Biology subjects

Lu, S.

Publications and source records attributed to Lu, S..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Molecular architecture of SARS-CoV-2 envelope by integrative modeling

Despite tremendous efforts by the research community during the COVID-19 pandemic, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. Being a key structural component of the SARS-CoV-2 virion, the envelope encapsulates viral RNA and is composed of three structural proteins, spike (S), membrane (M), and envelope (E), which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multiscale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns formed by Ms transmembrane and intravirion (endo) domains. These results are in good agreement with current experimental data, demonstrating a generic and versatile integrative approach to model the structure of a virus de novo. We anticipate our work to provide insights into critical roles of structural proteins in the viral assembly and integration, proposing new targets for the antiviral therapies.

biophysics↗

bioRxiv2021

iCn3D: from Web-based 3D Viewer to Structure Analysis Tool in Batch Mode

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node.js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node.js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node.js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode. This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.

bioinformatics↗

bioRxiv2021

A Sequence-based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

Antibodies are proteins which play a vital role in the immune system by recognizing and neutralizing antigens. The region on the antibody binding to an antigen, known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consuming and expensive. In this article, we propose a sequence-based method for antibody paratope prediction by combing local and global information of antibody sequence and partner features from partner antigen sequence. Convolution Neural Networks(CNNs) and a sliding window approach on antibody sequence are used to extract local information. Attention-based Bidirectional Long Short-Term Memory (Att-BLSTM) on antibody sequence are used to extract global information. Also, the partner antigen is vital for paratope prediction, and we employ Att-BLSTM on the partner antigen sequence as well. The outputs of CNNs and Att-BLSTM networks are combined to predict antibody paratope by fully-connected networks. The experiments show that our proposed method achieves superior performance over the state-of-the-art sequenced-based antibody paratope prediction methods on benchmark datasets.

bioinformatics↗

bioRxiv2021

A Structure-based B-cell Epitope Prediction Model Through Combing Local and Global Features

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks(GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory(Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

bioinformatics↗

bioRxiv2021

WDR82-binding long non-coding RNA lncEry controls mouse erythroid differentiation and maturation

Hematopoietic differentiation is controlled by both genetic and epigenetic regulators. Long non-coding RNAs (lncRNAs) have been demonstrated to be important for normal hematopoiesis, but their function in erythropoiesis needs to be further explored. We profiled the transcriptomes of 16 murine hematopoietic cell populations by deep RNA-sequencing and identified a novel lncRNA, Gm15915, that was highly expressed in erythroid-related progenitors and erythrocytes. For this reason, we named it lncEry. We also identified a novel lncEry isoform, which was also the principal transcript that has not been reported before. LncEry depletion impaired erythropoiesis, indicating the important role of the lncRNA in regulating erythroid differentiation and maturation. Mechanistically, we found that lncEry interacted with WD repeat-containing protein 82 (WDR82) to promote the transcription of Klf1 and globin genes and thus control the early and late stages of erythropoiesis, respectively. These findings identified lncEry as an important player in the transcriptional regulation of erythropoiesis.

cell biology↗

bioRxiv2021

Attention-based convolutional neural networks for protein-protein interaction site prediction

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not been distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP significantly improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.

bioinformatics↗

bioRxiv2021

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. In this regard, the role of individual genes in disease-relevant mechanisms can be hypothesized with transcriptome-wide association studies (TWAS), which have represented a significant step forward in testing the mediating role of gene expression in GWAS associations. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, and our approach was accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we found that functionally important players lacked TWAS associations but were prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.

bioinformatics↗

bioRxiv2021

Comprehensive analysis of RNA-seq and whole genome sequencing data reveals no evidence for SARS-CoV-2 integrating into host genome

SARS-CoV-2, as the causation of severe epidemic of COVID-19, is one kind of positive single-stranded RNA virus with high transmissibility. However, whether or not SARS-CoV-2 can integrate into host genome needs thorough investigation. Here, we performed both RNA sequencing (RNA-seq) and whole genome sequencing on SARS-CoV-2 infected human and monkey cells, and investigated the presence of host-virus chimeric events. Through RNA-seq, we did detect the chimeric host-virus reads in the infected cells. But further analysis using mixed libraries of infected cells and uninfected zebrafish embryos demonstrated that these reads are falsely generated during library construction. In support, whole genome sequencing also didnt identify the existence of chimeric reads in their corresponding regions. Therefore, the evidence for SARS-CoV-2s integration into host genome is lacking. One-Sentence SummarySARS-CoV-2 does not integrate into host genome through whole genome sequencing.

genomics↗

bioRxiv2021

Structural basis for SARS-CoV-2 Nucleocapsid protein recognition by single-domain antibodies

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, is the most severe public health event of the twenty-first century. While effective vaccines against SARS-CoV-2 have been developed, there remains an urgent need for diagnostics to quickly and accurately detect infections. Antigen tests, particularly those that detect the abundant SARS-CoV-2 Nucleocapsid protein, are a proven method for detecting active SARS-CoV-2 infections. Here we report high-resolution crystal structures of three llama-derived single-domain antibodies that bind the SARS-CoV-2 Nucleocapsid protein with high affinity. Each antibody recognizes a specific folded domain of the protein, with two antibodies recognizing the N-terminal RNA binding domain and one recognizing the C-terminal dimerization domain. The two antibodies that recognize the RNA binding domain affect both RNA binding affinity and RNA-mediated phase separation of the Nucleocapsid protein. All three antibodies recognize highly-conserved surfaces on the Nucleocapsid protein, suggesting that they could be used to develop affordable diagnostic tests to detect all circulating SARS-CoV-2 variants.

immunology↗

bioRxiv2021

Non-neutralizing antibodies targeting the immunogenic regions of HIV-1 envelope reduce mucosal infection and virus burden in humanized mice

Antibodies are principal immune components elicited by vaccines to induce protection from microbial pathogens. In the Thai RV144 HIV-1 vaccine trial, vaccine efficacy was 31% and the sole primary correlate of reduced risk was shown to be vigorous antibody response targeting the V1V2 region of HIV-1 envelope. Antibodies against V3 also were inversely correlated with infection risk in subsets of vaccinees. Antibodies recognizing these regions, however, do not exhibit potent neutralizing activity. Therefore, we examined the antiviral potential of poorly neutralizing monoclonal antibodies (mAbs) against immunodominant V1V2 and V3 sites by passive administration of human mAbs to humanized mice engrafted with CD34+ hematopoietic stem cells, followed by mucosal challenge with an HIV-1 infectious molecular clone (IMC) expressing the envelope of a tier 2 resistant HIV-1 strain. Treatment with anti-V1V2 mAb 2158 or anti-V3 mAb 2219 did not prevent infection, but both reduced the virus burden, and V3 mAb 2219 displayed a superior potency compared to V1V2 mAb 2158. While these mAbs had no or weak neutralizing activity and elicited undetectable levels of antibody-dependent cellular cytotoxicity (ADCC), V3 mAb 2219 displayed a greater capacity to bind virus- and cell-associated HIV-1 envelope and to mediate antibody-dependent cellular phagocytosis (ADCP) and C1q complement binding as compared to V1V2 mAb 2158. Mutations in the Fc region of 2219 abolished these effector activities and abrogated virus control in humanized mice. These results demonstrate the importance of Fc functions other than ADCC for antibodies without potent neutralizing activity.

immunology↗

bioRxiv2021

Structural basis for enhanced infectivity and immune evasion of SARS-CoV-2 variants

Several fast-spreading variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have become the dominant circulating strains that continue to fuel the COVID-19 pandemic despite intensive vaccination efforts throughout the world. We report here cryo-EM structures of the full-length spike (S) trimers of the B.1.1.7 and B.1.351 variants, as well as their biochemical and antigenic properties. Mutations in the B.1.1.7 protein increase the accessibility of its receptor binding domain and also the binding affinity for receptor angiotensin-converting enzyme 2 (ACE2). The enhanced receptor engagement can account for the increased transmissibility and risk of mortality as the variant may begin to infect efficiently infect additional cell types expressing low levels of ACE2. The B.1.351 variant has evolved to reshape antigenic surfaces of the major neutralizing sites on the S protein, rendering complete resistance to some potent neutralizing antibodies. These findings provide structural details on how the wide spread of SARS-CoV-2 enables rapid evolution to enhance viral fitness and immune evasion. They may guide intervention strategies to control the pandemic.

biochemistry↗

bioRxiv2021

A Chromosome-level Assembly of a Wild Castor Genome Provides New Insights into the Adaptive Evolution in a Tropical Desert

Wild castor grows in the high-altitude tropical desert of the African Plateau, a region known for high ultraviolet radiations, strong lights and extremely dry conditions. To investigate the potential genetic basis of adaptation to both highland and tropical deserts, we generated a chromosome-level genome sequence of the wild castor accession WT05, with genome size of 316 Mb and scaffold and contig N50 sizes of 31.93 Mb and 8.96 Mb, respectively. Compared with cultivated castor and other Euphorbiaceae species, the wild castor exhibits positive selection and gene family expansion for genes involved in DNA repair, photosynthesis and abiotic stress responses. Genetic variations associated with positive selection were identified in several key genes, such as LIG1, DDB2, and RECG1, involved in nucleotide excision repair. Moreover, a study of genomic diversity among wild and cultivated accessions revealed genomic regions containing selection signatures associated with the adaptation to extreme environments. The identification of the genes and alleles with selection signatures provides insights into the genetic mechanisms underlying the adaptation of wild castor to the high-altitude tropical desert and would facilitate direct improvement of modern castor varieties.

genomics↗

bioRxiv2021

MLG: Multilayer graph clustering for multi-condition scRNA-seq data

Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present Multi Layer Graph Clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale bench-marking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.

genomics↗

bioRxiv2021

Occurrence of COVID-19 symptoms during SARS-CoV-2 infection defines waning of humoral immunity

Approximately half of the SARS-CoV-2 infections occur without apparent symptoms, raising questions regarding long-term humoral immunity in asymptomatic individuals. Plasma levels of immunoglobulin G (IgG) and M (IgM) against the viral spike or nucleoprotein were determined for 25,091 individuals enrolled in a surveillance program in Wuhan, China. We compared 405 asymptomatic individuals with 459 symptomatic COVID-19 patients. The well-defined duration of the SARS-CoV-2 endemic in Wuhan allowed a side-by-side comparison of antibody responses following symptomatic and asymptomatic infections without subsequent antigen re-exposure. IgM responses rapidly declined in both groups. However, both the prevalence and durability of IgG responses and neutralizing capacities correlated positively with symptoms. Regardless of sex, age, and body weight, asymptomatic individuals lost their SARS-CoV-2-specific IgG antibodies more often and rapidly than symptomatic patients. These findings have important implications for immunity and favour immunization programs including individuals after asymptomatic infections. One-Sentence SummaryPrevalence and durability of SARS-CoV-2-specific IgG responses and neutralizing capacities correlate with COVID-19 symptoms.

immunology↗

bioRxiv2021

A viral genome packaging ring-ATPase is a flexibly coordinated pentamer

Multi-subunit ring-ATPases carry out a myriad of biological functions, including genome packaging in viruses. Though the basic structures and functions of these motors have been well-established, the mechanisms of ATPase firing and motor coordination are poorly understood. Here, by direct counting using single-molecule fluorescence, we have determined that the active bacteriophage T4 DNA packaging motor consists of five subunits of gp17. By systematically doping motors with an ATPase-defective subunit and selecting single motors containing a precise count of active/inactive subunit(s), we found, unexpectedly, that the packaging motor can tolerate an inactive sub-unit. However, motors containing an inactive subunit(s) exhibit fewer DNA engagements, a higher failure rate in encapsidation, reduced packaging velocity, and increased pausing. These findings suggest a new packaging model in which the motor, by re-adjusting its grip on DNA, can skip an inactive subunit and resume DNA translocation, contrary to the prevailing notion of strict coordination amongst motor subunits of other packaging motors.

biophysics↗

bioRxiv2021

De novo purine nucleotide biosynthesis mediated by amidophosphoribosyl transferase is required for conidiation and essential for the successful host colonization of Magnaporthe oryzae

Amidophosphoribosyl transferase catalyzes the first step of the purine nucleotide biosynthesis by converting 5-phosphoribosyl-1-pyrophosphate into 5-phosphoribosyl-1-amine. In this study, we identified and characterized the functions of MoAde4, an ortholog of yeast Ade4 in the rice blast fungus. MoAde4 is a 537-amino acid protein containing the GATase_6 and pribosyltran domains. Quantitative real-time PCR analysis showed MoADE4 transcripts were highly expressed during conidiation, early-infection, and late-infection stages of the fungus. Disruption of MoADE4 gene resulted in {Delta}Moade4 mutant exhibiting adenine, adenosine, and hypoxanthine auxotrophy on MM. Conidia quantification assays showed {Delta}Moade4 mutant was significantly reduced in sporulation. The conidia of {Delta}Moade4 mutant could still form appressoria but mostly failed to penetrate the rice cuticle. Pathogenicity test showed {Delta}Moade4 was completely nonpathogenic on rice and barley leaves which was attributed by failure of its infectious hyphae to colonize the host cells. The {Delta}Moade4 was defective in induction of strong host immunity and had its purine transporter genes repressed during in planta infection. Addition of exogenous adenine partially rescued conidiation and pathogenicity defects of the {Delta}Moade4 mutant on the barley and rice leaves. Localization assays showed that MoAde4 is located in the cytoplasm. Taken together, our results demonstrate that purine biosynthesis orchestrated by MoAde4 is required for fungal development, conidiation, more importantly, we found it to be essential for fungal pathogenicity not because of the appressorial formation, but appressorium penetration and host colonization during the plant infection of M. oryzae. Thus this findings suggests that purine biosynthesis could act as an important target for combating recalcitrant plant fungal pathogens.

molecular biology↗

bioRxiv2021

Constitutive G protein coupling profiles of understudied orphan GPCRs

A large number of GPCRs are potentially valuable drug targets but remain understudied. Many of these lack well-validated activating ligands and are considered "orphan" receptors, and G protein coupling profiles have not been defined for many orphan GPCRs. Here we asked if constitutive receptor activity can be used to determine G protein coupling profiles of orphan GPCRs. We monitored nucleotide-sensitive interactions between 48 understudied orphan GPCRs and five G proteins (240 combinations) using bioluminescence resonance energy transfer (BRET). No receptor ligands were used, but GDP was used as a common G protein ligand to disrupt receptor-G protein complexes. Constitutive BRET between the same receptors and {beta}-arrestins was also measured. We found sufficient GDP-sensitive BRET to generate G protein coupling profiles for 22 of the 48 receptors we studied. Altogether we identified 48 coupled receptor-G protein pairs, many of which have not been described previously. We conclude that receptor-G protein complexes that form spontaneously in the absence of guanine nucleotides can be used to profile G protein coupling of constitutively active GPCRs. This approach may prove useful for studying G protein coupling of other GPCRs for which activating ligands are not available.

pharmacology and toxicology↗

bioRxiv2021

Structural and immunological identification and antiviral infection experiment of the dominant cytotoxic T lymphocyte epitopes of the oncogenic Marek's disease virus

Mareks disease (MD) is a serious cancer caused by MDV in chickens, and it is also the first tumor disease that could be controlled by an attenuated vaccine in the world. However, the attenuated vaccine is able to inhibit only the formation of tumors but cannot prevent the infection of oncogenic viruses; instead, it leads to mutations and the emergence of a number of virulent strains. In this paper, the crystal structures of chicken MHC class I (pBF2*1501) molecules bound with 8-mer and 9-mer MDV peptides were solved. The results showed that the conformations of the 8-mer and 9-mer peptides in the antigen-binding groove (ABG) of pBF2*1501 are different; the ABG-8-mer is flat, and the ABG-9-mer has the "featured" M-type epitope morphology. Based on these results, multiple MDV epitopes were confirmed using the tetramer technique, and the immunoprotective effect of dominant epitope was confirmed using the protein adjuvant HSP108-N333 in BF2*1501-expressing chickens. The results showed that the two epitopes have obvious protective effects after a standard immunization program. After challenge, the mortality rate was only 20%, and the protective index (PI) reached 33% in the epitope group. The results verified that a single epitope could induce extremely strong specific antitumor T lymphocyte immunity. The results show that three key factors are crucial to obtain the best antitumor effect: accurate identification of dominant cytotoxic T lymphocyte (CTL) epitopes, efficient protein adjuvant and MHC matching. Meanwhile, the results also provided obvious advantages for the development of multiple epitope vaccines for tumor diseases. ImportanceMareks disease (MD) is the first tumor disease that can be controlled by an attenuated vaccine. However, the attenuated vaccine cannot prevent the infection of the oncogenic virus and instead leads to the emergence of a number of mutated virulent strains. First, the mechanism of the chicken BF2*1501 preferentially presents 9-mer peptide was clarified. Based on this result, multiple MDV epitopes were confirmed, and the dominant epitopes were identified. Subsequently, two epitope groups showed obvious protective effects after a standard immunization program, and the protective index (PI) of one of the epitope groups reached over 30%. The results showed that a single epitope could induce strong and efficient antitumor CTL immunity. Overall, three key factors are crucial to obtain the best antitumor effect: accurate identification of dominant epitopes, efficient protein adjuvant and MHC matching. Our results provide an obvious strategy for the development of multiple epitope vaccines in tumor diseases.

immunology↗