#METABOLOMICS WORKBENCH ReemAlMalki91_20230328_085203 DATATRACK_ID:3823 STUDY_ID:ST002552 ANALYSIS_ID:AN004202 PROJECT_ID:PR001644
VERSION             	1
CREATED_ON             	April 6, 2023, 3:03 pm
#PROJECT
PR:PROJECT_TITLE                 	Biomarker discovery in galactosemia: Metabolomics with UPLC/HRMS in dried blood
PR:PROJECT_TITLE                 	spots
PR:PROJECT_TYPE                  	newborn screening
PR:PROJECT_SUMMARY               	Galactosemia (GAL) is an autosomal recessive genetic disorder characterized by
PR:PROJECT_SUMMARY               	galactose metabolism disturbances. GAL develops non-preventable life-threatening
PR:PROJECT_SUMMARY               	complications even with a reduced content of galactose and lactose patient’s
PR:PROJECT_SUMMARY               	diet. Thus, the underlying pathophysiology of long-term complications in GAL
PR:PROJECT_SUMMARY               	remains poorly understood. The current study used a metabolomics approach using
PR:PROJECT_SUMMARY               	ultra-performance liquid chromatography coupled with high-resolution mass
PR:PROJECT_SUMMARY               	spectrometry to investigate the metabolomic changes in the dried blood spots of
PR:PROJECT_SUMMARY               	15 patients with GAL and 39 healthy individuals. Compared to the control group,
PR:PROJECT_SUMMARY               	2,819 metabolites underwent significant changes in patients with GAL. In all,
PR:PROJECT_SUMMARY               	480 human endogenous metabolites were identified, of which 209 and 271 were
PR:PROJECT_SUMMARY               	upregulated and downregulated, respectively. PA (8:0/LTE4) and ganglioside GT1c
PR:PROJECT_SUMMARY               	(d18:0/20:0) metabolites showed the most significant difference between GAL and
PR:PROJECT_SUMMARY               	the healthy group, with an area under the curve of 1 and 0.995, respectively.
PR:PROJECT_SUMMARY               	Additionally, our findings showed novel potential biomarkers for GAL, such as
PR:PROJECT_SUMMARY               	17-alpha-estradiol-3-glucuronide and 16-alpha-hydroxy DHEA 3-sulfatediphosphate.
PR:PROJECT_SUMMARY               	In conclusion, this metabolomics study deepened the understanding of the
PR:PROJECT_SUMMARY               	pathophysiology of GAL and presented metabolites that might serve as potential
PR:PROJECT_SUMMARY               	prognostic biomarkers to monitor the progression or support the clinical
PR:PROJECT_SUMMARY               	diagnosis of GAL.
PR:INSTITUTE                     	King Saud University
PR:DEPARTMENT                    	Metabolomics
PR:LABORATORY                    	Metabolomics
PR:LAST_NAME                     	AlMalki
PR:FIRST_NAME                    	Reem
PR:ADDRESS                       	King Fahad road, Riyadh, KSA, 00000, Saudi Arabia
PR:EMAIL                         	439203044@student.ksu.edu.sa
PR:PHONE                         	+966534045397
#STUDY
ST:STUDY_TITLE                   	Biomarker discovery in galactosemia: Metabolomics with UPLC/HRMS in dried blood
ST:STUDY_TITLE                   	spots
ST:STUDY_TYPE                    	Newborn screening
ST:STUDY_SUMMARY                 	Galactosemia (GAL) is an autosomal recessive genetic disorder characterized by
ST:STUDY_SUMMARY                 	galactose metabolism disturbances. GAL develops non-preventable life-threatening
ST:STUDY_SUMMARY                 	complications even with a reduced content of galactose and lactose patient’s
ST:STUDY_SUMMARY                 	diet. Thus, the underlying pathophysiology of long-term complications in GAL
ST:STUDY_SUMMARY                 	remains poorly understood. The current study used a metabolomics approach using
ST:STUDY_SUMMARY                 	ultra-performance liquid chromatography coupled with high-resolution mass
ST:STUDY_SUMMARY                 	spectrometry to investigate the metabolomic changes in the dried blood spots of
ST:STUDY_SUMMARY                 	15 patients with GAL and 39 healthy individuals. Compared to the control group,
ST:STUDY_SUMMARY                 	2,819 metabolites underwent significant changes in patients with GAL. In all,
ST:STUDY_SUMMARY                 	480 human endogenous metabolites were identified, of which 209 and 271 were
ST:STUDY_SUMMARY                 	upregulated and downregulated, respectively. PA (8:0/LTE4) and ganglioside GT1c
ST:STUDY_SUMMARY                 	(d18:0/20:0) metabolites showed the most significant difference between GAL and
ST:STUDY_SUMMARY                 	the healthy group, with an area under the curve of 1 and 0.995, respectively.
ST:STUDY_SUMMARY                 	Additionally, our findings showed novel potential biomarkers for GAL, such as
ST:STUDY_SUMMARY                 	17-alpha-estradiol-3-glucuronide and 16-alpha-hydroxy DHEA 3-sulfatediphosphate.
ST:STUDY_SUMMARY                 	In conclusion, this metabolomics study deepened the understanding of the
ST:STUDY_SUMMARY                 	pathophysiology of GAL and presented metabolites that might serve as potential
ST:STUDY_SUMMARY                 	prognostic biomarkers to monitor the progression or support the clinical
ST:STUDY_SUMMARY                 	diagnosis of GAL.
ST:INSTITUTE                     	King Saud University
ST:LAST_NAME                     	AlMalki
ST:FIRST_NAME                    	Reem
ST:ADDRESS                       	King Fahad road, Riyadh, KSA, 00000, Saudi Arabia
ST:EMAIL                         	439203044@student.ksu.edu.sa
ST:PHONE                         	+966534045397
ST:NUM_GROUPS                    	2
ST:PUBLICATIONS                  	Yes
#SUBJECT
SU:SUBJECT_TYPE                  	Human
SU:SUBJECT_SPECIES               	Homo sapiens
SU:TAXONOMY_ID                   	9606
SU:GENDER                        	Male and female
#SUBJECT_SAMPLE_FACTORS:         	SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Raw file names and additional sample data
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB1_20587853	Factor:Patient	RAW_FILE_NAME=GALT_AB1_20587853.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB2_21780341	Factor:Patient	RAW_FILE_NAME=GALT_AB2_21780341.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB3_21745506	Factor:Patient	RAW_FILE_NAME=GALT_AB3_21745506.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB4_21100457	Factor:Patient	RAW_FILE_NAME=GALT_AB4_21100457.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB6_21770900	Factor:Patient	RAW_FILE_NAME=GALT_AB6_21770900.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB8_21112212	Factor:Patient	RAW_FILE_NAME=GALT_AB8_21112212.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB9_21775172	Factor:Patient	RAW_FILE_NAME=GALT_AB9_21775172.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB12_21780943	Factor:Patient	RAW_FILE_NAME=GALT_AB12_21780943.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB13_21769782	Factor:Patient	RAW_FILE_NAME=GALT_AB13_21769782.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB14_21799507	Factor:Patient	RAW_FILE_NAME=GALT_AB14_21799507.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB15_21745524	Factor:Patient	RAW_FILE_NAME=GALT_AB15_21745524.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB16_20428037	Factor:Patient	RAW_FILE_NAME=GALT_AB16_20428037.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB17_20758512	Factor:Patient	RAW_FILE_NAME=GALT_AB17_20758512.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB18_21012310	Factor:Patient	RAW_FILE_NAME=GALT_AB18_21012310.raw
SUBJECT_SAMPLE_FACTORS           	Patient	GALT_AB19_21744172	Factor:Patient	RAW_FILE_NAME=GALT_AB19_21744172.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR1_21799701	Factor:Ctrl	RAW_FILE_NAME=GALT_NR1_21799701.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR2_21799835	Factor:Ctrl	RAW_FILE_NAME=GALT_NR2_21799835.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR3_21799729	Factor:Ctrl	RAW_FILE_NAME=GALT_NR3_21799729.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR4_21752056	Factor:Ctrl	RAW_FILE_NAME=GALT_NR4_21752056.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR5_21756742	Factor:Ctrl	RAW_FILE_NAME=GALT_NR5_21756742.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR6_21799686	Factor:Ctrl	RAW_FILE_NAME=GALT_NR6_21799686.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR7_21798410	Factor:Ctrl	RAW_FILE_NAME=GALT_NR7_21798410.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR8_21756982	Factor:Ctrl	RAW_FILE_NAME=GALT_NR8_21756982.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR9_21756292	Factor:Ctrl	RAW_FILE_NAME=GALT_NR9_21756292.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR10_21756779	Factor:Ctrl	RAW_FILE_NAME=GALT_NR10_21756779.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR11_21798863	Factor:Ctrl	RAW_FILE_NAME=GALT_NR11_21798863.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR12_21798942	Factor:Ctrl	RAW_FILE_NAME=GALT_NR12_21798942.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR13_21756636	Factor:Ctrl	RAW_FILE_NAME=GALT_NR13_21756636.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR14_21756609	Factor:Ctrl	RAW_FILE_NAME=GALT_NR14_21756609.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR15_21798906	Factor:Ctrl	RAW_FILE_NAME=GALT_NR15_21798906.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR16_21794987	Factor:Ctrl	RAW_FILE_NAME=GALT_NR16_21794987.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR17_21798924	Factor:Ctrl	RAW_FILE_NAME=GALT_NR17_21798924.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR18_21756973	Factor:Ctrl	RAW_FILE_NAME=GALT_NR18_21756973.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR19_21756645	Factor:Ctrl	RAW_FILE_NAME=GALT_NR19_21756645.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR20_21798933	Factor:Ctrl	RAW_FILE_NAME=GALT_NR20_21798933.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR21_21756964	Factor:Ctrl	RAW_FILE_NAME=GALT_NR21_21756964.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR22_21799695	Factor:Ctrl	RAW_FILE_NAME=GALT_NR22_21799695.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR23_21752092	Factor:Ctrl	RAW_FILE_NAME=GALT_NR23_21752092.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR24_21799792	Factor:Ctrl	RAW_FILE_NAME=GALT_NR24_21799792.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR25_21757006	Factor:Ctrl	RAW_FILE_NAME=GALT_NR25_21757006.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR26_21799932	Factor:Ctrl	RAW_FILE_NAME=GALT_NR26_21799932.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR28_21799871	Factor:Ctrl	RAW_FILE_NAME=GALT_NR28_21799871.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR29_21798155	Factor:Ctrl	RAW_FILE_NAME=GALT_NR29_21798155.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR30_21799710	Factor:Ctrl	RAW_FILE_NAME=GALT_NR30_21799710.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR31_21756654	Factor:Ctrl	RAW_FILE_NAME=GALT_NR31_21756654.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR32_21799969	Factor:Ctrl	RAW_FILE_NAME=GALT_NR32_21799969.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR33_21798474	Factor:Ctrl	RAW_FILE_NAME=GALT_NR33_21798474.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR34_21799978	Factor:Ctrl	RAW_FILE_NAME=GALT_NR34_21799978.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR35_21799853	Factor:Ctrl	RAW_FILE_NAME=GALT_NR35_21799853.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR36_21799941	Factor:Ctrl	RAW_FILE_NAME=GALT_NR36_21799941.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR37_21798960	Factor:Ctrl	RAW_FILE_NAME=GALT_NR37_21798960.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR38_21783126	Factor:Ctrl	RAW_FILE_NAME=GALT_NR38_21783126.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR39_21799677	Factor:Ctrl	RAW_FILE_NAME=GALT_NR39_21799677.raw
SUBJECT_SAMPLE_FACTORS           	Ctrl	GALT_NR40_21798479	Factor:Ctrl	RAW_FILE_NAME=GALT_NR40_21798479.raw
#COLLECTION
CO:COLLECTION_SUMMARY            	Fifty-four DBS samples were collected from genetically and biochemically
CO:COLLECTION_SUMMARY            	confirmed GAL (n = 15) patients at King Faisal Specialist Hospital and Research
CO:COLLECTION_SUMMARY            	center (KFSHRC) and healthy controls (n = 39).
CO:COLLECTION_PROTOCOL_FILENAME  	Characteristics of the study population and metabolites extraction
CO:SAMPLE_TYPE                   	Blood (plasma)
#TREATMENT
TR:TREATMENT_SUMMARY             	no treatment use
#SAMPLEPREP
SP:SAMPLEPREP_SUMMARY            	Metabolites extraction The polar metabolites were extracted from DBS samples
SP:SAMPLEPREP_SUMMARY            	using our developed standard protocol (Jacob et al., 2018). Five 3 mm size DBS
SP:SAMPLEPREP_SUMMARY            	disks were used for metabolite extraction using methanol, acetonitrile, and
SP:SAMPLEPREP_SUMMARY            	water (40:40:20%) for protein precipitation. The mixture was mixed at 25°C and
SP:SAMPLEPREP_SUMMARY            	600 rpm for 2 hours in a thermomixer (Eppendorf, Germany). Pooled QC samples
SP:SAMPLEPREP_SUMMARY            	were prepared using aliquots from the study samples. Afterward, the supernatants
SP:SAMPLEPREP_SUMMARY            	were transferred to another set of tubes, evaporated in SpeedVacc (Christ, City,
SP:SAMPLEPREP_SUMMARY            	Germany), and stored at −80°C until LCMS analysis.
SP:SAMPLEPREP_PROTOCOL_FILENAME  	Metabolites extraction
SP:PROCESSING_STORAGE_CONDITIONS 	-20℃
SP:EXTRACT_STORAGE               	Room temperature
#CHROMATOGRAPHY
CH:CHROMATOGRAPHY_TYPE           	Reversed phase
CH:INSTRUMENT_NAME               	Waters Acquity
CH:COLUMN_NAME                   	Waters Acquity UPLC XSelect HSS C18 (100 × 2.1mm, 2.5um)
CH:SOLVENT_A                     	100% water; 0.1% formic acid
CH:SOLVENT_B                     	50% methanol/50% acetonitrile; 0.1% formic acid
CH:FLOW_GRADIENT                 	0–16 min 95%–5% A, 16–19 min 5% A, 19–20 min 5%–95% A, and 20–22
CH:FLOW_GRADIENT                 	min, 95%– 95% A
CH:FLOW_RATE                     	300 μl/min.
CH:COLUMN_TEMPERATURE            	55
CH:METHODS_FILENAME              	UPLCHRMS
#ANALYSIS
AN:ANALYSIS_TYPE                 	MS
#MS
MS:INSTRUMENT_NAME               	Waters Xevo-G2-S
MS:INSTRUMENT_TYPE               	QTOF
MS:MS_TYPE                       	ESI
MS:ION_MODE                      	POSITIVE
MS:MS_COMMENTS                   	The DIA data were collected with a Masslynx™ V4.1 workstation in continuum
MS:MS_COMMENTS                   	mode (Waters Inc., Milford, MA, USA). The raw MS data were processed following a
MS:MS_COMMENTS                   	standard pipeline using the Progenesis QI v.3.0 software.
MS:MS_RESULTS_FILE               	ST002552_AN004202_Results.txt	UNITS:peak area 	Has m/z:Yes	Has RT:No	RT units:Minutes
#END