Determination of nitrogen deficiency-related microRNAs in plants using fluorescence quenching of graphene oxide nanosheets

A smart multiplexed microRNA biosensor based on FRET for the prediction of mechanical damage and storage period of strawberry fruits

Today, measuring the concentration of various microRNAs in fruits has been introduced to model the storage conditions of agricultural products. However, there is a limiting factor in the extensive utilization of such techniques: the existing methods for measuring microRNA sequences, including PCR and microarrays, are time-consuming and expensive and do not allow for simultaneous measurement of several microRNAs. In this study, a biosensor based on the Förster resonance energy transfer (FRET) of fluorescence dyes that can lead to the hybridization of oligonucleotide probes labeled with such dyes by using an excitation wavelength has been used to simultaneously measure microRNAs. Three microRNA compounds, i.e., miRNA-164, miRNA-167, and miRNA-399a, which play significant roles in the postharvest characteristics of strawberry fruits were measured. The simultaneous measurement was performed using three fluorescence dyes which exert various emission wavelengths at 570, 596, and 670 nm. In the following, machine learning methods including artificial neural networks (ANNs) and support vector machines (SVMs), with hyperparameter values optimized ​with the help of metaheuristic optimization algorithms, were used to predict the amount of mechanical loading on strawberry fruits and their storage period having the microRNA concentrations. The results showed that the SVM with Gaussian kernel, which was optimized by the Harris hawks optimization, is capable of predicting the mechanical stress and storage period of strawberry fruits with a coefficient of determination (R2) of 0.89 and 0.92, respectively. The findings of this study reveal the application of combining FRET-based biosensors and machine learning methods in fruit storage quality assessment.

Omics approaches to understand the MADS-box gene family in common bean (Phaseolus vulgaris L.) against drought stress

MADS-box genes are known to play important roles in diverse aspects of growth/devolopment and stress response in several plant species. However, no study has yet examined about MADS-box genes in P. vulgaris. In this study, a total of 79 PvMADS genes were identified and classified as type I and type II according to the phylogenetic analysis. While both type I and type II PvMADS classes were found to contain the MADS domain, the K domain was found to be present only in type II PvMADS proteins, in agreement with the literature. All chromosomes of the common bean were discovered to contain PvMADS genes and 17 paralogous gene pairs were identified. Only two of them were tandemly duplicated gene pairs (PvMADS-19/PvMADS-23 and PvMADS-20/PvMADS-24), and the remaining 15 paralogous gene pairs were segmentally duplicated genes. These duplications were found to play an important role in the expansion of type II PvMADS genes. Moreover, the RNAseq and RT-qPCR analyses showed the importance of PvMADS genes in response to drought stress in P. vulgaris.

Physical and physiological characteristics, as well as miRNA concentrations, are affected by the storage time of tomatoes

This study aims to investigate the potential of miRNA measurements in indicating tomato quality during transportation and storage. The impact of storage temperature, duration, and mechanical loading on tomato senescence, carotenoid content, total soluble solids, fruit firmness, and relevant miRNA concentrations were examined. Significant two-way or three-way interactions were observed between storage conditions and physical/physiological characteristics (excluding carotenoids). Remarkably, significant three-way interactions were found between storage conditions and miRNA concentrations. Strong correlations were observed between the physiological characteristics of the tomatoes and their miRNA concentrations. These findings suggest that measuring miRNAs could serve as a convenient and portable method for evaluating postharvest fruit quality, reducing reliance on labor-intensive laboratory techniques.

Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics

BACKGROUND

Specific detection of the type and severity of plant abiotic stresses helps prevent yield loss by considering timely actions. This study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions. Various features, i.e., morphological (image textural features), physiological/biochemical (relative water content, chlorophyll, catalase activity, anthocyanins, phenol content, and proline), as well as miRNA characteristics (the concentration of miRNA-156a, miRNA-166i, miRNA-399g, and miRNA-477b) were extracted from plant leaves, and machine learning methods were used to predict the type and severity of stress by having these features. Support vector machine (SVM) with parameters optimized by genetic algorithm (GA) and particle swarm optimization (PSO) was used for machine learning.

RESULTS

The coefficient of determination of predicting the stress type and severity in plants under both stresses was 0.61, 0.82, and 0.99 using morphological, physiological/biochemical, and miRNA characteristics, respectively. This reveals machine learning methods optimized by metaheuristic optimization techniques can provide specific detection of salt and drought stresses in cucumber plants based on miRNA characteristics. Among the study miRNAs, miRNA-477b and miRNA-399g had the highest and lowest contribution to salt and drought stresses, respectively.

CONCLUSIONS

Comapred to conventional plant traits, miRNAs are more reliable features for providing us with valuable information about plant abiotic diseases at early stages. Using an electrochemical miRNA biosensor similar to one used in this work to measure the miRNA concentration in plant leaves and using a machine learning algorithm such as SVM enable farmers to detect the salt and drought stress at early stages in cucumber plants with very high accuracy.

Gold nanoparticles-mediated ratiometric fluorescence aptasensor for ultra-sensitive detection of Abscisic Acid

Herein, a novel ratiometric aptasensor based on carbon quantum dots@2-Methylimidazole zinc salt (CQDs@ZIF-8) and aptamer-functionalized gold nanoparticles (Apt-AuNPs) was developed for highly sensitive detection of ABA by fluorescence spectrometry. The CQDs@ZIF-8 nanomaterials displayed dual-emission properties at 490 nm and 657 nm with excitation at 420 nm were synthesized for the first time. ZIF-8 not only served as an anchor point for CQDs but also acted as a modulator to regulate fluorescence signals of CQDs. Interestingly, introduction of ZIF-8 changed the quenching properties of the AuNPs on CQDs. The AuNPs quenched the fluorescence of CQDs@ZIF-8 at 490 nm but not at the second peak of 657 nm. Few studies have been reported on the ineffectiveness of AuNPs in fluorescence quenching as far as we know. In this study, we found that incorporation of ABA triggered the aggregation of AuNPs due to the specific ABA-aptamer recognition and this changed the fluorescence intensity of the ratiometric probe (CQDs@ZIF-8@Apt-AuNPs). The proposed probe increased the sensitivity and selectivity of determining ABA levels in rice seeds in the range of 0.100-150 ng/mL with an LOD of 30.0 ng/L. Importantly, the method proposed here offers a new unique strategy for the construction of ratiometric probes and ultra-sensitive measurement of biomolecules.

Potassium Deficiency Significantly Affected Plant Growth and Development as Well as microRNA-Mediated Mechanism in Wheat (Triticum aestivum L.)

It is well studied that potassium (K⁺) deficiency induced aberrant growth and development of plant and altered the expression of protein-coding genes. However, there are not too many systematic investigations on root development affected by K⁺ deficiency, and there is no report on miRNA expression during K⁺ deficiency in wheat. In this study, we found that K⁺ deficiency significantly affected wheat seedling growth and development, evidenced by reduced plant biomass and small plant size. In wheat cultivar AK-58, up-ground shoots were more sensitive to K⁺ deficiency than roots. K⁺ deficiency did not significantly affect root vitality but affected root development, including root branching, root area, and root size. K⁺ deficiency delayed seminal root emergence but enhanced seminal root elongation, total root length, and correspondingly total root surface area. K⁺ deficiency also affected root and leaf respiration at the early exposure stage, but these effects were not observed at the later stage. One potential mechanism causing K⁺ deficiency impacts is microRNAs (miRNAs), one important class of small regulatory RNAs. K⁺ deficiency induced the aberrant expression of miRNAs and their targets, which further affected plant growth, development, and response to abiotic stresses, including K⁺ deficiency. Thereby, this positive root adaption to K⁺ deficiency is likely associated with the miRNA-involved regulation of root development.