# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9076 | 0 | 0.9621 | ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/. | 2021 | 33495705 |
| 9079 | 1 | 0.9616 | Review, Evaluation, and Directions for Gene-Targeted Assembly for Ecological Analyses of Metagenomes. Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth. | 2019 | 31749830 |
| 5097 | 2 | 0.9607 | Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. INTRODUCTION: Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. METHODS: The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNs were used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. RESULTS: The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. CONCLUSION: Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment. | 2024 | 39347119 |
| 9078 | 3 | 0.9601 | MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota. MOTIVATION: Antibiotic resistance is an important global public health problem. Human gut microbiota is an accumulator of resistance genes potentially providing them to pathogens. It is important to develop tools for identifying the mechanisms of how resistance is transmitted between gut microbial species and pathogens. RESULTS: We developed MetaCherchant-an algorithm for extracting the genomic environment of antibiotic resistance genes from metagenomic data in the form of a graph. The algorithm was validated on a number of simulated and published datasets, as well as applied to new 'shotgun' metagenomes of gut microbiota from patients with Helicobacter pylori who underwent antibiotic therapy. Genomic context was reconstructed for several major resistance genes. Taxonomic annotation of the context suggests that within a single metagenome, the resistance genes can be contained in genomes of multiple species. MetaCherchant allows reconstruction of mobile elements with resistance genes within the genomes of bacteria using metagenomic data. Application of MetaCherchant in differential mode produced specific graph structures suggesting the evidence of possible resistance gene transmission within a mobile element that occurred as a result of the antibiotic therapy. MetaCherchant is a promising tool giving researchers an opportunity to get an insight into dynamics of resistance transmission in vivo basing on metagenomic data. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at https://github.com/ctlab/metacherchant. The code is written in Java and is platform-independent. COTANCT: ulyantsev@rain.ifmo.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. | 2018 | 29092015 |
| 9083 | 4 | 0.9590 | ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract. | 2024 | 38725076 |
| 8259 | 5 | 0.9590 | Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria. For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the 'Secondary Metabolite Transcriptomic Pipeline' (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest. | 2022 | 35580059 |
| 9072 | 6 | 0.9590 | PanGeT: Pan-genomics tool. A decade after the concept of Pan-genome was first introduced; research in this field has spread its tentacles to areas such as pathogenesis of diseases, bacterial evolutionary studies and drug resistance. Gene content-based differentiation of virulent and a virulent strains of bacteria and identification of pathogen specific genes is imperative to understand their physiology and gain insights into the mechanism of genome evolution. Subsequently, this will aid in identifying diagnostic targets and in developing and selecting vaccines. The root of pan-genomic studies, however, is to identify the core genes, dispensable genes and strain specific genes across the genomes belonging to a clade. To this end, we have developed a tool, "PanGeT - Pan-genomics Tool" to compute the 'pan-genome' based on comparisons at the genome as well as the proteome levels. This automated tool is implemented using LaTeX libraries for effective visualization of overall pan-genome through graphical plots. Links to retrieve sequence information and functional annotations have also been provided. PanGeT can be downloaded from http://pranag.physics.iisc.ernet.in/PanGeT/ or https://github.com/PanGeTv1/PanGeT. | 2017 | 27851981 |
| 8393 | 7 | 0.9589 | The draft genome of whitefly Bemisia tabaci MEAM1, a global crop pest, provides novel insights into virus transmission, host adaptation, and insecticide resistance. BACKGROUND: The whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is among the 100 worst invasive species in the world. As one of the most important crop pests and virus vectors, B. tabaci causes substantial crop losses and poses a serious threat to global food security. RESULTS: We report the 615-Mb high-quality genome sequence of B. tabaci Middle East-Asia Minor 1 (MEAM1), the first genome sequence in the Aleyrodidae family, which contains 15,664 protein-coding genes. The B. tabaci genome is highly divergent from other sequenced hemipteran genomes, sharing no detectable synteny. A number of known detoxification gene families, including cytochrome P450s and UDP-glucuronosyltransferases, are significantly expanded in B. tabaci. Other expanded gene families, including cathepsins, large clusters of tandemly duplicated B. tabaci-specific genes, and phosphatidylethanolamine-binding proteins (PEBPs), were found to be associated with virus acquisition and transmission and/or insecticide resistance, likely contributing to the global invasiveness and efficient virus transmission capacity of B. tabaci. The presence of 142 horizontally transferred genes from bacteria or fungi in the B. tabaci genome, including genes encoding hopanoid/sterol synthesis and xenobiotic detoxification enzymes that are not present in other insects, offers novel insights into the unique biological adaptations of this insect such as polyphagy and insecticide resistance. Interestingly, two adjacent bacterial pantothenate biosynthesis genes, panB and panC, have been co-transferred into B. tabaci and fused into a single gene that has acquired introns during its evolution. CONCLUSIONS: The B. tabaci genome contains numerous genetic novelties, including expansions in gene families associated with insecticide resistance, detoxification and virus transmission, as well as numerous horizontally transferred genes from bacteria and fungi. We believe these novelties likely have shaped B. tabaci as a highly invasive polyphagous crop pest and efficient vector of plant viruses. The genome serves as a reference for resolving the B. tabaci cryptic species complex, understanding fundamental biological novelties, and providing valuable genetic information to assist the development of novel strategies for controlling whiteflies and the viruses they transmit. | 2016 | 27974049 |
| 8394 | 8 | 0.9583 | Expanding Diversity of Firmicutes Single-Strand Annealing Proteins: A Putative Role of Bacteriophage-Host Arms Race. Bacteriophage-encoded single strand annealing proteins (SSAPs) are recombinases which can substitute the classical, bacterial RecA and manage the DNA metabolism at different steps of phage propagation. SSAPs have been shown to efficiently promote recombination between short and rather divergent DNA sequences and were exploited for in vivo genetic engineering mainly in Gram-negative bacteria. In opposition to the conserved and almost universal bacterial RecA protein, SSAPs display great sequence diversity. The importance for SSAPs in phage biology and phage-bacteria evolution is underlined by their role as key players in events of horizontal gene transfer (HGT). All of the above provoke a constant interest for the identification and study of new phage recombinase proteins in vivo, in vitro as well as in silico. Despite this, a huge body of putative ssap genes escapes conventional classification, as they are not properly annotated. In this work, we performed a wide-scale identification, classification and analysis of SSAPs encoded by the Firmicutes bacteria and their phages. By using sequence similarity network and gene context analyses, we created a new high quality dataset of phage-related SSAPs, substantially increasing the number of annotated SSAPs. We classified the identified SSAPs into seven distinct families, namely RecA, Gp2.5, RecT/Redβ, Erf, Rad52/22, Sak3, and Sak4, organized into three superfamilies. Analysis of the relationships between the revealed protein clusters led us to recognize Sak3-like proteins as a new distinct SSAP family. Our analysis showed an irregular phylogenetic distribution of ssap genes among different bacterial phyla and specific phages, which can be explained by the high rates of ssap HGT. We propose that the evolution of phage recombinases could be tightly linked to the dissemination of bacterial phage-resistance mechanisms (e.g., abortive infection and CRISPR/Cas systems) targeting ssap genes and be a part of the constant phage-bacteria arms race. | 2021 | 33959107 |
| 9073 | 9 | 0.9583 | EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains. In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets. | 2020 | 32431712 |
| 9068 | 10 | 0.9582 | TnCentral: a Prokaryotic Transposable Element Database and Web Portal for Transposon Analysis. We describe here the structure and organization of TnCentral (https://tncentral.proteininformationresource.org/ [or the mirror link at https://tncentral.ncc.unesp.br/]), a web resource for prokaryotic transposable elements (TE). TnCentral currently contains ∼400 carefully annotated TE, including transposons from the Tn3, Tn7, Tn402, and Tn554 families; compound transposons; integrons; and associated insertion sequences (IS). These TE carry passenger genes, including genes conferring resistance to over 25 classes of antibiotics and nine types of heavy metal, as well as genes responsible for pathogenesis in plants, toxin/antitoxin gene pairs, transcription factors, and genes involved in metabolism. Each TE has its own entry page, providing details about its transposition genes, passenger genes, and other sequence features required for transposition, as well as a graphical map of all features. TnCentral content can be browsed and queried through text- and sequence-based searches with a graphic output. We describe three use cases, which illustrate how the search interface, results tables, and entry pages can be used to explore and compare TE. TnCentral also includes downloadable software to facilitate user-driven identification, with manual annotation, of certain types of TE in genomic sequences. Through the TnCentral homepage, users can also access TnPedia, which provides comprehensive reviews of the major TE families, including an extensive general section and specialized sections with descriptions of insertion sequence and transposon families. TnCentral and TnPedia are intuitive resources that can be used by clinicians and scientists to assess TE diversity in clinical, veterinary, and environmental samples. IMPORTANCE The ability of bacteria to undergo rapid evolution and adapt to changing environmental circumstances drives the public health crisis of multiple antibiotic resistance, as well as outbreaks of disease in economically important agricultural crops and animal husbandry. Prokaryotic transposable elements (TE) play a critical role in this. Many carry "passenger genes" (not required for the transposition process) conferring resistance to antibiotics or heavy metals or causing disease in plants and animals. Passenger genes are spread by normal TE transposition activities and by insertion into plasmids, which then spread via conjugation within and across bacterial populations. Thus, an understanding of TE composition and transposition mechanisms is key to developing strategies to combat bacterial pathogenesis. Toward this end, we have developed TnCentral, a bioinformatics resource dedicated to describing and exploring the structural and functional features of prokaryotic TE whose use is intuitive and accessible to users with or without bioinformatics expertise. | 2021 | 34517763 |
| 8136 | 11 | 0.9580 | Recent progress in CRISPR/Cas9-based genome editing for enhancing plant disease resistance. Nowadays, agricultural production is strongly affected by both climate change and pathogen attacks which seriously threaten global food security. For a long time, researchers have been waiting for a tool allowing DNA/RNA manipulation to tailor genes and their expression. Some earlier genetic manipulation methods such as meganucleases (MNs), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) allowed site directed modification but their successful rate was limited due to lack of flexibility when targeting a 'site-specific nucleic acid'. The discovery of clustered regularly interspaced short palindrome repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system has revolutionized genome editing domain in different living organisms during the past 9 years. Based on RNA-guided DNA/RNA recognition, CRISPR/Cas9 optimizations have offered an unrecorded scientific opportunity to engineer plants resistant to diverse pathogens. In this report, we describe the main characteristics of the primary reported-genome editing tools ((MNs, ZFNs, TALENs) and evaluate the different CRISPR/Cas9 methods and achievements in developing crop plants resistant to viruses, fungi and bacteria. | 2023 | 36871676 |
| 106 | 12 | 0.9576 | Genomic evidence of the illumination response mechanism and evolutionary history of magnetotactic bacteria within the Rhodospirillaceae family. BACKGROUND: Magnetotactic bacteria (MTB) are ubiquitous in natural aquatic environments. MTB can produce intracellular magnetic particles, navigate along geomagnetic field, and respond to light. However, the potential mechanism by which MTB respond to illumination and their evolutionary relationship with photosynthetic bacteria remain elusive. RESULTS: We utilized genomes of the well-sequenced genus Magnetospirillum, including the newly sequenced MTB strain Magnetospirillum sp. XM-1 to perform a comprehensive genomic comparison with phototrophic bacteria within the family Rhodospirillaceae regarding the illumination response mechanism. First, photoreceptor genes were identified in the genomes of both MTB and phototrophic bacteria in the Rhodospirillaceae family, but no photosynthesis genes were found in the MTB genomes. Most of the photoreceptor genes in the MTB genomes from this family encode phytochrome-domain photoreceptors that likely induce red/far-red light phototaxis. Second, illumination also causes damage within the cell, and in Rhodospirillaceae, both MTB and phototrophic bacteria possess complex but similar sets of response and repair genes, such as oxidative stress response, iron homeostasis and DNA repair system genes. Lastly, phylogenomic analysis showed that MTB cluster closely with phototrophic bacteria in this family. One photoheterotrophic genus, Phaeospirillum, clustered within and displays high genomic similarity with Magnetospirillum. Moreover, the phylogenetic tree topologies of magnetosome synthesis genes in MTB and photosynthesis genes in phototrophic bacteria from the Rhodospirillaceae family were reasonably congruent with the phylogenomic tree, suggesting that these two traits were most likely vertically transferred during the evolution of their lineages. CONCLUSION: Our new genomic data indicate that MTB and phototrophic bacteria within the family Rhodospirillaceae possess diversified photoreceptors that may be responsible for phototaxis. Their genomes also contain comprehensive stress response genes to mediate the negative effects caused by illumination. Based on phylogenetic studies, most of MTB and phototrophic bacteria in the Rhodospirillaceae family evolved vertically with magnetosome synthesis and photosynthesis genes. The ancestor of Rhodospirillaceae was likely a magnetotactic phototrophic bacteria, however, gain or loss of magnetotaxis and phototrophic abilities might have occurred during the evolution of ancestral Rhodospirillaceae lineages. | 2019 | 31117953 |
| 8263 | 13 | 0.9576 | CRISPR/Cas9: A Novel Weapon in the Arsenal to Combat Plant Diseases. Plant pathogens like virus, bacteria, and fungi incur a huge loss of global productivity. Targeting the dominant R gene resulted in the evolution of resistance in pathogens, which shifted plant pathologists' attention toward host susceptibility factors (or S genes). Herein, the application of sequence-specific nucleases (SSNs) for targeted genome editing are gaining more importance, which utilize the use of meganucleases (MN), zinc finger nucleases (ZFNs), transcription activator-like effector-based nucleases (TALEN) with the latest one namely clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9). The first generation of genome editing technologies, due to their cumbersome nature, is becoming obsolete. Owing to its simple and inexpensive nature the use of CRISPR/Cas9 system has revolutionized targeted genome editing technology. CRISPR/Cas9 system has been exploited for developing resistance against virus, bacteria, and fungi. For resistance to DNA viruses (mainly single-stranded DNA viruses), different parts of the viral genome have been targeted transiently and by the development of transgenic plants. For RNA viruses, mainly the host susceptibility factors and very recently the viral RNA genome itself have been targeted. Fungal and bacterial resistance has been achieved mainly by targeting the host susceptibility genes through the development of transgenics. In spite of these successes CRISPR/Cas9 system suffers from off-targeting. This and other problems associated with this system are being tackled by the continuous discovery/evolution of new variants. Finally, the regulatory standpoint regarding CRISPR/Cas9 will determine the fate of using this versatile tool in developing pathogen resistance in crop plants. | 2018 | 30697226 |
| 8135 | 14 | 0.9576 | Harnessing Genome Editing Techniques to Engineer Disease Resistance in Plants. Modern genome editing (GE) techniques, which include clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) system, transcription activator-like effector nucleases (TALENs), zinc-finger nucleases (ZFNs) and LAGLIDADG homing endonucleases (meganucleases), have so far been used for engineering disease resistance in crops. The use of GE technologies has grown very rapidly in recent years with numerous examples of targeted mutagenesis in crop plants, including gene knockouts, knockdowns, modifications, and the repression and activation of target genes. CRISPR/Cas9 supersedes all other GE techniques including TALENs and ZFNs for editing genes owing to its unprecedented efficiency, relative simplicity and low risk of off-target effects. Broad-spectrum disease resistance has been engineered in crops by GE of either specific host-susceptibility genes (S gene approach), or cleaving DNA of phytopathogens (bacteria, virus or fungi) to inhibit their proliferation. This review focuses on different GE techniques that can potentially be used to boost molecular immunity and resistance against different phytopathogens in crops, ultimately leading to the development of promising disease-resistant crop varieties. | 2019 | 31134108 |
| 8405 | 15 | 0.9574 | Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies. Soybean is one of the most valuable agricultural crops in the world. Besides, this legume is constantly attacked by a wide range of pathogens (fungi, bacteria, viruses, and nematodes) compromising yield and increasing production costs. One of the major disease management strategies is the genetic resistance provided by single genes and quantitative trait loci (QTL). Identifying the genomic regions underlying the resistance against these pathogens on soybean is one of the first steps performed by molecular breeders. In the past, genetic mapping studies have been widely used to discover these genomic regions. However, over the last decade, advances in next-generation sequencing technologies and their subsequent cost decreasing led to the development of cost-effective approaches to high-throughput genotyping. Thus, genome-wide association studies applying thousands of SNPs in large sets composed of diverse soybean accessions have been successfully done. In this chapter, a comprehensive review of the majority of GWAS for soybean diseases published since this approach was developed is provided. Important diseases caused by Heterodera glycines, Phytophthora sojae, and Sclerotinia sclerotiorum have been the focus of the several GWAS. However, other bacterial and fungi diseases also have been targets of GWAS. As such, this GWAS summary can serve as a guide for future studies of these diseases. The protocol begins by describing several considerations about the pathogens and bringing different procedures of molecular characterization of them. Advice to choose the best isolate/race to maximize the discovery of multiple R genes or to directly map an effective R gene is provided. A summary of protocols, methods, and tools to phenotyping the soybean panel is given to several diseases. We also give details of options of DNA extraction protocols and genotyping methods, and we describe parameters of SNP quality to soybean data. Websites and their online tools to obtain genotypic and phenotypic data for thousands of soybean accessions are highlighted. Finally, we report several tricks and tips in Subheading 4, especially related to composing the soybean panel as well as generating and analyzing the phenotype data. We hope this protocol will be helpful to achieve GWAS success in identifying resistance genes on soybean. | 2022 | 35641772 |
| 9182 | 16 | 0.9573 | Harnessing CRISPR/Cas9 in engineering biotic stress immunity in crops. There is significant potential for CRISPR/Cas9 to be used in developing crops that can adapt to biotic stresses such as fungal, bacterial, viral, and pest infections and weeds. The increasing global population and climate change present significant threats to food security by putting stress on plants, making them more vulnerable to diseases and productivity losses caused by pathogens, pests, and weeds. Traditional breeding methods are inadequate for the rapid development of new plant traits needed to counteract this decline in productivity. However, modern advances in genome-editing technologies, particularly CRISPR/Cas9, have transformed crop protection through precise and targeted modifications of plant genomes. This enables the creation of resilient crops with improved resistance to pathogens, pests, and weeds. This review examines various methods by which CRISPR/Cas9 can be utilized for crop protection. These methods include knocking out susceptibility genes, introducing resistance genes, and modulating defense genes. Potential applications of CRISPR/Cas9 in crop protection involve introducing genes that confer resistance to pathogens, disrupting insect genes responsible for survival and reproduction, and engineering crops that are resistant to herbicides. In conclusion, CRISPR/Cas9 holds great promise for advancing crop protection and ensuring food security in the face of environmental challenges and increasing population pressures. The most recent advancements in CRISPR technology for creating resistance to bacteria, fungi, viruses, and pests are covered here. We wrap up by outlining the most pressing issues and technological shortcomings, as well as unanswered questions for further study. | 2025 | 40663257 |
| 8256 | 17 | 0.9573 | Revolutionizing Tomato Cultivation: CRISPR/Cas9 Mediated Biotic Stress Resistance. Tomato (Solanum lycopersicon L.) is one of the most widely consumed and produced vegetable crops worldwide. It offers numerous health benefits due to its rich content of many therapeutic elements such as vitamins, carotenoids, and phenolic compounds. Biotic stressors such as bacteria, viruses, fungi, nematodes, and insects cause severe yield losses as well as decreasing fruit quality. Conventional breeding strategies have succeeded in developing resistant genotypes, but these approaches require significant time and effort. The advent of state-of-the-art genome editing technologies, particularly CRISPR/Cas9, provides a rapid and straightforward method for developing high-quality biotic stress-resistant tomato lines. The advantage of genome editing over other approaches is the ability to make precise, minute adjustments without leaving foreign DNA inside the transformed plant. The tomato genome has been precisely modified via CRISPR/Cas9 to induce resistance genes or knock out susceptibility genes, resulting in lines resistant to common bacterial, fungal, and viral diseases. This review provides the recent advances and application of CRISPR/Cas9 in developing tomato lines with resistance to biotic stress. | 2024 | 39204705 |
| 9075 | 18 | 0.9573 | CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype . | 2023 | 37474912 |
| 801 | 19 | 0.9572 | Redox-sensitive transcriptional regulator SoxR directly controls antibiotic production, development and thiol-oxidative stress response in Streptomyces avermitilis. The redox-sensitive transcriptional regulator SoxR is conserved in bacteria. Its role in mediating protective response to various oxidative stresses in Escherichia coli and related enteric bacteria has been well established. However, functions and regulatory mechanisms of SoxR in filamentous Streptomyces, which produce half of known antibiotics, are unclear. We report here that SoxR pleiotropically regulates antibiotic production, morphological development, primary metabolism and thiol-oxidative stress response in industrially important species Streptomyces avermitilis. SoxR stimulated avermectin production by directly activating ave structural genes. Four genes (sav_3956, sav_4018, sav_5665 and sav_7218) that are homologous to targets of S. coelicolor SoxR are targeted by S. avermitilis SoxR. A consensus 18-nt SoxR-binding site, 5'-VSYCNVVMHNKVKDGMGB-3', was identified in promoter regions of sav_3956, sav_4018, sav_5665, sav_7218 and target ave genes, leading to prediction of the SoxR regulon and confirmation of 11 new targets involved in development (ftsH), oligomycin A biosynthesis (olmRI), primary metabolism (metB, sav_1623, plcA, nirB, thiG, ndh2), transport (smoE) and regulatory function (sig57, sav_7278). SoxR also directly activated three key developmental genes (amfC, whiB and ftsZ) and promoted resistance of S. avermitilis to thiol-oxidative stress through activation of target trx and msh genes. Overexpression of soxR notably enhanced antibiotic production in S. avermitilis and S. coelicolor. Our findings expand our limited knowledge of SoxR and will facilitate improvement of methods for antibiotic overproduction in Streptomyces species. | 2022 | 33951287 |