Biology subjects

Huang, Y.

Publications and source records attributed to Huang, Y..

At least 361 records · Page 20Linked to original sources

bioRxiv2025

Machine Learning-Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data

In this study, we generated label-free data-independent acquisition (DIA)-based liquid chromatography (LC)-mass spectrometry (MS) proteomics data from 261 renal cell carcinomas (RCC) and 195 normal adjacent tissues (NAT). The RCC tumors included 48 non-clear cell renal cell carcinomas (non-ccRCC) and 213 ccRCC. A total of 219,740 peptides and 11,943 protein groups were identified with 9,787 protein groups per sample on average. We adopted a comprehensive approach to select representative samples with different mutation sites, considering histopathological, immune, methylation, and non-negative matrix factorization (NMF)-based subtypes, along with clinical characteristics (gender, grade, and stage) to capture the complexity and diversity of ccRCC tumors. We used machine learning identified 55 protein signatures that distinguish RCC tumors from NATs. Furthermore, 39 protein signatures that differentiate different RCC tumor subtypes were also identified. Our findings offer an extensive perspective of the proteomic landscape in RCC, illuminating specific proteins that serve to distinguish RCC tumors from NATs and among various RCC tumor subtypes.

cancer biology↗

bioRxiv2025

Pooled screening identifies combinatorial CAR signaling domains for next-generation CAR-M immunotherapies

Chimeric antigen receptor-engineered macrophages (CAR-Ms) hold great promise for solid tumor immunotherapy. The intracellular domains (ICDs) of CARs determine the phenotypic output of therapeutic macrophages but remain largely unexplored. Here, we constructed a CAR library containing 131 unique signaling domains derived from native immune receptors and identified 17 ICDs that enhance macrophage phagocytosis, inflammatory responses, or tumor infiltration in vitro and in vivo. We further developed a scalable 3 barcode technology, CARode, to uniquely label and trace ICD variants within large-scale combinatorial CAR library and applied it to single-cell RNA sequencing and single-cell CAR analysis to assess the synergetic effects of ICD combinations on macrophage activation. Our approach uncovered a novel CD40-LY9-FCRL1 chimeric receptor that modulates the tumor microenvironment and improves solid tumor clearance. In conclusion, our findings demonstrate that pooled screening can accelerate the discovery of complex ICD constructs, providing a powerful platform for engineering macrophage-based immunotherapies.

bioengineering↗

bioRxiv2025

cfDecon: Accurate and Interpretable methylation-based cell type deconvolution for cell-free DNA

Cell-free DNA (cfDNA) analysis has emerged as a powerful tool for noninvasive diagnostics and monitoring of aberrant methylation. However, computational deconvolution of cfDNA remains challenging due to the complexity of methylation data, the diverse cellular compositions in cfDNA mixtures, and the limited interpretability of current methods. We present cfDecon, an efficient deep-learning framework for cfDNA deconvolution at the resolution of individual reads. cfDecon employs a multichannel autoencoder core module and an iterative refinement process to estimate cell-type proportions and generate condition-aware cell-type-specific methylation profiles. We rigorously evaluate cfDecon through comprehensive simulation experiments, including scenarios with normal cellular compositions, rare cell types, and unknown cell types, where cfDecon consistently outperforms previous state-of-the-art methods in deconvolution. Using strict data separation and careful control for potential data leakage, cfDecon shows scalability and applicability to large-scale studies by raising Lins Concordance Correlation Coefficient (CCC) over 33% on an atlas-level reference dataset. We further validate cfDecons utility in disease diagnosis on two clinical datasets, Amyotrophic Lateral Sclerosis (ALS) and Hepatocellular Carcinoma (HCC). cfDecon raises the performance of disease detection from 0.53 to 0.79 compared to existing methods on the ALS dataset and 0.55 to 0.77 on the HCC dataset. The capacity of cfDecon to predict condition-specific DNA methylation patterns across cell types offers a valuable framework for studying differentially methylated CpGs. Furthermore, integrating cfDecon with enrichment analysis facilitates the exploration of functional variations in CpGs across different conditions. cfDecon represents a significant advancement in cfDNA analysis, offering improved accuracy, interpretability, and potential for personalized medicine applications. The codebase is available at https://github.com/Susanxuan/cfDecon.

bioinformatics↗

bioRxiv2025

Triple antimicrobial combinations with potent synergistic activity against M. abscessus

Synergy of antimicrobial combinations was tested against contemporary Mycobacteroides abscessus isolates using 2-D and 3-D checkerboard assays. Triple combinations of omadacycline-azithromycin-clofazimine, tigecycline-azithromycin-clofazimine, omadacycline-azithromycin-linezolid and omadacycline-azithromycin-contezolid demonstrated synergy (FIC [≤] 0.5) against 33%, 31%, 62%, and 66% of isolates, respectively. Notably, in all triple combinations, macrolide resistant M. abscessus subsp. abscessus and M. abscessus subsp. bolletii isolates were fully sensitized to azithromycin at the FIC index, as were isolates with elevated clofazimine MICs.

microbiology↗

bioRxiv2025

A Comparative Evaluation of Computational Models for RNA modification detection using Nanopore sequencing with RNA004 Chemistry

Direct RNA sequencing from Oxford Nanopore Technologies (ONT) has become a valuable method for studying RNA modifications such as N6-methyladenosine (m6A), pseudouridine ({psi}), and 5-methylcytosine (m5C). Recent advancements in the RNA004 chemistry substantially reduce sequencing errors compared to previous chemistries (e.g., RNA002), thereby promising enhanced accuracy for epitranscriptomic analysis. In this study, we benchmark the performance of two state-of-the-art RNA modification detection models capable of handling RNA004 data - ONTs Dorado and m6Anet - using two wild-type (WT) cell lines, HEK293T and HeLa, with respective ground truths from GLORI and eTAM-seq, and their paired in vitro transcribed (IVT) RNA as negative controls. We found that under default settings and considering sites with [≥]10% modification ratio and [≥]10X coverage, Dorado has higher recall ([~]0.92) than m6Anet ([~]0.51) for m6A detection. Among the overlapping methylated sites between ground truth and computational predictions, there are high correlations of site-specific m6A modification stoichiometry, with correlation coefficient of [~]0.89 for Dorado-truth comparison and [~]0.72 for m6Anet-truth comparison. However, combined assessment of WT and IVT datasets show that while the per-site false positive rate (FPR) can be lower ([~]8% for Dorado and [~]33% for m6Anet), both computational tools can have high per-site false discovery rate (FDR) of m6A ([~]40% for Dorado and [~]80% for m6Anet) due to the low prevalence of m6A in transcriptome, with a similar trend observed for pseudouridine ([~]95% FDR for Dorado). Additional motif analysis reveals that both Dorado and m6Anet exhibit high heterogeneity of false positive calls across sequence contexts, suggesting that sequence contexts help determine accuracy of specific modification calls. There is also a substantial overlap of false positive calls between the two IVT samples, suggesting a post-filtering strategy to improve modification calling by compiling a set of low-confidence sites with a probabilistic model from several IVT samples across diverse cells/tissues. Our analysis highlights key strengths and limitations of the current generation of m6A detection algorithms and offers insights into optimizing thresholds and interpretability. The IVT datasets generated by the RNA004 chemistry provides a publicly available benchmark resource for further development and refinement of computational methods.

bioinformatics↗

bioRxiv2025

Curvature generation and engineering principles from Shewanella oneidensis multi-flagellin flagellum

Motility driven by nanoscale flagella is vital to microbial survival and spread in fluid and structured environments. Absence of native flagellum structures, however, has limited our understanding of the mechanisms of microbial motility, hindering efforts to engineer microbe-based microbots for applications. Here, by cryogenic electron tomography (cryoET) and microscopy (cryoEM), we determined the structural basis of motility driven by the single flagellum anchored to one pole of Shewanella oneidensis MR-1 (S. oneidensis), an electrogenic bacterium commonly used in biotechnology. The structures of the curved flagellum, representing the conformation during motion, are captured, allowing delineation of molecular interactions among the subunits of its three components--filament, hook, and hook-filament junction. The structures of the filament, i.e., the propeller, reveal a varying composition of the flagellin isoforms FlaA and FlaB throughout the filament. Distinct inter-subunit interactions are identified at residues 129 and 134, which are the major determinants of functional differences in motility for the two isoforms. The hook--the universal joint--has a significantly larger curvature than that of the filament, despite both containing 11 curvature-defining conformers of their subunits. Transition between the propeller and universal joint is mediated by hook-filament junction, composed of 11 subunits of FlgK and FlgL, reconciling incompatibility between the filament and hook. Correlating these compositional and structural transitions with varying levels of curvature in flagellar segments reveals molecular mechanism enabling propulsive motility. Mechanistic understandings from S. oneidensis suggest engineering principles for nanoscale biomimetic systems. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/637127v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@17933forg.highwire.dtl.DTLVardef@1c44ab1org.highwire.dtl.DTLVardef@3d56dorg.highwire.dtl.DTLVardef@1b126b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

DAZAP2 functions as a pan-coronavirus restriction factor by inhibiting viral entry and genomic replication

The SARS-CoV-2 pandemic and the emergence of novel variants underscore the need to understand host-virus interactions and identify host factors that restrict viral infection. Here, we perform a genome-wide CRISPR knockout screen to identify host restriction factors for SARS-CoV-2, revealing DAZAP2 as a potent antiviral gene. DAZAP2, previously implicated in SARS-CoV-2 restriction, is found to inhibit viral entry by blocking virion fusion with both endolysosomal and plasma membranes. Additionally, DAZAP2 suppresses genomic RNA replication without affecting the primary translation of viral replicases. We demonstrate that DAZAP2 functions as a pan-coronavirus restriction factor across four genera of coronaviruses. Importantly, knockout of DAZAP2 enhances SARS-CoV-2 infection in mouse models and in human primary airway epithelial cells, confirming its physiological relevance. Mechanistically, antiviral activity of DAZAP2 appears to be indirect, potentially through the regulation of host gene expression, as it primarily localizes to the nucleus. Our findings provide new insights into the host defense system against coronaviruses and highlight DAZAP2 as a potential target for host-directed antiviral therapies. IMPORTANCEDuring viral infection, the host defense response is mediated by a variety of host factors through distinct mechanisms that have yet to be fully elucidated. Although DAZAP2 was previously implicated in SARS-CoV-2 restriction, its mechanisms of action and in vivo relevance remain unclear. In this study, we identify the DAZAP2 as a potent pan-coronavirus restriction factor that inhibits viral infection through dual mechanisms: blocking virion fusion with both endolysosomal and plasma membranes, and suppressing genomic RNA replication. We confirm its physiological relevance in host defense using mouse models and primary cell cultures. This study advances our understanding of host-pathogen interactions. Targeting DAZAP2 or its regulatory pathways could provide a new approach to enhance host defense against current and future coronavirus threats.

microbiology↗

bioRxiv2025

Dnmt3b deficiency in adipocyte progenitor cells ameliorates obesity in female mice

Obesity arises from chronic energy imbalance where energy intake exceeds energy expenditure. Emerging evidence supports a key role of DNA methylation in the regulation of adipose tissue development and metabolism. We recently discovered a key role of DNA methylation, catalyzed by DNA methyltransferase 1 or 3a (Dnmt1 or 3a), in the regulation of adipocyte differentiation and metabolism. Here, we aimed to investigate the role of adipocyte progenitor cell Dnmt3b, an enzyme mediating de novo DNA methylation, in energy metabolism and obesity. We generated a genetic model with Dnmt3b knockout in adipocyte progenitor cells (PD3bKO) by crossing Dnmt3b-floxed mice with platelet-derived growth factor receptor alpha (Pdgfr)-Cre mice. Dnmt3b gene deletion in adipocyte progenitors enhanced thermogenic gene expression in brown adipose tissue, increased overall energy expenditure, and mitigated high-fat diet (HFD)-induced obesity in female mice. PD3bKO mice also displayed a lower respiratory exchange ratio (RER), indicative of a metabolic shift favoring fat utilization as an energy source. Furthermore, female PD3bKO mice exhibited improved insulin sensitivity alongside their lean phenotype. In contrast, male PD3bKO mice showed no changes in body weight but demonstrated decreased insulin sensitivity, revealing a sexually dimorphic metabolic response to Dnmt3b deletion in adipocyte progenitor cells. These findings underscore the critical role of Dnmt3b in regulating energy homeostasis, body weight, and metabolic health, with significant implications for understanding sex-specific mechanisms of obesity and metabolism.

systems biology↗

bioRxiv2025

Single-Cell Genomics Elucidates Molecular Variations and Regulatory Mechanisms in Circulating Immune Cells

The human peripheral blood displays diverse molecular characteristics across populations, understanding the drivers and underlying mechanisms of which remains challenging. Here, we introduce the Chinese Immune Multi-Omics Atlas (CIMA), elucidating sex-, age-, and genetic-related molecular variations by analyzing multi-omics data from 428 adults with over 10 million immune cells. CIMA generated an enhancer-driven gene regulatory network, identifying 237 high-quality regulons and revealing cell type-specific regulatory mechanisms. Additionally, 11,521 lead cis-expression quantitative trait loci (eQTLs) and 46,339 chromatin accessibility QTLs (caQTLs) were identified at cell type level. CIMA also uncovered pleiotropic associations among immune-related disease risk loci, eQTLs, and caQTLs in a cell type-specific manner. Lastly, a novel cell language model, CIMA-CLM, was developed to predict chromatin accessibility and noncoding variant effects using chromatin sequences and gene expressions. This work represents a population-scale multi-omics resource of human immune cells, providing a valuable reference for future investigation of immune-related diseases.

immunology↗

bioRxiv2025

SpatialFormer: Universal Spatial Representation Learning from Subcellular Molecular to Multicellular Landscapes

Spatial transcriptomics quantifies gene expression within its spatial context, significantly advancing biomedical research. Understanding gene spatial expression and the organization of multicellular systems is vital for disease diagnosis and studying biological processes. However, existing models often struggle to integrate gene expression data with cellular spatial information effectively. In this study, we introduce SpatialFormer, a hybrid framework combining convolutional networks and transformers to learn single-cell multimodal and multi-scale information in the niche context, including expression data and subcellular gene spatial distribution. Pre-trained on 300 million cell pairs from 12 million spatially resolved single cells across 62 Xenium slides, SpatialFormer merges gene spatial expression profiles with cell niche information via the pair-wise training strategy. Our findings demonstrate that SpatialFormer distills biological signals across various tasks, including single-cell batch correction, cell-type annotation, co-localization detection, and identifying gene pairs that are critical for the immune cell-cell interactions involved in the regulation of lung fibrosis. These advancements enhance our understanding of cellular dynamics and offer new pathways for applications in biomedical research.

bioinformatics↗

bioRxiv2025

Carbon monoxide exposure stimulates growth and activity of primary producers in diverse soil ecosystems

Carbon monoxide (CO) is both a potent poison for many aerobic organisms and a desirable energy source for diverse microorganisms. Atmospheric emissions of this gas have increased since industrialization and their levels are highly elevated in many urban and natural environments; however, it is unresolved whether elevated levels of CO at environmentally relevant concentrations are primarily stimulatory or inhibitory to soil microbial communities. Here, we showed that CO exposure minimally affects microbial abundance, richness, and composition in diverse ecosystem soils, suggesting most microbes are tolerant of this gas. Genome-resolved metagenomic profiling showed that these soils harbored diverse bacteria capable of using CO an electron donor for aerobic respiration and carbon fixation. CO stimulated the growth of several of these putative CO-oxidizing bacteria in a dose-dependent manner, especially widespread proteobacterial and actinobacterial lineages. Additionally, we found a strong relationship between CO oxidation and carbon fixation and observed an enrichment of several carboxydotrophic MAGs capable of carbon fixation in most soil types. These findings highlight that environmentally relevant CO levels do not inhibit soil microbes, but instead foster the growth of distinct carboxydotrophic bacteria, suggesting a robust soil CO sink that could help mitigate anthropogenic CO emissions and contribute to carbon cycling in terrestrial ecosystems.

biochemistry↗

bioRxiv2025

On the need of individually optimizing temporal interference stimulation of human brains due to inter-individual variability

IntroductionTranscranial temporal interference stimulation (TI, TIS, or tTIS), also known as interferential stimulation (IFS), is able to focally stimulate deep brain regions, provided it is properly optimized. We previously presented an algorithm for optimizing TI using two arrays of electrodes and showed that it can achieve more focal stimulation compared to optimized high-definition transcranial electrical stimulation (HD-TES) and conventional optimized TI using two pairs of electrodes, especially in the deep brain areas such as the hippocampus. However, those modeling studies were only performed on an averaged head (MNI152 template) and three individual heads without exploring inter-individual variability. Existing TI works in the literature mostly utilize a common (possibly optimized) montage of two pairs of electrodes on different individual heads without considering inter-individual variability. Material and methodHere we aim to study the inter-individual variability of optimized TI by applying the same optimization algorithms on N = 25 heads using their individualized head models. Specifically, we compared the focality achieved by different stimulation techniques at six different regions of interest (ROI; right hippocampus, left dorsolateral prefrontal cortex, left motor cortex, right amygdala, right caudate, and left thalamus) under both individually optimized and unoptimized montages. We also conducted numerical sensitivity analysis on the individual optimization and performed phantom recordings to test our models. ResultsAs expected, there is a variability in focality achieved by TI of up to 1.2 cm at the same ROI across subjects due to inter-individual differences in the head anatomy and tissue conductivity. We show that optimized TI using two arrays of electrodes achieves higher focality than that from optimized HD-TES at the same level of modulation intensity at 5 of the 6 ROIs. Compared to using a common montage either optimized from the MNI152 template or from the literature, individually optimized TI using two pairs of electrodes improves the focality by up to 4.4 cm, and by up to 1.1 cm if using two arrays of electrodes. Focality achieved by the individual optimization is sensitive to random changes and can vary up to 9.3 cm due to the non-lienarity of TI physics. Experimental recordings on a head phantom confirms the drop in TI stimulation strength when using unoptimized montages as predicted by our in silico models. ConclusionThis work demonstrates the need of individually optimizing TI to target deep brain areas, and advocates against using a common head model and montage for TI modeling and experimental studies.

biophysics↗

bioRxiv2024

Elastase mediated white matter damage in cerebral small vessel disease: Microglia - neutrophils pas de deux

Cerebral small vessel disease (CSVD) leads to an extensive white matter damage associated with cognitive decline, yet the underlying damaging mechanisms remain incompletely understood. Here we established a positive correlation between plasma levels of serine proteinase elastase ELANE and periventricular white matter hyperintensity (PV-WMH) in a cohort of CSVD patients. In a CSVD murine model induced by bilateral carotid artery stenosis (BCAS), upregulated ELANE was detected both in microglia and peripheral blood neutrophils. Genetic ELANE deficiency significantly alleviated oligodendrocyte loss, thereby reducing white matter lesions (WMLs) as well as ameliorating sensorimotor and cognitive impairments in BCAS mice. In vitro studies demonstrated that ELANE triggered time-dependent and dose-dependent oligodendrocyte lineage cell death. Bone marrow transplantation showed that ELANE from microglia and peripheral blood both contributed to WML development and BCAS-induced neurological deficits. Mechanistically, ELANE, accumulated by oligodendrocytes, cleaved the phosphodiesterase domain of 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase). Pharmacological inhibition of ELANE with Sivelestat reduced oligodendrocyte loss and WMLs leading to the restoration of white matter integrity and neurological improvements in BCAS mice. In post-mortem brain specimens of CSVD patients ELANE accumulated within WMLs being predominantly localized in microglia (and hence defined as microglial ELANE) rather than in the brain-infiltrating neutrophils. We therefore posit microglial ELANE as an instigator of whiter matter injury in CSVD and suggest its potential therapeutic relevance.

neuroscience↗

bioRxiv2024

Actin dysregulation induces immune evasion via oxidative stress-activated PD-L1 in gastric cancer

Diffuse gastric adenocarcinoma (DGAC) is an aggressive malignancy with limited therapeutic options, poor prognosis, and poorly understood biology. CRACD, an actin polymerization regulator, is often inactivated in gastric cancer, including DGAC. We found that genetic engineering of murine gastric organoids with Cracd ablation combined with Kras mutation and Trp53 loss induced aberrant cell plasticity, hyperproliferation, and hypermucinosis, the features that recapitulate DGAC transcriptional signatures. Notably, CRACD inactivation remodeled the immune landscape for immune evasion through PD-L1 enrichment in tumor cells. Mechanistically, CRACD loss disrupted actin dynamics, generating reactive oxygen species that activated HIF1, which transactivated PD-L1. Pharmacologic inhibition of HIF1 or PD-L1 restored immune surveillance and suppressed tumorigenesis. These findings reveal a novel role of actin homeostasis in limiting cell plasticity and immune evasion, position CRACD as a potential biomarker for stratifying patients with DGAC, and highlight HIF1 and PD-L1 as actionable therapeutic targets.

cancer biology↗

bioRxiv2024

Domain Structure and Interface Control of Mechanical Stiffness in Sustainable Cellulose Bio-nanocomposites

Renewable and biodegradable plastics derived from soy protein isolate (SPI) offer a promising alternative to conventional petroleum-based plastics, particularly for film-grade bioplastics applications such as plastic bags. However, even with reinforcement from cellulose nanocrystals (CNCs), their mechanical properties including stiffness lag behind those of petroleum-based plastics. To identify pathways for improving CNC-reinforced SPI composites, we studied stiffening mechanisms by interpreting experimental data using homogenization models that accounted for CNC agglomeration and the formation of CNC/SPI interphases. To model effects of surface modification of CNCs with polydopamine (polyDOPA), we incorporated two key mechanisms: enhanced CNC dispersion and modified CNC-SPI interfacial interactions. Models accounted for interphases surrounding CNCs, arising from physicochemical interactions with the polyDOPA-modified CNC surfaces. Consistent wih experimental observations of polyDOPA modification enhancing mechanical properties through both increased spatial distribution of CNCs and matrix-filler interactions, results demonstrated that improved dispersion and interfacial bonding contribute to increased composite stiffness. Results highlight the potential of biodegradable CNC/SPI bio-nanocomposites as sustainable plastic alternatives, and suggest pathways for further enhancing their mechanical properties.

biophysics↗

bioRxiv2024

Episodic slow breathing in mice markedly reduces fear responses

We sought to delineate neural mechanisms underlying the effects of controlled breathing in humans, such as in meditation or breathwork, which can reduce depression, anxiety, stress, and pain. Thus, we developed a murine model, where breathing frequency in awake mice can be substantially slowed. When done for 30 min/day for 4 weeks, these mice had significant reductions in stress-related changes in behavior compared to control mice. We conclude that slow breathing effects on emotional state are present in mice, and which cannot be attributed directly to top-down influences such as volitional or emotional control or placebo effects. Our study paves the way for investigations of the neural mechanisms underlying body-brain interactions related to the effects of controlled breathing as well as a platform for optimization of its therapeutic use for amelioration of ordinary and pathological stress and anxiety in humans.

neuroscience↗

bioRxiv2024

ProCyon: A multimodal foundation model for protein phenotypes

Characterizing human proteins remains a major challenge: approximately 29% of human proteins lack experimentally validated functions and even well-annotated proteins often lack context-specific phenotypic insights. To enable universal modeling of protein phenotypes, we present PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW, a multimodal foundation model that utilizes protein sequence, structure, and natural language for generating and predicting protein phenotypes across diverse knowledge domains. PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW is trained on our novel dataset, PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW-IO_SCPLOWNSTRUCTC_SCPLOW, with 33 million protein phenotype instructions. On dozens of benchmarking tasks, PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW performs competitively against single-modal and multimodal models. Further, PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW conditionally retrieves proteins via mechanisms of action of small molecule drugs and disease contexts, and it generates candidate phenotypic descriptions for poorly characterized proteins, including those implicated in Parkinsons disease that were identified after PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOWs knowledge cutoff date. We experimentally confirm PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOWs predictions in multiple sclerosis using post-mortem brain RNA-seq, identifying novel MS genes and elucidating associated pathway mechanisms consistent with cortical pathology. PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW paves the way toward a general approach to generate functional insights into the human proteome.

bioinformatics↗

bioRxiv2024

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease

Alzheimers disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/627436v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1e67fd5org.highwire.dtl.DTLVardef@1d2b779org.highwire.dtl.DTLVardef@10b6efforg.highwire.dtl.DTLVardef@1a44394_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗