Multi-omics Database Research Articles

A multi-omics database for the biological study of Osmanthus fragrans

Osmanthus fragrans Lour. is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds. To fully utilize O. fragrans resources, we established an O. fragrans multi-omics database called the O. fragrans Information Resource (OfIR: http://yanglab.hzau.edu.cn/OfIR/home/). OfIR is a convenient and comprehensive multi-omics database that efficiently integrates phenotype and genetic variation from 127 O. fragrans cultivars, and provides many easy-to-use analysis tools, including primer design, sequence extraction, multi-sequence alignment, GO and KEGG enrichment analysis, variation annotation, and electronic PCR. Two case studies were used to demonstrate its power to mine candidate genetic variation sites or genes associated with specific traits or regulatory networks. In summary, the multi-omics database OfIR provides a convenient and user-friendly platform for researchers in mining functional genes and contributes to the genetic breeding of O. fragrans.

Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.).

Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein-protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix-loop-helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.

1294: A Multi-Omics Database Using Clinical Trial Data for Rectal Cancer Radiotherapy Outcome Prediction

1294: A Multi-Omics Database Using Clinical Trial Data for Rectal Cancer Radiotherapy Outcome Prediction

Abstract Mo078: Identifying Genetic Determinants of Phytosterol Levels: A Genome-Wide Association and Meta-Analysis Study Unveils a New Locus Influencing Campesterol Serum Concentrations

Background: Sitosterolemia, a rare inherited disorder, stems from mutations in the ABCG5/ABCG8 genes, which play a critical role in plant sterol transport. These genetic anomalies result in reduced phytosterol excretion, causing an accumulation in the body that can lead to serious health issues, such as premature coronary artery disease. Hypothesis: The study aimed to uncover the genetic determinants influencing plant sterol levels within the Middle East/North Africa (MENA) population, focusing on individuals from Qatar. It hypothesized that unique genetic variations might influence these levels, contributing to the development of sitosterolemia. Aims: To conduct the first genome-wide association study (GWAS) in the MENA region to identify genetic variants affecting plant sterol levels in the Qatari population, and to compare these findings with those from populations of European ancestry. Methods: The study utilized the Metabolon platform for analyzing serum levels of β-sitosterol and campesterol, combined with genome sequence data from the Qatar Genome Program. A trans-ancestry meta-analysis compared our findings with those from a large European cohort, while conditional analysis helped identify specific single nucleotide polymorphisms (SNPs) associated with these sterols. Results: The research identified two independent SNPs related to β-sitosterol ((rs145164937 and rs4299376) and two to campesterol (rs7598542 and rs75901165) in the Qatari population, alongside previously reported variants ( P <5.0 ×10 -8 ) .Notably, one variant (rs75901165) was located in the Intraflagellar Transport 43 (IFT43) gene, diverging from the common ABCG5/8 locus. The study also supported the association of variants in the SCARB1 and ABO genes with sitosterolemia and assessed a polygenic risk score's efficacy based on European GWAS data in the Qatari cohort. Conclusion(s): The findings underscore the significance of conducting population-specific genetic research to understand better and manage conditions like sitosterolemia. This research highlights the importance of personalized treatment approaches for hyperlipidemia and the value of establishing comprehensive multi-omics databases tailored to specific populations.

Analysis of the interlink between glucose-6-phosphate dehydrogenase (G6PD) and lung cancer through multi-omics databases

Glucose-6-Phosphate Dehydrogenase (G6PD) is a crucial enzyme that executes the pentose phosphate pathway. Due to its critical nodal position in the metabolic network, it is associated with different forms of cancer tumorigeneses and progression. Nonetheless, its functional role and molecular mechanism in lung cancer remain unknown. The present study provides intricate information associated with G6PD and Lung Cancer. Varieties of public datasets were retrieved by us, including UALCAN, TCGA, cBioPortal, and the UCSC Xena browser. The data obtained were used to assess the expression of G6PD, its clinical features, epigenetic regulation, relationship with tumour infiltration, tumour mutation burden, microsatellite instability, tumour microenvironment, immune checkpoint genes, genomic alteration, and patient's overall survival rate. The present study revealed that the G6PD expression was correlated with the clinical features of lung cancer including disease stage, race, sex, age, smoking habits, and lymph node metastasis. Moreover, the expression profile of G6PD also imparts epigenetic changes by modulating the DNA promoter methylation activity. Methylation of promoters changes the expression of various transcription factors, genes leading to an influence on the immune system. These events linked with G6PD-related mutational gene alterations (FAM3A, LAG3, p53, KRAS). The entire circumstance influences the patient's overall survival rate and poor prognosis. Functional investigation using STRING, GO, and KEGG found that G6PD primarily engages in hallmark functions (metabolism, immunological responses, proliferation, apoptosis, p53, HIF-1, FOXO, PI3K-AKT signaling). This work provides a wide knowledge of G6PD's function in lung cancer, as well as a theoretical foundation for possible prognostic therapeutic markers.

A near-complete cucumber reference genome assembly and Cucumber-DB, a multi-omics database

A near-complete cucumber reference genome assembly and Cucumber-DB, a multi-omics database

RNA-binding protein transcripts as potential biomarkers for detecting Primary Sclerosing Cholangitis and for predicting its progression to Cholangiocarcinoma.

Primary Sclerosing Cholangitis (PSC) is a persistent inflammatory liver condition that affects the bile ducts and is commonly diagnosed in young individuals. Despite efforts to incorporate various clinical, biochemical and molecular parameters for diagnosing PSC, it remains challenging, and no biomarkers characteristic of the disease have been identified hitherto. PSC is linked with an uncertain prognosis, and there is a pressing need to explore multiomics databases to establish a new biomarker panel for the early detection of PSC's gradual progression into Cholangiocarcinoma (CCA) and for the development of effective therapeutic interventions. Apart from non-coding RNAs, other components of the Ribonucleoprotein (RNP) complex, such as RNA-Binding Proteins (RBPs), also hold great promise as biomarkers due to their versatile expression in pathological conditions. In the present review, an update on the RBP transcripts that show dysregulated expression in PSC and CCA is provided. Moreover, by utilizing a bioinformatic data mining approach, we give insight into those RBP transcripts that also exhibit differential expression in liver and gall bladder, as well as in body fluids, and are promising as biomarkers for diagnosing and predicting the prognosis of PSC. Expression data were bioinformatically extracted from public repositories usingTCGA Bile Duct Cancer dataset for CCA and specific NCBI GEO datasets for both PSC and CCA; more specifically, RBPs annotations were obtained from RBP World database. Interestingly, our comprehensive analysis shows an elevated expression of the non-canonical RBPs, FANCD2, as well as the microtubule dynamics regulator, ASPM, transcripts in the body fluids of patients with PSC and CCA compared with their respective controls, with the same trend in expression being observed in gall bladder and liver cancer tissues. Consequently, the manipulation of tissue expression of RBP transcripts might be considered as a strategy to mitigate the onset of CCA in PSC patients, and warrants further experimental investigation. The analysis performed herein may be helpful in the identification of non-invasive biomarkers for the early detection of PSC and for predicting its progression into CCA. In conclusion, future clinical research should investigate in more depth the full potential of RBP transcripts as biomarkers for human pathologies.

Abstract PO1-14-06: Integrated analysis reveals the impact of obesity on triple-negative breast cancer

Abstract Background: Obesity and overweight status, which has been growing rapidly over the past few decades, is considered as a risk factor for many types of cancers including breast cancer. Despite the multi-omics profile of triple-negative breast cancer (TNBC) has been comprehensively characterized, the impact of obesity on molecular features of TNBC is not fully appreciated. Methods: We applied an integrative analysis on clinicopathological data and molecular data (including genomic, transcriptomic, proteomic and metabolomic profiling) using the multi-omics database of TNBC (N = 465) from Fudan University Shanghai Cancer Center (FUSCC) for associations with patient body mass index (BMI). Patients were categorized into overweight/obese (OW/OB, BMI ≥ 24 kg/m2) and normal (NL, BMI < 24 kg/m2) group according to the Chinese criteria of BMI. The clinical and molecular differences between OW/OB and NL patients were systematically explored. We also constructed high-fat diet (HFD)-induced obese mouse tumor models and used single-cell RNA sequencing to investigate the impact of obesity on the tumor microenvironment. Furthermore, we analyzed the efficacy of anti-PD-1 immunotherapy on TNBC tumors in both obese and normal mice. Results: OW/OB patients exhibited higher proportion of metabolic syndrome, more adipose tissue in the breast and worse survival than NL patients. Among most frequently mutated genes, OBSCN showed statistically significantly less mutated in the OW/OB group (3.2% vs 9.6%), while TP53 (68.3% vs 76.9%) and PIK3CA (21.4% vs 14.1%) had tendency to be different. In terms of copy number alterations, we found OW/OB patients had a higher amplified or gained frequency of 13q14.11 (FOXO1) and a lower frequency of deletion or loss of chromosomal region 7p22.1 (FOXK1). We further dissect the expression profile of TNBC. Differentially expressed gene analysis and pathway enrichment analysis demonstrated that immune and metabolic pathways were the major distinction between OW/OB and NL tumors. OW/OB tumors were characterized with elevated inflammation of tumor microenvironment, as well as higher expression of immune checkpoints. Moreover, analyses focusing on metabolic heterogeneity using transcriptomic, proteomic and metabolomic data revealed upregulation of lipid metabolism and reactive oxygen species pathway in OW/OB group. In addition, our in vivo experiments demonstrated that TNBC in the obese mice displayed faster growth rates. Flow cytometry analysis and single-cell RNA sequencing showed that higher proportion of immunosuppressive myeloid cells and exhausted CD8+ T cells and upregulation of lipid metabolism in HFD group. Applying anti-PD-1 immunotherapy in both obese and normal mice displayed that tumors in the obese mice showed more sensitive to anti-PD-1 immunotherapy. Conclusion: Our study systematically revealed that obesity might play a significant role in the molecular heterogeneity of TNBC and showed distinct sensitivities to immunotherapy, which should be taken in account in the field of precision medicine. Keywords: triple-negative breast cancer, obesity, immune, tumor microenvironment, metabolism Citation Format: Yue Gong, Peng Ji, Huai-liang Wu, Li-Hua He, Ming-Liang Jin, Xin Hu, Yi-Zhou Jiang, Zhiming Shao. Integrated analysis reveals the impact of obesity on triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-14-06.

BjuIR: A multi-omics database with various tools for accelerating functional genomics research in Brassica juncea

BjuIR: A multi-omics database with various tools for accelerating functional genomics research in Brassica juncea

Yak genome database: a multi-omics analysis platform

BackgroundThe yak (Bos grunniens) is a large ruminant species that lives in high-altitude regions and exhibits excellent adaptation to the plateau environments. To further understand the genetic characteristics and adaptive mechanisms of yak, we have developed a multi-omics database of yak including genome, transcriptome, proteome, and DNA methylation data.DescriptionThe Yak Genome Database (http://yakgenomics.com/) integrates the research results of genome, transcriptome, proteome, and DNA methylation, and provides an integrated platform for researchers to share and exchange omics data. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The genome module provides access to yak genome sequences, gene annotations and variant information. The transcriptome module offers transcriptome data from various tissues of yak and cattle strains at different developmental stages. The proteome module presents protein profiles from diverse yak organs. Additionally, the DNA methylation module shows the DNA methylation information at each base of the whole genome. Functions of data downloading and browsing, functional gene exploration, and experimental practice were available for the database.ConclusionThis comprehensive database provides a valuable resource for further investigations on development, molecular mechanisms underlying high-altitude adaptation, and molecular breeding of yak.

LettuceDB: an integrated multi-omics database for cultivated lettuce.

Crop genomics has advanced rapidly during the past decade, which generated a great abundance of omics data from multi-omics studies. How to utilize the accumulating data becomes a critical and urgent demand in crop science. As an attempt to integrate multi-omics data, we developed a database, LettuceDB (https://db.cngb.org/lettuce/), aiming to assemble multidimensional data for cultivated and wild lettuce germplasm. The database includes genome, variome, phenome, microbiome and spatial transcriptome. By integrating user-friendly bioinformatics tools, LettuceDB will serve as a one-stop platform for lettuce research and breeding in the future. Database URL: https://db.cngb.org/lettuce/.

The Prognostic Importance of Ki-67 in Gastrointestinal Carcinomas: A Meta-analysis and Multi-omics Approach.

This study aimed to determine if Ki-67, a commonly used marker to measure tumor proliferation, is a reliable prognostic factor in various types of gastrointestinal (GI) cancers based on current high-quality multivariable evidence. A comprehensive search was conducted in PubMed, Embase, Scopus, and ISI Web of Science databases to investigate the association between Ki-67 positivity and overall survival (OS) and disease/recurrence-free survival (DFS/RFS) in GI cancers. Heterogeneity was assessed using Chi-square-based Q and I2 analyses and publication bias using funnel plots and Egger's analysis. In addition, Ki-67 levels in different GI cancers were examined by different platforms. The prognostic capability of Ki-67, gene ontology (GO), and pathway enrichment analysis were obtained from GEPIA2 and STRING. Totally, 61 studies, involving 13,034 patients, were deemed eligible for our evaluation. The combined hazard ratios (HRs) demonstrated the prediction ability of overexpressed Ki-67 for a worse OS (HR: 1.67, P < 0.001; HR: 1.37, P = 0.021) and DFS/RFS (HR: 2.06, P < 0.001) in hepatocellular and pancreatic malignancies, respectively, as confirmed by multi-omics databases. However, similar correlation was not found in esophageal, gastric, and colorectal cancers. Furthermore, most of the associations were identified to be robust based on different subcategories and publication bias assessment. Finally, enriched Ki-67-related genes were found to be involved in various important signaling pathways, such as cell cycle, P53 signaling network, and DNA damage responses. This study supports that Ki-67 can serve as an independent prognostic biomarker for pancreatic and hepatocellular malignancies in clinical settings.

From genomic spectrum of NTRK genes to adverse effects of its inhibitors, a comprehensive genome-based and real-world pharmacovigilance analysis.

Introduction: The discovery of neurotrophic tyrosine receptor kinase (NTRK) gene fusions has facilitated the development of precision oncology. Two first-generation NTRK inhibitors (larotrectinib and entrectinib) are currently approved for the treatment of patients with solid tumors harboring NTRK gene fusions. Nevertheless, comprehensive NTRK profiling at the pan-cancer genomic level and real-world studies pertaining to the adverse events of NTRK inhibitors are lacking. Methods: We characterize the genome of NTRK at the pan-cancer level through multi-omics databases such as The Cancer Genome Atlas (TCGA). Through the FDA Adverse Event Reporting System (FAERS) database, we collect reports of entrectinib and larotrectinib-induced adverse events and perform a pharmacovigilance analysis using various disproportionality methods. Results: NTRK1/2/3 expression is lower in most tumor tissues, while they have higher methylation levels. NTRK gene expression has prognostic value in some cancer types, such as breast invasive carcinoma (BRCA). The cancer type with highest NTRK alteration frequency is skin cutaneous melanoma (SKCM) (31.98%). Thyroid carcinoma (THCA) has the largest number of NTRK fusion cases, and the most common fusion pair is ETV6-NTRK3. Adverse drug events (ADEs) obtained from the FAERS database for larotrectinib and entrectinib are 524 and 563, respectively. At the System Organ Class (SOC) level, both drugs have positive signal value for "nervous system disorder". Other positive signals for entrectinib include "cardiac disorders", "metabolism and nutrition disorders", while for larotrectinib, it is "hepatobiliary disorders". The unexpected signals are also listed in detail. ADEs of the two NTRK inhibitors mainly occur in the first month. The median onset time of ADEs for entrectinib and larotrectinib was 16 days (interquartile range [IQR] 6-86.5) and 44 days ([IQR] 7-136), respectively. Conclusion: Our analysis provides a broad molecular view of the NTRK family. The real-world adverse drug event analysis of entrectinib and larotrectinib contributes to more refined medication management.

MODMS: a multi-omics database for facilitating biological studies on alfalfa (Medicago sativa L.).

Alfalfa (Medicago sativa L.) is a globally important forage crop. It also serves as a vegetable and medicinal herb because of its excellent nutritional quality and significant economic value. Multi-omics data on alfalfa continue to accumulate owing to recent advances in high-throughput techniques, and integrating this information holds great potential for expediting genetic research and facilitating advances in alfalfa agronomic traits. Therefore, we developed a comprehensive database named MODMS (multi-omics database of M. sativa) that incorporates multiple reference genomes, annotations, comparative genomics, transcriptomes, high-quality genomic variants, proteomics, and metabolomics. This report describes our continuously evolving database, which provides researchers with several convenient tools and extensive omics data resources, facilitating the expansion of alfalfa research. Further details regarding the MODMS database are available at https://modms.lzu.edu.cn/.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding.

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibilityand chromosomal interaction, as well as genetic variation data from 24501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

CfOmics: a cell-free multi-Omics database for diseases.

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

Emphasizing the Role of Multi-omics Approach to Increase Survival Rate of Breast and Prostate Cancer Patients

The understanding of cancer biology has greatly advanced since the advent of genomics. A remarkable heterogeneity at the whole-genome (or omics) level exists amongst even histologically comparable cancers, demonstrating the enormous complexity of the cancer genome. A powerful resource that has the potential to translate high-throughput omics to better and quick overall survival is the massive accrual and public accessibility of multi-omics databases with accompanying clinical annotation, including tumor histology, patient response, and outcome. In this new era of high-throughput omics, this paper emphasizes the distinct benefits of a multidimensional approach to genomic analysis. It discusses the implications of translational omics research for the cancer population. Single-level data analysis of high-throughput technologies has constraints because it only displays a small window of cellular processes. Understanding the links across several cellular organization levels made possible by data integration across various platforms, including genomes, epigenomics, transcriptomics, proteomics, and metabolomics, is important. This review examines a few popular frameworks for integrating multi-omics data. It provides a general overview of multi-omics applications in tumor classification, prognosis, diagnostics, and the function of data integration in searching for novel biomarkers and treatment options.

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada

Abstract. The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at https://doi.org/10.5281/zenodo.8002909 (Aslam et al., 2023a) and https://doi.org/10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively.

Milletdb: a multi-omics database to accelerate the research of functional genomics and molecular breeding of millets.

Millets are a class of nutrient-rich coarse cereals with high resistance to abiotic stress; thus, they guarantee food security for people living in areas with extreme climatic conditions and provide stress-related genetic resources for other crops. However, no platform is available to provide a comprehensive and systematic multi-omics analysis for millets, which seriously hinders the mining of stress-related genes and the molecular breeding of millets. Here, a free, web-accessible, user-friendly millets multi-omics database platform (Milletdb, http://milletdb.novogene.com) has been developed. The Milletdb contains six millets and their one related species genomes, graph-based pan-genomics of pearl millet, and stress-related multi-omics data, which enable Milletdb to be the most complete millets multi-omics database available. We stored GWAS (genome-wide association study) results of 20 yield-related trait data obtained under three environmental conditions [field (no stress), early drought and late drought] for 2 years in the database, allowing users to identify stress-related genes that support yield improvement. Milletdb can simplify the functional genomics analysis of millets by providing users with 20 different tools (e.g., 'Gene mapping', 'Co-expression', 'KEGG/GO Enrichment' analysis, etc.). On the Milletdb platform, a gene PMA1G03779.1 was identified through 'GWAS', which has the potential to modulate yield and respond to different environmental stresses. Using the tools provided by Milletdb, we found that the stress-related PLATZs TFs (transcription factors) family expands in 87.5% of millet accessions and contributes to vegetative growth and abiotic stress responses. Milletdb can effectively serve researchers in the mining of key genes, genome editing and molecular breeding of millets.

Discovery of unique CYP716C oxidase involved in pentacyclic triterpene biosynthesis from Camptotheca acuminata

Dozens of triterpenes have been isolated from Camptotheca acuminata, however, triterpene metabolism in this plant remains poorly understood. The common C28 carboxy located in the oleanane-type and ursane-type triterpenes indicates the existence of a functionally active triterpene, C28 oxidase, in this plant. Thorough mining and screening of the CYP716 genes were initiated using the multi-omics database for C. acuminata. Two CYP716A (CYP716A394 and CYP716A395) and three CYP716C (CYP716C80–CYP716C82) were identified based on conserved domain analyses and hierarchical cluster analyses. CYP716 microsomal proteins were prepared and their enzymatic activities were evaluated in vitro. The CYP716 classified into the CYP716C subfamily displays β-amyrin oxidation activity, and CYP716A displays α-amyrin and lupeol oxidation activity, based on gas chromatography-mass spectrometry analyses. The oxidation products were determined based on their mass and nuclear magnetic resonance spectrums. The optimum reaction conditions and kinetic parameters for CYP716C were determined, and functions were verified in Nicotiana benthaminana. Relative quantitative analyses revealed that these CYP716C genes were enriched in the leaves of C. acuminata plantlets after 60 d. These results indicate that CYP716C plays a dominant role in oleanane-type triterpene metabolism in the leaves of C. acuminata via a substrate-specific manner, and CYP716A is responsible for ursane- and lupane-type triterpene metabolism in fruit. This study provides valuable insights into the unique CYP716C-mediated oxidation step of pentacyclic triterpene biosynthesis in C. acuminata.