An improved method for RNA isolation and cDNA library construction from immature seeds of Jatropha curcas L

<p>Abstract</p> <p>Background</p> <p>RNA quality and quantity is sometimes unsuitable for cDNA library construction, from plant seeds rich in oil, polysaccharides and other secondary metabolites. Seeds of jatropha (<it>Jatropha curcas </it>L.) are rich in fatty acids/lipids, storage proteins, polysaccharides, and a number of other secondary metabolites that could either bind and/or co-precipitate with RNA, making it unsuitable for downstream applications. Existing RNA isolation methods and commercial kits often fail to deliver high-quality total RNA from immature jatropha seeds for poly(A)⁺RNA purification and cDNA synthesis.</p> <p>Findings</p> <p>A protocol has been developed for isolating good quality total RNA from immature jatropha seeds, whereby a combination of the CTAB based RNA extraction method and a silica column of a commercial plant RNA extraction kit is used. The extraction time was reduced from two days to about 3 hours and the RNA was suitable for poly(A)⁺RNA purification, cDNA synthesis, cDNA library construction, RT-PCR, and Northern hybridization. Based on sequence information from selected clones and amplified PCR product, the cDNA library seems to be a good source of full-length jatropha genes. The method was equally effective for isolating RNA from mustard and rice seeds.</p> <p>Conclusions</p> <p>This is a simple CTAB + silica column method to extract high quality RNA from oil rich immature jatropha seeds that is suitable for several downstream applications. This method takes less time for RNA extraction and is equally effective for other tissues where the quality and quantity of RNA is highly interfered by the presence of fatty acids, polysaccharides and polyphenols.</p

Expression of fatty acid and lipid biosynthetic genes in developing endosperm of Jatropha curcas

BACKGROUND: Temporal and spatial expression of fatty acid and lipid biosynthetic genes are associated with the accumulation of storage lipids in the seeds of oil plants. In jatropha (Jatropha curcas L.), a potential biofuel plant, the storage lipids are mainly synthesized and accumulated in the endosperm of seeds. Although the fatty acid and lipid biosynthetic genes in jatropha have been identified, the expression of these genes at different developing stages of endosperm has not been systemically investigated. RESULTS: Transmission electron microscopy study revealed that the oil body formation in developing endosperm of jatropha seeds initially appeared at 28 days after fertilization (DAF), was actively developed at 42 DAF and reached to the maximum number and size at 56 DAF. Sixty-eight genes that encode enzymes, proteins or their subunits involved in fatty acid and lipid biosynthesis were identified from a normalized cDNA library of jatropha developing endosperm. Gene expression with quantitative reverse-transcription polymerase chain reaction analysis demonstrated that the 68 genes could be collectively grouped into five categories based on the patterns of relative expression of the genes during endosperm development. Category I has 47 genes and they displayed a bell-shaped expression pattern with the peak expression at 28 or 42 DAF, but low expression at 14 and 56 DAF. Category II contains 8 genes and expression of the 8 genes was constantly increased from 14 to 56 DAF. Category III comprises of 2 genes and both genes were constitutively expressed throughout endosperm development. Category IV has 9 genes and they showed a high expression at 14 and 28 DAF, but a decreased expression from 42 to 56 DAF. Category V consists of 2 genes and both genes showed a medium expression at 14 DAF, the lowest expression at 28 or 42 DAF, and the highest expression at 56 DAF. In addition, genes encoding enzymes or proteins with similar function were differentially expressed during endosperm development. CONCLUSION: The formation of oil bodies in jatropha endosperm is developmentally regulated. The expression of the majority of fatty acid and lipid biosynthetic genes is highly consistent with the development of oil bodies and endosperm in jatropha seeds, while the genes encoding enzymes with similar function may be differentially expressed during endosperm development. These results not only provide the initial information on spatial and temporal expression of fatty acid and lipid biosynthetic genes in jatropha developing endosperm, but are also valuable to identify the rate-limiting genes for storage lipid biosynthesis and accumulation during seed development

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called ‘LodgeNet,’ that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, ‘Haze &amp; Gamma Adjustment,’ minimize atmospheric haze and adjusts the gamma, while the second, ‘Stretching Contrast Limits,’ extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field

Tasco®, a Product of Ascophyllum nodosum, Imparts Thermal Stress Tolerance in Caenorhabditis elegans

Tasco®, a commercial product manufactured from the brown alga Ascophyllum nodosum, has been shown to impart thermal stress tolerance in animals. We investigated the physiological, biochemical and molecular bases of this induced thermal stress tolerance using the invertebrate animal model, Caenorhabiditis elegans. Tasco® water extract (TWE) at 300 μg/mL significantly enhanced thermal stress tolerance as well as extended the life span of C. elegans. The mean survival rate of the model animals under thermal stress (35 °C) treated with 300 μg/mL and 600 μg/mL TWE, respectively, was 68% and 71% higher than the control animals. However, the TWE treatments did not affect the nematode body length, fertility or the cellular localization of daf-16. On the contrary, TWE under thermal stress significantly increased the pharyngeal pumping rate in treated animals compared to the control. Treatment with TWE also showed differential protein expression profiles over control following 2D gel-electrophoresis analysis. Furthermore, TWE significantly altered the expression of at least 40 proteins under thermal stress; among these proteins 34 were up-regulated while six were down-regulated. Mass spectroscopy analysis of the proteins altered by TWE treatment revealed that these proteins were related to heat stress tolerance, energy metabolism and a muscle structure related protein. Among them heat shock proteins, superoxide dismutase, glutathione peroxidase, aldehyde dehydrogenase, saposin-like proteins 20, myosin regulatory light chain 1, cytochrome c oxidase RAS-like, GTP-binding protein RHO A, OS were significantly up-regulated, while eukaryotic translation initiation factor 5A-1 OS, 60S ribosomal protein L18 OS, peroxiredoxin protein 2 were down regulated by TWE treatment. These results were further validated by gene expression and reporter gene expression analyses. Overall results indicate that the water soluble components of Tasco® imparted thermal stress tolerance in the C. elegans by altering stress related biochemical pathways

λ-Carrageenan Suppresses Tomato Chlorotic Dwarf Viroid (TCDVd) Replication and Symptom Expression in Tomatoes

The effect of carrageenans on tomato chlorotic dwarf viroid (TCDVd) replication and symptom expression was studied. Three-week-old tomato plants were spray-treated with iota(ɩ)-, lambda(λ)-, and kappa(κ)-carrageenan at 1 g·L−1 and inoculated with TCDVd after 48 h. The λ-carrageenan significantly suppressed viroid symptom expression after eight weeks of inoculation, only 28% plants showed distinctive bunchy-top symptoms as compared to the 82% in the control group. Viroid concentration was reduced in the infected shoot cuttings incubated in λ-carrageenan amended growth medium. Proteome analysis revealed that 16 tomato proteins were differentially expressed in the λ-carrageenan treated plants. Jasmonic acid related genes, allene oxide synthase (AOS) and lipoxygenase (LOX), were up-regulated in λ-carrageenan treatment during viroid infection. Taken together, our results suggest that λ-carrageenan induced tomato defense against TCDVd, which was partly jasmonic acid (JA) dependent, and that it could be explored in plant protection against viroid infection

A pin-based probe for electronic moisture meters to determine moisture content in a single wheat kernel

Abstract Background Optimum moisture in straw and grain at maturity is important for timely harvesting of wheat. Grain harvested at the right time has reduced chance of being affected by adverse weather conditions which is important to maintain grain quality and end use functionality. Wheat varieties with a short dry down period could help in timely harvest of the crop. However, measuring single kernel moisture in wheat and other small grain crops is a phenotyping bottleneck which requires characterising moisture content of the developing kernel at physiological maturity. Results Here we report developing a pin-based probe to detect moisture in a developing wheat kernel required for determining physiological maturity. An in-house designed pin-based probe was used with different commercially available electronic moisture meters to assess the moisture content of the individual kernels in spikes with high accuracy (R2 = 0.73 to 0.94, P < 0.001) compared with a reference method of oven drying. The average moisture values varied among different electronic moisture meters and the oven-dry method and differences in values were minimized at low kernel moisture content (< 50%). The single kernel moisture probe was evaluated in the field to predict the physiological maturity in wheat using 38% moisture content as the reference and visible notes on kernel stage. Conclusion The pin-based moisture probe is a reliable tool for wheat physiologists and breeders to conveniently and accurately measure moisture content in developing grain that will aid in identifying wheat germplasm with fast dry-down characteristics

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

The current epidemic, population growth, and decreasing arable lands lead to a severe food crisis, which calls for productive and efficient agricultural methods to ensure a sustainable food supply for mankind. Crop monitoring is considered to be a potential solution for the improvement of food production. Current crop monitoring combines agriculture methodologies with other advanced technologies, including sensing technology, geographical information systems (GIS), Internet of Things (IoT), information and communication technology (ICT), robotics, and drone techniques to increase production with low labor cost. The high-throughput plant phenotyping is crucial for crop monitoring on the data acquisition of large-scale crop characteristics. The high-throughput plant phenotyping studies aim to achieve fast and precise large-scale crop monitoring techniques with minimum environmental impact by applying special plant phenotyping platforms. The phenotyping platforms are integrated with various sensors and data communication systems, which can help to achieve automatic data acquisition and transmission. This paper reviews the current high-throughput plant phenotyping development in crop monitoring, including sensors, communication protocols, data management, and plant phenotyping platforms. State-of-art challenges are reviewed and discussed. Also, the paper provides discussions on the current situation, upcoming challenges, and possible future trends for researchers in this field

Field-Scale Precision: Predicting Grain Yield of Diverse Wheat Breeding Lines Using High-Throughput UAV Multispectral Imaging

This study explored how to use UAV-based multispectral imaging, a plot detection model, and machine learning (ML) algorithms to predict wheat grain yield at the field scale. Multispectral data were collected over several weeks using the MicaSense RedEdge-P camera. Ground truth data on vegetation indices were collected utilizing portable phenotyping instruments, and agronomic data were collected manually. The YOLOv8 detection model was utilized for field-scale wheat plot detection. Four ML algorithms&#x2014;decision tree (DT), random forest (RF), gradient boosting (GB), and extreme GB (XGBoost were used to evaluate wheat grain yield prediction using normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI) data. The results demonstrated the RF algorithm&#x0027;s predicting ability across all growth stages, with a root-mean-square error (RMSE) of 43 grams per plot (g/p) and a coefficient of determination ($R^{2}$) value of 0.90 for NDVI data. For NDRE data, DT outperformed other models, with an RMSE of 43 g/p and an $R^{2}$ of 0.88. GB exhibited the highest predictive accuracy for G-NDVI data, with an RMSE of 42 g/p and an $R^{2}$ value of 0.89. The study integrated isogenic bread wheat sister lines and checked cultivars differing in grain yield, grain protein, and other agronomic traits to facilitate the identification of high-yield performers. The results show the potential use of UAV-based multispectral imaging combined with a detection model and ML in various precision agriculture applications, including wheat breeding, agronomy research, and broader agricultural practices