Data-Driven Decoupling of Transmembrane Transport Pathways for Sulfonamide Antibiotics: Mining Plant Transporter for Improved Phytoremediation

Jia-Heng Zhao; Zhuang-Hao Hou; Wei-Qiang Lin; Yi Hu; Yan-Yun Hu; Guo-Ping Sheng

doi:10.1016/j.eng.2026.03.008

Engineering ›› :202603008 DOI: 10.1016/j.eng.2026.03.008

Article

research-article

Data-Driven Decoupling of Transmembrane Transport Pathways for Sulfonamide Antibiotics: Mining Plant Transporter for Improved Phytoremediation

Author information +

History +

PDF

Abstract

Emerging organic contaminants, including pharmaceuticals, are increasingly threatening global ecological systems. Phytoremediation is a sustainable strategy for remove pharmaceuticals, and its efficiency depends on the transporters capable of handling pollutants. However, the transporter identification is constrained by the high cost of developing mutant libraries that require a well-characterized plant genome, and the trial-and-error inefficiencies arising from unguided transporter screening without prior assessment of dominant transmembrane pathways. Herein, we developed a machine learning-molecular dynamics convergence framework that mechanistically decouples sulfonamide transport pathways in plants. Machine learning successfully predicted the overall transmembrane ability of five sulfonamides by training a Random Forest model on the transmembrane capabilities of 1366 pharmaceuticals. Molecular dynamics simulations quantified the passive diffusion capabilities of five sulfonamides. The discrepancies between overall transport and passive diffusion suggested the significance of active transport in sulfonamide uptake by plants. Transcriptome analysis, molecular docking, and heterologous expression identified LrABCG35 as a transporter for five sulfonamide antibiotics, which exhibits potential to enhance plant-mediated pollutant transport. Our approach eliminates futile engineering trials and accelerates the development of precision phytoremediation for polluted water bodies by pre-screening contaminants for transporter dependency, paving the way for more effective environmental remediation strategies.

Keywords

data-driven / sulfonamide antibiotics / plant uptake / transmembrane / transporter

Cite this article

Download citation ▾

Jia-Heng Zhao, Zhuang-Hao Hou, Wei-Qiang Lin, Yi Hu, Yan-Yun Hu, Guo-Ping Sheng. Data-Driven Decoupling of Transmembrane Transport Pathways for Sulfonamide Antibiotics: Mining Plant Transporter for Improved Phytoremediation. Engineering 202603008 DOI:10.1016/j.eng.2026.03.008

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Schwarzenbach RP, Egli T, Hofstetter TB, Von Gunten U, Wehrli B. Global water pollution and human health. Annu Rev Environ Resour 2010; 35(1):109-36.

[2]	Svensson Grape E, Chacón-García AJ, Rojas S, Pérez Y, Jaworski A, Nero M, et al. Removal of pharmaceutical pollutants from effluent by a plant-based metal-organic framework. Nat Water 2023; 1(5):433-42.

[3]	Adeleye AS, Xue J, Zhao Y, Taylor AA, Zenobio JE, Sun Y, et al. Abundance, fate, and effects of pharmaceuticals and personal care products in aquatic environments. J Hazard Mater 2022;424(Pt B):127284.

[4]	Chen XC, Huang L, Chang THA, Ong BL, Ong SL, Hu J. Plant traits for phytoremediation in the tropics. Engineering 2019; 5(5):841-8.

[5]	Li F, Fang L, Wu F. A roadmap for sustainable agricultural soil remediation under China’s carbon neutrality vision. Engineering 2023; 25:28-31.

[6]	Zhang Z, Cui Q, Chen L, Zhu X, Zhao S, Duan C, et al. A critical review of microplastics in the soil-plant system: distribution, uptake, phytotoxicity and prevention. J Hazard Mater 2022;424(Pt D):127750.

[7]	Muerdter CP, Powers MM, Webb DT, Chowdhury S, Roach KE, LeFevre GH. Functional group properties and position drive differences in xenobiotic plant uptake rates, but metabolism shares a similar pathway. Environ Sci Technol Lett 2023; 10(7):596-603.

[8]	Qi Y, Liu H, Chen X, Liu L. Engineering microbial membranes to increase stress tolerance of industrial strains. Metab Eng 2019; 53:24-34.

[9]	Chen L, Duan L, Sun M, Yang Z, Li H, Hu K, et al. Current trends and insights on EMS mutagenesis application to studies on plant abiotic stress tolerance and development. Front Plant Sci 2023; 13:1052569.

[10]	Gaillochet C, Develtere W, Jacobs TB. CRISPR screens in plants: approaches, guidelines, and future prospects. Plant Cell 2021; 33(4):794-813.

[11]	Liu T, Zhang X, Li K, Yao Q, Zhong D, Deng Q, et al. Large-scale genome editing in plants: approaches, applications, and future perspectives. Curr Opin Biotechnol 2023; 79:102875.

[12]	Gomes MP, Gonçalves CA, de Brito JCM, Souza AM, da Silva Cruz FV, Bicalho EM, et al. Ciprofloxacin induces oxidative stress in duckweed (implications for energy metabolism and antibiotic-uptake ability. J Hazard Mater 2017; 328:140-9.

[13]	Malchi T, Maor Y, Tadmor G, Shenker M, Chefetz B. Irrigation of root vegetables with treated wastewater: evaluating uptake of pharmaceuticals and the associated human health risks. Environ Sci Technol 2014; 48(16):9325-33.

[14]	Xiao Y, Zhang H, Li Z, Huang T, Akihiro T, Xu J, et al. An amino acid transporter-like protein (OsATL15) facilitates the systematic distribution of thiamethoxam in rice for controlling the brown planthopper. Plant Biotechnol J 2022; 20(10):1888-901.

[15]	Nagy I, Veeckman E, Liu C, Bel MV, Vandepoele K, Jensen CS, et al. Chromosome-scale assembly and annotation of the perennial ryegrass genome. BMC Genomics 2022; 23(1):505.

[16]	Li S, Zhu Y, Zhong G, Huang Y, Jones KC. Comprehensive assessment of environmental emissions, fate, and risks of veterinary antibiotics in China: an environmental fate modeling approach. Environ Sci Technol 2024; 58(12):5534-47.

[17]	Kumar K, Gupta SC. A framework to predict uptake of trace organic compounds by plants. J Environ Qual 2016; 45(2):555-64.

[18]	Lanevskij K, Didziapetris R. Physicochemical QSAR analysis of passive permeability across Caco-2 monolayers. J Pharm Sci 2019; 108(1):78-86.

[19]	Artursson P, Karlsson J. Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun 1991; 175(3):880-5.

[20]	Karlsson J, Artursson P. A method for the determination of cellular permeability coefficients and aqueous boundary layer thickness in monolayers of intestinal epithelial (Caco-2) cells grown in permeable filter chambers. Int J Pharm 1991; 71(1-2):55-64.

[21]	Durant JL, Leland BA, Henry DR, Nourse JG. Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 2002; 42(6):1273-80.

[22]	Zhao J. The observed values of permeability coefficients of drugs, drug-like compounds, and other small molecules [Internet]. Geneva: Zenodo; 2025

[23]	[ 2026 May 18. Available from: https://doi.org/10.5281/zenodo.17254535.]

[24]	Tayyebi A, Alshami AS, Rabiei Z, Yu X, Ismail N, Talukder MJ, et al. Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models. J Cheminform 2023; 15(1):99.

[25]	Kim D, Jeong J, Choi J. Identification of optimal machine learning algorithms and molecular fingerprints for explainable toxicity prediction models using ToxCast/Tox21 bioassay data. ACS Omega 2024; 9(36):37934-41.

[26]	Sanches-Neto FO, Dias-Silva JR, Keng Queiroz Junior LH, Carvalho-Silva VH. “pySiRC”: machine learning combined with molecular fingerprints to predict the reaction rate constant of the radical-based oxidation processes of aqueous organic contaminants. Environ Sci Technol 2021; 55(18):12437-48.

[27]	Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2020; 2(1):56-67.

[28]	Bajusz D, Rácz A, Héberger K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 2015; 7(1):20.

[29]	Sahigara F, Ballabio D, Todeschini R, Consonni V. Defining a novel K-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions. J Cheminform 2013; 5(1):27.

[30]	Hu Y, Habibul N, Hu YY, Meng FL, Sheng GP. Chemical speciation of ciprofloxacin in aqueous solution regulates its phytotoxicity and uptake by rice (Oryza sativa L.). Sci Total Environ 2021; 771:144787.

[31]	Zheng X, Hou Z, Qian Y, Zhang Y, Cui Q, Wang X, et al. Tumors evade immune cytotoxicity by altering the surface topology of NK cells. Nat Immunol 2023; 24(5):802-13.

[32]	Lee CT, Comer J, Herndon C, Leung N, Pavlova A, Swift RV, et al. Simulation-based approaches for determining membrane permeability of small compounds. J Chem Inf Model 2016; 56(4):721-33.

[33]	Wu EL, Cheng X, Jo S, Rui H, Song KC, Dávila-Contreras EM, et al. CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem 2014; 35(27):1997-2004.

[34]	Kumar S, Bouzida D, Swendsen RH, Kollman PA, Rosenberg JM.The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem 1992; 13(8):1011-21.

[35]	Diamond JM, Katz Y. Interpretation of nonelectrolyte partition coefficients between dimyristoyl lecithin and water. J Membr Biol 1974; 17(1):121-54.

[36]	Roe DR, Cheatham TE 3rd. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 2013; 9(7):3084-95.

[37]	Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 2010; 26(1):136-8.

[38]	Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2021; 2(3):100141.

[39]	Banasiak J, Jasiński M. ATP-binding cassette transporters in nonmodel plants. New Phytol 2022; 233(4):1597-612.

[40]	Xia JQ, Nazish T, Javaid A, Ali M, Liu QQ, Wang L, et al. A gain-of-function mutation of the MATE family transporter DTX6 confers paraquat resistance in Arabidopsis. Mol Plant 2021; 14(12):2126-33.

[41]	Tegeder M. Transporters for amino acids in plant cells: some functions and many unknowns. Curr Opin Plant Biol 2012; 15(3):315-21.

[42]	Huang Z, Wu Z, Gu X, Ji L. Diagnosis, toxicological mechanism, and detoxification for hepatotoxicity induced by pyrrolizidine alkaloids from herbal medicines or other plants. Crit Rev Toxicol 2024; 54(2):123-33.

[43]	Lee JM, Roche JR, Donaghy DJ, Thrush A, Sathish P. Validation of reference genes for quantitative RT-PCR studies of gene expression in perennial ryegrass (Lolium perenne L.). BMC Mol Biol 2010; 11(1):8.

[44]	Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873):583-9.

[45]	Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS, Croll TI, et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci 2021; 30(1):70-82.

[46]	Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31(2):455-61.

[47]	Huang H, Zhang S, Lv J, Wen B, Wang S, Wu T. Experimental and theoretical evidence for diastereomer-and enantiomer-specific accumulation and biotransformation of HBCD in maize roots. Environ Sci Technol 2016; 50(22):12205-13.

[48]	Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 2018; 10(1):39.

[49]	Thomas S, Maynard ND, Gill J. DNA library construction using Gibson Assembly®. Nat Methods 2015; 12(11):i-ii.

[50]	Larsen B, Xu D, Halkier BA, Nour-Eldin HH. Advances in methods for identification and characterization of plant transporter function. J Exp Bot 2017; 68(15):4045-56.

[51]	Cornish-Bowden A. One hundred years of Michaelis-Menten kinetics. Perspect Sci 2015; 4:3-9.

[52]	Nikaido H, Pagès JM. Broad-specificity efflux pumps and their role in multidrug resistance of Gram-negative bacteria. FEMS Microbiol Rev 2012; 36(2):340-63.

[53]	Fang XZ, Fang SQ, Ye ZQ, Liu D, Zhao KL, Jin CW. NRT1.1 dual-affinity nitrate transport/signalling and its roles in plant abiotic stress resistance. Front Plant Sci 2021; 12:715694.

[54]	Gräfe K, Schmitt L. The ABC transporter G subfamily in Arabidopsis thaliana. J Exp Bot 2021; 72(1):92-106.

[55]	Dhara A, Raichaudhuri A. ABCG transporter proteins with beneficial activity on plants. Phytochemistry 2021; 184:112663.

[56]	Zhou Q, Yang Y, Yang Z. Molecular dissection of cadmium-responsive transcriptome profile in a low-cadmium-accumulating cultivar of Brassica parachinensis. Ecotoxicol Environ Saf 2019; 176:85-94.

[57]	Kang J, Hwang JU, Lee M, Kim YY, Assmann SM, Martinoia E, et al. PDR-type ABC transporter mediates cellular uptake of the phytohormone abscisic acid. Proc Natl Acad Sci USA 2010; 107(5):2355-60.

[58]	Zhang Y, Kilambi HV, Liu J, Bar H, Lazary S, Egbaria A, et al. ABA homeostasis and long-distance translocation are redundantly regulated by ABCG ABA importers. Sci Adv 2021; 7(43):eabf6069.