Potential of Jatropha curcas as a Biofuel, Animal Feed and Health Products

Biotechnological Research Progress in Jatropha, a Biodiesel-Yielding Plant

Environmental pollution is one of the most pressing challenges in today's world. The main cause of this pollution is fuel emissions from automobiles and other sources. As industrialization progresses, we will be unable to compromise on the use of energy to power heavy machines and will be forced to seek out the best options. As a consequence, utilizing green fuel, such as biodiesel derived from natural sources, is a realistic option. Jatropha curcas L. (Euphorbiaceae) is recognized as the greatest feedstock for biodiesel production throughout the world, and it has gained a huge market value in the recent years. Conventional cultivation alone will not be sufficient to meet the global need for the plant's biomass for the production of biodiesel. Adoption of plant tissue culture techniques that improve the biomass availability is an immediate need. The present review provides detailed information regarding in-vitro plant propagation (direct and indirect organogenesis), somatic embryogenesis, and acclimatization protocols of plantlets for stabilized production of biomass. The review also focuses on biotechnological approaches such as gene transformation studies, production of haploids, and double haploids for developing elite germplasm for high biomass and improved traits for the production of biodiesel.

Benefits and Challenges of Jatropha Meal as Novel Biofeed for Animal Production

Jatropha curcas L. has gained importance as a source of seed oil for biodiesel production. The meal contained about 60% protein with a good balance of essential amino acids, containing various bioactive compounds, including saponins, phytic acids, trypsin inhibitors, lectins, phenolics, and flavonoids, which render it as a potential biofeed for animal production. The Jatropha meal demonstrated various biological activities, including antioxidant, antibacterial, and anti-inflammatory effects which enhance its property as a bio-feed. The levels of these bioactive compounds in the seeds are dependent on the genotypes. The J. curcas possessed different varieties which are either toxic or non-toxic according to the presence of phorbol esters. The presence of phorbol esters in the meal confirmed the toxic variety of Jatropha resulting in the limited application of meal as a biofeed. The Jatropha meal devoid of phorbol esters could be applied as a biofeed in the animal production industry, and for the toxic varieties, various techniques such as physicochemical and biological treatments have been introduced to the industry to remove the phorbol esters from Jatropha meal. Several studies employing various cells and animals confirmed the toxicity of the phorbol esters. The molecular mechanism of action of phorbol esters is through up-regulation of PKC-β II gene, overexpression of down-stream proto-oncogenes resulted in inflammation and oxidative stress ending by apoptotic cell death. Despite the presence of valuable bioactive compounds in the Jatropha meal, its nutritional application is not recommended unless the phorbol esters are completely removed.

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρ_g ≤ 0.33), moderate (0.34 ≤ ρ_g ≤ 0.66), and high magnitude (ρ_g ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

Random regression for modeling yield genetic trajectories in Jatropha curcas breeding

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

Exploitation of Hi-C sequencing for improvement of genome assembly and in-vitro validation of differentially expressing genes in Jatropha curcas L

Jatropha curcas is one of the major sources of renewable energy due to potential use of its oil as a biofuel. The genome of this crop is constituted by the high content of repetitive elements. We employed the Hi-C proximity ligation technique to re-scaffold our existing hybrid genome assembly of an elite genotype (RJC1) developed using Illumina and Pacbio technologies. We assembled 99.81% of non-truncated reads to achieve 266.80 Mbp of the genome with an N50 value of 1.58 Mb. Furthermore, we compared the efficiency of Hi-C-augmented genome assembly with the hybrid genome assembly and observed a ~ 50% reduction in scaffolds and a tenfold increase in the N50 value. The gene ontology analysis revealed the identification of terms for molecular function (45.52%), cellular component (33.47%), and biological function (20.99%). Comparative genomic analysis of 13-plant species showed the conservation of 414 lipid metabolizing genes identified in the KEGG pathway analysis. Differential gene expression (DGE) studies were conducted in the healthy and Jatropha mosaic virus-infected leaves via RNA-seq analysis and observed gene expression changes for 2185 genes. Out of these, we observed 546 genes having more than two-fold change of transcript level and among these 259 genes were down-regulated and 287 genes were up-regulated. To validate RNA-seq data, two DEGs were selected for gene expression analysis using qRT-PCR and the data was in correlation with in silico results. RNA-seq analysis further shows the identification of some of the candidate genes and may be useful to develop JMV resistant plants after functional validation. This Hi-C genome assembly provides a detailed accurate reference genome which could be utilized to improve Jatropha and other economically important Euphorbiaceae family members.

Isolation and Purification of Bioactive Compounds from the Stem Bark of Jatropha podagrica

This paper reports the successive isolation and purification of bioactive compounds from the stem bark of Jatropha podagrica, a widely known medicinal plant. The ethyl acetate extract of the stem bark exhibited the strongest antioxidant activity assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging, and ferric reducing antioxidant power (FRAP) assays (IC₅₀ = 46.7, 66.0, and 492.6, respectively). By column chromatography (CC) with elution of hexane and ethyl acetate at 8:2, 7:3, and 6:4 ratios, the isolation of this active extract yielded five fractions (C1–C5). Chemical structures of the constituents included in C1–C5 were elucidated by gas chromatography-mass spectrometry (GC-MS), electrospray ionization-mass spectrometry (ESI-MS), and nuclear magnetic resonance (NMR) and resolved as methyl gallate (C1, C2, C3, C4), gallic acid (C1, C2), fraxetin (C2, C3, C4, C5), and tomentin (C3). Mixture C2 (IC₅₀ DPPH and ABTS = 2.5 µg/mL) and C3 (IC₅₀ FRAP = 381 µg/mL) showed the highest antioxidant properties. Among the isolated fractions, C4 was the most potential agent in growth inhibition of six bacterial strains including Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Listeria monocytogenes,Bacillus subtilis, and Proteus mirabilis (MIC = 5, 20, 30, 20, 25, and 20 mg/mL, respectively). All identified constituents exerted an inhibitory activity on the growth of Lactuca sativa, of which the mixture C3 performed the maximal inhibition on shoot (IC₅₀ = 49.4 µg/mL) and root (IC₅₀ = 47.1 µg/mL) growth. Findings of this study suggest that gallic acid, methyl gallate, fraxetin, and tomentin isolated from J. podagrica possessed antioxidant, antibacterial, and growth inhibitory potentials.

Jatropha half-sib family selection with high adaptability and genotypic stability

Jatropha (Jatropha curcas) has become one of the most important species for producing biofuels. Currently, Genotype x Environment (GxE) interaction is the biggest challenge that breeders should solve to increase the section accuracy in the plant breeding. Therefore, the objectives in this study were to estimate the parameters in the 180 half-sib families in Jatropha evaluated for five production years, to verify the significance of the GxE interaction variance, to evaluate the adaptability and stability for each family based on three prediction methods, to select superior half-sib families based on the adaptability and stability analyses, and to predict the accuracy for the sixth production year. Jatropha half-sib families were classified and selected using the follow adaptability and stability methods: linear regression, bi-segmented linear regression and mixed models concepts called harmonic mean of the relative performance of genetic values (HMRPGV). The prediction accuracy was estimated by the Pearson correlation between the predicted genetic values by adaptability and stability methods and the phenotypic value in the sixth production year. In result, most half-sib families were classified as general adaptability and general stability for the evaluated traits. The selection gain obtained via HMRPGV was higher than other methods. The prediction accuracy for the sixth production year was 0.45. Therefore, HMRPGV is efficient to maximize the genetic gain, and it can be a useful strategy to select genotype with high adaptability and stability in Jatropha breeding as well as other species that should be evaluated for many years to take a suitable selection accuracy.

Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models

Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.

Complex Approach to Conceptual Design of Machine Mechanically Extracting Oil from Jatropha curcas L. Seeds for Biomass-Based Fuel Production

One of important sources of biomass-based fuel is Jatropha curcas L. Great attention is paid to the biofuel produced from the oil extracted from the Jatropha curcas L. seeds. A mechanised extraction is the most efficient and feasible method for oil extraction for small-scale farmers but there is a need to extract oil in more efficient manner which would increase the labour productivity, decrease production costs, and increase benefits of small-scale farmers. On the other hand innovators should be aware that further machines development is possible only when applying the systematic approach and design methodology in all stages of engineering design. Systematic approach in this case means that designers and development engineers rigorously apply scientific knowledge, integrate different constraints and user priorities, carefully plan product and activities, and systematically solve technical problems. This paper therefore deals with the complex approach to design specification determining that can bring new innovative concepts to design of mechanical machines for oil extraction. The presented case study as the main part of the paper is focused on new concept of screw of machine mechanically extracting oil from Jatropha curcas L. seeds.

Bayesian Multi-Trait Analysis Reveals a Useful Tool to Increase Oil Concentration and to Decrease Toxicity in Jatropha curcas L

The biggest challenge for jatropha breeding is to identify superior genotypes that present high seed yield and seed oil content with reduced toxicity levels. Therefore, the objective of this study was to estimate genetic parameters for three important traits (weight of 100 seed, oil seed content, and phorbol ester concentration), and to select superior genotypes to be used as progenitors in jatropha breeding. Additionally, the genotypic values and the genetic parameters estimated under the Bayesian multi-trait approach were used to evaluate different selection indices scenarios of 179 half-sib families. Three different scenarios and economic weights were considered. It was possible to simultaneously reduce toxicity and increase seed oil content and weight of 100 seed by using index selection based on genotypic value estimated by the Bayesian multi-trait approach. Indeed, we identified two families that present these characteristics by evaluating genetic diversity using the Ward clustering method, which suggested nine homogenous clusters. Future researches must integrate the Bayesian multi-trait methods with realized relationship matrix, aiming to build accurate selection indices models.