Biology subjects

Liu, Q.

Publications and source records attributed to Liu, Q..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

The Rab32-LRMDA-Retriever Complex is a Key Regulator of Intestinal Immune Homeostasis

Maintaining intestinal homeostasis relies on the intricate interplay among the mucosal epithelium, immune system, and host microbiome. A key question is how innate immune cells sense and process microbes in the gut lumen, eliciting appropriate protective responses without causing tissue injury. Clearance of invading microbes and initiation of downstream inflammatory responses are central to this process and require proper function of the endolysosomal system. Dysfunction of this system can predispose the host to chronic inflammatory disorders and acute infections. Here, through forward genetic screening of N-ethyl-N-nitrosourea (ENU)-mutagenized mice and CRISPR/Cas9 validation, we identify Lrmda, encoding leucine-rich melanocyte differentiation-associated protein (LRMDA), as a key regulator of intestinal homeostasis. Using hematopoietic chimera and conditional knockouts, we show that LRMDA functions primarily in CD11c+ cells, including mucosal dendritic cells (DCs) and macrophages, but not in non-hematopoietic cells. Proteomic, cellular, and biochemical analyses reveal that LRMDA directly and cooperatively interacts with the endolysosome-specific small GTPase Rab32 and the endosomal recycling complex Retriever. Loss of LRMDA or Retriever function increases susceptibility to dextran sodium sulfate (DSS)-induced colitis and impairs clearance of Listeria monocytogenes. Together, our findings establish the Rab32-LRMDA-Retriever complex as a critical regulator of endolysosomal trafficking in innate immune cells, essential for maintaining intestinal immune homeostasis.

immunology↗

bioRxiv2025

A new double reporter strategy reveals a subset of non-migratory hematopoietic stem cells localized in a dynamic bone marrow niche.

Interactions of hematopoietic stem cells (HSCs) with the bone marrow microenvironment are critical to regulate stem cell function in homeostasis, emergency hematopoiesis and ageing. The dynamic behavior of endogenous HSCs in their niches has been challenging to study due to the complexity of markers required to define pure HSCs. Multiple recently developed reporter strategies advanced our capacity to identify HSCs in situ. Yet, they provide different levels of HSC enrichment and are frequently not stable in situations of perturbed hematopoiesis, leading to contradictory observations of HSC migratory behavior. Here we employed a new double reporter strategy by combining the Hoxb5-mKO2 and Vwf-GFP reporters, that are stably expressed in homeostasis, stress and ageing. Hoxb5+Vwf+ cells represent a subset of highly pure and potent long-term HSCs with a platelet-biased pattern of differentiation, that are included in the populations identified by other reporter strategies. Intravital microscopy revealed Hoxb5+Vwf+ cells to be non-migratory in homeostasis, following platelet depletion when these cells are actively proliferating, and during ageing. This non-migratory behavior of HSCs in homeostasis and stress indicates that their activation does not inherently depend on relocation to alternative niches eliciting proliferation, as previously proposed. We found Hoxb5+Vwf+ HSCs in direct contact with vasculature and LepR+ perivascular cells but not preferentially closer to megakaryocytes. Nevertheless, increased megakaryopoiesis following platelet depletion brings megakaryocytes into close proximity of Hoxb5+Vwf+ HSCs, revealing previously unrecognized niche dynamics in HSC regulation during regeneration. DATA SHARING STATEMENTOriginal data will be made available upon reasonable request. The RNA sequencing data used in this study has been published before35 and is available from GEO (GSE81682). The code used for analysis is available from https://github.com/loversaber/Secchi_Paper_RNAseq_Analysis.git. KEY POINTSO_LICombined expression of Hoxb5-mKO2 and Vwf-GFP reporters defines a population of highly enriched long-term HSCs in situ and in vivo. C_LIO_LIHoxb5+Vwf+ HSCs are non-migratory in homeostasis, stress hematopoiesis and ageing, while their niche is highly dynamic. C_LI

cell biology↗

bioRxiv2025

EZpred: improving deep learning-based enzyme function prediction using unlabeled sequence homologs

Features extracted from sequence homologs significantly enhance the accuracy of deep learning-based protein structure prediction. Indeed, models such as AlphaFold, which extracts features from sequence homologs, generally produce more accurate protein structures compared to single sequence-based methods like ESMfold. In contrast, features from sequence homologs are seldom employed for deep learning-based protein function prediction. Although a small number of models also incorporate function labels from sequence homologs, they cannot utilize features extracted from sequence homologs that lack function labels. To address this gap, we propose EZpred, which is the first deep learning model to use unlabeled sequence homologs for protein function prediction. Starting with the target sequence and homologs identified by MMseqs2, EZpred extracts sequence features using the ESMC protein language model. These features are then fed into a deep learning model to predict the Enzyme Commission (EC) numbers of the target protein. For 753 enzymes, the F1-score of EZpred EC number prediction is 4% higher than a similar model that does not use sequence homologs and at least 10% higher that state-of-the-art EC number prediction models. These results demonstrate the strong positive impact of sequence homologs in deep learning-based enzyme function prediction. Significance StatementMultiple sequence alignment (MSA) of homologous sequences is the most important source of input feature for deep learning-based protein structure prediction. Yet, this kind of feature is rarely used for protein function prediction. We propose the first deep learning model that significantly improves protein function prediction using features from sequence homologs without functional labels. This shows the utility of an important set of features that are often overlooked by previous studies.

bioinformatics↗

bioRxiv2025

An efficient beet severe curly top virus-based VIGS vector in Beta vulgaris

Sugar beet (Beta vulgaris L.) is one of the most important sugar crops worldwide. However, studies on sugar beet gene functions are still lagging compared to other crops due to the lack of an effective genetic manipulation. In this study, we generated an infectious clone of beet sever curly top virus (BSCTV) and engineered it into a VIGS vector. BSCTV-BvPDS can induce strong and persistent bleached PDS-silencing phenotype in 8 tested sugar beet varieties. We further efficiently silenced B. vulgaris MYB1 gene that regulating the betalain pathway in sugar beet by BSCTV-based VIGS system. Finally, we used this system to silence the effector CbNip1 of Cercospora beticola through cross-kingdom RNAi, resulting in reduced disease symptoms in sugar beet plants. Therefore, the BSCTV VIGS system can be used to study sugar beet-pathogen interactions. In summary, we have established an easy and efficiency BSCTV-based VIGS vector in sugar beet. This system will be an attractive tool for functional genomic studies for sugar beet.

plant biology↗

bioRxiv2025

O-GlcNAcylation-Ubiquitin Crosstalk of METTL1 Drives m7G Epitranscriptomic Collapse and Lipid Metabolic Reprogramming in Diabetic Cardiomyopathy

BackgroundMetabolic remodelling and memory in cardiomyocytes is a pivotal mechanism underlying cardiomyopathy pathogenesis. Clinical observations demonstrate persistent progression of hyperglycaemia-induced multiorgan damage following blood glucose stabilization, which is predominantly mediated through epigenetic regulation. While prior studies have identified epigenetic contributions to hyperglycaemic myocardial injury, the involvement of RNA methylation-particularly N7-methylguanosine (m7G) modification-in this regulatory network remains undefined. MethodsClinical specimens were collected from diabetic patients, and cardiomyocyte-specific METTL1 and ob/ob knockout murine models were established in parallel. Multiomic profiling (proteomics, glycoproteomics, ubiquitinomics, m7G-MeRIP sequencing, and metabolomics) was systematically conducted. The molecular mechanisms governing METTL1 regulation via O-GlcNAcylation and ubiquitination were elucidated through integrated in vitro and in vivo assays. A DUB siRNA library and computational strategies combining molecular docking with molecular dynamics simulations were employed for screening drugs targeting METTL1 O-GlcNAcylation, followed by in vivo therapeutic validation. ResultsComparative analysis of diabetic murine and human samples revealed strong METTL1 downregulation in cardiomyopathy contexts. Tamoxifen-inducible METTL1 knockout mice presented exacerbated diabetic cardiomyopathy phenotypes, confirming its cardioprotective function. Multiomic integration demonstrated that METTL1-mediated m7G modification critically regulates cardiomyocyte fatty acid metabolism. Mechanistically, hyperglycaemia was found to induce O-GlcNAcylation at the METTL1-T268 residue, suppressing m7G methyltransferase activity by 38% (p < 0.01). Subsequent investigations revealed that USP5 deubiquitinase activity is impaired under hyperglycaemic conditions, leading to accelerated METTL1 degradation. Notably, administration of the first-in-class small drug HIT106265621 significantly attenuated cardiomyopathy-associated pathological alterations in ob/ob mice in vivo. ConclusionHyperglycaemia promotes METTL1 O-GlcNAcylation, which impedes USP5-mediated deubiquitination, consequently reducing cardiomyocyte METTL1 protein levels and m7G modification. METTL1 deficiency drives diabetic cardiomyopathy progression through fatty acid metabolic dysregulation, inflammatory activation, and myocardial hypertrophy. Pharmacological inhibition of the OGT-METTL1 interaction using HIT106265621 has therapeutic potential for metabolic cardiomyopathy intervention.

pathology↗

bioRxiv2025

DiffMethylTools: a toolbox of the detection, annotation and visualization of differential DNA methylation

DNA methylation is a fundamental epigenetic mechanism, and its significant changes (i.e., differential methylation) regulate gene expression, cell-type specification and disease progression without altering the underlying DNA sequence. Differential methylation was usually detected via existing statistical tools by comparing two groups of methyomes (i.e. whole-genome methylation profiles) and has wide applications of various downstream investigations for human disease studies. However, few toolboxes were available to efficiently streamline methylation investigation by integrating robust detection, annotation and visualization of differential methylation. Also, differential methylation detected via tools has poor reproducibility and no tools were tested on the increasing volume of long read methylomes. To address these issues, we introduced DiffMethylTools, an end-to-end solution to eliminate analytical and computational difficulties for differential methylation dissection. Comparison of detection performance on six datasets including three long-read methylomes demonstrated that DiffMethylTools achieved overall better performance of detecting differential methylation than existing tools like MethylKit, DSS, MethylSig, and bsseq. Besides, DiffMethylTools supported versatile input formats for seamless transition from upstream methylation detection tools, and offered diverse annotations and visualizations to facilitate downstream investigations. DiffMethylTools therefore offered a robust, interpretable, and user-friendly solution for differential methylation investigation, benefiting the dissection of methylations roles in human disease studies.

bioinformatics↗

bioRxiv2025

Ambient Proteins: Training Diffusion Models on Low Quality Structures

We present Ambient Protein Diffusion, a framework for training protein diffusion models that generates structures with unprecedented diversity and quality. State-of-the-art generative models are trained on computationally derived structures from AlphaFold2 (AF), as experimentally determined structures are relatively scarce. The resulting models are therefore limited by the quality of synthetic datasets. Since the accuracy of AF predictions degrades with increasing protein length and complexity, de novo generation of long, complex proteins remains challenging. Ambient Protein Diffusion overcomes this problem by treating low-confidence AF structures as corrupted data. Rather than simply filtering out low-quality AF structures, our method adjusts the diffusion objective for each structure based on its corruption level, allowing the model to learn from both high and low quality structures. Empirically, Ambient Protein Diffusion yields major improvements: on proteins with 700 residues, diversity increases from 45% to 86% from the previous state-of-the-art, and designability improves from 68% to 86%. We will make all of our code, models and datasets available under the following repository: https://github.com/jozhang97/ambient-proteins.

bioengineering↗

bioRxiv2025

Cryo-electron microscopy structures of a high-affinity zinc ABC transporter

Zn2+ is an essential micronutrient to all living organisms and plays a key role in various physiological functions. Microorganisms employ high-affinity Zn2+ ABC transporters to uptake zinc from the environment when it is scarce. However, the mechanism of zinc uptake and its regulation remain poorly understood. Here, we report the cryo-electron microscopy structure of the Zn2+ ABC transporter complex ZnuB-ZnuC from Escherichia Coli. The complex has two ZnuB subunits for transport and two ZnuC subunits for regulation. The ZnuB homodimer is in an outward-facing, closed conformation with a large hydrophilic cavity at the dimer interface. The ZnuC subunits contain an N-terminal ATP-binding cassette (ABC) domain (NBD) and a C-terminal zinc-sensing domain (ZSD). Zn2+ binding to the ZSD locks the transporter in a closed state regardless of nucleotide, whereas under low-Zn2+ conditions, ZSD C-terminal disorder permits ATP-driven zinc uptake. High-affinity zinc ABC transporters are ubiquitously utilized by pathogenic bacteria to evade host immune systems in competing for essential Zn2+. These findings highlight new pharmaceutical targets for disrupting Zn2+ homeostasis in antibiotic-resistant pathogens. One-sentence summaryHigh-affinity zinc ABC transporter structures show a self-regulating mechanism via a built-in zinc sensor.

biochemistry↗

bioRxiv2025

OocystMeter, a machine-learning algorithm to count and measure Plasmodium oocysts, reveals clustering patterns in the Anopheles midgut

We present OocystMeter, a machine learning-based software developed to automate the segmentation of malaria oocysts from images of mosquito midguts stained with mercurochrome. Existing bioimage analysis tools, including machine learning-based ones, often struggle with the unique staining patterns, complex midgut backgrounds, and variable morphology of oocysts, making the determination of oocyst size and numbers cumbersome. To overcome these challenges, we curated a high-quality dataset comprised of 11,178 Plasmodium falciparum oocysts in Anopheles gambiae midguts annotated by expert parasitologists. Using this dataset, we fine-tuned a Mask R-CNN object detection model to achieve segmentation accuracy comparable to human parasitologists (Spearmans correlation of 0.998 for oocyst counts and 0.978 for size measurements). Applying this tool in conjunction with spatial analysis, we uncovered a non-random, clustered spatial distribution of oocysts independent of the midguts anatomical regions or geometric axes, particularly in infections with fewer than 75 oocysts/midgut. Our workflow significantly accelerates malaria oocyst intensity and size analysis, reduces human bias, and provides spatial coordinates for advanced parasitology studies. OocystMeter is freely available at https://github.com/duopeng/OocystMeter, and as a web tool at http://Oocystmeter.org/, offering a valuable resource for researchers investigating the oocyst stage of malaria development.

bioinformatics↗

bioRxiv2025

Substrate binding and channeling allosterically modulate the interactions within the AlkB-AlkG electron transfer complex

The transmembrane alkane monooxygenase AlkB and rubredoxin AlkG form an electron transfer complex that hydroxylates terminal alkanes to produce alcohols. The recent cryoEM study of Fontimonas thermophila AlkB-AlkG complex (FtAlkBG) revealed its architecture, including a dodecane (D12) substrate in the diiron active site. However, the molecular mechanism of action of FtAlkBG remains unknown. Here, we examined the FtAlkBG dynamics and interactions by multiscale computations, including molecular dynamics (MD) simulations, elastic network model (ENM), and QM/MM study of the oxidative mechanism at the catalytic site of AlkB. D12 remained stably bound to the catalytic site during MD runs, coordinated by hydrophobic residues I267, L263, L264, and I133, critical to substrate stabilization. MD simulations further showed that D12 could exit the catalytic site via a well-defined translocation pathway gated by I54, S49, and F46, through two intermediate states before nearly leaving the protein, and diffuse back to the active site. Several salt bridges and conserved AlkB-AlkG contacts were enhanced upon substrate binding. Substrate binding also mediated the association/dissociation events required for efficient electron transfer. It promoted tighter coupling between the diiron center in AlkB and the iron in AlkG. The allosteric effects relevant to enzymatic activity regulated by the substrate binding and channeling were further delineated by ENM analysis which confirmed a strong coupling spanning the site of entry from the membrane, the catalytic site and the AlkB-AlkG interface. Our study provides new insights into key sites that could be targeted for developing AlkB-variants with desirable alkane conversion functions.

molecular biology↗

bioRxiv2025

Versatile Vasculature Chips for Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) has revolutionized microvasculature imaging by surpassing the diffraction limit via microbubbles. ULM demonstrates exceptional potential to resolve micrometer-scale vascular structures in both preclinical and clinical studies. However, its performance evaluation remains challenging primarily due to the lack of reference microvascular phantoms featuring micrometer-scale, hierarchical branches, and realistic vascular structures. Inspired by microfluidic chip techniques, we present an organ-on-a-chip protocol for fabricating agarose-based micro-vessel network phantoms with ground truth. The vasculature pattern offers design versatility, enabling on-demand customization. We experimentally demonstrated the feasibility of the vasculature phantom using two adapted patterns. The first was a leaf pattern, which exhibited intrinsic quasi-two-dimensional venation network with hierarchical and branching channels similar to animal vasculature. The second was a kidney pattern, which was based on a two-dimensional projection of real human vasculature obtained from micro computed tomography. The microbubble solution was perfused into the phantoms by capillary force and gravity. The ULM-reconstructed vasculature maps agreed well with the ground truth. ULM achieved a high sensitivity of 0.97 and 0.95, but a low precision of 0.37 and 0.60, for the leaf and kidney phantom, respectively. The results indicated the capability of ULM to reconstruct vessel structures while making many false positive predictions. The proposed protocol holds significant promise for the development and optimization of ultrasound microvascular imaging techniques.

bioengineering↗

bioRxiv2025

Single oocyte full-length isoform sequencing unveils the impact of transposable elements on RNA diversity and stability during oocyte maturation

The oocyte-specific isoforms play crucial roles in oocyte maturation, while current understanding of the oocyte transcriptome is mainly focused on gene level. Here, we utilized single cell full-length isoform sequencing based on third generation sequencing to detect entire transcripts in human and mouse oocytes. Isoform diversity during oocyte maturation was systematically profiled, including 6,736 and 4,902 putative novel human and mouse transcripts, respectively. More than half of novel isoforms were categorized as the novel-not-in-catalog (NNC) and may serve specific functions in oocytes, including novel isoforms of ARHGAP18, colocalized with microtubules and targeted knockdown disrupting oocyte maturation. Moreover, over 25% of NNC isoforms were derived from transposable elements (TEs), and their incorporation within transcripts could enhance isoform stability during oocyte maturation. Altogether, our findings represent a valuable resource showcasing the complexity and diversity of RNA isoforms in oocytes, as well as TE co-option for novel isoform generation and isoform stability enhancement.

developmental biology↗

bioRxiv2025

Scavenger Receptor class F member 2 is an intracellular receptor for Hepatitis B virus

Hepatitis B virus (HBV) infects hepatocytes by specific binding to the cell-surface receptor --sodium taurocholate cotransporting polypeptide (NTCP)--through the preS1 region of its large envelop protein, followed by a less well understood transport process across the cytoplasm to the nucleus. Here, we report that scavenger receptor class F member 2 (SCARF2), a single-pass transmembrane protein, functions as an intracellular receptor for HBV. SCARF2 binds to a preS1 region downstream of the NTCP binding site through its N-terminal EGF-like domains 4-6, and its proline-rich C-terminal domain also plays an indispensable role in the infection. The internalized HBV virions are transported to the cytoplasmic side of nuclear pore complexes (NPCs) within the SCARF2-containing endosomes. HBV capsid release from the endosomal vesicles is significantly impaired by knockdown of SCARF2. These results suggest a model that SCARF2 conveys HBV to the periphery of NPCs and ultimately leads to viral capsid release for nuclear entry.

microbiology↗

bioRxiv2025

Unbiased recording of clonal potency reveals species-specific regulation of mammalian intestine

The mammalian intestine regenerates rapidly after damage, yet the clonal dynamics and species-specific regulation of different populations remain poorly understood. Here we used synthetic or naturally occurring DNA alterations to reconstruct clonal histories of the mouse and human intestinal epithelium at single-cell resolution. In mice, we uncovered the clonal architecture of different cell types and their roles in regeneration, supporting a hierarchical regenerative response model. We identified a rare embryonic precursor population that persisted in the adult and was crucial for regeneration after irradiation. This population was marked by Tob2, which is required for nuclear transport of Ascl2. A parallel clonal analysis of 65 human colonic biopsies revealed secretory lineage bias and an age-associated decline in clonal diversity in the distal colon. Unlike highly proliferative murine Lgr5+ stem cells, human LGR5+ cells were found largely quiescent, revealing species-specific difference in clonal potency, and suggesting a distinct regulation of intestinal stemness.

genomics↗

bioRxiv2025

Ultra-wide-field, deep, adaptive two-photon microscopy

Observing the activity patterns of large neural populations throughout the brain is essential for understanding brain function. However, capturing neural interactions across widely distributed brain regions from both superficial and deep cortical layers remains challenging with existing microscopy technologies. Here, we introduce a state-of-the-art two-photon microscopy system, ULTRA, capable of single-cell resolution imaging across an ultra-large field of view (FOV) exceeding 50 mm{superscript 2}, enabling deep and very wide field in vivo imaging. To demonstrate its capabilities, we conducted a series of experiments under multiple imaging conditions, successfully visualizing brain structures and neuronal activities spanning a spatial range of over 7 mm from superficial layers to depths of up to 900 m, while covering a volume of 45.24 mm3 in the mouse brain. This versatile imaging platform overcomes traditional spatial constraints, providing a powerful tool for comprehensive exploration of neuronal circuitry over extensive spatial scales with cellular resolution.

neuroscience↗

bioRxiv2025

{Psi}-Atlas: an integrated atlas for pseudouridine epitranscriptome

Pseudouridine ({Psi}) is a C5 glycoside isomer of uridine, formed by breaking the N1 glycosyl bond and undergoing a 180{degrees} base rotation. This modification stands as one of the most widespread post-transcriptional alterations in RNA and is universally distributed among diverse RNA species. The pervasiveness of this modification enhances RNA structural integrity, bestows unique structural and functional attributes upon the RNA molecules it adorns, and facilitates additional hydrogen bonding. However, the absence of a convenient and integrated intuitive visualization database encompassing all currently reported species and RNA types is evident. Here, we present the {Psi}-Atlas, an extensive database meticulously curated for the comprehensive collection and annotation of RNA pseudouridine. This database encompasses 554,895 pseudouridine modification sites across various RNA categories, including mRNA, ncRNA, tRNA, rRNA, and others, in 55 distinct species reported in current literature. The sequencing methodologies employed comprise the next-generation sequencing (NGS) techniques such as {Psi}-Seq, Pseudo-Seq, CeU-Seq, PSI-Seq, RBS-Seq, HydraPsiSeq, BID-Seq, PRAISE-Seq, as well as third-generation sequencing methods like Direct RNA sequencing (DRS). {Psi}-Atlas as the most comprehensive and integrated resource for RNA pseudouridine modifications to date. And the {Psi}-Atlas database offers an intuitive interface for information display and a myriad of analytical tools, including PsiVar and PsiFinder. In essence, this platform serves as a robust search and visualization tool for the study of pseudouridylation in epitranscriptomics. {Psi}-Atlas is available at https://rnainformatics.org.cn/PsiAtlas.

bioinformatics↗

bioRxiv2025

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

Here, we present OriGene, a self-evolving multi-agent system that functions as a virtual disease biologist, systematically identifying original and mechanistically grounded therapeutic targets at scale. OriGenes architecture integrates over 600 specialized tools through a Model Context Protocol (MCP), enabling it to reason across diverse data modalities including genomics, protein networks, pharmacology, clinical records and literature evidence, to generate and prioritize target discovery hypotheses. We implemented a strategy combining a knowledge graph-based Tool RAG with an advanced agent selection mechanism to enable dynamic, context-aware tool deployment. Through a self-evolving framework, OriGene continuously integrates human and experimental feedback to iteratively refine its core thinking templates, tool composition, and analytical protocols, thereby enhancing both accuracy and adaptability over time. To comprehensively evaluate its performance, we established TRQA, an original benchmark comprising over 1,900 expert-level question-answer pairs spanning a wide range of diseases and target classes. OriGene consistently outperforms human experts, leading research agents, and state-of-the-art large language models in accuracy, recall, and robustness, particularly under conditions of data sparsity or noise. Critically, OriGene nominated previously underexplored therapeutic targets for liver (GPR160) and colorectal cancer (ARG2), which demonstrated significant anti-tumor activity in patient-derived organoid and tumor fragment models mirroring human clinical exposures. These findings demonstrate OriGenes potential as a scalable and adaptive platform for AI-driven discovery of mechanistically grounded therapeutic targets, offering a new paradigm to accelerate drug development.

bioinformatics↗

bioRxiv2025

Dendritic computation for rule-based flexible categorization

A hallmark of intelligent behavior is the ability to flexibly respond to external sensory inputs based on dynamically changing rules. A central question is how neurons in the brain implement computations underlying intelligent behaviors. The neocortical pyramidal neurons use their elaborated dendritic arbors to segregate a plethora of inputs and dynamically integrate them--a process known as dendritic computation--which may play important roles in rule-dependent sensory processing. However, evidence directly linking dendritic computation with intelligent cognitive behaviors has been absent. Here we combine two-photon imaging and a rule-switching flexible categorization task in mice to show that a projectome-defined extratelencephalic (ET) cortical layer 5 (L5) neurons in the auditory cortex integrate dendritic rule information and somatic sensory input to enable rule-dependent flexible categorization. The apical dendrite and soma within the same ET neurons exhibit distinct compartmental representations for sensory and rule information, with the soma predominantly encoding sensory information and the dendrites representing inferred task rule. Simultaneous optogenetic dendritic inhibition and two-photon imaging revealed that dendritic rule coding is essential for somatic output of flexible categorization. Our findings indicate that nonlinear dendritic integration of rule and sensory information constitutes a neuronal computational mechanism underlying rule-switching flexible decision-making.

neuroscience↗