Biology subjects

Liu, Q.

Publications and source records attributed to Liu, Q..

At least 199 records · Page 11Linked to original sources

bioRxiv2025

Desiccation-induced DNA damage facilitates drug resistance in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is an obligate human pathogen that depends on its ability to spread from host-to-host to survive as a species. Yet, knowledge of transmission-specific traits remains lacking. Here, we report the discovery of a specific adaptive response to desiccation, a stress intrinsically linked to the generation of the aerosol droplets within which Mtb transmits. We show that desiccation inflicts oxidative damage and activates Mtbs DNA repair responses but that this repair is imperfect and results in mutations. We further show that activation of these DNA repair responses is accompanied by increased expression of the transcription-coupled repair factor, mfd, but that this expression serves to buffer the fitness cost of specific resistance-conferring mutations in rpoB, the target of the frontline drug rifampin, rather than to facilitate transcription-coupled DNA repair. Silencing mfd during aerosolization impairs survival of strains harboring the rifampin resistance allele S450L. This function is further supported by whole genome sequence data from over 50,000 clinically circulating strains. These studies indicate that Mtb has evolved transmission-specific stress responses that have enabled it to leverage desiccation-induced DNA damage as a potential source of genetic diversification and drug resistance.

microbiology↗

bioRxiv2025

Marker genes for predicting cytokine release syndrome in vitro before CAR T cell infusion

Cytokine release syndrome (CRS) and neurotoxicity are common adverse events of the Chimeric antigen receptor (CAR) T cell therapy. Assessing the cytotoxicity associated biomarkers would be essential for therapy design to avoid developing severe toxicities. In this study, we re-analyzed previously published RNAseq results of CAR T cells before infusion and combined it with the clinical response post infusion. We observed that CAR T cells from patients who developed severe CRS displayed a higher expression of TCL6, HPCAL4, CCDC144B, and SIRPG, but lower levels of IL2, IL21, and HSPA1B when stimulated with anti-CAR19 idiotypic antibody in vitro. Interestingly, the upregulated gene SIRPG is positively correlated with CRS severity. In addition, without stimulation, CAR T cells from CRS group showed a higher levels of IFNAR1, IL7R, ZNF69, and USP32P1 but lower levels of CCL3, IL4, IL17A, IL23R, IL13, CD70, and IFNGR2. These results provided insights to evaluate the adverse events of CAR T products before treatments, which could be beneficial for designing therapy plans.

immunology↗

bioRxiv2025

Aromatic Microbial Metabolite Hippuric Acid Potentiates Pro-Inflammatory Responses in Macrophages through TLR-MyD88 Signaling and Lipid Remodeling

The gut microbiome generates a diverse array of metabolites that actively shape host immunity, yet the pro-inflammatory potential of microbial metabolites remains poorly understood. In this study, we identified hippuric acid, an aromatic gut microbe-derived metabolite, as a potent enhancer of pro-inflammatory responses using a murine bacterial infection model and a non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. Administering hippuric acid intraperitoneally in murine models of Escherichia coli infection or LPS-induced inflammation significantly heightened pro-inflammatory responses and innate immune cell activation. In vitro, hippuric acid selectively potentiated M1-like macrophage polarization (LPS + IFN{gamma}) but had no effect on M2-like polarization (IL-4). Hippuric acid further enhanced responses to diverse MyD88-dependent TLR ligands, but not TRIF-dependent TLR3, implicating a possible mechanism of action via activation of TLR-MyD88 signaling. Genetic deletion of MyD88 abrogated the pro-inflammatory effects of hippuric acid both in vitro and in vivo, confirming its dependence on the MyD88 pathway. Transcriptomic and lipidomic analyses revealed that hippuric acid promoted cholesterol biosynthesis and lipid accumulation, linking microbial metabolism to lipid-driven immune activation. Notably, hippuric acid similarly enhanced pro-inflammatory responses in human macrophages, and its elevated levels correlated with increased sepsis mortality, highlighting its potential clinical relevance. These findings establish hippuric acid as a previously unrecognized microbial-derived inflammatory modulator, bridging gut microbial metabolism, lipid remodeling, and innate immune signaling, and offer new insights into its role in infection and inflammation.

immunology↗

bioRxiv2025

Riemann-GNN: Causal Reasoning on Hyperbolic Riemannian Manifolds for Interpretable Drug-Disease Prediction

The high cost and low success rate of de novo drug development have driven increasing interest in drug repurposing. Computational approaches to drug repurposing leverage large-scale biomedical knowledge graphs (KGs) to identify new indications for existing drugs. However, the structural heterogeneity, relation sparsity, and limited interpretability of current models remain significant challenges. In this study, we propose Riemann-GNN, an end-to-end framework that combines hyperbolic geometry, graph neural networks (GNNs), and causal inference for interpretable drug repurposing. Riemann-GNN embeds multi-relational biomedical KGs into a hyperbolic Riemannian manifold modeled by the Poincare ball, effectively capturing the hierarchical structure of biomedical entities. On top of this geometry, we introduce a causal reasoning module that extracts disease-centric subgraphs and applies intervention-based inference using structural causal models. This module provides mechanistic explanations for predicted drug-disease associations. We evaluate our model on PrimeKG, a comprehensive biomedical KG with over 129,000 nodes and 4 million edges, achieving AUROC = 0.971, AUPRC = 0.963, and Recall@20 = 0.847, significantly outperforming several state-of-the-art baselines. In a case study on amyotrophic lateral sclerosis (ALS), Riemann-GNN identifies 20 novel candidate drugs--such as Bosutinib, Masitinib, and Tamoxifen--and provides interpretable causal paths explaining their therapeutic relevance. Several predictions without existing ALS labels in the KG are supported by literature evidence, while others suggest new hypotheses for future validation. These results demonstrate the utility of integrating hyperbolic representation learning with causal semantics for robust and explainable biomedical AI.

bioinformatics↗

bioRxiv2025

Deregulated Myt3 translation predisposes islet β-cells to dysfunction under obesity-induced metabolic stress

In response to obesity-related metabolic stress, islet {beta}-cells adapt (or compensate) by increasing their secretory function and mass. Yet, for unknown reasons, this compensation is reversed in some individuals at some point to induce {beta}-cell failure and overt type 2 diabetes (T2D). We have previously shown that transcription factor Myt3 (St18) and its paralogs, Myt1 and Myt2, prevent {beta}-cell failure. Myt3 was induced at post-transcriptional levels by obesity-related stress in both mouse and human {beta} cells and its downregulation accompanied {beta}-cell dysfunction during T2D development. Single-nucleotide polymorphisms in MYT3 were associated with an increased risk of developing human diabetes. We now demonstrate that Myt3 translation is regulated by an upstream open-reading frame that overlaps with the main Myt3 open-reading frame in mice. Disrupting this overlap enhances Myt3 translation in mouse {beta} cells without metabolic stress but decreases it under high-fat-diet challenges. Consequently, this deregulation results in {beta}-cell dysfunction and glucose intolerance in mice, accompanied by compromised expression of several {beta}-cell function genes. These findings suggest that stress-induced Myt3 translation is part of the compensation mechanism that prevents {beta}-cell failure.

genetics↗

bioRxiv2025

Granuloma Dual RNA-Seq Reveals Composite Transcriptional Programs Driven by Neutrophils and Necrosis within Tuberculous Granulomas

Mycobacterial granulomas lie at the center of tuberculosis (TB) pathogenesis and represent a unique niche where infecting bacteria survive in nutrient-restricted conditions and in the face of a host immune response. The granulomas necrotic core, where bacteria reside extracellularly in humans, is difficult to assess in many experimentally tractable models. Here, using necrotic mycobacterial granulomas in adult zebrafish, we develop dual RNA-seq across different host genotypes to identify the transcriptional alterations that enable bacteria to survive within this key microenvironment. Through pharmacological and genetic interventions, we find that neutrophils within mature, necrotic granulomas promote bacterial growth, in part through upregulation of the bacterial devR regulon. We identify conserved suites of bacterial transcriptional programs induced only in the context of this unique necrotic extracellular niche, including bacterial modules related to K+ transport and rpf genes. Analysis of Mycobacterium tuberculosis strains across diverse lineages and human populations suggests that granuloma-specific transcriptional modules are targets for bacterial genetic adaptation in the context of human infection. Summary sentenceDual host-pathogen transcriptional profiling defines granuloma-specific programs during mycobacterial infection.

immunology↗

bioRxiv2025

BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical semantics distributed across text, tables, and figures, but also reconstructing chemically exact ligand structures (e.g., Markush structures). To address this bottleneck, we introduce BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW, a multi-modal extraction framework that explicitly separates bioactivity semantic interpretation from ligand structure construction. Within BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm, in which multi-modal large language models operate on chemically grounded visual representations to infer inter-structure relationships, and exact molecular construction is delegated to domain chemistry tools. For rigorous evaluation and method development, we further establish BO_SCPLOWIOC_SCPLOWVO_SCPLOWISTAC_SCPLOW, a comprehensive benchmark comprising 16,457 bioactivity entries curated from 500 publications. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW validates its extraction ability and provides a quantitative baseline, achieving an F1 score of 0.32 for bioactivity triplets. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOWs practical utility is demonstrated via three applications: (1) extracting 82,262 data from 11,683 papers to build a pre-training database that improves downstream models performance by 3.9%; (2) enabling a human-in-the-loop workflow that doubles the number of high-quality NLRP3 bioactivity data, helping 38.6% improvement over 28 QSAR models and identification of 16 hit candidates with novel scaffolds; and (3) accelerating protein-ligand complex bioactivity annotation, achieving a 5.59-fold speed increase and 5.75% accuracy improvement over manual workflows in PoseBusters dataset. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW and BO_SCPLOWIOC_SCPLOWVO_SCPLOWISTAC_SCPLOW provide a scalable extraction methodology and a rigorous benchmark, paving the way to unlock bioactivity data that previously required extensive human effort. All data and code are available at GitHub.

bioinformatics↗

bioRxiv2025

Sustained cross-species transmission of gammacoronavirus in wild birds reveled by viral characterization in China

Gammacoronavirus ({gamma}-CoV) primarily infects poultry, wild birds, and marine mammals. The widespread distribution and circulation of {gamma}-CoV in the ecological environment may lead to sustained transmission and economic loss. To better understand the diversity of {gamma}-CoV in wild birds, we collect 482 wild-bird fecal samples from Yunnan, encompassing fourteen bird species. We detected twelve {gamma}-CoV positive samples in five bird species, with the characterization of five complete genomes - HNU5-1, HNU5-2, HNU5-3, HNU6-1, and HNU6-2-indicating that these genomes represent two viral species. The HNU5 strains were derived from Black-headed gull (Chroicocephalus ridibundus), while the HNU6 strains were came from Mallard (Anas platyrhynchos), and both of those were recombinant. The HNU5 strain exhibited the highest sequence identity (95.45%) with a {gamma}-CoV strain isolated from Numenius phaeopus (GenBank accession: PP845452). Similarly, the HNU6 strain showed 95.18% nucleotide identity with a {gamma}-CoV strain (GenBank accession: PP845437) derived from Anas platyrhynchos. Taxonomic analysis confirmed that HNU6s belong to the Gammacoronavirus anatis species, while HNU5s attributed to a new species. Cross-species analysis revealed active host-switching events among {gamma}-CoVs, indicating potential transmission of {gamma}-CoVs from marine mammals to wild bird, from wild bird to poultry, and inter-wild bird and inter-poultry transmission. In summary, we report five new {gamma}-CoV strains in wild birds and outline the cross-species transmission of {gamma}-CoVs. Our findings link {gamma}-CoV hosts across different natural environments and provide new insights for exploring {gamma}-CoVs.

microbiology↗

bioRxiv2025

The identification and characterization of hillocks in the postnatal mouse and human airway

Adult lung regeneration has been deeply scrutinized over the past decade. In contrast, the injury resistance mechanisms of the neonatal and pediatric airway have received considerably less attention despite the manifest clinical importance. We recently reported the discovery of the airway hillock in adult murine and human airways. Adult hillocks are stratified structures with luminal squamous barrier cells overlying a dedicated basal stem cell population. Functionally, hillocks serve as an injury-resistant reservoir of dedicated hillock stem cells that can resurface and repopulate the airway epithelium after severe damage. Indeed, hillock basal stem cells undergo massive clonal expansion in the process of repopulating denuded airway epithelium. Since the postnatal lung encounters injuries that can result in airway epithelial denudation in the setting of respiratory infection or aspiration, we sought to assess whether hillocks are present in the neonatal airway. In this manuscript we identify and characterize hillocks in postnatal mouse and human pediatric airways. We show that hillocks are present in mice from postnatal day 3 onwards and that they expand in size through adulthood. The earliest hillocks are functionally immature, but they acquire their injury resistance properties over the course of postnatal maturation. By re-analyzing published pediatric scRNAseq data, we identify cells with a hillock squamous cell gene signature that is conserved across species. Finally, we identify bona fide hillocks in an 8-month-old infant and an 8-year-old child. We now wonder whether the presence and maturation of hillocks has implications for disorders of the neonatal and childhood airway. More specifically, we hypothesize that the incomplete maturation of hillocks could create a window of vulnerability in neonates who may be particularly susceptible to airway damage in the setting of infection or aspiration. Thus, there is a need to define when human hillocks first form and establish the time window during which they functionally mature as a prelude to determining whether the appearance and properties of hillocks correlate to clinical phenotypes and outcomes.

developmental biology↗

bioRxiv2025

Transfer Learning and Permutation-Invariance improving Predicting Genome-wide, Cell-Specific and Directional Interventions Effects of Complex Systems

With the advent of precision medicine, single-drug treatments may not fully satisfy the pursuit of precision medicine. However, single-drug treatments have accumulated a large amount of data and a considerable number of deep learning models. In this context, using transfer learning to effectively leverage the existing vast amount of single-compound intervention effect data to build models that can accurately predict the intervention effects of complex systems is highly worth investigating. In this study, we used a deep model based on permutation-invariance as the core module, pre-trained on a large amount of single-compound intervention data in cell lines, and fine-tuned on a small amount of complex system (like natural products) intervention data in cell lines, resulting in a predictive model named SETComp (the Concat version with ~200M parameters and the Add version with ~173M parameters). The two versions of SETComp achieved an accuracy of 93.86% and 92.70%, respectively, on the complex system-cell-gene association test set, improving by 5.82% to 27.59% compared to the baseline. When predicting the intervention effects of those complex systems the model had never encountered before, the accuracy increased by up to 24.83% compared to the baseline. In our in vitro validation, up to 88.65% of the predictions were confirmed to be correct, and the models output showed a significant positive correlation with the real-world foldchange. We further observed SETComps potential in various biomedical scenarios, achieving good performance in applications such as mechanism uncovering, repositioning, and compound synergy discovery.

systems biology↗

bioRxiv2025

A subset of Haematopoietic Stem Cells resists Plasmodium infection-induced stress by uncoupling interferon sensing and metabolic activation

Hematopoietic stem cells (HSCs) sustain lifelong haematopoiesis as their progeny differentiate into all blood cell lineages. Homeostatic HSCs are mostly quiescent and only rarely divide, however their proliferation and differentiation rates can be modulated by external factors. Acute and chronic infections from a wide range of pathogens are known to challenge HSCs at the population level, being forced to respond to inflammation-mediated organismal demand to replenish the myeloid cell pool. However, less is known about the degree of heterogeneity in the HSCs response to inflammation at the single cell level. Here, using a natural murine malaria model and an NHS-ester biotin dilution assay we identify two subsets of HSCs, BiotinLo and BiotinHi, with distinct proliferation kinetics. Using combined functional, single-cell transcriptomics and phenotypic analyses, we uncover that BiotinHi HSCs remain highly functional despite expressing strong interferon response signatures. These infection-resistant HSCs express high levels of MHC II and are metabolically distinct from the remaining HSCs as they maintain less active mitochondria. These findings demonstrate that a likely reserve pool of HSCs remains highly functional during Plasmodium infection not because cells are shielded, but because they maintain a stemness associated metabolic profile despite effectively sensing inflammation.

cell biology↗

bioRxiv2025

Benchmarking single-cell multi-modal data integrations

Single-cell multi-modal omics, remarked as "Method of the Year 2019" by Nature Methods, provides valuable insights into complex tissues. Recent advances have enabled the generation both unpaired (separate profiling) and paired (simultaneous measurement) multi-modal datasets, driving rapid development of single-cell multi-modal integration tools. Nevertheless, there is a pressing need for a comprehensive benchmark to assess algorithms under varying integrated dataset types, integrated modalities, dataset sizes, and data quality. Here, we present a systematic benchmark for 40 single-cell multi-modal integration algorithms involving modalities of DNA, RNA, protein and spatial omics for paired, unpaired and mosaic datasets (a mixture of paired and unpaired datasets). We evaluated usability, accuracy, and robustness to assist researchers in selecting suitable integration methods tailored to their datasets and applications. Our benchmark provides valuable guidance in the ever-evolving field of single-cell multi-omics. The Stage 1 protocol for this Registered Report was accepted in principle on 3rd Jul 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Benchmarking_single-cell_multi-modal_data_integrations/26789572.

bioinformatics↗

bioRxiv2025

Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma

BackgroundAsthma is driven by complex interactions amongst structural airway cells, cells of the immune system, and the environmental. While sputum cell characterization has been instrumental in studying asthma pathogenesis and refining treatment strategies, the nuances of cellular transcriptomes and intercellular communication in asthmatic sputum remain poorly understood. MethodsWe employed single-cell RNA sequencing to analyze cells isolated form the sputum from 16 asthma patients and 8 non-asthmatic controls. Cell identities were established using curated marker genes and SingleR annotation. We compared cell-specific gene expression and communication networks between asthmatic and control groups, correlating findings with distinct pathways that were dysregulated in asthma. Findings37,565 cellular transcriptomes were captured and analyzed. 15 distinct cell populations were identified, including various macrophages, monocytes, dendritic cells, and lymphocytes, along with rare cell types such as mast cells, innate lymphoid cells, bronchial epithelial cells, and eosinophils. Intercellular communication analysis indicated heightened signaling activity in asthma compared to controls, particularly in CD4+ T cells and dendritic cells which exhibited the most significant increases in RNA expression of outgoing signaling molecules. Notably, the ADAM12-SDC4 and CCL22-CCR4 ligand-receptor pathways demonstrated the strongest shifts between asthma and control subjects, particularly between dendritic cells and CD4 lymphocytes. InterpretationSC RNA seq profiling the asthma cellular transcriptome analysis of sputum highlights both innate and adaptive immune mechanisms that are significantly amplified in asthma. The elevated expression of ADAM12-SCD4 and CCL22-CC4 point to their critical role in asthma pathogenesis, suggesting potential avenues for targeted therapies and improved management of this chronic condition. Research in contextO_ST_ABSEvidence Before This StudyC_ST_ABSAsthma is a chronic inflammatory disease of the airways driven by intricate interactions between airway structural and immune cells. Previous transcriptomic studies have focused on bulk RNA samples from the airway, leaving significant gaps in our understanding of the cellular dynamics that characterize the disease. Added Value of This StudyThis study pioneers the use of single-cell RNA sequencing on sputum samples from patients with asthma, revealing a detailed landscape of cell phenotypes and dynamic communication patterns that distinguish asthmatic individuals from those without the disease. Notably, heightened intercellular communication was observed in asthma, particularly between CD4+ T cells and dendritic cells, confirming that there is a robust network of interactions between immune and structural cells. The notable increase of ADAM12-CCR4 communication from dendritic cells to other cell populations further emphasizes the dysregulation present in asthma. Implications of All Available EvidenceOur transcriptomic profiling illuminates distinct and amplified communication pathways involving CD4+ T cells and dendritic cells, aligning with established paradigms of both adaptive and innate immune responses in asthma pathogenesis. The identification of ADAM12 and CCR4 pathway dysregulation adds a critical layer to our understanding of the molecular mechanisms underpinning asthma, paving the way for potential therapeutic targets and personalized treatment strategies. Single cell profiling of the sputum has the capacity to characterize the breadth of cellular phenotypes, their functional status, and the communication in the airway at a level not previously attainable.

genomics↗

bioRxiv2025

Robust Phylogenetics

We present a robust phylogenetic inference method, called the trimmed log-likelihood method, which effectively identifies fast-evolving, saturated, or erroneous sites in both simulated and empirical multiple sequence alignments. This method avoids circularity by dynamically identifying and removing sites without relying on an initial tree, allowing the specific sites removed to change as tree topology and branch lengths are estimated. Our analyses demonstrate that this method outperforms existing approaches, such as the Slow-Fast method, Tree Independent Generation of Evolutionary Rate approach, and Le Quesne Probability statistics, by removing fewer sites while still inferring phylogenies with comparable or greater accuracy. Implemented in IQ-TREE2, the trimmed log-likelihood method is user-friendly with a simple command-line interface. However, challenges remain in addressing heterogeneous evolutionary processes including compositional biases, such as GC bias. Despite these challenges, our approach offers a practical solution for improving phylogenetic inference by automatically and dynamically identifying sites to down-weight during phylogenetic analyses. We recommended that researchers compare trees inferred by varying the proportion of down-weighted sites to monitor changes in tree topology and to identify a set of candidate tree topologies for further consideration.

evolutionary biology↗

bioRxiv2025

A reference genome assembly of the alpine forage grass Elymus nutans

Elymus nutans Griseb. (Poaceae: Triticeae, 2n=6x=42) is a dominant perennial plant species in the Qinghai-Tibetan Plateau in China, which is an important forage resource in high-altitude and cold regions and is the most popular species used for high yield artificial grassland planting because of its rich nutritional value and good palatability for herbivorous ruminant animals. In this study, using advanced sequencing technology, we generated an allohexaploid reference genome for E. nutans, representing the three sets of chromosomes (subgenomes St, Y, and H). This is the first study to our knowledge to confirm the origin of the Y subgenome, which shares a close relationship with the V subgenome. We predict that E. nutans arose via two-step hybridization and that its speciation occurred [~]3.16 MYA. The whole genome in this study will update the Triticeae family genomics information and provide theoretical basis for the evolutionary of its related speicies.

plant biology↗

bioRxiv2025

The Arabidopsis TIRome informs the design of artificial TIR (Toll/interleukin-1 receptor) domain proteins

The TIR (Toll/interleukin-1 receptor) domain is an ancient protein module that functions in immune and cell death responses across the Tree of Life. TIR domains encoded by plants and prokaryotes function as enzymes to produce diverse small molecule immune signals. Plant genomes can encode hundreds of TIR-domain containing proteins - many of which confer important agricultural disease resistance as TIR-NLR (nucleotide-binding, leucine-rich repeat) immune receptors. Despite their importance, how natural variation influences TIR enzymatic output and immunity-associated cell death is largely unexplored. We assayed a complete collection of the TIR domains of Arabidopsis thaliana Col-0 (the "AtTIRome") to explore variation in TIR metabolite production and cell death signaling. Roughly half of the AtTIRome triggered cell death in transient assays. Artificial TIR proteins designed based on consensus sequences of the AtTIRomes cell death phenotypic classes revealed polymorphisms controlling variation in TIR cell death elicitation and metabolite production. Structure-function analyses of artificial TIRs revealed that natural variation in the "BB-loop", a flexible region overlying the catalytic pocket, determines differences in function across Arabidopsis TIR-containing proteins. We further demonstrate that artificial TIRs are functional on an NLR chassis and that BB-loop variation can tune the activity of a natural TIR-NLR protein. These findings shed light on the diversity of TIR outputs and reveal methods to design and engineer TIR-based immune receptors. Significance StatementTIR (Toll/interleukin-1 receptors) domain proteins perform immune signaling across the Tree of Life. TIR domains are enzymes that can process NAD+ to generate diverse small molecule signals. In order to better understand TIR-based signaling, we leveraged Arabidopsis natural variation across [~]150 TIR proteins to inform the design of artificial TIRs, define features that control output, and tune the output of a natural TIR immune receptor. The engineering of plant immune pathways will enable the optimization of disease resistance to safeguard yields.

plant biology↗

bioRxiv2025

MF-PENS: An Efficient and Versatile Nuclear Extraction Method for Plant Single-Cell Sequencing

The widespread adoption of single-cell transcriptomics in plants remains constrained by the challenges of isolating high-quality nuclei from tissues with rigid cell walls and complex metabolomes. Here, we present methanol fixation and Percoll-enriched nuclei sorting (MF-PENS), a robust strategy designed to tackle these limitations. Distinct from conventional protocols, MF-PENS circumvents the need for fluorescence activated cell sorting (FACS) and effectively reduce RNA leakage, consistently yielding high-quality single-nucleus RNA sequencing (snRNA-seq) data. MF-PENS has been validated across various plant tissues, including fresh and frozen rice roots and leaves, as well as recalcitrant samples such as soybean root nodules, soybean seeds, and tomato fruits. Benchmarking results demonstrate that MF-PENS significantly enhances gene capture efficiency, cell clustering performance, and cell type annotation accuracy. With its broad applicability and superior performance, MF-PENS represents a robust and versatile tool for plant single-cell research, facilitating deeper insights into plant development and biology processes.

plant biology↗

bioRxiv2025

Energy inefficiency underpinning brain state dysregulation in individuals with major depressive disorder

Disruptions in brain state dynamics are a hallmark of major depressive disorder (MDD), yet their underlying mechanisms remain unclear. This study, building on network control theory, revealed that decreased state stability and increased state-switching frequency in MDD are driven by elevated energy costs and reduced control stability, indicating energy inefficiency. Key brain regions, including the left dorsolateral prefrontal cortex, exhibited impaired energy regulation capacity, and these region-specific energy patterns were correlated with depressive symptom severity. Neurotransmitter and gene expression association analyses linked these energy deficits to intrinsic biological factors, notably the 5-HT2a receptor and excitatory-inhibitory balance. These findings shed light on the energetic mechanism underlying brain state dysregulation in MDD and its associated biological underpinnings, highlighting brain energy dynamics as a potential biomarker by which to explore therapeutic targets and advance precise interventions for restoring healthy brain dynamics in depression.

neuroscience↗