紫花苜蓿/豆科植物 BADH基因家族全基因组鉴定

Betaine aldehyde dehydrogenase (BADH), a member of family 10 of the aldehyde dehydrogenase superfamily, catalyzes the second oxidation step in the biosynthesis of glycine betaine (GB), which participates in a variety of critical processes that help plants tolerate abiotic stress. Nevertheless, it is still unclear how BADH functions in rice under pesticide stress. To look at the roles that the rice BADH family plays when under pesticide stress, three BADH genes were identified in transcriptome datasets of GB + oxyfluorfen (OFF)-treated rice. Using sequence alignment and phylogenetic analysis, the two subfamilies of the BADH gene family (ALDH10 and ALDH22) among rice, Arabidopsis, soybean, wheat, maize, barley, and sorghum were found. An examination of chromosomal position revealed that segmental duplication had a role in the expansion of OsBADH genes, and that the rice BADH genes were irregularly distributed on 3 of the 12 rice chromosomes. In collinearity analyses, rice BADH genes exhibited collinearity with those of wheat, maize, barley, and sorghum. The genes also showed a variety of conserved domains, cis-elements, motif compositions, and gene architectures that made it possible for them to encode different biotic and abiotic stress response proteins. Compared to the effects of OFF alone, BADH activity in rice roots and shoots increased 1.50-fold and 1.81-fold, respectively, following treatment with GB and 0.15 mg/L OFF. Analysis of protein–protein interaction networks provided more evidence for the involvement of OsBADH proteins in OFF metabolism. Overall, these findings demonstrate that BADH genes respond effectively to OFF-induced stress by producing GB, highlighting their potential roles in regulating pesticide degradation.

SQUAMOSA promoter-binding protein-like (SPL) transcription factors are widely present in plants and are involved in signal transduction, the stress response and development. The SPL gene family has been characterized in several model species, such as A. thaliana and G. max. However, there is no in-depth analysis of the SPL gene family in forage, especially alfalfa (Medicago sativa L.), one of the most important forage crops worldwide. In total, 76 putative MsSPL genes were identified in the alfalfa genome with an uneven distribution. Based on their identity and gene structure, these MsSPLs were divided into eight phylogenetic groups. Seventy-three MsSPL gene pairs arose from segmental duplication events, and the MsSPLs on the four subgenomes of individual chromosomes displayed high collinearity with the corresponding M. truncatula genome. The prediction of the cis-elements in the promoter regions of the MsSPLs detected two copies of ABA (abscisic acid)-responsive elements (ABREs) on average, implying their potential involvement in alfalfa adaptation to adverse environments. The transcriptome sequencing of MsSPLs in roots and leaves revealed that 54 MsSPLs were expressed in both tissues. Upon salt treatment, three MsSPLs (MsSPL17, MsSPL23 and MsSPL36) were significantly regulated, and the transcription level of MsSPL36 in leaves was repressed to 46.6% of the control level. In this study, based on sequence homology, we identified 76 SPL genes in the alfalfa. The SPLs with high identity shared similar gene structures and motifs. In total, 71.1% (54 of 76) of the MsSPLs were expressed in both roots and leaves, and the majority (74.1%) preferred underground tissues to aerial tissues. MsSPL36 in leaves was significantly repressed under salt stress. These findings provide comprehensive information regarding the SPB-box gene family for improve alfalfa tolerance to high salinity.

Alfalfa (Medicago sativa) is a high-quality legume forage crop worldwide, and alfalfa production is often threatened by abiotic environmental stresses. GRAS proteins are important transcription factors that play a vital role in plant development, as well as in response to environmental stress. In this study, the availability of alfalfa genome “Zhongmu No.1” allowed us to identify 51 GRAS family members, i.e., MsGRAS. MsGRAS proteins could be classified into nine subgroups with distinct conserved domains, and tandem and segmental duplications were observed as an expansion strategy of this gene family. In RNA-Seq analysis, 14 MsGRAS genes were not expressed in the leaf or root, 6 GRAS genes in 3 differentially expressed gene clusters were involved in the salinity stress response in the leaf. Moreover, qRT-PCR results confirmed that MsGRAS51 expression was induced under drought stress and hormone treatments (ABA, GA and IAA) but down-regulated in salinity stress. Collectively, our genome-wide characterization, evolutionary, and expression analysis suggested that the MsGRAS proteins might play crucial roles in response to abiotic stresses and hormonal cues in alfalfa. For the breeding of alfalfa, it provided important information on stress resistance and functional studies on MsGRAS and hormone signaling.

BACKGROUND: Alfalfa (Medicago sativa) is the most widely planted legume forage and one of the most economically valuable crops in the world. The periodic changes in its growth and development and abiotic stress determine its yield and economic benefits. Auxin controls many aspects of alfalfa growth by regulating gene expression, including organ differentiation and stress response. Auxin response factors (ARF) are transcription factors that play an essential role in auxin signal transduction and regulate the expression of auxin-responsive genes. However, the function of ARF transcription factors is unclear in autotetraploid-cultivated alfalfa. RESULT: A total of 81 ARF were identified in the alfalfa genome in this study. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed, identifying that ARF genes are mainly involved in transcriptional regulation and plant hormone signal transduction pathways. Phylogenetic analysis showed that MsARF was divided into four clades: I, II, III, and IV, each containing 52, 13, 7, and 9 genes, respectively. The promoter region of the MsARF gene contained stress-related elements, such as ABRE, TC-rich repeats, MBS, LTR. Proteins encoded by 50 ARF genes were localized in the nucleus without guide peptides, signal peptides, or transmembrane structures, indicating that most MsARF genes are not secreted or transported but only function in the nucleus. Protein structure analysis revealed that the secondary and tertiary structures of the 81 MsARF genes varied. Chromosomal localization analysis showed 81 MsARF genes were unevenly distributed on 25 chromosomes, with the highest distribution on chromosome 5. Furthermore, 14 segmental duplications and two sets of tandem repeats were identified. Expression analysis indicated that the MsARF was differentially expressed in different tissues and under various abiotic stressors. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis showed that the expression profiles of 23 MsARF genes were specific to abiotic stresses such as drought, salt, high temperature, and low temperature, as well as tissue-specific and closely related to the duration of stress. CONCLUSION: This study identified MsARF in the cultivated alfalfa genome based on the autotetraploid level, which GO, KEGG analysis, phylogenetic analysis, sequence characteristics, and expression pattern analysis further confirmed. Together, these findings provide clues for further investigation of MsARF functional verification and molecular breeding of alfalfa. This study provides a novel approach to systematically identify and characterize ARF transcription factors in autotetraploid cultivated alfalfa, revealing 23 MsARF genes significantly involved in response to various stresses.

Groundnut or peanut (Arachis hypogaea) is a legume crop. Its seeds are rich in protein and oil. Aldehyde dehydrogenase (ALDH, EC: 1.2.1.3) is an important enzyme involved in detoxification of aldehyde and cellular reactive oxygen species, as well as in attenuation of lipid peroxidation-meditated cellular toxicity under stress conditions. However, few studies have been identified and analyzed about ALDH members in Arachis hypogaea. In the present study, 71 members of the ALDH superfamily (AhALDH) were identified using the reference genome obtained from the Phytozome database. A systematic analysis of the evolutionary relationship, motif, gene structure, cis-acting elements, collinearity, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and expression patterns was conducted to understand the structure and function of AhALDHs. AhALDHs exhibited tissue-specific expression, and quantitative real-time PCR identified significant differences in the expression levels of AhALDH members under saline-alkali stress. The results revealed that some AhALDHs members could be involved in response to abiotic stress. Our findings on AhALDHs provide insights for further study.

… In this paper, we identify members of the ALDH gene superfamily in … gene families. The soybean genome contains 18 unique ALDH sequences encoding members of five ALDH families …

… Betaine aldehyde dehydrogenase (BADH) is widely considered as a key enzyme in glycine betaine metabolism in higher plants. Several paralogous genes encoding different isozymes …

… gene patterns and conduct a bioinformatics analysis across various fields. The expression sequence of the betaine aldehyde dehydrogenase (BADH) gene … One important gene family …

… A total of twenty ALDH genes were identified in the rice genome. They were grouped into 10 distinct families based on protein sequence identity. The whole genome duplication (WGD) …

Aldehyde dehydrogenases (ALDHs) represent a group of enzymes that detoxify aldehydes by facilitating their oxidation to carboxylic acids, and have been shown to play roles in plant response to abiotic stresses. However, the comprehensive analysis of ALDH superfamily in soybean (Glycine max) has been limited. In present study, a total of 53 GmALDHs were identified in soybean, and grouped into 10 ALDH families according to the ALDH Gene Nomenclature Committee and phylogenetic analysis. These groupings were supported by their gene structures and conserved motifs. Soybean ALDH superfamily expanded mainly by whole genome duplication/segmental duplications. Gene network analysis identified 1146 putative co-functional genes of 51 GmALDHs. Gene Ontology (GO) enrichment analysis suggested the co-functional genes of these 51 GmALDHs were enriched (FDR < 1e-3) in the process of lipid metabolism, photosynthesis, proline catabolism, and small molecule catabolism. In addition, 22 co-functional genes of GmALDHs are related to plant response to water deprivation/water transport. GmALDHs exhibited various expression patterns in different soybean tissues. The expression levels of 13 GmALDHs were significantly up-regulated and 14 down-regulated in response to water deficit. The occurrence frequencies of three drought-responsive cis-elements (ABRE, CRT/DRE, and GTGCnTGC/G) were compared in GmALDH genes that were up-, down-, or non-regulated by water deficit. Higher frequency of these three cis-elements was observed for the group of up-regulated GmALDH genes as compared to the group of down- or non- regulated GmALDHs by drought stress, implying their potential roles in the regulation of soybean response to drought stress. A total of 53 ALDH genes were identified in soybean genome and their phylogenetic relationships and duplication patterns were analyzed. The potential functions of GmALDHs were predicted by analyses of their co-functional gene networks, gene expression profiles, and cis-regulatory elements. Three GmALDH genes, including GmALDH3H2, GmALDH12A2 and GmALDH18B3, were highly induced by drought stress in soybean leaves. Our study provides a foundation for future investigations of GmALDH gene function in soybean.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.