GenoMine: a CRISPR-Cas9-based kill switch for biocontainment of Pseudomonas putida.

Synthetic genetic circuits have revolutionised our capacity to control cell viability by conferring microorganisms with programmable functionalities to limit survival to specific environmental conditions. Here, we present the GenoMine safeguard, a CRISPR-Cas9-based kill switch for the biotechnological workhorse Pseudomonas putida that employs repetitive genomic elements as cleavage targets to unleash a highly genotoxic response. To regulate the system's activation, we tested various circuit-based mechanisms including the digitalised version of an inducible expression system that operates at the transcriptional level and different options of post-transcriptional riboregulators. All of them were applied not only to directly control Cas9 and its lethal effects, but also to modulate the expression of two of its inhibitors: the AcrIIA4 anti-CRISPR protein and the transcriptional repressor TetR. Either upon direct induction of the endonuclease or under non-induced conditions of its inhibitors, the presence of Cas9 suppressed cell survival which could be exploited beyond biocontainment in situations where further CRISPR genome editing is undesirable.

Identification of genes associated with the high-temperature fermentation trait in the Saccharomyces cerevisiae natural isolate BCC39850.

The fermentative model yeast Saccharomyces cerevisiae has been extensively used to study the genetic basis of stress response and homeostasis. In this study, we performed quantitative trait loci (QTL) analysis of the high-temperature fermentation trait of the progeny from the mating of the S. cerevisiae natural isolate BCC39850 (haploid#17) and the laboratory strain CEN.PK2-1C. A single QTL on chromosome X was identified, encompassing six candidate genes (GEA1, PTK2, NTA1, NPA3, IRT1, and IML1). The functions of these candidates were tested by reverse genetic experiments. Deletion mutants of PTK2, NTA1, and IML1 showed growth defects at 42 °C. The PTK2 knock-out mutant also showed significantly reduced ethanol production and plasma membrane H+ ATPase activity and increased sensitivity to acetic acid, ethanol, amphotericin B (AMB), and ß-1,3-glucanase treatment. The CRISPR-Cas9 system was used to construct knock-in mutants by replacement of PTK2, NTA1, IML1, and NPA3 genes with BCC39850 alleles. The PTK2 and NTA1 knock-in mutants showed increased growth and ethanol production titers at 42 °C. These findings suggest an important role for the PTK2 serine/threonine protein kinase in regulating plasma membrane H+ ATPase activity and the NTA1 N-terminal amidase in protein degradation via the ubiquitin-proteasome system machinery, which affects tolerance to heat stress in S. cerevisiae.

Integrative protocol for quantifying cholesterol-related sterols in human serum samples and building decision support systems.

The growing interest in clinical diagnostics has recently focused on metabolic biomarkers. Here, we present a protocol for sample preparation, extraction of cholesterol-related sterols, and quantification of 10 sterols in human blood serum samples using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). We also describe steps of machine learning techniques to develop novel decision-making systems that offer potential benefits in disease monitoring and surveillance by measuring metabolic pathways. For complete details on the use and execution of this protocol, please refer to Kocar et al.1 and Skubic et al.2.

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae.

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

Pseudomonas putida as a platform for medium-chain length α,ω-diol production: Opportunities and challenges.

Medium-chain-length α,ω-diols (mcl-diols) play an important role in polymer production, traditionally depending on energy-intensive chemical processes. Microbial cell factories offer an alternative, but conventional strains like Escherichia coli and Saccharomyces cerevisiae face challenges in mcl-diol production due to the toxicity of intermediates such as alcohols and acids. Metabolic engineering and synthetic biology enable the engineering of non-model strains for such purposes with P. putida emerging as a promising microbial platform. This study reviews the advancement in diol production using P. putida and proposes a four-module approach for the sustainable production of diols. Despite progress, challenges persist, and this study discusses current obstacles and future opportunities for leveraging P. putida as a microbial cell factory for mcl-diol production. Furthermore, this study highlights the potential of using P. putida as an efficient chassis for diol synthesis.

Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Genes controlling hydrolysate toxin tolerance identified by QTL analysis of the natural Saccharomyces cerevisiae BCC39850.

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. KEY POINTS: • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins.

Metagenomics harvested genus-specific single-stranded DNA-annealing proteins improve and expand recombineering in Pseudomonas species.

The widespread Pseudomonas genus comprises a collection of related species with remarkable abilities to degrade plastics and polluted wastes and to produce a broad set of valuable compounds, ranging from bulk chemicals to pharmaceuticals. Pseudomonas possess characteristics of tolerance and stress resistance making them valuable hosts for industrial and environmental biotechnology. However, efficient and high-throughput genetic engineering tools have limited metabolic engineering efforts and applications. To improve their genome editing capabilities, we first employed a computational biology workflow to generate a genus-specific library of potential single-stranded DNA-annealing proteins (SSAPs). Assessment of the library was performed in different Pseudomonas using a high-throughput pooled recombinase screen followed by Oxford Nanopore NGS analysis. Among different active variants with variable levels of allelic replacement frequency (ARF), efficient SSAPs were found and characterized for mediating recombineering in the four tested species. New variants yielded higher ARFs than existing ones in Pseudomonas putida and Pseudomonas aeruginosa, and expanded the field of recombineering in Pseudomonas taiwanensisand Pseudomonas fluorescens. These findings will enhance the mutagenesis capabilities of these members of the Pseudomonas genus, increasing the possibilities for biotransformation and enhancing their potential for synthetic biology applications. .

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems.

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Crossing bacterial boundaries: The carbon catabolite repression system Crc-Hfq of Pseudomonas putida KT2440 as a tool to control translation in E. coli.

As a global regulatory mechanism, carbon catabolite repression allows bacteria and eukaryal microbes to preferentially utilize certain substrates from a mixture of carbon sources. The mechanism varies among different species. In Pseudomonas spp., it is mainly mediated by the Crc-Hfq complex which binds to the 5' region of the target mRNAs, thereby inhibiting their translation. This molecular mechanism enables P. putida to rapidly adjust and fine-tune gene expression in changing environments. Hfq is an RNA-binding protein that is ubiquitous and highly conserved in bacterial species. Considering the characteristics of Hfq, and the widespread use and rapid response of Crc-Hfq in P. putida, this complex has the potential to become a general toolbox for post-transcriptional multiplex regulation. In this study, we demonstrate for the first time that transplanting the pseudomonal catabolite repression protein, Crc, into E. coli causes multiplex gene repression. Under the control of Crc, the production of a diester and its precursors was significantly reduced. The effects of Crc introduction on cell growth in both minimal and rich media were evaluated. Two potential factors - off-target effects and Hfq-sequestration - could explain negative effects on cell growth. Simultaneous reduction of off-targeting and increased sequestration of Hfq by the introduction of the small RNA CrcZ, indicated that Hfq sequestration plays a more prominent role in the negative side-effects. This suggests that the negative growth effect can be mitigated by well-controlled expression of Hfq. This study reveals the feasibility of controlling gene expression using heterologous regulation systems.

Identification of Aspergillus niger Aquaporins Involved in Hydrogen Peroxide Signaling.

Aspergillus niger is a robust microbial cell factory for organic acid production. However, the regulation of many industrially important pathways is still poorly understood. The regulation of the glucose oxidase (Gox) expression system, involved in the biosynthesis of gluconic acid, has recently been uncovered. The results of that study show hydrogen peroxide, a by-product of the extracellular conversion of glucose to gluconate, has a pivotal role as a signaling molecule in the induction of this system. In this study, the facilitated diffusion of hydrogen peroxide via aquaporin water channels (AQPs) was studied. AQPs are transmembrane proteins of the major intrinsic proteins (MIPs) superfamily. In addition to water and glycerol, they may also transport small solutes such as hydrogen peroxide. The genome sequence of A. niger N402 was screened for putative AQPs. Seven AQPs were found and could be classified into three main groups. One protein (AQPA) belonged to orthodox AQP, three (AQPB, AQPD, and AQPE) were grouped in aquaglyceroporins (AQGP), two (AQPC and AQPF) were in X-intrinsic proteins (XIPs), and the other (AQPG) could not be classified. Their ability to facilitate diffusion of hydrogen peroxide was identified using yeast phenotypic growth assays and by studying AQP gene knock-outs in A. niger. The X-intrinsic protein AQPF appears to play roles in facilitating hydrogen peroxide transport across the cellular membrane in both Saccharomyces cerevisiae and A. niger experiments.

Systemic immune activation profiles in streptococcal necrotizing soft tissue infections: A prospective multicenter study.

OBJECTIVE: Early stages with streptococcal necrotizing soft tissue infections (NSTIs) are often difficult to discern from cellulitis. Increased insight into inflammatory responses in streptococcal disease may guide correct interventions and discovery of novel diagnostic targets. METHODS: Plasma levels of 37 mediators, leucocytes and CRP from 102 patients with ß-hemolytic streptococcal NSTI derived from a prospective Scandinavian multicentre study were compared to those of 23 cases of streptococcal cellulitis. Hierarchical cluster analyses were also performed. RESULTS: Differences in mediator levels between NSTI and cellulitis cases were revealed, in particular for IL-1ß, TNFα and CXCL8 (AUC >0.90). Across streptococcal NSTI etiologies, eight biomarkers separated cases with septic shock from those without, and four mediators predicted a severe outcome. CONCLUSION: Several inflammatory mediators and wider profiles were identified as potential biomarkers of NSTI. Associations of biomarker levels to type of infection and outcomes may be utilized to improve patient care and outcomes.

Exploring the Impact of Tensions in Stakeholder Norms on Designing for Value Change: The Case of Biosafety in Industrial Biotechnology.

Synthetic biologists design and engineer organisms for a better and more sustainable future. While the manifold prospects are encouraging, concerns about the uncertain risks of genome editing affect public opinion as well as local regulations. As a consequence, biosafety and associated concepts, such as the Safe-by-design framework and genetic safeguard technologies, have gained notoriety and occupy a central position in the conversation about genetically modified organisms. Yet, as regulatory interest and academic research in genetic safeguard technologies advance, the implementation in industrial biotechnology, a sector that is already employing engineered microorganisms, lags behind. The main goal of this work is to explore the utilization of genetic safeguard technologies for designing biosafety in industrial biotechnology. Based on our results, we posit that biosafety is a case of a changing value, by means of further specification of how to realize biosafety. Our investigation is inspired by the Value Sensitive Design framework, to investigate scientific and technological choices in their appropriate social context. Our findings discuss stakeholder norms for biosafety, reasonings about genetic safeguards, and how these impact the practice of designing for biosafety. We show that tensions between stakeholders occur at the level of norms, and that prior stakeholder alignment is crucial for value specification to happen in practice. Finally, we elaborate in different reasonings about genetic safeguards for biosafety and conclude that, in absence of a common multi-stakeholder effort, the differences in informal biosafety norms and the disparity in biosafety thinking could end up leading to design requirements for compliance instead of for safety.

Computational Approaches for Peroxisomal Protein Localization.

Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization.

Paving the way for synthetic C1 - Metabolism in Pseudomonas putida through the reductive glycine pathway.

One-carbon (C1) compounds such as methanol, formate, and CO2 are alternative, sustainable microbial feedstocks for the biobased production of chemicals and fuels. In this study, we engineered the carbon metabolism of the industrially important bacterium Pseudomonas putida to modularly assimilate these three substrates through the reductive glycine pathway. First, we demonstrated the functionality of the C1-assimilation module by coupling the growth of auxotrophic strains to formate assimilation. Next, we extended the module in the auxotrophic strains from formate to methanol-dependent growth using both NAD and PQQ-dependent methanol dehydrogenases. Finally, we demonstrated, for the first time, engineered CO2-dependent formation of part of the biomass through CO2 reduction to formate by the native formate dehydrogenase, which required short-term evolution to rebalance the cellular NADH/NAD + ratio. This research paves the way to further engineer P. putida towards full growth on formate, methanol, and CO2 as sole feedstocks, thereby substantially expanding its potential as a sustainable and versatile cell factory.

A tunable metabolic valve for precise growth control and increased product formation in Pseudomonas putida.

Metabolic engineering of microorganisms aims to design strains capable of producing valuable compounds under relevant industrial conditions and in an economically competitive manner. From this perspective, and beyond the need for a catalyst, biomass is essentially a cost-intensive, abundant by-product of a microbial conversion. Yet, few broadly applicable strategies focus on the optimal balance between product and biomass formation. Here, we present a genetic control module that can be used to precisely modulate growth of the industrial bacterial chassis Pseudomonas putida KT2440. The strategy is based on the controllable expression of the key metabolic enzyme complex pyruvate dehydrogenase (PDH) which functions as a metabolic valve. By tuning the PDH activity, we accurately controlled biomass formation, resulting in six distinct growth rates with parallel overproduction of excess pyruvate. We deployed this strategy to identify optimal growth patterns that improved the production yield of 2-ketoisovalerate and lycopene by 2.5- and 1.38-fold, respectively. This ability to dynamically steer fluxes to balance growth and production substantially enhances the potential of this remarkable microbial chassis for a wide range of industrial applications.

OrganelX web server for sub-peroxisomal and sub-mitochondrial protein localization and peroxisomal target signal detection.

We present the OrganelX e-Science Web Server that provides a user-friendly implementation of the In-Pero and In-Mito classifiers for sub-peroxisomal and sub-mitochondrial localization of peroxisomal and mitochondrial proteins and the Is-PTS1 algorithm for detecting and validating potential peroxisomal proteins carrying a PTS1 signal sequence. The OrganelX e-Science Web Server is available at https://organelx.hpc.rug.nl/fasta/.

Metabolic engineering of Pseudomonas putida KT2440 for medium-chain-length fatty alcohol and ester production from fatty acids.

Medium-chain-length fatty alcohols have broad applications in the surfactant, lubricant, and cosmetic industries. Their acetate esters are widely used as flavoring and fragrance substances. Pseudomonas putida KT2440 is a promising chassis for fatty alcohol and ester production at the industrial scale due to its robustness, versatility, and high oxidative capacity. However, P. putida has also numerous native alcohol dehydrogenases, which lead to the degradation of these alcohols and thereby hinder its use as an effective biocatalyst. Therefore, to harness its capacity as a producer, we constructed two engineered strains (WTΔpedFΔadhP, GN346ΔadhP) incapable of growing on mcl-fatty alcohols by deleting either a cytochrome c oxidase PedF and a short-chain alcohol dehydrogenase AdhP in P. putida or AdhP in P. putida GN346. Carboxylic acid reductase, phosphopantetheinyl transferase, and alcohol acetyltransferase were expressed in the engineered P. putida strains to produce hexyl acetate. Overexpression of transporters further increased 1-hexanol and hexyl acetate production. The optimal strain G23E-MPAscTP produced 93.8 mg/L 1-hexanol and 160.5 mg/L hexyl acetate, with a yield of 63.1%. The engineered strain is applicable for C6-C10 fatty alcohols and their acetate ester production. This study lays a foundation for P. putida being used as a microbial cell factory for sustainable synthesis of a broad range of products based on medium-chain-length fatty alcohols.

Tailoring and optimizing fatty acid production by oleaginous yeasts through the systematic exploration of their physiological fitness.

BACKGROUND: The use of palm oil for our current needs is unsustainable. Replacing palm oil with oils produced by microbes through the conversion of sustainable feedstocks is a promising alternative. However, there are major technical challenges that must be overcome to enable this transition. Foremost among these challenges is the stark increase in lipid accumulation and production of higher content of specific fatty acids. Therefore, there is a need for more in-depth knowledge and systematic exploration of the oil productivity of the oleaginous yeasts. In this study, we cultivated Cutaneotrichosporon oleaginosus and Yarrowia lipolytica at various C/N ratios and temperatures in a defined medium with glycerol as carbon source and urea as nitrogen source. We ascertained the synergistic effect between various C/N ratios of a defined medium at different temperatures with Response Surface Methodology (RSM) and explored the variation in fatty acid composition through Principal Component Analysis. RESULTS: By applying RSM, we determined a temperature of 30 °C and a C/N ratio of 175 g/g to enable maximal oil production by C. oleaginosus and a temperature of 21 °C and a C/N ratio of 140 g/g for Y. lipolytica. We increased production by 71% and 66% respectively for each yeast compared to the average lipid accumulation in all tested conditions. Modulating temperature enabled us to steer the fatty acid compositions. Accordingly, switching from higher temperature to lower cultivation temperature shifted the production of oils from more saturated to unsaturated by 14% in C. oleaginosus and 31% in Y. lipolytica. Higher cultivation temperatures resulted in production of even longer saturated fatty acids, 3% in C. oleaginosus and 1.5% in Y. lipolytica. CONCLUSIONS: In this study, we provided the optimum C/N ratio and temperature for C. oleaginosus and Y. lipolytica by RSM. Additionally, we demonstrated that lipid accumulation of both oleaginous yeasts was significantly affected by the C/N ratio and temperature. Furthermore, we systematically analyzed the variation in fatty acids composition and proved that changing the C/N ratio and temperature steer the composition. We have further established these oleaginous yeasts as platforms for production of tailored fatty acids.

Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections.

INTRODUCTION: Necrotizing Soft Tissue Infections (NSTI) are severe infections with high mortality affecting a heterogeneous patient population. There is a need for a clinical decision support system which predicts outcomes and provides treatment recommendations early in the disease course. METHODS: To identify relevant clinical needs, interviews with eight medical professionals (surgeons, intensivists, general practitioner, emergency department physician) were conducted. This resulted in 24 unique questions. Mortality was selected as first endpoint to develop a machine learning (Random Forest) based prediction model. For this purpose, data from the prospective, international INFECT cohort (N = 409) was used. RESULTS: Applying a feature selection procedure based on an unsupervised algorithm (Boruta) to the  > 1000 variables available in INFECT, including baseline, and both NSTI specific and NSTI non-specific clinical data yielded sixteen predictive parameters available on or prior to the first day on the intensive care unit (ICU). Using these sixteen variables 30-day mortality could be accurately predicted (AUC = 0.91, 95% CI 0.88-0.96). Except for age, all variables were related to sepsis (e.g. lactate, urine production, systole). No NSTI-specific variables were identified. Predictions significantly outperformed the SOFA score(p < 0.001, AUC = 0.77, 95% CI 0.69-0.84) and exceeded but did not significantly differ from the SAPS II score (p = 0.07, AUC = 0.88, 95% CI 0.83-0.92). The developed model proved to be stable with AUC  > 0.8 in case of high rates of missing data (50% missing) or when only using very early (<1 h) available variables. CONCLUSIONS: This study shows that mortality can be accurately predicted using a machine learning model. It lays the foundation for a more extensive, multi-endpoint clinical decision support system in which ultimately other outcomes and clinical questions (risk for septic shock, AKI, causative microbe) will be included.