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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., Antibiotic resistance spreads among bacteria through horizontal transfer of antibiotic resistance genes (ARGs). Here, Ellabaan et al. use a statistical approach to identify putative mobilisation elements and other features associated with ARG transfer among bacterial clades to predict the potential future dissemination of known ARGs.

We characterized the global transcriptome of Escherichia coli MG1655:: tetA grown in the presence of ½ MIC (14 mg/L) of OTC, and for comparison WT MG1655 strain grown with 1//2 MIC of OTC (0.25 mg/L OTC). 1646 genes changed expression significantly (FDR > 0.05) in the resistant strain, the majority of which (1246) were also regulated in WT strain. Genes involved in purine synthesis and ribosome structure and function were top-enriched among up-regulated genes, and anaerobic respiration, nitrate metabolism and aromatic amino acid biosynthesis genes among down-regulated genes. Blocking of the purine-synthesis- did not affect resistance phenotypes (MIC and growth rate with OTC), while blocking of protein synthesis using low concentrations of chloramphenicol or gentamicin, lowered MIC towards OTC. Metabolic-modeling, using a novel model for MG1655 and continuous weighing factor that reflected the degree of up or down regulation of genes encoding a reaction, identified 102 metabolic reactions with significant change in flux in MG1655:: tetA when grown in the presence of OTC compared to growth without OTC. These pathways could not have been predicted by simply analyzing functions of the up and down regulated genes, and thus this work has provided a novel method for identification of reactions which are essential in the adaptation to growth in the presence of antimicrobials.

Sugarcane ethanol fermentation represents a simple microbial community dominated by S. cerevisiae and co-occurring bacteria with a clearly defined functionality. In this study, we dissect the microbial interactions in sugarcane ethanol fermentation by combinatorically reconstituting every possible combination of species, comprising approximately 80% of the biodiversity in terms of relative abundance. Functional landscape analysis shows that higher-order interactions counterbalance the negative effect of pairwise interactions on ethanol yield. In addition, we find that Lactobacillus amylovorus improves the yeast growth rate and ethanol yield by cross-feeding acetaldehyde, as shown by flux balance analysis and laboratory experiments. Our results suggest that Lactobacillus amylovorus could be considered a beneficial bacterium with the potential to improve sugarcane ethanol fermentation yields by almost 3%. These data highlight the biotechnological importance of comprehensively studying microbial communities and could be extended to other microbial systems with relevance to human health and the environment., Industrial sugarcane ethanol fermentations are accomplished by a microbial community dominated by S. cerevisiae and co-occurring bacteria. Here, the authors investigate how microbial community composition contributes to community function and reveal the role of acetaldehyde in improving yeast growth rate and ethanol production.

The gut microbiota has been implicated in the therapeutic effects of antidiabetics. It is unclear if antidiabetics directly influences gut microbiome-host interaction. Oral peroxisome proliferator-activated receptor-γ (PPAR-γ) agonists, such as rosiglitazone, are potent insulin sensitizers used in the treatment of type 2 diabetes (T2D). PPAR-γ is abundantly expressed in the intestine, making it possible that PPAR-γ agonists directly influences gut microbiome-host homeostasis. The presented study therefore aimed to characterize local gut microbiome and intestinal transcriptome responses in diabetic db/db mice following rosiglitazone treatment. Diabetic B6.BKS(D)- Leprdb/J (db/db) mice (8 weeks of age) received oral dosing once daily with vehicle (n = 12) or rosiglitazone (3 mg/kg, n = 12) for 8 weeks. Gut segments (duodenum, jejunum, ileum, caecum, and colon) were sampled for paired analysis of gut microbiota and host transcriptome signatures using full-length bacterial 16S rRNA sequencing and RNA sequencing (n = 5–6 per group). Treatment with rosiglitazone improved glucose homeostasis without influencing local gut microbiome composition in db/db mice. In contrast, rosiglitazone promoted marked changes in ileal and colonic gene expression signatures associated with peroxisomal and mitochondrial lipid metabolism, carbohydrate utilization and immune regulation. In conclusion, rosiglitazone treatment markedly affected transcriptional markers of intestinal lipid metabolism and immune regulation but had no effect on the gut microbiome in diabetic db/db mice.

Oral antibiotics are commonly prescribed to non-hospitalized adults. However, antibiotic-induced changes in the human gut microbiome are often investigated in cohorts with preexisting health conditions and/or concomitant medication, leaving the effects of antibiotics not completely understood. We used a combination of omic approaches to comprehensively assess the effects of antibiotics on the gut microbiota and particularly the gut resistome of a small cohort of healthy adults. We observed that 3 to 19 species per individual proliferated during antibiotic treatment and Gram-negative species expanded significantly in relative abundance. While the overall relative abundance of antibiotic resistance gene homologs did not significantly change, antibiotic-specific gene homologs with presumed resistance toward the administered antibiotics were common in proliferating species and significantly increased in relative abundance. Virome sequencing and plasmid analysis showed an expansion of antibiotic-specific resistance gene homologs even 3 months after antibiotic administration, while paired-end read analysis suggested their dissemination among different species. These results suggest that antibiotic treatment can lead to a persistent expansion of antibiotic resistance genes in the human gut microbiota and provide further data in support of good antibiotic stewardship. Abbreviation: ARG – Antibiotic resistance gene homolog; AsRG – Antibiotic-specific resistance gene homolog; AZY – Azithromycin; CFX – Cefuroxime; CIP – Ciprofloxacin; DOX – Doxycycline; FDR – False discovery rate; GRiD – Growth rate index value; HGT – Horizontal gene transfer; NMDS – Non-metric multidimensional scaling; qPCR – Quantitative polymerase chain reaction; RPM – Reads per million mapped reads; TA – Transcriptional activity; TE – Transposable element; TPM – Transcripts per million mapped reads

To tackle the global antibiotic resistance crisis, antibiotic resistance acquired either vertically by chromosomal mutations or horizontally through antibiotic resistance genes (ARGs) have been studied. Yet, little is known about the interactions between the two, which may impact the evolution of antibiotic resistance. Here, we develop a multiplexed barcoded approach to assess the fitness of 144 mutant-ARG combinations in Escherichia coli subjected to eight different antibiotics at 11 different concentrations. While most interactions are neutral, we identify significant interactions for 12% of the mutant-ARG combinations. The ability of most ARGs to confer high-level resistance at a low fitness cost shields the selective dynamics of mutants at low drug concentrations. Therefore, high-fitness mutants are often selected regardless of their resistance level. Finally, we identify strong negative epistasis between two unrelated resistance mechanisms: the tetA tetracycline resistance gene and loss-of-function nuo mutations involved in aminoglycoside tolerance. Our study highlights important constraints that may allow better prediction and control of antibiotic resistance evolution., The authors study the interactions between chromosomal mutations and horizontally acquired genes in the evolution of antibiotic resistance in experimental evolution assays. They identify constraints that may allow better prediction and control of antibiotic resistance evolution.

Nature is a diverse and rich source of bioactive pathways or novel building blocks for synthetic biology. In this Perspective, we describe the emerging research field in which metagenomes are functionally interrogated using synthetic biology. This approach substantially expands the set of identified biological activities and building blocks. In reviewing this field, we find that its potential for new biological discovery is dramatically increasing. Functional metagenomic mining using genetic circuits has led to the discovery of novel bioactivity such as amidases, NF-κB modulators, naphthalene degrading enzymes, cellulases, lipases and transporters. Using these genetic circuits as a template, improvements are made by designing biosensors, such as in vitro-evolved riboswitches and computationally redesigned transcription factors. Thus, powered by the rapidly expanding repertoire of biosensors and streamlined processes for automated genetic circuit design, a greater variety of complex selection circuits can be built, with resulting impacts on drug discovery and industrial biotechnology.

Understanding and engineering solute transporters is important for metabolic engineering and the development of therapeutics. However, limited available experimental data on membrane transporters makes sequence-function relationships complex to predict. Here we apply ligand-responsive biosensor systems that enable selective growth of E. coli cells only if they functionally express an importer that is specific to the biosensor ligand. Using this system in a directed evolution framework, we successfully engineer the specificity of nicotinamide riboside transporters, PnuC, to accept thiamine as a substrate. Our results provide insight into the molecular determinants of substrate recognition of the PnuC transporter family and demonstrate how synthetic biology can be deployed to engineer the substrate spectrum of small molecule transporters.

Bio-production of chemicals is an important driver of the societal transition toward sustainability. However, fermentations with heavily engineered production organisms can be challenging to scale to industrial volumes. Such fermentations are subject to evolutionary pressures that select for a wide range of genetic variants that disrupt the biosynthetic capacity of the engineered organism. Synthetic product addiction that couples high-yield production of a desired metabolite to expression of nonconditionally essential genes could offer a solution to this problem by selectively favoring cells with biosynthetic capacity in the population without constraining the medium. We constructed such synthetic product addiction by controlling the expression of two nonconditionally essential genes with a mevalonic acid biosensor. The product-addicted production organism retained high-yield mevalonic acid production through 95 generations of cultivation, corresponding to the number of cell generations required for >200-m3 industrial-scale production, at which time the nonaddicted strain completely abolished production. Using deep DNA sequencing, we find that the product-addicted populations do not accumulate genetic variants that compromise biosynthetic capacity, highlighting how synthetic networks can be designed to control genetic population heterogeneity. Such synthetic redesign of evolutionary forces with endogenous processes may be a promising concept for realizing complex cellular designs required for sustainable bio-manufacturing.

A transition toward sustainable bio-based chemical production is important for green growth. However, productivity and yield frequently decrease as large-scale microbial fermentation progresses, commonly ascribed to phenotypic variation. Yet, given the high metabolic burden and toxicities, evolutionary processes may also constrain bio-based production. We experimentally simulate large-scale fermentation with mevalonic acid-producing Escherichia coli. By tracking growth rate and production, we uncover how populations fully sacrifice production to gain fitness within 70 generations. Using ultra-deep (>1000×) time-lapse sequencing of the pathway populations, we identify multiple recurring intra-pathway genetic error modes. This genetic heterogeneity is only detected using deep-sequencing and new population-level bioinformatics, suggesting that the problem is underestimated. A quantitative model explains the population dynamics based on enrichment of spontaneous mutant cells. We validate our model by tuning production load and escape rate of the production host and apply multiple orthogonal strategies for postponing genetically driven production declines., The declining performance of scale-up bioreactor cultures is commonly attributed to phenotypic and physical heterogeneities. Here, the authors reveal multiple recurring intra-pathway error modes that limit engineered E. coli mevalonic acid production over time- and industrial-scale fermentations.

Transcription factor-based biosensors are used to identify producer strains, a critical bottleneck in cell factory engineering. Here, we address two challenges with this methodology: transplantation of heterologous transcriptional regulators into new hosts to generate functional biosensors and biosensing of the extracellular product concentration that accurately reflects the effective cell factory production capacity. We describe the effects of different translation initiation rates on the dynamic range of a p-coumaric acid biosensor based on the Bacillus subtilis transcriptional repressor PadR by varying its ribosomal binding site. Furthermore, we demonstrate the functionality of this p-coumaric acid biosensor in Escherichia coli and Corynebacterium glutamicum. Finally, we encapsulate yeast p-coumaric acid-producing cells with E. coli-biosensing cells in picoliter droplets and, in a microfluidic device, rapidly sort droplets containing yeast cells producing high amounts of extracellular p-coumaric acid using the fluorescent E. coli biosensor signal. As additional biosensors become available, such approaches will find broad applications for screening of an extracellular product.

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., Some antibiotic resistance genes found in pathogenic bacteria might derive from antibiotic-producing actinobacteria. Here, Jiang et al. provide bioinformatic and experimental evidence supporting this hypothesis, and propose a specific mechanism for the transfer of these genes between bacterial phyla.

Background Presence of corneal cystine crystals is the main ocular manifestation of cystinosis, although controversial findings concerning the corneal layer with the highest density have been reported. The aim of this study was the analysis of the characteristics of crystal arrangement in different corneal layers and the assessment of corneal morphological changes with age. Methods A cross sectional study was carried out in three children and three adults who had nephropathic cystinosis and corneal cystine depositions. All patients underwent a comprehensive ophthalmological examination including best corrected distance visual acuity, slit-lamp examination, in vivo confocal microscopy and anterior segment optical coherence tomography. An evaluation of the depth of crystal deposits and crystal density in different corneal layers was also performed. Due to the low number of subjects no statistical comparison was performed. Results Anterior segment optical coherence tomography images revealed deposition of hyperreflective crystals from limbus to limbus in each patient. Crystals appeared as randomly oriented hyperreflective, elongated structures on in vivo confocal microscopy images in all corneal layers except the endothelium. In children the deposits occurred predominantly in the anterior stroma, while in adults, the crystals were mostly localized in the posterior corneal stroma with the depth of crystal deposition showing an increasing tendency with age (mean depth of crystal density was 353.17 ± 49.23 μm in children and it was 555.75 ± 25.27 μm in adults). Mean crystal density of the epithelium was 1.47 ± 1.17 (median: 1.5; interquartile range: 0.3–2.4). Mean crystal density of the anterior and posterior stroma of children and adults was 3.37 ± 0.34 (median: 3.4; interquartile range: 3.25–3.55) vs. 1.23 ± 0.23 (median: 1.2; interquartile range: 1.05–1.35) and 0.76 ± 0.49 (median: 0.7; interquartile range: 0.4–1.15) vs. 3.63 ± 0.29 (median: 3.7; interquartile range: 3.45–3.8), respectively. Endothelium had intact structure in all cases. Some hexagonal crystals were observed in two subjects. Conclusions In vivo confocal microscopy and anterior segment optical coherence tomography confirmed an age-related pattern of crystal deposition. In children, crystals tend to locate anteriorly, while in adults, deposits are found posteriorly in corneal stroma.

Enteroendocrine L-cell derived peptide hormones, notably glucagon-like peptide-1 (GLP-1) and glucagon-like peptide-2 (GLP-2), have become important targets in the treatment of type 2 diabetes, obesity and intestinal diseases. As gut microbial imbalances and maladaptive host responses have been implicated in the pathology of obesity and diabetes, this study aimed to determine the effects of pharmacologically stimulated GLP-1 and GLP-2 receptor function on the gut microbiome composition in diet-induced obese (DIO) mice. DIO mice received treatment with a selective GLP-1 receptor agonist (liraglutide, 0.2 mg/kg, BID) or dual GLP-1/GLP-2 receptor agonist (GUB09–145, 0.04 mg/kg, BID) for 4 weeks. Both compounds suppressed caloric intake, promoted a marked weight loss, improved glucose tolerance and reduced plasma cholesterol levels. 16S rDNA sequencing and deep-sequencing shotgun metagenomics was applied for comprehensive within-subject profiling of changes in gut microbiome signatures. Compared to baseline, DIO mice assumed phylogenetically similar gut bacterial compositional changes following liraglutide and GUB09-145 treatment, characterized by discrete shifts in low-abundant species and related bacterial metabolic pathways. The microbiome alterations may potentially associate to the converging biological actions of GLP-1 and GLP-2 receptor signaling on caloric intake, glucose metabolism and lipid handling.

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 β-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., Development of bacterial resistance to an antibiotic can lead to collateral sensitivity to another drug. Here, the authors study collateral sensitivity conferred by mutations in the horizontally acquired β-lactamase CTX-M-15, and identify antibiotic combinations that constrain the evolution of resistance.

The gut microbiota has been established as an important player influencing many aspects of human physiology. Breast milk, the first diet for an infant, contains human milk oligosaccharides (HMO) that shape the infant’s gut microbiota by selectively stimulating the growth of specific bacteria, especially bifidobacteria. In addition to their bifidogenic activity, the ability of HMO to modulate immune function and the gut barrier makes them prime candidates to restore a beneficial microbiota in dysbiotic adults and provide health benefits. We conducted a parallel, double-blind, randomised, placebo-controlled, HMO-supplementation study in 100 healthy, adult volunteers, consuming chemically produced 2′-O-fucosyllactose (2′FL) and/or lacto-N-neotetraose (LNnT) at various daily doses and mixes or placebo for 2 weeks. All participants completed the study without premature discontinuation. Supplementation of 2′FL and LNnT at daily doses up to 20 g was shown to be safe and well tolerated, as assessed using the gastrointestinal symptoms rating scale. 16S rRNA sequencing analysis showed that HMO supplementation specifically modified the adult gut microbiota with the primary impact being substantial increases in relative abundance of Actinobacteria and Bifidobacterium in particular and a reduction in relative abundance of Firmicutes and Proteobacteria. This study provides the first set of data on safety, tolerance and impact of HMO on the adult gut microbiota. Collectively, the results from this study show that supplementing the diet with HMO is a valuable strategy to shape the human gut microbiota and specifically promote the growth of beneficial bifidobacteria.

Only 25% of bacterial membrane transporters have functional annotation owing to the difficulty of experimental study and of accurate prediction of their function. Here we report a sequence-independent method for high-throughput mining of novel transporters. The method is based on ligand-responsive biosensor systems that enable selective growth of cells only if they encode a ligand-specific importer. We developed such a synthetic selection system for thiamine pyrophosphate and mined soil and gut metagenomes for thiamine-uptake functions. We identified several members of a novel class of thiamine transporters, PnuT, which is widely distributed across multiple bacterial phyla. We demonstrate that with modular replacement of the biosensor, we could expand our method to xanthine and identify xanthine permeases from gut and soil metagenomes. Our results demonstrate how synthetic-biology approaches can effectively be deployed to functionally mine metagenomes and elucidate sequence-function relationships of small-molecule transport systems in bacteria.

Nature Biotechnology asks a selection of researchers about the most exciting frontier in their field and the most needed technologies for advancing knowledge and applications.

Background The spatial and temporal dynamics of SARS-CoV-2 have been described in case series and retrospective studies. In this study, we provide a coherent overview of the duration of viral detection and viral RNA load in COVID-19 patients, stratified by specimen type, clinical severity, and age. Method We systematically searched PubMed/MEDLINE and Cochrane review database for studies published between 1.11.2019 and 23.04.2020. We pooled the data of selected studies (22/7226 (650 patients) for meta-analysis) to estimate duration of viral detection and visualized viral load over time. Findings Our analysis showed consistent viral detection from specimen from the upper respiratory tract (URT), the lower respiratory tract (LRT), and faeces, irrespective of the clinical severity of COVID-19. Our analysis suggests that SARS-CoV-2 persists for a longer duration in the LRT compared to the URT in adult patients (5•7 days in mild; 5•9 days in moderate-severe patients). The differences in the duration of viral detection between mild and moderate-severe patients is limited in the LRT, but an indication of longer duration of viral detection for moderate-severe patients was observed in feces (15 days in mild vs. 21 days in moderate-severe patients) and the URT (12 days in mild vs. 16 days in moderate-severe patients). Further, viral load was demonstrated to peak in earlier stages of infection in the URT compared to LRT. Interpretation This review may aid mathematical modelling and help in defining appropriate endpoints for clinical trails with antivirals in COVID-19. Funding The project has received funding support from Innovation Fund Denmark.

High productivity of biotechnological strains is important to industrial fermentation processes and can be constrained by precursor availability and substrate uptake rate. Adaptive laboratory evolution (ALE) of Escherichia coli MG1655 to glucose minimal M9 medium has been shown to increase strain fitness, mainly through a key mutation in the transcriptional regulator rpoB, which increases flux through central carbon metabolism and the glucose uptake rate. We wanted to test the hypothesis that a substrate uptake enhancing rpoB mutation can translate to increased productivity in a strain possessing a heterologous metabolite pathway. When engineered for heterologous mevalonate production, we found that E. coli rpoB E672K strains displayed 114–167% higher glucose uptake rates and 48–77% higher mevalonate productivities in glucose minimal M9 medium. This improvement in heterologous mevalonate productivity of the rpoB E672K strain is likely mediated by the elevated glucose uptake rate of such strains, which favors overflow metabolism toward acetate production and availability of acetyl-CoA as precursor. These results demonstrate the utility of adaptive laboratory evolution (ALE) to generate a platform strain for an increased production rate for a heterologous product.

We comprehensively assessed the contribution of the Shine-Dalgarno sequence to protein expression and used the data to develop EMOPEC (Empirical Model and Oligos for Protein Expression Changes; http://emopec.biosustain.dtu.dk). EMOPEC is a free tool that makes it possible to modulate the expression level of any Escherichia coli gene by changing only a few bases. Measured protein levels for 91% of our designed sequences were within twofold of the desired target level.

Escherichia coli strain WG5 is a widely used host for phage detection, including somatic coliphages employed as standard ISO method 10705-1 (2000). Here, we present the complete genome sequence of a commercial E. coli WG5 strain.

Background Developing novel microbial cell factories requires careful testing of candidates under industrially relevant conditions. However, this frequently occurs late during the strain development process. The availability of laboratory media that simulate industrial-like conditions might improve cell factory development, as they allow for strain construction and testing in the laboratory under more relevant conditions. While sugarcane molasses is one of the most important substrates for the production of biofuels and other bioprocess-based commodities, there are no defined media that faithfully simulate it. In this study, we tested the performance of a new synthetic medium simulating sugarcane molasses. Results Laboratory scale simulations of the Brazilian ethanol production process, using both sugarcane molasses and our synthetic molasses (SM), demonstrated good reproducibility of the fermentation performance, using yeast strains, PE-2 and Ethanol Red™. After 4 cycles of fermentation, the final ethanol yield (gp gs−1) values for the SM ranged from 0.43 ± 0.01 to 0.44 ± 0.01 and from 0.40 ± 0.01 to 0.46 ± 0.01 for the molasses-based fermentations. The other fermentation parameters (i.e., biomass production, yeast viability, and glycerol and acetic acid yield) were also within similar value ranges for all the fermentations. Sequential pairwise competition experiments, comparing industrial and laboratory yeast strains, demonstrated the impact of the media on strain fitness. After two sequential cocultivations, the relative abundance of the laboratory yeast strain was 5-fold lower in the SM compared to the yeast extract-peptone-dextrose medium, highlighting the importance of the media composition on strain fitness. Conclusions Simulating industrial conditions at laboratory scale is a key part of the efficient development of novel microbial cell factories. In this study, we have developed a synthetic medium that simulated industrial sugarcane molasses media. We found good agreement between the synthetic medium and the industrial media in terms of the physiological parameters of the industrial-like fermentations. Electronic supplementary material The online version of this article (10.1186/s13068-018-1221-x) contains supplementary material, which is available to authorized users.

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.

The opportunistic Gram-negative pathogen Pseudomonas aeruginosa , known for its intrinsic and acquired antibiotic resistance, has a notorious ability to form biofilms, which often facilitate chronic infections. The evolutionary paths to antibiotic resistance have mainly been investigated in planktonic cultures and are less studied in biofilms. We experimentally evolved P. aeruginosa PAO1 colony biofilms and stationary-phase planktonic cultures for seven passages in the presence of subinhibitory levels (0.1 mg/liter) of ciprofloxacin (CIP) and performed a genotypic (whole-bacterial population sequencing) and phenotypic assessment of the populations. We observed a higher proportion of CIP resistance in the CIP-evolved biofilm populations than in planktonic populations exposed to the same drug concentrations. However, the MICs of ciprofloxacin were lower in CIP-resistant isolates selected from the biofilm population than the MICs of CIP-resistant isolates from the planktonic cultures. We found common evolutionary trajectories between the different lineages, with mutations in known CIP resistance determinants as well as growth condition-dependent adaptations. We observed a general trend toward a reduction in type IV-pilus-dependent motility (twitching) in CIP-evolved populations and a loss of virulence-associated traits in the populations evolved in the absence of antibiotic. In conclusion, our data indicate that biofilms facilitate the development of low-level mutational resistance, probably due to the lower effective drug exposure than in planktonic cultures. These results provide a framework for the selection process of resistant variants and the evolutionary mechanisms involved under the two different growth conditions.

Flow-seq combines flexible genome engineering methods with flow cytometry-based cell sorting and deep DNA sequencing to enable comprehensive interrogation of genotype to phenotype relationships. One application is to study the effect of specific regulatory elements on protein expression. Constructing targeted genomic variation around genomically integrated fluorescent marker genes enables rapid elucidation of the contribution of specific sequence variants to protein expression. Such an approach can be used to characterize the impact of modifications to the Shine-Dalgarno sequence in Escherichia coli.

Summary The skin functions as the primary interface between the human body and the external environment. To understand how the microbiome varies within urban mass transit and influences the skin microbiota, we profiled the human palm microbiome after contact with handrails within the Hong Kong Mass Transit Railway (MTR) system. Intraday sampling time was identified as the primary determinant of the variation and recurrence of the community composition, whereas human-associated species and clinically important antibiotic resistance genes (ARGs) were captured as p.m. signatures. Line-specific signatures were notably correlated with line-specific environmental exposures and city characteristics. The sole cross-border line appeared as an outlier in most analyses and showed high relative abundance and a significant intraday increment of clinically important ARGs (24.1%), suggesting potential cross-border ARG transmission, especially for tetracycline and vancomycin resistance. Our study provides an important reference for future public health strategies to mitigate intracity and cross-border pathogen and ARG transmission.

Most antibiotics are derived from the soil, but their catabolism there, which is necessary to close the antibiotic carbon cycle, remains uncharacterized. We report the first draft genome sequences of soil Proteobacteria identified for subsisting solely on β-lactams as their carbon sources. The genomes encode multiple β-lactamases, although their antibiotic catabolic pathways remain enigmatic.

The soil microbiome can produce, resist, or degrade antibiotics and even catabolize them. While resistance genes are widely distributed in the soil, there is a dearth of knowledge concerning antibiotic catabolism. Here we describe a pathway for penicillin catabolism in four isolates. Genomic and transcriptomic sequencing revealed β-lactamase, amidase, and phenylacetic acid catabolon upregulation. Knocking out part of the phenylacetic acid catabolon or an apparent penicillin utilization operon (put) resulted in loss of penicillin catabolism in one isolate. A hydrolase from the put operon was found to degrade in vitro benzylpenicilloic acid, the β-lactamase penicillin product. To test the generality of this strategy, an Escherichia coli strain was engineered to co-express a β-lactamase and a penicillin amidase or the put operon, enabling it to grow using penicillin or benzylpenicilloic acid, respectively. Elucidation of additional pathways may allow bioremediation of antibiotic-contaminated soils and discovery of antibiotic-remodeling enzymes with industrial utility.

Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood. Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.

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, chloramphenicol and ciprofloxacin), as well as antibiotic combinations (amikacin + chloramphenicol and chloramphenicol + ciprofloxacin). 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.

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.

Elucidating the adaptive strategies and plasticity of bacterial genomes in situ is crucial for understanding the epidemiology and evolution of pathogens threatening human health. While much is known about the evolution of Escherichia coli in controlled laboratory environments, less effort has been made to elucidate the genome dynamics of E. coli in its native settings. Here, we follow the genome dynamics of co-existing E. coli lineages in situ of the infant gut during the first year of life. One E. coli lineage causes a urinary tract infection (UTI) and experiences several alterations of its genomic content during subsequent antibiotic treatment. Interestingly, all isolates of this uropathogenic E. coli strain carried a highly stable plasmid implicated in virulence of diverse pathogenic strains from all over the world. While virulence elements are certainly beneficial during infection scenarios, their role in gut colonization and pathogen persistence is poorly understood. We performed in vivo competitive fitness experiments to assess the role of this highly disseminated virulence plasmid in gut colonization, but found no evidence for a direct benefit of plasmid carriage. Through plasmid stability assays, we demonstrate that this plasmid is maintained in a parasitic manner, by strong first-line inheritance mechanisms, acting on the single-cell level, rather than providing a direct survival advantage in the gut. Investigating the ecology of endemic accessory genetic elements, in their pathogenic hosts and native environment, is of vital importance if we want to understand the evolution and persistence of highly virulent and drug resistant bacterial isolates.

Summary: Production of chemicals in microbes often employs potent biosynthetic enzymes, which can interact with the microbial native metabolism to affect cell fitness and product yield. However, production optimization largely relies on data collected from wild-type strains in the absence of metabolic perturbations, thus limiting their relevance to specific conditions. Here, we address this issue by coupling cell fitness to the production of thiamine diphosphate in Escherichia coli using a synthetic RNA biosensor. We use this strategy to interrogate a library of transposon mutants and elucidate the native gene network influencing both cell fitness and thiamine production. Ultimately, we identify effectors of the OxyR-Fur stress response that limit thiamine biosynthesis via alternative regulation of iron storage and Fe-S cluster inclusion in enzymes. This study presents a new approach for the reliable high-throughput identification of genetic targets of both known and unknown function that are directly relevant to a specific biosynthetic process. : Cardinale et al. investigate the specific impact on the cell of synthetic metabolic pathways. By linking the growth of bar-coded mutants with end-product biosensing in E. coli, the authors identify genetic targets in the cellular Fe-S cluster biosynthesis specifically affecting the production of recombinant thiamine. Keywords: RNA biosensors, thiamine biosynthesis, transposon mutagenesis, oxidative stress response, Fe-S clusters

Background Plasmids are widely used and essential tools in molecular biology. However, plasmids often impose a metabolic burden and are only temporarily useful for genetic engineering, bio-sensing and characterization purposes. While numerous techniques for genetic manipulation exist, a universal tool enabling rapid removal of plasmids from bacterial cells is lacking. Results Based on replicon abundance and sequence conservation analysis, we show that the vast majority of bacterial cloning and expression vectors share sequence similarities that allow for broad CRISPR-Cas9 targeting. We have constructed a universal plasmid-curing system (pFREE) and developed a one-step protocol and PCR procedure that allow for identification of plasmid-free clones within 24 h. While the context of the targeted replicons affects efficiency, we obtained curing efficiencies between 40 and 100% for the plasmids most widely used for expression and engineering purposes. By virtue of the CRISPR-Cas9 targeting, our platform is highly expandable and can be applied in a broad host context. We exemplify the wide applicability of our system in Gram-negative bacteria by demonstrating the successful application in both Escherichia coli and the promising cell factory chassis Pseudomonas putida. Conclusion As a fast and freely available plasmid-curing system, targeting virtually all vectors used for cloning and expression purposes, we believe that pFREE has the potential to eliminate the need for individualized vector suicide solutions in molecular biology. We envision the application of pFREE to be especially useful in methodologies involving multiple plasmids, used sequentially or simultaneously, which are becoming increasingly popular for genome editing or combinatorial pathway engineering. Electronic supplementary material The online version of this article (doi:10.1186/s12934-017-0748-z) contains supplementary material, which is available to authorized users.

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.

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.

We describe the development of an optimized glycolytic flux biosensor and its application in detecting altered flux in a production strain and in a mutant library. The glycolytic flux biosensor is based on the Cra-regulated ppsA promoter of E. coli controlling fluorescent protein synthesis. We validated the glycolytic flux dependency of the biosensor in a range of different carbon sources in six different E. coli strains and during mevalonate production. Furthermore, we studied the flux-altering effects of genome-wide single gene knock-outs in E. coli in a multiplex FlowSeq experiment. From a library consisting of 2126 knock-out mutants, we identified 3 mutants with high-flux and 95 mutants with low-flux phenotypes that did not have severe growth defects. This approach can improve our understanding of glycolytic flux regulation improving metabolic models and engineering efforts., Graphical abstract fx1

Wide application of antibiotics has contributed to the evolution of multi-drug resistant human pathogens, resulting in poorer treatment outcomes for infections. In the marine environment, seawater samples have been investigated as a resistance reservoir; however, no studies have methodically examined sponges as a reservoir of antibiotic resistance. Sponges could be important in this respect because they often contain diverse microbial communities that have the capacity to produce bioactive metabolites. Here, we applied functional metagenomics to study the presence and diversity of functional resistance genes in the sponges Aplysina aerophoba, Petrosia ficiformis, and Corticium candelabrum. We obtained 37 insert sequences facilitating resistance to D-cycloserine (n = 6), gentamicin (n = 1), amikacin (n = 7), trimethoprim (n = 17), chloramphenicol (n = 1), rifampicin (n = 2) and ampicillin (n = 3). Fifteen of 37 inserts harbored resistance genes that shared Pseudovibrio with 41% global amino acid identity to the closest β-lactamase with demonstrated functionality, and subsequently classified into a new family termed PSV. Taken together, our results show that sponge microbiota host diverse and novel resistance genes that may be harnessed by phylogenetically distinct bacteria.

Summary Chronic Pseudomonas aeruginosa infections evade antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P. aeruginosa toward clinically relevant antibiotics leads to phenotypic convergence toward distinct states. These states are associated with collateral sensitivity toward several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In-depth investigation of chronic P. aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections., Graphical Abstract, Highlights • Collateral sensitivity can evolve from diverse genetic and phenotypic starting points • Collateral effects of resistance evolution converges to distinct phenotypic states • Genetic markers associated with convergent states were linked to nfxB mutations • nfxB mutants were eradicated in vivo from the lung of a CF patient during treatment, The evolution of antibiotic resistance of Pseudomonas infection in cystic fibrosis patients confers predictable sensitivities to other classes of antibiotics, suggesting new ways to optimize treatments for chronic infection.

The emergence of antibiotic resistance in human pathogens has become a major threat to modern medicine and in particular hospitalized patients. The outcome of antibiotic treatment can be affected by the composition of the gut resistome either by enabling resistance gene acquisition of infecting pathogens or by modulating the collateral effects of antibiotic treatment on the commensal microbiome. Accordingly, knowledge of the gut resistome composition could enable more effective and individualized treatment of bacterial infections. Yet, rapid workflows for resistome characterization are lacking. To address this challenge we developed the poreFUME workflow that deploys functional metagenomic selections and nanopore sequencing to resistome mapping. We demonstrate the approach by functionally characterizing the gut resistome of an ICU patient. The accuracy of the poreFUME pipeline is >97 % sufficient for the reliable annotation of antibiotic resistance genes. The poreFUME pipeline provides a promising approach for efficient resistome profiling that could inform antibiotic treatment decisions in the future.

Background Tetracyclines are among the most used antibiotics in livestock worldwide. Resistance is widely disseminated in Escherichia coli, where it is generally mediated by tetracycline efflux pumps, such as TetA. Expression of tetracycline efflux pumps is tightly controlled by the repressor TetR, which has been shown to be tetracycline-responsive at sub-MIC tetracycline concentrations. The objective of this study was to investigate the effects of increasing tetracycline concentrations on the growth of TetA-producing E. coli, and to determine how expression of tetA and tetR related to each other in different growth phases in the presence of tetracycline. Results A tetracycline resistant E. coli strain containing tetA and tetR on the chromosome was constructed and cultured in the presence of increasing concentrations of tetracycline. Expression of tetR and tetA was measured at four time points in different growth phases by quantitative real-time PCR. The TetA-producing E. coli exhibited prolonged lag phase with increasing concentrations of tetracycline, while expression of tetA and tetR increased and decreased, respectively, with increasing tetracycline concentration. The levels of tetA and tetR mRNA varied depending on growth phase, resulting in a gradual decrease of the tetA/tetR ratio from approximately 4 in the lag phase to approximately 2 in the stationary phase. Conclusion This study shows that the expression of tetR and tetA is tetracycline concentration- and growth phase-dependent, contributing to improved understanding of the relationships between E. coli growth, tetracycline exposure and expression of tetracycline resistance. Electronic supplementary material The online version of this article (doi:10.1186/s12866-016-0649-z) contains supplementary material, which is available to authorized users.

A bottleneck in metabolic engineering and systems biology approaches is the lack of efficient genome engineering technologies. Here, we combine CRISPR/Cas9 and λ Red recombineering based MAGE technology (CRMAGE) to create a highly efficient and fast method for genome engineering of Escherichia coli. Using CRMAGE, the recombineering efficiency was between 96.5% and 99.7% for gene recoding of three genomic targets, compared to between 0.68% and 5.4% using traditional recombineering. For modulation of protein synthesis (small insertion/RBS substitution) the efficiency was increased from 6% to 70%. CRMAGE can be multiplexed and enables introduction of at least two mutations in a single round of recombineering with similar efficiencies. PAM-independent loci were targeted using degenerate codons, thereby making it possible to modify any site in the genome. CRMAGE is based on two plasmids that are assembled by a USER-cloning approach enabling quick and cost efficient gRNA replacement. CRMAGE furthermore utilizes CRISPR/Cas9 for efficient plasmid curing, thereby enabling multiple engineering rounds per day. To facilitate the design process, a web-based tool was developed to predict both the λ Red oligos and the gRNAs. The CRMAGE platform enables highly efficient and fast genome editing and may open up promising prospective for automation of genome-scale engineering.

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts can not 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.

Homologous recombination of single-stranded oligonucleotides is a highly efficient process for introducing precise mutations into the genome of E. coli and other organisms when mismatch repair (MMR) is disabled. This can result in the rapid accumulation of off-target mutations that can mask desired phenotypes, especially when selections need to be employed following the generation of combinatorial libraries. While the use of inducible mutator phenotypes or other MMR evasion tactics have proven useful, reported methods either require non-mobile genetic modifications or costly oligonucleotides that also result in reduced efficiencies of replacement. Therefore a new system was developed, Transient Mutator Multiplex Automated Genome Engineering (TM-MAGE), that solves problems encountered in other methods for oligonucleotide-mediated recombination. TM-MAGE enables nearly equivalent efficiencies of allelic replacement to the use of strains with fully disabled MMR and with an approximately 12- to 33-fold lower off-target mutation rate. Furthermore, growth temperatures are not restricted and a version of the plasmid can be readily removed by sucrose counterselection. TM-MAGE was used to combinatorially reconstruct mutations found in evolved salt-tolerant strains, enabling the identification of causative mutations and isolation of strains with up to 75% increases in growth rate and greatly reduced lag times in 0.6 M NaCl.

Predictable integration of foreign biological signals and parts remains a key challenge in the systematic engineering of synthetic cellular actuations, and general methods to improve signal transduction and sensitivity are needed. To address this problem we modeled and built a molecular signal buffer network in Saccharomyces cerevisiae inspired by chemical pH buffer systems. The molecular buffer system context-insulates a riboswitch enabling synthetic control of colony formation and modular signal manipulations. The riboswitch signal is relayed to a transcriptional activation domain of a split transcription factor, while interacting DNA-binding domains mediate the transduction of signal and form an interacting molecular buffer. The molecular buffer system enables modular signal inversion through integration with repressor modules. Further, tuning of input sensitivity was achieved through perturbation of the buffer pair ratio guided by a mathematical model. Such buffered signal tuning networks will be useful for domestication of RNA-based sensors enabling tunable outputs and library-wide selections for drug discovery and metabolic engineering.

The aim of the study was to determine how ESBL-producing Escherichia coli change the expression of metabolic and biosynthesis genes when adapting to inhibitory concentrations of cefotaxime. Secondly, it was investigated whether significantly regulated pathways constitute putative secondary targets that can be used to combat the resistant bacteria.Strains of E. coli MG1655 encoding blaCTX-M-1 from an IncI1 plasmid and from the chromosome were challenged with cefotaxime corresponding to inhibitory concentrations, and transcriptional patterns were compared with growth without or with very low concentrations of cefotaxime by RNA sequencing. Significantly regulated pathways were inhibited with suitable inhibitors, or genes encoding the enzymes of the regulated pathways were knocked out. The ability of the bacteria to grow in the presence of cefotaxime was determined. Chequerboard assays were utilized to confirm synergies between treatments.Genes belonging to 16 different functional gene classes were significantly regulated. Protein and peptidoglycan syntheses were up-regulated and low concentrations of chloramphenicol or d-cycloserine, targeting these systems, strongly reduced the MIC of cefotaxime (32-fold). Inhibition and/or mutations in other genes that were significantly regulated, belonging to energy synthesis, purine synthesis, proline uptake or potassium uptake, also rendered the resistant bacteria more susceptible to cefotaxime.The results show that ESBL-producing E. coli adapt to treatment with cefotaxime by changing their gene expression patterns and furthermore that targeting regulated adaptive pathways may be a suitable way to identify targets for drugs that will specifically inhibit the resistant bacteria.

Riboswitches are small structural elements within messenger RNA (mRNA) that can change their conformation in response to specific environmental exposures. These changes can alter mRNA transcription or translation. Such riboregulators are emerging as a substantial contributor to bacterial gene control. Yet such RNA-based regulation remains challenging to study, in part because of a lack of effective high-throughput technologies for their unbiased identification. On page 187 of this issue, Dar et al. describe a novel method for genome-wide experimental identification of genes that are regulated by conditional transcription termination, which likely is a result of RNA structural switching ( 1 ). This work further enhances our understanding of bacterial gene regulation and expands the universe of RNA-based regulatory devices that Can be deployed in synthetic biology applications.