Abstract
Arsenic is known as the king of poisons due to its toxic nature and is used as pesticide and fertilizers. Accumulation of arsenic in plants causes toxic reactions, negative impact on growth and productivity. Increment in arsenic uptake is directly proportional to oxidative stress resulting in the production of reactive oxygen species. However, few plants have developed mechanism for tolerance by expressing genes or proteins such as antioxidants enzymes, arsenite oxidase, that playing some beneficial role in detoxification against arsenic effect. Pteris vittata is one such example of plants which can tolerate both forms of Arsenic i.e., arsenate (+5) and arsenite (+3). Tolerance to arsenic in P. vittata is due to the activity of three genes i.e., Glyceraledhyde 3-phosphate dehydrogenase C1 (PvGAPC1), Organic cation/carnitine transporter 4 (PvOCT4), and Glutathione S transferase (PvGSTF1). Knowing the importance of these genes in arsenic tolerance, in the present investigation we have characterized and predicted the tertiary structure of these proteins using computational approaches. Further to understand the mechanism of arsenic tolerance, we have compared the 3D structure of PvGAPC1, PvOCT4 and PvGSTF1 with its counterpart predicted from arsenic sensitive Oryza sativa and Arabidopsis thaliana. Thus, crop improvement techniques using these genes of Pteris vittata be utilized for developing arsenic tolerance in important cultivated crops.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242
Binder JX, Pletscher-Frankild S, Tsafou K, Stolte C, O’Donoghue SI, Schneider R, Jensen LJ (2014) Compartments: unification and visualization of protein subcellular localization evidence. Database 2014
Boyce WT, Sokolowski MB, Robinson GE (2020) Genes and environments, development and time. Proc Natl Acad Sci 117(38):23235–23241
Brown E, Mengmeng Z, Taotao F, Juanli W, Junbo N (2018) Mechanisms of bacterial degradation of arsenic. Indian J Microbiol Res 5:436–441
Cai C, Lanman NA, Withers KA, DeLeon AM, Wu Q, Gribskov M, Salt DE, Banks JA (2019) Three genes define a bacterial-like arsenic tolerance mechanism in the arsenic hyperaccumulating fern Pteris vittata. Current Biol 29(10):1625–1633. e1623
Chakrabarty N (2015) Arsenic toxicity: prevention and treatment. CRC Press
Chakraborti D, Rahman MM, Ahamed S, Dutta RN, Pati S, Mukherjee SC (2016) Arsenic groundwater contamination and its health effects in Patna district (capital of Bihar) in the middle Ganga plain, India. Chemosphere 152:520–529
DeLano WL (2002) Pymol: an open-source molecular graphics tool. CCP4 Newslett Protein Crystallogr 40(1):82–92
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32(5):1792–1797
Eisenberg D, Lüthy R, Bowie J (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. In: Methods in enzymology, vol 277. Academic Press, pp 396–404. doi 10.s0076-6879
Engwa GA, Ferdinand PU, Nwalo FN, Unachukwu MN (2019) Mechanism and health effects of heavy metal toxicity in humans. Poisoning Mod World-New Tricks Old Dog 10
Finnegan P, Chen W (2012) Arsenic toxicity: the effects on plant metabolism. Front Physiol 3:182
Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A (2005) Protein identification and analysis tools on the ExPASy server. In: The proteomics protocols handbook, pp 571–607
Geourjon C, Deleage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6):681–684
Ghosh P, Rathinasabapathi B, Teplitski M, Ma LQ (2015) Bacterial ability in AsIII oxidation and AsV reduction: relation to arsenic tolerance, P uptake, and siderophore production. Chemosphere 138:995–1000
Guex N, Peitsch MC, Schwede T (2009) Automated comparative protein structure modeling with SWISS-MODEL and Swiss-Pdb viewer: a historical perspective. Electrophoresis 30(S1):S162–S173
Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35(6):1547
Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291
Ma LQ, Komar KM, Tu C, Zhang W, Cai Y, Kennelley ED (2001) A fern that hyperaccumulates arsenic. Nature 409(6820):579–579
Mandal BK, Suzuki KT (2002) Arsenic round the world: a review. Talanta 58(1):201–235
Mirza N, Mahmood Q, Maroof Shah M, Pervez A, Sultan S (2014) Plants as useful vectors to reduce environmental toxic arsenic content. The Scientific World Journal 2014
Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer EL, Tosatto SC, Paladin L, Raj S, Richardson LJ (2021) Pfam: the protein families database in 2021. Nucleic Acids Res 49(D1):D412–D419
Popov M, Zemanová V, Sácký J, PavlÃk M, Leonhardt T, MatouÅ¡ek T, Kaňa A, PavlÃková D, Kotrba P (2021) Arsenic accumulation and speciation in two cultivars of Pteris cretica L. and characterization of arsenate reductase PcACR2 and arsenite transporter PcACR3 genes in the hyperaccumulating cv. Albo-lineata. Ecotoxicol Environ Safety 216:112196
Porter E, Peterson P (1975) Arsenic accumulation by plants on mine waste (United Kingdom). Sci Total Environ 4(4):365–371
Purty R, Sachar M, Chatterjee S (2017) Structural and expression analysis of salinity stress responsive phosphoserine phosphatase from Brassica juncea L. J Proteom Bioinform 10:119–127
Purty RS, Jha AN, Chatterjee S (2020) Presence of heavy metal in water (Yamuna River), soil and vegetables in delhi and to examine the effect of phyto-accumulating capacity ofEichhornia crassipes. Plant Cell Biotechnol Mol Biol 21(3–4):22–36
Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4(4):406–425
Schultz J, Milpetz F, Bork P, Ponting CP (1998) SMART, a simple modular architecture research tool: identification of signaling domains. Proc Natl Acad Sci 95(11):5857–5864
Shah MT, Suleman M, Abdul Baqi S, Sattar A, Khan N (2020) 1. Determination of heavy metals in drinking water and their adverse effects on human health. A review. Pure Appl Biol (PAB) 9(1):96–104
Shaji E, Santosh M, Sarath K, Prakash P, Deepchand V, Divya B (2020) Arsenic contamination of groundwater: a global synopsis with focus on the Indian Peninsula. Geosci Front 12:101079
Shankar S, Shanker U (2014) Arsenic contamination of groundwater: a review of sources, prevalence, health risks, and strategies for mitigation. Sci World J 2014
Sharma P, Jha AB, Dubey RS (2021) Arsenic toxicity and tolerance mechanisms in crop plants. In: Handbook of plant and crop physiology. CRC Press, pp 831–873
Souri Z, Karimi N, Sandalio LM (2017) Arsenic hyperaccumulation strategies: an overview. Front Cell Dev Biol 5:67
Strawn DG (2018) Review of interactions between phosphorus and arsenic in soils from four case studies. Geochem Trans 19(1):1–13
Tawfik DS, Viola RE (2011) Arsenate replacing phosphate: alternative life chemistries and ion promiscuity. Biochemistry 50(7):1128–1134
Tu C, Ma LQ (2003) Effects of arsenate and phosphate on their accumulation by an arsenic-hyperaccumulator Pteris vittata L. Plant Soil 249(2):373–382
Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ (2009) Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25(9):1189–1191
Winter D, Vinegar B, Nahal H, Ammar R, Wilson GV, Provart NJ (2007) An electronic fluorescent pictograph browser for exploring and analyzing large-scale biological data sets. PloS one 2(8):e718
Xu Q, Zhu C, Fan Y, Song Z, Xing S, Liu W, Yan J, Sang T (2016) Population transcriptomics uncovers the regulation of gene expression variation in adaptation to changing environment. Sci Rep 6(1):1–10
Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinform 9(1):1–8
Acknowledgements
Authors like to thank GGS Indraprastha University, New Delhi and IIT Kharagpur, for all the support and encouragement.
Competing Interests
The authors declare that there are no conflicts of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Deogam, R., Pipil, N.K., Chakraborty, N., Chatterjee, S., Purty, R.S. (2022). In Silico Characterization and Structural Modeling of Proteins Involved in Arsenic Tolerance of Hyper Accumulating Fern Pteris Vittata. In: Ratan, J.K., Sahu, D., Pandhare, N.N., Bhavanam, A. (eds) Advances in Chemical, Bio and Environmental Engineering. CHEMBIOEN 2021. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-96554-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-96554-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96553-2
Online ISBN: 978-3-030-96554-9
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)