Integration of multi-omics technologies for crop improvement: Status and prospects

The role of omics in improving the orphan crop tef

Tef or teff [Eragrostis tef (Zucc.) Trotter] is a cereal crop indigenous to the Horn of Africa, where it is a staple food for a large population. The popularity of tef arises from its resilience to environmental stresses and its nutritional value. For many years, tef has been considered an orphan crop, but recent research initiatives from across the globe are helping to unravel its undisclosed potential. Advanced omics tools and techniques have been directed toward the exploration of tef's diversity with the aim of increasing its productivity. In this review, we report on the most recent advances in tef omics that brought the crop into the spotlight of international research.

Mapping and Omics Integration: Towards Precise Rice Disease Resistance Breeding

Rice (Oryza sativa), as a staple crop feeding a significant portion of the global population, particularly in Asian countries, faces constant threats from various diseases jeopardizing global food security. A precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain an accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. The integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with a better understanding of their functional roles in disease resistance. The improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because a better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to a more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can result in a transformative impact on rice breeding for enhancing disease resistance.

Multi-Omics Analysis Reveals the Distinct Features of Metabolism Pathways Supporting the Fruit Size and Color Variation of Giant Pumpkin

Pumpkin (Cucurbita maxima) is an important vegetable crop of the Cucurbitaceae plant family. The fruits of pumpkin are often used as directly edible food or raw material for a number of processed foods. In nature, mature pumpkin fruits differ in size, shape, and color. The Atlantic Giant (AG) cultivar has the world's largest fruits and is described as the giant pumpkin. AG is well-known for its large and bright-colored fruits with high ornamental and economic value. At present, there are insufficient studies that have focused on the formation factors of the AG cultivar. To address these knowledge gaps, we performed comparative transcriptome, proteome, and metabolome analysis of fruits from the AG cultivar and a pumpkin with relatively small fruit (Hubbard). The results indicate that up-regulation of gene-encoded expansins contributed to fruit cell expansion, and the increased presence of photoassimilates (stachyose and D-glucose) and jasmonic acid (JA) accumulation worked together in terms of the formation of large fruit in the AG cultivar. Notably, perhaps due to the rapid transport of photoassimilates, abundant stachyose that was not converted into glucose in time was detected in giant pumpkin fruits, implying that a unique mode of assimilate unloading is in existence in the AG cultivar. The potential molecular regulatory network of photoassimilate metabolism closely related to pumpkin fruit expansion was also investigated, finding that three MYB transcription factors, namely CmaCh02G015900, CmaCh01G018100, and CmaCh06G011110, may be involved in metabolic regulation. In addition, neoxanthin (a type of carotenoid) exhibited decreased accumulation that was attributed to the down-regulation of carotenoid biosynthesis genes in AG fruits, which may lead to pigmentation differences between the two pumpkin cultivars. Our current work will provide new insights into the potential formation factors of giant pumpkins for further systematic elucidation.

Comparative genomics and bioinformatics approaches revealed the role of CC-NBS-LRR genes under multiple stresses in passion fruit

Passion fruit is widely cultivated in tropical, subtropical regions of the world. The attack of bacterial and fungal diseases, and environmental factors heavily affect the yield and productivity of the passion fruit. The CC-NBS-LRR (CNL) gene family being a subclass of R-genes protects the plant against the attack of pathogens and plays a major role in effector-triggered immunity (ETI). However, no information is available regarding this gene family in passion fruit. To address the underlying problem a total of 25 and 21 CNL genes have been identified in the genome of purple (Passiflora edulis Sims.) and yellow (Passiflora edulis f. flavicarpa) passion fruit respectively. Phylogenetic tree was divided into four groups with PeCNLs present in 3 groups only. Gene structure analysis revealed that number of exons ranged from 1 to 9 with 1 being most common. Most of the PeCNL genes were clustered at the chromosome 3 and underwent strong purifying selection, expanded through segmental (17 gene pairs) and tandem duplications (17 gene pairs). PeCNL genes contained cis-elements involved in plant growth, hormones, and stress response. Transcriptome data indicated that PeCNL3, PeCNL13, and PeCNL14 were found to be differentially expressed under Cucumber mosaic virus and cold stress. Three genes were validated to be multi-stress responsive by applying Random Forest model of machine learning. To comprehend the biological functions of PeCNL proteins, their 3D structure and gene ontology (GO) enrichment analysis were done. Our research analyzed the CNL gene family in passion fruit to understand stress regulation and improve resilience. This study lays the groundwork for future investigations aimed at enhancing the genetic composition of passion fruit to ensure robust growth and productivity in challenging environments.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Genomic data integration tutorial, a plant case study

BACKGROUND

The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.

RESULTS

To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.

CONCLUSION

The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Green Biologics: Harnessing the Power of Plants to Produce Pharmaceuticals

Plants are increasingly used for the production of high-quality biological molecules for use as pharmaceuticals and biomaterials in industry. Plants have proved that they can produce life-saving therapeutic proteins (Elelyso™-Gaucher's disease treatment, ZMapp™-anti-Ebola monoclonal antibodies, seasonal flu vaccine, Covifenz™-SARS-CoV-2 virus-like particle vaccine); however, some of these therapeutic proteins are difficult to bring to market, which leads to serious difficulties for the manufacturing companies. The closure of one of the leading companies in the sector (the Canadian biotech company Medicago Inc., producer of Covifenz) as a result of the withdrawal of investments from the parent company has led to the serious question: What is hindering the exploitation of plant-made biologics to improve health outcomes? Exploring the vast potential of plants as biological factories, this review provides an updated perspective on plant-derived biologics (PDB). A key focus is placed on the advancements in plant-based expression systems and highlighting cutting-edge technologies that streamline the production of complex protein-based biologics. The versatility of plant-derived biologics across diverse fields, such as human and animal health, industry, and agriculture, is emphasized. This review also meticulously examines regulatory considerations specific to plant-derived biologics, shedding light on the disparities faced compared to biologics produced in other systems.

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.

The chromosome-scale reference genome and transcriptome analysis of Solanum torvum provides insights into resistance to root-knot nematodes

Solanum torvum (Swartz) (2n = 24) is a wild Solanaceae plant with high economic value that is used as a rootstock in grafting for Solanaceae plants to improve the resistance to a soil-borne disease caused by root-knot nematodes (RKNs). However, the lack of a high-quality reference genome of S. torvum hinders research on the genetic basis for disease resistance and application in horticulture. Herein, we present a chromosome-level assembly of genomic sequences for S. torvum combining PacBio long reads (HiFi reads), Illumina short reads and Hi-C scaffolding technology. The assembled genome size is ~1.25 Gb with a contig N50 and scaffold N50 of 38.65 Mb and 103.02 Mb, respectively as well as a BUSCO estimate of 98%. GO enrichment and KEGG pathway analysis of the unique S. torvum genes, including NLR and ABC transporters, revealed that they were involved in disease resistance processes. RNA-seq data also confirmed that 48 NLR genes were highly expressed in roots and fibrous roots and that three homologous NLR genes (Sto0288260.1, Sto0201960.1 and Sto0265490.1) in S. torvum were significantly upregulated after RKN infection. Two ABC transporters, ABCB9 and ABCB11 were identified as the hub genes in response to RKN infection. The chromosome-scale reference genome of the S. torvum will provide insights into RKN resistance.

Integrated multi-locus genome-wide association studies and transcriptome analysis for seed yield and yield-related traits in Brassica napus

Rapeseed (Brassica napus L.), the third largest oil crop, is an important source of vegetable oil and biofuel for the world. Although the breeding and yield has been improved, rapeseed still has the lowest yield compared with other major crops. Thus, increasing rapeseed yield is essential for the high demand of vegetable oil and high-quality protein for live stocks. Silique number per plant (SN), seed per pod (SP), and 1000-seed weight (SW) are the three important factors for seed yield in rapeseed. Some yield-related traits, including plant height (PH), flowering time (FT), primary branch number (BN) and silique number per inflorescence (SI) also affect the yield per plant (YP). Using six multi-locus genome-wide association study (ML-GWAS) approaches, a total of 908 yield-related quantitative trait nucleotides (QTNs) were identified in a panel consisting of 403 rapeseed core accessions based on whole-genome sequencing. Integration of ML-GWAS with transcriptome analysis, 79 candidate genes, including BnaA09g39790D (RNA helicase), BnaA09g39950D (Lipase) and BnaC09g25980D (SWEET7), were further identified and twelve genes were validated by qRT-PCRs to affect the SW or SP in rapeseed. The distribution of superior alleles from nineteen stable QTNs in 20 elite rapeseed accessions suggested that the high-yielding accessions contained more superior alleles. These results would contribute to a further understanding of the genetic basis of yield-related traits and could be used for crop improvement in B. napus.