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Late embryogenesis abundant (LEA) proteins are widely known to be present in higher plants and are believed to play important functional roles in embryonic development and abiotic stress responses. However, there is a current lack of systematic analyses on the LEA protein gene family in tea plant. In this study, a total of 48 LEA genes were identified using Hidden Markov Model profiles in C. sinensis, and were classified into seven distinct groups based on their conserved domains and phylogenetic relationships. Genes in the CsLEA_2 group were found to be the most abundant. Gene expression analyses revealed that all the identified CsLEA genes were expressed in at least one tissue, and most had higher expression levels in the root or seed relative to other tested tissues. Nearly all the CsLEA genes were found to be involved in seed development, and thirty-nine might play an important role in tea seed maturation concurrent with dehydration. However, only sixteen CsLEA genes were involved in seed desiccation, and furthermore, most were suppressed. Additionally, forty-six CsLEA genes could be induced by at least one of the tested stress treatments, and they were especially sensitive to high temperature stress. Furthermore, it was found that eleven CsLEA genes were involved in tea plant in response to all tested abiotic stresses. Overall, this study provides new insights into the formation of CsLEA gene family members and improves our understanding on the potential roles of these genes in normal development processes and abiotic stress responses in tea plant, particularly during seed development and desiccation. These results are beneficial for future functional studies of CsLEA genes that will help preserve the recalcitrant tea seeds for a long time and genetically improve tea plant.
Pubmed ID: 31575979
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Suite of motif-based sequence analysis tools to discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences; search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN; compare a motif to all motifs in a database of motifs; associate motifs with Gene Ontology terms via their putative target genes, and analyze motif enrichment using SpaMo or CentriMo. Source code, binaries and a web server are freely available for noncommercial use.
View all literature mentionsA database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).
View all literature mentionsTool for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.
View all literature mentionsSoftware tool to calculate various physicochemical parameters for given protein stored in Swiss-Prot or TrEMBL or for user entered protein sequence. Protein can either be pecified as Swiss-Prot/TrEMBL accession number or ID, or in form of raw sequence. Computed parameters include molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index and grand average of hydropathicity.
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