Category Archives: ORIGINAL ARTICLE

Draft genome sequence of Colletotrichum fructicola causing leaf spot on tea plants (Camellia sinensis)

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Draft genome sequence of Colletotrichum fructicola causing leaf spot on tea plants (Camellia sinensis)

Draft whole-genome sequence of Colletotrichum fructicola SX-6 annotated 15,243 predicted protein-coding genes; four gene families whose genes were significantly more abundant were identified by comparative genome analyses.


Abstract

Colletotrichum fructicola, one of the dominant pathogens isolated from the main tea region in China, causes leaf spot in mature leaves of tea plants, affecting their growth and yield. Here, we present the draft whole-genome sequence of the C. fructicola strain SX-6 previously used for morphological and transcriptomic analyses. The assembly consists of 510 contigs with an estimated genome size of 56.8 Mb. A total of 15,243 predicted protein-coding genes in the SX-6 genome were annotated using NR, Swiss-Prot, KEGG, KOG, TCDB, GO, PHI, DFVF, P450, SignalP and CAZy databases. We identified 833 carbohydrate-active enzymes, 1803 secreted proteins, 79 secondary metabolite gene clusters and 576 fungal virulence factors that may be involved in the pathogenicity of this fungus. Comparative genome analyses with 25 Colletotrichum species revealed their evolutionary relationships via a constructed phylogenetic tree and identified four gene families whose genes were significantly more abundant in strain SX-6. The resulting assembly will provide a valuable resource for further research on the gene functions of C. fructicola.

Coat protein genealogy and complete genome characterization of field isolates of rice yellow mottle virus from Zambia

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Abstract

Rice yellow mottle virus (RYMV) is widespread in mainland Africa and adjoining islands but to date its occurrence in Zambia is unknown. In March 2022, field surveys were conducted in Luapula, Northern, Western and Eastern provinces of Zambia to determine the occurrence of RYMV and its genetic relationship with global isolates of the virus. Thirty-three paddy rice fields were visited and 108 leaf tissue samples were collected for analysis. The incidence of yellow mottle symptoms ranged from 25% to 43% in 10 (30.3%) of 33 fields and RYMV was detected in 35 (32.4%) of 108 samples by RT-PCR with virus-specific primers. A subset of 27 RYMV-positive samples was constituted from which full-length (720 bp) coat protein (CP) regions were amplified followed by bidirectional Sanger sequencing. Phylogenetic analysis of the CP cistron revealed distinct clustering of the isolates from Zambia in a monophyletic clade as subtype of the RYMV strain S4. Three isolates were randomly selected and used to obtain complete RYMV genomes of 4448–4449 nucleotides (nt). Comparative analysis of both the CP and the complete RYMV genomes from Zambia with their corresponding sequences in GenBank revealed that they shared 92%–93.2% nt identities with strain S4 isolates. These results confirm for the first time occurrence of RYMV strain S4 in Zambia and further reinforce the need for phytosanitary vigilance to safeguard rice production in Zambia.

Characterisation of starch properties and physical characteristics in buckwheat (Fagopyrum esculentum Moench.) mutant lacking accumulation of ‘granule‐bound starch synthase a’

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Abstract

The concentration of amylose, which is synthesised using granule-bound starch synthase, affects the physical properties of food. However, no studies have focused on starch properties and physical characteristics of low-amylose buckwheat. Here, we hypothesised that low-amylose buckwheat would be useful to produce new buckwheat products because low-amylose characteristics change the texture of buckwheat food. In this study, we bred relatively low-amylose buckwheat compared to wild type and investigated the causative genes of the traits, starch properties and physical properties of noodles. In the GBSSa mutant, the amylose concentration was lower than that in the wild type. Compared with the wild type, the mutant exhibited the following traits: Amylose concentration decreased by approximately 2%, setback in the Rapid Visco Analyzer decreased by 30 points and the physical characteristics of noodles in the sensory analysis were soft and sticky. These results suggest that this trait may be useful for changing the texture of foods. In addition, the mutant is promising for producing new foods with physical characteristics that are different from those of the wild type.

Genetic dissection of endosperm hydration in malting barley (Hordeum vulgare)

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Abstract

Hydration of the endosperm is a critical part of the malting process that ensures proper modification of the grain. However, little is known about the genetic controls of endosperm hydration and its relationship to agronomic and malt quality traits. The extent of endosperm hydration is estimated through hydration index (HYI). We measured HYI, agronomic, and malt quality traits on a 169-line subset of the NSGC Barley Core Panel, which includes global malt lines, some dating from the inception of European breeding programmes. Utilizing GWAS, 61 QTLs were identified for HYI, dormancy, agronomic, and malt quality traits. Of these, six were found to be related to HYI and were located on 1H, 2H, 3H, 6H, and 7H. We found HYI QTLs cosegregating with kernel size and hardness (1H and 3H), malting quality (2H and 6H), and dormancy (2H and 6H). These results indicate that endosperm hydration after steeping can be improved by selecting high HYI alleles on 2H, 6H, and 7H, positively impacting malting quality without negatively impacting kernel size or dormancy.

Extending Finlay–Wilkinson regression with environmental covariates

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Abstract

Finlay–Wilkinson regression is a popular method for analysing genotype–environment interaction in series of plant breeding and variety trials. It involves a regression on the environmental mean, indexing the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This paper reviews the development of such methods. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Two new methods are proposed for obtaining such synthetic covariates, which may be integrated into genotype-specific regression models, that is, criss-cross regression and a factor-analytic approach. The main advantage of such explicit modelling is that predictions can be made also for new environments where trials have not been conducted. A published dataset is employed to illustrate the proposed methods.

A new approach to Fourier transform Raman: Identification of haploids in maize (Zea mays)

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Abstract

The objective of this work was to adapt the FT Raman spectroscopy analysis in the differentiation of haploid and diploid kernels in maize, developing a new efficient, agile, precise, and nondestructive methodology. The main difference observed in FT Raman readings was a peak in the region between 1600 and 1700 cm−1 in possibly haploid kernels. It was possible to correlate the characteristics of the kernels with the presence of the R1-nj gene and the readings obtained in the Raman spectrometry technique. Most of the kernels previously classified as haploid showed positive values for principal component analysis (PCAs), indicating a correlation in the identification of haploids by the techniques adopted. The identification of haploids by R-Navajo was superior to FT Raman. However, FT Raman spectroscopy is an agile analysis technique that enables the development of non-invasive and non-destructive analytical methods in maize kernels, in addition to providing relevant information about the chemical structures present in the composition of the samples.

Responses of differentially expressed proteins and endogenous hormones in winter rapeseed (Brassica rapa L.) roots under water deficit stress

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Abstract

Winter rapeseed (Brassica rapa L.) can well-adapt to environmental conditions such as barrenness, water deficit and low temperature in arid and semi-arid planting regions and is the preferred rapeseed type. In this study, we analysed changes of root system morphology, antioxidant enzyme activity, endogenous hormone contents and differentially expressed proteins (DEPs) under control (CK), slight water deficit (SWD, 50–55% of maximum field water capacity), moderate water deficit (MWD, 40–45% of maximum field water capacity) and high water deficit (HWD, 30–35% of maximum field water capacity) conditions. Winter rapeseed experienced taproot elongation, decreased taproot diameter and increased lateral root number, under water deficit stress. The accumulation of reactive oxygen species (ROS) can cause membrane system peroxidation, and antioxidant enzyme activity increases to remove ROS. Changes in jasmonic acid (JA), salicylic acid (SA), cytokinin (CTK), auxin (IAA) and gibberellin (GA) levels promote the absorption of water and minerals by driving changes in the root system architecture to resist water deficit stress. A proteomic analysis has shown that DEPs are involved in energy metabolism, antioxidation response, osmotic regulation, hormone signal transduction, protein metabolism and the stress response, and these proteins are located in the peroxisome, chloroplast, mitochondrion, cell wall, vacuole, cytoplasm, extracellular space and cell membrane. In this study, multiple DEPs (malate dehydrogenase cytoplasmic 1 OS, 14-3-3-like protein GF14 Psi, GA 3-beta-dioxygenase, glutathione reductase and jasmonate-inducible protein) were involved in the root system architecture, revealing the complexity of the root response to water deficit. Significant/extremely significant synergistic relationships were observed between antioxidant enzyme activity and endogenous hormone contents. In conclusion, ROS, endogenous hormones and stress-related proteins work synergistically to control the root system architecture of winter rapeseed roots in response to water deficit stress.

Hybrid breeding for fall armyworm resistance: Combining ability and hybrid prediction

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Abstract

Fall armyworm (FAW, Spodoptera frugiperda) emerged as a major lepidopteran pest destroying maize in sub-Saharan Africa. A diallel mating design was used to generate 210 experimental hybrids from 21 lines. Experimental hybrids and four checks were evaluated in two locations. Commercial checks suffered higher foliar and ear damage compared to the top 15 hybrids. Mean squares associated with the genotypic variation were higher than genotype-by-environment interactions for foliar and ear damage traits. Heritabilities were moderate to high. Significant correlations were observed between grain yield (GY) with ear rot (−0.54) and ear damage (−0.45). Positive and significant GCA effects were observed for GY in seven parental lines, which were developed from multiple insect resistance breeding programmes. CKSBL10153 has the highest GCA value for GY and shows significant GCA effects for foliar and ear damage traits. These lines were identified as the ideal combiners for GY and FAW resistance and are therefore recommended for utilization as testers in the development of FAW-resistant three-way cross-hybrid maize with correlated response for increased GY. GCA and marker-based prediction correlations of GY were 0.79 and 0.96, respectively. Both GCA effects and marker-based models were effective in predicting hybrid performance for FAW resistance.

Combined transcriptome and proteome analyses between the cytoplasmic male sterile line and its maintainer line in Welsh onion (Allium fistulosum)

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Abstract

Cytoplasmic male sterility (CMS) is dispensable for research of heterosis and investigation of nuclear–cytoplasmic interaction in Welsh onion. The molecular mechanism of the cytoplasmic male sterile line and its maintainer line was investigated by combined transcriptome and proteome analyses in Welsh onion. The 410,585 full-length non-chimeric (FLNC, full-length readsnon-Chimeric) sequences were obtained, and AS events and lncRNAs were identified by full-length transcriptome. Towards a comprehensive understanding and identification of related genes for male sterility, the transcriptome and proteome sequencing were utilized to explore the differences between the cytoplasmic male sterile line and its maintainer line. The 287 differentially expressed genes (DEGs) and 49 differentially expressed proteins (DEPs) were identified, and we furthermore explored the function of genes and proteins. The heat map preliminarily demonstrated the expression change of the key genes and proteins in two cultivars. CMS-related genes were screened form differentially expressed genes, and verified by quantitative real-time quantitative polymerase chain reaction (qRT-PCR). Our research found the CMS regulatory genes, proteins and related pathways. The transcriptome and proteome datasets contribute to accelerate the development of CMS gene clones and functional genomics research on Welsh onions.

Designing optimal training sets for genomic prediction using adversarial validation with probit regression

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Abstract

Genomic selection (GS) is a disruptive methodology that is revolutionizing animal and plant breeding. However, its practical implementation is challenging since many times there is a mismatch in the distribution of the training and testing sets. Adversarial validation is an approach popular in machine learning to detect and address the difference between the training and testing distributions. For this reason, the adversarial validation method in this research was implemented using probit regression to detect the mismatch in distributions and also to select an optimal training set. We evaluated the proposed method with 14 datasets, and the results were benchmarked regarding of using the whole reference population and simple random samples. We found that the proposed method is effective for detecting the mismatch in distributions and outperformed in prediction accuracy by 11.67% (in terms of mean square error) and by 5.35% (in terms of normalized mean square error) when the whole reference population was used as training sets. Also, in general, this outperformed some existing methods for optimal training designs in the context of GS.