Biology subjects

Sung, K.

Publications and source records attributed to Sung, K..

At least 19 recordsLinked to original sources

bioRxiv2026

A knockout screening platform for interferon-stimulated genes in respiratory cells identifies LY6E as a dominant antiviral effector against coronaviruses

Human coronaviruses (HCoVs) cause pathogenic outcomes ranging from mild illness to severe respiratory disease. Determining how respiratory cells coordinate an early antiviral response is necessary to understand successful control of HCoV replication. The Type I Interferon (IFN) response is a major component of innate viral immunity. IFN signaling leads to the upregulation of hundreds of interferon-stimulated genes (ISGs) that can have antiviral activity, though the individual ISGs that are responsible for restricting mildly pathogenic HCoVs in the respiratory epithelium are not clear. Here, we developed a targeted CRISPR-Cas9 knockout sgRNA library (termed the respISG library), focusing on ISGs that are upregulated across a panel of respiratory cells, to screen for innate host factors that restrict the mildly pathogenic human coronavirus, HCoV-OC43. We executed cell death-based screens in immortalized human small airway epithelial cells and identified lymphocyte antigen 6E (LY6E) as a top hit. We confirmed LY6E activity in validation studies and found it regulates entry of various SARS-CoV-2 Spike-pseudotyped lentiviruses. In addition to demonstrating a key role for LY6E in blocking a variety of HCoVs in small airway epithelial cells, this study establishes a loss-of-function CRISPR-Cas9 screening platform for identification of HCoV antiviral ISGs in respiratory cells.

microbiology↗

bioRxiv2026

Programmable Recruitment of RNA-Binding Proteins Enables Small Molecule-Directed Destabilization of Nuclear Pre-mRNA

Chemically induced proximity has not been systematically applied to control RNA fate. Here, a programmable platform was developed to identify RNA-binding proteins (RBPs) that can be recruited by small molecules to destabilize RNA. Using microtubule-associated protein Tau (MAPT) pre-mRNA as a model target, a heterobifunctional molecule was designed to bind both a ligandable structure in MAPT pre-mRNA and FKBP12F36V-tagged RBPs. Screening of a library of tagged RBPs identified several proteins that reduced MAPT RNA levels, including zinc finger protein 36 (ZFP36) and nanos C2HC-type zinc finger 3 (NANOS3). The approach was then extended from engineered proteins to an endogenous RBP. Using small molecule ligandability maps, a cysteine-reactive ligand for ZFP36 was identified. When this ligand was linked to the MAPT-binding small molecule, endogenous ZFP36 was recruited to MAPT mRNA, reducing its abundance in cells. Genetic and chemical controls demonstrated that activity was dependent on both RNA binding and ZFP36 recruitment, supporting an induced-proximity mechanism. These studies establish a general strategy for identifying new recruitable RBP effectors and should advance ribonuclease-targeting chimera (RiboTAC) technology by expanding the repertoire of effector proteins that can be harnessed for RNA degradation. More broadly, new effectors can be discovered through model reporter-based screens and translated to endogenous systems by mining known protein binders and ligandability maps, providing a systematic path to develop small molecules that control RNA stability, including RNAs targeted through structured regions of nuclear pre-mRNAs.

biochemistry↗

bioRxiv2026

A chemoinformatics-guided platform for efficient discovery of RNA-binding small molecules: Proof-of-concept for myotonic dystrophy type 1

Structured RNAs cause human diseases but remain challenging to target selectively with small molecules. Here, we report a chemoinformatics-guided discovery framework that integrates fingerprint-based molecular design, experimental validation, and mechanistic profiling to identify small molecules that bind highly structured, disease-associated RNAs. Using an RNA-binder fingerprint derived from known ligands, a Tversky similarity screen of >8 million compounds yielded a 150-member library enriched in chemical space for RNA-active scaffolds. Target engagement and cell-based assays identified multiple selective ligands for the pathogenic expanded triplet repeat, r(CUG)exp, that causes myotonic dystrophy type 1 (DM1) by binding and sequestering the RNA-binding protein muscleblind-like 1 (MBNL1). Biophysical and single-molecule analyses revealed that the small molecules bind the 1x1 nucleotide U/U internal loops formed when r(CUG)exp folds, partially block MBNL1 binding, and modulate RNA folding equilibria. Two optimized scaffolds rescued MBNL1-dependent splicing in patient-derived myotubes with micromolar potency and minimal cytotoxicity. This study establishes a generalizable, data-driven platform for discovering drug-like RNA-binding lead small molecules and demonstrates its application to the toxic repeat expansion RNA underlying DM1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/723748v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1a87b41org.highwire.dtl.DTLVardef@340a14org.highwire.dtl.DTLVardef@81b583org.highwire.dtl.DTLVardef@1b3ba14_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioinformatics↗

bioRxiv2026

Entrenchment of germline amino-acid differences in antibody affinity maturation

Entrenchment -- epistasis that locks in amino acid differences between homologous proteins, so each disfavors substitutions toward the others state -- has been demonstrated along individual protein lineages over deep evolutionary time. Antibodies offer a unique system for studying entrenchment: multiple homologous germline V gene paralogs provide diverse starting points, and the rapid somatic evolution of affinity maturation generates dense phylogenies from which selection on germline-encoded residues can be inferred. Using DASM, a deep-learning model that separates selection from mutation in antibody repertoire data, we test for entrenchment across immunoglobulin heavy chain variable (IGHV) genes. We detect entrenchment at two levels of germline divergence, driven by different sources of epistasis. Within V gene families (up to [~]20% amino acid divergence), entrenched sites cluster at the borders of the complementarity-determining regions (CDRs, the antigen-binding loops) and show high germline diversity. These sites contact antigen, light chain, and the heavy chain CDR3 loop, all of which are encoded independently of the IGHV germline. This pattern is consistent with epistasis from genetically uncoupled partners. Between V gene families, at deeper levels of divergence (25-40%), entrenchment additionally includes positions in the framework scaffold distant from binding interfaces, suggesting a larger contribution from intra-heavy-chain structural constraints. Observed mutation frequencies in human repertoires corroborate these predictions where data are sufficient. Together, these results demonstrate that the rapid somatic evolution of antibodies can serve as a lens for revealing epistatic constraints acting on germline-encoded residues, including constraints imposed by genetically uncoupled partners assembled during B cell development.

evolutionary biology↗

bioRxiv2026

Correlations of 276 missense variants of PSEN1, PSEN2, and APP on the production of Aβ peptides against variant effect predictors and biophysical structures

INTRODUCTIONEarly-onset Alzheimers disease (EOAD) is mostly caused by mutations in Presenilin-1,2 (PSEN1, PSEN2), and amyloid precursor protein (APP) genes. Both genetic and experimental evidence has supported the PSEN-APP amyloid hypothesis as the leading pathogenesis of AD. Thus far, 276 missense variants from PSEN1,2, and APP have been identified. Herein, we report the development of algorithm platforms for predicting the degrees of pathogenic potentials of these various missense mutations in AD. METHODSWe performed extensive pairwise correlations between 276 in vitro functional assays of missense variants from PSEN1, PSEN2, and APP and clinical data among EOAD patients against 37 variant effect predictors (VEPs). Furthermore, we used the structural models of all three proteins to refine understanding of the molecular basis of missense variants in dominant negative (DN) or loss-of-function (LoF) effects. RESULTSWe found that VEPs were consistently correlated with age of onset (AAO) among EOAD patients and the A{beta}42/A{beta}40 ratio biomarker. This study also identified discrepancies in the predictor variable leading to an increase in the A{beta}42/A{beta}40 ratio; The increased A{beta}42/A{beta}40 ratio was due to decreased A{beta}40 levels by Sun et al., (2016), while Petit et al., (2022) argued that increased A{beta}42 level was the culprit. We also identified that with increased predicted pathogenicity, there were decreased A{beta}38 levels. Conversely, this study found no direct correlations with A{beta}37 and A{beta}43. Lastly, structural studies show characteristics of either DN and LoF mechanisms, as there were numerous variants located in buried hydrophobic domains and those on charged surfaces of the proteins. DISCUSSIONCurrently, a major limitation in the field is the lack of reliable approaches for predicting the pathogenicity of variants in EOAD. We show that VEPs are promising in deciphering the effects of variants in PSEN1, PSEN2, and APP genes, especially between multiple pathogenic mutations. Our study also illuminates the need for experimental studies to identify the predictor variable causing the increased A{beta}42/A{beta}40 biomarker. Our biophysical studies also identify the need to characterize whether the mechanism of pathogenesis caused by DN or LoF effects is in scope of the presenilin hypothesis. Research in ContextO_LISystematic review: The authors reviewed experimental evidence of pathogenic PSEN1, PSEN2, and APP variants in scope of the presenilin hypothesis. To our knowledge, the pathogenesis of EOAD remains incompletely understood. There are also no predictive tools/models available for informing clinical translation. C_LIO_LIInterpretation: VEPs demonstrate strong correlations with AAO and A{beta} levels from pairwise correlations with 276 missense variants. Structurally, it appears that mutations demonstrate characteristics of dominant negative and loss-of-function effects. C_LIO_LIFuture Direction: Larger studies are necessary of VEPs for realistic clinical usage. C_LI HighlightsO_LIWe performed correlations of 37 VEPs against in vitro data of EOAD variants. C_LIO_LIVEPs demonstrate potential as diagnostic tools for early onset Alzheimers disease. C_LIO_LIPathogenic missense variants show characteristics of DN and LoF effects. C_LI

neuroscience↗

bioRxiv2025

Separating selection from mutation in antibody language models

Antibodies are encoded by nucleotide sequences that are generated by V(D)J recombination and evolve according to mutation and selection processes. Existing antibody language models, however, focus exclusively on antibodies as strings of amino acids and are fitted using standard language modeling objectives such as masked or autoregressive prediction. In this paper, we first show that fitting models using this objective implicitly incorporates nucleotide-level mutation processes as part of the protein language model, which degrades performance when predicting effects of mutations on functional properties of antibodies. To address this limitation, we devise a new framework: a Deep Amino acid Selection Model (DASM) that learns the selection effects of amino-acid mutations while explicitly factoring out the nucleotide-level mutation process. By fitting selection as a separate term from the mutation process, the DASM exclusively quantifies functional effects: effects that change some aspect of the function of the antibody. This factorization leads to substantially improved performance on standard functional benchmarks. Moreover, our model is an order of magnitude smaller and multiple orders of magnitude faster to evaluate than existing approaches, as well as being readily interpretable.

evolutionary biology↗

bioRxiv2025

Getting to Know You: Neural Representations of Other People Grow More Perceiver-Specific Over Time

Mental representations of others are central to social behavior, yet little is known about how these representations evolve over significant periods of time as people get to know one another. In this longitudinal functional magnetic resonance imaging study, we tracked how neural representations of familiar peers changed over the course of a school year among two cohorts of first-year high school students (N = 150) embedded in newly formed social networks. We assessed the extent to which individuals neural representations of their classmates converged with group-level norms--capturing the balance between information that is idiosyncratic to each perceiver and that which is shared across perceivers--and how this changed over time. We identified brain regions where neural representations of familiar peers were well aligned across perceivers, including several areas implicated in person perception, social cognition, and visual processing. Across both cohorts of participants, neural representations in the hippocampus and lateral prefrontal cortex began as highly aligned across participants, then grew more idiosyncratic to each perceiver over time. Similar effects were observed in the ventral medial prefrontal cortex, but only in one cohort of participants. Regions often linked to social cognition (temporoparietal junction, superior temporal sulcus) evinced strong encoding for group normative information at both timepoints. Taken together, these findings suggest that neural representations of familiar others may initially be predominantly driven by information that is consistent across perceivers (e.g., physical appearance, face-based evaluations, and trait impressions), then become more unique over time as each perceiver gains their own experiences with and impressions of these individuals. Significance StatementUnderstanding how humans form mental representations of others is fundamental to the study of social behavior. Yet we know little about how these representations evolve as people get to know each other over time. Using brain imaging in a real-world social network of first-year high school students, we tracked how participants neural representations of their classmates changed over the course of a year. While some brain regions encoded stable, shared representations of peers, others showed increasingly individualized patterns. These findings shed light on how real-world social experiences shape representations of familiar others and delineate how the brain balances both personal experience and shared social information. This work informs theories of social cognition by showing how mental representations evolve over time.

neuroscience↗

bioRxiv2025

Deciphering gain-of-function from loss-of-function variants with AlphaMissense: A case study with the mechanosensitive PIEZO1 ion channel protein

Pathogenic missense mutations are commonly found in protein-coding regions of DNA, and often alter protein function. In the past decade, enormous experimental efforts have been undertaken through the development of functional assays, mutagenesis screenings, and computational prediction algorithms to characterize these variants. Indeed, most efforts have been focused towards identifying degrees of loss-of-function (LoF) effects on the three-dimensional structures of proteins, but gain-of-function (GoF) mutations remain poorly understood. Herein, we performed a case study of the PIEZO1 mechanosensitive ion channel protein whose GoF variants (n = 56) are implicated in hereditary xerocytosis (HX) and LoF variants (n = 6) in lymphatic dysplasia (LD), respectively. This study evaluated the abilities of AlphaMissense (AM) to decipher both mutation types, and benchmarked its performance against other algorithmic approaches, including Combined Annotation Dependent Depletion (CADD) v1.7, evolutionary model of variant effect (EVE), and Evolutionary Scale Modeling-1b (ESM-1B). We found that all approaches excelled in identifying LoF variants but were often ambiguous in their predictions for GoF PIEZO1 variants. ESM-1b was a notable exception that demonstrated balance sensitivity to both GoF and LoF variants as it likely identified certain sequential features not utilized in other approaches. Secondly, our findings suggest that GoF variants of HX do not significantly destabilize the PIEZO1 structure and are not generally identified by conventional signatures of damage from these algorithms. GoF variants are not synonymous with direct changes in changes in free energy upon mutations. Furthermore, we validated computational structure predictions of PIEZO1 against resolved cryo-EM structures, and provided biophysical data for variants located on unresolved residues of this ion channel. We formulated a weighted ensemble model that performed similarly to AM and outperformed all other traditional approaches evaluated in this study. Overall, this is the first study to directly evaluate the capabilities of pathogenicity prediction algorithms for deciphering GoF and LoF variants, and underscores the limitations present in current approaches.

biophysics↗

bioRxiv2025

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

Antibodies play a crucial role in adaptive immunity. They develop as B cell receptors (BCRs): membrane-bound forms of antibodies that are expressed on the surfaces of B cells. BCRs are refined through affinity maturation, a process of somatic hypermutation (SHM) and natural selection, to improve binding to an antigen. Computational models of affinity maturation have developed from two main perspectives: molecular evolution and language modeling. The molecular evolution perspective focuses on nucleotide sequence context to describe mutation and selection; the language modeling perspective involves learning patterns from large data sets of protein sequences. In this paper, we compared models from both perspectives on their ability to predict the course of antibody affinity maturation along phylogenetic trees of BCR sequences. This included models of SHM, models of SHM combined with an estimate of selection, and protein language models. We evaluated these models for large human BCR repertoire data sets, as well as an antigen-specific mouse experiment with a pre-rearranged cognate naive antibody. We demonstrated that precise modeling of SHM, which requires the nucleotide context, provides a substantial amount of predictive power for predicting the course of affinity maturation. Notably, a simple nucleotide-based convolutional neural network modeling SHM outperformed state-of-the-art protein language models, including one trained exclusively on antibody sequences. Furthermore, incorporating estimates of selection based on a custom deep mutational scanning experiment brought only modest improvement in predictive power. To support further research, we introduce EPAM (Evaluating Predictions of Affinity Maturation), a benchmarking framework to integrate evolutionary principles with advances in language modeling, offering a road map for understanding antibody evolution and improving predictive models.

immunology↗

bioRxiv2025

Evaluating the pathogenicity, stability, and stereochemical effects of variants of unknown significance in TREM2 using orthogonal structural analyses

Triggering receptor expressed on myeloid cells 2 (TREM2), an immune receptor expressed on the surface of microglia, has been identified through genome-wide association studies to be one of the risk factors in Alzheimers disease (AD). Several studies have also identified missense variants of TREM2 to be associated with frontotemporal dementia and Nasu-Hakola disease (NHD). To date, 51 novel missense variants of TREM2 have been identified in the literature, with the disease risk profiles of most variants still unknown. Assessing and classifying the pathogenicity of these variants is essential to investigate the disease mechanisms and develop effective treatments. Herein, we classified 32 missense variants involved in TREM2 using structural bioinformatic data with AlphaFold2. Using the protocol described in our previous work (Pillai et al., 2025), we determined the structural, stability, and potential functional effects of these variants. Our evaluations of the mutations were divided into those (i) implicated in NHD, (ii) located on the transmembrane domain, (iii) surface of the IgV-like domain, and (iii) buried in the receptor. Our analysis of variants involved in NHD suggests that, while V126G imposes the greatest effects, the T66M variant exerts significantly less effects on the TREM2 structures compared to the other remaining variants. Variants in the transmembrane domain of TREM2 did not impose significant alterations to the three-dimensional structure. Outside of known variants in the IgV-like domain, we identified 10 variants that imposed significant destabilizing effects to the structure and are of potential interest. Overall, the baseline biochemical data provided from this study may be informative to experimental efforts to better classify rare coding variants of TREM2 that are of unknown biological and clinical significance. HighlightsUsing our previously published protocol, we characterized the structure, stability, and functional effects of 27 unique variants of unknown biological significance in TREM2. Our analysis was divided into variants located on the transmembrane domain, surface of the IgV-like domain, buried in the IgV-like domain, and those leading to Nasu-Hakola disease. Excluding AD- and NHD-causing variants, we identified 10 TREM2 variants that cause significant destabilizing effects to the receptor. The in-silico data provided from this study serve as a baseline, and can guide future experimental efforts involving TREM2 variants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/656054v1_ufig1.gif" ALT="Figure 1000"> View larger version (35K): org.highwire.dtl.DTLVardef@c426b1org.highwire.dtl.DTLVardef@1c859f1org.highwire.dtl.DTLVardef@88ea70org.highwire.dtl.DTLVardef@d777f4_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Discovery of RNA-Reactive Small Molecules Guides Design of Electrophilic Modules for RNA-Specific Covalent Binders

RNA is a key drug target that can be modulated by small molecules, however covalent binders of RNA remain largely unexplored. Using a high-throughput mass spectrometry screen of 2,000 electrophilic compounds, we identified ligands that react with RNA in a binding-dependent manner. RNA reactivity was influenced by both the reactive group and the RNA-binding scaffold. Electrophilic modules such as 3-chloropivalamide, bis(2-chloroethyl)amine, chloroacetamide, and N-acylimidazole that react with proteins also cross-linked to RNA, especially when paired with aromatic heterocycles, particularly those with a thieno[3,2-c]pyridinium core. These results suggest that electrophiles commonly used for protein targeting can also covalently modify RNA, potentially contributing to both on- and off-target effects. This insight enabled the design of an RNA-specific covalent compound by modifying a Hoechst scaffold, originally identified to bind DNA, to react selectively with the expanded triplet repeat RNA, r(CUG)exp, that causes myotonic dystrophy type 1 (DM1). Selectivity appears to arise from binding to the RNA major groove near the reactive site. Overall, this study highlights the potential of rationally designing covalent RNA-targeting small molecules. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=54 SRC="FIGDIR/small/649986v1_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@2049c7org.highwire.dtl.DTLVardef@1cbc49aorg.highwire.dtl.DTLVardef@d88363org.highwire.dtl.DTLVardef@99fef8_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Thrifty wide-context models of B cell receptor somatic hypermutation

Somatic hypermutation (SHM) is the diversity-generating process in antibody affinity maturation. Probabilistic models of SHM are needed for analyzing rare mutations, for understanding the selective forces guiding affinity maturation, and for understanding the underlying biochemical process. High throughput data offers the potential to develop and fit models of SHM on relevant data sets. In this paper we model SHM using modern frameworks. We are motivated by recent work suggesting the importance of a wider context for SHM, however, assigning an independent rate to each k-mer leads to an exponential proliferation of parameters. Thus, using convolutions on 3-mer embeddings, we develop "thrifty" models of SHM of various sizes; these can have fewer free parameters than a 5-mer model and yet have a significantly wider context. These offer a slight performance improvement over a 5-mer model, and other modern model elaborations worsen performance. We also find that a per-site effect is not necessary to explain SHM patterns given nucleotide context. Also, the two current methods for fitting an SHM model -- on out-of-frame sequence data and on synonymous mutations -- produce significantly different results, and augmenting out-of-frame data with synonymous mutations does not aid out-of-sample performance.

immunology↗

bioRxiv2024

Learning antibody sequence constraints from allelic inclusion

Antibodies and B-cell receptors (BCRs) are produced by B cells, and are built of a heavy chain and a light chain. Although each B cell could express two different heavy chains and four different light chains, usually only a unique pair of heavy chain and light chain is expressed--a phenomenon known as allelic exclusion. However, a small fraction of naive-B cells violate allelic exclusion by expressing two productive light chains, one of which has impaired function; this has been called allelic inclusion. We demonstrate that these B cells can be used to learn constraints on antibody sequence. Using large-scale single-cell sequencing data from humans, we find examples of light chain allelic inclusion in thousands of naive-B cells, which is an order of magnitude larger than existing datasets. We train machine learning models to identify the abnormal sequences in these cells. The resulting models correlate with antibody properties that they were not trained on, including polyreactivity, surface expression, and mutation usage in affinity maturation. These correlations are larger than what is achieved by existing antibody modeling approaches, indicating that allelic inclusion data contains useful new information. We also investigate the impact of similar selection forces on the heavy chain in mouse, and observe that pairing with the surrogate light chain significantly restricts heavy chain diversity.

immunology↗

bioRxiv2024

GluN2B-mediated regulation of silent synapses for receptor specification and addiction memory

Psycho-stimulants including cocaine elicit stereotyped, addictive behaviors. Re-emergence of silent synapses containing only NMDA-type glutamate receptors (NMDARs) is a critical mediator of addiction memory and seeking behaviors. Despite the predominant abundance of GluN2B-containing NMDARs in silent synapses, their operational mechanisms are not fully understood. Using conditional depletion/deletion of GluN2B at D1-expressing accumbal medium spiny neurons, we examine the synaptic and behavioral actions that silent synapses incur after repeated exposure to cocaine. GluN2B ablation reduces the proportion of silent synapses, but some of them can persist by substitution to GluN2C, which drives the aberrantly-facilitated synaptic incorporation of calcium-impermeable AMPA-type glutamate receptors (AMPARs). The resultant precocious maturation of silent synapses impairs addiction memory but elevates locomotor activity, which can be normalized by blockade of calcium-impermeable AMPAR trafficking. Collectively, GluN2B supports the competence of cocaine-induced silent synapses for specifying subunit composition of AMPARs, and thereby expression of addition memory and the related behaviors.

neuroscience↗

bioRxiv2024

The S2 subunit of spike encodes diverse targets for functional antibody responses to SARS-CoV-2

The SARS-CoV-2 virus responsible for the COVID-19 global pandemic has exhibited a striking capacity for viral evolution that drives continued evasion from vaccine and infection-induced immune responses. Mutations in the receptor binding domain of the S1 subunit of the spike glycoprotein have led to considerable escape from antibody responses, reducing the efficacy of vaccines and monoclonal antibody (mAb) therapies. Therefore, there is a need to interrogate more constrained regions of Spike, such as the S2 subdomain. Here, we describe a collection of S2 mAbs from two SARS-CoV-2 convalescent individuals that target multiple regions in the S2 subdomain and can be grouped into at least five epitope classes. Most did not neutralize SARS-CoV-2 with the exception of C20.119, which bound to a highly conserved epitope in the fusion peptide and showed broad binding and neutralization activity across SARS-CoV-2, SARS-CoV-1, and closely related zoonotic sarbecoviruses. Several of the S2 mAbs tested mediated antibody-dependent cellular cytotoxicity (ADCC) at levels similar to the S1 mAb S309 that was previously authorized for treatment of SARS-CoV-2 infections. Three of the mAbs with ADCC function also bound to spike trimers from HCoVs, such as MERS-CoV and HCoV-HKU1. Our findings suggest there are diverse epitopes in S2, including functional S2 mAbs with HCoV and sarbecovirus breadth that likely target functionally constrained regions of spike. These mAbs could be developed for potential future pandemics, while also providing insight into ideal epitopes for eliciting a broad HCoV response. AUTHOR SUMMARYThe early successes of vaccines and antibody therapies against SARS-CoV-2, the virus responsible for the COVID-19 global pandemic, leveraged the considerable antibody response to the viral entry protein, spike, after vaccination or infection. These initial interventions were highly effective at protecting from infection and reducing severe disease or death. However, SARS-CoV-2 has shown no sign of abating, with the continued rise of new variants that have escaped some of the antibody defense due to distinct alterations most significantly in regions of the spike protein that elicit most of the anti-viral, functional antibody response. These findings suggest a critical need to identify vaccine approaches and therapies that provide the broadest possible antibody responses, focused on regions of spike critical for SARS-CoV-2 infection and, therefore, do not undergo changes that could lead to immune evasion. Our study describes a panel of functional antibodies, from individuals after SARS-CoV-2 infection, that recognize the S2 spike subdomain that is responsible for carrying out viral fusion with host cells. These regions in S2 are generally well conserved across SARS-CoV-2 variants and other closely related viruses and thus, could guide more effective vaccine design in the face of continued viral evolution.

immunology↗

bioRxiv2023

Elevated binding and functional antibody responses to SARS-CoV-2 in infants versus mothers

Infant antibody responses to viral infection can differ from those in adults. However, data on the specificity and function of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in infants, and direct comparisons between infants and adults are limited. We characterized antibody binding and functionality in convalescent plasma from postpartum women and their infants infected with SARS-CoV-2 from a vaccine-naive prospective cohort in Nairobi, Kenya. Antibody titers against SARS-CoV-2 Spike, receptor binding domain and N-terminal domain, and Spike-expressing cell-surface staining levels were significantly higher in infants than in mothers. Plasma antibodies from mothers and infants bound to similar regions of the Spike S2 subunit, including the fusion peptide (FP) and stem helix-heptad repeat 2. However, infants displayed higher antibody levels and more consistent antibody escape pathways in the FP region compared to mothers. Finally, infants had significantly higher levels of antibody-dependent cellular cytotoxicity (ADCC), though, surprisingly, neutralization titers between infants and mothers were similar. These results suggest infants develop distinct SARS-CoV-2 binding and functional antibody repertoires and reveal age-related differences in humoral immunity to SARS-CoV-2 infection that could be relevant to protection and COVID-19 disease outcomes.

immunology↗

bioRxiv2022

Identification of a novel HASPIN inhibitor and its synergism with the PLK1 inhibitor

BackgroundHASPIN, a mitotic kinase for Histone H3, is a promising target for anti-cancer therapy. However, as HASPIN is an atypical kinase with low similarity to eukaryotic protein kinases, development of a HASPIN inhibitor from the conventional pharmacophore of kinase inhibitors would be technically challenging. MethodsChemical modifications of a cytotoxic 4-thioadenosine analogue with high genotoxicity and multiple kinomescan profiles were performed to produce a novel non-genotoxic kinase inhibitor, LJ4827. The mode of action of this inhibitor with clear anti-cancer activity was inferred based on transcriptomic and chemical similarity to known drugs. ResultsThe specificity and potency of LJ4827 as a HASPIN inhibitor were validated by in vitro kinase screening and subsequent X-ray crystallography. As predicted, LJ4827 treatment delayed mitosis by clear inhibition of the recruitment of Aurora B at the centromere in cancer cells, without a genotoxic response. Through transcriptome analysis of lung cancer patients, PLK1 was predicted as a druggable synergistic partner to complement HASPIN inhibition. Cotreatment with the PLK1 inhibitor BI2536 and LJ4827 led to pronounced cytotoxicity of lung cancers in vitro and in vivo. ConclusionSimultaneous inhibition of both HASPIN and PLK1 is a promising therapeutic strategy for lung cancers.

pharmacology and toxicology↗

bioRxiv2022

Npas4-mediated dopaminergic regulation of fear memory states

Amygdala circuitry encodes associations between conditioned stimuli and aversive unconditioned stimuli, and also controls fear expression (Pape and Pare, 2010). However, whether and how irrelevant information for unpaired conditioned stimuli (CS-) is discretely processed, and how it was influenced by stress remain unknown. CS- memory is retrievable immediately after fear conditioning, but then becomes silent after memory consolidation in mice. Synaptic pathway from the lateral to the anterior basal amygdala gates the expression of CS- memory, depending upon Npas4-mediated Drd4 synthesis. The upregulation of Npas4-Drd4 axis, which is precluded by corticosterone, shifts functional states of neural engrams for CS- memory toward silent states and limits its retrievability. In here, we demonstrate the cellular and molecular mechanisms regulating the functional states of neural engrams, which can be switched or maintained, supporting discriminative memory.

neuroscience↗