{"entity": "researcher", "timestamp": "2026-07-20T13:01:41.143Z", "family": "Jonsson", "given": "P\u00e4r", "initials": "P", "orcid": "0000-0001-8357-5018", "affiliations": ["Department of Chemistry, Ume\u00e5 University, SE-901 87 Ume\u00e5, Sweden."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/fd630ff903db4a61af5bf65a46c9c098.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/fd630ff903db4a61af5bf65a46c9c098"}}, "publications": [{"entity": "publication", "iuid": "86224b9927024f2dbe91734530bc518a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/86224b9927024f2dbe91734530bc518a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/86224b9927024f2dbe91734530bc518a"}}, "title": "Time-Resolved Hierarchical Modeling Highlights Metabolites Influencing Productivity and Cell Death in Chinese Hamster Ovary Cells.", "authors": [{"family": "Eriksson", "given": "Andreas", "initials": "A", "orcid": "0009-0009-7323-192X", "researcher": {"href": "https://publications.scilifelab.se/researcher/b9ea928d916c48a68e0e996bf3dc76d7.json"}}, {"family": "Richelle", "given": "Anne", "initials": "A"}, {"family": "Trygg", "given": "Johan", "initials": "J"}, {"family": "Scholze", "given": "Steffi", "initials": "S"}, {"family": "Pijeaud", "given": "Shanti", "initials": "S"}, {"family": "Antti", "given": "Henrik", "initials": "H"}, {"family": "Zehe", "given": "Christoph", "initials": "C"}, {"family": "Surowiec", "given": "Izabella", "initials": "I", "orcid": "0009-0002-3709-2458", "researcher": {"href": "https://publications.scilifelab.se/researcher/22e6f70588334990bb3eddc2c48b8d9c.json"}}, {"family": "Jonsson", "given": "P\u00e4r", "initials": "P", "orcid": "0000-0001-8357-5018", "researcher": {"href": "https://publications.scilifelab.se/researcher/fd630ff903db4a61af5bf65a46c9c098.json"}}], "type": "journal article", "published": "2025-03-00", "journal": {"title": "Biotechnol J", "issn": "1860-7314", "volume": "20", "issue": "3", "pages": "e202400624", "issn-l": "1860-6768"}, "abstract": "Biopharmaceuticals are medical compounds derived from biological sources and are often manufactured by living cells, primarily Chinese hamster ovary (CHO) cells. CHO cells display variation among cell clones, leading to growth and productivity differences that influence the product's quantity and quality. The biological and environmental factors behind these differences are not fully understood. To identify metabolites with a consistent relationship to productivity or cell death over time, we analyzed the extracellular metabolome of 11 CHO clones with different growth and productivity characteristics over 14 days. However, in bioreactor processes, metabolic profiles and process variables are both strongly time-dependent, confounding the metabolite-process variable relationship. To address this, we customized an existing hierarchical approach for handling time dependency to highlight metabolites with a consistent correlation to a process variable over a selected timeframe. We benchmarked this new method against conventional orthogonal partial least squares (OPLS) models. Our hierarchical method highlighted several metabolites consistently related to productivity or cell death that the conventional method missed. These metabolites were biologically relevant; most were known already, but some that had not been reported in CHO literature before, such as 3-methoxytyrosine and succinyladenosine, had ties to cell death in studies with other cell types. The metabolites showed an inverse relationship with the response variables: those positively correlated with productivity were typically negatively correlated with the death rate, or vice versa. For both productivity and cell death, the citrate cycle and adjacent pathways (pyruvate, glyoxylate, pantothenate) were among the most important. In summary, we have proposed a new method to analyze time-dependent omics data in bioprocess production. This approach allowed us to identify metabolites tied to cell death and productivity that were not detected with traditional models.", "doi": "10.1002/biot.202400624", "pmid": "40065671", "labels": {"Swedish NMR Centre": "Service", "Swedish Metabolomics Centre": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC11894446"}], "notes": [], "created": "2025-06-11T13:34:21.410Z", "modified": "2026-02-27T08:32:11.278Z"}, {"entity": "publication", "iuid": "62345871056a4551be2da7b386d2bc0b", "links": {"self": {"href": "https://publications.scilifelab.se/publication/62345871056a4551be2da7b386d2bc0b.json"}, "display": {"href": "https://publications.scilifelab.se/publication/62345871056a4551be2da7b386d2bc0b"}}, "title": "Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank.", "authors": [{"family": "Borgm\u00e4stars", "given": "Emmy", "initials": "E", "orcid": "0000-0001-9521-4463", "researcher": {"href": "https://publications.scilifelab.se/researcher/a3abe4844a5d4f82af345733fc62115d.json"}}, {"family": "Jacobson", "given": "Sara", "initials": "S", "orcid": "0000-0002-1542-1970", "researcher": {"href": "https://publications.scilifelab.se/researcher/65109046b0a8472c8090c0c3c84a7420.json"}}, {"family": "Simm", "given": "Maja", "initials": "M", "orcid": "0000-0002-7145-5913", "researcher": {"href": "https://publications.scilifelab.se/researcher/693493f6eaca4cb683129493393194ef.json"}}, {"family": "Johansson", "given": "Mattias", "initials": "M"}, {"family": "Billing", "given": "Ola", "initials": "O"}, {"family": "Lundin", "given": "Christina", "initials": "C"}, {"family": "Nystr\u00f6m", "given": "Hanna", "initials": "H"}, {"family": "\u00d6hlund", "given": "Daniel", "initials": "D", "orcid": "0000-0002-5847-2778", "researcher": {"href": "https://publications.scilifelab.se/researcher/42e9e473f68c460098a37e22d0a41369.json"}}, {"family": "Lubovac-Pilav", "given": "Zelmina", "initials": "Z", "orcid": "0000-0001-6427-0315", "researcher": {"href": "https://publications.scilifelab.se/researcher/af98c65fa2964875bd6d46f9c2488e6b.json"}}, {"family": "Jonsson", "given": "P\u00e4r", "initials": "P", "orcid": "0000-0001-8357-5018", "researcher": {"href": "https://publications.scilifelab.se/researcher/fd630ff903db4a61af5bf65a46c9c098.json"}}, {"family": "Franklin", "given": "Oskar", "initials": "O", "orcid": "0000-0002-3777-6887", "researcher": {"href": "https://publications.scilifelab.se/researcher/3ef2966672d941f881f588707c1edd37.json"}}, {"family": "Sund", "given": "Malin", "initials": "M", "orcid": "0000-0002-7516-9543", "researcher": {"href": "https://publications.scilifelab.se/researcher/a6d2313e3364447789d52b5d4a44b318.json"}}], "type": "journal article", "published": "2024-04-30", "journal": {"title": "J Gastrointest Oncol", "issn": "2078-6891", "volume": "15", "issue": "2", "pages": "755-767", "issn-l": null}, "abstract": "Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.\n\nWe performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.\n\nAmong twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].\n\nIndoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.", "doi": "10.21037/jgo-23-930", "pmid": "38756646", "labels": {"Swedish Metabolomics Centre": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC11094504"}, {"db": "pii", "key": "jgo-15-02-755"}], "notes": [], "created": "2024-11-26T10:28:08.517Z", "modified": "2025-10-17T13:03:13.219Z"}, {"entity": "publication", "iuid": "a6c74c26d13a4608b4806234c6d580d5", "links": {"self": {"href": "https://publications.scilifelab.se/publication/a6c74c26d13a4608b4806234c6d580d5.json"}, "display": {"href": "https://publications.scilifelab.se/publication/a6c74c26d13a4608b4806234c6d580d5"}}, "title": "Identification of Pre-Diagnostic Metabolic Patterns for Glioma Using Subset Analysis of Matched Repeated Time Points.", "authors": [{"family": "Jonsson", "given": "P\u00e4r", "initials": "P", "orcid": "0000-0001-8357-5018", "researcher": {"href": "https://publications.scilifelab.se/researcher/fd630ff903db4a61af5bf65a46c9c098.json"}}, {"family": "Antti", "given": "Henrik", "initials": "H"}, {"family": "Sp\u00e4th", "given": "Florentin", "initials": "F"}, {"family": "Melin", "given": "Beatrice", "initials": "B"}, {"family": "Bj\u00f6rkblom", "given": "Benny", "initials": "B", "orcid": "0000-0001-9347-5790", "researcher": {"href": "https://publications.scilifelab.se/researcher/acf29b039dfc496fb33c0cf7cb1d587c.json"}}], "type": "journal article", "published": "2020-11-12", "journal": {"title": "Cancers (Basel)", "issn": "2072-6694", "volume": "12", "issue": "11", "pages": "3349", "issn-l": "2072-6694"}, "abstract": "Here, we present a strategy for early molecular marker pattern detection-Subset analysis of Matched Repeated Time points (SMART)-used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease.", "doi": "10.3390/cancers12113349", "pmid": "33198241", "labels": {"Bioinformatics Support for Computational Resources": "Service", "Swedish Metabolomics Centre": "Service"}, "xrefs": [{"db": "pii", "key": "cancers12113349"}, {"db": "pmc", "key": "PMC7696703"}], "notes": [], "created": "2020-12-11T12:04:50.936Z", "modified": "2025-10-17T13:03:16.601Z"}]}