Biology subjects

Lin, H.

Publications and source records attributed to Lin, H..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Spatial transcriptomics identifies novel P. aeruginosa virulence factors

To holistically unravel the complexity of pathogen-host interactions within infected tissues we leverage a dual spatial transcriptomic approach that, for the first time, simultaneously captures the expression of Pseudomonas aeruginosa genes alongside the entire host transcriptome in a model of ocular infection. This innovative method reveals differential pathogen and host-specific gene expression patterns across specific anatomical regions generating a unified transcriptional map of infection. By integrating these data, we developed a predictive ridge regression model trained on images from infected tissues. The model achieved an R{superscript 2} score of 0.923 in predicting bacterial burden distributions by using host features thereby predicting novel biomarkers associated with disease severity. Our analysis revealed a complex interplay between P. aeruginosa nutritional requirements and protective host responses and identified novel interactions between bacterial metabolite transport proteins and host autophagy. Among an array of iron acquisition gene transcripts that showed significant enrichment at the host-pathogen interface, we discovered a novel virulence mediator PA2590. This study highlights the power of spatial transcriptomics, particularly in combining bacterial and host transcriptomes, to uncover novel host-pathogen interactions, advance our understanding of bacterial virulence mechanisms, and point to druggable molecules.

microbiology↗

bioRxiv2024

At the onset of active whisking, the input layer of barrel cortex exhibits a 24 h window of increased excitability that depends on prior experience.

The development of motor control over sensory organs is a critical milestone, enabling active explo-ration and shaping of the sensory environment. Whether the onset of sensory organ motor control directly influences the development of corresponding sensory cortices remains unknown. Here, we confirm and exploit the late onset of whisking behavior in mice to address this question in the somatosensory system. Using ex vivo electrophysiology, we describe a transient increase in the intrinsic excitability of excitatory neurons in layer IV of the barrel cortex, which processes whisker input, immediately following the onset of active whisking around on postnatal days 13 and 14. This increase in neuronal gain is specific to layer IV, independent of changes in synaptic strength, and requires prior sensory experience. Further, these effects are not expressed in inhibitory in-terneurons in barrel cortex. The transient increase in excitability is not evident in layer II/III of barrel cortex or in the visual cortex upon eye opening, suggesting a unique interaction between the development of active sensing and the thalamocortical input layer in the somatosensory iso-cortex. Predictive modeling indicates that, immediately following the onset of active whisking, changes in active membrane conductances alone can reliably distinguish neurons in control but not whisker-deprived hemispheres. Our findings demonstrate an experience-dependent, lamina-specific refinement of neuronal excitability tightly linked to the emergence of active whisking. This tran-sient increase in the gain of the thalamic input layer coincides with a critical period for synaptic plasticity in downstream layers, suggesting a role in cortical maturation and sensory processing.

neuroscience↗

bioRxiv2024

ECloudGen: Access to Broader Chemical Space for Structure-based Molecule Generation

Structure-based molecule generation represents a significant advancement in AI-aided drug design (AIDD). However, progress in this domain is constrained by the scarcity of structural data on protein-ligand complexes, a challenge we term the Paradox of Sparse Chemical Space Generation. To address this limitation, we propose a novel latent variable approach that bridges the data gap between ligand-only and protein-ligand complexes, enabling the target-aware generative models to explore a broader chemical space and enhancing the quality of molecular generation. Drawing inspiration from quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables--an innovative integration of physical principles into deep learning frameworks. ECloudGen incorporates modern techniques, including latent diffusion models, Llama architectures, and a newly proposed contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a significantly broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in a case study designing V2R inhibitors. Furthermore, ECloudGens structurally ordered modeling of chemical space enables the development of a model-agnostic optimizer, extending its utility to molecular optimization tasks. This capability has been validated through a single-objective oracle benchmark and a complex multi-objective optimization scenario involving the redesign of endogenous BRD4 ligands. In conclusion, ECloudGen effectively addresses the Paradox of Sparse Chemical Space Generation through its integration of theoretical insights, advanced generative techniques, and real-world validation. The newly proposed technique of leveraging physical entities (such as electron clouds) as latent variables within a deep learning framework may prove useful for computational biology fields beyond AIDD.

bioinformatics↗

bioRxiv2024

Lysineless HiBiT and NanoLuc Tagging Systems as Alternative Tools Monitoring Targeted Protein Degradation

Target protein degradation (TPD) has emerged as a revolutionary approach in drug discovery, leveraging the cells intrinsic machinery to selectively degrade disease-associated proteins. Proteolysis-Targeting Chimeras (PROTACs) exemplify this strategy, exploiting heterobifunctional molecules to induce ubiquitination and subsequent degradation of target proteins. The clinical advancement of PROTACs underscores their potential in therapeutic intervention, with numerous projects progressing through clinical stages. However, monitoring subtle changes in protein abundance induced by TPD molecules demands highly sensitive assays. Nano-luciferase (nLuc) fusion proteins, or the NanoBiT technology derived from it, offer a robust screening platform due to their high sensitivity and stability. Despite these advantages, concerns have arisen regarding potential degradation artifacts introduced by tagging systems due to the presence of lysine residues on them, prompting the development of alternative tools. In this study, we introduce HiBiT-RR and nLucK0, variants devoid of lysine residues, to mitigate such artifacts. Our findings demonstrate that HiBiT-RR maintains similar sensitivity and binding affinity with the original HiBiT. Moreover, the comparison between nLucWT and nLucK0 constructs reveals variations in degradation patterns induced by certain PROTAC molecules, emphasizing the importance of choosing appropriate tagging systems to ensure the reliability of experimental outcomes in studying protein degradation processes.

pharmacology and toxicology↗

bioRxiv2024

KBTBD4 Cancer Hotspot Mutations Drive Neomorphic Degradation of HDAC1/2 Corepressor Complexes

Cancer mutations can create neomorphic protein-protein interactions to drive aberrant function1. As a substrate receptor of the CULLIN3-RBX1 E3 ubiquitin ligase complex, KBTBD4 is recurrently mutated in medulloblastoma (MB)2, the most common embryonal brain tumor in children, and pineoblastoma3. These mutations impart gain-of-function to KBTBD4 to induce aberrant degradation of the transcriptional corepressor CoREST4. However, their mechanism of action remains unresolved. Here, we elucidate the mechanistic basis by which KBTBD4 mutations promote CoREST degradation through engaging HDAC1/2, the direct neomorphic target of the substrate receptor. Using deep mutational scanning, we systematically map the mutational landscape of the KBTBD4 cancer hotspot, revealing distinct preferences by which insertions and substitutions can promote gain-of-function and the critical residues involved in the hotspot interaction. Cryo-electron microscopy (cryo-EM) analysis of two distinct KBTBD4 cancer mutants bound to LSD1-HDAC1-CoREST reveals that a KBTBD4 homodimer asymmetrically engages HDAC1 with two KELCH-repeat propeller domains. The interface between HDAC1 and one of the KBTBD4 propellers is stabilized by the MB mutations, which directly insert a bulky side chain into the active site pocket of HDAC1. Our structural and mutational analyses inform how this hotspot E3-neo-substrate interface can be chemically modulated. First, our results unveil a converging shape complementarity-based mechanism between gain-of-function E3 mutations and a molecular glue degrader, UM171. Second, we demonstrate that HDAC1/2 inhibitors can block the mutant KBTBD4-HDAC1 interface, the aberrant degradation of CoREST, and the growth of KBTBD4-mutant MB models. Altogether, our work reveals the structural and mechanistic basis of cancer mutation-driven neomorphic protein-protein interactions and pharmacological strategies to modulate their action for therapeutic applications.

molecular biology↗

bioRxiv2024

Protection against Clostridioides difficile disease by a naturally avirulent C. difficile strain

Clostridioides difficile (C. difficile) strains belonging to the epidemic BI/NAP1/027 (RT027) group have been associated with increased transmissibility and disease severity. In addition to the major toxin A and toxin B virulence factors, RT027 strains also encode the CDT binary toxin. Our lab previously identified a toxigenic RT027 isolate, ST1-75, that is avirulent in mice despite densely colonizing the colon. Here, we show that co-infecting mice with the avirulent ST1-75 and virulent R20291 strains protects mice from colitis due to rapid clearance of the virulent strain and persistence of the avirulent strain. Although avirulence of ST1-75 is due to a mutation in the cdtR gene, which encodes a response regulator that modulates the production of all three C. difficile toxins, the ability of ST1-75 to protect against acute colitis is not directly attributable to the cdtR mutation. Metabolomic analyses indicate that the ST1-75 strain depletes amino acids more rapidly than the R20291 strain and supplementation with amino acids ablates ST1-75s competitive advantage, suggesting that the ST1-75 strain limits the growth of virulent R20291 bacteria by amino acid depletion. Since the germination kinetics and sensitivity to the co-germinant glycine are similar for the ST1-75 and R20291 strains, our results identify the rapidity of in vivo nutrient depletion as a mechanism providing strain-specific, virulence-independent competitive advantages to different BI/NAP1/027 strains. They also suggest that the ST1-75 strain may, as a biotherapeutic agent, enhance resistance to CDI in high-risk patients. ImportanceClostridioides difficile infections (CDI) are prevalent in healthcare settings and are associated with high recurrence rates. Therapies to prevent CDI, including recent FDA-approved live biotherapeutic products, are costly and have not been used to prevent primary infections. While a nontoxigenic C. difficile strain (NTCD-M3) protects against virulent CDI in animals and reduced CDI recurrence in a phase 2 clinical trial, protection against CDI recurrence in humans was variable and required high doses of the nontoxigenic strain. Here we show that an avirulent C. difficile isolate, ST1-75, efficiently outcompetes virulent C. difficile strains in mice when co-infected at a 1:1 ratio. Our data suggest that inter-strain competition results from ST1-75s more rapid depletion of amino acids than the virulent R20291 strain. Our study identifies inter-strain nutrient depletion as a potentially exploitable mechanism to reduce the incidence of CDI.

microbiology↗

bioRxiv2024

Orbitofrontal cortex to dorsal striatum circuit is critical for incubation of oxycodone craving after forced abstinence

Relapse is a major challenge in treating opioid addiction, including oxycodone. During abstinence, oxycodone seeking progressively increases, and we previously demonstrated a causal role of the orbitofrontal cortex (OFC) in this incubation of oxycodone craving after forced abstinence. Here, we explored critical downstream targets of OFC by focusing on dorsal striatum (DS). We first examined dorsal striatal Fos (a neuronal activity marker) expression associated with oxycodone seeking after abstinence. Using a dopamine D1 receptor (D1R) antagonist, we also tested the causal role of DS in incubated oxycodone seeking. Next, we combined fluorescence-conjugated cholera toxin subunit B (CTb-555, a retrograde tracer) with Fos to assess whether the activation of OFC[->]DS projections was associated with incubated oxycodone seeking. We then used a series of pharmacological procedures to examine the causal role of the interaction between glutamatergic projections from OFC and D1R signaling in DS in incubation of oxycodone craving. We found that dorsal striatal Fos expression in DS exhibited a time-dependent increase in parallel with incubation of oxycodone craving, and DS inactivation decreased incubated oxycodone seeking. Moreover, OFC[->]DS projections were activated during incubated oxycodone seeking, and anatomical disconnection of OFC[->]DS projections, but not unilateral inactivation of OFC or DS, decreased incubated oxycodone seeking. Lastly, contralateral disconnection of OFC[->]DS projections had no effect on oxycodone seeking on abstinence day 1. Together, these results demonstrated a causal role of OFC[->]DS projections in incubation of oxycodone craving.

neuroscience↗

bioRxiv2024

Decoding region-level visual functions from invasive EEG monkey data

Decoding vision is an ambitious task as it aims to transform scalar brain activity into dynamic images with refined shapes, colors and movements. In familiar environments, the brain may trigger activity that resembles specific pattern, thereby facilitating decoding. Can an artificial neural network (ANN) decipher such latent patterns? Here, we explore this question using invasive electroencephalography data from monkeys. By decoding multiregion brain activity, ANN effectively captures individual regions functional roles as a consequence of minimizing visual errors. For example, ANN recognizes that regions V4 and LIP are involved in visual color and shape processing while MT predominantly handles visual motion, aligning with regional visual functions evident in the brain. ANN likely reconstructs vision by seizing hidden spike patterns, representing stimuli distinctly in a two-dimensional plane. Furthermore, during the encoding process of transforming visual stimuli into neuronal activity, optimal performance is achieved in regions closely associated with vision processing.

neuroscience↗

bioRxiv2024

Loss of the Familial Dysautonomia gene Elp1 in cerebellar granule cell progenitors leads to ataxia in mice

Familial Dysautonomia (FD) is an autosomal recessive disorder caused by a splice site mutation in the gene ELP1, which disproportionally affects neurons. While classically characterized by deficits in sensory and autonomic neurons, neuronal defects in the central nervous system have been described. ELP1 is highly expressed in the normal developing and adult cerebellum, but its role in cerebellum development is unknown. To investigate the cerebellar function of Elp1, we knocked out Elp1 in cerebellar granule cell progenitors (GCPs) and examined the outcome on animal behavior and cellular composition. We found that GCP-specific conditional knockout of Elp1 (Elp1cKO) resulted in ataxia by 8 weeks of age. Cellular characterization showed that the animals had smaller cerebella with fewer granule cells. This defect was already apparent 7 days after birth, when Elp1cKO animals also exhibited fewer mitotic GCPs and shorter Purkinje dendrites. Through molecular characterization, we found that loss of Elp1 was associated with an increase in apoptotic cell death and cell stress pathways in GCPs. Our study demonstrates the importance of ELP1 within the developing cerebellum, and suggests that Elp1 loss in the GC lineage may also play a role in the progressive ataxia phenotypes of FD patients.

neuroscience↗

bioRxiv2024

Hour-long, kilohertz sampling rate 3D single-virus tracking in live cells enabled by StayGold fluorescent protein fusions

The viral infection process covers a large range of spatiotemporal scales. Tracking the viral infection process with fluorescent labels over long durations while maintaining a fast sampling rate requires bright and highly photostable labels. StayGold is a recently identified green fluorescent protein that has a greater photostability and higher signal intensity under identical illumination conditions as compared to existing fluorescence protein variants. Here, StayGold protein fusions were used to generate virus-like particles (StayGold-VLPs) to achieve hour-long 3D single-virus tracking (SVT) with one thousand localizations per second (kHz sampling rate) in live cells. The expanded photon budget from StayGold protein fusions prolonged the tracking duration, facilitating a comprehensive study of viral trafficking dynamics with high temporal resolution over long timescales. The development of StayGold-VLPs presents a simple and general VLP labeling strategy for better performance in SVT, enabling exponentially more information to be collected from single trajectories and allowing for the future possibility of observing the whole life cycle of a single virus. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/585070v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@96584corg.highwire.dtl.DTLVardef@7f1e4corg.highwire.dtl.DTLVardef@19e38ddorg.highwire.dtl.DTLVardef@a75aed_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Effectiveness and efficiency: label-aware hierarchical subgraph learning for protein-protein interaction (laruGL-PPI)

Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods for PPIs prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they usually suffer from performance degradation under complex real-world scenarios. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. In addition, with the emergence of high-throughput experimental methods such as mass spectrometry and protein chip technology, the amount of available PPI data is exploding. The huge data growth leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose a label-aware hierarchical subgraph learning method (laruGL-PPI) that can effectively infer PPIs while being both interpretable and generalizable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments on PPI datasets of different scales demonstrate that laruGL-PPI outperforms state-of-the-art PPI prediction methods, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.

bioinformatics↗

bioRxiv2024

Comprehensive analyses of a large human gut Bacteroidales culture collection reveal species and strain level diversity and evolution

Species of the Bacteroidales order are among the most abundant and stable bacterial members of the human gut microbiome with diverse impacts on human health. While Bacteroidales strains and species are genomically and functionally diverse, order-wide comparative analyses are lacking. We cultured and sequenced the genomes of 408 Bacteroidales isolates from healthy human donors representing nine genera and 35 species and performed comparative genomic, gene-specific, mobile gene, and metabolomic analyses. Families, genera, and species could be grouped based on many distinctive features. However, we also show extensive DNA transfer between diverse families, allowing for shared traits and strain evolution. Inter- and intra-specific diversity is also apparent in the metabolomic profiling studies. This highly characterized and diverse Bacteroidales culture collection with strain-resolved genomic and metabolomic analyses can serve as a resource to facilitate informed selection of strains for microbiome reconstitution.

microbiology↗

bioRxiv2024

PPFlow: Target-aware Peptide Design with Torsional Flow Matching

Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method called PPFO_SCPLOWLOWC_SCPLOW, based on conditional flow matching on torus manifolds, to model the internal geometries of torsion angles for the peptide structure design. Besides, we establish a protein-peptide binding dataset named PPBench2024 to fill the void of massive data for the task of structure-based peptide drug design and to allow the training of deep learning methods. Extensive experiments show that PPFO_SCPLOWLOWC_SCPLOW reaches state-of-the-art performance in tasks of peptide drug generation and optimization in comparison with baseline models, and can be generalized to other tasks including docking and side-chain packing.

molecular biology↗

bioRxiv2024

Structural insights into sphingosine 1-phosphate receptor 4 activation

S1PR4 is one of five subtypes of sphingosine 1-phosphate receptors (S1PRs) that regulate immune cell functioning, with functional distinctions to other subtypes. S1PR1-targeted modulators caused serious cardiac and vascular adverse effects because S1PR1 was expressed throughout the whole body. Since S1PR4 was only expressed in lung and lymphoid cells, S1PR4-targeted modulators might not trigger these side effects. However, the development of S1PR4-specific agonists is greatly hindered because of the lack of activated S1PR4 structure. Here, we resolved cryo-EM structures of activated S1PR4 and revealed the structural mechanism of ligand recognition, receptor activation, and Gi coupling. Our results offered structural templates for the development of selective S1PR4 agonists with improved safety profiles.

biochemistry↗

bioRxiv2024

PIKfyve controls dendritic cell function and tumor immunity

The modern armamentarium for cancer treatment includes immunotherapy and targeted therapy, such as protein kinase inhibitors. However, the mechanisms that allow cancer-targeting drugs to effectively mobilize dendritic cells (DCs) and affect immunotherapy are poorly understood. Here, we report that among shared gene targets of clinically relevant protein kinase inhibitors, high PIKFYVE expression was least predictive of complete response in patients who received immune checkpoint blockade (ICB). In immune cells, high PIKFYVE expression in DCs was associated with worse response to ICB. Genetic and pharmacological studies demonstrated that PIKfyve ablation enhanced DC function via selectively altering the alternate/non-canonical NF-{kappa}B pathway. Both loss of Pikfyve in DCs and treatment with apilimod, a potent and specific PIKfyve inhibitor, restrained tumor growth, enhanced DC-dependent T cell immunity, and potentiated ICB efficacy in tumor-bearing mouse models. Furthermore, the combination of a vaccine adjuvant and apilimod reduced tumor progression in vivo. Thus, PIKfyve negatively controls DCs, and PIKfyve inhibition has promise for cancer immunotherapy and vaccine treatment strategies.

cancer biology↗

bioRxiv2024

SIRT2-Mediated ACSS2 K271 Deacetylation Suppresses Lipogenesis Under Nutrient Stress

De novo lipogenesis is associated with the development of human diseases such as cancer, diabetes, and obesity. At the core of lipogenesis lies acetyl coenzyme A (CoA), a metabolite that plays a crucial role in fatty acid synthesis. One of the pathways contributing to the production of cytosolic acetyl-CoA is mediated by acetyl-CoA synthetase 2 (ACSS2). Here, we reveal that when cells encounter nutrient stress, particularly a deficiency in amino acids, Sirtuin 2 (SIRT2) catalyzes the deacetylation of ACSS2 at the lysine residue K271. This results in K271 ubiquitination and subsequently proteasomal degradation of ACSS2. Substitution of K271 leads to decreased ubiquitination of ACSS2, increased ACSS2 protein level, and thus increased lipogenesis. Our study uncovers a mechanism that cells employ to efficiently manage lipogenesis during periods of nutrient stress.

biochemistry↗

bioRxiv2024

Characterization of the SARS-CoV-2 BA.5 Variants in H11-K18-hACE2 Hamsters

This study aims to comprehensively characterize the SARS-CoV-2 BA.5 variants using K18 hACE2 transgenic mice and golden hamsters as model organisms. Previous research on SARS-CoV-2 has utilized both mouse and hamster models, leading to conflicting results concerning the viruss lethality. In our study, the finding suggests that H11-K18 hACE2 golden hamsters closely mimic the disease progression observed in human COVID-19 cases caused by BA.5 variants, demonstrating consistent severity and symptoms comparable to severe infections. Additionally, hamsters exhibit heightened respiratory viral replication, accurately reflecting the clinical viral kinetics observed in humans. The study emphasizes the critical importance of selecting an appropriate animal model for SARS-CoV-2 research, while also providing robust support for the hypothesis that BA.5 variants contribute to fatal outcomes in COVID-19 cases. These findings highlight the pivotal role of the golden hamster model in advancing our understanding of the pathogenic mechanisms underlying SARS-CoV-2 variants, as well as in the development of targeted therapeutic strategies. Significance StatementOur research work explores groundbreaking insights that could reshape our understanding of COVID-19 and pave the way for targeted therapies. We use golden hamster models to express the possibility of different animal models could contribute to human diseases. We hope this finding could clarify some conflicts existed, and help further development of medication for COVID.

immunology↗

bioRxiv2024

Tissue Tension and Strain as Indicators of Suction-mediated Cutaneous DNA Transfection: A Parametric Study

Cutaneous suction-based transfection is a recently developed technique that is painless and simple-to-use for the delivery of DNA for nucleic-acid-based vaccines. The technique promises high efficiency for both antigen expression and immunogenicity as demonstrated in both animal studies and human clinical trials. To realize this promise, a parametric study and systematic evaluation on the efficacy of cutaneous suction as a transfection method was performed. Using Green Fluorescent Protein (GFP) plasmid expression as a transfection reporter in a rat model, the expression level as a function of both suction nozzle size and suction pressure was quantified. A numerical model was employed to compute skin deformation in terms of strain, which was used to correlate with GFP expression. Based on these results, two quantities, total integrated strain and tissue tension, are proposed as indicators of expression level that can be used to guide protocol development and optimization. These indicators are also discussed in relation to possible cellular uptake mechanisms.

bioengineering↗