EC Microbiology

Origin of metabolic pathways is an important milestone in evolution, linking pre-biotic/abiotic era to biotic era, and genic-encoded enzymatic reactions is at the core. Studies imply that de novo genic origin of early enzymes is plausible. There are also suggestions that large cells may be at the origin of life. However, the questions of whether unlinked enzymatic reactions link to multi-step biochemical pathways and whether large cells are necessary remain. Here, we use digital organisms to examine the emergence of multi-step biochemical pathways from independently genic-encoded enzymatic reactions. Our simulation results suggest that independently genic-encoded enzymatic reactions can randomly link into multi-step biochemical pathways in the absence of large cell selective pressure (p-value > 0.05). This suggests that genic-encoded multi-step biochemical pathways may arise randomly once enzymes are prevalent.

 Keywords: Genic-Encoded Enzymatic Reactions; Biochemical Pathways; Pre-Biotic/Abiotic Era; Biotic Era

1. Fani R. “The origin and evolution of metabolic pathways: why and how did primordial cells construct metabolic routes?” Evolution: Education and Outreach3 (2012): 367-381.
2. Fani R and Fondi M. “Origin and evolution of metabolic pathways”. Physics of Life Reviews1 (2009): 23-52.
3. Scossa F and Fernie AR. “The evolution of metabolism: how to test evolutionary hypotheses at the genomic level”. Computational and Structural Biotechnology Journal 18 (2020): 482-500.
4. Lazcano A and Miller SL. “On the origin of metabolic pathways”. Journal of Molecular Evolution4 (1999): 424-431.
5. Becerra A. “The semi-enzymatic origin of metabolic pathways: inferring a very early stage of the evolution of life”. Journal of Molecular Evolution3 (2021): 183-188.
6. Keller MA., et al. “The widespread role of non-enzymatic reactions in cellular metabolism”. Current Opinion in Biotechnology 34 (2015): 153-161.
7. Muchowska KB., et al. “Nonenzymatic metabolic reactions and life’s origins”. Chemical Reviews15 (2020): 7708-7744.
8. Prosdocimi F., et al. “Prebiotic chemical refugia: multifaceted scenario for the formation of biomolecules in primitive earth”. Theory in Biosciences4 (2022): 339-347.
9. Keller MA., et al. “Non-enzymatic glycolysis and pentose phosphate pathway-like reactions in a plausible archean ocean”. Molecular Systems Biology4 (2014): 725.
10. Ralser M. “An appeal to magic? the discovery of a non-enzymatic metabolism and its role in the origins of life”. The Biochemical Journal16 (2018): 2577-2592.
11. Rauscher SA and Moran J. “Hydrogen drives part of the reverse krebs cycle under metal or meteorite catalysis”. Angewandte Chemie International Edition 51 (2022): e202212932.
12. Tagami S and Li P. “The origin of life: RNA and Protein co-evolution on the ancient earth”. Development, Growth and Differentiation3 (2023): 167-174.
13. Sim BK and Ling MH. “Possibility of abiotic genesis of biochemistry”. EC Microbiology6 (2020): 104-109.
14. Ling MH. “De novo putative protein domains from random peptides”. Acta Scientific Microbiology4 (2019): 109-112.
15. Thong-Ek C., et al. “Potential de novo origins of archaebacterial glycerol-1-phosphate dehydrogenase (G1PDH)”. Acta Scientific Microbiology6 (2019): 106-110.
16. Kwek BZ., et al. “Random sequences may have putative beta-lactamase properties”. Acta Scientific Medical Sciences7 (2019): 113-117.
17. Ardhanari-Shanmugam KD., et al. “De novo origination of Bacillus subtilis 168 promoters from random sequences”. Acta Scientific Microbiology11 (2019): 07-10.
18. Usman S., et al. “Pseudomonas balearica DSM 6083T promoters can potentially originate from random sequences”. MOJ Proteomics and Bioinformatics2 (2019): 66-70.
19. Neo CY and Ling MH. “Prevalence and length of open reading frames vary across randomly generated sequences of different nucleotide compositions”. EC Microbiology7 (2020): 72-78.
20. Stetter KO. “Hyperthermophiles in the history of life”. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences1474 (2006): 1837-1842; discussion 1842-1843.
21. Volland J-M., et al. “A centimeter-long bacterium with DNA contained in metabolically active, membrane-bound organelles”. Science 6600 (2022): 1453-1458.
22. Langton CG. “Studying artificial life with cellular automata”. Physica D: Nonlinear Phenomena1-3 (1986): 120-149.
23. Elena SF and Sanjuán R. “The effect of genetic robustness on evolvability in digital organisms”. BMC Evolutionary Biology 8 (2008): 284.
24. Ortega R., et al. “Ontology for the avida digital evolution platform”. Scientific Data1 (2023): 608.
25. Anderson CJR and Harmon L. “Ecological and mutation-order speciation in digital organisms”. The American Naturalist 2 (2014): 257-268.
26. 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.
27. 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.
28. Yao Y., et al. “Using digital organisms to study the evolutionary consequences of whole genome duplication and polyploidy”. PloS One7 (2019): e0220257.
29. 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.
30. Wilke CO and Adami C. “The biology of digital organisms”. Trends in Ecology and Evolution11 (2002): 528-532.
31. 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.
32. Ang DG and Ling MH. “Sudden and steep harsh environment results in over-compensation in digital organisms”. EC Microbiology7 (2021): 104-113.
33. Sooriya Kannan KS., et al. “Nutrient availability impacts intracellular metabolic profiles in digital organisms”. Acta Scientific Microbiology5 (2022): 18-25.
34. Dolson E and Ofria C. “Digital evolution for ecology research: a review”. Frontiers in Ecology and Evolution 9 (2021): 750779.
35. Mozhayskiy V and Tagkopoulos I. “Microbial evolution in vivo and in silico: methods and applications”. Integrative Biology2 (2013): 262-277.
36. O’Neill B. “Digital evolution”. PLoS Biology1 (2003): E18.
37. Koh YZ and Ling MH. “On the liveliness of artificial life”. iConcept Journal of Human-Level Intelligence 3 (2013): 1.
38. 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.
39. 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.
40. Ling MH. “Ragaraja 1.0: The genome interpreter of digital organism simulation environment (DOSE)”. The Python Papers Source Codes 4 (2012): 2.
41. Maitra A and Ling MH. “DOSSIER: A toolkit to extract data from digital life simulations using dose”. Acta Scientific Computer Sciences 7 (2022): 37-40.
42. Latora V and Marchiori M. “Efficient behavior of small-world networks”. Physical Review Letters19 (2001): 198701.
43. Latora V and Marchiori M. “Economic small-world behavior in weighted networks”. The European Physical Journal B - Condensed Matter and Complex Systems 2 (2003): 249-263.
44. Schavemaker PE and Muñoz-Gómez SA. “The role of mitochondrial energetics in the origin and diversification of eukaryotes”. Nature Ecology and Evolution9 (2022): 1307-1317.
45. Williams RJP. “A system’s view of the evolution of life”. Journal of the Royal Society, Interface17 (2007): 1049-1070.