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. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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 .
  8. 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.
  9. 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.
  10. 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 .
  11. 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 .
  12. 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 .
  13. 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.
  14. 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 .
  15. Iqbal, K, Dietrich, S, Wittenbecher, C, Krumsiek, J, Kühn, T, Lacruz, ME et al.. Comparison of metabolite networks from four German population-based studies. Int J Epidemiol. 2018;47 (6):2070-2081. doi: 10.1093/ije/dyy119. PubMed PMID:29982629 PubMed Central PMC6280930.
  16. Bonifacio, E, Beyerlein, A, Hippich, M, Winkler, C, Vehik, K, Weedon, MN et al.. Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children. PLoS Med. 2018;15 (4):e1002548. doi: 10.1371/journal.pmed.1002548. PubMed PMID:29614081 PubMed Central PMC5882115.
  17. Lange, T, Budde, K, Homuth, G, Kastenmüller, G, Artati, A, Krumsiek, J et al.. Comprehensive Metabolic Profiling Reveals a Lipid-Rich Fingerprint of Free Thyroxine Far Beyond Classic Parameters. J. Clin. Endocrinol. Metab. 2018;103 (5):2050-2060. doi: 10.1210/jc.2018-00183. PubMed PMID:29546278 .
  18. Wahl, A, van den Akker, E, Klaric, L, Štambuk, J, Benedetti, E, Plomp, R et al.. Genome-Wide Association Study on Immunoglobulin G Glycosylation Patterns. Front Immunol. 2018;9 :277. doi: 10.3389/fimmu.2018.00277. PubMed PMID:29535710 PubMed Central PMC5834439.
  19. Hege, MA, Veit, R, Krumsiek, J, Kullmann, S, Heni, M, Rogers, PJ et al.. Eating less or more - Mindset induced changes in neural correlates of pre-meal planning. Appetite. 2018;125 :492-501. doi: 10.1016/j.appet.2018.03.006. PubMed PMID:29524474 .
  20. Benedetti, E, Pučić-Baković, M, Keser, T, Wahl, A, Hassinen, A, Yang, JY et al.. Publisher Correction: Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway. Nat Commun. 2018;9 (1):706. doi: 10.1038/s41467-017-02379-2. PubMed PMID:29440641 PubMed Central PMC5811429.
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