From Eat to trEat : engineering the mitochondrial Eat1 enzyme for enhanced ethyl acetate production in Escherichia coli

Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable organic solvent with low toxicity. While ethyl acetate is produced by several natural yeast species, the main mechanism of production has remained elusive until the discovery of Eat1 in Wickerhamomyces anomalus. Unlike other yeast alcohol acetyl transferases (AATs), Eat1 is located in the yeast mitochondria, suggesting that the coding sequence contains a mitochondrial pre-sequence. For expression in prokaryotic hosts such as E. coli, expression of heterologous proteins with eukaryotic signal sequences may not be optimal. Results Unprocessed and synthetically truncated eat1 variants of Kluyveromyces marxianus and Wickerhamomyces anomalus have been compared in vitro regarding enzyme activity and stability. While the specific activity remained unaffected, half-life improved for several truncated variants. The same variants showed better performance regarding ethyl acetate production when expressed in E. coli. Conclusion By analysing and predicting the N-terminal pre-sequences of different Eat1 proteins and systematically trimming them, the stability of the enzymes in vitro could be improved, leading to an overall improvement of in vivo ethyl acetate production in E. coli. Truncated variants of eat1 could therefore benefit future engineering approaches towards efficient ethyl acetate production.publishedVersio

From Eat to trEat : engineering the mitochondrial Eat1 enzyme for enhanced ethyl acetate production in Escherichia coli

Genetic engineering of microorganisms has become a common practice to establish microbial cell factories for a wide range of compounds. Ethyl acetate is an industrial solvent that is used in several applications, mainly as a biodegradable organic solvent with low toxicity. While ethyl acetate is produced by several natural yeast species, the main mechanism of production has remained elusive until the discovery of Eat1 in Wickerhamomyces anomalus. Unlike other yeast alcohol acetyl transferases (AATs), Eat1 is located in the yeast mitochondria, suggesting that the coding sequence contains a mitochondrial pre-sequence. For expression in prokaryotic hosts such as E. coli, expression of heterologous proteins with eukaryotic signal sequences may not be optimal. Results Unprocessed and synthetically truncated eat1 variants of Kluyveromyces marxianus and Wickerhamomyces anomalus have been compared in vitro regarding enzyme activity and stability. While the specific activity remained unaffected, half-life improved for several truncated variants. The same variants showed better performance regarding ethyl acetate production when expressed in E. coli. Conclusion By analysing and predicting the N-terminal pre-sequences of different Eat1 proteins and systematically trimming them, the stability of the enzymes in vitro could be improved, leading to an overall improvement of in vivo ethyl acetate production in E. coli. Truncated variants of eat1 could therefore benefit future engineering approaches towards efficient ethyl acetate production

Ischemic Burden Reduction and Long-Term Clinical Outcomes After Chronic Total Occlusion Percutaneous Coronary Intervention

Objectives: The authors sought to evaluate the impact of ischemic burden reduction after chronic total occlusion (CTO) percutaneous coronary intervention (PCI) on long-term prognosis and cardiac symptom relief. Background: The clinical benefit of CTO PCI is questioned. Methods: In a high-volume CTO PCI center, 212 patients prospectively underwent quantitative [ 15O]H 2O positron emission tomography perfusion imaging before and three months after successful CTO PCI between 2013-2019. Perfusion defects (PD) (in segments) and hyperemic myocardial blood flow (hMBF) (in ml · min −1 · g −1) allocated to CTO areas were related to prognostic outcomes using unadjusted (Kaplan-Meier curves, log-rank test) and risk-adjusted (multivariable Cox regression) analyses. The prognostic endpoint was a composite of all-cause death and nonfatal myocardial infarction. Results: After a median [interquartile range] of 2.8 years [1.8 to 4.3 years], event-free survival was superior in patients with ≥3 versus 2.3 versus ≤2.3 ml · min −1 · g −1 (p 2.3 ml · min −1 · g −1 were more frequently free of angina and dyspnea on exertion at long-term follow-up (p = 0.04). Conclusions: Patients with extensive ischemic burden reduction and no residual ischemia after CTO PCI had lower rates of all-cause death and nonfatal myocardial infarction. Long-term cardiac symptom relief was associated with normalization of hMBF levels after CTO PCI

A toolbox of machine learning software to support microbiome analysis

Peer reviewed: TrueAcknowledgements: This article is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies,” CA18131, supported by COST (European Cooperation in Science and Technology), www.cost.eu.The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.</jats:p

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.Peer reviewe