Biology subjects

Pan, X.

Publications and source records attributed to Pan, X..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery

The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu), an evolvable, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically, PantheonOS enables agentic code evolution, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1-Nodal inhibition in proximal-distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond. Websitehttps://pantheonos.stanford.edu Ecosystemhttps://github.com/aristoteleo SummaryLarge language model-powered agent systems are driving a paradigm shift in scientific discovery by automating, scaling, and accelerating data analysis. This transformation is particularly profound in biology, where the rapid expansion of single-cell and spatial genomics has effectively reshaped the field into a data-intensive science. However, existing biological agent systems are typically constrained to single-agent designs, are narrowly specialized, or are overly general without sufficient domain expertise, limiting their applicability to routine or shallow analyses. Here, we introduce PantheonOS (https://pantheonos.stanford.edu), an evolvable, privacy-preserving, and general-purpose multi-agent framework designed to reconcile generality with deep domain specificity. PantheonOS provides an abstract, extensible architecture that enables customized agent composition and supports end-to-end single-cell and multi-omics analysis, spanning reinforcement-learning-augmented gene panel design, raw FASTQ processing, multimodal data integration, and three-dimensional spatial genomics reconstruction. Central to this framework, Pantheon-Evolve enables agentic code evolution, allowing the system to autonomously improve state-of-the-art batch-correction algorithms and new reinforcement-learning based gene panel design algorithms, achieving performance beyond manually designed baselines. We demonstrate the power of PantheonOS across multiple biological domains. In early mouse embryogenesis, PantheonOS automatically reconstructs three-dimensional spatial gene expression landscapes and resolves asymmetric Cer1 expression and paracrine Cer1-Nodal inhibition, revealing a robust proximal-distal axis at embryonic day six (E6.0). In human development, PantheonOS integrates fetal heart single-cell multi-omics with whole-heart 3D MERFISH+ data at post-conception week 12, uncovering spatially resolved molecular programs underlying heart disease ontogeny. Finally, an intelligent model-routing mechanism enables PantheonOS to adaptively select optimal virtual cell models across heterogeneous tasks, revealing minimal regulatory networks of cardiogenesis and predicting spatially resolved perturbation effects in the developing heart. Together, PantheonOS establishes a foundation for fully automated, evolvable, and domain-aware agentic analysis in genomics, and points toward a future in which scientific discovery is increasingly driven by self-improving AI systems across biology and beyond.

bioinformatics↗

bioRxiv2026

Synthetic analogue of adrenocorticotropic hormone, ACTH(4-7)PGP delays neurological manifestations in diseases of mucopolysaccharidosis III spectrum by reducing neuroinflammation and rescuing neurotransmission, synaptogenesis, and axonal demyelination

Mucopolysaccharidosis III (MPS III or Sanfilippo disease) is a spectrum of 4 genetic disorders (MPS IIIA-D), caused by defects in the genes SGSH, NAGLU, HGSNAT and GNS encoding enzymes involved in degradation of heparan sulfate (HS). HS accumulates in brain tissues and causes neuronal dysfunction and neurodegeneration leading to neuropsychiatric problems, developmental delays, childhood dementia, blindness and death during the second decade of life. Previously, we demonstrated that pathophysiological mechanisms, underlying MPS IIIC in mouse models, involves functional pathological changes, affecting synaptogenesis and synaptic transmission and leading to learning and memory deficits. These results suggested that a treatment for MPS III could be developed by using compounds inducing synaptogenesis. In the current study, we tested the efficacy of a synthetic peptide ACTH(4-7)PGP, an analog of adrenocorticotropic hormone fragment, previously used as a neuroprotective and anti-inflammatory medication for treatment of acute neurological conditions, including stroke. We show that intranasal administration of ACTH(4-7)PGP restores defective synaptic transmission in CA1 pyramidal neurons of MPS IIIA and MPS IIIC mouse models and rescues the decrease in synaptic proteins in cultured MPS IIIC mouse hippocampal neurons and iPSC-derived neurons of human MPS IIIA, MPS IIIB and MPS IIIC patients. Furthermore, daily intranasal administration of ACTH(4-7)PGP to MPS IIIC and MPS IIIA mice reduces hyperactivity and rescues defects in working and spatial memory, delays progression of CNS pathology including neuroinflammation and axonal demyelination, and increases the lifespan. Together with the absence of any adverse reactions to ACTH(4-7)PGP in the MPS III and WT mice, our results justify testing the drugs efficacy in clinical settings.

genetics↗

bioRxiv2026

Computational modeling of head direction cells in three-dimensional space: directional encoding and visual cue manipulation

Head direction (HD) cells can fire selectively as a function of the animals head azimuth direction and form an internal compass for navigation. They were found in the mammalian limbic system including the dorsal presubiculum and entorhinal cortex. The underlying network updates its directional estimate in a self-organized fashion and can be recalibrated by external sensory cues. Although ring-attractor models were proposed to account for azimuth coding on horizontal planes, they cannot explain conjunctive azimuth-and-pitch tuning observed in HD cells of the presubiculum of flying bats navigating in three-dimensional (3-D) space. Based on the 3-D electrophysiological recordings, we developed a toroidal continuous attractor network that jointly encodes horizontal azimuth and vertical pitch angle of head direction. The model can reproduce the experimentally recorded tuning curves of individual HD cells, and accurately encode the 3-D dynamic head direction of bat by HD cell population. The model also simulate the influence of horizontal visual cue manipulation on the HD system in 3-D space and predicts how horizontal landmark rotation induces a sustained azimuthal offset that persists after the cue is removed, which is comparable to two-dimensional experimental findings. This research clarifies how conjunctive 3-D direction codes are generated and modified by vestibular input, visual information and recurrent connectivity. It also uncovers the computational principles and properties of the brains navigation functions in realistic 3-D environments and offers new theoretical reference for future studies. Author summaryA central function of the brains navigation system is to track head direction, which in terrestrial mammals is largely confined to a horizontal plane. However, for animals like bats that navigate freely in three-dimensional (3-D) space, the neural code for head direction has been found to be high-dimensional. While ring attractor network models elegantly explain horizontal azimuth coding, how the 3-D head direction code is formed and affected by sensory information remains unknown. Here, we develop a toroidal continuous attractor network model with vestibular and visual modules that explains the jointly encoding of azimuth and pitch angles, and the effect of visual cue. We introduce the toroidal topology to the continuous attractor network, and the high-dimensional angular velocity signal from vestibular system and visual input are used to update the population dynamics of the network. Our model is constrained by and reproduces 3-D electrophysiological data from flying bats, captures the unique tuning curves of individual head direction cells, and further achieves the accurate encoding of dynamic 3-D head movements by population activity. Evidences in the rodent have suggested visual cue manipulation recalibrates head direction coding horizontally. Our model further successfully simulates the influence of visual cue rotation in 3-D space, predicting a persistent, global offset in the azimuth encoding--an effect that endures after cue removal and aligns with 2-D rodent experimental phenomena. This work provides a mechanistic, network-level explanation for 3-D head direction encoding and reveals how multimodal cues are integrated to form the internal compass in a volumetric world.

neuroscience↗

bioRxiv2026

Yin Yang 1-Dependent PcG Function is Essential for TET2 Expression and Early T cell Development

Yin Yang 1 (YY1) is a multifunctional transcription factor and mammalian Polycomb Group (PcG) protein critical for lymphocyte development. While YY1 is required for early T-cell development and survival, the underlying mechanisms remain incompletely defined. Herein, we utilize the YY1 REPO domain conditional knockout mouse model (Yy1-/{Delta}REPO) to further dissect the YY1-PcG domain dependent epigenetic regulation in early T cell development. Yy1-/{Delta}REPO mice show a developmental block at the double-negative (DN) 3 to DN4 T cell transition, with expansion of the DN3 population and reduced TCR{beta}+ DN4 T cells. The genetic network governing T cell differentiation is dysregulated in Yy1-/{Delta}REPO DN3 T cells. YY1 binds directly to the Tet2 promoter, and deletion of the YY1 REPO domain leads to downregulation of the DNA demethylase TET2 in DN3 T cells. Although deletion of the YY1 REPO domain does not impair YY1 binding at the Tet2 promoter, H3K4me3 enrichment at the promoter is reduced. Pharmacologic inhibition of TET catalytic activity in wild-type DN thymocytes partially recapitulates the developmental defects in Yy1-/{Delta}REPO DN thymocytes, whereas re-expression of TET2 catalytic domain in Yy1-/{Delta}REPO DN thymocytes partially rescues T cell development. Collectively, these findings reveal a previously unappreciated link between YY1 REPO domain-dependent regulation and TET2-mediated epigenetic control during early T-cell development.

molecular biology↗

bioRxiv2026

YY1-Mediated Polycomb Group Function Safeguards Hematopoietic Stem Cells from Premature Aging

Hematopoietic stem cells (HSCs) undergo functional decline with age, characterized by myeloid-biased differentiation, loss of quiescence, and altered metabolic homeostasis. The molecular mechanisms driving these changes remain incompletely understood. Yin Yang 1 (YY1) is a multifunctional transcription factor and mammalian Polycomb group (PcG) protein that recruits PcG complexes to specific genomic loci via its 26-amino acid REPO (Recruitment of Polycomb) domain. To define the role of YY1 PcG function in adult HSCs, we generated a conditional YY1 REPO domain knockout mouse model (Yy1-/{Delta}REPO). Deletion of the REPO domain led to premature HSC aging, with expansion of immunophenotypic HSCs but loss of long-term self-renewal capacity. Yy1-/{Delta}REPO HSCs exhibited myeloid-biased output, expansion of myeloid-primed multipotent progenitors, increased myeloid colony formation, and an elevated myeloid-to-lymphoid ratio in peripheral blood. These cells displayed reduced quiescence, elevated reactive oxygen species, increased mitochondrial oxidative capacity, and enhanced {beta}-galactosidase activity--hallmarks of cellular aging. RNA-seq demonstrated dysregulation of gene networks governing HSC metabolism. Together, these findings establish YY1 PcG activity as a key epigenetic mechanism that preserves metabolic quiescence, sustains long-term self-renewal, and delays HSC aging. Our studies reveal a fundamental PcG-dependent epigenetic mechanism that dictate cell fate decisions and function decline during HSC aging.

molecular biology↗

bioRxiv2026

Integrative Inference of Spatially Resolved Cell Lineage Trees using LineageMap

Understanding the spatio-temporal processes of tissue growth, including how new cell types emerge and how cells form the tissue architecture, is a fundamental problem in biology. The emerging spatially resolved lineage tracing data, where three modalities, lineage barcodes, gene expression profiles, and spatial locations, are measured for each single cell, provides an unprecedented opportunity to understand these processes. Computational methods that take advantage of all three modalities to reconstruct cell lineage tree and ancestral cell states and locations are needed. We introduce LineageMap, a hybrid lineage inference algorithm that integrates the scalability of distance-based tree reconstruction methods with the flexibility of likelihood-based methods under a unified probabilistic framework. The input to LineageMap is spatially resolved lineage tracing data, where for each single cell, the gene expression, lineage barcode and spatial locations are available. LineageMap enables accurate, interpretable, and scalable inference of high-resolution lineage trees as well as locations of ancestral cells from the tri-modality single-cell data. Across simulated and experimental datasets, LineageMap consistently outperforms existing methods in the accuracy of reconstructed cell lineage trees, while revealing biologically coherent spatiotemporal trajectories. Our framework bridges molecular lineage tracing with spatial and transcriptomic information, advancing computational reconstruction of dynamic cellular ancestries in both time and space. LineageMap is available at: https://github.com/ZhangLabGT/LineageMap.

developmental biology↗

bioRxiv2026

PRADA-DTI: A Prototype-Retrieval Augmented Domain-Adaptation Framework for Drug-Target Interaction Prediction

Drug target interaction (DTl) prediction is a fundamental task in computational drug discovery. However, most existing DTI models assume a static learning environment, whereas real-world biomedical data are dynamic, characterized by the continuous emergence of new protein families and interaction patterns. This poses major challenges for model generalization and continual adaptation, especially under privacy and data-access constraints. To address these issues, we introduce PRADA-DTI, a retrieval-augmented, parameter-efficient framework for domain-incremental DTI learning. Built on a shared physicochemical backbone, PRADA-DTI learns domain-specialized prompts for representation-level modulation, coupled with Low-Rank Adaptation (LoRA) modules for parameter-space adaptation. At inference, protein embeddings query a compact prototype memory without storing raw molecular data, retrieving similar domains to dynamically compose the relevant prompts and LoRA parameters without requiring domain labels. This retrieval-guided composition enables continual learning from new protein domains while mitigating catastrophic forgetting on previous ones. On BindingDB and BIOSNAP benchmarks, PRADA-DTI substantially outperforms state-of-the-art continual learning and parameter-efficient baselines in both predictive accuracy and forgetting mitigation with minimal parameter updates. Interpretability analysis through residue-level attribution visualization demonstrates that the model correctly attends to binding pocket regions across different protein domains, confirming that the retrieval-guided adaptation mechanism captures biologically relevant structural patterns. These results demonstrate the effectiveness of retrievalaugmented parameter adaptation for continual drug discovery.

bioinformatics↗

bioRxiv2025

IgG-Bridging-Seeded Synergistic Aggregation of SARS-CoV-2 Spikes Underlies Potent Neutralization by A Low-Affinity Antibody

Mechanistic studies of viral neutralization typically prioritize high-affinity antibodies, relegating low-affinity binders to the sidelines. We report P5-1C8, a Class 1 SARS-CoV-2 antibody that exemplifies this underexplored "low-affinity yet high-potency" phenotype, retaining strong neutralization of Omicron JN.1 despite markedly weakened trimer binding (KD = 225 nM; IC50 = 0.06 nM). Structural and biophysical analyses reveal that P5-1C8 engages WT and BA.1 spikes through canonical intra-spike bivalency, but with JN.1 it induces aggregation. Using virion-like nanoparticles displaying multiple spikes, we show that IgG remains bound with no detectable dissociation and triggers pronounced aggregation. Coarse-grained molecular dynamics delineate the stepwise pathway in which weak IgG-spike contacts seed aggregation via transient inter-spike bridging. Together, these findings establish the first mechanistic framework demonstrating how weak-binding antibodies can nonetheless achieve potent neutralization through higher-order aggregation, thereby expanding the conceptual landscape of antibody function and opening new directions for antibody evaluation and design. Graphical AbstractLow-affinity antibodies are frequently disregarded in discovery pipelines. This work reports P5-1C8, a Class 1 SARS-CoV-2 antibody with weak trimer binding (KD-to-IC50 > 3,700-fold) yet potent neutralization of Omicron JN.1. Structural, biophysical, functional and coarse-grained simulations collectively demonstrate that transient inter-spike IgG bridging seeds higher-order aggregation, which in turn drives neutralization and provides a mechanistic framework. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/691454v1_ufig1.gif" ALT="Figure 1000"> View larger version (79K): org.highwire.dtl.DTLVardef@1d4f132org.highwire.dtl.DTLVardef@1274400org.highwire.dtl.DTLVardef@e186a6org.highwire.dtl.DTLVardef@4f1870_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Deconvolution of Sparse-count RNA Sequencing Data for Tumor Cells Using Embedded Negative Binomial Distributions

Estimating tumor-specific transcript proportions from mixed bulk samples has potential to inform novel biology. However, estimation accuracy using existing methods in sparse-count data such as microRNA-seq and spatial transcriptomics has yet to be established. We generated a mixed small RNA benchmark dataset to demonstrate analytical challenges. To resolve them, we developed DeMixNB, a semi-reference-based deconvolution model assuming a sum of negative binomial distributions. Applications to miRNA-seq from 856 patients with breast cancer and 3,755 spatial spots from lung cancer generated either clinical or mechanistic insights into tumor cell plasticity. This supports the important utility of DeMixNB to investigate cancer RNomes.

bioinformatics↗

bioRxiv2025

Dual-Channel Event Microscopy for Ultrafast Biological Imaging

Many fundamental biological processes--spanning immune-tumor interactions, neuronal signaling, and microvascular flow--exhibit fast, multiscale dynamics among diverse cell types within three-dimensional tissue environments. Capturing such activity requires imaging systems that simultaneously achieve high temporal resolution, multicolor capability, and volumetric coverage over large fields of view (FOVs). However, existing modalities remain limited by trade-offs among imaging speed, spectral capacity, depth of field (DOF), and spatial resolution. Here, we present Dual-Channel Event Microscopy (DEM), which integrates digital micromirror device (DMD)-based pulsed illumination, extended-DOF optics, and event-based sensing for ultrafast, dual-channel volumetric imaging across a 2.3 x 1.3 mm2 FOV with an effective 200 {micro}m DOF. Using dual-color fluorescent phantoms and microsphere flow assays, DEM achieves accurate spectral separation and reconstruction of rapid motion at kilohertz frame rates. In vivo, DEM enables simultaneous visualization of neutrophils and premalignant tumors in freely swimming zebrafish. In immobilized specimens, it provides robust, sensor-level optical sectioning near the heart, suppressing diffuse background to reveal fine vascular networks and active blood circulation into and out of the cardiac chambers. DEM further enables quantitative mapping of blood-flow dynamics in the zebrafish tail, resolving arterial-venous differences and capturing heartbeat-driven oscillations that reflect cardiac pumping with high temporal fidelity. By uniting ultrafast acquisition, dual-channel capability, volumetric coverage, and intrinsic optical sectioning within a single event-driven architecture, DEM offers a powerful platform for visualizing rapid multicellular interactions and physiological dynamics in living systems.

bioengineering↗

bioRxiv2025

The two-component nuclease-active KELShedu system confers broad antiphage activity via abortive infection

Bacteriophages and bacteria engage in a continuous evolutionary arms race, driving the development of intricate bacterial defense systems such as CRISPR-Cas, BREX, Gabija, and Shedu. Here, we characterize a two-component KELShedu system in Escherichia coli that confers resistance to phages via abortive infection. The KELShedu system comprises KELA, a dsDNA-binding protein, and KELB, a metal ion-dependent nuclease harboring the DUF4263 domain. In addition, we find that physiological levels of NTP inhibit the DNA cleavage activity of the KELShedu system, suggesting that KELShedus activation depends on reduced intracellular NTP levels during phage invasion. Our research demonstrates that the KELShedu system responds to nucleotide depletion triggered by phage replication, leading to non-specific degradation of cellular DNA and ultimately inducing abortive infection. These insights into the KELShedu system expand the repertoire of bacterial anti-phage mechanisms and lay the groundwork for novel applications in microbial engineering and therapeutic development. TeaserThe KELShedu system exerts anti-phage function by aborting infection in response to physiological changes following phage infection

microbiology↗

bioRxiv2025

Fast Multi-objective RNA Optimization with Autoregressive Reinforcement Learning

Codon optimization is essential in mRNA vaccine development, while existing tools face limitations in the computational efficiency, sequence diversity and universality. To address these challenges, we develop RNAJog (RNA Joint Optimization with autoregressive Generative model), a framework integrating autoregressive generation with reinforcement learning to optimize codon sequences for minimum free energy (MFE), codon adaptation index (CAI) and GC content, even enabling sequence design without requiring annotated training data. Evaluations in both in silico and wet-lab experiments have confirmed RNAJogs effectiveness and efficiency, with two orders of magnitude faster than traditional algorithm (LinearDesign) for long RNA sequence and about a 10-fold increase in antibody titer compared to the wild-type mRNA for Influenza virus hemagglutinin (HA) mRNA vaccine design in mouse. RNAJog also supports biological constraints for sequence optimization. Using this feature, we minimized m6A modification motifs in Bmp2 coding sequence for enhancing the translational efficiency and RNA stability, which are validated in cell-based experiments.

bioinformatics↗

bioRxiv2025

Whole-transcriptome-scale and isoform-resolved spatial imaging of single cells in complex tissues

Cell and tissue functions arise from complex interactions among numerous genes, and a systematic understanding of these functions requires isoform-resolved, whole-transcriptome-scale analysis of single cells with high spatial resolution. Here, we introduce a spatial transcriptomics method based on a novel in situ RNA amplification strategy, enabling short RNA sequence detection and hence spatially resolved expression profiling of individual cells at the whole-transcriptome scale with splice-isoform resolution. Using this approach, we imaged [~]33,000 distinct RNAs--including [~]23,000 genes and [~]10,000 isoform-defining transcripts--in the mouse brain. Our data enabled systematic analyses of region- and cell-type-specific gene programs and ligand-receptor-based cell-cell communications. These data further revealed a rich spatial diversity and cell-type specificity in isoform usage across numerous genes and identified brain structures particularly enriched for specific isoform usage. We anticipate broad application of this method for molecular and cellular analysis of tissues, unlocking previously inaccessible discoveries in cell and organismal biology.

genomics↗

bioRxiv2025

Temporal control of sgRNA library activation unlocks large-scale in vivo CRISPR screens

Functional genomics screens have illuminated genetic dependencies in cancer, but conventional in vitro approaches fail to capture vulnerabilities shaped by the tumor microenvironment. Here, we implement CRISPR-StAR (Stochastic Activation by Recombination), a next-generation inducible CRISPR screening platform for large-scale in vivo applications. The system uses a dual lox-based recombination system to enable guide-level normalization and clonal knockout phenotyping. To analyze the rich (barcode-embedded) sequencing output, we developed UMIBB, a superior Bayesian statistical framework for quantifying gene-level dropout and enrichment compared to conventional software packages. Screening a 30,000-sgRNA library in A549 xenografts, followed by clone representation and dropout correlation analyses, showed high fidelity and reproducibility with dropout phenotypes resolvable using as few as 30 tumors for this size library. Validation across multiple tumor models demonstrated that a single tumor can provide reliable, functional annotation for [~]1,000 genes leveraging intra-tumor library controls for normalization. Comparing in vivo and in vitro screens revealed that a substantial subset of tumor suppressor genes exerts strong phenotypic effects only observable in vivo. For example, single-gene knockout and transcriptomic profiling confirmed that KMT2C and KMT2D have contrasting impacts on tumor growth - an insight that would have been overlooked in standard cell culture. Looking ahead, CRISPR-StAR screening, combined with our user-friendly analysis pipeline available on GitHub (R-package), offer an integrated framework for creating in vivo dependency maps that can complement existing vitro datasets like DepMap and Achilles. Critically, our approach reduces animal use by up to 7-fold compared to conventional in vivo dropout screens. This represents a significant ethical and methodological advancement - achieving genome-scale resolution with far fewer animals and greater reproducibility.

genomics↗

bioRxiv2025

Predicting the Evolutionary and Functional Landscapes of Viruses with a Unified Nucleotide-Protein Language Model: LucaVirus

Predicting viral evolution and function remains a central challenge in biology, hindered by high sequence divergence and limited knowledge compared to cellular organisms. Here, we introduce LucaVirus, a multi-modal foundation model for viruses, trained on 25.4 billion nucleotide and amino acid tokens covering nearly all known viruses. LucaVirus learns biologically meaningful representations capturing relationships between sequences, protein/gene homology, and evolutionary divergence. Using these embeddings, we developed downstream models that address key virology tasks: identifying hidden viruses in genomic "dark matter", annotating enzymatic activities of uncharacterized proteins, predicting viral evolvability, and identifying antibody candidates for emerging viruses. LucaVirus achieves state-of-the-art results in three tasks and matches leading models in the fourth with one-third the parameters. Together, these findings demonstrate the power of a unified foundation model to comprehensively decode the viral world and establish LucaVirus as an efficient and versatile platform for AI-driven virology, from virus discovery to functional and therapeutic predictions.

bioinformatics↗

bioRxiv2025

Mitochondrial calcium influx-driven bioenergetics in the dopaminergic system selectively enable drug reward

Drugs of abuse hijack the brains reward system, driving pathological dopamine surges that underlie compulsive behavior and addiction. However, directly targeting dopamine signaling for treatment risks disrupting natural reward processes. Here, we identify a bioenergetic mechanism that selectively promotes addiction-related dopamine release and behaviors. Opioids and methamphetamine, but not natural rewards, induce mitochondrial calcium (Ca2+) influx via the mitochondrial calcium uniporter (MCU) in dopaminergic terminals of the nucleus accumbens. Optogenetic stimulation reveals that this mitochondrial Ca2+ influx occurs exclusively during high-intensity dopaminergic neuronal activation. This Ca2+ influx drives rapid ATP production, compensating for energy deficits caused by neuronal hyperactivity and enabling sustained dopamine release. Genetic deletion or pharmacological inhibition of MCU in dopaminergic neurons selectively reduces drug-induced dopamine release and prevents addictive behaviors while sparing natural reward processing in mice. These findings uncover a distinct mitochondrial bioenergetic mechanism underlying drug reward and propose MCU as a promising therapeutic target for addiction treatment.

neuroscience↗

bioRxiv2025

A genetically encoded bifunctional enzyme mitigates redox imbalance and lipotoxicity via engineered Gro3P-Glycerol shunt

Dihydroxyacetone phosphate (DHAP), glycerol-3-phosphate (Gro3P) and reduced/oxidized nicotinamide adenine dinucleotide (NADH/NAD+) are key metabolites of the Gro3P shuttle system that forms a redox circuit, allowing transfer of reducing equivalents between cytosol and mitochondria. Targeted activation of Gro3P biosynthesis was recently identified as a promising strategy to alleviate reductive stress by promoting NAD+ recycling, including in cells with an impaired mitochondrial complex I. However, because Gro3P constitutes the backbone of triglycerides under some circumstances, its accumulation can lead to excessive fat deposition. Here, we present the development of a novel genetically encoded tool based on a di-domain glycerol-3-phosphate dehydrogenase from algae Chlamydomonas reinhardtii (CrGPDH), which is a bifunctional enzyme that can recycle NAD+ while converting DHAP to Gro3P. In addition, this enzyme possesses an N-terminal domain which cleaves Gro3P into glycerol and inorganic phosphate (Pi) (in humans and other organisms, this reaction is catalyzed by a separate glycerol-3-phosphate phosphatase, a reaction also known as "glycerol shunt"). When expressed in mammalian cells, CrGPDH diminished Gro3P levels and boosted the TCA cycle and fatty acid {beta}-oxidation in mitochondria. CrGPDH expression alone supported proliferation of HeLa cells under conditions of either inhibited activity of the mitochondrial electron transport chain or hypoxia. Moreover, human kidney cancer cells, which exhibit abnormal lipid accumulation, had decreased triglycerides levels when expressing CrGPDH. Our findings suggest that the coordinated boosting of both Gro3P biosynthesis and glycerol shunt may be a viable strategy to alleviate consequences of redox imbalance and associated impaired lipogenesis in a wide repertoire of conditions, ranging from primary mitochondrial diseases to obesity, type 2 diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD).

bioengineering↗

bioRxiv2025

Alignment-driven Cross-Graph Modeling for 3D RNA Inverse Folding

The 3D RNA inverse folding task aims to design RNA sequences that can fold into a target tertiary structure. This task is complicated by the inherent structural complexity and flexibility of RNA molecules. Considering that conserved RNA structures often exhibit sequence similarity, we present AlignIF, a pipeline designed to generate RNA sequences conditioned on the target RNA 3D structure and its multiple structure alignment (MStA), explicitly incorporating structural evolutionary conservation. AlignIF introduces a novel MStA backbone encoder designed to model multiple geometric graphs derived from MStA, which can better capture meaningful information from MStA structures. Additionally, AlignIF uses a random-ordered autoregressive sequence decoder to generate candidate RNA sequences. In our constructed benchmark dataset, AlignIF achieves a perplexity of 2.32 and a recovery rate of 57. 08%, respectively, a relative improvement of 17.8% and 11.8% over the state- of-the-art methods. In addition, AlignIF excels in diversity and ranking capability. We further demonstrate the added value of the incorporated MStA structures along with the proposed encoder architecture.

bioinformatics↗