Metabolomics of Vitreous Humour from Retinoblastoma Patients · biosynthesis of ether lipids starts...
1
Figure 3. A pair wise analysis between samples (9 patients and 2 controls) from LCMS analysis. Control1 Control 2 P3 (N) P5 (N) P6 (N) P9 (L) P4 (H) P8 (H) P2(H) P7 (L) P1 (H) P1 (H) P7 (L) P2 (H) P8 (H) P4 (H) P9(L) P6 (N) P5 (N) P3 (N) Control 2 Control 1 Statistical analysis Statistical analysis on vitreous humour samples reveal 350 differential metabolites as shown in the volcano plot (Figure 4) and hierarchical clustering between samples is shown in Figure 5. Metabolomics of Vitreous Humour from Retinoblastoma Patients Seetaramanjaneyulu Gundimeda 1 , Syed Salman Lateef 1 , Nilanjan Guha 1 , Deepak SA 1 , Arunkumar Padmanaban 1 , Ashwin Mallipatna 2 , Arkasubhra Ghosh 2 . 1. Agilent Technologies India Pvt. Ltd, Bangalore, Karnataka, India 2. GROW Research Laboratory, Narayana Nethralaya Foundation, Bangalore, Karnataka, India Summary Experimental Pathway Analysis Pathway analysis was carried out using the Pathway Analysis Module in GeneSpring 13.1. The differentially expressed entity list (p ≤0.05 and fold change ≥2.0) was selected for pathway analysis. Curated pathways from the KEGG were used for pathway analysis. Results and Discussion Results and Discussion Introduction This study illustrates a metabolomics approach to study molecular events leading to progression of retinoblastoma. Retinoblastoma is a pediatric ocular cancer affecting children usually less than five years of age. It is a complex disease predisposed primarily by mutations in the RB1 gene. From a cohort of 9 patients undergoing enucleation of the affected eyes, we obtained tumor, aqueous humor, vitreous humor and tear samples. We obtained retina, aqueous humor and vitreous humor from enucleated eyes of 2 deceased pediatric controls, whose cause of death is not due to any eye related disease. The results show overlap of key cellular pathways which can be mechanistically linked to disease progression. The study provide new biological insights that are made accessible by combining data from different biological and biochemical domains with a comprehensive integrated method. The information is useful not only to correlate expression markers with disease mechanism but also to better predict appropriate chemotherapy regimens and identify new mechanisms to treat even advanced stages of retinoblastoma. Method The samples from 9 patient and 2 controls were extracted using methanol: ethanol (1:1 v/v). The extracted samples were subjected to LC/QTOF-MS and GC/QTOF-MS analysis. For LC-QTOF analysis, data was acquired using electrospray ionization in positive and negative ion modes using modified polar reverse phase C18 column, and HILIC column. Molecular features were searched against METLIN database and confirmed by METLIN library using data dependent MS/MS acquisition. For GC-QTOF analysis, data was acquired using EI source on a DB-5ms column. The results were searched against Fiehn RTL library. Gene expression microarray studies were performed using SurePrint G3 Human GE 8X60K V2 Microarray while miRNA studies were performed using Agilent SurePrint G3 Human v16 miRNA 8X60K Microarray kit. The metabolomics and gene expression results were combined and analyzed using pathway architect module of the GeneSpring 13.1 MPP. The metabolomics workflow in shown in Figure 2. Conclusions Retinoblastoma (Rb) is the most common malignant tumor of the eye in children. Inactivation of both copies of the RB1 gene in retina is known to be the cause of cancer. Here, we present metabolomic studies on vitreous humor samples to identify differential metabolites in Rb patients that can provide a direct or indirect link to the pathways found in cancerous tissue. 9 patient and 2 controls samples were used. The extracted samples were subjected to LC/QTOF-MS and GC/QTOF-MS analysis. More than 1000 features were identified using these two techniques . Wide variety of compounds ranging from amino acids, carbohydrates, nucleobases and lipids were identified. Among lipids, Phosphatidyl cholines (PC), ether linked phosphatidyl ethanolamines (PE), ceramides, sphingomyelins and sphinganines were identified. Lipids, especially PCs and ether linked PEs were found to be up regulated in patient samples. Many of the ether lipids found to be 5 folds more in patient samples. Carnitines and free fatty acids were also up regulated in patient samples. As the biosynthesis of ether lipids starts in peroxisomes, this study suggests an altered peroxisomal metabolism in these patients. Database and Library search The accurate mass database search for LC/Q-TOF and GC/Q-TOF data resulted in the detection of about 1000 and 200 compounds respectively. Differential compounds are identified by database search. Compounds are further confirmed by matching with the spectra of authentic compounds from MSMS spectral library. As an example, comparison of acquired and library spectra of Guanine were shown in figure 6. SimLipid was used for the identification of lipids using MS/MS data along with accurate mass information to get unambiguous hits. Sample-Sample correlation A pair wise analysis between samples (9 patient and 2 controls) within vitreous humor (C18 Pos) metabolomics experiments is shown in figure 3. The 9 patient samples are classified based on clinical and pathological risk as high risk (H), low risk (L) and no risk (N). The correlation analysis followed by clustering showed the relationship between the three groups of 9 patients. The results showed that high risk group patients correlate positively with each other marked by red color. Most of the other samples showed no (yellow) correlation or negative (blue) correlation with controls. Figure 1. The location of vitreous humour. Table 1. Lipid identification using SimLipid software Figure 6. LC/MS/MS results of guanine showing MS/MS spectra (A), mirror plot (B) and library spectra from PCDL (C) Figure 4. Volcano plot showing statistical analysis and fold change. GCMS data analysis GCMS acquisition was performed using Fiehn RTL method using Agilent 7200 Q- TOF mass spectrometer. The data analysis results using Agilent Unknown analysis software is shown in Figure 7. A selected list of GCMS metabolites include galactosamine, 3-hydroxy-3-methylglutaric acid, glucose, sorbose, pantothenic acid, trehalose, glutamic acid and 3- (4-hydroxyphenyl)lactic acid. Metabolomics 2015 Poster 018 Figure 7. Unknown analysis results showing the extracted ion chromatogram (A) and the header-to-tail plot of L-Sorbose (B). [A] [B] Component RT: 17.2097 Acquisition Time (min) 17.18 17.2 17.22 Counts 6 x10 0 1 2 3 4 5 6 7 TIC Component 73.0480 217.1096 147.0676 103.0410 129.0386 Library spectra Acquired spectra Pathways ABC transporters Glycolysis / Gluconeogenesis AMPK signaling pathway Glyoxylate and dicarboxylate metabolism Alanine, aspartate and glutamate metabolism HIF-1 signaling pathway Arachidonic acid metabolism Inositol phosphate metabolism Arginine and proline metabolism Insulin secretion Bile secretion Metabolism of xenobiotics by cytochrome P450 Cysteine and methionine metabolism Nicotinate and nicotinamide metabolism Fatty acid biosynthesis Pantothenate and CoA biosynthesis Fatty acid elongation Pentose phosphate pathway Fructose and mannose metabolism Phenylalanine metabolism Galactose metabolism Phosphatidylinositol signaling system Glycerolipid metabolism Purine metabolism Serotonergic synapse Tyrosine metabolism Type II diabetes mellitus Valine, leucine and isoleucine biosynthesis Figure 8. Spingolipid pathway showing the metabolism of ceramides which was significantly up regulated. Genomics and metabolomics data were co-visualized in the pathway context using the Multi-Omics Analysis tool of GeneSpring 13.1, which enabled simultaneous viewing of the differential entities from both gene expression and metabolomics. Table 2 shows the list of predominant pathways as revealed by combined analysis of LCMS of vitreous humor and gene expression. Table 2. Predominant pathways revealed by combined LCMS and gene expression multi-omic analysis in vitreous humor Figure 9. Genomic and metabolomics data were co-visualized in the pathway context using the Multi-Omics Analysis tool of GeneSpring 13.1 Combined analysis of transcriptomics and metabolomics data shows glycerophospholipid pathway to be significantly affected. 2 x10 0 1 Product Ion Guanine 152.0570 135.0303 110.0346 55.0289 80.0245 170.0655 2 x10 0 Mirror plot 152.0570 110.0346 55.0289 80.0245 170.0655 2 x10 0 1 Guanine C5H5N5O + Product Ion, Metlin_Metabolites_AM_PCDL spectra 152.0567 135.0301 110.0349 Counts vs. Mass-to-Charge (m/z) 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 A B C Figure 5. hierarchial clustering of control and patient samples and box and whiskers plots of Guanine (A), PC (16:0/16:1) (B) and Hydroxy Lauric acid (C) identified among control, high risk and low risk groups Figure 2. Metabolomics workflow for retinoblastoma study LCMS analysis Derivatization followed by GCMS analysis LCMS Agilent 6550 QTOF Column1: Agilent ZORBAX RRHD SB-Aq Column2: Agilent Poroshell 120 HILIC Mode: Both pos and neg. MS and MS/MS analysis GC: Agilent 7200, MS:Agilent 5975C Column: DB-5MS (P N: 122-5532G) Method: Ref manual G1676-80000 25 µL of vitreous Extraction solvent for metabolomics: Methanol:Ethanol (1:1 v/v) Data analysis Data analysis MassHunter Qualitative Analysis Software and MassHunter Profinder Software • METLIN Library confirmation Agilent Unknown Analysis Software (Fiehn RTL library) Agilent GeneSpring 13.1-MPP Differential Analysis METLIN search using ID Browser KEGG Pathway Searches using Pathway architect Statistics GCMS • Vitreous humour being in closest proximity to the retinoblastoma tissue showed characteristics exo-metabolites from the cancer tissue. • Different classes of metabolites were confirmed using LCMS and GCMS techniques. • Accurate mass LCMS libraries along with SimLipid software facilitated analysis of various class of metabolites including lipids. •Pathway search of differential metabolites using GeneSpring software yielded key biological pathways which were also reflected in the genomics study (data not shown). •Up regulation of phosphatidyl cholines ,free fatty acids and lipid transporters like carnitine Indicates an altered lipid metabolism in the patients. • Although retrieval of vitreous humour would require evasive procedures, a more detailed study using other biological fluids such as tears are underway.
Transcript of Metabolomics of Vitreous Humour from Retinoblastoma Patients · biosynthesis of ether lipids starts...
Figure 3. A pair wise analysis between samples (9 patients and 2 controls) fromLCMS analysis.
Control1
Control 2
P3 (N)
P5 (N)
P6 (N)
P9 (L)
P4 (H)
P8 (H)
P2(H)
P7 (L)
P1 (H)
P1(H
)
P7(L
)
P2(H
)
P8(H
)
P4(H
)
P9(L
)
P6(N
)
P5(N
)
P3(N
)
Cont
rol2
Cont
rol1
Statistical analysis
Statistical analysis on vitreous humour samples reveal 350 differentialmetabolites as shown in the volcano plot (Figure 4) and hierarchical clusteringbetween samples is shown in Figure 5.
Metabolomics of Vitreous Humour from Retinoblastoma Patients Seetaramanjaneyulu Gundimeda1, Syed Salman Lateef1, Nilanjan Guha1, Deepak SA1, Arunkumar Padmanaban1, Ashwin Mallipatna2, Arkasubhra Ghosh2.1. Agilent Technologies India Pvt. Ltd, Bangalore, Karnataka, India 2. GROW Research Laboratory, Narayana Nethralaya Foundation, Bangalore, Karnataka, India
Summary
Experimental
Pathway Analysis
Pathway analysis was carried out using the Pathway Analysis Module inGeneSpring 13.1. The differentially expressed entity list (p ≤0.05 and fold change≥2.0) was selected for pathway analysis. Curated pathways from the KEGG wereused for pathway analysis.
Results and Discussion Results and Discussion
Introduction
This study illustrates a metabolomics approach to study molecular eventsleading to progression of retinoblastoma. Retinoblastoma is a pediatric ocularcancer affecting children usually less than five years of age. It is a complexdisease predisposed primarily by mutations in the RB1 gene. From a cohort of 9patients undergoing enucleation of the affected eyes, we obtained tumor,aqueous humor, vitreous humor and tear samples. We obtained retina, aqueoushumor and vitreous humor from enucleated eyes of 2 deceased pediatriccontrols, whose cause of death is not due to any eye related disease. Theresults show overlap of key cellular pathways which can be mechanisticallylinked to disease progression. The study provide new biological insights that aremade accessible by combining data from different biological and biochemicaldomains with a comprehensive integrated method. The information is useful notonly to correlate expression markers with disease mechanism but also to betterpredict appropriate chemotherapy regimens and identify new mechanisms totreat even advanced stages of retinoblastoma.
Method
The samples from 9 patient and 2 controls were extracted using methanol:ethanol (1:1 v/v). The extracted samples were subjected to LC/QTOF-MS andGC/QTOF-MS analysis. For LC-QTOF analysis, data was acquired usingelectrospray ionization in positive and negative ion modes using modified polarreverse phase C18 column, and HILIC column. Molecular features weresearched against METLIN database and confirmed by METLIN library using datadependent MS/MS acquisition. For GC-QTOF analysis, data was acquired usingEI source on a DB-5ms column. The results were searched against Fiehn RTLlibrary. Gene expression microarray studies were performed using SurePrint G3Human GE 8X60K V2 Microarray while miRNA studies were performed usingAgilent SurePrint G3 Human v16 miRNA 8X60K Microarray kit. Themetabolomics and gene expression results were combined and analyzed usingpathway architect module of the GeneSpring 13.1 MPP. The metabolomicsworkflow in shown in Figure 2.
Conclusions
Retinoblastoma (Rb) is the most common malignant tumor of the eye inchildren. Inactivation of both copies of the RB1 gene in retina is known to bethe cause of cancer. Here, we present metabolomic studies on vitreous humorsamples to identify differential metabolites in Rb patients that can provide adirect or indirect link to the pathways found in cancerous tissue. 9 patient and 2controls samples were used. The extracted samples were subjected toLC/QTOF-MS and GC/QTOF-MS analysis. More than 1000 features wereidentified using these two techniques . Wide variety of compounds ranging fromamino acids, carbohydrates, nucleobases and lipids were identified. Amonglipids, Phosphatidyl cholines (PC), ether linked phosphatidyl ethanolamines (PE),ceramides, sphingomyelins and sphinganines were identified. Lipids, especiallyPCs and ether linked PEs were found to be up regulated in patient samples.Many of the ether lipids found to be 5 folds more in patient samples. Carnitinesand free fatty acids were also up regulated in patient samples. As thebiosynthesis of ether lipids starts in peroxisomes, this study suggests an alteredperoxisomal metabolism in these patients.
Database and Library search
The accurate mass database search for LC/Q-TOF and GC/Q-TOF data resultedin the detection of about 1000 and 200 compounds respectively. Differentialcompounds are identified by database search. Compounds are further confirmedby matching with the spectra of authentic compounds from MSMS spectrallibrary. As an example, comparison of acquired and library spectra of Guaninewere shown in figure 6. SimLipid was used for the identification of lipids usingMS/MS data along with accurate mass information to get unambiguous hits.
Sample-Sample correlation
A pair wise analysis between samples (9 patient and 2 controls) within vitreoushumor (C18 Pos) metabolomics experiments is shown in figure 3. The 9 patientsamples are classified based on clinical and pathological risk as high risk (H),low risk (L) and no risk (N). The correlation analysis followed by clusteringshowed the relationship between the three groups of 9 patients. The resultsshowed that high risk group patients correlate positively with each othermarked by red color. Most of the other samples showed no (yellow) correlationor negative (blue) correlation with controls.
Figure 1. The location of vitreous humour.
Table 1. Lipid identification using SimLipid software
Figure 6. LC/MS/MS results of guanine showing MS/MS spectra (A), mirrorplot (B) and library spectra from PCDL (C)
Figure 4. Volcano plot showing statistical analysis and fold change.
GCMS data analysis
GCMS acquisition was performed using Fiehn RTL method using Agilent 7200 Q-TOF mass spectrometer. The data analysis results using Agilent Unknownanalysis software is shown in Figure 7. A selected list of GCMS metabolitesinclude galactosamine, 3-hydroxy-3-methylglutaric acid, glucose, sorbose,pantothenic acid, trehalose, glutamic acid and 3- (4-hydroxyphenyl)lactic acid.
Metabolomics 2015Poster 018
Figure 7. Unknown analysis results showing the extracted ion chromatogram(A) and the header-to-tail plot of L-Sorbose (B).
[A] [B]Component RT: 17.2097
Acquisition Time (min)17.18 17.2 17.22
Co
un
ts 6x10
0
1
2
3
4
5
6
7
TIC
Component
73.0480
217.1096
147.0676
103.0410
129.0386
Library spectra
Acquired spectra
Pathways
ABC transporters Glycolysis / GluconeogenesisAMPK signaling pathway Glyoxylate and dicarboxylate metabolismAlanine, aspartate and glutamate metabolism HIF-1 signaling pathwayArachidonic acid metabolism Inositol phosphate metabolismArginine and proline metabolism Insulin secretionBile secretion Metabolism of xenobiotics by cytochrome P450Cysteine and methionine metabolism Nicotinate and nicotinamide metabolismFatty acid biosynthesis Pantothenate and CoA biosynthesisFatty acid elongation Pentose phosphate pathwayFructose and mannose metabolism Phenylalanine metabolismGalactose metabolism Phosphatidylinositol signaling systemGlycerolipid metabolism Purine metabolismSerotonergic synapse Tyrosine metabolismType II diabetes mellitus Valine, leucine and isoleucine biosynthesis
Figure 8. Spingolipid pathway showing the metabolism of ceramides which wassignificantly up regulated.
Genomics and metabolomics data were co-visualized in the pathway contextusing the Multi-Omics Analysis tool of GeneSpring 13.1, which enabledsimultaneous viewing of the differential entities from both gene expression andmetabolomics. Table 2 shows the list of predominant pathways as revealed bycombined analysis of LCMS of vitreous humor and gene expression.Table 2. Predominant pathways revealed by combined LCMS and geneexpression multi-omic analysis in vitreous humor
Figure 9. Genomic and metabolomics data were co-visualized in the pathwaycontext using the Multi-Omics Analysis tool of GeneSpring 13.1 Combinedanalysis of transcriptomics and metabolomics data shows glycerophospholipidpathway to be significantly affected.
2x10
0
1
Product Ion Guanine
152.0570
135.0303110.034655.0289 80.0245 170.0655
2x10
0
Mirror plot152.0570
110.034655.0289 80.0245 170.0655
2x10
0
1
Guanine C5H5N5O + Product Ion, Metlin_Metabolites_AM_PCDL spectra152.0567
135.0301110.0349
Counts vs. Mass-to-Charge (m/z)10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
A
B
C
Figure 5. hierarchial clustering of control and patient samples and box andwhiskers plots of Guanine (A), PC (16:0/16:1) (B) and Hydroxy Lauric acid(C) identified among control, high risk and low risk groups
Figure 2. Metabolomics workflow for retinoblastoma study
LCMS analysis Derivatizationfollowed by GCMS analysis
LCMSAgilent 6550 QTOFColumn1: Agilent ZORBAX RRHD SB-AqColumn2: Agilent Poroshell 120 HILICMode: Both pos and neg. MS and MS/MS analysis
GC: Agilent 7200, MS:Agilent 5975CColumn: DB-5MS (P N: 122-5532G)Method: Ref manual G1676-80000
25 µL of vitreous
Extraction solvent for metabolomics:Methanol:Ethanol(1:1 v/v)
Data analysis Data analysisMassHunter Qualitative Analysis Software and MassHunter ProfinderSoftware• METLIN Library confirmation
Agilent Unknown Analysis Software(Fiehn RTL library)
Agilent GeneSpring 13.1-MPP Differential AnalysisMETLIN search using ID BrowserKEGG Pathway Searches using Pathway architect
Statistics
GCMS• Vitreous humour being in closest proximity to the retinoblastoma tissue showed characteristics exo-metabolites from the cancer tissue.• Different classes of metabolites were confirmed using LCMS and GCMS techniques.• Accurate mass LCMS libraries along with SimLipid software facilitated analysis of various class of metabolites including lipids.•Pathway search of differential metabolites using GeneSpring software yielded key biological pathways which were also reflected in the genomics study (data not shown).•Up regulation of phosphatidyl cholines ,free fatty acids and lipid transporters like carnitine Indicates an altered lipid metabolism in the patients. • Although retrieval of vitreous humour would require evasive procedures, a more detailed study using other biological fluids such as tears are underway.
