| Literature DB >> 30366456 |
Lianhui Zhao1, Yunfei Huang2, Yiyang Dong3, Xutiange Han4, Sai Wang5, Xingguo Liang6,7.
Abstract
Marine biotoxins distribute widely, have high toxicity, and can be easily accumulated in water or seafood, exposing a serious threat to consumer health. Achieving specific and sensitive detection is the most effective way to prevent emergent issues caused by marine biotoxins; however, the previous detection methods cannot meet the requirements because of ethical or technical drawbacks. Aptamers, a kind of novel recognition element with high affinity and specificity, can be used to fabricate various aptasensors (aptamer-based biosensors) for sensitive and rapid detection. In recent years, an increasing number of aptamers and aptasensors have greatly promoted the development of marine biotoxins detection. In this review, we summarized the recent aptamer-related advances for marine biotoxins detection and discussed their perspectives. Firstly, we summarized the sequences, selection methods, affinity, secondary structures, and the ion conditions of all aptamers to provide a database-like information; secondly, we summarized the reported aptasensors for marine biotoxins, including principles, detection sensitivity, linear detection range, etc.; thirdly, on the basis of the existing reports and our own research experience, we forecast the development prospects of aptamers and aptasensors for marine biotoxins detection. We hope this review not only provides a comprehensive summary of aptamer selection and aptasensor development for marine biotoxins, but also arouses a broad readership amongst academic researchers and industrial chemists.Entities:
Keywords: aptamer; aptasensor; food safety; marine biotoxin; rapid detection
Mesh:
Substances:
Year: 2018 PMID: 30366456 PMCID: PMC6265707 DOI: 10.3390/toxins10110427
Source DB: PubMed Journal: Toxins (Basel) ISSN: 2072-6651 Impact factor: 4.546
Detailed information of the aptamers selected for marine biotoxins.
| Num. | Target | Aptamer Name | Selection Method | Year | Sequence (5′–3′) | Affinity (Kd, nM) | Secondary Structure | Folding Reference Condition | Ref. |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Palytoxin | PTX-13 | Mag-beads-SELEX | 2017 | GGAGGTGGTGGGGACTTTGCTTGTACTGGGCGCCCGGTTGAA | 84.3 |
| 20 mM Tris, 100 mM NaCl, 2 mM MgCl2, 5 mM KCl, pH 7.5 | [ |
| 2 | Okadaic acid (OA) C1 | OA34 | Beads-SELEX | 2013 | GGTCACCAACAACAGGGAGCGCTACGCGAAGGGTCAATGTGACGTCATGCGGATGTGTGG | 77 |
| 50 mM Tris, 150 mM NaCl, 2 mM MgCl2, pH 7.5 | [ |
| 3 | Brevetoxin 2 (BTX-2) C2 | BT10 | Beads-SELEX | 2015 | GGCCACCAAACCACACCGTCGCAACCGCGAGAACCGAAGTAGTGATCATGTCCCTGCGTG | 92 |
| 50 mM Tris, 10 mM MgCl2, pH 7.5 | [ |
| 4 | Brevetoxin 2 (BTX-2) C2 | Bap5 | Microwell-SELEX | 2016 | GAGGCAGCACTTCACACGATCTGTGAAGTTTTTGTCATGGTTTGGGGGTGGTAGGGGTGTTGTCTGCGTAATGACTGTAGAGATG | 4830 |
| 20 mM Hepes, 120 mM NaCl, 5 mM KCl, 1 mM CaCl2, 1 mM MgCl2 | [ |
| 5 | Microcystin-LR (MC-LR) C2 | AN6 | Beads-SELEX | 2012 | GGCGCCAAACAGGACCACCATGACAATTACCCATACCACCTCATTATGCCCCATCTCCGC | 50 |
| 50 mM Tris, 150 mM NaCl, 2 mM MgCl2, pH 7.5 | [ |
| 6 | Microcystin-LA (MC-LA) C2 | RC4 | Beads-SELEX | 2012 | CACGCACAGAAGACACCTACAGGGCCAGATCACAATCGGTTAGTGAACTCGTACGGCGCG | 76 |
| 50 mM Tris, 150 mM NaCl, 2 mM MgCl2, pH 7.5 | [ |
| 7 | Microcystin-YR (MC-YR) C2 | HC1 | Beads-SELEX | 2012 | GGACAACATAGGAAAAAGGCTCTGCTACCGGATCCCTGTTGTATGGGCATATCTGTTGAT | 193 |
| 50 mM Tris, 150 mM NaCl, 2 mM MgCl2, pH 7.5 | [ |
| 9 | Tetrodotoxin (TTX) C3 | G11-T ⁑ | Truncation | 2012 | AAAAATTTCACACGGGTGCCTCGGCTGTCC | N/A |
| 250 mM NaCl, 1 mM MgCl2, 0.1 mM EDTA, 1 mM DTT, 20 mM Tris-HCl, pH 7.5 | [ |
| 10 | Tetrodotoxin (TTX) C3 | A3 | Beads-SELEX | 2014 | GGGAGCTCAGAATAA ACGCTCAACCCTGCCGGGGGCTTCTCCTTGCTGCTCTGCTCTGTTCGACATGAGGCCCGGATC | N/A |
| 10 mM PBS, pH 7.5 | [ |
| 11 | Saxitoxin (STX) C3 | APTSTX | Mag-beads-SELEX | 2013 | GGTATTGAGGGTCGCATCCCGTGGAAACATGTTCATTGG GCGCACTCCGCTTTCTGTAGATGGCTCTAACTCTCCTCT | 3840 |
| 10 mM phosphate buffer, 2.7 mM KCl, 140 mM NaCl, 0.05% Tween-20, pH 7.4 | [ |
| 12 | Saxitoxin(STX) C3 | M-30f | Truncation | 2015 | TTGAGGGTCGCATCCCGTGGAAACAGGTTCATTG | 133 |
| 10 mM phosphate buffer, 2.7 mM KCl, 140 mM NaCl, 0.05% Tween-20, pH 7.4 | [ |
| 13 | Anatoxin-a (ATX-a) C3 | ATX8 | Beads-SELEX | 2015 | TGGCGACAAGAAGACGTACAAACACGCACCAGGCCGGAGTGGAGTATTCTGAGGTCGG | 81.378 |
| 50 mM Tris, pH 7.5, 150 mM NaCl, 2 mM MgCl2, pH 7.5 | [ |
| 14 | Gonyautoxin1/4 (GTX1/4) C3 | GO18-T | GO-SELEX | 2016 | AGCAGCACAGAGGTCAGATGCAATCGGAACGAGTAACCTTTGGTCGGGCAAGGTAGGTTGCCTATGCGTGCTACCGTGAA | 62 |
| 20 mM Tris-HCl, 100 mM NaCl, 2 mM MgCl2, 5 mM KCl, pH 7.5 | [ |
| 15 | Gonyautoxin1/4 (GTX1/4) C3 | GO18-T-d | Truncation | 2016 | AACCTTTGGTCGGGCAAGGTAGGTT | 8.1 |
| 20 mM Tris–HCl and 10 mM MgCl2, pH 7.5 | [ |
Num., number; Ref., reference; N/A, not available; ⁑ The aptamer was named as G11-T in this review, because several papers cited the sequence but it was not wholly consist with that in the original selection paper; C1, polyether toxins; C2, polypeptide toxins; C3, alkaloid toxins. Secondary structure of each aptamer was predicted using the Mfold online sever, with the parameters listed in the “Folding reference condition”.
Figure 1Principle of aptamer selection (SELEX process).
Figure 2Procedure of positive selection process in Mag-beads-SELEX.
Figure 3Procedure of positive selection process in Microwell-SELEX. T, target. BSA, bovine serum albumin.
Figure 4Procedure of positive selection in Graphene-SELEX (GO-SELEX).
Detailed information of the developed aptasensors for marine biotoxin detection.
| Target | Aptamer | Aptasensor | Year | Linear detection Range (ng/mL) a | LOD (ng/mL) a | Samples | References |
|---|---|---|---|---|---|---|---|
| GTX1/4 | GO18-T-d | BLI-based | 2016 | 0.2~200 | 0.05 | shellfish | [ |
| STX | M-30f | BLI-based | 2017 | 0.1~0.8 | 0.5 | shellfish | [ |
| PTX | PTX-13 | BLI-based | 2017 | 0.2~0.7 | 0.00004 | shellfish, seawater | [ |
| OA | OA34 | EC-based | 2013 | 0.1~60 | 0.07 | shellfish | [ |
| ATX | ATX8 | EC-based | 2015 | 1~100 | 0.5 | drinking water, certified samples | [ |
| BTX-2 | BT10 | EC-based | 2015 | 0.01~2000 | 0.106 | shellfish, mussel | [ |
| OA | OA34 | EC-based | 2017 | 5~100 | 1 | buffer | [ |
| MC-LR | AN6 | EC-based | 2018 | 5.0 × 10−5~248.8 | 2.0 | water | [ |
| TTX | G11-T | FL-based | 2017 | 0.1~100,000 | 0.06 | fish | [ |
| MC-LR | AN6 | FL-based | 2017 | 0.01~50 | 0.002 | water | [ |
| MC-LR and OA | AN6 for MC-LR and OA34 for OA | FL-based | 2015 | 0.1~50 | 0.025 for MC-LR and 0.05 for OA | water, shrimps, fish | [ |
| MC-LR | AN6 | FL-based | 2017 | 0.4~1194 | 0.137 | water, serum samples | [ |
| STX | APTSTX | FL-based | 2015 | 15~3000 | 7.5 | gastric juice, serum, urine | [ |
| OA | OA34 | FL-based | 2017 | 0.001~100 | 0.001 | shellfish | [ |
| BTX-2 | Bap5 | ELAA-based | 2016 | 3.125~200 | 3.125 | buffer | [ |
a converted to be ng/mL for easy comparison. LOD, limit of detection.
Figure 5Biolayer Interferometry (BLI)-based aptasensors for marine biotoxin detection. (a) Scheme of a label-free BLI-based aptasensor for GTX1/4 detection (Scheme was drawn according to the text description of Ref. [72]); (b) Scheme of a label-free and competitive BLI-based aptasensor for STX detection (Scheme was drawn according to the text description and the original Figure 2 of Ref. [80]); (c) Scheme of a competitive and signal-amplified BLI-based aptasensor for PTX detection (Scheme was drawn according to the text description and the original Figure 2 of Ref. [61]).
Figure 6Electrochemistry (EC)-based aptasensors based on gold electrodes. (a) Scheme of a label-free EC-based aptasensor for OA detection (Scheme was drawn according to the text description and the original Scheme 1 of Ref. [62]). (b) Scheme of a competitive EC-based aptasensor for BTX-2 detection (Scheme was drawn according to the text description and the original Scheme 1 of Ref. [63]). EIS, electrochemical impedance spectroscopy.
Figure 7Scheme of a competitive gap-based electrochemical aptasensor for OA detection (Scheme was drawn according to the text description and the original Figure 1 of Ref. [84]).
Figure 8Scheme of a photoelectrochemical aptasensor for detection of microcystin-LR (MC-LR) (Scheme was drawn according to the text description and the original Scheme 1 of Ref. [85]).
Figure 9Fluorescence (FL)-based aptasensors using up-conversion fluorescence or down-conversion fluorescence. (a) Scheme of a Fe3O4/aptamer/CDs nanocomposites-based aptasensor for okadaic acid (OA) detection (Scheme was drawn according to the text description and the original Scheme 1 of Ref. [66]). CDs, carbon dots. UCF, up-conversion fluorescence. (b) Scheme of a CS-UCNPs and MoS2-assisted FL-based aptasensor for MC-LR detection (Scheme was drawn according to the text description and the original Scheme 1 of Ref. [91]). (c) Scheme of a dual FRET aptasensor for simultaneous detection of MC-LR and OA (Scheme was drawn according to the text description and the original Figure 1 of Ref. [92]).
Figure 10Scheme of a single-walled carbon nanotubes (SWNTs)-assisted fluorescence (FL)-based aptasensor for MC-LR detection (Scheme was drawn according to the text description and the original Scheme 1 of Ref. [93]).
Figure 11Scheme of a competitive fluorophore-linked aptasensor based on rolling circle amplification (RCA) for okadaic acid (OA) detection (Scheme was drawn according to the text description and the original Figure 1 of Ref. [94]).
Figure 12Scheme of an indirect competitive enzyme-linked aptamer assay (ELAA)-based aptasensor for BTX-2 detection (Scheme was drawn according to the text description of Ref. [64]).