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In Silico Characterization and Structural Modeling of Proteins Involved in Arsenic Tolerance of Hyper Accumulating Fern Pteris Vittata

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Advances in Chemical, Bio and Environmental Engineering (CHEMBIOEN 2021)

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.

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References

  • Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • Boyce WT, Sokolowski MB, Robinson GE (2020) Genes and environments, development and time. Proc Natl Acad Sci 117(38):23235–23241

    Article  CAS  Google Scholar 

  • Brown E, Mengmeng Z, Taotao F, Juanli W, Junbo N (2018) Mechanisms of bacterial degradation of arsenic. Indian J Microbiol Res 5:436–441

    Google Scholar 

  • 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

    Google Scholar 

  • Chakrabarty N (2015) Arsenic toxicity: prevention and treatment. CRC Press

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • DeLano WL (2002) Pymol: an open-source molecular graphics tool. CCP4 Newslett Protein Crystallogr 40(1):82–92

    Google Scholar 

  • Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32(5):1792–1797

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Finnegan P, Chen W (2012) Arsenic toxicity: the effects on plant metabolism. Front Physiol 3:182

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • Geourjon C, Deleage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6):681–684

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Ma LQ, Komar KM, Tu C, Zhang W, Cai Y, Kennelley ED (2001) A fern that hyperaccumulates arsenic. Nature 409(6820):579–579

    Article  CAS  Google Scholar 

  • Mandal BK, Suzuki KT (2002) Arsenic round the world: a review. Talanta 58(1):201–235

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Popov M, Zemanová V, Sácký J, Pavlík M, Leonhardt T, MatouÅ¡ek T, Kaňa A, Pavlíková 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

    Google Scholar 

  • Porter E, Peterson P (1975) Arsenic accumulation by plants on mine waste (United Kingdom). Sci Total Environ 4(4):365–371

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4(4):406–425

    CAS  PubMed  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Shankar S, Shanker U (2014) Arsenic contamination of groundwater: a review of sources, prevalence, health risks, and strategies for mitigation. Sci World J 2014

    Google Scholar 

  • 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

    Google Scholar 

  • Souri Z, Karimi N, Sandalio LM (2017) Arsenic hyperaccumulation strategies: an overview. Front Cell Dev Biol 5:67

    Article  Google Scholar 

  • Strawn DG (2018) Review of interactions between phosphorus and arsenic in soils from four case studies. Geochem Trans 19(1):1–13

    Article  Google Scholar 

  • Tawfik DS, Viola RE (2011) Arsenate replacing phosphate: alternative life chemistries and ion promiscuity. Biochemistry 50(7):1128–1134

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinform 9(1):1–8

    Article  Google Scholar 

Download references

Acknowledgements

Authors like to thank GGS Indraprastha University, New Delhi and IIT Kharagpur, for all the support and encouragement.

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The authors declare that there are no conflicts of interest.

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Correspondence to Ram Singh Purty .

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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

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