Biology subjects

Xiong, M.

Publications and source records attributed to Xiong, M..

At least 19 recordsLinked to original sources

bioRxiv2026

FMR1 Perturbations Disrupt Sensory Corticostriatal Circuits and Mitochondrial Energetics in Fragile X Syndrome

Fragile X syndrome (FXS) features sensory, social, and cognitive dysfunction, but how fragile X messenger ribonucleoprotein 1 (FMR1) deficiency disrupts sensory corticostriatal circuitry and whether distinct FMR1 perturbations converge on shared cellular mechanisms remain incompletely defined. We integrated diffusion magnetic resonance imaging (MRI) in individuals with FXS and Fmr1-knockout (KO) mice with projection-defined calcium recording, bidirectional optogenetic manipulation, primary somatosensory cortex (S1)-targeted expression of three patient-associated FMR1 missense variants, regional transcriptomics, and optical manipulation of mitochondrial energetic state. Individuals with FXS exhibited disrupted sensory corticostriatal structural connectivity, with mouse analyses further localizing abnormalities to S1-striatal pathways. Fmr1 deficiency impaired recruitment of the S1-dorsomedial striatum (DMS) pathway during social interaction, and projection-specific manipulation demonstrated that S1-DMS dysfunction contributes to social and cognitive deficits. Three patient-associated FMR1 variants, I234T, E329G, and R586Q, converged on social behavioral deficits, which were improved by S1-DMS activation. Transcriptomic analyses revealed convergent oxidative phosphorylation (OXPHOS)-related mitochondrial remodeling in S1 and DMS across Fmr1 deficiency and variant models. Systemic treatment with the mitochondria-targeted antioxidant MitoQ attenuated novel-location-recognition, social-interaction, and social-novelty deficits in Fmr1-KO mice. Moreover, mt-EcGAPR-based Mitochondrial OptoEnergetic Activation (Mito-OptoEA) enhanced mitochondrial energetic state in S1 and improved novel-location recognition and sociability in both Fmr1-KO and I234T models. Together, these findings define a sensory corticostriatal circuit-metabolic framework for FXS in which S1-DMS circuit dysfunction and convergent mitochondrial energetic abnormalities contribute to FMR1-related social and cognitive phenotypes.

neuroscience↗

bioRxiv2026

Population Intelligence: Smallest-Model Numerical Experiment

We introduce a minimal individual based model for population intelligence that retains three structures absent from standard single agent descriptions: a manifold valued intelligence state, endogenous population turnover, and heritable lineage dependent reproductive fitness. The validated S^1 intelligence diffusion was extended with neutral logistic birth death dynamics. Each individual occupies an angle on the circle S^1; noisy mean field attraction produces collective synchronization; logistic birth death dynamics regulate population size; and reproductive rates can depend on lineage and position relative to designated safe or unsafe directions. This nested construction is intentionally small enough to admit an analytic equilibrium benchmark while remaining capable of selection, invasion, and safety transitions. Population size returned toward carrying capacity from (N(0)=200,800,1600), with tail means (775.7,800.3,805.1) for K=800. Across K=100 to 1600, the intelligence equilibrium error scaled as K^{-0.513} , consistent with the canonical K^{-1/2} fluctuation rate, while the extinction rate was zero in 120 runs over the simulated horizon. The mean order parameter remained close to the original theoretical (r*=0.7242), as neutral demography predicts. Directional reproductive selection shifts a safety score to +0.7447 or -0.7448 while synchronization remains approximately 0.75, demonstrating that coordination is not equivalent to safety. These are controlled numerical results, not asymptotic proofs.

bioinformatics↗

bioRxiv2026

Manifold Agentic Reasoning: Extending Agentic POMDPs and Post-Training Reasoning to Riemannian State and Reasoning Spaces

Agentic reasoning systems increasingly interact with environments whose states are only partially observed, dynamically evolving, and constrained by physical, biological, or logical structure. Existing agentic reasoning frameworks often model internal reasoning, tool use, and post-training adaptation using flat latent representations and struggle in curved manifold space environments. However, many scientific and embodied domains naturally lie on curved state spaces, including tissue geometry, developmental trajectories, protein conformations, robotic configuration spaces, and constrained physical systems. We introduce Manifold Agentic Reasoning, a geometric framework that extends agentic reasoning from Euclidean latent spaces to Riemannian manifolds. In the proposed framework, observations are encoded as manifold-valued states, memory is retrieved by geodesic similarity, candidate hypotheses are generated in tangent spaces, predicted transitions are projected by exponential maps, and decisions are admitted through verification-gated commitment or repaired by manifold self-correction. We further extend the framework to graph-agentic manifold reasoning, where node states live on manifolds and neighbor information is transported by logarithmic maps before attention-based aggregation. Manifold agent reasoning moves AI past brittle, prompt-chained templates to solve four critical production flaws: silent hallucinations and reasoning drift, brittle tool and context misuse, the black-box evaluation problem and stiff behavior profiles. To evaluate the framework, we introduce a Curved Tissue Manipulation and Recovery benchmark in which an agent must repair damaged tissue on a curved manifold. Simulated results show that the full manifold-agent substantially outperforms both a baseline reasoning agent and a full flat-agent reasoning system, achieving higher recovery success, lower geodesic shape error, lower pattern error, and fewer invalid transitions. Curvature and ablation studies indicate that the performance gain is driven by geometry-aware reasoning, verification, memory, and self-repair. These results suggest that manifold-aware agentic reasoning provides a principled foundation for reliable scientific and embodied AI systems operating in curved, constrained, or mechanistic domains.

bioinformatics↗

bioRxiv2026

Synthesis of Ultra-Large Fibrous Proteins from Bacteria via a Looped-Translation System

High molecular weight fibrous proteins such as silk, elastin, and collagens, are fundamental for providing shape to macroscopic biological structures, yet their recombinant production remains challenging because of their extreme size and sequence repetitiveness. Here, we report a circular RNA-based ribosome translation platform that enables iterative ribosome synthesis of fibrous proteins through continuously "looped" translation. To promote efficient circularization of repetitive fibrous protein transcripts, we combined a synonymous codon locker sequence strategy with RNA circularization chaperones. Guided by a ribosome traffic model, we further optimized the translation bottlenecks within the circular RNA, substantially improving translation yields. The established looped translation platform is applicable to at least six classes of fibrous proteins and generated products with molecular weight exceeding titin at 3.8 MDa. The synthesized polypeptides were characterized through electron microscopy, bulk material fabrication, and mechanical analysis, demonstrating properties associated with ultra-high molecular weight polypeptides. Finally, we coupled looped translation to secretion through a programmed ribosomal frameshift, enabling export of fibrous protein across cellular membranes in both Escherichia coli and Bacillus subtilis. We envision that the genetic tools presented here could find a range of applications in bioplastics and engineered living materials.

synthetic biology↗

bioRxiv2026

GCBM-DCT-HV-Bio-NL-Grow-CHG-CSM-RHEC: A Unified Geometric, Biological, Causal, and Regenerative Framework for Mechanism-Aware Tissue and Connectome Modeling

Modern biological prediction problems increasingly require models that go beyond Euclidean feature regression and local graph smoothing. Tissue, cellular, and connectome systems are nonlinear, geometry-dependent, intervention-sensitive, history-dependent, and subject to regenerative or homeostatic constraints. We propose GCBM-DCT-HV-Bio-NL-Grow-CHG-CSM-RHEC, a unified model for mechanism-aware biological prediction. The model integrates geometric connectome dynamics, differentiable charted tissue geometry, Hamiltonian latent transport, nonlinear biological kinetics, nested latent memory, continual growth without overwriting, causal hypergraph structure, causal structure modeling, and regenerative homeostatic error correction. Unlike Euclidean baselines, which treat observations as flat vectors, and local graph baselines, which use neighborhood smoothing without mechanistic structure, the proposed model represents biological states (Trapnell 2015) as coupled geometric, dynamical, causal, and regenerative objects. We evaluate the model on four synthetic toy studies, Toy A-D, designed to reflect increasing biological complexity: local Euclidean structure, nonlinear mechano-chemical interaction, causal intervention response, and out-of-distribution regenerative shift. Compared with Euclidean and local graph baselines, the full model achieves the lowest mean squared error across all four toy studies. Relative to the Euclidean baseline, the full model reduces MSE by approximately 63.0%, 89.1%, 89.0%, and 90.9% on Toy A, Toy B, Toy C, and Toy D, respectively. These results support the value of integrating geometry, mechanism, causal structure, adaptive growth, and regenerative correction into a single predictive architecture (Figure 1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/734320v1_fig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@9e9eorg.highwire.dtl.DTLVardef@add398org.highwire.dtl.DTLVardef@1eb1f1org.highwire.dtl.DTLVardef@1346720_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO GCBM-DCT-HV-Bio-NL-Grow-CHG-CSM-RHEC, a unified model for mechanism-aware biological prediction. C_FIG

bioinformatics↗

bioRxiv2026

Hippocampal Glucuronyl C5-epimerase promotes stress resilience by directly engaging PI3K through a non-enzymatic mechanism

Major depressive disorder (MDD) seriously affects human physical and mental health and causes global socioeconomic burdens. Stress-induced depressive etiology is linked to dendritic atrophy in the hippocampus, however, the underlying mechanism was poorly understood. Here, we identify that glucuronyl C5-epimerase (Glce), a heparan sulfae-modifying enzyme, as a critical regulator of hippocampal dendritic integrity and stress resilience. Genetic ablation of Glce in the hippocampal excitatory neurons is sufficient to induce dendritic atrophy and depressive-like behaviors, whereas its restoration rescues these deficits. Notably, Glce levels are reduced in the plasma of depressed individuals. We further find that Glce functions independently of its enzymatic activity, instead binding directly to and activating PI3K (p85/p110), thereby triggering an Akt/CREB/BDNF signaling cascade. Together, our findings uncover a non-canonical role for Glce and establish that Glce-PI3K-BDNF axis is essential for maintaining hippocampal structure and behavioral resilience, thereby highlighting a critical role of Glce in the pathophysiology of depression.

neuroscience↗

bioRxiv2026

Mechanotransduction-Aware Causal Omics on Tissue Scaffolds: A Controlled Mechanochemical Framework for Identifying Disease Genes Beyond Pure Omics Analysis

Omics-based disease-gene discovery is typically performed as if molecular states evolve independently of tissue mechanics. Most current pipelines analyze transcriptomic or multimodal molecular data alone and identify abnormal genes using differential expressions, latent trajectories, or association-based recovery under treatment. However, in mechanically active tissues, gene expression is shaped not only by internal regulatory networks but also by mechanotransduction arising from strain, curvature, force transmission, and scaffold geometry. This raises a fundamental question: should disease-gene identification in tissues be treated as a pure omics association problem, or as a causal mechanochemical inference problem? We introduce a mechanotransduction-aware causal omics framework on a Cosserat tissue scaffold. Gene expression evolves through intrinsic regulatory dynamics, spatial diffusion, external control, and a mechanotransduction term driven by scaffold mechanics. To distinguish causation from association, we define a hidden mechano-drug rescue channel in the true data-generating system and compare predictive models that either include or omit mechanotransduction. We show that association-based rankings can incorrectly elevate downstream homeostatic or repair genes, even when the disease gene is the true direct mechanochemical target. By contrast, a causal ranking based on reconstruction of the direct mechanotransduction intervention effect correctly identifies the disease gene as the strongest beneficiary. These results argue that popular pure-omics analysis is insufficient for disease-gene discovery in mechanically structured tissues. Mechanotransduction should be modeled as part of the causal structure of tissue biology rather than treated as a secondary covariate or omitted entirely.

bioinformatics↗

bioRxiv2026

Dendritic excitations primarily mediate back-propagation in CA1 pyramidal neurons during behavior

Dendrites integrate synaptic inputs to trigger action potentials, and dendrites carry back-propagating action potentials (bAPs) to synapses where these signals contribute to plasticity. Despite strong evidence for a rich repertoire of nonlinear dendritic excitations, the in vivo roles of these excitations in dendritic integration and back-propagation remain uncertain. Here, we used high-speed voltage imaging through a chronically implanted microprism to map membrane potential dynamics from basal to apical dendrites of CA1 neurons in mice navigating in a virtual reality environment. Despite complex dendritic branch morphology, the dynamics were largely captured by 2 or 3 electrical compartments: basal, soma, and apical. Fast dendritic spikes almost always started from bAPs, indicating that dendritic spikes are primarily a consequence rather than a cause of somatic spiking. These fast spikes sometimes triggered slower apical dendritic plateau depolarizations, which drove complex spikes at the soma. We found that the biophysics of dendritic excitability determined the distribution of simple and complex spikes across a place field. Our results show how CA1 pyramidal neurons convert synaptic inputs to spiking outputs and suggest a primary role of dendritic nonlinearities in mediating activity-dependent plasticity.

neuroscience↗

bioRxiv2025

Missense Mutation ADNP p.C687R Disrupts Chromatin Regulation and GABAergic Differentiation in HVDAS

Activity-dependent neuroprotective protein (ADNP) is a key regulator of neurodevelopment and a high-risk gene for autism spectrum disorder (ASD). Most pathogenic variants cause loss-of-function in Helsmoortel-Van der Aa syndrome (HVDAS), yet the impact of missense mutations remains unclear. Here, we characterized a ADNP missense mutation (p.C687R) identified from a HVDAS patient, predicted to disrupt its ninth zinc finger domain. Overexpression of p.C687R altered subnuclear localization and affected wild-type ADNP distribution. In utero electroporation revealed impaired neuronal migration and cortical branching. CUT&Tag profiling showed mutant- specific chromatin binding, preferentially targeting histone modification genes through both direct and distal interactions. Patient-derived iPSCs carrying p.C687R exhibited upregulated bivalent histone marks (H3K4me3 and H3K27me3) on neurodevelopmental genes, including regulators of GABAergic differentiation, which became transcriptionally activated in neural progenitors. These findings reveal a gain-of-function mechanism by which ADNP p.C687R perturbs chromatin regulation and disrupts GABAergic lineage specification, expanding the molecular framework of ADNP pathogenesis. The Paper Explained ProblemMutations in the gene ADNP cause Helsmoortel-Van der Aa syndrome (HVDAS), a severe neurodevelopmental disorder, and also increase the risk for autism spectrum disorder (ASD). While most known ADNP mutations lead to loss of protein function, many missense mutations that alter a single amino acid remain poorly understood regarding their effects on brain development and disease contribution. ResultsWe characterized a specific missense mutation, ADNP p.C687R, from a patient diagnosed with HVDAS, and found that it does not simply inactivate the protein but instead confers a disruptive new function. Across multiple models, from cells to mice, the p.C687R mutant exhibits abnormal chromatin binding, altered subnuclear localization, and impaired neuronal migration and differentiation. Importantly, in patient-derived iPSCs, it marks distinct neurodevelopmental genes with bivalent histone modifications, which become aberrantly activated during differentiation into GABAergic neural progenitor cells. ImpactThese findings confirm the pathogenicity of the missense mutation ADNP p.C687R in HVDAS, reveal a gain-of-function mechanism whereby the mutant disrupts chromatin regulation and GABAergic lineage specification, and advance our understanding of ADNP-related neurodevelopmental disorders.

neuroscience↗

bioRxiv2025

Machine learning-based definition of cellular senescence reveals pro-senescence potential implication in lung adenocarcinoma

Despite growing evidence implicating cellular senescence in tumor progression, methodological challenges in objectively quantifying senescent cell burden across cancer types continue to limit mechanistic studies and clinical translation. To address this, we developed the Predictive Cellular Senescence Model (PreCSenM), a machine learning-based tool that assigns a CS score by integrating senescence-associated features across 888 samples. Benchmarking analyses confirmed that PreCSenM markedly outperforms existing methodologies in reliably predicting senescent states within both normal and cancerous transcriptomic datasets. In lung adenocarcinoma (LUAD), PreCSenM identified the CS score as a robust predictor of clinical outcomes. Multi-omics analyses further revealed that lower CS levels correlate with decreased genomic stability and enhanced immune responses, aligning with clinical observations. Notably, drug discovery using PreCSenM identified histone deacetylase inhibitors (HDACis) as potent inducers of CS in LUAD. Transcriptional and epigenetic profiling of HDACi-treated cells pinpointed FOSB as a core transcription factor driving CS, and FOSB knockdown blocked HDACi-induced senescence. Overall, our study establishes PreCSenM as a multidimensional senescence quantification tool, bridging computational prediction with clinical relevance and mechanistic validation to enable senescence-directed therapies in precision oncology.

bioinformatics↗

bioRxiv2025

Optogenetic construction of de novo integrin-adhesion complexes reveals role for biocondensation in adhesion nucleation

Integrin-adhesion complexes (IACs) form spontaneously in cells on extracellular matrix substrates, allowing them to sense matrix composition and transduce force. However, IACs often do not form uniformly across a cell, which begs the question: What is required to nucleate an adhesion, and what factors enable the stabilization of an IAC once it has formed? Many factors have been suggested to promote formation and the subsequent stabilization of IACs. It is difficult to explicitly test these factors in vivo as IACs undergo constant remodeling. Here, we employ optogenetics to explicitly test the ability of talin in different activity and phase states to nucleate and stabilize IACs in regions where none are present. We find that fusion of intrinsically disordered regions directly to talin enhances its adhesion nucleation potential and allows new adhesions to be produced in response to optogenetic talin clustering. Similarly, expression of factors previously shown to enhance biocondensation in vitro, such as paxillin, the paxillin N-terminus, or unfolding of talin, allows for adhesion nucleation and biocondensation of talin. We show that these biocondensates of talin can cluster and activate integrins even in the absence of extracellular matrix. By applying optogenetic activation to regions of the cell with or without ventral actomyosin, we demonstrate actomyosin engagement promotes the formation and stability of adhesions. These results are corroborated by theoretical modelling which shows that phase separation of talin is enhanced by differential clutch formation in the presence of actomyosin thus enabling peripheral adhesion formation and stability. This work establishes a model in which increased cooperativity of talin enables IAC nucleation through talin biocondensation, which clusters and activates integrins. In addition to these findings, we generate multiple optogenetic tools that enable local nucleation or enhancement of IACs. HighlightsO_LIOptogenetic tools mediating talin biocondensation can locally induce focal adhesion formation C_LIO_LIPaxillin LD domains enable biocondensation of talin C_LIO_LIBiocondensation of talin enables IAC formation C_LIO_LIPhase separation of talin can activate integrins independently of ECM C_LIO_LIComputational model reconciles spatial variance in LLPS and IAC formation. C_LI

cell biology↗

bioRxiv2025

Cross-species phenotypic profiling uncovers functional determinants of bacterial cold shock adaptation

Temperature shifts impose broad physiological stress, requiring precise and dynamic regulatory programs to restore cellular homeostasis. While the heat shock response is well characterized, the mechanisms underlying cold shock response (CSR) remain less understood. To identify genes critical for cold adaptation, we applied transposon sequencing (Tn-seq) to monitor mutant fitness across the full course of CSR and sustained low-temperature growth in two mesophilic bacteria, Escherichia coli and Bacillus subtilis. In B. subtilis, phenotypic profiling revealed a temporally structured program: membrane fluidity and cell wall remodeling were most critical in the early stage of CSR, whereas post-transcriptional regulation became essential during late-stage recovery to reprogram gene expression and restore growth. Cross-species comparison uncovered both conserved and species-specific mechanisms, with RNA metabolism and ribosome/translation regulators playing broad roles. Specifically, we identified a conserved synergy between two ribosomal RNA methyltransferases, RsmA and RsmH, in promoting cold adaptation. In B. subtilis, mutants lacking these enzymes exhibited significant delay in translation recovery following cold-induced global inhibition. Together, these findings provide a comparative, systems-level view of bacterial cold adaptation and establish a framework for exploring stress responses in pathogens and extremophiles.

microbiology↗

bioRxiv2025

Luminos: open-source software for bidirectional microscopy

Bidirectional microscopy (BDM) combines simultaneous targeted optical perturbation and imaging of biophysical or biochemical signals (e.g. membrane voltage, Ca2+, or signaling molecules). A core challenge in BDM is precise spatial and temporal alignment of stimulation, imaging, and other experimental parameters. Here we present Luminos, an open-source MATLAB library for modular and precisely synchronized control of BDM experiments. The system supports hardware-triggered synchronization across stimulation, recording, and imaging channels with microsecond accuracy. Source code and documentation for Luminos are available online at https://www.luminosmicroscopy.com and https://github.com/adamcohenlab/luminos-microscopy. This library will facilitate development of bidirectional microscopy methods across the biological sciences.

bioengineering↗

bioRxiv2024

Human cartilage model of the precocious osteoarthritis-inducing COL2A1 p.Arg719Cys reveals pathology-driving matrix defects and a failure of the ER proteostasis network to recognize the defective procollagen-II

Mutations in the COL2A1 gene, encoding procollagen-II, cause various chondrodysplasias, including precocious osteoarthritis with mild spondyloepiphyseal dysplasia engendered by the p.Arg719Cys substitution. The molecular mechanisms underlying these disorders remain incompletely understood, largely owing to the absence of models faithfully recapitulating the human disease. Here, we developed an in vitro human cartilage model using isogenic induced pluripotent stem cell (iPSC) lines carrying either wild-type or Arg719Cys COL2A1. Directed differentiation into chondrocytes yielded cartilage tissues that were analyzed by immunohistochemistry, electron microscopy, SDS-PAGE, and RNA-sequencing. Tissues derived from Arg719Cys heterozygotes displayed a deficient matrix, closely reflecting the human disease phenotype. Arg719Cys procollagen-II was excessively post-translationally modified and partially retained within the endoplasmic reticulum (ER), leading to ER distention. Notably, despite introduction of an aberrant cysteine residue--expected to engage redox-sensitive folding and quality control pathways--Arg719Cys procollagen-II was not detectably recognized by the ER proteostasis network. The resulting inability to mount a quality control response, including activation of the unfolded protein response, indicates a failure in cellular surveillance. As a result, malformed procollagen-II both accumulates intracellularly and is secreted, contributing to the deposition of a structurally compromised extracellular matrix that drives disease pathology. The iPSC-derived cartilage model presented here provides a genetically defined and expandable, human-based system for dissecting the mechanisms of failed proteostasis in collagenopathies. These findings shed light on the types of substitutions in procollagen that cells can or cannot recognize, and underscore the therapeutic potential of targeting cellular surveillance and collagen quality control pathways in COL2A1-related disorders and beyond. SIGNIFICANCEThe p.Arg719Cys substitution in COL2A1 is known to cause dominantly inherited precocious osteoarthritis with mild spondyloepiphyseal dysplasia, but its molecular pathogenesis remains poorly understood. Prior work suggests disrupted fibrillogenesis as the molecular etiology, yet phenotypically relevant human models are lacking. Here, we use human iPSC-derived cartilage to uncover how the Arg719Cys substitution alters procollagen-II folding, modification, trafficking, and matrix deposition. We observe hallmark disease phenotypes, including a sparse collagen-II matrix and distended endoplasmic reticulum (ER), consistent with intracellular accumulation of procollagen-II. Surprisingly, the mutant procollagen-II does not appear to be differentially engaged by the ER proteostasis network, despite the incorporation of an aberrant and apparently non-disulfide bond-forming Cys residue, suggesting a fundamental failure of cellular surveillance. This unrecognized misfolding leads to secretion of defective collagen culminating in matrix dysfunction. These findings establish a mechanistic basis for disease etiology and highlight collagen quality control as a potentially tractable therapeutic target. The iPSC-based model provides a scalable, human-relevant platform for dissecting disease mechanisms and developing therapies for COL2A1-related disorders and other collagenopathies.

cell biology↗

bioRxiv2024

Dynamic Models of Neural Population Dynamics

The recent developments in artificial intelligence (AI) increase the hope that AI can provide a powerful tool to facilitate scientific discovery and to generate and validate new ideas for scientific research autonomously. Large Language Models (LLMs), such as ChatGPT4 have demonstrated remarkable capabilities in understanding and generating human-like text. Their potential extends beyond simple language tasks, offering transformative possibilities in scientific research of all fields. By leveraging vast amounts of data and advanced computational power, LLMs can assist researchers in generating novel ideas, automating routine tasks, and fostering interdisciplinary collaborations. On September 12, 2024, OpenAI released their updated generative artificial intelligence system called ChatGPTo1. This new AI system, built upon chain-of-thought and reinforcement learning, has greatly enhanced logical reasoning abilities and can effectively solve various complex problems from elementary-level mathematical problems to modern scientific research issues in physics, chemistry, and biology. Unlike previous LLMs in which logical reasoning and data analysis abilities are developed through training on actual data, ChatGPTo1 logical reasoning ability and capacity to generate new scientific ideas are primarily acquired through chain-of-thought processes and reinforcement learning rather than pre-training. To examine this, we specifically tested ChatGPTo1 current reasoning and scientific discovery capabilities by developing theoretically complex and quantitatively challenging scientific equations in various fields of neuroscience, such as dynamical systems, nonlinear dynamical systems, dynamical systems on differential manifolds, neural field theory, nonlinear divergence theorems, nonlinear heat conduction equations and Laplace equations and their extensions on differential manifolds, nonlinear statistical analysis methods, deep learning, and other topics involving multiple fields. The current large language models may illustrate a certain degree of general intelligence, even if fundamentally it may be different from human intelligence. However, it does not mean the current LLMs can fully apply such ability in practical applications or that their reasoning potential can be fully tapped. It is essential to explore specific pathways and methods to cultivate their potential for scientific discovery. To accomplish this, we consider how to integrate them with common search engines (such as Google) capabilities and ChatGPT4o cross-modal abilities to better understand new disciplines and scientific discoveries. To this point, the major shortcoming of ChatGPTo1 is that it is not an end-to-end scientific discovery method and lacks the ability to achieve full automation. It also lacks methods for image analysis and full-scale data analysis, making it difficult to use simulation and data analysis to evaluate and test proposed new theories and methods.

neuroscience↗

bioRxiv2024

Tumor relapse-free survival prognosis related consistency between cancer tissue and adjacent normal tissue in drug repurposing for solid tumor via connectivity map

Traditional drug discovery encounters challenges, including high costs, time-intensive processes, and inherent risks. Drug repurposing emerges as a compelling alternative strategy to identify new indications for investigational or approved drugs, circumventing these obstacles. Among the various drug repurposing methods, the Disease-specific Signature-based Connectivity Map (Cmap) approach is widely utilized. However, the commonly employed method for constructing disease-specific signatures, known as Differentially Expressed Genes (DEG), faces issues related to inconsistencies between dysregulated genes and the prognosis of genes in tumor tissue, as well as discrepancies in prognosis genes between tumor and normal tissues. In this study, we propose a novel approach, Prognosis Consistency Scoring (PCS), aimed at addressing these inconsistencies. PCS measures the consistency of gene prognosis between tumor and normal tissues by combining the Recurrence-Free Survival (RFS) prognosis power of genes in both contexts. Disease-specific signatures are then constructed based on PCS, and drug repurposing is performed using the Cmap and Lincs Unified Environment (CLUE). Validation of predicted drugs is conducted using data from DrugBank and ClinicalTrials databases. Our findings reveal that the aforementioned inconsistencies are pervasive. Compared to signatures based on DEGs, PCS-based signatures exhibit superior performance, identifying more drugs with higher prediction accuracy, as confirmed by DrugBank annotations. Notably, a significant proportion of predicted drugs without corresponding indications were subsequently validated in the ClinicalTrials database. Additionally, PCS-based signatures demonstrate elevated disease specificity and association with Drug Related Gene (DRG).

bioinformatics↗

bioRxiv2023

The tRNA Gm18 Methyltransferase TARBP1 Promotes Hepatocellular Carcinoma Progression via Metabolic Reprogramming of Glutamine

Cancer cells rely on metabolic reprogramming to sustain the prodigious energetic requirements for rapid growth and proliferation. Glutamine metabolism is frequently dysregulated in cancers and is being exploited as a potential therapeutic target. In current study, we identified TARBP1 (TAR (HIV-1) RNA Binding Protein 1) as a novel driver gene critical for glutamine metabolic reprogramming in tumor through the CRISPRi/Cas9 screening. Our in vivo and in vitro assays demonstrated that TARBP1 is the methyltransferase of Guanosine 2-O-methylation targeting position 18 (G18) of tRNAGln (TTG/CTG) and tRNASer (TGA/GCT), and loss of Gm18 modification diminishes the stability of tRNAs. Therefore, TARBP1 is critical for maintaining efficient translation of mRNA, in particular the glutamine transportor-ASCT2 (also known as SCL1A5). Importantly, TARBP1 is frequently amplified and overexpressed in HCC, consequentially promotes the protein synthesis of ASCT2 and glutamine import to fuel the growth of cancer cell, which is associated with poor patient survival. Taken together, this study reveals the critical role of TARBP1 in HCC progression through glutamine metabolic reprogramming and provides a potential target for tumor therapy.

cancer biology↗

bioRxiv2021

The endoplasmic reticulum adopts two distinct tubule forms

The endoplasmic reticulum (ER) is a versatile organelle with diverse functions. Through super-resolution microscopy, we show that the peripheral ER in the mammalian cell adopts two distinct forms of tubules. Whereas an ultrathin form, R1, is consistently covered by ER-membrane curvature-promoting proteins, e.g., Rtn4 in the native cell, in the second form, R2, Rtn4 and analogs are arranged into two parallel lines at a conserved separation of [~]105 nm over long ranges. The two tubule forms together account for [~]90% of the total tubule length in the cell, with either one being dominant in different cell types. The R1-R2 dichotomy and the final tubule geometry are both co-regulated by Rtn4 (and analogs) and the ER sheet-maintaining protein Climp63, which respectively define the edge curvature and lumen height of the R2 tubules to generate a ribbon-like structure of well-defined width. Accordingly, the R2 tubule width correlates positively with the Climp63 intralumenal size. The R1 and R2 tubules undergo active remodeling at the second/sub-second time scales as they differently accommodate proteins, with the former effectively excluding ER-luminal proteins and ER-membrane proteins with large intraluminal domains. We thus uncover a dynamic structural dichotomy for ER tubules with intriguing functional implications.

cell biology↗