| Literature DB >> 35268582 |
Saeed Ranjbar1,2, Francisco Xavier Malcata1,2.
Abstract
Contamination of the biosphere by heavy metals has been rising, due to accelerated anthropogenic activities, and is nowadays, a matter of serious global concern. Removal of such inorganic pollutants from aquatic environments via biological processes has earned great popularity, for its cost-effectiveness and high efficiency, compared to conventional physicochemical methods. Among candidate organisms, microalgae offer several competitive advantages; phycoremediation has even been claimed as the next generation of wastewater treatment technologies. Furthermore, integration of microalgae-mediated wastewater treatment and bioenergy production adds favorably to the economic feasibility of the former process-with energy security coming along with environmental sustainability. However, poor biomass productivity under abiotic stress conditions has hindered the large-scale deployment of microalgae. Recent advances encompassing molecular tools for genome editing, together with the advent of multiomics technologies and computational approaches, have permitted the design of tailor-made microalgal cell factories, which encompass multiple beneficial traits, while circumventing those associated with the bioaccumulation of unfavorable chemicals. Previous studies unfolded several routes through which genetic engineering-mediated improvements appear feasible (encompassing sequestration/uptake capacity and specificity for heavy metals); they can be categorized as metal transportation, chelation, or biotransformation, with regulation of metal- and oxidative stress response, as well as cell surface engineering playing a crucial role therein. This review covers the state-of-the-art metal stress mitigation mechanisms prevalent in microalgae, and discusses putative and tested metabolic engineering approaches, aimed at further improvement of those biological processes. Finally, current research gaps and future prospects arising from use of transgenic microalgae for heavy metal phycoremediation are reviewed.Entities:
Keywords: bioremediation; genetic engineering; heavy metals; microalgae; phycoremediation
Mesh:
Year: 2022 PMID: 35268582 PMCID: PMC8911655 DOI: 10.3390/molecules27051473
Source DB: PubMed Journal: Molecules ISSN: 1420-3049 Impact factor: 4.411
Figure 1Main sources of heavy metal pollution and biological toxicity thereof.
Figure 2Heavy metal bioremediation mechanisms adapted by microalgae.
Genetic and metabolic engineering tools available for microalgal strain improvement.
| Genetic and Metabolic Engineering Tools | Type | Specifics | Advantages and Disadvantages |
|---|---|---|---|
| CRISPR/Cas systems | Gene editing | A single guide RNA (sgRNA) drives the Cas protein to a matching DNA sequence on the host cell genome—which is degraded by Cas protein to create knockout and deletion mutants; and replaced by a new gene to generate insertion and knockdown mutants, with the aid of non-homologous, end-joining machinery | Allows manipulation of any DNA sequences; |
| ZFNs | Gene editing | An array of site-specific DNA-binding domains that recognizes two sequences flanking a specific site, attached to the endonuclease domain of bacterial FokI restriction enzyme; upon binding, FokI domains dimerize and cleave DNA at the site, which will be repaired by the DNA repair machinery of the cell | Comparably high probability of off-target events; complicated programming is required; |
| TALENs | Gene editing | Tandem arrays with 10 to 30 repeats that bind and recognize extended DNA sequences, attached to the endonuclease domain of bacterial FokI restriction enzyme; upon binding, FokI domains dimerize and cleave DNA at the site, which will be repaired by the DNA repair machinery of the cell | Comparably high probability of off-target events; complicated programming is required; |
| Cre/ | Gene editing | Two | Simple and efficient; |
| Modular cloning systems | Synthetic biology | Design and construction of expression vectors by providing a library of genetic building blocks (e.g., promoters, UTRs, terminators, tags, reporters, antibiotic resistance genes, introns) | Simple and efficient; |
| RNAi technology | Gene silencing | Small RNA molecules bind to target mRNAs to form double-stranded RNAs, which are degraded by RNA-induced silencing complex (RISC)—and cause sequence-specific suppression of gene expression, through translational or transcriptional repression | Simple and efficient; |
| Multiomics technologies | Omics | Analysis of whole-cell biochemical information of cell through genomics, transcriptomics, proteomics, metabolomics, interactomics, phenomics, meta-omics, etc. | Provide snapshot of response of cell or whole ecosystem to environmental changes; |
| Online databases | Bioinformatics | Integrated online platforms e.g., ChlamyCys, Greenhouse, Diatom EST database, Alga-PrAs, Cyan-Omics, and KEGG | Functional interpretation of genes and elucidation of their underlying biological themes via integrated annotation and expression data; |
| Flux balance analysis | Systems | All relevant metabolic information of an organism (e.g., genes, enzymes, reactions) are collected, and analyzed with the aid of a mathematical model within the perspective of the entire network, and applied to make predictions and genome reconstruction | Allows tailor-made design of cell factories aimed at maximum efficiency; |
| Illumina microarray technology | Sequencing | Tiny silica microbeads are housed in carefully etched microwells, and coated with multiple copies of an oligonucleotide probe targeting a specific DNA or RNA sequence; upon excitation by laser, binding of each probe to a complementary sequence in sample results in signal that conveys information to the detector | Fast and robust; |
Genetic engineering targets anticipated to improve microalgal HM bioremediation capacity.
| Approach | Targets |
|---|---|
|
| NRAMP, ZRT, IRT, ZIP, FTR, CTR, CDF, HMA, FPN, Ccc1/VIT1, PTA, AQP, MTP, PMA, V-ATPase, V-PPase, MRP, ATM/HMT, PDR, YSL, CAX, MFS |
|
| MTs, PCs, GSH, PPK, VTC, PPX, Pro (P5CS), His (HISN3), Cys (HAL2), Ser (SDC1), glycine-betaine, and organic acids |
|
| ChrR, arsC, CrACR2s, MerA/B/P/C/T/F, SMT, CYPs |
|
| Trx, HSPs, carotenoids, SOD, POD, CAT, GPX, GST |
|
| MTF-1, C2H2, AP2, MYB, bHLH, YABBY, bZIP, SBP, HB, WRKY13, CRR1, ABI5, GATA, CKs (IPT and CKX1), GA, ABA, BRs, JA, ET, SA, miRNAs (miR398, miR319, miR390, miR393, miR171, miR395, miR397, miR408, and miR857) |
|
| MTs, PCs, 6x-His, CXXEE, MerR, GST, NikRm, ModE |