Biology subjects

Liu, H.

Publications and source records attributed to Liu, H..

At least 505 records · Page 28Linked to original sources

bioRxiv2023

High-affinity binding to the SARS-CoV-2 spike trimer by a nanostructured, trivalent protein-DNA synthetic antibody

Multivalency enables nanostructures to bind molecular targets with high affinity. Although IgG antibodies can be generated against a wide range of antigens, their shape and size cannot be tuned to match a given target. DNA nanotechnology provides an attractive approach for designing customized multivalent scaffolds due to the addressability and programmability of the nanostructure shape and size. Here, we use computational simulation to guide the design and synthesis of a DNA nanostructure-based synthetic antibody ("nano-synbody"). The nano-synbody is comprised of a three-helix bundle DNA nanostructure with three identical arms terminating in a mini-binder protein that targets the SARS-CoV-2 spike protein. The structure was designed to match the valence and distance between the three receptor binding domains (RBDs) in the spike trimer, in order to enhance binding through avidity effects. Moreover, the design allowed for the display of one, two, or three protein-displaying arms, thereby systematically probing the effect of multivalency on binding affinity. The binding strength of the nano-synbody increased with the increasing number of arms, yielding 11.2 pM affinity ([~]100-fold enhancement over monovalent binding) for the wild-type spike protein for the three-arm structure. Moreover, the multivalency was able to yield a 95 pM affinity for the Omicron variant, a mutant against which the monovalent protein was ineffective. The nano-synbody could also block infection of a spike protein-bearing pseudovirus, and similarly demonstrated effective inhibition of the Omicron variant when trimerized. The structure of the three-arm nano-synbody bound to the Omicron variant spike trimer was solved by negative-stain transmission electron microscopy reconstruction, and shows the protein-DNA nanostructure with all three arms bound to the RBD domains, confirming the intended trivalent attachment. Finally, nano-synbody binding could be reversed by removing one, two, or three arms in a programmable fashion, via toehold-mediated strand displacement. The ability to tune the size and shape of the nano-synbody, as well as its potential ability to attach (and then remove) two or more different binding ligands, will enable the high-affinity binding of a range of proteins, and pave the way towards their manipulation using DNA-based nano-robotic devices.

bioengineering↗

bioRxiv2023

Identification of astrocyte-driven pseudolineages reveals clinical stratification and therapeutic targets in Glioblastoma

The growth of a tumor is tightly linked to the distribution of its cells along a continuum of activation states. Here, we systematically decode the activation state architecture (ASA) in a glioblastoma (GBM) patient cohort through comparison to adult murine neural stem cells. Modelling of these data forecast how tumor cells organize to sustain growth and identifies rate of activation as the main predictor of growth. Accordingly, patients with a higher quiescence fraction exhibit improved outcomes. Further, DNA methylation arrays enable ASA-related patient stratification. Comparison of healthy and malignant gene expression dynamics reveals dysregulation of the Wnt-antagonist SFRP1 at the quiescence to activation transition. SFRP1 overexpression renders GBM quiescent and increases overall survival of tumor-bearing mice. Surprisingly, it does so through reprogramming the tumors stem-like methylome into an astrocyte-like one. Our findings offer a novel patient stratification with clinical impact, new biomarkers and therapeutic avenues to halt GBM progression.

cancer biology↗

bioRxiv2023

Engineering an artificial catch bond using mechanical anisotropy

Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociated the complex using five precisely controlled anchor geometries. When tension was applied through residue #13 on CohE and the N-terminus of DocG, the complex behaved as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaved as a slip bond. We used a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrated that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.

biophysics↗

bioRxiv2023

Epigenetic regulation of p63 blocks squamous-to-neuroendocrine transdifferentiation in esophageal development and malignancy

While cell fate determination and maintenance are important in establishing and preserving tissue identity and function during development, aberrant cell fate transition leads to cancer cell heterogeneity and resistance to treatment. Here, we report an unexpected role for the transcription factor p63 (Trp63/TP63) in the fate choice of squamous versus neuroendocrine lineage in esophageal development and malignancy. Deletion of p63 results in extensive neuroendocrine differentiation in the developing mouse esophagus and esophageal progenitors derived from human embryonic stem cells. In human esophageal neuroendocrine carcinoma (eNEC) cells, p63 is transcriptionally silenced by EZH2-mediated H3K27 trimethylation (H3K27me3). Upregulation of the major p63 isoform {Delta}Np63, through either ectopic expression or EZH2 inhibition, promotes squamous transdifferentiation of eNEC cells. Together these findings uncover p63 as a rheostat in coordinating the transition between squamous and neuroendocrine cell fates during esophageal development and tumor progression.

cell biology↗

bioRxiv2023

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

BackgroundGlioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. MethodAfter collecting 18 RCD-related signatures from the opening literatures, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. ResultHere, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed a RCD gene pair scoring system, named RCD.GP signature. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified the SLC43A3 by q-PCR in glioma grades and glioma cell lines. ConclusionOur study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

bioinformatics↗

bioRxiv2023

A phylogenetic approach to explore the Aspergillus fumigatus conidial surface-associated proteome and its role in pathogenesis

Aspergillus fumigatus, an important pulmonary fungal pathogen causing several diseases collectively called aspergillosis, relies on asexual spores or conidia for initiating host infection. Here, we used a phylogenomic approach to compare proteins in the conidial surface of A. fumigatus, two closely related non-pathogenic species, Aspergillus fischeri and Aspergillus oerlinghausenensis, and the cryptic pathogen Aspergillus lentulus. After identifying 62 proteins uniquely expressed on the A. fumigatus conidial surface, we deleted 42 genes encoding conidial proteins. We found deletion of 33 of these genes altered susceptibility to macrophage killing, penetration and damage to epithelial cells, and cytokine production. Notably, a gene that encodes glycosylasparaginase, which modulates levels of the host pro-inflammatory cytokine IL-1{beta}, is important for infection in an immunocompetent murine model of fungal disease. These results suggest that A. fumigatus conidial surface proteins and effectors are important for evasion and modulation of the immune response at the onset of fungal infection.

microbiology↗

bioRxiv2023

Voxel-wise regression without intensity normalizationprovides better sensitivity when identifying injuryregions after stroke in FDG-PET images

BackgroundIn the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET), intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of an appropriate normalization method in stroke studies remains unclear, leading to inconsistent findings in the literature. Materials and methodsHere, we propose a regression-based and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other comprising 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination was calculated to measure the linearity. The results between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those having reduced linearity. Our method was compared with existing intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. ResultsThe proposed method detected more injury areas compared to existing approaches, as confirmed by the ground truth provided by TTC. The area under the curve (AUC) of the average receiver operating characteristic (ROC) curves using our method reached 0.84, whereas the AUCs using existing methods ranged from 0.77 [~] 0.79. The average false positive rate (FPR) and true positive rate (TPR) of the individual analysis results using our method (FPR = 0.06, TPR = 0.56) were better than the group-wise analysis results using t-tests (FPR = 0.10, TPR = 0.51). The identified injury regions were consistent in the two independent data sets. Some of them were confirmed by other publications. ConclusionsThe proposed method offers a new quantitative approach to analyzing FDG-PET images. The calculation does not involve intensity normalization and can be applied to a single subject. The method yields more sensitive results than existing intensity normalization methods.

neuroscience↗

bioRxiv2023

Iterative Machine Learning for Classification and Discovery of Single-molecule Unfolding Trajectories from Force Spectroscopy Data

We report the application of machine learning techniques to accelerate classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) where a user classifies a small number of repeatable unfolding patterns encoded as image data, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multi-domain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm, and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis, and generate new insights in protein biophysics.

biophysics↗

bioRxiv2023

Large-scale population genomics of Malayan pangolins reveals deep diversification and a new species

BackgroundArchipelagos and oceanic islands often present high percentage of endemism due to rapid speciation. The Malayan pangolin is a species distributing at both mainland (southern Yunnan, China) and oceanic islands via Malayan peninsula, which may result in deep differentiation among populations. In-depth investigation of population structure and genetic consequences for such species is of vital importance for their protection and conservation, practically for the critically endangered Malayan pangolin that is suffering from poaching, illegal trade, and habitat loss. ResultsHere we carried out a large-scale population genomic analysis for Malayan pangolins, and revealed three highly distinct genetic populations in this species, two of which are now being reported for the first time. Based on multiple lines of genomic and morphological evidence, we postulate the existence of a new pangolin species (Manis_1). Genetic diversity and recent inbreeding were both at a moderate level for both Malayan pangolins and Manis_1, but mainland Malayan pangolins presented relatively lower genetic diversity, higher inbreeding and fitness cost than island populations. ConclusionsWe found extremely deep and graded differentiation in Malayan pangolins, with two newly discovered genetic populations and a new pangolin species that is closely related to the Philippine pangolin than the typical Malayan pangolin, but a distant relative of the Indian pangolin. Anthropogenic factors did not significantly weaken the basis of genetic sustainability for Malayan pangolins, but mainland Malayan pangolins should be paid more attention for conservation due to higher genetic risks than island populations.

genomics↗

bioRxiv2023

EpiVar Browser: advanced exploration of epigenomics data under controlled access

MotivationHuman epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets. ResultsGuided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed a tool to facilitate the exploration of epigenomics datasets aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because the information about individual genotypes is not accessible and aggregated in the output that is made available, no identifiable data is released, yet the interface allows for dynamic genotype - epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalisable strategy to facilitate responsible access to sensitive epigenomics data. Availability and implementationOnline portal instance: https://computationalgenomics.ca/tools/epivar Source code: https://github.com/c3g/epivar-browser

genomics↗

bioRxiv2023

The Influenza B Virus Victoria and Yamagata Lineages Display Distinct Cell Tropism and Infection Induced Host Gene Expression in Human Nasal Epithelial Cell Cultures

Understanding Influenza B virus infections is of critical importance in our efforts to control severe influenza and influenza-related disease. Until 2020, two genetic lineages of influenza B virus - Yamagata and Victoria - circulated in the population. These lineages are antigenically distinct but differences in virus replication or the induction of host cell responses after infection have not been carefully studied. Recent IBV clinical isolates of both lineages were obtained from influenza surveillance efforts of the Johns Hopkins Center of Excellence in Influenza Research and Response and characterized in vitro. B/Victoria and B/Yamagata clinical isolates were recognized less efficiently by serum from influenza-vaccinated individuals in comparison to the vaccine strains. B/Victoria lineages formed smaller plaques on MDCK cells compared to B/Yamagata, but infectious virus production in primary human nasal epithelial cell (hNEC) cultures showed no differences. While ciliated epithelial cells were the dominant cell type infected by both lineages, B/Victoria lineages had a slight preference for MUC5AC-positive cells, while B/Yamagata lineages infected more basal cells. Finally, while both lineages induced a strong interferon response 48 hours after infection of hNEC cultures, the B/Victoria lineages showed a much stronger induction of interferon related signaling pathways compared to B/Yamagata. This demonstrates that the two influenza B virus lineages differ not only in their antigenic structure but in their ability to induce host innate immune responses.

microbiology↗

bioRxiv2023

A versatile framework for drug-target interaction prediction by considering domain specific features

Predicting drug-target interaction (DTI) is a critical and rate-limiting step in drug discovery. Traditional wet-lab experiments are reliable but expensive and time-consuming. Recently, deep learning has revealed itself as a new and promising tool for accelerating the DTI prediction process because its powerful performance. Due to the vast chemical space, the DTI prediction models are typically expected to discover drugs or targets that are absent from the training set. However, generalizing prediction performance to novel drug-target pairs that belong to different distributions is a challenge for deep learning methods. In this work, we propose an Ensemble of models that capture both Domain-generIc and domain-Specific features (E-DIS) to learn diversity domain features and adapt to out-of-distribution (OOD) data. We employed Mixture-of-Experts (MOE) as a domain-specific feature extractor for the raw data to prevent the loss of any crucial features by the encoder during the learning process. Multiple experts are trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. We evaluate our approach using four benchmark datasets under both in-domain and cross-domain settings and compare it with advanced approaches for solving OOD generalization problems. The results demonstrate that E-DIS effectively improves the robustness and generalizability of DTI prediction models by incorporating diversity domain features.

bioinformatics↗

bioRxiv2023

Modeling Human Spine-Spinal Cord Organogenesis by hPSC-Derived Neuromesodermal Progenitors

Human trunk development, including spine and spinal cord organogenesis, is a coordinated, orderly, and interdependent process with spatiotemporal tissue patterning. However, the underlying cellular and molecular mechanisms remain largely unclear due to the lack of an effective model that can simulate the early development of human body axis. Here, we reported the long-term patterning and dynamic morphogenesis of human trunk through the formation of spine-spinal cord organoids (SSCOs) self-organized from three-dimensional culture of human PSC-derived neuromesodermal progenitors (NMPs). The SSCOs resembled the morphogenetic features of spine and spinal cord along the anterior-posterior axis, and showed the chondro-osteogenic and neural trajectories consistent with developmental dynamics of spine and spinal cord in gestational embryo through single-cell RNA sequencing (scRNA-seq). In addition, we identified a new HMMR+ bipotent cell population with self-renewal ability and neural/mesodermal competence but distinct from NMPs, which may be involved in trunk development and represent an invaluable tool for disease modeling of spine- and spinal cord-related disorders. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/549829v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@14bfd30org.highwire.dtl.DTLVardef@1db7fe8org.highwire.dtl.DTLVardef@1aa66a1org.highwire.dtl.DTLVardef@380932_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

EHMT2/G9a-Inhibition Reprograms Cancer-Associated Fibroblasts (CAFs) to a More Differentiated, Less Proliferative and Invasive State

Cancer-associated fibroblasts (CAFs) have previously been shown to play a pivotal role in multiple cancer dynamics, including mediating tumor cell invasion: their pro-invasive secretory profile and ability to remodel the extracellular matrix (ECM) architecture particularly promote tumor progression through tumor cell invasion into surrounding tissue areas and beyond. Given that reduced CAF abundance in tumors correlates with improved outcomes in various cancers, we set out to identify epigenetic targets involved in CAF activation in the tumor-stromal margin to reduce overall tumor aggressiveness. Using the GLAnCE (Gels for Live Analysis of Compartmentalized Environments) co-culture platform, we performed an image-based, phenotypic screen and identified EHMT2 (also known as G9a), an epigenetic enzyme that targets the methylation of histone 3 lysine 9 (H3K9), as the most potent modulator of CAF abundance and CAF-mediated tumor cell invasion. Transcriptomic and functional analysis of EHMT2-inhibited CAFs revealed the involvement of EHMT2 in driving CAFs towards a pro-invasive phenotype. Further, EHMT2 signaling mediated CAF hyperproliferation, a feature that is typically associated with activated fibroblasts present in tumors, but the molecular basis for which has not thus far been identified. This study suggests a role for EHMT2 as a regulator of CAF hyperproliferation within the tumor mass, which in turn magnifies CAF-induced pro-invasive effects on tumor cells.

cancer biology↗

bioRxiv2023

Rapid identification of key genes for the rod-shaped morphology in bacteria using multi-species genomes

Accurate identification of key genes is pivotal in biological research. Here, we introduce machine learning to the field of functional gene identification, enabling precise prediction of bacterial shape based on genomic information. Our machine learning model successfully predicts bacterial shape, and we determine the influence of various protein domains on shape using the model. This approach facilitates the identification of candidate genes involved in regulating bacterial shape. Through targeted knockout experiments on eight potential key regulatory genes (pal, yicC, mreB, tolQ, ftsX, amiC, yddB, and rpoZ) in Escherichia coli, we observe significant alterations in rod-shaped morphology upon individual knockout of pal and mreB genes. E. coli transitions from rod-shaped to spherical or cell wall-deficient protoplasmic states. Experimental validations validate the robustness of our newly developed method. This study establishes an innovative avenue for exploring functional genes, harnessing large-scale genomic information to promptly uncover key genes governing shared traits across species.

genetics↗

bioRxiv2023

Manipulation of Bacterial ROS Production Leads to Self-escalating DNA Damage and Resistance-resistant Lethality for Intracellular Mycobacteria

The high prevalence of drug resistance in mycobacteria calls for antimicrobial mechanisms that suppresses the development of resistance. As a structurally conserved multi-site bio-macromolecule, DNA is presumed to be an ideal candidate for such resistance-resistant drug target. However, survey of marketed and investigational DNA interactors indicates that they are not immune to resistance development. Here, we report our strategy to achieve real resistance-resistant DNA targeting by incurring "catastrophic" DNA damage with an organoruthenium-natural product hybrid. The dual-mode DNA damage, in the form of strong tri-valent binding and concomitant oxidative modification, is achieved by manipulating of bacterias native endogenous ROS production mechanism upon lethal stress (such as DNA binding). Such self-escalating DNA damage, together with precise targeting of intracellular bacteria via vacuole fusion, thus endows the hybrids resistance-resistant lethality against mycobacteria and in vivo efficacy in animal models.

microbiology↗

bioRxiv2023

Human Immune Cell Epigenomic Signatures in Response to Infectious Diseases and Chemical Exposures

The epigenomic landscape of human immune cells is dynamically shaped by both genetic factors and environmental exposures. However, the relative contributions of these elements are still not fully understood. In this study, we employed single-nucleus methylation sequencing and ATAC-seq to systematically explore how pathogen and chemical exposures, along with genetic variation, influence the immune cell epigenome. We identified distinct exposure-associated differentially methylated regions (eDMRs) corresponding to each exposure, revealing how environmental factors remodel the methylome, alter immune cell states, and affect transcription factor binding. Furthermore, we observed a significant correlation between changes in DNA methylation and chromatin accessibility, underscoring the coordinated response of the epigenome. We also uncovered genotype-associated DMRs (gDMRs), demonstrating that while eDMRs are enriched in regulatory regions, gDMRs are preferentially located in gene body marks, suggesting that exposures and genetic factors exert differential regulatory control. Notably, disease-associated SNPs were frequently colocalized with meQTLs, providing new cell-type-specific insights into the genetic basis of disease. Our findings underscore the intricate interplay between genetic and environmental factors in sculpting the immune cell epigenome, offering a deeper understanding of how immune cell function is regulated in health and disease.

genomics↗

bioRxiv2023

Photosynthetic acclimation mediates exponential growth of a desert plant in Death Valley summer

Heat waves, now more frequent and longer due to climate change, devastate plant productivity. Although rare, thermophilic plants could hold keys to engineering heat resilience in crop plants. Tidestromia oblongifolia is a thermophilic flowering plant that thrives at temperatures above 45{degrees}C. When exposed to Death Valley summer conditions, T. oblongifolia increased its thermal optimum of photosynthesis within a day and accelerated growth within 10 days. The physiological changes were accompanied by morphological, anatomical, and gene expression changes revealed by a newly sequenced genome. In bundle sheath cells where Rubisco fixes CO2, mitochondria relocated to chloroplasts and novel, cup-shaped chloroplasts appeared. Understanding how this plant acclimates under heat may afford new ways of engineering heat tolerance in crop plants. One-Sentence SummaryTidestromia oblongifolias acclimation to Death Valley is accompanied by changes in gene expression, organellar dynamics, and photosynthesis.

plant biology↗