Biology subjects

Zhang, F.

Publications and source records attributed to Zhang, F..

At least 325 records · Page 18Linked to original sources

bioRxiv2021

VP-Detector: A 3D convolutional neural network for automated macromolecule localization and classification in cryo-electron tomograms

MotivationCryo-electron tomography (Cryo-ET) with sub-tomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. However, current tomographic reconstructions suffer the low signal-to-noise (SNR) ratio and the missing wedge artifacts. Hence, automatic and accurate macromolecule localization and classification become the bottleneck problem for structural determination by STA. Here, we propose a 3D multi-scale dense convolutional neural network (MSDNet) for voxel-wise annotations of tomograms. Weighted focal loss is adopted as a loss function to solve the class imbalance. The proposed network combines 3D hybrid dilated convolutions (HDC) and dense connectivity to ensure an accurate performance with relatively few trainable parameters. 3D HDC expands the receptive field without losing resolution or learning extra parameters. Dense connectivity facilitates the re-use of feature maps to generate fewer intermediate feature maps and trainable parameters. Then, we design a 3D MSDNet based approach for fully automatic macromolecule localization and classification, called VP-Detector (Voxel-wise Particle Detector). VP-Detector is efficient because classification performs on the pre-calculated coordinates instead of a sliding window. ResultsWe evaluated the VP-Detector on simulated tomograms. Compared to the state-of-the-art methods, our method achieved a competitive performance on localization with the highest F1-score. We also demonstrated that the weighted focal loss improves the classification of hard classes. We trained the network on a part of training sets to prove the availability of training on relatively small datasets. Moreover, the experiment shows that VP-Detector has a fast particle detection speed, which costs less than 14 minutes on a test tomogram. Contactzsh@amss.ac.cn, xfcui@email.sdu.edu.cn, zhangfa@ict.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Single cell analysis of RA synovial B cells reveals a dynamic spectrum of ectopic lymphoid B cell activation and hypermutation characterized by NR4A nuclear receptor expression

Ectopic lymphoid structures (ELS) can develop in rheumatoid arthritis (RA) synovial tissue, but the precise pathways of B cell activation and selection are not well understood. Here, we identified a unique B cell population in the synovium characterized by co-expression of a family of orphan nuclear receptors, NR4A1 (also known as NUR77), NR4A2 (NURR1) and NR4A3 (NOR1), that is highly enriched at both early and late stages of RA. NR4A B cells are rare in healthy peripheral blood, RA blood, and SLE kidney, but share markers with blood transcriptomic signatures that peak during RA disease flare. Using combined single cell transcriptomics and B cell receptor (BCR) sequencing, we demonstrate that NR4A synovial B cells have an activated transcriptomic profile that significantly overlaps with germinal center (GC) light zone (LZ) B cells and an accrual of somatic hypermutation that correlates with loss of naive B cell status. NR4A B cells uniquely co-express lymphotoxin {beta} and IL6, supporting important functions in ELS promotion and pro-inflammatory cytokine production. Further, the presence of shared clones in this activated B cell state and NR4A expressing synovial plasma cells (PC) and the rapid up-regulation with BCR stimulation points to in situ differentiation. Taken together, we identified a dynamic progression of B cell activation in RA synovial ELS, with NR4A transcription factors having an important role in antigen activation and local adaptive immune responses. One sentence summaryB cells in the rheumatoid arthritis synovium undergo a spectrum of in situ activation, with the NR4A family of transcription factors having an important role in antigen stimulation, local adaptive immunity, and pathological B cell responses.

immunology↗

bioRxiv2021

Noise-Transfer2Clean: Denoising cryo-EM images based on noise modeling and transfer

MotivationCryo-electron microscopy (cryo-EM) is a widely-used technology for ultrastructure determination, which constructs the three-dimensional (3D) structures of protein and macromolecular complex from a set of two-dimensional (2D) micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. ResultsHere, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly discovering the noise model of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noises true properties. To our knowledge, NT2C is the first denoising method that resolves the complex noise model in cryo-EM images. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising and specimen signal restoration, comparing with the state-of-art methods. A real-world case study shows that NT2C can improve the recognition rate on hard-to-identify particles by 19% in the particle picking task.

bioinformatics↗

bioRxiv2021

Circadian Control of Heparan Sulfate Levels Times Phagocytosis of Amyloid Beta Aggregates

Alzheimers Disease (AD) is a neuroinflammatory disease characterized partly by the inability to clear, and subsequent build-up, of amyloid-beta (A{beta}). A{beta} clearance is regulated by several pathways and has a circadian component. However, the mechanism underlying the circadian clearance of A{beta} has not been defined. Myeloid-based phagocytosis, a key mechanism in the metabolism of A{beta}, is circadianly-regulated, presenting a potential mechanism for the circadian clearance of A{beta}. In this work, we revealed that the phagocytosis of A{beta}42 undergoes a daily oscillation that is dependent on the circadian clock. We found the circadian timing of global heparan sulfate proteoglycan (HSPG) biosynthesis was the molecular timer for the clock-controlled phagocytosis of A{beta} and that both HSPG binding and A{beta}42 aggregation were essential for this oscillation. These data highlight that circadian regulation in immune cells may play a role in the intricate relationship between the circadian clock and AD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/442651v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@bca9b3org.highwire.dtl.DTLVardef@29575corg.highwire.dtl.DTLVardef@2673c5org.highwire.dtl.DTLVardef@7a8daa_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Transcriptomic Changes During Stage Progression of Mycosis Fungoides

BackgroundMycosis fungoides (MF) is the most common cutaneous T cell lymphoma, which in the early patch/plaque stages runs an indolent course. However, ~25% of MF patients develop skin tumors, a hallmark of progression to the advanced stage and associated with high mortality. The mechanisms involved in stage progression are poorly elucidated. MethodsWe performed whole-transcriptome and whole-exome sequencing of malignant MF cells from skin biopsies obtained by laser-capture microdissection. We compared three types of MF lesions: early-stage plaques (ESP, n=12), and plaques and tumors from patients in late-stage disease (late-stage plaques, LSP, n=10, and tumors, TMR, n=15). Gene Ontology (GO) and KEGG analysis were used to determine pathway changes specific for different lesions which we linked to the recurrent somatic mutations overrepresented in MF tumors. ResultsThe key upregulated pathways during stage progression were those related to cell proliferation and survival (MEK/ERK, Akt-mTOR), Th2/Th9 signaling (IL4, STAT3, STAT5, STAT6), meiomitosis (CT45A1, CT45A3, STAG3, GTSF1, and REC8) and DNA repair (PARP1, MYCN, OGG1). Principal coordinate clustering of the transcriptome revealed extensive gene expression differences between early (ESP) and advanced-stage lesions (LSP and TMR). LSP and TMR showed remarkable similarities at the level of the transcriptome, which we interpreted as evidence of cell percolation between lesions via hematogenous self-seeding. ConclusionStage progression in MF is associated with Th2/Th9 polarization of malignant cells, activation of proliferation, survival, as well as increased genomic instability. Global transcriptomic changes in multiple lesions are probably caused by hematogenous cell percolation between discrete skin lesions.

bioinformatics↗

bioRxiv2021

Surrogate production of genome edited sperm from a different subfamily by spermatogonial stem cell transplantation

The surrogate reproduction technique provides a powerful tool for production of allogenic or xenogeneic gametes derived from endangered species or those with valuable genetic traits. Production of functional donor-derived gametes through intra- or inter-specific spermatogonial stem cell transplantation (SSCT) has been achieved in many species. However, generation of functional gametes from a phylogenetically distant species such as from a different subfamily by SSCT has never been successful. Here, using two small cyprinid fishes, Chinese rare minnow (gobiocypris rarus, for brief: Gr) and zebrafish (danio rerio), which belong to different subfamilies, as donors and recipients for SSCT, we optimized the SSCT technique and successfully obtained Gr-derived sperm carrying targeted genome modifications in zebrafish. We revealed that the transplanted Gr spermatogonia supported the host gonadal development and underwent normal spermatogenesis, resulting in a reconstructed fertile testis containing Gr spermatids and zebrafish testicular somatic cells. Interestingly, the surrogate spermatozoa resembled those of host zebrafish but not donor Gr in morphology and swimming behavior. Finally, we showed that Gr-derived genome edited sperm was successfully produced in zebrafish by cross-subfamily SSCT, when the pou5f3 and chd gene knockout Gr SSCs were used as surrogate donors. This is the first report demonstrating the surrogate production of genome edited sperm from a phylogenetically distant species, and this method is feasible to be applied to future breeding of commercial fishes.

developmental biology↗

bioRxiv2021

Genetic diversity, evolutionary dynamics and pathogenicity of ferret badger rabies virus variants in mainland China, 2008-2018

In contrast to dog associated human rabies cases decline year by year due to the rabies vaccination coverage rates increase in China, ferret badger (FB, Melogale moschata)-associated human rabies cases emerged in the 1990s, and are now an increasingly recognized problem in southeast China. To investigate epidemiology, temporal evolution dynamics, transmission characterization and pathogenicity of FB-associated rabies viruses (RABVs), from 2008 to 2018, we collected 3,622 FB brain samples in Jiangxi and Zhejiang Province, and detected 112 RABV isolates. Four FB-related lineages were identified by phylogenetic analysis (lineages A-D), the estimated Times to Most Recent Common Ancestor were 1941, 1990, 1937 and 1997 for lineages A-D respectively. Furthermore, although no FB-associated human rabies case has been reported there apart from Wuyuan area, FB-RABV isolates are mainly distributed in Jiangxi Province. Pathogenicity of FB-RABVs was assessed using peripheral inoculation in mice and in beagles with masseter muscles, mortality-rates ranging from 20% to 100% in mice and 0 to 20% in beagles in the groups infected with the various isolates. Screening of sera from humans with FB bites and no postexposure prophylaxis to rabies, revealed that 5 of 9 were positive for neutralizing antibodies of RABV. All the results above indicated that FB-RABV variants caused a lesser pathogenicity in mice, beagles and even humans. Vaccination in mice suggests that inactivated vaccine or recombinant subunit vaccine products can be used to control FB- associated rabies, however, oral vaccines for stray dogs and wildlife need to be developed and licensed in China urgently. IMPORTANCEIn recent years FB-associated rabies virus has been identified as a major life-threatening pathogen in some districts in China. To understand the risk to public health and the contemporary dynamics, the present study conducted extensive investigations on FB rabies, distribution, virus isolation, phylogeny analysis, pathogenicity determination of various FB rabies virus strains, besides, serologic epidemiology survey to those whom bit by FB was also collected. The results show that the majority of FBs dwell in southeast China, like Jiangxi and Zhejiang Province, Phylogenetic analysis indicates that all isolated FB RABVs evolved from dogs, and the FB RABV can separate into 4 distinct lineages distributed relatively independent in different areas. The isolate strains differ in pathogenicity, although they have relatively lower pathogenicity compared to dog rabies virus according to our study, the need for further study to licensed oral vaccines and FB RABV pathogenesis is emphasized in order to control rabies.

pathology↗

bioRxiv2021

Flux vs burden: biosensor optimization for dynamic pathway engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors.

synthetic biology↗

bioRxiv2021

Phytophthora capsici sterol reductase PcDHCR7 has a role in mycelium development and pathogenicity

The de novo biosynthesis of sterols is critical for eukaryotes, however, some organisms lack this pathway including most oomycetes. Phytophthora spp. are sterol auxotroph but remarkably, have retained a few genes encoding enzymes in the sterol biosynthesis pathway. Here we investigated the function of PcDHCR7, a gene in Phytophthora capsici predicted to encode the {bigtriangleup}7-sterol reductase. When expressed in Saccharomyces cerevisiae, PcDHCR7 showed a {bigtriangleup}7-sterol reductase activity. Knocking out PcDHCR7 in P. capsici resulted in loss of the capacity to transform ergosterol into brassicasterol, which means PcDHCR7 has a {bigtriangleup}7-sterol reductase activity in P. capsici itself. This enables P. capsici to transform sterols recruited from the environment for better use. Biological characteristics were compared between wild-type isolate and PcDHCR7 knock-out transformants. The results indicated that PcDHCR7 plays a key role in mycelium development and pathogenicity of zoospores in P. capsici.

microbiology↗

bioRxiv2021

Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification by data independent acquisition mass spectrometry

Identification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of >0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively. Significance StatementA better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data.

bioinformatics↗

bioRxiv2021

Whole-Genome Sequencing on 220 Alfalfa (Medicago sativa L.) Accessions Identified Association of DREB1C Gene with Fall Dormancy Height

Fall dormancy (FD) is one of the most important traits of alfalfa (Medicago sativa) for cultivar selection to overcome winter stress. Although transcriptomics, proteomics analysis, and QTL mapping have revealed some important genes correlated with FD, the genetic architecture of this trait is still unclear. There are no applicable genes or markers for selection, which hinders progress in the genetic research and molecular breeding for the trait. We conducted whole-genome sequencing (WGS) on 220 alfalfa accessions at 10x depth. Among the 875,023 SNPs, four of them were associated with FD height using GWAS. One SNP located on chromosome 6 is in linkage disequilibrium with dehydration-responsive element-binding protein 1C (DREB1C). Furthermore, seven DREB genes are clustered in this region, one of which has previously been shown to enhance freezing tolerance in the model plant Medicago truncatula. The candidate genes uncovered by our research will benefit the transgenic and CRISPR-Cas9 research of FD in alfalfa. This gene will also be useful for molecular marker development and marker-associated breeding of FD for alfalfa.

genomics↗

bioRxiv2021

Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression.

Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by superenhancers (SEs). The transcription of genes at SEs is enabled by the formation of phase-separated condensates by transcription factors and co-activators with intrinsically disordered regions. The androgen receptor (AR), main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the co-activator MED1 to SEs to promote oncogenic programs. In this work, we show that dynamic AR-rich, liquid-like foci form in PCa models upon androgen stimulation and correlate with AR transcriptional activity. The co-activator MED1 plays an essential role in the formation of AR foci while AR antagonists hinder their formation. These results suggest that enhanced compartmentalization of AR and co-activators at SEs may play an important role in the activation of oncogenic transcription programs in PCa. A better understanding of the assembly and the regulation of these AR-rich compartments may provide novel therapeutic options.

cancer biology↗

bioRxiv2021

piRNA pathway is essential for generating functional oocytes in golden hamster

Piwi-interacting RNAs (piRNAs) are small RNAs predominantly expressed in germ cells that are critical for gametogenesis in various species. However, PIWI-deficient female mice are fertile and mouse oocytes express a panel of small RNAs that do not appear widely representative of mammals, and piRNA function in the oogenesis of other mammals has therefore remained elusive. Recent studies revealed the small RNA and PIWI transcriptional profiles in golden hamster oocytes more closely resemble that of humans than mice. Herein, we generated PIWIL1-, PLD6- and MOV10L1-deficient golden hamsters and found that all female mutants were sterile, with embryos arrested at the two-cell stage. In PIWIL1 mutant oocytes, we observed transposon accumulation and broad transcriptomic dysregulation, while zygotic gene activation was impaired in early embryos. Intriguingly, PIWIL1-piRNAs exhibited a unique, preferential silencing of endogenous retroviruses (ERVs), whereas silencing LINE1s depended on both PIWIL1- and PIWIL3-piRNAs. Moreover, we showed that piRNAs participate in the degradation of maternal mRNAs in MII oocytes and embryos via partially complementary targets. Together, our findings demonstrate that piRNAs are indispensable for generating functional oocytes in golden hamster, and show the informative value of this model for functional and mechanistic investigations of piRNAs, especially those related to female infertility.

developmental biology↗

bioRxiv2021

Discovery of SARS-CoV-2 papain-like protease inhibitors through a combination of high-throughput screening and FlipGFP-based reporter assay

The papain-like protease (PLpro) of SARS-CoV-2 is a validated antiviral drug target. PLpro is involved in the cleavage of viral polyproteins and antagonizing host innate immune response through its deubiquitinating and deISG15ylating activities, rendering it a high profile antiviral drug target. Through a FRET-based high-throughput screening, several hits were identified as PLpro inhibitors with IC50 values at the single-digit micromolar range. Subsequent lead optimization led to potent inhibitors with IC50 values ranging from 0.56 to 0.90 {micro}M. To help prioritize lead compounds for the cellular antiviral assay against SARS-CoV-2, we developed the cell-based FlipGFP assay that is suitable for quantifying the intracellular enzymatic inhibition potency of PLpro inhibitors in the BSL-2 setting. Two compounds selected from the FlipGFP-PLpro assay, Jun9-53-2 and Jun9-72-2, inhibited SARS-CoV-2 replication in Caco-2 hACE2 cells with EC50 values of 8.89 and 8.32 {micro}M, respectively, which were 3-fold more potent than GRL0617 (EC50 = 25.1 {micro}M). The X-ray crystal structures of PLpro in complex with GRL0617 showed that binding of GRL0617 to SARS-CoV-2 induced a conformational change in the BL2 loop to the more closed conformation. Overall, the PLpro inhibitors identified in this study represent promising starting points for further development as SARS-CoV-2 antivirals, and FlipGFP-PLpro assay might be a suitable surrogate for screening PLpro inhibitors in the BSL-2 setting.

pharmacology and toxicology↗

bioRxiv2021

Natural genetic variation drives microbiome selection in the Caenorhabditis elegans gut

Host genetic landscapes can shape microbiome assembly in the animal gut by contributing to the establishment of distinct physiological environments. However, the genetic determinants contributing to the stability and variation of these microbiome types remain largely undefined. Here, we use the free-living nematode Caenorhabditis elegans to identify natural genetic variation among wild strains of C. elegans strains that drives assembly of distinct microbiomes. To achieve this, we first established a diverse model microbiome that represents the phylogenetic and functional diversity naturally found in the C. elegans microbiome. Using this community, we show that C. elegans utilizes immune, xenobiotic and metabolic signaling pathways to favor the assembly of different microbiome types. Variations in these pathways were associated with the enrichment for specific commensals, including the Alphaproteobacteria Ochrobactrum. Using RNAi and mutant strains, we showed that host selection for Ochrobactrum is mediated specifically by host insulin signaling pathways. Ochrobactrum recruitment is blunted in the absence of daf-2/IGFR and requires the insulin signaling transcription factors daf-16/FOXO and pqm-1/SALL2. Further, the ability of C. elegans to enrich for Ochrobactrum is correlated positively with host outcomes, as animals that develop faster are larger and have higher gut Ochrobactrum colonization as adults. These results highlight a new role for the highly conserved insulin signaling pathways in the regulation of microbiome composition in C. elegans.

microbiology↗

bioRxiv2021

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding

MotivationLong non-coding RNAs (IncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. ResultsWe proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences, and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared to traditional machine learning models with k-mer features and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also provided a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. AvailabilityThe DeepLncLoc web server, source code and datasets are freely available at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and https://github.com/CSUBioGroup/DeepLncLoc. Contactlimin@mail.csu.edu.cn

bioinformatics↗

bioRxiv2021

Spike mutations in SARS-CoV-2 variants confer resistance to antibody neutralization

New SARS-CoV-2 variants continue to emerge from the current global pandemic, some of which can replicate faster and with greater transmissibility and pathogenicity. In particular, UK501Y.V1 identified in UK, SA501Y.V2 in South Africa, and BR501Y.V3 in Brazil are raising serious concerns as they spread quickly and contain spike protein mutations that may facilitate escape from current antibody therapies and vaccine protection. Here, we constructed a panel of 28 SARS-CoV-2 pseudoviruses bearing single or combined mutations found in the spike protein of these three variants, as well as additional nine mutations that within or close by the major antigenic sites in the spike protein identified in the GISAID database. These pseudoviruses were tested against a panel of monoclonal antibodies (mAbs), including some approved for emergency use to treat SARS-CoV-2 infection, and convalescent patient plasma collected early in the pandemic. SA501Y.V2 pseudovirus was the most resistant, in magnitude and breadth, against mAbs and convalescent plasma, followed by BR501Y.V3, and then UK501Y.V1. This resistance hierarchy corresponds with Y144del and 242-244del mutations in the N-terminal domain as well as K417N/T, E484K and N501Y mutations in the receptor binding domain (RBD). Crystal structural analysis of RBD carrying triple K417N-E484K-N501Y mutations found in SA501Y.V2 bound with mAb P2C-1F11 revealed a molecular basis for antibody neutralization and escape. SA501Y.V2 and BR501Y.V3 also acquired substantial ability to use mouse and mink ACE2 for entry. Taken together, our results clearly demonstrate major antigenic shifts and potentially broadening the host range of SA501Y.V2 and BR501Y.V3, which pose serious challenges to our current antibody therapies and vaccine protection.

immunology↗

bioRxiv2021

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners.

neuroscience↗