Biology subjects

Duan, H.

Publications and source records attributed to Duan, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Predicting Enzyme Functions Using Contrastive Learning with Hierarchical Enzyme Structure Information

Enzyme functional annotation is a fundamental challenge in biology, and many computational tools have been developed. Accurate function prediction of enzymes relies heavily on sequence and structural information, providing critical insights into enzyme activity and specificity. However, for less studied proteins or proteins with previously uncharacterized functions or multiple activities, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) numbers. At the same time, functional hierarchical information between enzyme species categorized based on EC numbers has not been sufficiently investigated. To address these challenges, we propose a machine learning algorithm named EnzHier, which assigns EC numbers to enzymes with better accuracy and reliability compared to state-of-the-art tools. EnzHier cleverly learns the functional hierarchy of enzymes by optimizing triplet loss, enabling it to annotate understudied enzymes confidently and identify confounding enzymes with two or more EC numbers. By incorporating both sequence and structural information, EnzHier enhances its predictive capabilities. We experimentally demonstrate its excellent performance. We anticipate that this tool will be widely used to predict the function of uncharacterized enzymes, thereby advancing many fields such as drug design and discovery and medical diagnostics.

bioinformatics↗

bioRxiv2024

Comparative Transcriptomics of Arabidopsis, Medicago, Brachypodium and Setaria species during Phosphorus limitation

Translating biological knowledge from Arabidopsis to crop species is important to advance agriculture and secure food production in the face of dwindling fertilizer resources, biotic and abiotic stresses. However, it is often not trivial to identify functional homologs (orthologs) of Arabidopsis genes in crops. Combining sequence and expression data can improve the correct prediction of orthologs. Here, we conducted a large-scale RNA sequencing based transcriptomics study of Arabidopsis, Medicago, Brachypodium and Setaria grown side-by-side in Phosphorus (P)-sufficient and P-limited conditions to generate comparable transcriptomics datasets. Comparison of top 200 P-limitation induced genes in Arabidopsis revealed that [~]80% of these genes have identifiable close homologs in the other three species but only [~]50% retain their P-limitation response in the legume and grasses. Most of the hallmark genes of the P-starvation response were found conserved in all four species. This study reveals many known, novel, unannotated, conserved and species-specific regulations of the transcriptional P-starvation response. Identification and experimental verification of expressologs by independent RT-qPCR for P-limitation marker genes in Prunus showed the usefulness of comparative transcriptomics in pinpointing the functional orthologs in diverse crop species. This study provides an unprecedented resource for functional genomics and translational research to create P-efficient crops. HIGHLIGHTComparative transcriptomics reveals novel, known, conserved and specific transcriptome responding to Phosphorus limitation in Arabidopsis, Medicago, Brachypodium and Setaria to facilitate translational research in crops.

molecular biology↗

bioRxiv2024

Stearoyl coenzyme A desaturase 1 (SCD1) regulates foot-and-mouth disease virus replication by modulating host cell lipid metabolism and 2C-mediated replication complex formation

The life cycle of foot-and-mouth disease virus (FMDV) is tightly regulated by host cell lipid metabolism. In a previous study, we successfully established a BHK-21 cell model (BHK-Op) for persistent FMDV infection by single-cell clone selection and isolated a virus-negative cell line (BHK-VEC cells) from high-passage BHK-Op cells. BHK-VEC cells could be not infected with FMDV. By conforming the transcriptome data of BHK-VEC cells and BHK-21 cells, we identified that the stearoyl coenzyme A desaturase 1 (SCD1), a key enzyme for the fatty acid metabolism, regulates FMDV replication. SCD1 overexpression or exogenous addition of oleic acid (OA), a product of SCD1 enzyme activity, promoted or upregulated FMDV replication in BHK-21 cells or SCD1 knockdown cells, respectively. Interestingly, overexpression of SCD1 or exogenous addition of OA restored the FMDV infection and replication in BHK-VEC cells. Exogenous addition of OA also promoted FMDV replication in BHK-Op. SCD1 recruited the nonstructural FMDV protein 2C to the detergent-resistant membrane located in the perinuclear nucleus to form replication complex. Inhibition of SCD1 enzyme activity resulted in significantly decreased number of FMDV replication complexes with also abnormal morphology. Importantly, inhibition of SCD1 enzyme activity effectively suppressed replication of other positive-sense RNA viruses, such as REO176, PV1 and EV71. Our results demonstrated that, as a key host regulator of RNA virus replication, SCD1 is a potential target for developing novel drugs against positive-sense RNA virus infection.

microbiology↗

bioRxiv2024

SRSF2 is a key player in orchestrating the directional migration and differentiation of MyoD progenitors during skeletal muscle development

SRSF2 plays a dual role, functioning both as a transcriptional regulator and a key player in alternative splicing. The absence of SRSF2 in MyoD+ progenitors resulted in perinatal mortality in mice, accompanied by severe skeletal muscle defects. SRSF2 deficiency disrupts the directional migration of MyoD progenitors, causing them to disperse into both muscle and non-muscle regions. Single-cell RNA-sequencing analysis revealed significant alterations in SRSF2-deficient myoblasts, including a reduction in extracellular matrix components, diminished expression of genes involved in ameboid-type cell migration and cytoskeleton organization, mitosis irregularities, and premature differentiation. Notably, one of the targets regulated by SRSF2 is the serine/threonine kinase Aurka. Knockdown of Aurka led to reduced cell proliferation, disrupted cytoskeleton, and impaired differentiation, reflecting the effects seen with SRSF2 knockdown. Crucially, the introduction of exogenous Aurka in SRSF2-knockdown cells markedly alleviated the differentiation defects caused by SRSF2 knockdown. Furthermore, our research unveiled the role of SRSF2 in controlling alternative splicing within genes associated with human skeletal muscle diseases, such as BIN1, DMPK, FHL1, and LDB3. Specifically, the precise knockdown of the Bin1 exon17-containing variant, which is excluded following SRSF2 depletion, profoundly disrupted C2C12 cell differentiation. In summary, our study offers valuable insights into the role of SRSF2 in governing MyoD progenitors to specific muscle regions, thereby controlling their differentiation through the regulation of targeted genes and alternative splicing during skeletal muscle development.

developmental biology↗

bioRxiv2024

MetaDIA: A Novel Database Reduction Strategy for DIA Human Gut Metaproteomics

BackgroundMicrobiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a significant challenge, as it involves matching peptides to mass spectra within an enormous search space for complex and unknown samples. This poses difficulties for both the accuracy and the speed of identification. Specifically, analysis of data-independent acquisition (DIA) datasets has relied on libraries constructed from prior data-dependent acquisition (DDA) results. This approach requires running the samples in DDA mode to construct a library from the identified results, which can then be used for the DIA data. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified by DDA. These limitations restrict the application of DIA in metaproteomics research. ResultsWe introduced a novel strategy to reduce the search space by utilizing species abundance and functional abundance information from the microbiome to score each peptide and prioritize those most likely to be detected. Employing this strategy, we have developed and optimized a workflow called MetaDIA for analysis of microbiome DIA data, which operates independently of DDA assistance. Our method demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. ConclusionOur approach successfully created a smaller, yet sufficient database for DIA data search requirements in metaproteomics, showing high consistency with results from the conventional DDA-based library. We believe this method can facilitate the application of DIA in metaproteomics.

bioinformatics↗

bioRxiv2024

Gating of Memory to Behavior by the Locus Coeruleus

An essential function of memory is to guide behavior for survival and adaptation. While considerable knowledge has been accumulated on memory formation, much less is understood about how retrieved memories direct behavior/action. In the auditory Pavolovian threat conditioning paradigm, retrieval of conditioned threat memory activates dorsomedial prefrontal (dmPFC) neurons exhibiting transient responses (T-neurons), which activate both dmPFC neurons exhibiting sustained responses (S-neurons) and locus coeruleus (LC) neurons. Auditory inputs to S-neurons enable the conversion from transient to sustained responses so that the freezing durations match those of the auditory cues. Activation of LC neurons is required for the conversion by enhancing S-neuron responses, which, interestingly, opens a short time window during which non-conditioned cues also lead to freezing. The transition from memory to behavior thus hinges on the integration of retrieved memory, sensory inputs, and emotional/body state cues to generate a selective, adequate, and finely tuned behavior. Significance statementThis study provides new insights into the neural circuitry and mechanisms of how retrieved memories direct the execution of behavior in response to conditioned threatening stimuli. It reveals how different neuron types in the dmPFC interact with LC neurons to determine and modulate the duration and intensity of defensive responses. It also shows that activation of LC neurons can induce the generalization of freezing to non-threatening cues, which may have implications for understanding anxiety disorders. This study contributes to the field of neuroscience by advancing the understanding of memory-behavior conversion and role of the dmPFC and LC in conditioned threat/fear behavior.

neuroscience↗

bioRxiv2024

Integrated Analyses of Multi-omic Data Derived from Paired Primary Lung Cancer and Brain Metastasis Reveals the Metabolic Vulnerability as a Novel Therapeutic Target

Lung cancer brain metastases (LC-BrMs) are frequently associated with dismal mortality rates in patients with lung cancer; however, standard of care therapies for LC-BrMs are still limited in their efficacy. A deep understanding of molecular mechanisms and tumor microenvironment of LC-BrMs will provide us with new insights into developing novel therapeutics for treating patients with LC-BrMs. Here, we performed integrated analyses of genomic, transcriptomic, proteomic and metabolomic data which were derived from a total number of 174 patients with paired and unpaired primary lung cancer and LC-BrM, spanning four published and two newly generated patient cohorts on both bulk and single cell levels. We uncovered that LC-BrMs exhibited significantly higher intra-tumor heterogeneity. We also observed that mutations in a subset of genes were almost always shared by both primary lung cancers and LC-BrM lesions, including TTN, TP53, MUC16, LRP1B, RYR2, and EGFR. In addition, the genome-wide landscape of somatic copy number alterations was similar between primary lung cancers and LC-BrM lesions. Nevertheless, several regions of focal amplification were significantly enriched in LC-BrMs, including 5p15.33 and 20q13.33. Intriguingly, integrated analyses of transcriptomic, proteomic and metabolomic data revealed mitochondrial-specific metabolism was activated but tumor immune microenvironment was suppressed in LC-BrMs. Subsequently, we validated our results by conducting real-time quantitative reverse transcription PCR experiments, immunohistochemistry and multiplexed immunofluorescence staining of patients paired tumor specimens. Patients with a higher expression of mitochondrial metabolism genes but a lower expression of immune genes in their LC-BrM lesions tended to have a worse survival outcome. Therapeutically, targeting oxidative phosphorylation with gamitrinib in patient-derived organoids specific to LC-BrMs induced apoptosis and inhibited cell proliferation. The combination of gamitrinib plus anti-PD-1 immunotherapy significantly improved survival of mice bearing LC-BrMs. In conclusion, our findings not only provide comprehensive and integrated perspectives of molecular underpinnings of LC-BrMs but also contribute to the development of a potential, rationale-based combinatorial therapeutic strategy with the goal of translating it into clinical trials for patients with LC-BrMs.

cancer biology↗

bioRxiv2023

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

The identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.

bioinformatics↗

bioRxiv2023

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Key PointsEarly and Accurate Diagnosis Essential: Acute pulmonary embolism (PE) is a critical condition that demands prompt and precise diagnosis for effective treatment. Limitations of Current Diagnostics: Existing diagnostic methods like Computed Tomography Pulmonary Angiography (CTPA) have certain limitations, leading to the exploration of alternative approaches. Potential of Blood-Based Biomarkers: A recent study focused on identifying blood-based biomarkers for PE. This involved using gene ontology analysis and machine learning methods to analyze gene expression data from both PE patients and healthy controls. Gene Selection and Analysis: The study selected 20 genes for detailed analysis. These included various coagulation factors, fibrinolytic genes, and inflammation markers. Gene Ontology enrichment analysis was performed to understand the biological processes and molecular functions of these genes. Machine Learning for Diagnosis: Supervised machine learning algorithms were utilized to create classification models using the expression levels of these 20 genes. The models demonstrated promising results in distinguishing PE patients from healthy individuals. Acute pulmonary embolism (PE) is a life-threatening condition requiring early and accurate diagnosis. Current diagnostic methods like CTPA have limitations, and a study aimed to identify potential blood-based biomarkers for PE using gene ontology analysis and machine learning methods. Gene expression data of PE patients and healthy controls were obtained from the Gene Expression Omnibus database. A total of 20 genes were selected for further analysis, including coagulation factors F7, F10, F12, fibrinolytic genes PLAT, SERPINE1 and SERPINE2, and inflammation markers SELE, VCAM1 and ICAM. Gene Ontology enrichment analysis was performed to identify biological processes and molecular functions overrepresented among the candidate genes. Supervised machine learning algorithms were applied to build classification models using the expression levels of the 20 genes as features. Nested cross-validation was employed to assess model performance. The RF model achieved the highest area under the receiver operating characteristic curve of 0.89, indicating excellent discrimination between PE patients and controls based on the gene expression signature. Validation in larger cohorts is warranted to clinically translate these findings into a non-invasive diagnostic test for PE.

genomics↗

bioRxiv2023

A Fuzzy sequencer for rapid DNA fragment counting and genotyping

High-throughput sequencing technologies generate a vast number of DNA sequence reads simultaneously, which are subsequently analyzed using the information contained within these fragmented reads. The assessment of sequencing technology relies on information efficiency, which measures the amount of information entropy produced per sequencing reaction cycle. In this study, we propose a fuzzy sequencing strategy that exhibits information efficiency more than twice of currently prevailing cyclic reversible terminator sequencing methods. To validate our approach, we developed a fully functional and high-throughput fuzzy sequencer. This sequencer implements a highly efficient fluorogenic sequencing-by-synthesis chemistry and underwent testing across various application scenarios, including copy-number variation detection, noninvasive prenatal testing, transcriptome profiling, mutation genotyping, and metagenomic profling. Our findings unequivocally demonstrate that the fuzzy sequencing strategy outperforms existing methods in terms of information efficiency and delivers accurate resequencing results with faster turnaround times. One Sentence SummaryA fuzzy sequencer can exceed current limit of information efficiency of DNA sequencers for resequencing applications.

genomics↗

bioRxiv2023

Engineered dsRNA-protein nanoparticles for effective long-distance transport, delivery and gene silencing in plants

Long-distance transport of exogenous biologically active RNA molecules in higher plants has not been reported. Here, we report that cationized bovine serum albumin (cBSA) avidly binds double-stranded beta-glucuronidase RNA (dsGUS RNA) to form nucleic acid-protein nanocomplexes. Using tobacco and poplar plants, we have shown effective uptake and long-distance transport of cBSA/dsGUS RNA nanocomplexes from basal ends of leaf petioles to leaf blades as well as from basal ends of shoots to their apexes and apical leaves. We have further demonstrated that the cBSA/dsGUS RNA nanocomplexes are highly effective in silencing both conditionally inducible DR5-GUS gene and constitutively active 35S-GUS gene in leaf, shoot and shoot meristem tissues. This cBSA/dsRNA delivery technology may provide a convenient, fast, and inexpensive tool for characterizing gene functions in plants, and potentially for in planta gene-editing.

molecular biology↗

bioRxiv2023

Highfold: accurately predicting cyclic peptide monomers and complexes with AlphaFold

In recent years, cyclic peptides have gained growing traction as a therapeutic modality owing to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes can provide valuable insights. However, experimental observation needs much time and money, and there still are many limitations to CADD methods. As for DL-based models, the scarcity of training data poses a formidable challenge in predicting cyclic peptides and their complexes. In this work, we present "High-fold," an AlphaFold-based algorithm that addresses this issue. By incorporating pertinent information about head-to-tailed circular and disulfide bridge structures, Highfold reaches the best performance in comparison to other various approaches. This model enables accurate prediction of cyclic peptides and their complexes, making a step to-wards resolving its structure-activity research.

bioinformatics↗

bioRxiv2023

Spatio-temporal patterns of ovarian development and VgR gene silencing reduced fecundity of parthenogenetic Artemia

Halophilic zooplankton brine shrimp Artemia has been used as an experimental animal in multidisciplinary research. However, the reproductive patterns and regulation mechanisms of Artemia remain a mystery. In this study, the ovarian development process of parthenogenetic Artemia (P. Artemia) was distinguished into five stages, and oogenesis or egg formation process was identified with six phases. The mode of oogenesis was assumed polytrophic. Moreover, we traced the dynamic translocation of germline stem cells (GSCs) by EdU labeling, elucidated several key cytological events in oogenesis through HE staining and florescent image. Notably, distinguished from the ovary structure of insects and crustaceans, P. Artemia germarium originated from ovariole buds and located in the base of the ovarioles. RNA-seq based on five stages of ovarian development revealed that 2657 up-regulated genes related to reproduction were obtained by pair-to-pair comparison of RNA-seq. Furthermore, Gbb, Dpp, piwi, vasa, nanos, VgA and VgR associated with GSCs recognition and reproductive development genes were screened and also verified by qPCR. Silencing of the VgR gene in P. Artemia (PA-VgR) at stage L of ovarian development led to a low level of gene expression (<10%) within five days, which resulted variations of oogenesis-related gene expression and significantly inhibited vitellogenesis, impeded oocyte maturation, and eventually decreased the offspring number. In conclusion, we illustrated the patterns of ovarian development, outlined the key spatio-temporal features of oogenesis and established a platform for gene function research using P. Artemia as an experimental animal. These results lay the foundation for studying the reproductive biology of invertebrates, may also provide a novel insight in the reproductive regulation mechanism of aquatic animals in extreme environments. SUMMARY STATEMENTArtemia is a novel experimental platform and potential "model" crustacean for studying specific process and mechanism of reproduction.

developmental biology↗

bioRxiv2023

CcBHLA: pan-specific peptide-HLA class I binding prediction via Convolutional and BiLSTM features

Human major histocompatibility complex (MHC) proteins are encoded by the human leukocyte antigen (HLA) gene complex. When exogenous peptide fragments form peptide-HLA (pHLA) complexes with HLA molecules on the outer surface of cells, they can be recognized by T cells and trigger an immune response. Therefore, determining whether an HLA molecule can bind to a given peptide can improve the efficiency of vaccine design and facilitate the development of immunotherapy. This paper regards peptide fragments as natural language, we combine textCNN and BiLSTM to build a deep neural network model to encode the sequence features of HLA and peptides. Results on independent and external test datasets demonstrate that our CcBHLA model outperforms the state-of-the-art known methods in detecting HLA class I binding peptides. And the method is not limited by the HLA class I allele and the length of the peptide fragment. Users can download the model for binding peptide screening or retrain the model with private data on github (https://github.com/hongliangduan/CcBHLA-pan-specific-peptide-HLA-class-I-binding-prediction-via-Convolutional-and-BiLSTM-features.git).

bioinformatics↗

bioRxiv2023

Nucleotide excision repair deficiency is a targetable therapeutic vulnerability in clear cell renal cell carcinoma.

PurposeDue to a demonstrated lack of DNA repair deficiencies, clear cell renal cell carcinoma (ccRCC) has not benefitted from targeted synthetic lethality-based therapies. We investigated whether nucleotide excision repair (NER) deficiency is present in an identifiable subset of ccRCC cases that would render those tumors sensitive to therapy targeting this specific DNA repair pathway aberration. Experimental DesignWe used functional assays that detect UV-induced 6-4 pyrimidine-pyrimidone photoproducts to quantify NER deficiency in ccRCC cell lines. We also measured sensitivity to irofulven, an experimental cancer therapeutic agent that specifically targets cells with inactivated transcription-coupled nucleotide excision repair (TC-NER). In order to detect NER deficiency in clinical biopsies, we assessed whole exome sequencing data for the presence of an NER deficiency associated mutational signature previously identified in ERCC2 mutant bladder cancer. ResultsFunctional assays showed NER deficiency in ccRCC cells. Irofulven sensitivity increased in some cell lines. Prostaglandin reductase 1 (PTGR1), which activates irofulven, was also associated with this sensitivity. Next generation sequencing data of the cell lines showed NER deficiency-associated mutational signatures. A significant subset of ccRCC patients had the same signature and high PTGR1 expression. ConclusionsccRCC cell line based analysis showed that NER deficiency is likely present in this cancer type. Approximately 10% of ccRCC patients in the TCGA cohort showed mutational signatures consistent with ERCC2 inactivation associated NER deficiency and also substantial levels of PTGR1 expression. These patients may be responsive to irofulven, a previously abandoned anticancer agent that has minimal activity in NER-proficient cells. Translational relevanceDNA repair deficiencies can be therapeutically targeted by synthetic lethal-based strategies in cancer. However, clear cell renal cell carcinoma (ccRCC) has not benefitted from this therapeutic approach due to a lack of evidence for the presence of specific DNA repair pathway deficiencies. Here, we demonstrate that ccRCC harbors a therapeutically targetable DNA repair pathway aberration, nucleotide excision repair (NER) deficiency. ccRCC cell lines displayed robust signs of NER deficiency as determined by functional assays and some of these cell lines also displayed NER deficiency induced mutational signatures. These cell lines are also sensitive to irofulven, an abandoned anticancer agent that creates DNA lesions which can only be repaired by the NER pathway. We estimate that up to 10% of ccRCC cases may respond to NER-directed therapy with irofulven based on NER deficiency associated mutational signatures and PTGR1 expression levels, which is an enzyme required to activate irofulven.

cancer biology↗

bioRxiv2022

eIF3 mRNA selectivity profiling reveals eIF3k as a cancer-relevant regulator of ribosome content

eIF3, whose subunits are frequently overexpressed in cancer, regulates mRNA translation from initiation to termination, but mRNA-selective functions of individual subunits remain poorly defined. Using multi-omic profiling upon acute depletion of eIF3 subunits, we observed that while eIF3a, b, e, and f markedly differed in their impact on eIF3 holo-complex formation and translation, they were each required for cancer cell proliferation and tumor growth. Remarkably, eIF3k showed the opposite pattern with depletion promoting global translation, cell proliferation, tumor growth, and stress resistance through repressing the synthesis of ribosomal proteins, especially RPS15A. Whereas ectopic expression of RPS15A mimicked the anabolic effects of eIF3k depletion, disruption of eIF3 binding to the 5-UTR of RSP15A mRNA negated them. Supported by mathematical modeling, our data uncovers eIF3k-l as a mRNA-specific module which, through controlling RPS15A translation, serves as a rheostat of ribosome content to secure spare translational capacity that can be mobilized during stress.

cell biology↗

bioRxiv2022

Single-cell functional genomics of natural killer cell evasion in blood cancers

Natural killer (NK) cells are emerging as a promising therapeutic option in cancer. To better understand how cancer cells evade NK cells, we studied interacting NK and blood cancer cells using single-cell and genome-scale functional genomics screens. At single-cell resolution, interaction of NK and cancer cells induced distinct activation states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to more resistant B-lymphoid cancers. CRISPR screens uncovered cancer cell-intrinsic genes driving sensitivity and resistance, including antigen presentation and death receptor signaling mediators, adhesion molecules, protein fucosylation genes, and transcriptional regulators. CRISPR screens with a single-cell transcriptomic readout revealed how these cancer cell genes influenced the gene expression landscape of both cell types, including regulation of activation states in both cancer and NK cells by IFN{gamma} signaling. Our findings provide a resource for rational design of NK cell-based therapies in blood cancers. HIGHLIGHTSO_LITranscriptomic states of interacting NK cells and cancer cells depend on cancer cell lineage C_LIO_LIMolecular correlates of increased sensitivity of myeloid compared to B-lymphoid cancers include activating receptor ligands NCR3LG1, PVR, and ULBP1 C_LIO_LINew regulators of NK cell resistance from 12 genome-scale CRISPR screens include blood cancer-specific regulators SELPLG, SPN, and MYB C_LIO_LISingle-cell transcriptomics CRISPR screens targeting 65 genome-wide screen hits identify MHC-I, IFNy, and NF-{kappa}B regulation as underlying mechanisms C_LI

immunology↗

bioRxiv2021

An ultrapotent RBD-targeted biparatopic nanobody neutralizes broad SARS-CoV-2 variants

The wide transmission and host adaptation of SARS-CoV-2 have led to the rapid accumulation of mutations, posing significant challenges to the effectiveness of vaccines and therapeutic antibodies. Although several neutralizing antibodies were authorized for emergency clinical use, convalescent patients derived natural antibodies are vulnerable to SARS-CoV-2 Spike mutation. Here, we describe the screen of a panel of SARS-CoV-2 receptor-binding domain (RBD) targeted nanobodies (Nbs) from a synthetic library and the design of a biparatopic Nb, named Nb1-Nb2, with tight affinity and super wide neutralization breadth against multiple SARS-CoV-2 variants of concern. Deep-mutational scanning experiments identify the potential binding epitopes of the Nbs on the RBD and demonstrate that biparatopic Nb1-Nb2 has a strong escape resistant feature against more than 60 tested RBD amino acid substitutions. Using pseudovirion-based and trans-complementation SARS-CoV-2 tools, we determine that the Nb1-Nb2 broadly neutralizes multiple SARS-CoV-2 variants, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Lambda (C.37), Kappa (B.1.617.1) and Mu (B.1.621). Furthermore, a heavy chain antibody is constructed by fusing the human IgG1 Fc to Nb1-Nb2 (designated as Nb1-Nb2-Fc) to improve its neutralization potency, yield, stability and potential half-life extension. For the new Omicron variant (B.1.1.529) that harbors unprecedented multiple RBD mutations, Nb1-Nb2-Fc keeps a firm affinity (KD < 1.0x10-12 M) and strong neutralizing activity (IC50 = 0.0017 nM). Together, we developed a tetravalent biparatopic human heavy chain antibody with ultrapotent and broad-spectrum SARS-CoV-2 neutralization activity which highlights the potential clinical applications.

microbiology↗