Roland Nilsson Group

We are a research group specializing in detailed analyses of cellular metabolism using modern measurement techniques and mathematical models. Our work aims for a deeper and more quantitative understanding of how various cell types in the human body process nutrients to harvest energy and synthesize macromolecules. In particular, we are interested in the metabolic derangements that occur in cancer cells, but also in specialized modes metabolism unique to other cell types.

Our work involves large-scale data integration, mathematical models of metabolism, transcriptomics, mass-spectrometry based metabolite profiling, and cellular physiology. We are working in close collaboration with colleagues at the University of California, San Diego, as well as Harvard University and the Broad Institute (Boston).

Roland Nilsson, Ph.D, roland.nilsson@ki.se

Nina Grankvist, Ph.D, Lab manager, nina.grankvist@ki.se

Custom cell culture media, isotope tracing, biochemistry.

Mohamed Elhusseiny, Ph.D, mohamed.elhusseiny@ki.se

Methionine metabolism.

Deniz Seçilmiş, Ph.D, deniz.secilmis@ki.se

Computational mass spectrometry.

Sebastian Joseph, B.Sc, sebastian.joseph@ki.se

Adipocyte metabolism.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors. Sundqvist, et al.  PLoS Computational Biology, 18(4):e1009999, 2022.

Functional Analysis of Immune Signature Genes in Th1* Memory Cells Links ISOC1 and Pyrimidine Metabolism to IFN-γ and IL-17 Production. Kushnareva, et al.  The Journal of Immunology 206:1181-1193, 2021.

The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters. Schober, et al.  Science Advances 7:eabf0717, 2021.

Inhibition of fatty acid synthesis induces differentiation and reduces tumor burden in childhood neuroblastoma. Ruiz-Perez, et al. iScience 24:102128, 2021.

Mapping choline metabolites in normal and transformed cells. Irena Roci, et al.  Metabolomics 16:125, 2020.

Validity of natural isotope abundance correction for metabolic flux analysis. Roland Nilsson.  Mathematical Biosciences 330:108481, 2020.  preprint

Mapping metabolic oscillations during cell cycle progression. Irena Roci, et al. Cell Cycle 19:2676-2684, 2020.   preprint

Yeast homologs of human MCUR1 regulate mitochondrial proline metabolism. Zulkifli, et al.  Nature Communications 11:4866, 2020.

Cellular sensing of extracellular purine nucleosides triggers an innate IFN-β response. Dhanwani, et al. Science Advances  6:eaba3688XX, 2020.

Profiling of the plasma proteome across different stages of human heart failure. Egerstedt, et al.  Nature Communications 10:5830, 2019.

Mitochondrial MTHFD isozymes display distinct expression, regulation, and association with cancer. Costas Koufaris, et al. Gene 7:144032, 2019.

Large-Scale Profiling of Cellular Metabolic Activities Using Deep ¹³C Labeling Medium. Nina Grankvist, et al. Methods in Molecular Biology 2088:72-93, 2020.

MYCN-enhanced oxidative and glycolytic metabolism reveals vulnerabilities for targeting neuroblastoma. Oliynyk et al. iScience, 21:188-204, 2019.

Mapping metabolic events in the cancer cell cycle reveals arginine catabolism in the committed SG2M phase. Irena Roci, et al. Cell Reports 26:P1691-1700.e5, 2019.  LCMS data set

Gabapentin can suppress cell proliferation independent of the cytosolic branched-chain amino acid transferase 1 (BCAT1).  Nina Grankvist, et al. Biochemistry 58: 6762-6766, 2018.

Profiling the metabolism of human cells by deep ¹³C labeling. Nina Grankvist, et al. Cell Chemical Biology 25:1415-1427, 2018.  Preprint | LCMS data set

Protein interaction and functional data indicate MTHFD2 involvement in RNA processing and translation. Costas Koufaris & Roland Nilsson. Cancer & Metabolism 6:12, 2018.

Metabolic reprogramming of acute lymphoblastic leukemia cells in response to glucocorticoid treatment. Dyczynski, et al. Cell Death & Disease 9:846, 2018.

A Web Service Framework for Interactive Analysis of Metabolomics Data. Yaroslav Lyutvinskiy, et al. Analytical Chemistry,  89:5713–5718, 2017.

Estimation of flux ratios without uptake or release data: application to serine and methionine metabolism. Roland Nilsson, et al. Metabolic Engineering 43:137-146, 2017.

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing. Irena Roci, et al. Journal of Visualized Experiments 120:e55011, 2017.

Simultaneous tracing of carbon and nitrogen isotopes in human cells. Roland Nilsson and Mohit Jain. Molecular Biosystems 12:1929-1937, 2016.

Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells. Irena Roci, et al.  Analytical Chemistry, 88:2707–2713,  2016.

The folate-coupled enzyme MTHFD2 is a nuclear protein and promotes cell proliferation. Nina Gustafsson Sheppard, et al. Scientific Reports 5:15029, 2015.

A systematic survey of lipids across mouse tissues. Mohit Jain, et al. AJP Endocrinology and Metabolism 306:8, E854-E868, 2014.

Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer. Roland Nilsson, Mohit Jain, Nikhil Madhusudhan, Nina Gustafsson Sheppard, Laura Strittmatter,et al. Nature Communications 5:3128, 2014.

An siRNA screen for NFAT activation identifies septins as coordinators of store-operated Ca2+ entry. Sonia Sharma, et al. Nature 499:238-242, 2013.

Identification of a novel flow-mediated gene expression signature in patients with bicuspid aortic valve. Shohreh Maleki, Hanna M Björck, Lasse Folkersen, Roland Nilsson, Johan Renner, et al. Journal of Molecular Medicine 91:129–139, 2013.

Metabolite Profiling Identifies a Key Role for Glycine in Rapid Cancer Cell Proliferation. Mohit Jain & Roland Nilsson, et al. Science 336:1040-1044, 2012.

Mitochondrial and nuclear genomic responses to loss of LRPPRC expression. Vishal M. Gohil, Roland Nilsson, et al. Journal of Biological Chemistry 285:13742–13747, 2010.

Nutrient-sensitized screening for drugs that shift energy metabolism from mitochondrial respiration to glycolysis. Vishal M. Gohil & Sunil A. Sheth, Roland Nilsson, Andrew P. Wojtovich, Jeong H. Lee JH, et al. Nature Biotechnology 28:249–255, 2010.

A Computational Screen for Regulators of Oxidative Phosphorylation Implicates SLIRP in Mitochondrial RNA Homeostasis. Joshua M. Baughman, Roland Nilsson, et al. PLoS Genetics 5:8, 2009.

Discovery of Genes Essential for Heme Biosynthesis through Large-Scale Gene Expression Analysis. Roland Nilsson & Iman Schulz, Eric L. Pierce, Kathleen A. Soltis, Amornrat Naranuntaratet, et al.  Cell Metabolism 10:2, 119–130, 2009.

Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. An Algorithm for Reading The Journal of Machine Learning Research 10, 1071–1094, 2009.

On reliable discovery of molecular signatures. Roland Nilsson, Johan Björkegren and Jesper Tegnér. BMC Bioinformatics 10:38, 2009.

ApoB100-LDL Acts as a Metabolic Signal from Liver to Peripheral Fat Causing Inhibition of Lipolysis in Adipocytes. Josefin Skogsberg, Andrea Dicker, Mikael Rydén, Gaby Åström, Roland Nilsson, et al.  PLoS ONE 3:11, 2008.

Transcriptional Profiling Uncovers a Network of Cholesterol-Responsive Atherosclerosis Target Genes. Josefin Skogsberg, Jesper Lundström, Alexander Kovacs, Roland Nilsson, Peri Noori, et al.  PLoS Genetics 4:3, 2008.

Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia. Alex Kovacs, Per Tornvall, Roland Nilsson, Jesper Tegnér, Anders Hamsten, et al. PNAS 104:34, 13768-13773, 2007.

Consistent feature selection for pattern recognition in polynomial time. Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Journal of Machine Learning Research 8, pp. 589–612, 2007 .

Detecting multivariate differential expression patterns. Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. BMC Bioinformatics 8:150, 2007.

Towards scalable and data efficient learning of Markov boundaries. José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. International Journal of Approximate Reasoning 45:2, 211–232, 2007.

Systems biology of innate immunity. Jesper Tegnér, Roland Nilsson, Vladimir B. Bajic, Johan Björkegren and Timothy Ravasi. Cellular Immunology 244:2, 105–109, 2006.

Transcriptional network dynamics in macrophage activation. Roland Nilsson, Vladimir B. Bajic, Harukazu Suzuki, Diego di Bernardo, Johan Björkegren, et al. Genomics 88:2, pp. 133–42, 2006.

Evaluating feature selection for SVMs in high dimensions. Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. In proc. of the 17th European Conference on Machine Learning, pp. 719–726, 2006.

Identifying relevant nodes without learning the model. José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér.  In proc. of the 22nd Conference on Uncertainty in Artificial Intelligence, pp. 367–374, 2006.

Reading dependencies from the minimal undirected independence map of a graphoid that satisfies weak transitivity. José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. In proc. of the 3rd European Workshop on Probabilistic Graphical Models, pp. 247–254, 2006.

A flexible implementation for support vector machines. Roland Nilsson, Johan Björkegren and Jesper Tegnér. The Mathematica Journal 10:1, pp. 114–127, 2006.

A powerful differential expression test for probe-level oligonucleotide microarray data. Roland Nilsson, Johan Björkegren and Jesper Tegnér. In proc. of the 2nd IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 10–14, 2004.