Drug-Driven Phenotypic Convergence Supports Rational Treatment Strategies of Chronic Infections.

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.

Saccharomyces boulardii promoters for control of gene expression in vivo.

BACKGROUND: Interest in the use of engineered microbes to deliver therapeutic activities has increased in recent years. The probiotic yeast Saccharomyces boulardii has been investigated for production of therapeutics in the gastrointestinal tract. Well-characterised promoters are a prerequisite for robust therapeutic expression in the gut; however, S. boulardii promoters have not yet been thoroughly characterised in vitro and in vivo. RESULTS: We present a thorough characterisation of the expression activities of 12 S. boulardii promoters in vitro in glucose, fructose, sucrose, inulin and acetate, under both aerobic and anaerobic conditions, as well as in the murine gastrointestinal tract. Green fluorescent protein was used to report on promoter activity. Promoter expression was found to be carbon-source dependent, with inulin emerging as a favourable carbon source. Furthermore, relative promoter expression in vivo was highly correlated with expression in sucrose (R = 0.99). CONCLUSIONS: These findings provide insights into S. boulardii promoter activity and aid in promoter selection in future studies utilising S. boulardii to produce therapeutics in the gut.

Compatibility of Evolutionary Responses to Constituent Antibiotics Drive Resistance Evolution to Drug Pairs.

Antibiotic combinations are considered a relevant strategy to tackle the global antibiotic resistance crisis since they are believed to increase treatment efficacy and reduce resistance evolution (WHO treatment guidelines for drug-resistant tuberculosis: 2016 update.). However, studies of the evolution of bacterial resistance to combination therapy have focused on a limited number of drugs and have provided contradictory results (Lipsitch, Levin BR. 1997; Hegreness et al. 2008; Munck et al. 2014). To address this gap in our understanding, we performed a large-scale laboratory evolution experiment, adapting eight replicate lineages of Escherichia coli to a diverse set of 22 different antibiotics and 33 antibiotic pairs. We found that combination therapy significantly limits the evolution of de novode novo resistance in E. coli, yet different drug combinations vary substantially in their propensity to select for resistance. In contrast to current theories, the phenotypic features of drug pairs are weak predictors of resistance evolution. Instead, the resistance evolution is driven by the relationship between the evolutionary trajectories that lead to resistance to a drug combination and those that lead to resistance to the component drugs. Drug combinations requiring a novel genetic response from target bacteria compared with the individual component drugs significantly reduce resistance evolution. These data support combination therapy as a treatment option to decelerate resistance evolution and provide a novel framework for selecting optimized drug combinations based on bacterial evolutionary responses.

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.

Synthetic addiction extends the productive life time of engineered Escherichia coli populations.

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.

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

Evolution of Antibiotic Resistance in Biofilm and Planktonic Pseudomonas aeruginosa Populations Exposed to Subinhibitory Levels of Ciprofloxacin.

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.

Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries.

Bioethanol is a sustainable energy alternative and can contribute to global greenhouse-gas emission reductions by over 60%. Its industrial production faces various bottlenecks, including sub-optimal efficiency resulting from bacteria. Broad-spectrum removal of these contaminants results in negligible gains, suggesting that the process is shaped by ecological interactions within the microbial community. Here, we survey the microbiome across all process steps at two biorefineries, over three timepoints in a production season. Leveraging shotgun metagenomics and cultivation-based approaches, we identify beneficial bacteria and find improved outcome when yeast-to-bacteria ratios increase during fermentation. We provide a microbial gene catalogue which reveals bacteria-specific pathways associated with performance. We also show that Limosilactobacillus fermentum overgrowth lowers production, with one strain reducing yield by ~5% in laboratory fermentations, potentially due to its metabolite profile. Temperature is found to be a major driver for strain-level dynamics. Improved microbial management strategies could unlock environmental and economic gains in this US $ 60 billion industry enabling its wider adoption.

Engineered phage with antibacterial CRISPR-Cas selectively reduce E. coli burden in mice.

Antibiotic treatments have detrimental effects on the microbiome and lead to antibiotic resistance. To develop a phage therapy against a diverse range of clinically relevant Escherichia coli, we screened a library of 162 wild-type (WT) phages, identifying eight phages with broad coverage of E. coli, complementary binding to bacterial surface receptors, and the capability to stably carry inserted cargo. Selected phages were engineered with tail fibers and CRISPR-Cas machinery to specifically target E. coli. We show that engineered phages target bacteria in biofilms, reduce the emergence of phage-tolerant E. coli and out-compete their ancestral WT phages in coculture experiments. A combination of the four most complementary bacteriophages, called SNIPR001, is well tolerated in both mouse models and minipigs and reduces E. coli load in the mouse gut better than its constituent components separately. SNIPR001 is in clinical development to selectively kill E. coli, which may cause fatal infections in hematological cancer patients.

Biocontainment strategies for in vivo applications of Saccharomyces boulardii.

The human gastrointestinal tract is a complex and dynamic environment, playing a crucial role in human health. Microorganisms engineered to express a therapeutic activity have emerged as a novel modality to manage numerous diseases. Such advanced microbiome therapeutics (AMTs) must be contained within the treated individual. Hence safe and robust biocontainment strategies are required to prevent the proliferation of microbes outside the treated individual. Here we present the first biocontainment strategy for a probiotic yeast, demonstrating a multi-layered strategy combining an auxotrophic and environmental-sensitive strategy. We knocked out the genes THI6 and BTS1, causing thiamine auxotrophy and increased sensitivity to cold, respectively. The biocontained Saccharomyces boulardii showed restricted growth in the absence of thiamine above 1 ng/ml and exhibited a severe growth defect at temperatures below 20°C. The biocontained strain was well tolerated and viable in mice and demonstrated equal efficiency in peptide production as the ancestral non-biocontained strain. In combination, the data support that thi6∆ and bts1∆ enable biocontainment of S. boulardii, which could be a relevant chassis for future yeast-based AMTs.

Cold Exposure and Oral Delivery of GLP-1R Agonists by an Engineered Probiotic Yeast Strain Have Antiobesity Effects in Mice.

Advanced microbiome therapeutics (AMTs) holds promise in utilizing engineered microbes such as bacteria or yeasts for innovative therapeutic applications, including the in situ delivery of therapeutic peptides. Glucagon-like peptide-1 receptor agonists, such as Exendin-4, have emerged as potential treatments for type 2 diabetes and obesity. However, current administration methods face challenges with patient adherence and low oral bioavailability. To address these limitations, researchers are exploring improved oral delivery methods for Exendin-4, including utilizing AMTs. This study engineered the probiotic yeast Saccharomyces boulardii to produce Exendin-4 (Sb-Exe4) in the gastrointestinal tract of male C57BL/6 mice to combat diet-induced obesity. The biological efficiency of Exendin-4 secreted by S. boulardii was analyzed ex vivo on isolated pancreatic islets, demonstrating induced insulin secretion. The in vivo characterization of Sb-Exe4 revealed that when combined with cold exposure (8 °C), the Sb-Exe4 yeast strain successfully suppressed appetite by 25% and promoted a 4-fold higher weight loss. This proof of concept highlights the potential of AMTs to genetically modify S. boulardii for delivering active therapeutic peptides in a precise and targeted manner. Although challenges in efficacy and regulatory approval persist, AMTs may provide a transformative platform for personalized medicine. Further research in AMTs, particularly focusing on probiotic yeasts such as S. boulardii, holds great potential for novel therapeutic possibilities and enhancing treatment outcomes in diverse metabolic disorders.

Physiology of Saccharomyces cerevisiae during growth on industrial sugar cane molasses can be reproduced in a tailor-made defined synthetic medium.

Fully defined laboratory media have the advantage of allowing for reproducibility and comparability of results among different laboratories, as well as being suitable for the investigation of how different individual components affect microbial or process performance. We developed a fully defined medium that mimics sugarcane molasses, a frequently used medium in different industrial processes where yeast is cultivated. The medium, named 2SMol, builds upon a previously published semi-defined formulation and is conveniently prepared from some stock solutions: C-source, organic N, inorganic N, organic acids, trace elements, vitamins, Mg + K, and Ca. We validated the 2SMol recipe in a scaled-down sugarcane biorefinery model, comparing the physiology of Saccharomyces cerevisiae in different actual molasses-based media. We demonstrate the flexibility of the medium by investigating the effect of nitrogen availability on the ethanol yield during fermentation. Here we present in detail the development of a fully defined synthetic molasses medium and the physiology of yeast strains in this medium compared to industrial molasses. This tailor-made medium was able to satisfactorily reproduce the physiology of S. cerevisiae in industrial molasses. Thus, we hope the 2SMol formulation will be valuable to researchers both in academia and industry to obtain new insights and developments in industrial yeast biotechnology.

Changes in liver metabolic pathways demonstrate efficacy of the combined dietary and microbial therapeutic intervention in MASLD mouse model.

OBJECTIVE: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most prevalent liver disease globally, yet no therapies are approved. The effects of Escherichia coli Nissle 1917 expressing aldafermin, an engineered analog of the intestinal hormone FGF19, in combination with dietary change were investigated as a potential treatment for MASLD. METHODS: MASLD was induced in C57BL/6J male mice by American lifestyle-induced obesity syndrome diet and then switched to a standard chow diet for seven weeks. In addition to the dietary change, the intervention group received genetically engineered E. coli Nissle expressing aldafermin, while control groups received either E. coli Nissle vehicle or no treatment. MASLD-related plasma biomarkers were measured using an automated clinical chemistry analyzer. The liver steatosis was assessed by histology and bioimaging analysis using Fiji (ImageJ) software. The effects of the intervention in the liver were also evaluated by RNA sequencing and liquid-chromatography-based non-targeted metabolomics analysis. Pathway enrichment studies were conducted by integrating the differentially expressed genes from the transcriptomics findings with the metabolites from the metabolomics results using Ingenuity pathway analysis. RESULTS: After the intervention, E. coli Nissle expressing aldafermin along with dietary changes reduced body weight, liver steatosis, plasma aspartate aminotransferase, and plasma cholesterol levels compared to the two control groups. The integration of transcriptomics with non-targeted metabolomics analysis revealed the downregulation of amino acid metabolism and related receptor signaling pathways potentially implicated in the reduction of hepatic steatosis and insulin resistance. Moreover, the downregulation of pathways linked to lipid metabolism and changes in amino acid-related pathways suggested an overall reduction of oxidative stress in the liver. CONCLUSIONS: These data support the potential for using engineered microbial therapeutics in combination with dietary changes for managing MASLD.

Screening for effective cell-penetrating peptides with minimal impact on epithelial cells and gut commensals in vitro.

One of the biggest challenges for oral drug absorption is the epithelial barrier of the gastrointestinal tract. The use of cell-penetrating peptides (CPPs) to modulate the epithelial barrier function is known to be an effective strategy to improve drug absorption and bioavailability. In this study we compare side-by-side, 9 most promising CPPs to study their cytotoxicity (Cytotox Red dye staining) and cell viability (AlamarBlue staining) on epithelial cells and their effects on paracellular permeability of the intestinal barrier in vitro in a differentiated Caco-2 epithelial monolayer model. The data revealed that 4 out of 9 well-studied CPPs significantly improved Caco-2 paracellular permeability without compromising on cellular health. To assess the impact of CPPs on the human microbiota we studied the antimicrobial effects of the 4 effective CPPs from our permeation studies against 10 representative strains of the gut microbiota in vitro using microbroth dilution. Our data revealed that these 4 CPPs affected the growth of almost all tested commensal strains. Interestingly, we found that two synthetic CPPs (Shuffle and Penetramax) outperformed all the other CPPs in their ability to increase intestinal paracellular permeability at 50 µM and had only a small to moderate effect on the tested gut commensal strains. Based on these data Shuffle and Penetramax represent relevant CPPs to be further characterized in vivo for safe delivery of poorly absorbed therapeutics while minimizing negative impacts on the gut microbiota.

Characterization of Eight Bacterial Biosensors for Microbial Diagnostic and Therapeutic Applications.

The engineering of microbial cells to produce and secrete therapeutics directly in the human body, known as advanced microbial therapeutics, is an exciting alternative to current drug delivery routes. These living therapeutics can be engineered to sense disease biomarkers and, in response, deliver a therapeutic activity. This strategy allows for precise and self-regulating delivery of a therapeutic that adapts to the disease state of the individual patient. Numerous sensing systems have been characterized for use in prokaryotes, but a very limited number of advanced microbial therapeutics have incorporated such sensors. We characterized eight different sensors that respond to physiologically relevant conditions and molecules found in the human body in the probiotic strain Escherichia coli Nissle 1917. The resulting sensors were characterized under aerobic and anaerobic conditions and were demonstrated to be functional under gut-like conditions using the nematode Caenorhabditis elegans as an in vivo model. We show for the first time how a biosensor is able to detect in vivo the bile acid-like molecule Δ4-dafachronic acid, a small molecule in C. elegans that regulates lifespan. Furthermore, we exemplify how bacterial sensors can be used to dynamically report on changes in the intestinal environment of C. elegans, by demonstrating the use of a biosensor able to detect changes in lactate concentrations in the gut lumen of individual C. elegans. The biosensors presented in this study allow for dynamic control of expression in vivo and represent a valuable tool in further developing advanced microbiome therapeutics.

Laboratory performance prediction using virtual reality behaviometrics.

In this study, we show that virtual reality (VR) behaviometrics can be used for the assessment of compliance and physical laboratory skills. Drawing on approaches from machine learning and classical statistics, significant behavioral predictors were deduced from a logistic regression model that classified students and biopharma company employees as experts or novices on pH meter handling with 77% accuracy. Specifically, the game score and number of interactions in VR tasks requiring practical skills were found to be performance predictors. The study provides biopharma companies and academic institutions the possibility of assessing performance using an automatic, reliable, and simple alternative to traditional in-person assessment methods. Integrating the assessment into the training tool renders such laborious post-training assessments unnecessary.

Effects of broad-spectrum antibiotics on the colonisation of probiotic yeast Saccharomyces boulardii in the murine gastrointestinal tract.

Mouse models are commonly used to study the colonisation profiles of microorganisms introduced to the gastrointestinal tract. Three commonly used mouse models include conventional, germ-free, and antibiotic-treated mice. However, colonisation resistance in conventional mice and specialised equipment for germ-free mice are usually limiting factors in their applications. In this study, we sought to establish a robust colonisation model for Saccharomyces boulardii, a probiotic yeast that has caught attention in the field of probiotics and advanced microbiome therapeutics. We characterised the colonisation of S. boulardii in conventional mice and mice treated with a cocktail of broad-spectrum antibiotics, including ampicillin, kanamycin, metronidazole and vancomycin. We found colonisation levels increased up to 10,000-fold in the antibiotic-treated mice compared to nonantibiotic-treated mice. Furthermore, S. boulardii was detected continuously in more than 75% of mice for 10 days after the last administration in antibiotic-treated mice, in contrast to in nonantibiotic-treated mice where S. boulardii was undetectable in less than 2 days. Finally, we demonstrated that this antibiotic cocktail can be used in two commonly used mouse strains, C57BL/6 and ob/ob mice, both achieving ~ 108 CFU/g of S. boulardii in faeces. These findings highlight that the antibiotic cocktail used in this study is an advantageous tool to study S. boulardii based probiotic and advanced microbiome therapeutics.

A Novel Efficient L-Lysine Exporter Identified by Functional Metagenomics.

Lack of active export system often limits the industrial bio-based production processes accumulating the intracellular product and hence complexing the purification steps. L-lysine, an essential amino acid, is produced biologically in quantities exceeding two million tons per year; yet, L-lysine production is challenged by efficient export system at high titers during fermentation. To address this issue, new exporter candidates for efficient efflux of L-lysine are needed. Using metagenomic functional selection, we identified 58 genes encoded on 28 unique metagenomic fragments from cow gut microbiome library that improved L-lysine tolerance. These genes include a novel L-lysine transporter, belonging to a previously uncharacterized EamA superfamily, which is further in vivo characterized as L-lysine exporter using Xenopus oocyte expression system as well as Escherichia coli host. This novel exporter improved L-lysine tolerance in E. coli by 40% and enhanced yield, titer, and the specific production of L-lysine in an industrial Corynebacterium glutamicum strain by 7.8%, 9.5%, and 12%, respectively. Our approach allows the sequence-independent discovery of novel exporters and can be deployed to increase titers and productivity of toxicity-limited bioprocesses.

Identification and Optimization of Novel Small-Molecule Cas9 Inhibitors by Cell-Based High-Throughput Screening.

CRISPR/Cas9 has revolutionized several areas of life science; however, methods to control the Cas9 activity are needed for both scientific and therapeutic applications. Anti-CRISPR proteins are known to inhibit the CRISPR/Cas adaptive immunity; however, in vivo delivery of such proteins is problematic. Instead, small-molecule Cas9 inhibitors could serve as useful tools due to their permeable, proteolytically stable, and non-immunogenic nature. Here, we identified a small-molecule ligand with anti-CRISPR/Cas9 activity through a high-throughput screening utilizing an Escherichia coli selection system. Extensive structure-activity relationship studies, which involved a deconstruction-reconstruction strategy, resulted in a range of analogues with significant improvements in the inhibitory activity. Based on NMR and electrophoretic mobility shift assays, we propose that the inhibitory action of these compounds likely results from direct binding to apo-Cas9, preventing Cas9:gRNA complex formation. These molecules may find use as Cas9 modulators in various applications.

Exploring Selective Pressure Trade-Offs for Synthetic Addiction to Extend Metabolite Productive Lifetimes in Yeast.

Engineered microbes often suffer from reduced fitness resulting from metabolic burden and various stresses. The productive lifetime of a bioreactor with engineered microbes is therefore susceptible to the rise of nonproductive mutants with better fitness. Synthetic addiction is emerging as a concept to artificially couple the growth rate of the microbe to production to tackle this problem. However, only a few successful cases of synthetic addiction systems have been reported to date. To understand the limitations and design constraints in long-term cultivations, we designed and studied conditional synthetic addiction circuits in Saccharomyces cerevisiae. This allowed us to probe a range of selective pressure strengths and identify the optimal balance between circuit stability and production-to-growth coupling. In the optimal balance, the productive lifetime was greatly extended compared with suboptimal circuit tuning. With a too-high or -low pressure, we found that production declines mainly through homologous recombination. These principles of trade-off in the design of synthetic addition systems should lead to the better control of bioprocess performance.