Biology subjects

He, B.

Publications and source records attributed to He, B..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Integrating simultaneous motor imagery and spatial attention for EEG-BCI control

Objective: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control. Methods: We evaluated a cohort of 25 human subjects ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI). Results: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subjects best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA. Conclusion: Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects. Significance: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users BCI performance.

bioengineering↗

bioRxiv2023

Comprehensive Multi-omics Analysis Reveals the Core Role of Glycerophospholipid Metabolism in Rheumatoid Arthritis Development

ObjectivesRheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. MethodsA total of 162 subjects were included in our study, 121 subjects were used for 16S rRNA, ITS sequencing and metabolomics analysis and 41 subjects were used for transcriptomics analysis. Enrichment analysis was based on significant difference metabolites and genes. Protein-protein interaction network and correlation analyses were performed to interpret the interactions among intestinal flora, metabolites and genes. We applied three models to distinguish between NORA and CRA using computational OR values, LASSO and random forest, respectively. ResultsOur results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 28 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. ConclusionsThese findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

immunology↗

bioRxiv2023

Spike substitution T813S increases Sarbecovirus fusogenicity by enhancing the usage of TMPRSS2.

SARS-CoV Spike (S) protein shares considerable homology with SARS-CoV-2 S, especially in the conserved S2 subunit (S2). S protein mediates coronavirus receptor binding and membrane fusion, and the latter activity can greatly influence coronavirus infection. We observed that SARS-CoV S is less effective in inducing membrane fusion compared with SARS-CoV-2 S. We identify that S813T mutation is sufficient in S2 interfering with the cleavage of SARS-CoV-2 S by TMPRSS2, reducing spike fusogenicity and pseudoparticle entry. Conversely, the mutation of T813S in SARS-CoV S increased fusion ability and viral replication. Our data suggested that residue 813 in the S was critical for the proteolytic activation, and the change from threonine to Serine at 813 position might be an evolutionary feature adopted by SARS-2-related viruses. This finding deepened the understanding of Spike fusogenicity and could provide a new perspective for exploring Sarbecovirus evolution. Author SummaryThe Spike strain of SARS-CoV-2 has accumulated many mutations during its time in circulation, most of which have occurred in the S1 region, and more specifically in the RBD, in an effort to either improve the viruss affinity for the receptor ACE2 or to enhance its ability to evade the immune system. Mutations in the Spike S2 region have more far-reaching effects than those in the S1 region because it is more conserved across sarbecoviruses. By comparing SARS and SARS2, we found that an important substitution at amino acid position 813 in the S2 region (T813S) disrupts the utilization of TMPRSS2 and can significantly influence viral entry into cells. This discovery deepens our knowledge of S proteins and provides new prospects for tracing the evolution of Sarbecoviruses.

microbiology↗

bioRxiv2023

Botrytis cinerea small RNAs are associated with tomato AGO1 and silence tomato target genes supporting cross-kingdom RNAi between the fungal pathogen B. cinerea and its tomato host

Cross-kingdom or cross-species RNA interference (RNAi) is broadly present in many interacting systems between microbes/parasites and their plant and animal hosts. A recent study by Qin et al. (2022) performed correlation analysis using global sRNA- and mRNA-deep sequencing data of cultured B. cinerea and B. cinerea-infected tomato leaves and claimed that cross-kingdom RNAi may not occur in B. cinerea-tomato interaction (Qin et al., 2022). Here, we use experimental evidence and additional bioinformatics analysis of the datasets produced by Qin et al. (2022) to identify the key reasons why a discrepancy between the conclusion of Qin et al. 2022 and previously published findings occurred. We also provided additional experimental evidence to support the presence of cross-kingdom RNAi between tomato and B. cinerea. We believe it is important to clarify the basic concept and mechanism of cross-kingdom/cross-species sRNA trafficking and illustrate proper bioinformatics analyses in this regard for all the scientists and researchers in this field.

plant biology↗

bioRxiv2022

Low-intensity transcranial focused ultrasound changes pain-associated behaviors by modulating pain processing brain circuits

There is an urgent and unmet clinical need to develop non-pharmacological interventions for chronic pain management due to the critical side effects of opioids. Low-intensity transcranial focused ultrasound is an emerging non-invasive neuromodulation technology with high spatial specificity and deep brain penetration. Here, we developed a tightly-focused 128-element ultrasound transducer to specifically target small mouse brains, employing dynamic focus steering. We demonstrate that transcranial focused ultrasound stimulation at pain processing brain circuits can significantly alter pain-associated behaviors in mouse models in vivo. Our findings indicate that a single-session focused ultrasound stimulation to the primary somatosensory cortex (S1) significantly attenuates heat pain sensitivity in wild-type mice and modulates heat and mechanical hyperalgesia in a humanized mouse model of chronic pain in sickle cell disease. Results further revealed a sustained behavioral change associated with heat hypersensitivity by targeting deeper cortical structures (e.g., insula) and multi-session focused ultrasound stimulation to S1 and insula. Analyses of brain electrical rhythms through electroencephalography demonstrated a significant change in noxious heat hypersensitive- and chronic hyperalgesia-associated neural signals following focused ultrasound treatment. Validation of efficacy was carried out through control experiments, tuning ultrasound parameters, adjusting inter-experiment intervals, and investigating effects on age, gender, genotype, and in a head-fixed awake model. Importantly, transcranial focused ultrasound was shown to be safe, causing no adverse effects on motor function and brain neuropathology. In conclusion, the rich experimental evidence validates the ability of novel focused ultrasound neuromodulation to suppress pain, presenting significant translational potential for next-generation chronic pain treatment without adverse effects. Key pointsO_LINovel non-invasive neuromodulation of brains pain processing circuits with submillimeter spatial precision for pain management C_LIO_LITranscranial focused ultrasound significantly modulates pain-related behaviors and brain electrical rhythms of pain in humanized SCD mice C_LI

bioengineering↗

bioRxiv2022

Personalized Deep Learning based Source Imaging Framework Improves the Imaging of Epileptic Sources from MEG Interictal Spikes

Electromagnetic source imaging (ESI) has been widely used to image brain activities for research and clinical applications from MEG and EEG. It is a challenging task due to the ill-posedness of the problem and the complexity of modeling the underlying brain dynamics. Deep learning has gained attention in the ESI field for its ability to model complex distributions and has successfully demonstrated improved imaging performance for ESI. In this work, we investigated the capability of imaging epileptic sources from MEG interictal spikes using deep learning-based source imaging framework (DeepSIF). A generic DeepSIF model was first trained with a generic head model using a template MRI. A fine-tuning procedure was proposed to introduce personalized head model information into the neural network for a personalized DeepSIF model. Two models were evaluated and compared in extensive computer simulations. The MEG-DeepSIF approach was further rigorously validated for imaging epileptogenic regions from interictal spike recordings in focal epilepsy patients. We demonstrated that DeepSIF can be successfully applied to MEG recordings and the additional fine-tuning step for personalized DeepSIF can alleviate the impact of head model variations and further improve the performance significantly. In a cohort of 29 drug-resistant focal epilepsy patients, the personalized DeepSIF model provided a sublobar concordance of 93%, sublobar sensitivity of 77% and specificity of 99%, respectively. When compared to the seizure-onset-zone defined by intracranial recordings, the localization error is 15.78 {+/-} 5.54 mm; and when compared with resection volume in seizure free patients, the spatial dispersion is 8.19 {+/-} 8.14 mm. DeepSIF enables an accurate and robust imaging of spatiotemporal brain dynamics from MEG recordings, suggesting its unique value to neuroscience research and clinical applications.

bioengineering↗

bioRxiv2022

Single-cell imaging based prognosis prediction identifies new breast cancer survival subtypes

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction. STATEMENT OF TRANSLATIONAL RELEVANCEOur findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

bioinformatics↗

bioRxiv2022

DeepAIR: a deep-learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens is one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR consists of three feature encoders (a trainable-embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder), a gating-based attention mechanism to extract important features, and a tensor fusion mechanism to integrate obtained features. We train and evaluate DeepAIR on three downstream prediction tasks, including the prediction of AIR-antigen binding affinity, the prediction of AIR-antigen binding reactivity, and the classification of the immune repertoire. On five representative datasets, DeepAIR shows outstanding prediction performance in terms of AUC (area under the ROC curve) in predicting the binding reactivity to various antigens, as well as the classification of immune repertoire for nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD). DeepAIR is freely available for academic purposes at https://github.com/TencentAILabHealthcare/DeepAIR. We anticipate that DeepAIR can serve as a useful tool for characterizing and profiling antigen binding AIRs, thereby informing the design of personalized immunotherapy. HighlightsO_LIIntegrating predicted AIR structures using AlphaFold2 significantly improves the prediction accuracy of the binding reactivity between AIRs and antigens. C_LIO_LIDeepAIR is featured by a novel deep learning architecture that leverages both the gating-based attention mechanism and tensor fusion mechanism to effectively extract and integrate informative features from three feature encoders, including a trainable embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder. C_LIO_LIDeepAIR is implemented as a biologically interpretable deep learning framework that highlights the key residues in both and {beta} chains that are critical for predicting the AIR-antigen binding. C_LI

immunology↗

bioRxiv2022

PI 4-kinases promote cell surface expansion and facilitate tissue morphogenesis during Drosophila cellularization and gastrulation

During epithelial morphogenesis, dynamic cell shape change driven by intrinsic or extrinsic forces requires prompt regulation of cell surface area. Using Drosophila ventral furrow formation as a model, we identified the PI 4-kinase Fwd as an important regulator for apical constriction-mediated cell shape changes. These morphological changes involve prompt lateral surface expansion in the constricting cells and apical surface expansion in the non-constricting cells adjacent to the constriction domain, both of which are impeded upon depletion of Fwd. Computer modeling demonstrates that restricting apical and lateral cell surface expansion will result in specific tissue-level morphological abnormalities during furrow formation, which well predicts the phenotypes observed in the fwd deficient embryos. Fwd also promotes cell surface expansion during cellularization, but this function is largely redundant with another PI 4-kinase, PI4KII. Together, our findings uncover an important role of Fwd in facilitating cell surface expansion in support of dynamic epithelial remodeling.

developmental biology↗

bioRxiv2022

Dlg1 regulates subcellular distribution of non-muscle myosin II during Drosophila germband extension

Elongation of the body axis through convergent extension is a conserved developmental process that is mediated by cell intercalation. During convergent extension of the germband epithelium in Drosophila embryos, planar polarized activation of non-muscle myosin II ("myosin") promotes cell intercalation by facilitating patterned remodeling of adherens junctions. Here, we report that loss of the basolateral determinant Dlg1 leads to defects in the subcellular distribution of myosin during germband extension, and consequently, impairs proper junctional remodeling and apical area maintenance during cell intercalation. In dlg1 mutant embryos, ectopic accumulation of myosin is observed at the medioapical domain and along the lateral membrane, whereas junctional myosin is greatly reduced. Analogous myosin mis-localization patterns are observed upon knockdown of other basolateral determinants, Scrib and Lgl, but not the apical determinants. The function of Dlg1 in regulating the spatial distribution of myosin requires its intact SH3 and GUK domains and involves the Rho1 GEF Cyst, active Rho1 and Rok. We propose that Dlg1 facilitates correct junctional remodeling and prevents undesired apical area variation during cell intercalation by regulating the subcellular location of myosin activation.

developmental biology↗

bioRxiv2022

A systematic investigation of lactic acid bacteria-derived biosynthetic gene clusters reveals diverse antagonistic bacteriocins in the human microbiome

Lactic acid bacteria (LAB) produce various bioactive secondary metabolites (SMs), which endow LAB with a protective role for the host. However, the biosynthetic potentials of LAB-derived SMs remain elusive, particularly in their diversity, abundance, and distribution in the human microbiome. To gain an insight into the biosynthetic capacity of LAB, we analyzed the biosynthetic gene clusters (BGCs) from 31,977 LAB genomes and 748 human microbiome metagenomes, identifying 130,051 BGCs. The found BGCs were clustered into 2,849 gene cluster families (GCFs), most of which are species-specific, niche-specific, and uncharacterized yet. We found that most LAB BGCs encoded bacteriocins with pervasive antagonistic activities predicted by machine learning models, potentially playing protective roles in the human microbiome. Class II bacteriocins, the most abundant LAB SMs, are particularly enriched and predominant in vaginal microbiomes. Together with experimental validation, our metagenomic and metatranscriptomic analysis showed that class II bacteriocins with antagonistic potential might regulate microbial communities in the vagina, thereby contributing to homeostasis. These discoveries of the diverse and prevalent antagonistic SMs are expected to stimulate the mechanism study of LABs protective roles in the host and highlight the potential of LAB as a new source of antibacterial SMs.

bioinformatics↗

bioRxiv2022

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

Resting state electromagnetic recordings have been analyzed in epilepsy patients aiding presurgical evaluation. However, it has been rarely explored how pathological networks can be separated and thus used for epileptogenic focus localization purpose. We proposed here a resting state EEG/MEG analysis framework, to disentangle brain functional networks represented by electrophysiological oscillations. Firstly, by using an Embedded Hidden Markov Model (EHMM), we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. After that, functional connectivity analysis along with graph theory were applied on the extracted brain states to quantify the network features of the extracted brain states, and we determine the source location of pathological states based on these features. The EHMM model was rigorously evaluated using computer simulations. Our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We than validated the entire framework as compared with clinical ground truth in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings. We segmented the resting state MEG recordings into a few brain states with diverse connectivity patterns and extracted pathological brain states by applying graph theory on the constructed functional networks. We showed reasonable localization results using the extracted pathological brain states in 6/10 patients, as compared to the invasive clinical findings. The framework can serve as an objective tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings. It promises to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

neuroscience↗

bioRxiv2022

Efficacy of Parainfluenza Virus 5 (PIV5)-vectored Intranasal COVID-19 Vaccine as a Single Dose Vaccine and as a Booster against SARS-CoV-2 Variants

Immunization with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines has greatly reduced coronavirus disease 2019 (COVID-19)-related deaths and hospitalizations, but waning immunity and the emergence of variants capable of immune escape indicate the need for novel SARS-CoV-2 vaccines. An intranasal parainfluenza virus 5 (PIV5)-vectored COVID-19 vaccine CVXGA1 has been proven efficacious in animal models and blocks contact transmission of SARS-CoV-2 in ferrets. CVXGA1 vaccine is currently in human clinical trials in the United States. This work investigates the immunogenicity and efficacy of CVXGA1 and other PIV5-vectored vaccines expressing additional antigen SARS-CoV-2 nucleoprotein (N) or SARS-CoV-2 variant spike (S) proteins of beta, delta, gamma, and omicron variants against homologous and heterologous challenges in hamsters. A single intranasal dose of CVXGA1 induces neutralizing antibodies against SARS-CoV-2 WA1 (ancestral), delta variant, and omicron variant and protects against both homologous and heterologous virus challenges. Compared to mRNA COVID-19 vaccine, neutralizing antibody titers induced by CVXGA1 were well-maintained over time. When administered as a boost following two doses of a mRNA COVID-19 vaccine, PIV5-vectored vaccines expressing the S protein from WA1 (CVXGA1), delta, or omicron variants generate higher levels of cross-reactive neutralizing antibodies compared to three doses of a mRNA vaccine. In addition to the S protein, the N protein provides added protection as assessed by the highest body weight gain post-challenge infection. Our data indicates that PIV5-vectored COVID-19 vaccines, such as CVXGA1, can serve as booster vaccines against emerging variants. ImportanceWith emerging new variants of concern (VOC), SARS-CoV 2 continues to be a major threat to human health. Approved COVID-19 vaccines have been less effective against these emerging VOCs. This work demonstrates the protective efficacy, and strong boosting effect, of a new intranasal viral-vectored vaccine against SARS-CoV-2 variants in hamsters.

microbiology↗

bioRxiv2022

Deep multi-omic network fusion for marker discovery of Alzheimer's Disease

Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored to interpret findings from genome wide association studies of Alzheimers disease (AD) and to gain more insight of the disease mechanism. However, each -omics data type is usually examined individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore give rise to challenges in interpretation. To address this problem, we propose a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set. It aims to identify a subnetwork of functional interactions predictive of AD evidenced by multi-omic measures. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using multi-omic data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded multi-omic sub-networks related to innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. Around 50% of these findings were replicated in another independent cohort. Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron-neuron or neuron-glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage.

systems biology↗

bioRxiv2022

Ultrasound Neuromodulation and Correlation Change in the Rat Somatosensory Cortex

Transcranial focused ultrasound (tFUS) is a neuromodulation technique which has been the focus of increasing interest for noninvasive brain stimulation with high spatial specificity. Its ability to excite and inhibit neural circuits as well as to modulate perception and behavior has been demonstrated, however, we currently lack understanding of how tFUS modulates the ways neurons interact with each other. This understanding would help explain tFUSs mechanism of high-level neuromodulation and allow future development of therapies for neurological disorders. In this study we investigate how tFUS modulates neural interaction and response to peripheral electrical limb stimulation through intracranial multi-electrode recordings in the rat somatosensory cortex. We deliver ultrasound in a pulsed pattern to attempt to induce frequency dependent plasticity in a manner similar to that found following electrical stimulation. We show that neural firing in response to peripheral electrical stimulation is increased after ultrasound stimulation at all frequencies, showing tFUS induced excitation in individual neurons in vivo. We demonstrate tFUS frequency dependent pairwise correlation changes between neurons, with both potentiation and depression observed at different frequencies. These results extend previous research showing tFUS to be capable of inducing synaptic depression and demonstrate its ability to modulate network dynamics as a whole.

neuroscience↗

bioRxiv2022

De-heterogeneity of the eukaryotic viral reference database (EVRD) improves the accuracy and efficiency of viromic analysis

Widespread in public databases, the notorious contamination in virus reference databases often leads to confusing even wrong conclusions in applications like viral disease diagnosis and viromic analysis, highlighting the need of a high-quality database. Here, we report the comprehensive scrutiny and the purification of the largest viral sequence collections of GenBank and UniProt by detection and characterization of heterogeneous sequences (HGSs). A total of 766 nucleotide- and 276 amino acid-HGSs were determined with length up to 6,605 bp, which were widely distributed in 39 families, with many involving highly public health-related viruses, such as hepatitis C virus, Crimea-Congo hemorrhagic fever virus and filovirus. Majority of these HGSs are sequences of a wide range of hosts including humans, with the rest resulting from vectors, misclassification and laboratory components. However, these HGSs cannot be simply considered as exotic contaminants, since part of which are resultants of natural occurrence or artificial engineering of the viruses. Nevertheless, they significantly disturb the genomic analysis, and hence were deleted from the database. A further augmentation was implemented with addition of the risk and vaccine sequences, which finally results in a high-quality eukaryotic virus reference database (EVRD). EVRD showed higher accuracy and less time-consuming without coverage compromise by reducing false positives than other integrated databases in viromic analysis. EVRD is freely accessible with favorable application in viral disease diagnosis, taxonomic clustering, viromic analysis and novel virus detection.

microbiology↗

bioRxiv2022

Early zygotic gene product Dunk interacts with anillin to regulate Myosin II during Drosophila cleavage

Drosophila cellularization is a special form of cleavage that converts syncytial embryos into cellular blastoderms by partitioning the peripherally localized nuclei into individual cells. An early event in cellularization is the recruitment of non-muscle myosin II ("myosin") to the basal tip of cleavage furrows, where myosin forms an interconnected basal array before reorganizing into individual cytokinetic rings. The initial recruitment and organization of basal myosin are regulated by a cellularization-specific gene, dunk, but the underlying mechanism is unclear. Through a genome-wide yeast two-hybrid screen, we identified anillin (Scraps in Drosophila), a conserved scaffolding protein in cytokinesis, as the primary binding partner of Dunk. We show that Dunk regulates the localization of anillin at the cleavage furrows during early cellularization and functionally interacts with anillin in regulating basal myosin. Furthermore, we show that anillin colocalizes with myosin since the onset of cellularization and is required for the initial recruitment and maintenance of myosin at the basal array, prior to the well-documented function of anillin in regulating cytokinetic ring assembly. Based on these results, we propose that Dunk regulates myosin recruitment and organization during early cellularization by interacting with and regulating anillin.

developmental biology↗

bioRxiv2021

High-fidelity Cas13 variants for targeted RNA degradation with minimal collateral effect

CRISPR-Cas13 systems have recently been employed for targeted RNA degradation in various organisms. However, collateral degradation of bystander RNAs has imposed a major barrier for their in vivo applications. We designed a dual-fluorescent reporter system for detecting collateral effects and screening Cas13 variants in mammalian cells. Among over 200 engineered variants, several Cas13 variants (including Cas13d and Cas13X) exhibit efficient on-target activity but markedly reduced collateral activity. Furthermore, transcriptome-wide off-targets and cell growth arrest induced by Cas13 are absent for these variants. Importantly, high-fidelity Cas13 variants show comparable RNA knockdown activity with wild-type Cas13 but no detectable collateral damage in transgenic mice and adeno-associated virus-mediated somatic cell targeting. Thus, high-fidelity Cas13 variants with minimal collateral effect are now available for targeted degradation of RNAs in basic research and therapeutic applications.

molecular biology↗