Biology subjects

Zhang, T.

Publications and source records attributed to Zhang, T..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

Segmented poly(A) tails with microRNA target sites confer tissue-specific regulation for mRNA therapeutics

Targeted delivery and controlled expression of mRNA-LNPs are critical for the development of safe and effective mRNA medicines. However, efficient post-delivery regulation of mRNA-LNP expression remains challenging. In this study, we engineered segmented poly(A) tail variants that function as gene-specific regulatory elements for synthetic mRNAs. Specifically, we inserted microRNA target sites (MTS) for miR-122 or miR-142 at various positions of the poly(A) tail of synthesized luciferase reporter mRNA. These modifications significantly reduced luciferase expression in non-target tissues in vitro and in vivo, demonstrating position-dependent selective expression control. Furthermore, by incorporating triple-MTS sequences for miR-142, miR-126, and miR-148a in all possible combinations at the 5 end of the poly(A) tail, we identified triple-MTS arrangements that simultaneously decrease luciferase activities in three non-target hepatic cell types, while preserving robust expression in hepatocytes. The results highlighted the importance of MTS insertion order for optimal mRNA silencing. These triple-MTS modules significantly expanded the utility of single miRNA-responsive elements, enabling cell-type selective regulation for mRNA therapeutics. By complementing tissue-tropic delivery LNPs, our mRNA cargo regulatory elements have the potential to improve tissue and cell type selectivity as a novel platform for post-delivery regulation of mRNA-LNP.

bioengineering↗

bioRxiv2025

Molecular Architecture and Function Mechanism of Tri-heteromeric GluN1-N2-N3A NMDA Receptors

N-methyl-D-aspartate receptors (NMDARs) play a pivotal role in brain development and synaptic function. Previous studies have focused on GluN1-N2 (2A-2D) and GluN1- N3 (3A and 3B) di-heteromeric (di-) NMDARs, leaving the activation mechanism and stoichiometry of GluN1-N2-N3 tri-heteromeric (tri-) NMDARs largely unexplored. In this study, we employed a two-step affinity-tagged chromatography approach to purify recombinantly expressed GluN1-N2A-N3A tri-NMDARs and determined their cryo-EM structure. Based on the proteoliposome single-channel recording, we discovered GluN1-N2A-N3A can be activated upon co-binding of glycine and glutamate, exhibiting two distinct conductance levels. Furthermore, leveraging structural-based click- chemistry, we introduced photo-crosslinker p-azido-phenylalanine (AzF) into the N- terminal domain of GluN2A and GluN2B, enabling the crosslinking with GluN3A subunit both in vitro and in vivo. These findings provide molecular insights into the subunit arrangement, native architecture and activation mechanism of GluN1-N2-N3A tri- NMDARs and also highlight the complexity of NMDAR assembly and function in the brain.

neuroscience↗

bioRxiv2025

Core gastric microbiota linked to pathogenesis and preserved across age stratified cohorts

Mounting evidences reveal a complex microbial niche in gastric environment. Nevertheless, the phylogenetic stability of gastric microbiota remains undetermined. Here, using full-length 16S rRNA and ITS amplicon sequencing applied to gastric brushes from a pediatric endoscopic cohort (n=243), we identified a core microbiota comprising 11 genera, which clustered into two antagonistic communities (CST1A and CST1B). CST1A was enriched in healthy subjects, while CST1B correlated with gastroduodenal disease. Notably, this bipartite architecture arose through bacterial interactions, with fungal taxa demonstrating minimal integration. Further stratified analyses revealed that H. pylori infection preferentially depleted CST1A taxa. The subclusters exhibited divergent metabolic profiles, with CST1A specializing in motility processes and CST1B in nucleotide/peptidoglycan synthesis. Cross-cohort validation of six datasets (n=1,183) confirmed the persistent relevance of the CST1A-CST1B structure across disease stages. Collectively, our findings define a core gastric microbiota established in early life, linked to gastric disease risk, and persisting into adulthood. HighlightsO_LIIdentification of a gastric bacterial network established in early life. C_LIO_LIDysbiosis in two subclusters within the core microbiota is associated with pediatric peptic ulcers. C_LIO_LIHelicobacter pylori infection reduces the abundance of the core microbiota, with a particularly greater inhibitory effect on CST1A. C_LIO_LIThe core microbiota is preserved across age stratified cohorts and is associated with gastric cancer. C_LI

microbiology↗

bioRxiv2025

Deep Learning for Molecular and Genomic Characterization of Lung Cancer in Never-Smokers Using Hematoxylin and Eosin-Stained Images

Despite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI. Key architectural modifications included a simplified two-layer residual block without bottleneck layers, selective shortcut connections, and a sigmoid-based classification head for independent prediction of each alteration, designed to reduce computational complexity while maintaining predictive accuracy. The model was trained and evaluated on 495 WSIs from the Sherlock-Lung study (70% training with 10% internal test set for 10-fold cross-validation, and 30% held-out validation set for final evaluation). For the held-out validation data, our model achieved high areas under the receiver operating characteristic curve [AUROC] values =0.84-0.93 for detecting 11 features: EGFR, KRAS, TP53, RBM10 mutations, MDM2 amplification, kataegis, CDKN2A deletion, ALK fusion, whole-genome doubling, and EGFR hotspot mutations (p.L858R and p.E746_A750del). Performance was low to moderate for tumor mutational burden (AUROC=0.67), APOBEC mutational signature (AUROC=0.57), and KRAS hotspot mutations (p.G12C: AUROC=0.74, p.G12V: AUROC=0.55, p.G12D: AUROC=0.43). Compared to results from established architectures such as Inception-v3 on the same WSIs, our model demonstrated significantly improved performance for most features. With further optimization, our model could support triaging for molecular testing and inform precision treatment strategies for NS-LUAD patients.

pathology↗

bioRxiv2025

A prognostic signature for lung adenocarcinoma in people who have never smoked

Knowledge of tumor cell dynamics can inform prognosis and treatment yet is largely lacking for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA-seq data from 684 NS-LUAD and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histological data further characterized the subtypes. Steady, marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver genes alterations, is linked to prolonged survival and low immune evasion. Proliferative shows high proliferation markers, TP53 mutations, and gene fusions. Chaotic, with high epithelial-to-mesenchymal transition markers, has the worst prognosis even within stage I tumors. Lacking known molecular or histological characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the overall classification and strongly predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving NS-LUAD prognostication in clinical settings. SignificanceThe transcriptome of 684 lung adenocarcinomas in people who have never smoked (NS-LUAD) identifies three subtypes with different cellular dynamics, and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a clinically applicable tool for treatment decision making in NS-LUAD patients.

genomics↗

bioRxiv2025

RESM: Capturing sequence and structure encoding of RNAs by mapped transfer learning from ESM (evolutionary scale modeling) protein language model

RNA sequences exhibit lower evolutionary conservation than proteins due to their informationally constrained four-letter alphabet, compared to the 20-letter code of proteins. More limited information makes unsupervised learning of structural and functional evolutionary patterns more challenging from single RNA sequences. We overcame this limitation by mapping RNA sequences to pseudo-protein sequences to allow effective transfer training from a protein language model (protein Evolution-Scale Model 2, protESM-2). The resulting RNA ESM (RESM) outperforms 12 existing RNA language models in zero-shot prediction, not only in sequence classification but also in RNA secondary structure and RNA-RNA interaction prediction. Further supervised fine-tuning demonstrates RESMs generalizability and superior performance over the existing models compared across multiple downstream tasks, including mRNA ribosome loading efficiency and gene expression prediction, despite RESM being trained exclusively on noncoding RNAs. Moreover, RESM can generalize to unseen sequences beyond its 1,024-nucleotide training limit, achieving 81.3% improvement over state-of-the-art methods in supervised secondary structure prediction for RNAs up to 4,000 nucleotides, limited only by the available GPU memory, while providing >1000-fold speedup compared to MSA-based approaches. RESM provides a robust foundation for deciphering RNA sequence-structure-function relationships, with broad implications for RNA biology.

bioinformatics↗

bioRxiv2025

CHAMP1 Complex Promotes Heterochromatin Assembly and Reduces Replication Stress

Replication stress is a major driver of genomic instability and a hallmark of cancer cells. Although dynamic heterochromatin remodeling has been implicated in replication stress response, the precise mechanisms remain unclear. Here, we identify the CHAMP1 complex, composed of CHAMP1, POGZ, HP1, and the H3K9 methyltransferase SETDB1, as a critical regulator of heterochromatin assembly at stalled replication forks. Upon replication stress, the CHAMP1 complex is recruited to stalled forks where it facilitates H3K9me3 deposition, creating a repressive chromatin environment that shields replication forks from MRE11-mediated degradation. The complex promotes the recruitment of the origin recognition complex (ORC) to sites of replication stress, such as the telomeric heterochromatin in alternative lengthening of telomeres (ALT)-positive tumor cells, thereby supporting efficient telomeric DNA replication. Loss of CHAMP1 disrupts ORC2 recruitment and impairs fork restart, leading to increased micronuclei formation and heightened sensitivity to replication stress. Notably, CHAMP1 deficiency induces synthetic lethality with FANCM inhibition in ALT-positive tumor cells, and the CHAMP1 complex is essential for the survival of CCNE1-amplified ovarian cancers. These findings uncover a chromatin-based mechanism of replication fork stabilization and suggest that CHAMP1 may represent a candidate therapeutic vulnerability in cancers with elevated replication stress.

molecular biology↗

bioRxiv2025

Uranium mining fuels evolution in deep groundwater microbiomes

Deep-biosphere microbes have been underpinning global biogeochemical cycles throughout Earth history, yet their evolutionary responses to long-term anthropogenic disturbance remain poorly understood. Neutral-pH in-situ leaching (ISL), the dominant uranium-mining strategy, imposes persistent radiochemical and redox stress offering rare natural experiments across decades for observing subsurface microbial evolution. Here, by exploiting metagenomics and metatranscriptomics in examining microbial responses through various mining stages, we show that neutral U ISL causes substantial changes in microbial communities spanning 2,294 strains accompanied by diversification and selection of active microbial species. These changes occurred during elevated dissolved uranium and radiological activity which correlated with a substantial transcriptional change in energy metabolism, and with an overexpression of specific genes for oxidative-stress defence and DNA-repair pathways. Increased nucleotide diversity in 101 out of 392 species clusters and nonsynonymous/synonymous polymorphism ratios greater than one in 139 species clusters were predominantly observed in metatranscriptomes with elevated radiation, highlighting positive selection of transcriptionally active populations. These findings demonstrate that neutral U ISL drives functional and genetic diversification of subsurface microbiomes, revealing a dynamic and evolutionarily responsive deep biosphere.

genomics↗

bioRxiv2025

MViewEMA: Efficient Global Accuracy Estimation for Protein Complex Structural Models Using Multi-View Representation Learning

Estimation of model accuracy (EMA) is crucial for the reliable application of protein structure models in biological research. With the rapid advancement of protein structure prediction techniques and the explosive growth of predicted structural data, existing EMA methods struggle to balance computational efficiency with estimation performance. Here, we present MViewEMA, a single-model EMA method that leverages a multi-view representation learning framework to integrate residue-residue interaction features from micro-environment, meso-environment, and macro-environment levels for global accuracy assessment of protein complex models. Benchmark results demonstrate that MViewEMA outperforms state-of-the-art EMA methods in global accuracy assessment, achieving more than a 10-fold improvement in computational efficiency compared to our previous method, DeepUMQA3. This method enables efficient selection of high-quality protein complex models from large-scale structural datasets and achieved top performance in model selection tracks during the CASP16 blind test, demonstrating its potential to enhance the accuracy of complex structure prediction when integrated into modern frameworks such as AlphaFold-Multimer, AlphaFold3, and DiffDock-PP.

bioinformatics↗

bioRxiv2025

ProteoMeter: A pipeline for integrating multi-PTM and limited proteolysis data to reveal modification-structure coupling at the residue level

Systemic perturbations trigger extensive changes across the proteome-altering protein abundance, post-translational modifications (PTMs), conformational states, and complex assembly. Interpreting these effects demands computational pipelines capable of integrating diverse proteomics modalities, such as multi-PTM profiling, limited proteolysis mass spectrometry (LiP-MS), and cross-linking mass spectrometry (XL-MS), within a unified and interoperable framework. Because instrument data are quantified at the peptide level, mapping these measurements to individual residue or modification site is essential for biologically meaningful interpretation. We introduce ProteoMeter, an open-source Python library designed to integrate multi-modal proteomics datasets and map them to single-residue resolution using a standardized coordinate framework. We showcase its capabilities in a combined multi-PTM and LiP-MS analysis profiling the proteomic response to human coronavirus 229e (HCoV-229E) infection.ProteoMeteris actively maintained and is freely available-including all source code and figure-generation scripts-at the following repository: https://github.com/PNNL-Predictive-Phenomics/ProteoMeter.

systems biology↗

bioRxiv2025

Much higher covariation with foveation timing by superior colliculus than primary visual cortical neuronal activity

The superior colliculus (SC) is a potent driver for orienting movements, but it also possesses short-latency visual responses. Even though such responses largely derive from direct primary visual cortical (V1) input, their relationship to V1 activity has never been analyzed with the same experimental subjects and stimuli. Here, we revisited pioneering observations that trial-by-trial variability in either SC or V1 visual responses may predict eye movement timing variability. We found that V1 covariation with behavioral variability was virtually non-existent in comparison to the SC, whether with respect to visual response onset latency, visual response strength, or pre-stimulus state. By far, our largest predictor of behavioral variability was visual response strength in SC visual-motor neurons. These results suggest that the SC reformats its sensory inputs for exploitation of the SCs proximity to the motor periphery; V1 aids in jumpstarting the sensing process, but the SC much more directly supports visually-driven orienting.

neuroscience↗

bioRxiv2025

Light-up nanostructures with allosteric DNA mimics of GFP

In this study, we present a systematic approach for the rational design of synthetic allosteric DNA aptamers. This methodology enables precise control over the allosteric ON-OFF transition in fluorescent DNA aptamers, allowing for the engineering of aptamers with highly tunable fluorescent properties. When combined with toehold-mediated strand displacement, we have developed a series of allosteric aptamers in which the target sequence functions as a specific allosteric modulator. Furthermore, these aptamers have been applied in synthetic DNA computing and in the construction of responsive nanostructures that light up upon activation.

synthetic biology↗

bioRxiv2025

Vertical Transmission of Bovine H5N1 during Pregnancy and Lactation in Mice

Avian influenza H5N1 has pandemic potential and historically has caused more severe disease in pregnant women than the general population. With increasing transmission of H5N1 detected among placental mammals, animal models are necessary for testing countermeasures, including during pregnancy. Pregnant outbred mice were infected with a contemporary strain of bovine H5N1 during the second or third trimester equivalent. Second trimester infection caused in utero transmission, with infectious virus detected in the uterus, placenta, and fetus. Birth following third trimester infection resulted in offspring with decreased size, neurodevelopmental delays, and adolescent behavioral impairments, with infectious virus detected in the neonatal milk ring and lungs, as well as mammary tissues. H5N1 viral protein colocalized with trophoblast cells in the placenta and epithelial cells in mammary tissue that spatially overlapped with lectins for 2,3-linked SA. With the pandemic potential of H5N1, our vertical transmission model in placental mammals is essential for understanding viral spread and evaluating treatments during pregnancy.

microbiology↗

bioRxiv2025

Integrated landscape genomics reveals biogeography and climate-driven local adaptation in high-altitudes

High-elevation environments harbor unique species adapted to altitudinal and environmental extremes. However, due to logistical challenges of sampling such species throughout their distribution across steep and varied terrains, studies often lack comprehensive evidence to understand patterns of population divergence, local adaptation, and how current adaptations may influence future vulnerability. In this study, we utilized Tibetan Partridge (Perdix hodgsoniae), a high-altitude endemic bird found between 2800-5000 meters across the arid western and humid northeastern regions of the Sino-Himalayan landscape. This regions complex topography-characterized by tall mountains, deep valleys, and contrasting climatic conditions provided opportunities for investigating population divergence, local adaptation, and climate-related vulnerability. We integrated population-scale whole-genome sequencing with ecological, climatic, landscape, and morphological data to examine current patterns of local adaptation and forecast future risks. Our findings show that both biogeographic barriers and climatic variation drive rapid population divergence in P. hodgsoniae, reflected in distinct morphological traits and population genetic structure. Western populations, inhabiting dry and fragmented landscapes, exhibit adaptations to temperature extremes with low genetic diversity, reduced habitat suitability, limited gene flow, and weak connectivity--factors that increase their vulnerability to future environmental changes. In contrast, northeastern populations, living in more humid regions, show genetic adaptations linked to precipitation, maintain high genetic diversity and habitat connectivity, and may serve as evolutionary refugia under future climate scenarios. This study underscores the value of integrating genomic, ecological, and landscape data to reveal mechanisms of divergence and adaptation, and to develop robust predictions for conservation planning under rapidly changing environmental conditions.

evolutionary biology↗

bioRxiv2025

Visual-like Template Diffusion: Boosting Single-Sequence Protein Structure Prediction by Adapting Image Diffusion Models

Single-sequence protein structure prediction has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here, we propose a visual-like 2D geometric template* diffusion method, named TDFold, to generate high-quality pairwise geometries (including pairwise distances and orientations) for achieving accurate and highly efficient single-sequence 3D structure prediction for proteins. Given a protein sequence, TDFold initially generates high-quality inter-residue geometries from a probabilistic diffusion perspective. Since inter-residue geometries can be encoded as multi-channel feature matrices, analogous to image feature maps, we construct an image-level 2D geometric template diffusion module by adapting the stable diffusion (SD) model from text-vision generation to sequencegeometry diffusion for proteins. Subsequently, a lightweight sequencegeometry collaborative learning (SCL) network is constructed to facilitate accurate and efficient protein structure prediction. As a result, TDFold possesses three highlights: (i) better single-sequence prediction performance: TDFold greatly outperforms existing protein language models (PLMs, e.g. ESMFold and OmegaFold) and homology-based methods (e.g. AlphaFold2, AlphaFold3 and RoseTTAFold) on homologyinsufficient datasets such as Orphan and Orphan25, while also achieving promising results on the popular CASP14, CASP15 and CASP16 benchmarks; (ii) low resource consumption: By utilizing the lightweight SCL architecture, the GPU memory consumption of TDFold is generally lower than that of popular methods such as AlphaFold2 and ESMFold; (iii) higher efficiency in training and inference: TDFold can be trained within a week using a single NVIDIA 4090 GPU. Furthermore, the inference time of TDFold is significantly shorter (about 10x to 100x) than that of existing methods (ESMFold, AlphaFold2 and AlphaFold3) for long protein sequences. This work demonstrates the effectiveness of leveraging powerful vision diffusion models to enhance protein 2D geometric template generation, thereby establishing a new paradigm for single-sequence protein structure prediction. It also accelerates protein-related research, particularly for resource-limited universities and academic institutions. The code has been released to speed up biological research.

bioinformatics↗

bioRxiv2025

Floral innovation through modifications in stem cell peptide signaling.

Understanding how evolution shapes genetic networks to create new developmental forms is a central question in biology. Asteraceae (sunflower family) comprise 10% of flowering plants and have capitula, a novel flowering shoot (inflorescence) that mimics a single flower (1, 2). During capitulum development, shoot stem cells undergo prolonged proliferation relative to other species (3, 4). Here we show that capitulum evolution paralleled decreases in CLAVATA3 peptide (CLV3p) signaling, a conserved repressor of stem cell proliferation. Asteraceae CLV3p displays reduced receptor binding and downstream transcriptional outputs. Reversion of CLV3 to a more active form impairs Asteraceae stem cell regulation and capitulum development. Lastly, we trace CLV3 evolution across the Asterales allowing inferences on capitulum evolution. Our findings reveal novel evolutionary mechanisms in plant reproduction and suggest approaches for engineering crops.

plant biology↗

bioRxiv2025

Longitudinal Imaging of Experimental Intracerebral Hemorrhage Pathology with Iodine-enhanced Micro-CT

BackgroundIodine-enhanced micro-computed tomography (Micro-CT) enables high-resolution three-dimensional imaging of brain architecture. This study aimed to characterize both acute and chronic pathological changes following intracerebral hemorrhage (ICH) using iodine-enhanced micro-CT. MethodExperimental ICH was induced in 8- to 10-week-old C57BL/6 mice (n = 76) via stereotaxic injection of either 0.075 U collagenase IV or 30 l autologous blood. Iodine-enhanced micro-CT imaging was performed at 4 hours, 1, 3, and 7 days after intracerebral hemorrhage post-ICH to evaluate hematoma formation and erythrolysis. Chronic alterations, including ventriculomegaly and ipsilateral lesion, were assessed at 28 days post-ICH. In parallel, MRI was conducted at 1, 7, and 28 days following autologous blood injection, followed by micro-CT, to facilitate cross-modality quantitative analysis. Lesion volumes were compared between imaging modalities over time. ResultsMicro-CT enabled quantification of hematoma volume and erythrolysis in ICH models. Hematomas extended along perivascular pathways toward the cerebral surface in both collagenase- and autologous blood-induced ICH models. At 28 days post-ICH, micro-CT detected ventriculomegaly and hypodense lesions without concurrent expansion of the choroid plexus. Lesion volume measurements derived from micro-CT correlated with those from MRI, enabling quantitatively assessment of iron deposition and hematoma size alterations after ICH. ConclusionIodine-enhanced micro-CT provides a robust and high-resolution imaging platform for evaluating hematoma evolution, hemolysis, ventricular enlargement, and chronic brain lesions in experimental ICH. When integrated with MRI, these multimodal imaging approaches enhance the characterization of both hematoma volume and iron deposition following ICH.

neuroscience↗

bioRxiv2025

SORLA upregulation suppresses global pathological effects in aged tauopathy mouse brain

A role for the trafficking receptor SORLA in reducing A{beta} levels has been well-established, however, relatively little is known with respect to whether and how SORLA can potentially affect tau pathology in vivo. Here, we show that transgenic SORLA upregulation (SORLA TG) can reverse pathological effects in aged PS19 (P301S tau) mouse brain, including tau phosphorylation and seeding, ventricle dilation, synapse loss, LTP impairment and glial hyperactivation. Proteomic analysis indicates reversion of PS19 profiles in PS19/SORLA TG hippocampus, including pathological changes in synapse-related proteins as well as key drivers of synaptic dysfunction such as Apoe and C1q. snRNA-seq analysis reveals suppression of PS19- signatures with SORLA upregulation, including proinflammatory induction of Plxnb1/Plxnb2 in glia. Tau seeding and aggregation, neuroinflammation, as well as PlxnB1/B2 induction are exacerbated in PS19 hippocampus with SORLA deletion. These results implicate a global role for SORLA in neuroprotection from tau toxicity in PS19 mouse brain.

neuroscience↗