Browsing by Author "Gu, Haiwei"

Now showing 1 - 8 of 8
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    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 Medicine
    Introduction: 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.
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    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 Medicine
    Both 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.
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    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 Medicine
    Amino 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.
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    SOX17 Deficiency Mediates Pulmonary Hypertension: At the Crossroads of Sex, Metabolism, and Genetics
    (American Thoracic Society, 2023) Sangam, Shreya; Sun, Xutong; Schwantes-An, Tae-Hwi; Yegambaram, Manivannan; Lu, Qing; Shi, Yinan; Cook, Todd; Fisher, Amanda; Frump, Andrea L.; Coleman, Anna; Sun, Yanan; Liang, Shuxin; Crawford, Howard; Lutz, Katie A.; Maun, Avinash D.; Pauciulo, Michael W.; Karnes, Jason H.; Chaudhary, Ketul R.; Stewart, Duncan J.; Langlais, Paul R.; Jain, Mohit; Alotaibi, Mona; Lahm, Tim; Jin, Yan; Gu, Haiwei; Tang, Haiyang; Nichols, William C.; Black, Stephen M.; Desai, Ankit A.; Medical and Molecular Genetics, School of Medicine
    Rationale: Genetic studies suggest that SOX17 (SRY-related HMG-box 17) deficiency increases pulmonary arterial hypertension (PAH) risk. Objectives: On the basis of pathological roles of estrogen and HIF2α (hypoxia-inducible factor 2α) signaling in pulmonary artery endothelial cells (PAECs), we hypothesized that SOX17 is a target of estrogen signaling that promotes mitochondrial function and attenuates PAH development via HIF2α inhibition. Methods: We used metabolic (Seahorse) and promoter luciferase assays in PAECs together with the chronic hypoxia murine model to test the hypothesis. Measurements and Main Results: Sox17 expression was reduced in PAH tissues (rodent models and from patients). Chronic hypoxic pulmonary hypertension was exacerbated by mice with conditional Tie2-Sox17 (Sox17EC-/-) deletion and attenuated by transgenic Tie2-Sox17 overexpression (Sox17Tg). On the basis of untargeted proteomics, metabolism was the top pathway altered by SOX17 deficiency in PAECs. Mechanistically, we found that HIF2α concentrations were increased in the lungs of Sox17EC-/- and reduced in those from Sox17Tg mice. Increased SOX17 promoted oxidative phosphorylation and mitochondrial function in PAECs, which were partly attenuated by HIF2α overexpression. Rat lungs in males displayed higher Sox17 expression versus females, suggesting repression by estrogen signaling. Supporting 16α-hydroxyestrone (16αOHE; a pathologic estrogen metabolite)-mediated repression of SOX17 promoter activity, Sox17Tg mice attenuated 16αOHE-mediated exacerbations of chronic hypoxic pulmonary hypertension. Finally, in adjusted analyses in patients with PAH, we report novel associations between a SOX17 risk variant, rs10103692, and reduced plasma citrate concentrations (n = 1,326). Conclusions: Cumulatively, SOX17 promotes mitochondrial bioenergetics and attenuates PAH, in part, via inhibition of HIF2α. 16αOHE mediates PAH development via downregulation of SOX17, linking sexual dimorphism and SOX17 genetics in PAH.
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    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 Medicine
    Hepatitis 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.
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    Targeted metabolomics reveals plasma biomarkers and metabolic alterations of the aging process in healthy young and older adults
    (Springer, 2023) Jasbi, Paniz; Nikolich‑Žugich, Janko; Patterson, Jeffrey; Knox, Kenneth S.; Jin, Yan; Weinstock, George M.; Smith, Patricia; Twigg, Homer L., III; Gu, Haiwei; Medicine, School of Medicine
    With the exponential growth in the older population in the coming years, many studies have aimed to further investigate potential biomarkers associated with the aging process and its incumbent morbidities. Age is the largest risk factor for chronic disease, likely due to younger individuals possessing more competent adaptive metabolic networks that result in overall health and homeostasis. With aging, physiological alterations occur throughout the metabolic system that contribute to functional decline. In this cross-sectional analysis, a targeted metabolomic approach was applied to investigate the plasma metabolome of young (21-40y; n = 75) and older adults (65y + ; n = 76). A corrected general linear model (GLM) was generated, with covariates of gender, BMI, and chronic condition score (CCS), to compare the metabolome of the two populations. Among the 109 targeted metabolites, those associated with impaired fatty acid metabolism in the older population were found to be most significant: palmitic acid (p < 0.001), 3-hexenedioic acid (p < 0.001), stearic acid (p = 0.005), and decanoylcarnitine (p = 0.036). Derivatives of amino acid metabolism, 1-methlyhistidine (p = 0.035) and methylhistamine (p = 0.027), were found to be increased in the younger population and several novel metabolites were identified, such as cadaverine (p = 0.034) and 4-ethylbenzoic acid (p = 0.029). Principal component analysis was conducted and highlighted a shift in the metabolome for both groups. Receiver operating characteristic analyses of partial least squares-discriminant analysis models showed the candidate markers to be more powerful indicators of age than chronic disease. Pathway and enrichment analyses uncovered several pathways and enzymes predicted to underlie the aging process, and an integrated hypothesis describing functional characteristics of the aging process was synthesized. Compared to older participants, the young group displayed greater abundance of metabolites related to lipid and nucleotide synthesis; older participants displayed decreased fatty acid oxidation and reduced tryptophan metabolism, relative to the young group. As a result, we offer a better understanding of the aging metabolome and potentially reveal new biomarkers and predicted mechanisms for future study.
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    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 Medicine
    Colorectal 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.
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    Zeaxanthin Drives Dynamic Changes in the Mouse Metabolome Through Gut Microbiome Shift
    (Elsevier, 2021) Lu, Peiran; Wong, Siau Yen; Chai, Jianmin; Jasbi, Paniz; Wu, Lei; Lyu, Yi; Tang, Minghua; Smith, Brenda; Lucas, Edralin; Clarke, Stephen L.; Chowanadisai, Winyoo; Shen, Xinchun; He, Hui; Zhao, Jiangchao; Gu, Haiwei; Conway, Tyrrell; Wyss, Adrian; Lin, Dingbo; Obstetrics and Gynecology, School of Medicine
    Objectives: Zeaxanthin, an oxygenized carotenoid, exerts antioxidant properties in human nutrition and metabolism. Like other carotenoids, zeaxanthin is poorly absorbed in the small intestine. The large portion of zeaxanthin reaches the colon and is not fully recovered in the colon. In this study, we aimed to investigate the association of zeaxanthin intake with the gut microbiome homeostasis and metabolomic responses in mice. Methods: Six-week-old male and female C57BL/6J wild type (WT), beta-carotene oxygenase 2 (BCO2) knockout mice were fed with AIN93M chow diets supplemented with or without zeaxanthin (0.02% w/w) for 10 weeks. At the termination of the study, mice were fasted for 3 hrs prior to euthanization. Cecal contents, colon, serum, feces, and other tissues were collected for laboratory assessments.16S rRNA sequencing and LC-MS/MS were performed for gut microbiota profiling and serum and fecal metabolomics analysis, respectively. Results: Significant zeaxanthin accumulation occurred in BCO2 KO, but not WT mice. Zeaxanthin accumulation was associated with the alteration of colonic gut microbiota composition, for example, zeaxanthin-increased abundance in Lachnospiraceae, Proteobacteria, and Parabacteroides, indicating enhanced short-chain production, improved intestinal integrity, and anaerobic bacterial colonization. The results of fecal and serum metabolomics revealed that zeaxanthin significantly altered tyrosine metabolism, branched-chain fatty acid oxidation, fatty acid biosynthesis, and phospholipid biosynthesis, and suppressed levels of kynurenine and trimethylamine N-oxide (TMAO). Conclusions: The results suggested that zeaxanthin accumulation promotes gut microbiome homeostasis and alters the gut microbial metabolites as signals in stimulating the host-gut microbe interplay.