Biology subjects

Lang, Y.

Publications and source records attributed to Lang, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Generation of hypoimmunogenic gastric insulin-secreting organoids

Gastric insulin-secreting organoids (GINS) represent a promising source of {beta}-like cells for type 1 diabetes (T1D) therapy. In same-donor comparisons with induced pluripotent stem cell-derived islets (iPSC-islets), GINS displayed robust glucose responsiveness and reduced expression of key T1D autoantigens. Importantly, GINS exhibited decreased susceptibility to cytotoxicity mediated by engineered HLA-matched preproinsulin-specific effector T cells (Avatar Teffs) and a distinct transcriptional profile enriched for immune-modulatory and stress-adaptive gene programs. To enhance immune evasion, we engineered gastric stem cells to overexpress Programmed Death Ligand 1 (PD-L1) in an inducible manner. PD-L1+ GINS maintained normal functionality, while exhibiting improved survival under allogeneic Avatar Teff challenge in a MHC class I-independent fashion. We evaluated PD-L1-mediated protection against autologous Avatar Teff attack using an endothelialized microfluidic platform recapitulating physiologic immune interactions. T cells show reduced infiltration into PD-L1 GINS, resulting in significantly higher organoid viability compared to control GINS. Together, these findings identify GINS as a functional and engineerable {beta}-like cell platform with intrinsic hypoimmunogenic features, and support PD-L1 engineering as a strategy to enhance immune protection for both allogeneic and autologous transplantation in T1D.

immunology↗

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

High-Affinity Protein Binder Design via Flow Matching and In Silico Maturation

The de novo design of high-affinity protein binders remains a central challenge in protein engineering and therapeutic discovery. While deep generative models have advanced backbone generation and interface design, achieving picomolar or nanomolar binding affinities typically demands extensive experimental screening or iterative in vitro maturation. A general computational approach for the direct production of high-affinity binders has remained elusive. Here, we introduce an integrated framework that synergizes PPIFlow, a flow-matching-based generative model, with a novel in silico maturation strategy. PPIFlow employs a pairformer architecture to explicitly reason over pair-wise geometric and chemical interactions, modeling protein backbone rigid-body transformations as continuous flows. To bridge the affinity gap, we implement a dedicated in silico affinity maturation stage that combines interface rotamer enrichment with partial flow refinement to optimize energetic packing. This pipeline is further accelerated by AF3Score, a score-only adaptation of AlphaFold3 that enables high-fidelity and computationally efficient candidate prioritization. Across a diverse set of therapeutic targets, this synergistic approach consistently produces picomolar and nanomolar affinity binders without experimental affinity maturation. Notably, the framework proves highly effective for the de novo design of single-domain antibodies (VHHs), producing sub-nanomolar binders across multiple targets. These results establish that coupling robust backbone generation with focused in silico maturation renders the purely computational design of high-affinity binders feasible.

biochemistry↗

bioRxiv2025

De Novo Computational Design of VHH Nanobodies Against LGR5

VHH discovery traditionally relies on animal immunization or large-scale library screening, methods that are slow, costly, and often ineffective for challenging targets such as GPCRs. We present a fully de novo computational pipeline for epitope-directed VHH design, integrating generative backbone modeling, deep learning-based sequence optimization, and iterative experimental feedback. Using LGR5 as a model, we progressed from in silico design to functional binders without structural templates. Across three design-test-learn cycles, millions of candidates were reduced to epitope-specific binders with nanomolar affinity and high thermal stability (melting temperature [Tm] > 65 {degrees}C). Cryogenic electron microscopy (cryo-EM) confirmed atomic-level agreement (RMSD {approx} 2.2 [A]). This structure-validated approach accelerates timelines, reduces cost, and is broadly applicable to GPCRs and other membrane proteins, enabling "on-demand" therapeutic antibody generation.

bioengineering↗

bioRxiv2025

AI-guided design of common light chains to enable manufacturable bispecific antibodies

Bispecific antibodies (BsAbs) offer therapeutic advantages but face manufacturing bottlenecks from light chain mispairing, which can generate a substantial fraction of incorrect products and increase manufacturing complexity and cost of goods. Common light chains (cLC) eliminate mispairing, yet existing approaches require screening thousands of variants per target. We present an AI-driven framework that computationally designs cLCs through structure-guided pairing of non-cognate VH-VL interfaces, reducing experimental screening by three orders of magnitude. The platform successfully engineers therapeutic antibodies lacking experimental structures, expanding applicability beyond crystallographic databases. Among 10 therapeutic targets, we successfully generated designs for 7 targets, comprising 55 unique BsAb pairs. Of these, 43.6% (24/55) were successfully as BsAb cLCs. Three bispecific antibodies reached production-ready specifications: >90% purity and 1.6-1.8 g/L titers. This platform democratizes bispecific antibody development, expanding access beyond well-resourced programs.

bioengineering↗

bioRxiv2025

piqtree: A Python Package for Seamless Phylogenetic Inference with IQ-TREE

piqtree (pronounced pie-cue-tree) is an easy to use, open-source Python package that provides Python script based control of IQ-TREEs phylogenetic inference engine. piqtree builds IQ-TREE as a Python package, presenting a library of Python functions for performing many of IQ-TREEs capabilities including phylogenetic reconstruction, ultrafast bootstrapping, branch length optimization, model selection, rapid neighbor-joining, alignment simulation, and more. As piqtree explicitly uses IQ-TREEs phylogenetic algorithms, the computational and statistical performance of piqtree equal that of IQ-TREE. Modestly higher memory usage may be expected owing to the Python runtime and the need to load the alignment in Python. By exposing IQ-TREEs algorithms within Python, piqtree offers users a greatly simplified experience in development of phylogenetic workflows through seamless interoperability with other Python libraries and tools mediated by the cogent3 package. It enables users to perform interactive phylogenetic analyses and visualization using, for instance, Jupyter notebooks. We present the key features available in the piqtree library and a small case study that showcases its interoperability. piqtree is distributed for use as a standard Python package at https://pypi.org/project/piqtree/, documentation is available at https://piqtree.readthedocs.io and source code at https://github.com/iqtree/piqtree.

bioinformatics↗

bioRxiv2025

Engineering a mouse-adapted SADS-CoV and establishing a neonatal mouse model to study its infection

Swine acute diarrhea syndrome coronavirus (SADS-CoV) is an emerging bat-origin alphacoronavirus causing severe disease in neonatal piglets, with significant economic losses to the swine industry as a consequence. The virus exhibits a broad species tropism, infecting cells derived from pigs, humans and mice, highlighting its potential for cross-species transmission. Due to drawbacks associated with the use of young piglets, there is a need for an appropriate small animal model to study SADS-CoV biology. Here we established a mouse infection model based on a murinized mutant of the virus, mSADS-CoV, in which the ectodomain of the viral spike protein was replaced by that of the murine coronavirus mouse hepatitis virus (MHV). This chimeric virus, generated through targeted RNA recombination, replicated efficiently in murine cell cultures and exhibited an age-dependent infection in neonatal mice that was lethal in 2-day-old BALB/c mice affecting various organs, notably the intestine. We validated our infection model by successfully verifying the efficacy of the RNA-dependent RNA polymerase inhibitor remdesivir (RDV). The model will serve as a valuable tool for studying SADS-CoV pathogenesis and for elucidating the roles of host factors in viral replication as well as for preclinical evaluation of vaccine candidates and antiviral compounds targeting the viral replication machinery. Author SummarySADS-CoV poses a dual threat to the swine industry and public health because of its broad species tropism and potential for cross-species transmission. The emergence of other bat-derived coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV), SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV), underscores the need for robust models to study these pathogens. The successful rescue of mSADS-CoV and the development of a mouse infection model represent significant advancements in SADS-CoV research. This model not only enables the evaluation of antiviral therapeutics such as RDV but also provides a powerful platform for investigating viral replication mechanisms and host-pathogen interactions, offering critical insights for pandemic preparedness.

microbiology↗

bioRxiv2024

Benchmarking Inverse Folding Models for Antibody CDR Sequence Design

Antibody-based therapies are at the forefront of modern medicine, addressing diverse challenges across oncology, autoimmune diseases, infectious diseases, and beyond. The ability to design antibodies with enhanced functionality and specificity is critical for advancing next-generation therapeutics. Recent advances in artificial intelligence (AI) have propelled the field of antibody engineering, particularly through inverse folding models for Complementarity-Determining Region (CDR) sequence design. These models aim to generate novel antibody sequences that fold into desired structures with high antigen-binding affinity. However, current evaluation metrics, such as amino acid recovery rates, are limited in their ability to assess the structural and functional accuracy of designed sequences. This study benchmarks state-of-the-art inverse folding models--ProteinMPNN, ESM-IF, LM-Design, and AntiFold--using comprehensive datasets and alternative evaluation metrics like sequence similarity. By systematically analyzing recovery rates, mutation prediction capabilities, and amino acid composition biases, we identify strengths and limitations across models. AntiFold exhibits superior performance in Fab antibody design, particularly in variable regions like CDRH3, whereas LM-Design demonstrates adaptability across diverse antibody types, including VHH antibodies. In contrast, models trained on general protein datasets (e.g., ProteinMPNN and ESM-IF) struggle with antibody-specific nuances. Key insights include the models varying reliance on antigen structure and their distinct capabilities in capturing critical residues for antigen binding. Our findings highlight the need for enhanced training datasets, integration of functional data, and refined evaluation metrics to advance antibody design tools. By addressing these challenges, future models can unlock the full potential of AI-driven antibody engineering, paving the way for innovative therapeutic applications. Author SummaryAntibodies play a vital role in modern medicine, offering targeted therapies for diseases ranging from cancer to infectious diseases. Designing new antibodies with specific and enhanced functionalities remains a key challenge in advancing therapeutic applications. In this study, we benchmarked cutting-edge artificial intelligence models for antibody sequence design, focusing on their ability to generate sequences for the critical antigen-binding regions of antibodies, known as Complementarity-Determining Regions (CDRs). Our findings reveal that specialized models like AntiFold excel in designing human antibody fragments, particularly in complex regions, while other models such as LM-Design demonstrate versatility across different antibody types. Importantly, we identified the limitations of models trained on general protein datasets, highlighting the need for antibody-specific training data to capture the unique features critical for therapeutic effectiveness. By evaluating these models against robust datasets and diverse metrics, our work underscores the importance of improving training data and evaluation methods to advance AI-driven antibody design. These insights pave the way for more accurate and effective tools, ultimately supporting the development of next-generation antibody-based therapeutics.

bioengineering↗

bioRxiv2024

Construction of a macrophage-tropic subtype C HIV-1-mGreenLantern reporter virus for studies on HIV-1 rep-lication and the impact of methamphetamine

HIV-1 subtype C viruses are responsible for 50% of global HIV burden. However, nearly all currently available reporter viruses widely used in HIV research are based on subtype B. We constructed and characterized a replication competent HIV-1 subtype C reporter virus expressing mGreenLantern. mGreenLantern sequences were inserted in-frame with nef ATG in HIV-1IndieC1. As controls, we employed HIV-1IndieC1, HIV-1ADA, and HIV-1NLAD8-GFP-Nef viruses. HIV-1IndieC1-mGreenLantern (HIV-1IndieC1-mGL) exhibited characteristics of the parental HIV-1IndieC1 virus, including its infectivity in TZMbl reporter cells and replication competence in macrophages. To further characterize HIV-1IndieC1-mGL virus, we tested its responsiveness to CCL2 levels, a characteristic feature of subtype B HIV-1 that is missing in subtype C. CCL2 immunodepletion inhibited the production of HIV-1ADA and HIV-1NLAD8-GFP-Nef as expected, but not that of HIV-1IndieC1-mGL as previously reported. We also tested the effect of Methamphetamine, as its effect is mediated by NF-{kappa}B and since subtype C viruses carry an additional copy of NF{kappa}B. We found that methamphetamine increased the replication of all viruses tested in macrophages, however, its effect was much more robust for HIV-1IndieC1 and HIV-1IndieC1-mGL. Our studies established that HIV-1IndieC1-mGL retains all the characteristics of the parental HIV-1IndieC1 and can be a useful tool for HIV-1 subtype C investigations.

microbiology↗

bioRxiv2023

Discovery and characterization of a specific inhibitor of serine-threonine kinase cyclin dependent kinase-like 5 (CDKL5) demonstrates role in hippocampal CA1 physiology

Pathological loss-of-function mutations in cyclin-dependent kinase-like 5 (CDKL5) cause CDKL5 deficiency disorder (CDD), a rare and severe neurodevelopmental disorder associated with severe and medically refractory early-life epilepsy, motor, cognitive, visual and autonomic disturbances in the absence of any structural brain pathology. Analysis of genetic variants in CDD have indicated that CDKL5 kinase function is central to disease pathology. CDKL5 encodes a serine-threonine kinase with significant homology to GSK3{beta}, which has also been linked to synaptic function. Further, Cdkl5 knock-out rodents have increased GSK3{beta} activity and often increased long-term potentiation (LTP). Thus, development of a specific CDKL5 inhibitor must be careful to exclude cross-talk with GSK3{beta} activity. We synthesized and characterized specific, high-affinity inhibitors of CDKL5 that do not have detectable activity for GSK3{beta}. These compounds are very soluble in water but blood-brain barrier penetration is low. In rat hippocampal brain slices, acute inhibition of CDKL5 selectively reduces post-synaptic function of AMPA-type glutamate receptors in a dose-dependent manner. Acute inhibition of CDKL5 reduces hippocampal LTP. These studies provide new tools and insights into the role of CDKL5 as a newly appreciated, key kinase necessary for synaptic plasticity. Comparisons to rodent knock-out studies suggest that compensatory changes have limited the understanding of the roles of CDKL5 in synaptic physiology, plasticity and human neuropathology.

neuroscience↗

bioRxiv2023

Sialoglycan binding triggers spike opening in a human coronavirus

Coronavirus (CoV) spikes mediate receptor binding and membrane fusion, making them prime targets for neutralising antibodies. In the cases of SARS-CoV, SARS-CoV-2, and MERS-CoV, spikes transition freely between open and closed conformations to balance host cell attachment and immune evasion. The open conformation exposes domain S1B, allowing it to bind to proteinaceous cell surface receptors. It also facilitates protein refolding during spike-mediated membrane fusion. However, with a single exception, the pre-fusion spikes of all other CoVs studied so far have been observed exclusively in the closed state. This raises the possibility of regulation, where spikes more commonly transition to open states in response to specific cues, rather than spontaneously. In our study, using cryo-EM and molecular dynamics simulations, we show that the spike protein of the common cold human coronavirus HKU1 undergoes local and long-range conformational changes upon binding a sialoglycan-based primary receptor to domain S1A. This binding triggers the transition of S1B domains to the open state via allosteric inter-domain cross-talk. Our findings paint a more elaborate picture of CoV attachment, with possibilities of dual receptor usage and priming of entry as a means of immune escape.

microbiology↗

bioRxiv2022

Brevicidine, a bacterial non-ribosomally produced cyclic antimicrobial lipopeptide with a unique modus operandi

Due to the accelerated appearance of antibiotic-resistant (AMR) pathogens in clinical infections, new first-in-class antibiotics, operating via novel modes of action, are desperately-needed. Brevicidine, a bacterial non-ribosomally produced cyclic lipopeptide, has shown potent and selective antimicrobial activity against Gram-negative pathogens. However, before our investigations, little was known about how brevicidine exerts its potent bactericidal effect against Gram-negative pathogens. In this study, we find that brevicidine has potent antimicrobial activity against AMR Enterobacteriaceae pathogens, with a MIC value ranging between 0.5M (0.8mg/L) and 2M (3.0mg/L). In addition, brevicidine showed potent anti-biofilm activity against the Enterobacteriaceae pathogens, with same 100% inhibition and 100% eradication concentration of 4M (6.1mg/L). Further mechanistic studies showed that brevicidine exerts its potent bactericidal activity via interacting with lipopolysaccharide in the outer membrane, targeting phosphatidylglycerol and cardiolipin in the inner membrane, and dissipating the proton motive force of bacteria. This results in metabolic perturbation, including inhibition of adenosine triphosphate synthesis, inhibits the dehydrogenation of nicotinamide adenine dinucleotides, accumulation of reactive oxygen species in bacteria, and inhibition of protein synthesis. Lastly, brevicidine showed a good therapeutic effect in a mouse peritonitis-sepsis model. Our findings pave the way for further research on clinical applications of brevicidine, to combat the prevalent infections caused by AMR Gram-negative pathogens worldwide.

microbiology↗

bioRxiv2021

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2

The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social and economic stability. In this study, we established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus S protein and its host receptor ACE2. This platform is a rapid, sensitive, specific, and high throughput system. With this platform, we screened two compound libraries of 2,864 molecules and identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S-protein and the human cell ACE2 receptor. This data suggests that TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug. SIGNIFICANCEThe ongoing pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious threat to public health worldwide. Given the urgency of the situation, researchers are attempting to repurpose existing drugs for treating COVID-19. In this present study, we screened two compound libraries of 2,864 molecules and identified a potent inhibitor (TS-984) for blocking the coronavirus S-protein and the human cell ACE2 receptor. TS-984 might have the potential to be developed into an effective anti-coronavirus drug for treating COVID-19.

pharmacology and toxicology↗

bioRxiv2020

Bat influenza vectored NS1-truncated live vaccine protects pigs against heterologous virus challenge

Swine influenza is an important disease for the swine industry. Currently used whole inactivated virus (WIV) vaccines can induce vaccine-associated enhanced respiratory disease (VAERD) in pigs when the vaccine strains mismatch with the infected viruses. Live attenuated influenza virus vaccine (LAIV) is effective to protect pigs against homologous and heterologous swine influenza virus infections without inducing VAERD, but has safety concerns due to potential reassortment with circulating viruses. Herein, we used a chimeric bat influenza Bat09:mH3mN2 virus, which contains both surface HA and NA gene open reading frames of the A/swine/Texas/4199-2/1998 (H3N2) and six internal genes from the novel bat H17N10 virus, to develop modified live-attenuated viruses (MLVs) as vaccine candidates which cannot reassort with canonical influenza A viruses by co-infection. Two attenuated MLV vaccine candidates including the virus that expresses a truncated NS1 (Bat09:mH3mN2-NS1-128, MLV1) or expresses both a truncated NS1 and the swine IL-18 (Bat09:mH3mN2-NS1-128-IL-18, MLV2) were generated and evaluated in pigs against a heterologous H3N2 virus using the WIV vaccineb as a control. Compared to the WIV vaccine, both MLV vaccines were able to reduce lesions and virus replication in lungs and limit nasal virus shedding without VAERD, also induced significantly higher levels of mucosal IgA response in lungs and significantly increased numbers of antigen-specific IFN-{gamma} secreting cells against the challenge virus. However, no significant difference was observed in efficacy between the MLV1 and MLV2. These results indicate that bat influenza vectored MLV vaccines can be used as a safe live vaccine to prevent swine influenza.

microbiology↗

bioRxiv2020

Coronavirus hemagglutinin-esterase and spike proteins co-evolve for functional balance and optimal virion avidity

Human coronaviruses OC43 and HKU1 are respiratory pathogen of zoonotic origin that have gained worldwide distribution. OC43 apparently emerged from a bovine coronavirus (BCoV) spill-over. All three viruses attach to 9-O-acetylated sialoglycans via spike protein S with hemagglutinin-esterase HE acting as a receptor-destroying enzyme. In BCoV, an HE lectin domain promotes esterase activity towards clustered substrates. OC43 and HKU1, however, lost HE lectin function as an adaptation to humans. Replaying OC43 evolution, we knocked-out BCoV HE lectin function and performed forced evolution-population dynamics analysis. Loss of HE receptor-binding selected for second-site mutations in S, decreasing S binding affinity by orders of magnitude. Irreversible HE mutations selected for cooperativity in virus swarms with low-affinity S minority variants sustaining propagation of high-affinity majority phenotypes. Salvageable HE mutations induced successive second-site substitutions in both S and HE. Apparently, S and HE are functionally interdependent and co-evolve to optimize the balance between attachment and release. This mechanism of glycan-based receptor usage, entailing a concerted, fine-tuned activity of two envelope protein species, is unique among CoVs, but reminiscent of that of influenza A viruses (IAVs). Apparently, general principles fundamental to virion-sialoglycan interactions prompted convergent evolution of two important groups of human and animal pathogens.

microbiology↗

bioRxiv2020

Swine Promyelocytic Leukemia Isoform II Inhibits Pseudorabies Virus Infection by Suppressing Viral Gene Transcription in PML-NBs

Promyelocytic leukaemia nuclear bodies (PML-NBs) possess an important intrinsic antiviral activity against -herpesvirus infection. PML is the structural backbone of NBs, comprising different isoforms. However, the contribution of each isoform to -herpesvirus restriction is not well understood. Here, we report the role of PML-NBs and swine PML (sPML) isoforms in pseudorabies virus (PRV) infection in its natural host swine cells. We found that sPML-NBs exhibit an anti-PRV activity in the context of increasing the expression level of endogenous sPML. Of four sPML isoforms cloned and examined, only isoform sPML-II/IIa, not sPML-I and IVa, expressed in a sPML knockout cells inhibits PRV infection. Both the unique 7b region of sPML-II and sumoylation-dependent normal formation of PML-NBs are required. 7b possesses a transcriptional repression activity and suppresses viral gene transcription during PRV infection with the cysteine residue 589 and 599 being critically involved. We conclude that sPML-NBs inhibit PRV infection by repressing viral gene transcription through the 7b region of sPML-II. IMPORTANCEPML-NBs are nuclear sites that mediate the antiviral restriction of -herpesvirus gene expression and replication. However, the contrition of each PML isoform to this activity of PML-NBs is not well characterized. Using PRV and its natural host swine cells as a system, we have discovered that the unique C-terminus of sPML isoform II is required for PML-NBs to inhibit PRV infection by directly engaging in repression of viral gene transcription. Our study not only confirms in swine cells that PML-NBs have an anti-viral function, but also presents a mechanism to suggest that PML-NBs inhibit viral infection in an isoform specific manner.

immunology↗

bioRxiv2020

Cryo-EM structure of coronavirus-HKU1 haemagglutinin esterase reveals architectural changes arising from prolonged circulation in humans

The human betacoronaviruses HKU1 and OC43 (subgenus Embecovirus) arose from separate zoonotic introductions, OC43 relatively recently and HKU1 apparently much longer ago. Embecovirus particles are studded with two types of surface projections called S (for spike) and HE (for haemagglutinin-esterase), with S mediating receptor-binding and membrane fusion and HE acting as a receptor-destroying enzyme. Together, they promote dynamic virion attachment to glycan-based receptors with 9-O-acetylated sialic acid as main constituent. We recently showed that adaptation of HKU1 and OC43 to replication in the human respiratory tract involved loss-of-function mutations in the lectin domain of HE. Here we present the cryo-EM structure of the [~]80 kDa, heavily glycosylated HKU1 HE at a global resolution of 3.4 [A]. Comparison to existing HE structures reveals a drastically truncated lectin domain, incompatible with sialic acid binding, but with the structure and function of the HE esterase domain left intact. Our cryo-EM structure, in combination with mass spectrometry analysis, also describes the extent of glycosylation on the now redundant lectin domain, which forms a putative glycan shield. The findings further our insight into the evolution and host adaptation of human embecoviruses and also demonstrate the utility of cryo-EM for studying small, heavily glycosylated proteins which are intractable to X-ray crystallography.

microbiology↗

bioRxiv2020

Neural correlates of newsvendor-based decision making in the human brain: An exploratory study to link neuroeconomics with neuroimaging using fNIRS

Neuroeconomics with neuroimaging is a novel approach involving economics and neuroscience. The newsvendor problem (NP) is a prevalent economics concept that may be used to map brain activations during NP-evoked risky decision making. In this study, we hypothesized that key brain regions responsible for NP are dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). Twenty-seven human subjects participated in the study using 40 NP trials; the participants were randomly assigned to a group with a low-profit margin (LM) or high-profit margin (HM) treatment. Cerebral hemodynamic responses were recorded simultaneously during the NP experiments from all participants with a 77-channel functional Near-infrared Spectroscopy (fNIRS) system. After data preprocessing, general linear model was applied to generate brain activation maps, followed by statistical t-tests. The results showed that: (a) DLPFC and OFC were significantly evoked by NP versus baseline regardless of treatment types; (b) DLPFC and OFC were activated by HM versus baseline; and (c) DLPFC was activated during LM versus baseline. Furthermore, significant deactivation in right DLPFC was shown due to LM with respect to HM. This study affirms that DLPFC and OFC are two key cortical regions when solving NP. In particular, right DLPFC was found to be more deactivated under challenging risk decision making.

neuroscience↗