Propionic acid (propionate) is a commercially valuable carboxylic acid produced through microbial fermentation. Propionic acid is mainly used in the food industry but has recently found applications in the cosmetic, plastics and pharmaceutical industries. Propionate can be produced via various metabolic pathways, which can be classified into three major groups: fermentative pathways, biosynthetic pathways, and amino acid catabolic pathways. The current review provides an in-depth description of the major metabolic routes for propionate production from an energy optimization perspective. Biological propionate production is limited by high downstream purification costs which can be addressed if the target yield, productivity and titre can be achieved. Genome shuffling combined with high throughput omics and metabolic engineering is providing new opportunities, and biological propionate production is likely to enter the market in the not so distant future. In order to realise the full potential of metabolic engineering and heterologous expression, however, a greater understanding of metabolic capabilities of the native producers, the fittest producers, is required.

BackgroundPropionic acid is used primarily as a food preservative with smaller applications as a chemical building block for the production of many products including fabrics, cosmetics, drugs, and plastics. Biological production using propionibacteria would be competitive against chemical production through hydrocarboxylation of ethylene if native producers could be engineered to reach near-theoretical yield and good productivity. Unfortunately, engineering propionibacteria has proven very challenging. It has been suggested that activation of the sleeping beauty operon in Escherichia coli is sufficient to achieve propionic acid production. Optimising E. coli production should be much easier than engineering propionibacteria if tolerance issues can be addressed.ResultsPropionic acid is produced in E. coli via the sleeping beauty mutase operon under anaerobic conditions in rich medium via amino acid degradation. We observed that the sbm operon enhances amino acids degradation to propionic acid and allows E. coli to degrade isoleucine. However, we show here that the operon lacks an epimerase reaction that enables propionic acid production in minimal medium containing glucose as the sole carbon source. Production from glucose can be restored by engineering the system with a methylmalonyl-CoA epimerase from Propionibacterium acidipropionici (0.23 ± 0.02 mM). 1-Propanol production was also detected from the promiscuous activity of the native alcohol dehydrogenase (AdhE). We also show that aerobic conditions are favourable for propionic acid production. Finally, we increase titre 65 times using a combination of promoter engineering and process optimisation.ConclusionsThe native sbm operon encodes an incomplete pathway. Production of propionic acid from glucose as sole carbon source is possible when the pathway is complemented with a methylmalonyl-CoA epimerase. Although propionic acid via the restored succinate dissimilation pathway is considered a fermentative process, the engineered pathway was shown to be functional under anaerobic and aerobic conditions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-017-0735-4) contains supplementary material, which is available to authorized users.

Production of propionic acid by fermentation of propionibacteria has gained increasing attention in the past few years. However, biomanufacturing of propionic acid cannot compete with the current oxo-petrochemical synthesis process due to its well-established infrastructure, low oil prices and the high downstream purification costs of microbial production. Strain improvement to increase propionic acid yield is the best alternative to reduce downstream purification costs. The recent generation of genome-scale models for a number of Propionibacterium species facilitates the rational design of metabolic engineering strategies and provides a new opportunity to explore the metabolic potential of the Wood-Werkman cycle. Previous strategies for strain improvement have individually targeted acid tolerance, rate of propionate production or minimisation of by-products. Here we used the P. freudenreichii subsp. shermanii and the pan-Propionibacterium genome-scale metabolic models (GEMs) to simultaneously target these combined issues. This was achieved by focussing on strategies which yield higher energies and directly suppress acetate formation. Using P. freudenreichii subsp. shermanii, two strategies were assessed. The first tested the ability to manipulate the redox balance to favour propionate production by over-expressing the first two enzymes of the pentose-phosphate pathway (PPP), Zwf (glucose-6-phosphate 1-dehydrogenase) and Pgl (6-phosphogluconolactonase). Results showed a 4-fold increase in propionate to acetate ratio during the exponential growth phase. Secondly, the ability to enhance the energy yield from propionate production by over-expressing an ATP-dependent phosphoenolpyruvate carboxykinase (PEPCK) and sodium-pumping methylmalonyl-CoA decarboxylase (MMD) was tested, which extended the exponential growth phase. Together, these strategies demonstrate that in silico design strategies are predictive and can be used to reduce by-product formation in Propionibacterium. We also describe the benefit of carbon dioxide to propionibacteria growth, substrate conversion and propionate yield.

Native to propionibacteria, the Wood–Werkman cycle enables propionate production via succinate decarboxylation. Current limitations in engineering propionibacteria strains have redirected attention toward the heterologous production in model organisms. Here, we report the functional expression of the Wood–Werkman cycle in Escherichia coli to enable propionate and 1‐propanol production. The initial proof‐of‐concept attempt showed that the cycle can be used for production. However, production levels were low (0.17 mM). In silico optimization of the expression system by operon rearrangement and ribosomal‐binding site tuning improved performance by fivefold. Adaptive laboratory evolution further improved performance redirecting almost 30% of total carbon through the Wood–Werkman cycle, achieving propionate and propanol titers of 9 and 5 mM, respectively. Rational engineering to reduce the generation of byproducts showed that lactate (∆ldhA) and formate (∆pflB) knockout strains exhibit an improved propionate and 1‐propanol production, while the ethanol (∆adhE) knockout strain only showed improved propionate production.

Propionibacteria have been studied extensively since the early 1930s due to their relevance to industry and importance as human pathogens. Still, their unique metabolism is far from fully understood. This is partly due to their signature high GC content, which has previously hampered the acquisition of quality sequence data, the accurate annotation of the available genomes, and the functional characterization of genes. The recent completion of the genome sequences for several species has led researchers to reassess the taxonomical classification of the genus Propionibacterium, which has been divided into several new genres. Such data also enable a comparative genomic approach to annotation and provide a new opportunity to revisit our understanding of their metabolism. Using pan-genome analysis combined with the reconstruction of the first high-quality Propionibacterium genome-scale metabolic model and a pan-metabolic model of current and former members of the genus Propionibacterium, we demonstrate that despite sharing unique metabolic traits, these organisms have an unexpected diversity in central carbon metabolism and a hidden layer of metabolic complexity. This combined approach gave us new insights into the evolution of Propionibacterium metabolism and led us to propose a novel, putative ferredoxin-linked energy conservation strategy. The pan-genomic approach highlighted key differences in Propionibacterium metabolism that reflect adaptation to their environment. Results were mathematically captured in genome-scale metabolic reconstructions that can be used to further explore metabolism using metabolic modeling techniques. Overall, the data provide a platform to explore Propionibacterium metabolism and a tool for the rational design of strains.

Mitochondrial diseases are a group of inherited diseases with highly varied and complex clinical presentations. Here, we report four individuals, including two siblings, affected by a progressive mitochondrial encephalopathy with biallelic variants in the cardiolipin biosynthesis gene CRLS1. Three affected individuals had a similar infantile presentation comprising progressive encephalopathy, bull’s eye maculopathy, auditory neuropathy, diabetes insipidus, autonomic instability, cardiac defects and early death. The fourth affected individual presented with chronic encephalopathy with neurodevelopmental regression, congenital nystagmus with decreased vision, sensorineural hearing loss, failure to thrive and acquired microcephaly. Using patient-derived fibroblasts, we characterised cardiolipin synthase 1 (CRLS1) dysfunction that impaired mitochondrial morphology and biogenesis, providing functional evidence that the CRLS1 variants cause a mitochondrial phenotype. Lipid profiling in fibroblasts from two patients further confirmed the functional defect demonstrating reduced cardiolipin levels, altered acyl-chain composition and significantly increased levels of phosphatidylglycerol, the substrate of CRLS1. Proteomic profiling of patient cells and mouse Crls1 knockout cell lines identified both endoplasmic reticular and mitochondrial stress responses, and key features that distinguish between varying degrees of cardiolipin insufficiency. These findings support that deleterious variants in CRLS1 cause an autosomal recessive mitochondrial disease, presenting as a severe encephalopathy with multisystemic involvement. Furthermore, we identify key signatures in cardiolipin and proteome profiles across various degrees of cardiolipin loss, facilitating the use of omics technologies to guide a diagnosis for this mitochondrial disease.

Summary We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. Results We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible. Availability and implementation Our framework along with documentation is available on https://github.com/biosustain/multitfa. Supplementary information Supplementary data are available at Bioinformatics online.

Background: Propionibacteria have been studied extensively since the early 1930’s due to their relevance to industry and importance as human pathogens. Still, their unique metabolism is far from fully understood. This is partly due to their signature high GC content which has hampered the acquisition of quality sequence data, the accurate annotation of the few available genomes and the functional characterization of genes. The recent completion of the genome sequences for several species enable a comparative genomic approach to annotation and provides a new opportunity to revisit our understanding of their metabolism. Results: Using pan-genome analysis combined with the reconstruction of the first high-quality Propionibacterium genome-scale metabolic model and a pan-Propionibacterium metabolic model we demonstrate that Propionibacterium have an unexpected diversity in central carbon metabolism and a hidden layer of metabolic complexity. This combined approach gave us new insights into the evolution of Propionibacterium metabolism and led us to propose a novel, putative ferredoxin-linked energy conservation strategy. The pan-genomic approach highlighted key differences in Propionibacterium metabolism that reflect adaptation to their environment. Results were mathematically captured in genome-scale metabolic reconstructions which can be used to further explore metabolism using metabolic modelling techniques. Conclusions: Overall, the data provides a platform to explore Propionibacterium metabolism and provides a tool for the rational design of strains.