Biology subjects

Ma, A.

Publications and source records attributed to Ma, A..

At least 19 recordsLinked to original sources

bioRxiv2026

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

systems biology↗

bioRxiv2026

CD24-dependent innate immune checkpoint targeting chimera disrupts TGF-β-driven immunosuppression to enhance anti-tumor therapy

The immunosuppressive tumor microenvironment (TME) poses formidable challenges for cancer immunotherapies. Among the complex TME landscape, the cytokine TGF-{beta}1 is an attractive soluble target for reinvigoration of anti-tumor immunity due to its pivotal function in immune cell suppression, extracellular matrix remodeling, and epithelial-to-mesenchymal transition (EMT) promotion, while immune checkpoint receptors exemplified by PD-1 and CD24 are promising surface targets. Though adaptive immune checkpoint inhibition is well established in clinical practice, TGF-{beta} blockade or innate immune checkpoint blockade is yet to be harnessed for tumor treatment. Here, leveraging CD24 overexpression on multiple tumor tissues and its unique recycling kinetics, we designed an innate Immune Checkpoint TArgeting Chimera (ICTAC) G01, a bifunctional CD24 antibody-TGF-{beta}1 receptor fusion protein that disrupts TGF-{beta}1 signaling by TGF-{beta}1 degradation instead of simple blockade and simultaneously inhibits the "don't eat me" CD24 signaling. We demonstrated that G01 effectively suppressed TGF-{beta}1-Smad signaling, reversed the EMT, and attenuated fibrotic collagen deposition. Additionally, it antagonized CD24 signaling to unleash robust antibody-dependent effector activity, further reprogramming the immunologically cold TME toward inflammatory phenotype. By selectively targeting malignant cells while sparing CD24-expressing B cells, G01 demonstrated a favorable safety profile. Notably, in patient-derived tumor fragments (PDTFs), G01 also inflamed the TME, underscoring its translational potential. Together, these results establish ICTAC as an effective strategy for tumor-specific receptor-mediated extracellular protein depletion, which supports its application for reversing TGF-{beta}1; and innate immune checkpoint-mediated tumor immunosuppression by concurrent targeting of both surface and soluble tumor-promoting factors.

immunology↗

bioRxiv2026

An agentic AI environment to support biomedical research in collaborative academic environments

Agentic artificial intelligence (AAI) is increasingly used by biomedical scientists, where it has substantially lowered the difficulty of integrating computational, statistical and data-science approaches into day-to-day research activities, assisting with project design, analysis and manuscript preparation. Significant barriers remain, however, which are unlikely to be removed solely by releasing more capable AI models. Meaningful use still demands a technical fluency that many biomedical researchers lack: a PI and their lab members must learn how to use the AAI system, establish standard operating procedures (SOPs) for managing, sharing and curating data, and enforce rules that address data privacy and security. Existing commercial AAI systems, which were designed primarily for software engineering purposes, need to be significantly re-purposed for biomedical-related data science. We present Murmurent, shared software that sits beneath the agentic AI and provides the skills, procedures and protections to address the concerns above, removing the need for a lab to develop them independently. This includes (1) infrastructure to support projects and so-called choreographies} involving multiple group members; (2) a set of specialized agents each dedicated to typical biomedical data science tasks including software and statistical development, data visualization, literature search, equity, diversity, inclusion, and decolonization (EDID) review, and others; (3) a tiered memory which can retain important research decisions, intermediate, derived outputs and sensitive data, and which can exploit this information to automatically build better contexts in AAI sessions; (4) traceability records which are used by the AAI session to better plan and execute data analyses and software builds by, for example, helping to avoid repeating decisions that lead to dead-ends; (5) enforcement of SOPs for data maintenance and data governance across all lab members; and (6) multi-user capacity to allow groups to interact and collaborate. We use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), a protein with a shallow catalytic site making it difficult to target. We describe the construction of several approaches to identify Pin1 inhibitors and the results they yield. Murmurent is open source.

bioinformatics↗

bioRxiv2026

A self-limiting, TREM2-dependent anabolic program confers microglial resilience in Alzheimer's disease

Microglia are key drivers of Alzheimer's disease (AD), and TREM2, one of the strongest genetic risk factors, enables the disease-associated microglia (DAM) state required for plaque engagement. How TREM2 sustains this protection, and why it falters as disease advances, remain unclear. Using proteomics, single-cell transcriptomics, and in vivo metabolic labeling, we show that plaque-associated microglia mount a TREM2-dependent anabolic program coupling nascent protein synthesis to mitochondrial biogenesis. This program, not the DAM signature, marks phagocytically competent microglia; its loss is associated with proteostatic overload and mitochondrial dysfunction. Unexpectedly, this anabolic program is self-limiting, peaking at low amyloid burden and declining as burden rises. This decline is conserved in humans: across 523 donors, anabolic capacity tracks TREM2 but falls at advanced Braak stage even as DAM signature keeps rising. Thus, anabolic capacity, rather than activation state, marks microglial resilience in AD, and sustained stimulation of phagocytosis by anti-amyloid antibodies risks biosynthetic exhaustion and self-limiting efficacy.

neuroscience↗

bioRxiv2026

FAM136A is an essential chaperone for mitochondrial membrane protein biogenesis

The metabolic and signaling function of mitochondria rely on a network of chaperones within the inner membrane space (IMS) that regulate the biogenesis of nascent mitochondrial proteins. Using a genome wide CRISPRi screen we found that in human cells FAM136A is required for biogenesis of all three voltage-dependent anion channel (VDAC) paralogs, an abundant and essential family of {beta}-barrel metabolite transporters in the outer mitochondrial membrane (OM). FAM136A is a ubiquitously expressed essential gene, that is conserved in metazoa and plants. Using a combination of experiments in human cells and in vitro reconstitution, we determined that FAM136A associates with unfolded VDACs in the IMS; solubilizes nascent VDAC through a direct interaction; and facilitates insertion of VDAC into the OM. FAM136A also binds and chaperones a subset of -helical subunits of the electron transport chain. We therefore conclude that FAM136A is an IMS-resident chaperone, necessary and sufficient to maintain nascent membrane proteins in a folding-competent state to mediate their integration into the bilayer.

molecular biology↗

bioRxiv2026

An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid

The retinal pigment epithelium and choroid are critical for supporting the function and maintaining the homeostasis of the outer retina, and their dysfunction underlies a range of inherited and complex ocular diseases. To comprehensively characterize the cellular, transcriptomic, and epigenomic heterogeneity and dynamics within these tissues, we assembled an integrated multi-omics reference atlas comprising 719,813 single-cell/single-nucleus transcriptomes and 234,007 snATAC-seq profiles from 102 ancestrally diverse donors spanning 0 to 99 years of age, including cells from both the macula and periphery. This atlas resolves 48 distinct cell types or states and catalogs 448,567 open chromatin regions. Specifically, we resolved five distinct RPE subpopulations organized along a central-to-peripheral spatial axis, alongside two distinct stress/senescence states. We reconstructed the transcriptomic and epigenetic zonation of endothelial cells and expanded choroidal stromal heterogeneity by characterizing 11 fibroblast and two pericyte types. Age-associated compositional analysis revealed a significant fractional depletion of melanocytes, PI16+ fibroblasts, and venule endothelial cells with age, alongside a modest relative loss of central RPE and a corresponding increase in far-peripheral RPE. Cell-type-specific aging transcriptomics uncovered shared pathways related to inflammatory responses alongside distinct cell-type-specific signatures. Notably, significant age-associated epigenetic changes concentrated in the macula during the transition from early-to-middle adulthood and remained stable into old age, with transcription factors from the AP-1/bZIP family emerging as the dominant enriched motifs. Finally, integrating this atlas with AMD GWAS data provides novel variant-to-gene evidence implicating LIPG and COL4A3 in AMD pathogenesis. Together, this multi-omics atlas serves as both an invaluable community reference and a powerful discovery engine that translates genetic risk signals into localized target cells and candidate mechanisms, laying a foundation for understanding RPE/choroid biology in health and disease.

genomics↗

bioRxiv2026

Syntrophic microbiomes associated with methane-suppressive irrigation in rice

Rice, a staple crop of nearly half of the world population, is grown predominantly in flooded paddies which are one of the largest contributors to methane emissions. An effective approach is to minimise the anaerobic flooded conditions that favour the growth of methanogenic archaea. Empirical measurements showed that controlled irrigation regime reduces methane emissions by 70% to 90%. The soil microbiomes of both flood and drip irrigated soil were characterised using whole-genome shotgun metagenomics. Controlled irrigation was shown to suppress methanogens and lower methane emissions. While emissions are correlated with mcrA gene abundance, empty flooded fields exhibited relatively high mcrA levels above baseline despite undetectable methane emissions. Rice cultivar genotype had no significant effect on the soil microbiomes. Co-occurrence network analysis indicates that soil microbial communities stratify according to their oxygen preferences along a gradient. Methanogens were increased in flooded paddies, and methane production attributed to the microorganisms involved in the anaerobic decay of organic matter. Controlled irrigation altered the microbiome by raising the soil redox potential by enhancing aeration and promoting ammonia oxidation and nitrification pathways. IMPORTANCEThe temporal dynamics of microbial communities in drip-irrigated rice fields remain poorly characterized to-date. Empirical measurements demonstrate that controlled drip irrigation effectively suppresses methanogens and lowers methane emissions by up to 90%. Statistical analysis further revealed a moderate correlation between methane emissions and the mcrA gene with R = 0.6 and p-value = 2.9e-05. The correlation plot showed that the outliers corresponded to samples from empty flooded fields, where high mcrA gene abundance was observed despite low methane emissions. Methane produced in the soil is likely released into the atmosphere via transport through the aerenchyma of rice plants. Controlled irrigation is shown to be climate friendly as it reduces methane emissions by improving soil aeration and increasing the soil redox potential.

microbiology↗

bioRxiv2026

Disentangling polymer confinement from specific-folding interactions reveals the drivers of E. coli chromosome organization

The three-dimensional organization of the bacterial chromosome is critical for gene regulation. Chromosome conformation capture (Hi-C) has enabled genome-wide mapping of chromosomal folding, yet ensemble-averaged contact maps entangle biologically specific-folding interactions (SFIs) with nonspecific polymer compaction and mask single-cell heterogeneity. Here, we developed a polymer-based simulation framework in E. coli to address these limitations. We found that a null model of 300,000 random polymer configurations recapitulated the global chromosomal organization observed in Hi-C data, establishing that most Hi-C signals reflect generic polymer behavior in a confined volume. Contrasting null-model predictions with experimental Hi-C data isolated a small subset of SFIs (< 7%) and generated a specific-fold ensemble of 20,000 single-cell conformations that reproduced chromosomal interaction domains and single-cell heterogeneity. SFIs were enriched in the ter region, reduced nucleoid accessibility, colocalized with cryptic prophages, and depleted in positively supercoiled regions. H-NS and MatP emerged as major chromosome-wide and local determinants of SFIs, respectively. Furthermore, high-SFI regions correlated with stress-adaptive genes, whereas low-SFI regions harbored housekeeping genes. Together, our results established that a small number of biologically encoded SFIs superimposed on a polymer background shape the E. coli chromosome and gene expression, providing a quantitative framework for dissecting chromosome architecture and function.

genomics↗

bioRxiv2026

SpaFlow depicts the dynamics of ligand-receptor interaction in spatial transcriptomics data

Spatial transcriptomics (ST) enables the study of cell-cell communication in native tissue context, but current methods for the ligand-receptor interaction (LRI) inference generally rely on static, distance-based assumptions. Here we present SpaFlow, a reaction-diffusion framework that models ligand diffusion, binding, dissociation, production and degradation to infer spatially resolved LRI activity and hotspots from ST data. Across paired 10x Visium and CosMx metastatic renal cell carcinoma datasets, SpaFlow outperformed existing methods in recovering spatially coherent LRIs, with inferred LRI activity showing stronger association with downstream signaling. In hepatocellular carcinoma after neoadjuvant immunotherapy, SpaFlow identified CXCL12-CXCR4 hotspots enriched at immune-rich tumor boundaries in responders. In aging mouse heart, SpaFlow resolved niche-specific pro-fibrotic and senescence-associated signaling, highlighting Postn-Itgav/Itgb5 as an additional pro-fibrotic axis and Angptl2-Pirb as a candidate mediator of inter-niche senescence-related communication. In human idiopathic pulmonary fibrosis lung, SpaFlow localized CXCL12-CXCR4 signaling between adventitial fibroblasts and CD4 T cells, CD8 T cells, and B cells in the fibrotic surrounding regions. Together, SpaFlow provides a physically informed framework for quantifying spatially constrained cell-cell communication and mechanistically interpreting signaling patterns in complex tissues.

bioinformatics↗

bioRxiv2026

Preferential formation of NUP98-KDM5A condensates at specific H3K4me3-rich loci drives leukemogenic gene expression

Chromosomal translocations involving NUP98 generate fusion proteins that alter gene expression programs, yet the fundamental principles governing their gene targeting and condensate behavior remain poorly understood. Using NUP98-KDM5A as a model, we integrate cellular imaging, in vitro reconstitution, and genomic analyses to dissect how chromatin engagement shapes condensate formation. We find that NUP98-KDM5A forms sub-diffraction-limited, gel-like condensates whose assembly is potentiated by binding to H3K4me3. This interaction creates a quantitative targeting mechanism in which, at the native expression level, condensates preferentially form at genomic loci with high local H3K4me3 density. Such local density-dependent recruitment explains selective enrichment at the leukemogenic HOX gene clusters, despite widespread presence of H3K4me3 across the genome. Analysis of single-cell sequencing data from patients further supports a correlation between local H3K4me3 density and transcriptional activation in NUP98-KDM5A-driven leukemia. Together, our findings reveal how activating chromatin marks and condensate-forming proteins synergize to generate specificity within euchromatin, offering a generalizable framework for understanding how chromatin-associated condensates interpret epigenetic landscapes.

cell biology↗

bioRxiv2026

A multi-agent platform for assessment and improvement of bioinformatics software documentation

Rapid advances in bioinformatics have transformed biomedical research in areas such as single-cell and spatial omics, digital pathology, and multi-modal data integration, yet software usability and reproducibility have not kept pace with the growing complexity and proliferation of computational tools. Inconsistent, incomplete, or inaccessible documentation remains a pervasive and underappreciated barrier, limiting tool adoption, hindering reproducibility across laboratories, and reducing the long-term impact of computational methods. Here, we introduce BioGuider, a multi-agent platform designed to systematically evaluate and improve documentation quality in bioinformatics software. Rather than treating documentation as ancillary text, BioGuider models it as a first-class, testable object. The platform implements a modular pipeline for documentation collection, assessment, reporting, and optional correction, with specialized agents that emulate real-world user interactions. BioGuider evaluates documentation against standardized, task-oriented criteria spanning installation, configuration, usage, and tutorials, and supports iterative, constraint-aware refinement while preserving code integrity and biological context. We benchmark BioGuider using a controlled error-injection framework that introduces realistic documentation failures across general, biology-specific, and configuration-related categories. Across multiple large language models, BioGuider demonstrates robust error detection and correction, with strong performance maintained under severe documentation degradation. Applying BioGuider to 47 widely used bioinformatics tools, we observe a positive association between documentation quality and citation frequency, highlighting documentation as a previously under-quantified driver of software adoption and scientific impact.

bioinformatics↗

bioRxiv2026

Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging

DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation. However, direct measurements of DNA methylation, such as whole-genome bisulfite sequencing or DNA methylation arrays, are costly and require substantial DNA input, limiting their scalability for large cohorts and their applicability to emerging modalities such as single-cell and spatially resolved transcriptomics. In this study, motivated by the fact that DNA methylation is fundamentally a metabolic process, we investigate whether sample-wise DNA methylation activity can be inferred directly from transcriptomic profiles of genes involved in methionine and one-carbon metabolism. We show that a compact metabolic model comprising seven core reaction steps and 98 genes accurately predicts total DNA methylation activity across matched transcriptomic and methylation datasets from CCLE, TCGA, GTEx, and an independent single-cell multi-omics data set. Building on this framework, we develop Total DNA Methylation Activity (TDMA), a physics-informed neural network-based score that enables robust estimation of DNA methylation activity from bulk, single-cell, and spatial transcriptomics data. We demonstrate that TDMA captures methylation-dependent transcriptional regulation and identifies genes and pathways under epigenetic control. Applying TDMA to GTEx, we further reveal strong associations between the predicted total methylation activity, chronological aging, and established epigenetic clocks. We also demonstrated that TDMA can serve as a transcriptomics-derived epigenetic clock and highlights age-dependent roles of folate and glutathione metabolism in epigenetic aging. Applying TDMA to single cell and spatial transcriptomics data collected from pancreatic adenocarcinoma (PDAC), we identified that methionine metabolism and DNA methylation regulates T cell cytotoxicity in the tumor microenvironment of PDAC. Together, this work establishes a scalable, modality-agnostic framework for estimating DNA methylation activity from transcriptomics and provides new insights into the metabolic regulation of epigenetic aging.

bioinformatics↗

bioRxiv2025

A Chromatin-Structure-Guided Framework for Predictive and Interpretable Regulatory Genomics

Chromatin organization shapes gene regulation by linking distal elements across megabase scales, yet most predictive genomics models still treat the genome as linear, without incorporating three-dimensional structure. Hi-C provides genome-wide chromatin conformation information, but its contact maps are population-averaged, distance-biased, and noisy, obscuring the biologically specific contacts. We present CHROME, a framework built on a self-avoiding polymer ensemble null model that identifies physically specific, non-random Hi-C contacts. By integrating these contacts into graph representations, CHROME enables efficient information transfer across spatially connected loci. It integrates sequence, chromatin accessibility, or pre-trained embeddings into a graph attention architecture to predict cell line-specific ChIP-seq profiles, consistently outperforming local encoder baselines and generalizing to an unseen cell line. The resulting graph embeddings also enhance prediction on tissue-specific eQTL and ClinVar variant pathogenicity, outperforming local sequence-based embeddings. Beyond predictive performance, CHROME provides interpretability through attention-derived neighbor-to-center contributions that reveal how spatially connected loci influence local regulatory activity over multi-megabase distances. Together, these results show that incorporating physically validated chromatin interactions enables more accurate and interpretable modeling of gene regulation and variant effects.

genomics↗

bioRxiv2025

A20 restriction of nitric oxide production restores macrophage bioenergetic balance

Macrophages play critical roles in regulating host responses to microbial pathogens and other forms of tissue stress and can acquire pro-inflammatory or tissue reparative phenotypes. A20, or TNFAIP3, is a potent regulator of innate immune cell functions and is extensively linked to human inflammatory and autoimmune diseases. We now find that A20 is a powerful regulator of both glycolytic and mitochondrial respiration in macrophages. Differentiated macrophages that are acutely rendered A20 deficient exhibit increased glycolytic activity and markedly decreased mitochondrial respiration after LPS stimulation. These cells are unable to repolarize from an M1-like to M2-like phenotype. Compromised mitochondrial oxygen consumption in A20 deficient macrophages is caused by increased nitric oxide production. Inhibition or genetic ablation of inducible nitric oxide synthase (iNOS) restores mitochondrial oxidative phosphorylation and lactate production in these cells. These metabolic perturbations occur independently of exaggerated cytokine production and despite robust production of IL-10. Therefore, A20 prevents Warburg-like aerobic glycolysis and restores macrophage homeostasis.

immunology↗

bioRxiv2025

An Integrated Decarbonising Approach to Mitigate Methane Emissions and Enhance Productivity in Rice Cultivation

Flooded rice paddies contribute approximately 12% of global anthropogenic methane emissions, accounting for 1.5% of the total warming effect from all greenhouse gases. With the rising global demand for rice due to population growth, the need for effective methane mitigation strategies in rice cultivation is increasingly critical. This study investigates the combined impact of irrigation methods, fertiliser combinations, and varietal differences on productivity, water use and methane emissions in rice. Field trials were conducted across five land parcels covering 8 Ha in the Sathyamangalam region of Tamil Nadu, India, from October 2024 to January 2025. Results revealed that drip irrigation significantly reduced seasonal methane emissions by up to 68% (128 kg/ha/season) compared to continuous flood irrigation (402.32 kg/ha/season) offering a sustainable solution to address climate change. Furthermore, our modified package of practices coupled with tailored fertiliser combination, led to a 28% reduction in methane emissions (222 kg/ha/season) relative to continuous flooding methods used by the farmers in the region (309 kg/ha/season). Methane emission differences of up to 23.8% were also evident across rice varieties ADT-45 (229.7 kg/ha/season and BPT-5204 (301.7 kg/ha/season) for. Although flood irrigation yielded a 5-6% higher grain productivity than drip irrigation, the TLL fertiliser package under flood irrigation still provided distinct benefits with a yield increase of 5.4%. Notably, water usage under drip irrigation was 42.5% lower on average across the five locations, with minimal impact on yield, resulting in a marked improvement in water use efficiency (0.62 kg.m-3 under drip vs. 0.39 kg.m-3 under flood irrigation). Our findings highlight the value of integrating modern irrigation techniques, optimizing fertiliser management, and appropriate varietal selection with higher environmental sustainability and improved farm productivity to mitigate climate impact in rice cultivation.

plant biology↗

bioRxiv2025

Base-editing a single missense mutation in A20 enhances CAR-T cell efficacy

T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss-of-function screening in repetitively stimulated human T cells and identified the mulitfunctional ubiquitin-modifying protein A20/TNFAIP3 as a major negative regulator of exhausted T cell persistence. Protein large language modeling, deep base-editing mutagenesis, and studies in immunocompetent mice with domain-specific inactivating mutations revealed A20s non-enzymatic M1 ubiquitin-binding zinc finger 7 (A20ZF7) motif as critical to suppression of anti-tumor immunity. A20ZF7-deficient CD8+ tumor-infiltrating lymphocytes (TILs) resisted terminal exhaustion and circumvented an unappreciated mechanism restraining perforin degranulation in terminally exhausted cells. Human chimeric antigen receptor (CAR)-T cells engineered via base-editing to inactivate A20ZF7 via a single missense mutation also resisted exhaustion, secreted more perforin and robustly suppressed cancer in vivo. These studies pinpoint A20ZF7 as a novel T cell checkpoint and reveal precision base-editing of missense mutations as an effective approach to enhance CAR-T cell therapy.

immunology↗

bioRxiv2025

Fibroblast orchestration of inflammaging via NF-kB activation

Aged tissue is characterized by chronic inflammation known as "inflammaging". While this aging immune phenotype supposedly drives some of the most common diseases affecting the elderly, little is known about the structural drivers of inflammaging. In this study, we demonstrate that age-dependent activation of NF-kB in tissue fibroblasts remodels the immune architecture, promoting the emergence of an exhausted T cell population (GZMK+/CD8+) recently identified in normal aging, as well as autoimmunity and cancer. Fibroblast-specific NF-kB activation triggered a fibroblast-macrophage-T cell circuit to form tertiary lymphoid structures in the lung and promoted the emergence of exhausted GZMK+ T cells. Fibroblastic activation of NF-kB increased host susceptibility to acute lung injury and mimics severe pneumonia commonly seen in elderly patients, which was alleviated by deletion of GZMK+ T cells. Our data provide a structural basis for inflammaging, where fibroblasts orchestrate the complex immune aging phenotype in non-immune tissues, increasing susceptibility to age-related diseases. Highlights- Bronchus-associated lymphoid tissue (BALT) enriched for GZMK+ T cells develop with age - Lung adventitial fibroblasts demonstrate increased NF-kB activation with age. - Fibroblast activation of NF-kB in young animals recapitulates multiple features of normal lung immune aging - Depletion of GZMK+ cells decreases lung inflammation in a mouse model of acute respiratory distress syndrome (ARDS)

immunology↗

bioRxiv2025

A cross model spatial and single-cell atlas reveals the conserved involvement of osteopontin in polycystic kidney disease

Polycystic kidney disease (PKD) arises from mutations in cilia-associated genes, such as PKD1 and PKD2, expressed in renal epithelial cells, leading to progressive kidney dysfunction and end- stage kidney disease (ESKD). PKD patients exhibit significant heterogeneity in disease progression, largely due to genetic and environmental modifiers. Like patients, mouse models of PKD also exhibit significant heterogeneity with regards to the gene mutated, age of disease onset, and rate of disease progression. To elucidate the cellular and molecular consequences of these variables, we constructed an integrated single-cell and spatial transcriptomics atlas across mouse models of PKD, mapping changes in cell type composition, gene expression, and intercellular signaling networks across the whole atlas and within individual models. Consistently across models, single cell RNA sequencing (scRNAseq) data revealed increased Spp1 (osteopontin) expression and signaling from PKD-enriched clusters to Ly6clo monocytes. Global deletion of Spp1 in Pkd1RC/RC mice resulted in reduced cyst severity, improved kidney function, and reduced Ly6clo monocyte numbers, suggesting that SPP1 signaling to Ly6clo monocytes promotes PKD progression. We also created a freely available, searchable website (https://bmblx.bmi.osumc.edu/scPKD/) that can be used to identify cross- and intra-model specific changes in gene expression, guiding researchers to new therapeutic targets for treating PKD.

bioinformatics↗