Genomic insights into selection for heterozygous alleles and woody traits in Populus tomentosa

Multi-omics analysis reveals genetic architecture and local adaptation of coumarins metabolites in Populus

BACKGROUND

Accumulation of coumarins plays key roles in response to immune and abiotic stress in plants, but the genetic adaptation basis of controlling coumarins in perennial woody plants remain unclear.

RESULTS

We detected 792 SNPs within 334 genes that were significantly associated with the phenotypic variations of 15 single-metabolic traits and multiple comprehensive index, such as principal components (PCs) of coumarins metabolites. Expression quantitative trait locus mapping uncovered that 337 eQTLs associated with the expression levels of 132 associated genes. Selective sweep revealed 55 candidate genes have potential selective signature among three geographical populations, highlighting that the coumarins biosynthesis have been encountered forceful local adaptation. Furthermore, we constructed a genetic network of seven candidate genes that coordinately regulate coumarins biosynthesis, revealing the multiple regulatory patterns affecting coumarins accumulation in Populus tomentosa. Validation of candidate gene variations in a drought-tolerated population and DUF538 heterologous transformation experiments verified the function of candidate genes and their roles in adapting to the different geographical conditions in poplar.

CONCLUSIONS

Our study uncovered the genetic regulation of the coumarins metabolic biosynthesis of Populus, and offered potential clues for drought-tolerance evaluation and regional improvement in woody plants.

Reference-based genome assembly and comparative genomics of Calamus Brandisii Becc. for unveiling sex-specific genes for early gender detection

Calamus brandisii Becc. is an endangered rattan species indigenous to the Western Ghats of India and used in the furniture and handicraft industries. However, its dioecious nature and longer flowering time pose challenges for conservation efforts. Developing markers for early gender detection in seedlings is crucial for maintaining viable populations for in-situ and ex-situ conservation. Currently, no sex chromosomes or gender-specific genes have been reported in the species. We report the first comprehensive comparative genomics study between the male and female genomes of C. brandisii to identify polymorphisms and potential genes for gender determination. Reference-based assembly was conducted and the male and female genomes were predicted to contain 43,810 and 50,493 protein-coding genes respectively. The haploid genome size was ∼691 Mb and ∼884 Mb for male and female genomes respectively. Comparative analysis revealed significant genetic variation between the two genomes including 619,776 SNPs, 73,659 InDels, 212,123 Structural variants (SVs) and 305 copy number variations (CNVs). A total of 5 male-specific and 11 female-specific genes linked to the sex determining region was predicted. The genomic variants identified between the two genomes could be used in development of markers for early gender identification in C. brandisii for restoration programs. The gender-specific genes identified in this study also provide new insights into the mechanisms of sex determination and differentiation in rattans.

Population genetics analysis of Diospyrosmun A.Chev. ex Lecomte (Ebenaceae) based on EST-SSR markers derived from a novel transcriptome

Diospyrosmun A.Chev. ex Lecomte (Ebenaceae), a native evergreen tree in Vietnam, has important economic and ecological values. The absence of effective and reliable molecular markers has hampered the study of D.mun's genetic diversity and population structure, even though it is an endemic and endangered species. Therefore, significant enrichment of genomic resources is urgently needed to uncover and better understand the genetic architecture of D.mun. This study aims to demonstrate an efficient and reliable tool to explore the polymorphism within D.mun germplasm. It provides a valuable platform for the breeding and conservation of this species and other endangered species worldwide. The Illumina HiSeq™ 4000 sequencing technology was applied for the transcriptomic analysis, genetic differentiation and population structure of D.mun in Vietnam. In this study, the transcriptomes of D.mun were analysed using the Illumina HiSeqTM 4000 sequencing system and a total of 5,588,615,700 base pairs were generated. De novo assembly indicated that 91,134 unigenes were generated (average length = 645.55 bp, N50 = 957 bp, Q20 = 98.08% and Q30 = 94.51%). A total of 92,798 and 21,134 unigenes had significant similarities amongst Nr and Swiss-Prot, respectively. In the GO database, 19,929 unigenes were annotated and these genes were divided into three major categories and 50 subcategories. In the KOG analysis, 18,499 unigenes were annotated and divided into 25 gene function categories. In the KEGG analysis, 12,017 unigenes were annotated. According to the related pathways involved, they could be classified into 56 subclasses. In this study, we have identified a total of 9,391 EST-SSR markers. Ten microsatellite loci were employed to assess the genetic diversity and structure of 82 adult D.mun trees across three populations in Vietnam. The results indicated moderate levels of genetic diversity with PIC = 0.77, NA = 3.9, NE = 2.8, Ho = 0.56 and HE = 0.58 and the fixation index value was recorded as positive for three populations (NS, NH and CP). Genetic differentiation among populations was low (FST = 0.045), suggesting limited gene flow (Nm = 5.34). This result indicates gene exchange between the populations of ancient D.mun from different geographical areas and regions. The analysis of molecular variance (AMOVA) showed that high genetic variation existed within individuals (91%) compared to amongst populations (4%). Genetic structure analysis, DAPC and the NJ tree indicated that the three populations were divided into three main clusters. With this study, we provide a molecular resoureces for the breeding and conservation of D.mun.

Rare variations within the serine/arginine-rich splicing factor PtoRSZ21 modulate stomatal size to determine drought tolerance in Populus

Rare variants contribute significantly to the 'missing heritability' of quantitative traits. The genome-wide characteristics of rare variants and their roles in environmental adaptation of woody plants remain unexplored. Utilizing genome-wide rare variant association study (RVAS), expression quantitative trait loci (eQTL) mapping, genetic transformation, and molecular experiments, we explored the impact of rare variants on stomatal morphology and drought adaptation in Populus. Through comparative analysis of five world-wide Populus species, we observed the influence of mutational bias and adaptive selection on the distribution of rare variants. RVAS identified 75 candidate genes correlated with stomatal size (SS)/stomatal density (SD), and a rare haplotype in the promoter of serine/arginine-rich splicing factor PtoRSZ21 emerged as the foremost association signal governing SS. As a positive regulator of drought tolerance, PtoRSZ21 can recruit the core splicing factor PtoU1-70K to regulate alternative splicing (AS) of PtoATG2b (autophagy-related 2). The rare haplotype PtoRSZ21hap2 weakens binding affinity to PtoMYB61, consequently affecting PtoRSZ21 expression and SS, ultimately resulting in differential distribution of Populus accessions in arid and humid climates. This study enhances the understanding of regulatory mechanisms that underlie AS induced by rare variants and might provide targets for drought-tolerant varieties breeding in Populus.

Systems genetic analysis of lignin biosynthesis in Populus tremula

The genetic control underlying natural variation in lignin content and composition in trees is not fully understood. We performed a systems genetic analysis to uncover the genetic regulation of lignin biosynthesis in a natural 'SwAsp' population of aspen (Populus tremula) trees. We analyzed gene expression by RNA sequencing (RNA-seq) in differentiating xylem tissues, and lignin content and composition using Pyrolysis-GC-MS in mature wood of 268 trees from 99 genotypes. Abundant variation was observed for lignin content and composition, and genome-wide association study identified proteins in the pentose phosphate pathway and arabinogalactan protein glycosylation among the top-ranked genes that are associated with these traits. Variation in gene expression and the associated genetic polymorphism was revealed through the identification of 312 705 local and 292 003 distant expression quantitative trait loci (eQTL). A co-expression network analysis suggested modularization of lignin biosynthesis and novel functions for the lignin-biosynthetic CINNAMYL ALCOHOL DEHYDROGENASE 2 and CAFFEOYL-CoA O-METHYLTRANSFERASE 3. PHENYLALANINE AMMONIA LYASE 3 was co-expressed with HOMEOBOX PROTEIN 5 (HB5), and the role of HB5 in stimulating lignification was demonstrated in transgenic trees. The systems genetic approach allowed linking natural variation in lignin biosynthesis to trees´ responses to external cues such as mechanical stimulus and nutrient availability.

Genomic and transcriptome analyses reveal potential contributors to erucic acid biosynthesis in seeds of rapeseed (Brassica napus)

KEY MESSAGE

Through comprehensive genomic and transcriptomic analyses, we identified a set of 23 genes that act up- or downstream of erucic acid content (EAC) production in rapeseed seeds. We selected example genes to showcase the distribution of single nucleotide polymorphisms, haplotypes associated with EAC phenotypes, and the creation of molecular markers differentiating low EAC and high EAC genotypes. Erucic acid content (EAC) is a crucial trait in rapeseed, with low LEAC oil recognized for its health benefits and high EA oil holding industrial value. Despite its significance, the genomic consequences of intensive LEAC-cultivar selection and the genetic basis underlying EA regulation remain largely unexplored. To address this knowledge gap, we conducted selective signal analyses, genome-wide association studies (GWAS), and transcriptome analyses. Our investigation unveiled the genetic footprints resulting from LEAC selection in germplasm populations, drawing attention to specific loci that contribute to enriching diversity. By integrating GWAS and transcriptome analyses, we identified a set of 23 genes that play a significant role in determining EAC in seeds or are downstream consequences of EA-level alterations. These genes have emerged as promising candidates for elucidating the potential mechanisms governing EAC in rapeseed. To exemplify the findings, we selected specific genes to demonstrate the distribution of single nucleotide polymorphisms and haplotypes associated with different EAC phenotypes. Additionally, we showcased to develop molecular markers distinguishing between LEAC and high EAC genotypes.

Temporal dynamics of genetic architecture governing leaf development in Populus

Leaf development is a multifaceted and dynamic process orchestrated by a myriad of genes to shape the proper size and morphology. The dynamic genetic network underlying leaf development remains largely unknown. Utilizing a synergistic genetic approach encompassing dynamic genome-wide association study (GWAS), time-ordered gene co-expression network (TO-GCN) analyses and gene manipulation, we explored the temporal genetic architecture and regulatory network governing leaf development in Populus. We identified 42 time-specific and 18 consecutive genes that displayed different patterns of expression at various time points. We then constructed eight TO-GCNs that covered the cell proliferation, transition, and cell expansion stages of leaf development. Integrating GWAS and TO-GCN, we postulated the functions of 27 causative genes for GWAS and identified PtoGRF9 as a key player in leaf development. Genetic manipulation via overexpression and suppression of PtoGRF9 revealed its primary influence on leaf development by modulating cell proliferation. Furthermore, we elucidated that PtoGRF9 governs leaf development by activating PtoHB21 during the cell proliferation stage and attenuating PtoLD during the transition stage. Our study provides insights into the dynamic genetic underpinnings of leaf development and understanding the regulatory mechanism of PtoGRF9 in this dynamic process.

PPGR: a comprehensive perennial plant genomes and regulation database

Perennial woody plants hold vital ecological significance, distinguished by their unique traits. While significant progress has been made in their genomic and functional studies, a major challenge persists: the absence of a comprehensive reference platform for collection, integration and in-depth analysis of the vast amount of data. Here, we present PPGR (Resource for Perennial Plant Genomes and Regulation; https://ngdc.cncb.ac.cn/ppgr/) to address this critical gap, by collecting, integrating, analyzing and visualizing genomic, gene regulation and functional data of perennial plants. PPGR currently includes 60 species, 847 million protein-protein/TF (transcription factor)-target interactions, 9016 transcriptome samples under various environmental conditions and genetic backgrounds. Noteworthy is the focus on genes that regulate wood production, seasonal dormancy, terpene biosynthesis and leaf senescence representing a wealth of information derived from experimental data, literature mining, public databases and genomic predictions. Furthermore, PPGR incorporates a range of multi-omics search and analysis tools to facilitate browsing and application of these extensive datasets. PPGR represents a comprehensive and high-quality resource for perennial plants, substantiated by an illustrative case study that demonstrates its capacity in unraveling gene functions and shedding light on potential regulatory processes.

Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch

Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37-0.39 vs. 0.14-0.25), and RKHS was more suitable for traits of type II (PAs = 0.23-0.37 vs. 0.07-0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traits and lay the foundation for the development of genomic selection strategies in Japanese larch.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.