A Two-Level Factorial Design for Screening Factors that Influence the Growth of Pseudomonas sp. Strain Dry135 on Acrylamide

Authors

  • M.F.A. Rahman Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia.
  • Mohd Ezuan Khayat Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia.
  • Aisami Abubakar Department of Biochemistry, Faculty of Science, Gombe State University, P.M.B 127, Tudun Wada, Gombe, Gombe State, Nigeria.
  • Hafeez Muhammad Yakasai Department of Biochemistry, Faculty of Basic Medical Sciences, College of Health Sciences, Bayero University Kano, Nigeria.
  • Mohd Yunus Shukor Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia.

DOI:

https://doi.org/10.54987/jemat.v10i2.771

Keywords:

Acrylamide, Pseudomonas sp., Bioremediation, Screening, Two-Level Factorial Design

Abstract

Polyacrylamide is one of the most important sources of acrylamide in soil because it degrades into acrylamide over time. The breakdown of acrylamide by bacteria has experienced a steady but consistent increase in interest all over the world as a bioremediation technique. In this investigation, a previously obtained molybdenum-reducing bacterium with amide-degrading capabilities was found on critical parameters leading to optimum growth on acrylamide utilizing a two-level factorial design. The two-level factorial design was used in the screening of five independent parameters impacting the bacterium's growth on acrylamide. These variables include pH, temperature, incubation period, acrylamide concentration, and ammonium sulphate concentration. The two-factor factorial design was successful in identifying major contributing parameters in the growth of this bacterium on acrylamide, which were acrylamide concentration, pH, and incubation time (p<0.05), which can be further optimized using RSM in future research. ANOVA, Pareto's chart, pertubation's plot, and other diagnostic plots were used to analyze the significant contributing components or parameters. Diagnostic plots such as half-normal, Cook's distance, residual vs runs, leverage vs runs, Box-Cox, DFFITS, and DFBETAS all supported the two-level factorial result. The acrylamide range used in this study was well within the range reported to being tolerated by the majority of acrylamide-degrading bacteria. Incubation time is an expected finding because longer incubation time allows for higher growth, and incubation time ranging from two to five days for optimized growth has been documented in numerous acrylamide-degrading bacteria. Most acrylamide-degrading microorganisms grow well in near-neutral environments, and the results obtained in this investigation are consistent with published literature trends.

References

Spencer P, Schaumburg HH. Nervous system degeneration produced by acrylamide monomer. Environ Health Perspect. 1975 Jun 1;11:129-33.

Tareke E, Rydberg P, Karlsson P, Eriksson S, Törnqvist M. Analysis of acrylamide, a carcinogen formed in heated foodstuffs. J Agric Food Chem. 2002;50(17):4998-5006.

Tyl RW, Friedman MA. Effects of acrylamide on rodent reproductive performance. Reprod Toxicol. 2003 Jan 1;17(1):1-13.

Yang HJ, Lee SH, Jin Y, Choi JH, Han CH, Lee MH. Genotoxicity and toxicological effects of acrylamide on reproductive system in male rats. J Vet Sci. 2005 Jun;6(2):103-9.

Backer LC, Dearfield KL, Erexson GL, Campbell JA, Westbrook?Collins B, Allen JW. The effects of acrylamide on mouse germ-line and somatic cell chromosomes. Environ Mol Mutagen. 1989;13(3):218-26.

Mottram, DS, Wedzicha BL, Dobson AT. Acrylamide is formed in the Maillard reaction. Nature. 2002;419:448-9.

Zamora R, Delgado RM, Hidalgo FJ. Strecker aldehydes and ?-keto acids, produced by carbonyl-amine reactions, contribute to the formation of acrylamide. Food Chem. 2011;128(2):465-70.

Shukor MY, Gusmanizar N, Azmi NA, Hamid M, Ramli J, Shamaan NA, et al. Isolation and characterization of an acrylamide-degrading Bacillus cereus. J Enviromental Biol. 2009;30(1):57-64.

Hagmar L, Törnqvist M, Nordander C, Rosén I, Bruze M, Kautiainen A, et al. Health effects of occupational exposure to acrylamide using hemoglobin adducts as biomarkers of internal dose. Scand J Work Environ Health. 2001;27(4):219-26.

Igisu H, Goto I, Kawamura Y, Kato M, Izumi K. Acrylamide encephaloneuropathy due to well water pollution. J Neurol Neurosurg Psychiatry. 1975;38(6):581-4.

Eikmann T, Herr C. How dangerous is actually acrylamide exposure for the population. Umweltmed Forsch Prax. 2002;7(6):307-8.

Pruser KN, Flynn NE. Acrylamide in health and disease. Front Biosci - Sch. 2011;3 S(1):41-51.

Pennisi M, Malaguarnera G, Puglisi V, Vinciguerra L, Vacante M, Malaguarnera M. Neurotoxicity of acrylamide in exposed workers. Int J Environ Res Public Health. 2013;10(9):3843-54.

Rahim MBH, Syed MA, Shukor MY. Isolation and characterization of an acrylamide-degrading yeast Rhodotorula sp . strain MBH23 KCTC 11960BP. J Basic Microbiol. 2012;52(5):573-81.

Wakaizumi M, Yamamoto H, Fujimoto N, Ozeki K. Acrylamide degradation by filamentous fungi used in food and beverage industries. J Biosci Bioeng. 2009;108(5):391-3.

Wampler DA, Ensign SA. Photoheterotrophic metabolism of acrylamide by a newly isolated strain of Rhodopseudomonas palustris. Appl Environ Microbiol. 2005;71(10):5850-7.

Buranasilp K, Charoenpanich J. Biodegradation of acrylamide by Enterobacter aerogenes isolated from wastewater in Thailand. J Environ Sci. 2011;23(3):396-403.

Charoenpanich J, Tani A. Proteome analysis of acrylamide-induced proteins in a novel acrylamide-degrader Enterobacter aerogenes by 2D electrophoresis and MALDI-TOF-MS. Chiang Mai Univ J Nat Sci. 2014;13(1):11-22.

Gusmanizar N, Shukor Y, Ramli J, Syed MA. Isolation and characterization of an acrylamide-degrading Burkholderia sp. strain DR.Y27. J Ris Kim. 2015 Feb 11;2(1):34.

Yu F, Fu R, Xie Y, Chen W. Isolation and characterization of polyacrylamide-degrading bacteria from dewatered sludge. Int J Environ Res Public Health. 2015;12(4):4214-30.

Bedade DK, Singhal RS. Biodegradation of acrylamide by a novel isolate, Cupriavidus oxalaticus ICTDB921: Identification and characterization of the acrylamidase produced. Bioresour Technol. 2018 Aug 1;261:122-32.

Aisami A, Gusmanizar N. Characterization of an acrylamide-degrading bacterium isolated from hydrocarbon sludge. Bioremediation Sci Technol Res. 2019 Dec 28;7(2):15-9.

Othman AR, Rahim MBHA. Modelling the Growth Inhibition Kinetics of Rhodotorula sp. strain MBH23 (KCTC 11960BP) on Acrylamide. Bioremediation Sci Technol Res. 2019 Dec 28;7(2):20-5.

Rusnam, Gusmanizar N. An Acrylamide-degrading Bacterial Consortium Isolated from Volcanic Soil. J Biochem Microbiol Biotechnol. 2021 Dec 31;9(2):19-24.

Rusnam, Gusmanizar N. Characterization of An Acrylamide-degrading Bacterium Isolated from Volcanic Soil. J Environ Bioremediation Toxicol. 2022 Aug 5;5(1):32-7.

Hedayat AS, Pesotan H. Designs for two-level factorial experiments with linear models containing main effects and selected two-factor interactions. J Stat Plan Inference. 1997;64(1):109-24.

Leitnaker MG, Mee RW. Analytic use of two-level factorials in incomplete blocks to examine the stability of factor effects. Qual Eng. 2001;14(1):49-58+xii.

Chang FK, Ting CP. Optimal two-level fractional factorial designs for location main effects with dispersion factors. Commun Stat - Theory Methods. 2011;40(11):2035-43.

Besseris GJ. Order statistics for the two-level eight-run saturated-unreplicated fractional-factorial screening: Corrections for asymmetry in the three-and four-factor multi-contrasting. Int J Qual Eng Technol. 2013;3(3):236-42.

Parui S, Parsad R, Mandal BN. Efficient block designs for incomplete factorial experiments for two factors with unequal block sizes. Commun Stat - Theory Methods. 2021;50(11):2531-45.

Haida Z, Ab Ghani S, Juju Nakasha J, Hakiman M. Determination of experimental domain factors of polyphenols, phenolic acids and flavonoids of lemon (Citrus limon) peel using two-level factorial design: Determination of experimental domain factors. Saudi J Biol Sci. 2022;29(1):574-82.

Yakasai MH, Abd Rahman MF, Abd Rahim MBH, Khayat ME, Shamaan NA, Shukor MY. Isolation and characterization of a metal-reducing Pseudomonas sp. strain 135 with amide-degrading capability. Bioremediation Sci Technol Res. 2017;5(2):32-8.

Bonate PL. Linear Models and Regression. In: Bonate PL, editor. Pharmacokinetic-Pharmacodynamic Modeling and Simulation [Internet]. Boston, MA: Springer US; 2011 [cited 2022 Dec 24]. p. 61-100. Available from: https://doi.org/10.1007/978-1-4419-9485-1_2

Mundry R. From nonparametric tests to mixed models: A brief overview of statistical tools frequently used in comparative psychology. In: APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior, Vol 1. Washington, DC, US: American Psychological Association; 2017. p. 157-77. (APA handbooks in psychology®).

Fox J. Regression Diagnostics: An Introduction. SAGE Publications; 2019. 138 p.

Dey A, Suen CY, Das A. Asymmetric fractional factorial plans optimal for main effects and specified two-factor interactions. Stat Sin. 2005;15(3):751-65.

Bailey RA, ?acka A. Nested row-column designs for near-factorial experiments with two treatment factors and one control treatment. J Stat Plan Inference. 2015;165:63-77.

Lawson J. Comparison of conditional main effects analysis to the analysis of follow-up experiments for separating confounded two-factor interaction effects in 2 IVk?p fractional factorial experiments. Qual Reliab Eng Int. 2020;36(4):1454-72.

Chobisa D. Design of Experiments for the Development of Injectable Drug Products. In: Beg S, editor. Design of Experiments for Pharmaceutical Product Development: Volume II?: Applications and Practical Case studies [Internet]. Singapore: Springer; 2021 [cited 2022 Dec 24]. p. 69-96. Available from: https://doi.org/10.1007/978-981-33-4351-1_5

Draper NR, Stoneman DM. Factor Changes and Linear Trends in Eight-Run Two-Level Factorial Designs. Technometrics. 1968;10(2):301-11.

Wei J, Carroll RJ, Harden KK, Wu G. Comparisons of treatment means when factors do not interact in two-factorial studies. Amino Acids. 2012;42(5):2031-5.

Cornell JA. Classical and Modern Regression With Applications. Technometrics. 1987 Aug 1;29(3):377-8.

Aydin D, Alma ÖG. Diagnostics of influential observations in partially linear models. Erzincan Univ J Sci Technol. 2015 Jan 2;8(1):51-68.

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Published

31.12.2022

How to Cite

Rahman, M., Khayat, M. E., Abubakar, A., Yakasai, H. M., & Shukor, M. Y. (2022). A Two-Level Factorial Design for Screening Factors that Influence the Growth of Pseudomonas sp. Strain Dry135 on Acrylamide. Journal of Environmental Microbiology and Toxicology, 10(2), 34–41. https://doi.org/10.54987/jemat.v10i2.771

Issue

Vol. 10 No. 2 (2022)

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Articles

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