Biology subjects

Luo, S.

Publications and source records attributed to Luo, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2022

The protist ubiquitin ligase effector PbE3-2 targets RD21A to impede plant immunity

Clubroot caused by the soil-borne protist pathogen Plasmodiophora brassicae is one of the most devastating diseases of Brassica oil and vegetable crops worldwide. Understanding the pathogen infection strategy is crucial for the development of disease control. However, the molecular mechanism by which this pathogen promotes infection remains largely unknown. Here, we identified a P. brassicae-secreted effector PbE3-2 that impedes plant immunity by ubiquitinating the immune regulator RD21A for degradation. Overexpression of PbE3-2 in Arabidopsis thaliana resulted in higher susceptibility to P. brassicae and decreases in chitin-triggered reactive oxygen species burst and expression of marker genes in salicylic acid signaling. PbE3-2 interacted with and ubiquitinated RD21A in vitro and in vivo. Mutant plants deficient in RD21A exhibited similar susceptibility and compromsied immune responses as in PbE3-2 overexpression plants. These results suggest that P. brassicae promotes clubroot disease through RD21A degradation mediated by the effector PbE3-2. As PbE3-2 is widely conserved across different P. brassicae pathotypes, the degradation of RD21A by PbE3-2 might be a prevalent infection strategy in this pathogen.

microbiology↗

bioRxiv2022

Humanized V(D)J-rearranging and TdT-expressing Mouse Vaccine Models with Physiological HIV-1 Broadly Neutralizing Antibody Precursors

Antibody heavy chain (HC) and light chain (LC) variable region exons are assembled by V(D)J recombination. V(D)J junctional regions encode complementarity-determining-region 3 (CDR3), an antigen-contact region immensely diversified through non-templated nucleotide additions ("N-regions") by terminal deoxynucleotidyl transferase (TdT). HIV-1 vaccine strategies seek to elicit human HIV-1 broadly neutralizing antibodies (bnAbs), such as the potent CD4-binding site VRC01-class bnAbs. Mice with primary B cells that express receptors (BCRs) representing bnAb precursors are used as vaccination models. VRC01-class bnAbs uniformly use human HC VH1-2 and commonly use human LCs V{kappa}3-20 or V{kappa}1-33 associated with an exceptionally short 5-amino-acid (5-aa) CDR3. Prior VRC01-class models had non-physiological precursor levels and/or limited precursor diversity. Here, we describe VRC01-class rearranging mice that generate more physiological primary VRC01-class BCR repertoires via rearrangement of VH1-2, as well as V{kappa}1-33 and/or V{kappa}3-20 in association with diverse CDR3s. Human-like TdT expression in mouse precursor B cells increased LC CDR3 length and diversity and also promoted generation of shorter LC CDR3s via N-region suppression of dominant microhomology-mediated V{kappa}-to-J{kappa} joins. Priming immunization with eOD-GT8 60mer, which strongly engages VRC01 precursors, induced robust VRC01-class germinal center (GC) B cell responses. V{kappa}3-20-based responses were enhanced by N-region addition, which generates V{kappa}3-20-to-J{kappa} junctional sequence combinations that encode VRC01-class 5-aa CDR3s with a critical E residue. VRC01-class-rearranging models should facilitate further evaluation of VRC01-class prime and boost immunogens. These new VRC01-class mouse models establish a prototype for generation of vaccine-testing mouse models for other HIV-1 bnAb lineages that employ different HC or LC Vs. Significance StatementMouse models that express human precursors of HIV-1 broadly neutralizing antibodies (bnAbs) are useful for evaluating vaccination strategies for eliciting such bnAbs in humans. Prior models were handicapped by non-physiological frequency and/or diversity of B lymphocytes that express the bnAb precursors. We describe a new class of mouse models in which the mice express humanized bnAb precursors at a more physiologically relevant level through developmental rearrangement of both antibody heavy and light chain gene segments that encode the precursors. The model also incorporated a human enzyme that diversifies the rearranging gene segments and promotes generation of certain variable region sequences needed for the response. This new class of mouse models should facilitate preclinical evaluation of candidate human HIV-1 vaccination strategies.

immunology↗

bioRxiv2022

Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq data. We proposed a data analysis framework to prioritize prognostic-associated subpopulations based on relative expression orderings (REOs). Cell type specific gene pairs (C-GPs) were identified to evaluate prognostic value for each cell type. We found REOs-based signatures could accurately classify most cell subtypes. C-GPs achieves higher precision compared with four current methods. Moreover, we developed single-cell gene pair signatures to predict recurrence risk for patients individually. Fibro_SGK1 cells and IgA+ IGLC2+ B cells were novel prognostic-associated subpopulations. A user-friendly toolkit, scRankXMBD (https://github.com/xmuyulab/scRank-XMBD), was developed to enable implementation of this framework. Our work facilitate the application of the rank-based method in scRNA-seq data for prognostic biomarker discovery and precision oncology.

bioinformatics↗

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

High-resolution de novo structure prediction from primary sequence

Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) to accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan proteins or fast-evolving proteins like antibodies, and a protein typically folds in a natural setting from its primary amino acid sequence into its three-dimensional structure, suggesting that evolutionary information and MSAs should not be necessary to predict a proteins folded form. Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures. OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution. Our study fills a much-encountered gap in structure prediction and brings us a step closer to understanding protein folding in nature.

bioinformatics↗

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

Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models

Antibodies are immune system proteins that protect the host by binding to specific antigens such as viruses and bacteria. The binding between antibodies and antigens is mainly determined by the complementarity-determining regions (CDR) of the antibodies. In this work, we develop a deep generative model that jointly models sequences and structures of CDRs based on diffusion probabilistic models and equivariant neural networks. Our method is the first deep learning-based method that generates antibodies explicitly targeting specific antigen structures and is one of the earliest diffusion probabilistic models for protein structures. The model is a "Swiss Army Knife" capable of sequence-structure co-design, sequence design for given backbone structures, and antibody optimization. We conduct extensive experiments to evaluate the quality of both sequences and structures of designed antibodies. We find that our model could yield competitive results in binding affinity measured by biophysical energy functions and other protein design metrics.

bioinformatics↗

bioRxiv2022

Identification and targeting of a unique NaV1.7 domain driving chronic pain

Despite identification of several small molecules directly targeting the voltage-gated sodium channel NaV1.7, none has been clinically successful. We reported that preventing addition of a small ubiquitin-like modifier (SUMO) on the NaV1.7-interacting cytosolic collapsin response mediator protein 2 (CRMP2) blocked NaV1.7 functions and was antinociceptive in rodents. Here, we discovered a 15 amino acid CRMP2 regulatory sequence (CRS) unique to NaV1.7 that is essential for this regulatory coupling. CRMP2 preferentially bound to the NaV1.7 CRS over other isoforms. Substitution of the NaV1.7 CRS with the homologous domains from the other eight voltage-gated sodium channel isoforms decreased tetrodotoxin-sensitive NaV1.7 currents in rodent sensory neurons. A cell-penetrant version of NaV1.7-CRS reduced NaV1.7 currents and trafficking, decreased presynaptic NaV1.7 localization, reduced spinal neurotransmitter release, and reversed mechanical allodynia in a rat spared nerve injury model of neuropathic pain. Interfering with NaV1.7-CRMP2 coupling did not produce motor impairment and spared thermal, inflammatory, and post-surgical nociception. As proof-of-concept for NaV1.7-targeted gene therapy, we found that NaV1.7-CRS packaged into an adeno-associated virus recapitulated the effects on NaV1.7 function in both rodent and rhesus macaque sensory neurons and both reversed and prevented the development of mechanical allodynia in a neuropathic pain model in male and female rodents. One Sentence SummaryA novel regulatory domain on the voltage gated sodium channel NaV1.7 that can be targeted to produce analgesia.

neuroscience↗

bioRxiv2022

Altered virome structrue and function characterization in Helicobacter pylori-driven colorectal carcinogenesis and H. pylori eradication

The understanding of gut virome and its role in Helicobacter pylori-driven colorectal cancer (CRC), as well as the long-term impact of H. pylori eradication via antibiotic treatment on it could contribute to better understanding the mechanisms of the disruption of gut bacteriome homeostasis involved in H. pylori-driven colorectal carcinogenesis and antibiotic therapy for H. pylori eradication. In the dynamic analysis of viral genome shotgun metagenomic of samples from lower gastrointestinal tract of the Apc+/1638N and C57BL/6 mice with H. pylori infection and eradication, stable viral abundance and replacement of bursted unique viral contigs in infected and uninfected Apc+/1638N mice were observed. Temperate phages, which encoding comprehensive microbial functional genes and targeting various susceptible hosts, were expanded extremely prior to cancer exacerbation. In addition, short-term antibiotic exposure for H. pylori eradication was able to alter the gut virome and thrive the antibiotic resistance genes (ARGs) in the viral genome for at least 6 months. Collectively, these results point toward a potential role of the altered, but dynamically balanced gut virome, characterized by the expanded temperate phages, in contributing to the H. pylori-driven CRC, and indicate that viral genome may act as ARG reservoir for the antibiotic resistance of bacteria after the antibiotics therapy to H. pylori eradication.

microbiology↗

bioRxiv2022

Humanized antibody potently neutralizes all SARS-CoV-2 variants by a novel mechanism

SARS-CoV-2 Omicron variants have generated a world-wide health crisis due to resistance to most approved SARS-CoV-2 neutralizing antibodies and evasion of antibodies induced by vaccination. Here, we describe the SARS-CoV-2 neutralizing SP1-77 antibody that was generated from a humanized mouse model with a single human VH1-2 and V{kappa}1-33-associated with immensely diverse complementarity-determining-region-3 (CDR3) sequences. SP1-77 potently and broadly neutralizes SARS-CoV-2 variants of concern and binds the SARS-CoV-2 spike protein receptor-binding-domain (RBD) via a novel-CDR3-based mode. SP1-77 does not block RBD-binding to the ACE2-receptor or endocytosis step of viral entry, but rather blocks membrane fusion. Our findings provide the first mechanistic insight into how a non-ACE2 blocking antibody potently neutralizes SARS-CoV-2, which may inform strategies for designing vaccines that robustly neutralize current and future SARS-CoV-2 variants.

immunology↗

bioRxiv2022

DNA packaging via hierarchical chromatin structures revealed by live-cell 3D imaging

Single-molecule localization microscopy is a powerful superresolution imaging technique to study biological questions by visualizing subcellular fine structures with nanometer-scale precision. However, its application in live-cell imaging studies has been impeded by the paucity of self-blinking organic fluorophores that enable high spatiotemporal resolution and labeling/localization density at a moderate laser intensity. Herein, we report a self-blinking Si-rhodamine dye 6-HESiR with a suitably increased "ON" fraction and a fluorogenic self-blinking dsDNA probe 6-HoeHESiR as a powerful tool for 3D superresolution imaging of native chromatin in eukaryotes without the use of photoswitching buffer and high laser intensity. With the probe 6-HoeHESiR, 3D superresolution imaging of in vitro reconstituted nucleosomal arrays and chromatin fibers yielded results consistent with EM analysis. Similar euchromatin and heterochromatin structures were visualized in fixed and live cells with high spatiotemporal resolution and labeling density, providing the first live-cell evidence for a hierarchical model of chromatin organization. 3D imaging results obtained in the presence of selective inhibitors of histone deacetylases also corroborate chromatin fiber decompaction upon hyperacetylation of histones.

cell biology↗

bioRxiv2022

A therapeutically targetable NOTCH1-SIRT1-KAT7 axis in T-cell Leukemia

T-cell Acute Lymphoblastic Leukemia (T-ALL) is a NOTCH1-driven disease in need of novel therapies. Here, we identify a NOTCH1-SIRT1-KAT7 link as a therapeutic vulnerability in T-ALL, in which SIRT1 is overexpressed downstream of a novel NOTCH1-bound enhancer. SIRT1 loss impairs leukemia generation, while SIRT1 overexpression accelerates leukemia and confers resistance to NOTCH1 inhibition in a deacetylase-dependent manner. Moreover, secondary SIRT1 loss extends survival and synergizes with NOTCH1 inhibition. Global acetyl-proteomics upon SIRT1 loss uncovered hyperacetylation of KAT7 and BRD1, subunits of a histone acetyltransferase complex targeting H4K12. Metabolic and gene expression profiling revealed a metabolic crisis together with a transcriptional signature resembling KAT7 deletion. Consistently, SIRT1 loss resulted in reduced H4K12ac, and overexpression of a non-acetylatable KAT7 mutant partly rescued SIRT1 loss-induced proliferation defects. The newly unveiled NOTCH1-SIRT1-KAT7 axis uncovers novel therapeutic targets in T-ALL and reveals a circular feedback mechanism balancing deacetylase/acetyltransferase activation with potentially broad relevance in cancer. Statement of significanceWe identified a novel axis in T-ALL whereby NOTCH1 activates SIRT1 through an enhancer region, and SIRT1 deacetylates and activates KAT7. Targeting SIRT1 shows antileukemic effects, partly mediated by KAT7 inactivation. Our results identify novel therapeutic targets and uncover a rheostat mechanism between deacetylase/acetyltransferase activities with potentially broader cancer relevance.

cancer biology↗

bioRxiv2022

Tree diversity effects on productivity depend on mycorrhizae and life strategies in a temperate forest experiment

Tree species are known to predominantly interact either with arbuscular mycorrhizal (AM) or ectomycorrhizal (EM) fungi. However, there is a knowledge gap whether these mycorrhizae differently influence biodiversity-ecosystem functioning (BEF) relationships and whether a combination of both can increase community productivity. In 2015, we established a tree-diversity experiment by growing tree communities with varying species-richness levels (1, 2, or 4 species), and either with AM or EM tree species, or a combination of both. We investigated basal area and annual basal area increment from 2015 to 2020 as proxy for community productivity. We found significant positive relationships between tree species richness and community productivity, which strengthened over time. Further, AM and EM tree species differently influenced productivity; however, there was no overyielding when AM and EM trees grew together. EM tree communities were characterized by low productivity in the beginning, but an increase of increment over time, and showed overall strong biodiversity effects. For AM tree communities the opposite was true. While young trees did not benefit from the presence of the other mycorrhizal type, dissimilar mechanisms underlying BEF relationships in AM and EM trees indicate that maximizing tree and mycorrhizal diversity may increase ecosystem functioning in the long run.

ecology↗

bioRxiv2022

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, and results in a series of discontinuous bursts of mRNAs. A challenge is understanding how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this issue, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpsons paradoxes, i.e., genome-wide burst kinetics exhibited different characteristics in two cases without and with distinguishing feedback regulations. We show that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

systems biology↗

bioRxiv2022

Comparative analysis about the structure of complete chloroplast genomes in genus Mangifera and accuracy verification about phylogenetic analysis based on gene ycf2 in genus level

Mango is an evergreen plant belonging to the genus Mangifera of the Anacardiaceae family. Genus Mangifera has 69 species of Mangifera around the world that are mainly distributed in tropical and subtropical countries, including India, Indonesia, the Malay Peninsula, Thailand, and South China. It is a popular tropical fruit known as the "King of Tropical Fruits". However, the study of the structure information of the complete chloroplast genome of Mangifera was microscopic, there was no report about the comparison of SSR, Ka/Ks, codons analysis and RNA editing, so in this study, we sequenced the 6 Mangifera samples and used three different ways to analyze the relationship of 6 species of Mangifera. Then we got some results, through the RNA editing and Ka/Ks calculating, we found the species could be divided into two groups, and the difference between the two groups was protein-coding gene ccsA. Moreover, all RNA editing occurred conversion of C to T and the gene ndhB had the most RNA editing sites in all species. In Ka/Ks analysis, the gene atpB, cemA, clpP, ndhD, petD, petB and ycf15 would be suffered from the positive selection after divergence. We also find the IR regions in these seven samples were very conservation through IR contraction and expansion and Sequence Divergence Analysis. Finally, we tried to confirm the relationship between 7 samples of Mangifera in Angiosperms in 3 different ways. Then we got that ML210 and MP090 had a closer relationship than others, MS796 had a closer relationship with ML210 and MP090 than others. At the same time, the method of phylogenetic analysis based on the gene ycf2 was not more accurate at the genus level than the method based on complete cp genome and proteincoding genes.

molecular biology↗

bioRxiv2022

Fertilizer reduction with bio-organic fertilizer to regulate the root soil microbial community structure to improve Baby Chinese cabbage yield

Using high-throughput sequencing, this study aimed to explore the response of soil microbial community and Baby Chinese cabbage yield to the reduction of chemical fertilizers combined with bio-organic fertilizer in the Gansu plateau, China. Our experiments consisted of conventional fertilizer (CK), 30% chemical fertilizer reduction + 6,000 kg bio-organic fertilizer (T1), 30% chemical fertilizer reduction + 9,000 kg bio-organic fertilizer (T2), 40% chemical fertilizer reduction + 6,000 kg bio-organic fertilizer (T3), and 40% chemical fertilizer reduction + 9,000 kg bio-organic fertilizer (T4). Compared with CK, soil microbial diversity and richness were higher for all treatments with added bio-organic fertilizer. PCoA showed that the bacterial and fungal communities in T2 and T4 were similar to each other. Redundancy and Spearmans correlation analyses of microbial communities and soil physicochemical properties revealed that reductions in chemical fertilizer rate combined with bio-organic fertilizer had a stronger impact on the fungal than the bacterial community. They also increased the relative abundance of the dominant bacterial and fungal phyla. Baby Chinese cabbage yield was relatively higher under the combined bio-organic fertilizer plus reduced chemical fertilizer rate with T2 showing the highest yield. Therefore, this approach is feasible for sustainable agricultural, cost-effective and profitable crop production. ImportanceO_LIBio-organic + moderately reduced chemical fertilizer raised Chinese cabbage yield C_LIO_LIBio-organic + chemical fertilizer was more efficacious than either one alone C_LIO_LIPresence of bio-organic fertilizer enhanced overall rhizosphere physicochemistry C_LIO_LIBio-organic fertilizer improved beneficial bacterial & fungal abundance & diversity C_LIO_LIFertilizer combination sustainably & cost-effectively improves crop & soil quality C_LI

microbiology↗

bioRxiv2022

TrieDedup: A fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing

BackgroundHigh-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, Ns. PCR duplicates introduced in library preparation should usually be removed in genomics studies, and several deduplication tools have been developed for this purpose. However, two identical reads may appear different due to ambiguous bases and the existing tools cannot address Ns correctly or efficiently. ResultsHere we proposed and implemented TrieDedup, which uses trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base Ns, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage. ConclusionsTrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment and repertoire diversity analysis of large scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors. AvailabilityTrieDedup is available at https://github.com/lolrenceH/TrieDedup

bioinformatics↗

bioRxiv2022

A highly attenuated SARS-CoV-2 related pangolin coronavirus variant has a 104nt deletion at the 3'-terminus untranslated region

SARS-CoV-2 related coronaviruses (SARS-CoV-2r) from Guangdong and Guangxi pangolins have been implicated in the emergence of SARS-CoV-2 and future pandemics. We previously reported the culture of a SARS-CoV-2r GX_P2V from Guangxi pangolins. Here we report the GX_P2V isolate rapidly adapted to Vero cells by acquiring two genomic mutations: an alanine to valine substitution in the nucleoprotein and a 104-nucleotide deletion in the hypervariable region (HVR) of the 3-terminus untranslated region (3-UTR). We further report the characterization of the GX_P2V variant in in vitro and in vivo infection models. In cultured Vero and BGM cells, the GX_P2V variant produced minimal cell damage and small plaques. The GX_P2V variant infected golden hamsters and BALB/c mice but was highly attenuated. Golden hamsters infected intranasally had a short duration of productive infection. These productive infections induced neutralizing antibodies against pseudoviruses of GX_P2V and SARS-CoV-2. Collectively, our data show that the GX_P2V variant is highly attenuated in in vitro and in vivo infection models. Attenuation of the variant is likely due to the 104-nt deletion in the HVR in the 3-UTR. This study furthers our understanding of pangolin coronaviruses pathogenesis and provides novel insights for the design of live attenuated vaccines against SARS-CoV-2.

microbiology↗