Biology subjects

Lu, J.

Publications and source records attributed to Lu, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Cancer pathway connectivity resolved by drug perturbation and RNA sequencing

Pathway inhibitors are a backbone of cancer treatment. The configuration of pathway dependencies varies from tumour to tumour. Better treatment for individual patients could be designed if the pathway wiring were readily measurable. Here, we characterise the transcriptional responses of 116 lymphoma patient samples exposed to ten drug perturbations. We used factor analysis to decompose individual and shared drug effects, thereby generating a pathway connectivity map of chronic lymphocytic leukemia (CLL). The expression profiles of the major disease subgroups, defined by IGHV mutation status, became more similar to each other after BTK inhibition, consistent with B-cell receptor (BCR) signalling as a driver of their phenotypic difference. An even stronger convergence was observed with combined IRAK4 and BTK inhibition, indicating cooperation of BCR and toll-like receptor (TLR) signalling in CLL. We identified genetic aberrations (BRAF, TP53, deletion 17p, deletion 15q, trisomy 12) that modulated drug effects in CLL and constituted specific interaction patterns. IRAK4 inhibition effects depended on the presence of trisomy 12, a finding that suggests that the trisomy 12 driver event in CLL acts by gene dosage-dependent IRAK4 upregulation and amplification of the BCR/TLR cooperation. Our results highlight the potential of systematic drug perturbation assays with transcriptome readout to map pathway interconnectivity and functionally annotate tumour drivers.

systems biology↗

bioRxiv2025

Graded BMP signals modulate yellow and red color in fishes impacting adult pigment pattern and behavior

Among the most interesting adult traits are those with roles in animal communication. Yet developmental mechanisms by which genes drive cell behaviors in building the final forms of such traits are rarely known. In this context, pigmentation is useful because colors and patterns often provide signals in mate choice, predation avoidance and other behaviors and pigmentation is unusually accessible to observation and manipulation. Here we focus on some of the most prominent signaling colors--red, orange and yellow--and show how BMP signaling at the cellular level allows for a very different kind of signal at the organismal level. Using pearl danio, Danio albolineatus, we find that spatially and temporally graded BMP signals promote development of yellow/orange xanthophores over red erythrophores in the fin of this species and a distantly related minnow, Tanichthys albonubes, and that conserved mechanisms, involving BMP co-receptor Rgmb, regulate differentiation of other pigment cell types in corresponding locations of zebrafish, D. rerio. We further use mutants of D. albolineatus with more red or more yellow cells than wild-type to demonstrate female responsiveness to carotenoid-based color differences between males in shoaling preference assays, and we show the existence of polygenic standing variation for this pigmentary trait. Our findings illustrate a chain of function spanning hierarchical levels and provide a deeper understanding of pigmentary form and function and its evolution. HIGHLIGHTSO_LIFate specification of alternative red or yellow pigment cell types in the fin of a minnow, pearl danio, depends on thresholds and gradients in BMP signaling during fin outgrowth. C_LIO_LIGenetic loss of BMP signaling leads to production of red over yellow carotenoids with resulting "super-red" fish preferred by females in shoaling assays. C_LIO_LIBMP-dependence of pigmentary traits is conserved across species with standing, polygenic variation for fin pattern and color in pearl danio. C_LI

developmental biology↗

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

Single-cell transcriptomic dynamics exposes hidden survival trajectory under antibiotic treatment

Heterogeneity plays a major role in bacterial resistance to antibiotic treatment while the mechanism at single-cell level is largely unknown. Here, we employed a robust integration of bulk and bacterial single-cell RNA-seq (scRNA-seq) to uncover how individual cells of Acinetobacter baumannii reorganise the heterogenous transcriptome in response to antibiotic. Using polymyxin as a representative, bulk RNA-seq showed canonical envelope- and efflux-centred responses but obscured underlying heterogeneity. Single-cell profiling resolved these averages into discrete subpopulations whose abundances shifted with concentration and time. Specifically, in early time an envelope-stress programme predominated survival at the low concentration, whereas an outer-membrane repair/efflux programme dominated survival at the high concentration. These patterns revealed structured and time-resolved heterogeneity, highlighting an ingenious bacterial stress responsive strategy. Through trajectory inference, we further revealed a concentration-dependent bifurcation of cell fates: low-concentration treated cells detoured through a transient stress state and rejoined growth, whereas high-concentration treated survivors diverted into a slow-growing, tolerant branch. Perturbing marker genes from these programmes altered fitness eventually and rapidly increased permeability, depolarisation and reactive-oxygen burden, linking state to survival capability. Collectively, we map a dynamic, concentration-structured landscape of antibiotic responses at the single-cell level, revealing diverse survival trajectories that are obscured in conventional population-averaged analyses. Our developed single-cell based framework revealed tolerant bacterial cells with rewiring of the transcriptional landscape, causing emergence of antibiotic resistance. Importantly, these findings urge precision antimicrobial therapy in patients to minimise emergence of antibiotic resistance.

microbiology↗

bioRxiv2025

Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights

Interactions between T cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) are central to adaptive immunity. Recent advances in structure prediction tools have enabled atomic-level modeling of TCR-pMHC interactions. However, the lack of systematic evaluation forces practitioners to invest substantial resources in selecting appropriate tools. Here, we present a comprehensive benchmark of TCR-pMHC structure prediction with 70 previously unseen complexes and ten models spanning MSA-based, PLM-based, and docking-based approaches, revealing the superior modeling accuracy and docking quality of MSA-based methods, especially AlphaFold3. To further enhance the utility of AlphaFold3 predictions, we identify the pLDDT score of the TCR CDR3 region as an informative indicator of both structural correctness and functional relevance. Specifically, it enables up to 4.3% Top-1 success gain through reranking and captures mutation-induced affinity changes in over 80% of cases. Overall, our analysis would facilitate the practical usage of immune structure prediction models and guide the advancement of these models.

bioinformatics↗

bioRxiv2025

A unified genetic perturbation language for human cellular programming

Evolution simultaneously and combinatorially explores complex genetic changes across perturbation classes, including gene knockouts, knockdowns, overexpression, and the creation of new genes from existing domains. Separate technologies are capable of genetic perturbations at scale in human cells, but these methods are largely mutually incompatible. Here we present CRISPR-All, a unified genetic perturbation language for programming of any major type of genetic perturbation simultaneously, in any combination, at genome scale, in primary human cells. This is enabled by a standardized molecular architecture for each major perturbation class, development of a functional syntax for combining arbitrary numbers of elements across classes, and linkage to unique single cell compatible barcodes. To facilitate use, CRISPR-All converts high level descriptions of desired complex genetic changes into a single DNA sequence that can rewire genomic programs within a cell. Using the CRISPR-All language allowed for head-to-head functional comparisons across perturbation types in a comprehensive analysis of all previously identified genetic enhancements of human CAR-T cells. Combining CRISPR-All programs with single cell RNA sequencing revealed a greater diversity of phenotypic states, including improved functional performance, only accessible through distinct perturbation classes. Finally, CRISPR-All combinatorial genome scale screening of up to four distinct perturbations simultaneously revealed additive functional improvements in human T cells accessible only through iterative multiplexing of modifications across perturbation classes. CRISPR-All enables exploration of a combinatorial genetic perturbation space, which may be impactful for biological and clinical applications.

genetics↗

bioRxiv2025

Brain endothelial PRMT5-ANGPTL4 axis regulates cerebellar inhibitory synaptogenesis and motor coordination

Brain vasculature is essential for central nervous system function, but its specific roles in synaptic development and motor function regulation are poorly understood. In this study, we identify a cerebrovascular signaling axis wherein endothelial PRMT5 critically regulates inhibitory synaptogenesis and motor coordination. Cerebrovascular-specific deletion of Prmt5 gene leads to excessive inhibitory synaptic input onto Purkinje cells and progressive motor deficits in mice. Mechanistically, PRMT5 deficiency epigenetically upregulates the expression of the secreted factor ANGPTL4 through reduced symmetric dimethylation of H3R8 and H4R3, along with increased H3K9 acetylation at the Angptl4 promoter, thereby driving excessive inhibitory synaptogenesis onto Purkinje cells. Importantly, Angptl4 deletion in brain endothelium normalizes inhibitory synaptic input onto Purkinje cells and restores motor coordination in PRMT5-deficient mice. Together, these findings define the endothelial PRMT5-ANGPTL4 axis as a key regulator of cerebellar inhibitory circuitry and motor function, highlighting cerebrovascular mechanisms as potential therapeutic targets for motor coordination disorders. Teaser Cerebrovascular PRMT5 prevents excessive inhibitory synaptogenesis via epigenetic repression of Angptl4.

neuroscience↗

bioRxiv2025

Prognosis prediction using autophagy gene expression in osteosarcoma

Defining the transition from localized to metastatic osteosarcoma (OS) before overt dissemination is fundamental for improving survival. However, effective early diagnostic tools remain scarce, largely due to limited exploitation of the pre-metastatic tumor microenvironments own record of molecular barcode exposures encoded in cell-intrinsic stress states. We analyzed autophagy-dependent transcriptional datasets from 139 individuals to develop Auto-RS, a computational classifier that can integrate age and sex to deliver individualized risk management. The prognosis-interpretable features of Auto-RS recapitulate established molecular trajectories of metastasis at the single-cell level, capturing tumor cells intrinsic shift from proliferative to invasive states and revealing cooperative programs among cancer-associated fibroblasts and immune cells. More significantly, Auto-RS can expose chemotherapy vulnerabilities of newer drugs, providing a framework to prioritize therapeutics without direct testing in children. This framework brings a potential inflection point, where metastasis prediction and therapeutic stratification may converge to improve OS outcomes.

cell biology↗

bioRxiv2025

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗

bioRxiv2025

Mid-Infrared Photothermal Mesoscopy with Millimeter Field of View and Sub-micron Spatial Resolution

By optically sensing mid-infrared absorption through a visible probe beam, mid-infrared photothermal (MIP) microscopy has emerged as a powerful tool for chemical imaging with micromolar sensitivity and submicron spatial resolution. The adoption of spatially multiplexed camera-based widefield detection further enhanced the imaging speed. However, current widefield MIP systems suffer from a small field of view (FOV)--typically tens of square micrometers, which constrains their utility in large-area tissue imaging applications. Here, we report a laser-scan MIP mesoscope that achieves millimeter-scale FOV while preserving submicron resolution. By leveraging an all-reflective laser scanning architecture, low-magnification and medium numerical-aperture objectives, and a defocused signal collection scheme, our MIP mesoscope achieves a 1.2 x 1.2 mm2 FOV, 650 nm lateral resolution, and microsecond-scale pixel dwell time. In vivo chemical imaging of whole Caenorhabditis elegans and high-throughput detection of beta-amyloids in both mouse and human brain tissues associated with Alzheimers disease are demonstrated.

bioengineering↗

bioRxiv2025

Cross-Species Insights from ART-D to Uncover Evolutionarily Conserved Oncogenic Mechanisms

Cancer arises from oncogenic clones, yet the dynamic mechanisms governing their stepwise evolution toward malignancy remain incompletely understood. Here, we establish the Atlas of Ras-driven Tumors in Drosophila (ART-D), a systematic, cross-species platform that dissects the molecular and phenotypic trajectories of tumorigenesis through ten genetically defined RasV12-driven models. By integrating longitudinal phenotypic profiling, we define three conserved stages of tumor development--initiation, promotion, and progression--distinguished by dynamic changes in tumor burden and tumor-induced cachexia. Transcriptomic dynamics reveal stage-specific signaling rewiring: early tumorigenesis is marked by co-activation of JAK/STAT, NF-{kappa}B/Toll, and MAPK pathways, whereas malignant progression is driven by Notch hyperactivation and Hippo pathway inactivation. Through integrative multi-omics and machine learning, we uncover an evolutionarily conserved pathogenic network involving coordinated JNK, NF-{kappa}B/Toll, Notch, and Hippo signaling that is functionally validated across species. ART-D provides a transformative resource that bridges Drosophila genetics and human cancer biology, offering a framework for decoding conserved oncogenic principles and enabling precision targeting of stage-specific vulnerabilities in RAS-driven cancers.

genetics↗

bioRxiv2025

Emergence of population-level feedback control by transposon-plasmid coevolution

The origins of adaptive functions remain poorly understood, despite considerable interest1-5. Here, we report the de novo evolution of population-level feedback control in clonal Escherichia coli strains containing high-copy plasmids, enabling these strains to express green fluorescent protein (GFP) in response to tetracycline. Selection maintains tetA+ and tetA- plasmids within single cells, due to negative feedback from the toxic effects of a tetA-gfp tetracycline resistance transposon. At high plasmid copy numbers, the intracellular equilibrium of tetA+ and tetA- plasmids robustly responds to tetracycline through rapid evolutionary dynamics6-8. Theory predicts that the GFP response to tetracycline is determined by the covariance between GFP expression and bacterial fitness. Our findings show that small mutational changes can result in large qualitative changes to population-level behavior. Here, the evolution of polymorphic intracellular populations of mobile genetic elements allows host populations to dynamically respond to antibiotic in the environment.

systems biology↗

bioRxiv2025

Genomic and genetic insights into speciation and pigment pattern diversification in Danio fishes

The Danioninae subfamily of teleost fishes boasts up to four hundred distinct species that have evolved to display a stunning diversity of morphological forms. Here we use newly assembled genome sequences of four laboratory and wild zebrafish strains as well as eleven species of the Danio and Danionella genera to explore their phylogenetic history and the genetic basis of pigment pattern diversification. Phylogenomic analyses uncover extensive introgression and incomplete lineage sorting that have obscured phylogenetic relationships within Danio and corroborate an ancient hybrid origin of zebrafish. Whereas D. rerio inherited ancestral horizontal stripes, relatives repeatedly evolved spots and vertical bars. Interspecific complementation tests reveal functional divergence of the adhesion molecule gene igsf11 and the gap junction gene gja5b between the striped zebrafish and Danio species with divergent patterns. Comparative genomic and transcriptomic analyses suggest that protein and regulatory evolution have accompanied pigment pattern diversification. Our analyses elucidate complex genetic changes underlying the phylogenetic history and morphological diversification in the Danio genus. Resolved phylogenetic relationships, available genome assemblies, transcriptomes, and genetic tractability establish Danio fish species as excellent models for biomedical research in vertebrates.

genomics↗

bioRxiv2025

Optimal transport modeling uncovers spatial domain dynamics in spatiotemporal transcriptomics studies

Spatiotemporal transcriptomics is an emerging and powerful approach that adds a temporal dimension to spatial transcriptomics, enabling the characterization of dynamic changes in tissue architecture during development or disease progression. Here, we present SpaDOT (Spatial Domain Transition detection), a computational method designed to identify spatial domains and infer their temporal dynamics across time points for spatiotemporal transcriptomics studies. SpaDOT employs a variational autoencoder (VAE) framework to capture a low-dimensional representation of the data, incorporating hidden clustering variables to define distinct spatial domains. In the process, SpaDOT integrates a Gaussian Process prior and graph neighbor information to explicitly model both global spatial continuity and local structural heterogeneity among spatial locations within each time point, while using optimal transport (OT) to derive time-varying embeddings and infer the relationships between spatial domains across time points. Through simulation and real data applications, we demonstrate the superiority of SpaDOT in spatial domain detection, latent space preservation, and domain transition tracking over time. In real data applications, SpaDOT accurately captures dynamic domain transitions, including the disappearance and re-emergence of domains due to technical variation. Notably, SpaDOT uncovers key aspects of valvulogenesis in the developing heart, revealing the splitting of a single valve structure into two distinct functional valves -- the atrioventricular valve and the semilunar valve.

bioinformatics↗

bioRxiv2025

msBayesImpute: A Versatile Framework for Addressing Missing Values in Biomedical Mass Spectrometry Proteomics Data

Advancements in mass spectrometry (MS) technologies have significantly improved the ability to quantify proteins and analyse their modifications. However, MS-based proteomics datasets frequently encounter missing values due to a complex interplay of missing at random (MAR) and missing not at random (MNAR) mechanisms. If unaddressed, such missing data can result in information loss and biased outcomes in data pre-processing, such as normalisation, as well as subsequent analyses and interpretations. Few approaches effectively address both MAR and MNAR, and those that do often necessitate manual tuning of mixture percentages between them or rely on two-group experimental designs. To enhance the handling of missing values, we developed msBayesImpute, an innovative computational method that integrates Bayesian factorization with probabilistic dropout models. We evaluated msBayesImpute against several popular imputation methods using both simulated missing values and those generated through a dilution series experiment on samples from lung cancer patients. Our comprehensive benchmark demonstrated superior performance in reconstructing missing values, estimating normalization factors, and identifying differentially expressed proteins across varying levels of missingness. Notably, msBayesImpute does not require predefined experimental designs and is scalable to large-scale studies. This versatility positions msBayesImpute as an effective and robust tool for enhancing the utility of MS datasets in biological research.

bioinformatics↗

bioRxiv2025

Comparative population genomics reveals adaptive convergence in two Drosophila species across global environments

The extent to which evolution is predictable across lineages remains unclear. To address this question, we investigated convergent adaptation in a pair of globally distributed sibling species, Drosophila melanogaster and D. simulans. We integrated whole-genome data from approximately 2,000 strains sampled across major continents, and revealed a more recent global colonization of D. simulans relative to D. melanogaster. Using a suite of complementary selection scans, we quantified signatures of positive selection across evolutionary timescales and genomic contexts. Despite substantial divergence, approximately 9-13% of adaptively evolving genes were shared between species across methods, revealing widespread convergence at the gene and pathway levels. Convergence was particularly pronounced for insecticide resistance genes, and was also evident in oxidative stress experiment. This study provides a quantitative, multiscale framework for dissecting molecular convergence, offering insights into the predictability of evolution, the constraints imposed by genomic architecture, and the dynamics of adaptation under global environmental change.

evolutionary biology↗

bioRxiv2025

Generation of Region-specific Airway Basal Stem Cells from Human Pluripotent Stem Cells via Regulation of BMP-NOGGIN Axis

Basal cells (BCs) are the primary stem cell population of adult human airways and a key target for hPSC-based models of airway development, disease and regenerative medicine. Recent studies have revealed substantial regional differences between human proximal and distal airway cell types, including BCs. Here, we show that the NOGGIN-BMP signaling axis governs proximal-distal patterning of hPSC-derived lung progenitors leading to the generation of region-specific induced BCs (iBCs). Continuous BMP inhibition through NOGGIN, coupled with tapered WNT activation, generates proximal iBCs that molecularly and functionally resemble human proximal airway BCs and differentiate in vitro and in vivo into the full repertoire of specialized proximal airway cell types, including ionocytes and pulmonary neuroendocrine cells. In sharp contrast, BMP activation with tapered WNT generates distal airway-like cells and distal BCs with limited ionocyte differentiation potential. Progeny of proximal iBCs derived from G551D CFTR mutant hiPSCs also recapitulate a cystic fibrosis-associated ionocyte phenotype.

cell biology↗

bioRxiv2025

Absolute quantification of TCA cycle intermediates in mouse ocular tissues reveals distinct tissue- and sex-specific mitochondrial metabolism

ObjectiveMitochondrial tricarboxylic acid (TCA) cycle is central to energy production and redox balance in the eye, which must sustain high metabolic activity to support vision. Retinal neurons, the retinal pigment epithelium (RPE), cornea, and lens each have distinct physiological roles and metabolic demands, yet the absolute concentrations of key TCA intermediates and their variation by tissue, sex, and time of day are not well-defined. MethodsTargeted gas chromatography-mass spectrometry was employed to quantify the absolute concentrations of TCA cycle metabolites in mouse ocular tissues collected at 10 AM and 2 PM to capture diurnal variations. Key metabolite ratios were subsequently calculated to provide insight into TCA cycle dynamics across eye tissues. ResultsThe retina showed the highest concentrations of TCA metabolites among all ocular tissues, particularly succinate, citrate, and malate, consistent with its high energy demands. The RPE/choroid demonstrated well-balanced intermediates with the highest -ketoglutarate (-KG)/Isocitrate ratio, reflecting its efficient mitochondrial oxidation and reductive carboxylation. Corneal metabolism was featured by dominant malate, especially in females, suggesting a metabolic adaptation for redox regulation and oxidative stress defense. The lens had uniformly low metabolite levels except for succinate, indicating minimal mitochondrial activity under physiologically low oxygen conditions. Notably, both the cornea and lens showed significant sex-dependent and diurnal variations in TCA cycle intermediates. ConclusionThis study demonstrates distinct tissue-specific mitochondrial metabolism in the eye, reflecting the unique functional and biochemical demands of each tissue. These metabolic signatures may underlie their susceptibility to mitochondrial dysfunction in various ocular diseases.

biochemistry↗