Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 235 records · Page 13Linked to original sources

bioRxiv2026

Tirzepatide improves pancreatic β-cell function in mice and patients with type 2 diabetes.

The dual incretin receptor agonist tirzepatide improves {beta}-cell function in T2D patients, but the underlying mechanism remains unclear. This study aimed to elucidate the molecular pathway through which tirzepatide restores {beta}-cell functional improvement. High-fat diet (HFD)-fed C57BL/6J mice were treated with vehicle, a GIP analogue, semaglutide or tirzepatide. Tirzepatide significantly reduced body weight and improved glucose tolerance in HFD-fed mice without altering {beta}-cell mass, proliferation, or apoptosis. Instead, tirzepatide reversed {beta}-cell dedifferentiation, as indicated by reduced ALDH1A3 expression and restored levels of the identity transcription factors PDX1 and MAFA. Single-cell RNA sequencing (scRNA seq) and in vitro studies revealed that tirzepatide up-regulated FOXO1, reactivating the FOXO1-PDX1/MAFA axis. In T2D patients, tirzepatide improved glycemic control, reduced insulin demand, increased HOMA-{beta}, and decreased HOMA-IR. Improvement in HOMA-{beta} correlated positively with baseline insulin resistance. Hence, our study suggested that tirzepatide restores {beta}-cell function in T2D by reprogramming stressed {beta} cells and re-establishing {beta}-cell identity through FOXO1-dependent transcriptional reactivation. These findings provide a mechanistic basis for the superior efficacy of dual incretin receptor agonism in T2D management. ARTICLE HIGHLIGHTSO_LITirzepatide restores {beta} cell identity and function without altering {beta} cell mass in HFD induced diabetic mice. C_LIO_LITirzepatide reverses {beta}-cell dedifferentiation and restores key {beta}-cell transcription factors (PDX1, MAFA) through reactivation of the AKT-FOXO1 signaling pathway. C_LIO_LITirzepatide increases HOMA-{beta} and decreases HOMA-IR in T2D patients, and improvements in HOMA-{beta} positively correlate with baseline insulin resistance. C_LIO_LIThese results demonstrate that tirzepatides therapeutic benefits are not only metabolic but also involve direct restoration of {beta}-cell identity and function. This highlights {beta}-cell reprogramming as a novel therapeutic avenue, thus supporting the broader clinical adoption of dual incretin receptor agonists. C_LI

physiology↗

bioRxiv2026

Anti-IL1RAP Antibodies for Pan-Inhibition of IL-1 Family Cytokine Signaling in Inflammatory Diseases and Oncology

The interleukin-1 (IL-1) superfamily of cytokines--comprising the IL-1, IL-33, and IL-36 subfamilies--orchestrates a vast array of innate and adaptive immune responses. While physiological activation of these pathways is essential for host defense and tissue repair, their chronic dysregulation is pathognomonic of a broad spectrum of autoimmune diseases and malignancies. Current therapeutic modalities largely rely on the neutralization of single cytokines, an approach often limited by the functional redundancy and compensatory signaling loops inherent to the IL-1 superfamily. The Interleukin-1 Receptor Accessory Protein (IL1RAP) serves as an obligatory co-receptor for IL-1, IL-33, and IL-36 subfamilies, functioning as a molecular bottleneck for pro-inflammatory signaling. Herein, we report the development and comprehensive characterization of two humanized monoclonal antibodies, DXP-006 and DXP-106, which target a unique epitope on domain 2 of IL1RAP. Cryo-electron microscopy studies at 3.55 [A] resolution reveal that these antibodies lock the critical c2d2 loop of IL1RAP, sterically occluding its recruitment to IL-1R1, ST2 and IL-36R binary complexes and potently blockade IL-1, IL33, and IL-36 signaling. DXP-006, engineered with a half-life-extended and effector-silenced Fc, exerts profound efficacy in murine models of psoriasis, superior to the clinical benchmark bimekizumab. Conversely, DXP-106, engineered with a defucosylated Fc to enhance antibody-dependent cellular cytotoxicity, exhibits potent antitumor activity in non-small cell lung cancer xenografts. These findings position DXP-006 and DXP-106 as ideal therapeutics for IL-1 family-driven autoimmunity and IL1RAP-positive cancers. One Sentence SummaryAnti-IL1RAP antibodies block multiple IL-1 family cytokines simultaneously, showing superior efficacy in inflammation and cancer models.

immunology↗

bioRxiv2026

Modeling a Shared Reality of Tractography through Varied Structural Imaging

Though diffusion MRI (dMRI) is the gold standard for white matter tractography, fundamental questions remain about whether captured patterns reflect diffusion-specific phenomena or general structural properties accessible through alternative imaging approaches. This work investigates structural probabilities within the human brain as a complex manifold and examines structural-functional relationships of anatomical bundles to clarify what dMRI specifically captures in white matter architecture. We introduce a framework to extract white matter pathways from FLAIR images without additional subject-specific anatomical context. Using a teacher-student model, we capture systemic information from dMRI-based tractography to guide FLAIR-based tractogram creation. The teacher model trains on dMRI features to generate diffusion tractography, while the student utilizes frozen teacher layers to extract tractography features using only FLAIR input. In our pilot analysis of 14 randomly selected subjects from the Baltimore Longitudinal Study of Aging (BLSA), we performed additional inference on 9 withheld subjects to evaluate robustness. We assessed FLAIR-template generated streamlines using bundle adjacency and Dice coefficient at the voxel level across 39 white matter bundles compared to gold standard diffusion streamlines. Statistical evaluations compared our method against other non-diffusion tractography algorithms using T1-weighted and FLAIR images with subject-specific anatomical context. Results demonstrate our proposed method offers statistically similar performance to other non-diffusion methods when compared to diffusion streamlines These findings suggest that without diffusion data, our method captures unconditional subject-specific prior probabilities of tractography, indicating that tractography patterns may sample from a shared latent space of structural information not unique to any single imaging sequence.

bioengineering↗

bioRxiv2026

Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC

Batch effect correction is a common and often necessary step in data analysis to reduce bias due to technical and experimental factors when combining multiple batches of data. The severity of the batch effects dictates the correction strategy; therefore, a careful assessment of each datasets batch effects is necessary. BatchQC is an R package that provides reproducible tools and visualizations for quantitatively and qualitatively addressing batch effects across a broad range of data types. BatchQC integrates with standardized Bioconductor data structures and features an object-oriented design, enabling the application of workflows that can freely evaluate and process data within and outside the package tools. Common batch evaluation methods, along with novel quantitative metrics, help determine the benefits of batch correction for each dataset and enable direct comparisons between methods. Here, we present BatchQC as the first comprehensive batch-correction R package, with independent tools, reproducible workflows, visualization, and novel statistics.

bioinformatics↗

bioRxiv2026

The genome of an aneuploid cultivar facilitates elucidating the genetic bases of trait polymorphisms in Phalaenopsis orchids

As an important ornamental crop, Phalaenopsis orchids exhibit extraordinary trait diversity with unclear genetic bases. Here, we present a haplotype-resolved, chromosome-level genome for an aneuploid cultivar Santiago. We reveal pervasive variation in the number and sequence among homoeologous genes. When serving as a reference, this genome facilitates the genetic understanding of trait variation in a hybrid population that is highly representative in trait polymorphisms. Specifically, nucleotide polymorphisms in a potential long-distance enhancer and the consequential expression variation of PsAGL6-2, presence/absence of PsMYB12 and b-type homoeologous gene of PsMYB2 determine lip morphology variation, presence/absence of venation-associated stripes and background pink color, respectively. Diverse functions of PsMYBx1 in repressing anthocyanin accumulation across different cultivars further enhances the color patterning diversity in Phalaenopsis. Our study provides a practical framework for using a highly heterogeneous, haplotype-resolved genome to decode phenotypic diversity and has the potential to promote marker-assisted breeding in Phalaenopsis.

genomics↗

bioRxiv2026

Lipid monounsaturation confers cold tolerance and improves tissue viability during hypothermic storage

The rapid loss of tissue viability during hypothermic storage frequently results in organ failure and high discard rates in transplantation. In contrast, ectothermic animals can tolerate prolonged exposure to low temperatures in their natural environments. Elucidating the mechanisms underlying this cold tolerance may therefore inform improved strategies for tissue preservation. Here, we investigate proteomic and metabolomic responses to cold exposure in the livers of two frog species from distinct habitats: the cold-sensitive African clawed frog (Xenopus laevis) and the cold-tolerant Northeastern Asian brown frog (Rana dybowskii). Cold exposure induced lipid mobilization in X. laevis, whereas it promoted phospholipid mono-unsaturation in R. dybowskii. Notably, treatment with monounsaturated fatty acids (MUFAs) or overexpression of stearoyl-CoA desaturase (SCD) reduced cell death during cold storage. Moreover, MUFA infusion significantly improved cell viability in mouse liver tissue under hypothermic conditions. Together, these findings suggest that lipid mono-unsaturation, an adaptive feature of cold-tolerant frogs, can be leveraged to enhance tissue preservation during cold storage.

biochemistry↗

bioRxiv2026

PlantMDCS: A code-free, modular toolkit for rapid deployment of plant multi-omics databases

With the rapid accumulation of diverse omics datasets, achieving efficient management and integrative analysis of plant multi-omics data remains a major challenge. Conventional solutions rely on constructing web-based databases, which often demand substantial programming expertise and long-term financial support. To address these limitations, we developed the Plant Multi-omics Database Construction System (PlantMDCS)-a locally deployable, user-friendly, and collaborative platform that unifies database construction and downstream multi-omics analysis within a graphical environment. PlantMDCS adopts a decoupled front-end/back-end architecture. The back end serves as the core engine for data management and computation, and is responsible for the storage, preprocessing, integration, and hierarchical association of multi-omics data. Once initialized, the front end supports the complete research workflow, including data import, querying, integrative analysis and visualization. All operations can be performed without programming, while local resource usage is dominated by disk storage required for user-provided datasets rather than sustained computational overhead. Benchmarking across plant species ranging from Arabidopsis to hexaploid wheat demonstrated that database construction can be completed within minutes, independent of genome size or data complexity. PlantMDCS is designed for local deployment to ensure data security, while allowing multi-user collaboration within local networks and supporting controlled remote access for teams distributed across different regions. Overall, PlantMDCS offers a secure and sustainable framework that integrates data management and analysis within a unified system. This design shifts multi-omics research away from fragmented file-based processing toward persistent, database-driven exploration, thereby enhancing analytical efficiency and reproducibility.

bioinformatics↗

bioRxiv2026

DynMoCo: a Novel AI Framework to Reveal Modular Substructures of Protein From Molecular Dynamics

Proteins are dynamic molecular machines whose functions are determined by their structures. While static structures can offer initial insights or hypotheses about protein function, they are often insufficient for a detailed mechanistic understanding. Molecular dynamics (MD) simulations provide atomistic view of proteins dynamic motion and conformational change, but the resulting high-dimensional data are challenging to interpret. Traditional summary statistics and dimensionality-reduction methods often focus on global motions and can overlook regional, yet functionally critical motions. Inspired by approaches from social network science, we introduce a novel perspective for analyzing MD simulations through dynamic community detection, where molecules are modeled as time-evolving graphs, and communities of residues or atoms that move coherently or exhibit functional coupling are identified. We present DynMoCo, a novel deep learning framework that integrates graph convolutional networks with recurrent models for end-to-end dynamic community detection on molecular graphs. Given a MD trajectory, DynMoCo identifies spatially grounded substructures, tracks their evolution over time, and can incorporate structural knowledge to ensure physically meaningful communities. We provide a library of custom-written scripts to allow users to extract and visualize these communites on the MD simulated molecules in motion. We demonstrate the method on force-ramp and force-clamp steered MD simulations of three integrin systems, revealing modular substructures within known domains and characterizing their conformational rearrangements during force-induced unbending. By reducing high-dimensional MD data into interpretable communities, this approach offers new insights into the intrinsic organization and dynamic function of complex biomolecular systems. SIGNIFICANCEProteins often perform their functions through dynamic, locally coordinated motions. Molecular dynamics simulations provide detailed views of these motions but produce high-dimensional data that are challenging to analyze and interpret. We present a novel deep learning model that analyzes molecular dynamics simulations data and identifies structurally coherent and potentially functionally related communities, while tracking their temporal evolution. This analysis tool provides a novel way to analyze MD data transforming it into interpretable representations of modular dynamic, enabling discovery of new mechanistic insights and advancing our understanding of how molecular motions drive biological function.

biophysics↗

bioRxiv2026

Multiple redundant mechanisms account for the majority of gene silencing downstream of DNA methylation

DNA methylation is a conserved epigenetic modification crucial for silencing genes and transposable elements (TEs). However, the mechanisms that cause silencing remain unclear, partly because methyl reader protein mutants in both plants and animals show minimal transcriptional changes. To explore the possibility of redundancy among these silencing mechanisms, we generated combinatorial mutants of H1.1, H1.2, ADCP1, MOM1, MBD2, MBD5, and MBD6 lacking key methyl readers and related silencing pathways. We observed massive derepression of genes and TEs at DNA-methylated loci, showing that these pathways account for 73% of silencing compared to DNA methylation-free mutants. We also observed that immune response genes were upregulated, causing an imbalance between growth and defense. Loss of downstream silencing pathways further disrupted 3D genome organization, leading to increased euchromatin-heterochromatin interactions. These findings highlight the cooperative action of multiple downstream mechanisms in DNA methylation-mediated silencing and genome organization.

molecular biology↗

bioRxiv2026

ATLAS: Population-Level Disease Locus Discovery via Differential Attention in Genomic Language Models

Identifying disease-associated genetic variants remains a key challenge in genomics, especially in small cohorts or for rare and complex mutation types where genome-wide association studies (GWAS) often fall short. We introduce ATLAS, a population-level framework that leverages attention signals from pretrained genomic language models (gLMs) to detect disease-associated genes and loci directly from raw sequences--without requiring explicit variant calls or supervised training. ATLAS first performs gene-level differential attention analysis to prioritize candidate genes, followed by base-level analysis to localize disease-associated regions at single-haplotype resolution. We validate ATLAS on synthetic and {beta}-thalassemia datasets, demonstrating robust performance across diverse allele frequencies (down to 10%), cohort sizes (below 200 individuals per group), and genomic scales. Compared to GWAS, ATLAS achieves higher recall of known loci and captures haplotype-specific signals missed by traditional methods. Cross-model benchmarking further shows that precise localization depends on both model size and pretraining on diverse human genomes. In summary, ATLAS offers a scalable, sequence-native alternative to traditional statistical genetics.

genetics↗

bioRxiv2026

Tetrabromobisphenol A (TBBPA) inhibits Bacillus subtilis as a Membrane Active Antibacterial Agent

Antimicrobial resistance (AMR) is a pressing global public health crisis, necessitating novel antimicrobial agents and mechanistic insights. Tetrabromobisphenol A (TBBPA), a widely used brominated flame retardant, exhibits potent activity against Gram-positive bacteria including methicillin-resistant Staphylococcus aureus (MRSA) without inducing resistance, yet its mode of action remains unclear. Using Bacillus subtilis as a model, we investigated TBBPAs antibacterial mechanism via extensive bacterial cytological profiling, fluorescence imaging, and mutant validation. We found that TBBPA causes membrane depolarization and disruption, based on Thioflavin T release, MinD mislocalization, FM5-95 fluorescence aggregation, and propidium iodide penetration. In fact, TBBPA can destabilizes giant unilamellar lipid vesicles in vitro. The induced membrane damage triggers several downstream effects, including MreB immobilization, which impairs cell wall synthesis, inhibition of DNA replication and translation, and increased autolysin activation leading to cell lysis. In conclusion, this study suggests that TBBPA directly targets the cell membrane, causing disruption of multiple essential processes, and leads to activation of autolysins, resulting in lysis. These findings highlight TBBPAs prospective utility as an anti-Gram-positive agent; nevertheless, concerns over its potential side effects necessitate further investigations into its safety profile prior to clinical translation.

microbiology↗

bioRxiv2026

SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types

There is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize senotypes and senescent cell heterogeneity. Here, we profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog we termed SenCat. We found that, while senescent cells from all primary tissue types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Machine learning analysis of the SenCat transcriptomic and proteomic datasets successfully identified independent sets of senescent human cells, and senescent-like cells in mouse lung and kidney. In sum, SenCat represents a much-needed resource to identify senescent cells across tissues in the body. HIGHLIGHTSO_LIIdentifying senescent cells in organisms in vivo remains a challenge C_LIO_LIWe created SenCat: a catalog transcriptomes and proteomes of senescent primary cells C_LIO_LIMachine learning (ML) analysis of SenCat identified robust senescence scores C_LIO_LIML-derived senescence scores uncovered senescent-like cell dynamics in vivo C_LI

molecular biology↗

bioRxiv2026

The BELL-type homeobox transcription factor RLC3/OsBLH4 controls leaf rolling and drought tolerance via KNOX-BELL-lignin regulatory network in rice

O_LIModerate leaf rolling in rice is crucial for plant architecture and stress adaptation, but its molecular regulation remains unclear. We investigated the role of RLC3/OsBLH4, a BELL-type homeobox transcription factor, in controlling leaf rolling and drought tolerance, addressing gaps in lignin biosynthesis and cell wall development mechanisms. C_LIO_LIWe used gene map-based cloning (rlc3-1, rlc3-2), CRISPR/Cas9 knockout lines (rlc3-ko#11, rlc3-ko#12), and allelic complementation to validate RLC3s function. Additionally, we employed biochemical assays, gene expression analysis, and protein interaction studies to explore its regulatory network. C_LIO_LIRLC3 mutations impaired lignin biosynthesis and secondary cell wall formation, reducing bulliform cells area and causing midrib defects. These structural abnormalities accelerated water loss, leading to excessive leaf rolling and compromised drought tolerance. Mechanistically, RLC3 directly activates lignin synthesis genes (OsPAL5, OsCOMT5, OsCCR4, OsCAld5H1) and interacts with KNOX transcription factors (OSH1, OSH45, OSH71) to form a KNOX-BELL complex, further regulating lignin content and cell wall development. C_LIO_LIRLC3 orchestrates lignin deposition and secondary cell wall development to control leaf rolling, water transport, and drought tolerance. This study reveals a novel KNOX-BELL-lignin regulatory module governing leaf morphology and stress adaptation, offering targets for crop improvement under drought conditions. C_LI

plant biology↗

bioRxiv2026

Characterizing the metabolomes of microglia, astrocytes, and neurons in aging and Alzheimer's brains

Neurons and glia are distinct in their morphology, development, and function, possessing unique transcriptomes and proteomes, but little is known about their metabolomes. The challenge of brain cell metabolic profiling is to obtain a large number of cells for reliable analysis. Here, we purified microglia, astrocytes, and neurons from mouse brains, identifying >70 metabolites through targeted metabolomics and 9,854 metabolite features via untargeted metabolomics. We systematically characterized cell type-enriched metabolites and metabolic pathways, revealing an enrichment of glutathione (GSH) and polyamine metabolism in microglia. This enrichment was validated in vivo and showed significant decreases with aging and in an Alzheimers disease (AD) model. Notably, GSH and polyamine metabolism correlated strongly with chemokine-related gene expression. Disrupting the GSH pathway in microglia resulted in downregulation of chemokine-related genes, aberrant morphogenesis, and {beta}-amyloid deposition. Our results provide a valuable resource (https://metabolismocean.org/braincell) for metabolic studies related to aging, AD, and other neurological diseases.

neuroscience↗

bioRxiv2026

eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolution

The human genome is densely populated with cis-regulatory elements (CREs), yet deciphering their functional regulatory syntax and combinational logic remains a fundamental challenge. Here, we integrate 379 genome-scale CRISPR screen experiments, encompassing 21 million perturbations across 23 cell types, to construct a compendium of 41,239 high-confidence functional CREs from 530,527 candidates. Leveraging this resource, we develop eScreen, a deep learning model built on the StripedHyena2 architecture to functionally decode the regulatory genome at single-nucleotide resolution. eScreen achieves three primary functions: (1) predicts genome-wide cell-type-specific CRE functional activity with high accuracy, outperforming existing models; (2) provides mechanistic interpretation of regulatory syntax at single-nucleotide resolution; (3) dissects the functional organization of enhancer clusters through in silico perturbation analysis. We perform multiple independent CRISPR knockout, CRISPR interference (CRISPRi), and base editing screens to validate these functions of eScreen both at scale and on individual cases. Furthermore, we provide an interactive web server (https://escreen.huanglabxmu.com/) for the community to access the integrated CRISPR screen resources and eScreen functions. Collectively, our work establishes a highly precise and convenient tool to decode the causal effects of the regulatory genome.

genomics↗

bioRxiv2026

Deconvolving mutation effects on protein stability and function with disentangled protein language models

Understanding how evolutionary constraints shape protein sequences is fundamental to deciphering the molecular mechanisms underlying protein stability and function, which has broad implications in protein engineering and therapeutics development. Recent advances in protein language models (pLMs) have enabled accurate prediction of mutation effects through evolutionary information, effectively capturing the selective pressure that governs protein sequence variation. A critical challenge, however, remains in disentangling the intertwined mutation effects on protein stability and function, as evolutionary signals conflate both stability-driven and function-driven pressures, obscuring the mechanistic basis of mutation effects and limiting their utility for rational protein engineering. In this work, we introduce DETANGO, a novel deep learning framework that explicitly deconvolves the mutation effects on protein functions by removing components attributable to stability perturbations from the pLM-predicted mutation effects. Guided by computational or experimental stability measurements, DETANGO estimates a functional plausibility score for each single-point mutation that is the component of the mutation effect not accounted for by changes in stability. Single-point mutations with low functional plausibility are predicted to be stable-but-inactive (SBI) variants, whose compromised activities are caused by direct perturbations on functional mechanisms rather than structural stability. Residues enriched for such variants are inferred to be functionally critical, as indicated by the strong evolutionary pressures to maintain protein function. Through extensive benchmarking experiments, we show that DETANGO accurately identifies SBI variants and pinpoints functionally important residues across contexts, including ligand binding, catalysis, and allostery. Moreover, extending DETANGO from individual proteins to homologous protein families reveals shared and distinctive functional patterns across protein families. Collectively, these results establish DETANGO as a biologically grounded framework for disentangling evolutionary constraints on protein stability and function, advancing mechanistic understanding of protein function, and informing rational protein engineering.

bioinformatics↗

bioRxiv2026

An agentic framework turns patient-sourced records into a multimodal map of ALS heterogeneity

ALS progression is multidimensional, yet fragmented records and scalar outcomes obscure how patients move through disease states and how those states relate to molecular variation. MEDSTREM converts patient-held medical-record images into standardised longitudinal data, enabling bottom-up cohort construction. Using MEDSTREM-structured records from more than 8,000 AskHelpU participants together with PRO-ACT and Answer ALS, we developed DynaALS, the ALS Disease Dynamics Atlas. DynaALS represents ALS as a dynamic patient-state manifold that captures distinct directions of deterioration and patient movement between them over time. DynaALS retrieved population-referenced future states and decoded them into multidimensional clinical profiles without requiring patient-specific longitudinal histories. Motor-neuron RNA and chromatin profiles linked DynaALS states to developmental and regulatory programs, while neuromuscular-organoid single-cell multi-omics converged on a neural-developmental Netrin-DCC signalling axis across interacting cell types. By coupling MEDSTREM-enabled data construction to dynamic state modelling, DynaALS establishes a transferable patient-state engine for predictive and biologically interpretable disease models.

bioinformatics↗

bioRxiv2026

Direct interactions of CEACAM1 and CD36 with LPS and each other

Micellar lipopolysaccharide (mLPS), a major shed product of gram-negative bacteria in the intestine, may result in TLR4-mediated sepsis in the circulation. CEACAM1 and CD36, expressed on both epithelia and immune cells, regulate TLR4 signaling and inflammation, suggesting a shared role in innate immunity. Furthermore, CEACAM1 associates with CD36 on hepatocytes, regulating lipid storage and bile acids (BAs) in the liver, where LPS detoxification occurs. Using E. coli mLPS-Ra as a model and soluble forms sCEACAM1 or sCD36, we assessed their direct binding to mLPS by SPR, SEC, and TEM. SPR derived steady state affinity constants (KD= 1.47 x 10-6 M and KD= 2.23 x 10-6 M) were obtained for mLPS-Ra binding to sCEACAM1 and sCD36, respectively. mLPS-Ra binding to sCEACAM1 and sCD36 was reduced by bile acids in order of sodium deoxycholate > sodium cholate. mLPS exhibited dose dependent binding to sCEACAM and sCD36 by SEC and the isolated complexes formed well defined micelles on TEM. A KD of 5.28 x 10-8 M was obtained for sCEACAM1 binding to immobilized sCD36. Antibody-based-proximity ligation assays (PLA) demonstrated the association of the ectodomains of CEACAM1 and CD36 on hepatic (P< 0.001) and HEK cells (P< 0.018), while biotin-based PLA demonstrated association of the cytoplasmic domains of CEACAM1 and a CD36-BioID2 fusion protein in HEK cells. Alpha Fold predicted CD36 binding in cis to CEACAM1 in a membrane model, in agreement with the results of the PLAs. Based on the SPR, SEC and TEM binding studies, CEACAM1 and CD36 share a cooperative mLPS binding function distinct from their regulation of TLR4 binding.

biochemistry↗