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Annals of Clinical and Experimental Metabolism

Metabolomics Usefulness in Mosquito Borne Infectious Disease

Editorial | Open Access Volume 2 | Issue 3 |

  • 1. Department of Medicine, Hainan Medical University, China
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Corresponding Authors
Viroj Wiwanitkit, Department of Medicine, Hainan Medical University, Wiwanikit House, Bangkhae, Bangkok, Thailand.
CITATION

Wiwanitkit V (2017) Metabolomics Usefulness in Mosquito Borne Infectious Disease. Ann Clin Exp Metabol 2(3): 1023.

EDITORIAL

Metabolism is an important biological process in any living things. The normal physiology links to normal metabolism whereas the disease and pathology is usually related to abnormal metabolism. Hence, understanding metabolism is a basic step for understanding any disease. To study metabolism, the classical way is based on the classical physiological and biochemical approach. That approach requires times and attempt in in vitro and in vivo studies. Due to the influx of new biomedical technology, in silico approach becomes the new approach in medicine. The bioinformatics is the approach based on computational approach and is the direct way for the in silico study. There are many new bioinformatics techniques such as genomics and proteomics. Of several new bioinformatics, the metabolomics is the new omics science focusing on metabolism via computational analysis of metabolome. Metabolomics is an important new bioinformatics technology that can be applied in medicine. The tracking on metabolism is the main approach of metabolomics that can be useful for diagnostic and therapeutic purpose. The application in tropical medicine is possible. The usefulness of metabolomics in mosquito borne infectious disease should be mentioned. Birungi et al., mentioned for the “application of metabolomics to improve understanding of the effect of dengue infection on endothelial cells’ metabolome [1].” With use of metabolomics, “expressive predominancy of amino acids, organic acids, carbohydrates, nucleosides, lipids, fatty acids, and derivatives” can be studied [2].

The good example is the use of metabolomics in dengue. The identifications of metabolome by metabolomics technique can help better understand the pathophysiology of dengue [3] as well as difference of clinical feature among different groups of patients [4]. The approach can be useful for finding new diagnostic and therapeutic tools. For example, Voge et al., recently purposed the finding of biomarker from using metabolomics approach [5]. In another study, Shrinet et al., performed a serum metabolomics analysis of patients with chikungunya and dengue mono/coinfections and could finding distinct metabolite signatures in the three disease conditions [6]. The use of metabolomics is also the basic approach for finding new vaccine [7]. Similar to dengue, there are also many new interesting reports on metabolomics study for malaria. The use of metabolomics in study of pathophysiology and finding new diagnostic and therapeutic approach of malaria can be seen in the many publications [8,9]. The clarification of pathogenesis of malaria as well as host response to the infection by metabolomics approach is possible [10]. The good example is the report on using metabolomics for clarification of the drug resistance phenomenon in malaria [11]. It is no doubt that metabolomics approach is useful in study on mosquito borne infectious disease. In the present day, due to the improved computational technology, the new online tool is already available for metabolomics analysis and can be approach for the case of mosquito borne infectious disease. The good example is the new online tool namely “Met Tailor [12].” The example of using this new tool on dengue was recently reported in the publication by Chen et al. [12].

REFERENCES

1. Birungi G, Chen SM, Loy BP, Ng ML, Li SF. Metabolomics approach for investigation of effects of dengue virus infection using the EA.hy926 cell line. J Proteome Res. 2010; 9: 6523-6534.

2. Canuto GA, da Cruz PL, Faccio AT, Klassen A, Tavares MF. Neglected diseases prioritized in Brazil under the perspective of metabolomics: A review. Electrophoresis. 2015.

3. Cui L, Lee YH, Kumar Y, Xu F, Lu K, Ooi EE, et al. Serum metabolome and lipidome changes in adult patients with primary dengue infection. PLoS Negl Trop Dis. 2013; 7: 2373.

4. Shahfiza N, Osman H, Hock TT, Shaari K, Abdel-Hamid AH. Metabolomics for characterization of gender differences in patients infected with dengue virus. Asian Pac J Trop Med. 2015; 8: 451-456.

5. Voge NV, Perera R, Mahapatra S, Gresh L, Balmaseda A, LoronoPino MA, et al. Metabolomics-Based Discovery of Small Molecule Biomarkers in Serum Associated with Dengue Virus Infections and Disease Outcomes. PLoSNegl Trop Dis. 2016; 10: 0004449.

6. Shrinet J, Shastri JS, Gaind R, Bhavesh NS, Sunil S. Serum metabolomics analysis of patients with chikungunya and dengue mono/co-infections reveals distinct metabolite signatures in the three disease conditions. Sci Rep. 2016; 6: 36833.

7. González-Romo F, Picazo JJ. Development of new vaccines. Enferm Infecc Microbiol Clin. 2015; 33: 557-568.

8. Creek DJ, Chua HH, Cobbold SA, Nijagal B, MacRae JI, Dickerman BK, et al. Metabolomics-Based Screening of the Malaria Box Reveals both Novel and Established Mechanisms of Action. Antimicrob Agents Chemother. 2016; 60: 6650-6663.

9. Allman EL, Painter HJ, Samra J, Carrasquilla M, Llinas M. Metabolomic Profiling of the Malaria Box Reveals Antimalarial Target Pathways. Antimicrob Agents Chemother. 2016; 60: 6635-6649.

10.Sengupta A, Ghosh S, Das BK, Panda A, Tripathy R, Pied S, et al. Host metabolic responses to Plasmodium falciparum infections evaluated by 1 H NMR metabolomics. Mol Biosyst. 2016; 12: 3324-3332.

11.Uppal K, Salinas JL, Monteiro WM, Val F, Cordy RJ, Liu K, et al. Plasma metabolomics reveals membrane lipids, aspartate/asparagine and nucleotide metabolism pathway differences associated with chloroquine resistance in Plasmodium vivax malaria. PLoS One. 2017; 12: 0182819.

12.Chen G, Cui L, Teo GS, Ong CN, Tan CS, Choi H. Met Tailor: dynamic block summary and intensity normalization for robust analysis of mass spectrometry data in metabolomics. Bioinformatics. 2015; 31: 3645-3652.

Wiwanitkit V (2017) Metabolomics Usefulness in Mosquito Borne Infectious Disease. Ann Clin Exp Metabol 2(3): 1023.

Received : 23 Oct 2017
Accepted : 04 Nov 2017
Published : 06 Nov 2017
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