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Background Plasmids play a major role in the transfer of antimicrobial resistance (AMR) genes among bacteria via horizontal gene transfer. The identification of plasmids in short-read assemblies is a challenging problem and a very active research area. Plasmid binning aims at detecting, in a draft genome assembly, groups (bins) of contigs likely to originate from the same plasmid. Several methods for plasmid binning have been developed recently, such as PlasBin-flow, HyAsP, gplas, MOB-suite, and plasmidSPAdes. This motivates the problem of evaluating the performances of plasmid binning methods, either against a given ground truth or between them. Results We describe PlasEval, a novel method aimed at comparing the results of plasmid binning tools. PlasEval computes a dissimilarity measure between two sets of plasmid bins, that can originate either from two plasmid binning tools, or from a plasmid binning tool and a ground truth set of plasmid bins. The PlasEval dissimilarity accounts for the contig content of plasmid bins, the length of contigs and is repeat-aware. Moreover, the dissimilarity score computed by PlasEval is broken down into several parts, that allows to understand qualitative differences between the compared sets of plasmid bins. We illustrate the use of PlasEval by benchmarking four recently developed plasmid binning tools—PlasBin-flow, HyAsP, gplas, and MOB-recon—on a data set of 53 E. coli bacterial genomes. Conclusion Analysis of the results of plasmid binning methods using PlasEval shows that their behaviour varies significantly. PlasEval can be used to decide which specific plasmid binning method should be used for a specific dataset. The disagreement between different methods also suggests that the problem of plasmid binning on short-read contigs requires further research. We believe that PlasEval can prove to be an effective tool in this regard. PlasEval is publicly available at https://github.com/acme92/PlasEval

Background The selection pressure exercised by antibiotic drugs is an important consideration for the wise stewardship of antimicrobial treatment programs. Treatment decisions are currently based on crude assumptions, and there is an urgent need to develop a more quantitative knowledge base that can enable predictions of the impact of individual antibiotics on the human gut microbiome and resistome. Results Using shotgun metagenomics, we quantified changes in the gut microbiome in two cohorts of hematological patients receiving prophylactic antibiotics; one cohort was treated with ciprofloxacin in a hospital in Tübingen and the other with cotrimoxazole in a hospital in Cologne. Analyzing this rich longitudinal dataset, we found that gut microbiome diversity was reduced in both treatment cohorts to a similar extent, while effects on the gut resistome differed. We observed a sharp increase in the relative abundance of sulfonamide antibiotic resistance genes (ARGs) by 148.1% per cumulative defined daily dose of cotrimoxazole in the Cologne cohort, but not in the Tübingen cohort treated with ciprofloxacin. Through multivariate modeling, we found that factors such as individual baseline microbiome, resistome, and plasmid diversity; liver/kidney function; and concurrent medication, especially virostatic agents, influence resistome alterations. Strikingly, we observed different effects on the plasmidome in the two treatment groups. There was a substantial increase in the abundance of ARG-carrying plasmids in the cohort treated with cotrimoxazole, but not in the cohort treated with ciprofloxacin, indicating that cotrimoxazole might contribute more efficiently to the spread of resistance. Conclusions Our study represents a step forward in developing the capability to predict the effect of individual antimicrobials on the human microbiome and resistome. Our results indicate that to achieve this, integration of the individual baseline microbiome, resistome, and mobilome status as well as additional individual patient factors will be required. Such personalized predictions may in the future increase patient safety and reduce the spread of resistance. Trial registration ClinicalTrials.gov, NCT02058888 . Registered February 10 2014

Whole genome sequencing (WGS) of Salmonella supports both molecular typing and detection of antimicrobial resistance (AMR). Here, we evaluated the correlation between phenotypic antimicrobial susceptibility testing (AST) and in silico prediction of AMR from WGS in Salmonella enterica (n = 1321) isolated from human infections in Canada. Phenotypic AMR results from broth microdilution testing were used as the gold standard. To facilitate high-throughput prediction of AMR from genome assemblies, we created a tool called Staramr, which incorporates the ResFinder and PointFinder databases and a custom gene-drug key for antibiogram prediction. Overall, there was 99% concordance between phenotypic and genotypic detection of categorical resistance for 14 antimicrobials in 1321 isolates (18,305 of 18,494 results in agreement). We observed an average sensitivity of 91.2% (range 80.5–100%), a specificity of 99.7% (98.6–100%), a positive predictive value of 95.4% (68.2–100%), and a negative predictive value of 99.1% (95.6–100%). The positive predictive value of gentamicin was 68%, due to seven isolates that carried aac(3)-IVa, which conferred MICs just below the breakpoint of resistance. Genetic mechanisms of resistance in these 1321 isolates included 64 unique acquired alleles and mutations in three chromosomal genes. In general, in silico prediction of AMR in Salmonella was reliable compared to the gold standard of broth microdilution. WGS can provide higher-resolution data on the epidemiology of resistance mechanisms and the emergence of new resistance alleles.

The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/ . CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools. High-quality gRNA activity data is needed for accurate on-target efficiency predictions. Here the authors generate activity data for over 10,000 gRNA and build a deep learning model CRISPRon for improved performance predictions.

Acinetobacter baumannii is one of the major pathogenic ESKAPE bacterium, which is responsible for about more than 722,000 cases in a year, globally. Despite the alarming increase in multidrug resistance, a safe and effective vaccine for Acinetobacter infections is still not available. Hence in the current study, a multiepitope vaccine construct was developed using linear B cell, cytotoxic T cell, and helper T cell epitopes from the antigenic and well-conserved lipopolysaccharide assembly proteins employing systematic immunoinformatics and structural vaccinology strategies. The multi-peptide vaccine was predicted to be highly antigenic, non-allergenic, non-toxic, and cover maximum population coverage worldwide. Further, the vaccine construct was modeled along with adjuvant and peptide linkers and validated to achieve a high-quality three-dimensional structure which was subsequently utilized for cytokine prediction, disulfide engineering, and docking analyses with Toll-like receptor (TLR4). Ramachandran plot showed 98.3% of the residues were located in the most favorable and permitted regions, thereby corroborating the feasibility of the modeled vaccine construct. Molecular dynamics simulation for a 100 ns timeframe further confirmed the stability of the binding vaccine-receptor complex. Finally, in silico cloning and codon adaptation were also performed with the pET28a (+) plasmid vector to determine the efficiency of expression and translation of the vaccine. Immune simulation studies demonstrated that the vaccine could trigger both B and T cell responses and can elicit strong primary, secondary, and tertiary immune responses. The designed multi-peptide subunit vaccine would certainly expedite the experimental approach for the development of a vaccine against A. baumannii infection.

Population dynamic models that explain and predict the spread of conjugative plasmids are pivotal for understanding microbial evolution and engineering microbiomes. However, prediction uncertainty of these models has rarely been assessed. We adopt a Bayesian approach, employing Markov Chain Monte Carlo (MCMC), to parameterize and model plasmid conjugation dynamics. This approach treats model parameters as random variables whose probability distributions are informed by data on plasmid population dynamics. These distributions allow us to estimate credible intervals of the model’s parameters and predictions. We validated this approach using synthetic population dynamic data with known parameter values and experimental population dynamic data of mini-RK2, a miniaturized counterpart of the well-characterized and widely used RK2 conjugation plasmid. Our methodology accurately estimated the parameters of synthetic data, and model predictions were robust across time scales and initial conditions. Incorporating long-term population dynamic data enhances the precision of parameter estimates related to plasmid loss and the accuracy of long-term population dynamic predictions. For experimental data, the model correctly explained and predicted most population dynamic trends, albeit with broader credible intervals. Incorporating long-term data also improves credible ranges of most parameters. However, in some cases, such as with the growth parameter of cells with the conjugative plasmid, the inclusion of long-term data can lead to stronger correlations and potential identifiability issues between key parameters. Overall, our method allows for deeper investigation of plasmid population dynamics and could potentially be generalized to study population dynamics of other mobile genetic elements.

Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), 13C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs. To commercialize recombinant organisms for renewable chemical production, it is essential to characterize the cost and benefit of metabolic burden using metabolic flux analysis tools. Genome-scale modeling can incorporate 13C-fluxome information and machine learning to predict the metabolic burden of synthetic biology modules. Modularized expression of native or recombinant pathways using a variety of experimental tools for controlling expression can substantially reduce the metabolic burden introduced by these pathways. The development of a standard synthetic-biology publication database may allow the use of machine learning or artificial intelligence to harness past knowledge for future rational design. Detailed computational methods have been developed to model macromolecule synthesis (DNA, RNA, proteins) to account for the maintenance costs associated with basal cellular function. Systems-level dynamic simulations and design algorithms can inform new approaches to engineering microbial production strains.

Burkholderia stabilis strain FERMP-21014 secretes cholesterol esterase (BsChe), which is used in clinical settings to determine serum cholesterol levels. Previously, we constructed an expression plasmid with an endogenous constitutive promoter to enable the production of recombinant BsChe. In this study, we obtained one mutant strain with 13.1-fold higher BsChe activity than the wild type, using N-methyl-N′-nitro-N-nitrosoguanidine as a mutagen. DNA-sequencing analysis revealed that the strain had lost chromosome 3 (∆Chr3), suggesting that the genes hindering BsChe production may be encoded on Chr3. We also identified common mutations in the functionally unknown BSFP_068720/30 genes in the top 10 active strains generated during transposon mutagenesis. As BSFP_068720/30/40 comprised an operon on Chr3, we created the BSFP_068720/30/40 disruption mutant and confirmed that each disruption mutant containing the expression plasmid exhibited ~ 16.1-fold higher BsChe activity than the wild type. Quantitative PCR showed that each disruption mutant and ΔChr3 had a ~ 9.4-fold higher plasmid copy number than the wild type. Structural prediction models indicate that BSFP_068730/40 is structurally homologous to the structural maintenance of chromosomes (SMC) protein MukBE, which is responsible for chromosome segregation during cell division. Conversely, BSFP_068720/30/40 disruption did not lead to a Chr3 drop-out. These results imply that BSFP_068720/30/40 is not a SMC protein but is involved in destabilizing foreign plasmids to prevent the influx of genetic information from the environment. In conclusion, the disruption of BSFP_068720/30/40 improved plasmid stability and copy number, resulting in exceptionally high BsChe production. Key points • Disruption of BSFP_068720/30/40 enabled mass production of Burkholderia Che/Lip. • BSFP_068730/40 is an SMC protein homolog not involved in chromosome retention. • BSFP_068720/30/40 is likely responsible for the exclusion of exogenous plasmids.

Using a metagenomic approach, three types of CRISPR–Cas systems have been discovered in uncultivated bacterial and archaeal hosts from a variety of different environments. CRISPR–Cas systems from uncultured microbes Current CRISPR–Cas technology is based on systems identified from cultured bacteria, whereas the enzymes from the numerous prokaryotes that have not been cultured have remained unexplored. By using cultivation-independent genome-resolved metagenomics, Jillian Banfield, Jennifer Doudna and colleagues identify and then functionally characterize new CRISPR–Cas systems. These include the first reported Cas9 in the archaeal domain of life, which was thought to lack such systems, as well as compact CRISPR–CasX and CRISPR–CasY systems. Genomic exploration of environmental microbial communities gives access to unprecedented genome diversity with the potential to revolutionize microbe-based biotechnologies. CRISPR–Cas systems provide microbes with adaptive immunity by employing short DNA sequences, termed spacers, that guide Cas proteins to cleave foreign DNA 1 , 2 . Class 2 CRISPR–Cas systems are streamlined versions, in which a single RNA-bound Cas protein recognizes and cleaves target sequences 3 , 4 . The programmable nature of these minimal systems has enabled researchers to repurpose them into a versatile technology that is broadly revolutionizing biological and clinical research 5 . However, current CRISPR–Cas technologies are based solely on systems from isolated bacteria, leaving the vast majority of enzymes from organisms that have not been cultured untapped. Metagenomics, the sequencing of DNA extracted directly from natural microbial communities, provides access to the genetic material of a huge array of uncultivated organisms 6 , 7 . Here, using genome-resolved metagenomics, we identify a number of CRISPR–Cas systems, including the first reported Cas9 in the archaeal domain of life, to our knowledge. This divergent Cas9 protein was found in little-studied nanoarchaea as part of an active CRISPR–Cas system. In bacteria, we discovered two previously unknown systems, CRISPR–CasX and CRISPR–CasY, which are among the most compact systems yet discovered. Notably, all required functional components were identified by metagenomics, enabling validation of robust in vivo RNA-guided DNA interference activity in Escherichia coli . Interrogation of environmental microbial communities combined with in vivo experiments allows us to access an unprecedented diversity of genomes, the content of which will expand the repertoire of microbe-based biotechnologies.

Cyanobacteria are widely-diverse, environmentally crucial photosynthetic prokaryotes of great interests for basic and applied science. Work to date has focused mostly on the three non-nitrogen fixing unicellular species Synechocystis PCC 6803, Synechococcus PCC 7942, and Synechococcus PCC 7002, which have been selected for their genetic and physiological interests summarized in this review. Extensive “omics” data sets have been generated, and genome-scale models (GSM) have been developed for the rational engineering of these cyanobacteria for biotechnological purposes. We presently discuss what should be done to improve our understanding of the genotype-phenotype relationships of these models and generate robust and predictive models of their metabolism. Furthermore, we also emphasize that because Synechocystis PCC 6803, Synechococcus PCC 7942, and Synechococcus PCC 7002 represent only a limited part of the wide biodiversity of cyanobacteria, other species distantly related to these three models, should be studied. Finally, we highlight the need to strengthen the communication between academic researchers, who know well cyanobacteria and can engineer them for biotechnological purposes, but have a limited access to large photobioreactors, and industrial partners who attempt to use natural or engineered cyanobacteria to produce interesting chemicals at reasonable costs, but may lack knowledge on cyanobacterial physiology and metabolism.