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Cheng, C.-Y.

Publications and source records attributed to Cheng, C.-Y..

At least 19 recordsLinked to original sources

Dopamine neurons drive basal forebrain reward prediction in milliseconds

Phasic reward prediction signals in ventral tegmental area (VTA) dopamine (DA) neurons and basal forebrain (BF) noncholinergic bursting neurons are remarkably similar despite the distinct circuit organizations and functions of the two systems. Whether this similarity reflects independent computation within each system, inheritance from common upstream circuitry, or causal propagation from one system to the other remains unknown. Here we show that reward prediction signals are tightly coordinated across these two systems, with VTA DA activity causally driving BF bursting neurons on a millisecond timescale. Simultaneous recordings from freely moving rats revealed strong coupling between putative DA neurons and BF bursting neurons on individual trials, both in response amplitude and spike timing. DA activity consistently preceded BF bursting neuron activity by approximately 10 ms across well-trained behavior, learning, and even outside overt task events. In DAT-Cre mice, optogenetic activation of VTA DA neurons rapidly recruited BF bursting neurons at latencies matching those observed during natural DA-BF coupling, while activation of their BF terminals produced responses at still shorter latencies. Conversely, inhibition of DA neurons or their BF terminals suppressed both cue-evoked and, notably, ongoing activity in a substantial subset of BF bursting neurons. Fast DA-evoked BF recruitment persisted after D1/D2 receptor blockade, and anatomical tracing identified glutamatergic DA-to-BF synapses, supporting glutamate co-release as a candidate substrate. Together, these findings reveal a previously unrecognized causal link that enables rapid propagation of reward prediction signals between functionally distinct subcortical systems.

neuroscience↗

BiLSTM-Powered Bilinear Attention for Protein-Ligand Prediction

Rapid and accurate prediction of protein-ligand bindings is essential for drug discovery. While generative AI has driven rapid advancements in structure-based approaches, sequence-based methods remain significantly faster and more cost-effective. Here, we present a weakly supervised deep learning framework integrating graph convolutional networks (GCN) for molecular encoding and bidirectional long short-term memory (BiLSTM) for protein modeling. The latter represents long-range dependencies better than the widely used convolutional neural network (CNN). Leveraging a bilinear attention network (BAN), this model learns protein-ligand pairwise interactions without requiring three-dimensional structural supervision. By using the publicly available BindingDB dataset, the model was trained, solely on affinity labels, and successfully classified binder and non-binders with AUROC of 0.96 and an AUPRC of 0.95. The model generates interpretable attention maps that serve as a "GPS" to locate binding sites. Remarkably, despite the lack of structural training data, it can pinpoint key contact residues confirmed by crystal structures. Our method could function as a scalable filter for giga-scale libraries, allowing rapid screening of drug candidates with direct structural insights into the protein-ligand interface.

bioinformatics↗

Nuclear mTOR-Polycomb synergism at developmental gene promoters prone to activation

Pluripotent cells hold the capacity to differentiate into cells with diverse functions, enabling development and regeneration. The balance between local epigenetic repression and maintaining the potential for prompt transcriptional activation supports pluripotency while allowing imminent differentiation. How the activation of developmental pathways is correctly timed in sync with the environment remains poorly understood. Here we find that the cellular growth regulator mTOR selectively binds developmental gene promoters in pluripotent mouse embryonic stem cells. mTOR binding at target genes correlates with and depends on histone H2AK119 monoubiquitination (H2AK119ub1) deposited by the Polycomb Repressor Complex 1 (PRC1). Acute depletion of the whole PRC1 complex or its catalytic activity leads to depletion of mTOR at target gene promoters, whereas forced PRC1 recruitment to an artificial site brings along mTOR. At target genes, mTOR colocalizes with components of the transcription machinery and we find that mTOR-bound genes are distinctly characterized by high levels of RNA Polymerase II pausing. Our findings reveal a role of mTOR in pluripotency regulation and highlight the tight crosstalk between gene regulatory and cell growth machineries in stem cells.

molecular biology↗

HSPB8 regulates CTP synthase filaments to couple nucleotide metabolism and autophagy in tumors

Metabolic adaptation and proteostasis are essential for tumor survival under nutrient stress, yet how these processes are mechanistically integrated remains unclear. Here, we identify CTP synthase (CTPS) filament dynamics as a regulatory nexus linking nucleotide metabolism to autophagic flux. Under glutamine deprivation--a hallmark of poorly vascularized solid tumors--CTPS undergoes polymerization into filamentous assemblies that exhibit reduced enzymatic activity. We demonstrate that filament formation is driven by intracellular asparagine availability and stabilized by the accumulation of misfolded proteins. Using APEX2-based proximity labeling and proteomics, we identify the small heat shock protein HSPB8 as a filament-associated regulator. HSPB8, acting within the chaperone-assisted selective autophagy (CASA) pathway, antagonizes CTPS polymerization by promoting clearance of misfolded proteins, thereby restoring soluble, catalytically active CTPS. Filament disassembly increases CTP production and enhances synthesis of autophagy-related phospholipids, including phosphatidylinositol and phosphatidylethanolamine, resulting in accelerated autophagic flux. Cells expressing filament-deficient CTPS mutants display elevated autophagosome formation and increased LC3-II accumulation upon lysosomal blockade, confirming enhanced flux. In vivo, disruption of CTPS filament assembly impairs xenograft tumor growth and is associated with excessive autophagy. Analysis of TCGA datasets further reveals that high CTPS and low HSPB8 expression correlate with poor patient survival across multiple cancers. Collectively, our findings establish CTPS filament dynamics as a proteostasis-sensitive metabolic switch that coordinates nucleotide biosynthesis with autophagy, revealing a previously unrecognized vulnerability in tumors adapting to nutrient limitation.

cell biology↗

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations (cis-pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

bioinformatics↗

National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

The whole-of-nation National Precision Medicine (NPM) programme in Singapore aims to integrate a genomics backbone into standard healthcare. Phase II recently completed the sequencing of 102,202 genomes from the diverse Asian population of Singapore, generating the PRECISE-SG100K dataset. The scaling from the pilot SG10K_Health phase to national production scale demanded a central shift: from earlier generation workflows to an integrated, industrial grade "Genomic Engine". The initiative was co-developed under a public-private partnership model. Our Genomic Engine was designed with an automation-first approach, as a modular platform linking a purpose-built, centralized biospecimen processing and DNA extraction facility (EXTRACT Lab), a high-throughput sequencing operation (Illumina Data Generation Infrastructure, IDGI), with the cloud-native analytics (GRIDS) running on a fully integrated PRECISE Sample Tracking System (STS). Here we describe the key design features of our Genomic Engine. Highlighting the Stress Test phase where the automated DNA extraction yields failed to meet expectations. We share the operational adjustments which were implemented to stabilize production and resulted in a three-fold reduction in sample triage due to improved DNA recovery. Without establishing the operational guardrails during the Stress Test, the Engine would not have reached a steady-state production evidenced by >3,000 genomes per month, median turnaround time 11 days, 99.6% operational success, within in six months. This framework offers a practical blueprint for other population-scale initiatives seeking to bridge the gap between scientific piloting and industrial delivery.

genomics↗

TISCalling: Leveraging Machine Learning to Identify Translational Initiation Sites in Plants and Viruses

The recognition of translational initiation sites (TISs) offers complementary insights into identifying genes encoding novel proteins or small peptides. Conventional computational methods primarily identify Ribo-seq-supported TISs and lack the capacity of systematical and global identification of TIS, especially for non-AUG sites in plants. Additionally, these methods are often unsuitable for evaluating the importance of mRNA sequence features for TIS determination. In this study, we present TISCalling, a robust framework that combines machine learning (ML) models and statistical analysis to identify and rank novel TISs across eukaryotes. TISCalling generalized and ranks important features common to multiple plant and mammalian species while identifying kingdom-specific features such as mRNA secondary structures and G-contents. Furthermore, TISCalling achieved high predictive power for identifying novel viral TISs. Importantly, TISCalling provides prediction scores for putative TIS along plant transcripts, enabling prioritization of those of interest for further validation. We offer TISCalling as a command-line-based package [https://github.com/yenmr/TIScalling], capable of generating prediction models and identifying key sequence features. Additionally, we provide web tools [https://predict.southerngenomics.org/TIScalling] for visualizing pre-computed potential TISs, making it accessible to users without programming experience. The TISCalling framework offers a sequence-aware and interpretable approach for decoding genome sequences and exploring functional proteins in plants and viruses.

bioinformatics↗

Assessment of Genetic Resources to Major Tomato Diseases: Insights from wild germplasm and public S. lycopersicum collections

Tomato (Solanum lycopersicum L.) production is severely impacted by biotic stresses, leading to significant yield losses. Developing genetically resistant cultivars presents a sustainable alternative to chemical control, which is often costly and ineffective against evolving pathogens. This study utilized molecular markers to assess genetic resistance to key tomato diseases, including Fusarium wilt, late blight, bacterial wilt, root-knot nematode, Tomato Mosaic Virus (ToMV), and Tomato Yellow Leaf Curl Virus (TYLCV), across 964 tomato accessions comprising both wild germplasm and cultivated varieties. Wild germplasm exhibited the highest frequencies, with S. peruvianum identified as a rich reservoir of resistance loci. Markers associated with TYLCV (Ty-1/3, Ty2) and Fusarium wilt (I2(OH)) were particularly prevalent in wild species. The potential for gene pyramiding potential was evident, with some wild accessions carrying up to six resistance loci. In contrast, resistance loci were limited in Solanum lycopersicum. Two S. lycopersicum germplasm collections were screened: a geographically representative collection from four Southeast Asian national genebanks and a genetically representative core collection from global public genebanks. The highest resistance frequencies in S. lycopersicum were observed for bacterial wilt-associated markers SLM12-2 and SLM12-10. However, the overall scarcity of resistance alleles in S. lycopersicum emphasizes the need for further introgression of resistance genes from wild relatives. This study provides valuable genetic insights into tomato germplasm for combating biotic stress, forming a foundation for sustainable breeding strategies to enhance disease resistance and safeguard global tomato production.

plant biology↗

Evolutionarily Conserved Heat-Induced Chromatin Dynamics Drive Heat Stress Responses in Plants

Eukaryotic organisms remodel chromatin landscapes to regulate gene expression in response to environmental stress. In plants, heat stress (HS) induces widespread chromatin changes, yet the role of heat-responsive Heat Shock Transcription Factors (HSFs) in chromatin remodeling and their evolutionary conservation remains unclear. Using chromatin accessibility profiling and transcriptomics in Marchantia polymorpha hsf mutants, we identify HSFA1 as a key determinant in positioning cis-regulatory elements (CREs) for HS-induced gene activation, a mechanism conserved across land plants, mice, and human cells. By integrating gene regulatory network modeling, we identify parallel transcription factor subnetworks, with MpWRKY10 and MpABI5B acting as indirect regulators of HS responses via phenylpropanoid pathways and general stress signaling. We further explore crosstalk between HS and abscisic acid (ABA) signaling, showing that while ABA modulates gene expression in an HSFA1-dependent manner, it does not induce broad chromatin remodeling, positioning it as a downstream regulator rather than a primary determinant of chromatin dynamics. To extend these insights, we develop a cross-species and cross-condition machine learning framework that accurately predicts chromatin accessibility and gene expression, demonstrating a conserved regulatory logic of stress-responsive chromatin and transcription dynamics. Our findings provide a conceptual framework for understanding how TFs coordinate chromatin architecture to drive stress adaptation in plants and potentially other eukaryotes.

plant biology↗

Combinatorial microRNA activity is essential for the transition of pluripotent cells from proliferation into dormancy

Dormancy is a key feature of stem cell function in adult tissues as well as embryonic cells in the context of diapause. The establishment of dormancy is an active process that involves extensive transcriptional, epigenetic, and metabolic rewiring1,2. How these processes are coordinated to successfully transition cells to the resting dormant state remains unclear. Here we show that microRNA activity, which is otherwise dispensable for pre-implantation development, is essential for the adaptation of early mouse embryos to the dormant state of diapause. In particular, the pluripotent epiblast depends on miRNA activity, the absence of which results in loss of pluripotent cells. Through integration of high-sensitivity small RNA expression profiling of individual embryos and protein expression of miRNA targets with public data of protein-protein interactions, we constructed the miRNA-mediated regulatory network of mouse early embryos specific to diapause. We find that individual miRNAs contribute to the combinatorial regulation by the network and the perturbation of the network compromises embryo survival in diapause. Without miRNAs, nuclear and cytoplasmic bodies show aberrant expression, concurrent with splicing defects. We identified the nutrient-sensitive transcription factor TFE3 as an upstream regulator of diapause-specific miRNAs, linking cytoplasmic mTOR activity to nuclear miRNA biogenesis. Our results place miRNAs as a critical regulatory layer for the molecular rewiring of early embryos to establish dormancy.

molecular biology↗

Investigating the validity and interpretability of longitudinal brain age underlying cognition in Asian children and older adults

Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore, most brain age models are trained and tested on cross-sectional data from primarily Caucasian, adult participants. It is thus unclear how well these models generalize to non-Caucasian participants, especially children. Here, we tested a previously published deep learning model on Singaporean elderly participants (55 - 88 years old) and children (4 - 11 years old). We found that the model directly generalized to the elderly participants, but model finetuning was necessary for children. After finetuning, we found that the rate of change in brain age gap was associated with future executive function performance in both elderly participants and children. We further found that lateral ventricles and frontal areas contributed to brain age prediction in elderly participants, while white matter and posterior brain regions were more important in predicting brain age of children. Taken together, our results suggest that there is potential for generalizing brain age models to diverse populations. Moreover, the longitudinal change in brain age gap reflects developing and aging processes in the brain, relating to future cognitive function.

neuroscience↗

VPS8D, a CORVET subunit, is required to maintain the contractile vacuole complex in Tetrahymena thermophila

Contractile vacuole complexes (CVCs) are complex osmoregulatory organelles, with vesicular (bladder) and tubular (spongiome) subcompartments. The mechanisms that underlie their formation and maintenance within the eukaryotic endomembrane network are poorly understood. In the Ciliate Tetrahymena thermophila, six differentiated CORVETs (class C core vacuole/endosome tethering complexes), with Vps8 subunits designated A-F, are likely to direct endosomal trafficking. Vps8Dp localizes to both bladder and spongiome. We show by inducible knockdown that VPS8D is essential to CVC organization and function. VPS8D knockdown increased susceptibility to osmotic shock, tolerated in the wildtype but triggering irreversible lethal swelling in the mutant. The knockdown rapidly triggered contraction of the spongiome and lengthened the period of the bladder contractile cycle. More prolonged knockdown resulted in disassembly of both the spongiome and bladder, and dispersal of proteins associated with those compartments. In stressed cells where the normally singular bladder is replaced by numerous vesicles bearing bladder markers, Vps8Dp concentrated conspicuously at long-lived inter-vesicle contact sites, consistent with tethering activity. Similarly, Vps8Dp in cell-free preparations accumulated at junctions formed after vacuoles came into close contact. Also consistent with roles for Vps8Dp in tethering and/or fusion were the emergence in knockdown cells of multiple vacuole-related structures, replacing the single bladder. SynopsisIn the Ciliate Tetrahymena thermophila, VPS8D, which encodes a subunit of a non-conventional CORVET complex, is an essential determinant of the contractile vacuole complex (CVC). VPS8D knockdown results in retraction and dispersal of the spongiome, and disappearance of the bladder, reinforcing the view that CVCs arise from endosomal trafficking. Intermediate knockdown phenotypes and Vps8Dp localization support a role in homotypic tethering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/566071v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@b652c6org.highwire.dtl.DTLVardef@1f456f7org.highwire.dtl.DTLVardef@79ab8dorg.highwire.dtl.DTLVardef@1edbf74_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

Structure and dynamics of the contractile vacuole complex in Tetrahymena thermophila

The contractile vacuole complex (CVC) is a dynamic and morphologically complex membrane organelle, comprised of a large vesicle (bladder) linked with a tubular reticulum (spongiome). CVCs provide key osmoregulatory roles across diverse eukaryotic lineages, but probing the mechanisms underlying the structure and function is hampered by the limited tools available for in vivo analysis. In the experimentally tractable ciliate Tetrahymena thermophila, we describe four proteins that, as endogenously tagged constructs, localize specifically to distinct CVC zones. The DOPEY homolog Dop1p and the CORVET subunit Vps8Dp localize both to the bladder and spongiome but with different local distributions that are sensitive to osmotic perturbation, while the lipid scramblase Scr7p co-localizes with Vps8Dp. The H+- ATPase subunit Vma4 is spongiome-specific. The live imaging permitted by these probes revealed dynamics at multiple scales including rapid exchange of CVC-localized and soluble protein pools vs. lateral diffusion in the spongiome, spongiome extension and branching, and CVC formation during mitosis. While the association with DOP1 and VPS8D implicate the CVC in endosomal trafficking, both the bladder and spongiome are isolated from bulk endocytic input. Summary statementIn the ciliate Tetrahymena thermophila, four proteins are shown to provide markers for different zones of the contractile vacuole complex. They shed light on its formation and maintenance by enabling in vivo analysis of its dynamics.

cell biology↗

RAPTOR: A Five-Safes approach to a secure, cloud native and serverless genomics data repository

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a "five-safes" framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets

genomics↗

Anti-spike antibody response to natural infection with SARS-CoV-2 and its activity against emerging variants

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has substantially impacted human health globally. Spike-specific antibody response plays a major role in protection against SARS-CoV-2. Here, we demonstrated that acute SARS-CoV-2 infection elicits rapid and robust spike-binding and ACE2-blocking antibody responses, which wane approximately 11 months after infection. Serological responses were found to be correlated with the frequency of spike-specific memory B cell responses to natural infections. Further, significantly higher spike-binding, ACE2-blocking, and memory B cell responses were detected in patients with fever and pneumonia. Spike-specific antibody responses were found to be greatly affected by spike mutations in emerging variants, especially the Beta and Omicron variants. These results warrant continued surveillance of spike-specific antibody responses to natural infections and highlight the importance of maintaining functional anti-spike antibodies through immunization. ImportanceAs spike protein-specific antibody responses play a major role in protection against SARS-CoV-2, we examined the spike-binding and ACE2-blocking antibody responses in SARS-CoV-2 infection at different time points. We found robust responses following acute infection, which waned approximately 11 months after infection. Further, the serological responses were correlated with the frequency of spike-specific memory B cell responses to natural infections. Patients with fever and pneumonia showed significantly stronger spike-binding, ACE2-blocking antibody, and memory B cell responses. Moreover, the spike-specific antibody responses were substantially affected by the emerging variants, especially the Beta and Omicron variants. These results warrant continued surveillance of spike-specific antibody responses to natural infections and highlight the importance of maintaining functional anti-spike antibodies through immunization.

immunology↗

Rare coding variants in 35 genes associate with circulating lipid levels: a multi-ancestry analysis of 170,000 exomes

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency<1%) predicted damaging coding variation using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

genetics↗

Integrated Analysis of Cell Shape and Movement in Moving Frame

The cell movement and morphological change are two interrelated cellular processes. The integrated analysis is needed to explore the relationship between them. However, it has been challenging to investigate them as a whole. The cell trajectory can be described by its speed, curvature, and torsion. On the other hand, the 3-Dimensional (3D) cell shape can be studied by using a shape descriptor such as Spherical harmonic (SH) descriptor which is an extension of Fourier transform in 3D space. To integrate these shape-movement data, we propose a method to use a parallel-transport (PT) moving frame as the 3D-shape coordinate system. This moving frame is purely determined by the velocity vector. On this moving frame, the movement change will influence the coordinate system for shape analysis. By analyzing the change of the SH coefficients over time in the moving frame, we can observe the relationship between shape and movement. We illustrate the application of our approach using simulational and real datasets in this paper.

bioinformatics↗

Plasmablast-derived antibody response to acute SARS-CoV-2 infection in humans

Serological and plasmablast responses and plasmablast-derived IgG monoclonal antibodies (MAbs) have been analysed in three COVID-19 patients with different clinical severities. Potent humoral responses were detected within 3 weeks of onset of illness in all patients and the serological titre was elicited soon after or concomitantly with peripheral plasmablast response. An average of 13.7% and 13.0% of plasmablast-derived MAbs were reactive with virus spike glycoprotein or nucleocapsid, respectively. A subset of anti-spike (10 of 32) and over half of anti-nucleocapsid (19 of 35) antibodies cross-reacted with other betacoronaviruses tested and harboured extensive somatic mutations, indicative of an expansion of memory B cells upon SARS-CoV-2 infection. Fourteen of 32 anti-spike MAbs, including five anti-RBD, three anti-non-RBD S1 and six anti-S2, neutralised wild-type SARS-CoV-2 in independent assays. Anti-RBD MAbs were further grouped into four cross-inhibiting clusters, of which six antibodies from three separate clusters blocked the binding of RBD to ACE2 and five were neutralising. All ACE2-blocking anti-RBD antibodies were isolated from two patients with prolonged fever, which is compatible with substantial ACE2-blocking response in their sera. At last, the identification of non-competing pairs of neutralising antibodies would offer potential templates for the development of prophylactic and therapeutic agents against SARS-CoV-2.

microbiology↗