In silico characterization of a metatranscriptome-derived stress-responsive putative DnaJ/HSP40-domain-containing fragment associated with heavy metal homeostasis

Priyanka Sharma Bhupendra Narayan Singh Yadav Shristy Maurya Rajiv Kumar Yadav   

Open Access   

Published:  Aug 05, 2026

DOI: 10.7324/jabb.2026.311545
Abstract

Heavy metal (HM)-contaminated soils impose severe stress on the environment, yet certain soil microbes exhibit remarkable tolerance, revealing adaptive mechanisms in stress-adapted communities. Micro-eukaryotic communities in such environments exhibit substantial transcriptional reprogramming to mitigate stress-induced cellular damage. This study explores the molecular basis of HM stress adaptation based on transcript-level evidence using RNA-Seq-based metatranscriptomics combined with comprehensive in silico characterization. Exp_525919, a highly expressed transcript, was annotated against the UniProt proteome database using BLAST and was predicted as an uncharacterized protein from Achlya hypogyna (ACHHYP_04605), showing 57% sequence identity and a significant e-value (7.6e-24). Integrative analyses, including functional annotation, structural modeling, domain architecture, and evolutionary conservation, suggest that Exp_525919 is a putative DnaJ/heat shock protein 40 (HSP40)-domain-containing fragment, potentially associated with cellular protein quality control processes under HM stress conditions. Secondary structure revealed a predominance of coil regions with interspersed β-strands and a central α-helix. GO annotation predicted potential associations with transport, nucleic acid binding, and ribosomal association. Structural modeling generated a high-confidence model (96% coverage, 99.5% confidence), consistent with predicted chaperone-like proteins. Protein–protein interaction analysis revealed that the predicted uncharacterized protein ACHHYP_04605 is closely associated with major molecular chaperones, including HSP70, DnaJ/HSP40, and HSP90, suggesting its involvement in HSP70-centered stress-responsive networks. Network clustering revealed a molecular chaperone architecture in which core protein folding components were identified as central network hubs potentially associated with stress-adaptive responses. Overall, RNA-Seq-based metatranscriptomic analysis identified transcriptionally responsive potential genes and pathways underlying HM tolerance in micro-eukaryotic communities and provided a robust computational foundation for future experimental validation.


Keyword:     RNA-Seq soil metatranscriptome heavy metal in silico characterization heat shock proteins molecular chaperones


Citation:

Sharma P, Singh Yadav BN, Maurya S, Yadav RK. In silico characterization of a metatranscriptome-derived stress-responsive putative DnaJ/HSP40-domain-containing fragment associated with heavy metal homeostasis. J Appl Biol Biotech 2026. Article in Press. http://doi.org/10.7324/jabb.2026.311545

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

HTML Full Text
Reference

1. Wuana RA, Okieimen FE. Heavy metals in contaminated soils: a review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol. 2011; 1–20; doi: https://doi.org/10.5402/2011/402647

2. Kokkinos E, Zouboulis A. Hydrometallurgical recovery of CR(III) from tannery waste: optimization and selectivity investigation. Water (Switzerland) 2020;12(3):1–10; doi: https://doi.org/10.3390/w12030719

3. Bellion M, Courbot M, Jacob C, Guinet F, Blaudez D, Chalot M. Metal induction of a Paxillus involutus metallothionein and its heterologous expression in Hebeloma cylindrosporum. New Phytologist 2007;174(1):151–8; doi: https://doi.org/10.1111/j.1469-8137.2007.01973.x

4. Ruotolo R, Marchini G, Ottonello S. Membrane transporters and protein traffic networks differentially affecting metal tolerance: a genomic phenotyping study in yeast. Genome Biol 2008;9(4): R67; doi: https://doi.org/10.1186/gb-2008-9-4-r67

5. Pócsi I. Toxic metal/metalloid tolerance in fungi: a biotechnology-oriented approach. In: Banfalvi, G. (ed.). Cellular effects of heavy metals. Springer, Dordrecht, Netherlands, p 31, 2011; doi: https://doi.org/10.1007/978-94-007-0428-2_2

6. Cohen MD, Kargacin B, Klein CB, Costa M. Mechanisms of chromium carcinogenicity and toxicity. Crit Rev Toxicol 1993;23(3):255–81; doi: https://doi.org/10.3109/10408449309105012

7. Joutey NT, Sayel H, Bahafid W, Ghachtouli N El. Mechanisms of hexavalent chromium resistance and removal by microorganisms. Rev Environ Contam Toxicol 2015;233:45–69; doi: https://doi.org/10.1007/978-3-319-10479-9_2

8. Yadav BNS, Sharma P, Maurya S, Yadav RK. Metagenomics and metatranscriptomics as potential driving forces for the exploration of diversity and functions of micro-eukaryotes in soil. 3 Biotech 2023;13(12): 423; doi: https://doi.org/10.1007/s13205-023-03841-3

9. Oliverio AM, Geisen S, Delgado-Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv 2020;6:eaax8787; doi: https://doi.org/10.1126/sciadv.aax8787

10. Schloter M, Nannipieri P, Sørensen SJ, Van Elsas JD. Microbial indicators for soil quality. Biol Fertil Soils 2018;54(1):1–10; doi: https://doi.org/10.1007/s00374-017-1248-3

11. Huang S, Lentendu G, Fujinuma J, Shiono T, Kubota Y, Mitchell EAD. Soil micro-eukaryotic diversity patterns along elevation gradient are best estimated by increasing the number of elevation steps rather than within elevation band replication. Microb Ecol 2023;86(4):2606–17; doi: https://doi.org/10.1007/s00248-023-02259-x

12. Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS One 2008;3(6): e2527; doi: https://doi.org/10.1371/journal.pone.0002527

13. Stark R, Grzelak M, Hadfield J.RNA sequencing: the teenage years. Nat Rev Genet 2019;20(11):631–56; doi: https://doi.org/10.1038/s41576-019-0150-2

14. Mukherjee A, Reddy MS. Metatranscriptomics: an approach for retrieving novel eukaryotic genes from polluted and related environments. 3 Biotech 2020;10(2): 71; doi: https://doi.org/10.1007/s13205-020-2057-1

15. Tang H, Xiang G, Xiao W, Yang Z, Zhao B. Microbial mediated remediation of heavy metals toxicity: mechanisms and future prospects. Front Plant Sci 2024;15:1420408; doi: https://doi.org/10.3389/fpls.2024.1420408

16. Walkley AJ, Black IA. Estimation of soil organic carbon by the chromic acid titration method. Soil Sci 1934;37:29–38.

17. Piper CS. Soil and plant analysis: a laboratory manual of methods for the examination of soils and the determination of the inorganic constituents of plants. 7th ed. The University of Adelaide, Adelaide, Australia, 1996.

18. Kitson RE, Mellon MG. Colorimetric determination of phosphorous as molybdivanadophosphoric acid. Ind Eng Chem Anal 1944;16:379–83; doi: https://doi.org/10.1021/i560130a017

19. Jackson ML. Soil chemical analysis. Prentice Hall of India Pvt Ltd, New Delhi, India, p 498, 1973.

20. USEPA. Method 3060A, Alkaline Digestion for Hexavalent Chromium. Tests Methods for Evaluating Solid Waste, Physical/Chemical Methods SW 846. Washington DC: US Government Printing Office (GPO); 1996.

21. Yu Z, Pei Y, Zhao S, Kakade A, Khan A, Sharma M, et al. Metatranscriptomic analysis reveals active microbes and genes responded to short-term Cr(VI) stress. Ecotoxicology 2021;30(8):1527–37; doi: https://doi.org/10.1007/s10646-020-02290-5

22. Andrews S. FastQC: a quality control tool for high throughput sequence data; 2017. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

23. Krueger F. Trim Galore: a wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. Babraham bioinformatics; 2019. Available from: https://github.com/FelixKrueger/TrimGalore

24. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. MetaSPAdes: a new versatile metagenomics assembler. Genome Res 2017;27(5):824– 34; doi: https://doi.org/10.1101/gr.213959.116

25. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011;27(6):863–4; doi: https://doi.org/10.1093/bioinformatics/btr026

26. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012;28(23):3150–2; doi: https://doi.org/10.1093/bioinformatics/bts565

27. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012;9(4):357–9; doi: https://doi.org/10.1038/nmeth.1923

28. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010;11(10):R106; doi: https://doi.org/10.1186/gb-2010-11-10-r106

29. Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res 2010;38(12): e132; doi: https://doi.org/10.1093/nar/gkq275

30. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015;12(1):59–60; doi: https://doi.org/10.1038/nmeth.3176

31. National Center for Biotechnology Information (NCBI). ORFfinder: Open Reading Frame Finder. Available from: https://www.ncbi.nlm.nih.gov/orffinder/

32. GenomeNet. MOTIF: searching protein sequence motifs. Available at: https://www.genome.jp/tools/motif/

33. Buchan DWA, Moffat L, Lau A, Kandathil SM, Jones DT. Deep learning for the PSIPRED protein analysis workbench. Nucleic Acids Res 2024;52(W1):W287–93; doi: https://doi.org/10.1093/nar/gkae328

34. Powell HR, Islam SA, David A, Sternberg MJE. Phyre2.2: a community resource for template-based protein structure prediction. J Mol Biol 2025;437(15):168960; doi: https://doi.org/10.1016/j.jmb.2025.168960

35. Bertoni D, Tsenkov M, Magana P, Nair S, Pidruchna I, Querino Lima Afonso M, et al. AlphaFold protein structure database 2025: a redesigned interface and updated structural coverage. Nucleic Acids Res 2026;54(1):D358–62; doi: https://doi.org/10.1093/nar/gkaf1226

36. Blum M, Andreeva A, Florentino LC, Chuguransky SR, Grego T, Hobbs E, et al. InterPro: the protein sequence classification resource in 2025. Nucleic Acids Res 2025;53(D1):D444–56; doi: https://doi.org/10.1093/nar/gkae1082

37. Tegenfeldt F, Kuznetsov D, Manni M, Berkeley M, Zdobnov EM, Kriventseva EV. OrthoDB and BUSCO update: annotation of orthologs with wider sampling of genomes. Nucleic Acids Res 2025;53(D1):D516–22; doi: https://doi.org/10.1093/nar/gkae987

38. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13(11):2498– 504; doi: https://doi.org/10.1101/gr.1239303

39. Zha S, Yu A, Wang Z, Shi Q, Cheng X, Liu C, et al. Microbial strategies for effective hexavalent chromium removal: a comprehensive review. Chem Eng J 2024;489:151457; doi: https://doi.org/10.1016/j.cej.2024.151457

40. Kumar V, Dwivedi SK. Hexavalent chromium reduction ability and bioremediation potential of Aspergillus flavus CR500 isolated from electroplating wastewater. Chemosphere 2019;237: 124567; doi: https://doi.org/10.1016/j.chemosphere.2019.124567

41. Bennett RM, Cordero PRF, Bautista GS, Dedeles GR. Reduction of hexavalent chromium using fungi and bacteria isolated from contaminated soil and water samples. Chem Ecol 2013;29(4):320–8; doi: https://doi.org/10.1080/02757540.2013.770478

42. Bahafid W, Tahri Joutey N, Sayel H, Ghachtouli N EL. Mechanism of hexavalent chromium detoxification using Cyberlindnera fabianii yeast isolated from contaminated site in Fez (Morocco). J Mater Environ Sci 2013;4(6):840–7.

43. Shakya M, Lo CC, Chain PSG. Advances and challenges in metatranscriptomic analysis. Front Genet 2019; 10:904; doi: https://doi.org/10.3389/fgene.2019.00904

44. Dotaniya ML, Meena VD, Rajendiran S, Coumar MV, Saha JK, Kundu S. Geo-accumulation indices of heavy metals in soil and groundwater of kanpur, India under long term irrigation of tannery effluent. Bull Environ Contam Toxicol 2017;98(5):706–11; doi: https://doi.org/10.1007/s00128-016-1983-4

45. WHO. Permissible limits of heavy metals in soil and plants. Geneva: World Health Organization; 1996.

46. Yadav BNS, Sharma P, Maurya S, Yadav A, Tiwari N, Reddy MS, et al. Spatial and seasonal variations of active micro-eukaryotic community structure in heavy metal-contaminated soils. 3 Biotech 2026;16(1):6; doi: https://doi.org/10.1007/s13205-025-04605-x

47. Szebesczyk A, S?owik J.Heat shock proteins and metal ions – Reaction or interaction?. Comput Struct Biotechnol J 2023;21:3103– 8; doi: https://doi.org/10.1016/j.csbj.2023.05.024

48. Díaz-Pérez C, Cervantes C, Campos-García J, Julián-Sánchez A, Riveros-Rosas H. Phylogenetic analysis of the chromate ion transporter (CHR) superfamily. FEBS J 2007;274(23):6215–27; doi: https://doi.org/10.1111/j.1742-4658.2007.06141.x

49. Wang X, Wang C, Sheng H, Wang Y, Zeng J, Kang H, et al. Transcriptome-wide identification and expression analyses of ABC transporters in dwarf polish wheat under metal stresses. Biol Plant 2017;61(2):293–304; doi: https://doi.org/10.1007/s10535-016-0697-0

50. Gao Y, Yang F, Liu J, Xie W, Zhang L, Chen Z, et al. Genome-wide identification of metal tolerance protein genes in Populus trichocarpa and their roles in response to various heavy metal stresses. Int J Mol Sci 2020;21(5): 1680; doi: https://doi.org/10.3390/ijms21051680

51. Ferrari M, Cozza R, Marieschi M, Torelli A. Role of sulfate transporters in chromium tolerance in Scenedesmus acutus M. (Sphaeropleales). Plants 2022;11(2): 223; doi: https://doi.org/10.3390/plants11020223

52. Smith AT, Smith KP, Rosenzweig AC. Diversity of the metal-transporting P1B-type ATPases. J Biol Inorganic Chem 2014;19(6):947–60; doi: https://doi.org/10.1007/s00775-014-1129-2

53. Yu XZ, Lin YJ, Zhang Q. Metallothioneins enhance chromium detoxification through scavenging ROS and stimulating metal chelation in Oryza sativa. Chemosphere 2019;220:300–13; doi: https://doi.org/10.1016/j.chemosphere.2018.12.119

54. Mukherjee A, Yadav R, Marmeisse R, Fraissinet-Tachet L, Reddy MS. Heavy metal hypertolerant eukaryotic aldehyde dehydrogenase isolated from metal contaminated soil by metatranscriptomics approach. Biochimie 2019;160:183–92; doi: https://doi.org/10.1016/j.biochi.2019.03.010

55. Singh V, Singh N, Verma M, Kamal R, Tiwari R, Sanjay Chivate M, et al. Hexavalent-Chromium-Induced Oxidative Stress and the Protective Role of Antioxidants against Cellular Toxicity. Antioxidants 2022;11(12): 2375; doi: https://doi.org/10.3390/antiox11122375

56. Legatzki A, Grass G, Anton A, Rensing C, Nies DH. Interplay of the Czc system and two P-type ATPases in conferring metal resistance to Ralstonia metallidurans. J Bacteriol 2003;185(15):4354–61; doi: https://doi.org/10.1128/JB.185.15.4354-4361.2003

57. Brace JL, Lester RL, Dickson RC, Rudin CM. SVF1 regulates cell survival by affecting sphingolipid metabolism in Saccharomyces cerevisiae. Genetics 2007;175(1):65–76; doi: https://doi.org/10.1534/genetics.106.064527

58. Yan L, Jin H, Raza A, Huang Y, Gu DP, Zou X. Natural resistance-associated macrophage proteins (NRAMPs) are involved in cadmium enrichment in peanut (Arachis hypogaea L.) under cadmium stress. Plant Growth Regul 2024;102(3):619–32; doi: https://doi.org/10.1007/s10725-023-01091-0

59. Yadav BNS, Sharma P, Maurya S, Yadav RK. Functional metatranscriptomics reveals a novel multi-metal tolerant gene of nramp-family, isolated from metal-contaminated soil. Appl Biol Res 2024;26(2):186–98; doi: https://doi.org/10.48165/abr.2024.26.01.22

60. Gu J, He Y, He C, Zhang Q, Huang Q, Bai S, et al. Advances in the structures, mechanisms and targeting of molecular chaperones. Sig Transduct Target Ther 2025;10:84; doi: https://doi.org/10.1038/s41392-025-02166-2

61. Hartl FU, Hayer-Hartl M. Converging concepts of protein folding in vitro and in vivo. Nat Struct Mol Biol 2009;16:574–81; doi: https://doi.org/10.1038/nsmb.1591

62. Frydman J.Folding of newly translated proteins in vivo: the role of molecular chaperones. Annu Rev Biochem 2001;70:603–47; doi: https://doi.org/10.1146/annurev.biochem.70.1.603

63. Wu J, Liu T, Rios Z, Mei Q, Lin X, Cao S. Heat Shock Proteins and Cancer. Trends Pharmacol Sci 2017;38(3):226–56; doi: https://doi.org/10.1016/j.tips.2016.11.009

64. Lubkowska A, Pluta W, Stro?ska A, Lalko A. Role of Heat Shock Proteins (HSP70 and HSP90) in Viral Infection. Int J Mol Sci 2021;22(17):9366; doi: https://doi.org/10.3390/ijms22179366

65. Keller A. A review on heat shock proteins (HSP70 and HSP90) in protein folding and stress response. Int J Mol Biol Biochem 2025;7(1):57–62; doi: https://doi.org/10.33545/26646501.2025.v7.i1a.85

66. Vierling E. The Roles of Heat Shock Proteins in Plants. Annu Rev Plant Biol 1991;42:579–620; doi: https://doi.org/10.1146/annurev.pp.42.060191.003051

67. Mayer MP, Bukau B. Hsp70 chaperones: cellular functions and molecular mechanism. Cell Mol Life Sci 2005;62(6):670–84; doi: https://doi.org/10.1007/s00018-004-4464-6

68. Sharma SS, Dietz KJ.The relationship between metal toxicity and cellular redox imbalance. Trends Plant Sci 2009;14(1):43–50; doi: https://doi.org/10.1016/j.tplants.2008.10.007

69. Ogura T, Wilkinson AJ.AAA+ superfamily ATPases: common structure—diverse function. Genes Cells 2001;6(7):575–97; doi: https://doi.org/10.1046/j.1365-2443.2001.00447.x

70. Hennessy F, Nicoll WS, Zimmermann R, Cheetham ME, Blatch GL. Not all J domains are created equal: implications for the specificity of Hsp40-Hsp70 interactions. Protein Sci 2005;14(7):1697–709; doi: https://doi.org/10.1110/ps.051406805

71. Kampinga HH, Craig EA. The HSP70 chaperone machinery: j proteins as drivers of functional specificity. Nat Rev Mol Cell Biol 2010;11(8):579–92; doi: https://doi.org/10.1038/nrm2941

72. Walsh P, Bursa? D, Law YC, Cyr D, Lithgow T. The J-protein family: modulating protein assembly, disassembly and translocation. EMBO Rep 2004;5(6):567–71; doi: https://doi.org/10.1038/sj.embor.7400172

73. Pellecchia M, Szyperski T, Wall D, Georgopoulos C, Wüthrich K. NMR structure of the J-domain and the Gly/Phe-rich region of the Escherichia coli DnaJ chaperone. J Mol Biol 1996;260(2):236–50; doi: https://doi.org/10.1006/jmbi.1996.0395

74. Genevaux P, Schwager F, Georgopoulos C, Kelley WL. Scanning mutagenesis identifies amino acid residues essential for the in vivo activity of the Escherichia coli DnaJ (HSP40) J-domain. Genetics 2002;162(3):1045–53; doi: https://doi.org/10.1093/genetics/162.3.1045

75. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 2023;51(D1):D638–46; doi: https://doi.org/10.1093/nar/gkac1000

76. Han JDJ, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, et al. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 2004;430:88–93; doi: https://doi.org/10.1038/nature02555

77. Barabási AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004;5:101–13; doi: https://doi.org/10.1038/nrg1272

78. Taipale M, Jarosz DF, Lindquist S. HSP90 at the hub of protein homeostasis: emerging mechanistic insights. Nat Rev Mol Cell Biol 2010;11(7):515–28; doi: https://doi.org/10.1038/nrm2918

79. Powers ET, Morimoto RI, Dillin A, Kelly JW, Balch WE. Biological and Chemical Approaches to Diseases of Proteostasis Deficiency. Annu Rev Biochem 2009;78:959–91; doi: https://doi.org/10.1146/annurev.biochem.052308.114844

80. Kim YE, Hipp MS, Bracher A, Hayer-Hartl M, Ulrich Hartl F. Molecular Chaperone Functions in Protein Folding and Proteostasis. Annu Rev Biochem 2013;82:323–55; doi: https://doi.org/10.1146/annurev-biochem-060208-092442

81. Velasco-Carneros L, Bernardo-Seisdedos G, Maréchal JD, Millet O, Moro F, Muga A. Pseudophosphorylation of single residues of the J domain of DNAJA2 regulates the holding/folding balance of the Hsc70 system. Protein Sci 2024;33(8):e5105; doi: https://doi.org/10.1002/pro.5105

82. Kandasamy G, Andréasson C. HSP70-HSP110 chaperones deliver ubiquitin-dependent and -independent substrates to the 26S proteasome for proteolysis in yeast. J Cell Sci 2018;131(6):jcs210948; doi: https://doi.org/10.1242/jcs.210948

83. Havalová H, Ondrovi?ová G, Keresztesová B, Bauer JA, Pevala V, Kutejová E, et al. Mitochondrial HSP70 chaperone system—the influence of post-translational modifications and involvement in human diseases. Int J Mol Sci 2021;22:8077; doi: https://doi.org/10.3390/ijms22158077

84. Piva F. Gaining new insights on the HSP90 regulatory network. Bioinformation 2020;16:17–20; doi: https://doi.org/10.6026/97320630016017

Article Metrics
13 Views 4 Downloads 17 Total

Year

Month

Related Search

By author names

Similar Articles

Bioremediation of heavy metals from aquatic environment through microbial processes: A potential role for probiotics?

Marie Andrea Laetitia Huët, Daneshwar Puchooa

Evaluation of heavy metals in selected fruits in Umuahia market, Nigeria: Associating toxicity to effect for improved metal risk assessment

Uroko Robert Ikechukwu, Victor Eshu Okpashi, Uchenna Nancy Oluomachi, Nwuke Chunedu Paulinus, Nduka Florence Obiageli, Ogbonnaya Precious

Effect of different industrial and domestic effluents on growth, yield, and heavy metal accumulation in Turnip (Brassica rapa L.)

Noor ul Ain, Qurat ul Ain, Sadaf Javeria, Sana Ashiq, Kanwal Ashiq, Muhammad Sufyan Akhtar

Bioaccumulation of heavy metal lead (Pb) in different tissues of brackish water fish Mugil cephalus (Linnaeus, 1758)

Vardi Venkateswarlu, Chenji Venkatrayulu

Arsenic-induced antibiotic response in bacteria isolated from an arsenic resistance estuary

Dhanasekaran Padmanabhan, Zerubabel Stephen, Somanathan Karthiga Reshmi, Subbiah Kavitha

Analytical study on hexavalent chromium accumulation in plant parts of Pongamia pinnata (L.) Pierre and remediation of contaminated soil

Pratyush Kumar Das, Bidyut Prava Das, Patitapaban Dash

Microbes-mediated alleviation of heavy metal stress in crops: Current research and future challenges

Rubee Devi, Tanvir Kaur, Divjot Kour, Macie Hricovec, Rajinikanth Mohan, Neelam Yadav, Pankaj Kumar Rai, Ashutosh Kumar Rai, Ashok Yadav, Manish Kumar, Ajar Nath Yadav

Nanotechnology for the bioremediation of heavy metals and metalloids

Urja Sharma, Jai Gopal Sharma

Effect of heavy metals on germination, biochemical, and L-DOPA content in Mucuna pruriens (L.) DC.

Akshatha Banadka, Praveen Nagella

Ex-situ biofilm mediated approach for bioremediation of selected heavy metals in wastewater of textile industry

Anu Kumar, Shivani, Bhanu Krishan, Mrinal Samtiya, Tejpal Dhewa

Bacillus species for sustainable management of heavy metals in soil: Current research and future challenges

Diyashree Karmakar, Shanu Magotra, Rajeshwari Negi, Sanjeev Kumar, Sarvesh Rustagi, Sangram Singh, Ashutosh Kumar Rai, Divjot Kour, Ajar Nath Yadav,

Alkali-modified Ficus benghalensis fruit waste: An effective biosorbent for heavy metal removal from aqueous solutions - A comparative study

Harshala Kasalkar, Namrata Kislay, Rahul Kumar, Asmita Jadhav, Nilesh Wagh, Geeta Malbhage

Evaluation of chromium stress tolerance in endophytic bacteria isolated from chickpea root nodules and their plant growth-promoting traits

Sudipta Majhi, Mausumi Sikdar

Xeno-estrogenic metal-induced alterations in antioxidant enzyme activities, vitellogenin, and estrogen-receptor alpha in Channa striata brooders

Nazura Usmani, Muizzah Fatima

Changes in the embryonic protein profile and hatching as a response to thermal stress in the Eri silkworm, Samia cynthia ricini

Punyavathi, Koushik Hullahalli Kumar, Sentimenla Moatemjen, Likhith Gowda Mahadevegowda, Manjunatha Hosaholalu Boregowda