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Zhou, T.

Publications and source records attributed to Zhou, T..

At least 145 records · Page 8Linked to original sources
bioRxiv2022

Latitude influences stability via stabilizing mechanisms in naturally-assembled forest ecosystems at different spatial grains

Ecosystem stability reveals how ecosystems respond to global change over time. Yet, the focus of past research on small spatial grains and extents overlooks scale dependence and how broad-scale environmental gradients shape stability. Here, we use forest inventory data covering a broad latitudinal gradient from the temperate to the tropical zone to examine cross-scale variation in stability of aboveground biomass and underlying stabilizing mechanisms. While stability did not shift systematically with latitude at either spatial grain, we found evidence that species asynchrony increased towards the tropics at the small spatial grain while species stability decreased at both spatial grains. Moreover, latitude stabilized forest communities via its effects on both stabilizing mechanisms, which compensated for the weak and destabilizing effects of species richness. Yet, the trade-off in the relative importance of species stability and species asynchrony for stability was not mediated by latitude, suggesting that context-dependent factors - to a greater extent than macroecological ones - underlie large-scale patterns of stability. Our results highlight the crucial role of species asynchrony and species stability in determining ecosystem stability across broad-scale environmental gradients, suggesting that conserving biodiversity alone may not be sufficient for stabilizing naturally-assembled forest ecosystems.

ecology↗
bioRxiv2022

MC1R drives cutaneous repair by promoting angiogenesis and lymphangiogenesis

Cutaneous healing results in scarring with significant functional and psychological sequelae, while chronic non-healing wounds represent repair failure often with devastating consequences, including amputation and death. Due to a lack of effective therapies, novel interventions addressing scarring and chronic wounds are urgently needed. Here, we demonstrate that harnessing melanocortin 1 receptor with a selective agonist (MC1R-Ag) confers multifaceted benefits to wound repair. MC1R-Ag accelerates wound closure and re-epithelialization while improving wound bed perfusion and lymphatic drainage by promoting angiogenesis and lymphangiogenesis. Concomitant reductions in oxidative stress, inflammation and scarring were also observed. To evaluate the therapeutic potential of targeting MC1R in pathological healing, we established a novel murine model that recapitulates the hallmarks of human non-healing wounds. This model combines advanced age and locally elevated oxidative stress. Remarkably, topical application of MC1R-Ag restored repair, whereas disrupting MC1R signalling exacerbated the chronic wound phenotype. Our study highlights MC1R agonism as a promising therapeutic approach for scarring and non-healing wound pathologies, and our chronic wound model as a valuable tool for elucidating ulcer development mechanisms.

pathology↗
bioRxiv2022

The critical role of BTRC in hepatic steatosis as an ATGL E3 ligase

ObjectiveNon-alcoholic fatty liver disease (NAFLD), characterized by hepatic steatosis, is one of the most common causes of liver dysfunction. ATGL is closely related to hepatic steatosis as the speed-limited triacylglycerol lipase. Nevertheless, the expression and regulation of ATGL in NAFLD remain unclear. MethodsUsing immunohistochemistry and qRT-PCR to detect the expression of ATGL and BTRC in different models with hepatic steatosis. Co-IP evaluated the binding of ATGL and BTRC. Knockdown of BTRC employed by adenoviruses and then analyzed the ATGL expression, triglyceride levels, and lipid droplets accumulation. ResultsOur results revealed that ATGL protein level was decreased in animal and cellular models of hepatic steatosis and the liver tissues of cholangioma/hepatic carcinoma patients with hepatic steatosis, while the ATGL mRNA level had hardly changed; which means the decreased ATGL mainly degraded through the proteasome pathway. BTRC was identified as the E3 ligase for ATGL, up-regulated, and negatively correlated with ATGL level. Moreover, adenovirus-mediated knockdown of BTRC ameliorated hepatic steatosis via up-regulating ATGL level. ConclusionsOur study demonstrates a crucial role of elevated BTRC in hepatic steatosis through promoting ATGL proteasomal degradation as a new ATGL E3 ligase and suggests BTRC may serve as a potential therapeutic target for NAFLD. FundingThis study was supported by The National Natural Science Foundation of China (Grants 82070888, 82070882, 82100917, 81872165, 82273116, 82203661, 81901557 and 81902693); Key Project of Nature Science Foundation of Guangdong Province, China (Grant 2019B1515120077); National Key R&D Program of China (Grant 2018YFA0800403); Guangdong Special Support Program for Young Top Scientist (Grant 201629046); Guangdong Natural Science Fund (Grant 2019A1515011810, 2021A1515010434, 2022A1515012423 and 2022A1515012513); Key Sci-Tech Research Project of Guangzhou Municipality (Grants 202201010820) Fundamental Research Funds for the Central Universities (Grant 50000-31620106); China Postdoctoral Science Foundation (Grant 2021M703679, 2020M683110).

cell biology↗
bioRxiv2022

Default But Not Rest: Topological Discrimination Defines the Default Mode Network

The default mode network (DMN), a set of transmodal cortical regions, has historically been argued to serve the internal functions of brain. The discovery of this network highlighted the brains intrinsic operations. The DMN generally decreases its activity during tasks and increases its activity during relaxed non-task states. It is important to investigate the nature of the DMN in order to understand the human brain in health and disease. In the current study, we discovered a task-related cortical network we called the Topological Discrimination Network (TDN), which was consistently revealed by contrasting activations from topological discrimination tasks with local geometric discrimination tasks. The TDN and the DMN consist of essentially the same group of brain regions and the fMRI response of topological discrimination in those regions exhibited consistent temporal dynamics with resting state. The robustness of the TDN is supported by multiple experiments performed at different field strengths (3T and 7T MRI scanner) as well as different types of signals measured (BOLD and CBF). The collective results suggest that the process of topological discrimination could almost be considered as a functional "default mode" of our brain. The TDN, like the DMN, could define the functional baseline of brain, with the advantage of functional consistency across participants and experimental sessions.

neuroscience↗
bioRxiv2022

ConstrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals

In recent years, supervised machine learning models trained on videos of animals with pose estimation data and behavior labels have been used for automated behavior classification. Applications include, for example, automated detection of neurological diseases in animal models. However, there are two problems with these supervised learning models. First, such models require a large amount of labeled data but the labeling of behaviors frame by frame is a laborious manual process that is not easily scalable. Second, such methods rely on handcrafted features obtained from pose estimation data that are usually designed empirically. In this paper, we propose to overcome these two problems using contrastive learning for self-supervised feature engineering on pose estimation data. Our approach allows the use of unlabeled videos to learn feature representations and reduce the need for handcrafting of higher-level features from pose positions. We show that this approach to feature representation can achieve better classification performance compared to handcrafted features alone, and that the performance improvement is due to contrastive learning on unlabeled data rather than the neural network architecture. Author SummaryAnimal models are widely used in medicine to study diseases. For example, the study of social interactions between animals such as mice are used to investigate changes in social behaviors in neurological diseases. The process of manually annotating animal behaviors from videos is slow and tedious. To solve this problem, machine learning approaches to automate the video annotation process have become more popular. Many of the recent machine learning approaches are built on the advances in pose-estimation technology which enables accurate localization of key points of the animals. However, manual labeling of behaviors frame by frame for the training set is still a bottleneck that is not scalable. Also, existing methods rely on handcrafted feature engineering from pose estimation data. In this study, we propose ConstrastivePose, an approach using contrastive learning to learn feature representation from unlabeled data. We demonstrate the improved performance using the features learnt by our method versus handcrafted features for supervised learning. This approach can be helpful for work seeking to build supervised behavior classification models where behavior labelled videos are scarce.

animal behavior and cognition↗
bioRxiv2022

Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from RNA-seq datasets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hotspots were identified for DVG recombination and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single cell RNA-seq analysis indicated the IFN stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the NGS dataset from a published cohort study and observed significantly higher DVG amount and frequency in symptomatic patients than that in asymptomatic patients. Finally, we observed unusually high DVG frequency in one immunosuppressive patient up to 140 days after admitted to hospital due to COVID-19, first-time suggesting an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. ImportanceDefective viral genomes (DVGs) are ubiquitously generated in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide them the potential for novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex and the recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hotspots for non-homologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide the evidence to harness DVGs immunostimulatory potential in the development of vaccine and antivirals for SARS-CoV-2.

microbiology↗
bioRxiv2022

Treatment history shapes the evolution of complex carbapenem-resistant phenotypes in Klebsiella spp.

Antibiotic resistance is driven by selection, but how a bacterial strains evolutionary history shapes drug-resistance remains an open question. Here we reconstruct the genetic and evolutionary mechanisms of carbapenem resistance in a clinical isolate of Klebsiella. A combination of short and long read sequencing, machine learning, genetic and enzymatic analyses established that this carbapenem-resistant strain carries no carbapenemase-encoding genes. Genetic reconstruction of the resistance phenotype confirmed that two distinct genetic loci are necessary for the strain to acquire carbapenem resistance. Experimental evolution of the carbapenem-resistant strains in growth conditions without the antibiotic revealed that both loci confer a significant cost, and are readily lost by de novo mutation resulting in the rapid evolution of a carbapenem-sensitive phenotype. Thus, historical contingency - a patients treatment history - can shape the evolution of antibiotic resistance and suggests that the strategic combinations of antibiotics could direct the evolution of low-fitness, drug-resistant genotypes.

microbiology↗
bioRxiv2022

Silent transcription intervals and translational bursting lead to diverse phenotypic switching

Bimodality of gene expression, as a mechanism generating phenotypic diversity, enhances the survival of cells in a fluctuating environment. Growing experimental evidence suggests that silent transcription intervals and translational bursting play important roles in regulating phenotypic switching. Characterizing these kinetics is challenging. Here, we develop an interpretable and tractable model, the generalized telegraph model (GTM), which considers silent transcription intervals described by a general waiting-time distribution and translational bursting characterized by an arbitrary distribution. Using methods of queuing theory, we derive analytical expressions of all moment statistics and distribution of protein counts. We show that non-exponential inactive times and translational bursting can lead to two nonzero bimodalities that cannot be captured in the classical telegraph model (CTM). In addition, we find that both silent-intervals noise and translational burst-size noise can amplify gene expression noise, as well as induce diverse dynamic expressions. Our results not only provide an alternative mechanism for phenotypic switching driven by silent transcription intervals and translational bursting, but also can be used to infer complex promoter structures based on experimental data. SIGNIFICANCEUnderstanding how phenotypic diversity arises among isogenic cell populations is a fundamental problem in biology. Previous studies have shown that the bimodality of gene expression contributing to phenotypic variations is mostly caused by the intrinsic or extrinsic regulations of underlying systems. It is unclear whether bimodality occurs in the absence of these regulations. The CTM has made great success in interpreting many experimental phenomena, but it cannot capture the bimodal distributions with two nonzero peaks that have been demonstrated in experiments. In particular, recent single-cell studies have shown non-exponential inactive periods and non-geometric translational bursting in gene expression. How to model these kinetics is challenging. We develop a stochastic gene model, namely the GTM, to model the silent transcription intervals by a general waiting-time distribution and translational bursting by an arbitrary distribution. By mapping the GTM into a queuing model, we derive the steady-state distribution of gene products that can be used for analyzing phenotypic switching. We find that non-exponential inactive times and translational bursting can lead to two nonzero bimodalities that cannot be captured in the CTM. These results indicate that both silent transcription intervals and translational bursting have important roles in controlling cell phenotypic variations in fluctuating environments.

systems biology↗
bioRxiv2022

A multispecific antibody confers pan-reactive SARS-CoV-2 neutralization and prevents immune escape

Summary ParagraphDespite effective countermeasures, SARS-CoV-2 persists worldwide due to its ability to diversify and evade human immunity1. This evasion stems from amino-acid substitutions, particularly in the receptor-binding domain of the spike, that confer resistance to vaccines and antibodies 2-16. To constrain viral escape through resistance mutations, we combined antibody variable regions that recognize different receptor binding domain (RBD) sites17,18 into multispecific antibodies. Here, we describe multispecific antibodies, including a trispecific that prevented virus escape >3000-fold more potently than the most effective clinical antibody or mixtures of the parental antibodies. Despite being generated before the evolution of Omicron, this trispecific antibody potently neutralized all previous variants of concern and major Omicron variants, including the most recent BA.4/BA.5 strains at nanomolar concentrations. Negative stain electron microscopy revealed that synergistic neutralization was achieved by engaging different epitopes in specific orientations that facilitated inter-spike binding. An optimized trispecific antibody also protected Syrian hamsters against Omicron variants BA.1, BA.2 and BA.5, each of which uses different amino acid substitutions to mediate escape from therapeutic antibodies. Such multispecific antibodies decrease the likelihood of SARS-CoV-2 escape, simplify treatment, and maximize coverage, providing a strategy for universal antibody therapies that could help eliminate pandemic spread for this and other pathogens.

immunology↗
bioRxiv2022

Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model

MotivationGene expression has inherent stochasticity resulting from transcriptions burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions (i.e., exponentially distributed dwell time in ON and OFF states). However, growing evidence suggests that the gene-state dwell times are nonexponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. ResultsWe develop an interpretable and tractable model, the generalized telegraph model (GTM), to carve transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework (BayesGTM). BayesGTM demonstrates efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of BayesGTM to genome-wide data from mouse embryonic fibroblasts reveals that CTM would overestimate burst frequency and underestimate burst size. In conclusion, the GTM and the corresponding BayesGTM are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.

systems biology↗
bioRxiv2022

Dissecting the genetic basis of the heterosis of Y900, an elite super-hybrid rice

Y900 is one of the top hybrid rice varieties with a yield exceeding 15 t/hm2. To dissect the mechanism of heterosis, the male parent line R900 and female parent line Y58S were sequenced using long-read and Hi-C technology. High-quality reference genomes of sizes of 396.41 Mb and 398.24 Mb were obtained for R900 and Y58S, respectively. Genome-wide variations between the parents were systematically identified, including 1,367,758 SNPs and 299,149 Indels. No megabase level structural variations exist. >75% of genes exhibited variation between the two parents. Compared with other two-line hybrids sharing the same female parent, the Geng/japonica-type genetic components from different male parents showed an increasing trend from phase 2-4 super-hybrid rice; Transcriptome analysis revealed that additive and dominance effects are the main genetic effects that constitute the heterosis of Y900. Allele-specific expression patterns and expression regulation patterns are quite dynamic in different tissues. For young panicle tissues, cis-regulation is dominant, while trans-regulation is more popular in leaf issues. Overdominance is more likely regulated by the trans-regulation mechanism. The differential gene expression and regulation pattern are closely related to Geng/japonica introgression. Additionally, R900 contained several excellent japonica haplotypes, such as NAL1, OsSPL13, Ghd8, OsBRI1, and DTH2, which make a good complement to Y58S. The fine tune mechanism through dynamic expression or regulation pattern change, especially on some key functional genes, is the base for heterosis.

plant biology↗
bioRxiv2022

Molecular basis for the pheromone-mediated feeding preference of early-instar Mythimna separata larvae

Insect sex pheromones are important chemical signals in sexual communication, they are traditionally thought to be inactive to larvae. However, it was found that some lepidopteran larvae can be significantly attracted by sex pheromones of the same species in a food context. Here we reported that the host-feeding choice of early-instar Mythimna separata larvae can be effectively regulated by the sex pheromone component (Z)-11-hexadecenal (Z11-16: Ald). Further exploration revealed that two olfactory proteins, M. separata general odorant binding protein 2 (MsGOBP2) and olfactory receptor 3 (MsOR3), were molecular basis of the host-feeding choice regulation mediated by Z11-16: Ald. Silencing either MsGOBP2 or MsOR3 led to a sharply reduced preference for Z11-16: Ald-spiked hosts. It is possible that the Z11-16: Ald-based preference of the M. separata larvae for host is governed by the interplay between MsGOBP2 and MsOR3. In the present research, the interactions between MsGOBP2 and Z11-16: Ald were also discussed using molecular dynamics-based approaches. Our research explored insight into the Z11-16: Ald-mediated host-feeding choice regulation of M. separata larvae, all the results would aid in developing olfaction-based methods for controlling pests in larval stage.

ecology↗
bioRxiv2022

A novel cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

BackgroundCuproptosis has recently been considered a novel form of programmed cell death. To date, factors crucial to the regulation of this process remain unelucidated. Here, we aimed to identify long-chain non-coding RNAs (lncRNAs) associated with cuproptosis in order to predict the prognosis of patients with hepatocellular carcinoma (HCC). MethodsUsing RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related mRNAs and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, TMB (tumor mutation burden), and pharmacologic options were made between high- and low-risk groups. ResultsOur prognostic risk model was constructed using the cuproptosis-related PICSAR, FOXD2-AS1, and AP001065.1 lncRNAs. The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95% CI = 1.063- 1.270; p < 0.001). Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as internal validation cohorts. A prognostic nomogram developed considering CupRLSig data and a number of clinical characteristics were found to exhibit adequate performance in survival risk stratification. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses. Finally, differences in immune checkpoint expression and responses to chemotherapy as well as in targeted therapy among CupRLSig stratified high- and low-risk groups were explored. ConclusionsThe lncRNA signature constructed in this study is valuable in prognostic estimation in the setting of HCC.

bioinformatics↗
bioRxiv2022

The immunogenomic landscape of cattle

Here, we report the de novo assembly of a cattle genome using ultra-long-read nanopore sequencing in conjunction with other advanced technologies. The assembled genome contains only 145 contigs (N50 [~] 74.0 Mb). Compare to the current reference cattle genome ARS-UCD1.2, 154 gaps are closed, and 467 scaffolds are further placed in our assembly. Importantly, except two remained gaps in the T-cell receptor /{delta} (TRA/TRD) region, the gene loci of other TRs and immunoglobulins (IGs) are seamlessly assembled and exquisitely annotated. With the characterization of 258 IG genes and 626 TR genes that distributed in seven genomic loci, we illustrate the highest immune gene diversity in mammals to our knowledge. Moreover, the gene structures of major histocompatibility complex (MHC) are integrally depicted with properly phased haplotypes. Thus, our work not only reports a cattle genome with the most continuity and completeness, but also provide a comprehensive view of the complex immune- genome.

genomics↗
bioRxiv2022

Genomic insights into the adaptive and convergent evolution of Leuciscus waleckii inhabiting extremely alkaline environments

Leuciscus waleckii is widely distributed in Northeast Asia and has high economic value. Different from its freshwater counterparts, the population in Lake Dali Nur has a strong alkalinity tolerance and can adapt to extremely alkaline-saline water with bicarbonate over 50 mmol/L (pH 9.6), thus providing an exceptional model with which to explore the mechanisms of adaptive evolution under extreme alkaline environments. Here, we assembled a high quilty chromosome-level reference genome for L. waleckii from Lake Dali Nur, which provides an important genomic resource for the exploitation of alkaline water fishery resources and adaptive evolution research across teleost fish. Notably, we identified significantly expanded long terminal repeats (LTRs) and long interspersed nuclear elements (LINEs) in L. waleckii compared to other Cypriniformes fish, suggesting their more recent insertion into the L. waleckii genome. We also identified expansions in genes encoding gamma-glutamyltransferase, which possibly underlie the adaptation to extreme environmental stress. Based on the resequencing of 85 L.waleckii individuals from divergent populations, the historical population size of L.waleckii in Lake Dali Nur dramatically expanded in a thousand years approximately 13,000 years ago, and experienced a cliff recession in the process of adapting to the alkaline environment of Lake Dali Nur approximately 6,000 years ago. Genome scans further revealed the significant selective sweep regions from Lake Dali Nur, which harbour a set of candidate genes involved in hypoxia tolerance, ion transport, acid-base regulation and nitrogen metabolism. In particular, 5 alkali population specific nonsynonymous mutations were identified in CA15 gene copies. In addition, two sites with convergent amino acid mutation were detected in the RHCG-a gene among several alkali environment adapted Cypriniformes fish, this mutation may increase the NH3 excretion rate of the RHCG channel. Our findings provide comprehensive insight into the genomic mechanisms of L. waleckii and reveal their adaptative evolution under extreme alkaline environments.

evolutionary biology↗
bioRxiv2022

Prefusion-Stabilized Lassa Virus Trimer Identifies Neutralizing Nanobodies and Reveals an Apex-Situated Site of Vulnerability

Lassa virus (LASV) is responsible for 100,000-300,000 zoonotic infections annually and poses a threat to public health. Development of antibody-based therapeutics or vaccines has been challenging because neutralizing antibodies - even among Lassa hemorrhagic fever survivors - are generally of low titer, and the target of neutralizing antibodies, the trimeric glycoprotein complex (GPC), a type 1-fusion machine with GP1 and GP2 subunits, has been difficult to produce. Here, we use structure-based design to obtain a soluble LASV GPC by engineering an inter-protomer disulfide (R207GCGP1-L326CGP2) and appending the T4-fibritin trimerization domain. We verified the antigenicity of this prefusion-stabilized LASV GPC against a panel of human antibodies and used electron microscopy (EM) to confirm its trimeric association. We panned the prefusion-stabilized LASV GPC against single domain nanobody libraries and identified one of camel origin, which we named D5, which bound GPC with 27 nM affinity and neutralized the Josiah strain of LASV with an IC50 of 12 {micro}g/ml when formatted into a bivalent IgG2a context. The cryo-EM structure of a ternary complex of the D5 nanobody, the antigen-binding fragment of human antibody 8.11G, and LASV GPC revealed D5 to recognize a site-of-vulnerability at the trimer apex. The recognized site appeared to be specific to GPC lacking cleavage of between GP1 and GP2 subunits. Collectively, our findings suggest that GPC-cleavage intermediates may be targets for LASV neutralization and define an apex-situated site of vulnerability for vaccine development. SignificanceLassa virus (LASV) infection is expanding outside its traditionally endemic areas in West Africa, posing a biothreat to the world. LASV-neutralizing antibodies, moreover, have proven difficult to elicit. To gain insight into requirements for antibody-mediated neutralization of LASV, we developed a prefusion-stabilized LASV glycoprotein trimer (GPC), panned it against phage libraries comprised single-domain antibodies or nanobodies from shark and camel, and identified one, D5, which - when placed into bivalent IgG2a context - could neutralize LASV. Cryo-EM analysis revealed D5 to recognize a cleavage-dependent site-of-vulnerability at the trimer apex. We propose this apex-situated site to be an attractive target for LASV vaccine and therapeutic development.

immunology↗
bioRxiv2022

Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi

Single-cell Hi-C (scHi-C) technologies can probe three-dimensional (3D) genome structures in single cells and their cell-to-cell variability. However, existing scHi-C analysis methods are hindered by the data quality and the complex 3D genome patterns. The lack of computational scalability and interpretability poses further challenges for large-scale scHi-C analysis. Here, we introduce Fast-Higashi, an ultrafast and interpretable method based on tensor decomposition that can jointly identify cell identities and chromatin meta-interactions. Fast-Higashi is able to simultaneously model multiple tensors with unmatched features of different sizes. A new partial random walk with restart (Partial RWR) algorithm in Fast-Higashi efficiently mitigates data sparseness. Extensive evaluations on real scHi-C datasets demonstrate the advantage of Fast-Higashi over existing methods for embedding, leading to improved delineation of rare cell types and better reconstruction of developmental trajectories. Fast-Higashi can directly infer chromatin meta-interactions, identify 3D genome features that define distinct cell types, and help elucidate cell type-specific connections between genome structure and function. Moreover, Fast-Higashi can be generalized to incorporate other single-cell omics data. Fast-Higashi provides a highly efficient and interpretable scHi-C analysis solution that is applicable to a broad range of biological contexts.

bioinformatics↗