The impact of trichoderma spp. on agriculture and their identification

Fungi belonging to the genus Trichoderma were discovered in the late 18th century and they have been utilized ever since their biocontrol potential was uncovered. Trichoderma species have greatly assisted the blooming of agricultural industries due to their aggressive characteristics against plant pathogens. Their role as a biocontrol agent is owed to their mode of mechanisms: induction of the plant’s defence system, mycoparasitism, the production of secondary metabolites, and rhizosphere competence. Meanwhile, their role as a biofertilizer became evident when studies conducted hitherto showed that they could increase plant’s nutrient uptake, improve the yield of crops, enhance plant’s tolerance to external stresses, and induce the germination of seeds. Since this genus is hyperdiverse, accurate identification of them is indispensable. In the past, Trichoderma spp. were identified via their morphological characteristics. However, the emergence of molecular technology has made the identification of Trichoderma isolates more precise, explicit and rapid. Hence, this paper briefly reviews the accumulated knowledge in respect of this genus. Nevertheless, an extensive study must be done in order to explore the potential in improving the natural strains of Trichoderma

Strategies to alter DNA methylation patterns in plants

Epigenetic regulation is achieved through cytosine DNA methylation and histone modification. Epigenetic regulation is not only responsible for regulating gene-coding regions, it is also involved in silencing harmful transposable elements and repetitive elements. Naturally, DNA methylation patterns may vary between individual plants of the same species, influenced by difference exposures to environmental stresses. These changes are heritable, as the plants adapt to challenges in their growth environment. The dynamics and heritability of DNA methylation changes makes producing an epi-mutant variety of crop plants interesting. New epi-varieties may potentially carry interesting phenotypes, with high commercial values. Establishment and maintenance of DNA methylation is controlled by DNA methyltransferases, which creates an opportunity for inducing DNA methylation changes by interfering with the expression of DNA methyltransferases in plants. In this study, we used different strategies in various plant species to induce DNA methylation changes. The first strategy used inverted repeats to silence the MET1 gene, and indicates the importance of having the appropriate level of MET1 expression in maize for plant growth and development. The second strategy employed the TALEN and CRISPR genome editing tools for inducing point mutagenesis in the tomato MET1 gene. However, high dependency of tomato to MET1 gene have inhibited callus regeneration. The third strategy used over-expression of the CMT2 gene to induce phenotype and methylation pattern changes. In addition to using the available strategies, we developed a novel tool for the proof-of-concept targeted demethylation of stable methylated regions in Arabidopsis, which could be extended as epigenome editing tools

The chloroplast genome inheritance pattern of the Deli-Nigerian prospection material (NPM) × Yangambi population of Elaeis guineensis Jacq

Background The chloroplast genome has the potential to be genetically engineered to enhance the agronomic value of major crops. As a crop plant with major economic value, it is important to understand every aspect of the genetic inheritance pattern among Elaeis guineensis individuals to ensure the traceability of agronomic traits. Methods Two parental E. guineensis individuals and 23 of their F1 progenies were collected and sequenced using the next-generation sequencing (NGS) technique on the Illumina platform. Chloroplast genomes were assembled de novo from the cleaned raw reads and aligned to check for variations. The sequences were compared and analyzed with programming language scripting and relevant bioinformatic softwares. Simple sequence repeat (SSR) loci were determined from the chloroplast genome. Results The chloroplast genome assembly resulted in 156,983 bp, 156,988 bp, 156,982 bp, and 156,984 bp. The gene content and arrangements were consistent with the reference genome published in the GenBank database. Seventy-eight SSRs were detected in the chloroplast genome, with most located in the intergenic spacer region.The chloroplast genomes of 17 F1 progenies were exact copies of the maternal parent, while six individuals showed a single variation in the sequence. Despite the significant variation displayed by the male parent, all the nucleotide variations were synonymous. This study show highly conserve gene content and sequence in Elaeis guineensis chloroplast genomes. Maternal inheritance of chloroplast genome among F1 progenies are robust with a low possibility of mutations over generations. The findings in this study can enlighten inheritance pattern of Elaeis guineensis chloroplast genome especially among crops’ scientists who consider using chloroplast genome for agronomic trait modifications

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML