Biology subjects

Yin, R.

Publications and source records attributed to Yin, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Propofol overcome Tamoxifen resistance in breast cancer by modulating tumorogenesis, immune response and metabolism

Although 70% of estrogen receptor (ER)-positive breast cancer patients benefit from Tamoxifen therapy, the developing resistance to Tamoxifen leads to high rates of metastasis and poor prognosis. Propofol, a commonly used anesthetic, has been shown to inhibit the occurrence and development of breast cancer. However, its role in anti-endocrine resistance in ER-positive breast cancer remains unclear. In this study, we found that Propofol significantly promotes cell cycle arrest, induces apoptosis, and inhibits proliferation in Tamoxifen-resistant breast cancer cells. Furthermore, transcriptome sequencing analysis revealed 1065 differentially expressed genes (DEGs) between Propofol-treated Tamoxifen-resistant MCF-7 (MCF7-TR) cells and none-treated MCF7-TR cells. Gene ontology annotation enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis (KEGG), and gene set enrichment analysis (GSEA) showed that Propofol affects the expression of genes located in the plasma membrane and cell periphery, mainly regulating signals involved in cell cycle regulation, immune response modulation, and metabolism. Our results provide new insights into the mechanism by which Propofol controls resistance to Tamoxifen in breast cancer progression and offer a theoretical basis for clinical treatment.

cancer biology↗

bioRxiv2024

Decoding Continuous Character-based Language from Non-invasive Brain Recordings

Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge. Previous non-invasive decoders either require multiple experiments with identical stimuli to pinpoint cortical regions and enhance signal-to-noise ratios in brain activity, or they are limited to discerning basic linguistic elements such as letters and words. We propose a novel approach to decoding continuous language from single-trial non-invasive fMRI recordings, in which a three-dimensional convolutional network augmented with information bottleneck is developed to automatically identify responsive voxels to stimuli, and a character-based decoder is designed for the semantic reconstruction of continuous language characterized by inherent character structures. The resulting decoder can produce intelligible textual sequences that faithfully capture the meaning of perceived speech both within and across subjects, while existing decoders exhibit significantly inferior performance in cross-subject contexts. The ability to decode continuous language from single trials across subjects demonstrates the promising applications of non-invasive language brain-computer interfaces in both healthcare and neuroscience.

neuroscience↗

bioRxiv2023

Mechanisms analysis for Formononetin counteracted-Osimertinib resistance in Non-small-cell lung cancer cells: from the insight into the gene transcriptional level

BackgroundsOsimertinib resistance in Non-Small-Cell Lung Cancers (NSCLC) is a difficult problem to be solved in clinic. Formononetin is one of the main isoflavone components, which has strong anti-cancer effects in NSCLC. However, the potential effects and the mechanisms of Formononetin to counteract the Osimertinib resistant in NSCLC is remain to be uncovered. MethodsOsimertinib-resistant NSCLC cell line (H1975_OR) was generated by increasingly adding of Osimertinib in H1975 cell culture medium. The Formononetin was used to induce H1975_OR cell apoptosis an to inhibit cell proliferation and clonal formation. The RNA-sequencing was used to study the potential mechanisms for Formononetin to counteract the Osimertinib resistant in NSCLC. ResultsFormononetin could significantly induced cell apoptosis, whereas, dramatically inhibited cell proliferation and clinal formation in H1975_OR cells. Formononetin induced tremendous alterations of gene expression in H1975_OR cells and these genes can be clustered into unique GO, KEGG and GSEA pathways. ConclusionOur results preliminarily uncovered the underlying mechanisms for Formononetin counteracted Osimertinib-resistance in NSCLC cells and provided a potential treatment method for Osimertinib resistant NSCLC patients.

cancer biology↗

bioRxiv2023

How persistent infection overcomes peripheral tolerance mechanisms to cause T cell-mediated autoimmune disease

T cells help orchestrate immune responses to pathogens, and their aberrant regulation can trigger autoimmunity. Recent studies highlight that a threshold number of T cells (a quorum) must be activated in a tissue to mount a functional immune response. These collective effects allow the T cell repertoire to respond to pathogens while suppressing autoimmunity due to circulating autoreactive T cells. Our computational studies show that increasing numbers of pathogenic peptides targeted by T cells during persistent or severe viral infections increase the probability of activating T cells that are weakly reactive to self-antigens (molecular mimicry). These T cells are easily re-activated by the self-antigens and contribute to exceeding the quorum threshold required to mount autoimmune responses. Rare peptides that activate many T cells are sampled more readily during severe/persistent infections than in acute infections, which amplifies these effects. Experiments in mice to test predictions from these mechanistic insights are suggested.

immunology↗

bioRxiv2023

Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFolds antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.

molecular biology↗

bioRxiv2023

The Sequence Basis for Selectivity of Ephrin-B2 Ligand for Eph Receptors and Pathogenic Henipavirus G Glycoproteins

Ephrin-B2 (EFNB2) is a ligand for six Eph receptors in humans and functions as a cell entry receptor for several henipaviruses including Nipah virus (NiV), a pathogenic zoonotic virus with pandemic potential. To understand the sequence basis of promiscuity for EFNB2 binding to the attachment glycoprotein of NiV (NiV-G) and Eph receptors, we performed deep mutagenesis on EFNB2 to identify mutations that enhance binding to NiV-G over EphB2, one of the highest affinity Eph receptors. The mutations highlight how different EFNB2 conformations are selected by NiV-G versus EphB2. Specificity mutations are enriched at the base of the G-H binding loop of EFNB2, especially surrounding a phenylalanine hinge upon which the G-H loop pivots, and at a phenylalanine hook that rotates away from the EFNB2 core to engage Eph receptors. One EFNB2 mutant, D62Q, possesses pan-specificity to the attachment glycoproteins of closely related henipaviruses and has markedly diminished binding to the six Eph receptors. However, EFNB2-D62Q has high residual binding to EphB3 and EphB4. A second deep mutational scan of EFNB2 identified combinatorial mutations to further enhance specificity to NiV-G. A triple mutant of soluble EFNB2, D62Q-Q130L-V167L, has minimal binding to Eph receptors but maintains binding, albeit reduced, to NiV-G. Soluble EFNB2 decoy receptors carrying the specificity mutations were potent neutralizers of chimeric henipaviruses. These findings demonstrate how specific residue changes at the shared binding interface of a promiscuous ligand (EFNB2) can influence selectivity for multiple receptors, and may also offer insight towards the development of henipavirus therapeutics and diagnostics. IMPORTANCEEphrin-B2 (EFNB2) is a ligand for six Eph receptors in humans and regulates multiple cell developmental and signaling processes. It also functions as the cell entry receptor for Nipah virus and Hendra virus, zoonotic viruses that can cause respiratory and/or neurological symptoms in humans with high mortality. Here, we investigate the sequence basis of EFNB2 specificity for binding the Nipah virus attachment G glycoprotein over Eph receptors. We then use this information to engineer EFNB2 as a soluble decoy receptor that specifically binds the attachment glycoproteins of Nipah virus and other related henipaviruses to neutralize infection. These findings further mechanistic understanding of protein selectivity and may facilitate the development of diagnostics or therapeutics against henipavirus infection.

microbiology↗

bioRxiv2023

Low-threshold, high-resolution, chronically stable intracortical microstimulation by ultraflexible electrodes

Intracortical microstimulation (ICMS) enables applications ranging from neuroprosthetics to causal circuit manipulations. However, the resolution, efficacy, and chronic stability of neuromodulation is often compromised by the adverse tissue responses to the indwelling electrodes. Here we engineer ultraflexible stim-Nanoelectronic Threads (StimNETs) and demonstrate low activation threshold, high resolution, and chronically stable ICMS in awake, behaving mouse models. In vivo two-photon imaging reveals that StimNETs remain seamlessly integrated with the nervous tissue throughout chronic stimulation periods and elicit stable, focal neuronal activation at low currents of 2 A. Importantly, StimNETs evoke longitudinally stable behavioral responses for over eight months at markedly low charge injection of 0.25 nC/phase. Quantified histological analysis show that chronic ICMS by StimNETs induce no neuronal degeneration or glial scarring. These results suggest that tissue-integrated electrodes provide a path for robust, long-lasting, spatially-selective neuromodulation at low currents which lessen risks of tissue damage or exacerbation of off-target side-effects.

neuroscience↗

bioRxiv2023

Sustainable land management enhances ecological and economic multifunctionality under ambient and future climate

1.Anthropogenic activity is threatening ecosystem multifunctionality, i.e. the ability of ecosystems to provide multiple functions and services which are vital for human well-being. Here we assess how multifunctionality of agroecosystems in Central Germany depends on land-use type and climate change. Our analysis included 13 ecosystem functions in a large-scale field experiment with five different land-use types (three grassland and two farmland types either sustainably or intensively managed) under two different climate scenarios (ambient and future climate). We consider ecological measures of multifunctionality using averaging approaches with different weights, i.a. reflecting preferences of farmers and environmentalists, and assess an economic multifunctionality measure based on the total value of ecosystem services. Results show that intensive management and future climate decrease ecological multifunctionality for multiple weighting scenarios in both grassland and farmland. Only under a weighting according to farmers preferences, intensively-managed grassland shows higher multifunctionality as compared to sustainably-managed grassland. The economic multifunctionality measure, which includes economic benefits for society at large, reveals a multifunctionality about [~]1.7 times higher for sustainable compared to intensive management for both grassland and farmland. Above-belowground biodiversity correlates positively with ecosystem multifunctionality and is expected to be one of its main drivers. Based on these findings, we suggest to promote and economically incentivise sustainable land management that enhances both ecological and economic multifunctionality, also under future climatic conditions.

ecology↗

bioRxiv2022

Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?

Approaches to in silico prediction of protein structures have been revolutionized by AlphaFold2, while those to predict interfaces between proteins are relatively underdeveloped, owing to the overly complicated yet relatively limited data of protein-protein complexes. In short, proteins are 1D sequences of amino acids folding into 3D structures, and interact to form assemblies to function. We believe that such intricate scenarios are better modeled with additional indicative information that reflects their multi-modality nature and multi-scale functionality. To improve binary prediction of inter-protein residue-residue contacts, we propose to augment input features with multi-modal representations and to synergize the objective with auxiliary predictive tasks. (i) We first progressively add three protein modalities into models: protein sequences, sequences with evolutionary information, and structure-aware intra-protein residue contact maps. We observe that utilizing all data modalities delivers the best prediction precision. Analysis reveals that evolutionary and structural information benefit predictions on the difficult and rigid protein complexes, respectively, assessed by the resemblance to native residue contacts in bound complex structures. (ii) We next introduce three auxiliary tasks via self-supervised pre-training (binary prediction of protein-protein interaction (PPI)) and multi-task learning (prediction of inter-protein residue-residue distances and angles). Although PPI prediction is reported to benefit from predicting inter-contacts (as causal interpretations), it is not found vice versa in our study. Similarly, the finer-grained distance and angle predictions did not appear to uniformly improve contact prediction either. This again reflects the high complexity of protein-protein complex data, for which designing and incorporating synergistic auxiliary tasks remains challenging.

bioinformatics↗

bioRxiv2022

Robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing (MGA-Seq)

Genomic abnormalities, including structural variation (SV), copy number variation (CNV), single-nucleotide polymorphism (SNP), homogenously staining regions (HSR) and extrachromosomal DNA (ecDNA), are strongly associated with cancer, rare diseases and infertility. A robust technology to simultaneously detect these genomic abnormalities is highly desired for clinical diagnosis and basic research. In this study, we developed a simple and cost-effective method - multiple genetic abnormality sequencing (MGA-Seq) - to simultaneously detect SNPs, CNVs, SVs, ecDNA and HSRs in a single tube. This method has been successfully applied in both cancer cell lines and clinical tumour samples and revealed that focal amplification in tumour tissue is substantially heterogeneous. Notably, we delineated the architecture of focal amplification and the ecDNA network by MGA-Seq, which facilitated the exploration of the regulation of gene expression in ecDNA. This method could be extensively applied for diagnosis and may greatly facilitate the investigation of the genomic mechanism for genetic diseases.

genomics↗

bioRxiv2022

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

bioinformatics↗

bioRxiv2022

Deciphering Spatial Domains by Integrating Histopathological Image and Transcriptomics via Contrastive Learning

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histopathological images of the tissues. Deciphering the spatial domains of spots in the tissues is a vital step for various downstream tasks in spatial transcriptomics analysis. Existing methods have been developed for this purpose by combining gene expression and histopathological images to conquer noises in gene expression. However, current methods only use the histopathological images to construct spot relations without updating in the training stage, or simply concatenate the information from gene expression and images into one feature vector. Here, we propose a novel method ConGI to accurately decipher spatial domains by integrating gene expression and histopathological images, where the gene expression is adapted to image information through contrastive learning. We introduce three contrastive loss functions within and between modalities to learn the common semantic representations across all modalities while avoiding their meaningless modality-private noise information. The learned representations are then used for deciphering spatial domains through a clustering method. By comprehensive tests on tumor and normal spatial transcriptomics datasets, ConGI was shown to outperform existing methods in terms of spatial domain identification. More importantly, the learned representations from our model have also been used efficiently for various downstream tasks, including trajectory inference, clustering, and visualization.

bioinformatics↗

bioRxiv2022

Longitudinal neural and vascular recovery following ultraflexible neural electrode implantation in aged mice

Flexible neural electrodes improve the recording longevity and quality of individual neurons by promoting tissue-electrode integration. However, the intracortical implantation of flexible electrodes inevitably induces tissue damage. Understanding the longitudinal neural and vascular recovery following the intracortical implantation is critical for the ever-growing applications of flexible electrodes in both healthy and disordered brains. Aged animals are of particular interest because they play a key role in modeling neurological disorders, but their tissue-electrode interface remains mostly unstudied. Here we integrate in-vivo two-photon imaging and electrophysiological recording to determine the time-dependent neural and vascular dynamics after the implantation of ultraflexible neural electrodes in aged mice. We find heightened angiogenesis and vascular remodeling in the first two weeks after implantation, which coincides with the rapid increase in local field potentials and unit activities detected by electrophysiological recordings. Vascular remodeling in shallow cortical layers preceded that in deeper layers, which often lasted longer than the recovery of neural signals. By six weeks post-implantation vascular abnormalities had subsided, resulting in normal vasculature and microcirculation. Putative cell classification based on firing pattern and waveform shows similar recovery time courses in fast-spiking interneurons and pyramidal neurons. These results elucidate how structural damages and remodeling near implants affecting recording efficacy, and support the application of ultraflexible electrodes in aged animals at minimal perturbations to endogenous neurophysiology.

neuroscience↗

bioRxiv2022

ViPal: A Framework for Virulence Prediction of Influenza Viruses with Prior Viral Knowledge Using Genomic Sequences

Influenza viruses pose significant threats to public health and cause enormous economic loss every year. Previous work has revealed the viral factors that influence the virulence of influenza viruses. However, taking prior viral knowledge represented by heterogeneous categorical and discrete information into account is scarce in the existing work. How to make full use of the preceding domain knowledge into virulence study is challenging but beneficial. This paper proposes a general framework named ViPal for virulence prediction that incorporates discrete prior viral mutation and reassortment information based on all eight influenza segments. The posterior regularization technique is leveraged to transform prior viral knowledge to constraint features and integrated into the machine learning models. Experimental results on influenza genomic datasets validate that our proposed framework can improve virulence prediction performance over baselines. The comparison between ViPal and other existing methods shows the computational efficiency of our framework with superior performance. Moreover, the interpretable analysis through SHAP identifies the scores of constraint features contributing to the prediction. We hope this framework could provide assistance for the accurate detection of influenza virulence and facilitate flu surveillance.

bioinformatics↗

bioRxiv2022

Single-cell transcriptome reveals the redifferentiation trajectories of the early stage of de novo shoot regeneration in Arabidopsis thaliana

De novo shoot regeneration from a callus plays a crucial role in both plant biotechnology and the fundamental research of plant cell totipotency. Recent studies have revealed many regulatory factors involved in this developmental process. However. our knowledge of the cell heterogeneity and cell fate transition during de novo shoot regeneration is still limited. Here, we performed time-series single-cell transcriptome experiments to reveal the cell heterogeneity and redifferentiation trajectories during the early stage of de novo shoot regeneration. Based on the single-cell transcriptome data of 35,669 cells at five-time points, we successfully determined seven major cell populations in this developmental process and reconstructed the redifferentiation trajectories. We found that all cell populations resembled root identities and undergone gradual cell-fate transitions. In detail, the totipotent callus cells differentiated into pluripotent QC-like cells and then gradually developed into less differentiated cells that have multiple root-like cell identities, such as pericycle-like cells. According to the reconstructed redifferentiation trajectories, we discovered that the canonical regeneration-related genes were dynamically expressed at certain stages of the redifferentiation process. Moreover, we also explored potential transcription factors and regulatory networks that might be involved in this process. The transcription factors detected at the initial stage, QC-like cells, and the end stage provided a valuable resource for future functional verifications. Overall, this dataset offers a unique glimpse into the early stages of de novo shoot regeneration, providing a foundation for a comprehensive analysis of the mechanism of de novo shoot regeneration.

plant biology↗

bioRxiv2021

Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

High resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases had highly accurate models generated as top-ranked predictions, greatly surpassing the performance of unbound protein-protein docking, whereas antibody-antigen docking was largely unsuccessful. While AlphaFold-generated accuracy predictions were able to discriminate near-native models, previously developed scoring protocols improved performance. Our study demonstrates that end-to-end deep learning can accurately model transient protein complexes, and identifies areas for improvement to guide future developments to reliably model any protein-protein interaction of interest.

bioinformatics↗

bioRxiv2021

Single-cell Stereo-seq enables cell type-specific spatial transcriptome characterization in Arabidopsis leaves

Understanding the complex functions of plant leaves requires spatially resolved gene expression profiling with single-cell resolution. However, although in situ gene expression profiling technologies have been developed, this goal has not yet been achieved. Here, we present the first in situ single-cell transcriptome profiling in plant, scStereo-seq (single-cell SpaTial Enhanced REsolution Omics-sequencing), which enabled the bona fide single-cell spatial transcriptome of Arabidopsis leaves. We successfully characterized subtle but significant transcriptomic differences between upper and lower epidermal cells. Furthermore, with high-resolution location information, we discovered the cell type-specific spatial gene expression gradients from main vein to leaf edge. By reconstructing those spatial gradients, we show for the first time the distinct spatial developmental trajectories of vascular cells and guard cells. Our findings show the importance of incorporating spatial information for answering complex biological questions in plant, and scStereo-seq offers a powerful single cell spatially resolved transcriptomic strategy for plant biology.

plant biology↗

bioRxiv2021

Structural basis for recognition of two HLA-A2-restricted SARS-CoV-2 spike epitopes by public and private T cell receptors

T cells play a vital role in combatting SARS-CoV-2 and in forming long-term memory responses. Whereas extensive structural information is available on neutralizing antibodies against SARS-CoV-2, such information on SARS-CoV-2-specific T cell receptors (TCRs) bound to their peptide-MHC targets is lacking. We determined structures of a public and a private TCR from COVID-19 convalescent patients in complex with HLA-A2 and two SARS-CoV-2 spike protein epitopes (YLQ and RLQ). The structures revealed the basis for selection of particular TRAV and TRBV germline genes by the public but not the private TCR, and for the ability of both TCRs to recognize natural variants of YLQ and RLQ but not homologous epitopes from human seasonal coronaviruses. By elucidating the mechanism for TCR recognition of an immunodominant yet variable epitope (YLQ) and a conserved but less commonly targeted epitope (RLQ), this study can inform prospective efforts to design vaccines to elicit pan-coronavirus immunity.

immunology↗