Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide, causing a global pandemic. Bat-origin RaTG13 is currently the most phylogenetically related virus. Here we obtained the complex structure of the RaTG13 receptor binding domain (RBD) with human ACE2 (hACE2) and evaluated binding of RaTG13 RBD to 24 additional ACE2 orthologs. By substituting residues in the RaTG13 RBD with their counterparts in the SARS-CoV-2 RBD, we found that residue 501, the major position found in variants of concern (VOCs) 501Y.V1/V2/V3, plays a key role in determining the potential host range of RaTG13. We also found that SARS-CoV-2 could induce strong cross-reactive antibodies to RaTG13 and identified a SARS-CoV-2 monoclonal antibody (mAb), CB6, that could cross-neutralize RaTG13 pseudovirus. These results elucidate the receptor binding and host adaption mechanisms of RaTG13 and emphasize the importance of continuous surveillance of coronaviruses (CoVs) carried by animal reservoirs to prevent another spillover of CoVs.

Spatial barcoding technologies have the potential to reveal histological details of transcriptomic profiles; however, they are currently limited by their low resolution. Here, we report Seq-Scope, a spatial barcoding technology with a resolution comparable to an optical microscope. Seq-Scope is based on a solid-phase amplification of randomly barcoded single-molecule oligonucleotides using an Illumina sequencing platform. The resulting clusters annotated with spatial coordinates are processed to expose RNA-capture moiety. These RNA-capturing barcoded clusters define the pixels of Seq-Scope that are ∼0.5-0.8 μm apart from each other. From tissue sections, Seq-Scope visualizes spatial transcriptome heterogeneity at multiple histological scales, including tissue zonation according to the portal-central (liver), crypt-surface (colon) and inflammation-fibrosis (injured liver) axes, cellular components including single-cell types and subtypes, and subcellular architectures of nucleus and cytoplasm. Seq-Scope is quick, straightforward, precise, and easy-to-implement and makes spatial single-cell analysis accessible to a wide group of biomedical researchers.

Update of medRxiv. 2021 Mar 09;:

Antibodies against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein prevent SARS-CoV-2 infection. However, the effects of antibodies against other spike protein domains are largely unknown. Here, we screened a series of anti-spike monoclonal antibodies from coronavirus disease 2019 (COVID-19) patients and found that some of antibodies against the N-terminal domain (NTD) induced the open conformation of RBD and thus enhanced the binding capacity of the spike protein to ACE2 and infectivity of SARS-CoV-2. Mutational analysis revealed that all of the infectivity-enhancing antibodies recognized a specific site on the NTD. Structural analysis demonstrated that all infectivity-enhancing antibodies bound to NTD in a similar manner. The antibodies against this infectivity-enhancing site were detected at high levels in severe patients. Moreover, we identified antibodies against the infectivity-enhancing site in uninfected donors, albeit at a lower frequency. These findings demonstrate that not only neutralizing antibodies but also enhancing antibodies are produced during SARS-CoV-2 infection.

Structural variations (SVs) and gene copy number variations (gCNVs) have contributed to crop evolution, domestication, and improvement. Here, we assembled 31 high-quality genomes of genetically diverse rice accessions. Coupling with two existing assemblies, we developed pan-genome-scale genomic resources including a graph-based genome, providing access to rice genomic variations. Specifically, we discovered 171,072 SVs and 25,549 gCNVs and used an Oryza glaberrima assembly to infer the derived states of SVs in the Oryza sativa population. Our analyses of SV formation mechanisms, impacts on gene expression, and distributions among subpopulations illustrate the utility of these resources for understanding how SVs and gCNVs shaped rice environmental adaptation and domestication. Our graph-based genome enabled genome-wide association study (GWAS)-based identification of phenotype-associated genetic variations undetectable when using only SNPs and a single reference assembly. Our work provides rich population-scale resources paired with easy-to-access tools to facilitate rice breeding as well as plant functional genomics and evolutionary biology research.

Comment in Cell Metab. 2021 Jul 6;33(7):1271-1273. Nat Rev Endocrinol. 2021 Oct;17(10):579-580.

Comment in Cell. 2021 Jun 24;184(13):3358-3360.

Update of bioRxiv. 2021 Jan 27;:

Mutations in leucine-rich repeat kinase 2 (LRRK2) are commonly implicated in the pathogenesis of both familial and sporadic Parkinson's disease (PD). LRRK2 regulates critical cellular processes at membranous organelles and forms microtubule-based pathogenic filaments, yet the molecular basis underlying these biological roles of LRRK2 remains largely enigmatic. Here, we determined high-resolution structures of full-length human LRRK2, revealing its architecture and key interdomain scaffolding elements for rationalizing disease-causing mutations. The kinase domain of LRRK2 is captured in an inactive state, a conformation also adopted by the most common PD-associated mutation, LRRK2G2019S. This conformation serves as a framework for structure-guided design of conformational specific inhibitors. We further determined the structure of COR-mediated LRRK2 dimers and found that single-point mutations at the dimer interface abolished pathogenic filamentation in cells. Overall, our study provides mechanistic insights into physiological and pathological roles of LRRK2 and establishes a structural template for future therapeutic intervention in PD.

Comment in Cell. 2021 Jun 24;184(13):3356-3357. Cell. 2021 Nov 24;184(24):5839-5841. Cell. 2021 Nov 24;184(24):5842-5844.

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

Author information: (1)Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA. (2)Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany. (3)Icahn School of Medicine at Mount Sinai, New York, NY, USA. (4)Genome Institute of Singapore, A(∗)STAR, Singapore, Singapore. (5)Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain. (6)ETH Zurich, Department of Computer Science, Biomedical Informatics Group, Zurich, Switzerland; University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland. (7)Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Kenya Medical Research Institute - Kisumu, Kisumu, Kenya. (8)Department of Energy, Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. (9)Independent Researcher, Zurich, Switzerland. (10)Massachusetts Institute of Technology, McGovern Institute for Brain Research, Cambridge, MA, USA. (11)Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India; Centre for Artificial Intelligence and Machine Learning, Indian Statistical Institute, Kolkata, India. (12)University of Minnesota, Minneapolis, MN, USA. (13)Universidad Andres Bello, Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Santiago, Chile. (14)California State University, Sacramento, Sacramento, CA, USA. (15)Department of Agricultural Sciences, Division of Microbiology, University of Naples Federico II, Naples, Italy; Task Force on Microbiome Studies, University of Naples Federico II, Naples, Italy. (16)University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA. (17)Aix-Marseille Université, Mediterranean Institute of Oceanology, Université de Toulon, CNRS, IRD, UM 110, Marseille, France. (18)Medical Genomics group, A.C.Camargo Cancer Center, São Paulo - SP, Brazil. (19)Norwegian Defence Research Establishment FFI, Kjeller, Norway. (20)Department of Biology, Lund University, Lund, Sweden. (21)Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine, Kyiv, Ukraine; Kyiv Academic University, Kyiv, Ukraine. (22)University of Applied Sciences Vienna, Vienna, Austria. (23)Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK. (24)University of Colorado at Boulder, Boulder, CO, USA. (25)Microbial Genomics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay; Center for Integrative Biology, Universidad Mayor, Santiago de Chile, Santiago, Chile; Wellcome Sanger Institute, Hinxton, UK. (26)Institut Pasteur Korea, Seoul, South Korea. (27)ETH Zurich, Department of Computer Science, Biomedical Informatics Group, Zurich, Switzerland; Kyiv Academic University, Kyiv, Ukraine; C+, Research Center in Technologies for Society, School of Engineering, Universidad del Desarrollo, Santiago, Chile. (28)State Key Laboratory of Genetic Engineering (SKLGE) and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland; Boku University Viennna, Vienna, Austria. (29)School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China. (30)Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden. (31)Microba, 388 Queen St, Brisbane City, QLD 4000, Australia. (32)University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA. (33)Fundação Oswaldo Cruz, Rio de Janeiro - RJ, Brazil. (34)University of São Paulo, Ribeirão Preto Medical School, Ribeirão Preto - SP, Brazil. (35)Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal. (36)Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany. (37)Applied Environmental Metagenomics and Infectious Diseases Research (AEMIDR), Department of Biological Sciences, Elizade University, Ilara-Mokin, Nigeria. (38)Acibadem Mehmet Ali Aydınlar University, Istanbul, Turkey. (39)Microbial Paleogenomics Unit, Institut Pasteur, CNRS UMR2000, Paris 75015, France. (40)Sorbonne University, Faculty of Science, Institute of Biology Paris-Seine, Laboratory of Computational and Quantitative Biology, Paris, France; Robert Koch Institute, Berlin, Germany. (41)Robert Koch Institute, Berlin, Germany. (42)Center for Pharmacogenomics, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering (SKLGE) and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China. (43)The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China. (44)University of Medicine and Health Sciences, St. Kitts, West Indies and American University in Cairo, Cairo, Egypt. (45)108 Military Central Hospital, Hanoi, Vietnam; Vietnamese-German Center for Medical Research (VG-CARE), Hanoi, Vietnam. (46)Keio University, Tokyo, Japan. (47)University of Vermont, Burlington, VT, USA. (48)Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden; SciLife EVP, Department of Aquatic Sciences Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden. (49)Millennium Initiative for Collaborative Research on Bacterial Resistance, Santiago, Chile; C+, Research Center in Technologies for Society, School of Engineering, Universidad del Desarrollo, Santiago, Chile. (50)Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski," Sofia, Bulgaria. (51)Institute of Arctic Biology, University of Alaska, Fairbanks, Fairbanks, AK, USA. (52)Institute of Tropical Medicine, Univeristätsklinikum Tübingen, Tübingen, Germany; Faculty of Medicine, Duy Tan University, Da Nang, Vietnam. (53)Corporación Corpogen-Research Center, Bogotá, Colombia. (54)State Key Laboratory of Genetic Engineering (SKLGE) and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. (55)Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA; The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA. Electronic address: chm2042@med.cornell.edu.

Surface epithelia provide a critical barrier to the outside world. Upon a barrier breach, resident epithelial and immune cells coordinate efforts to control infections and heal tissue damage. Inflammation can etch lasting marks within tissues, altering features such as scope and quality of future responses. By remembering inflammatory experiences, tissues are better equipped to quickly and robustly respond to barrier breaches. Alarmingly, in disease states, memory may fuel the inflammatory fire. Here, we review the cellular communication networks in barrier tissues and the integration between tissue-resident and recruited immune cells and tissue stem cells underlying tissue adaptation to environmental stress.

Comment on Cell. 2021 Jun 24;184(13):3528-3541.e12.

Comment on Cell. 2021 Jun 24;184(13):3394-3409.e20.

The capping of mRNA and the proofreading play essential roles in SARS-CoV-2 replication and transcription. Here, we present the cryo-EM structure of the SARS-CoV-2 replication-transcription complex (RTC) in a form identified as Cap(0)-RTC, which couples a co-transcriptional capping complex (CCC) composed of nsp12 NiRAN, nsp9, the bifunctional nsp14 possessing an N-terminal exoribonuclease (ExoN) and a C-terminal N7-methyltransferase (N7-MTase), and nsp10 as a cofactor of nsp14. Nsp9 and nsp12 NiRAN recruit nsp10/nsp14 into the Cap(0)-RTC, forming the N7-CCC to yield cap(0) (7MeGpppA) at 5' end of pre-mRNA. A dimeric form of Cap(0)-RTC observed by cryo-EM suggests an in trans backtracking mechanism for nsp14 ExoN to facilitate proofreading of the RNA in concert with polymerase nsp12. These results not only provide a structural basis for understanding co-transcriptional modification of SARS-CoV-2 mRNA but also shed light on how replication fidelity in SARS-CoV-2 is maintained.

Crimean-Congo hemorrhagic fever virus (CCHFV) is a World Health Organization priority pathogen. CCHFV infections cause a highly lethal hemorrhagic fever for which specific treatments and vaccines are urgently needed. Here, we characterize the human immune response to natural CCHFV infection to identify potent neutralizing monoclonal antibodies (nAbs) targeting the viral glycoprotein. Competition experiments showed that these nAbs bind six distinct antigenic sites in the Gc subunit. These sites were further delineated through mutagenesis and mapped onto a prefusion model of Gc. Pairwise screening identified combinations of non-competing nAbs that afford synergistic neutralization. Further enhancements in neutralization breadth and potency were attained by physically linking variable domains of synergistic nAb pairs through bispecific antibody (bsAb) engineering. Although multiple nAbs protected mice from lethal CCHFV challenge in pre- or post-exposure prophylactic settings, only a single bsAb, DVD-121-801, afforded therapeutic protection. DVD-121-801 is a promising candidate suitable for clinical development as a CCHFV therapeutic.

To control viral infection, vertebrates rely on both inducible interferon responses and less well-characterized cell-intrinsic responses composed of "at the ready" antiviral effector proteins. Here, we show that E3 ubiquitin ligase TRIM7 is a cell-intrinsic antiviral effector that restricts multiple human enteroviruses by targeting viral 2BC, a membrane remodeling protein, for ubiquitination and proteasome-dependent degradation. Selective pressure exerted by TRIM7 results in emergence of a TRIM7-resistant coxsackievirus with a single point mutation in the viral 2C ATPase/helicase. In cultured cells, the mutation helps the virus evade TRIM7 but impairs optimal viral replication, and this correlates with a hyperactive and structurally plastic 2C ATPase. Unexpectedly, the TRIM7-resistant virus has a replication advantage in mice and causes lethal pancreatitis. These findings reveal a unique mechanism for targeting enterovirus replication and provide molecular insight into the benefits and trade-offs of viral evolution imposed by a host restriction factor.

Comment in Nat Cell Biol. 2021 Nov;23(11):1108.

Comment in Nat Rev Cardiol. 2021 Sep;18(9):613. Cell Res. 2021 Sep;31(9):947-948.

Comment on Cell. 2021 Jun 10;184(12):3222-3241.e26.

Comment on Cell. 2021 Jun 10;184(12):3163-3177.e21.

Northern East Asia was inhabited by modern humans as early as 40 thousand years ago (ka), as demonstrated by the Tianyuan individual. Using genome-wide data obtained from 25 individuals dated to 33.6-3.4 ka from the Amur region, we show that Tianyuan-related ancestry was widespread in northern East Asia before the Last Glacial Maximum (LGM). At the close of the LGM stadial, the earliest northern East Asian appeared in the Amur region, and this population is basal to ancient northern East Asians. Human populations in the Amur region have maintained genetic continuity from 14 ka, and these early inhabitants represent the closest East Asian source known for Ancient Paleo-Siberians. We also observed that EDAR V370A was likely to have been elevated to high frequency after the LGM, suggesting the possible timing for its selection. This study provides a deep look into the population dynamics of northern East Asia.

Searching for factors to improve knockin efficiency for therapeutic applications, biotechnology, and generation of non-human primate models of disease, we found that the strand exchange protein RAD51 can significantly increase Cas9-mediated homozygous knockin in mouse embryos through an interhomolog repair (IHR) mechanism. IHR is a hallmark of meiosis but only occurs at low frequencies in somatic cells, and its occurrence in zygotes is controversial. Using multiple approaches, we provide evidence for an endogenous IHR mechanism in the early embryo that can be enhanced by RAD51. This process can be harnessed to generate homozygotes from wild-type zygotes using exogenous donors and to convert heterozygous alleles into homozygous alleles without exogenous templates. Furthermore, we identify additional IHR-promoting factors and describe features of IHR events. Together, our findings show conclusive evidence for IHR in mouse embryos and describe an efficient method for enhanced gene conversion.

Comment in Cell. 2021 Jun 10;184(12):3083-3085.

Comment in Nat Rev Drug Discov. 2021 Jul;20(7):505. Cell. 2021 Jun 10;184(12):3081-3083.

NIH has acknowledged and committed to ending structural racism. The framework for NIH's approach, summarized here, includes understanding barriers; developing robust health disparities/equity research; improving its internal culture; being transparent and accountable; and changing the extramural ecosystem so that diversity, equity, and inclusion are reflected in funded research and the biomedical workforce.

Erratum in Cell. 2021 Aug 19;184(17):4593-4595.

Erratum in Cell. 2021 Sep 16;184(19):5070.

Gene expression by RNA polymerase II (RNAPII) is tightly controlled by cyclin-dependent kinases (CDKs) at discrete checkpoints during the transcription cycle. The pausing checkpoint following transcription initiation is primarily controlled by CDK9. We discovered that CDK9-mediated, RNAPII-driven transcription is functionally opposed by a protein phosphatase 2A (PP2A) complex that is recruited to transcription sites by the Integrator complex subunit INTS6. PP2A dynamically antagonizes phosphorylation of key CDK9 substrates including DSIF and RNAPII-CTD. Loss of INTS6 results in resistance to tumor cell death mediated by CDK9 inhibition, decreased turnover of CDK9 phospho-substrates, and amplification of acute oncogenic transcriptional responses. Pharmacological PP2A activation synergizes with CDK9 inhibition to kill both leukemic and solid tumor cells, providing therapeutic benefit in vivo. These data demonstrate that fine control of gene expression relies on the balance between kinase and phosphatase activity throughout the transcription cycle, a process dysregulated in cancer that can be exploited therapeutically.

The N6-methyladenosine (m6A) RNA modification is used widely to alter the fate of mRNAs. Here we demonstrate that the C. elegans writer METT-10 (the ortholog of mouse METTL16) deposits an m6A mark on the 3' splice site (AG) of the S-adenosylmethionine (SAM) synthetase pre-mRNA, which inhibits its proper splicing and protein production. The mechanism is triggered by a rich diet and acts as an m6A-mediated switch to stop SAM production and regulate its homeostasis. Although the mammalian SAM synthetase pre-mRNA is not regulated via this mechanism, we show that splicing inhibition by 3' splice site m6A is conserved in mammals. The modification functions by physically preventing the essential splicing factor U2AF35 from recognizing the 3' splice site. We propose that use of splice-site m6A is an ancient mechanism for splicing regulation.

Comment on Cell. 2021 Jun 10;184(12):3109-3124.e22.

Comment in Nat Methods. 2021 Jul;18(7):710.

Comment in Cell Res. 2021 Oct;31(10):1043-1044.

Monoclonal antibodies (mAbs) are a focus in vaccine and therapeutic design to counteract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Here, we combined B cell sorting with single-cell VDJ and RNA sequencing (RNA-seq) and mAb structures to characterize B cell responses against SARS-CoV-2. We show that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells. Cryo-electron microscopy structures of selected nAbs from these two clusters complexed with SARS-CoV-2 spike trimers show recognition of various receptor-binding domain (RBD) epitopes. One of these mAbs, BG10-19, locks the spike trimer in a closed conformation to potently neutralize SARS-CoV-2, the recently arising mutants B.1.1.7 and B.1.351, and SARS-CoV and cross-reacts with heterologous RBDs. Together, our results characterize transcriptional differences among SARS-CoV-2-specific B cells and uncover cross-neutralizing Ab targets that will inform immunogen and therapeutic design against coronaviruses.

Comment in Cell. 2021 Jun 10;184(12):3080-3081. Nat Rev Genet. 2021 Aug;22(8):480. Traffic. 2021 Oct;22(10):362-363.

Comment in Nat Rev Neurosci. 2021 Jul;22(7):385.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is initiated by binding of the viral Spike protein to host receptor angiotensin-converting enzyme 2 (ACE2), followed by fusion of viral and host membranes. Although antibodies that block this interaction are in emergency use as early coronavirus disease 2019 (COVID-19) therapies, the precise determinants of neutralization potency remain unknown. We discovered a series of antibodies that potently block ACE2 binding but exhibit divergent neutralization efficacy against the live virus. Strikingly, these neutralizing antibodies can inhibit or enhance Spike-mediated membrane fusion and formation of syncytia, which are associated with chronic tissue damage in individuals with COVID-19. As revealed by cryoelectron microscopy, multiple structures of Spike-antibody complexes have distinct binding modes that not only block ACE2 binding but also alter the Spike protein conformational cycle triggered by ACE2 binding. We show that stabilization of different Spike conformations leads to modulation of Spike-mediated membrane fusion with profound implications for COVID-19 pathology and immunity.