Biology subjects

Tao, S.

Publications and source records attributed to Tao, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Unique Gluing Effect of ASXL1 K351 Monoubiquitination Stimulates PR-DUB-Mediated Nucleosomal H2AK119Ub Deubiquitination

Protein ubiquitination plays a critical role in regulating fundamental biological processes through both proteolytic and nonproteolytic mechanisms. While classically known for its role in targeting proteins for degradation, ubiquitination can also modulate enzymatic activity. However, all of the known mechanisms of such modulation involve spatially constrained mechanisms that take place near the enzyme-substrate interfaces. Here, we report an unprecedented regulatory paradigm mediated by ubiquitination that is located distal to the enzyme-substrate interface but can still activate the polycomb repressive deubiquitinase (PR-DUB) complex. Using cryo-electron microscopy, molecular dynamics (MD) simulations, hydrogen-deuterium exchange mass spectrometry (HDX-MS), and kinetics assays, we determined that the ASXL1 K351 monoubiquitination promotes PR-DUB-mediated nucleosomal H2AK119Ub deubiquitination by stabilizing the catalytic pocket of PR-DUB, causing significant increase in maximal catalytic velocity (Vmax) but almost no change in the substrate binding affinity (Km). Structural analysis revealed that the ubiquitin moiety at ASXL1 K351 bridges the BAP1 and ASXL1 subunits, acting as an intramolecular "glue" that constrains the conformational dynamics of these subunits without altering the nucleosome binding interface. Further MD and HDX-MS results demonstrated the monoubiquitination of ASXL1 K351 enables PR-DUB to effectively maintain a spatial arrangement of the catalytic conformation that closely aligns with substrate cleavage. This study expands our understanding of the mechanisms of ubiquitin function to include intramolecular fastening with a molecular gluing effect and provides a deeper mechanistic understanding of PR-DUB activation in nucleosomal H2AK119Ub removal.

biochemistry↗

bioRxiv2025

MiT4SL: multi-omics triplet representation learning for cancer cell line-adapted prediction of synthetic lethality

Synthetic lethality (SL) offers a promising approach for targeted cancer therapies. Current SL prediction models heavily rely on extensive labeled data for specific cell lines to accurately identify SL pairs. However, a major limitation is the scarcity of SL labels across most cell lines, which makes it challenging to predict SL pairs for target cell lines with limited or even no available labels in real-world scenarios. Furthermore, gene interactions could be opposite between training and test cell lines, i.e. SL vs. non-SL, which further aggravates the challenge of generalization among cell lines. A promising strategy is to transfer knowledge learned from cell lines with relatively abundant SL labels to those with limited SL labels for the discovery of novel SL pairs, i.e., cell line-adapted SL prediction. Here, we propose MiT4SL, a multi-omics triplet representation learning model for cell line-adapted SL prediction. The core idea of MiT4SL is to model cell lineage information as embeddings, which are generated by combining a protein-protein interaction network representation tailored to each cell line with the corresponding protein sequence embeddings. We then combine these cell line embeddings with gene pair representations derived from a biomedical knowledge graph and protein sequences. This triplet representation learning strategy enables MiT4SL to capture both shared biological mechanisms across cell lines and those unique to each cell line, effectively mitigating distribution shift and improving generalization to target cell lines. Additionally, explicit cell line embeddings provide the necessary signals for MiT4SL to effectively differentiate between cell line contexts, enabling it to adjust predictions and mitigate possible label conflicts for the same gene pair across different cell lines. Experimental results across various cell line-adapted scenarios show that MiT4SL outperforms six state-of-the-art models. To the best of our knowledge, MiT4SL is the first deep learning model designed specifically for cancer cell line-adapted SL prediction. AvailabilityThe code of our work is available at https://github.com/JieZheng-ShanghaiTech/MiT4SL.

bioinformatics↗

bioRxiv2025

brain network dynamics during multi-task demands predict children math achievement

Dynamic reconfiguration of neural network and flexible information integration across multiple tasks has been considered critical to characterize individual difference in complex cognition and general intelligence. A promising and underexplored question is how these neurocognitive processes related to childrens academic achievements, a hallmark of high-order cognitive abilities that integrate attention, memory and problem-solving. By using of a multitasking paradigm which bridging outside- in and inside-out approaches, we investigated the dynamic neural mechanisms underlying two core domains of academic performance: math and reading. We first apply partial least squares regression (PLSR) to examine static neural patterns and find that the first latent component--reflecting a generalized brain functional system--predicts math achievement but not reading. The multiple-demand system and the somato-cognitive action network (SCAN) are consistently engaged across diverse task demands. Furthermore, we use a Hidden Markov Model (HMM) to examine dynamic features of brain activity and identify distinct integrated and segregated brain states. Notably, the segregated state--characterized by heightened cortical network segregation--is associated with better math performance. Information-theoretic analyses further reveal that greater complexity in the temporal sequence of the segregated brain functional networks, along with stronger cerebrocerebellar functional coupling, correlates with higher math achievement. By means of multitasks design, these findings suggest that flexible engagement of specialized brain network and automatic information processing is crucial for math learning in children.

neuroscience↗

bioRxiv2025

Visible-Light-Controlled Residue-Selective Cross-Linking for Deciphering Protein Complexes and Dynamic Protein-Protein Interactions in Live Cells

Cross-linking mass spectrometry (XL-MS) has emerged as an attractive technology for investigating protein complexes and protein-protein interactions (PPIs). However, commonly used cross-linking strategies present significant challenges for precise analysis of protein complexes and dynamic PPIs in native biological environments. Here we present the visible-light-controlled lysine-selective cross-linking (VL-XL) strategy for in-depth analysis of protein complexes and dynamic PPIs both in vitro and in live cells, building on light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) chemistry. We demonstrate that the VL-XL strategy effectively explores the dynamic dimerization of PD-L1 stimulated by exogenous modulators. Moreover, the VL-XL strategy successfully profiles the time-resolved EGF-stimulated EGFR interactome, providing valuable insights into the regulatory mechanisms of EGFR signaling and intracellular trafficking. Importantly, the VL-XL strategy efficiently deciphers the molecular glue (MG) induced dynamic PPIs and substrate profile of MG degrader, opening an innovative avenue for identifying neo-substrates. By harnessing the advantages of temporal controllability, good biocompatibility, and lysine selectivity, the VL-XL method simplifies MS data analysis and facilitates the acquisition of accurate structural information of protein complexes and the elucidation of elusive PPIs in live cells. Overall, the VL-XL strategy expands the XL-MS toolbox, and realizes in-depth analysis of protein complexes and dynamic PPIs, which will inspire innovative solutions for protein interactomes research and structural systems biology.

biochemistry↗

bioRxiv2025

The Adhesion GPCR ADGRL2 engages Gα13 to Enable Epidermal Differentiation

Homeostasis relies on signaling networks controlled by cell membrane receptors. Although G-protein-coupled receptors (GPCRs) are the largest family of transmembrane receptors, their specific roles in the epidermis are not fully understood. Dual CRISPR-Flow and single cell Perturb-seq knockout screens of all epidermal GPCRs were thus performed, uncovering an essential requirement for adhesion GPCR ADGRL2 (latrophilin 2) in epidermal differentiation. Among potential downstream guanine nucleotide-binding G proteins, ADGRL2 selectively activated G13. Perturb-seq of epidermal G proteins and follow-up tissue knockouts verified that G13 is also required for epidermal differentiation. A cryo-electron microscopy (cryo-EM) structure in lipid nanodiscs showed that ADGRL2 engages with G13 at multiple interfaces, including via a novel interaction between ADGRL2 intracellular loop 3 (ICL3) and a G13-specific QQQ glutamine triplet sequence in its GTPase domain. In situ gene mutation of this interface sequence impaired epidermal differentiation, highlighting an essential new role for an ADGRL2-G13 axis in epidermal differentiation.

cell biology↗

bioRxiv2025

Learning universal knowledge graph embedding for predicting biomedical pairwise interactions

Predicting biomedical interactions is crucial for understanding various biological processes and drug discovery. Graph neural networks (GNNs) are promising in identifying novel interactions when extensive labeled data are available. However, labeling biomedical interactions is often time-consuming and labor-intensive, resulting in low-data scenarios. Furthermore, distribution shifts between training and test data in real-world applications pose a challenge to the generalizability of GNN models. Recent studies suggest that pre-training GNN models with self-supervised learning on unlabeled data can enhance their performance in predicting biomedical interactions. Here, we propose LukePi, a novel self-supervised pre-training frame-work that pre-trains GNN models on biomedical knowledge graphs (BKGs). LukePi is trained with two self-supervised tasks: topology-based node degree classification and semantics-based edge recovery. The former is to predict the degree of a node from its topological context and the latter is to infer both type and existence of a candidate edge by learning semantic information in the BKG. By integrating the two complementary tasks, LukePi effectively captures the rich information from the BKG, thereby enhancing the quality of node representations. We evaluate the performance of LukePi on two critical link prediction tasks: predicting synthetic lethality and drug-target interactions, using four benchmark datasets. In both distribution-shift and low-data scenarios, LukePi significantly outperforms 15 baseline models, demonstrating the power of the graph pre-training strategy when labeled data are sparse.

bioinformatics↗

bioRxiv2025

Functional dynamics reveal the response of the crabapple (Malus sp.) phyllosphere microbiome to Gymnosporangium yamadae infection

The plant microbiome is crucial for maintaining plant health under disease stress. While considerable research has focused on belowground systems, the phyllosphere microbiome remains underexplored, particularly regarding its functional responses to pathogen infection. In this study, we investigated the phyllosphere microbiome dynamics of crabapple (Malus Kelsey) infected by Gymnosporangium yamadae using metatranscriptomic sequencing across six stages of rust disease progression. Our analysis revealed a general increase in the diversity of fungi, bacteria and viruses in infected leaves. Notably, fungal diversity negatively correlated with bacterial diversity, reflecting competitive interactions during disease development. Microbial taxa in diseased leaves exhibited heightened expression activity compared to healthy leaves, with fungi progressively dominating the microbial community. Functional co-occurrence networks of the phyllosphere microbiome in infected leaves were more complex than in healthy leaves, suggesting adaptive reorganization in response to pathogen invasion. Differentially expressed genes at each stage were significantly enriched in carbohydrate metabolism pathways and gene regulation-related functions, enabling functional adaptations to rust diseases. Random forest modeling identified key microbial transcripts associated with pathogen abundance, including beneficial microbes like Saitozyma podzolica, which secretes glucan-degrading enzymes, and Mortierella elongata, involved in sterol biosynthesis and plant resistance. Conversely, Alternaria alternata emerged as a major pathobiome contributor, secreting enzymes that degrade plant cell wall components (e.g., pectin, cellulose, lignin, and xylan), and engaging in MAPK signaling pathways critical for pathogenesis. Our findings underscore the vital role of the phyllosphere microbiome in mediating plant-pathogen interactions and shaping disease progression, providing a foundation for microbiome-based strategies to enhance plant resilience. ImportanceOur findings reveal the dynamic shifts in the expression patterns and adaptive functional strategies of crabapple phyllosphere microbiome in response to the Gymnosporangium yamadae infection. We identified several key microbes that may play vital roles in pathogenesis and speculated on their roles and functions in plant-pathogen interactions. In conclusion, our study highlights the potential of the phyllosphere microbiome in regulating plant health.

microbiology↗

bioRxiv2024

Accurate structure prediction of immune proteins using parameter-efficient transfer learning

Accurate prediction of immune protein structures is crucial for understanding the immune system and advancing immunotherapy development. While deep learning methods have significantly advanced protein structure prediction by extracting evolutionary constraints from homologous sequences of a target protein, they struggle with immune proteins due to the limited number of known structures and the lack of homologous sequences in hypervariable regions. To address this challenge, we propose ImmuneFold, a transfer learning approach that fine-tunes ESMFold specifically for immune proteins. We leverage low-rank adaption (LoRA), a parameter-efficient fine-tuning technique that requires considerably less memory and substantially fewer parameters. Evaluations on various immune proteins, including T-cell receptors, antibodies, and nanobodies, demonstrate that ImmuneFold outperforms existing methods in prediction accuracy. Furthermore, we apply ImmuneFold to develop a zero-shot protocol for TCR-epitope binding prediction. Unlike previous supervised methods suffering from severe overfitting due to limited experimental binding data, our approach first predicts TCR-epitope structure using ImmuneFold and then directly estimates the binding affinity by calculating Rosseta energy. Evaluations on experimental binding datasets suggest that our method is robust and accurate in predicting TCR-epitope binding. In summary, ImmuneFold demonstrates accurate predictions of immune protein structures and TCR-epitope binding, highlighting its potential to advance the development of immunotherapies.

bioinformatics↗

bioRxiv2024

Disease-Linked Regulatory DNA Variants and Homeostatic Transcription Factors in Epidermis

Identifying noncoding single nucleotide variants (SNVs) in regulatory DNA linked to polygenic disease risk, the transcription factors (TFs) they bind, and the target genes they dysregulate is a goal in polygenic disease research. Massively parallel reporter gene analysis (MPRA) of 3,451 SNVs linked to risk for polygenic skin diseases characterized by disrupted epidermal homeostasis identified 355 differentially active SNVs (daSNVs). daSNV target gene analysis, combined with daSNV editing, underscored dysregulated epidermal differentiation as a pathomechanism shared across common polygenic skin diseases. CRISPR knockout screens of 1772 human TFs revealed 108 TFs essential for epidermal progenitor differentiation, uncovering novel roles for ZNF217, CXXC1, FOXJ2, IRX2 and NRF1. Population sampling CUT&RUN of 27 homeostatic TFs identified allele-specific DNA binding (ASB) differences at daSNVs enriched near epidermal homeostasis and monogenic skin disease genes, with notable representation of SP/KLF and AP-1/2 TFs. This resource implicates dysregulated differentiation in risk for diverse polygenic skin diseases.

genetics↗

bioRxiv2024

CXXC-finger protein 1 associates with FOXP3 to stabilize homeostasis and suppressive functions of regulatory T cells

FOXP3-expressing regulatory T (Treg) cells play a pivotal role in maintaining immune homeostasis and tolerance, with their activation being crucial for preventing various inflammatory responses. However, the mechanisms governing the epigenetic program in Treg cells during their dynamic activation remain unclear. In this study, we demonstrate that CXXC finger protein 1 (CXXC1) interacts with the transcription factor FOXP3 and facilitates the regulation of target genes by modulating H3K4me3 deposition. Cxxc1 deletion in Treg cells leads to severe inflammatory disease and spontaneous T-cell activation, with impaired immunosuppressive function. As a transcriptional regulator, CXXC1 promotes the expression of key Treg functional markers under steady-state conditions, which are essential for the maintenance of Treg cell homeostasis and their suppressive functions. Epigenetically, CXXC1 binds to the genomic regulatory regions of Treg program genes in mouse Treg cells, overlapping with FOXP3 binding sites. Given its critical role in Treg cell homeostasis, CXXC1 presents itself as a promising therapeutic target for autoimmune diseases.

immunology↗

bioRxiv2024

Macrophages redeploy functional cancer cell surface proteins following phagocytosis

Macrophage-mediated phagocytosis is a vital innate immune process altered in cancer. We show here that tumor-associated macrophages (TAMs) redeploy intact cell surface proteins from cancer cells to their own cell surface. We initially observed the canonical epithelial cancer surface marker EpCAM on the surface of TAMs in primary human solid tumors but not paired peripheral blood macrophages. In a murine model of metastatic breast cancer, we also observed EpCAM on the surface of primary TAMs that have phagocytosed breast cancer cells. In a model of a myeloproliferative neoplasm, we again found engulfed cell-derived surface proteins on the surface of macrophages following phagocytosis. A co-culture system and proteomics assay that tags proteins based on their cell-of-origin revealed hundreds of cell surface proteins synthesized in cancer cells are present and fully intact on the surface of macrophages following phagocytosis. Using a biotin transfer assay, we determined that these proteins were on the surface of the cancer cell prior to redeployment by the macrophage following phagocytosis. Furthermore, macrophages that redeploy a neutral amino acid transporter correspondingly show increased transport of an unnatural amino acid substrate. Widespread acquisition of proteins from engulfed cells may contribute to two critical TAM phenotypes: the inability to phagocytose and reprogrammed metabolism.

immunology↗

bioRxiv2024

Feasibility of laminar functional quantitative susceptibility mapping

Layer fMRI is an increasingly utilized technique that provides insights into the laminar organization of brain activity. However, both blood-oxygen-level-dependent (BOLD) fMRI and vascular space occupancy data (VASO) have certain limitations, such as bias towards larger cortical veins in BOLD fMRI and high specific absorption rate in VASO. This study aims to explore the feasibility of whole-brain laminar functional quantitative susceptibility mapping (fQSM) compared to laminar BOLD fMRI and VASO at ultra-high field. Data were acquired using 3D EPI techniques. Complex data were denoised with NORDIC and susceptibility maps were computed using 3D path-based unwrapping, the variable-kernel sophisticated harmonic artifact reduction as well as the streaking artifact reduction for QSM algorithms. To assess layer-specific activation, twenty layers were segmented in the somatosensory and motor cortices, obtained from a finger tapping paradigm, and further averaged into six anatomical cortical layers. The magnitude of signal change and z-scores were compared across layers for each technique. fQSM showed the largest activation-dependent mean susceptibility decrease in Layers II/III in M1 and Layers I/ II in S1 with up to -1.3 ppb while BOLD fMRI showed the strongest mean signal increase in Layer I. Our data suggest that fQSM demonstrates less bias towards activation in superficial layers compared to BOLD fMRI. Moreover, activation-based susceptibility change was comparable to VASO data. Studying whole-brain, layer-dependent activation with submillimeter fQSM is feasible, and reduces bias towards venous drainage effects on the cortical surface compared to BOLD fMRI, thereby enabling better localization of laminar activation.

neuroscience↗

bioRxiv2024

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

SummaryThe price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data. AvailabilityMetapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA.

bioinformatics↗

bioRxiv2024

The continuous differentiation of multiscale structural gradients from childhood to adolescence correlates with the maturation of cortical morphology and functional specialization

From childhood to adolescence, the structural organization of the human brain undergoes dynamic and regionally heterogeneous changes across multiple scales, from synaptic pruning to the reorganization of large-scale anatomical wiring. However, during this period, the developmental process of multiscale structural architecture, its association with cortical morphological changes, and its role in the maturation of functional organization remain largely unknown. Here, we utilized a longitudinal multimodal imaging dataset including 276 children aged 6 to 14 years to investigate the developmental process of multiscale cortical wiring. We used an in vivo model of cortical wiring that combines features of white matter tractography, cortico-cortical proximity, and microstructural similarity to construct a multiscale brain structural connectome. By employing the gradient mapping method, the gradient space derived from the multiscale structural connectome effectively recapitulated the sensory-association axis and anterior-posterior axis. Our findings revealed a continuous expansion of the multiscale structural gradient space during development, with the principal gradient increasingly distinguishing between primary and transmodal regions. This age-related differentiation coincided with regionally heterogeneous changes in cortical morphology. Furthermore, our study revealed that developmental changes in coupling between multiscale structural and functional connectivity were correlated with functional specialization refinement, as evidenced by changes in the participation coefficient. We also found that the differentiation of the principal multiscale structural gradient was associated with improved cognitive abilities, such as enhanced working memory and attention performance, and potentially supported by molecular processes related to synaptic functions. These findings advance our understanding of the intricate maturation process of brain structural organization and its implications for cognitive performance.

neuroscience↗

bioRxiv2024

Development of the overlapping network modules in the human brain

Developmental connectomic studies have shown that the modular organization of functional networks in the human brain undergoes substantial reorganization with age to support cognitive growth. However, these studies implicitly assume that each brain region belongs to one and only one specific network module, ignoring the potential spatial overlap between functional modules. How the overlapping functional modular architecture develops and whether this development is related to structural signatures remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6-14 years), we investigated the development of the overlapping modular architecture of functional networks, and further explored their structural associations. Specifically, an edge-centric network model was used to identify the overlapping functional modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a remarkable regional inhomogeneity in module overlap in children, with higher entropy in the ventral attention, somatomotor, and subcortical networks and lower entropy in the visual and default-mode networks. Furthermore, the overlapping modules developed in a linear, spatially dissociable manner from childhood to adolescence, with significantly reduced entropy in the prefrontal cortex and putamen and increased entropy in the parietal lobules. Personalized overlapping modular patterns capture individual brain maturity as characterized by brain age. Finally, the overlapping functional modules can be significantly predicted by integrating gray matter morphology and white matter network properties. Our findings highlight the maturation of overlapping network modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

neuroscience↗

bioRxiv2024

Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD

BackgroundNeuroimaging studies suggest substantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD). However, how these individual-level brain phenotypes contribute to the identification of ADHD biotypes and whether these biotypes have different treatment outcomes and neurobiological underpinnings remain largely unknown. MethodsWe collected multisite, high-quality structural magnetic resonance imaging data from 1,006 children aged 6-14 years, including 351 children with ADHD and 655 typically developing children. Normative growth models of cortical thickness were established for 219 regions in the typically developing children. Individual-level deviations from these normal references were quantified and clustered to identify ADHD biotypes. We validated the replicability and generalizability of the ADHD biotypes using two independent datasets and evaluated the associations of the biotypes with symptomatic, cognitive, and gene expression profiles, as well as follow-up treatment outcomes. FindingsNo more than 10% of children with ADHD had extreme deviations in cortical thickness in a single region, suggesting high heterogeneity among individuals with ADHD. On the basis of the brain deviation maps, we discovered two robust ADHD biotypes, an infra-normal subtype with cortical thinning associated with ADHD symptoms and a supranormal subtype with cortical thickening associated with cognition. Patients with the infra-normal subtype responded better to methylphenidate than to atomoxetine, although both subtypes showed treatment efficacy. Brain deviations in the infra-normal subtype were explained by the expression levels of genes enriched in presynaptic and axonal development and polygenic risk of ADHD. InterpretationWe identified anatomically distinct, clinically valuable, and biologically informed ADHD subtypes, providing insight into the neurobiological basis of clinical heterogeneity and facilitating a personalized medication strategy for ADHD patients. Panel: Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSubstantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD) motivates the need to discover homogeneous biotypes. We searched PubMed for research articles on ADHD biotypes using brain MRI published before December 1, 2023, using the search terms ((attention deficit hyperactivity disorder [Title/Abstract]) OR (ADHD [Title/Abstract])) AND ((subtypes [Title/Abstract]) OR (subgroups [Title/Abstract]) OR (subtyping [Title/Abstract])) AND ((MRI [Title/Abstract]) OR (neuroimaging [Title/Abstract]) OR (brain [Title/Abstract])) without language restrictions. Of the eight included studies, two identified ADHD biotypes using structural morphology, four used functional activity, and two used multimodal features. However, none of these studies considered the developmental effect of the brain phenotypes, examined treatment response, or investigated the genetic correlates of the biotypes. Added value of this studyThis study is the first to use individualized brain measures extracted from normative models to investigate ADHD biotypes in a large sample of more than 1,000 children. We identified two reproducible ADHD biotypes, characterized by distinct symptomatic, cognitive, and gene expression profiles, as well as differential treatment responses. This study advances our understanding of the neurobiological basis underlying the clinical heterogeneity of ADHD and highlights the critical need to discover ADHD biotypes using an unbiased and individualized approach. Implications of all the available evidenceThis study revealed remarkable neuroanatomical heterogeneity in ADHD patients and identified anatomically distinct, clinically valuable, and biologically informed ADHD biotypes. Our findings have potential value for the investigation of data-driven biotypes to evaluate treatment efficacy and facilitate personalized treatment. We also highlight the need for future studies to move beyond the understanding of ADHD solely based on the "average patient" perspective.

neuroscience↗

bioRxiv2024

Stably-Inverted Apical-Out Human Upper Airway Organoids for SARS-CoV-2 Infection and Therapeutic Testing

While breakthroughs with organoids have emerged as next-generation in vitro tools, standardization for drug discovery remains a challenge. This work introduces human airway organoids with reversed biopolarity (AORBs), cultured and analyzed in a high-throughput, single-organoid-per-well format, enabling milestones towards standardization. AORBs exhibit a spatio-temporally stable apical-out morphology, facilitating high-yield direct intact-organoid virus infection. Single-cell RNA sequencing and immunohistochemistry confirm the physiologically relevant recapitulation of differentiated human airway epithelia. The cellular tropism of five severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains along with host response differences between Delta, Washington, and Omicron variants, as observed in transcriptomic profiles, also suggest clinical relevance. Dose-response analysis of three well-studied SARS-CoV-2 antiviral compounds (remdesivir, bemnifosbuvir, and nirmatrelvir) demonstrates that AORBs efficiently predict human efficacy, comparable to gold-standard air-liquid interface cultures, but with higher throughput ([~]10-fold) and fewer cells ([~]100-fold). This combination of throughput and relevance allows AORBs to robustly detect false negative results in efficacy, preventing irretrievable loss of promising lead compounds. While this work leverages the SARS-CoV-2 study as a proof-of-concept application, the standardization capacity of AORB holds broader implications in line with regulatory efforts to push alternatives to animal studies.

bioengineering↗

bioRxiv2024

Capturing Snapshots of Nucleosomal H2A K13/K15 Ubiquitination Mediated by the Monomeric E3 Ligase RNF168

AbstractsThe DNA damage repair regulatory protein RNF168, a monomeric RING-type E3 ligase, plays a crucial role in regulating cell fate and DNA repair by specific and efficient ubiquitination of the adjacent Lys13 and Lys15 sites at the H2A N-terminal tail. However, understanding how RNF168 coordinates with its cognate E2 enzyme UbcH5c to ubiquitinate H2AK13/15 site-specifically has long been hampered by the lack of high-resolution structures of RNF168 and UbcH5c[~]Ub in complex with nucleosomes. Here, we developed mechanism-based chemical trapping strategies and determined the cryo-EM structures of the RNF168/UbcH5c-Ub/NCP complex captured in transient H2AK13/15 monoubiquitination and adjacent dual-monoubiquitination reactions. Our structural analysis revealed that RNF168 stably binds to the nucleosomal H2A-H2B acidic patch through a basic helix with multiple interactions, which positions the UbcH5c active centre directly over the H2A N-terminus, providing a "helix-anchoring" mode for monomeric E3 ligase RNF168 on nucleosome in contrast to the "compass-binding" mode of dimeric E3 ligases. Furthermore, our chemically synthesized ubiquitinated histones have enabled the elucidation of the efficiency of Ub installation and the interplay between the initial and subsequent Ub modifications on the adjacent H2A K13 and K15 sites. Overall, our work not only provides structural snapshots of H2A K13/K15 site-specific monoubiquitination and adjacent dual-monoubiquitination, but also offers a near-atomic resolution structural framework for understanding how pathogenic mutations or physiological modifications affect the molecular function of RNF168 in H2A K13/15 ubiquitination.

biochemistry↗