Jan Krumsiek, Ph.D.

Assistant Professor of Physiology and Biophysics

  • Assistant Professor of Computational Genomics in Computational Biomedicine in the Institute for Computational Biomedicine

646-962-4152

1305 York Avenue, Room Y-13.15A
New York, NY 10021


Techniques

Research Areas


Research Summary:

Dr. Krumsiek develops and applies novel methods for the analysis of metabolomics and multi-omics data. This includes pathway-based methods, computational simulations, and machine learning techniques. As special focus of his work is the inference of metabolic networks from data, providing a condition-specific, unbiased in vivo view on metabolism. He has mainly published on etiology and risk prediction in the field of diabetes and obesity research, and is now transitioning to novel projects in the cancer research. The lab elucidates metabolic associations of drug treatment and clinical parameters in different types of cancer.

https://pubs.acs.org/doi/abs/10.1021/pr501130a

Recent Publications:

  1. Wörheide, MA, Krumsiek, J, Kastenmüller, G, Arnold, M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta. 2021;1141 :144-162. doi: 10.1016/j.aca.2020.10.038. PubMed PMID:33248648 .
  2. Benedetti, E, Pučić-Baković, M, Keser, T, Gerstner, N, Büyüközkan, M, Štambuk, T et al.. A strategy to incorporate prior knowledge into correlation network cutoff selection. Nat Commun. 2020;11 (1):5153. doi: 10.1038/s41467-020-18675-3. PubMed PMID:33056991 PubMed Central PMC7560866.
  3. Benedetti, E, Gerstner, N, Pučić-Baković, M, Keser, T, Reiding, KR, Ruhaak, LR et al.. Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference. Metabolites. 2020;10 (7):. doi: 10.3390/metabo10070271. PubMed PMID:32630764 PubMed Central PMC7408386.
  4. Noordam, R, van Heemst, D, Suhre, K, Krumsiek, J, Mook-Kanamori, DO. Proteome-wide assessment of diabetes mellitus in Qatari identifies IGFBP-2 as a risk factor already with early glycaemic disturbances. Arch Biochem Biophys. 2020;689 :108476. doi: 10.1016/j.abb.2020.108476. PubMed PMID:32585310 .
  5. Arnold, M, Nho, K, Kueider-Paisley, A, Massaro, T, Huynh, K, Brauner, B et al.. Sex and APOE ε4 genotype modify the Alzheimer's disease serum metabolome. Nat Commun. 2020;11 (1):1148. doi: 10.1038/s41467-020-14959-w. PubMed PMID:32123170 PubMed Central PMC7052223.
  6. Otto, L, Budde, K, Kastenmüller, G, Kaul, A, Völker, U, Völzke, H et al.. Associations between adipose tissue volume and small molecules in plasma and urine among asymptomatic subjects from the general population. Sci Rep. 2020;10 (1):1487. doi: 10.1038/s41598-020-58430-8. PubMed PMID:32001750 PubMed Central PMC6992585.
  7. Altenbuchinger, M, Zacharias, HU, Solbrig, S, Schäfer, A, Büyüközkan, M, Schultheiß, UT et al.. A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study. Sci Rep. 2019;9 (1):13954. doi: 10.1038/s41598-019-50346-2. PubMed PMID:31562371 PubMed Central PMC6764972.
  8. Playdon, MC, Joshi, AD, Tabung, FK, Cheng, S, Henglin, M, Kim, A et al.. Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS). Metabolites. 2019;9 (7):. doi: 10.3390/metabo9070145. PubMed PMID:31319517 PubMed Central PMC6681081.
  9. Chu, SH, Huang, M, Kelly, RS, Benedetti, E, Siddiqui, JK, Zeleznik, OA et al.. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites. 2019;9 (6):. doi: 10.3390/metabo9060117. PubMed PMID:31216675 PubMed Central PMC6630728.
  10. Quell, JD, Römisch-Margl, W, Haid, M, Krumsiek, J, Skurk, T, Halama, A et al.. Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics. Metabolites. 2019;9 (6):. doi: 10.3390/metabo9060109. PubMed PMID:31181753 PubMed Central PMC6631474.
  11. Matejka, K, Stückler, F, Salomon, M, Ensenauer, R, Reischl, E, Hoerburger, L et al.. Dynamic modelling of an ACADS genotype in fatty acid oxidation - Application of cellular models for the analysis of common genetic variants. PLoS One. 2019;14 (5):e0216110. doi: 10.1371/journal.pone.0216110. PubMed PMID:31120904 PubMed Central PMC6532850.
  12. Do, KT, Wahl, S, Raffler, J, Molnos, S, Laimighofer, M, Adamski, J et al.. Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics. 2018;14 (10):128. doi: 10.1007/s11306-018-1420-2. PubMed PMID:30830398 PubMed Central PMC6153696.
  13. Zacharias, HU, Altenbuchinger, M, Schultheiss, UT, Samol, C, Kotsis, F, Poguntke, I et al.. A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease. J Proteome Res. 2019;18 (4):1796-1805. doi: 10.1021/acs.jproteome.8b00983. PubMed PMID:30817158 .
  14. Sachs, S, Zarini, S, Kahn, DE, Harrison, KA, Perreault, L, Phang, T et al.. Intermuscular adipose tissue directly modulates skeletal muscle insulin sensitivity in humans. Am J Physiol Endocrinol Metab. 2019;316 (5):E866-E879. doi: 10.1152/ajpendo.00243.2018. PubMed PMID:30620635 PubMed Central PMC6580171.
  15. Kindt, A, Liebisch, G, Clavel, T, Haller, D, Hörmannsperger, G, Yoon, H et al.. The gut microbiota promotes hepatic fatty acid desaturation and elongation in mice. Nat Commun. 2018;9 (1):3760. doi: 10.1038/s41467-018-05767-4. PubMed PMID:30218046 PubMed Central PMC6138742.
  16. Hertel, J, Rotter, M, Frenzel, S, Zacharias, HU, Krumsiek, J, Rathkolb, B et al.. Dilution correction for dynamically influenced urinary analyte data. Anal Chim Acta. 2018;1032 :18-31. doi: 10.1016/j.aca.2018.07.068. PubMed PMID:30143216 .
  17. Herder, C, Kannenberg, JM, Carstensen-Kirberg, M, Strom, A, Bönhof, GJ, Rathmann, W et al.. A Systemic Inflammatory Signature Reflecting Cross Talk Between Innate and Adaptive Immunity Is Associated With Incident Polyneuropathy: KORA F4/FF4 Study. Diabetes. 2018;67 (11):2434-2442. doi: 10.2337/db18-0060. PubMed PMID:30115651 .
  18. Pietzner, M, Budde, K, Homuth, G, Kastenmüller, G, Henning, AK, Artati, A et al.. Hepatic Steatosis Is Associated With Adverse Molecular Signatures in Subjects Without Diabetes. J Clin Endocrinol Metab. 2018;103 (10):3856-3868. doi: 10.1210/jc.2018-00999. PubMed PMID:30060179 .
  19. Do, KT, Rasp, DJN, Kastenmüller, G, Suhre, K, Krumsiek, J. MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions. Bioinformatics. 2019;35 (3):532-534. doi: 10.1093/bioinformatics/bty650. PubMed PMID:30032270 PubMed Central PMC6361241.
  20. Pitchika, A, Jolink, M, Winkler, C, Hummel, S, Hummel, N, Krumsiek, J et al.. Associations of maternal type 1 diabetes with childhood adiposity and metabolic health in the offspring: a prospective cohort study. Diabetologia. 2018;61 (11):2319-2332. doi: 10.1007/s00125-018-4688-x. PubMed PMID:30008062 .
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