รศ.ดร.ศักดิ์ดา คุ้มหรั่ง (Affiliated Faculty)

  • Post Doc; Systems and Synthetic Biology, Chalmers University of Technology, Sweden
  • Dr. rer. nat. (Ph.D.) in Chemistry; Karl-Franzens University, Austria
  • วท.ม. (เคมีวิเคราะห์) มหาวิทยาลัยสงขลานครินทร์
  • Diploma (Environmental Analysis); Technical University of Denmark, Denmark
  • วท.บ. (เคมี) มหาวิทยาลัยสงขลานครินทร์

ศูนย์ชีวโมเลกุลและฟีโนมศิริราช (Siriraj Metabolomics and Phenomics Center)
1034 อาคารศรีสวรินทิรา ชั้น 10
โรงพยาบาลศิริราช, เขตบางกอกน้อย, กรุงเทพฯ 10700

Laboratory : อาคารศรีสวรินทิรา ชั้น 9

Phone : (66) 02-419-5505

E-Mail : sakda.kho@mahidol.edu

ติดต่อ : ศูนย์ชีวโมเลกุลและฟีโนมศิริราช

งานวิจัย

Metabolomics and systems biology

            Our research focus is on applying metabolomics and systems biology for a better understanding of biology, especially in the area of medical research. Since 2007, we have been working on developing tools for metabolomics and systems biology (both wet- and dry-lab) as well as applying the methods to various biological problems. The established methods are capable of quantitative, semi-quantitative and high-throughput analysis. These advanced technologies allow dissecting the complexity of biology. The ultimate goal of our research group is to build an integrated platform encompassing analytical methods (MS- and NMR-based metabolomics) and bioinformatics components, software and databases, necessary for enhancing the capacity and efficiency of metabolomics and molecular phenotyping study in clinical research. Subsequently, the platform will be applied to address different biological problems such as glomerular disease and chronic kidney diseases, vaccine research, food and nutrition, human health, traditional medicine, and natural products.

1. Khoomrung, S. (2006). Analysis of total arsenic in soil and edible plant samples from Ronphibun sub-district, Nakorn Si Thammarat province by hydride generation atomic absorption spectrophotometry (Doctoral dissertation).
2. Khoomrung, S., Laoteng, K., Jitsue, S., & Cheevadhanarak, S. (2008). Significance of fatty acid supplementation on profiles of cell growth, fatty acid, and gene expression of three desaturases in Mucor rouxii. Applied microbiology and biotechnology, 80(3), 499–506. https://doi.org/10.1007/s00253-008-1569-0
3. Raber, G., Khoomrung, S., Taleshi, M. S., Edmonds, J. S., & Francesconi, K. A. (2009). Identification of arsenolipids with GC/MS. Talanta, 78(3), 1215–1218. https://doi.org/10.1016/j.talanta.2009.01.013
4. Shi, S., Valle-Rodríguez, J. O., Khoomrung, S., Siewers, V., & Nielsen, J. (2012). Functional expression and characterization of five wax ester synthases in Saccharomyces cerevisiae and their utility for biodiesel production. Biotechnology for biofuels, 5, 7. https://doi.org/10.1186/1754-6834-5-7
5. Khoomrung, S., Chumnanpuen, P., Jansa-ard, S., Nookaew, I., & Nielsen, J. (2012). Fast and accurate preparation fatty acid methyl esters by microwave-assisted derivatization in the yeast Saccharomyces cerevisiae. Applied microbiology and biotechnology, 94(6), 1637–1646. https://doi.org/10.1007/s00253-012-4125-x
6. Khoomrung, S., Chumnanpuen, P., Jansa-Ard, S., Ståhlman, M., Nookaew, I., Borén, J., & Nielsen, J. (2013). Rapid quantification of yeast lipid using microwave-assisted total lipid extraction and HPLC-CAD. Analytical chemistry, 85(10), 4912–4919. https://doi.org/10.1021/ac3032405
7. Hussain, A., Nookaew, I., Khoomrung, S., Andersson, L., Larsson, I., Hulthén, L., Jansson, N., Jakubowicz, R., Nilsson, S., Sandberg, A. S., Nielsen, J., & Holmäng, A. (2013). A maternal diet of fatty fish reduces body fat of offspring compared with a maternal diet of beef and a post-weaning diet of fish improves insulin sensitivity and lipid profile in adult C57BL/6 male mice. Acta physiologica (Oxford, England), 209(3), 220–234. https://doi.org/10.1111/apha.12130
8. Hussain, A., Olausson, H., Nilsson, S., Nookaew, I., Khoomrung, S., Andersson, L., Koskela, A., Tuukkanen, J., Ohlsson, C., & Holmäng, A. (2013). Maternal beef and postweaning herring diets increase bone mineral density and strength in mouse offspring. Experimental biology and medicine (Maywood, N.J.), 238(12), 1362–1369. https://doi.org/10.1177/1535370213506436
9. Knuf, C., Nookaew, I., Remmers, I., Khoomrung, S., Brown, S., Berry, A., & Nielsen, J. (2014). Physiological characterization of the high malic acid-producing Aspergillus oryzae strain 2103a-68. Applied microbiology and biotechnology, 98(8), 3517–3527. https://doi.org/10.1007/s00253-013-5465-x
10. Khoomrung, S., Raber, G., Laoteng, K., & Francesconi, K. A. (2014). Identification and characterization of fish oil supplements based on fatty acid analysis combined with a hierarchical clustering algorithm. European Journal of Lipid Science and Technology, 116(7), 795–804. https://doi.org/10.1002/ejlt.201300369
11. Nicastro, R., Tripodi, F., Guzzi, C., Reghellin, V., Khoomrung, S., Capusoni, C., Compagno, C., Airoldi, C., Nielsen, J., Alberghina, L., & Coccetti, P. (2015). Enhanced amino acid utilization sustains growth of cells lacking Snf1/AMPK. Biochimica et biophysica acta, 1853(7), 1615–1625. https://doi.org/10.1016/j.bbamcr.2015.03.014
12. Qin, J., Zhou, Y. J., Krivoruchko, A., Huang, M., Liu, L., Khoomrung, S., Siewers, V., Jiang, B., & Nielsen, J. (2015). Modular pathway rewiring of Saccharomyces cerevisiae enables high-level production of L-ornithine. Nature communications, 6, 8224. https://doi.org/10.1038/ncomms9224
13. Khoomrung, S., Martínez, J. L., Tippmann, S., Jansa-Ard, S., Buffing, M. F., Nicastro, R., & Nielsen, J. (2015). Expanded metabolite coverage of Saccharomyces cerevisiae extract through improved chloroform/methanol extraction and tert-butyldimethylsilyl derivatization. Analytical Chemistry Research, 6, 9-16. https://doi.org/10.1016/j.ancr.2015.10.001
14. Tippmann, S., Nielsen, J., & Khoomrung, S. (2016). Improved quantification of farnesene during microbial production from Saccharomyces cerevisiae in two-liquid-phase fermentations. Talanta, 146, 100–106. https://doi.org/10.1016/j.talanta.2015.08.031
15. Olafsdottir, T. A., Lindqvist, M., Nookaew, I., Andersen, P., Maertzdorf, J., Persson, J., Christensen, D., Zhang, Y., Anderson, J., Khoomrung, S., Sen, P., Agger, E. M., Coler, R., Carter, D., Meinke, A., Rappuoli, R., Kaufmann, S. H., Reed, S. G., & Harandi, A. M. (2016). Comparative Systems Analyses Reveal Molecular Signatures of Clinically tested Vaccine Adjuvants. Scientific reports, 6, 39097. https://doi.org/10.1038/srep39097
16. Fletcher, E., Feizi, A., Bisschops, M. M. M., Hallström, B. M., Khoomrung, S., Siewers, V., & Nielsen, J. (2017). Evolutionary engineering reveals divergent paths when yeast is adapted to different acidic environments. Metabolic engineering, 39, 19–28. https://doi.org/10.1016/j.ymben.2016.10.010
17. Rodriguez, A., Chen, Y., Khoomrung, S., Özdemir, E., Borodina, I., & Nielsen, J. (2017). Comparison of the metabolic response to over-production of p-coumaric acid in two yeast strains. Metabolic engineering, 44, 265–272. https://doi.org/10.1016/j.ymben.2017.10.013
18. Khoomrung, S., Wanichthanarak, K., Nookaew, I., Thamsermsang, O., Seubnooch, P., Laohapand, T., & Akarasereenont, P. (2017). Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine. Frontiers in pharmacology, 8, 474. https://doi.org/10.3389/fphar.2017.00474
19. Grapov, D., Fahrmann, J., Wanichthanarak, K., & Khoomrung, S. (2018). Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. Omics : a journal of integrative biology, 22(10), 630–636. https://doi.org/10.1089/omi.2018.0097
20. Guo, Z. P., Khoomrung, S., Nielsen, J., & Olsson, L. (2018). Changes in lipid metabolism convey acid tolerance in Saccharomyces cerevisiae. Biotechnology for biofuels, 11, 297. https://doi.org/10.1186/s13068-018-1295-5
21. Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N., & Khoomrung, S. (2019). Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Computational and structural biotechnology journal, 17, 611–618. https://doi.org/10.1016/j.csbj.2019.04.009
22. Jariyasopit, N., Tung, P., Su, K., Halappanavar, S., Evans, G. J., Su, Y., Khoomrung, S., & Harner, T. (2019). Polycyclic aromatic compounds in urban air and associated inhalation cancer risks: A case study targeting distinct source sectors. Environmental pollution (Barking, Essex : 1987), 252(Pt B), 1882–1891. https://doi.org/10.1016/j.envpol.2019.06.015
23. Jeennor, S., Anantayanon, J., Panchanawaporn, S., Khoomrung, S., Chutrakul, C., & Laoteng, K. (2019). Reengineering lipid biosynthetic pathways of Aspergillus oryzae for enhanced production of γ-linolenic acid and dihomo-γ-linolenic acid. Gene, 706, 106–114. https://doi.org/10.1016/j.gene.2019.04.074
24. Khoomrung, S., Nookaew, I., Sen, P., Olafsdottir, T. A., Persson, J., Moritz, T., Andersen, P., Harandi, A. M., & Nielsen, J. (2020). Metabolic Profiling and Compound-Class Identification Reveal Alterations in Serum Triglyceride Levels in Mice Immunized with Human Vaccine Adjuvant Alum. Journal of proteome research, 19(1), 269–278. https://doi.org/10.1021/acs.jproteome.9b00517
25. Liu, Y., Liu, Q., Krivoruchko, A., Khoomrung, S., & Nielsen, J. (2020). Engineering yeast phospholipid metabolism for de novo oleoylethanolamide production. Nature chemical biology, 16(2), 197–205. https://doi.org/10.1038/s41589-019-0431-2
26. Hodge, K., Makjaroen, J., Robinson, J., Khoomrung, S., & Pisitkun, T. (2020). Deep Proteomic Deconvolution of Interferons and HBV Transfection Effects on a Hepatoblastoma Cell Line. ACS omega, 5(27), 16796–16810. https://doi.org/10.1021/acsomega.0c01865
27. Pomyen, Y., Wanichthanarak, K., Poungsombat, P., Fahrmann, J., Grapov, D., & Khoomrung, S. (2020). Deep metabolome: Applications of deep learning in metabolomics. Computational and structural biotechnology journal, 18, 2818–2825. https://doi.org/10.1016/j.csbj.2020.09.033
28. Sen, P., Lamichhane, S., Mathema, V. B., McGlinchey, A., Dickens, A. M., Khoomrung, S., & Orešič, M. (2021). Deep learning meets metabolomics: a methodological perspective. Briefings in bioinformatics, 22(2), 1531–1542. https://doi.org/10.1093/bib/bbaa204
29. Limjiasahapong, S., Kaewnarin, K., Jariyasopit, N., Hongthong, S., Nuntasaen, N., Robinson, J. L., Nookaew, I., Sirivatanauksorn, Y., Kuhakarn, C., Reutrakul, V., & Khoomrung, S. (2021). UPLC-ESI-MRM/MS for Absolute Quantification and MS/MS Structural Elucidation of Six Specialized Pyranonaphthoquinone Metabolites From Ventilago harmandiana. Frontiers in plant science, 11, 602993. https://doi.org/10.3389/fpls.2020.602993
30. Jariyasopit, N., Khamsaeng, S., Panya, A., Vinaisuratern, P., Metem, P., Asawalertpanich, W., Visessanguan, W., Sirivatanauksorn, V., & Khoomrung, S. (2021). Quantitative analysis of nutrient metabolite compositions of retail cow’s milk and milk alternatives in Thailand using GC–MS. Journal of Food Composition and Analysis, 97, Article 103785. https://doi.org/10.1016/j.jfca.2020.103785
31. Kaewnarin, K., Limjiasahapong, S., Jariyasopit, N., Anekthanakul, K., Kurilung, A., Wong, S. C. C., Sirivatanauksorn, Y., Visessanguan, W., & Khoomrung, S. (2021). High-Resolution QTOF-MRM for Highly Accurate Identification and Quantification of Trace Levels of Triterpenoids in Ganoderma lucidum Mycelium. Journal of the American Society for Mass Spectrometry, 32(9), 2451–2462. https://doi.org/10.1021/jasms.1c00175
32. Anekthanakul, K., Manocheewa, S., Chienwichai, K., Poungsombat, P., Limjiasahapong, S., Wanichthanarak, K., Jariyasopit, N., Mathema, V. B., Kuhakarn, C., Reutrakul, V., Phetcharaburanin, J., Panya, A., Phonsatta, N., Visessanguan, W., Pomyen, Y., Sirivatanauksorn, Y., Worawichawong, S., Sathirapongsasuti, N., Kitiyakara, C., & Khoomrung, S. (2021). Predicting lupus membranous nephritis using reduced picolinic acid to tryptophan ratio as a urinary biomarker. iScience, 24(11), 103355. https://doi.org/10.1016/j.isci.2021.103355
33. Indrati, N., Sumpavapol, P., Samakradhamrongthai, R. S., Phonsatta, N., Poungsombat, P., Khoomrung, S., & Panya, A. (2022).Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference. International Journal of Food Science & Technology, 57(6), 3760–3770. https://doi.org/10.1111/ijfs.15703
34. Mathema, V. B., Duangkumpha, K., Wanichthanarak, K., Jariyasopit, N., Dhakal, E., Sathirapongsasuti, N., Kitiyakara, C., Sirivatanauksorn, Y., & Khoomrung, S. (2022). CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics. Briefings in bioinformatics, 23(2), bbab550. https://doi.org/10.1093/bib/bbab550
35. Indrati, N., Phonsatta, N., Poungsombat, P., Khoomrung, S., Sumpavapol, P., & Panya, A. (2022). Metabolic profiles alteration of Southern Thailand traditional sweet pickled mango during the production process. Frontiers in nutrition, 9, 934842. https://doi.org/10.3389/fnut.2022.934842
36. Duangkumpha, K., Jariyasopit, N., Wanichthanarak, K., Dhakal, E., Wisanpitayakorn, P., Thotsiri, S., Sirivatanauksorn, Y., Kitiyakara, C., Sathirapongsasuti, N., & Khoomrung, S. (2022). GC × GC-TOFMS metabolomics analysis identifies elevated levels of plasma sugars and sugar alcohols in diabetic mellitus patients with kidney failure. The Journal of biological chemistry, 298(10), 102445. https://doi.org/10.1016/j.jbc.2022.102445
37. Jariyasopit, N., Limjiasahapong, S., Kurilung, A., Sartyoungkul, S., Wisanpitayakorn, P., Nuntasaen, N., Kuhakarn, C., Reutrakul, V., Kittakoop, P., Sirivatanauksorn, Y., & Khoomrung, S. (2022). Traveling Wave Ion Mobility-Derived Collision Cross Section Database for Plant Specialized Metabolites: An Application to Ventilago harmandiana Pierre. Journal of proteome research, 21(10), 2481–2492. https://doi.org/10.1021/acs.jproteome.2c00413
38. Wanichthanarak, K., Nookaew, I., Pasookhush, P., Wongsurawat, T., Jenjaroenpun, P., Leeratsuwan, N., Wattanachaisaereekul, S., Visessanguan, W., Sirivatanauksorn, Y., Nuntasaen, N., Kuhakarn, C., Reutrakul, V., Ajawatanawong, P., & Khoomrung, S. (2023). Revisiting chloroplast genomic landscape and annotation towards comparative chloroplast genomes of Rhamnaceae. BMC plant biology, 23(1), 59. https://doi.org/10.1186/s12870-023-04074-5
39. Mathema, V. B., Sen, P., Lamichhane, S., Orešič, M., & Khoomrung, S. (2023). Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine. Computational and structural biotechnology journal, 21, 1372–1382. https://doi.org/10.1016/j.csbj.2023.01.043
40. Suta, S., Surawit, A., Mongkolsucharitkul, P., Pinsawas, B., Manosan, T., Ophakas, S., Pongkunakorn, T., Pumeiam, S., Sranacharoenpong, K., Sutheeworapong, S., Poungsombat, P., Khoomrung, S., Akarasereenont, P., Thaipisuttikul, I., Suktitipat, B., & Mayurasakorn, K. (2023). Prolonged Egg Supplement Advances Growing Child's Growth and Gut Microbiota. Nutrients, 15(5), 1143. https://doi.org/10.3390/nu15051143
41. Jariyasopit, N., & Khoomrung, S. (2023). Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids. Computational and structural biotechnology journal, 21, 4777–4789. https://doi.org/10.1016/j.csbj.2023.09.032
42. Suta, S., Ophakas, S., Manosan, T., Honwichit, O., Charoensiddhi, S., Surawit, A., Pongkunakorn, T., Pumeiam, S., Mongkolsucharitkul, P., Pinsawas, B., Sutheeworapong, S., Puangsombat, P., Khoomrung, S., & Mayurasakorn, K. (2023). Influence of Prolonged Whole Egg Supplementation on Insulin-like Growth Factor 1 and Short-Chain Fatty Acids Product: Implications for Human Health and Gut Microbiota. Nutrients, 15(22), 4804. https://doi.org/10.3390/nu15224804
43. Thongkongkaew, T., Jariyasopit, N., Khoomrung, S., Siritutsoontorn, S., Jitrapakdee, S., Kittakoop, P., & Ruchirawat, S. (2023). Anti-Xanthine Oxidase 5'-Hydroxyhericenes A-D from the Edible Mushroom Hericium erinaceus and Structure Revision of 3-[2,3-Dihydroxy-4-(hydroxymethyl)tetrahydrofuran-1-yl]-pyridine-4,5-diol. ACS omega, 8(48), 46284–46291. https://doi.org/10.1021/acsomega.3c07792
44. Kurilung, A., Limjiasahapong, S., Kaewnarin, K., Wisanpitayakorn, P., Jariyasopit, N., Wanichthanarak, K., Sartyoungkul, S., Wong, S. C. C., Sathirapongsasuti, N., Kitiyakara, C., Sirivatanauksorn, Y., & Khoomrung, S. (2024). Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples. Journal of pharmaceutical analysis, 14(5), 100921. https://doi.org/10.1016/j.jpha.2023.12.011
45. Wisanpitayakorn, P., Sartyoungkul, S., Kurilung, A., Sirivatanauksorn, Y., Visessanguan, W., Sathirapongsasuti, N., & Khoomrung, S. (2024). Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion's Polarizability and Molecular Mass with Limited Data. Journal of chemical information and modeling, 64(5), 1533–1542. https://doi.org/10.1021/acs.jcim.3c01491
46. Thamlikitkul, L., Manocheewa, S., Limjiasahapong, S., Wanichthanarak, k., Panya, A., Poungvarin, N., Pomyen, Y., Khoomrung S. (2024)Abstract 1068: Plasma metabolite biomarkers for EGFR-mutated non-small cell lung cancer (NSCLC) Cancer Research 2024.
47. Manokasemsan, W., Jariyasopit, N., Poungsombat, P., Kaewnarin, K., Wanichthanarak, K., Kurilung, A., Duangkumpha, K., Limjiasahapong, S., Pomyen, Y., Chaiteerakij, R., Tansawat, R., Srisawat, C., Sirivatanauksorn, Y., Sirivatanauksorn, V., & Khoomrung, S. (2024). Quantifying fecal and plasma short-chain fatty acids in healthy Thai individuals. Computational and structural biotechnology journal, 23, 2163–2172. https://doi.org/10.1016/j.csbj.2024.05.007
48. Wanichthanarak, K., In-On, A., Fan, S., Fiehn, O., Wangwiwatsin, A., & Khoomrung, S. (2024). Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0. GigaScience, 13, giae005. https://doi.org/10.1093/gigascience/giae005
49. Kurilung, A., Limjiasahapong, S., Kaewnarin, K., Wisanpitayakorn, P., Jariyasopit, N., Wanichthanarak, K., Sartyoungkul, S., Wong, S. C. C., Sathirapongsasuti, N., Kitiyakara, C., Sirivatanauksorn, Y., & Khoomrung, S. (2024). Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples. Journal of pharmaceutical analysis, 14(5), 100921. https://doi.org/10.1016/j.jpha.2023.12.011
50. Sureram, S., Chutiwitoonchai, N., Pooprasert, T., Sangsopha, W., Limjiasahapong, S., Jariyasopit, N., Sirivatanauksorn, Y., Khoomrung, S., Mahidol, C., Ruchirawat, S., & Kittakoop, P. (2024). Discovery of procyanidin condensed tannins of (-)-epicatechin from Kratom, Mitragyna speciosa, as virucidal agents against SARS-CoV-2. International journal of biological macromolecules, 273(Pt 1), 133059. https://doi.org/10.1016/j.ijbiomac.2024.133059
51. Indrati, N., Phonsatta, N., Poungsombat, P., Khoomrung, S., Panya, A., & Sumpavapol, P. (2025). Investigation of southern Thailand sweet pickled mango metabolic profiles related to deterioration. Food chemistry, 478, 143663. https://doi.org/10.1016/j.foodchem.2025.143663
52. Kurilung, A., Limjiasahapong, S., Wanichthanarak, K., Manokasemsan, W., Kaewnarin, K., Duangkumpha, K., Manocheewa, S., Tansawat, R., Chaiteerakij, R., Nookaew, I., Sirivatanauksorn, Y., & Khoomrung, S. (2025). LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis.
Computational and structural biotechnology journal, 27, 3079–3089. https://doi.org/10.1016/j.csbj.2025.07.009
53. Indrati, N., Phonsatta, N., Poungsombat, P., Khoomrung, S., Panya, A., & Sumpavapol, P. (2025)Investigation of southern Thailand sweet pickled mango metabolic profiles related to deterioration.
Food chemistry, 478, 143663. https://doi.org/10.1016/j.foodchem.2025.143663
54. Saxena, K., Andersson, R., Widlund, P. O., Khoomrung, S., Hanzén, S., Nielsen, J., Kumar, N., Molin, M., & Nyström, T. (2025). Perturbations in L-serine metabolism regulate protein quality control through the sensor of the retrograde response pathway RTG2 in Saccharomyces cerevisiae. The Journal of biological chemistry, 301(7), 110329. https://doi.org/10.1016/j.jbc.2025.110329
55. Kurilung, A., Limjiasahapong, S., Wanichthanarak, K., Manokasemsan, W., Kaewnarin, K., Duangkumpha, K., Manocheewa, S., Tansawat, R., Chaiteerakij, R., Nookaew, I., Sirivatanauksorn, Y., & Khoomrung, S. (2025). LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis.
Computational and structural biotechnology journal, 27, 3079–3089. https://doi.org/10.1016/j.csbj.2025.07.009
56. Thamlikitkul, L., Wanichthanarak, K., Manocheewa, S., Limjiasahapong, S., Phonsatta, N., Thangvichien, S., Panya, A., Sirivatanauksorn, Y., Poungvarin, N., & Khoomrung, S. (2025). Plasma metabolomic analysis in Thai EGFR-mutated non-small cell lung cancer patients. Computational and structural biotechnology journal, 27, 4321–4331. https://doi.org/10.1016/j.csbj.2025.10.010
57. Manokasemsan, W., Jariyasopit, N., Wanichthanarak, K., Poungsombat, P., Kurilung, A., Limjiasahapong, S., Thapa, K., Sirivatanaukson, Y., Ruksasuk, S., Srithongkul, T., Kitiyakara, C., & Khoomrung, S. (2025). LC-MS/MS Identifies Elevated Imidazole Propionate and Gut-Derived Metabolite Alterations in Peritoneal Dialysis.
Computational and Structural Biotechnology, 27, 5271–5280.
58. Wisanpitayakorn, P., Konsue, A., Sartyoungkul, T., In-On, A., Sirivatanauksorn, Y., Gang, D. R., Kittakoop, P., & Khoomrung, S. (2025). Spatial Mapping of Stereoisomeric and Isobaric Alkaloids in Mitragyna speciosa Tissues by High-Resolution DESI-cIM-MS. Analytical chemistry, 10.1021/acs.analchem.5c04730.
Advance online publication. https://doi.org/10.1021/acs.analchem.5c04730
59. Sangwongchai, W., Wanichthanarak, K., In-on, A., Natee, S., Champasri, C., Sa-ingthong, N., Beckles, D. M., Khoomrung, S., & Thitisaksakul, M. (2025). Unveiling distinct storage composition and starch properties in developing indica rice grains via transcriptional profiling and enzymatic activity analysis.
Computational and Structural Biotechnology Journal, 27, 4898-4914. https://doi.org/10.1016/j.csbj.2025.11.011