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973600.9775Coevolutionary phage training leads to greater bacterial suppression and delays the evolution of phage resistance. The evolution of antibiotic-resistant bacteria threatens to become the leading cause of worldwide mortality. This crisis has renewed interest in the practice of phage therapy. Yet, bacteria's capacity to evolve resistance may debilitate this therapy as well. To combat the evolution of phage resistance and improve treatment outcomes, many suggest leveraging phages' ability to counter resistance by evolving phages on target hosts before using them in therapy (phage training). We found that in vitro, λtrn, a phage trained for 28 d, suppressed bacteria ∼1,000-fold for three to eight times longer than its untrained ancestor. Prolonged suppression was due to a delay in the evolution of resistance caused by several factors. Mutations that confer resistance to λtrn are ∼100× less common, and while the target bacterium can evolve complete resistance to the untrained phage in a single step, multiple mutations are required to evolve complete resistance to λtrn. Mutations that confer resistance to λtrn are more costly than mutations for untrained phage resistance. Furthermore, when resistance does evolve, λtrn is better able to suppress these forms of resistance. One way that λtrn improved was through recombination with a gene in a defunct prophage in the host genome, which doubled phage fitness. This transfer of information from the host genome is an unexpected but highly efficient mode of training phage. Lastly, we found that many other independently trained λ phages were able to suppress bacterial populations, supporting the important role training could play during phage therapeutic development.202134083444
509710.9770Comparing 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.202439347119
907620.9759ResiDB: 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/.202133495705
973330.9757The 2018 Garrod Lecture: Preparing for the Black Swans of resistance. The need for governments to encourage antibiotic development is widely agreed, with 'market entry rewards' being suggested. Unless these are to be spread widely-which is unlikely given the $1 billion sums proposed-we should be wary, for this approach is likely to evolve into one of picking, or commissioning, a few 'winners' based on extrapolation of current resistance trends. The hazard to this is that whilst the evolution of resistance has predictable components, notably mutation, it also has completely unpredictable ones, contingent upon 'Black Swan' events. These include the escape of 'new' resistance genes from environmental bacteria and the recruitment of these genes by promiscuous mobile elements and epidemic strains. Such events can change the resistance landscape rapidly and unexpectedly, as with the rise of Escherichia coli ST131 with CTX-M ESBLs and the emergence of 'impossible' VRE. Given such unpredictability, we simply cannot say with any certainty, for example, which of the four current approaches to combating MBLs offers the best prospect of sustainable prizeworthy success. Only time will tell, though it is encouraging that multiple potential approaches to overcoming these problematic enzymes are being pursued. Rather than seeking to pick winners, governments should aim to reduce development barriers, as with recent relaxation of trial regulations. In particular, once β-lactamase inhibitors have been successfully trialled with one partner drug, there is scope to facilitate licensing them for partnering with other established β-lactams, thereby insuring against new emerging resistance.201830351434
973440.9757Combination of genetically diverse Pseudomonas phages enhances the cocktail efficiency against bacteria. Phage treatment has been used as an alternative to antibiotics since the early 1900s. However, bacteria may acquire phage resistance quickly, limiting the use of phage treatment. The combination of genetically diverse phages displaying distinct replication machinery in phage cocktails has therefore become a novel strategy to improve therapeutic outcomes. Here, we isolated and studied lytic phages (SPA01 and SPA05) that infect a wide range of clinical Pseudomonas aeruginosa isolates. These relatively small myophages have around 93 kbp genomes with no undesirable genes, have a 30-min latent period, and reproduce a relatively high number of progenies, ranging from 218 to 240 PFU per infected cell. Even though both phages lyse their hosts within 4 h, phage-resistant bacteria emerge during the treatment. Considering SPA01-resistant bacteria cross-resist phage SPA05 and vice versa, combining SPA01 and SPA05 for a cocktail would be ineffective. According to the decreased adsorption rate of the phages in the resistant isolates, one of the anti-phage mechanisms may occur through modification of phage receptors on the target cells. All resistant isolates, however, are susceptible to nucleus-forming jumbophages (PhiKZ and PhiPA3), which are genetically distinct from phages SPA01 and SPA05, suggesting that the jumbophages recognize a different receptor during phage entry. The combination of these phages with the jumbophage PhiKZ outperforms other tested combinations in terms of bactericidal activity and effectively suppresses the emergence of phage resistance. This finding reveals the effectiveness of the diverse phage-composed cocktail for reducing bacterial growth and prolonging the evolution of phage resistance.202337264114
840550.9756Mapping 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.202235641772
506960.9755MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples. Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene bla(NDM) and bla(KPC), colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-enhanced recombinase polymerase amplification (RPA) and a refined Chelex-100 lysis method with HRP-catalyzed lateral flow immunoassay (LFIA), we developed an MCL-PRPA-HLFIA cascade assay system for detecting these genes in UTI samples. The refined Chelex-100 lysis method extracts target DNA from UTI samples in 20 min without high-speed centrifugation or pre-incubation of urine samples. Following optimization, the cascade detection system achieved an LOD of 10(2) CFU/mL with satisfactory specificity and could detect these genes in both simulated and actual UTI samples. It takes less than an hour to complete the process without the use of high-speed centrifuges or other specialized equipment, such as PCR amplifiers. The MCL-PRPA-HLFIA cascade assay system provides new ideas for the construction of rapid detection methods for pan-drug-resistant genes in clinical UTI samples and provides the necessary medication guidance for UTI treatment.202337047757
999370.9754Lysozyme Resistance in Clostridioides difficile Is Dependent on Two Peptidoglycan Deacetylases. Clostridioides (Clostridium) difficile is a major cause of hospital-acquired infections leading to antibiotic-associated diarrhea. C. difficile exhibits a very high level of resistance to lysozyme. Bacteria commonly resist lysozyme through modification of the cell wall. In C. difficile, σ(V) is required for lysozyme resistance, and σ(V) is activated in response to lysozyme. Once activated, σ(V), encoded by csfV, directs transcription of genes necessary for lysozyme resistance. Here, we analyze the contribution of individual genes in the σ(V) regulon to lysozyme resistance. Using CRISPR-Cas9-mediated mutagenesis we constructed in-frame deletions of single genes in the csfV operon. We find that pdaV, which encodes a peptidoglycan deacetylase, is partially responsible for lysozyme resistance. We then performed CRISPR inhibition (CRISPRi) to identify a second peptidoglycan deacetylase, encoded by pgdA, that is important for lysozyme resistance. Deletion of either pgdA or pdaV resulted in modest decreases in lysozyme resistance. However, deletion of both pgdA and pdaV resulted in a 1,000-fold decrease in lysozyme resistance. Further, muropeptide analysis revealed that loss of either PgdA or PdaV had modest effects on peptidoglycan deacetylation but that loss of both PgdA and PdaV resulted in almost complete loss of peptidoglycan deacetylation. This suggests that PgdA and PdaV are redundant peptidoglycan deacetylases. We also used CRISPRi to compare other lysozyme resistance mechanisms and conclude that peptidoglycan deacetylation is the major mechanism of lysozyme resistance in C. difficileIMPORTANCEClostridioides difficile is the leading cause of hospital-acquired diarrhea. C. difficile is highly resistant to lysozyme. We previously showed that the csfV operon is required for lysozyme resistance. Here, we used CRISPR-Cas9 mediated mutagenesis and CRISPRi knockdown to show that peptidoglycan deacetylation is necessary for lysozyme resistance and is the major lysozyme resistance mechanism in C. difficile We show that two peptidoglycan deacetylases in C. difficile are partially redundant and are required for lysozyme resistance. PgdA provides an intrinsic level of deacetylation, and PdaV, encoded by a part of the csfV operon, provides lysozyme-induced peptidoglycan deacetylation.202032868404
937880.9754Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics. The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary - and largely uncharacterized - genetics of adsorption, injection, cell take-over and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40% . Feature selection revealed key phage λ and E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The method's success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.202438260415
844490.9752Whole genome resequencing and complementation tests reveal candidate loci contributing to bacterial wilt (Ralstonia sp.) resistance in tomato. Tomato (Solanum lycopersicum) is one of the most economically important vegetable crops worldwide. Bacterial wilt (BW), caused by the Ralstonia solanacearum species complex, has been reported as the second most important plant pathogenic bacteria worldwide, and likely the most destructive. Extensive research has identified two major loci, Bwr-6 and Bwr-12, that contribute to resistance to BW in tomato; however, these loci do not completely explain resistance. Segregation of resistance in two populations that were homozygous dominant or heterozygous for all Bwr-6 and Bwr-12 associated molecular markers suggested the action of one or two resistance loci in addition to these two major QTLs. We utilized whole genome sequence data analysis and pairwise comparison of six BW resistant and nine BW susceptible tomato lines to identify candidate genes that, in addition to Bwr-6 and Bwr-12, contributed to resistance. Through this approach we found 27,046 SNPs and 5975 indels specific to the six resistant lines, affecting 385 genes. One sequence variant on chromosome 3 captured by marker Bwr3.2dCAPS located in the Asc (Solyc03g114600.4.1) gene had significant association with resistance, but it did not completely explain the resistance phenotype. The SNP associated with Bwr3.2dCAPS was located within the resistance gene Asc which was inside the previously identified Bwr-3 locus. This study provides a foundation for further investigations into new loci distributed throughout the tomato genome that could contribute to BW resistance and into the role of resistance genes that may act against multiple pathogens.202235589778
9083100.9752ARGNet: 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.202438725076
8749110.9752Analysis of Defense-Related Gene Expression in Citrus Hybrids Infected by Xylella fastidiosa. Resistance to Xylella fastidiosa was evaluated in 264 hybrids of crosses between Murcott tangor (Citrus reticulata × Citrus sinensis) and Pera sweet orange (C. sinensis) under field conditions. Uninfected hybrids were grafted with buds collected from Pera sweet orange plants infected with X. fastidiosa, forming a plant with two scions (i.e., hybrid branches and Pera sweet orange branches). From these plants, we chose 10 genotypes with three biological replicates. We evaluated gene expression, bacterial multiplication, and citrus variegated chlorosis (CVC) symptom development in both scions. X. fastidiosa was not detected in most hybrid scions and none showed disease symptoms. In contrast, all Pera sweet orange scions were infected with X. fastidiosa and expressed symptoms of CVC. We quantified the expression of 12 defense-related genes by qPCR comparing resistant to susceptible scions. We suggest that some of these genes are involved in resistance of the hybrids to X. fastidiosa, since their expression was significantly higher in the resistant hybrid scions than in tolerant hybrids and scions originated from CVC symptomatic Pera sweet orange buds. However, we note that these data should be interpreted carefully, as the plant genotypes tested are related but necessarily distinct (hybrids of C. reticulata and C. sinensis, in relation to a C. sinensis control). A principal component analysis revealed a relationship between the expression of these genes and hybrid scions, and between scions that originated from infected buds and the presence of the bacteria and plant symptoms. Multiyear field trials are necessary to develop plant resistance to X. fastidiosa. While the experimental design used here had limitations, it allowed us to identify a set of genes potentially involved in Citrus sp. resistance to this pathogen. Future work on the role of these genes in plant defenses to X. fastidiosa infection is necessary to confirm their importance in the displayed resistance phenotype.201930480473
8758120.9751Genome-wide association mapping for resistance to bacterial blight and bacterial leaf streak in rice. Using genome-wide SNP association mapping, a total of 77 and 7 loci were identified for rice bacterial blight and bacterial leaf streak resistance, respectively, which may facilitate rice resistance improvement. Bacterial blight (BB) and bacterial leaf streak (BLS) caused by Gram-negative bacteria Xanthomonas oryzae pv. oryzae (Xoo) and X. oryzae pv. oryzicola (Xoc), respectively, are two economically important diseases negatively affecting rice production. To mine new sources of resistance, a set of rice germplasm collection consisting of 895 re-sequenced accessions from the 3000 Rice Genomes Project (3 K RGP) were screened for BB and BLS resistance under field conditions. Higher levels of BB resistance were observed in aus/boro subgroup, whereas the japonica, temperate japonica and tropical japonica subgroups possessed comparatively high levels of resistance to BLS. A genome-wide association study (GWAS) mined 77 genomic loci significantly associated with BB and 7 with BLS resistance. The phenotypic variance (R(2)) explained by these loci ranged from 0.4 to 30.2%. Among the loci, 7 for BB resistance were co-localized with known BB resistance genes and one for BLS resistance overlapped with a previously reported BLS resistance QTL. A search for the candidates in other novel loci revealed several defense-related genes that may be involved in resistance to BB and BLS. High levels of phenotypic resistance to BB or BLS could be attributed to the accumulation of the resistance (R) alleles at the associated loci, indicating their potential value in rice resistance breeding via gene pyramiding. The GWAS analysis validated the known genes underlying BB and BLS resistance and identified novel loci that could enrich the current resistance gene pool. The resources with strong resistance and significant SNPs identified in this study are potentially useful in breeding for BB and BLS resistance.202133830376
9735130.9751Arms race and fluctuating selection dynamics in Pseudomonas aeruginosa bacteria coevolving with phage OMKO1. Experimental evolution studies have examined coevolutionary dynamics between bacteria and lytic phages, where two models for antagonistic coevolution dominate: arms-race dynamics (ARD) and fluctuating-selection dynamics (FSD). Here, we tested the ability for Pseudomonas aeruginosa to coevolve with phage OMKO1 during 10 passages in the laboratory, whether ARD versus FSD coevolution occurred, and how coevolution affected a predicted phenotypic trade-off between phage resistance and antibiotic sensitivity. We used a unique "deep" sampling design, where 96 bacterial clones per passage were obtained from the three replicate coevolving communities. Next, we examined phenotypic changes in growth ability, susceptibility to phage infection and resistance to antibiotics. Results confirmed that the bacteria and phages coexisted throughout the study with one community undergoing ARD, whereas the other two showed evidence for FSD. Surprisingly, only the ARD bacteria demonstrated the anticipated trade-off. Whole genome sequencing revealed that treatment populations of bacteria accrued more de novo mutations, relative to a control bacterial population. Additionally, coevolved bacteria presented mutations in genes for biosynthesis of flagella, type-IV pilus and lipopolysaccharide, with three mutations fixing contemporaneously with the occurrence of the phenotypic trade-off in the ARD-coevolved bacteria. Our study demonstrates that both ARD and FSD coevolution outcomes are possible in a single interacting bacteria-phage system and that occurrence of predicted phage-driven evolutionary trade-offs may depend on the genetics underlying evolution of phage resistance in bacteria. These results are relevant for the ongoing development of lytic phages, such as OMKO1, in personalized treatment of human patients, as an alternative to antibiotics.202236168737
8446140.9751Genome-wide association study for resistance to Pseudomonas syringae pv. garcae in Coffea arabica. Bacteria halo blight (BHB), a coffee plant disease caused by Pseudomonas syringae pv. garcae, has been gaining importance in producing mountain regions and mild temperatures areas as well as in coffee nurseries. Most Coffea arabica cultivars are susceptible to this disease. In contrast, a great source of genetic diversity and resistance to BHB are found in C. arabica Ethiopian accessions. Aiming to identify quantitative trait nucleotides (QTNs) associated with resistance to BHB and the influence of these genomic regions during the domestication of C. arabica, we conducted an analysis of population structure and a Genome-Wide Association Study (GWAS). For this, we used genotyping by sequencing (GBS) and phenotyping for resistance to BHB of a panel with 120 C. arabica Ethiopian accessions from a historical FAO collection, 11 C. arabica cultivars, and the BA-10 genotype. Population structure analysis based on single-nucleotide polymorphisms (SNPs) markers showed that the 132 accessions are divided into 3 clusters: most wild Ethiopian accessions, domesticated Ethiopian accessions, and cultivars. GWAS, using the single-locus model MLM and the multi-locus models mrMLM, FASTmrMLM, FASTmrEMMA, and ISIS EM-BLASSO, identified 11 QTNs associated with resistance to BHB. Among these QTNs, the four with the highest values of association for resistance to BHB are linked to g000 (Chr_0_434_435) and g010741 genes, which are predicted to encode a serine/threonine-kinase protein and a nucleotide binding site leucine-rich repeat (NBS-LRR), respectively. These genes displayed a similar transcriptional downregulation profile in a C. arabica susceptible cultivar and in a C. arabica cultivar with quantitative resistance, when infected with P. syringae pv. garcae. However, peaks of upregulation were observed in a C. arabica cultivar with qualitative resistance, for both genes. Our results provide SNPs that have potential for application in Marker Assisted Selection (MAS) and expand our understanding about the complex genetic control of the resistance to BHB in C. arabica. In addition, the findings contribute to increasing understanding of the C. arabica domestication history.202236330243
8759150.9751Genetic and transcriptomic dissection of host defense to Goss's bacterial wilt and leaf blight of maize. Goss's wilt, caused by the Gram-positive actinobacterium Clavibacter nebraskensis, is an important bacterial disease of maize. The molecular and genetic mechanisms of resistance to the bacterium, or, in general, Gram-positive bacteria causing plant diseases, remain poorly understood. Here, we examined the genetic basis of Goss's wilt through differential gene expression, standard genome-wide association mapping (GWAS), extreme phenotype (XP) GWAS using highly resistant (R) and highly susceptible (S) lines, and quantitative trait locus (QTL) mapping using 3 bi-parental populations, identifying 11 disease association loci. Three loci were validated using near-isogenic lines or recombinant inbred lines. Our analysis indicates that Goss's wilt resistance is highly complex and major resistance genes are not commonly present. RNA sequencing of samples separately pooled from R and S lines with or without bacterial inoculation was performed, enabling identification of common and differential gene responses in R and S lines. Based on expression, in both R and S lines, the photosynthesis pathway was silenced upon infection, while stress-responsive pathways and phytohormone pathways, namely, abscisic acid, auxin, ethylene, jasmonate, and gibberellin, were markedly activated. In addition, 65 genes showed differential responses (up- or down-regulated) to infection in R and S lines. Combining genetic mapping and transcriptional data, individual candidate genes conferring Goss's wilt resistance were identified. Collectively, aspects of the genetic architecture of Goss's wilt resistance were revealed, providing foundational data for mechanistic studies.202337652038
196160.9750A specialized citric acid cycle requiring succinyl-coenzyme A (CoA):acetate CoA-transferase (AarC) confers acetic acid resistance on the acidophile Acetobacter aceti. Microbes tailor macromolecules and metabolism to overcome specific environmental challenges. Acetic acid bacteria perform the aerobic oxidation of ethanol to acetic acid and are generally resistant to high levels of these two membrane-permeable poisons. The citric acid cycle (CAC) is linked to acetic acid resistance in Acetobacter aceti by several observations, among them the oxidation of acetate to CO2 by highly resistant acetic acid bacteria and the previously unexplained role of A. aceti citrate synthase (AarA) in acetic acid resistance at a low pH. Here we assign specific biochemical roles to the other components of the A. aceti strain 1023 aarABC region. AarC is succinyl-coenzyme A (CoA):acetate CoA-transferase, which replaces succinyl-CoA synthetase in a variant CAC. This new bypass appears to reduce metabolic demand for free CoA, reliance upon nucleotide pools, and the likely effect of variable cytoplasmic pH upon CAC flux. The putative aarB gene is reassigned to SixA, a known activator of CAC flux. Carbon overflow pathways are triggered in many bacteria during metabolic limitation, which typically leads to the production and diffusive loss of acetate. Since acetate overflow is not feasible for A. aceti, a CO(2) loss strategy that allows acetic acid removal without substrate-level (de)phosphorylation may instead be employed. All three aar genes, therefore, support flux through a complete but unorthodox CAC that is needed to lower cytoplasmic acetate levels.200818502856
8264170.9750Anti-CRISPR Phages Cooperate to Overcome CRISPR-Cas Immunity. Some phages encode anti-CRISPR (acr) genes, which antagonize bacterial CRISPR-Cas immune systems by binding components of its machinery, but it is less clear how deployment of these acr genes impacts phage replication and epidemiology. Here, we demonstrate that bacteria with CRISPR-Cas resistance are still partially immune to Acr-encoding phage. As a consequence, Acr-phages often need to cooperate in order to overcome CRISPR resistance, with a first phage blocking the host CRISPR-Cas immune system to allow a second Acr-phage to successfully replicate. This cooperation leads to epidemiological tipping points in which the initial density of Acr-phage tips the balance from phage extinction to a phage epidemic. Furthermore, both higher levels of CRISPR-Cas immunity and weaker Acr activities shift the tipping points toward higher initial phage densities. Collectively, these data help elucidate how interactions between phage-encoded immune suppressors and the CRISPR systems they target shape bacteria-phage population dynamics.201830033365
5117180.9749Metagenomic sequencing of mpox virus clade Ib lesions identifies possible bacterial and viral co-infections in hospitalized patients in eastern DRC. Mpox is an emerging zoonotic disease that caused two public health emergencies of international concern within two years. Less is known about the interplay of microbial organisms in mpox lesions which could result in superinfections that exacerbate outcomes or delay recovery. We utilized a unified metagenomic sequencing approach involving slow-speed centrifugation and differential lysis on 19 mpox lesion swabs of hospitalized patients in South Kivu province (eastern DRC) to characterize bacteria, antimicrobial resistance genes, mpox virus (MPXV), and viral co-infections. High-quality MPXV whole-genome sequences were obtained until a Ct value of 27. Furthermore, co-infections with other clinically relevant viruses, such as varicella zoster virus and herpes simplex virus-2, were detected and confirmed by real-time PCR. In addition, metagenomic sequence analysis of the bacterial content showed the presence of bacteria associated with skin and soft tissue infection in 10 of the 19 samples analyzed. These bacteria had a high abundance of resistance genes, with possible implications for antimicrobial treatment based on the predicted antimicrobial resistance. In conclusion, we report the presence of bacterial and viral pathogens in mpox lesions and detection of widespread resistance genes to the standard antibiotic treatment. The possibility of a co-infection, including antimicrobial resistance, should be considered when discussing treatment options, along with the determination of the case-fatality ratio.IMPORTANCEThe mpox virus clade Ib lineage emerged in the eastern Democratic Republic of the Congo owing to continuous human-to-human transmission in a vulnerable patient population. A major challenge of this ongoing outbreak is its occurrence in regions with severely limited healthcare infrastructure. As a result, less is known about co-infections in affected patients. Identifying and characterizing pathogens, including their antimicrobial resistance, is crucial for reducing infection-related complications and improving antimicrobial stewardship. In this study, we applied a unified metagenomics approach to detect and characterize bacterial and viral co-infections in mpox lesions of hospitalized mpox patients in the eastern DRC.202540445195
5118190.9749Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.202235134132