18 results on '"Catanese, Lorenzo"'
Search Results
2. Predictive performance and clinical application of COV50, a urinary proteomic biomarker in early COVID-19 infection: a prospective multicentre cohort study
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Staessen, Jan A, Wendt, Ralph, Yu, Yu-Ling, Kalbitz, Sven, Thijs, Lutgarde, Siwy, Justyna, Raad, Julia, Metzger, Jochen, Neuhaus, Barbara, Papkalla, Armin, von der Leyen, Heiko, Mebazaa, Alexandre, Dudoignon, Emmanuel, Spasovski, Goce, Milenkova, Mimoza, Canevska-Taneska, Aleksandra, Lazo, Mercedes Salgueira, Psichogiou, Mina, Rajzer, Marek W, Fulawka, Lukasz, Dzitkowska-Zabielska, Magdalena, Weiss, Guenter, Feldt, Torsten, Stegemann, Miriam, Normark, Johan, Zoufaly, Alexander, Schmiedel, Stefan, Seilmaier, Michael, Rumpf, Benedikt, Banasik, Mirosław, Krajewska, Magdalena, Catanese, Lorenzo, Rupprecht, Harald, Czerwienska, Beata, Peters, Björn, Nilsson, Åsa, Rothfuss, Katja, Lübbert, Christoph, Mischak, Harald, Beige, Joachim, Ermisch, Jörg, Kellner, Nils, Peruth-Stutzmann, Lydia, Schroth, Stefanie, Schmidt, Jonathan, Schmidt, Ulrike, Breuer, Daniel, Abeud, Fariza, Fournier, Marie-Celine, Louadah, Badr, Molas, Rocio, Rojas, Fraile Loreto, García, Fabiola Alonso, Sánchez, Isabel Garcia, Hrom, Ioana Cezara, Więczek., Andrzej, Schwab, Matthias, K Asayama, Kei, Hansen, Tine W, Maestre, Gladys E, Basoulis, Dimitrios, Karamanakos., Georgios, Lis, Pawel, Olszanecka, Agnieszka, Bellmann-Weiler, Rosa, Lanser, Lucas, Edin, Alicia, Forsell, Matthias NE, Stegmayr, Bernd, Jensen, Björn-Erik Ole, Orth, Hans-Martin, Borstel, Sylke, Mikolajewska, Agata, Hecking, Manfred, Schmölz, Lukas, Hoffmann, Michał, Narkiewicz, Krzysztof, Matera-Witkiewicz, Agnieszka, Zachciał, Justyna, Litwin, Monika, Marciniak, Patrycja, Salgueira Lazo, Mercedes, Fuławka, Łukasz, Rupprecht, Harald D, and Czerwieńska, Beata
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- 2022
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3. Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers
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Rupprecht, Harald, primary, Catanese, Lorenzo, additional, Amann, Kerstin, additional, Hengel, Felicitas E., additional, Huber, Tobias B., additional, Latosinska, Agnieszka, additional, Lindenmeyer, Maja T., additional, Mischak, Harald, additional, Siwy, Justyna, additional, Wendt, Ralph, additional, and Beige, Joachim, additional
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- 2024
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4. Non-Invasive Biomarkers for Diagnosis, Risk Prediction, and Therapy Guidance of Glomerular Kidney Diseases: A Comprehensive Review
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Catanese, Lorenzo, primary, Rupprecht, Harald, additional, Huber, Tobias B., additional, Lindenmeyer, Maja T., additional, Hengel, Felicitas E., additional, Amann, Kerstin, additional, Wendt, Ralph, additional, Siwy, Justyna, additional, Mischak, Harald, additional, and Beige, Joachim, additional
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- 2024
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5. Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease.
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Mavrogeorgis, Emmanouil, He, Tianlin, Mischak, Harald, Latosinska, Agnieszka, Vlahou, Antonia, Schanstra, Joost P, Catanese, Lorenzo, Amann, Kerstin, Huber, Tobias B, Beige, Joachim, Rupprecht, Harald D, and Siwy, Justyna
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CHRONIC kidney failure ,KIDNEY disease diagnosis ,DIABETIC nephropathies ,MACHINE learning ,DIFFERENTIAL diagnosis - Abstract
Background and hypothesis Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies. Methods The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models. Results In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state. Conclusion Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Differentiating primary and secondary FSGS using non-invasive urine biomarkers.
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Catanese, Lorenzo, Siwy, Justyna, Wendt, Ralph, Amann, Kerstin, Beige, Joachim, Hendry, Bruce, Mischak, Harald, Mullen, William, Paterson, Ian, Schiffer, Mario, Wolf, Michael, and Rupprecht, Harald
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FOCAL segmental glomerulosclerosis , *URINE , *BIOMARKERS , *CHRONIC kidney failure , *INDEPENDENT sets - Abstract
Background Focal segmental glomerulosclerosis (FSGS) is divided into genetic, primary (p), uncertain cause, and secondary (s) forms. The subclasses differ in management and prognosis with differentiation often being challenging. We aimed to identify specific urine proteins/peptides discriminating between clinical and biopsy-proven pFSGS and sFSGS. Methods Sixty-three urine samples were collected in two different centers (19 pFSGS and 44 sFSGS) prior to biopsy. Samples were analysed using capillary electrophoresis-coupled mass spectrometry. For biomarker definition, datasets of age-/sex-matched normal controls (NC, n = 98) and patients with other chronic kidney diseases (CKDs, n = 100) were extracted from the urinary proteome database. Independent specificity assessment was performed in additional data of NC (n = 110) and CKD (n = 170). Results Proteomics data from patients with pFSGS were first compared to NC (n = 98). This resulted in 1179 biomarker (P < 0.05) candidates. Then, the pFSGS group was compared to sFSGS, and in a third step, pFSGS data were compared to data from different CKD etiologies (n = 100). Finally, 93 biomarkers were identified and combined in a classifier, pFSGS93. Total cross-validation of this classifier resulted in an area under the receiving operating curve of 0.95. The specificity investigated in an independent set of NC and CKD of other etiologies was 99.1% for NC and 94.7% for CKD, respectively. The defined biomarkers are largely fragments of different collagens (49%). Conclusion A urine peptide-based classifier that selectively detects pFSGS could be developed. Specificity of 95%–99% could be assessed in independent samples. Sensitivity must be confirmed in independent cohorts before routine clinical application. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease
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Mavrogeorgis, Emmanouil, primary, He, Tianlin, additional, Mischak, Harald, additional, Latosinska, Agnieszka, additional, Vlahou, Antonia, additional, Schanstra, Joost P, additional, Catanese, Lorenzo, additional, Amann, Kerstin, additional, Huber, Tobias B, additional, Beige, Joachim, additional, Rupprecht, Harald, additional, and Siwy, Justyna, additional
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- 2023
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8. Uniform Manifold Approximation and Projection-based Assessment of Chronic Kidney Disease Aetiologies based on Urinary Peptidomics
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Mavrogeorgis, Emmanouil, primary, He, Tianlin, additional, Mischak, Harald, additional, Latosinska, Agnieszka, additional, vlahou, antonia, additional, Schanstra, Joost P, additional, Catanese, Lorenzo, additional, Amann, Kerstin, additional, Huber, Tobias B, additional, Beige, Joachim, additional, Rupprecht, Harald, additional, and Siwy, Justyna, additional
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- 2023
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9. Recent Advances in Urinary Peptide and Proteomic Biomarkers in Chronic Kidney Disease: A Systematic Review
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Catanese, Lorenzo, primary, Siwy, Justyna, additional, Mischak, Harald, additional, Wendt, Ralph, additional, Beige, Joachim, additional, and Rupprecht, Harald, additional
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- 2023
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10. Predictive performance and clinical application of COV50, a urinary proteomic biomarker in early COVID-19 infection : a prospective multicentre cohort study
- Author
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Staessen, Jan A., Wendt, Ralph, Yu, Yu-Ling, Kalbitz, Sven, Thijs, Lutgarde, Siwy, Justyna, Raad, Julia, Metzger, Jochen, Neuhaus, Barbara, Papkalla, Armin, von der Leyen, Heiko, Mebazaa, Alexandre, Dudoignon, Emmanuel, Spasovski, Goce, Milenkova, Mimoza, Canevska-Taneska, Aleksandra, Salgueira Lazo, Mercedes, Psichogiou, Mina, Rajzer, Marek W., Fuławka, Łukasz, Dzitkowska-Zabielska, Magdalena, Weiss, Guenter, Feldt, Torsten, Stegemann, Miriam, Normark, Johan, Zoufaly, Alexander, Schmiedel, Stefan, Seilmaier, Michael, Rumpf, Benedikt, Banasik, Mirosław, Krajewska, Magdalena, Catanese, Lorenzo, Rupprecht, Harald D., Czerwieńska, Beata, Peters, Björn, Nilsson, Åsa, Rothfuss, Katja, Lübbert, Christoph, Mischak, Harald, Beige, Joachim, Staessen, Jan A., Wendt, Ralph, Yu, Yu-Ling, Kalbitz, Sven, Thijs, Lutgarde, Siwy, Justyna, Raad, Julia, Metzger, Jochen, Neuhaus, Barbara, Papkalla, Armin, von der Leyen, Heiko, Mebazaa, Alexandre, Dudoignon, Emmanuel, Spasovski, Goce, Milenkova, Mimoza, Canevska-Taneska, Aleksandra, Salgueira Lazo, Mercedes, Psichogiou, Mina, Rajzer, Marek W., Fuławka, Łukasz, Dzitkowska-Zabielska, Magdalena, Weiss, Guenter, Feldt, Torsten, Stegemann, Miriam, Normark, Johan, Zoufaly, Alexander, Schmiedel, Stefan, Seilmaier, Michael, Rumpf, Benedikt, Banasik, Mirosław, Krajewska, Magdalena, Catanese, Lorenzo, Rupprecht, Harald D., Czerwieńska, Beata, Peters, Björn, Nilsson, Åsa, Rothfuss, Katja, Lübbert, Christoph, Mischak, Harald, and Beige, Joachim
- Abstract
Background: The SARS-CoV-2 pandemic is a worldwide challenge. The CRIT-CoV-U pilot study generated a urinary proteomic biomarker consisting of 50 peptides (COV50), which predicted death and disease progression from SARS-CoV-2. After the interim analysis presented for the German Government, here, we aimed to analyse the full dataset to consolidate the findings and propose potential clinical applications of this biomarker. Methods: CRIT-CoV-U was a prospective multicentre cohort study. In eight European countries (Austria, France, Germany, Greece, North Macedonia, Poland, Spain, and Sweden), 1012 adults with PCR-confirmed COVID-19 were followed up for death and progression along the 8-point WHO scale. Capillary electrophoresis coupled with mass spectrometry was used for urinary proteomic profiling. Statistical methods included logistic regression and receiver operating characteristic curve analysis with a comparison of the area under curve (AUC) between nested models. Hospitalisation costs were derived from the care facility corresponding with the Markov chain probability of reaching WHO scores ranging from 3 to 8 and flat-rate hospitalisation costs adjusted for the gross per capita domestic product of each country. Findings: From June 30 to Nov 19, 2020, 228 participants were recruited, and from April 30, 2020, to April 14, 2021, 784 participants were recruited, resulting in a total of 1012 participants. The entry WHO scores were 1–3 in 445 (44%) participants, 4–5 in 529 (52%) participants, and 6 in 38 (4%) participants; and of all participants, 119 died and 271 had disease progression. The odds ratio (OR) associated with COV50 in all 1012 participants for death was 2·44 (95% CI 2·05–2·92) unadjusted and 1·67 (1·34–2·07) when adjusted for sex, age, BMI, comorbidities, and baseline WHO score; and for disease progression, the OR was 1·79 (1·60–2·01) when unadjusted and 1·63 (1·41–1·91) when adjusted (p<0·0001 for all). The predictive accuracy of the optimised COV50
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- 2022
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11. Sauerstoffabhängige Regulation des Wilms-Tumor Gens WT1 in Neuroblastomzellen
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Catanese, Lorenzo
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neuroblastoma ,hypoxia inducible factors ,hypoxia ,HIF ,600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit ,WT1 - Abstract
Sauerstoffmangel (Hypoxie) f��rdert ein aggressives Wachstum von Tumoren. Dies gilt insbesondere f��r das Neuroblastom, den h��ufigsten extrakraniellen soliden Tumor des Kindesalters. Neuroblastome sind Malignome des peripheren sympathischen Nervensystems, deren Tumorzellen von der embryonalen Neuralleiste abstammen. In Neuroblastomen korreliert die Akkumulation von Hypoxie-induzierbarem-Faktor-2�� (HIF-2��) mit einem hochmalignen Tumorph��notyp und einem ung��nstigen klinischen Verlauf. Hypoxie-induzierbare-Faktoren (HIF) bilden eine Familie von heterodimeren Transkriptionsfaktoren, die bei der zellul��ren Adaptation an ein niedriges Sauerstoffniveau in normalem Gewebe und in Tumoren eine zentrale Rolle spielen. Neben der Anreicherung von HIF-2�� ist auch eine hohe Expression von Wilms-Tumor-Gen 1 (WT1) mit einer schlechten Prognose von Neuroblastomen assoziiert. WT1 kodiert einen Transkriptionsfaktor, der urspr��nglich als Suppressor f��r das Wachstum von Nierentumoren bei Kindern (Wilms-Tumor, Nephroblastom) charakterisiert wurde. Neuere Untersuchungen haben ergeben, dass WT1 in vielen malignen Tumoren verst��rkt exprimiert wird und dort m��glicherweise onkogene Eigenschaften besitzt. Vor diesem Hintergrund sollte in der vorliegenden Arbeit ��berpr��ft werden, ob die Expression von WT1 in Neuroblastomzellen sauerstoffabh��ngig reguliert wird. Weiterhin sollte gegebenenfalls der molekulare Mechanismus analysiert werden, ��ber den ein Sauerstoffmangel die WT1-Expression stimuliert. Im Rahmen der durchgef��hrten Untersuchungen konnte gezeigt werden, dass die Expression von WT1 in Kelly-Neuroblastomzellen tats��chlich durch Hypoxie stimuliert wird. Der Knockdown von HIF-2�� mittels RNA-Interferenz bzw. Deletion von HIF-2�� mittels CRISPR/Cas9 bewirkte eine signifikante Abnahme der WT1 Expression in hypoxischen Kelly-Zellen. Die Inaktivierung von HIF-1�� hatte hingegen keinen signifikanten Einfluss auf das WT1 Niveau. Weiterhin wurde in silico ein HIF-Bindungsmotiv innerhalb eines DNase-sensitiven Bereichs in Intron 3 des WT1 Gens identifiziert. Mittels Chromatin-Immunpr��zipitation (ChIP) und Elektromobilit��tsshiftassays konnte eine Bindung von HIF-2 an diese Sequenz experimentell nachgewiesen werden. In Reportergenassays, die an transient transfizierten Kelly-Zellen durchgef��hrt wurden, ��bertrug ein 256 bp langes DNA-Fragment unter Einschluss des HIF-Bindungsmotivs aus Intron 3 des WT1-Gens eine Hypoxieempfindlichkeit auf die ansonsten hypoxieresistenten SV40- und WT1-Promotoren. Funktionell konnte nachgewiesen werden, dass WT1 die Migration von SK-NA-S-Neuroblastomzellen stimuliert. R2-Datenanalysen von gro��en Kohorten von Neuroblastompatienten zeigten, dass sowohl HIF-2�� als auch WT1 in Subgruppen von Neuroblastompatienten mit einer erh��hten Mortalit��t assoziiert sind. Mit diesen Ergebnissen wird erstmalig ein sauerstoffempfindlicher Enhancer in Intron 3 des WT1 Gens identifiziert. Unter hypoxischen Bedingungen bewirkt die Bindung von HIF-2 an das Enhancerelement eine WT1-Expression in Kelly-Neuroblastomzellen. Dieser neu entdeckte molekulare Mechanismus spielt m��glicherweise in der Pathophysiologie von Neuroblastomen eine Rolle., Oxygen shortage (hypoxia) favors aggressive tumor growth. This is particularly relevant to neuroblastoma, the most common extracranial solid tumor in childhood. Neuroblastoma arises from neural crest-derived cells in the peripheral sympathetic nervous system. High levels of hypoxia-inducible factor (HIF) 2�� in neuroblastoma correlate with an undifferentiated tumor phenotype and poor clinical outcome. Hypoxiainducible factors constitute a family of heterodimeric transcription factors that play a pivotal role in the adaptation of normal and tumor tissues to low oxygen conditions. Recent studies have shown that high expression levels of the Wilms tumor gene 1 (WT1) in neuroblastoma are associated with an aggressive tumor growth and poor prognosis. WT1 was initially identified as a tumor suppressor gene preventing the formation of childhood tumors of the kidney (Wilms tumor, nephroblastoma). However, WT1 is highly expressed in various types of cancer suggesting that it may have oncogenic properties in certain tissues. It was the purpose of this study to test whether WT1 expression in neuroblastoma cells is regulated by oxygen and, if so, to analyze the underlying molecular mechanism. Exposure of Kelly neuroblastoma cells to low ambient oxygen did indeed stimulate expression of the WT1 gene. Silencing of HIF-2�� by RNA interference and knockout of HIF-2�� using CRISPR/Cas9 genome editing significantly reduced WT1 levels in hypoxic Kelly cells. In contrast, inactivation of HIF-1�� had no significant effect on WT1 expression in hypoxic Kelly cells. In silico analyses revealed a HIF binding motif within a cluster of DNase hypersensitivity in intron 3 of the WT1 gene. Binding of HIF-2 to this sequence was experimentally proven by chromatin immunoprecipitation (ChIP) and electromobility shift assay. A 256 bp DNA sequence encompassing the identified HIF binding site conferred oxygen sensitivity to otherwise hypoxia resistant WT1 and SV40 promoter constructs in transiently transfected Kelly cells. In functional assays, WT1 was found to enhance the migratory capacity of SK-NA-S neuroblastoma cells. R2 data analysis of cohorts of neuroblastoma patients showed that HIF-2�� and WT1 are independently associated with increased mortality. In summary, these results identify an oxygen sensitive enhancer in intron 3 of the WT1 gene. Binding of HIF-2 to this newly discovered enhancer element stimulates WT1 expression in hypoxic Kelly neuroblastoma cells. It is suggested that this novel regulatory mechanism might play a role in the pathophysiology of neuroblastoma.
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- 2022
12. Predictive Performance and Clinical Application of COV50, A Urinary Proteomic Biomarker in Early COVID-19 Infection: A Cohort Study
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Staessen, Jan, primary, Wendt, Ralph, additional, Yu, Yu-Ling, additional, Kalbitz, Sven, additional, Thijs, Lutgarde, additional, Siwy, Justyna, additional, Raad, Julia, additional, Metzger, Jochen, additional, Neuhaus, Barbara, additional, Papkalla, Armin, additional, von der Leyen, Heiko, additional, Mebazaa, Alexandre, additional, Dudoignon, Emmanuel, additional, Spasovski, Goce, additional, Milenkova, Mimoza, additional, Canevska-Tanevska, Aleksandra, additional, Psichogiou, Mina, additional, Rajzer, Marek W., additional, Fulawka, Lukasz, additional, Dzitkowska-Zabielska, Magdalena, additional, Weiss, Günter, additional, Feldt, Torsten, additional, Stegemann, Miriam Songa, additional, Normark, Johan, additional, Zoufaly, Alexander, additional, Schmiedel, Stefan, additional, Seilmaier, Michael, additional, Rumpf, Benedikt, additional, Banasik, Miroslaw, additional, Krajewska, Magdalena, additional, Catanese, Lorenzo, additional, Rupprecht, Harald, additional, Czerwienska, Beata, additional, Peters, Björn, additional, Nilsson, Asa, additional, Rothfuss, Katja, additional, Lübbert, Christoph, additional, Mischak, Harald, additional, Beige, Joachim, additional, and Investigators, CRIT-Cov-U, additional
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- 2022
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13. Molecular Mapping of Urinary Complement Peptides in Kidney Diseases
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Wendt, Ralph, primary, Siwy, Justyna, additional, He, Tianlin, additional, Latosinska, Agnieszka, additional, Wiech, Thorsten, additional, Zipfel, Peter F., additional, Tserga, Aggeliki, additional, Vlahou, Antonia, additional, Rupprecht, Harald, additional, Catanese, Lorenzo, additional, Mischak, Harald, additional, and Beige, Joachim, additional
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- 2021
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14. Fructose-induced severe hypertriglyceridemia and diabetes mellitus: a cautionary tale
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Dugic, Ana, primary, Kryk, Michael, additional, Mellenthin, Claudia, additional, Braig, Christoph, additional, Catanese, Lorenzo, additional, Petermann, Sandy, additional, Kothmann, Jürgen, additional, and Mühldorfer, Steffen, additional
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- 2021
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15. A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease
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Catanese, Lorenzo, primary, Siwy, Justyna, additional, Mavrogeorgis, Emmanouil, additional, Amann, Kerstin, additional, Mischak, Harald, additional, Beige, Joachim, additional, and Rupprecht, Harald, additional
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- 2021
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16. Intermittent hypoxia: Friend and foe
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Krueger, Katharina, primary, Catanese, Lorenzo, additional, and Scholz, Holger, additional
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- 2019
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17. Deletion of an intronic HIF-2α binding site suppresses hypoxia-induced WT1 expression
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Krueger, Katharina, primary, Catanese, Lorenzo, additional, Sciesielski, Lina K., additional, Kirschner, Karin M., additional, and Scholz, Holger, additional
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- 2019
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18. Differentiating primary and secondary FSGS using non-invasive urine biomarkers.
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Catanese L, Siwy J, Wendt R, Amann K, Beige J, Hendry B, Mischak H, Mullen W, Paterson I, Schiffer M, Wolf M, and Rupprecht H
- Abstract
Background: Focal segmental glomerulosclerosis (FSGS) is divided into genetic, primary (p), uncertain cause, and secondary (s) forms. The subclasses differ in management and prognosis with differentiation often being challenging. We aimed to identify specific urine proteins/peptides discriminating between clinical and biopsy-proven pFSGS and sFSGS., Methods: Sixty-three urine samples were collected in two different centers (19 pFSGS and 44 sFSGS) prior to biopsy. Samples were analysed using capillary electrophoresis-coupled mass spectrometry. For biomarker definition, datasets of age-/sex-matched normal controls (NC, n = 98) and patients with other chronic kidney diseases (CKDs, n = 100) were extracted from the urinary proteome database. Independent specificity assessment was performed in additional data of NC ( n = 110) and CKD ( n = 170)., Results: Proteomics data from patients with pFSGS were first compared to NC ( n = 98). This resulted in 1179 biomarker ( P < 0.05) candidates. Then, the pFSGS group was compared to sFSGS, and in a third step, pFSGS data were compared to data from different CKD etiologies ( n = 100). Finally, 93 biomarkers were identified and combined in a classifier, pFSGS93. Total cross-validation of this classifier resulted in an area under the receiving operating curve of 0.95. The specificity investigated in an independent set of NC and CKD of other etiologies was 99.1% for NC and 94.7% for CKD, respectively. The defined biomarkers are largely fragments of different collagens (49%)., Conclusion: A urine peptide-based classifier that selectively detects pFSGS could be developed. Specificity of 95%-99% could be assessed in independent samples. Sensitivity must be confirmed in independent cohorts before routine clinical application., Competing Interests: J.S. is employee of Mosaiques Diagnostics. H.M. is co-founder and a shareholder of Mosaiques Diagnostics. B.H., M.W., and I.P. are employees of Travere Therapeutics., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)
- Published
- 2023
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