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1. Metabolic rewiring and biomass redistribution enable optimized mixotrophic growth in Chlamydomonas

   https://doi.org/10.1073/pnas.2522572123  

   Koley, Somnath; Foley, Kevin; Perrine, Zoee; Morley, Stewart A; Kambhampati, Shrikaar; Gomez, Olivia; Chu, Kevin L; Chou, Yi-Hsiang; Wei, Michael; Tzeng, Shin-Cheng; et al (January 2026, Proceedings of the National Academy of Sciences)

   Aquatic photosynthetic systems account for approximately one-half of all global carbon assimilation and could be a significant source of renewable fuels and feedstocks. However, rapid growth and biomass production in algae have not always translated into high product yields, partly because central metabolism is context specific, with metabolic fluxes being influenced by nutrient conditions and other environmental factors. In the green microalgaChlamydomonas reinhardtii(Chlamydomonas), mixotrophic cultures (acetate + light) grow far faster than phototrophic (light only) or heterotrophic (acetate + dark) cultures, even though acetate partially suppresses photosynthesis. Here, an isotopic dilution strategy with unlabeled acetate was combined with¹³CO₂transient labeling to perform isotopically nonstationary metabolic flux analysis (INST-MFA) and to directly compare autotrophic and mixotrophic metabolism in Chlamydomonas supported by data from transcriptomics, proteomics, and metabolomics. INST-MFA indicated that acetate induces a synergistic rewiring of metabolism, conserving carbon by using the glyoxylate cycle and suppressing gluconeogenesis, the latter of which was discordant with omics results and prior models. Additionally, our data provide a plausible rationale for the well-known suppression of photosynthesis by acetate. We propose that reduced total protein content in mixotrophic versus phototrophic cells, much of which is attributed to reduced levels of photosynthetic proteins, decreases the costly metabolic burden of protein synthesis and represents a growth rate optimization strategy. 

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2. SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

   https://doi.org/10.1038/s42003-024-05844-z  

   Kambhampati, Shrikaar; Hubbard, Allen H.; Koley, Somnath; Gomez, Javier D.; Marsolais, Frédéric; Evans, Bradley S.; Young, Jamey D.; Allen, Doug K. (December 2024, Communications Biology)

   Abstract The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy ofSIMPELis demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired withSIMPELand isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example,SIMPELwas paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package,SIMPELextends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux. 

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3. Model validation and selection in metabolic flux analysis and flux balance analysis

   https://doi.org/10.1002/btpr.3413  

   Kaste, Joshua_A M; Shachar‐Hill, Yair (January 2024, Biotechnology Progress)

   Abstract 13C‐Metabolic Flux Analysis (13C‐MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint‐based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state‐of‐the‐art in constraint‐based metabolic model validation and model selection. Applications and limitations of the χ²‐test of goodness‐of‐fit, the most widely used quantitative validation and selection approach in 13C‐MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C‐MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint‐based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology. 

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4. Overexpression of peroxisome proliferator-activated receptor co-activator-1⍺ (PGC-1⍺) in Chinese hamster ovary cells increases oxidative metabolism and IgG productivity

   https://doi.org/10.1016/j.ymben.2023.07.005  

   Sacco, Sarah A.; McAtee Pereira, Allison G.; Trenary, Irina; Smith, Kevin D.; Betenbaugh, Michael J.; Young, Jamey D. (September 2023, Metabolic Engineering)

   Chinese hamster ovary (CHO) cells are used extensively to produce protein therapeutics, such as monoclonal antibodies (mAbs), in the biopharmaceutical industry. MAbs are large proteins that are energetically demanding to synthesize and secrete; therefore, high-producing CHO cell lines that are engineered for maximum metabolic efficiency are needed to meet increasing demands for mAb production. Previous studies have identified that high-producing cell lines possess a distinct metabolic phenotype when compared to low-producing cell lines. In particular, it was found that high mAb production is correlated to lactate consumption and elevated TCA cycle flux. We hypothesized that enhancing flux through the mitochondrial TCA cycle and oxidative phosphorylation would lead to increased mAb productivities and final titers. To test this hypothesis, we overexpressed peroxisome proliferator-activated receptor 𝛾 co-activator-1⍺ (PGC-1⍺), a gene that promotes mitochondrial metabolism, in an IgG-producing parental CHO cell line. Stable cell pools overexpressing PGC-1⍺ exhibited increased oxygen consumption, indicating increased mitochondrial metabolism, as well as increased mAb specific productivity compared to the parental line. We also performed 13C metabolic flux analysis (MFA) to quantify how PGC-1⍺ overexpression alters intracellular metabolic fluxes, revealing not only increased TCA cycle flux, but global upregulation of cellular metabolic activity. This study demonstrates the potential of rationally engineering the metabolism of industrial cell lines to improve overall mAb productivity and to increase the abundance of high-producing clones in stable cell pools. 

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5. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

   https://doi.org/10.1093/bioinformatics/btad186  

   Kaste, Joshua A; Shachar-Hill, Yair (May 2023, Bioinformatics)

   Birol, Inanc (Ed.)

   Abstract Motivation The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Results Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant. Availability and implementation Code and data generated as part of this study are available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights. 

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