@article{MacintyreZhangViegelmannetal.2014,
  author    = {Macintyre, Lynsey and Zhang, Tong and Viegelmann, Christina and Martinez, Ignacio Juarez and Cheng, Cheng and Dowdells, Catherine and Abdelmohsen, Usama Ramadan and Gernert, Christine and Hentschel, Ute and Edrada-Ebel, RuAngelie},
  title     = {Metabolomic Tools for Secondary Metabolite Discovery from Marine Microbial Symbionts},
  series = {Marine Drugs},
  volume    = {12},
  journal   = {Marine Drugs},
  number    = {6},
  issn      = {1660-3397},
  doi       = {10.3390/md12063416},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-116097},
  pages     = {3416-3448},
  year      = {2014},
  abstract  = {Marine invertebrate-associated symbiotic bacteria produce a plethora of novel secondary metabolites which may be structurally unique with interesting pharmacological properties. Selection of strains usually relies on literature searching, genetic screening and bioactivity results, often without considering the chemical novelty and abundance of secondary metabolites being produced by the microorganism until the time-consuming bioassay-guided isolation stages. To fast track the selection process, metabolomic tools were used to aid strain selection by investigating differences in the chemical profiles of 77 bacterial extracts isolated from cold water marine invertebrates from Orkney, Scotland using liquid chromatography-high resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance (NMR) spectroscopy. Following mass spectrometric analysis and dereplication using an Excel macro developed in-house, principal component analysis (PCA) was employed to differentiate the bacterial strains based on their chemical profiles. NMR H-1 and correlation spectroscopy (COSY) were also employed to obtain a chemical fingerprint of each bacterial strain and to confirm the presence of functional groups and spin systems. These results were then combined with taxonomic identification and bioassay screening data to identify three bacterial strains, namely Bacillus sp. 4117, Rhodococcus sp. ZS402 and Vibrio splendidus strain LGP32, to prioritize for scale-up based on their chemically interesting secondary metabolomes, established through dereplication and interesting bioactivities, determined from bioassay screening.},
  language  = {en}
}
@article{ArltBiehlTayloretal.2011,
  author    = {Arlt, Wiebke and Biehl, Michael and Taylor, Angela E. and Hahner, Stefanie and Lib{\´e}, Rossella and Hughes, Beverly A. and Schneider, Petra and Smith, David J. and Stiekema, Han and Krone, Nils and Porfiri, Emilio and Opocher, Giuseppe and Bertherat, Jer{\^o}me and Mantero, Franco and Allolio, Bruno and Terzolo, Massimo and Nightingale, Peter and Shackleton, Cedric H. L. and Bertagna, Xavier and Fassnacht, Martin and Stewart, Paul M.},
  title     = {Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors},
  series = {The Journal of Clinical Endocrinology \& Metabolism},
  volume    = {96},
  journal   = {The Journal of Clinical Endocrinology \& Metabolism},
  number    = {12},
  doi       = {10.1210/jc.2011-1565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-154682},
  pages     = {3775 -- 3784},
  year      = {2011},
  abstract  = {Context: Adrenal tumors have a prevalence of around 2\% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11\% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. Objective: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. Design: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. Results: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90\% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88\% (area under the curve = 0.96) when using only the nine most differentiating markers. Conclusions: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.},
  language  = {en}
}