Skip to main content
NCBI home page
Plant Communications logoLink to Plant Communications
. 2022 Aug 12;4(1):100425. doi: 10.1016/j.xplc.2022.100425

LettuceGDB: The community database for lettuce genetics and omics

Zhonglong Guo 1,2,3,4,8, Bo Li 1,3,8, Jianjun Du 1,5,8, Fei Shen 1,3, Yongxin Zhao 1,3, Yang Deng 1,3, Zheng Kuang 1,3, Yihan Tao 2, Miaomiao Wan 2, Xianju Lu 1,5, Dong Wang 6, Ying Wang 1,2,3, Yingyan Han 7, Jianhua Wei 1, Lei Li 2, Xinyu Guo 1,5,∗, Chunjiang Zhao 1,5,∗∗, Xiaozeng Yang 1,3,∗∗∗
PMCID: PMC9860171  PMID: 35964156

Abstract

As a globally popular leafy vegetable and a representative plant of the Asteraceae family, lettuce has great economic and academic significance. In the last decade, high-throughput sequencing, phenotyping, and other multi-omics data in lettuce have accumulated on a large scale, thus increasing the demand for an integrative lettuce database. Here, we report the establishment of a comprehensive lettuce database, LettuceGDB (https://www.lettucegdb.com/). As an omics data hub, the current LettuceGDB includes two reference genomes with detailed annotations; re-sequencing data from over 1000 lettuce varieties; a collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies; re-analyses of 256 RNA sequencing datasets; a complete miRNAome; extensive metabolite information for representative varieties and wild relatives; epigenetic data on the genome-wide chromatin accessibility landscape; and various lettuce research papers published in the last decade. Five hierarchically accessible functions (Genome, Genotype, Germplasm, Phenotype, and O-Omics) have been developed with a user-friendly interface to enable convenient data access. Eight built-in tools (Assembly Converter, Search Gene, BLAST, JBrowse, Primer Design, Gene Annotation, Tissue Expression, Literature, and Data) are available for data downloading and browsing, functional gene exploration, and experimental practice. A community forum is also available for information sharing, and a summary of current research progress on different aspects of lettuce is included. We believe that LettuceGDB can be a comprehensive functional database amenable to data mining and database-driven exploration, useful for both scientific research and lettuce breeding.

Key words: lettuce, genome, multi-omics, germplasms, breeding, community

LettuceGDB, which contains multi-omics data and integrates rich germplasm resources with abundant phenotypic data, is a comprehensive lettuce database dedicated to data mining and database-driven exploration for scientific research and lettuce breeding. As a community database, LettuceGDB aims to provide information and promote knowledge sharing to serve the lettuce research community.

Introduction

Lettuce (Lactuca sativa) is a globally popular leafy vegetable from the Asteraceae family that has high economic value. The global production of lettuce, together with chicory, has increased by 60% in the last 20 years, exceeding 29 million tons in 2019 and ranking third among all leafy vegetables based on statistics from the Food and Agriculture Organization (http://www.fao.org/). Lettuce is one of the oldest domesticated vegetables and was first recorded on the mural paintings of Egyptian tombs dating back 4500 years (Lindqvist, 1960; de Vries, 1997). Recent research posits that lettuce was first domesticated in Western Asia, especially near the Caucasus, and prickly lettuce (Lactuca serriola) is the major genetic resource for modern cultivated lettuce (Wei et al., 2021). Continuous domestication and selection have yielded diverse modern lettuce varieties, including oilseed, romaine, butterhead, crisphead, stem, looseleaf, and numerous mixed types (Simko et al., 2013).

Lettuce has gradually become a model plant of the Asteraceae family and a suitable system for specific molecular biology studies owing to the availability of a high-quality reference genome and stable transformation systems (Darqui et al., 2021). Abundant transcriptomic datasets and hundreds of re-sequenced varieties have been used to study transcriptional differences and genetic relationships among different cultivars and wild relatives (Zhang et al., 2016, 2017; Wei et al., 2021). Lettuce is also a good system for studying leaf development and morphological differences because of the rich morphological differences among abundant varieties, especially in leaves and heading shapes (Zhang et al., 2007; Seki et al., 2020; Yu et al., 2020; Luo et al., 2021). The changeable leaf color of lettuce provides a good platform for studying the anthocyanin biosynthetic pathway and chloroplast development (Park et al., 2008; Zhang et al., 2018, 2021; Gurdon et al., 2019; Su et al., 2020), and lettuce has been a model for studying metabolites in Asteraceae plants (Kim et al., 2018; van Treuren et al., 2018; Yang et al., 2018; Zhang et al., 2020). In addition, lettuce is exploited for biopharmaceuticals because of its low processing cost, consistent and scalable production, and the excellent biosafety profile of its transgenic plants (Pniewski et al., 2017; Daniell et al., 2019; Zhang et al., 2019; Matsui et al., 2021).

Comprehensive and integrated scientific databases can provide coherent information to users and advance scientific and practical progress. There are numerous successful examples, such as MaizeGDB (https://www.maizegdb.org/) (Harper et al., 2016). By integrating different omics data, germplasm resource information, multiple tools, and communication platforms, MaizeGDB has greatly promoted scientific research on maize and facilitated the transition to the next breeding stage, i.e., breeding 4.0 (Portwood et al., 2019). The lettuce research community has made great efforts to generate a large number of germplasms, such as those stored in the Germplasm Resources Information Network (https://www.ars-grin.gov/) and the Centre for Genetic Resources (https://cgngenis.wur.nl). In addition, in the last decade, large amounts of lettuce data from multiple omics approaches, including genomics, transcriptomics, phenomics, metabonomics, and epigenomics, have been produced and collected in GenBank (https://www.ncbi.nlm.nih.gov-/genbank/), CGNB (https://www.cng-b.org/, the Lettuce Database), the Lettuce Genome Resource (https://lgr.genomecenter.ucdavis.edu/), and so forth. Therefore, an integrative lettuce database with more comprehensive annotations, greater data integrity, and convenient tools is highly desired to advance fundamental research and breeding practice.

Here, we describe the recently developed LettuceGDB (https://www.lettucegdb.com), which integrates lettuce data from public databases and data generated by our lettuce breeding group (LBG) at the Beijing Academy of Agriculture and Forestry Sciences. LettuceGDB includes various data types, such as multi-omics data (genome, re-sequencing, transcriptome, miRNAome, metabolome, and epigenome), and integrates rich germplasm resources with abundant phenotypic data collected manually and through high-throughput phenotyping platforms. Multiple easy-to-use access functions/interfaces and several built-in tools, including a data download center, have been designed to enable users to conveniently access these data. Finally, a community platform that systematically summarizes current research progress on different aspects of lettuce has been developed. Therefore, we believe that LettuceGDB can effectively serve the entire research community and assist with scientific research and lettuce breeding.

Results

Content of LettuceGDB

To better organize all the current data on lettuce in bulk and provide users with friendly interfaces and easy-to-use tools, LettuceGDB comprises three components: data, functions, and tools. All current public datasets were combined with our LBG data and grouped into ten different types (Supplemental Table 1; Figure 1). The current version of LettuceGDB contains two genomes and related annotations, re-sequencing data from 1048 lettuce varieties, more than 1300 germplasms, batched phenotypic data on key traits, 256 RNA sequencing (RNA-seq) datasets, a complete miRNAome based mainly on small RNA-seq (sRNA-seq) and parallel analysis of RNA ends (PARE)-seq annotation, metabolic information and epigenetic data on the genome-wide chromatin accessibility landscape, and lettuce research papers published in the last decade (Figure 1).

Figure 1.

Figure 1

Open in a new tab

Framework of three components at LettuceGDB.

Five major functions or interfaces have been developed to present the above datasets (Figure 1 and Supplemental Figure 1), enabling users to easily access the underlying data. These functions are 1) Genome, which provides detailed information on two reference genomes and related annotations; 2) Genotype, which presents all variations of the re-sequenced lettuce varieties via visual and searchable access ports; 3) Germplasm, which contains detailed information on all germplasm resources; 4) Phenotype, which organizes all the phenotypic records collected manually or through phenomics platforms; and 5) O-Omics, which contains data from other omics, including the transcriptome, miRNAome, metabolome, and epigenome.

The third component of LettuceGDB is designed to develop and integrate eight related tools with different functions or data to assist users in conveniently using and downloading the data (Figure 1 and Supplemental Figure 1). Tools related to various genomics data include Assembly Converter, Search Gene, BLAST, JBrowse, Primer Design, and Gene Annotation, and the Tissue Expression tool displays transcriptomic datasets. Users can search published research on lettuce and access different data types through the Literature and Data tools. Finally, a community platform has been developed to facilitate user communication and information sharing. In addition to these tools, there are many other functions, such as browsers, search engines, filters, etc., to facilitate the use of LettuceGDB.

Functions of LettuceGDB

Genome and genotype

The current Genome function includes information on the genomes of two cultivars, a crisphead variety (L. sativa cv. Salinas) (Reyes-Chin-Wo et al., 2017) and a stem lettuce variety (L. sativa cv. YL1), which was de novo assembled and first released by the LBG (Figure 2A). All information related to these two genomes, including detailed genomic annotations, can be downloaded using the Data tool. In addition, the Search Gene tool was developed to facilitate the use and download of information on individual genes (Figure 2B). It enables users to search all annotated lettuce genes; download the genomic, mRNA, coding, protein, and promoter region sequences of a specific gene; view the gene structure and sequence via a graphical panel; and browse expression of the gene in different lettuce varieties and wild relatives (Figure 2B). In addition, the Gene Annotation tool collects more functional annotations for each gene (Figure 2C), including detailed information on a specific gene protein family, homologous superfamily, domains, repeats, and Gene Ontology terms obtained through a similarity search of the InterPro database (Jones et al., 2014). Lastly, a pan-genome consisting of four other representative lettuce types (looseleaf, romaine, butterhead, and wild lettuce [L. serriola]) has been initialized, and these data will be available soon (Figure 2A).

Figure 2.

Figure 2

Open in a new tab

Genome and genotype functions at LettuceGDB.

(A) A phylogenetic tree represents the relationships among wild and cultivated lettuce. The two genomes available at LettuceGDB are indicated in dark cyan.

(B) Search Gene page at LettuceGDB.

(C) Sketch map showing annotation of protein-coding genes using InterProScan.

(D) Retrieval system for variations based on the results of 1048 re-sequencing datasets.

The Genotype function mainly includes re-sequencing data from different Lactuca accessions. The LBG has generated re-sequencing data for 1048 accessions, including 900 cultivars and over 100 wild relatives. These data include 70 792 508 SNPs and 5 869 237 insertions or deletions (indels), affecting more than 35 000 annotated gene loci. The genetic relationships among cultivated lettuces and their wild relatives and the structure and evolutionary relationships of different cultivar types/groups can be inferred from these data. To help users quickly find variants of interest, we have developed a query system (Figure 2D). Users can check for an SNP/indel in a specific region through the gene ID or genomic positions. Two additional filters, variant type and effect type, can also be added. The search results yield a wealth of information, such as specific SNP/indel locations, variation types, variation frequencies in different lettuce cultivars and wild relatives (sibling species), and gene regions affected by these variations (Figure 2D).

Germplasm

The Germplasm function contains two types of germplasms. The first type includes varieties collected from the wild or produced by traditional breeding methods. The current database includes 1333 varieties sourced globally (Figure 3A). This collection can be visualized on a world map with details on the sources and types of germplasms (Figure 3B). A search engine is also present to help users query these germplasms by accession, name, ID, and three additional filters (type, country, and species) (Figure 3C). A search returns a list with six fields: accession, LettuceGDB ID, Germplasm Resources Information Network ID, species, type, and country. A more detailed information page for the germplasm is obtained by clicking on the name or the LettuceGDB ID. This page contains 13 phenotypic records for the germplasm, including seed color, plant height, bolting time, leaf color, leaf shape, leaf margin, etc., as well as photos taken in the LBG breeding greenhouse or the field (Figure 3D).

Figure 3.

Figure 3

Open in a new tab

Germplasm and phenotype functions at LettuceGDB.

(A) Pie chart showing the proportion of each type of lettuce germplasm at LettuceGDB.

(B) World map showing the global distribution and statistics of lettuce germplasms. Cyan indicates the collection countries or regions, and the dark cyan circle shows an example in the USA in which detailed germplasm information is displayed.

(C) Retrieval system for lettuce germplasms at LettuceGDB.

(D) Detailed page showing the germplasm information and phenotypic records for a specific lettuce cultivar.

(E) Interface for transgenic lettuce germplasms at LettuceGDB.

The second germplasm type includes new germplasms generated by genetic manipulation methods, such as transgenes and genome editing (Figure 3E). At present, there is limited information on this type of germplasm, with the only available germplasms sourced from the LBG. However, this type of germplasm resource may grow rapidly in the future, as a stable transformation system has been established in lettuce.

Phenotype

Besides genotype information on the germplasms, phenotypic records are another significant aspect of the germplasm resources. Currently, the Phenotype function is divided into two categories. The first category includes data collected manually over the last 3 years through multi-site planting and growth cycles. These data have 13 features, including multiple important agronomic traits: morphological traits like leaf type and leaf edge, growth cycle related to bolting and flowering time, etc. (Figure 4A). These traits based on cultivar types have also been summarized and displayed in box plots and other formats (Figure 4B).

Figure 4.

Figure 4

Open in a new tab

Phenotype data collected manually and with high-throughput phenomics platforms at LettuceGDB.

(A) Sketch map showing three types of traits measured manually at LettuceGDB.

(B) Example showing a height comparison of different lettuce varieties.

(C) Schematic diagram of the high-throughput phenomics platform used in our experiment.

(D) Six continuously recorded phenotypes in C978 lettuce using the high-throughput phenomics platform.

PA, projected area; PP, projected perimeter; CA, convex area; CP, convex perimeter; PCD, projected circumcircle ratio; PPR, projected area perimeter ratio.

The second category includes data obtained through high-throughput phenotyping platforms. These data are divided into different scales and test environments (indoor data and field data). Indoor data are recorded via rail-mounted facilities implemented by infrared, far-infrared, visible light, and other imaging technologies, and field data are obtained using drones (which capture plant features from above via visible and hyper spectra) (Figure 4C). For instance, indoor data include leaf size, leaf color, etc., and processed data such as growth curves are calculated based on projected area, perimeter, and convex area at continuous time points (Du et al., 2020) (Figure 4D).

O-omics

O-omics includes mainly transcriptome, miRNAome, metabolome, and epigenome data, in addition to the above-mentioned data types. The transcriptome data are mainly sourced from Zhang’s work (Zhang et al., 2017) and contain leaf transcriptome data from 240 Lactuca accessions (Supplemental Table 2) and 16 other RNA-seq libraries from roots, stems, leaves, flowers, and seeds (Supplemental Table 3). The transcriptome data can be displayed through either the Search Gene or Tissue Expression tools. Search Gene summarizes gene expression information in different varieties and wild relatives (Figure 2B), whereas Tissue Expression displays the results in the form of a bar chart and heatmap (Figure 5A).

Figure 5.

Figure 5

Open in a new tab

O-omics function at LettuceGDB.

(A) Heatmap showing the expression levels of queried genes.

(B) Schematic view of the miRNAome page at LettuceGDB.

(C) Box plot showing comparison of a specific metabolite in different types of lettuce.

(D) Retrieval system for significant peaks based on the results of ATAC-seq data.

miRNAome mainly includes the latest research on lettuce microRNAs (miRNAs) (Deng et al., 2021). These data are based on two groups of datasets: 21 sRNA-seq datasets from different tissues and five PARE-seq datasets (Supplemental Tables 4 and 5). The current miRNAome includes 157 high-confidence miRNA genes and related annotations, including genomic positions and sequences, secondary structures, target genes obtained through prediction and experimental validation from PARE-seq datasets, conservation, etc. (Figure 5B). These data are also contained in the Plant MicroRNA Encyclopedia (PmiREN2.0) (Guo et al., 2021).

At present, LettuceGDB contains 4120 metabolites from 50 lettuce varieties belonging to six lettuce types that were obtained by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Among the detectable compounds, 533 are annotated and divided into 10 groups, including alkaloids, amino acids and derivatives, flavonoids, lignans and coumarins, lipids, nucleotides and derivatives, organic acids, phenolic acids, terpenoids, and others. LettuceGDB provides a retrieval system for specific metabolites and a visual comparison among different types of lettuce (Figure 5C).

The current epigenetic data are limited, containing only the datasets from the LBG. Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) was used to assess differences in transposase-accessible areas of chromatin in leaves before and after heat treatment. A search engine is also available to search the peaks accumulated by the ATAC-seq results. The locations of these peaks, the degree of change, the affected genes, and the specific locations of the affected genes can then be determined (Figure 5D).

Tools

Users can access the eight complementary tools (Assembly Converter, Search Gene, BLAST, JBrowse, Primer Design, Gene Annotation, Tissue Expression, and Literature) in the Tools and Data menus for data retrieval and analysis (Figures 6A–6E). Assembly Converter is used to find one-to-one orthologous genes between the crisphead lettuce genome and the stem lettuce genome. The Search Gene and Gene Annotation tools have been described above under their related functions, and the other five tools are described below.

Figure 6.

Figure 6

Open in a new tab

Schematic diagram of tools and community at LettuceGDB.

(A–G) BLAST (A), JBrowse (B), Gene Expression (C), Primer Design (D), Literature (E), Data (F), and (G) Community at LettuceGDB.

BLAST allows users to search homologous sequences of interest against the lettuce genomes (Figure 6A), either by pasting a sequence into the text box or by uploading a fasta file. Users can select one of five BLAST algorithms (blastn, blastp, blastx, tblastn, or tblastx) and configure their query with advanced options. LettuceGDB contains four BLAST databases (genome, mRNA, coding sequences, and protein sequences) for crisphead and stem lettuce. The BLAST hit output results are presented in a standard table (query name, target name, score, identities, percentage, and expect) as collapsible fields that can be explored further.

JBrowse is an open-source, pluggable, and comprehensive computational platform used to visualize and integrate biological data (Buels et al., 2016). JBrowse at LettuceGDB is used to show integrated information on lettuce genomes and annotated genomic datasets (Figure 6B). LettuceGDB currently offers data for two genomes, and users can also easily browse and explore information of interest such as miRNA loci, expression levels of specific genes, etc.

RNA-seq datasets were used to calculate the expression levels of each lettuce gene (Figure 6C). The Tissue Expression tool enables users to obtain the expression values of a given gene list in different tissues or libraries. Highcharts (https://www.highcharts.com) generates an interactive and dynamic heatmap for the visualization of expression data. The gene ID, SRR ID, fragments per kilobase per million fragments, and related information for each point are revealed by hovering the cursor over the heatmap.

A web-based PCR primer design interface, Primer Design, is implemented with primer3 as the core program to facilitate the users’ experimental practice (Figure 6D) (Chuang et al., 2013). Some novel features for genetic experimental design are available in addition to the typical primer design function. For instance, genomic, mRNA, coding, or promoter sequences can be automatically loaded into the input field by entering the gene ID. Users can also select various primer-design parameters of primer3.

LettuceGDB provides a professional literature search tool for lettuce research (Figure 6E) and consists of over 2000 papers published in the past 10 years, facilitating greater efficiency in literature triage and curation. The Literature search tool supports keyword searches for year, author, title, and journal, and hyperlinks to the full texts of the publications are provided in the search results list.

Data center and community

To enhance the user’s convenience, LettuceGDB provides a standalone page for downloading data, such as genomes, annotations, germplasms, etc., in compressed or Excel file formats. Users can browse all available data and download their required data in bulk by clicking on the data tag in the navigation bar (Figure 6F).

LettuceGDB provides a community forum for sharing and exchanging information and knowledge among members of the lettuce research community (Figure 6G). Here, researchers can post their latest research results, conduct academic discussions, and share other information. In addition, lettuce research from the past several years has been systematically summarized in the latest research section by our LBG team. At present, the studies encompass eight areas: heading in vegetable crops; research progress on lettuce metabolites; application of high-throughput phenomics in lettuce; lettuce seed germination; regulation factors of lettuce bolting and flowering; color regulation mechanism of lettuce leaves; genome and transcriptome of lettuce; and lettuce disease resistance (verticillium wilt and downy mildew). For instance, heading is a critical trait for the storage and shelf life of lettuce and other leafy vegetables. Therefore, systematic compilation of the current research on lettuce heading can enhance understanding of this research topic.

Discussion and perspectives

Lettuce is an important representative of leafy vegetables and Asteraceae plants, and omics and breeding data for lettuce have accumulated rapidly in the last two decades, especially omics data in the last 10 years. Therefore, it is necessary to construct a lettuce database that integrates all these data to accelerate lettuce research and breeding practices. Here, we developed LettuceGDB, which combines public multiple omics data (Supplemental Table 1) and all types of data generated by our LBG team. At present, data at LettuceGDB include genome, transcriptome, miRNAome, metabolome, and abundant germplasm resource information (Figure 1). Five functions corresponding to the different data types (Genome, Genotype, Germplasm, Phenotype, and O-omics) were developed. Besides summarizing and showing large amounts of data, the functions also provide convenient browsing and retrieval functions for these data. Various tools have been incorporated, especially genomic data tools, to enable users to conveniently use LettuceGDB (Figure 6). As a hub of lettuce multi-omics data, we believe that LettuceGDB can become a knowledge- and information-sharing center. For this reason, information in the Literature and Community tools was collected and organized, and cutting-edge research on lettuce was summarized.

One of the purposes of building LettuceGDB is to promote progress in lettuce breeding. Recently, scientists have proposed upcoming breeding approaches or stages such as the so-called 5G breeding approach (Varshney et al., 2020) and breeding 4.0 (Wallace et al., 2018). Wallace et al. indicated that crop breeding could be divided into four different stages based on the application of biotechnology in breeding practice. They also showed that current major crops such as corn and rice are transitioning from breeding 3.0 to 4.0. The most important features of these new breeding concepts are 1) the large amount of data accumulated for a specific crop, including all types of omics data and breeding information, which can be used to design new breeding strategies and efficiently enable the aggregation of valuable traits, and 2) new biotechnology techniques, such as genome editing, which can help breeders obtain needed traits more effectively than traditional methods. Although the various types of lettuce data are not comparable with those of major crops (rice and corn), we hope that LettuceGDB can become an important data center for lettuce breeding, providing researchers and breeders with rich data and convenient access, thus accelerating lettuce breeding practices to the next level.

However, the current version (v.1.1c) of LettuceGDB still has many limitations and requires more improvements in the future. For instance, there are limited case-by-case gene studies in lettuce. Genetic modification methods have generated only a few germplasm resources. In addition, data on genome-scale epigenetics are limited. Therefore, we hope that more people can participate in the construction of LettuceGDB and share more research results, data, and germplasm resources to promote its development. To this end, a data submission center was developed for the sharing of various data and submission of germplasm resources. We hope that colleagues in lettuce research can jointly promote the progress of LettuceGDB to accelerate and improve lettuce research and breeding.

Materials and methods

Hardware and software

The LettuceGDB website runs on a Linux server supported by cloud technology. The PHP language has been used to develop and support web applications. The database in the background was developed using MySQL. HTML, CSS, and JavaScript languages are used to develop the web interfaces of LettuceGDB. The Highcharts library (https://www.highcharts.com/) is used to create reliable data visualizations.

Reference genome and annotation resources

The Lettuce Genome Sequencing Project (Reyes-Chin-Wo et al., 2017) generated the assembled genome and related annotations of crisphead lettuce (Supplemental Table 1), which were downloaded from NCBI (GCF_002870075.3, https://www.ncbi.nlm.nih.gov/genome/352).

The genome of stem lettuce (unpublished data) was assembled by our LBG team. In brief, Falcon (v.0.4) (Chin et al., 2016) was used to construct the initial contigs from PacBio-only long reads. Arrow (v.2.2.3) was then used to polish the contigs with these long reads. Pilon (v.1.2) (Walker et al., 2014) was used to further correct the contigs with accurate Illumina short reads. Bionano Solve (v.3.0.1) (https://bionanogenomics.com) was used to assemble Bionano optical maps into consensus physical maps. Finally, Hi-C sequencing data were used to anchor the hybrid scaffolds onto chromosomes with Juicer (v.1.5) (Durand et al., 2016) and 3D-DNA (Dudchenko et al., 2017).

A total of 38 910 protein-coding genes were annotated in the genome of crisphead lettuce, and the LBG predicted 40 341 genes in the stem lettuce genome (unpublished data). LettuceGDB retained the original gene names from crisphead lettuce with the prefix “Lsat,” whereas genes from stem lettuce were given names beginning with “LSA” to distinguish genes from the two genomes.

Genotyping of re-sequencing data

Minimap2 (Li, 2018) was used to map clean reads to the stem lettuce genome after removing adaptors. A standard Sentieon variant-calling pipeline was then used to call variants. SNPs or indels with missing genotype rate <0.8 and minor allele frequency >0.05 were used for further analysis. Beagle (v.3.3.2) (Browning et al., 2021) was used for missing genotype imputation and phasing to obtain a high-quality genotype dataset containing SNPs in more than 95% of germplasms with a posterior probability of >0.5. Finally, ANOVAR was used with the stem lettuce gene models (v.1.1c) to identify variants in exons, introns, intergenic regions, 5′ UTRs, and 3′ UTRs.

RNA-seq analysis

A total of 256 RNA-seq datasets were downloaded from the NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra) or generated by our LBG group (Supplemental Tables 2 and 3). The original compressed files were converted into Fastq format using Linux v.2.8.2 with the SRA toolkit (https://www.ncbi.nlm.nih.gov/sra/docs/sradownload/). FastQC (Brown et al., 2017) was used for quality control. Trim Galore (v.0.5.0) (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to trim adapter sequences with the parameters “-q 20 --stringency 3 --length 20”. Reads with >100 bases were retained after trimming. HISAT2 (Kim et al., 2019) was used to map the clean reads to the corresponding genome, and StringTie v.1.3.3 (Pertea et al., 2015) was used to assemble the transcripts and quantify each mRNA-seq dataset. The expression values of transcripts were normalized using fragments per kilobase per million fragments, and the StringTie results were extracted.

ATAC-seq analysis

ATAC-seq libraries were sequenced in paired-end mode on the Illumina NovaSeq 6000 sequencing platform. The FastQC program was used to process raw reads for quality control. Trim Galore (v.0.5.0) was used to trim adapter sequences with a minimum base quality of 20. Bowtie 2 (Langmead and Salzberg, 2012) was used to align clean reads to the stem lettuce genome using the parameter “--local”. Reads aligned to chloroplast or mitochondrial sequences were filtered, and significant peaks were then called using MACS2 (Zhang et al., 2008) with the parameters “--nomodel --shift 100 --extsize 200”. The cutoff of the q value was 0.05.

sRNA-seq analyses

Raw data from 21 sRNA-seq libraries were downloaded from the SRA (Supplemental Table 3). The original compressed files were converted into Fastq format using Linux v.2.8.2 with the SRA toolkit. Trim Galore (v.0.5.0) was used to remove adapter sequences with the parameters “–length 18 –max_length 28 –small_rna”. After quality control, the trimmed Fastq files were converted into Fasta format, with identical reads collapsed using an in-house Perl script. Reads that mapped to non-coding RNAs, including tRNA, rRNA, snRNA, and snoRNA sequences in the Rfam database (v.13.0) with ≤1 mismatch, were filtered to reduce the noise in the annotation. The remaining sequences were then mapped to the genome of crisphead lettuce. miRDeep-P2 software (Kuang et al., 2019) was used to detect candidate miRNAs. To annotate miRNAs, all predicted mature miRNA sequences with ±1 nt adjacent nucleotides were aligned to mature miRNAs in PmiREN2.0 (Guo et al., 2020) using Bowtie (1.1.2) (Langmead, 2010) with no more than two mismatches allowed.

Prediction of miRNA target genes

Two suites of plant miRNA target prediction methods, psRNATarget (Dai et al., 2018) and RNAhybrid (Krüger and Rehmsmeier, 2006), were used to predict miRNA target genes. The identified mature lettuce miRNA sequences and transcripts of crisphead lettuce were uploaded to the psRNATarget webserver. The newest default parameters of Schema V2 (2017 release) (Dai et al., 2018) were used, except that the default expectation threshold of 5 was reduced to a more restrictive value of 3. RNAhybrid was used to predict energetically plausible miRNA:mRNA duplexes with plant-specific constraints, as described previously (Xia et al., 2009; Yi et al., 2019). The cutoff value for minimum free energy/minimum duplex energy was 0.70.

The miRNA targets were also predicted using five PARE-seq datasets downloaded from SRA (Supplemental Table 5). Putative miRNA cleavage sites were identified using CleaveLand4 (Addo-Quaye et al., 2009) software at default settings. The reads were aligned to transcript sequences of crisphead lettuce to generate density files. The predicted mature miRNA sequences were aligned to transcript sequences to identify potential miRNA target sites. The density distribution of reads and miRNA–mRNA alignment were used to classify miRNA target candidates. All potential miRNA targets were assigned to one of five categories. Only results from categories 0, 1, and 2 were retained to reduce false positives.

Metabolite profiling

The 50 most representative samples of different types of lettuce were selected based on genetic relationships, geographical origins, and phenotypic differences. These materials were planted in an open field, and leaves were obtained 35 days after transplant (at least three plants per sample). The freeze-dried leaves were crushed using a mixer mill (MM 400, Retsch) with zirconia beads at 30 Hz for 1.5 min. The powder (50 ± 10 mg) was mixed with 1.0 mL of 70% aqueous methanol at 4°C overnight, then centrifuged at 12 000 RPM for 10 min. The extracts were then filtered (SCAA-104, 0.22-μm pore size; MetWare, Wuhan, China) before LC-MS/MS analysis.

An LC-ESI-MS/MS system (HPLC, ExionLC AD UPHLC; MS, Applied Biosystems SCIEX QTRAP 6500; MS, Applied Biosystems SCIEX TripleTOF 6600) was used for LC-MS/MS analysis (Fang and Luo, 2019). The entire analysis procedure was as follows: first, the lettuce-related literature was searched and the literature materials sorted; second, mixed samples were collected to select extreme samples of high-resolution MS data. A preliminary screening of the lettuce metabolites in multi-reaction monitoring mode was conducted based on the MetWare standard database containing 10 000 plant metabolites. Finally, the collected data were integrated to optimize deduplication, thus obtaining the final metabolites.

Gene annotation via InterProScan

InterProScan (5.30) (Jones et al., 2014) was used to identify functional domains in all protein models. Each protein-coding gene was assigned a page with comprehensive information on homologs, family, domains, repeats, and Gene Ontology terms.

Synteny analysis

The synteny analysis was carried out using MCScanX (Wang et al., 2012). First, Fasta and GFF files that included all protein-coding genes from the two genomes were generated. Amino acid sequences of protein-coding genes were then used as a query to search against each other using blastp (Camacho et al., 2009) with an E value of 1e−5. The GFF files and BLAST output files of all protein-coding genes were imported into MCScanX to scan for collinear pairs with default parameters.

Literature collection

Python scripts were implemented to automatically search for “lettuce” in PubMed using BioPython’s Entrez library (Cock et al., 2009). We then manually curated and retained 2286 publications with high relevance to lettuce, which were further classified into 27 types.

Funding

This work was supported by the Beijing Academy of Agriculture and Forestry Sciences (KJCX201907-2 to J.W., KJCX201917 to C.Z., and KJCX20200204 and KJCX20220105 to X.Y.), the Beijing Postdoctoral Research Foundation (2021-ZZ-133 to B.L.), and the National Natural Science Foundation of China (31621001 to X.Y.).

Author contributions

X.Y., X.G., and C.Z. designed the project; Z.G., B.L., Y.T., M.W., D.W., and Z.K. designed and developed the database; F.S., Y.D., and Z.K. performed the genome assembly, genotyping, and miRNAome analyses; B.L. and J.D. collected phenotypic records manually and with phenotyping platforms; Y.Z. and Y.W. provided the epigenetic data; J.W., Y.H., and L.L. provided conceptual advice and comments; X.Y., Z.G., and B.L. wrote the manuscript; and all authors commented on the manuscript.

Acknowledgments

We thank all members of Dr. Yang’s laboratories for their comments and suggestions on this study. A portion of the analysis was performed on the High Performance Computing Platform of the Center for Life Sciences (Peking University). No conflict of interest is declared.

Published: August 12, 2022

Footnotes

Published by the Plant Communications Shanghai Editorial Office in association with Cell Press, an imprint of Elsevier Inc., on behalf of CSPB and CEMPS, CAS.

Supplemental information is available at Plant Communications Online.

Contributor Information

Xinyu Guo, Email: guoxy@nercita.org.cn.

Chunjiang Zhao, Email: zhaocj@nercita.org.cn.

Xiaozeng Yang, Email: yangxiaozeng@baafs.net.cn.

Supplemental information

Document S1. Supplemental Figure 1
mmc1.pdf (63.6KB, pdf)
Supplemental Data 1. Supplemental Tables 1–5
mmc2.xlsx (39.2KB, xlsx)
Document S2. Article plus supplemental information
mmc3.pdf (1.5MB, pdf)

Data availability

LettuceGDB is freely available at https://lettucegdb.com/.

References

  1. Addo-Quaye C., Miller W., Axtell M.J. CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics. 2009;25:130–131. doi: 10.1093/bioinformatics/btn604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Brown J., Pirrung M., McCue L.A. FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool. Bioinformatics. 2017;33:3137–3139. doi: 10.1093/bioinformatics/btx373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Browning B.L., Tian X., Zhou Y., Browning S.R. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 2021;108:1880–1890. doi: 10.1016/j.ajhg.2021.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Buels R., Yao E., Diesh C.M., Hayes R.D., Munoz-Torres M., Helt G., Goodstein D.M., Elsik C.G., Lewis S.E., Stein L., et al. JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol. 2016;17:66. doi: 10.1186/s13059-016-0924-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Camacho C., Coulouris G., Avagyan V., Ma N., Papadopoulos J., Bealer K., Madden T.L. BLAST+: architecture and applications. BMC Bioinf. 2009;10:421. doi: 10.1186/1471-2105-10-421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chin C.S., Peluso P., Sedlazeck F.J., Nattestad M., Concepcion G.T., Clum A., Dunn C., O'Malley R., Figueroa-Balderas R., Morales-Cruz A., et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods. 2016;13:1050–1054. doi: 10.1038/nmeth.4035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chuang L.Y., Cheng Y.H., Yang C.H. Specific primer design for the polymerase chain reaction. Biotechnol. Lett. 2013;35:1541–1549. doi: 10.1007/s10529-013-1249-8. [DOI] [PubMed] [Google Scholar]
  8. Cock P.J.A., Antao T., Chang J.T., Chapman B.A., Cox C.J., Dalke A., Friedberg I., Hamelryck T., Kauff F., Wilczynski B., et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25:1422–1423. doi: 10.1093/bioinformatics/btp163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dai X., Zhuang Z., Zhao P.X. psRNATarget: a plant small RNA target analysis server (2017 release) Nucleic Acids Res. 2018;46:W49–W54. doi: 10.1093/nar/gky316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Daniell H., Rai V., Xiao Y. Cold chain and virus-free oral polio booster vaccine made in lettuce chloroplasts confers protection against all three poliovirus serotypes. Plant Biotechnol. J. 2019;17:1357–1368. doi: 10.1111/pbi.13060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Darqui F.S., Radonic L.M., Beracochea V.C., Hopp H.E., López Bilbao M. Peculiarities of the transformation of Asteraceae family species: the cases of sunflower and lettuce. Front. Plant Sci. 2021;12:767459. doi: 10.3389/fpls.2021.767459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. de Vries I.M. Origin and domestication of Lactuca sativa L. Genet. Resour. Crop Evol. 1997;44:165–174. doi: 10.1023/A:1008611200727. [DOI] [Google Scholar]
  13. Deng Y., Qin Y., Yang P., Du J., Kuang Z., Zhao Y., Wang Y., Li D., Wei J., Guo X., et al. Comprehensive annotation and functional exploration of MicroRNAs in lettuce. Front. Plant Sci. 2021;12:781836. doi: 10.3389/fpls.2021.781836. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Du J., Lu X., Fan J., Qin Y., Yang X., Guo X. Image-based high-throughput detection and phenotype evaluation method for multiple lettuce varieties. Front. Plant Sci. 2020;11:563386. doi: 10.3389/fpls.2020.563386. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dudchenko O., Batra S.S., Omer A.D., Nyquist S.K., Hoeger M., Durand N.C., Shamim M.S., Machol I., Lander E.S., Aiden A.P., et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science. 2017;356:92–95. doi: 10.1126/science.aal3327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Durand N.C., Shamim M.S., Machol I., Rao S.S.P., Huntley M.H., Lander E.S., Aiden E.L. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 2016;3:95–98. doi: 10.1016/j.cels.2016.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fang C., Luo J. Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. Plant J. 2019;97:91–100. doi: 10.1111/tpj.14097. [DOI] [PubMed] [Google Scholar]
  18. Guo Z., Kuang Z., Wang Y., Zhao Y., Tao Y., Cheng C., Yang J., Lu X., Hao C., Wang T., et al. PmiREN: a comprehensive encyclopedia of plant miRNAs. Nucleic Acids Res. 2020;48:D1114–D1121. doi: 10.1093/nar/gkz894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Guo Z., Kuang Z., Zhao Y., Deng Y., He H., Wan M., Tao Y., Wang D., Wei J., Li L., Yang X. PmiREN2.0: from data annotation to functional exploration of plant microRNAs. Nucleic Acids Res. 2021;50:D1475–D1482. doi: 10.1093/nar/gkab811. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gurdon C., Poulev A., Armas I., Satorov S., Tsai M., Raskin I. Genetic and phytochemical characterization of lettuce flavonoid biosynthesis mutants. Sci. Rep. 2019;9:3305. doi: 10.1038/s41598-019-39287-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Harper L., Gardiner J., Andorf C., Lawrence C.J. MaizeGDB: the maize genetics and genomics database. Methods Mol. Biol. 2016;1374:187–202. doi: 10.1007/978-1-4939-3167-5_9. [DOI] [PubMed] [Google Scholar]
  22. Jones P., Binns D., Chang H.Y., Fraser M., Li W., McAnulla C., McWilliam H., Maslen J., Mitchell A., Nuka G., et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–1240. doi: 10.1093/bioinformatics/btu031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kim D., Paggi J.M., Park C., Bennett C., Salzberg S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019;37:907–915. doi: 10.1038/s41587-019-0201-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kim D.E., Shang X., Assefa A.D., Keum Y.S., Saini R.K. Metabolite profiling of green, green/red, and red lettuce cultivars: variation in health beneficial compounds and antioxidant potential. Food Res. Int. 2018;105:361–370. doi: 10.1016/j.foodres.2017.11.028. [DOI] [PubMed] [Google Scholar]
  25. Krüger J., Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34:W451–W454. doi: 10.1093/nar/gkl243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kuang Z., Wang Y., Li L., Yang X. miRDeep-P2: accurate and fast analysis of the microRNA transcriptome in plants. Bioinformatics. 2019;35:2521–2522. doi: 10.1093/bioinformatics/bty972. [DOI] [PubMed] [Google Scholar]
  27. Langmead B. Aligning short sequencing reads with Bowtie. Curr. Protoc. Bioinformatics. 2010 doi: 10.1002/0471250953.bi1107s32. Chapter 11, Unit 11.7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Langmead B., Salzberg S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–3100. doi: 10.1093/bioinformatics/bty191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lindqvist K. On the origin of cultivated lettuce. Hereditas. 1960;46:319–350. doi: 10.1111/j.1601-5223.1960.tb03091.x. [DOI] [Google Scholar]
  31. Luo C., Wang S., Ning K., Chen Z., Wang Y., Yang J., Wang Q. LsAP2 regulates leaf morphology by inhibiting CIN-like TCP transcription factors and repressing LsKAN2 in lettuce. Hortic. Res. 2021;8:184. doi: 10.1038/s41438-021-00622-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Matsui T., Takita E., Oiwa S., Yokoyama A., Kato K., Sawada K. Lettuce-based production of an oral vaccine against porcine edema disease for the seed lot system. Plant Biotechnol. 2021;38:239–246. doi: 10.5511/plantbiotechnology.21.0414a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Park J.S., Kim J.B., Cho K.J., Cheon C.I., Sung M.K., Choung M.G., Roh K.H. Arabidopsis R2R3-MYB transcription factor AtMYB60 functions as a transcriptional repressor of anthocyanin biosynthesis in lettuce (Lactuca sativa) Plant Cell Rep. 2008;27:985–994. doi: 10.1007/s00299-008-0521-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pertea M., Pertea G.M., Antonescu C.M., Chang T.C., Mendell J.T., Salzberg S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015;33:290–295. doi: 10.1038/nbt.3122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Pniewski T., Czyż M., Wyrwa K., Bociąg P., Krajewski P., Kapusta J. Micropropagation of transgenic lettuce containing HBsAg as a method of mass-scale production of standardised plant material for biofarming purposes. Plant Cell Rep. 2017;36:49–60. doi: 10.1007/s00299-016-2056-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Portwood J.L., 2nd, Woodhouse M.R., Cannon E.K., Gardiner J.M., Harper L.C., Schaeffer M.L., Walsh J.R., Sen T.Z., Cho K.T., Schott D.A., et al. MaizeGDB 2018: the maize multi-genome genetics and genomics database. Nucleic Acids Res. 2019;47:D1146–D1154. doi: 10.1093/nar/gky1046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Reyes-Chin-Wo S., Wang Z., Yang X., Kozik A., Arikit S., Song C., Xia L., Froenicke L., Lavelle D.O., Truco M.J., et al. Genome assembly with in vitro proximity ligation data and whole-genome triplication in lettuce. Nat. Commun. 2017;8:14953. doi: 10.1038/ncomms14953. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Seki K., Komatsu K., Tanaka K., Hiraga M., Kajiya-Kanegae H., Matsumura H., Uno Y. A CIN-like TCP transcription factor (LsTCP4) having retrotransposon insertion associates with a shift from Salinas type to Empire type in crisphead lettuce (Lactuca sativa L.) Hortic. Res. 2020;7:15. doi: 10.1038/s41438-020-0241-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Simko I., Atallah A.J., Ochoa O.E., Antonise R., Galeano C.H., Truco M.J., Michelmore R.W. Identification of QTLs conferring resistance to downy mildew in legacy cultivars of lettuce. Sci. Rep. 2013;3:2875. doi: 10.1038/srep02875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Su W., Tao R., Liu W., Yu C., Yue Z., He S., Lavelle D., Zhang W., Zhang L., An G., et al. Characterization of four polymorphic genes controlling red leaf colour in lettuce that have undergone disruptive selection since domestication. Plant Biotechnol. J. 2020;18:479–490. doi: 10.1111/pbi.13213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. van Treuren R., van Eekelen H.D.L.M., Wehrens R., de Vos R.C.H. Metabolite variation in the lettuce gene pool: towards healthier crop varieties and food. Metabolomics. 2018;14:146. doi: 10.1007/s11306-018-1443-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Varshney R.K., Sinha P., Singh V.K., Kumar A., Zhang Q., Bennetzen J.L. 5Gs for crop genetic improvement. Curr. Opin. Plant Biol. 2020;56:190–196. doi: 10.1016/j.pbi.2019.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Walker B.J., Abeel T., Shea T., Priest M., Abouelliel A., Sakthikumar S., Cuomo C.A., Zeng Q., Wortman J., Young S.K., et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9:e112963. doi: 10.1371/journal.pone.0112963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wallace J.G., Rodgers-Melnick E., Buckler E.S. On the Road to Breeding 4.0: Unraveling the Good, the Bad, and the Boring of Crop Quantitative Genomics. Annu. Rev. Genet. 2018;52:421–444. doi: 10.1146/annurev-genet-120116-024846. [DOI] [PubMed] [Google Scholar]
  45. Wang Y., Tang H., Debarry J.D., Tan X., Li J., Wang X., Lee T.H., Jin H., Marler B., Guo H., et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40:e49. doi: 10.1093/nar/gkr1293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wei T., van Treuren R., Liu X., Zhang Z., Chen J., Liu Y., Dong S., Sun P., Yang T., Lan T., et al. Whole-genome resequencing of 445 Lactuca accessions reveals the domestication history of cultivated lettuce. Nat. Genet. 2021;53:752–760. doi: 10.1038/s41588-021-00831-0. [DOI] [PubMed] [Google Scholar]
  47. Xia W., Cao G., Shao N. Progress in miRNA target prediction and identification. Sci. China C Life Sci. 2009;52:1123–1130. doi: 10.1007/s11427-009-0159-4. [DOI] [PubMed] [Google Scholar]
  48. Yang X., Wei S., Liu B., Guo D., Zheng B., Feng L., Liu Y., Tomás-Barberán F.A., Luo L., Huang D. A novel integrated non-targeted metabolomic analysis reveals significant metabolite variations between different lettuce (Lactuca sativa. L) varieties. Hortic. Res. 2018;5:33. doi: 10.1038/s41438-018-0050-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Yi Y., Liu Y., Wu W., Wu K., Zhang W. Reconstruction and analysis of circRNA-miRNA-mRNA network in the pathology of cervical cancer. Oncol. Rep. 2019;41:2209–2225. doi: 10.3892/or.2019.7028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Yu C., Yan C., Liu Y., Liu Y., Jia Y., Lavelle D., An G., Zhang W., Zhang L., Han R., et al. Upregulation of a KN1 homolog by transposon insertion promotes leafy head development in lettuce. Proc. Natl. Acad. Sci. U. S. A. 2020;117:33668–33678. doi: 10.1073/pnas.2019698117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Zhang F.Z., Wagstaff C., Rae A.M., Sihota A.K., Keevil C.W., Rothwell S.D., Clarkson G.J.J., Michelmore R.W., Truco M.J., Dixon M.S., et al. QTLs for shelf life in lettuce co-locate with those for leaf biophysical properties but not with those for leaf developmental traits. J. Exp. Bot. 2007;58:1433–1449. doi: 10.1093/jxb/erm006. [DOI] [PubMed] [Google Scholar]
  52. Zhang L., Qian J., Han Y., Jia Y., Kuang H., Chen J. Alternative splicing triggered by the insertion of a CACTA transposon attenuates LsGLK and leads to the development of pale-green leaves in lettuce. Plant J. 2021 doi: 10.1111/tpj.15563. [DOI] [PubMed] [Google Scholar]
  53. Zhang L., Su W., Tao R., Zhang W., Chen J., Wu P., Yan C., Jia Y., Larkin R.M., Lavelle D., et al. RNA sequencing provides insights into the evolution of lettuce and the regulation of flavonoid biosynthesis. Nat. Commun. 2017;8:2264. doi: 10.1038/s41467-017-02445-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Zhang S., Sang X., Hou D., Chen J., Gu H., Zhang Y., Li J., Yang D., Zhu H., Yang X., et al. Plant-derived RNAi therapeutics: a strategic inhibitor of HBsAg. Biomaterials. 2019;210:83–93. doi: 10.1016/j.biomaterials.2019.04.033. [DOI] [PubMed] [Google Scholar]
  55. Zhang W., Alseekh S., Zhu X., Zhang Q., Fernie A.R., Kuang H., Wen W. Dissection of the domestication-shaped genetic architecture of lettuce primary metabolism. Plant J. 2020;104:613–630. doi: 10.1111/tpj.14950. [DOI] [PubMed] [Google Scholar]
  56. Zhang Y., Liu T., Meyer C.A., Eeckhoute J., Johnson D.S., Bernstein B.E., Nusbaum C., Myers R.M., Brown M., Li W., et al. Model-based analysis of ChIP-seq (MACS) Genome Biol. 2008;9:R137. doi: 10.1186/gb-2008-9-9-r137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zhang Y., Xu S., Cheng Y., Peng Z., Han J. Transcriptome profiling of anthocyanin-related genes reveals effects of light intensity on anthocyanin biosynthesis in red leaf lettuce. PeerJ. 2018;6:e4607. doi: 10.7717/peerj.4607. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Zhang Y.Z., Xu S.Z., Cheng Y.W., Ya H.Y., Han J.M. Transcriptome analysis and anthocyanin-related genes in red leaf lettuce. Genet. Mol. Res. 2016;15 doi: 10.4238/gmr.15017023. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Supplemental Figure 1
mmc1.pdf (63.6KB, pdf)
Supplemental Data 1. Supplemental Tables 1–5
mmc2.xlsx (39.2KB, xlsx)
Document S2. Article plus supplemental information
mmc3.pdf (1.5MB, pdf)

Data Availability Statement

LettuceGDB is freely available at https://lettucegdb.com/.

Articles from Plant Communications are provided here courtesy of Elsevier

RESOURCES