Biology subjects

Zhang, J.

Publications and source records attributed to Zhang, J..

At least 181 records · Page 10Linked to original sources

bioRxiv2026

Robust Conditional Diffusion with Noisy Templates for Antibody Sequence-Structure Design

Antibodies specifically recognize antigens and play a central role in therapeutic discovery. Designing antibodies for a given antigen remains challenging because antigen-antibody complex data are limited, whereas the sequence and conformational spaces of complementarity-determining regions (CDRs) are large. Retrieved CDR templates from databases or candidate libraries can narrow the design space and improve controllability, but retrieval for novel antigens is often sparse and imperfect; treating retrieved templates as hard conditions can bias the denoising process and cause negative transfer. To address this problem, we propose Robust Conditional Diffusion with Noisy Templates for antibody sequence-structure design (NT-ABDiff), a joint diffusion framework that treats candidate CDR-only templates as optional and potentially unreliable conditions. NT-ABDiff uses reliability-aware template modulation to estimate the context-conditioned usefulness of each candidate and to adaptively reweight and fuse multiple templates during conditioning. We further train the model with mixed-quality and corrupted templates as conditional perturbation regularization, encouraging the denoiser to exploit informative templates while remaining stable when templates are uninformative. Experiments under controlled template shifts and a train-set retrieval evaluation show that NT-ABDiff improves CDR-H3 sequence recovery and structural accuracy over strong baselines, while retaining robustness to missing, mismatched, and corrupted templates. Under a stringent random-template CDR-H3 evaluation, NT-ABDiff improves amino-acid recovery (AAR) from 30.03% to 39.47% and reduces RMSD from 3.160 to 2.915 [A]; with train-set retrieval candidates, it achieves 39.50% AAR and 2.76 [A] RMSD. Code, processed splits, configuration files, and evaluation scripts are available at https://github.com/ShiDeng7rz/NT-ABDiff.

bioinformatics↗

bioRxiv2026

Incorporation of single-neuron projectome-based connectivity motifs enhances the cortex-specific performance of artificial neural networks

The organizational principles of natural neural networks could inspire the new architecture design of artificial neural networks (ANNs). Analysis of single-neuron connectomes of mouse brains revealed distinct profiles of three-node connectivity motifs in various cortical areas and hippocampal formation. A connectome-informed neural network algorithm ("CINA") was developed to incorporate natural connectivity motifs into ANN algorithms represented by recurrent neural network (RNN) and transformer-based large language model (LLM). We found that incorporation of the average profile of cortical motifs improved the RNNs performance in noise-resistant categorization and motor learning benchmark tasks, as compared with RNNs with random connectivity. Notably, incorporating cortex-specific motifs further elevated the RNNs performance in tasks related to the cortical function, and this effect was enhanced by artificially increasing the bias in the motif profile. Similar experimental results were verified on an LLM using Motif-Transformer for natural language question answering and brain-signal decoding tasks. Graph-theoretic analyses showed that incorporating natural motifs drove the emergence of modular and small-world properties in ANNs. Together, we demonstrated not only connectome-inspired optimization of ANN architecture but also functional significance of specific motif profiles in various cortices.

neuroscience↗

bioRxiv2026

Cognitive function depends upon Satb2 gene dosage in cortical projection neurons

SATB2-associated syndrome (SAS) is a severe neurodevelopmental disorder caused by de novo heterozygous SATB2 mutations, yet how haploinsufficiency disrupts brain development remains poorly understood. While homozygous Satb2 loss causes profound embryonic cell-fate defects, we demonstrate using a heterozygous mouse model that SAS phenotypes emerge primarily during postnatal circuit maturation. Integrating chromatin profiling, transcriptomics, electrophysiology, and behavior, we show that SATB2 acts as a dose-sensitive chromatin regulator that binds conserved enhancer-promoter landscapes to orchestrate networks linked to human intelligence. Although excitatory neuron subtype specification is preserved, Satb2 heterozygotes adopt an intermediate epigenetic state that drives cell-type-specific dysregulation of genes enriched for intellectual disability risk variants. Consequently, mutant neurons exhibit simplified dendritic arborization, reduced intrinsic excitability, and weakened layer 2/3-to-layer 5 intracortical connectivity. These circuit deficits culminate in the disorganization of the somatosensory barrel cortex and severe impairments in whisker-dependent texture discrimination. Finally, by restricting Satb2 heterozygosity to the cortex, we decouple these cortical sensory deficits from subcortical vocalization phenotypes. Together, our work links SATB2 dosage to chromatin architecture and postnatal circuit maturation, revealing a critical, post-mitotic therapeutic window for intervention in SAS.

neuroscience↗

bioRxiv2026

Lineage Memory and Convergent Adaptation Dictate the Single-Cell and Spatial Architecture of Brain Malignancies

How diverse cancer lineages navigate the draconian central nervous system environment--whether constrained by ancestral ontogeny or driven by convergent adaptation--remains a fundamental biological paradox. To decode this, we integrated extensive in-house and public resources to construct the largest single-cell atlas of primary and secondary brain malignancies (>550,000 cells). We demonstrate that malignant cells balance strict lineage imprinting with shared brain-adaptive programs, powerfully converging upon a dominant pan-cancer mesenchymal (MES)-like state alongside a SYT1+ pioneer subpopulation exploiting neuronal mimicry. Concurrently, the microenvironment undergoes lineage-constrained divergence. The myeloid landscape shifts from a resident-dominated architecture in gliomas to extensive blood-borne infiltration in metastases. Lymphoid responses parallel this, crowning laryngocarcinoma as an ultra-hot subtype and revealing that massive T cell influx in highly infiltrated niches is paradoxically driven into terminal exhaustion, contrasting with severe immune exclusion in cold tumors. Furthermore, stromal-vascular adaptation couples pro-angiogenic tip-endothelial activation with a dynamic opposition between structural extracellular matrix (ECM) deposition and focal proteolytic degradation. Anchored by the pan-cancer MES adaptation, we leveraged subcellular spatial transcriptomics to redefine the microenvironment around four MES-like architectural niches, mapping an evolutionary trajectory from perivascular entry to immunosuppressive stromal remodeling. Finally, projecting these architectures onto independent clinical cohort establishes the MES-S2 proliferative niche as the primary driver of severe clinical deterioration across both transcriptomic and proteomic dimensions.

Cancer Biology↗

bioRxiv2026

A multimodal foundation model linking histopathology and DNA methylation

Hematoxylin and eosin (H&E) slides are routinely available in cancer care, but molecular profiling often requires additional tissue processing and turnaround time. We introduce HistoMethyl, a DNA methylation-aware pathology foundation model that aligns whole-slide histopathology with matched genome-scale methylation beta-value profiles during pre-training while requiring only an H&E slide at inference. We evaluated HistoMethyl across four task groups: gene mutation prediction, morphology-associated classification, overall survival prediction, and direct DNA methylation beta-value recovery. Evaluation spanned TCGA cross-validation, a disease-held-out lower-grade glioma cohort, and external cohort validation in CPTAC glioblastoma and SurGen rectal adenocarcinoma, totaling 14 cancer cohorts and 81 gene mutation tasks. The best-performing configuration improved mean mutation AUROC by 5.03 percentage points. By converting DNA methylation supervision into an H&E-only representation, His-toMethyl could support an early molecular triage layer that helps prioritize cases for confirmatory sequencing, methylation profiling, immunohistochemistry, and molecular tumor board review. These image-only predictions are intended to accelerate downstream molecular testing and tissue allocation while leaving final diagnosis and treatment selection anchored in validated molecular assays.

Cancer Biology↗

bioRxiv2026

Ecosystem disservices are underrepresented in literature on annual crop agroecosystems

Maximising ecosystem service (ES) benefits while minimising ecosystem disservices (EDS) is essential for ecological intensification in annual crop systems. Yet ecosystem disservices are often underreported in the scientific literature, potentially biasing how agroecosystem functioning is understood. To test this, we built a predicted network of plausible links between ecosystem service providers (taxa or functional groups that can deliver ecosystem services or disservices), and the ES or EDS they may provide. We then compared this with a realised network based on systematic Scopus searches of annual crop literature. The predicted network contained 47 nodes and 103 links, whereas the realised network contained 33 nodes and 58 links, representing declines of 29.8% in nodes and 43.7% in links. Overall connectivity declined, especially for highly connected nodes, and four of the nine predicted disservice nodes were absent from the literature. ES links were more likely to be documented than EDS links, and EDS links were three times more likely to be absent. Across all links, ES were reported in 6.6 times more papers than EDS. Projected networks, which map indirect connections by linking ES directly to EDS if they share common providers, showed that these bundled interactions were strongly reduced, obscuring multifunctionality and trade-offs. This systematic underrepresentation of EDS, reflecting a cognitive bias, can inflate perceived benefits, distort the evaluation of key taxa and interactions, and create unrealistic expectations about intervention outcomes in biological crop protection. Addressing EDS alongside ES is therefore essential for more balanced assessments of crop-system management and better-informed decisions.

ecology↗

bioRxiv2026

Combinatorial docking and molecular generation to navigate over 100-billion molecules for prospective ligand discovery

Commercially available make-on-demand libraries now exceed 100 billion compounds, requiring over 50 years to screen on 2,000 CPU cores using conventional docking. We present two complementary approaches to address this challenge. CombiDOCK, a combinatorial docking framework, enables exhaustive screening at the 100-billion scale within 40 days. MINT-Dock, a generative framework, accelerates navigation of this space by integrating CombiDOCK with Monte Carlo Tree Search. Benchmarked on 46 diverse targets, CombiDOCK matched full-molecule docking accuracy, and MINT-Dock achieved a 4,800-fold enrichment over random selection. Compared with prior billion-scale brute-force campaigns against {sigma}2, VMAT2, and VAChT, prospective CombiDOCK screens of the 100-billion-molecule library yielded higher hit rates and more potent ligands, while MINT-Dock achieved comparable outcomes across single- and multi-target objectives with >20-fold computational cost reductions. Docking-predicted poses of the best VAChT-binding compounds were confirmed by cryo-EM structures. These methods provide exhaustive and generative paths for navigating the trillion-molecule frontier of drug discovery.

bioinformatics↗

bioRxiv2026

Oxidized protein states define metastatic fitness in lung cancer

AbstractReactive oxygen species (ROS) are a pervasive feature of human cancers, yet the protein targets through which ROS-regulated cell states shape tumor biology remains poorly understood. Here, using cysteine chemical proteomics, we define signatures of protein states under distinct cellular ROS environments that capture protein oxidation and conformational changes. Quantifying these signatures in primary lung tumors and brain metastases revealed a surprising enrichment of oxidative states in metastasis. To determine how these states support fitness, we performed genome-wide CRISPR screens and identified the mitochondrial Complex I subunit NDUFA10 as a key oxidation-dependent vulnerability. Oxidation of NDUFA10*Cys253 supports Complex I function through a previously unrecognized nucleotide kinase activity that maintains mitochondrial DNA levels. Enforcing a reduced conformation in NDUFA10 disrupts brain metastatic colonization in vivo. These findings establish ROS regulated protein states as a functional layer of tumor fitness, providing a framework for identifying redox-dependent mechanisms that support cancer progression.

Cancer Biology↗

bioRxiv2026

DigiMus: a connectome-informed spiking framework for multi-region mouse neural-behavior modeling

Computational models are increasingly used to relate mouse brain structure, neural activity and behavior, but most models still learn from task data with limited constraints from biological circuit organization. Here we present DigiMus, a connectome-informed spiking framework for multi-region-capable mouse neural-behavior modeling. DigiMus combines leaky integrate-and-fire spiking dynamics with brain-region-specific motif regularization in a trainable sequence-modeling architecture, allowing directed three-node circuit motifs derived from 38,481 reconstructed neuronal morphologies across approximately 50 brain regions to guide recurrent coupling during learning. We evaluate DigiMus on 18 rule-based cognitive tasks spanning sensorimotor mapping and perceptual decision-making, and on three mouse neural decoding datasets involving auditory discrimination, fixed-interval licking and visual decoding. Across synthetic tasks, DigiMus showed stable performance relative to TCN, LSTM and Transformer baselines, with stronger advantages in more complex decision-making settings. In real neural datasets, single-region instantiations of DigiMus produced small, consistent and dataset-dependent improvements over a structure-free sequence baseline, while retaining motif-prior signatures in trained connectivity. Internal state analyses further linked task-dependent state dynamics to behavioral error patterns. These results suggest that connectome-derived structural priors can shape neural sequence models, and establish DigiMus as a modular, connectome-informed workflow for mouse neural-behavior modeling and hypothesis generation, rather than a complete digital reconstruction.

neuroscience↗

bioRxiv2026

The Origin and Evolution of Protein Synthesis: A Co-Adaptation Flexible-Rigid Docking Model Based on First-Principles Reasoning

Although the "RNA world" hypothesis suggests that RNA played a crucial role in the origin of life [7], the functional framework of RNA in prebiotic protein synthesis and the mechanisms of genetic code formation during the prebiotic period remain poorly understood. Here, using the prebiotic "primordial soup" as a model, we reconstructed the detailed steps that would yield a protein with a stable ordered amino-acid sequence in the "primordial soup" at the prebiotic period. In the "primordial soup", a large number of medium- to large-sized biomolecule-like substances--such as RNA-like and protein-like molecules of various sizes and shapes, as well as related polymers like amino-acid-RNA-like etc.--did generate and accumulate. Moreover, protein-like and RNA-like molecules formed even more intricate complexes. These complexes bound free mRNA-like molecules through complementary base pairing. Subsequently, with an extremely low probability, two adjacent amino-acid-RNA-like molecules became bound to this free mRNA-like molecule, and their amino acids underwent a condensation reaction by the complexes, producing peptides and eventually proteins or polypeptides. This free mRNA-like molecule exhibits a certain flexible structure, whereas the super-large complexes formed by protein-like and RNA-like molecules (which possess certain activities) and the amino-acid-RNA molecules exhibit relatively rigid structures. Long-term evolution and mutual selection led to the emergence of proteins with stable amino acid sequences and moderate catalytic activity. In this way, the nucleotide information embedded in such mRNA-like molecules indirectly express through protein synthesis--a process we term the "A Co-Adaptation Flexible-Rigid Docking Model", where flexible mRNA-like molecules dock onto rigid complexes to enable ordered peptide formation. Finally, we show how trinucleotide codons emerge naturally from the flexible-rigid docking constraints.

evolutionary biology↗

bioRxiv2026

STN-DBS frequency-tuned beta subband dynamics are associated with speech tempo in Parkinson's disease

In Parkinsons disease, subthalamic nucleus deep brain stimulation (STN-DBS) frequency modulates narrow beta subbands, with low-frequency stimulation (LFS) selectively attenuating 15-23 Hz power. The negative association between this subband and speech tempo, together with the LFS-related improvement in articulatory rate, suggests that 15-23 Hz subband dynamics may serve as a candidate neural marker associated with speech tempo modulation.

neuroscience↗

bioRxiv2026

Architecture of a portal complex embedded in the poxvirus core

Gene transcription within the viral core is a unique feature of poxvirus replication. Following entry into the cytoplasm, the poxvirus core dissociates from the lateral bodies and undergoes expansion, functioning as a compartment for early transcription. However, the mechanisms governing molecular exchange between the viral core and the host cytoplasm remain poorly understood. Here, we determine the structures of the portal complex and its pore on the poxvirus core at 7.1 [A] and 4.9 [A] resolution, respectively, using cryo-electron tomography and sub-tomogram averaging. The pore is assembled from three viral proteins, E8, E6, and L3, for which we constructed an atomic model. Structural and channel analyses reveal that the pore satisfies the geometric and electrostatic requirements for the transport of RNA and smaller negatively charged molecules, while excluding double-stranded DNA and cytosolic DNA sensors. Together, our findings establish a structural framework for understanding the assembly and function of the poxvirus portal complex and identify potential targets for antiviral intervention.

biophysics↗

bioRxiv2026

Accurate de novo design of peptides from programming biophysical landscape

Peptides regulate virtually every cellular process and emerge as transformative modalities in bioengineering and therapeutics. However, the de novo design of functional peptides remains challenging, as peptide-protein interactions are governed by delicate and dynamic biophysics that are difficult to model, and experimental datasets are scarce. Here we present NeoPep, a generative deep-learning framework that encodes biophysical principles to accurately design functional peptides de novo. By integrating over 5 million peptide-protein complexes spanning experimentally determined, sequence mimics, and structure ensembles, NeoPep learns the complex biophysical landscape governing peptide function and enables its programmable control. In prospective application across 10 diverse and challenging targets, NeoPep generated potent peptide binders, agonists, and antagonists with hit rates of 12.5-66.7%, even in the absence of defined binding sites or structure information. Beyond de novo co-design, NeoPep supports standalone structure or sequence redesign, readily discriminating subtle context differences. In the structure redesign mode, it generates highly selective peptides with atomic-level conformational accuracy (C RMSD < 2.0 [A] to our solved cryo-EM structure). This structural precision circumvents the need for experimental structure determination, further accelerating iterative sequence redesign to yield a 43.3-fold improvement in potency. These findings establish a general framework for translating biophysical principles into actionable peptide functions, with broad implications for basic research, bioengineering, and medicine.

molecular biology↗

bioRxiv2026

Drivers of space use in a large semi-urban feral ungulate under seasonally fluctuating resource conditions

Resource scarcity prompts animals to adjust their space use in ways that enhance their survival. In wild herbivores, seasonal habitat shifts are well studied; however, little is known about how large herbivores navigate human-dominated landscapes under fluctuating resource conditions. In Hong Kong, feral water buffalo (Bubalus bubalis; henceforth buffalo) experience declines in body condition score during the dry season. While buffalo expand spatial ranges in the dry season, whether this expansion reflects access to improved ecological conditions, and how key anthropogenic factors (like distance to roads and human density) further influence space use, remains understudied. We observed six buffalo herds (n=92 known individuals) across one wet (July-September 2023) and one dry (January-March 2024) season. We recorded herd locations and extracted remotely sensed habitat and environmental data. We used Normalized Difference Vegetation Index (NDVI) as a proxy of vegetation productivity and included elevation, distance to roads, and human density as additional predictors. We hypothesized that dry-season space use would shift toward higher vegetation productivity, and that anthropogenic factors would have weak influence on space use due to the adaption of buffalo in human-dominated landscapes. We found that buffalo used areas with higher vegetation productivity and higher elevation in the dry season than in the wet. Distance to roads and human density had no detectable effect. Our findings reveal that space use by a large herbivore like buffalo in human-dominated landscapes is strategic and resource driven, and that these seasonal shifts may have important implications for local biodiversity and human-animal interactions.

zoology↗

bioRxiv2026

Ignet 2.0 and Vignet: An Ontology-Driven Web Platform for Biomedical Gene Interaction Discovery and Visualization

BackgroundThe expansion of biomedical literature demands systematic ontology-guided discovery of gene interactions, vaccine mechanisms, drug associations, and adverse events. Existing platforms such as STRING, DisGeNET, and PubTator fall short of providing a unified, freely accessible system that integrates ontology-based semantic interaction classification, vaccine-focused heterogeneous network construction, and Artificial Intelligence-assisted evidence retrieval. ResultsIgnet 2.0 and Vignet are freely accessible dual-platform systems that combine PubMed literature mining, BioBERT-based interaction scoring for millions of gene-gene co-occurrence pairs and integrate three biomedical ontologies and one curated drug resource, Interaction Network Ontology (INO), Vaccine Ontology (VO), Human Disease Ontology (HDO), and DrugBank. Ignet 2.0 supports gene interaction discovery, gene set enrichment retrieval of BioBERT-scored GenePair evidence, and AI-assisted summarization through BioSummarAI. Vignet extends these features with VO-guided Vaccine Exploration, VacPair interaction scoring, and the creation of vaccine, gene, drug, and disease networks in VacNet. A public Representational State Transfer Application Programming Interface (REST API) and Model Context Protocol (MCP) endpoint enable real-time integration, fostering trust in biomedical knowledge discovery. ConclusionIgnet 2.0 and Vignet are scalable, ontology-guided biomedical knowledge platforms that facilitate evidence-based gene interaction analysis, vaccine-focused semantic exploration, and AI-assisted knowledge discovery. Their real-time PubMed data integration ensures up-to-date insights; however, users should consider validation processes and potential lags in incorporating the latest experimental data, which may affect the reliability of immediate data. AvailabilityIgnet 2.0: https://ignet.org/ignet; Vignet: https://ignet.org/vignet/

bioinformatics↗

bioRxiv2026

Desert hedgehog enhances endothelial resilience and prevents atherosclerosis by mitigating PAI-1 signaling

BACKGROUNDAtherosclerotic cardiovascular disease remains the leading cause of death worldwide. Most current pharmacotherapies target conventional risk factors that promote atherosclerosis (e.g., hyperlipidemia) rather than intrinsic resilience factors that protect against atherosclerosis in the face of risk factors. Here, we investigated the role of desert hedgehog (DHH), a canonical ligand of the hedgehog signaling pathway, as a novel resilience factor that restrains endothelial mesenchymal transition (EndoMT) and protects against atherosclerosis. METHODSSingle-cell RNA sequencing (scRNA-seq) was performed on atheroprone and atheroprotective regions of the ApoE knockout mouse aorta to identify mechanoresponsive genes associated with atherosclerosis. Endothelial cell-specific Dhh knockout mice were subjected to partial carotid ligation and hypercholesterolemic conditions to investigate the role of endothelial Dhh in atherosclerosis progression. scRNA-seq, bulk RNA sequencing, endothelial lineage tracing, immunoprecipitation-coupled mass spectrometry, and surface plasmon resonance were used to in-vestigate the role and mechanism of DHH in EndoMT. Pharmacological interventions and recombinant DHH administration were performed in vivo to evaluate therapeutic potential of DHH targeting. DHH expression was also examined in human atherosclerotic arteries and serum samples from patients with coronary artery disease. RESULTSDHH protein expression was enriched in arterial endothelium from mice, porcine, and humans. However, DHH expression was significantly reduced in atherosclerotic arteries and serum from patients with coronary artery disease. scRNA-seq of atheroprone and atheroresistant region of mouse aorta identified Dhh as a novel mechanoresponsive gene enriched in aortic regions exposed to unidirectional laminar flow. Endothelial cell-specific Dhh knockout (DhhecKO) mice exhibited increased atherosclerotic lesion area, large necrotic cores, and reduced collagen content following partial carotid ligation. Similarly, under hypercholesterolemic conditions, both male and female DhhecKO mice showed aggravated atherosclerosis progression. scRNA-seq of DhhecKO mouse aortas revealed an increased proportion of endothelial cells undergoing mesenchymal transition, indicating enhanced EndoMT. These findings were corroborated by bulk RNA-sequencing of DHH depleted HUVECs and endothelial lineage tracing in inducible DhhecKO mice. Mechanistically, DHH directly interacted with plasminogen activator inhibitor type 1 (PAI-1) and suppressed PAI-1-induced EndoMT. PAI-1 promoted EndoMT in ECs through activation of canonical TGF-{beta} signaling (SMAD2/3) and noncanonical AKT/ERK1/2 signaling via interaction with low-density lipoprotein receptor-related protein (LRP1). Neutralization of PAI-1 or inhibition of LRP1, AKT/ERK1/2, or SMAD3 signaling abolished DHH deficiency-induced EndoMT. DHH competitively inhibited PAI-1 binding to LRP1, thereby attenuating downstream pro-EndoMT signaling. Intriguingly, treatment with PAI-1 inhibitor TM5275 mitigated endothelial Dhh deficiency induced EndoMT in vivo. Of translational relevance, recombinant mouse DHH protein administration reduced atherosclerosis progression, stabilized plaque, and decreased the expression of EndoMT markers in ApoE knockout mice. CONCLUSIONSDesert hedgehog (DHH) is an intrinsic endothelial cell-enriched resilience factor that protects against EndoMT and atherosclerosis by preventing PAI-1 signaling. The present study implicates endothelial DHH as a potential therapeutic target for atherosclerotic cardiovascular disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/729715v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@194c8a3org.highwire.dtl.DTLVardef@1004d52org.highwire.dtl.DTLVardef@d47c73org.highwire.dtl.DTLVardef@368866_HPS_FORMAT_FIGEXP M_FIG C_FIG Desert hedgehog (DHH) is an intrinsic endothelial cell-enriched resilience factor that protects against EndoMT and atherosclerosis by preventing PAI-1 binding to LRP1 and downstream AKT/ERK, as well as SMAD2/3 signaling. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIDHH was identified as a flow-responsive resilience gene that is downregulated by disturbed flow in endothelial cells. C_LIO_LIEndothelial-specific deletion of Dhh promoted endothelial-to-mesenchymal transition (En-doMT) and atherosclerosis. C_LIO_LIDHH directly interacts with PAI-1 and preclude PAI-1 mediated pro-EndoMT signaling. C_LI What Are the Clinical Implications?O_LIDHH maintains endothelial homeostasis during atherosclerosis. C_LIO_LITargeting endothelial DHH-PAI-1 axis may represent a potential therapeutic strategy to reduce EndoMT and limit plaque progression. C_LIO_LILower circulating DHH level may serve as a potential biomarker of endothelial dysfunction and plaque vulnerability in atherosclerotic disease. C_LI

biochemistry↗

bioRxiv2026

Lifespan Trajectories of Resting State EEG power

Trajectories of resting state monopolar EEG power were analyzed using the means, variances, and correlations of data in seven frequency bands obtained from 17 electrodes of the 10-20 system from a sample of 5238 participants from the Collaborative Study on the Genetics of Alcoholism (COGA) with 11906 observations from ages 12 through 70. The values for the individual observations were calculated by standard Fourier transform based methods. In order to make the study more useful for understanding EEG in the general population only subjects who were never diagnosed with alcohol use disorder were included in the study. The trajectories of power show a clear pattern of decrease in all frequency bands and both sexes from ages 12 to 20. Subsequently there is considerable amount of variation between frequency bands and between males and females. In females, the generally lower rate of decrease after age 20 is reversed at age 35 in anterior and central regions in the alpha and beta bands, and stabilized in the theta bands. In males, the decreases continue in the theta and high alpha bands, but there are elements of the reversal in the low alpha and beta bands. Examination of the derivatives of the trajectories show that sex differences begin in the mid-twenties in alpha and beta but not until the mid-thirties in theta. In contrast to the varied power value trajectories, the trajectories of inter-frequency correlation show a pervasive increase with age of the correlation between high alpha and each of the other frequency bands except high beta. This increase is primarily anterior in the alpha-theta correlations and more regionally uniform in the alpha-beta correlations. Sex differences are very small. Trajectories of intra-frequency correlations for comparable between region pairs and within region pairs were generally high and stable across age. This is the first of a series of studies which will provide similar analyses of bipolar EEG power and bipolar EEG coherence in this sample. We know of no other study of resting state EEG power which combines the analysis of power with the analysis of both inter-frequency and intra-frequency correlation of power values.

neuroscience↗

bioRxiv2026

Deep learning-guided design of hydrolases for crystalline PET depolymerization

Poly(ethylene terephthalate) (PET), a ubiquitous polyester used in packaging and textiles, persists in the environment due to its high crystallinity and stability, contributing substantially to global plastic pollution. Enzymatic depolymerization by PET hydrolase (PETase) offers a chemically precise and environmentally sustainable route to convert PET into its monomeric building blocks, enabling recycling. However, practical implementation remains hindered by the rigid, crystalline architecture of PET, which restricts enzyme access and necessitates energy-intensive pretreatment to enable efficient depolymerization. In addition, most PETases achieve only partial depolymerization with low terephthalic acid (TPA) yields and accumulate inhibitory intermediates, while limited thermostability and slow surface kinetics further restrict efficiency. To overcome these barriers, we report VenusPETase, an engineered variant of KbPETase designed using PET-Flow, a state-of-the-art computational framework for PETase engineering. Compared with KbPETase, VenusPETase exhibits a 3.4-fold increase in hydrolytic activity, up to a 27-fold improvement after heat treatment of protein, and a 12 {degrees}C enhancement in thermostability. VenusPETase exhibits rapid degradation across a wide crystallinity range (8%-50%) at 50 {degrees}C, effectively spanning the entire spectrum of commercial PET products. Moreover, VenusPETase outperformed nine high-performance PETases under their respective optimal conditions and degraded untreated PET substrates across 8%-50% crystallinity, producing TPA as over 95% of the released products with minimal intermediate accumulation. X-ray crystallography and molecular dynamics simulations suggest that dynamic modulation, elevated surface electrostatic potential enhance the interaction of VenusPETase with crystalline PET, thereby lowering the hydrolytic energy barrier and improving catalytic performance. We also demonstrate that untreated, postconsumer-PET from nine different products can all be degraded by VenusPETase. The recovered monomers can be directly repolymerized into virgin-quality PET, demonstrating a closed-loop enzymatic recycling process. In a 100 L bioreactor, VenusPETase completely depolymerizes post-consumer crystalline PET (28% crystallinity) within 24 h under 50 {degrees}C. These results establish VenusPETase as a robust biocatalyst that enables efficient, closed-loop recycling of crystalline PET under mild conditions.

biochemistry↗