Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories

The integration of bio-catalysis and electrocatalysis to produce fuels and chemicals from carbon dioxide

The dependence on fossil fuels has caused excessive emissions of greenhouse gases (GHGs), leading to climate changes and global warming. Even though the expansion of electricity generation will enable a wider use of electric vehicles, biotechnology represents an attractive route for producing high-density liquid transportation fuels that can reduce GHG emissions from jets, long-haul trucks and ships. Furthermore, to achieve immediate alleviation of the current environmental situation, besides reducing carbon footprint it is urgent to develop technologies that transform atmospheric CO2 into fossil fuel replacements. The integration of bio-catalysis and electrocatalysis (bio-electrocatalysis) provides such a promising avenue to convert CO2 into fuels and chemicals with high-chain lengths. Following an overview of different mechanisms that can be used for CO2 fixation, we will discuss crucial factors for electrocatalysis with a special highlight on the improvement of electron-transfer kinetics, multi-dimensional electrocatalysts and their hybrids, electrolyser configurations, and the integration of electrocatalysis and bio-catalysis. Finally, we prospect key advantages and challenges of bio-electrocatalysis, and end with a discussion of future research directions

In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

Turnover numbers (kcat values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (kapp values) in bacteria, a large-scale estimation of kapp in eukaryotes is lacking. Here, we estimated kapp of the yeast Saccharomyces cerevisiae under diverse conditions. By comparing the maximum kapp across conditions with in vitro kcat we found a weak correlation in log scale of R2 = 0.28, which is lower than for Escherichia coli (R2 = 0.62). The weak correlation is caused by the fact that many in vitro kcat values were measured for enzymes obtained through heterologous expression. Removal of these enzymes improved the correlation to R2 = 0.41 but still not as good as for E. coli, suggesting considerable deviations between in vitro and in vivo enzyme activities in yeast. By parameterizing an enzyme-constrained metabolic model with our kapp dataset we observed better performance than the default model with in vitro kcat in predicting proteomics data, demonstrating the strength of using the dataset generated here

Mathematical modeling of proteome constraints within metabolism

Genome-scale metabolic models (GEMs) are widely used to predict phenotypes with the aid of constraint-based modeling. In order to improve the predictive power of these models, there have been many efforts on imposing biological constraints, among which proteome constraints are of particular interest. Here we describe the concept of proteome constraints and review proteome-constrained GEMs, as well as their advantages and applications. In addition, we discuss a key issue in the field, i.e., low coverage of enzyme-specific turnover rates, and subsequently provide a few solutions to solve it. We end with a discussion on the trade-off between model complexity and capability

Yeast has evolved to minimize protein resource cost for synthesizing amino acids

Proteins, as essential biomolecules, account for a large fraction of cell mass, and thus the synthesis of the complete set of proteins (i.e., the proteome) represents a substantial part of the cellular resource budget. Therefore, cells might be under selective pressures to optimize the resource costs for protein synthesis, particularly the biosynthesis of the 20 proteinogenic amino acids. Previous studies showed that less energetically costly amino acids are more abundant in the proteomes of bacteria that survive under energy-limited conditions, but the energy cost of synthesizing amino acids was reported to be weakly associated with the amino acid usage in Saccharomyces cerevisiae. Here we present a modeling framework to estimate the protein cost of synthesizing each amino acid (i.e., the protein mass required for supporting one unit of amino acid biosynthetic flux) and the glucose cost (i.e., the glucose consumed per amino acid synthesized). We show that the logarithms of the relative abundances of amino acids in S. cerevisiae\u27s proteome correlate well with the protein costs of synthesizing amino acids (Pearson\u27s r = 20.89), which is better than that with the glucose costs (Pearson\u27s r = 20.5). Therefore, we demonstrate that S. cerevisiae tends to minimize protein resource, rather than glucose or energy, for synthesizing amino acids

Finding directionality and gene-disease predictions in disease associations

Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders. Methods: The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases. Results: 880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology. Conclusions: The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications

Biobased production of alkanes and alkenes through metabolic engineering of microorganisms

Advancement in metabolic engineering of microorganisms has enabled bio-based production of a range of chemicals, and such engineered microorganism can be used for sustainable production leading to reduced carbon dioxide emission there. One area that has attained much interest is microbial hydrocarbon biosynthesis, and in particular, alkanes and alkenes are important high-value chemicals as they can be utilized for a broad range of industrial purposes as well as 'drop-in' biofuels. Some microorganisms have the ability to biosynthesize alkanes and alkenes naturally, but their production level is extremely low. Therefore, there have been various attempts to recruit other microbial cell factories for production of alkanes and alkenes by applying metabolic engineering strategies. Here we review different pathways and involved enzymes for alkane and alkene production and discuss bottlenecks and possible solutions to accomplish industrial level production of these chemicals by microbial fermentation

Innovation trends in industrial biotechnology

Microbial fermentations are used for the sustainable production of a range of products. Due to increasing trends in the food sector toward plant-based foods and meat and dairy product substitutes, microbial fermentation will have an increasing role in this sector, as it will enable a sustainable and scalable production of valuable foods and food ingredients. Microbial fermentation will also be used to advance and expand the production of sustainable chemicals and natural products. Much of this market expansion will come from new start-ups that translate academic research into novel processes and products using state-of-the art technologies. Here, we discuss the trends in innovation and technology and provide recommendations for how to successfully start and grow companies in industrial biotechnology