LOD of 1.04E7 EVs/mL and an LOQ of 3.21E7 EVs/mL. We therefore developed this assay into a testing kit and demonstrated that this EV quantification ELISA kit is capable of ensuring minimal interference from impurities and supporting the process development and in-process control in EV production.

biochemistry↗

bioRxiv2024

FLEXIBLE, PRODUCTION-SCALE, HUMAN WHOLE GENOME SEQUENCING ON A BENCHTOP SEQUENCER

Human whole-genome sequencing (hWGS) provides comprehensive genomic information that can potentially help guide research in disease prevention and treatment and ultimately improve human health. Recent advancements in sequencing technology have improved sequencing quality and further reduced sequencing costs on bench-top sized instruments, making whole-genome sequencing an accessible technology for broader use. Here, we demonstrate the feasibility of a large whole genome sequencing project using a benchtop sequencer in a small laboratory setting, on a scale previously reserved for production-scale factory-sized machines. In this project, 807 samples were prepared and sequenced across 313 flow cells, with high sequencing quality at a median %Q30 of 96.6% and a median %Q40 of 89.31%. To screen library quality and maximize sample yield, we utilized 48-plex sample pre-pool QC runs to provide >1x coverage per sample prior to sample pooling and full-depth sequencing. With this strategy, we consistently achieved >30x human whole genome sequencing of three-plex sample trios with standard settings or up to 4 samples per run with a high-throughput run setting. Additionally, this low-pass data provided valuable sample-level insights, allowing for detection of chromosome copy number variations (CNV) prior to full-depth sequencing. With three instruments running concurrently, >2,800 30x human whole genomes could be sequenced per year. To further demonstrate additional flexibility present in the platform, we also explored two different use cases 1) large insert sizes (1kb+) library to achieve superior genome coverage; 2) a proof of concept for rapid WGS sequencing to minimize sample to answer turnaround time for time-critical sequencing applications. Sequencing of a 2x100 >30x human WGS can be achieved in <12 hours and subsequent generation of fastq, bam and vcf in <1 additional hour. This study provides a cost-effective and flexible real-world demonstration of achieving both high quality hWGS sequencing and instrument flexibility without the need for complex batching schemes or factory-sized sequencers.

molecular biology↗

bioRxiv2024

Defective Neurogenesis in Lowe Syndrome is Caused by Mitochondria Loss and Cilia-related Sonic Hedgehog Defects

Human brain development requires tight coordination of metabolic and signaling pathways. Lowe syndrome (LS) is a recessive X-linked disorder characterized by proximal tubular renal disease, congenital cataracts, glaucoma, and neurodevelopmental delays. While LS results from mutations in the OCRL gene, which encodes an inositol polyphosphate 5-phosphatase, the cellular mechanisms driving neuronal dysfunction remain poorly understood. In this study, using patient-derived iPSC neurons, an OCRL knockout mouse model, and an independent zebrafish OCRL-deficient model, we identified mitochondrial dysfunction as a conserved phenotype of OCRL loss across species. Collectively, our findings showed that OCRL deficiency leads to reduced mitochondrial activity, decreased mtDNA levels, reduced mitochondrial content (TOM20), and increased oxidative stress. We further showed that OCRL-deficient neural cells exhibited an altered balance of neuronal versus astrocytic differentiation, rather than a defect in neurogenesis. Additionally, we observed impaired Sonic Hedgehog (Shh) signaling and ciliary homeostasis. Thus, we propose that mitochondrial dysfunction-induced oxidative stress acts as a central mediator linking OCRL loss to altered cell fate and disrupted Shh signaling, providing a unifying framework for these phenotypes.

cell biology↗

bioRxiv2024

Microbial succession, community assembly and adaptation over five years in a newly discovered deep-sea cold seep

Deep-sea cold seeps, characterized by the release of hydrocarbon-rich fluids such as methane from the seafloor, create unique ecosystems with evolving biological communities. Microorganisms serve as pioneers, initially colonizing early-stage cold seeps and providing essential matter and energy for subsequent colonizers. However, microbial succession, community assembly, and adaptation in these environments remain largely unexplored due to challenges associated with locating early-stage cold seeps. Here, we collected sediment samples from a newly discovered early-stage cold seep site, QDN-W07, in the South China Sea at varying depths over five years. We also sampled two previously reported cold seep sites, QDN-S18 and HM-ROV01, for comparative analysis. Amplicon sequencing revealed a high relative abundance of ANME-3 (up to 99%) and Methylomonadaceae (up to 83%) in surface sediments, and indicated changes in microbial community structures over time. Dispersal limitation dominated community assembly, while the contribution of homogeneous selection decreased over time, accompanied by a declined in the intensity of microbial association. Metagenomic analysis of core microbiome, comprising 172 metagenomic-assembled genomes at QDN-W07, revealed tolerance to low temperatures and oxygen. Core microbiome genomes were enriched with genes related to aerobic methane oxidation, urea metabolism, and the oxidation of sulfur, ammonia and carbon monoxide. These results indicate that core microorganisms obtain energy from the oxidation of inorganic compounds, allowing them to thrive in cold, methane-rich and unstable conditions. Overall, this study enhances our understanding of deep biosphere life, biogeochemical cycling, and the ecological management of cold seep environments.

microbiology↗

bioRxiv2024

Promiscuous and unbiased recombination underlies the sequence-discrete species of the SAR11 lineage in the deep ocean

Surveys of microbial communities (metagenomics) or isolate genomes have revealed sequence-discrete species. That is, members of the same species usually show >95% Average Nucleotide Identity (ANI) of shared genes among themselves vs. <83% ANI to members of other species while genome pairs showing between 83-95% ANI are comparatively rare. In these surveys, aquatic bacteria of the ubiquitous SAR11 clade (Class Alphaproteobacteria), which play a major role in carbon cycling, are an outlier and often do not exhibit discrete species boundaries, suggesting the potential for alternate modes of genetic differentiation. To explore evolution in SAR11, we analyzed high-quality, single-cell amplified genomes (SAGs) and companion metagenomes from an oxygen minimum zone (OMZ) in the Eastern Tropical Pacific Ocean, where the SAR11 make up [~]20% of the total microbial community. Our results show that SAR11 do form several sequence-discrete species, but their ANI range of discreteness is shifted to lower identities between 86-91%, with intra-species ANI ranging between 91-100%. Measuring recent gene exchange among these genomes based on a newly developed methodology revealed higher frequency of homologous recombination within compared to between species that affects sequence evolution at least twice as much as diversifying point mutation across the genome. Recombination in SAR-11 appears to be more promiscuous compared to other prokaryotic species and has facilitated the spreading of adaptive mutations within the species, further promoting the high intra-species diversity observed. These results implicate rampant, genome-wide homologous recombination as the mechanism that underlies the evolution of SAR11 into discrete species. SignificanceDistinguishing "species" is a pressing issue in microbiology, partly because the mechanisms that create and maintain clusters of genetically similar microbes are sparsely documented for natural populations. By leveraging high quality single-cell genomic data and a novel method for assessing homologous recombination, we show that rampant homologous recombination maintains species-level clusters of genomes for the most abundant order of marine bacteria, suggesting that these genomes may be evolving sexually to a much greater extent than previously thought. Therefore, our results identify a mechanism explaining the evolution of species in a major microbial group and have implications for understanding microbial diversity and the species concept more broadly.

microbiology↗

bioRxiv2024

Inhibition of Androgen Receptor Exposes Replication Stress Vulnerability in Prostate Cancer

Standard initial systemic treatment for patients with metastatic prostate cancer includes agents that target androgen receptor (AR) signaling. Despite an initial positive response to these AR pathway inhibitors (ARPIs), acquired resistance remains a significant challenge. We show that treatment of AR-positive prostate cancer cells with the frontline ARPI enzalutamide induces DNA replication stress. Such stress is exacerbated by suppression of translesion DNA synthesis (TLS), leading to aberrant accumulation of single-stranded DNA (ssDNA) gaps and persistent DNA damage biomarkers. We further demonstrate that the TLS inhibitor, JH-RE-06, markedly sensitizes AR-positive prostate cancer cells, but not AR-negative benign cells, to enzalutamide in vitro. Combination therapy with enzalutamide and JH-RE-06 significantly suppresses cancer growth in a syngeneic murine tumor model over vehicle control or individual treatment groups. These findings suggest that AR inhibition broadly triggers DNA replication stress in hormone-sensitive prostate cancer, thereby exposing a unique vulnerability that can be exploited by a TLS-disrupting adjuvant for targeted therapy.

cancer biology↗

bioRxiv2024

Systems genetic dissection of Alzheimer's disease brain gene expression networks

In Alzheimers disease (AD), changes in the brain transcriptome are hypothesized to mediate the impact of neuropathology on cognition. Gene expression profiling from postmortem brain tissue is a promising approach to identify causal pathways; however, there are challenges to definitively resolve the upstream pathologic triggers along with the downstream consequences for AD clinical manifestations. We have functionally dissected 30 AD-associated gene coexpression modules using a cross-species strategy in fruit fly (Drosophila melanogaster) models. Integrating longitudinal RNA-sequencing and behavioral phenotyping, we interrogated the unique and shared transcriptional responses to amyloid beta (A{beta}) plaques, tau neurofibrillary tangles, and/or aging, along with potential links to progressive neuronal dysfunction. Our results highlight hundreds of conserved, differentially expressed genes mapping to human AD regulatory networks. To confirm causal modules and pinpoint AD network drivers, we performed systematic in vivo genetic manipulations of 357 conserved, prioritized targets, identifying 141 modifiers of A{beta}- and/or tau-induced neurodegeneration. We discover an up-regulated network that is significantly enriched for both AD risk variants and markers of immunity / inflammation, and which promotes A{beta} and tau-mediated neurodegeneration based on fly genetic manipulations in neurons. By contrast, a synaptic regulatory network is strongly downregulated in human brains with AD and is enriched for loss-of-function suppressors of A{beta}/tau in Drosophila. Additional experiments suggest that this human brain transcriptional module may respond to and modulate A{beta}-induced glutamatergic hyperactivation injury. In sum, our cross-species, systems genetic approach establishes a putative causal chain linking AD pathology, large-scale gene expression perturbations, and ultimately, neurodegeneration.

systems biology↗