Biology subjects

Qin, G.

Publications and source records attributed to Qin, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Deep Learning Unravels Potential Antibiotic-Resistance Drugs against Klebsiella Pneumoniae

BackgroundKlebsiella pneumoniae (KP) is a critical pathogen recognized by the World Health Organization due to its high mortality rates and resistance to antibiotics. Developing effective treatments for KP is essential to mitigate the growing threat of multidrug-resistant infections. This study leverages deep learning techniques to identify and validate potential drug candidates against KP, accelerating the drug discovery process and reducing associated risks. ResultsA comprehensive dataset was constructed, comprising 3,475 drugs selected from the DrugBank database, filtered for their potential efficacy and safety. Using convolutional neural networks (CNNs) for drug-drug interaction analysis, a prediction accuracy of 72% was achieved. Evolutionary Scale Modeling (ESM) was employed to calculate molecular similarities between drugs and KP strains, identifying five promising candidates with structural similarities exceeding 85% to known KP targets. A drug repurposing knowledge graph (DRKG) further validated these findings, with the PairRE model outperforming other approaches in ranking potential drug candidates. ConclusionsThis study presents a scalable and efficient framework for identifying and validating drug candidates to combat antibiotic-resistant KP. By integrating deep learning models, molecular similarity analysis, and knowledge graph techniques, this approach shortens the drug discovery timeline from years to months. The identified candidates hold significant promise for preclinical and clinical testing, offering a pathway to effective therapies against KP and other multidrug-resistant pathogens.

bioinformatics↗

bioRxiv2024

Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data

As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy. Effective transformation of data into information requires harmonization of stakeholder goals, implementation, enforcement of standards regarding quality and completeness, and availability of resources for maintenance and updates. Systems such as the Biomedical Data Translator leverage knowledge graphs (KGs), structured and machine learning readable knowledge representation, to encode knowledge extracted through inference. We focus here on the transformation of data from multiomics datasets and EHRs into compact knowledge, represented in a KG data structure. We demonstrate this data transformation in the context of the Translator ecosystem, including clinical trials, drug approvals, cancer, wellness, and EHR data. These transformations preserve individual privacy. We provide access to the five resulting KGs through the Translator framework. We show examples of biomedical research questions supported by our KGs, and discuss issues arising from extracting biomedical knowledge from multiomics data.

bioinformatics↗

bioRxiv2024

Deep Learning with Evolutionary Scale Modeling to discover Antibiotic-Resistance Drug against Klebsiella Pneumoniae

The development of effective antibiotics is crucial in combating antibiotic-resistant pathogens such as Klebsiella pneumoniae (KP). This study utilizes deep learning, specifically Evolutionary Scale Modeling (ESM), to identify potential drug candidates. By analyzing molecular similarities between known drugs and KP strains, we aim to accelerate the discovery of new treatments and reduce the risks associated with clinical trials. This approach may shed new lights in the field of drug repositioning.

bioinformatics↗

bioRxiv2024

Aryl-hydrocarbon receptor in smooth muscle cells protect against dioxin induced adverse remodeling of atherosclerosis

IntroductionEnvironmental exposure to dioxin has been linked to increased myocardial infarction. Smooth muscle cells (SMC) in the coronary vasculature play a critical role in atherosclerotic plaque remodeling due to their phenotypic plasticity, however, the detailed mechanism linking dioxin exposure to adverse SMC modulation is not well understood. MethodsSingle-cell RNA and ATAC sequencing and histological analyses were performed on the aorta from mouse models of atherosclerosis exposed to 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) or control. Primary human coronary artery SMC (HCASMC) treated in culture with TCDD were used to perform RNA-Seq, ATAC-Seq, and functional phenotypic assays. ChIP-Seq was performed with antibodies against Aryl-hydrocarbon receptor (AHR) and TCF21, two of known SMC modulating transcription factors. ResultsModulated SMC were the most transcriptionally responsive cell type to dioxin in the atherosclerotic aorta. Dioxin accelerated disease phenotype by promoting a modulated SMC phenotype early, resulting in increased lesion size, migration of SMC, and macrophage recruitment to the lesion. We found C3 expressing modulated SMCs to be likely contributing to the increased macrophage recruitment and inflammation. Analysis of the RNA-Seq data from HCASMC treated with TCDD showed differential enrichment of biological pathways related to cell migration, localization, and inflammation. Furthermore, ATAC-Seq data showed a significant activation for pathways regulating vascular development, cell migration, inflammation, and apoptosis. With TCDD treatment, there was also enrichment of AHR ChIP-Seq peaks, while the TCF21 enrichment decreased significantly. The SMC-specific Ahr knockout resulted in increased oxidative stress in SMC, increased lesion size and macrophage content, and loss of SMC lineage cells in the lesion cap when exposed to TCDD, consistent with a more vulnerable plaque phenotype. ConclusionDioxin adversely remodels atherosclerotic plaque by accelerating the SMC- phenotypic modulation, and increasing inflammation and oxidative stress resulting in increased macrophage recruitment and lesion size. Dioxin may adversely affect the SMC phenotype and disease state by affecting the TCF21 occupancy in the open chromatin regions. Furthermore, we observed that SMC-specific deletion of Ahr in mice resulted in worsening of dioxin mediated SMC modulation and atherosclerosis, suggesting that Ahr in SMC confers protection against dioxin by promoting a stable plaque phenotype and reducing dioxin induced oxidative stress. SummaryO_LIExposure to dioxin, an environmental pollutant present in tobacco smoke and air pollution, accelerates smooth muscle cell modulation, and atherosclerosis. C_LIO_LIDioxin exposure leads to inflammatory smooth muscle cell phenotype characterized by complement pathway activation and increased macrophage recruitment to plaque C_LIO_LIAryl-hydrocarbon receptor in SMC protects against oxidative stress, and promotes a stable plaque phenotype C_LI

molecular biology↗

bioRxiv2024

Merging logical models: An application in Acute Myeloid Leukemia modeling

MotivationGene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of automated approaches for merging existing models. ResultsWe present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (d) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases. Availability and implementationThe workflow and accompanying tools, including modules for model standardization, automated logical model merging, and evaluation, are available at https://github.com/IlyaLab/LogicModelMerger/.

systems biology↗

bioRxiv2024

A drought stress-responsive metabolite malate modulates stomatal responses through G-protein-dependent pathway

Drought stress is a significant environmental threat to global agricultural production and distribution. Plant adaptation to dehydration stress involves intricate biological processes with substantial changes in metabolite composition. In this study, we investigated the role of tricarboxylic acid (TCA) cycle metabolites in drought tolerance in grapevine and Arabidopsis by metabolome, live cell imaging, electrophysiological and pharmacological approaches. Metabolome analysis revealed that amount of malate, citrate, and isocitrate increased over time in detached grapevine leaves. Ca2+ imaging and ion channel measurements indicated that fumarate, malate, and -ketoglutarate induced cytosolic free Ca2+ concentration ([Ca2+]cyt) elevation in guard cells and directly activated a guard-cell anion channel SLAC1. However, only malate induced stomatal closure, which required increases in [Ca2+]cyt in guard cells and activation of SLAC1. Through pharmacological experiments and reverse genetics analyses, G-proteins were identified as essential components of malate signaling by regulating second messenger production. These results indicate that TCA cycle metabolites are sensed individually by guard cells and that malate plays a key role in connecting metabolic regulation and drought tolerance through G-protein-dependent signal cascades.

plant biology↗

bioRxiv2024

Super Pangenome of Grapevines Empowers Improvement of the Oldest Domesticated Fruit

Grapevine (Vitis) is the oldest domesticated fruit crop with great cultural and economic importance. Here, we assemble and annotate haplotype-resolved genomes of 72 Vitis accessions including 25 wild and 47 cultivated grapevines, and a haplotype-resolved complete genome of V. vinifera. Coalescent phylogenomics of 142 haplotype genomes disentangles the mysterious hybridization history of grapevines, revealing enormous genetic diversity among species. Pangenome analysis together with phenotyping data reveals that European cultivars, more susceptible to the most destructive disease downy mildew (DM), had a smaller repertoire of disease resistance genes of NLR family. Through extensive structural variation (SV) characterization, phenotyping, transcriptome profiling of 113 Vitis accessions, and SV-eQTL analysis, we have identified over 79 SVs and their relevant genes significantly associated with DM resistance, exemplified by a lysine histidine transporter, VvLHT8. This haplotype-resolved complete genome and pangenome of Vitis genus will accelerate grapevine breeding and enrich our understanding of the evolution and biology of grapevines.

plant biology↗

bioRxiv2024

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

DNA methylation (DNAm) has been shown to mediate causal effects from traumatic experiences to post-traumatic stress disorder (PTSD). However, the scientific question about whether the mediation effect changes over time remains unclear. In this paper, we develop time-varying structural equation models to identify cytosine-phosphate-guanine (CpG) sites where DNAm mediates the effect of trauma exposure on PTSD, and to capture dynamic changes in mediation effects. The proposed methodology is motivated by the Detroit Neighborhood Health Study (DNHS) with high-dimensional and longitudinal DNAm measurements. To handle the non-monotone missing DNAm in the dataset, we propose a novel Longitudinal Multiple Imputation (LMI) method utilizing dependency among repeated measurements, and employ the generalized method of moments to integrate the multiple imputations. Simulations confirm that the proposed method outperforms existing approaches in various longitudinal settings. In DNHS data analysis, our method identifies several CpG sites where DNAm exhibits dynamic mediation effects. Some of the corresponding genes have been shown to be associated with PTSD in the existing literature, and our findings on their time-varying effects could deepen the understanding of the mediation role of DNAm on the causal path from trauma exposure to PTSD risk.

bioinformatics↗

bioRxiv2023

Armillaria korhonenii, the sixteenth biological species of Armillaria from China

Armillaria korhonenii, designated as Chinese biological species Q (CBSQ) here, is described as a new species based on phylogenetic and morphological evidence as well as the results of mating tests. This species is the sixteenth biological species of Armillaria reported from China following the discovery of 15 previously known biological species. Phylogenetically, A. korhonenii is the earliest divergent lineage of the genus Armillaria as inferred from the nucleotide sequences of the Elongation Factor 1-Alpha (EF1a). Morphologically, it is characterized by its ellipsoid to elongate basidiospores, yellowish orange to honey yellow pileus with fibrillose squamules. Mating tests indicated that A. korhonenii is a tetrapolar heterogeneous species and is inter-sterile with all the tested species except partial compatibility with CBSH.

microbiology↗

bioRxiv2023

Ras-mediated homeostatic control of front-back signaling dictates cell polarity

Studies in the model systems, Dictyostelium amoebae and HL-60 neutrophils, have shown that local Ras activity directly regulates cell motility or polarity. Localized Ras activation on the membrane is spatiotemporally regulated by its activators, RasGEFs, and inhibitors, RasGAPs, which might be expected to create a stable front and back, respectively, in migrating cells. Focusing on C2GAPB in amoebae and RASAL3 in neutrophils, we investigated how Ras activity along the cortex controls polarity. Since existing gene knockout and overexpression studies can be circumvented, we chose optogenetic approaches to assess the immediate, local effects of these Ras regulators on the cell cortex. In both cellular systems, optically targeting the respective RasGAPs to the cell front extinguished existing protrusions and changed the direction of migration, as might be expected. However, when the expression of C2GAPB was induced globally, amoebae polarized within hours. Furthermore, within minutes of globally recruiting either C2GAPB in amoebae or RASAL3 in neutrophils, each cell type polarized and moved more rapidly. Targeting the RasGAPs to the cell backs exaggerated these effects on migration and polarity. Overall, in both cell types, RasGAP-mediated polarization was brought about by increased actomyosin contractility at the back and sustained, localized F-actin polymerization at the front. These experimental results were accurately captured by computational simulations in which Ras levels control front and back feedback loops. The discovery that context-dependent Ras activity on the cell cortex has counterintuitive, unanticipated effects on cell polarity can have important implications for future drug-design strategies targeting oncogenic Ras.

cell biology↗

bioRxiv2023

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing, ex vivo drug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predict ex vivo drug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients. One Sentence SummaryIntegrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

cell biology↗

bioRxiv2023

Syntaxin of plants 32 regulates pollen wall development and pollen tube cell wall integrity via controlling secretory pathway

Pollen tubes (PTs) elongate in a polar way to deliver sperm cells to the ovule. Pollen wall development and PT cell wall integrity (CWI) maintenance are critical for PT growth and double fertilization. Pollen wall development mainly relies on secretion of exine precursors in tapetum. RALF4/19-ANX/BUPS-MRI and RALF4/19-LRX-AUN are two distinct signaling pathways but converge to fine-tune CWI during PT growth. Here, we discovered that atsyp32+/-, AtSYP32 RNAi and AtSYP3132 RNAi lines were male sterile. The tapetum development in these lines were disturbed, and the pollen wall structure was impaired resulting in pollen grain and tube bursting and less PTs navigated to micropyles. Strikingly, there were numerous ectopic secretory vesicles retained in pollen cytoplasm, and the abundance or distribution of polysaccharides and AGPs altered significantly in PTs of the mutants and RNAi lines. AtSYP32 interacted with the vesicle transport regulators SEC31B, SEC22 and BET12, the PT CWI regulators RALF19 and LRX11, and the XyG xylosyltransferase XXT5, in the Golgi apparatus. Transcription of some genes related to pollen wall biosynthesis and PT CWI maintenance were seriously affected by AtSYP32 downregulation. Our findings illustrate that AtSYP32 plays essential roles in pollen wall development and PT CWI maintenance via controlling secretory pathway. IN A NUTSHELLO_ST_ABSBackgroundC_ST_ABSPollen wall is the most complex cell wall. Pollen wall development mainly relies on secretion of precursors of exine and pollen coat in tapetal cells. Pollen tubes (PTs) grow in a polar way to deliver sperm cells to the ovule. Maintenance of PT cell wall integrity (CWI) is critical for PT elongation and double fertilization. RALF4/19 ligands interact with BUPS-ANX receptors, signaling it in an autocrine manner to maintain CWI during PT elongation. RALF4/19-LRX-AUN pathway is distinct with RALF4/19-ANX/BUPS-MRI pathway but they converge to fine-tune CWI during PT growth. Biosynthesis of PT cell wall involves multiple subcellular compartments and vesicle transport pathways. Golgi apparatus acts as a hub in vesicle trafficking. Golgi-syntaxin AtSYP31 and AtSYP32 regulate pollen development by controlling intra-Golgi transport and Golgi morphology QuestionWhat is AtSYP32 role in pollen wall and tapetum development? Who are the AtSYP32 partners that regulate secretion of cell wall biosynthesis materials? FindingsWe found that no homozygote progeny was obtained from self-pollinated atsyp32+/- alleles due to pollen sterile. The tapetum development and degeneration in atsyp32+/- mutants was severely delayed, and the pollen wall and PT wall structure were impaired. Strikingly, there were numerous ectopic secretory vesicles retained in pollen cytoplasm in atsyp32+/- mutants, and the abundance or distribution of PT wall polysaccharides and AGPs altered obviously. AtSYP32 interacted with the vesicle transport regulators SEC31B, SEC22 and BET12, the PT CWI regulators RALF19 and LRX11, and XyG xylosyltransferase XXT5, in the Golgi. All these highlight that AtSYP32 regulates pollen wall development and maintenance of PT CWI via controlling secretory pathway. Next stepsThe biological significances and the molecular mechanisms of AtSYP32 interacting with XXT5, RALF19 and LRX11 are elusive but thought-provoking. We are going to clarify the mechanisms.

molecular biology↗

bioRxiv2022

ABLs and TMKs are co-receptors for extracellular auxin

Extracellular perception of auxin, an essential phytohormone in plants, has been debated for decades. Auxin binding protein 1 (ABP1) physically interacts with quintessential transmembrane kinases (TMKs) and was proposed to act as an extracellular auxin receptor, but its role was disputed because abp1 knockout mutants lack obvious morphological phenotypes. Here we identified two new auxin-binding proteins, ABL1 and ABL2, that are localized to the apoplast and directly interact with the extracellular domain of TMKs in an auxin-dependent manner. Furthermore, functionally redundant ABL1 and ABL2 genetically interact with TMKs and exhibit functions that are overlapping with those of ABP1 as well as independent of ABP1. Importantly, the extracellular domain of TMK1 itself binds auxin and synergizes with either ABP1 or ABL1 in auxin binding. Thus, our findings discovered new auxin receptors ABL1 and ABL2 having functions overlapping with but distinct from ABP1 and acting together with TMKs as co-receptors for extracellular auxin.

plant biology↗

bioRxiv2022

Subcellular Redox Responses Reveal Different Cu-dependent Antioxidant Defenses between Mitochondria and Cytosol

Excess intracellular Cu perturbs cellular redox balance and thus causes diseases. However, the relationship between cellular redox status and Cu homeostasis and how such an interplay is coordinated within cellular compartments has not yet been well established. Using combined approaches of organelle-specific redox sensor Grx1-roGFP2 and non-targeted proteomics, we investigate the real-time Cu-dependent antioxidant defenses of mitochondria and cytosol in live HEK293 cells. The Cu-dependent real-time imaging experiments show that CuCl2 treatment results in increased oxidative stress in both cytosol and mitochondria. In contrast, subsequent Cu depletion by BCS, a Cu chelating reagent, lowers oxidative stress in mitochondria but causes even higher oxidative stress in the cytosol. The proteomic data reveal that several mitochondrial proteins, but not cytosolic ones, undergo significant abundance change under Cu treatments. The proteomic analysis also shows that proteins with significant changes are related to mitochondrial oxidative phosphorylation and glutathione synthesis. The differences in redox behaviors and protein profiles in different cellular compartments reveal distinct mitochondrial and cytosolic response mechanisms upon Cu-induced oxidative stress. These findings provide insights into how redox and Cu homeostasis interplay by modulating specific protein expressions at the subcellular levels, shedding light on understanding the effects of Cu-induced redox misregulation on the diseases. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/500983v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@849766org.highwire.dtl.DTLVardef@efb723org.highwire.dtl.DTLVardef@1e097d5org.highwire.dtl.DTLVardef@105c62_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Near telomere-to-telomere nuclear phased chromosomes of the dikaryotic wheat fungus Rhizoctonia cerealis

Rhizoctonia cerealis (Rce), which causes sharp eyespot, is one of the most destructive wheat pathogens. However, the genetic and molecular virulence mechanisms of Rce have not been elucidated. As a dikaryotic organism, the haplotype phasing of this fungus has not been completed so far. We applied a haplotype phasing algorithm to generate a high-quality near telomere-to-telomere nuclear-phased genome sequence of Rce strain R0301. Sixteen pairs of chromosomes were assigned to the A and B genomes with a total size of 83 Mb. Based on a dual-time course RNA-seq, 25308 genes were predicted. Genes for steroid biosynthesis and starch and sucrose metabolism were significantly enriched, together with many genes encoding carbohydrate-active enzymes (CAZymes) and secreted effector proteins, which should be involved in infection of wheat plants. Population genomic analysis of 31 isolates collected in China during the last forty years suggests that this population has not undergone substantial differentiation over time. ImportanceThe finished genome reference is the basis of revealing pathogens biology base. Many efforts have been made to produce the chromosome-scale assembly of fungi. However, the reference of many pathogenic fungi is highly fragmented, which prevents the analysis of genome structure variation, evolution and import pathogenicity genes. Here, we assembly the only chromosome-scale haplotype-phased reference of dikaryotic fungus so far. This assembly achieves the gold standard based on many evaluation software, which indicates that the pipeline developed in this study can be applied to assemble references for other dikaryotic organisms. This work can also promote the research on the globes destructive wheat pathogens, sharp eyespot, caused by R. cerealis.

genomics↗

bioRxiv2021

SL-Cloud: A Computational Resource to Support Synthetic Lethal Interaction Discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, an integrated resource and framework to facilitate the prediction of context-specific SLIs by using cloud-based technologies. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the multiple comparable prediction approaches available. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential SLI partners, using multiple computational strategies. Context-specific synthetic lethality potential can also be compared using the framework. We demonstrate various use cases for our cloud-based computational resource and the utility of this approach for customizable and extensible computational inference of SLIs.

bioinformatics↗

bioRxiv2021

N6-methyladenosine RNA modification regulates strawberry fruit ripening in an ABA-dependent manner

BackgroundEpigenetic marks, such as DNA methylation, play pivotal roles in regulating ripening of both climacteric and non-climacteric fruits. However, it remains unclear whether mRNA m6A methylation, the epitranscriptome, is functionally conserved for ripening control. ResultsHere we show that m6A methylation, which has been revealed to regulate ripening of tomato, a typical climacteric fruit, displays a dramatic change at ripening onset of strawberry, a classical non-climacteric fruit. The m6A modification in the coding sequence (CDS) regions appears to be ripening-specific and tends to stabilize the mRNAs, whereas m6A around the stop codons and within the 3 untranslated regions is generally negatively correlated with the abundance of the mRNAs. We identified thousands of transcripts with m6A hypermethylation in the CDS regions, including those of NCED5, ABAR, and AREB1 in the abscisic acid (ABA) biosynthesis and signaling pathway. We demonstrated that the methyltransferases MTA and MTB are indispensable for normal ripening of strawberry fruit, and MTA-mediated m6A modification promotes mRNA stability of NCED5 and AREB1, while facilitates translation of ABAR. ConclusionOur findings uncover that m6A methylation regulates ripening of non-climacteric strawberry fruit by targeting ABA pathway, which is distinct from that in climacteric tomato fruit.

developmental biology↗

bioRxiv2020

A novel high-accuracy genome assembly method utilizing a high-throughput workflow

Across domains of biological research using genome sequence data, high-quality reference genome sequences are essential for characterizing genetic variation and understanding the genetic basis of phenotypes. However, the construction of genome assemblies for various species is often hampered by complexities of genome organization, especially repetitive and complex sequences, leading to mis-assembly and missing regions. Here, we describe a high-throughput gold standard genome assembly workflow using a large-scale bacterial artificial chromosome (BAC) library with a refined two-step pooling strategy and the Lamp assembler algorithm. This strategy minimizes the laborious processes of physical map construction and clone-by-clone sequencing, enabling inexpensive sequencing of several thousand BAC clones. By applying this strategy with a minimum tiling path BAC clone library for the short arm of chromosome 2D (2DS) of bread wheat, 98% of BAC sequences, covering 92.7% of the 2DS chromosome, were assembled correctly for this species with a highly complex and repetitive genome. We also identified 48 large mis-assemblies in the reference wheat genome assembly (IWGSC RefSeq v1.0) and corrected these large mis-assemblies in addition to filling 92.2% of the gaps in RefSeq v1.0. Our 2DS assembly represents a new benchmark for the assembly of complex genomes with both high accuracy and efficiency.

genomics↗