{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST002253","ANALYSIS_ID":"AN003681","VERSION":"1","CREATED_ON":"03-01-2023"},

"PROJECT":{"PROJECT_TITLE":"Metabolomic profiles of T. spiralis-infected mouse serum at 0, 2, 4, 8 weeks","PROJECT_SUMMARY":"Trichinellosis is the zoonosis affected people worldwide, caused by parasitic nematode in Genus Trichinella. After ingesting raw meat containing infective larvae of Trichinella spp., patients may show signs of myalgia, headaches, facial and periorbital edema. In severe cases, patients develop myocarditis, heart failure, and possibly death. The standard method for diagnosis of Trichinella infection is immunological techniques, which lack of sensitivity and timeliness. Metabolomics has been extensively used to identify compounds with diagnostic potential in many diseases, however, there is no study regarding biomarker discovery in trichinellosis yet. Therefore, this study aims to identify potential biomarkers of trichinellosis using metabolomics. Mice were infected with larvae stage of T. spiralis and their serum were collected before, 2 weeks, 4 weeks, and 8 weeks after infection. Metabolites in serum were extracted and identified using mass spectrometer in untargeted manner. Metabolomic data was annotated with XCMS online platform and analyzed with Metaboanalyst version 5.0. A total of 4,688 and 5,533 metabolite features were identified from positive and negative mode, respectively. The 1,139 features were significantly changed metabolites and further used for pathway analysis and biomarker selection. Glycerophospholipid metabolism was the major pathway affected by Trichinella infection and these lipid species were the main lipid class identified. The Receiver operating characteristic (ROC) revealed 247 molecules with diagnostic power of trichinellosis. Phosphatidylserine was the major lipid class from ROC analysis, for example, PS(12:0/15:0), PS(18:0/19:0)[U]. Our study suggested glycerophospholipid and phosphatidylserine species as the potential markers of trichinellosis. Findings of this study are the initial step for biomarker discovery in trichinellosis, which would be a benefit for improvement of disease diagnosis in the future.","INSTITUTE":"Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy","LAST_NAME":"Chienwichai","FIRST_NAME":"Peerut","ADDRESS":"906, Kamphaeng Phet 6 Rd., Lak Si, Bangkok, 10210, Thailand","EMAIL":"peerut.chi@cra.ac.th","PHONE":"+6681687460","PUBLICATIONS":"https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0011119#pntd.0011119.ref035","DOI":"http://dx.doi.org/10.21228/M8FX4R"},

"STUDY":{"STUDY_TITLE":"Metabolomic profiles of T. spiralis-infected mouse serum at 0, 2, 4, 8 weeks","STUDY_SUMMARY":"Trichinellosis is the zoonosis affected people worldwide, caused by parasitic nematode in Genus Trichinella. After ingesting raw meat containing infective larvae of Trichinella spp., patients may show signs of myalgia, headaches, facial and periorbital edema. In severe cases, patients develop myocarditis, heart failure, and possibly death. The standard method for diagnosis of Trichinella infection is immunological techniques, which lack of sensitivity and timeliness. Metabolomics has been extensively used to identify compounds with diagnostic potential in many diseases, however, there is no study regarding biomarker discovery in trichinellosis yet. Therefore, this study aims to identify potential biomarkers of trichinellosis using metabolomics. Mice were infected with larvae stage of T. spiralis and their serum were collected before, 2 weeks, 4 weeks, and 8 weeks after infection. Metabolites in serum were extracted and identified using mass spectrometer in untargeted manner. Metabolomic data was annotated with XCMS online platform and analyzed with Metaboanalyst version 5.0. A total of 4,688 and 5,533 metabolite features were identified from positive and negative mode, respectively. The 1,139 features were significantly changed metabolites and further used for pathway analysis and biomarker selection. Glycerophospholipid metabolism was the major pathway affected by Trichinella infection and these lipid species were the main lipid class identified. The Receiver operating characteristic (ROC) revealed 247 molecules with diagnostic power of trichinellosis. Phosphatidylserine was the major lipid class from ROC analysis, for example, PS(12:0/15:0), PS(18:0/19:0)[U]. Our study suggested glycerophospholipid and phosphatidylserine species as the potential markers of trichinellosis. Findings of this study are the initial step for biomarker discovery in trichinellosis, which would be a benefit for improvement of disease diagnosis in the future.","INSTITUTE":"Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy","LAST_NAME":"Chienwichai","FIRST_NAME":"Peerut","ADDRESS":"906, Kamphaeng Phet 6 Rd., Lak Si, Bangkok, 10210, Thailand","EMAIL":"peerut.chi@cra.ac.th","PHONE":"+6681687460","SUBMIT_DATE":"2022-04-28"},

"SUBJECT":{"SUBJECT_TYPE":"Mammal","SUBJECT_SPECIES":"Mus musculus","TAXONOMY_ID":"10090"},
"SUBJECT_SAMPLE_FACTORS":[
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 1",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS2_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS2_1"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 2",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS2_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS2_2"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 3",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS2_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS2_3"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 4",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS3_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS3_1"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 5",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS3_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS3_2"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 6",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS3_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS3_3"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 7",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS4_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS4_1"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 8",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS4_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS4_2"}
},
{
"Subject ID":"-",
"Sample ID":"2-Week post-infection 9",
"Factors":{"Experimental factor":"2 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_2wk_TS4_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_2wk_TS4_3"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 1",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS2_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS2_1"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 2",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS2_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS2_2"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 3",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS2_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS2_3"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 4",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS3_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS3_1"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 5",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS3_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS3_2"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 6",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS3_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS3_3"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 7",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS4_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS4_1"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 8",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS4_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS4_2"}
},
{
"Subject ID":"-",
"Sample ID":"4-Week post-infection 9",
"Factors":{"Experimental factor":"4 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_4wk_TS4_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_4wk_TS4_3"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 1",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS2_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS2_1"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 2",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS2_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS2_2"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 3",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS2_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS2_3"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 4",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS3_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS3_1"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 5",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS3_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS3_2"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 6",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS3_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS3_3"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 7",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS4_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS4_1"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 8",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS4_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS4_2"}
},
{
"Subject ID":"-",
"Sample ID":"8-Week post-infection 9",
"Factors":{"Experimental factor":"8 weeks after infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_8wk_TS4_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_8wk_TS4_3"}
},
{
"Subject ID":"-",
"Sample ID":"Control 1",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS2_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS2_1"}
},
{
"Subject ID":"-",
"Sample ID":"Control 2",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS2_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS2_2"}
},
{
"Subject ID":"-",
"Sample ID":"Control 3",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS2_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS2_3"}
},
{
"Subject ID":"-",
"Sample ID":"Control 4",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS3_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS3_1"}
},
{
"Subject ID":"-",
"Sample ID":"Control 5",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS3_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS3_2"}
},
{
"Subject ID":"-",
"Sample ID":"Control 6",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS3_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS3_3"}
},
{
"Subject ID":"-",
"Sample ID":"Control 7",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS4_1","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS4_1"}
},
{
"Subject ID":"-",
"Sample ID":"Control 8",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS4_2","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS4_2"}
},
{
"Subject ID":"-",
"Sample ID":"Control 9",
"Factors":{"Experimental factor":"No infection"},
"Additional sample data":{"RAW_FILE_NAME":"20210115_Met_IDA_Pos_NI_TS4_3","RAW_FILE_NAME Neg":"20210116_Met_IDA_Neg_NI_TS4_3"}
}
],
"COLLECTION":{"COLLECTION_SUMMARY":"5 Mice were infected with T. spiralis and serum samples were collected at 0, 2, 4, and 8 weeks after infection. Metabolite profiling was performed with mass spectrometer.","SAMPLE_TYPE":"Blood (serum)"},

"TREATMENT":{"TREATMENT_SUMMARY":"Blood was collected at 0, 2,4, and 8 weeks after infection"},

"SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"20 μL serum was mixed with 80 μL cold methanol and vortexed for 1 minute. This mixture was then incubated at 4°C for 20 minutes and centrifuged at 12,000 rpm for 10 minutes. Next, the supernatant was collected and dried with a speed vacuum (Tomy Digital Biology, Tokyo, Japan). Samples were stored at −80°C until further analysis"},

"CHROMATOGRAPHY":{"INSTRUMENT_NAME":"Agilent 1260","COLUMN_NAME":"Waters Acquity BEH C8 (100 x 2.1mm,1.7um)","CHROMATOGRAPHY_TYPE":"Reversed phase"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS"},

"MS":{"INSTRUMENT_NAME":"ABI Sciex 5600+ TripleTOF","INSTRUMENT_TYPE":"QTOF","MS_TYPE":"ESI","MS_COMMENTS":"Information-dependent acquisition mode composed of a TOF-MS scan and 10 dependent product ion scans were used in the high sensitivity mode with dynamic background subtraction. The mass range of the TOF-MS scan was m/z 100–1,000 and the product ion scan was set to m/z 50−1,000. Equal aliquots of each metabolite sample were pooled to form the quality control (QC) samples. The QC samples were injected before, during, and after sample analysis to assess the system performance.","ION_MODE":"NEGATIVE","MS_RESULTS_FILE":"ST002253_AN003681_Results.txt UNITS:m/z Has m/z:Yes Has RT:Yes RT units:Minutes"}

}