Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins.

Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.

HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer.

BACKGROUND: Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS: In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS: The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.

Heparin mimetics as potential intervention for COVID-19 and their bio-manufacturing.

The COVID-19 pandemic has caused severe health problems worldwide and unprecedented decimation of the global economy. Moreover, after more than 2 years, many populations are still under pressure of infection. Thus, a broader perspective in developing antiviral strategies is still of great importance. Inspired by the observed multiple benefits of heparin in the treatment of thrombosis, the potential of low molecular weight heparin (LMWH) for the treatment of COVID-19 have been explored. Clinical applications found that LMWH decreased the level of inflammatory cytokines in COVID-19 patients, accordingly reducing lethality. Furthermore, several in vitro studies have demonstrated the important roles of heparan sulfate in SARS-CoV-2 infection and the inhibitory effects of heparin and heparin mimetics in viral infection. These clinical observations and designed studies argue for the potential to develop heparin mimetics as anti-SARS-CoV-2 drug candidates. In this review, we summarize the properties of heparin as an anticoagulant and the pharmaceutical possibilities for the treatment of virus infection, focusing on the perspectives of developing heparin mimetics via chemical synthesis, chemoenzymatic synthesis, and bioengineered production by microbial cell factories. The ultimate goal is to pave the eminent need for exploring novel compounds to treat coronavirus infection-caused diseases.

A Pan-Draft Metabolic Model Reflects Evolutionary Diversity across 332 Yeast Species.

Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.0. We found that draft GEMs could reflect the difference in yeast metabolic potentials, and therefore, could be utilized to probe the evolutionary trend of metabolism among 332 yeast species. We created a pan-draft metabolic model to account for the metabolic capacity of every sequenced yeast species by merging all draft GEMs. Further analysis showed that the pan-reactome of yeast has a "closed" property, which confirmed the great conservatism that exists in yeast metabolic evolution. Lastly, the quantitative correlations among trait similarity, evolutionary distances, genotype, and model similarity were thoroughly investigated. The results suggest that the evolutionary distance and genotype, to some extent, determine model similarity, but not trait similarity, indicating that multiple mechanisms shape yeast trait evolution. A large-scale reconstruction and integrative analysis of yeast draft GEMs would be a valuable resource to probe the evolutionary mechanism behind yeast trait variety and to further refine the existing yeast species-specific GEMs for the community.

Multiscale models quantifying yeast physiology: towards a whole-cell model.

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger.

BACKGROUND: Nicotinamide adenine dinucleotide phosphate (NADPH) is an important cofactor ensuring intracellular redox balance, anabolism and cell growth in all living systems. Our recent multi-omics analyses of glucoamylase (GlaA) biosynthesis in the filamentous fungal cell factory Aspergillus niger indicated that low availability of NADPH might be a limiting factor for GlaA overproduction. RESULTS: We thus employed the Design-Build-Test-Learn cycle for metabolic engineering to identify and prioritize effective cofactor engineering strategies for GlaA overproduction. Based on available metabolomics and 13C metabolic flux analysis data, we individually overexpressed seven predicted genes encoding NADPH generation enzymes under the control of the Tet-on gene switch in two A. niger recipient strains, one carrying a single and one carrying seven glaA gene copies, respectively, to test their individual effects on GlaA and total protein overproduction. Both strains were selected to understand if a strong pull towards glaA biosynthesis (seven gene copies) mandates a higher NADPH supply compared to the native condition (one gene copy). Detailed analysis of all 14 strains cultivated in shake flask cultures uncovered that overexpression of the gsdA gene (glucose 6-phosphate dehydrogenase), gndA gene (6-phosphogluconate dehydrogenase) and maeA gene (NADP-dependent malic enzyme) supported GlaA production on a subtle (10%) but significant level in the background strain carrying seven glaA gene copies. We thus performed maltose-limited chemostat cultures combining metabolome analysis for these three isolates to characterize metabolic-level fluctuations caused by cofactor engineering. In these cultures, overexpression of either the gndA or maeA gene increased the intracellular NADPH pool by 45% and 66%, and the yield of GlaA by 65% and 30%, respectively. In contrast, overexpression of the gsdA gene had a negative effect on both total protein and glucoamylase production. CONCLUSIONS: This data suggests for the first time that increased NADPH availability can indeed underpin protein and especially GlaA production in strains where a strong pull towards GlaA biosynthesis exists. This data also indicates that the highest impact on GlaA production can be engineered on a genetic level by increasing the flux through the pentose phosphate pathway (gndA gene) followed by engineering the flux through the reverse TCA cycle (maeA gene). We thus propose that NADPH cofactor engineering is indeed a valid strategy for metabolic engineering of A. niger to improve GlaA production, a strategy which is certainly also applicable to the rational design of other microbial cell factories.

Author Correction: A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Multi-scale data-driven engineering for biosynthetic titer improvement.

Industrial biosynthesis is a very complex process which depends on a range of different factors, from intracellular genes and metabolites, to extracellular culturing conditions and bioreactor engineering. The identification of species that improve the titer of some reaction is akin to the task of finding a needle in a haystack. This review aims to summarize state-of-the-art biosynthesis titer improvement on different scales separately, particularly regarding the advancement of metabolic pathway rewiring and data-driven process optimization and control. By integrating multi-scale data and establishing a mathematical replica of a real biosynthesis, more refined quantitative insights can be gained for achieving a higher titer than ever.

Improving cytosolic aspartate biosynthesis increases glucoamylase production in Aspergillus niger under oxygen limitation.

BACKGROUND: Glucoamylase is one of the most industrially applied enzymes, produced by Aspergillus species, like Aspergillus niger. Compared to the traditional ways of process optimization, the metabolic engineering strategies to improve glucoamylase production are relatively scarce. RESULTS: In the previous study combined multi-omics integrative analysis and amino acid supplementation experiment, we predicted four amino acids (alanine, glutamate, glycine and aspartate) as the limited precursors for glucoamylase production in A. niger. To further verify this, five mutants namely OE-ala, OE-glu, OE-gly, OE-asp1 and OE-asp2, derived from the parental strain A. niger CBS 513.88, were constructed respectively for the overexpression of five genes responsible for the biosynthesis of the four kinds of amino acids (An11g02620, An04g00990, An05g00410, An04g06380 and An16g05570). Real-time quantitative PCR revealed that all these genes were successfully overexpressed at the mRNA level while the five mutants exhibited different performance in glucoamylase production in shake flask cultivation. Notably, the results demonstrated that mutant OE-asp2 which was constructed for reinforcing cytosolic aspartate synthetic pathway, exhibited significantly increased glucoamylase activity by 23.5% and 60.3% compared to CBS 513.88 in the cultivation of shake flask and the 5 L fermentor, respectively. Compared to A. niger CBS 513.88, mutant OE-asp2 has a higher intracellular amino acid pool, in particular, alanine, leucine, glycine and glutamine, while the pool of glutamate was decreased. CONCLUSION: Our study combines the target prediction from multi-omics analysis with the experimental validation and proves the possibility of increasing glucoamylase production by enhancing limited amino acid biosynthesis. In short, this systematically conducted study will surely deepen the understanding of resources allocation in cell factory and provide new strategies for the rational design of enzyme production strains.

Publisher Correction: Harnessing the intracellular triacylglycerols for titer improvement of polyketides in Streptomyces.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Harnessing the intracellular triacylglycerols for titer improvement of polyketides in Streptomyces.

Pharmaceutically important polyketides such as avermectin are mainly produced as secondary metabolites during the stationary phase of growth of Streptomyces species in fermenters. The source of intracellular metabolites that are funneled into polyketide biosynthesis has proven elusive. We applied multi-omics to reveal that intracellular triacylglycerols (TAGs), which accumulates in primary metabolism, are degraded during stationary phase. This process could channel carbon flux from both intracellular TAGs and extracellular substrates into polyketide biosynthesis. We devised a strategy named 'dynamic degradation of TAG' (ddTAG) to mobilize the TAG pool and increase polyketide biosynthesis. Using ddTAG we increased the titers of actinorhodin, jadomycin B, oxytetracycline and avermectin B1a in Streptomyces coelicolor, Streptomyces venezuelae, Streptomyces rimosus and Streptomyces avermitilis. Application of ddTAG increased the titer of avermectin B1a by 50% to 9.31 g l-1 in a 180-m3 industrial-scale fermentation, which is the highest titer ever reported. Our strategy could improve polyketide titers for pharmaceutical production.

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae--an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition.

Oxygen limitation is regarded as a useful strategy to improve enzyme production by mycelial fungus like Aspergillus niger. However, the intracellular metabolic response of A. niger to oxygen limitation is still obscure. To address this, the metabolism of A. niger was studied using multi-omics integrated analysis based on the latest GEMs (genome-scale metabolic model), including metabolomics, fluxomics and transcriptomics. Upon sharp reduction of the oxygen supply, A. niger metabolism shifted to higher redox level status, as well as lower energy supply, down-regulation of genes for fatty acid synthesis and a rapid decrease of the specific growth rate. The gene expression of the glyoxylate bypass was activated, which was consistent with flux analysis using the A. niger GEMs iHL1210. The increasing flux of the glyoxylate bypass was assumed to reduce the NADH formation from TCA cycle and benefit maintenance of the cellular redox balance under hypoxic conditions. In addition, the relative fluxes of the EMP pathway were increased, which possibly relieved the energy demand for cell metabolism. The above multi-omics integrative analysis provided new insights on metabolic regulatory mechanisms of A. niger associated with enzyme production under oxygen-limited condition, which will benefit systematic design and optimization of the A. niger microbial cell factory.

Integrated analysis of the yeast NADPH-regulator Stb5 reveals distinct differences in NADPH requirements and regulation in different states of yeast metabolism.

The Saccharomyces cerevisiae transcription factor (TF) Stb5 is known to be involved in regulating NADPH generation. We explored its role by combining DNA binding studies with transcriptome analysis at four environmental conditions that were selected to cover a range of different metabolic states. Using ChIP-exo, DNA binding targets of Stb5 were found to confirm many previously proposed binding targets, in particular genes encoding enzymes involved in NADPH generation and the pentose-phosphate (PP) pathway. Transcriptome analysis of an STB5 deletion strain revealed transcriptional changes in direct regulation targets of Stb5, including several PP pathway genes as well as additional novel regulatory targets, but interestingly not including the proposed PP pathway flux controlling enzyme Zwf1. Consistently, NADPH levels were found to decrease significantly with STB5 deletion in cultures with aerobic, glucose metabolism. We also found reduced growth for the STB5 deletion strain in similar conditions as those with reduced NADPH levels, supporting a role for Stb5 in NADPH generation through the PP pathway. We finally explored the flux distribution by genome scale modelling simulations and found a decreased flux in both NADPH generating as well as consuming reactions in the STB5 deletion strain.

Comprehensive reconstruction and evaluation of Pichia pastoris genome-scale metabolic model that accounts for 1243 ORFs.

BACKGROUND: Pichia pastoris is one of the most important cell factories for production of industrial enzymes and heterogenous proteins. The genome-scale metabolic model of high quality is crucial for comprehensive understanding of the P. pastoris metabolism. METHODS: In this paper, we upgraded P. pastoris genome-scale metabolic model based on the combination of latest genome annotations and literatures. Then the performance of the new model was evaluated using the Cobra Toolbox v2.0. RESULTS: Compared with the recently published model iMT1026, the reaction number in the new model iRY1243 was increased from 2035 to 2407 and the metabolite number was increased from 1018 to 1094. Accordingly, the unique ORF number was increased from 1026 to 1243. To improve the metabolic functions of P. pastoris genome-scale metabolic model, the biosynthesis pathways of vitamins and cofactors were carefully added. iRY1243 showed good performances when predicting the growth capability on most of the reported carbon and nitrogen sources, the metabolic flux distribution with glucose as a sole carbon source, the essential and partially essential genes, and the effects of gene deletion or overexpression on cell growth and S-adenosyl-l-methionine production. CONCLUSION: iRY1243 is an upgraded P. pastoris genome-scale metabolic model with significant improvements in the metabolic coverage and prediction ability, and thus it will be a potential platform for further systematic investigation of P. pastoris metabolism.

Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs.

Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, 13 C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from 13 C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc.

[Progress in omics research of Aspergillus niger].

Aspergillus niger, as an important industrial fermentation strain, is widely applied in the production of organic acids and industrial enzymes. With the development of diverse omics technologies, the data of genome, transcriptome, proteome and metabolome of A. niger are increasing continuously, which declared the coming era of big data for the research in fermentation process of A. niger. The data analysis from single omics and the comparison of multi-omics, to the integrations of multi-omics based on the genome-scale metabolic network model largely extends the intensive and systematic understanding of the efficient production mechanism of A. niger. It also provides possibilities for the reasonable global optimization of strain performance by genetic modification and process regulation. We reviewed and summarized progress in omics research of A. niger, and proposed the development direction of omics research on this cell factory.

[Construction and application of black-box model for glucoamylase production by Aspergillus niger].

Carbon-limited continuous culture was used to study the relationship between the growth of Aspergillus niger and the production of glucoamylase. The result showed that when the specific growth rate was lower than 0.068 h(-1), the production of glucoamylase was growth-associated, when the specific growth rate was higher than 0.068 h(-1), the production of glucoamylase was not growth-associated. Based on the result of continuous culture, the Monod dynamics model of glucose consumption of A. niger was constructed, Combining Herbert-Pirt equation of glucose and oxygen consumption with Luedeking-Piret equation of enzyme production, the black-box model of Aspergillus niger for enzyme production was established. The exponential fed-batch culture was designed to control the specific growth rate at 0.05 h(-1) by using this model and the highest yield for glucoamylase production by A. niger reached 0.127 g glucoamylase/g glucose. The black-box model constructed in this study successfully described the glucoamylase production by A. niger and the result of the model fitted the measured value well. The black-box model could guide the design and optimization of glucoamylase production by A. niger.

Integrated isotope-assisted metabolomics and (13)C metabolic flux analysis reveals metabolic flux redistribution for high glucoamylase production by Aspergillus niger.

BACKGROUND: Aspergillus niger is widely used for enzyme production and achievement of high enzyme production depends on the comprehensive understanding of cell's metabolic regulation mechanisms. RESULTS: In this paper, we investigate the metabolic differences and regulation mechanisms between a high glucoamylase-producing strain A. niger DS03043 and its wild-type parent strain A. niger CBS513.88 via an integrated isotope-assisted metabolomics and (13)C metabolic flux analysis approach. We found that A. niger DS03043 had higher cell growth, glucose uptake, and glucoamylase production rates but lower oxalic acid and citric acid secretion rates. In response to above phenotype changes, A. niger DS03043 was characterized by an increased carbon flux directed to the oxidative pentose phosphate pathway in contrast to reduced flux through TCA cycle, which were confirmed by consistent changes in pool sizes of metabolites. A higher ratio of ATP over AMP in the high producing strain might contribute to the increase in the PP pathway flux as glucosephosphate isomerase was inhibited at higher ATP concentrations. A. niger CBS513.88, however, was in a higher redox state due to the imbalance of NADH regeneration and consumption, resulting in the secretion of oxalic acid and citric acid, as well as the accumulation of intracellular OAA and PEP, which may in turn result in the decrease in the glucose uptake rate. CONCLUSIONS: The application of integrated metabolomics and (13)C metabolic flux analysis highlights the regulation mechanisms of energy and redox metabolism on flux redistribution in A. niger. Graphical abstract An integrated isotope-assisted metabolomics and (13)C metabolic flux analysis was was firstly systematically performed in A. niger. In response to enzyme production, the metabolic flux in A. niger DS03043 (high-producing) was redistributed, characterized by an increased carbon flux directed to the oxidative pentose phosphate pathway as well as an increased pool size of pentose. The consistency in (13)C metabolic flux analysis and metabolites quantification indicated that an imbalance of NADH formation and consumption led to the accumulation and secretion of organic acids in A. niger CBS513.88 (wild-type).