Bioinformatics Approach in Plant Genomic Research

Genetic Diversity and Population Structure of Chinese Chestnut (Castanea mollissima Blume) Cultivars Revealed by GBS Resequencing

Chinese chestnut (Castanea mollissima Bl.) is one of the earliest domesticated and cultivated fruit trees, and it is widely distributed in China. Because of the high quality of its nuts and its high resistance to abiotic and biotic stresses, Chinese chestnut could be used to improve edible chestnut varieties worldwide. However, the unclear domestication history and highly complex genetic background of Chinese chestnut have prevented the efficiency of breeding efforts. To explore the genetic diversity and structure of Chinese chestnut populations and generate new insights that could aid chestnut breeding, heterozygosity statistics, molecular variance analysis, ADMIXTURE analysis, principal component analysis, and phylogenetic analysis were conducted to analyze single nucleotide polymorphism data from 185 Chinese chestnut landraces from five geographical regions in China via genotyping by sequencing. Results showed that the genetic diversity level of the five populations from different regions was relatively high, with an observed heterozygosity of 0.2796-0.3427. The genetic diversity level of the population in the mid-western regions was the highest, while the population north of the Yellow River was the lowest. Molecular variance analysis showed that the variation among different populations was only 2.07%, while the intra-group variation reached 97.93%. The Chinese chestnut samples could be divided into two groups: a northern and southern population, separated by the Yellow River; however, some samples from the southern population were genetically closer to samples from the northern population. We speculate that this might be related to the migration of humans during the Han dynasty due to the frequent wars that took place during this period, which might have led to the introduction of chestnut to southern regions. Some samples from Shandong Province and Beijing City were outliers that did not cluster with their respective groups, and this might be caused by the special geographical, political, and economic significance of these two regions. The findings of our study showed the complex genetic relationships among Chinese chestnut landraces and the high genetic diversity of these resources.

Xylanase Inhibitors: Defense Players in Plant Immunity with Implications in Agro-Industrial Processing

Xylanase inhibitors (XIs) are plant cell wall proteins largely distributed in monocots that inhibit the hemicellulose degrading activity of microbial xylanases. XIs have been classified into three classes with different structures and inhibition specificities, namely Triticum aestivum xylanase inhibitors (TAXI), xylanase inhibitor proteins (XIP), and thaumatin-like xylanase inhibitors (TLXI). Their involvement in plant defense has been established by several reports. Additionally, these inhibitors have considerable economic relevance because they interfere with the activity of xylanases applied in several agro-industrial processes. Previous reviews highlighted the structural and biochemical properties of XIs and hypothesized their role in plant defense. Here, we aimed to update the information on the genomic organization of XI encoding genes, the inhibition properties of XIs against microbial xylanases, and the structural properties of xylanase-XI interaction. We also deepened the knowledge of XI regulation mechanisms in planta and their involvement in plant defense. Finally, we reported the recently studied strategies to reduce the negative impact of XIs in agro-industrial processes and mentioned their allergenicity potential.

Plant catalase in silico characterization and phylogenetic analysis with structural modeling

Background

Catalase (EC 1.11.1.6) is a heme-containing tetrameric enzyme that plays a critical role in signaling and hydrogen peroxide metabolism. It was the first enzyme to be crystallized and isolated. Catalase is a well-known industrial enzyme used in diagnostic and analytical methods in the form of biomarkers and biosensors, as well as in the textile, paper, food, and pharmaceutical industries. In silico analysis of CAT genes and proteins has gained increased interest, emphasizing the development of biomarkers and drug designs. The present work aims to understand the catalase evolutionary relationship of plant species and analyze its physicochemical characteristics, homology, phylogenetic tree construction, secondary structure prediction, and 3D modeling of protein sequences and its validation using a variety of conventional computational methods to assist researchers in better understanding the structure of proteins.

Results

Around 65 plant catalase sequences were computationally evaluated and subjected to bioinformatics assessment for physicochemical characterization, multiple sequence alignment, phylogenetic construction, motif and domain identification, and secondary and tertiary structure prediction. The phylogenetic tree revealed six unique clusters where diversity of plant catalases was found to be the largest for Oryza sativa. The thermostability and hydrophilic nature of these proteins were primarily observed, as evidenced by a relatively high aliphatic index and negative GRAVY value. The distribution of 5 sequence motifs was uniformly distributed with a width length of 50 with the best possible amino residue sequences that resemble the plant catalase PLN02609 superfamily. Using SOPMA, the predicted secondary structure of the protein sequences revealed the predominance of the random coil. The predicted 3D CAT model from Arabidopsis thaliana was a homotetramer, thermostable protein with 59-KDa weight, and its structural validation was confirmed by PROCHECK, ERRAT, Verify3D, and Ramachandran plot. The functional relationships of our query sequence revealed the glutathione reductase as the closest interacting protein of query protein.

Conclusions

This theoretical plant catalases in silico analysis provide insight into its physiochemical characteristics and functional and structural understanding and its evolutionary behavior and exploring protein structure-function relationships when crystal structures are unavailable.

Bioinformatics approaches and applications in plant biotechnology

BACKGROUND

In recent years, major advance in molecular biology and genomic technologies have led to an exponential growth in biological information. As the deluge of genomic information, there is a parallel growth in the demands of tools in the storage and management of data, and the development of software for analysis, visualization, modelling, and prediction of large data set.

MAIN BODY

Particularly in plant biotechnology, the amount of information has multiplied exponentially with a large number of databases available from many individual plant species. Efficient bioinformatics tools and methodologies are also developed to allow rapid genome sequence and the study of plant genome in the 'omics' approach. This review focuses on the various bioinformatic applications in plant biotechnology, and their advantages in improving the outcome in agriculture. The challenges or limitations faced in plant biotechnology in the aspect of bioinformatics approach that explained the low progression in plant genomics than in animal genomics are also reviewed and assessed.

CONCLUSION

There is a critical need for effective bioinformatic tools, which are able to provide longer reads with unbiased coverage in order to overcome the complexity of the plant's genome. The advancement in bioinformatics is not only beneficial to the field of plant biotechnology and agriculture sectors, but will also contribute enormously to the future of humanity.

RicENN: Prediction of Rice Enhancers with Neural Network Based on DNA Sequences

Enhancers are the primary cis-elements of transcriptional regulation and play a vital role in gene expression at different stages of plant growth and development. Having high locational variation and free scattering in non-encoding genomes, identification of enhancers is a crucial, but challenging work in understanding the biological mechanism of model plants. Recently, applications of neural network models are gaining increasing popularity in predicting the function of genomic elements. Although several computational models have shown great advantages to tackle this challenge, a further study of the identification of rice enhancers from DNA sequences is still lacking. We present RicENN, a novel deep learning framework capable of accurately identifying enhancers of rice, integrating convolution neural networks (CNNs), bi-directional recurrent neural networks (RNNs), and attention mechanisms. A combined-feature representation method was designed to extract the sequence features from original DNA sequences using six types of autocorrelation encodings. Moreover, we verified that the integrated model achieves the best performance by an ablation study. Finally, our deep learning framework realized a reliable prediction of the rice enhancers. The results show RicENN outperforms available alternative approaches in rice species, achieving the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of 0.960 and 0.960 on cross-validation, and 0.879 and 0.877 during independent tests, respectively. This study develops a hybrid model to combine the merits of different neural network architectures, which shows the potential ability to apply deep learning in bioinformatic sequences and contributes to the acceleration of functional genomic studies of rice. RicENN and its code are freely accessible at http://bioinfor.aielab.cc/RicENN/ .

An efficient chromatin immunoprecipitation (ChIP) protocol for studying histone modifications in peach reproductive tissues

BACKGROUND

Perennial fruit trees display a growth behaviour characterized by annual cycling between growth and dormancy, with complex physiological features. Rosaceae fruit trees represent excellent models for studying not only the fruit growth/patterning but also the progression of the reproductive cycle depending upon the impact of climate conditions. Additionally, current developments in high-throughput technologies have impacted Rosaceae tree research while investigating genome structure and function as well as (epi)genetic mechanisms involved in important developmental and environmental response processes during fruit tree growth. Among epigenetic mechanisms, chromatin remodelling mediated by histone modifications and other chromatin-related processes play a crucial role in gene modulation, controlling gene expression. Chromatin immunoprecipitation is an effective technique to investigate chromatin dynamics in plants. This technique is generally applied for studies on chromatin states and enrichment of post-transcriptional modifications (PTMs) in histone proteins.

RESULTS

Peach is considered a model organism among climacteric fruits in the Rosaceae family for studies on bud formation, dormancy, and organ differentiation. In our work, we have primarily established specific protocols for chromatin extraction and immunoprecipitation in reproductive tissues of peach (Prunus persica). Subsequently, we focused our investigations on the role of two chromatin marks, namely the trimethylation of histone H3 at lysine in position 4 (H3K4me3) and trimethylation of histone H3 at lysine 27 (H3K27me3) in modulating specific gene expression. Bud dormancy and fruit growth were investigated in a nectarine genotype called Fantasia as our model system.

CONCLUSIONS

We present general strategies to optimize ChIP protocols for buds and mesocarp tissues of peach and analyze the correlation between gene expression and chromatin mark enrichment/depletion. The procedures proposed may be useful to evaluate any involvement of histone modifications in the regulation of gene expression during bud dormancy progression and core ripening in fruits.

Plant genomics in Africa: present and prospects

Plants are the world's most consumed goods. They are of high economic value and bring many health benefits. In most countries in Africa, the supply and quality of food will rise to meet the growing population's increasing demand. Genomics and other biotechnology tools offer the opportunity to improve subsistence crops and medicinal herbs in the continent. Significant advances have been made in plant genomics, which have enhanced our knowledge of the molecular processes underlying both plant quality and yield. The sequencing of complex genomes of African plant species, facilitated by the continuously evolving next-generation sequencing technologies and advanced bioinformatics approaches, has provided new opportunities for crop improvement. This review summarizes the achievements of genome sequencing projects of endemic African plants in the last two decades. We also present perspectives and challenges for future plant genomic studies that will accelerate important plant breeding programs for African communities. These challenges include a lack of basic facilities, a lack of sequencing and bioinformatics facilities, and a lack of skills to design genomics studies. However, it is imperative to state that African countries have become key players in the plant genome revolution and genome derived-biotechnology. Therefore, African governments should invest in public plant genomics research and applications, establish bioinformatics platforms and training programs, and stimulate university and industry partnerships to fully deploy plant genomics, particularly in the fields of agriculture and medicine.

Deep-BSC: Predicting Raw DNA Binding Pattern in Arabidopsis Thaliana

Background:

With the rapid development of the sequencing methods in recent years, binding sites have been systematically identified in such projects as Nested-MICA and MEME. Prediction of DNA motifs with higher accuracy and precision has been a very important task for bioinformaticians. Nevertheless, experimental approaches are still time-consuming for big data set, making computational identification of binding sites indispensable.

Objective:

To facilitate the identification of the binding site, we proposed a deep learning architecture, named Deep-BSC (Deep-Learning Binary Search Classification), to predict binding sites in a raw DNA sequence with more precision and accuracy.

Methods:

Our proposed architecture purely relies on the raw DNA sequence to predict the binding sites for protein by using a convolutional neural network (CNN). We trained our deep learning model on binding sites at the nucleotide level. DNA sequence of A. thaliana is used in this study because it is a model plant.

Results:

The results demonstrate the effectiveness and efficiency of our method in the classification of binding sites against random sequences, using deep learning. We construct a CNN with different layers and filters to show the usefulness of max-pooling technique in the proposed method. To gain the interpretability of our approach, we further visualized binding sites in the saliency map and successfully identified similar motifs in the raw sequence. The proposed computational framework is time and resource efficient.

Conclusion:

Deep-BSC enables the identification of binding sites in the DNA sequences via a highly accurate CNN. The proposed computational framework can also be applied to problems such as operator, repeats in the genome, DNA markers, and recognition sites for enzymes, thereby promoting the use of Deep-BSC method in life sciences.

Research Tools for the Functional Genomics of Plant miRNAs During Zygotic and Somatic Embryogenesis

During early plant embryogenesis, some of the most fundamental decisions on fate and identity are taken making it a fascinating process to study. It is no surprise that higher plant embryogenesis was intensively analysed during the last century, while somatic embryogenesis is probably the most studied regeneration model. Encoded by the MIRNA, short, single-stranded, non-coding miRNAs, are commonly present in all Eukaryotic genomes and are involved in the regulation of the gene expression during the essential developmental processes such as plant morphogenesis, hormone signaling, and developmental phase transition. During the last few years dedicated to miRNAs, analytical methods and tools have been developed, which have afforded new opportunities in functional analyses of plant miRNAs, including (i) databases for in silico analysis; (ii) miRNAs detection and expression approaches; (iii) reporter and sensor lines for a spatio-temporal analysis of the miRNA-target interactions; (iv) in situ hybridisation protocols; (v) artificial miRNAs; (vi) MIM and STTM lines to inhibit miRNA activity, and (vii) the target genes resistant to miRNA. Here, we attempted to summarise the toolbox for functional analysis of miRNAs during plant embryogenesis. In addition to characterising the described tools/methods, examples of the applications have been presented.