Biology subjects

Hu, J.

Publications and source records attributed to Hu, J..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

Object categorization based on abstract concepts by monkeys, humans, and vision models

Humans categorize objects at multiple levels of abstraction--animate versus inanimate, big versus small, and many other attributes. Despite its apparent challenge, the advent of deep neural networks (DNNs) has demonstrated that complex visual processing alone can support such classification without language or human-specific knowledge. This raises a natural question: to what extent can non-human primates, without language, perform such categorization? Although basic object-recognition behavior in monkeys such as similarity judgment has been extensively studied, their ability to classify objects across diverse rules remains poorly characterized. Here, we developed a task paradigm that enabled us to train monkeys on a large battery of binary classification tasks using natural object images, spanning more than 10 rules, such as animate versus inanimate, natural versus man-made objects, and mammalian versus non-mammalian animals. Monkeys acquired each rule in a few days, generalized the learned rules to new images, and exhibited error patterns consistent with human judgments. At the same time, their classification performance correlated more strongly with that of visual DNNs trained without language input, whereas human performance was better explained by language-informed DNNs. These results provide an important benchmark for the capacity of biological neural networks to perform image classification without language and human-specific knowledge.

neuroscience↗

bioRxiv2025

A biofilm activator MmtA1 modulates multi-drug resistance in mycobacteria

When exposed to the antibiotic-induced stress, Mycobacterium species have the ability to survive by forming protective multicellular structures known as biofilms. However, the underlying mechanism of biofilm formation in response to antibiotics is not fully understood. In this study, we identified a transcriptional factor, MmtA1, which responds to the first-line anti-tuberculosis drug isoniazid and regulates genes involved in sugar and lipid transportation. This leads to increased biofilm formation and multi-drug resistance in M. smegmatis. These findings suggest that isoniazid may act as a molecular messenger, working in coordination with transcriptional regulatory factors to induce multidrug resistance under chemotherapy. ImportanceThis study elucidates a novel transcriptional regulatory mechanism by which Mycobacterium smegmatis develops multidrug resistance in response to the anti-tuberculosis drug isoniazid. By linking antibiotic stress to transcriptional regulation and metabolic adaptation, this work provides new insights into drug-induced tolerance in mycobacteria, suggesting antibiotic functional as inducer of biofilm formation, triggering a coordinated transcriptional response to enhance bacterial survival under antibiotic pressure.

microbiology↗

bioRxiv2025

MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis

Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge. Traditional machine vision approaches, despite their ability to detect spontaneous behaviors, fall short due to limited interpretability and reliance on manual labeling, which restricts the exploration of the full behavioral spectrum. Here, we introduce MouseGPT, a Vision-Language Model (VLM) that integrates visual cues with natural language to revolutionize mouse behavior analysis. Built upon our first-of-its-kind dataset--incorporating pose dynamics and open-vocabulary behavioral annotations across over 42 million frames of diverse psychiatric conditions--MouseGPT provides a novel, context-rich method for comprehensive behavior interpretation. Our holistic analysis framework enables detailed behavior profiling, clustering, and novel behavior discovery, offering deep insights without the need for labor-intensive manual annotation. Evaluations reveal that MouseGPT surpasses existing models in precision, adaptability, and descriptive richness, positioning it as a transformative tool for ethology and for unraveling complex behavioral dynamics in animal models.

animal behavior and cognition↗

bioRxiv2025

mcDETECT: Decoding 3D Spatial Synaptic Transcriptomes with Subcellular-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) is a powerful tool for studying the molecular basis of brain diseases. However, most current analyses focus only on nuclear or somatic expression, overlooking distally localized mRNAs, known as the "dark transcriptome." These transcripts, which make up nearly 40% of the brain transcriptome, are packaged into RNA granules and transported to polarized neuronal structures critical for neuronal function. Here, we present mcDETECT, a machine learning framework that leverages in situ ST (iST) data to detect RNA granules and resolve their RNA composition. Applying mcDETECT to simulated and real datasets from multiple iST platforms, we demonstrate robust granule detection and molecular subtyping. Granule-informed downstream analysis further revealed functionally distinct neuronal substates not captured by somatic expression alone. In an Alzheimers disease (AD) mouse model, mcDETECT uncovered alterations in RNA granule distributions and associated neuronal substates prior to measurable neuronal loss, highlighting potential therapeutic targets for early AD pathology.

bioinformatics↗

bioRxiv2025

Structural defects in amyloid-β fibrils drive secondary nucleation

The nucleation of amyloid fibrils from monomeric protein, catalyzed by the surface of existing fibrils, is an important driver of many disorders such as Alzheimers and Parkinsons diseases. The structural basis of this secondary nucleation process, however, is poorly understood. Here, we ask whether secondary nucleation sites are found predominantly at rare growth defects: defects in the fibril core structure generated during their original assembly. We first demonstrate using the specific inhibitor of secondary nucleation, Brichos, that secondary nucleation sites on Alzheimers disease-associated fibrils composed of A{beta}40 and A{beta}42 peptides are rare compared to the number of protein molecules they contain. We then grow A{beta}40 fibrils under conditions designed to eliminate most growth defects while leaving the regular fibril morphology unchanged, and confirm the latter using cryo-electron microscopy. We measure both the ability of these annealed fibrils to promote secondary nucleation and the stoichiometry of their secondary nucleation sites, finding that both are greatly reduced as predicted. Re-analysis of published data for other proteins suggests that fibril growth defects that expose monomer planes or other structural units may also drive secondary nucleation generally, across most or all amyloids. These findings could unlock structure-based drug design of therapeutics that aim to halt amyloid disorders by inhibiting secondary nucleation sites.

biophysics↗

bioRxiv2025

Cortactin regulates metastatic dormancy of circulating tumor cells by suppressing mTOR-dependent senescence

Metastatic dormancy often refers to the stable cell cycle arrest of disseminated tumor cells (DTCs) at distant sites. However, whether circulating tumor cells (CTCs) in the blood microenvironment can enter a dormant state prior to extravasation and becoming DTCs remains unclear. Using patient-derived melanoma CTC lines and animal explant models (CDX), we identified a previously unrecognized role of the cytoskeletal regulator cortactin (encoded by CTTN) in controlling mTOR/p53-dependent senescence and metastatic dormancy. Cortactin was localized to Rab7-postive endosomes and engaged in late endosomal tethering and homeostasis. The depletion of cortactin resulted in the accumulation of aberrantly enlarged late endosomal aggregates that were positive for Rab7 and mTOR. The mTOR protein complex was accumulated and activated within these abnormal vesicular structures, leading to robust p53 activation through phosphorylation at serine (S) 15 and S33 sites. Consequently, melanoma CTCs underwent G0/G1 cell cycle arrest and entered cellular senescence. This unusual oncogene-induced senescence (OIS) mechanism was characterized by SASP upregulation, {beta}-galactosidase activity, depletion of Ki-67 and Lamin B1, and elevated mitochondrial ROS (mtROS) levels. Notably, a positive feedback loop between p53 and mtROS was essential for maintaining stable senescence in CTCs. In preclinical CDX mouse models, we developed a sequential therapeutic strategy combining cortactin depletion with anti-Bcl-xL senolytic drugs. Such "One-two punch" treatment strategy effectively eliminated viable CTCs and suppressed metastatic tumor growth in vivo. Thus, targeting cortactin to induce CTC senescence, followed by senolytic therapy, may represent a promising strategy to block CTC-mediated metastatic progression.

cancer biology↗

bioRxiv2025

Pangenome analyses of tea plant reveal structural variations driven gene expression alterations and agronomic trait diversification

Tea plants, one of the worlds most economically significant beverage crops, exhibit extensive genetic diversity and a rich array of secondary metabolites. While structural variations (SVs) are key drivers of phenotypic innovation, their regulatory roles in transcriptional networks and agronomic trait diversification remain underexplored in this perennial crop. We constructed a pangenome from 21 representative tea accessions and their wild relatives. Comparative analysis revealed that wild relatives harbor a greater abundance of gibberellin-related genes, whereas cultivated accessions exhibit an expansion of defense-related genes. We identified 522,428 non-redundant SVs, primarily insertions/deletions (73.56%), which were enriched in promoter regions. These SVs influenced the expression of genes governing flavor compounds, including those involved in flavonoid, amino acid, and terpenoid biosynthesis. Population SV analysis of 275 tea accessions uncovered three haplotypes in the ANS3 gene promoter region, with Hap1, containing a 192 bp insertion, predominantly found in wild relatives but largely lost in modern cultivars. This insertion enhanced CtANS3 expression, increasing anthocyanin accumulation. Additionally, a 159 bp insertion in the CtLRR1 promoter reduced resistance to Colletotrichum gloeosporioides in wild relatives. Our findings underscore SVs as pivotal regulators of genomic diversity, flavor differentiation, and adaptive evolution in tea plant domestication.

genomics↗

bioRxiv2025

Deep contrastive feature compression with classical machine learning enables ligand discovery through efficient triage of large chemical libraries

Improving in silico compound-protein interaction (CPI) predictability is critical for productive drug discovery. Current deep learning approaches largely rely on end-to-end models trained on limited labeled CPI datasets, overlooking the representational power of large-scale biochemical foundation models. We present COMRADE (Contrastive Multirepresentation Accelerated Docking Engine), a hybrid virtual screening framework that accelerates docking by triaging compounds using CE-Screen (Contrastive Embedding-Screen). CE-Screen leverages seven high-dimensional pretrained representations - including those from protein language models and molecular transformers, along with an original physics-based interaction potential encoding - for rapid first-pass screening [~]100x faster than docking. Its contrastive compression neural network maps these inputs onto a single compact, discriminative representation optimized for CPI prediction via a lightweight ensemble classifier. CE-Screen outperforms state-of-the-art end-to-end models by up to 111.11% on retrospective benchmarks and is successfully used to triage [~]10.8 million compounds against five targets, yielding novel hits for each one - including a new scaffold for the branched-chain ketoacid dehydrogenase kinase (BCKDK), an understudied yet high-value target in metabolic disease and oncology.

bioinformatics↗

bioRxiv2025

DisConST: Deciphering Spatial Domains Using Distribution-aware Contrastive Learning for Spatial Transcriptomics

Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling the gene expression information together with the spatial locations, resulting in suboptimal accuracy. We introduce DisConST (Distribution-aware Contrastive Learning for Spatial Transcriptomics), a novel deep-learning method designed to improve spatial domain detection within spatial transcriptomics datasets. DisConST addresses key challenges, such as the high dropout rates and the complex integration of spatial and gene expression data, by incorporating contrastive learning strategies that are aware of the underlying data distributions. It employs the zero-inflated negative binomial (ZINB) distribution, along with graph contrastive learning, to generate more informative latent representations. These representations efficiently integrate spatial positions, transcriptomic profiles, and cell-type proportions within spots. We validated DisConST across diverse ST datasets of tissues, organs, and embryos from various sequencing platforms in both normal and disease states. Our results consistently demonstrated that DisConST achieves superior spatial domain recognition accuracy compared to existing state-of-the-art methods. Furthermore, our experiments highlighted the utility of DisConST in advancing research on tissue organization, embryonic development, and tumor immune microenvironment dissection. The source code for DisConST is freely available at https://github.com/Zhenpm/DisConST/.

bioinformatics↗

bioRxiv2025

Characterization of MEK1/2 Degraders Uncovers a Kinase-Independent Role for MEK1/2 in the Stabilization and Maturation of CRAF

Altered MAPK signaling frequently occurs in human disease. MEK1 and MEK2 (MEK1/2) are central protein kinases in the MAPK signaling cascade that phosphorylate ERK1/2 promoting cell growth. MEK1/2 degraders offer a strategy to characterize both kinase-dependent and independent functions of MEK1/2. Here, we discovered that MEK1/2 degradation, but not kinase inhibition, caused the subsequent degradation of upstream kinase CRAF via a cell-intrinsic mechanism. MEK1/2 binding to CRAF, but not MEK1/2 catalytic activity, was required for CRAF protein stability and maturation to a functional kinase. In the absence of MEK1/2, a minor pool of newly synthesized immature CRAF that had anti-apoptotic functions remained. Finally, we showed that a stable primed CRAF-MEK1/2 signaling complex existed in cells that required RAS binding to potentiate MEK-ERK phosphorylation. Together, weve discovered a previously unrecognized kinase-independent function of MEK1/2, while contextualizing MEK1/2 as an integral component of the CRAF activation cycle beyond the conventional CRAF-MEK kinase- substrate paradigm.

cancer biology↗

bioRxiv2025

Probing the substrate binding-induced conformational change of a ZIP metal transporter using a sandwich ELISA

Zrt-/Irt-like proteins (ZIPs), a family of divalent metal transporters, are crucial for maintaining the homeostasis of zinc, an essential trace element involved in numerous biological processes. While extensive research on the prototypical ZIP from Bordetella bronchiseptica (BbZIP) have suggested an elevator transport mechanism, the dynamic conformational changes during the transport cycle have not been thoroughly studied. In this work, we developed a sandwich ELISA using a custom anti-BbZIP monoclonal antibody to investigate the conformational change induced by the metal binding to the transport site. This was achieved by determining the accessibility of a cysteine residue introduced at a position exposed to the solvent only when the transporter adopts an outward-facing conformation. This assay allowed us to report the dissociation constants of BbZIP for Zn2+ and Cd2+ at low and sub-micromolar levels, respectively. Notably, the installation of a positive charge at the M2 site drastically reduced metal binding at the M1 site, consistent with an auxiliary role for the M2 site in metal transport. We also demonstrated that this assay can be used to rapidly screen variants for subsequent structural study. We anticipate that other transporters where substrate binding induces large conformational changes can also be studied using this method.

biochemistry↗

bioRxiv2025

Modulation of TTR Gene Expression in the Eye using Modified Duplex RNAs

Small interfering RNAs (siRNAs) are a proven therapeutic approach for controlling gene expression in the liver. Expanding the clinical potential of RNA interference (RNAi) requires developing strategies to enhance delivery to extra-hepatic tissues. In this study we examine inhibiting transthyretin (TRR) gene expression by short interfering RNAs (siRNAs) in the eye. Anti-TTR siRNAs have been developed as successful drugs to treat TTR amyloidosis. When administered systemically, anti-TTR siRNAs alleviate symptoms by blocking TTR expression in the liver. However, TTR amyloidosis also affects the eye, suggesting a need for reducing ocular TTR gene expression. Here, we demonstrate that C5 and 2-O-linked lipid-modified siRNAs formulated in saline can inhibit TTR expression in the eye when administered locally by intravitreal (IVT) injection. Modeling suggests that length and accessibility of the lipid chains contributes to in vivo silencing. GalNAc modified anti-dsRNAs also inhibit TTR expression, albeit less potently. These data support lipid modified siRNAs as an approach to treating the ocular consequences of TTR amyloidosis. Inhibition of TTR expression throughout the eye demonstrates that lipid-siRNA conjugates have the potential to be a versatile platform for ocular drug discovery. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/642595v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1e9ad27org.highwire.dtl.DTLVardef@67067borg.highwire.dtl.DTLVardef@1da7eadorg.highwire.dtl.DTLVardef@1b2a552_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Enzyme Co-Scientist: Harnessing Large Language Models for Enzyme Kinetic Data Extraction from Literature

The extraction of molecular annotations from scientific literature is critical for advancing data-driven research. However, traditional methods, which primarily rely on human curation, are labor-intensive and error-prone. Here, we present an LLM-based agentic workflow that enables automatic and efficient data extraction from literature with high accuracy. As a demonstration, our workflow successfully delivers a dataset containing over 91,000 enzyme kinetics entries from around 3,500 papers. It achieves an average F1 score above 0.9 on expert-annotated subsets of protein enzymes and can be extended to the ribozyme domain in fewer than 3 days at less than $90. This method opens up new avenues for accelerating the pace of scientific research.

biochemistry↗

bioRxiv2025

The stress response factor SigH mediates intrinsic resistance to multiple antibiotics in Mycobacterium abscessus

Mycobacterium abscessus (Mab) causes pulmonary diseases with limited treatment options due to its high level of intrinsic resistance to available drugs. Mab possesses complex and poorly understood drug resistance mechanisms. Identifying new drug targets and gaining a deeper understanding of drug resistance mechanisms are essential for discovering novel therapeutic alternatives. Here, we investigated the role of a putative sigma factor SigH in intrinsic multi-drug resistance in Mab. Mab SigH shares an 84% peptide sequence identity with Mycobacterium tuberculosis (Mtb) SigH, a well-known stress response protein and global transcriptional regulator. We constructed a sigH gene deletion strain of Mab ({Delta}sigH) and complemented strains by expressing either Mab sigH (CPMabsigH) or Mtb sigH (CPMtbsigH) in {Delta}sigH. The {Delta}sigH strain exhibited hypersensitivity to a broad range of antibiotics, including levofloxacin, moxifloxacin, tigecycline, tetracycline, amikacin, vancomycin, and rifabutin and all complemented strains restored the drug resistance phenotype. Additionally, {Delta}sigH showed increased sensitivity to oxidative and heat stress compared to the wild-type Mab and complemented strains. Transcriptomic analysis revealed that deletion of sigH disrupted the balance of gene expression, primarily elevating the expression of genes encoding YrbE and MCE family proteins and downregulating genes expressing ABC-type transporters, sigma and anti-sigma factors and other genes associated with antimicrobial resistance. Collectively, our findings indicate that SigH is a key regulator of global gene expression in response to environmental stresses, including antimicrobial treatment, and is crucial for the intrinsic drug resistance of Mab. SigH represents a promising target for the development of novel therapeutic strategies against Mab infections.

molecular biology↗

bioRxiv2025

Zinc Finger Repressors mediate widespread PRNP lowering in the nonhuman primate brain and profoundly extend survival in prion disease mice

Prion disease is a rapidly progressing and invariably fatal neurodegenerative disorder with no approved treatment. The disease is caused by the self-templated misfolding of the prion protein (PrP) into toxic species, ultimately leading to neurodegeneration and death. We evaluated a novel epigenetic regulation approach using Zinc Finger Repressors (ZFRs) to ablate PrP expression at the transcriptional level. When delivered using adeno-associated virus (AAV), ZFRs potently and specifically reduced prion mRNA expression by >95% in vitro and to near undetectable levels within single neurons in vivo. In wildtype mice, ZFRs stably lowered neuronal PrP expression throughout the central nervous system for at least 17 months. In mice inoculated with misfolded PrP, AAV-ZFRs given at either early or late disease stages profoundly extended lifespan, significantly reduced PrP in the brain, and improved an array of molecular, histological, biomarker, and behavioral readouts. Finally, we delivered a ZFR targeting the human prion gene (PRNP) to cynomolgus monkeys using a novel blood-brain-barrier penetrant AAV capsid. Extensive bulk and single-cell assessments revealed widespread ZFR expression and PRNP repression in all 35 brain regions assessed, providing the first demonstration of epigenetic regulation across the nonhuman primate neuraxis following a single intravenous (IV) dose. These results highlight the potential of a one-time IV administered ZFR treatment for prion disease and other neurological disorders.

neuroscience↗

bioRxiv2025

A Molecular Dynamics Protocol for Predicting EGFR Overactivation and Its Application to the Allosteric Mutations S768I, S768N, and D761N

Hyperactivation caused by mutations in the Epidermal Growth Factor Receptor (EGFR) kinase domain is implicated in various diseases, including cancer. However, the structural mechanisms underlying overactivation in many EGFR mutations remain poorly understood, and exploring these mechanisms through conventional experiments or in silico simulations is often labor- and cost-intensive. Here, we establish a Molecular Dynamics (MD) protocol capable of rapidly revealing EGFR mutant modes of action using multiple short simulations. We first simulated wild-type EGFR and the well-studied oncogenic mutations L858R and T790M/L858R under different simulation conditions, to derive a protocol which could recapitulate their experimentally established behavior. We then applied this protocol to three rare EGFR mutations: S768I, S768N, and D761N. Experimental studies have suggested that S768I and D761N are oncogenic, whereas S768N is likely a neutral mutation that does not significantly alter EGFR activity. Our simulation results were consistent with these functional indications and provided the corresponding molecular bases - S768I and S768N affect the orientation and stability of the catalytically important C-helix, while D761N introduces a new hydrogen bonding network between the C-helix and activation loop. Collectively, the protocol presented here provides a robust and rapid framework for characterizing EGFR mutation mechanisms and is readily adaptable to novel or uncharacterized variants.

bioinformatics↗

bioRxiv2025

Quantitative and large-scale investigation of human TCR-HLA Cross-Reactivity

The interaction between Human leukocyte antigens (HLA) and T cell receptor (TCR) is essential for adaptive immune recognition. While it is known that one TCR can map to multiple HLA alleles, the extent of this cross-reactivity remains poorly understood. Here, we introduce THNet, a TCR-based HLA similarity inference method, and performed a comprehensive analysis of HLA-TCR cross-reactivity. This method is built upon clustering over 47 million TCRs to identify over 9 million significant HLA-TCR pairs. We created similarity networks for both class I and class II HLA alleles, illustrating how peptide cross-presentation contributes to HLA-TCR cross-reactivity. This analysis revealed novel disease-susceptibilities missed by single-HLA enrichment analyses, especially in the Black populations. Finally, we demonstrated that THNet prioritized optimal HLA mismatch candidates for organ transplantation, thereby improving patient survival rates. Our investigation of HLA-TCR cross-reactive network might provide useful insights for autoimmune risk prediction and better transplantation outcomes. One Sentence SummaryWe introduced THNet, a large-scale TCR-based HLA similarity mapping network that uncovers previously unrecognized cross-reactivity patterns across HLA alleles and provides valuable insights into their influence on disease susceptibility and graft rejection.

bioinformatics↗

bioRxiv2025

Characterizing trajectory-like chromatin architectures with Fun2

Chromatin is intricately folded into dynamic 3D structures, orchestrating key DNA metabolic processes. DNA replication, a core chromatin metabolic event, is tightly linked to these chromatin architectures. Recently, a replication-associated chromatin interaction structure was discovered as fountains via Replication-associated in situ Hi-C (Repli-HiC), supporting that replication forks remain spatially coupled from initiation to termination. Chromatin fountains are pivotal for understanding DNA replication within the complex chromatin landscape. However, the characteristics of these fountains can vary due to factors such as origin firing efficiency and local chromatin organization. In this study, we introduce a reinforcement learning-based computational framework, integrated with Monte Carlo Tree Search (MCTS) and value gradient optimization, to develop the Fun2 algorithm. Fun2 enables a comprehensive characterization of trajectory-like chromatin architectures, including both chromatin fountains and stripes, with enhanced adaptability and accuracy. This tool facilitates systematic investigation of the spatiotemporal dynamics of DNA replication and extends to other chromatin remodeling processes, such as Cohesin-mediated loop extrusion. The Fun2 algorithm provides a versatile computational tool for deciphering dynamic chromatin architectures, offering new insights into genome organization and its regulatory mechanisms.

bioinformatics↗