Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Oral supplementation of nicotinamide riboside alters intestinal microbial composition in rats and mice, but not humans

The gut microbiota impacts systemic levels of multiple metabolites including NAD⁺ precursors through diverse pathways. Nicotinamide riboside (NR) is an NAD⁺ precursor capable of regulating mammalian cellular metabolism. Some bacterial families express the NR-specific transporter, PnuC. We hypothesized that dietary NR supplementation would modify the gut microbiota across intestinal sections. We determined the effects of 12 weeks of NR supplementation on the microbiota composition of intestinal segments of high-fat diet-fed (HFD) rats. We also explored the effects of 12 weeks of NR supplementation on the gut microbiota in humans and mice. In rats, NR reduced fat mass and tended to decrease body weight. Interestingly, NR increased fat and energy absorption but only in HFD-fed rats. Moreover, 16S rRNA gene sequencing analysis of intestinal and fecal samples revealed an increased abundance of species within Erysipelotrichaceae and Ruminococcaceae families in response to NR. PnuC-positive bacterial strains within these families showed an increased growth rate when supplemented with NR. The abundance of species within the Lachnospiraceae family decreased in response to HFD irrespective of NR. Alpha and beta diversity and bacterial composition of the human fecal microbiota were unaltered by NR, but in mice, the fecal abundance of species within Lachnospiraceae increased while abundances of Parasutterella and Bacteroides dorei species decreased in response to NR. In conclusion, oral NR altered the gut microbiota in rats and mice, but not in humans. In addition, NR attenuated body fat mass gain in rats, and increased fat and energy absorption in the HFD context.

Cell-free fetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): a prospective cohort study

BACKGROUND

One in four pregnancies end in a pregnancy loss. Although the effect on couples is well documented, evidence-based treatments and prediction models are absent. Fetal aneuploidy is associated with a higher chance of a next successful pregnancy compared with euploid pregnancy loss in which underlying maternal conditions might be causal. Ploidy diagnostics are therefore advantageous but challenging as they require collection of the pregnancy tissue. Cell-free fetal DNA (cffDNA) from maternal blood has the potential for evaluation of fetal ploidy status, but no large-scale validation of the method has been done.

METHODS

In this prospective cohort study, women with a pregnancy loss were recruited as a part of the Copenhagen Pregnancy Loss (COPL) study from three gynaecological clinics at public hospitals in Denmark. Women were eligible for inclusion if older than 18 years with a pregnancy loss before gestational age 22 weeks (ie, 154 days) and with an intrauterine pregnancy confirmed by ultrasound (including anembryonic sac), and women with pregnancies of unknown location or molar pregnancies were excluded. Maternal blood was collected while pregnancy tissue was still in situ or within 24 h after pregnancy tissue had passed and was analysed by genome-wide sequencing of cffDNA. Direct sequencing of the pregnancy tissue was done as reference.

FINDINGS

We included 1000 consecutive women, at the time of a pregnancy loss diagnosis, between Nov 12, 2020, and May 1, 2022. Results from the first 333 women with a pregnancy loss (recruited between Nov 12, 2020, and Aug 14, 2021) were used to evaluate the validity of cffDNA-based testing. Results from the other 667 women were included to evaluate cffDNA performance and result distribution in a larger cohort of 1000 women in total. Gestational age of fetus ranged from 35-149 days (mean of 70·5 days [SD 16·5], or 10 weeks plus 1 day). The cffDNA-based test had a sensitivity for aneuploidy detection of 85% (95% CI 79-90) and a specificity of 93% (95% CI 88-96) compared with direct sequencing of the pregnancy tissue. Among 1000 cffDNA-based test results, 446 (45%) were euploid, 405 (41%) aneuploid, 37 (4%) had multiple aneuploidies, and 112 (11%) were inconclusive. 105 (32%) of 333 women either did not manage to collect the pregnancy tissue or collected a sample classified as unknown tissue giving a high risk of being maternal.

INTERPRETATION

This validation of cffDNA-based testing in pregnancy loss shows the potential and feasibility of the method to distinguish euploid and aneuploid pregnancy loss for improved clinical management and benefit of future reproductive medicine and women's health research.

FUNDING

Ole Kirks Foundation, BioInnovation Institute Foundation, and the Novo Nordisk Foundation.

Identification and Engineering of Transporters for Efficient Melatonin Production in Escherichia coli

Transporter discovery and engineering play an important role in cell factory development. Decreasing the intracellular concentration of the product reduces product inhibition and/or toxicity. Lowering intracellular concentrations is especially beneficial for achieving a robust strain at high titers. However, the identification of transporters for xenobiotic chemicals in the host strain is challenging. Here we present a high-throughput workflow to discover Escherichia coli transporters responsible for the efflux of the inhibitory xenobiotic compound melatonin. We took advantage of the Keio collection and screened about 400 transporter knockouts in the presence of a high concentration of melatonin. We found five transporters that when knocked out showed decreased tolerance to melatonin, indicating they are exporters of melatonin. We overexpressed these five genes individually in the production strain and found that one of them, yhjV, encoding a transporter with unknown substrates, resulted in a 27% titer increase in cultivation mimicking fed-batch fermentation. This study demonstrates how microbial cell factories can be improved through transporter identification and engineering. Further, these results lay the foundation for the scale-up of melatonin production in E. coli.

A dual-reporter system for investigating and optimizing protein translation and folding in E. coli

Strategies for investigating and optimizing the expression and folding of proteins for biotechnological and pharmaceutical purposes are in high demand. Here, we describe a dual-reporter biosensor system that simultaneously assesses in vivo protein translation and protein folding, thereby enabling rapid screening of mutant libraries. We have validated the dual-reporter system on five different proteins and find an excellent correlation between reporter signals and the levels of protein expression and solubility of the proteins. We further demonstrate the applicability of the dual-reporter system as a screening assay for deep mutational scanning experiments. The system enables high throughput selection of protein variants with high expression levels and altered protein stability. Next generation sequencing analysis of the resulting libraries of protein variants show a good correlation between computationally predicted and experimentally determined protein stabilities. We furthermore show that the mutational experimental data obtained using this system may be useful for protein structure calculations.

Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published ¹³C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing

Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.

Elucidating aromatic acid tolerance at low pH in Saccharomyces cerevisiae using adaptive laboratory evolution

Toxicity from the external presence or internal production of compounds can reduce the growth and viability of microbial cell factories and compromise productivity. Aromatic compounds are generally toxic for microorganisms, which makes their production in microbial hosts challenging. Here we use adaptive laboratory evolution to generate Saccharomyces cerevisiae mutants tolerant to two aromatic acids, coumaric acid and ferulic acid. The evolution experiments were performed at low pH (3.5) to reproduce conditions typical of industrial processes. Mutant strains tolerant to levels of aromatic acids near the solubility limit were then analyzed by whole genome sequencing, which revealed prevalent point mutations in a transcriptional activator (Aro80) that is responsible for regulating the use of aromatic amino acids as the nitrogen source. Among the genes regulated by Aro80, ESBP6 was found to be responsible for increasing tolerance to aromatic acids by exporting them out of the cell. Further examination of the native function of Esbp6 revealed that this transporter can excrete fusel acids (byproducts of aromatic amino acid catabolism) and this role is shared with at least one additional transporter native to S. cerevisiae (Pdr12). Besides conferring tolerance to aromatic acids, ESBP6 overexpression was also shown to significantly improve the secretion in coumaric acid production strains. Overall, we showed that regulating the activity of transporters is a major mechanism to improve tolerance to aromatic acids. These findings can be used to modulate the intracellular concentration of aromatic compounds to optimize the excretion of such products while keeping precursor molecules inside the cell.

Adaptive laboratory evolution of Pseudomonas putida KT2440 improves p-coumaric and ferulic acid catabolism and tolerance

Pseudomonas putida KT2440 is a promising bacterial chassis for the conversion of lignin-derived aromatic compound mixtures to biofuels and bioproducts. Despite the inherent robustness of this strain, further improvements to aromatic catabolism and toxicity tolerance of P. putida will be required to achieve industrial relevance. Here, tolerance adaptive laboratory evolution (TALE) was employed with increasing concentrations of the hydroxycinnamic acids p-coumaric acid (pCA) and ferulic acid (FA) individually and in combination (pCA ​+ ​FA). The TALE experiments led to evolved P. putida strains with increased tolerance to the targeted acids as compared to wild type. Specifically, a 37 ​h decrease in lag phase in 20 ​g/L pCA and a 2.4-fold increase in growth rate in 30 ​g/L FA was observed. Whole genome sequencing of intermediate and endpoint evolved P. putida populations revealed several expected and non-intuitive genetic targets underlying these aromatic catabolic and toxicity tolerance enhancements. PP_3350 and ttgB were among the most frequently mutated genes, and the beneficial contributions of these mutations were verified via gene knockouts. Deletion of PP_3350, encoding a hypothetical protein, recapitulated improved toxicity tolerance to high concentrations of pCA, but not an improved growth rate in high concentrations of FA. Deletion of ttgB, part of the TtgABC efflux pump, severely inhibited growth in pCA ​+ ​FA TALE-derived strains but did not affect growth in pCA ​+ ​FA in a wild type background, suggesting epistatic interactions. Genes involved in flagellar movement and transcriptional regulation were often mutated in the TALE experiments on multiple substrates, reinforcing ideas of a minimal and deregulated cell as optimal for domesticated growth. Overall, this work demonstrates increased tolerance towards and growth rate at the expense of hydroxycinnamic acids and presents new targets for improving P. putida for microbial lignin valorization.

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Pseudomonas putida was evolved in increasing hydroxycinnamic acid concentrations.

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The lag phase in high p-coumaric acid concentration was reduced.

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The growth rate and tolerance to high ferulic acid concentration was increased.

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PP_3350 and ttgB contribute to the observed phenotypes.

CRISPR interference of nucleotide biosynthesis improves production of a single-domain antibody in Escherichia coli

Growth decoupling can be used to optimize the production of biochemicals and proteins in cell factories. Inhibition of excess biomass formation allows for carbon to be utilized efficiently for product formation instead of growth, resulting in increased product yields and titers. Here, we used CRISPR interference to increase the production of a single‐domain antibody (sdAb) by inhibiting growth during production. First, we screened 21 sgRNA targets in the purine and pyrimidine biosynthesis pathways and found that the repression of 11 pathway genes led to the increased green fluorescent protein production and decreased growth. The sgRNA targets pyrF, pyrG, and cmk were selected and further used to improve the production of two versions of an expression‐optimized sdAb. Proteomics analysis of the sdAb‐producing pyrF, pyrG, and cmk growth decoupling strains showed significantly decreased RpoS levels and an increase of ribosome‐associated proteins, indicating that the growth decoupling strains do not enter stationary phase and maintain their capacity for protein synthesis upon growth inhibition. Finally, sdAb production was scaled up to shake‐flask fermentation where the product yield was improved 2.6‐fold compared to the control strain with no sgRNA target sequence. An sdAb content of 14.6% was reached in the best‐performing pyrG growth decoupling strain.

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

Background

Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.

Results

A genome-scale metabolic model of L. reuteri JCM 1112^T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.

Conclusion

We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112^T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

MARSI: metabolite analogues for rational strain improvement

Summary

Metabolite analogues (MAs) mimic the structure of native metabolites, can competitively inhibit their utilization in enzymatic reactions, and are commonly used as selection tools for isolating desirable mutants of industrial microorganisms. Genome-scale metabolic models representing all biochemical reactions in an organism can be used to predict effects of MAs on cellular phenotypes. Here, we present the metabolite analogues for rational strain improvement (MARSI) framework. MARSI provides a rational approach to strain improvement by searching for metabolites as targets instead of genes or reactions. The designs found by MARSI can be implemented by supplying MAs in the culture media, enabling metabolic rewiring without the use of recombinant DNA technologies that cannot always be used due to regulations. To facilitate experimental implementation, MARSI provides tools to identify candidate MAs to a target metabolite from a database of known drugs and analogues.

Availability and implementation

The code is freely available at https://github.com/biosustain/marsi under the Apache License V2. MARSI is implemented in Python.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolytica

Fatty alcohols are widely used in various applications within a diverse set of industries, such as the soap and detergent industry, the personal care, and cosmetics industry, as well as the food industry. The total world production of fatty alcohols is over 2 million tons with approximately equal parts derived from fossil oil and from plant oils or animal fats. Due to the environmental impact of these production methods, there is an interest in alternative methods for fatty alcohol production via microbial fermentation using cheap renewable feedstocks. In this study, we aimed to obtain a better understanding of how fatty alcohol biosynthesis impacts the host organism, baker’s yeast Saccharomyces cerevisiae or oleaginous yeast Yarrowia lipolytica. Producing and non-producing strains were compared in growth and nitrogen-depletion cultivation phases. The multi-omics analysis included physiological characterization, transcriptome analysis by RNAseq, ¹³Cmetabolic flux analysis, and intracellular metabolomics. Both species accumulated fatty alcohols under nitrogen-depletion conditions but not during growth. The fatty alcohol–producing Y. lipolytica strain had a higher fatty alcohol production rate than an analogous S. cerevisiae strain. Nitrogen-depletion phase was associated with lower glucose uptake rates and a decrease in the intracellular concentration of acetyl–CoA in both yeast species, as well as increased organic acid secretion rates in Y. lipolytica. Expression of the fatty alcohol–producing enzyme fatty acyl–CoA reductase alleviated the growth defect caused by deletion of hexadecenal dehydrogenase encoding genes (HFD1 and HFD4) in Y. lipolytica. RNAseq analysis showed that fatty alcohol production triggered a cell wall stress response in S. cerevisiae. RNAseq analysis also showed that both nitrogen-depletion and fatty alcohol production have substantial effects on the expression of transporter encoding genes in Y. lipolytica. In conclusion, through this multi-omics study, we uncovered some effects of fatty alcohol production on the host metabolism. This knowledge can be used as guidance for further strain improvement towards the production of fatty alcohols.

Generation of an E. coli platform strain for improved sucrose utilization using adaptive laboratory evolution

Background

Sucrose is an attractive industrial carbon source due to its abundance and the fact that it can be cheaply generated from sources such as sugarcane. However, only a few characterized Escherichia coli strains are able to metabolize sucrose, and those that can are typically slow growing or pathogenic strains.

Methods

To generate a platform strain capable of efficiently utilizing sucrose with a high growth rate, adaptive laboratory evolution (ALE) was utilized to evolve engineered E. coli K-12 MG1655 strains containing the sucrose utilizing csc genes (cscB, cscK, cscA) alongside the native sucrose consuming E. coli W.

Results

Evolved K-12 clones displayed an increase in growth and sucrose uptake rates of 1.72- and 1.40-fold on sugarcane juice as compared to the original engineered strains, respectively, while E. coli W clones showed a 1.4-fold increase in sucrose uptake rate without a significant increase in growth rate. Whole genome sequencing of evolved clones and populations revealed that two genetic regions were frequently mutated in the K-12 strains; the global transcription regulatory genes rpoB and rpoC, and the metabolic region related to a pyrimidine biosynthetic deficiency in K-12 attributed to pyrE expression. These two mutated regions have been characterized to confer a similar benefit when glucose is the main carbon source, and reverse engineering revealed the same causal advantages on M9 sucrose. Additionally, the most prevalent mutation found in the evolved E. coli W lineages was the inactivation of the cscR gene, the transcriptional repression of sucrose uptake genes.

Conclusion

The generated K-12 and W platform strains, and the specific sets of mutations that enable their phenotypes, are available as valuable tools for sucrose-based industrial bioproduction in the facile E. coli chassis.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1165-2) contains supplementary material, which is available to authorized users.

OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs

Biological production of chemicals is an attractive alternative to petrochemical-based production, due to advantages in environmental impact and the spectrum of feasible targets. However, engineering microbial strains to overproduce a compound of interest can be a long, costly and painstaking process. If production can be coupled to cell growth it is possible to use adaptive laboratory evolution to increase the production rate. Strategies for coupling production to growth, however, are often not trivial to find. Here we present OptCouple, a constraint-based modeling algorithm to simultaneously identify combinations of gene knockouts, insertions and medium supplements that lead to growth-coupled production of a target compound. We validated the algorithm by showing that it can find novel strategies that are growth-coupled in silico for a compound that has not been coupled to growth previously, as well as reproduce known growth-coupled strain designs for two different target compounds. Furthermore, we used OptCouple to construct an alternative design with potential for higher production. We provide an efficient and easy-to-use implementation of the OptCouple algorithm in the cameo Python package for computational strain design.

Building a global alliance of biofoundries

Biofoundries provide an integrated infrastructure to enable the rapid design, construction, and testing of genetically reprogrammed organisms for biotechnology applications and research. Many biofoundries are being built and a Global Biofoundry Alliance has recently been established to coordinate activities worldwide.

Analysis of Pseudomonas putida growth on non-trivial carbon sources using transcriptomics and genome-scale modelling

Pseudomonas putida is characterized by a versatile metabolism and stress tolerance traits that allow the bacterium to cope with different environmental conditions. In this work, the mechanisms that allow P. putida KT2440 to grow in the presence of four sole carbon sources (glucose, citrate, ferulic acid, serine) were investigated by RNA sequencing (RNA-seq) and genome-scale metabolic modelling. Transcriptomic data identified uptake systems for the four carbon sources, and candidates were subjected to preliminary experimental characterization by mutant strain growth to test their involvement in substrate assimilation. The OpdH and BenF-like porins were involved in citrate and ferulic acid uptake respectively. The citrate transporter (encoded by PP_0147) and the TctABC system were important for supporting cell growth in citrate; PcaT and VanK were associated with ferulic acid uptake; and the ABC transporter AapJPQM was involved in serine transport. A genome-scale metabolic model of P. putida KT2440 was used to integrate and analyze the transcriptomic data, identifying and confirming the active catabolic pathways for each carbon source. This study reveals novel information about transporters that are essential for understanding bacterial adaptation to different environments.

Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations

BACKGROUND

The selection of bioengineering platform strains and engineering strategies to improve the stress resistance of Saccharomyces cerevisiae remains a pressing need in bio-based chemical production. Thus, a systematic effort to exploit genotypic and phenotypic diversity to boost yeast's industrial value is still urgently needed.

RESULTS

We analyzed 5,400 growth curves obtained from 36 S. cerevisiae strains and comprehensively profiled their resistances against 13 industrially relevant stresses. We observed that bioethanol and brewing strains exhibit higher resistance against acidic conditions; however, plant isolates tend to have a wider range of resistance, which may be associated with their metabolome and fluxome signatures in the tricarboxylic acid cycle and fatty acid metabolism. By deep genomic sequencing, we found that industrial strains have more genomic duplications especially affecting transcription factors, showing that they result from disparate evolutionary paths in comparison with the environmental strains, which have more indels, gene deletions, and strain-specific genes. Genome-wide association studies coupled with protein-protein interaction networks uncovered novel genetic determinants of stress resistances.

CONCLUSIONS

These resistance-related engineering targets and strain rankings provide a valuable source for engineering significantly improved industrial platform strains.

Coupling S-adenosylmethionine-dependent methylation to growth: Design and uses

We present a selection design that couples S-adenosylmethionine–dependent methylation to growth. We demonstrate its use in improving the enzyme activities of not only N-type and O-type methyltransferases by 2-fold but also an acetyltransferase of another enzyme category when linked to a methylation pathway in Escherichia coli using adaptive laboratory evolution. We also demonstrate its application for drug discovery using a catechol O-methyltransferase and its inhibitors entacapone and tolcapone. Implementation of this design in Saccharomyces cerevisiae is also demonstrated.

Many important biological processes require methylation, e.g., DNA methylation and synthesis of flavoring compounds, neurotransmitters, and antibiotics. Most methylation reactions in cells are catalyzed by S-adenosylmethionine (SAM)–dependent methyltransferases (Mtases) using SAM as a methyl donor. Thus, SAM-dependent Mtases have become an important enzyme category of biotechnological interests and as healthcare targets. However, functional implementation and engineering of SAM-dependent Mtases remains difficult and is neither cost effective nor high throughput. Here, we are able to address these challenges by establishing a synthetic biology approach, which links Mtase activity to cell growth such that higher Mtase activity ultimately leads to faster cell growth. We show that better-performing variants of the examined Mtases can be readily obtained by growth selection after repetitive cell passages. We also demonstrate the usefulness of our approach for discovery of Mtase-specific drug candidates. We further show our approach is not only applicable in bacteria, exemplified by Escherichia coli, but also in eurkaryotic organisms such as budding yeast Saccharomyces cerevisiae.

Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains

Background

Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design.

Results

Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain.

Conclusions

This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes

Microbial cell factories have proven to be an economical means of production for many bulk, specialty, and fine chemical products. However, we still lack both a holistic understanding of organism physiology and the ability to predictively tune enzyme activities in vivo, thus slowing down rational engineering of industrially relevant strains. An alternative concept to rational engineering is to use evolution as the driving force to select for desired changes, an approach often described as evolutionary engineering. In evolutionary engineering, in vivo selections for a desired phenotype are combined with either generation of spontaneous mutations or some form of targeted or random mutagenesis. Evolutionary engineering has been used to successfully engineer easily selectable phenotypes, such as utilization of a suboptimal nutrient source or tolerance to inhibitory substrates or products. In this review, we focus primarily on a more challenging problem-the use of evolutionary engineering for improving the production of chemicals in microbes directly. We describe recent developments in evolutionary engineering strategies, in general, and discuss, in detail, case studies where production of a chemical has been successfully achieved through evolutionary engineering by coupling production to cellular growth.

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .

Computational Methods to Assess the Production Potential of Bio-Based Chemicals

Elevated costs and long implementation times of bio-based processes for producing chemicals represent a bottleneck for moving to a bio-based economy. A prospective analysis able to elucidate economically and technically feasible product targets at early research phases is mandatory. Computational tools can be implemented to explore the biological and technical spectrum of feasibility, while constraining the operational space for desired chemicals. In this chapter, two different computational tools for assessing potential for bio-based production of chemicals from different perspectives are described in detail. The first tool is GEM-Path: an algorithm to compute all structurally possible pathways from one target molecule to the host metabolome. The second tool is a framework for Modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes, and economic impact assessment. Integrating GEM-Path and MuSIC will play a vital role in supporting early phases of research efforts and guide the policy makers with decisions, as we progress toward planning a sustainable chemical industry.

Generation of a platform strain for ionic liquid tolerance using adaptive laboratory evolution

Background

There is a need to replace petroleum-derived with sustainable feedstocks for chemical production. Certain biomass feedstocks can meet this need as abundant, diverse, and renewable resources. Specific ionic liquids (ILs) can play a role in this process as promising candidates for chemical pretreatment and deconstruction of plant-based biomass feedstocks as they efficiently release carbohydrates which can be fermented. However, the most efficient pretreatment ILs are highly toxic to biological systems, such as microbial fermentations, and hinder subsequent bioprocessing of fermentative sugars obtained from IL-treated biomass.

Methods

To generate strains capable of tolerating residual ILs present in treated feedstocks, a tolerance adaptive laboratory evolution (TALE) approach was developed and utilized to improve growth of two different Escherichia coli strains, DH1 and K-12 MG1655, in the presence of two different ionic liquids, 1-ethyl-3-methylimidazolium acetate ([C₂C₁Im][OAc]) and 1-butyl-3-methylimidazolium chloride ([C₄C₁Im]Cl). For multiple parallel replicate populations of E. coli, cells were repeatedly passed to select for improved fitness over the course of approximately 40 days. Clonal isolates were screened and the best performing isolates were subjected to whole genome sequencing.

Results

The most prevalent mutations in tolerant clones occurred in transport processes related to the functions of mdtJI, a multidrug efflux pump, and yhdP, an uncharacterized transporter. Additional mutations were enriched in processes such as transcriptional regulation and nucleotide biosynthesis. Finally, the best-performing strains were compared to previously characterized tolerant strains and showed superior performance in tolerance of different IL and media combinations (i.e., cross tolerance) with robust growth at 8.5% (w/v) and detectable growth up to 11.9% (w/v) [C₂C₁Im][OAc].

Conclusion

The generated strains thus represent the best performing platform strains available for bioproduction utilizing IL-treated renewable substrates, and the TALE method was highly successful in overcoming the general issue of substrate toxicity and has great promise for use in tolerance engineering.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0819-1) contains supplementary material, which is available to authorized users.

Isolation and characterization of the E. coli membrane protein production strain Mutant56(DE3)

Membrane protein production is usually toxic to E. coli. However, using genetic screens strains can be isolated in which the toxicity of membrane protein production is reduced, thereby improving production yields. Best known examples are the C41(DE3) and C43(DE3) strains, which are both derived from the T7 RNA polymerase (P)-based BL21(DE3) protein production strain. In C41(DE3) and C43(DE3) mutations lowering t7rnap expression levels result in strongly reduced T7 RNAP accumulation levels. As a consequence membrane protein production stress is alleviated in the C41(DE3) and C43(DE3) strains, thereby increasing membrane protein yields. Here, we isolated Mutant56(DE3) from BL21(DE3) using a genetic screen designed to isolate BL21(DE3)-derived strains with mutations alleviating membrane protein production stress other than the ones in C41(DE3) and C43(DE3). The defining mutation of Mutant56(DE3) changes one amino acid in its T7 RNAP, which weakens the binding of the T7 RNAP to the T7 promoter governing target gene expression rather than lowering T7 RNAP levels. For most membrane proteins tested yields in Mutant56(DE3) were considerably higher than in C41(DE3) and C43(DE3). Thus, the isolation of Mutant56(DE3) shows that the evolution of BL21(DE3) can be promoted towards further enhanced membrane protein production.

Systems biology solutions for biochemical production challenges

There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity compared to basic science research requires pushing systems biology strategies to their limits and often spurs innovative developments that benefit fields outside metabolic engineering. Here we survey recent advanced applications of systems biology methods in engineering microbial production strains for biofuels and -chemicals.

Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.

Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes

Escherichia coli strains are widely used in academic research and biotechnology. New technologies for quantifying strain-specific differences and their underlying contributing factors promise greater understanding of how these differences significantly impact physiology, synthetic biology, metabolic engineering, and process design. Here, we quantified strain-specific differences in seven widely used strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics, and genome-scale modeling. Metabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titer. These differences could be linked to differential regulatory structure. Analyzing high-flux reactions and expression of encoding genes resulted in a correlated and quantitative link between these sets, with strain-specific caveats. Integrated modeling revealed that certain strains are better suited to produce given compounds or express desired constructs considering native expression states of pathways that enable high-production phenotypes. This study yields a framework for quantitatively comparing strains in a species with implications for strain selection.

Engineering and systems-level analysis of Saccharomyces cerevisiae for production of 3-hydroxypropionic acid via malonyl-CoA reductase-dependent pathway

Background

In the future, oil- and gas-derived polymers may be replaced with bio-based polymers, produced from renewable feedstocks using engineered cell factories. Acrylic acid and acrylic esters with an estimated world annual production of approximately 6 million tons by 2017 can be derived from 3-hydroxypropionic acid (3HP), which can be produced by microbial fermentation. For an economically viable process 3HP must be produced at high titer, rate and yield and preferably at low pH to minimize downstream processing costs.

Results

Here we describe the metabolic engineering of baker’s yeast Saccharomyces cerevisiae for biosynthesis of 3HP via a malonyl-CoA reductase (MCR)-dependent pathway. Integration of multiple copies of MCR from Chloroflexus aurantiacus and of phosphorylation-deficient acetyl-CoA carboxylase ACC1 genes into the genome of yeast increased 3HP titer fivefold in comparison with single integration. Furthermore we optimized the supply of acetyl-CoA by overexpressing native pyruvate decarboxylase PDC1, aldehyde dehydrogenase ALD6, and acetyl-CoA synthase from Salmonella entericaSEacs^L641P. Finally we engineered the cofactor specificity of the glyceraldehyde-3-phosphate dehydrogenase to increase the intracellular production of NADPH at the expense of NADH and thus improve 3HP production and reduce formation of glycerol as by-product. The final strain produced 9.8 ± 0.4 g L⁻¹ 3HP with a yield of 13 % C-mol C-mol⁻¹ glucose after 100 h in carbon-limited fed-batch cultivation at pH 5. The 3HP-producing strain was characterized by ¹³C metabolic flux analysis and by transcriptome analysis, which revealed some unexpected consequences of the undertaken metabolic engineering strategy, and based on this data, future metabolic engineering directions are proposed.

Conclusions

In this study, S. cerevisiae was engineered for high-level production of 3HP by increasing the copy numbers of biosynthetic genes and improving flux towards precursors and redox cofactors. This strain represents a good platform for further optimization of 3HP production and hence an important step towards potential commercial bio-based production of 3HP.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0451-5) contains supplementary material, which is available to authorized users.

EasyCloneMulti: A Set of Vectors for Simultaneous and Multiple Genomic Integrations in Saccharomyces cerevisiae

Saccharomyces cerevisiae is widely used in the biotechnology industry for production of ethanol, recombinant proteins, food ingredients and other chemicals. In order to generate highly producing and stable strains, genome integration of genes encoding metabolic pathway enzymes is the preferred option. However, integration of pathway genes in single or few copies, especially those encoding rate-controlling steps, is often not sufficient to sustain high metabolic fluxes. By exploiting the sequence diversity in the long terminal repeats (LTR) of Ty retrotransposons, we developed a new set of integrative vectors, EasyCloneMulti, that enables multiple and simultaneous integration of genes in S. cerevisiae. By creating vector backbones that combine consensus sequences that aim at targeting subsets of Ty sequences and a quickly degrading selective marker, integrations at multiple genomic loci and a range of expression levels were obtained, as assessed with the green fluorescent protein (GFP) reporter system. The EasyCloneMulti vector set was applied to balance the expression of the rate-controlling step in the β-alanine pathway for biosynthesis of 3-hydroxypropionic acid (3HP). The best 3HP producing clone, with 5.45 g.L^-1 of 3HP, produced 11 times more 3HP than the lowest producing clone, which demonstrates the capability of EasyCloneMulti vectors to impact metabolic pathway enzyme activity.

Transient overexpression of DNA adenine methylase enables efficient and mobile genome engineering with reduced off-target effects

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.

Modeling the Contribution of Allosteric Regulation for Flux Control in the Central Carbon Metabolism of E. coli

Modeling cellular metabolism is fundamental for many biotechnological applications, including drug discovery and rational cell factory design. Central carbon metabolism (CCM) is particularly important as it provides the energy and precursors for other biological processes. However, the complex regulation of CCM pathways has still not been fully unraveled and recent studies have shown that CCM is mostly regulated at post-transcriptional levels. In order to better understand the role of allosteric regulation in controlling the metabolic phenotype, we expand the reconstruction of CCM in Escherichia coli with allosteric interactions obtained from relevant databases. This model is used to integrate multi-omics datasets and analyze the coordinated changes in enzyme, metabolite, and flux levels between multiple experimental conditions. We observe cases where allosteric interactions have a major contribution to the metabolic flux changes. Inspired by these results, we develop a constraint-based method (arFBA) for simulation of metabolic flux distributions that accounts for allosteric interactions. This method can be used for systematic prediction of potential allosteric regulation under the given experimental conditions based on experimental data. We show that arFBA allows predicting coordinated flux changes that would not be predicted without considering allosteric regulation. The results reveal the importance of key regulatory metabolites, such as fructose-1,6-bisphosphate, in controlling the metabolic flux. Accounting for allosteric interactions in metabolic reconstructions reveals a hidden topology in metabolic networks, improving our understanding of cellular metabolism and fostering the development of novel simulation methods that account for this type of regulation.

Enhanced Protein Production in Escherichia coli by Optimization of Cloning Scars at the Vector-Coding Sequence Junction

Protein production in Escherichia coli is a fundamental activity for a large fraction of academic, pharmaceutical, and industrial research laboratories. Maximum production is usually sought, as this reduces costs and facilitates downstream purification steps. Frustratingly, many coding sequences are poorly expressed even when they are codon-optimized and expressed from vectors with powerful genetic elements. In this study, we show that poor expression can be caused by certain nucleotide sequences (e.g., cloning scars) at the junction between the vector and the coding sequence. Since these sequences lie between the Shine-Dalgarno sequence and the start codon, they are an integral part of the translation initiation region. To identify the most optimal sequences, we devised a simple and inexpensive PCR-based step that generates sequence variants at the vector-coding sequence junction. These sequence variants modulated expression by up to 1000-fold. FACS-seq analyses indicated that low GC content and relaxed mRNA stability (ΔG) in this region were important, but not the only, determinants for high expression.

Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production

In recent years, bio-based chemicals have gained traction as a sustainable alternative to petrochemicals. However, despite rapid advances in metabolic engineering and synthetic biology, there remain significant economic and environmental challenges. In order to maximize the impact of research investment in a new bio-based chemical industry, there is a need for assessing the technological, economic, and environmental potentials of combinations of biomass feedstocks, biochemical products, bioprocess technologies, and metabolic engineering approaches in the early phase of development of cell factories. To address this issue, we have developed a comprehensive Multi-scale framework for modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes and economic impact assessment. We demonstrate the use of the MuSIC framework in a case study where two major polymer precursors (1,3-propanediol and 3-hydroxypropionic acid) are produced from two biomass feedstocks (corn-based glucose and soy-based glycerol) through 66 proposed biosynthetic pathways in two host organisms (Escherichia coli and Saccharomyces cerevisiae). The MuSIC framework allows exploration of tradeoffs and interactions between economy-scale objectives (e.g. profit maximization, emission minimization), constraints (e.g. land-use constraints) and process- and cell-scale technology choices (e.g. strain design or oxygenation conditions). We demonstrate that economy-scale assessment can be used to guide specific strain design decisions in metabolic engineering, and that these design decisions can be affected by non-intuitive dependencies across multiple scales.

Analysis of genetic variation and potential applications in genome-scale metabolic modeling

Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

Evolution reveals a glutathione-dependent mechanism of 3-hydroxypropionic acid tolerance

Biologically produced 3-hydroxypropionic acid (3 HP) is a potential source for sustainable acrylates and can also find direct use as monomer in the production of biodegradable polymers. For industrial-scale production there is a need for robust cell factories tolerant to high concentration of 3 HP, preferably at low pH. Through adaptive laboratory evolution we selected S. cerevisiae strains with improved tolerance to 3 HP at pH 3.5. Genome sequencing followed by functional analysis identified the causal mutation in SFA1 gene encoding S-(hydroxymethyl)glutathione dehydrogenase. Based on our findings, we propose that 3 HP toxicity is mediated by 3-hydroxypropionic aldehyde (reuterin) and that glutathione-dependent reactions are used for reuterin detoxification. The identified molecular response to 3 HP and reuterin may well be a general mechanism for handling resistance to organic acid and aldehydes by living cells.

The YNP Metagenome Project: Environmental Parameters Responsible for Microbial Distribution in the Yellowstone Geothermal Ecosystem

The Yellowstone geothermal complex contains over 10,000 diverse geothermal features that host numerous phylogenetically deeply rooted and poorly understood archaea, bacteria, and viruses. Microbial communities in high-temperature environments are generally less diverse than soil, marine, sediment, or lake habitats and therefore offer a tremendous opportunity for studying the structure and function of different model microbial communities using environmental metagenomics. One of the broader goals of this study was to establish linkages among microbial distribution, metabolic potential, and environmental variables. Twenty geochemically distinct geothermal ecosystems representing a broad spectrum of Yellowstone hot-spring environments were used for metagenomic and geochemical analysis and included approximately equal numbers of: (1) phototrophic mats, (2) "filamentous streamer" communities, and (3) archaeal-dominated sediments. The metagenomes were analyzed using a suite of complementary and integrative bioinformatic tools, including phylogenetic and functional analysis of both individual sequence reads and assemblies of predominant phylotypes. This volume identifies major environmental determinants of a large number of thermophilic microbial lineages, many of which have not been fully described in the literature nor previously cultivated to enable functional and genomic analyses. Moreover, protein family abundance comparisons and in-depth analyses of specific genes and metabolic pathways relevant to these hot-spring environments reveal hallmark signatures of metabolic capabilities that parallel the distribution of phylotypes across specific types of geochemical environments.