A Two-Level Factorial Design for Screening Factors that Influence the Growth of Bacillus sp. Strain ZEID-14 on Acrylamide

Authors

  • Mohd Fadhil Abd Rahman Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia.
  • Motharasan Manogaran Malaysia Genome and Vaccine Institute (MGVI) National Institute of Biotechnolgy Malaysia (NIBM) Jalan Bangi, 43000 Kajang, Selangor, Malaysia.
  • Hafeez Muhammad Yakasai Department of Biochemistry, Faculty of Basic Medical Sciences, College of Health Sciences, Bayero University Kano, Nigeria.
  • Nur Adeela Yasid Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia.
  • 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/jebat.v5i2.762

Keywords:

Acrylamide, Bioremediation, Bacillus sp., Screening, Two-level factorial design

Abstract

Acrylamide is often used to strengthen soil structure and usually leachate from this application forms a major source of acrylamide pollution in the environment. The soil matrix means physicochemical methods of removal will be costly if not difficult. Bioremediation using acrylamide-degrading bacteria is an appealing technique. In this investigation, a previously isolated molybdenum-reducing bacterium with the ability to degrade amides was discovered based on critical characteristics contributing to optimal growth on acrylamide utilizing a two-level factorial design. Five independent parameters that influence the growth of the bacterium on acrylamide were evaluated using a two-level factorial design. Among these variables are pH, temperature, incubation period, acrylamide content, and glucose concentration. The two-factor factorial design was successful in identifying significant contributing parameters to the growth of this bacterium on acrylamide, namely acrylamide concentration, pH, and incubation time, which can be further otpimized using RSM in future studies. Using ANOVA, Pareto's chart, perturbation's plot, and other diagnostic plots, the significant contributing factors or parameters were examined. Half-normal, Cook's distance, residual vs runs, leverage versus runs, Box-Cox, DFFITS, and DFBETAS diagnostic plots all supported the two-level factorial result. This work was conducted using acrylamide concentrations well within the known tolerance range of most acrylamide-degrading bacteria. Incubation time is an expected consequence, as longer incubation time permits more growth. The majority of acrylamide-degrading microorganisms thrive under near-neutral circumstances, as indicated by the results of our investigation, which are consistent with previous 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.

Adnan M, Abu Zeid I, Ahmad SA, Effendi Halmi M, Abdullah S, Shukor M. A Molybdenum-reducing Bacillus sp. Strain Zeid 14 in Soils from Sudan that Could Grow on Amides and Acetonitrile. Malays J Soil Sci. 2016 Jan 1;20:111-34.

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.

Shah M. Microbial Degradation of Acrylamide by Enterobacter spp. Am J Water Resour. 2014 Nov 2;2:134-40.

Shimamura Y, Yui T, Horiike H, Masuda S. Toxicity of combined exposure to acrylamide and Staphylococcus aureus. Toxicol Rep. 2022 Jan 1;9:876-82.

Kulkarni NH, Muley AB, Bedade DK, Singhal RS. Cross-linked enzyme aggregates of arylamidase from Cupriavidus oxalaticus ICTDB921: process optimization, characterization, and application for mitigation of acrylamide in industrial wastewater. Bioprocess Biosyst Eng [Internet]. 2019; Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075085765&doi=10.1007%2fs00449-019-02240-4&partnerID=40&md5=12e064000a11176469878181f8642894

Lakshmikandan M, Sivaraman K, Elaiya Raja S, Vasanthakumar P, Rajesh RP, Sowparthani K, et al. Biodegradation of acrylamide by acrylamidase from Stenotrophomonas acidaminiphila MSU12 and analysis of degradation products by MALDI-TOF and HPLC. Int Biodeterior Biodegrad. 2014;94:214-21.

Chandrashekar V, Chandrashekar C, Shivakumar R, Bhattacharya S, Das A, Gouda B, et al. Assessment of acrylamide degradation potential of Pseudomonas aeruginosa BAC-6 isolated from industrial effluent. Appl Biochem Biotechnol. 2014;173(5):1135-44.

Emmanuel Joshua Jebasingh S, Lakshmikandan M, Rajesh RP, Raja P. Biodegradation of acrylamide and purification of acrylamidase from newly isolated bacterium Moraxella osloensis MSU11. Int Biodeterior Biodegrad. 2013;85:120-5.

Ansede JH, Pellechia PJ, Yoch DC. Metabolism of acrylate to beta-hydroxypropionate and its role in dimethylsulfoniopropionate lyase induction by a salt marsh sediment bacterium, Alcaligenes faecalis M3A. Appl Environ Microbiol. 1999 Nov;65(11):5075-81.

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

2022-12-31

How to Cite

Rahman, M. F. A., Manogaran, M., Yakasai, H. M., Yasid, N. A., & Shukor, M. Y. (2022). A Two-Level Factorial Design for Screening Factors that Influence the Growth of Bacillus sp. Strain ZEID-14 on Acrylamide. Journal of Environmental Bioremediation and Toxicology, 5(2), 17–24. https://doi.org/10.54987/jebat.v5i2.762

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