Multi-omics Database Research Articles

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.

ISMOD: an integrative browser for image-based single-cell multi-omics data.

Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.

PearMODB: a multiomics database forpear (Pyrus) genomics, genetics andbreeding study.

Pear (Pyrus ssp.) belongs to Rosaceae and is an important fruit tree widely cultivated around the world. Currently, challenges to cope with the burgeoning sets of multiomics data are rapidly increasing. Here, we constructed the Pear Multiomics Database (PearMODB) by integrating genome, transcriptome, epigenome and population variation data, and aimed to provide a portal for accessing and analyzing pear multiomics data. A variety of online tools were built including gene search, BLAST, JBrowse, expression heatmap, synteny analysis and primer design. The information of DNA methylation sites and single-nucleotide polymorphisms can be retrieved through the custom JBrowse, providing an opportunity to explore the genetic polymorphisms linked to phenotype variation. Moreover, different gene families involving transcription factors, transcription regulators and disease resistance (nucleotide-binding site leucine-rich repeat) were identified and compiled for quick search. In particular, biosynthetic gene clusters (BGCs) were identified in pear genomes, and specialized webpages were set up to show detailed information of BGCs, laying a foundation for studying metabolic diversity among different pear varieties. Overall, PearMODB provides an important platform for pear genomics, genetics and breeding studies. Database URL http://pearomics.njau.edu.cn.

GERDH: an interactive multi-omics database for cross-species data mining in horticultural crops.

Horticultural plants contribute immensely to the quality of humans' life. The rapid development of omics studies on horticultural plants has resulted in large volumes of valuable growth- and development-related data. Genes that are essential for growth and development are highly conserved in evolution. Cross-species data mining reduces the impact of species heterogeneity and has been extensively used for conserved gene identification. Owing to the lack of a comprehensive database for cross-species data mining using multi-omics data from all horticultural plant species, the current resources in this field are far from satisfactory. Here, we introduce GERDH (https://dphdatabase.com), a database platform for cross-species data mining among horticultural plants, based on 12,961 uniformly processed publicly available omics libraries from more than 150 horticultural plant accessions, including fruits, vegetables, and ornamental plants. Important and conserved genes that are essential for a specific biological process can be obtained by cross-species analysis module with interactive web-based data analysis and visualization. Moreover, GERDH is equipped with 7 online analysis tools, including gene expression, in-species analysis, epigenetic regulation, gene co-expression, enrichment/pathway and phylogenetic analysis. By interactive cross-species analysis, we identified key genes contributing to postharvest storage. By gene expression analysis, we explored new functions of CmEIN3 in flower development, which was validated by transgenic chrysanthemum analysis. We believe that GERDH will be a useful resource for key gene identification and will allow for omics big data to be more available and accessible to horticultural plant community members.

Osmoprotectants play a major role in the Portulaca oleracea resistance to high levels of salinity stress-insights from a metabolomics and proteomics integrated approach.

Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agri-industrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress-a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress. The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented. Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na+, Cl-, and K+, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants.

MDSi: Multi-omics Database for Setaria italica

BackgroundFoxtail millet (Setaria italica) harbors the small diploid genome (~ 450 Mb) and shows the high inbreeding rate and close relationship to several major foods, feed, fuel and bioenergy grasses. Previously, we created a mini foxtail millet, xiaomi, with an Arabidopsis-like life cycle. The de novo assembled genome data with high-quality and an efficient Agrobacterium-mediated genetic transformation system made xiaomi an ideal C4 model system. The mini foxtail millet has been widely shared in the research community and as a result there is a growing need for a user-friendly portal and intuitive interface to perform exploratory analysis of the data.ResultsHere, we built a Multi-omics Database for Setaria italica (MDSi, http://sky.sxau.edu.cn/MDSi.htm), that contains xiaomi genome of 161,844 annotations, 34,436 protein-coding genes and their expression information in 29 different tissues of xiaomi (6) and JG21 (23) samples that can be showed as an Electronic Fluorescent Pictograph (xEFP) in-situ. Moreover, the whole-genome resequencing (WGS) data of 398 germplasms, including 360 foxtail millets and 38 green foxtails and the corresponding metabolic data were available in MDSi. The SNPs and Indels of these germplasms were called in advance and can be searched and compared in an interactive manner. Common tools including BLAST, GBrowse, JBrowse, map viewer, and data downloads were implemented in MDSi.ConclusionThe MDSi constructed in this study integrated and visualized data from three levels of genomics, transcriptomics and metabolomics, and also provides information on the variation of hundreds of germplasm resources that can satisfies the mainstream requirements and supports the corresponding research community.

Abstract 1452: Big data analysis revealed signalling activity and key regulators in human prostate cancer cell lines

Abstract Background: Human prostate cancer (PCa) cell lines are the most used models for research. The commonly used PCa cell lines can be roughly categorized into 3 groups: androgen receptor (AR)+, neuroendocrine (NE)+ and double negative (AR- NE-) cell lines. However, although these cell lines have been extensively used in PCa research since their establishment decades ago, a global molecular characterization of these cell lines is still lacking, especially in the aspect of cell signaling activity and their clinical relevance, which requires a large scale of data mining. Methods: The RNA-seq, DNA-seq and proteomic datasets were gathered from public cohort and our lab’s collection. From these data, a PCa cell line multi-omics database (PCMD) was established. Single sample gene set enrichment analysis (ssGSEA) was used to reveal the signaling pathways enriched in each PCa cell line. Additionally, human PCa patient single cell RNAseq data-based deconvolution and human PCa patient bulk RNAseq data-based nearest-neighbor (NN) graph analysis were used to evaluate the clinical relevance of each PCa cell line. The DNA-seq, ATAC-seq, DNase HS-seq and Bisulfite-seq datasets were used to annotate the key genes. Results: The PCMD database was established by compiling more than 1000 RNASeq datasets derived from various PCa cell lines. The important signaling pathways and transcription factors were revealed for each PCa cell line. Further, the cell lines were annotated with relative clinical stages. Additionally, an interactive webApp was established for scientists to explore and visualize PCMD data. Conclusion: our research characterized PCa cell lines using unbiased strategies and large sample number. It is our hope that this study would aid researchers to develop hypothesis and choose appropriate cell line to use. Funding: This study is supported by: Feist-Weiller Cancer Center, LSU Health Shreveport. Citation Format: Siyuan Cheng, Lin Li, Xiuping Yu. Big data analysis revealed signalling activity and key regulators in human prostate cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1452.

Construction of Metastasis-Specific Regulation Network in Ovarian Cancer Based on Prognostic Stemness-Related Signatures.

WE aimed to reveal the correlation between ovarian cancer (OV) metastasis and cancer stemness in OV. RNA-seq data and clinical information of 591 OV samples (551 without metastasis and 40 with metastasis) were obtained from TCGA. The edgeR method was used to determine differentially expressed genes (DEGs) and transcription factors (DETFs). Then, mRNA expression-based stemness index was calculated using one-class logistic regression (OCLR). Weighted gene co-expression network analysis (WGCNA) was used to define stemness-related genes (SRGs). Univariate and multivariate Cox proportional hazard regression were conducted to identify the prognostic SRGs (PSRGs). PSRGs, DETFs, and 50 hallmark pathways quantified by gene set variation analysis (GSVA) were integrated into Pearson co-expression analysis. Significant co-expression interactions were utilized to construct an OV metastasis-specific regulation network. Cell communication analysis was carried out based on single cell RNA sequencing data to explore the molecular regulation mechanism of OV. Eventually, assay for targeting accessible-chromatin with high throughout sequencing (ATAC), chromatin immunoprecipitation sequencing (ChIP-seq) validation, and multiple data sets were used to validate the expression levels and prognostic values of key stemness-related signatures. Moreover, connectivity map (CMap) was used to identify potential inhibitors of stemness-related signatures. Based on edgeR, WGCNA, and Cox proportional hazard regression, 22 PSRGs were defined to construct a prognostic prediction model for metastatic OV. In the metastasis-specific regulation network, key TF-PSRS interaction pair was NR4A1-EGR3 (correlation coefficient = 0.81, p < 0.05, positive), and key PSRG-hallmark pathway interaction pair was EGR3-TNFα signaling via NFκB (correlation coefficient = 0.44, p < 0.05, positive), which were validated in multi-omics databases. Thioridazine was postulated to be the most significant compound in treatment of OV metastasis. PSRGs played critical roles in OV metastasis. Specifically, EGR3 was the most significant PSRG, which was positively regulated by DETF NR4A1, inducing metastasis via TNFα signaling.

BnIR: A multi-omics database with various tools for Brassica napus research and breeding

In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous “variation–gene expression–phenotype” associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

Interactive gene identification for cancer subtyping based on multi-omics clustering.

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L1 prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

Yersiniomics, a Multi-Omics Interactive Database for Yersinia Species.

The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species. It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis. In 2 decades, the number of genomic, transcriptomic, and proteomic studies on Yersinia grew massively, delivering a wealth of data. We developed Yersiniomics, an interactive web-based platform, to centralize and analyze omics data sets on Yersinia species. The platform allows user-friendly navigation between genomic data, expression data, and experimental conditions. Yersiniomics will be a valuable tool to microbiologists.

The burdock database: a multi-omic database for Arctium lappa, a food and medicinal plant

BackgroundBurdock is a biennial herb of Asteraceae found in Northern Europe, Eurasia, Siberia, and China. Its mature dry fruits, called Niu Bang Zi, are recorded in various traditional Chinese medicine books. With the development of sequencing technology, the mitochondrial, chloroplast, and nuclear genomes, transcriptome, and sequence-related amplified polymorphism (SRAP) fingerprints of burdock have all been reported. To make better use of this data for further research and analysis, a burdock database was constructed.ResultsThis burdock multi-omics database contains two burdock genome datasets, two transcriptome datasets, eight burdock chloroplast genomes, one burdock mitochondrial genome, one A. tomentosum chloroplast genome, one A. tomentosum mitochondrial genome, 26 phenotypes of burdock varieties, burdock rhizosphere-associated microorganisms, and chemical constituents of burdock fruit, pericarp, and kernel at different growth stages (using UPLC-Q-TOF–MS). The wild and cultivation distribution of burdock in China was summarized, and the main active components and pharmacological effects of burdock currently reported were concluded. The database contains ten central functional modules: Home, Genome, Transcriptome, Jbrowse, Search, Tools, SRAP fingerprints, Associated microorganisms, Chemical, and Publications. Among these, the “Tools” module can be used to perform sequence homology alignment (Blast), multiple sequence alignment analysis (Muscle), homologous protein prediction (Genewise), primer design (Primer), large-scale genome analysis (Lastz), and GO and KEGG enrichment analyses (GO Enrichment and KEGG Enrichment).ConclusionsThe database URL is http://210.22.121.250:41352/. This burdock database integrates molecular and chemical data to provide a comprehensive information and analysis platform for interested researchers and can be of immense help to the cultivation, breeding, and molecular pharmacognosy research of burdock.

METABOLOMICS ANALYSIS IDENTIFIED AN ORALLY ACTIVE METABOLITE FOR TREATING CROHN’S DISEASE-ASSOCIATED INTESTINAL FIBROSIS

Abstract BACKGROUND Intestinal fibrosis is a debilitating complication of Crohn’s disease (CD) patients. Surgical resection is used to treat intestinal strictures, but this approach may adversely affect the patients' quality of life. Based on the findings of our groups and others, we hypothesize that intestinal metabolites are associated with intestinal fibrosis. We aimed to discover novel anti-fibrogenic metabolites. METHODS We compared the intestinal metabolite profiles between non-IBD, stricturing CD, and non-stricturing CD patients in an inflammatory bowel disease (IBD) multi-omics database to identify CD stricture-related metabolites. We compared the fecal metabolite profiles between trinitrobenzene sulfonic acid (TNBS)-treated mice with and without anti-fibrogenic CSA13 treatment to identify mouse intestinal fibrosis-related metabolites. RESULTS Compared to non-stricturing CD patients and non-IBD patients, stricturing CD patients had reduced fecal levels of inosine. Compared to normal mice, TNBS-treated mice had reduced fecal levels of inosine. CSA13-exposed TNBS-treated mice had increased fecal inosine levels. Oral administration of inosine (10 mg/kg/day for 14 days) effectively reduced ileal fibrosis in fibrotic 40-week-old SAMP1/YitFc mice with 96-100% reduction of intestinal collagen mRNA expression and 87% reduction of overall ileal disease activity. Although inosine did not directly inhibit collagen expression in primary human ileal fibroblasts from stricturing CD patients (CD-HIF), it reduced collagen mRNA expression in fresh ileal tissues from stricturing CD patients. RNA-seq and proteomics identified that peroxiredoxin 2 (PRDX2) is negatively associated with intestinal strictures in CD patients. PRDX2 is a thiol-specific antioxidant gene that reduces peroxides to alcohols. Inosine increased the secretion of peroxiredoxin 2 (PRDX2) in primary human intestinal epithelial cells (HPEC). The inosine-mediated PRDX2 secretion was inhibited by the pretreatment of adenosine 2A receptor (A2AR) inhibitor ZM241385. PRDX2 reduced cell stress, induced cell stress-related protein (cyclooxygenase 2/COX-2), and inhibited collagen expression in the CD-HIF. Inhibition of COX2 by indomethacin reversed the anti-fibrogenic effect of PRDX2 in the CD-HIF. Inhibition of PRDX2 caused increased collagen expression in the CD-HIF. Lentiviral overexpression of Prdx2 ameliorated ileal fibrosis in the fibrotic SAMP1/YitFc mice. Fibrotic SAMP1/YitFc mice had reduced circulating free thiol, reflecting increased systemic oxidative stress. Inosine restored circulating free thiol levels in SAMP1/YitFc mice. CONCLUSIONS Inosine is an orally active anti-fibrogenic metabolite that promotes PRDX2 expression from intestinal epithelial cells. PRDX2 is a newly identified anti-fibrogenic protein, which may inhibit collagen expression by reducing cell stress.

Data-Driven Identification of Targets for Fluorescence-Guided Surgery in Non-Small Cell Lung Cancer.

Intraoperative identification of lung tumors can be challenging. Tumor-targeted fluorescence-guided surgery can provide surgeons with a tool for real-time intraoperative tumor detection. This study evaluated cell surface biomarkers, partially selected via data-driven selection software, as potential targets for fluorescence-guided surgery in non-small cell lung cancers: adenocarcinomas (ADC), adenocarcinomas in situ (AIS), and squamous cell carcinomas (SCC). Formalin-fixed paraffin-embedded tissue slides of resection specimens from 15 patients with ADC and 15 patients with SCC were used and compared to healthy tissue. Molecular targets were selected based on two strategies: (1) a data-driven selection using > 275 multi-omics databases, literature, and experimental evidence; and (2) the availability of a fluorescent targeting ligand in advanced stages of clinical development. The selected targets were carbonic anhydrase 9 (CAIX), collagen type XVII alpha 1 chain (collagen XVII), glucose transporter 1 (GLUT1), G protein-coupled receptor 87 (GPR87), transmembrane protease serine 4 (TMPRSS4), carcinoembryonic antigen (CEA), epithelial cell adhesion molecule (EpCAM), folate receptor alpha (FRα), integrin αvβ6 (αvβ6), and urokinase-type plasminogen activator receptor (uPAR). Tumor expression of these targets was assessed by immunohistochemical staining. A total immunostaining score (TIS, range 0-12), combining the percentage and intensity of stained cells, was calculated. The most promising targets in ADC were explored in six AIS tissue slides to explore its potential in non-palpable lesions. Statistically significant differences in TIS between healthy lung and tumor tissue for ADC samples were found for CEA, EpCAM, FRα, αvβ6, CAIX, collagen XVII, GLUT-1, and TMPRSS4, and of these, CEA, CAIX, and collagen XVII were also found in AIS. For SCC, EpCAM, uPAR, CAIX, collagen XVII, and GLUT-1 were found to be overexpressed. EpCAM, CAIX, and Collagen XVII were identified using concomitant use of data-driven selection software and clinical evidence as promising targets for intraoperative fluorescence imaging for both major subtypes of non-small cell lung carcinomas.

EDomics: a comprehensive and comparative multi-omics database for animal evo-devo.

Evolutionary developmental biology (evo-devo) has been among the most fascinating interdisciplinary fields for decades, which aims to elucidate the origin and evolution of diverse developmental processes. The rapid accumulation of omics data provides unprecedented opportunities to answer many interesting but unresolved evo-devo questions. However, the access and utilization of these resources are hindered by challenges particularly in non-model animals. Here, we establish a comparative multi-omics database for animal evo-devo (EDomics, http://edomics.qnlm.ac) containing comprehensive genomes, bulk transcriptomes, and single-cell data across 40 representative species, many of which are generally used as model organisms for animal evo-devo study. EDomics provides a systematic view of genomic/transcriptomic information from various aspects, including genome assembly statistics, gene features and families, transcription factors, transposable elements, and gene expressional profiles/networks. It also exhibits spatiotemporal gene expression profiles at a single-cell level, such as cell atlas, cell markers, and spatial-map information. Moreover, EDomics provides highly valuable, customized datasets/resources for evo-devo research, including gene family expansion/contraction, inferred core gene repertoires, macrosynteny analysis for karyotype evolution, and cell type evolution analysis. EDomics presents a comprehensive and comparative multi-omics platform for animal evo-devo community to decipher the whole history of developmental evolution across the tree of life.

Functional characterization of secologanin synthase-like homologous genes suggests their involvement in the biosynthesis of diverse metabolites in the secoiridoid biosynthetic pathway of Camptotheca acuminata Decne

The assignment of functions based on homology has recently been challenged by the frequent discovery of functional divergence among homologous gene family members of enzymes involved in plant secondary metabolism. Secologanin synthase (SLS) is the key CYP450 enzyme that acts critically in the biosynthesis of Strychnos alkaloid scaffold. In this study, to fully elucidate the mechanism that underlies metabolic variation, the CYP450 paralogs that participate in oxidative transformation of the secoiridoid pathway were functionally characterized by combining multitiered strategies of metabolite profiling, phylogenetic analyses, biochemistry assays and reverse genetics techniques. Five CaSLSs-like homologous genes were mined and isolated from an integrative multi-omics database of Camptotheca acuminata. Protein sequences, structural comparisons, and phylogenetic analyses confirmed that CaSLS1–2 and CaSLS4–5 were grouped into the SLS clade, and only CaSLS3 clustered into the 7DLH clade. Five homologs, including two previously identified enzymatic genes, were thus designated as CaSLAS1, CaSLAS2, Ca7DLH, CaSLS4 and CaSLS5. Enzymatic assays of the recombinant proteins in yeast showed that CaSLAS1 and CaSLAS2 displayed multi-catalytic activities of SLS, secologanic acid synthase (SLAS) and secoxyloganin synthase (SXS). Additionally, the reactions of CaSLASs enzymes generated stereospecific isomers of secoiridoid products, and a new product of secoxyloganin was observed. CaSLS5, a third SLS enzyme isoform that catalyzes the formation of secologanin, was reported for the first time. However, CaSXS enzymatic activities in vitro had little physiological impact on the biosynthesis of camptothecin (CPT) in Camptotheca acuminata. The primary and secondary roles of CaSLSs-like genes in secoiridoid metabolism were confirmed by virus-induced gene silencing (VIGS) in plant leaves. Efficient silencing and transcriptional downregulation of CaSLAS2, compared with the CaSLAS1 homologs, resulted in a greater reduction of the accumulation of CPT within silenced plants, and CaSLS5 had barely any effect on the contents of metabolites in planta. Thus, CaSLAS2, rather than CaSLAS1, appeared to function as a major participant in the biosynthesis of CPT, and there were redundant functions in the CaSLSs-like enzymes. Consistent with such roles, CaSLAS2 was ubiquitously expressed at very high levels in Camptotheca tissues, and CaSLAS2 was specifically expressed in young leaves. In contrast, CaSLS5 was poorly expressed in every tissue tested. Our findings demonstrate that homologs that belong to the CYP72 gene family are functionally diverse and exhibit divergence and thereby uncover an expanding group of enzymatic genes that determine the chemo-diversity of the iridoid pathway.

CottonMD: a multi-omics database for cotton biological study.

Cotton is an important economic crop, and many loci for important traits have been identified, but it remains challenging and time-consuming to identify candidate or causal genes/variants and clarify their roles in phenotype formation and regulation. Here, we first collected and integrated the multi-omics datasets including 25 genomes, transcriptomes in 76 tissue samples, epigenome data of five species and metabolome data of 768 metabolites from four tissues, and genetic variation, trait and transcriptome datasets from 4180 cotton accessions. Then, a cotton multi-omics database (CottonMD, http://yanglab.hzau.edu.cn/CottonMD/) was constructed. In CottonMD, multiple statistical methods were applied to identify the associations between variations and phenotypes, and many easy-to-use analysis tools were provided to help researchers quickly acquire the related omics information and perform multi-omics data analysis. Two case studies demonstrated the power of CottonMD for identifying and analyzing the candidate genes, as well as the great potential of integrating multi-omics data for cotton genetic breeding and functional genomics research.