Summary of Study ST002297
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001471. The data can be accessed directly via it's Project DOI: 10.21228/M8KH70 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002297 |
| Study Title | Comprehensive biotransformation analysis of phenylalanine-tyrosine metabolism reveals alternative routes of metabolite clearance in nitisinone-treated alkaptonuria (Urine metabolomic analysis) |
| Study Type | Urine metabolomic analysis (study 2 of 2) |
| Study Summary | Background: Metabolomic analyses in alkaptonuria (AKU) have recently revealed alternative pathways in phenylalanine-tyrosine (phe-tyr) metabolism from biotransformation of homo-gentisic acid (HGA), the active molecule in this disease. The aim of this research was to study the phe-tyr metabolic pathway and whether the metabolites upstream of HGA, increased in nitisinone-treated patients, also undergo phase 1 and 2 biotransformation reactions. Methods: Metabolomic analyses were performed on serum and urine from patients partaking in the SONIA 2 phase 3 international randomised-controlled trial of nitisinone in AKU (EudraCT no. 2013-001633-41). Serum and urine samples were taken from the same patients at baseline (pre-nitisinone) then at 24 and 48 months on nitisinone treatment (patients N = 47 serum; 53 urine) or no treatment (patients N = 45 serum; 50 urine). Targeted feature extraction was per-formed to specifically mine data for the entire complement of theoretically predicted phase 1 and 2 biotransformation products derived from phenylalanine, tyrosine, 4-hydroxyphenylpyruvic acid and 4-hydroxyphenyllactic acid, in addition to phenylalanine-derived metabolites with known increases in phenylketonuria. Results: In total, we ob-served 13 phase 1 and 2 biotransformation products from phenylalanine through to HGA. Each of these products were observed in urine and two were detected in serum. The derivatives of the metabolites upstream of HGA were markedly increased in urine of nitisinone-treated patients (fold change 1.2-16.2) and increases in 12 of these compounds were directly proportional to the degree of nitisinone-induced hypertyrosinaemia (correlation coefficient with serum tyrosine = 0.2-0.7). Increases in the urinary phenylalanine metabolites were also observed across consecutive visits in the treated group. Conclusions: Nitisinone treatment results in marked increases in a wider network of phe-tyr metabolites than shown before. This network comprises alternative biotransformation products from the major metabolites of this pathway, produced by reactions including hydration (phase 1) and bioconjugation (phase 2) of acetyl, methyl, acetylcysteine, glucuronide, glycine and sulfate groups. We propose that these alternative routes of phe-tyr metabolism, predominantly in urine, minimise tyrosinaemia as well as phenylalanaemia. |
| Institute | University of Liverpool Institute of Life Course & Medical Sciences |
| Department | Department of Musculoskeletal & Ageing Science |
| Last Name | Brendan |
| First Name | Norman |
| Address | William Henry Duncan Building, 6 West Derby Street, Liverpool, UK. L7 8TX |
| bnorman@liverpool.ac.uk | |
| Phone | +447809606497 |
| Submit Date | 2022-09-28 |
| Num Groups | 2 |
| Total Subjects | 103 |
| Num Males | 65 |
| Num Females | 38 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-10-14 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN003752 | AN003753 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | Reversed phase |
| Chromatography system | Agilent 1290 Infinity II | Agilent 1290 Infinity II |
| Column | Waters Atlantis dC18 (100 x 3mm,3um) | Waters Atlantis dC18 (100 x 3mm,3um) |
| MS Type | ESI | ESI |
| MS instrument type | QTOF | QTOF |
| MS instrument name | Agilent 6550 QTOF | Agilent 6550 QTOF |
| Ion Mode | NEGATIVE | POSITIVE |
| Units | peak area (pareto-scaled, log2-transformed, 24h creatinine normalised) | peak area (pareto-scaled, log2-transformed, 24h creatinine normalised) |
MS:
| MS ID: | MS003496 |
| Analysis ID: | AN003752 |
| Instrument Name: | Agilent 6550 QTOF |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | MS acquisition conditions detailed in attached Supplementary Materials. Raw data were mined using the targeted feature extraction function in Masshunter Profinder (build 10.00, Agilent) with mass targets based on chemical formulae of known/predicted phe-tyr pathway metabolites from the customised compound databases described below. A combined compound database was compiled using PCDL Manager (Agilent, build 08.00). Accurate mass retention time (AMRT) matched metabolites were present in our published AMRT database, which was generated from chemical standards using the same LC-QTOF-MS methodology employed here: phenylalanine, phenylethylamine, tyrosine, N-acetyl-tyrosine, tyramine, HPPA, HPLA and HGA. Other established phenylalanine metabolites added to the database for mining by accurate mass alone were hydroxyphenylacetic acid, phenylacetaldehyde, phenylacetamide, phenylacetic acid, phenylacetylglutamine, phenylethylamine, phenyllactic acid and phenylpyruvic acid. The remaining formulae were from non-established but theoretically possible phase 1 and 2 biotransformation products derived from phenylalanine (n=74), tyrosine (n=74), HPPA (n=67) and HPLA (n=67) predicted using the Biotransformation Mass Defects tool (Agilent), in addition to the HGA biotransformation products (n=7) previously established by our group. Feature extraction parameters were accurate mass match window ±5 ppm with addition of matched retention time (RT; window ±0.3 min) for AMRT database metabolites. Allowed ion species were: H+, Na+, and NH4+ in positive polarity, and H− and CHO2- in negative polarity. Charge state range was 1–2, and dimers were allowed. ‘Find by formula’ filters were: score >60 in at least 60 % of samples in at least one sample group. Where compounds were detected in both positive and negative ionisation, the polarity with the clearest signal was selected for further analysis. Extracted peak area intensity data were exported in .csv file format and imported into Mass Profiler Professional (MPP; build 15.1, Agilent), in which all statistical analyses were performed unless stated otherwise. In MPP, all data were log2 transformed and pareto scaled. Urine data were normalised to 24-h creatinine values. QC was performed based on compound signal intensity data from the pooled samples interspersed throughout each analytical sequence. Compounds were retained for subsequent statistical analyses if a) observed in 100 % of replicate injections for at least one sample group pool, and b) peak area coefficient of variation (CV) remained <30% across replicate injections for each sample group pool across batches 1 and 2 combined. |
| Ion Mode: | NEGATIVE |
| MS ID: | MS003497 |
| Analysis ID: | AN003753 |
| Instrument Name: | Agilent 6550 QTOF |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | MS acquisition conditions detailed in attached Supplementary Materials. Raw data were mined using the targeted feature extraction function in Masshunter Profinder (build 10.00, Agilent) with mass targets based on chemical formulae of known/predicted phe-tyr pathway metabolites from the customised compound databases described below. A combined compound database was compiled using PCDL Manager (Agilent, build 08.00). Accurate mass retention time (AMRT) matched metabolites were present in our published AMRT database, which was generated from chemical standards using the same LC-QTOF-MS methodology employed here: phenylalanine, phenylethylamine, tyrosine, N-acetyl-tyrosine, tyramine, HPPA, HPLA and HGA. Other established phenylalanine metabolites added to the database for mining by accurate mass alone were hydroxyphenylacetic acid, phenylacetaldehyde, phenylacetamide, phenylacetic acid, phenylacetylglutamine, phenylethylamine, phenyllactic acid and phenylpyruvic acid. The remaining formulae were from non-established but theoretically possible phase 1 and 2 biotransformation products derived from phenylalanine (n=74), tyrosine (n=74), HPPA (n=67) and HPLA (n=67) predicted using the Biotransformation Mass Defects tool (Agilent), in addition to the HGA biotransformation products (n=7) previously established by our group. Feature extraction parameters were accurate mass match window ±5 ppm with addition of matched retention time (RT; window ±0.3 min) for AMRT database metabolites. Allowed ion species were: H+, Na+, and NH4+ in positive polarity, and H− and CHO2- in negative polarity. Charge state range was 1–2, and dimers were allowed. ‘Find by formula’ filters were: score >60 in at least 60 % of samples in at least one sample group. Where compounds were detected in both positive and negative ionisation, the polarity with the clearest signal was selected for further analysis. Extracted peak area intensity data were exported in .csv file format and imported into Mass Profiler Professional (MPP; build 15.1, Agilent), in which all statistical analyses were performed unless stated otherwise. In MPP, all data were log2 transformed and pareto scaled. Urine data were normalised to 24-h creatinine values. QC was performed based on compound signal intensity data from the pooled samples interspersed throughout each analytical sequence. Compounds were retained for subsequent statistical analyses if a) observed in 100 % of replicate injections for at least one sample group pool, and b) peak area coefficient of variation (CV) remained <30% across replicate injections for each sample group pool across batches 1 and 2 combined. |
| Ion Mode: | POSITIVE |
