Die axiale Spondyloarthritis (axSpA) ist eine chronische entzündlich-rheumatische Erkrankung unbekannter Ursache, die durch Entzündungen in den Sakroiliakalgelenken (SIG) und an den Gelenken der Wirbelsäule gekennzeichnet ist. Darüber hinaus kann es im Krankheitsverlauf zu einer Ankylose in den SIG und zur Entwicklung von Syndesmophyten an den Wirbelkörpern kommen. Da ein Großteil der axSpA-Patienten auch sub-klinische mukosale Entzündungen aufweist, werden mukosale Antigene als Trigger der Entzündung diskutiert. Für die Diagnose und Prognose der axSpA existieren bisher wenige serologische Marker mit hoher Sensitivität. In dieser Arbeit konnte gezeigt werden, dass die Serumkonzentrationen von CTX-II, BMP-2 und LBP ein hohes diagnostisches Potenzial für die axSpA aufweisen, während die Serumkonzentrationen von BMP-2, PINP und VEGF als Marker für die Vorhersage röntgenolgischer Progression geeignet sein könnten. Weiterhin konnten erhöhte LPS-, LBP- und IL-6-Serum-Konzentrationen bei axSpA-Patienten nachgewiesen werden, die auf eine Translokation bakterieller Antigene hinweisen. Die Charakterisierung der Monoyzten zeigte erhöhte Frequenzen der CD14++CD16- und einen verminderten Anteil an CD14++CD16+ Monozyten in Patienten mit axSpA. Funktionell wiesen die Monozyten von axSpA-Patienten eine in vivo Präaktivierung mit erhöhter spontaner und durch suboptimale bakterielle Stimuli induzierter Freisetzung proinflammatorischer Zytokine bei gleichzeitig verminderter Reaktivität auf LPS in vitro auf. Diese Präaktivierung war bei Patienten unter Standardtherapie, nicht aber unter TNF-Blocker-Therapie, nachweisbar. Interessanterweise bestand bei Patienten unter Standardtherapie ein Zusammenhang zwischen der Krankheitsaktivität und der Frequenz zytokinproduzierender Monozyten. Somit konnten mit dieser Arbeit Biomarker mit diagnostischer und prognostischer Bedeutung für die axSpA identifiziert werden, deren Bedeutung in unabhängigen Kohorten weiter untersucht werden muss. / Axial Spondyloarthitis (axSpA) is a chronic inflammatory-rheumatic disease of unknown cause. It is characterized by inflammations of the sacroiliac joints (SIG) and the spinal joints. In addition an ankylosis in the SIG can develop in the progression of the disease as well as syndesmophytes at the vertebral body. Since the majority of axSpA-patients also have sub-clinical mucosal inflammations, mucosal anti-gens are discussed as triggers of the inflammation. For the diagnosis and prognosis of axSpA there are only a few serological markers with high sensitivity and specificity until now. In this thesis it could be shown that the serum concentration of CTX-II, BMP-2 and LBP exhibit a high diagnostic potential for axSpA, while the serum concentration of BMP-2, PINP and VEGF could be suitable markers for the forecast of radiographic progression. Furthermore increased LPS-, LBP- and IL-6-serum concentrations could be verified, which can be an indicator for the translocation of bacterial antigenes. The monocyte characterization showed increased frequencies of the pro-inflammatory CD14++CD16- sub population and a reduced portion of CD14++CD16+ monocytes in patients with axSpA. Functionally the monocytes of axSpA-patients showed an in vivo pre-activation with increased spontaneous and by suboptimal bacterial stimuli induced release of pro-inflammatory cytokines with simultaneously reduced reactivity towards LPS in vitro. This pre-activation could be detected for patients undergoing standard therapy, but not for those under TNF-blocker-therapy. Interestingly for the standard therapy patients there was a connection between the activity of the disease, meaning the BASDAI, and the frequency of the cytokine-producing monocytes. Therefore biomarkers with diagnostic and prognostic relevance could be identified in line with this thesis. The significance of these biomarkers has to be researched further in independent cohorts.

No description available.

Proteins and their interactions are fundamental to cellular life. Disruption of protein-protein, protein-RNA, or protein-DNA interactions can lead to disease, by affecting the function of protein complexes or by affecting gene regulation. A better understanding of these interactions on the molecular level gives rise to new methods to predict protein interaction, and is critical for the rational design of new therapeutic agents that disrupt disease-causing interactions. This thesis consists of three parts that focus on various aspects of protein interactions and their prediction in the context of disease. In the first part of this thesis, we classify interfaces of protein-protein interactions. We do so by systematically computing all binding sites between protein domains in protein complex structures solved by X-ray crystallography. The result is SCOPPI, the Structural Classification of Protein Protein Interfaces. Clustering and classification of geometrically similar interfaces reveals interesting examples comprising viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues, and diversity of interface localisations. We then develop a novel method to predict protein interactions which is based on these structural interface templates from SCOPPI. The method is applied in three use cases covering osteoclast differentiation, which is relevant for osteoporosis, the microtubule-associated network in meiosis, and proteins found deregulated in pancreatic cancer. As a result, we are able to reconstruct many interactions known to the expert molecular biologist, and predict novel high confidence interactions backed up by structural or experimental evidence. These predictions can facilitate the generation of hypotheses, and provide knowledge on binding sites of promising disease-relevant candidates for targeted drug development. In the second part, we present a novel algorithm to search for protein binding sites in RNA sequences. The algorithm combines RNA structure prediction with sequence motif scanning and evolutionary conservation to identify binding sites on candidate messenger RNAs. It is used to search for binding sites of the PTBP1 protein, an important regulator of glucose secretion in the pancreatic beta cell. First, applied to a benchmark set of mRNAs known to be regulated by PTBP1, the algorithm successfully finds significant binding sites in all benchmark mRNAs. Second, collaborators carried out a screen to identify changes in the proteome of beta cells upon glucose stimulation while inhibiting gene expression. Analysing this set of post-transcriptionally controlled candidate mRNAs for PTBP1 binding, the algorithm produced a ranked list of 11 high confident potential PTBP1 binding sites. Experimental validation of predicted targets is ongoing. Overall, identifying targets of PTBP1 and hence regulators of insulin secretion may contribute to the treatment of diabetes by providing novel protein drug targets or by aiding in the design of novel RNA-binding therapeutics. The third part of this thesis deals with gene regulation in disease. One of the great challenges in medicine is to correlate genotypic data, such as gene expression measurements, and other covariates, such as age or gender, to a variety of phenotypic data from the patient. Here, we address the problem of survival prediction based on microarray data in cancer patients. To this end, a computational approach was devised to find genes in human cancer tissue samples whose expression is predictive for the survival outcome of the patient. The central idea of the approach is the incorporation of background knowledge information in form of a network, and the use of an algorithm similar to Google s PageRank. Applied to pancreas cancer, it identifies a set of eight genes that allows to predict whether a patient has a poor or good prognosis. The approach shows an accuracy comparable to studies that were performed in breast cancer or lymphatic malignancies. Yet, no such study was done for pancreatic cancer. Regulatory networks contain information of transcription factors that bind to DNA in order to regulate genes. We find that including background knowledge in form of such regulatory networks gives highest improvement on prediction accuracy compared to including protein interaction or co-expression networks. Currently, our collaborators test the eight identified genes for their predictive power for survival in an independent group of 150 patients. Under a therapeutic perspective, reliable survival prediction greatly improves the correct choice of therapy. Whereas the live expectancy of some patients might benefit from extensive therapy such as surgery and chemotherapy, for other patients this may only be a burden. Instead, for this group, a less aggressive or different treatment could result in better quality of the remaining lifetime. Conclusively, this thesis contributes novel analytical tools that provide insight into disease-relevant interactions of proteins. Furthermore, this thesis work contributes a novel algorithm to deal with noisy microarray measurements, which allows to considerably improve prediction of survival of cancer patients from gene expression data.

Background Prostate cancer (PCa) is the most frequently diagnosed malignant disease among adult males in the USA and the second leading cause of cancer deaths in men. Due to the lack of diagnostic tools that are able to differentiate highly malignant and aggressive cases from indolent tumors, overtreatment has become very common in the era of prostate specific antigen (PSA) screening. New diagnostic methods to determine biological status, malignancy, aggressiveness and extent of PCa are urgently needed. 1H High Resolution Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy (1H HRMAS MRS) can be used to establish PCa metabolomic profiles while preserving tissue architecture for subsequent histopathological analysis. Immunohistochemistry (IHC), as opposed to conventional histopathology methods, has the potential to provide objective, more accurate and quantitative knowledge of tissue pathology. This diagnostic- accuracy study sought to evaluate a novel approach to quantitatively identify metabolomic markers of PCa by exploring the potential of PCa immunomarkers to quantify metabolomic profiles established by 1H HRMAS MRS. Material and Methods 1H HRMAS MRS was performed on tissue samples of 51 prostate cancer patients using a 14.1 Tesla NMR spectrometer (BRUKER Biospin, Billerica, MA) with a rotor synchronized CPMG pulse sequence. Spectral intensities of 36 regions of interest were measured as integrals of curve fittings with Lorentzian-Gaussian line shapes. Immunohistochemistry (IHC) was carried out following the spectroscopy scan, using three prostate immunomarkers to identify cancerous and benign glands: P504S (Alpha-methylacyl-CoA-racemace), CK903 (high-molecular weight cytokeratin) and p63. The immunostaining quality following 1H HRMAS MRS was evaluated and compared to unscanned sections of the same sample, to verify the stability and accessibility of the proposed immunomarkers. IHC images were automatically and quantitatively evaluated, using a quantitative image analysis program (QIAP), to determine the percentage of cancerous and benign epithelia in the tissue cross- sections. The results of the program were validated by a correlation with the results of a quantitative IHC review and quantitative conventional histopathology analysis performed by an experienced pathologist. Ultimately, spectral intensities and the cancer epithelium percentage, obtained from quantitative immunohistochemistry, were correlated in order to validate PCa metabolomic markers identified by 1H HRMAS MRS. Patient outcomes and incidence of recurrence were determined by retrospective review of medical records five years after initial surgery. Categories of recurrence were correlated to spectral intensities to explore potential metabolomic markers of recurrence in the cohort. Results Immunostainings with P504S and CK903 showed excellent staining quality and accessibility following 1H HRMAS MRS, suggesting these markers to be suitable for the presented quantitative approach to determine metabolomics profiles of PCa. In contrast, the quality of p63 IHC was impaired after previously performed spectroscopy. IHC using the immunomarkers P504S and CK903 on adjacent slides was found to present a feasible quantitative diagnostic method to distinguish between benign and cancerous conditions in prostate tissue. The cancer epithelium percentage as determined by QIAP showed a significant correlation to the results of quantitative IHC analysis performed by a pathologist (p < 0.001), as well as to a quantitative conventional histopathology review (p = 0.001). The same was true for the benign epithelium percentage (p < 0.001 and p = 0.0183), validating the presented approach. Two metabolomic regions showed a significant correlation between relative spectral intensities and the cancer epithelium percentage as determined by QIAP: 3.22 ppm (p = 0.015) and 2.68 ppm (p = 0.0144). The metabolites corresponding to these regions, phosphocholine and citrate, could be identified as metabolomic markers of PCa in the present cohort. 45 patients were followed for more than 12 months. Of these, 97.8% were still alive five years after initial surgery. 11 patients (24.4%) experienced a recurrence during the follow- up time. The categories of recurrence showed a correlation to the spectral intensities of two regions, 2.33 – 2.3 ppm (p = 0.0403) and 1.28 ppm (p = 0.0144), corresponding to the metabolites phosphocreatine and lipids. Conclusion This study introduces a method that allows an observer-independent, quantitative analysis of IHC to help establish metabolomic profiles and identify metabolomic markers of PCa from spectral intensities obtained with 1H HRMAS NMR Spectroscopy. The immunomarkers P504S and CK903 have been found suitable IHC analysis following 1H HRMAS MRS. A prospective in vivo application of PCa metabolite profiles and metabolomic markers determined by the presented method could serve as highly sensitive, non- invasive diagnostic tool. This observer- independent, computer- automated, quantitative analysis could help to distinguish highly aggressive tumors from low-malignant conditions, avoid overtreatment and reduce risks and complications for cancer patients in the future. Further studies are needed to verify the identified PCa metabolomic markers and to establish clinical applicability. / Einführung Prostatakrebs ist eine häufigsten Krebserkrankungen in den USA und die zweithäufigste malignom- assoziierte Todesursache männlicher Patienten weltweit. Seit der Einführung des Prostata- spezifischen Antigen (PSA)- Screeningtests wird diese Krebsart in früheren Stadien diagnostiziert und therapiert, wodurch die Mortalitätsrate in den letzten Jahren deutlich reduziert werden konnte. Da moderne diagnostische Methoden bislang jedoch nicht ausreichend in der Lage sind, suffizient zwischen hochmalignen und weniger aggressiven Varianten dieses bösartigen Krebsleidens zu unterscheiden, werden häufig auch Patienten aggressiv therapiert, deren niedriggradiges Prostatakarzinom keine klinische Relevanz gehabt hätte. Es besteht daher ein großes wissenschaftliches Interesse an der Entwicklung neuer diagnostischer Methoden zur akkuraten Bestimmung von biologischem Status, Malignität, Aggressivität und Ausmaß einer Prostatakrebserkrankung. \\\\\\\"1H High Resolution Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy\\\\\\\" (1H HRMAS MRS) ist eine vielversprechende diagnostische Methode, welche es ermöglicht, metabolomische Profile von Prostatakrebs zu erstellen, ohne die Gewebsstruktur der analysierten Proben zu zerstören. Durch anschließende histopathologische Begutachtung lassen sich die erstellten Metabolitprofile validieren und evaluieren. Im Gegensatz zu konventionellen histopathologischen Methoden können durch immunhistochemische Verfahren dabei objektivere, akkuratere und quantifizierbare histopathologische Erkenntnisse gewonnen werden. Die vorliegende Studie präsentiert einen neuentwickelten diagnostischen Ansatz zur quantitativen Bestimmung von metabolomischen Markern von Prostatakrebs, basierend auf der Durchführung von 1H HRMAS NMR Spektroskopie und quantitativer Immunhistochemie. Material und Methoden Einundfünfzig Gewebsproben von Prostatakrebspatienten wurden mittels 1H HRMAS MRS an einem 14.1 T BRUKER NMR Spektrometer unter Einsatz einer CPMG-Pulssequenz untersucht. Spektrale Intensitäten in 36 Metabolitregionen wurden gemessen. Anschließend wurden die analysierten Gewebeproben mit drei Immunfärbemarkern für sowohl malignes (P504S, Alpha-methylacyl-CoA-racemase) als auch benignes (CK903, High-molecular weight cytokeratin, und p63) Prostatagewebe angefärbt und quantitativ mit Hilfe eines Bildanalyseprogramms (QIAP) ausgewertet. Die Anwendbarkeit und Auswertbarkeit der genannten Immunomarker nach Spektroskopie wurde evaluiert und mit der Färbungsqualität von nicht- gescannten Schnitten verglichen. Die Resultate der automatischen Auswertung durch QIAP konnten durch einen erfahrenen Pathologen in einer quantitativen Analyse der Immunfärbungen sowie konventioneller histologischer Färbungen derselben Gewebsproben validiert werden. Die spektralen Intensitäten aus den Messungen mit 1H HRMAS MRS wurden mit den korrespondierenden Ergebnissen der quantitativen Auswertung der Immunfärbungen korreliert, um metabolomische Marker von Prostatakrebs zu identifizieren. Der klinische Verlauf und die Rezidivrate der Patienten wurden 5 Jahre nach der initialen Prostatektomie retrospektiv bestimmt. Rezidivkategorien wurden erstellt und mit den bestimmten spektralen Intensitäten korreliert, um metabolomische Marker für das Auftreten von Prostatakrebsrezidiven zu identifizieren. Ergebnisse Die Immunfärbungen mit P504S und CK903 zeigten exzellente Qualität und Auswertbarkeit nach vorheriger 1H HRMAS MRS. Beide Marker eigneten sich zur Durchführung von quantitativer Immunhistochemie an spektroskopierten Gewebeproben. Im Gegensatz dazu war die Qualität der Immunfärbungen mit p63 nach Spektroskopie vermindert. Quantitative Immunfärbungen unter Einsatz der Immunmarker P504S und CK903 stellten eine praktikable diagnostische Methode dar, um zwischen malignen und benignem Prostatagewebe zu unterscheiden. Der Anteil von bösartig verändertem Prostatagewebe, bestimmt durch QIAP, korrelierte signifikant mit den Ergebnissen der quantitativen Analyse der Immunfärbungen durch den Pathologen (p < 0.001), sowie mit der quantitativen Auswertung der konventionellen histopathologischen Färbung (p = 0.001). Ebenso ließ sich die Bestimmung des Anteils von benignem Gewebe mit QIAP zu den Ergebnissen der pathologischen Analyse korrelieren (p < 0.001 und p = 0.0183). Für zwei metabolomische Regionen konnte ein signifikante Korrelation zwischen relativen spektralen Intensitäten, bestimmt mit 1H HRMAS NMR Spektroskopie, und dem Anteil von malignem Epithelium in derselben Gewebeprobe, ermittelt durch QIAP, festgestellt werden: 3.22 ppm (p = 0.015) und 2.68 ppm (p = 0.0144). Die zu diesen Regionen korrespondierenden Metaboliten, Phosphocholin und Zitrat, konnten als potentielle metabolomische Marker für Prostatakrebs identifiziert werden. Die retrospektiven Analyse der klinischen Daten der Patienten fünf Jahre nach Prostatektomie ergab eine Überlebensrate von 97.8%. Elf dieser Patienten (24.4%) erlitten ein Rezidiv ihrer Erkrankung. Die bestimmten Rezidivkategorien korrelierten signifikant mit zwei metabolomischen Regionen (2.33 – 2.3 ppm, p = 0.0403 und 1.28 ppm, p = 0.0144), welche zu den Metaboliten Phosphokreatin und Lipiden korrespondierten. Schlussfolgerung Die vorliegende Studie präsentiert einen diagnostischen Ansatz zur objektiven und quantitativen Bestimmung metabolomischer Marker von Prostatakrebs unter Verwendung von 1H HRMAS MRS und Immunhistochemie. P504S und CK903 eignen sich als Immunmarker für quantitative Immunfärbungen nach vorheriger Durchführung von 1H HRMAS MRS. Die Metaboliten Phosphocholin und Zitrat konnten in der vorliegenden Patientenkohorte als potentielle metabolomische Marker für Prostatakrebs identifiziert werden. Eine mögliche in vivo Anwendung der gefundenen metabolomischen Marker könnte als hochsensitives, objektives und nicht- invasives diagnostisches Werkzeug der Prostatakrebsdiagnostik dienen. Der vorliegende untersucherunabhängige, automatisierte und quantitative diagnostischer Ansatz hat das Potential, zwischen hochmalignen und weniger aggressiven Krebsfällen zu unterscheiden und somit unnötige Risiken und Komplikationen für Prostatakrebspatienten zu reduzieren. Weitere Untersuchungen sind notwendig, um die identifizierten metabolomischen Marker zu verifizieren und eine klinische Anwendung zu etablieren.

Oral cancer is the sixth most common cancer worldwide and has been marked by high morbidity and poor survival rates that have changed little over the past few decades. Beyond prevention, early detection is the most crucial determinant for successful treatment and survival of cancer. Yet current methodologies for cancer diagnosis based upon pathological examination alone are insufficient for detecting early tumor progression and molecular transformation. Development of new diagnostic tools incorporating tumor biomarkers could enhance early detection by providing molecular-level insight into the biochemical and cellular changes associated with oral carcinogenesis. The work presented in this doctoral dissertation aims to address this clinical need through the development of new automated cellular analysis methods, incorporating lab-on-a-chip sensor techniques, for examination of molecular and morphological biomarkers associated with oral carcinogenesis. Using the epidermal growth factor receptor (EGFR) as a proof-of-principle biomarker, the sensor system demonstrated capacity to support rapid biomarker analysis in less than one-tenth the time of traditional methods and effectively characterized EGFR biomarker over-expression in oral tumor-derived cell lines. Successful extension from in vitro tumor cell lines to clinically relevant exfoliative brush cytology was demonstrated, providing a non-invasive method for sampling abnormal oral epithelium. Incorporation of exfoliative cytology further helped to define the important assay and imaging parameters necessary for dual molecular and morphological analysis in adherent epithelium. Next, this new sensor assay and method was applied in a small pilot study in order to secure an initial understanding of the diagnostic utility of such biosensor systems in clinical settings. Four cellular features were identified as useful indicators of cancerous or pre-cancerous conditions including, the nuclear area and diameter, nuclear-to-cytoplasm ratio, and EGFR biomarker expression. Further examination using linear regression and ROC curve analysis identified the morphological features as the best predictors of disease while a combination of all features may be ideal for classification of OSCC and pre-malignancy with high sensitivity and specificity. Further testing in a larger sample size is necessary to validate this regression model and the LOC sensor technique, but shows strong promise as a new diagnostic tool for early detection of oral cancer. / text

No description available.

Background: N-terminal pro-brain natriuretic peptide (NT-proBNP) is secreted by cardiac ventricular myocytes upon pressure and volume overload and is a prognostic marker to monitor the severity of precapillary pulmonary hypertension and the extent of right heart failure. Objectives: The impact of physical exercise on NT-proBNP levels in patients with left heart disease was demonstrated previously. No data regarding patients with isolated right heart failure and the influence of acute exercise on NT-proBNP serum levels exist. Methods: Twenty patients with precapillary pulmonary hypertension were examined. Hemodynamic parameters were measured during right heart catheterization. Serum NT-proBNP of patients was measured at rest, after a 6-min walking test, during ergospirometry and during recovery, all within 7 h. Significant differences in sequential NT-proBNP values, relative changes compared to values at rest and the correlation between NT-proBNP and obtained parameters were assessed. Results: At rest, the mean serum level of NT-proBNP was 1,278 ± 998 pg/ml. The mean level of NT-proBNP at maximal exercise was increased (1,592 ± 1,219 pg/ml), whereas serum levels decreased slightly during recovery (1,518 ± 1,170 pg/ml). The relative increase of serum NT-proBNP during exercise correlated with pulmonary vascular resistance (r = 0.45; p = 0.026) and cardiac output (r = –0.5; p = 0.015). Conclusions: In this study, we demonstrated acute changes in NT-proBNP levels due to physical exercise in a small group of patients with precapillary pulmonary hypertension. Our results also confirm the predominant usefulness of NT-proBNP as an intraindividual parameter of right heart load. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Proteomics as a modern comprehensive approach to the analysis of proteomes was applied in three projects aimed at diagnosis and therapy of cancer. The aim of the first the project was to find a new diagnostic biomarker for ovarian cancer. Two different comparative proteomic approaches were used for comparative analysis of sera from patients diagnosed with ovarian cancer and from healthy age-matched women. We identified -1-antitrypsin with increased concentration in patien sera, and apolipoprotein A4 and retinol-binding protein 4 (RBP4) with significantly decreased concentration in patients. The significantly decerased concentration of RBP4 in patients is a new observation. We propose that RBP4 is either decreased in ovarian cancer patients as a result of its reduced production by ovary or it may reflect less specific systemic changes, for instance early onset of cancer cachexia. The second project was focused on gaining insight into the molecular mechanism of cytarabine resistance in mantle cell lymphoma (MCL). Proteomic and transcriptomic analyses of cytarabine-resistant cells revealed marked downregulation of deoxycytidine kinase (DCK) - a protein essential to intracellular activation of purine and pyrimidine nucleosides and their analogues including cytarabine. The cytarabine-resistant MCL...

The thesis is focused on the development of the electrochemical system with microfluidic platform for the detection of multiple biomarkers. It analyses the use of biomarkers for the early diagnosis of cancer. The theoretical part contains basic information about voltammetric methods and microfluidic systems. The practical part provides solutions to the microfluidic chips, including the description of the used materials, designs, methodologies of preparation and conclusions from the testing of the manufactured microfluidic systems. The thesis describes the lock-in electrochemical system which measures the response of 4 electrochemical cells simultaneously. For the electrochemical system measurements, an electrochemical chip consisting of 64 electrochemical cells was used. The results of the analysis include the processing of the system tests and detected voltammetric curves of the Fe2+/Fe3+ solution and cysteine.

Although human biomedical and physiological information is readily available, such information for great apes is limited. We analyzed clinical chemical biomarkers in serum samples from 277 wild- and captive-born great apes and from 312 healthy human volunteers as well as from 20 rhesus macaques. For each individual, we determined a maximum of 33 markers of heart, liver, kidney, thyroid and pancreas function, hemoglobin and lipid metabolism and one marker of inflammation. We identified biomarkers that show differences between humans and the great apes in their average level or activity. Using the rhesus macaques as an outgroup, we identified human-specific differences in the levels of bilirubin, cholinesterase and lactate dehydrogenase, and bonobo-specific differences in the level of apolipoprotein A-I. For the remaining twenty-nine biomarkers there was no evidence for lineage-specific differences. In fact, we find that many biomarkers show differences between individuals of the same species in different environments. Of the four lineagespecific biomarkers, only bilirubin showed no differences between wild- and captive-born great apes. We show that the major factor explaining the human-specific difference in bilirubin levels may be genetic. There are human-specific changes in the sequence of the promoter and the protein-coding sequence of uridine diphosphoglucuronosyltransferase 1 (UGT1A1), the enzyme that transforms bilirubin and toxic plant compounds into water-soluble, excretable metabolites. Experimental evidence that UGT1A1 is down-regulated in the human liver suggests that changes in the promoter may be responsible for the human-specific increase in bilirubin. We speculate that since cooking reduces toxic plant compounds, consumption of cooked foods, which is specific to humans, may have resulted in relaxed constraint on UGT1A1 which has in turn led to higher serum levels of bilirubin in humans.