# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9083 | 0 | 0.9817 | 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 |
| 8136 | 1 | 0.9815 | 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 |
| 9075 | 2 | 0.9813 | 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 |
| 8187 | 3 | 0.9811 | Racial disparities in metastatic colorectal cancer outcomes revealed by tumor microbiome and transcriptome analysis with bevacizumab treatment. Background: Metastatic colorectal cancer (mCRC) is a heterogeneous disease, often associated with poor outcomes and resistance to therapies. The racial variations in the molecular and microbiological profiles of mCRC patients, however, remain under-explored. Methods: Using RNA-SEQ data, we extracted and analyzed actively transcribing microbiota within the tumor milieu, ensuring that the identified bacteria were not merely transient inhabitants but engaged in the tumor ecosystem. Also, we independently acquired samples from 12 mCRC patients, specifically, 6 White individuals and 6 of Black or African American descent. These samples underwent 16S rRNA sequencing. Results: Our study revealed notable racial disparities in the molecular signatures and microbiota profiles of mCRC patients. The intersection of these data showcased the potential modulating effects of specific bacteria on gene expression. Particularly, the bacteria Helicobacter cinaedi and Sphingobium herbicidovorans emerged as significant influencers, with strong correlations to the genes SELENBP1 and SNORA38, respectively. Discussion: These findings underscore the intricate interplay between host genomics and actively transcribing tumor microbiota in mCRC's pathogenesis. The identified correlations between specific bacteria and genes highlight potential avenues for targeted therapies and a more personalized therapeutic approach. | 2023 | 38357363 |
| 8263 | 4 | 0.9809 | 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 |
| 9078 | 5 | 0.9808 | 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 |
| 9182 | 6 | 0.9806 | 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 |
| 8401 | 7 | 0.9806 | LSTrAP-Crowd: prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data. BACKGROUND: Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis. RESULTS: In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. CONCLUSIONS: We identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced. | 2020 | 32883264 |
| 9076 | 8 | 0.9806 | 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 |
| 9189 | 9 | 0.9806 | CRISPR-Cas9 System: A Prospective Pathway toward Combatting Antibiotic Resistance. Antibiotic resistance is rising to dangerously high levels throughout the world. To cope with this problem, scientists are working on CRISPR-based research so that antibiotic-resistant bacteria can be killed and attacked almost as quickly as antibiotic-sensitive bacteria. Nuclease activity is found in Cas9, which can be programmed with a specific target sequence. This mechanism will only attack pathogens in the microbiota while preserving commensal bacteria. This article portrays the delivery methods used in the CRISPR-Cas system, which are both viral and non-viral, along with its implications and challenges, such as microbial dysbiosis, off-target effects, and failure to counteract intracellular infections. CRISPR-based systems have a lot of applications, such as correcting mutations, developing diagnostics for infectious diseases, improving crops productions, improving breeding techniques, etc. In the future, CRISPR-based systems will revolutionize the world by curing diseases, improving agriculture, and repairing genetic disorders. Though all the drawbacks of the technology, CRISPR carries great potential; thus, the modification and consideration of some aspects could result in a mind-blowing technique to attain all the applications listed and present a game-changing potential. | 2023 | 37370394 |
| 8256 | 10 | 0.9805 | 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 |
| 578 | 11 | 0.9805 | Characterization of radiation-resistance mechanism in Spirosoma montaniterrae DY10(T) in terms of transcriptional regulatory system. To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10[Formula: see text] and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation. | 2023 | 36959250 |
| 9744 | 12 | 0.9804 | PARGT: a software tool for predicting antimicrobial resistance in bacteria. With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies. | 2020 | 32620856 |
| 8185 | 13 | 0.9804 | RNA-cleaving DNAzymes as a diagnostic and therapeutic agent against antimicrobial resistant bacteria. The development of nucleic-acid-based antimicrobials such as RNA-cleaving DNAzyme (RCD), a short catalytically active nucleic acid, is a promising alternative to the current antibiotics. The current rapid spread of antimicrobial resistance (AMR) in bacteria renders some antibiotics useless against bacterial infection, thus creating the need for alternative antimicrobials such as DNAzymes. This review summarizes recent advances in the use of RCD as a diagnostic and therapeutic agent against AMR. Firstly, the recent diagnostic application of RCD for the detection of bacterial cells and the associated resistant gene(s) is discussed. The next section summarises the therapeutic application of RCD in AMR bacterial infections which includes direct targeting of the resistant genes and indirect targeting of AMR-associated genes. Finally, this review extends the discussion to challenges of utilizing RCD in real-life applications, and the potential of combining both diagnostic and therapeutic applications of RCD into a single agent as a theranostic agent. | 2022 | 34505182 |
| 8257 | 14 | 0.9804 | RNA interference in Colorado potato beetle: steps toward development of dsRNA as a commercial insecticide. Colorado potato beetle (CPB) is a notorious pest on potatoes and has a remarkable ability to detoxify plant chemicals and develop resistance against insecticides. dsRNA targeting CPB genes could be expressed in potato plants to control this pest. However, previous attempts at introducing transgenic potato plants to control CPB were not highly successful. Recent studies showed that feeding dsRNA expressed in bacteria works very well to kill CPB. To realize the potential of RNAi to control this and other economically important pests, more efficient methods for production and delivery of dsRNA need to be developed. Extensive research to determine off-target and non-target effects, environmental fate and potential for resistance development is also essential. | 2014 | 26705514 |
| 8898 | 15 | 0.9804 | Adaptive resistance in bacteria requires epigenetic inheritance, genetic noise, and cost of efflux pumps. Adaptive resistance emerges when populations of bacteria are subjected to gradual increases of antibiotics. It is characterized by a rapid emergence of resistance and fast reversibility to the non-resistant phenotype when the antibiotic is removed from the medium. Recent work shows that adaptive resistance requires epigenetic inheritance and heterogeneity of gene expression patterns that are, in particular, associated with the production of porins and efflux pumps. However, the precise mechanisms by which inheritance and variability govern adaptive resistance, and what processes cause its reversibility remain unclear. Here, using an efflux pump regulatory network (EPRN) model, we show that the following three mechanisms are essential to obtain adaptive resistance in a bacterial population: 1) intrinsic variability in the expression of the EPRN transcription factors; 2) epigenetic inheritance of the transcription rate of EPRN associated genes; and 3) energetic cost of the efflux pumps activity that slows down cell growth. While the first two mechanisms acting together are responsible for the emergence and gradual increase of the resistance, the third one accounts for its reversibility. In contrast with the standard assumption, our model predicts that adaptive resistance cannot be explained by increased mutation rates. Our results identify the molecular mechanism of epigenetic inheritance as the main target for therapeutic treatments against the emergence of adaptive resistance. Finally, our theoretical framework unifies known and newly identified determinants such as the burden of efflux pumps that underlie bacterial adaptive resistance to antibiotics. | 2015 | 25781931 |
| 8155 | 16 | 0.9803 | Gut bacteria enable prostate cancer growth. Testosterone-synthetizing gut bacteria drive resistance to therapy. | 2021 | 34618567 |
| 8335 | 17 | 0.9803 | Implementing Optogenetic-Controlled Bacterial Systems in Drosophila melanogaster for Alleviation of Heavy Metal Poisoning. Drosophila melanogaster (fruit fly) is an animal model chassis in biological and genetic research owing to its short life cycle, ease of cultivation, and acceptability to genetic modification. While the D. melanogaster chassis offers valuable insights into drug efficacy, toxicity, and mechanisms, several obvious challenges such as dosage control and drug resistance still limit its utility in pharmacological studies. Our research combines optogenetic control with engineered gut bacteria to facilitate the precise delivery of therapeutic substances in D. melanogaster for biomedical research. We have shown that the engineered bacteria can be orally administered to D. melanogaster to get a stable density of approximately 28,000 CFUs/per fly, leading to no detectable negative effects on the growth of D. melanogaster. In a model of D. melanogaster exposure to heavy metal, these orally administered bacteria uniformly express target genes under green light control to produce MtnB protein for binding and detoxifying lead, which significantly reduces the level of oxidative stress in the intestinal tract of Pb-treated flies. This pioneering study lays the groundwork for using optogenetic-controlled bacteria in the model chassis D. melanogaster to advance biomedical applications. | 2024 | 39312764 |
| 8400 | 18 | 0.9802 | Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis. BACKGROUND: Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. METHODS: In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l(2)-regularized logistic regression model. RESULTS: The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. CONCLUSIONS: The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways. | 2018 | 29954330 |
| 8172 | 19 | 0.9801 | From resistance to remedy: the role of clustered regularly interspaced short palindromic repeats system in combating antimicrobial resistance-a review. The growing challenge of antimicrobial resistance (AMR) poses a significant and increasing risk to public health worldwide, necessitating innovative strategies to restore the efficacy of antibiotics. The precise genome-editing abilities of the CRISPR-Cas system have made it a potent instrument for directly targeting and eliminating antibiotic resistance genes. This review explored the mechanisms and applications of CRISPR-Cas systems in combating AMR. The latest developments in CRISPR technology have broadened its potential use, encompassing programmable antibacterial agents and improved diagnostic methods for antibiotic-resistant infections. Nevertheless, several challenges must be overcome for clinical success, including the survival of resistant bacteria, generation of anti-CRISPR proteins that reduce effectiveness, and genetic modifications that change target sequences. Additionally, the efficacy of CRISPR-Cas systems differs across bacterial species, making their universal application challenging. After overcoming these challenges, CRISPR-Cas has the potential to revolutionize AMR treatment, restore antibiotic efficacy, and reshape infection control. | 2025 | 39404843 |