Engineering living and regenerative fungal-bacterial biocomposite structures.

Engineered living materials could have the capacity to self-repair and self-replicate, sense local and distant disturbances in their environment, and respond with functionalities for reporting, actuation or remediation. However, few engineered living materials are capable of both responsivity and use in macroscopic structures. Here we describe the development, characterization and engineering of a fungal-bacterial biocomposite grown on lignocellulosic feedstocks that can form mouldable, foldable and regenerative living structures. We have developed strategies to make human-scale biocomposite structures using mould-based and origami-inspired growth and assembly paradigms. Microbiome profiling of the biocomposite over multiple generations enabled the identification of a dominant bacterial component, Pantoea agglomerans, which was further isolated and developed into a new chassis. We introduced engineered P. agglomerans into native feedstocks to yield living blocks with new biosynthetic and sensing-reporting capabilities. Bioprospecting the native microbiota to develop engineerable chassis constitutes an important strategy to facilitate the development of living biomaterials with new properties and functionalities.

Forecasting the dissemination of antibiotic resistance genes across bacterial genomes.

Antibiotic resistance spreads among bacteria through horizontal transfer of antibiotic resistance genes (ARGs). Here, we set out to determine predictive features of ARG transfer among bacterial clades. We use a statistical framework to identify putative horizontally transferred ARGs and the groups of bacteria that disseminate them. We identify 152 gene exchange networks containing 22,963 bacterial genomes. Analysis of ARG-surrounding sequences identify genes encoding putative mobilisation elements such as transposases and integrases that may be involved in gene transfer between genomes. Certain ARGs appear to be frequently mobilised by different mobile genetic elements. We characterise the phylogenetic reach of these mobilisation elements to predict the potential future dissemination of known ARGs. Using a separate database with 472,798 genomes from Streptococcaceae, Staphylococcaceae and Enterobacteriaceae, we confirm 34 of 94 predicted mobilisations. We explore transfer barriers beyond mobilisation and show experimentally that physiological constraints of the host can explain why specific genes are largely confined to Gram-negative bacteria although their mobile elements support dissemination to Gram-positive bacteria. Our approach may potentially enable better risk assessment of future resistance gene dissemination.

Short and long-read ultra-deep sequencing profiles emerging heterogeneity across five platform Escherichia coli strains.

Reprogramming organisms for large-scale bioproduction counters their evolutionary objectives of fast growth and often leads to mutational collapse of the engineered production pathways during cultivation. Yet, the mutational susceptibility of academic and industrial Escherichia coli bioproduction host strains are poorly understood. In this study, we apply 2nd and 3rd generation deep sequencing to profile simultaneous modes of genetic heterogeneity that decimate engineered biosynthetic production in five popular E. coli hosts BL21(DE3), TOP10, MG1655, W, and W3110 producing 2,3-butanediol and mevalonic acid. Combining short-read and long-read sequencing, we detect strain and sequence-specific mutational modes including single nucleotide polymorphism, inversion, and mobile element transposition, as well as complex structural variations that disrupt the integrity of the engineered biosynthetic pathway. Our analysis suggests that organism engineers should avoid chassis strains hosting active insertion sequence (IS) subfamilies such as IS1 and IS10 present in popular E. coli TOP10. We also recommend monitoring for increased mutagenicity in the pathway transcription initiation regions and recombinogenic repeats. Together, short and long sequencing reads identified latent low-frequency mutation events such as a short detrimental inversion within a pathway gene, driven by 8-bp short inverted repeats. This demonstrates the power of combining ultra-deep DNA sequencing technologies to profile genetic heterogeneities of engineered constructs and explore the markedly different mutational landscapes of common E. coli host strains. The observed multitude of evolving variants underlines the usefulness of early mutational profiling for new synthetic pathways designed to sustain in organisms over long cultivation scales.

Impact of Fiber-Based Enteral Nutrition on the Gut Microbiome of ICU Patients Receiving Broad-Spectrum Antibiotics: A Randomized Pilot Trial.

OBJECTIVES: Dietary fiber increases the abundance of bacteria that metabolize fiber into short-chain fatty acids and confers resistance against gut colonization with multidrug-resistant bacteria. This pilot trial estimated the effect of fiber on gut short-chain fatty acid-producing bacteria in the ICU. DESIGN: Randomized, controlled, open label trial. SETTING: Medical ICU. PATIENTS: Twenty ICU adults receiving broad-spectrum IV antibiotics for sepsis. INTERVENTION: 1:1 randomization to enteral nutrition with mixed soy- and oat-derived fiber (14.3 g fiber/L) versus calorie- and micronutrient-identical enteral nutrition with 0 g/L fiber. MEASUREMENTS: Rectal swabs and whole stools were collected at baseline and on study Days 3, 7, 14, and 30. The primary outcome was within-individual change in the cumulative relative abundance of short-chain fatty acid-producing taxa from baseline to Day 3 based on 16S sequencing of rectal swabs. The secondary outcome was Day 3 cumulative short-chain fatty acid levels based on mass spectrometry of whole stools. Analyses were all intent to treat. MAIN RESULTS: By Day 3, the fiber group received a median of 32.1 g fiber cumulatively (interquartile range, 17.6-54.6) versus 0 g fiber (interquartile range, 0-4.0) in the no fiber group. The median within-individual change in short-chain fatty acid producer relative abundance from baseline to Day 3 was +61% (interquartile range -51 to +1,688) in the fiber group versus -46% (interquartile range, -78 to +13) in the no fiber group (p = 0.28). Whole stool short-chain fatty acid levels on Day 3 were a median of 707 µg short-chain fatty acids/g stool (interquartile range, 190-7,265) in the fiber group versus 118 µg short-chain fatty acids/g stool (interquartile range, 22-1,195) in the no fiber group (p = 0.16). CONCLUSIONS: Enteral fiber was associated with nonsignificant trends toward increased relative abundance of short-chain fatty acid-producing bacteria and increased short-chain fatty acid levels among ICU patients receiving broad-spectrum IV antibiotics. Larger studies should be undertaken and our results can be used for effect size estimates.

The effect of short-course antibiotics on the resistance profile of colonizing gut bacteria in the ICU: a prospective cohort study.

BACKGROUND: The need for early antibiotics in the intensive care unit (ICU) is often balanced against the goal of antibiotic stewardship. Long-course antibiotics increase the burden of antimicrobial resistance within colonizing gut bacteria, but the dynamics of this process are not fully understood. We sought to determine how short-course antibiotics affect the antimicrobial resistance phenotype and genotype of colonizing gut bacteria in the ICU by performing a prospective cohort study with assessments of resistance at ICU admission and exactly 72 h later. METHODS: Deep rectal swabs were performed on 48 adults at the time of ICU admission and exactly 72 h later, including patients who did and did not receive antibiotics. To determine resistance phenotype, rectal swabs were cultured for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE). In addition, Gram-negative bacterial isolates were cultured against relevant antibiotics. To determine resistance genotype, quantitative PCR (qPCR) was performed from rectal swabs for 87 established resistance genes. Within-individual changes in antimicrobial resistance were calculated based on culture and qPCR results and correlated with exposure to relevant antibiotics (e.g., did ß-lactam antibiotic exposure associate with a detectable change in ß-lactam resistance over this 72-h period?). RESULTS: Of 48 ICU patients, 41 (85%) received antibiotics. Overall, there was no increase in the antimicrobial resistance profile of colonizing gut bacteria during the 72-h study period. There was also no increase in antimicrobial resistance after stratification by receipt of antibiotics (i.e., no detectable increase in ß-lactam, vancomycin, or macrolide resistance regardless of whether patients received those same antibiotics). This was true for both culture and PCR. Antimicrobial resistance pattern at ICU admission strongly predicted resistance pattern after 72 h. CONCLUSIONS: Short-course ICU antibiotics made little detectable difference in the antimicrobial resistance pattern of colonizing gut bacteria over 72 h in the ICU. This provides an improved understanding of the dynamics of antimicrobial resistance in the ICU and some reassurance that short-course antibiotics may not adversely impact the stewardship goal of reducing antimicrobial resistance.

Recording mobile DNA in the gut microbiota using an Escherichia coli CRISPR-Cas spacer acquisition platform.

The flow of genetic material between bacteria is central to the adaptation and evolution of bacterial genomes. However, our knowledge about DNA transfer within complex microbiomes is lacking, with most studies of horizontal gene transfer (HGT) relying on bioinformatic analyses of genetic elements maintained on evolutionary timescales or experimental measurements of phenotypically trackable markers. Here, we utilize the CRISPR-Cas spacer acquisition process to detect DNA acquisition events from complex microbiota in real-time and at nucleotide resolution. In this system, an E. coli recording strain is exposed to a microbial sample and spacers are acquired from transferred plasmids and permanently stored in genomic CRISPR arrays. Sequencing and analysis of acquired spacers enables identification of the transferred plasmids. This approach allowed us to identify individual mobile elements without relying on phenotypic markers or post-transfer replication. We found that HGT into the recording strain in human clinical fecal samples can be extensive and is driven by different plasmid types, with the IncX type being the most actively transferred.

Collateral sensitivity constrains resistance evolution of the CTX-M-15 ß-lactamase.

Antibiotic resistance is a major challenge to global public health. Discovery of new antibiotics is slow and to ensure proper treatment of bacterial infections new strategies are needed. One way to curb the development of antibiotic resistance is to design drug combinations where the development of resistance against one drug leads to collateral sensitivity to the other drug. Here we study collateral sensitivity patterns of the globally distributed extended-spectrum ß-lactamase CTX-M-15, and find three non-synonymous mutations with increased resistance against mecillinam or piperacillin-tazobactam that simultaneously confer full susceptibility to several cephalosporin drugs. We show in vitro and in mice that a combination of mecillinam and cefotaxime eliminates both wild-type and resistant CTX-M-15. Our results indicate that mecillinam and cefotaxime in combination constrain resistance evolution of CTX-M-15, and illustrate how drug combinations can be rationally designed to limit the resistance evolution of horizontally transferred genes by exploiting collateral sensitivity patterns.

Chromosomal barcoding as a tool for multiplexed phenotypic characterization of laboratory evolved lineages.

Adaptive laboratory evolution is an important tool to evolve organisms to increased tolerance towards different physical and chemical stress. It is applied to study the evolution of antibiotic resistance as well as genetic mechanisms underlying improvements in production strains. Adaptive evolution experiments can be automated in a high-throughput fashion. However, the characterization of the resulting lineages can become a time consuming task, when the performance of each lineage is evaluated individually. Here, we present a novel method for the markerless insertion of randomized genetic barcodes into the genome of Escherichia coli using a novel dual-auxotrophic selection approach. The barcoded E. coli library allows multiplexed phenotyping of evolved strains in pooled competition experiments. We use the barcoded library in an adaptive evolution experiment; evolving resistance towards three common antibiotics. Comparing this multiplexed phenotyping with conventional susceptibility testing and growth-rate measurements we can show a significant positive correlation between the two approaches. Use of barcoded bacterial strain libraries for individual adaptive evolution experiments drastically reduces the workload of characterizing the resulting phenotypes and enables prioritization of lineages for in-depth characterization. In addition, barcoded clones open up new ways to profile community dynamics or to track lineages in vivo or situ.

Biochemical mechanisms determine the functional compatibility of heterologous genes.

Elucidating the factors governing the functional compatibility of horizontally transferred genes is important to understand bacterial evolution, including the emergence and spread of antibiotic resistance, and to successfully engineer biological systems. In silico efforts and work using single-gene libraries have suggested that sequence composition is a strong barrier for the successful integration of heterologous genes. Here we sample 200 diverse genes, representing >80% of sequenced antibiotic resistance genes, to interrogate the factors governing genetic compatibility in new hosts. In contrast to previous work, we find that GC content, codon usage, and mRNA-folding energy are of minor importance for the compatibility of mechanistically diverse gene products at moderate expression. Instead, we identify the phylogenetic origin, and the dependence of a resistance mechanism on host physiology, as major factors governing the functionality and fitness of antibiotic resistance genes. These findings emphasize the importance of biochemical mechanism for heterologous gene compatibility, and suggest physiological constraints as a pivotal feature orienting the evolution of antibiotic resistance.

Transfer and Persistence of a Multi-Drug Resistance Plasmid in situ of the Infant Gut Microbiota in the Absence of Antibiotic Treatment.

The microbial ecosystem residing in the human gut is believed to play an important role in horizontal exchange of virulence and antibiotic resistance genes that threatens human health. While the diversity of gut-microorganisms and their genetic content has been studied extensively, high-resolution insight into the plasticity, and selective forces shaping individual genomes is scarce. In a longitudinal study, we followed the dynamics of co-existing Escherichia coli lineages in an infant not receiving antibiotics. Using whole genome sequencing, we observed large genomic deletions, bacteriophage infections, as well as the loss and acquisition of plasmids in these lineages during their colonization of the human gut. In particular, we captured the exchange of multidrug resistance genes, and identified a clinically relevant conjugative plasmid mediating the transfer. This resistant transconjugant lineage was maintained for months, demonstrating that antibiotic resistance genes can disseminate and persist in the gut microbiome; even in absence of antibiotic selection. Furthermore, through in vivo competition assays, we suggest that the resistant transconjugant can persist through a fitness advantage in the mouse gut in spite of a fitness cost in vitro. Our findings highlight the dynamic nature of the human gut microbiota and provide the first genomic description of antibiotic resistance gene transfer between bacteria in the unperturbed human gut. These results exemplify that conjugative plasmids, harboring resistance determinants, can transfer and persists in the gut in the absence of antibiotic treatment.

Prediction of antibiotic resistance: time for a new preclinical paradigm?

Predicting the future is difficult, especially for evolutionary processes that are influenced by numerous unknown factors. Still, this is what is required of drug developers when they assess the risk of resistance arising against a new antibiotic candidate during preclinical development. In this Opinion article, we argue that the traditional procedures that are used for the prediction of antibiotic resistance today could be markedly improved by including a broader analysis of bacterial fitness, infection dynamics, horizontal gene transfer and other factors. This will lead to more informed preclinical decisions for continuing or discontinuing the development of drug candidates.

Time-Resolved Tracking of Mutations Reveals Diverse Allele Dynamics during Escherichia coli Antimicrobial Adaptive Evolution to Single Drugs and Drug Pairs.

Understanding the evolutionary processes that lead to antibiotic resistance can help to achieve better treatment strategies. Yet, little is known about the dynamics of the resistance alleles during adaptation. Here, we use population sequencing to monitor genetic changes in putative resistance loci at several time-points during adaptive evolution experiments involving five different antibiotic conditions. We monitor the mutational spectra in lineages evolved to be resistant to single antibiotics [amikacin (AMK), chloramphenicol (CHL), and ciprofloxacin (CIP)], as well as antibiotic combinations (AMK + CHL and CHL + CIP). We find that lineages evolved to antibiotic combinations exhibit different resistance allele dynamics compared with those of single-drug evolved lineages, especially for a drug pair with reciprocal collateral sensitivity. During adaptation, we observed interfering, superimposing and fixation allele dynamics. To further understand the selective forces driving specific allele dynamics, a subset of mutations were introduced into the ancestral wild type enabling differentiation between clonal interference and negative epistasis.

Dissemination of antibiotic resistance genes from antibiotic producers to pathogens.

It has been hypothesized that some antibiotic resistance genes (ARGs) found in pathogenic bacteria derive from antibiotic-producing actinobacteria. Here we provide bioinformatic and experimental evidence supporting this hypothesis. We identify genes in proteobacteria, including some pathogens, that appear to be closely related to actinobacterial ARGs known to confer resistance against clinically important antibiotics. Furthermore, we identify two potential examples of recent horizontal transfer of actinobacterial ARGs to proteobacterial pathogens. Based on this bioinformatic evidence, we propose and experimentally test a 'carry-back' mechanism for the transfer, involving conjugative transfer of a carrier sequence from proteobacteria to actinobacteria, recombination of the carrier sequence with the actinobacterial ARG, followed by natural transformation of proteobacteria with the carrier-sandwiched ARG. Our results support the existence of ancient and, possibly, recent transfers of ARGs from antibiotic-producing actinobacteria to proteobacteria, and provide evidence for a defined mechanism.

Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes.

Antibiotic resistance is a global threat to human health, wherefore it is crucial to study the mechanisms of antibiotic resistance as well as its emergence and dissemination. One way to analyze the acquisition of de novo mutations conferring antibiotic resistance is adaptive laboratory evolution. However, various evolution methods exist that utilize different population sizes, selection strengths, and bottlenecks. While evolution in increasing drug gradients guarantees high-level antibiotic resistance promising to identify the most potent resistance conferring mutations, other selection regimes are simpler to implement and therefore allow higher throughput. The specific regimen of adaptive evolution may have a profound impact on the adapted cell state. Indeed, substantial effects of the selection regime on the resulting geno- and phenotypes have been reported in the literature. In this study we compare the geno- and phenotypes of Escherichia coli after evolution to Amikacin, Piperacillin, and Tetracycline under four different selection regimes. Interestingly, key mutations that confer antibiotic resistance as well as phenotypic changes like collateral sensitivity and cross-resistance emerge independently of the selection regime. Yet, lineages that underwent evolution under mild selection displayed a growth advantage independently of the acquired level of antibiotic resistance compared to lineages adapted under maximal selection in a drug gradient. Our data suggests that even though different selection regimens result in subtle genotypic and phenotypic differences key adaptations appear independently of the selection regime.

Survival and Evolution of a Large Multidrug Resistance Plasmid in New Clinical Bacterial Hosts.

Large conjugative plasmids are important drivers of bacterial evolution and contribute significantly to the dissemination of antibiotic resistance. Although plasmid borne multidrug resistance is recognized as one of the main challenges in modern medicine, the adaptive forces shaping the evolution of these plasmids within pathogenic hosts are poorly understood. Here we study plasmid-host adaptations following transfer of a 73 kb conjugative multidrug resistance plasmid to naïve clinical isolates of Klebsiella pneumoniae and Escherichia coli. We use experimental evolution, mathematical modelling and population sequencing to show that the long-term persistence and molecular integrity of the plasmid is highly influenced by multiple factors within a 25 kb plasmid region constituting a host-dependent burden. In the E. coli hosts investigated here, improved plasmid stability readily evolves via IS26 mediated deletions of costly regions from the plasmid backbone, effectively expanding the host-range of the plasmid. Although these adaptations were also beneficial to plasmid persistence in a naïve K. pneumoniae host, they were never observed in this species, indicating that differential evolvability can limit opportunities of plasmid adaptation. While insertion sequences are well known to supply plasmids with adaptive traits, our findings suggest that they also play an important role in plasmid evolution by maintaining the plasticity necessary to alleviate plasmid-host constrains. Further, the observed evolutionary strategy consistently followed by all evolved E. coli lineages exposes a trade-off between horizontal and vertical transmission that may ultimately limit the dissemination potential of clinical multidrug resistance plasmids in these hosts.

Smoking Cessation and the Microbiome in Induced Sputum Samples from Cigarette Smoking Asthma Patients.

Asthma is a common disease causing cough, wheezing and shortness of breath. It has been shown that the lung microbiota in asthma patients is different from the lung microbiota in healthy controls suggesting that a connection between asthma and the lung microbiome exists. Individuals with asthma who are also tobacco smokers experience more severe asthma symptoms and smoking cessation is associated with improved asthma control. In the present study we investigated if smoking cessation in asthma patients is associated with a change in the bacterial community in the lungs, examined using induced sputum. We found that while tobacco smokers with asthma have a greater bacterial diversity in the induced sputum compared to non-smoking healthy controls, smoking cessation does not lead to a change in the microbial diversity.

Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis.

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts cannot predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases.

Limited dissemination of the wastewater treatment plant core resistome.

Horizontal gene transfer is a major contributor to the evolution of bacterial genomes and can facilitate the dissemination of antibiotic resistance genes between environmental reservoirs and potential pathogens. Wastewater treatment plants (WWTPs) are believed to play a central role in the dissemination of antibiotic resistance genes. However, the contribution of the dominant members of the WWTP resistome to resistance in human pathogens remains poorly understood. Here we use a combination of metagenomic functional selections and comprehensive metagenomic sequencing to uncover the dominant genes of the WWTP resistome. We find that this core resistome is unique to the WWTP environment, with <10% of the resistance genes found outside the WWTP environment. Our data highlight that, despite an abundance of functional resistance genes within WWTPs, only few genes are found in other environments, suggesting that the overall dissemination of the WWTP resistome is comparable to that of the soil resistome.

Collateral Resistance and Sensitivity Modulate Evolution of High-Level Resistance to Drug Combination Treatment in Staphylococcus aureus.

As drug-resistant pathogens continue to emerge, combination therapy will increasingly be relied upon to treat infections and to help combat further development of multidrug resistance. At present a dichotomy exists between clinical practice, which favors therapeutically synergistic combinations, and the scientific model emerging from in vitro experimental work, which maintains that this interaction provides greater selective pressure toward resistance development than other interaction types. We sought to extend the current paradigm, based on work below or near minimum inhibitory concentration levels, to reflect drug concentrations more likely to be encountered during treatment. We performed a series of adaptive evolution experiments using Staphylococcus aureus. Interestingly, no relationship between drug interaction type and resistance evolution was found as resistance increased significantly beyond wild-type levels. All drug combinations, irrespective of interaction types, effectively limited resistance evolution compared with monotreatment. Cross-resistance and collateral sensitivity were found to be important factors in the extent of resistance evolution toward a combination. Comparative genomic analyses revealed that resistance to drug combinations was mediated largely by mutations in the same genes as single-drug-evolved lineages highlighting the importance of the component drugs in determining the rate of resistance evolution. Results of this work suggest that the mechanisms of resistance to constituent drugs should be the focus of future resistance evolution work.

Prediction of resistance development against drug combinations by collateral responses to component drugs.

Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution.