The Role of MicroRNA-Based Strategies in Optimizing Plant Biomass Composition for Bio-Based Packaging Materials
Abstract
1. Introduction
2. Main Components of Plant Biomass and Their Impact on Packaging
3. Functions of miRNAs in Plants—Biogenesis, Gene Regulation, and Cell Wall Formation
4. Enhancing Plant Biomass Quality and Yield Through miRNAs
miRNA | Primary Target Genes/Pathways | Impact on Biomass | References |
---|---|---|---|
miR156 | SPL transcription factors (e.g., SPL3, SPL4, SPL9) | Extends the juvenile phase, increases branching, and delays flowering. Moderate overexpression enhances biomass yield, increases cellulose content, and reduces lignin. Excessive expression can cause dwarfism and delayed flowering. | [38,43,53] |
miR319 | TCP transcription factors (e.g., PvPCF5) | Suppresses TCP genes controlling leaf and stem growth, increasing stem length and plant height. Overexpression reduces lignin biosynthesis, lowers lignin percentage, and improves saccharification efficiency. | [48] |
miR397 | Laccase enzymes (LAC genes) | Directly represses laccase genes required for lignin polymerization. Overexpression decreases lignin content by 15–20%, increases relative cellulose proportion, and improves processing efficiency, though it may slightly reduce mechanical strength. | [4] |
miR408 | Laccases, plastocyanin (Cu-dependent proteins) | Delays secondary growth and lignification, increasing cell wall accessibility. Overexpression in poplar enhances saccharification without pretreatment and accelerates plant growth while slightly modifying the S/G lignin ratio. Also modulates copper–protein balance under stress. | [52] |
miR828/miR858 | MYB transcription factors (phenylpropanoid pathway) | Repress MYB factors regulating phenylpropanoid metabolism, altering lignin–anthocyanin balance. In Arabidopsis, the miR858a–MYB module coordinates lignin and flavonoid biosynthesis; in Populus, miR828 reduces lignin by targeting lignin-specific MYBs. Direct effects on biomass yield remain less studied but show potential for wood quality improvement. | [54,55] |
miR396 | Growth-regulating factors (GRFs) and GRF-interacting factors | Knock-out of MIR396e/f increases grain size, panicle branching, and above-ground biomass, especially under nitrogen limitation. | [56] |
miR160 | Auxin response factors (ARF10/16/17) | Regulates auxin signaling; modulation alters root and shoot development, affecting biomass partitioning and accumulation. | [57,58] |
miR167 | Auxin response factors ARF6/ARF8 | Controls lateral root growth and fertility; altering miR167 expression modulates root biomass and influences overall biomass yield. | [57,59] |
miR164 | NAC domain transcription factors (e.g., CUC genes) | Involved in lateral root formation and leaf senescence; altering its level influences root biomass and senescence timing. | [57,60] |
miR159 | MYB transcription factors | Regulates the transition from juvenile to adult phase and anther development; altering miR159 can modify flowering time and biomass allocation. | [57,61] |
miR171 | GRAS transcription factors (e.g., SCL6) | Controls axillary meristem development and shoot branching; influences tiller number and vegetative biomass. | [57,60] |
miR172 | APETALA2 (AP2)-like transcription factors | Promotes phase transition and flowering; altering miR172 influences biomass by shifting resources from vegetative growth to reproductive organs. | [57,62] |
miR393 | Auxin receptors (TIR1, AFB2, AFB3) | Suppression of auxin receptor genes reduces auxin sensitivity, modifies branching and root architecture, affecting biomass distribution. | [63] |
miR444 | MADS-box transcription factors in monocots | Regulates tillering and root development in rice; overexpression affects nitrogen uptake and biomass accumulation. | [64] |
miR528 | Laccases and auxin-responsive factors | In monocots (rice/maize), miR528 regulates lignin biosynthesis and stress responses; altering its expression can reduce lignin and improve biomass digestibility. | [65,66] |
miR399 | PHO2/UBC24 ubiquitin conjugase | Controls phosphate homeostasis; miR399 overexpression can increase shoot biomass under phosphate limitation by improving phosphate allocation. | [67,68] |
miR827 | NLA (nitrogen limitation adaptation) gene | Regulates nitrogen remobilization and phosphate transport; manipulation influences biomass yield under nutrient limitation. | [69] |
miR398 | Cu/Zn superoxide dismutases (CSD1/2), copper chaperones | Regulates reactive oxygen species homeostasis and copper distribution; modulation affects stress tolerance and indirectly alters biomass accumulation. | [70] |
miR395 | ATP sulfurylases (APS), sulfate transporters | Controls sulfate assimilation; overexpression can improve sulfur use efficiency and biomass production in sulfur-limited conditions. | [71,72] |
miR535 | SPL family genes | Modulate panicle branching and grain size in rice and sorghum; altering their levels affects biomass distribution between vegetative and reproductive organs. | [73,74] |
5. Biotechnological Approaches Using miRNAs to Enhance Plant Biomass Quality and Yield
5.1. In Silico Analysis of miRNA Target Genes
5.2. Biotechnological Strategies for Applying miRNAs
6. Prospects of Plant miRNA Research in Bio-Based Packaging Production
7. Challenges and Limitations of miRNA-Based Biomass Improvement
8. Conclusions and Future Perspectives
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AGO1 | ARGONAUTE 1 |
amiRNA | Artificial microRNA |
CRISPR | Clustered Regularly Interspaced Short Palindromic Repeats |
DCL1 | Dicer-like 1 |
HEN1 | HUA ENHANCER 1 |
HST1 | HASTY 1 protein |
HYL1 | HYPONASTIC LEAVES 1 |
miRNA | MicroRNA |
RISC | RNA-Induced Silencing Complex |
SE | SERRATE |
SPL | SQUAMOSA Promoter-Binding Protein-Like |
STTM | Short Tandem Target Mimic |
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Tool Name | Main Function/Description | URL |
---|---|---|
TargetFinder | Predicts plant miRNA–mRNA duplexes with high accuracy using position-based weighted scoring, accounting for mismatches and gaps [78,79]. | http://targetfinder.org (accessed on 6 September 2025) |
psRNATarget | Predicts binding of plant miRNAs to target mRNAs based on sequence complementarity and target site accessibility (UPE); customizable parameters; supports the analysis of large transcript libraries [80]. | https://www.zhaolab.org/psRNATarget/ (accessed on 6 September 2025) |
miRTarBase | Repository of experimentally validated miRNA–target interactions (MTIs); integrates expression profiles, tissue specificity, and sequence variants [81,82]. | https://mirtarbase.cuhk.edu.cn (accessed on 6 September 2025) |
RNAhybrid | Identifies miRNA–mRNA duplexes with the lowest free energy (ΔG), pinpointing critical binding regions [83,84]. | https://bio.tools/rnahybrid (accessed on 6 September 2025) |
TAPIR | Combines FASTA-based quick search and RNAhybrid analysis to detect imperfect duplexes; allows parameter tuning to find complex regulatory patterns, including target mimicry [85,86]. | https://bioinformatics.psb.ugent.be/webtools/tapir/ (accessed on 6 September 2025) |
MirTarget | Predicts potential binding sites within 5′UTR, CDS, and 3′UTR, considering non-canonical base pairs such as G–U and A–C [87,88,89,90]. | https://doi.org/10.6026/97320630010423; https://www.bioinformation.net/012/97320630012237.pdf |
Approach | Advantages | Disadvantages |
---|---|---|
Transgenic overexpression | Increases miRNA levels effectively; enables simultaneous downregulation of multiple target genes; relatively well-established method in plants [113,114]. | Possible off-target effects; may cause pleiotropic traits; requires transformation protocols; potential regulatory restrictions [113,115]. |
Target mimicry (STTM) | Highly specific miRNA inhibition; stable suppression; tissue-specific expression possible; allows functional analysis [116]. | Requires careful design to avoid unintended interactions; effect depends on expression stability; may not fully suppress highly abundant miRNAs [117]. |
Artificial miRNAs (amiRNAs) | Very high sequence specificity; minimal off-target effects; suitable for targeting single genes; adaptable to multiplex targeting [118,119]. | Time-consuming design; requires sequence knowledge; may lose efficacy with target gene mutations; transformation dependent [120,121]. |
CRISPR/Cas-based genome editing | Permanent and heritable changes; can knock out MIR genes or alter target recognition sites; enables multiplex editing; avoids transgene retention in some cases [122]. | Off-target genome edits possible; editing efficiency variable; regulatory and biosafety concerns; some targets difficult to edit [123]. |
Integration with breeding programs | No transgene introduction (when using natural alleles); suitable for long-term crop improvement; compatible with marker-assisted selection [124,125]. | Limited to available natural variation; slower than direct engineering; trait expression influenced by genetic background [126,127]. |
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Belkozhayev, A.M.; Abaildayev, A.; Kossalbayev, B.D.; Kerimkulova, A.; Kadirshe, D.K.; Toleutay, G. The Role of MicroRNA-Based Strategies in Optimizing Plant Biomass Composition for Bio-Based Packaging Materials. Plants 2025, 14, 2905. https://doi.org/10.3390/plants14182905
Belkozhayev AM, Abaildayev A, Kossalbayev BD, Kerimkulova A, Kadirshe DK, Toleutay G. The Role of MicroRNA-Based Strategies in Optimizing Plant Biomass Composition for Bio-Based Packaging Materials. Plants. 2025; 14(18):2905. https://doi.org/10.3390/plants14182905
Chicago/Turabian StyleBelkozhayev, Ayaz M., Arman Abaildayev, Bekzhan D. Kossalbayev, Aygul Kerimkulova, Danara K. Kadirshe, and Gaukhar Toleutay. 2025. "The Role of MicroRNA-Based Strategies in Optimizing Plant Biomass Composition for Bio-Based Packaging Materials" Plants 14, no. 18: 2905. https://doi.org/10.3390/plants14182905
APA StyleBelkozhayev, A. M., Abaildayev, A., Kossalbayev, B. D., Kerimkulova, A., Kadirshe, D. K., & Toleutay, G. (2025). The Role of MicroRNA-Based Strategies in Optimizing Plant Biomass Composition for Bio-Based Packaging Materials. Plants, 14(18), 2905. https://doi.org/10.3390/plants14182905