Proteomics and Metabolomics for AKI Diagnosis Q1 David Marx, MD,* ,† Jochen Metzger, PhD, ‡ Martin Pejchinovski, PhD, ‡ Ryan Bruce G Q4 il, § Q2 Maria Frantzi, PhD, ‡ Agnieszka Latosinska, PhD, ‡ Iwona Belczacka, ‡ Silke Heinzmann, PhD, § Q3 Holger Husi, PhD, || Jerome Zoidakis, PhD, ¶ Matthias Klingele, MD, #, ** and Stefan Herget-Rosenthal, MD †† Summary: Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention, and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and that the changes of molecular expression induced by AKI are difficult to distinguish from that of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI- associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI. Semin Nephrol ]:]]]-]]] C 2017 Elsevier Inc. All rights reserved. Keywords: Proteomics, metabolomics, pathway analysis, AKI diagnosis Q6 A cute kidney injury (AKI) is the most frequent acute Q7 renal condition and is associated with Q8 increased morbidity and mortality. 1,2 Currently, AKI is defined and classified by a rapid decrease in glomerular function and/or urine output based on increases of serum creatinine or decreases of urine production. 3 This definition is applied uniformly in clinical medicine and experimental AKI. In addition, patient history and physical examination, urine chem- istry and cytologic analysis, ultrasound, and very rarely kidney biopsy currently are used as diagnostic tools in AKI. However, these diagnostic methods have limi- tations. They do not permit early detection of AKI or prediction of the course of AKI. To improve patient outcome, it would be critical to have clinical tools that permit early detection of patients at risk of and those with evolving AKI. It also would be critical to have markers available to deter- mine AKI progression, assess response to therapy, subsequent requirement of renal replacement therapy, as well as the degree of renal regeneration or residual chronic kidney disease after an AKI episode. Finally, it would be ideal to have markers that also are mediators of the different pathophysiological pathways leading to AKI. Numerous biomarkers have been reported to enable the early detection of AKI. However, most of these biomarkers are linked closely to a single pathologic process, such as tubular injury. This may explain why these markers frequently have performed poorly in AKI populations with other pathophysiological mech- anisms or of heterogeneous origin. In this review, various biomarkers and the currently experimental approaches of proteomics and metabolomics in bio- fluids and kidney tissue are highlighted. Proteomic and metabolomic approaches may provide multimarker 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 0270-9295/ - see front matter & 2017 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.semnephrol.2017.09.007 Financial support: none. Conflict of interest statement: Jochen Metzger, Martin Pejchinovski, Maria Frantzi, Agnieszka Latosinska, and Iwona Belczacka are employees of Mosaiques Diagnostics GmbH. The remaining authors have no financial or nonfinancial competing interests to declare. * Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR Q5 7178, Strasbourg, France. † Nephrology-Transplantation Department, University Hospital, Strasbourg, France. ‡ Mosaiques Diagnostics GmbH, Hannover, Germany. § Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environment Health, Neuherberg, Germany. || Department of Diabetes and Cardiovascular Science, University of the Highlands and Islands, Inverness, United Kingdom. ¶ Biotechnology Division, Biomedical Research Foundation, Acad- emy of Athens, Athens, Greece. # Department of Internal Medicine, Nephrology and Hypertension, Saarland University Medical Centre, Homburg-Saar, Germany. ** Department of Internal Medicine, Hochtaunus Kliniken, Usingen, Germany. †† Department of Medicine and Nephrology, Rotes Kreuz Kranken- haus, Bremen, Germany. Address reprint requests to Jochen Metzger, PhD, Mosaiques Diagnostics GmbH, Rotenburger Straße 20, D-30659 Hannover, Germany. E-mail: metzger@mosaiques-diagnostics.com Seminars in Nephrology, Vol ], No ]]], Month 2017, pp ]]]–]]] 1