DEVELOPMENT OF BIOMARKERS OF EFFECT FROM CHRONIC TOBACCO USAGE: Part 3, Potential Metabolomics Biomarkers of Tobacco Effect

Transcription

1 DEVELOPMENT OF BIOMARKERS OF EFFECT FROM CHRONIC TOBACCO USAGE: Part 3, Potential Metabolomics Biomarkers of Tobacco Effect CORESTA Congress 3-7 September 1 Sapporo, Japan SSPT 4 G. L. Prasad Bobbette A. Jones Peter Chen Adam D. Kennedy 1

2 Purpose Explore global metabolomic changes in long-term smokers and long-term moist snuff consumers Discover metabolomic pathways and specific metabolites which could be used as potential biomarkers of effect

3 Clinical Study Samples Clinical Study Biomarker Discovery Study Study design, biomarkers of exposure and effect were presented in the preceding two platform presentations Three cohorts: Long-term Smokers (SMK, n=4) Long-term Moist Snuff Consumers (MSC, n=4) Non-Tobacco Consumers (NTC, n=4) Samples Analyzed Plasma (overnight fasting from food and tobacco) 4h urine ( normal diet and tobacco use) 3

4 Background Approach Conduct a global screen for a collection of metabolites present in a biological sample Why perform a global screen ( Omics )? To identify hitherto unknown candidate biomarkers To understand the underlying mechanisms Metabolomic Profiling Mass spectrometry-based metabolomic profiling and analysis were performed at Metabolon Inc. Details were presented in an accompanying poster 4

5 Overview of the Plasma and Urine Metabolomic Profiling Data Many known and unknown metabolites were detected in the plasma and urine samples of SMK, MSC and NTC. The metabolic profiles enabled separation of SMK, MSC and NTC cohorts. Several metabolites in diverse physiological pathways showed statistically significant differences in these generally healthy study cohorts. These results were presented in a poster (#3) at this conference: Development Of Biomarkers Of Effect From Chronic Tobacco Usage: Part 4, Metabolomic Profiles From Cigarette Smokers And Moist Snuff Consumers 5

6 Comparison of Metabolomic Profiles Random Forest Analysis is: A statistical technique used to bin cohorts based on overall characteristics Lower OOB (out-of-bag) and Class Error rates indicate that the cohorts can be separated accurately, whereas the higher error rates suggest incorrect binning (separation) among the study cohorts Simply stated: a sorting hat (from Harry Potter Movie) 6

7 Random Forest Analysis Comparisons of the Metabolomic Profiles 7

8 Order of Importance SMK vs. MSC vs. NTC (urine): All Metabolites cotinine N-oxide hydroxycotinine X X cotinine X X nicotine -ethylphenylsulfate o-cresol sulfate X ethylphenylsulfate X mandelate -methylhippurate X X X hydroxyhippurate (salicylurate) X X - 13 X X X X X X X X salicylate Color by SUPER_PATHWAY Amino acid Xenobiotics -missing- OOB Error Rate: 4.17% Predicted Group Random Forest SMK NTC MSC Actual Group y Class Error SMK NTC MSC Mean Decrease Accuracy All metabolites were utilized for this analysis. Nicotine metabolites drove the separation. Several structural unknowns were important for the separation. The overall separation between the three groups was excellent (4.17% error rate). The boxes contain nicotine metabolites. The X-compounds in the boxes were tentatively identified as nicotine metabolites. 8

9 SMK vs. MSC vs. NTC (urine): Nicotine Metabolites Excluded Order of Importance o-cresol sulfate X ethylphenylsulfate 3-ethylphenylsulfate X X mandelate -methylhippurate -hydroxyhippurate (salicylurate) X X X X - 13 X X X salicylate salicyluric glucuronide X X X X methyl-alpha-glucopyranoside X X X X X X X Color by SUPER_PATHWAY Amino acid Xenobiotics -missing- OOB Error Rate: 3.33% Predicted Group Random Forest SMK NTC MSC Actual Group Mean Decrease Accuracy Class Error SMK NTC MSC Without nicotine metabolites in the analysis SMK could still be separated from the non-smoking cohorts. However, the separation of MSC and NTC decreased. 9

10 MSC vs. SMK (urine): All Metabolites X ethylphenylsulfate X o-cresol sulfate X ethylphenylsulfate -methylhippurate mandelate X Color by SUPER_PATHWAY Amino acid Xenobiotics -missing- SMK can be separated from the MSC cohort in a two-way comparison with a low error rate. X y Order of Importance X X X - 13 X X X X X X X X X X hydroxycotinine X X ethylphenylsulfate X hydroxyhippurate (salicylurate) X OOB Error Rate: 5.% Predicted Group Random Forest SMK MSC Actual Group Mean Decrease Accuracy Class Error SMK 38.5 MSC

11 MSC vs. NTC (urine): All Metabolites X cotinine N-oxide hydroxycotinine X cotinine X nicotine o-cresol sulfate droxyhippurate (salicylurate) Order of Importance salicylate salicyluric glucuronide methyl-alpha-glucopyranoside 3-ethylphenylsulfate X X ethylphenylsulfate cis-urocanate X X X X X X hydroxy--ethylpropionate X X - 11 X N-acetyl-beta-alanine X X Color by SUPER_PATHWAY Amino acid Xenobiotics -missing- OOB Error Rate:.5% Random Forest Actual Group Predicted Group NTC Mean Decrease Accuracy MSC Class Error NTC MSC MSC and NTC cohorts can be separated with a high degree of accuracy when all metabolites are included in the analysis. Nicotine metabolites drive the separation of MSC and NTC cohorts. 11

12 -hydroxyhippurate (salicylurate) salicyluric glucuronide methyl-alpha-glucopyranoside Order of Importance MSC vs. NTC (urine): Nicotine Metabolites Excluded o-cresol sulfate salicylate 3-ethylphenylsulfate X ethylphenylsulfate X cis-urocanate X X X X X X N-acetyl-beta-alanine X X X X - 11 X X X X X X X X X Color by SUPER_PATHWAY Amino acid Xenobiotics -missing- OOB Error Rate: 31.5% Random Forest Actual Group Predicted Group NTC Mean Decrease Accuracy MSC Class Error NTC MSC Without nicotine metabolites in the analysis MSC and NTC cohorts are separated with lower accuracy on the basis of the other metabolites. This suggests that the metabolic profiles of MSC and NTC are similar. 1

13 Purpose Explore global metabolomic changes in long-term SMK and MSC Discover metabolomic pathways and specific metabolites which could be used as potential biomarkers of effect in SMK vs. MSC 13

14 Metabolites in Urine that Segregate SMK and MSC -methylhippurate hydroxyhippurate -hydroxyhippurate (salicylurate) 9 hydroxycotinine hydroxycotinine Non-Tobacco User Moist Snuff User Cigarette Smoker NTC MSC SMK Non-Tobacco User Moist Snuff User Cigarette Smoker NTC MSC SMK Non-Tobacco User Moist Snuff User Cigarette Smoker NTC MSC SMK o-cresol sulfate X X Non-Tobacco User Moist Snuff User Cigarette Smoker NTC MSC SMK Non-Tobacco User Moist Snuff User Cigarette Smoker These five metabolites segregate MSC and SMK NTC MSC SMK 14

15 Statistics to Biology. The SMK and MSC cohorts can be separated on metabolomic profiles. However Separation is based on too many unknown metabolites. Exposure biomarkers are also factored in the overall classification. Additional considerations are needed. Hence, we need to dig deeper into the data. The search for biomarkers of effect guides us to take a closer look at the biological/ physiological changes in tobacco consumers. 15

16 Arachidonic Acid Metabolism cell membrane phospholipids Plasma Arachidonate a ate ( 6) (: 4n6) NTC MSC SMK SMK > MSC cyclooxygenases COX-1 COX- phospholipase A arachidonic acid lipoxygenases 5-LO 1-LO 15-LO cytochrome P45 PGG 5-HETEs 1-HPETEs 15-HPETEs HETEs epoxides PGH LTA 4 1-HETEs 15-HETEs prostaglandins prostacyclin thromboxanes CysLTs LTC 4 LTD 4 LTE 4 LTB 4 lipoxins LXA 4 LXB 4 Arachidonic acid is a significant membrane fatty acid. lipoxins LXA 4 LXB 4 Arachidonic acid is a source of several inflammatory mediators. SMK exhibited higher levels of free arachidonic acid in plasma compared to MSC, suggesting an increased inflammatory state. 16

17 Oxidative Stress and Inflammation heme smoking heme NTC MSC SMK SMK > MSC biliverdin SMK < NTC = MSC Plasma bilirubin (E,E) N U M i ts ff S k SMK < NTC = MSC bilirubin (Z,Z) Biliverdin and bilirubin (antioxidants) were decreased in SMK, but not in MSC. Ability to combat oxidative stress could be impaired in smokers. N U M i ts ff S k SMK < NTC 17

18 Oxidative Stress and Purine Metabolism AMP GMP adenosine XMP guanosine inosine guanine O H O O H O hypoxanthine xanthine uric acid Xanthine oxidase xanthine oxidase Plasma Hypoxanthine SMK > MSC Urine Hypoxanthine Urine Xanthine NTC MSC SMK Purine degradation is a significant cellular source of inflammation. Retention of purine metabolites in plasma suggests increased oxidative stress and inflammation in SMK cohort. SMK < MSC S c a l e d I n t e n s i t y SMK < MSC = NTC 18

19 Lipid Signaling Plasma Palmitoyl Sphingomyelin cell membranes sphingomyelin ceramide lipid signaling SMK > MSC = NTC Lipids are important source of fuel and function as key signaling molecules (example, ceramide class of lipids). Palmitoyl sphingomyelin is a ceramide lipid. Increase in palmitoyl sphingomyelin in plasma of smokers may be an indication of inflammatory/ cellular signaling during the redox stress imposed by smoking. 19

20 Gut Microflora: Tryptophan Metabolism tryptophan 5 Urine 3-Indoxyl Sulfate indolepyruvate indole 4 indolelactate indoleacetate indoxyl liver 3 1 indolepropionate indoxyl sulfate Tryptophan is an amino acid which is metabolized by microflora and liver. Tobacco consumers, particularly SMK, showed the greatest change in tryptophan metabolites suggesting altered microflora and liver metabolism. SMK < MSC = NTC

21 Bile Acid Metabolism Bile acids: Synthesized from cholesterol Serve as emulsifiers in digestion of dietary lipids Modified by gut microflora Undergo modifications (example, sulfation) Alterations in the levels of bile acids may reflect intestinal and liver activity differences.. Plasma Glycocholenate Sulfate SMK > MSC = NTC Plasma Taurocholenate Sulfate SMK > MSC = NTC 1

22 Relative Abundance of Selected Metabolites: SMK vs. MSC Biochemical Group/Family Anti-Oxidant System biliverdin Redox Balance Purine Biochemical Urine Plasma bilirubin (E,E) cystathionine hypoxanthine High in SMK Low in SMK Tissue Reorganization Pro-hydroxy-pro Aromatic Amino Acids Bile Acid Benzoate/Aromatic Amino Acid Metabolism 3-hydroxy phenylacetate taurocholenate sulfate o-cresol sulfate -ethylphenylsulfate phenol sulfate

23 Key Findings: Metabolomic Profiling MSC and NTC cohorts resemble each other more closely. Without nicotine metabolites, the separation of MSC and NTC cohorts is difficult. Smokers exhibit distinct metabolic profiles from the non-smoking cohorts (MSC and NTC). Plasma and urine metabolomics reveal elevated oxidative stress and inflammation markers in Smokers. Several metabolites which differ significantly between Smoker and MSC cohorts have been identified. These metabolites could serve as potential biomarkers of effect. 3

24 Acknowledgements Study Partners Clinical Study Site High Point Clinical Trials Center, High Point NC (formerly Mendenhall Clinical Research Center) Analytical Laboratory Metabolon Inc., Durham NC Medical Monitor Clinical Study Monitor Gregory Tarleton, MD, PharmD Kathleen Doss, RN, MSN, CRA RJRT Colleagues Michael F. Borgerding, Angie Slater 4