Leptin Signaling: Decoding of Genetic Pathways using Bioinformatics; Shaping Bariatric Surgery Outcomes
DOI:
https://doi.org/10.6000/1929-6029.2024.13.35Keywords:
Leptin, leptin receptor, bioinformatics, SNPs, bariatric surgery, weight loss, pathway enrichmentAbstract
Background: Leptin, a hormone central to energy homeostasis and appetite regulation, plays a pivotal role in obesity and metabolic health. Single nucleotide polymorphisms (SNPs) in the leptin (LEP) and leptin receptor (LEPR) genes influence leptin signaling and may explain variability in outcomes following bariatric surgery. This bioinformatics-driven study examines the role of LEP and LEPR SNPs in modulating weight loss, metabolic changes, and hormonal responses post-surgery.
Methods: A total of 55 leptin SNPs and 216 leptin receptor SNPs were assessed for functional impact using SIFT, PolyPhen-2, and Mutation Assessor. Pathway enrichment analyses using DAVID and g:Profiler identified biological processes and signaling pathways linked to leptin function. Protein-protein interaction (PPI) networks were constructed via STRING and visualized in Cytoscape to explore molecular interactions. Statistical models evaluated associations between SNPs and surgical outcomes, including weight loss and metabolic improvements. Key pathways with false discovery rates (FDR) < 0.01 were highlighted to emphasize significance.
Results: Bioinformatics analyses revealed LEP and LEPR as critical variants associated with bariatric surgery outcomes. Specifically, LEP rs7799039 G allele carriers exhibited diminished weight loss (p < 0.05) and metabolic improvements. Functional prediction tools consistently indicated deleterious effects on leptin signaling. Pathway enrichment analyses identified leptin's involvement in critical pathways, including the adipocytokine signaling pathway (hsa04920, 2 of 68 genes, strength = 2.46, FDR = 0.0042)," "AMPK signaling pathway (hsa04152, 2 of 120 genes, strength = 2.22, FDR = 0.0064)," and "non-alcoholic fatty liver disease (NAFLD) pathway (hsa04932, 2 of 146 genes, strength = 2.13, FDR = 0.0064). PPI networks underscored leptin’s interactions with key metabolic and inflammatory regulators, such as TNF-α and IL-6, suggesting a broader impact on energy metabolism and inflammation.
Conclusion: This study demonstrates the utility of bioinformatics in elucidating the genetic basis of variable bariatric surgery outcomes. LEP and LEPR SNPs modulate critical pathways influencing weight loss and metabolic responses. Integrating genetic insights with bariatric care could advance precision medicine approaches for obesity management. Future studies with larger cohorts are warranted to confirm these findings and strengthen predictive models.
References
Cui H, López M, Rahmouni K. The cellular and molecular bases of leptin and ghrelin resistance in obesity. Nature Reviews Endocrinology 2017; 13(6): 338-51. https://doi.org/10.1038/nrendo.2016.222 DOI: https://doi.org/10.1038/nrendo.2016.222
Lee JE, Choi JH, Lee JH, Lee MG. Gene SNPs and mutations in clinical genetic testing: haplotype-based testing and analysis. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 2005; 573(1-2): 195-204. https://doi.org/10.1016/j.mrfmmm.2004.08.018 DOI: https://doi.org/10.1016/j.mrfmmm.2004.08.018
Krawczak M, Ball EV, Fenton I, Stenson PD, Abeysinghe S, Thomas N, et al. Human gene mutation database—a biomedical information and research resource. Human Mutation 2000; 15(1): 45-51. https://doi.org/10.1002/(SICI)1098-1004(200001)15:1<45::AID-HUMU10>3.0.CO;2-T DOI: https://doi.org/10.1002/(SICI)1098-1004(200001)15:1<45::AID-HUMU10>3.0.CO;2-T
Prokunina L, Alarcón-Riquelme ME. Regulatory SNPs in complex diseases: their identification and functional validation. Expert Reviews in Molecular Medicine 2004; 6(10): 1-5. https://doi.org/10.1017/S1462399404007690 DOI: https://doi.org/10.1017/S1462399404007690
Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, et al.The human gene mutation database: 2008 update. Genome Medicine 2009; 1(1): 1-6. https://doi.org/10.1186/gm13 DOI: https://doi.org/10.1186/gm13
Ramensky V, Bork P, Sunyaev S. Human non‐synonymous SNPs: server and survey. Nucleic Acids Research 2002; 30(17): 3894-900. https://doi.org/10.1093/nar/gkf493 DOI: https://doi.org/10.1093/nar/gkf493
Emahazion T, Feuk L, Jobs M, Sawyer SL, Fredman D, St Clair D,et al. SNP association studies in Alzheimer's disease highlight problems for complex disease analysis. TRENDS in Genetics 2001; 17(7): 407-13. https://doi.org/10.1016/S0168-9525(01)02342-3 DOI: https://doi.org/10.1016/S0168-9525(01)02342-3
Schork NJ, Fallin D, Lanchbury JS. Single nucleotide polymorphisms and the future of genetic epidemiology. Clinical Genetics 2000; 58(4): 250-64. https://doi.org/10.1034/j.1399-0004.2000.580402.x DOI: https://doi.org/10.1034/j.1399-0004.2000.580402.x
Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research 2003; 31(13): 3812-4. https://doi.org/10.1093/nar/gkg509 DOI: https://doi.org/10.1093/nar/gkg509
Ng PC, Henikoff S. Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet 2006; 7: 61-80. https://doi.org/10.1146/annurev.genom.7.080505.115630 DOI: https://doi.org/10.1146/annurev.genom.7.080505.115630
Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels.
Kono TJ, Lei L, Shih CH, Hoffman PJ, Morrell PL, Fay JC. Comparative genomics approaches accurately predict deleterious variants in plants. G3: Genes, Genomes, Genetics 2018; 8(10): 3321-9. https://doi.org/10.1534/g3.118.200563 DOI: https://doi.org/10.1534/g3.118.200563
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool J Mol Biol 215: 403-410. Find this article online. 1990. https://doi.org/10.1016/S0022-2836(05)80360-2 DOI: https://doi.org/10.1016/S0022-2836(05)80360-2
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10(1): 1-9. https://doi.org/10.1186/1471-2105-10-421 DOI: https://doi.org/10.1186/1471-2105-10-421
Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 2015; 31(16): 2745-7. https://doi.org/10.1093/bioinformatics/btv195 DOI: https://doi.org/10.1093/bioinformatics/btv195
Wagner A. Robustness and evolvability in living systems. Princeton university press; 2013. https://doi.org/10.1515/9781400849383 DOI: https://doi.org/10.1515/9781400849383
Pál C, Papp B, Lercher MJ. An integrated view of protein evolution. Nature Reviews Genetics 2006; 7(5): 337-48. https://doi.org/10.1038/nrg1838 DOI: https://doi.org/10.1038/nrg1838
Camps M, Herman A, Loh ER, Loeb LA. Genetic constraints on protein evolution. Critical Reviews in Biochemistry and Molecular Biology 2007; 42(5): 313-26. https://doi.org/10.1080/10409230701597642 DOI: https://doi.org/10.1080/10409230701597642
Yue P, Li Z, Moult J. Loss of protein structure stability as a major causative factor in monogenic disease. Journal of Molecular Biology 2005; 353(2): 459-73. https://doi.org/10.1016/j.jmb.2005.08.020 DOI: https://doi.org/10.1016/j.jmb.2005.08.020
Bloom JD, Silberg JJ, Wilke CO, Drummond DA, Adami C, Arnold FH. Thermodynamic prediction of protein neutrality. Proceedings of the National Academy of Sciences 2005; 102(3): 606-11. https://doi.org/10.1073/pnas.0406744102 DOI: https://doi.org/10.1073/pnas.0406744102
Zeldovich KB, Chen P, Shakhnovich EI. Protein stability imposes limits on organism complexity and speed of molecular evolution. Proceedings of the National Academy of Sciences 2007; 104(41): 16152-7. https://doi.org/10.1073/pnas.0705366104 DOI: https://doi.org/10.1073/pnas.0705366104
Huang LT, Gromiha MM, Ho SY. iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations. Bioinformatics 2007; 23(10): 1292-3. https://doi.org/10.1093/bioinformatics/btm100 DOI: https://doi.org/10.1093/bioinformatics/btm100
Parthiban V, Gromiha MM, Schomburg D. CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Research 2006; 34(suppl_2): W239-42. https://doi.org/10.1093/nar/gkl190 DOI: https://doi.org/10.1093/nar/gkl190
Vendruscolo M, Tartaglia GG. Towards quantitative predictions in cell biology using chemical properties of proteins. Molecular BioSystems 2008; 4(12): 1170-5. https://doi.org/10.1039/b805710a DOI: https://doi.org/10.1039/b805710a
Tokuriki N, Stricher F, Serrano L, Tawfik DS. How protein stability and new functions trade off. PLoS Computational Biology 2008; 4(2): e1000002. https://doi.org/10.1371/journal.pcbi.1000002 DOI: https://doi.org/10.1371/journal.pcbi.1000002
Dakal TC, Kala D, Dhiman G. Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene. Sci Rep 2017; 7: 6525. https://doi.org/10.1038/s41598-017-06575-4 DOI: https://doi.org/10.1038/s41598-017-06575-4
Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM. Positional cloning of the mouse obese gene and its human homologue. Nature 1994; 372: 425-32. https://doi.org/10.1038/372425a0 DOI: https://doi.org/10.1038/372425a0
Stefan N, Vozarova B, Del Parigi A, Ossowski V, Thompson DB, Hanson RL, et al. The Gln223Arg polymorphism of the leptin receptor in Pima Indians: influence on energy expenditure, physical activity and lipid metabolism. Int J Obes Relat Metab Disord 2002; 26: 1629-32. https://doi.org/10.1038/sj.ijo.0802161 DOI: https://doi.org/10.1038/sj.ijo.0802161
Clément K, Vaisse C, Lahlou N, Cabrol S, Pelloux V, Cassuto D, et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 1998; 392: 398-401. https://doi.org/10.1038/32911 DOI: https://doi.org/10.1038/32911
Farooqi IS, Wangensteen T, Collins S, Kimber W, Matarese G, Keogh JM, et al. Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. N Engl J Med 2007; 356: 237-47. https://doi.org/10.1056/NEJMoa063988 DOI: https://doi.org/10.1056/NEJMoa063988
Rosmond R, Chagnon YC, Holm G, Chagnon M, Pérusse L, Lindell K, et al. Hypertension in obesity and the leptin receptor gene locus. J Clin Endocrinol Metab 2000; 85: 3126-31. https://doi.org/10.1210/jc.85.9.3126 DOI: https://doi.org/10.1210/jc.85.9.3126
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
