journal article·2023-11-28Open access

An Integrated Multilevel Approach Unveils Complex Seed–Nanoparticle Interactions and Their Implications for Seed Priming

Elisa Cappetta, Carmine Del Regno, Marisa Conte, Christian Castro-Hinojosa, Susel Del Sol-Fernández, Chiara Vergata, Matteo Buti, Rossella Curcio, Anil Onder, Pierluigi Mazzei, Nicola Funicello, Salvatore De Pasquale, Mattia Terzaghi, Pasquale Del Gaudio, Antonietta Leone, Federico Martinelli, Maria Moros, Alfredo Ambrosone

ACS Nano· Volume 17· Issue 22

Abstract

Nanotechnology has the potential to revolutionize agriculture with the introduction of engineered nanomaterials. However, their use is hindered by high cost, marginal knowledge of their interactions with plants, and unpredictable effects related to massive use in crop cultivation. Nanopriming is an innovative seed priming technology able to match economic, agronomic, and environmental needs in agriculture. The present study was focused on unveiling, by a multilevel integrated approach, undisclosed aspects of seed priming mediated by iron oxide magnetic nanoparticles in pepper seeds ( Capsicum annuum ), one of the most economically important crops worldwide. Inductively coupled plasma atomic emission mass spectrometry and scanning electron microscopy were used to quantify the MNP uptake and assess seed surface changes. Magnetic resonance imaging mapped the distribution of MNPs prevalently in the seed coat. The application of MNPs significantly enhanced the root and vegetative growth of pepper plants, whereas seed priming with equivalent Fe concentrations supplied as FeCl 3 did not yield these positive effects. Finally, global gene expression by RNA-sequencing identified more than 2,200 differentially expressed genes, most of them involved in plant developmental processes and defense mechanisms. Collectively, these data provide evidence on the link between structural seed changes and an extensive transcriptional reprogramming, which boosts the plant growth and primes the embryo to cope with environmental challenges that might occur during the subsequent developmental and growth stages.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Seed Germination and Physiology · Legume Nitrogen Fixing Symbiosis · Plant Micronutrient Interactions and Effects

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Pharmacy, University of Salerno, Fisciano 84084, Italy

Instituto de Nanociencia y Materiales de Aragón, INMA (CSIC-Universidad de Zaragoza), Zaragoza 50009, Spain

Department of Biology, University of Florence, Sesto Fiorentino 50019, Italy

Department of Agriculture, Food, Environmental and Forestry Sciences (DAGRI), University of Florence, Firenze 50144, Italy

Department of Physics ‘E.R. Caianiello’, University of Salerno, Fisciano 84084, Italy

Department of Biosciences, Biotechnologies and Environment, University of Bari Aldo Moro, Bari 70121, Italy

Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid 28029, Spain

Funding

Agencia Estatal de Investigaci?n · Universit? degli Studi di Salerno · H2020 European Research Council

References (65)

Show source-provided references
1. 10.1016/j.tplants.2016.04.00510.1016/j.tplants.2016.04.005 ↗
2. 10.1021/acsnano.1c0616110.1021/acsnano.1c06161 ↗
3. 10.1038/s41565-020-00776-110.1038/s41565-020-00776-1 ↗
4. 10.1021/acsnano.6b0778110.1021/acsnano.6b07781 ↗
5. 10.1021/acs.jafc.9b0661510.1021/acs.jafc.9b06615 ↗
6. 10.1021/acsnano.7b0572310.1021/acsnano.7b05723 ↗
7. 10.1038/s41565-019-0470-610.1038/s41565-019-0470-6 ↗
8. 10.3389/fnano.2020.57995410.3389/fnano.2020.579954 ↗
9. 10.1016/j.bios.2016.09.09110.1016/j.bios.2016.09.091 ↗
10. 10.3389/fbioe.2019.0012010.3389/fbioe.2019.00120 ↗
11. 10.1146/annurev-arplant-042916-04113210.1146/annurev-arplant-042916-041132 ↗
12. 10.1016/j.tplants.2021.11.01510.1016/j.tplants.2021.11.015 ↗
13. 10.1186/s12951-022-01423-810.1186/s12951-022-01423-8 ↗
14. 10.3390/nano1102026710.3390/nano11020267 ↗
15. 10.1007/s00344-023-10981-610.1007/s00344-023-10981-6 ↗
16. 10.3390/agronomy1205098210.3390/agronomy12050982 ↗
17. 10.1016/j.scienta.2020.10953710.1016/j.scienta.2020.109537 ↗
18. 10.1007/s11356-020-09453-110.1007/s11356-020-09453-1 ↗
19. 10.1016/j.apsadv.2021.10016310.1016/j.apsadv.2021.100163 ↗
20. 10.2217/nnm.15.7010.2217/nnm.15.70 ↗
21. 10.1021/acsanm.2c0201410.1021/acsanm.2c02014 ↗
22. 10.3390/nano1111310610.3390/nano11113106 ↗
23. 10.1016/j.wasman.2019.06.02510.1016/j.wasman.2019.06.025 ↗
24. 10.1021/acssuschemeng.8b0601310.1021/acssuschemeng.8b06013 ↗
25. 10.1039/c0nr00104j10.1039/c0nr00104j ↗
26. 10.3389/fpls.2022.103529610.3389/fpls.2022.1035296 ↗
27. 10.3390/molecules2512293610.3390/molecules25122936 ↗
28. 10.1021/acs.jafc.7b0434010.1021/acs.jafc.7b04340 ↗
29. 10.1016/j.carbpol.2021.11874610.1016/j.carbpol.2021.118746 ↗
30. 10.1016/j.rinp.2019.10265110.1016/j.rinp.2019.102651 ↗
31. 10.1002/cmr.a.2013010.1002/cmr.a.20130 ↗
32. 10.1007/s11120-009-9486-310.1007/s11120-009-9486-3 ↗
33. 10.3389/fpls.2015.0016710.3389/fpls.2015.00167 ↗
34. 10.7717/peerj.624010.7717/peerj.6240 ↗
35. 10.1038/srep1338910.1038/srep13389 ↗
36. 10.3390/agronomy810021510.3390/agronomy8100215 ↗
37. 10.1016/j.plaphy.2022.11.00110.1016/j.plaphy.2022.11.001 ↗
38. 10.15192/PSCP.ASR.2014.3.1.121810.15192/PSCP.ASR.2014.3.1.1218 ↗
39. 10.1021/es402209w10.1021/es402209w ↗
40. 10.1039/D0EN00723D10.1039/D0EN00723D ↗
41. 10.1186/s12864-015-1530-410.1186/s12864-015-1530-4 ↗
42. 10.1016/j.jhazmat.2022.13042710.1016/j.jhazmat.2022.130427 ↗
43. 10.1104/pp.17.0049510.1104/pp.17.00495 ↗
44. 10.1016/j.cpb.2022.10023510.1016/j.cpb.2022.100235 ↗
45. 10.3389/fpls.2016.0098410.3389/fpls.2016.00984 ↗
46. 10.1042/BCJ2019015910.1042/BCJ20190159 ↗
47. 10.3389/fpls.2021.78173410.3389/fpls.2021.781734 ↗
48. 10.3390/ijms2101035110.3390/ijms21010351 ↗
49. 10.3390/ijms23181057010.3390/ijms231810570 ↗
50. 10.1007/s00425-012-1834-410.1007/s00425-012-1834-4 ↗
51. 10.1016/j.bbagrm.2011.10.00510.1016/j.bbagrm.2011.10.005 ↗
52. 10.1016/j.jplph.2020.15313410.1016/j.jplph.2020.153134 ↗
53. 10.1007/s10725-020-00571-x10.1007/s10725-020-00571-x ↗
54. 10.1016/j.jhazmat.2020.12241510.1016/j.jhazmat.2020.122415 ↗
55. 10.1039/D0EN00535E10.1039/D0EN00535E ↗
56. 10.1016/j.stress.2022.10010710.1016/j.stress.2022.100107 ↗
57. 10.1021/ja038085210.1021/ja0380852 ↗
58. 10.1021/nn204543c10.1021/nn204543c ↗
59. Andrews, S. Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Last accessed 30 June 2023.
60. 10.1093/bioinformatics/btu17010.1093/bioinformatics/btu170 ↗
61. 10.1038/ng.287710.1038/ng.2877 ↗
62. 10.1093/bioinformatics/btt65610.1093/bioinformatics/btt656 ↗
63. 10.1093/bioinformatics/btp61610.1093/bioinformatics/btp616 ↗
64. 10.1111/j.1365-313X.2004.02016.x10.1111/j.1365-313X.2004.02016.x ↗
65. 10.1093/nar/29.9.e4510.1093/nar/29.9.e45 ↗