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840000.9870Transferring 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.201829954330
902810.9868Efflux Pumps in Chromobacterium Species Increase Antibiotic Resistance and Promote Survival in a Coculture Competition Model. Members of the Chromobacterium genus include opportunistic but often-fatal pathogens and soil saprophytes with highly versatile metabolic capabilities. In previous studies of Chromobacterium subtsugae (formerly C. violaceum) strain CV017, we identified a resistance nodulation division (RND)-family efflux pump (CdeAB-OprM) that confers resistance to several antibiotics, including the bactobolin antibiotic produced by the soil saprophyte Burkholderia thailandensis Here, we show the cdeAB-oprM genes increase C. subtsugae survival in a laboratory competition model with B. thailandensis We also demonstrate that adding sublethal bactobolin concentrations to the coculture increases C. subtsugae survival, but this effect is not through CdeAB-OprM. Instead, the increased survival requires a second, previously unreported pump we call CseAB-OprN. We show that in cells exposed to sublethal bactobolin concentrations, the cseAB-oprN genes are transcriptionally induced, and this corresponds to an increase in bactobolin resistance. Induction of this pump is highly specific and sensitive to bactobolin, while CdeAB-OprM appears to have a broader range of antibiotic recognition. We examine the distribution of cseAB-oprN and cdeAB-oprM gene clusters in members of the Chromobacterium genus and find the cseAB-oprN genes are limited to the nonpathogenic C. subtsugae strains, whereas the cdeAB-oprM genes are more widely distributed among members of the Chromobacterium genus. Our results provide new information on the antibiotic resistance mechanisms of Chromobacterium species and highlight the importance of efflux pumps for saprophytic bacteria existing in multispecies communities.IMPORTANCE Antibiotic efflux pumps are best known for increasing antibiotic resistance of pathogens; however, the role of these pumps in saprophytes is much less well defined. This study describes two predicted efflux pump gene clusters in the Chromobacterium genus, which is comprised of both nonpathogenic saprophytes and species that cause highly fatal human infections. One of the predicted efflux pump clusters is present in every member of the Chromobacterium genus and increases resistance to a broad range of antibiotics. The other gene cluster has more narrow antibiotic specificity and is found only in Chromobacterium subtsugae, a subset of entirely nonpathogenic species. We demonstrate the role of both pumps in increasing antibiotic resistance and demonstrate the importance of efflux-dependent resistance induction for C. subtsugae survival in a dual-species competition model. These results have implications for managing antibiotic-resistant Chromobacterium infections and for understanding the evolution of efflux pumps outside the host.201931324628
973620.9868Coevolutionary 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
840330.9863Uncovering virulence factors in Cronobacter sakazakii: insights from genetic screening and proteomic profiling. The increasing problem of antibiotic resistance has driven the search for virulence factors in pathogenic bacteria, which can serve as targets for the development of new antibiotics. Although whole-genome Tn5 transposon mutagenesis combined with phenotypic assays has been a widely used approach, its efficiency remains low due to labor-intensive processes. In this study, we aimed to identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a pathogenic bacterium known for causing severe infections, particularly in infants and immunocompromised individuals. By employing a combination of genetic screening, comparative proteomics, and in vivo validation using zebrafish and rat models, we rapidly screened highly virulent strains and identified two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats. Proteomic profiling revealed upregulated proteins upon knockout of rcsA and treR, including FabH, GshA, GppA, GcvH, IhfB, RfaC, MsyB, and three unknown proteins. Knockout of their genes significantly weakened bacterial virulence, confirming their role as potential virulence factors. Our findings contribute to understanding the pathogenicity of C. sakazakii and provide insights into the development of targeted interventions and therapies against this bacterium.IMPORTANCEThe emergence of antibiotic resistance in pathogenic bacteria has become a critical global health concern, necessitating the identification of virulence factors as potential targets for the development of new antibiotics. This study addresses the limitations of conventional approaches by employing a combination of genetic screening, comparative proteomics, and in vivo validation to rapidly identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a highly pathogenic bacterium responsible for severe infections in vulnerable populations. The identification of two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats and the proteomic profiling upon knockout of rcsA and treR provides novel insights into the mechanisms underlying bacterial virulence. The findings contribute to our understanding of C. sakazakii's pathogenicity, shed light on the regulatory pathways involved in bacterial virulence, and offer potential targets for the development of novel interventions against this highly virulent bacterium.202337750707
840540.9863Mapping 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
509850.9863Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
955560.9863Bacteria.guru: Comparative Transcriptomics and Co-Expression Database for Bacterial Pathogens. While bacteria can be beneficial to our health, their deadly pathogenic potential has been an ever-present concern exacerbated by the emergence of drug-resistant strains. As such, there is a pressing urgency for an enhanced understanding of their gene function and regulation, which could mediate the development of novel antimicrobials. Transcriptomic analyses have been established as insightful and indispensable to the functional characterization of genes and identification of new biological pathways, but in the context of bacterial studies, they remain limited to species-specific datasets. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and researched bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, coexpression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid in discovering multi-faceted antibiotic targets and, overall, facilitate future bacterial research. AVAILABILITY: The database and coexpression networks are freely available from https://bacteria.guru/. Sample annotations can be found in the supplemental data.202234838806
840170.9863LSTrAP-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.202032883264
826380.9862CRISPR/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.201830697226
973590.9862Arms 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
8133100.9861Symbiotic bacteria confer insecticide resistance by metabolizing buprofezin in the brown planthopper, Nilaparvata lugens (Stål). Buprofezin, a chitin synthesis inhibitor, is widely used to control several economically important insect crop pests. However, the overuse of buprofezin has led to the evolution of resistance and exposed off-target organisms present in agri-environments to this compound. As many as six different strains of bacteria isolated from these environments have been shown to degrade buprofezin. However, whether insects can acquire these buprofezin-degrading bacteria from soil and enhance their own resistance to buprofezin remains unknown. Here we show that field strains of the brown planthopper, Nilaparvata lugens, have acquired a symbiotic bacteria, occurring naturally in soil and water, that provides them with resistance to buprofezin. We isolated a symbiotic bacterium, Serratia marcescens (Bup_Serratia), from buprofezin-resistant N. lugens and showed it has the capacity to degrade buprofezin. Buprofezin-susceptible N. lugens inoculated with Bup_Serratia became resistant to buprofezin, while antibiotic-treated N. lugens became susceptible to this insecticide, confirming the important role of Bup_Serratia in resistance. Sequencing of the Bup_Serratia genome identified a suite of candidate genes involved in the degradation of buprofezin, that were upregulated upon exposure to buprofezin. Our findings demonstrate that S. marcescens, an opportunistic pathogen of humans, can metabolize the insecticide buprofezin and form a mutualistic relationship with N. lugens to enhance host resistance to buprofezin. These results provide new insight into the mechanisms underlying insecticide resistance and the interactions between bacteria, insects and insecticides in the environment. From an applied perspective they also have implications for the control of highly damaging crop pests.202338091367
3767110.9861Transposon insertion sequencing reveals novel hypermutator genes in Acinetobacter baumannii. Mutation rates in bacteria are an important determinant of adaptation to new environments and success in different niches. In some bacterial pathogens, "hypermutator" variants-most often associated with mutations in components of the DNA mismatch repair system-are associated with increased antibiotic resistance and poorer patient outcomes. We report the serendipitous finding of novel hypermutator genes in Acinetobacter baumannii through genome-scale mutant fitness screening. Exposure of a transposon insertion mutant library of A. baumannii to extended weak antibiotic selection resulted in selection for mutations that directly increased fitness as expected, but also revealed genes where transposon insertion indirectly increased fitness due to elevated general mutation rates. Three novel hypermutator genes were confirmed in A. baumannii: nusB, encoding a transcription antiterminator; ABUW_0208, encoding a hypothetical protein; and ABUW_2121, which encodes a sulfite transporter. We find selection for hypermutator variants in transposon insertion sequencing (TIS) data sets from diverse bacteria under various antibiotic treatments. Our results expand the range of biological functions linked to hypermutator phenotypes in bacteria and provide a workflow for the identification of putative hypermutators by TIS.IMPORTANCEAll organisms have the capacity for evolution through mutation. Bacteria with high mutation rates have a survival advantage in some stressful environments because they generate beneficial mutations more frequently. "Hypermutators" are bacterial strains that carry gene inactivations that increase general mutation rates. These variants are important in chronic infections, as their increased genetic diversity allows higher drug resistance and prolonged survival in the host. Only a few different hypermutator genes are known, and there is no high-throughput method for their identification. We have made the serendipitous finding that hypermutator genes can be identified by genome-wide mutant fitness screening under specific selection conditions. We have identified novel hypermutator alleles in the notorious hospital pathogen Acinetobacter baumannii and show that hypermutator variants can be detected in screens of a wide range of pathogens.202540576344
9086120.9860Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.201829746491
9733130.9860The 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
9734140.9859Combination 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
9076150.9858ResiDB: 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
8259160.9858Secondary 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.202235580059
4899170.9858Chemiluminescent Carbapenem-Based Molecular Probe for Detection of Carbapenemase Activity in Live Bacteria. Carbapenemase-producing organisms (CPOs) pose a severe threat to antibacterial treatment due to the acquisition of antibiotic resistance. This resistance can be largely attributed to the antibiotic-hydrolyzing enzymes that the bacteria produce. Current carbapenem "wonder drugs", such as doripenem, ertapenem, meropenem, imipenem, and so on, are resistant to regular β-lactamases, but susceptible to carbapenemases. Even worse, extended exposure of bacteria to these drugs accelerates the spread of resistance genes. In order to preserve the clinical efficacy of antibacterial treatment, carbapenem drugs should be carefully regulated and deployed only in cases of a CPO infection. Early diagnosis is therefore of paramount importance. Herein, we report the design, synthesis, and activity of the first carbapenemase-sensitive chemiluminescent probe, CPCL, which may be used to monitor CPO activity. The design of our probe enables enzymatic cleavage of the carbapenem core, which is followed by a facile 1,8-elimination process and the emission of green light through rapid chemical excitation. We have demonstrated the ability of the probe to detect a number of clinically relevant carbapenemases and the successful identification of CPO present in bacterial cultures, such as those used for clinical diagnosis. We believe that our use of "turn-on" chemiluminescence activation will find significant application in future diagnostic assays and improve antibacterial treatment.202031957167
9083180.9858ARGNet: 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
9375190.9857Multistep diversification in spatiotemporal bacterial-phage coevolution. The evolutionary arms race between phages and bacteria, where bacteria evolve resistance to phages and phages retaliate with resistance-countering mutations, is a major driving force of molecular innovation and genetic diversification. Yet attempting to reproduce such ongoing retaliation dynamics in the lab has been challenging; laboratory coevolution experiments of phage and bacteria are typically performed in well-mixed environments and often lead to rapid stagnation with little genetic variability. Here, co-culturing motile E. coli with the lytic bacteriophage T7 on swimming plates, we observe complex spatiotemporal dynamics with multiple genetically diversifying adaptive cycles. Systematically quantifying over 10,000 resistance-infectivity phenotypes between evolved bacteria and phage isolates, we observe diversification into multiple coexisting ecotypes showing a complex interaction network with both host-range expansion and host-switch tradeoffs. Whole-genome sequencing of these evolved phage and bacterial isolates revealed a rich set of adaptive mutations in multiple genetic pathways including in genes not previously linked with phage-bacteria interactions. Synthetically reconstructing these new mutations, we discover phage-general and phage-specific resistance phenotypes as well as a strong synergy with the more classically known phage-resistance mutations. These results highlight the importance of spatial structure and migration for driving phage-bacteria coevolution, providing a concrete system for revealing new molecular mechanisms across diverse phage-bacterial systems.202236577749