{"entity": "journal", "iuid": "960672c6b68f4048a1f9d54a37e6166d", "timestamp": "2026-07-13T10:24:17.140Z", "links": {"self": {"href": "https://publications.scilifelab.se/journal/Metab.%20Eng..json"}, "display": {"href": "https://publications.scilifelab.se/journal/Metab.%20Eng."}}, "title": "Metab. Eng.", "issn": "1096-7184", "issn-l": "1096-7176", "publications_count": 6, "publications": [{"entity": "publication", "iuid": "068c0f554e1b4793a4400bc5e1d2dd6b", "links": {"self": {"href": "https://publications.scilifelab.se/publication/068c0f554e1b4793a4400bc5e1d2dd6b.json"}, "display": {"href": "https://publications.scilifelab.se/publication/068c0f554e1b4793a4400bc5e1d2dd6b"}}, "title": "Machine learning predicts system-wide metabolic flux control in cyanobacteria.", "authors": [{"family": "Kugler", "given": "Amit", "initials": "A"}, {"family": "Stensj\u00f6", "given": "Karin", "initials": "K"}], "type": "journal article", "published": "2024-03-00", "journal": {"title": "Metab. Eng.", "issn": "1096-7184", "volume": "82", "pages": "171-182", "issn-l": "1096-7176"}, "abstract": "Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin-Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.", "doi": "10.1016/j.ymben.2024.02.013", "pmid": "38395194", "labels": {"Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pii", "key": "S1096-7176(24)00029-6"}], "notes": [], "created": "2024-11-25T10:14:53.176Z", "modified": "2025-02-28T14:13:34.842Z"}, {"entity": "publication", "iuid": "4ca646911b414dd48a590dbfe40c64ca", "links": {"self": {"href": "https://publications.scilifelab.se/publication/4ca646911b414dd48a590dbfe40c64ca.json"}, "display": {"href": "https://publications.scilifelab.se/publication/4ca646911b414dd48a590dbfe40c64ca"}}, "title": "Fine-tuning of p-coumaric acid synthesis to increase (2S)-naringenin production in yeast.", "authors": [{"family": "Mao", "given": "Jiwei", "initials": "J"}, {"family": "Mohedano", "given": "Marta Tous", "initials": "MT"}, {"family": "Fu", "given": "Jing", "initials": "J"}, {"family": "Li", "given": "Xiaowei", "initials": "X"}, {"family": "Liu", "given": "Quanli", "initials": "Q"}, {"family": "Nielsen", "given": "Jens", "initials": "J"}, {"family": "Siewers", "given": "Verena", "initials": "V"}, {"family": "Chen", "given": "Yun", "initials": "Y"}], "type": "journal article", "published": "2023-09-00", "journal": {"title": "Metab. Eng.", "issn": "1096-7184", "volume": "79", "pages": "192-202", "issn-l": "1096-7176"}, "abstract": "(2S)-Naringenin is a key precursor for biosynthesis of various high-value flavonoids and possesses a variety of nutritional and pharmaceutical properties on human health. Systematic optimization approaches have been employed to improve (2S)-naringenin production in different microbial hosts. However, very few studies have focused on the spatiotemporal distribution of (2S)-naringenin and the related pathway intermediate p-coumaric acid, which is an important factor for efficient production. Here, we first optimized the (2S)-naringenin biosynthetic pathway by alleviating the bottleneck downstream of p-coumaric acid and increasing malonyl-CoA supply, which improved (2S)-naringenin production but significant accumulation of p-coumaric acid still existed extracellularly. We thus established a dual dynamic control system through combining a malonyl-CoA biosensor regulator and an RNAi strategy, to autonomously control the synthesis of p-coumaric acid with the supply of malonyl-CoA. Furthermore, screening potential transporters led to identification of Pdr12 for improved (2S)-naringenin production and reduced accumulation of p-coumaric acid. Finally, a titer of 2.05 g/L (2S)-naringenin with negligible accumulation of p-coumaric acid was achieved in a fed batch fermentation. Our work highlights the importance of systematic control of pathway intermediates for efficient microbial production of plant natural products.", "doi": "10.1016/j.ymben.2023.08.003", "pmid": "37611820", "labels": {"Chalmers Mass Spectrometry Infrastructure": "Service"}, "xrefs": [{"db": "pii", "key": "S1096-7176(23)00117-9"}], "notes": [], "created": "2024-01-03T13:11:34.579Z", "modified": "2024-01-03T13:11:34.583Z"}, {"entity": "publication", "iuid": "e3b2ca7aed6d470d88d8d03cebd458bf", "links": {"self": {"href": "https://publications.scilifelab.se/publication/e3b2ca7aed6d470d88d8d03cebd458bf.json"}, "display": {"href": "https://publications.scilifelab.se/publication/e3b2ca7aed6d470d88d8d03cebd458bf"}}, "title": "Suppressors of amyloid-\u03b2 toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism.", "authors": [{"family": "Chen", "given": "Xin", "initials": "X"}, {"family": "Li", "given": "Xiaowei", "initials": "X"}, {"family": "Ji", "given": "Boyang", "initials": "B"}, {"family": "Wang", "given": "Yanyan", "initials": "Y"}, {"family": "Ishchuk", "given": "Olena P", "initials": "OP"}, {"family": "Vorontsov", "given": "Egor", "initials": "E"}, {"family": "Petranovic", "given": "Dina", "initials": "D"}, {"family": "Siewers", "given": "Verena", "initials": "V"}, {"family": "Engqvist", "given": "Martin K M", "initials": "MKM"}], "type": "journal article", "published": "2022-07-00", "journal": {"title": "Metab. Eng.", "issn": "1096-7184", "volume": "72", "pages": "311-324", "issn-l": "1096-7176"}, "abstract": "High-level production of recombinant proteins in industrial microorganisms is often limited by the formation of misfolded proteins or protein aggregates, which consequently induce cellular stress responses. We hypothesized that in a yeast Alzheimer's disease (AD) model overexpression of amyloid-\u03b2 peptides (A\u03b242), one of the main peptides relevant for AD pathologies, induces similar phenotypes of cellular stress. Using this humanized AD model, we previously identified suppressors of A\u03b242 cytotoxicity. Here we hypothesize that these suppressors could be used as metabolic engineering targets to alleviate cellular stress and improve recombinant protein production in the yeast Saccharomyces cerevisiae. Forty-six candidate genes were individually deleted and twenty were individually overexpressed. The positive targets that increased recombinant \u03b1-amylase production were further combined leading to an 18.7-fold increased recombinant protein production. These target genes are involved in multiple cellular networks including RNA processing, transcription, ER-mitochondrial complex, and protein unfolding. By using transcriptomics and proteomics analyses, combined with reverse metabolic engineering, we showed that reduced oxidative stress, increased membrane lipid biosynthesis and repressed arginine and sulfur amino acid biosynthesis are significant pathways for increased recombinant protein production. Our findings provide new insights towards developing synthetic yeast cell factories for biosynthesis of valuable proteins.", "doi": "10.1016/j.ymben.2022.04.005", "pmid": "35508267", "labels": {"Glycoproteomics and MS Proteomics": "Service"}, "xrefs": [{"db": "pii", "key": "S1096-7176(22)00064-7"}], "notes": [], "created": "2023-03-07T14:35:13.196Z", "modified": "2024-01-16T13:46:28.785Z"}, {"entity": "publication", "iuid": "636d576384944722819703a146cf1555", "links": {"self": {"href": "https://publications.scilifelab.se/publication/636d576384944722819703a146cf1555.json"}, "display": {"href": "https://publications.scilifelab.se/publication/636d576384944722819703a146cf1555"}}, "title": "Genome scale metabolic modeling of cancer.", "authors": [{"family": "Nilsson", "given": "Avlant", "initials": "A"}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}], "type": "journal article", "published": "2017-09-00", "journal": {"volume": "43", "issn": "1096-7184", "issue": "Pt B", "pages": "103-112", "title": "Metab. Eng.", "issn-l": "1096-7176"}, "abstract": "Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future.", "doi": "10.1016/j.ymben.2016.10.022", "pmid": "27825806", "labels": {"Systems Biology": "Technology development", "Bioinformatics Support, Infrastructure and Training": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "S1096-7176(16)30212-9"}], "notes": [], "created": "2017-12-01T10:32:36.883Z", "modified": "2021-07-05T13:05:37.658Z"}, {"entity": "publication", "iuid": "15650b102c574ba3b445c90eef4e6c11", "links": {"self": {"href": "https://publications.scilifelab.se/publication/15650b102c574ba3b445c90eef4e6c11.json"}, "display": {"href": "https://publications.scilifelab.se/publication/15650b102c574ba3b445c90eef4e6c11"}}, "title": "Evolutionary engineering reveals divergent paths when yeast is adapted to different acidic environments.", "authors": [{"family": "Fletcher", "given": "Eugene", "initials": "E"}, {"family": "Feizi", "given": "Amir", "initials": "A"}, {"family": "Bisschops", "given": "Markus M M", "initials": "MMM"}, {"family": "Hallstr\u00f6m", "given": "Bj\u00f6rn M", "initials": "BM"}, {"family": "Khoomrung", "given": "Sakda", "initials": "S"}, {"family": "Siewers", "given": "Verena", "initials": "V"}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}], "type": "journal article", "published": "2017-01-00", "journal": {"title": "Metab. Eng.", "issn": "1096-7184", "issn-l": "1096-7176", "volume": "39", "issue": null, "pages": "19-28"}, "abstract": "Tolerance of yeast to acid stress is important for many industrial processes including organic acid production. Therefore, elucidating the molecular basis of long term adaptation to acidic environments will be beneficial for engineering production strains to thrive under such harsh conditions. Previous studies using gene expression analysis have suggested that both organic and inorganic acids display similar responses during short term exposure to acidic conditions. However, biological mechanisms that will lead to long term adaptation of yeast to acidic conditions remains unknown and whether these mechanisms will be similar for tolerance to both organic and inorganic acids is yet to be explored. We therefore evolved Saccharomyces cerevisiae to acquire tolerance to HCl (inorganic acid) and to 0.3M L-lactic acid (organic acid) at pH 2.8 and then isolated several low pH tolerant strains. Whole genome sequencing and RNA-seq analysis of the evolved strains revealed different sets of genome alterations suggesting a divergence in adaptation to these two acids. An altered sterol composition and impaired iron uptake contributed to HCl tolerance whereas the formation of a multicellular morphology and rapid lactate degradation was crucial for tolerance to high concentrations of lactic acid. Our findings highlight the contribution of both the selection pressure and nature of the acid as a driver for directing the evolutionary path towards tolerance to low pH. The choice of carbon source was also an important factor in the evolutionary process since cells evolved on two different carbon sources (raffinose and glucose) generated a different set of mutations in response to the presence of lactic acid. Therefore, different strategies are required for a rational design of low pH tolerant strains depending on the acid of interest.", "doi": "10.1016/j.ymben.2016.10.010", "pmid": "27815194", "labels": {"National Genomics Infrastructure": "Service", "NGI Stockholm (Genomics Applications)": "Service", "NGI Stockholm (Genomics Production)": "Service", "NGI Uppsala (Uppsala Genome Center)": "Service", "Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pii", "key": "S1096-7176(16)30175-6"}], "notes": [], "created": "2017-11-03T16:22:27.564Z", "modified": "2024-01-16T13:48:48.775Z"}, {"entity": "publication", "iuid": "1c365767e47244718692e25210b4b6a0", "links": {"self": {"href": "https://publications.scilifelab.se/publication/1c365767e47244718692e25210b4b6a0.json"}, "display": {"href": "https://publications.scilifelab.se/publication/1c365767e47244718692e25210b4b6a0"}}, "title": "Evolution reveals a glutathione-dependent mechanism of 3-hydroxypropionic acid tolerance.", "authors": [{"family": "Kildegaard", "given": "Kanchana R", "initials": "KR"}, {"family": "Hallstr\u00f6m", "given": "Bj\u00f6rn M", "initials": "BM"}, {"family": "Blicher", "given": "Thomas H", "initials": "TH"}, {"family": "Sonnenschein", "given": "Nikolaus", "initials": "N"}, {"family": "Jensen", "given": "Niels B", "initials": "NB"}, {"family": "Sherstyk", "given": "Svetlana", "initials": "S"}, {"family": "Harrison", "given": "Scott J", "initials": "SJ"}, {"family": "Maury", "given": "J\u00e9r\u00f4me", "initials": "J"}, {"family": "Herrg\u00e5rd", "given": "Markus J", "initials": "MJ"}, {"family": "Juncker", "given": "Agnieszka S", "initials": "AS"}, {"family": "Forster", "given": "Jochen", "initials": "J"}, {"family": "Nielsen", "given": "Jens", "initials": "J"}, {"family": "Borodina", "given": "Irina", "initials": "I"}], "type": "journal article", "published": "2014-11-00", "journal": {"volume": "26", "issn": "1096-7184", "issue": null, "pages": "57-66", "title": "Metab. Eng.", "issn-l": "1096-7176"}, "abstract": "Biologically produced 3-hydroxypropionic acid (3 HP) is a potential source for sustainable acrylates and can also find direct use as monomer in the production of biodegradable polymers. For industrial-scale production there is a need for robust cell factories tolerant to high concentration of 3 HP, preferably at low pH. Through adaptive laboratory evolution we selected S. cerevisiae strains with improved tolerance to 3 HP at pH 3.5. Genome sequencing followed by functional analysis identified the causal mutation in SFA1 gene encoding S-(hydroxymethyl)glutathione dehydrogenase. Based on our findings, we propose that 3 HP toxicity is mediated by 3-hydroxypropionic aldehyde (reuterin) and that glutathione-dependent reactions are used for reuterin detoxification. The identified molecular response to 3 HP and reuterin may well be a general mechanism for handling resistance to organic acid and aldehydes by living cells.", "doi": "10.1016/j.ymben.2014.09.004", "pmid": "25263954", "labels": {"National Genomics Infrastructure": null, "NGI Stockholm (Genomics Applications)": null, "NGI Stockholm (Genomics Production)": null}, "xrefs": [{"db": "pii", "key": "S1096-7176(14)00118-9"}], "notes": [], "created": "2017-05-04T14:58:43.697Z", "modified": "2020-01-21T13:56:00.973Z"}], "created": "2017-05-09T09:12:40.753Z", "modified": "2020-11-27T13:14:01.215Z"}