Katheresan S Sooriya Kannan^1,2, Tanmay Patil^1,2, Rohit Vij^1,2, Behnjemyn JK Loh^1,2 and Maurice HT Ling^1,2,3,4*

¹School of Life Sciences, Management Development Institute of Singapore, Singapore

*Corresponding Author: Maurice HT Ling, School of Life Sciences, Management Development Institute of Singapore, Singapore.

Received: April 26, 2022; Published: May 13, 2022

Abstract

The ability of organisms to utilize environmental chemicals as nutrients and adapt to changes in nutrient availability is a hallmark of life. Yet despite different environments, the concentration and osmolarity of intracellular metabolites are relatively constant across different organism. Although adaptation experiments can be performed, they are usually labour intensive and must be carried out in stepwise or gradual manner. On the other hand, digital organisms or computer-simulated organisms can be used to study adaptations to extreme conditions. Here, we examine the effects of nutrient levels on the metabolic profiles of organisms. Our results show that nutrient availability results in significantly different average intracellular metabolite amounts (F = 5166, p-value < 1E-200) at 1500^th generation despite the range within one order but there is significant decline of the impact of nutrient availability on the amounts of intracellular metabolites with increasing generations (r = -0.995, F = 385, p-value = 3.98E-05). However, mean intracellular amounts of specific metabolites are significantly different across all 12 nutrient availabilities (14 ≤ F ≤ 1927, 4.1E-304 ≤ p-value ≤ 1.6E-22). This suggests that the impact of nutrient availability is beyond the overall intercellular metabolite amounts but at the level of individual metabolites.

Keywords: Organisms; Metabolism; Nutrients

References

1. Li Z-Y., et al. “Metabolic Profiles of Prokaryotic and Eukaryotic Communities in Deep-Sea Sponge Neamphius huxleyi Indicated by Metagenomics”. Scientific Reports1 (2014): 3895.
2. Pocheville A. “The Ecological Niche: History and Recent Controversies”. Handbook of Evolutionary Thinking in the Sciences, eds Heams T, Huneman P, Lecointre G, Silberstein M (Springer Netherlands, Dordrecht) (2015): 547-586.
3. Huang W., et al. “Spontaneous Mutations and the Origin and Maintenance of Quantitative Genetic Variation”. eLife 5 (2016).
4. Durand E., et al. “Standing Variation and New Mutations Both Contribute to a Fast Response to Selection for Flowering Time in Maize Inbreds”. BMC Evolutionary Biology1 (2010): 2.
5. Xu S., et al. “Low Genetic Variation is Associated with Low Mutation Rate in the Giant Duckweed. Nature Communications1 (2019): 1243.
6. Maki H. “Origins of Spontaneous Mutations: Specificity and Directionality of Base-Substitution, Frameshift, and Sequence-Substitution Mutageneses”. Annual Review of Genetics1 (2002): 279-303.
7. Na G., et al. “The Effect of Environmental Factors and Migration Dynamics on the Prevalence of Antibiotic-Resistant Escherichia coli in Estuary Environments”. Scientific Reports1 (2018): 1663.
8. Pedraz L., et al. “Gradual Adaptation of Facultative Anaerobic Pathogens to Microaerobic and Anaerobic Conditions”. FASEB Journal2 (2020): 2912-2928.
9. Lee CH., et al. “Escherichia coli ATCC 8739 Adapts to the Presence of Sodium Chloride, Monosodium Glutamate, and Benzoic Acid After Extended Culture”. ISRN Microbiology (2012): 965356.
10. Loo BZL., et al. “Escherichia coli ATCC 8739 Adapts Specifically to Sodium Chloride, Monosodium Glutamate, and Benzoic Acid After Prolonged Stress”. Asia Pacific Journal of Life Sciences 7.3 (2013): 243.
11. Goh DJ., et al. “Gradual and Step-wise Halophilization Enables Escherichia coli ATCC 8739 to Adapt to 11% NaCl”. Electronic Physician3 (2012): 527-535.
12. Schmidt-Nielsen B. “Comparative Physiology of Cellular Ion and Volume Regulation”. The Journal of Experimental Zoology1 (2014): 207-219.
13. Fagerbakke KM., et al. “The Inorganic Ion content of Native Aquatic Bacteria”. Canadian Journal of Microbiology4 (1994): 304-311.
14. Park JO., et al. “Metabolite Concentrations, Fluxes and Free Energies Imply Efficient Enzyme Usage”. Nature Chemical Biology7 (2016): 482-489.
15. Pourmir A and Johannes TW. “Directed Evolution: Selection of the Host Organism. Computational and Structural Biotechnology Journal 2 (2012): e201209012.
16. English JG., et al. “VEGAS as a Platform for Facile Directed Evolution in Mammalian Cells. Cell3 (2019): 748-761.e17.
17. Langton CG. “Studying Artificial Life with Cellular Automata”. Physica D: Nonlinear Phenomena1-3 (1986): 120-149.
18. Elena SF and Sanjuán R. “The Effect of Genetic Robustness on Evolvability in Digital Organisms”. BMC Evolutionary Biology 8 (2008): 284.
19. Anderson CJR and Harmon L. “Ecological and Mutation-Order Speciation in Digital Organisms”. The American Naturalist2 (2014): 257-268.
20. Castillo CFG and Ling MHT. “Resistant Traits in Digital Organisms Do Not Revert Preselection Status Despite Extended Deselection: Implications to Microbial Antibiotics Resistance”. BioMed Research International (2014): 648389.
21. Ling MH. “Applications of Artificial Life and Digital Organisms in the Study of Genetic Evolution”. Advances in Computer Science: An International Journal4 (2014): 107-112.
22. Yao Y., et al. “Using Digital Organisms to Study the Evolutionary Consequences of Whole Genome Duplication and Polyploidy”. PloS One7 (2019): e0220257.
23. Castillo CF., et al. “Resistance Maintained in Digital Organisms Despite Guanine/Cytosine-Based Fitness Cost and Extended De-Selection: Implications to Microbial Antibiotics Resistance”. MOJ Proteomics and Bioinformatics2 (2015): 00039.
24. Wilke CO and Adami C. “The Biology of Digital Organisms”. Trends in Ecology and Evolution11 (2002): 528-532.
25. Chew SS., et al. “Rapid Genetic Diversity with Variability between Replicated Digital Organism Simulations and its Implications on Cambrian Explosion”. EC Clinical and Medical Case Reports11 (2020): 64-68.
26. Mozhayskiy V and Tagkopoulos I. “Microbial Evolution In Vivo and In Silico: Methods and Applications. Integrative Biology2 (2013): 262-277.
27. O’Neill B. “Digital Evolution”. PLoS Biology1 (2003): E18.
28. Koh YZ and Ling MH. “On the Liveliness of Artificial Life”. iConcept Journal of Human-Level Intelligence 3 (2013): 1.
29. Ang DG and Ling MH. “Sudden and Steep Harsh Environment Results in Over-Compensation in Digital Organisms”. EC Microbiology7 (2021): 104-113.
30. Castillo CF and Ling MH. “Digital Organism Simulation Environment (DOSE): A Library for Ecologically-Based In Silico Experimental Evolution”. Advances in Computer Science: An International Journal1 (2014): 44-50.
31. Castillo CF and Ling MH. “Digital Organism Simulation Environment (DOSE) Version 1.0.4”. Current STEM, Volume 1 (Nova Science Publishers, Inc.) (2018): 1-106.
32. Ling MH. “Ragaraja 1.0: The Genome Interpreter of Digital Organism Simulation Environment (DOSE)”. The Python Papers Source Codes 4 (2012): 2.
33. Baird FE., et al. “Tertiary Active Transport of Amino Acids Reconstituted by Coexpression of System A and L Transporters in Xenopus oocytes”. American Journal of Physiology-Endocrinology and Metabolism3 (2009): E822-E829.
34. Usman S., et al. “Pseudomonas balearica DSM 6083T Promoters Can Potentially Originate from Random Sequences”. MOJ Proteomics and Bioinformatics2 (2019): 66-70.
35. Ardhanari-Shanmugam KD., et al. “De Novo Origination of Bacillus subtilis 168 Promoters from Random Sequences”. Acta Scientific Microbiology11 (2019): 07-10.
36. Kwek BZ., et al. “Random Sequences May Have Putative Beta-Lactamase Properties”. Acta Scientific Medical Sciences7 (2019): 113-117.
37. Pulina S., et al. “Multiannual Decrement of Nutrient Concentrations and Phytoplankton Cell Size in a Mediterranean reservoir”. Nature Conservation 34 (2019): 163-191.
38. Martino ME., et al. “Bacterial Adaptation to the Host’s Diet Is a Key Evolutionary Force Shaping Drosophila-Lactobacillus Symbiosis”. Cell Host and Microbe1 (2018): 109-119.e6.
39. Chatterjee A and O’Brian MR. “Rapid Evolution of a Bacterial Iron Acquisition System”. Molecular Microbiology1 (2018): 90-100.
40. Huang Z., et al. “Effects of Culture Media on Metabolic Profiling of the Human Gastric Cancer Cell Line SGC7901”. Molecular BioSystems7 (2015): 1832-1840.
41. Sampaio BL., et al. “Effect of the Environment on the Secondary Metabolic Profile of Tithonia diversifolia: A Model for Environmental Metabolomics of Plants”. Scientific Reports1 (2016): 29265.

Citation

Citation: Maurice HT Ling., et al. “Nutrient Availability Impacts Intracellular Metabolic Profiles in Digital Organisms". Acta Scientific Microbiology 5.6 (2022): 18-25.

Copyright

Copyright: © 2022 Maurice HT Ling., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.