Browsing by Author "Raftery, Daniel"
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Item Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis(Springer Nature, 2017-11) Chen, Chen; Gowda, G. A. Nagana; Zhu, Jiangjiang; Deng, Lingli; Gu, Haiwei; Chiorean, E. Gabriela; Zaid, Mohammad Abu; Harrison, Marietta; Zhang, Dabao; Zhang, Min; Raftery, Daniel; Graduate Medical Education, IU School of MedicineIntroduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.Item Bile Acids Conjugation in Human Bile Is Not Random: New Insights from 1H-NMR Spectroscopy at 800 MHz(Springer, 2009-06) Nagana Gowda, G. A.; Shanaiah, Narasimhamurthy; Cooper, Amanda; Maluccio, Mary; Raftery, Daniel; Department of Surgery, School of MedicineBile acids constitute a group of structurally closely related molecules and represent the most abundant constituents of human bile. Investigations of bile acids have garnered increased interest owing to their recently discovered additional biological functions including their role as signaling molecules that govern glucose, fat and energy metabolism. Recent NMR methodological developments have enabled single-step analysis of several highly abundant and common glycine- and taurine- conjugated bile acids, such as glycocholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurocholic acid, taurodeoxycholic acid, and taurochenodeoxycholic acid. Investigation of these conjugated bile acids in human bile employing high field (800 MHz) (1)H-NMR spectroscopy reveals that the ratios between two glycine-conjugated bile acids and their taurine counterparts correlate positively (R2 = 0.83-0.97; p = 0.001 x 10(-2)-0.006 x 10(-7)) as do the ratios between a glycine-conjugated bile acid and its taurine counterpart (R2 = 0.92-0.95; p = 0.004 x 10(-3)-0.002 x 10(-10)). Using such correlations, concentration of individual bile acids in each sample could be predicted in good agreement with the experimentally determined values. These insights into the pattern of bile acid conjugation in human bile between glycine and taurine promise useful clues to the mechanism of bile acids' biosynthesis, conjugation and enterohepatic circulation, and may improve our understanding of the role of individual conjugated bile acids in health and disease.Item Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls(ACS Publications, 2016-08-16) Deng, Lingli; Gu, Haiwei; Zhu, Jiangjiang; Gowda, G. A. Nagana; Djukovic, Danijel; Chiorean, Gabriela; Raftery, Daniel; Department of Medicine, School of MedicineBoth nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.Item Detection of Hepatocellular Carcinoma in Hepatitis C Patients: Biomarker Discovery by LC-MS(Elsevier, 2014-09-01) Bowers, Jeremiah; Hughes, Emma; Skill, Nicholas; Maluccio, Mary; Raftery, Daniel; Department of Surgery, IU School of MedicineHepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed the best performance using p-values alone, the PLS-DA model was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between highrisk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLSDA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.Item Differentiating Hepatocellular Carcinoma from Hepatitis C Using Metabolite Profiling(MDPI, 2012-10-10) Wei, Siwei; Suryani, Yuliana; Gowda, G. A. Nagana; Skill, Nicholas; Maluccio, Mary; Raftery, Daniel; Surgery, School of MedicineHepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by 1H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer.Item Metabolomic Characterization of Human Model of Liver Rejection Identifies Aberrancies Linked to Cyclooxygenase (COX) and Nitric Oxide Synthase (NOS)(International Scientific Information, 2019-06-11) Skill, Nicholas J.; Elliott, Campbell M.; Ceballos, Brian; Saxena, Romil; Pepin, Robert; Bettcher, Lisa; Ellensberg, Matthew; Raftery, Daniel; Maluccio, Mary A.; Ekser, Burcin; Mangus, Richard S.; Kubal, Chandrashekhar A.; Surgery, IU School of MedicineBACKGROUND Acute liver rejection (ALR), a significant complication of liver transplantation, burdens patients, healthcare payers, and the healthcare providers due to an increase in morbidity, cost, and resources. Despite clinical resolution, ALR is associated with an increased risk of graft loss. A unique protocol of delayed immunosuppression used in our institute provided a model to characterize metabolomic profiles in human ALR. MATERIAL AND METHODS Twenty liver allograft biopsies obtained 48 hours after liver transplantation in the absence of immunosuppression were studied. Hepatic metabolites were quantitated in these biopsies by liquid chromatography and mass spectroscopy (LC/MS). Metabolite profiles were compared among: 1) biopsies with reperfusion injury but no histological evidence of rejection (n=7), 2) biopsies with histological evidence of moderate or severe rejection (n=5), and 3) biopsies with histological evidence of mild rejection (n=8). RESULTS There were 133 metabolites consistently detected by LC/MS and these were prioritized using variable importance to projection (VIP) analysis, comparing moderate or severe rejection vs. no rejection or mild rejection using partial least squares discriminant statistical analysis (PLS-DA). Twenty metabolites were identified as progressively different. Further PLS-DA using these metabolites identified 3 metabolites (linoleic acid, γ-linolenic acid, and citrulline) which are associated with either cyclooxygenase or nitric oxide synthase functionality. CONCLUSIONS Hepatic metabolic aberrancies associated with cyclooxygenase and nitric oxide synthase function occur contemporaneous with ALR. Additional studies are required to better characterize the role of these metabolic pathways to enhance utility of the metabolomics approach in diagnosis and outcomes of ALR.Item Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer(Wiley, 2013-06) Wei, Siwei; Liu, Lingyan; Zhang, Jian; Bowers, Jeremiah; Gowda, G.A. Nagana; Seeger, Harald; Fehm, Tanja; Neubauer, Hans J.; Vogel, Ulrich; Clare, Susan E.; Raftery, Daniel; Surgery, School of MedicineBreast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long‐term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–mass spectrometry (LC–MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC–MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients., ► Metabolomics differentiates response to neoadjuvant breast cancer chemotherapy.► Four serum metabolites are found to correlate with response to chemotherapy.► A 4‐metabolite model identifies 80% of the patients not showing complete response.► Additional studies on larger patient cohorts are needed to validate the findings.Item Metabolomics method to comprehensively analyze amino acids in different domains(The Royal Society of Chemistry, 2015-04-21) Gu, Haiwei; Du, Jianhai; Carnevale Neto, Fausto; Carroll, Patrick A.; Turner, Sally J.; Chiorean, E. Gabriela; Eisenman, Robert N.; Raftery, Daniel; Department of Medicine, IU School of MedicineAmino acids play essential roles in both metabolism and the proteome. Many studies have profiled free amino acids (FAAs) or proteins; however, few have connected the measurement of FAA with individual amino acids in the proteome. In this study, we developed a metabolomics method to comprehensively analyze amino acids in different domains, using two examples of different sample types and disease models. We first examined the responses of FAAs and insoluble-proteome amino acids (IPAAs) to the Myc oncogene in Tet21N human neuroblastoma cells. The metabolic and proteomic amino acid profiles were quite different, even under the same Myc condition, and their combination provided a better understanding of the biological status. In addition, amino acids were measured in 3 domains (FAAs, free and soluble-proteome amino acids (FSPAAs), and IPAAs) to study changes in serum amino acid profiles related to colon cancer. A penalized logistic regression model based on the amino acids from the three domains had better sensitivity and specificity than that from each individual domain. To the best of our knowledge, this is the first study to perform a combined analysis of amino acids in different domains, and indicates the useful biological information available from a metabolomics analysis of the protein pellet. This study lays the foundation for further quantitative tracking of the distribution of amino acids in different domains, with opportunities for better diagnosis and mechanistic studies of various diseases.Item Targeted Metabolic Profiling of Hepatocellular Carcinoma and Hepatitis C using LC-MS/MS(Wiley, 2013) Baniasadi, Hamid; Gowda, G. A. Nagana; Gu, Haiwei; Zeng, Ao; Zhuang, Shui; Skill, Nicholas; Maluccio, Mary; Raftery, Daniel; Surgery, School of MedicineHepatitis C virus (HCV) infection of the liver is a global health problem and a major risk factor for the development of hepatocellular carcinoma (HCC). Sensitive methods are needed for the improved and earlier detection of HCC, which would provide better therapy options. Metabolic profiling of the high-risk population (HCV patients) and those with HCC provides insights into the process of liver carcinogenesis and possible biomarkers for earlier cancer detection. Seventy-three blood metabolites were quantitatively profiled in HCC (n = 30) and cirrhotic HCV (n = 22) patients using a targeted approach based on LC-MS/MS. Sixteen of 73 targeted metabolites differed significantly (p < 0.05) and their levels varied up to a factor of 3.3 between HCC and HCV. Four of these 16 metabolites (methionine, 5-hydroxymethyl-2'-deoxyuridine, N2,N2-dimethylguanosine, and uric acid) that showed the lowest p values were used to develop and internally validate a classification model using partial least squares discriminant analysis. The model exhibited high classification accuracy for distinguishing the two groups with sensitivity, specificity, and area under the receiver operating characteristic curve of 97%, 95%, and 0.98, respectively. A number of perturbed metabolic pathways, including amino acid, purine, and nucleotide metabolism, were identified based on the 16 biomarker candidates. These results provide a promising methodology to distinguish cirrhotic HCV patients, who are at high risk to develop HCC, from those who have already progressed to HCC. The results also provide insights into the altered metabolism between HCC and HCV.Item Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring(Springer, 2015-10) Zhu, Jiangjiang; Djukovic, Danijel; Deng, Lingli; Gu, Haiwei; Himmati, Farhan; Abu Zaid, Mohammad; Chiorean, E. Gabriela; Raftery, Daniel; Department of Medicine, IU School of MedicineColorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.