Biology subjects

Mao, J.

Publications and source records attributed to Mao, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Drosophila Metaxin-2 controls beta-barrel biogenesis and muscle growth in a stage-dependent fashion

Metaxin-2 (Mtx2) is an evolutionarily conserved mitochondrial outer membrane protein. Mutations in human Mtx2 cause mandibuloacral dysplasia (MADaM), a progeroid disorder. However, the pathologic mechanisms of Mtx2 loss-of-function remain largely unknown. Using Drosophila, we show Mtx2 null mutants exhibit pupal lethality, rescued by Drosophila or human Mtx2, underscoring functional conservation across species. Tissue-specific conditional knockout and rescue experiments reveal muscle as a critical site of dMtx2 action, with alternations of myofibril assembly and myogenic proteins being observed in dMtx2 mutants. Structural and functional mitochondrial abnormalities are also detected, verifying dMtx2s function in mitochondrial homeostasis. Notably, Mtx2 deficiency affects beta-barrel protein biogenesis and muscle development in pupa but not in larva, demonstrating Mtx2s dynamic regulation in mitochondrial proteostasis and muscle development. Our results elucidate mitochondrial mechanisms driving potential muscle-autonomous defects in MADaM patients and highlights stage-specific Mtx2 function as a prospective therapeutic target for this progeroid syndrome.

developmental biology↗

bioRxiv2025

NeighbourNet: Scalable cell-specific co-expression networks for granular regulatory pattern discovery

Gene regulatory networks (GRNs) provide a fundamental framework for understanding the molecular mechanisms that govern gene expression. Advances in single-cell RNA sequencing (scRNA-seq) have enabled GRN inference at cellular resolution; however, most existing approaches rely on predefined clusters or cell states, implicitly assuming static regulatory programs and potentially missing subtle, dynamic variation in regulation across individual cells. To address these limitations, we introduce NeighbourNet (NNet), a method that constructs cell-specific co-expression networks. NNet first applies principal component analysis to embed gene expression into a low-dimensional space, followed by local regression within each cells k-nearest neighbourhood (KNN) to quantify co-expression. This approach improves computational efficiency and stabilises co-expression estimates, mitigating challenges posed by small sample sizes in KNN regression and the inherent noise and sparsity of scRNA-seq data. Beyond co-expression, NNet supports scalable downstream analyses, including (i) clustering and aggregating cell-specific networks into meta-networks that capture primary co-expression patterns, and (ii) integrating prior knowledge to annotate co-expression and infer active signalling interactions at the individual cell level. All functional modules of NNet are implemented with an efficient algorithm that enables application to large-scale single-cell datasets. We demonstrate NNets effectiveness through three case studies on transcription factor activity prediction, early haematopoiesis, and tumour microenvironments. Provided as an R package, NNet offers a novel framework for exploring cellular variation in co-expression and integrates seamlessly with existing single-cell analysis workflows.

bioinformatics↗

bioRxiv2025

Construction of Complete Telomere-to-Telomere Genome Assembly of the Rabbit Using Haploid Embryonic Stem Cells

The rabbit owns commercial importance in meat and fur production, and also has long served as a valuable animal model in biomedical research. Yet, the complete assembly of a high-quality rabbit reference genome has not been established. Here, we present a telomere-to-telomere (T2T) genome assembly of the New Zealand White (NZW) rabbit, the most complete and accurate rabbit genome to date. Using a haploid embryonic stem cells (haESCs) to overcame the challenges of homologous sequence interference and structural variation, we generate a genome with a contig N50 of 137.71 Mb, anchoring all chromosomes with only three gaps, through integrating long-read sequencing (PacBio HiFi, Oxford Nanopore), Hi-C, and Illumina data. Quality assessments reveal near-complete coverage of the BUSCO mammalian gene set, with 99.61% completeness. Notably, this complete assembly allowed for the first comprehensive annotation of the rabbit immunoglobulin loci and major histocompatibility complex region, which were previously unresolved in earlier genome versions. The NZW-T2T assembly based on haESCs not only provides a critical resource for advancing rabbit-based research, but also set a new benchmark for de novo genome assembly for other animals with complex genome.

genomics↗

bioRxiv2025

{Phi}-Space ST: a platform-agnostic method to identify cell states inspatial transcriptomics studies

We introduce {Phi}-Space ST, a platform-agnostic method to identify continuous cell states in spatial transcriptomics (ST) data using multiple scRNA-seq references. For ST with supercellular resolution, {Phi}-Space ST achieves interpretable cell type deconvolution with significantly faster computation. For subcellular resolution, {Phi}-Space ST annotates cell states without cell segmentation, leading to highly insightful spatial niche identification. {Phi}-Space ST harmonises annotations derived from multiple scRNA-seq references, and provides interpretable characterisations of disease cell states by leveraging healthy references. We validate {Phi}-Space ST in three case studies involving CosMx, Visium and Stereo-seq platforms for various cancer tissues. Our method revealed niche-specific enriched cell types and distinct cell type co-presence patterns that distinguish tumour from non-tumour tissue regions. These findings highlight the potential of {Phi}-Space ST as a robust and scalable tool for ST data analysis for understanding complex tissues and pathologies.

bioinformatics↗

bioRxiv2024

Engineered paediatric tumours retain patient tumour genotype and phenotype for precision medicine.

Precision medicine for paediatric and adult cancers that includes drug sensitivity profiling, can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts - this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from neuroblastoma and sarcoma patients identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to create patient-specific tumour organoids, modelling tumour growth conditions. Expanded tumour organoids recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourgenicity. Screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across cancer types.

cancer biology↗

bioRxiv2024

The tilt illusion arises from an efficient reallocation of neural coding resources at the contextual boundary

The tilt illusion -- a bias in the perceived orientation of a center stimulus induced by an oriented surround -- illustrates how context shapes visual perception. While the tilt illusion has been the subject of quantitative study for over 85 years, we still lack a comprehensive account of the phenomenon that connects its neural and behavioral characteristics. Here, we demonstrate that the tilt illusion originates from a dynamic change in neural coding precision induced by the surround context. We simultaneously obtained psychophysical and fMRI responses from human subjects while they viewed gratings in the absence and presence of an oriented surround, and extracted sensory encoding precision from their behavioral and neural data. Both measures show that in the absence of a surround, encoding reflects the natural scene statistics of orientation. However, in the presence of an oriented surround, encoding precision is significantly increased for stimuli similar to the surround orientation. This local change in encoding is sufficient to accurately predict the behavioral characteristics of the tilt illusion using a Bayesian observer model. The effect of surround modulation increases along the ventral stream, and is localized to the portion of the visual cortex with receptive fields at the center-surround boundary. The pattern of change in coding accuracy reflects the surround-conditioned orientation statistics in natural scenes, but cannot be explained by local stimulus configuration. Our results suggest that the tilt illusion naturally emerges from a dynamic coding strategy that efficiently reallocates neural coding resources based on the current stimulus context.

neuroscience↗

bioRxiv2024

Effects of Release of TSG-6 from Heparin Hydrogels on Supraspinatus Muscle Regeneration

Muscle degeneration after rotator cuff tendon tear is a significant clinical problem. In these experiments, we developed a poly(ethylene glycol)-based injectable granular hydrogel containing two heparin derivatives (fully sulfated (Hep) and fully desulfated (Hep-)) as well as a matrix metalloproteinase-sensitive peptide to promote sustained release of Tumor Necrosis Factor Stimulated Gene 6 (TSG-6) over 14+ days in vivo in a rat model of rotator cuff muscle injury. The hydrogel formulations demonstrated similar release profiles in vivo, thus facilitating comparisons between delivery from heparin derivatives on level of tissue repair in two different areas of muscle (near the myotendious junction (MTJ) and in the muscle belly (MB)) that have been shown previously to have differing responses to rotator cuff tendon injury. We hypothesized that sustained delivery of TSG-6 would enhance the anti-inflammatory response following rotator cuff injury through macrophage polarization, and that release from a fully sulfated heparin derivative (Hep) would potentiate this effect throughout the muscle. Inflammatory/immune cells, satellite cells, and fibroadipogenic progenitor cells, were analyzed by flow cytometery 3 and 7 days after injury and hydrogel injection, while metrics of muscle healing were examined via immunohistochemistry up to Day 14. Results showed controlled delivery of TSG-6 from Hep caused heightened macrophage response (Day 14 macrophages, 4.00 {+/-} 1.85% single cells, M2a, 3.27 {+/-} 1.95% single cells) and increased markers of early muscle regeneration (embryonic heavy chain staining) by Day 7, particularly in the MTJ region of the muscle, compared to release from desulfated heparin hydrogels. This work provides a novel strategy for localized, controlled delivery of TSG-6 to enhance muscle healing after rotator cuff tear. IMPACT STATEMENTRotator cuff tear is a significant problem that can cause muscle degeneration. In this study, a hydrogel particle system was developed for sustained release of an anti-inflammatory protein, Tumor Necrosis Factor Stimulated Gene 6 (TSG-6), to injured muscle. Release of the protein from a fully sulfated heparin hydrogel-based carrier demonstrated greater changes in amount inflammatory cells and more early regenerative effects than a less-sulfated carrier. Thus, this work provides a novel strategy for localized, controlled delivery of an anti-inflammatory protein to enhance muscle healing after rotator cuff tear.

bioengineering↗

bioRxiv2024

Finding the E-Channel Proton Loading Sites by Calculating the Ensemble of Protonation Microstates

The aerobic electron transfer chain builds a proton gradient by proton coupled electron transfer reactions through multiple proteins. Complex I is the first enzyme in this chain. On transfer of two electrons from NADH to quinone four protons are pumped from the N- (negative, higher pH) to the P- (positive, lower) side. Protons move pthrough three linear antiporter paths, with a few amino acids and waters providing the route and the E-channel, which is a complex of competing paths, with clusters of interconnected protonatable residues. Proton loading sites (PLS) are residues that transiently bind protons as they are transported from N- to P-compartments. PLS can be individual residues or extended clusters. The program MCCE uses Monte Carlos sampling to analyze the E-channel proton binding in equilibrium with individual Molecular Dynamics snapshots from trajectories of Thrmus thermuphillus Complex I in the apo, quinone or quinol bound states. At pH 7, the five E-channel subunits (Nqo4, Nqo7, Nqo8, Nqo10, and Nqo11) have accepted >25,000 protonation microstates, each with different residues protonated. The microstate explosion is tamed by analyzing interconnected clusters of residues along the proton transfer paths. A proton is bound and released to five coupled residues in a cluster on the protein N-side and to six coupled residues in the protein center. Loaded microstates bind protons to sites closer to the P-side in the forward pumping direction. MCCE microstate analysis shows the strong coupling of proton binding amongst individual residues in the two PLS clusters.

biophysics↗

bioRxiv2024

Miniaturizing, Modifying, and Augmenting Nature's Proteins with Raygun

Proteins have evolved over billions of years through extensive and coordinated substitutions, insertions and deletions (indels). Computational protein design cannot yet fully mimic natures ability to engineer new proteins from existing templates. Protein language models generate informative per-residue representations, but leveraging them to execute large-scale, function-preserving mutations and indels has remained beyond reach. We introduce Raygun, a generative AI framework that unlocks efficient miniaturization, modification, and augmentation of proteins, using a novel probabilistic encoding of protein sequences constructed from language model embeddings. Emulating evolution, Raygun shrinks proteins by 10-25% (sometimes over 50%) while preserving predicted structural integrity and fidelity, introduces extensive sequence diversity while preserving functional sites, and can expand proteins beyond their natural size. These capabilities unlock new opportunities in gene therapy and biotechnology. In cell-based validation, Raygun successfully miniaturized fluorescent proteins, two of which are smaller than 96% of fluorescent proteins reported in FPbase, as well as TurboID, a synthetic biotin ligase widely adopted for proteomics. It also successfully expanded Epidermal Growth Factor (EGF), a natural binding partner to the EGFR protein, generating EGF variants with higher binding affinity than the wildtype. Rayguns conceptual innovations in template-based protein design reveal that protein function can be encoded in a length-independent space. This fundamental insight bridges protein representation learning with evolutionary biology and could unlock the development of more efficient molecular tools and biological therapeutics.

bioinformatics↗

bioRxiv2024

Crk mediates Csk-Hippo signaling independently of Yap tyrosine phosphorylation to induce cell extrusion

Src family kinases (SFKs), including Src, Fyn and Yes, play important roles in development and cancer. Despite being first discovered as the Yes-associated protein, the regulation of Yap by SFKs remains poorly understood. Here, through single-cell analysis and genetic lineage tracing, we show that the pan-epithelial ablation of C-terminal Src kinase (Csk) in the lacrimal gland unleashes broad Src signaling but specifically causes extrusion and apoptosis of acinar progenitors at a time when they are shielded by myoepithelial cells from the basement membrane. Csk mutants can be phenocopied by constitutively active Yap and rescued by deleting Yap or Taz, indicating a significant functional overlap between Src and Yap signaling. Although Src-induced tyrosine phosphorylation has long been believed to regulate Yap activity, we find that mutating these tyrosine residues in both Yap and Taz fails to perturb mouse development or alleviate the Csk lacrimal gland phenotype. In contrast, Yap loses Hippo signaling-dependent serine phosphorylation and translocates into the nucleus in Csk mutants. Further chemical genetics studies demonstrate that acute inhibition of Csk enhances Crk/CrkL phosphorylation and Rac1 activity, whereas removing Crk/CrkL or Rac1/Rap1 ameliorates the Csk mutant phenotype. These results show that Src controls Hippo-Yap signaling through the Crk/CrkL-Rac/Rap axis to promote cell extrusion.

developmental biology↗

bioRxiv2024

{Phi}-Space: Continuous phenotyping of single-cell multi-omics data

Single-cell multi-omics technologies have empowered increasingly refined characterisation of the heterogeneity of cell populations. Automated cell type annotation methods have been developed to transfer cell type labels from well-annotated reference datasets to emerging query datasets. However, these methods suffer from some common caveats, including the failure to characterise transitional and novel cell states, sensitivity to batch effects and under-utilisation of phenotypic information other than cell types (e.g. sample source and disease conditions). We developed {Phi}-Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. In {Phi}-Space we adopt a highly versatile modelling strategy to continuously characterise query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualisations, clustering and cell type labelling. We demonstrate through three case studies that {Phi}-Space (i) characterises developing and out-of-reference cell states; (ii) is robust against batch effects in both reference and query; (iii) adapts to annotation tasks involving multiple omics types; (iv) overcomes technical differences between reference and query. The versatility of {Phi}-Space makes it applicable to a wide range analytical tasks beyond cell type transfer, and its ability to model complex phenotypic variation will facilitate biological discoveries from different omics types.

bioinformatics↗

bioRxiv2024

Genome sequencing of 'Fuji' apple clonal varieties reveals evolutionary history and genetic mechanism of the spur-type morphology

Somatic variations arising during asexual reproduction can lead to the emergence of bud sports exhibiting advantageous traits, forming the basis for bud sport breeding in perennial plants. Here, we report a near-complete, fully phased genome assembly of Fuji apple, enabling comprehensive identification of somatic variants across 74 clonally propagated Fuji varieties. Phylogenetic analysis of Fuji sport varieties indicates that the emergence of spur-type and early maturation traits results from multiple independent formation events. A number of putative functional somatic variants are identified, including one spur-type specific deletion located in the promoter of the TCP transcription factor gene MdTCP11. DNA methylation level of the deletion-associated miniature inverted-repeat transposable element is lower in the spur-type varieties compared to the standard-type varieties, while the expression of MdTCP11 is significantly higher. Overexpression of MdTCP11 in apple decreases the plant height, highlighting its important role in the development of spur-type apple varieties. This study sheds light on the 80-year cloning history of Fuji and provides valuable candidate causative mutations for apple breeding.

plant biology↗

bioRxiv2024

VGLL2 and TEAD1 fusion proteins drive YAP/TAZ-independent transcription and tumorigenesis by engaging p300

Studies on Hippo pathway regulation of tumorigenesis largely center on YAP and TAZ, the transcriptional co-regulators of TEAD. Here, we present an oncogenic mechanism involving VGLL and TEAD fusions that is Hippo pathway-related but YAP/TAZ-independent. We characterize two recurrent fusions, VGLL2-NCOA2 and TEAD1-NCOA2, recently identified in spindle cell rhabdomyosarcoma. We demonstrate that in contrast to VGLL2 and TEAD1, the fusion proteins are strong activators of TEAD-dependent transcription, and their function does not require YAP/TAZ. Furthermore, we identify that VGLL2 and TEAD1 fusions engage specific epigenetic regulation by recruiting histone acetyltransferase p300 to control TEAD-mediated transcriptional and epigenetic landscapes. We showed that small molecule p300 inhibition can suppress fusion proteins-induced oncogenic transformation both in vitro and in vivo. Overall, our study reveals a molecular basis for VGLL involvement in cancer and provides a framework for targeting tumors carrying VGLL, TEAD, or NCOA translocations.

cancer biology↗

bioRxiv2024

Adaptation optimizes sensory encoding of future stimuli

Sensory neurons continually adapt their response characteristics according to recent sensory input. However, it is unclear how such a reactive process shaped by sensory history can benefit the organism going forward. Here, we test the hypothesis that adaptation indeed acts proactively in the sense that it optimally adjusts sensory encoding for the future, i.e. for the next expected sensory input. We first quantified adaptation induced changes in sensory encoding by psychophysically measuring discrimination thresholds for visual orientation under different adaptation conditions. Using an information theoretic analysis, we found that adaptation consistently reallocates coding resources such that encoding accuracy peaks at the adaptor orientation while total coding capacity remains constant. We then asked whether this characteristic change in encoding accuracy is predicted by the temporal statistics of natural visual input. By analyzing the retinal input of freely behaving human subjects in natural environments, we found that the distribution of local visual orientations in the retinal input stream at any moment in time is also peaked at the mean orientation computed over a short input history leading up to that moment. We further tested our hypothesis with a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). We simulated our human adaptation experiment with PredNet while analyzing its internal sensory representation. We found that the network exhibited the same change in encoding accuracy as observed in human subjects, and as predicted by the natural input statistics. Taken together, our results suggest that adaptation induced changes in encoding accuracy are an attempt of the visual systems to be best possibly prepared for future sensory input.

neuroscience↗

bioRxiv2024

Exploring the conformational space of protein-protein complex with transformer-based generative model

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

bioinformatics↗

bioRxiv2024

Protein property prediction based on local environment by 3D equivariant convolutional neural networks

Predicting the properties of proteins is an important procedure in protein engineering. It determines the subspace of mutations for protein modifications, which is critical to the success of the project, but heavily relies on the knowledge and experience of scientists. In this study, we propose a novel deep 3D-CNN model, Eq3DCNN, specifically designed for local environment-related tasks in protein engineering. Eq3DCNN uses basic atom descriptors and their coordinates as inputs, utilizing customized data augmentations to enhance its training efficiency. To make the Eq3DCNN extracted features with more generalization capability, we incorporated a rotation equivariant module to get rotation invariant features. Using cross-validations with different data splitting strategies and under the scenarios of zero-shot predictions, we demonstrate that Eq3DCNN outperformed other 3D-CNN models in stability predictions, and also well-preformed on other prediction tasks, such as the binding pocket and the secondary structure predictions. Our results also identified the key factors that contribute to the models accuracy and the scope of its applications. These findings may help scientists in designing better mutation experiments and increasing the success rate in protein engineering.

bioinformatics↗

bioRxiv2023

A genome-wide association study of neonatal metabolites

The hereditary component significantly influences the concentration of metabolites in adults. Nevertheless, the precise influence of genetic factors on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified a total 17 previous reported associations and 13 novel discovered associations between single nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was calculated to be 76.2%, with a range of 69-78.8%. The aforementioned findings offer valuable insights pertaining to the genetic architecture of neonatal metabolism. In BriefLarge-scale genomes of maternal non-invasive prenatal testing provide insights into the genetic contribution to neonatal metabolism. HighlightsGWAS of 27,785 low-pass genomes revealed 13 novel associations of neonatal metabolic components. Estimated an average of 76.2% heritability of neonatal metabolic components and showed the individual concentration can be accurately predicted from polygenic risk scores. A total of 17 established relationships have been observed, providing evidence that maternal genomes can be utilized in neonatal metabolite GWAS.

genetics↗

bioRxiv2023

An interface-based molecular generative framework for protein-protein interaction inhibitors

Protein-protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge. In this work, we constructed a comprehensive dataset of PPI interfaces with active and inactive compound pairs. Based on this, we propose a novel molecular generative framework tailored to PPI interfaces, named GENiPPI. Our evaluation demonstrates that GENiPPI captures the implicit relationships between the PPI interfaces and the active molecules, and can generate novel compounds that target these interfaces. Moreover, GENiPPI can generate structurally diverse novel compounds with limited PPI interface modulators. To the best of our knowledge, this is the first exploration of a structure-based molecular generative model focused on PPI interfaces, which could facilitate the design of PPI modulators. The PPI interface-based molecular generative model enriches the existing landscape of structure-based (pocket/interface) molecular generative model.

bioinformatics↗