Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Metabolic reprogramming by caloric restriction enhances acute phase virological control and reduces chronic inflammation in SIV-infected rhesus macaques

Nutrient metabolism influences HIV-1 replication, antiviral immunity, and chronic inflammation, yet is difficult to leverage for therapeutic gain. We sought to modulate metabolism in the non-human primate model of HIV-1 by caloric restriction (CR), a modality canonically known for its antiaging benefits. Four months of 30% CR was safe and resulted in broad and systemic metabolic reprogramming in healthy adult male and female rhesus macaques. Relative to that of ad libitum-fed animals, CR lowered the frequencies of target CCR5+ CD4 T cells in the gut mucosa. Upon infection with SIV, CR reduced acute phase viremia, dampened type I interferon signaling, and overall permitted a more vigorous cycling of CD8+ T cells in lymphoid tissues. CR-induced protection from SIV was associated with a robust up-regulation of glycolysis, which supported an early reduction in viremia that ultimately waned over time. During virologic suppression with antiretroviral therapy (ART), CR significantly limited gastrointestinal (GI) immune activation, improved tricarboxylic acid cycle flux, and lowered concentrations of soluble CD14 and several TNF-related molecules in plasma. Blood SIV DNA levels however were unchanged by CR, suggesting that residual GI dysfunction and inflammation can be decoupled from viral persistence. Our findings highlight that a dietary modality can limit pathology in a primate lentiviral infection. They also reveal the robust but temporally constrained nature of glycolysis in supporting an acute antiviral response. SIGNIFICANCECaloric restriction (CR) is a safe dietary intervention known to confer anti-aging and health benefits across diverse animal models. However, its application in the context of infectious diseases has yielded mixed outcomes and has largely been limited to murine systems. In this study, we therefore employed CR to examine the impact of dietary modulation on SIV infection outcomes. Our findings demonstrate that CR reduced acute-phase viremia and attenuated markers of chronic inflammation following ART, effects that were associated with distinct metabolic signatures. Collectively, these findings underscore the importance of diet and nutrition in shaping chronic viral infection outcomes, such as SIV, within a clinically relevant non-human primate model.

immunology↗

bioRxiv2026

CEACAM5/6+ Tumor Cells and IL-1β+ Macrophages Drive Resistance to Chemo-immunotherapy in Gastric Cancer

Chemo-immunotherapy is a first-line treatment for advanced gastric cancer, yet response rates remain limited and resistance mechanisms are poorly defined. Here we generate a single-cell atlas of 542,121 cells from 35 patients treated with anti-PD-1 plus chemotherapy, profiling pre- and post-treatment tumors linked to clinical response. Integrating spatial transcriptomics, immunohistochemistry, and bulk RNA sequencing, we identify two temporally distinct resistance programs. Intrinsic resistance in pre-treatment non-responders is marked by enrichment of CEACAM5/6 tumor cells that form immune-excluded spatial niches characterized by macrophage recruitment and CD8 T-cell exhaustion. Acquired resistance in post-treatment non-responders is driven by expansion of IL-1{beta} macrophages, which induces coordinated NF-{kappa}B activation across tumor and stromal compartments, promoting PD-L1 upregulation, epithelial-mesenchymal transition, and chronic inflammation. These findings delineate an evolutionary trajectory of resistance and nominate CEACAM5/6 and IL-1{beta} as predictive biomarkers and therapeutic targets to improve anti-PD-1-based combination strategies.

cancer biology↗

bioRxiv2026

Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell

Predicting cellular responses to perturbations is crucial for therapeutic discovery, yet experimental screening is severely constrained by the combinatorial vastness of biological space. While computational simulations offer a scalable alternative, current models are limited by the incomplete latent representation----mainly relying on highly variable genes in feature representation; the poor genome-wide reconstruction fidelity; and the ungeneralizable dynamic laws across diverse contexts. Consequently, they fail to mechanistically transfer learned dynamics to unseen cellular contexts. To address these systemic flaws, we introduce AlphaCell, a generative Virtual Cell World Model that unifies genome-wise representation with continuous state transition modeling. AlphaCell achieves three synergistic innovations: (1) Latent Manifold Rectification, processing the full protein-coding transcriptome to construct a differentiable Virtual Cell Space, effectively filtering noise while preserving intrinsic cellular topology; (2) Biological Reality Reconstruction, utilizing a massive, knowledge-rich decoder to translate abstract latent states back into high-fidelity, genome-wide expression profiles; and (3) Universal State Transition, applying Optimal Transport Conditional Flow Matching to model perturbations as continuous, deterministic vector fields. By abstracting perturbation mechanisms into generalized dynamic laws, AlphaCell makes robust prediction of perturbation responses in a compositional generalization scenario and enables zero-shot prediction of cellular dynamics in entirely unseen cellular contexts, providing a foundational engine for cellular-context-generalizable perturbation prediction and perturbation-induced cellular dynamics simulation.

bioinformatics↗

bioRxiv2026

Rewiring of 3D Enhancer-Promoter Interactome Underlies Diabetic Endothelial Dysfunction

BackgroundDiabetic vascular complications are driven by endothelial dysfunction, yet the role of 3D genome organization in this process is unknown. We sought to define the alterations in chromatin architecture in diabetic endothelium and identify the key regulators involved. MethodsWe generated a high-resolution 3D epigenomic atlas of diabetic endothelial cells from mouse models and human subjects using H3K27ac HiChIP, complemented by ChIP-seq, ATAC-seq, and RNA-seq. A human cohort was used to assess protein expression in diabetic versus non-diabetic endothelial cells. To identify JUNB-interacting proteins, we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME), with protein-protein interaction validated by co-immunoprecipitation. Functional validation was performed using in vitro, ex vivo, and in vivo approaches, including endothelial-specific knockdown in a diabetic hindlimb ischemia model. ResultsMulti-omics profiling revealed extensive enhancer reprogramming in diabetic endothelium, with AP-1 binding motifs being consistently and selectively enriched in downregulated enhancers across three distinct diabetic models. Analysis of a human cohort confirmed significantly reduced JUNB protein levels in diabetic endothelial cells. We identified widespread disruption of JUNB-anchored enhancer-promoter interactions, which underlies transcriptional repression of key endothelial genes. RIME and co-immunoprecipitation established the E3 ubiquitin ligase RBBP6 as a direct JUNB interactor that promotes its polyubiquitination and proteasomal degradation in response to hyperglycemia. Human cohort analysis further showed reciprocal elevation of RBBP6 in diabetic endothelial cells. Either JUNB overexpression or RBBP6 knockdown restored enhancer-promoter connectivity, reactivated vasoprotective transcriptional programs, and rescued endothelial function. Critically, endothelial-specific knockdown of Rbbp6 in diabetic mice restored endothelium-dependent vasorelaxation and improved perfusion recovery after hindlimb ischemia, independent of systemic glucose levels. ConclusionsOur study unveils a novel mechanism whereby hyperglycemia induces enhancer reprogramming and disrupts endothelial 3D genome architecture through RBBP6-mediated degradation of JUNB. The RBBP6-JUNB axis is established as a crucial link between metabolic stress and epigenomic reprogramming in vascular disease, presenting a promising therapeutic target for diabetic vasculopathy.

genomics↗

bioRxiv2026

Global O-GlcNAcome Identifies O-GlcNAcylated Proteins Regulating Oligodendrocyte Precursor Cells Differentiation

Oligodendrocyte precursor cells (OPCs) differentiate into myelinating oligodendrocytes to form the myelin sheaths essential for neural function, a process frequently compromised during aging and in diseases like multiple sclerosis. However, the regulatory landscape of myelination remains incompletely understood. Here, we perform a comprehensive proteomic analysis of the O-GlcNAcome in postnatal day 0 mouse OPCs, identifying 165 O-GlcNAcylation sites across 118 proteins. These proteins are mostly nuclear and characterized by single-site modifications. Comparative analysis identifies 74 novel sites and 22 proteins uniquely O-GlcNAcylated in OPCs. Molecular docking suggestes that O-GlcNAcylation might act as a dynamic regulator of OPC differentiation. Notably, global O-GlcNAcylation levels increase during the OPC-to-oligodendrocyte transition. By integrating our findings with multiple sclerosis-related transcriptomic and proteomic datasets, we identify candidate proteins linking O-GlcNAc dysregulation to demyelination. Among these, Vimentin (Vim) exhibits a pronounced age-dependent discrepancy between decreased protein levels and elevated mRNA expression during differentiation. We validate Threonine 35 and 63 as key O-GlcNAcylation sites on Vimentin. Functional assays confirm that blocking Vimentin O-GlcNAcylation enhance the OPC differentiation. Collectively, our study establishes the first O-GlcNAc landscape for OPCs and suggests O-GlcNAcylation as a pivotal regulator for OPC differentiation.

neuroscience↗

bioRxiv2026

Pro-IL-1β activates NF-κB independently of maturation

Interleukin-1{beta} (IL-1{beta}) is an essential pro-inflammatory cytokine which functions as a key factor in innate immunity. The precursor protein, pro-IL-1{beta}, has long been regarded as an inactive form in innate immune responses. Here, we unveil the biological function and regulation of pro-IL-1{beta} in activating the NF-{kappa}B signaling pathway, which is distinct from IL-1{beta} signaling. The expression and release of pro-IL-1{beta} are induced by inflammatory stimuli and then pro-IL-1{beta} acutely activates the gene transcription driven by NF-{kappa}B in a dose dependent manner. This activity is resistant to IL-1 receptor antagonist (IL-1Ra). The signal transduction triggered by pro-IL-1{beta} relies on MyD88 and endocytosis. We further demonstrate that the N-terminal pro-peptide primarily contributes to this activity. Furthermore, we identify TLR7 and TLR8 as the binding partners of pro-IL-1{beta} in vitro and as the potential receptors mediating pro-IL-1{beta}-induced NF-{kappa}B activation. Collectively, this study sheds light on the unique cytokine function of pro-IL-1{beta} and provides new insights into the functional characterization of pro-cytokines in innate immunity.

biochemistry↗

bioRxiv2026

Type 2 diabetes amplifies environmental Pb toxicity and intergenerational risk

Type 2 diabetes (T2D) and environmental lead (Pb) exposure intersect as prevalent global health challenges. Prior studies report elevated blood Pb in T2D, but the underlying mechanism and directionality remain unresolved. Here we show that T2D fundamentally alters Pb toxicokinetics, converting otherwise modest exposure into progressive organ injury and heritable risk. In a human cohort, elevated blood Pb in T2D specifically associated with impaired renal function rather than glycaemic control or adiposity. In db/db mice, diabetes increased oral Pb absorption through delayed gastrointestinal transit and epithelial barrier dysfunction, leading to enhanced systemic retention. With sustained exposure, renal Pb accumulation induced tubular stress that reduced effective clearance, creating a feed-forward loop in which diabetes promotes Pb retention and retained Pb accelerates kidney injury. Chronic Pb exposure exacerbated diabetic kidney injury without inducing hyperglycaemia, and short-course chelation attenuated renal stress markers. Beyond somatic toxicity, combined paternal T2D and Pb exposure programmed renal and neurobehavioral phenotypes across generations via sperm RNA. These findings redefine Pb from a passive environmental exposure to a context-dependent driver of disease, identify T2D as a vulnerability state that reshapes toxicant handling, and establish a mechanistic link between metabolic disease, environmental exposure and intergenerational risk. This work highlights the need for vulnerability-informed clinical management and environmental regulation in populations burdened by T2D.

genetics↗

bioRxiv2026

Systematic Analysis of Human Tissue- and Cell-Specific Metabolic Models Identifies High-Sugar, High Fat Diet Induced Liver Dysregulation

Human tissues exhibit specialized metabolic functions that are essential for maintaining whole-body metabolic homeostasis. To systematically characterize organ- and cell-type-specific metabolic heterogeneity, we constructed 32 tissue-specific and 81 cell-type-specific enzyme-constrained genome-scale metabolic models (ecGEMs) by integrating the global human metabolic network with the tissue- and single-cell transcriptomic data from the Human Protein Atlas (HPA). Our analysis revealed pronounced differences in metabolic network architecture and activity across the human body, identifying key cell types that drive tissue metabolic functions. To demonstrate the applicability of these models, we employed the liver-specific ecGEM to investigate the metabolic reprogramming induced by a high-sugar, high-fat (HSHF) diet, a primary driver of metabolic dysfunction-associated fatty liver disease (MAFLD). Flux balance analysis revealed a fundamental transition in hepatic metabolism: from a flexible, multi-functional system toward a constrained, lipid-centric regime. This state is characterized by carbohydrate and lipid overload, mitochondrial respiratory dysfunction, and a compromised capacity for reactive oxygen species (ROS) detoxification. These computational predictions were validated through integrative analysis of transcriptomic data from a human MAFLD cohort and metabolomic profiles from an in vivo HSHF rat model. Together, this work provides a comprehensive atlas of human metabolic models, enabling the systematic investigation of metabolic features across tissues and conditions from a systems-level perspective. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/706568v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1015c7org.highwire.dtl.DTLVardef@5c200org.highwire.dtl.DTLVardef@3c982org.highwire.dtl.DTLVardef@a2894d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Nonanticoagulated Heparin Derivatives Mediate Goblet Cell Differentiation to Restore the Mucosal Barrier for Ulcerative Colitis Therapy

Epithelial mucus barrier dysfunction is a pathological hallmark of ulcerative colitis (UC), yet current clinical therapeutic strategies primarily suppress inflammation, lacking reliable approaches for restoration of the mucosal layer. Here, through a systematic screen of our established library of deanticoagulated heparins, we identified NALHP, a non-anticoagulant low-molecular-weight heparin derivative, and its representative fraction S6 as orally active epithelial repair-promoting glycans. NALHP/S6 restored crypt architecture, mucus production and epithelial barrier integrity in experimental colitis. In UC patient-derived colonic organoids, NALHP/S6 reduced aberrant stem/proliferative programs while promoting secretory, absorptive and junctional maturation. Single-cell transcriptomics and temporal validation identified a transient repair-secretory transitional state (RSTS) positioned between classical Wnt-associated stemness and mature barrier-forming epithelial states. Transcriptomic analysis revealed coordinated attenuation of Wnt and Notch signaling, whereas pharmacological reactivation of these pathways opposed NALHP/S6-induced epithelial state progression. These findings define mucosal repair as a regulated epithelial-state transition and identify NALHP/S6 as glycan-based modulators capable of restoring this progression in UC epithelium.

pathology↗

bioRxiv2026

Development of KASP Molecular Markers and Construction of Fingerprinting for Cowpea (Vigna unguiculata (L.) Walp) unguiculata Based on ddRAD-Seq

Cowpea (Vigna unguiculata (L.) Walp.) is a globally important legume crop. However, the scarcity of efficient molecular markers has hindered molecular breeding efforts and the protection of plant breeders rights. In this study, we employed double-digest restriction-site associated DNA sequencing (ddRAD-seq) to characterize the genetic diversity of 19 core cowpea germplasm accessions. We generated 791,621 SNPs, from which 13,469 high-quality SNPs were filtered. Population structure and phylogenetic analyses revealed that these accessions cluster into three distinct groups. To facilitate cost-effective and rapid genotyping, we developed a panel of KASP (Kompetitive Allele Specific PCR) markers. Through rigorous screening for polymorphism and stability, we identified six core KASP markers located in exonic regions. These six markers alone were sufficient to discriminate all 19 accessions. Based on these core markers, we constructed a unique DNA fingerprinting profile and assigned specific QR codes for each accession. This study demonstrates that extracting core KASP markers from ddRAD-seq data is a powerful strategy for germplasm identification. The developed fingerprinting system provides a robust, low-cost tool for seed purity testing, variety authentication, and marker-assisted selection in cowpea breeding programs.

plant biology↗

bioRxiv2026

An Integrated Pipeline for Cell-Type Annotation, Metabolic Profiling, and Spatial Communication Analysis in the Liver using Spatial Transcriptomics

The liver acts as a central metabolic hub, integrating systemic signals through a spatially organized pattern known as zonation, driven by the coordinated activity of diverse cell types including hepatocytes, stellate cells, Kupffer cells, endothelial cells, and immune populations. Spatial transcriptomics (ST) enables the profiling of thousands of cells with spatial resolution in a single experiment, facilitating the identification of novel gene markers, cell types, cellular states, and tissue neighborhoods across diverse tissues and organisms. By simultaneously capturing transcriptional and spatial heterogeneity, ST has become a powerful tool for understanding cellular and tissue biology. Given its advantages, there is growing demand for applying ST to uncover novel biological insights in the liver under various physiological and pathological conditions including obesity, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). However, to date no comprehensive and practical protocols currently exist for analyzing ST data specifically in the context of liver metabolism. Herein, we present a systematic and detailed protocol for ST data analysis using liver tissues from MASLD mouse models. This guide offers practical support for metabolic based researchers without advanced expertise in coding, mathematics and statistics enabling single-cell RNA-seq referencing for deconvolution-based annotation, curated liver cell type markers for manual annotation, and a GMT file of metabolic gene sets and flux balance analysis to analyze liver metabolic activity. This framework and integrated computational resources for decoding metabolic reprogramming and cellular heterogeneity will empower researchers to uncover novel biological pathways regulating liver metabolism in health and disease.

bioinformatics↗

bioRxiv2026

Mitochondrial Retrograde Signaling in Arabidopsis thaliana: heterogenous, spatial and polarised aspects

Organelle retrograde signaling studies have exclusively relied on bulk tissues, obscuring cell-type specificity, spatial organization, and heterogeneity within cell types. Here, we use single-nucleus and spatial RNA sequencing to define the cellular and spatial architecture of mitochondrial stress signaling in Arabidopsis thaliana. Inhibition of the mitochondrial electron transport chain by antimycin A and myxothiazol rapidly redistributed nuclei among transcriptional states within epidermal, leaf pavement, and mesophyll lineages, revealing that mitochondrial dysfunction reshapes cell identity trajectories rather than eliciting a uniform response. Stress-associated clusters were already present at low frequency in untreated samples, but the numbers of cells increased following stress treatments, indicating that stress increases the proportion of nuclei occupying pre-existing, primed transcriptional states. Gene-level analyses revealed distinct temporal dynamics and variable cell-state penetrance of the canonical mitochondrial stress markers and identified broadly responsive genes absent from earlier marker sets. Spatial transcriptomics resolved tissue-scale responses, spanning from pan-tissue induction to cell-type- and cluster-restricted activation, and uncovered pronounced adaxial-abaxial polarity in gene expression. Cell identity and spatial inferences were supported by extensive experimental validation using a custom 465-probe 10x Xenium panel and 52 promoter-GFP reporter lines. Together, these data provide a high-resolution framework for organelle-to-nucleus signaling and a resource of cell-type markers and spatial maps to dissect mitochondrial stress signaling and its integration with developmental and environmental pathways.

plant biology↗

bioRxiv2026

MetaKnogic-Alpha: A Hyper-Relational Knowledge Base for Grounded Metabolic Reasoning

The exponential trajectory of biomedical literature has precipitated a fundamental "synthesis gap" in metabolic research, where critical mechanistic insights remain fragmented across hundreds of thousands of disjointed full-text articles, preventing the consolidation of a global mechanistic view. Here, we present MetaKnogic-Alpha, a foundational mechanistic knowledge substrate designed to bridge this gap by transforming unstructured literature into a navigable, logic-based resource. MetaKnogic-Alpha synthesizes over 100K full-text articles into a hyper-relational hypergraph structure, preserving the n-ary relational logic inherent in complex metabolic pathways. To ensure biological rigor, we implemented a hierarchical discovery protocol: an autonomous reasoning agent first enriches query nomenclature for domain-specific precision, followed by a multi-hop topological expansion within the hypergraph to surface functional neighbors, such as enzymatic co-factors and distal regulators, often lost in traditional search paradigms. Crucially, the system subjects all literature-derived insights to a deterministic biochemical grounding against a curated metabolic reaction network, significantly mitigating the risk of probabilistic hallucinations common in standalone generative models. In rigorous benchmarking, MetaKnogic-Alpha achieved a mechanistic accuracy of 0.98 in scenarios where supporting evidence was present, providing a robustly attributable audit trail back to the primary literature via PubMed Central Identifiers. We designate this primary release as "alpha" to establish the foundational architectural logic for a burgeoning million-scale resource. By compressing the synthesis of thousands of papers from a multi-month manual effort into several hours of automated discovery, MetaKnogic-Alpha serves as a high-fidelity research companion that augments the human experts ability to resolve complex metabolic interactions and identify novel therapeutic drivers in precision oncology.

bioinformatics↗

bioRxiv2026

Inflammation in schizophrenia: Peripheral interleukin-6 levels-related disease-specific functional activity abnormalities

BackgroundPeripheral inflammation is implicated in the pathophysiology of schizophrenia, but how inflammatory signals map onto the large-scale brain organization remains incompletely understood. MethodsWe applied a supervised multimodal fusion approach guided by interleukin-6 (IL-6) to gray matter volume (GMV) and resting-state regional homogeneity (ReHo) from a population-based discovery cohort in the UK Biobank. Brain components related to IL-6 were identified and then projected onto an independent schizophrenia cohort to examine their relevance to the disease. Imaging-transcriptomic analyses using the Allen Human Brain Atlas characterize the molecular substrates underlying the disease-relevant pattern. ResultsTwo ReHo components were significantly associated with plasma IL-6, while no GMV components showed robust IL-6 correlations. One of the components (ReHo IC4) exhibited a conserved functional pattern characterized by enhanced visual synchrony and reduced synchrony in the medial prefrontal cortex. This pattern remained unchanged in both the healthy controls and patients. In contrast, another component (ReHo IC8) showed increased synchrony in the default mode network and reduced synchrony in sensorimotor networks, and its loadings were significantly elevated in patients with schizophrenia. Imaging-transcriptomic analysis revealed the molecular architecture of this disease-amplified pattern. The default mode region was enriched in synaptic signaling pathways, while the sensorimotor region was linked to mitochondrial bioenergetic processes; both patterns significantly enriched with gene sets related to schizophrenia. ConclusionsThis study identified an IL-6-associated functional brain pattern that is amplified in schizophrenia, linking peripheral inflammation to disease-specific network dysregulation. The findings provide a systems-level framework for understanding how peripheral inflammation interacts with large-scale brain network activities in schizophrenia.

neuroscience↗

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↗

bioRxiv2026

An AI-Native Biofoundry for Autonomous Enzyme Engineering: Integrating Active Learning with Automated Experimentation

The engineering of enzymes with novel functions is a cornerstone of synthetic biology but remains bottlenecked by the fragmentation between computational design and physical execution. While "self-driving" laboratories promise to resolve this, existing systems often rely on rigid, device-specific scripts that lack the flexibility to handle complex, evolving scientific tasks. Here, we report an AI-native autonomous biofoundry that fundamentally redefines laboratory automation through a "cloud-edge synergistic" architecture. The platform features an Agent-Native control system powered by Large Language Models (LLMs) and the Model Context Protocol (MCP), which bridges the semantic gap between abstract scientific intent and heterogeneous hardware execution. This architecture enables non-experts to orchestrate the entire Design-Build-Test-Learn (DBTL) cycle via natural language. By integrating deep phylogenetic mining, zero-shot protein language models (ESM-2), and supervised active learning, our system efficiently navigates rugged fitness landscapes. As a rigorous proof of concept, we applied this platform to evolve a Family B DNA polymerase for CoolMPS sequencing, a task requiring the incorporation of non-natural 3-blocked nucleotides. In just three autonomous rounds, the platform achieved a hit rate of >66% and identified variants with a 37% reduction in sequencing error rate compared to a commercial reference. This work demonstrates that AI-native infrastructures can not only accelerate trait evolution by orders of magnitude but also provide a scalable, brand-agnostic paradigm for the future of automated scientific discovery.

bioengineering↗

bioRxiv2026

Principles of in situ protein sequencing: expansion microscopy-adapted Edman degradation and amino acid recognition

The ability to map protein identity, with resolution sufficient to infer interactions, would support analysis of how proteins work together, or malfunction, in biological processes and diseases. Although several emerging technologies aim towards single-molecule protein sequencing, they require proteins to be removed from the nanoscale spatial context of cells and tissues. Expansion microscopy (ExM) has facilitated a diversity of chemical analyses by isotropically separating molecules throughout a specimen after permeation via a charged hydrogel, followed by gel swelling. Here, we adapt key protein sequencing steps - Edman degradation and amino acid recognition - to the ExM gel context. Using testbed peptides in ExM gels, we show that N-terminal amino acids can be recognized over multiple cycles of in-gel Edman degradation. This principle-oriented study demonstrates sequencing chemistry on defined synthetic constructs, rather than endogenous proteins in biological samples. These results establish principles of in situ protein sequencing and provide a framework for future in situ protein sequencing developments, including the development of higher specificity and affinity amino acid binders.

biochemistry↗

bioRxiv2026

Harnessing FBXO31 with terminal amide-functionalized molecules for targeted protein degradation

Targeted protein degradation (TPD) is a powerful strategy for controlling protein abundance. Here, we establish FBXO31 as a TPD-competent E3 ligase by exploiting its recognition of C-terminal amide-bearing degrons. Using an amidated Ala-Phe motif as a chemical recruiter, multiple small-molecule binders can be transformed into FBXO31-dependent degraders that induce rapid and potent target degradation. Mechanistic studies confirm FBXO31-mediated ternary complex formation and identify key residues in FBXO31 required for recruiter engagement and target degradation. We further show that an FBXO31-based multi-kinase degrader exhibits a distinct and broader degradation profile than a CRBN-based degrader, highlighting a potentially expanded degradable target space beyond CRBN.

biochemistry↗