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 accessibility and chromosomal interaction, as well as genetic variation data from 24 501 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.

AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes.

The Animal Meta-omics landscape database (AnimalMetaOmics, https://yanglab.hzau.edu.cn/animalmetaomics#/) is a comprehensive and freely available resource that includes metagenomic, metatranscriptomic, and metaproteomic data from various non-human animal species and provides abundant information on animal microbiomes, including cluster analysis of microbial cognate genes, functional gene annotations, active microbiota composition, gene expression abundance, and microbial protein identification. In this work, 55 898 microbial genomes were annotated from 581 animal species, including 42 924 bacterial genomes, 12 336 virus genomes, 496 archaea genomes and 142 fungi genomes. Moreover, 321 metatranscriptomic datasets were analyzed from 31 animal species and 326 metaproteomic datasets from four animal species, as well as the pan-genomic dynamics and compositional characteristics of 679 bacterial species and 13 archaea species from animal hosts. Researchers can efficiently access and acquire the information of cross-host microbiota through a user-friendly interface, such as species, genomes, activity levels, expressed protein sequences and functions, and pan-genome composition. These valuable resources provide an important reference for better exploring the classification, functional diversity, biological process diversity and functional genes of animal microbiota.

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.

Interaction between phenylpropane metabolism and oil accumulation in the developing seed of Brassica napus revealed by high temporal-resolution transcriptomes.

BACKGROUND: Brassica napus is an important oilseed crop providing high-quality vegetable oils for human consumption and non-food applications. However, the regulation between embryo and seed coat for the synthesis of oil and phenylpropanoid compounds remains largely unclear. RESULTS: Here, we analyzed the transcriptomes in developing seeds at 2-day intervals from 14 days after flowering (DAF) to 64 DAF. The 26 high-resolution time-course transcriptomes are clearly clustered into five distinct groups from stage I to stage V. A total of 2217 genes including 136 transcription factors, are specifically expressed in the seed and show high temporal specificity by being expressed only at certain stages of seed development. Furthermore, we analyzed the co-expression networks during seed development, which mainly included master regulatory transcription factors, lipid, and phenylpropane metabolism genes. The results show that the phenylpropane pathway is prominent during seed development, and the key enzymes in the phenylpropane metabolic pathway, including TT5, BAN, and the transporter TT19, were directly or indirectly related to many key enzymes and transcription factors involved in oil accumulation. We identified candidate genes that may regulate seed oil content based on the co-expression network analysis combined with correlation analysis of the gene expression with seed oil content and seed coat content. CONCLUSIONS: Overall, these results reveal the transcriptional regulation between lipid and phenylpropane accumulation during B. napus seed development. The established co-expression networks and predicted key factors provide important resources for future studies to reveal the genetic control of oil accumulation in B. napus seeds.

Functional genomics of Brassica napus: Progresses, challenges, and perspectives.

Brassica napus, commonly known as rapeseed or canola, is a major oil crop contributing over 13% to the stable supply of edible vegetable oil worldwide. Identification and understanding the gene functions in the B. napus genome is crucial for genomic breeding. A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B. napus. In this review, we present an overview of the progress made in the functional genomics of B. napus, including the availability of germplasm resources, omics databases and cloned functional genes. Based on the current progress, we also highlight the main challenges and perspectives in this field. The advances in the functional genomics of B. napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B. napus and will expedite the breeding of high quality, high resistance and high yield in B. napus varieties.

Comparative transcriptome profiling reveals the multiple levels of crosstalk in phytohormone networks in Brassica napus.

Plant hormones are the intrinsic factors that control plant development. The integration of different phytohormone pathways in a complex network of synergistic, antagonistic and additive interactions has been elucidated in model plants. However, the systemic level of transcriptional responses to hormone crosstalk in Brassica napus is largely unknown. Here, we present an in-depth temporal-resolution study of the transcriptomes of the seven hormones in B. napus seedlings. Differentially expressed gene analysis revealed few common target genes that co-regulated (up- and down-regulated) by seven hormones; instead, different hormones appear to regulate distinct members of protein families. We then constructed the regulatory networks between the seven hormones side by side, which allowed us to identify key genes and transcription factors that regulate the hormone crosstalk in B. napus. Using this dataset, we uncovered a novel crosstalk between gibberellin and cytokinin in which cytokinin homeostasis was mediated by RGA-related CKXs expression. Moreover, the modulation of gibberellin metabolism by the identified key transcription factors was confirmed in B. napus. Furthermore, all data were available online from http://yanglab.hzau.edu.cn/BnTIR/hormone. Our study reveals an integrated hormone crosstalk network in Brassica napus, which also provides a versatile resource for future hormone studies in plant species.

Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation.

Genotype imputation is a process that estimates missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs), boost the power to identify genetic association and promote the combination of genetic studies. However, there has been a lack of high-quality reference panels for most plants, which greatly hinders the application of genotype imputation. Here, we developed Plant-ImputeDB (http://gong_lab.hzau.edu.cn/Plant_imputeDB/), a comprehensive database with reference panels of 12 plant species for online genotype imputation, SNP and block search and free download. By integrating genotype data and whole-genome resequencing data of plants from various studies and databases, the current Plant-ImputeDB provides high-quality reference panels of 12 plant species, including ∼69.9 million SNPs from 34 244 samples. It also provides an easy-to-use online tool with the option of two popular tools specifically designed for genotype imputation. In addition, Plant-ImputeDB accepts submissions of different types of genomic variations, and provides free and open access to all publicly available data in support of related research worldwide. In general, Plant-ImputeDB may serve as an important resource for plant genotype imputation and greatly facilitate the research on plant genetic research.

Calling large indels in 1047 Arabidopsis with IndelEnsembler.

Large indels greatly impact the observable phenotypes in different organisms including plants and human. Hence, extracting large indels with high precision and sensitivity is important. Here, we developed IndelEnsembler to detect large indels in 1047 Arabidopsis whole-genome sequencing data. IndelEnsembler identified 34 093 deletions, 12 913 tandem duplications and 9773 insertions. Our large indel dataset was more comprehensive and accurate compared with the previous dataset of AthCNV (1). We captured nearly twice of the ground truth deletions and on average 27% more ground truth duplications compared with AthCNV, though our dataset has less number of large indels compared with AthCNV. Our large indels were positively correlated with transposon elements across the Arabidopsis genome. The non-homologous recombination events were the major formation mechanism of deletions in Arabidopsis genome. The Neighbor joining (NJ) tree constructed based on IndelEnsembler's deletions clearly divided the geographic subgroups of 1047 Arabidopsis. More importantly, our large indels represent a previously unassessed source of genetic variation. Approximately 49% of the deletions have low linkage disequilibrium (LD) with surrounding single nucleotide polymorphisms. Some of them could affect trait performance. For instance, using deletion-based genome-wide association study (DEL-GWAS), the accessions containing a 182-bp deletion in AT1G11520 had delayed flowering time and all accessions in north Sweden had the 182-bp deletion. We also found the accessions with 65-bp deletion in the first exon of AT4G00650 (FRI) flowered earlier than those without it. These two deletions cannot be detected in AthCNV and, interestingly, they do not co-occur in any Arabidopsis thaliana accession. By SNP-GWAS, surrounding SNPs of these two deletions do not correlate with flowering time. This example demonstrated that existing large indel datasets miss phenotypic variations and our large indel dataset filled in the gap.

Development and screening of EMS mutants with altered seed oil content or fatty acid composition in Brassica napus.

Brassica napus is an important oilseed crop in the world, and the mechanism of seed oil biosynthesis in B. napus remains unclear. In order to study the mechanism of oil biosynthesis and generate germplasms for breeding, an ethyl methanesulfonate (EMS) mutant population with ~100 000 M2 lines was generated using Zhongshuang 11 as the parent line. The EMS-induced genome-wide mutations in M2-M4 plants were assessed. The average number of mutations including single nucleotide polymorphisms and insertion/deletion in M2-M4 was 21 177, 28 675 and 17 915, respectively. The effects of the mutations on gene function were predicted in M2-M4 mutants, respectively. We screened the seeds from 98 113 M2 lines, and 9415 seed oil content and fatty acid mutants were identified. We further confirmed 686 mutants with altered seed oil content and fatty acid in advanced generation (M4 seeds). Five representative M4 mutants with increased oleic acid were re-sequenced, and the potential causal variations in FAD2 and ROD1 genes were identified. This study generated and screened a large scale of B. napus EMS mutant population, and the identified mutants could provide useful genetic resources for the study of oil biosynthesis and genetic improvement of seed oil content and fatty acid composition of B. napus in the future.

Association between red blood cell distribution width and long-term mortality among patients undergoing percutaneous coronary intervention with previous history of cancer.

Background: The number of patients suffering from coronary heart disease with cancer is rising. There is scarce evidence concerning the biomarkers related to prognosis among patients undergoing percutaneous coronary intervention (PCI) with cancer. Thus, the aim of this study was to investigate the association between red blood cell distribution width (RDW) and prognosis in this population.Methods: A total of 172 patients undergoing PCI with previous history of cancer were enrolled in this retrospective study. The endpoint was long-term all-cause mortality. According to tertiles of RDW, the patients were classified into three groups: Tertile 1 (RDW <12.8%), Tertile 2 (RDW ≥12.8% and <13.5%) and Tertile 3 (RDW ≥13.5%).Results: During an average follow-up period of 33.3 months, 29 deaths occurred. Compared with Tertile 3, mortality of Tertile 1 and Tertile 2 was significantly lower in the Kaplan-Meier analysis. In multivariate Cox regression analysis, RDW remained an independent risk factor of mortality (HR: 1.938, 95% CI: 1.295-2.655, p < 0.001). The all-cause mortality in Tertile 3 was significantly higher than that in Tertile 1 (HR: 5.766; 95% CI: 1.426-23.310, p = 0.014).Conclusions: An elevated RDW level (≥13.5%) was associated with long-term all-cause mortality among patients undergoing PCI with previous history of cancer.

Transcriptome Analysis Reveals Candidate Genes Associated with Leaf Etiolation of a Cytoplasmic Male Sterility Line in Chinese Cabbage (Brassica Rapa L. ssp. Pekinensis).

Cytoplasmic male sterility (CMS) is universally utilized in cruciferous vegetables. However, the Chinese cabbage hau CMS lines, obtained by interspecific hybridization and multiple backcrosses of the Brassica juncea (B. juncea) CMS line and Chinese cabbage, show obvious leaf etiolation, and the molecular mechanism of etiolation remains elusive. Here, the ultrastructural and phenotypic features of leaves from the Chinese cabbage CMS line 1409A and maintainer line 1409B are analyzed. The results show that chloroplasts of 1409A exhibit abnormal morphology and distribution. Next, RNA-sequencing (RNA-Seq) is used to identify 485 differentially expressed genes (DEGs) between 1409A and 1409B, and 189 up-regulated genes and 296 down-regulated genes are found. Genes that affect chloroplasts development, such as GLK1 and GLK2, and chlorophyll biosynthesis, such as PORB, are included in the down-regulated DEGs. Quantitative real-time PCR (qRT-PCR) analysis validate that the expression levels of these genes are significantly lower in 1409A than in 1409B. Taken together, these results demonstrate that leaf etiolation is markedly affected by chloroplast development and pigment biosynthesis. This study provides an effective foundation for research on the molecular mechanisms of leaf etiolation of the hau CMS line in Chinese cabbage (Brassica rapa L. ssp. pekinensis).

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes.

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.

Spatial Colocalization of Human Ohnolog Pairs Acts to Maintain Dosage-Balance.

Ohnologs -paralogous gene pairs generated by whole genome duplication- are enriched for dosage sensitive genes, that is, genes that have a phenotype due to copy number changes. Dosage sensitive genes frequently occur in the same metabolic pathway and in physically interacting proteins. Accumulating evidence reveals that functionally related genes tend to co-localize in the three-dimensional (3D) arrangement of chromosomes. We query whether the spatial distribution of ohnologs has implications for their dosage balance. We analyzed the colocalization frequency of ohnologs based on chromatin interaction datasets of seven human cell lines and found that ohnolog pairs exhibit higher spatial proximity in 3D nuclear organization than other paralog pairs and than randomly chosen ohnologs in the genome. We also found that colocalized ohnologs are more resistant to copy number variations and more likely to be disease-associated genes, which indicates a stronger dosage balance in ohnologs with high spatial proximity. This phenomenon is further supported by the stronger similarity of gene co-expression and of gene ontology terms of colocalized ohnologs. In addition, for a large fraction of ohnologs, the spatial colocalization is conserved in mouse cells, suggestive of functional constraint on their 3D positioning in the nucleus.

Bioinformatics Identification of Drug Resistance-Associated Gene Pairs in Mycobacterium tuberculosis.

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb). Due to the extensive use of anti-tuberculosis drugs and the development of mutations, the emergence and spread of multidrug-resistant tuberculosis is recognized as one of the most dangerous threats to global tuberculosis control. Some single mutations have been identified to be significantly linked with drug resistance. However, the prior research did not take gene-gene interactions into account, and the emergence of transmissible drug resistance is connected with multiple genetic mutations. In this study we use the bioinformatics software GBOOST (The Hong Kong University, Clear Water Bay, Kowloon, Hong Kong, China) to calculate the interactions of Single Nucleotide Polymorphism (SNP) pairs and identify gene pairs associated with drug resistance. A large part of the non-synonymous mutations in the drug target genes that were included in the screened gene pairs were confirmed by previous reports, which lent sound solid credits to the effectiveness of our method. Notably, most of the identified gene pairs containing drug targets also comprise Pro-Pro-Glu (PPE) family proteins, suggesting that PPE family proteins play important roles in the drug resistance of Mtb. Therefore, this study provides deeper insights into the mechanisms underlying anti-tuberculosis drug resistance, and the present method is useful for exploring the drug resistance mechanisms for other microorganisms.

Spatial features for Escherichia coli genome organization.

BACKGROUND: In bacterial genomes, the compactly encoded genes and operons are well organized, with genes in the same biological pathway or operons in the same regulon close to each other on the genome sequence. In addition, the linearly close genes have a higher probability of co-expression and their protein products tend to form protein-protein interactions. However, the organization features of bacterial genomes in a three-dimensional space remain elusive. The DNA interaction data of Escherichia coli, measured by the genome conformation capture (GCC) technique, have recently become available, which allowed us to investigate the spatial features of bacterial genome organization. RESULTS: By renormalizing the GCC data, we compared the interaction frequency of operon pairs in the same regulon with that of random operon pairs. The results showed that arrangements of operons in the E. coli genome tend to minimize the spatial distance between operons in the same regulon. A similar global organization feature exists for genes in biological pathways of E. coli. In addition, the genes close to each other spatially (even if they are far from each other on the genome sequence) tend to be co-expressed and form protein-protein interactions. These results provided new insights into the organization principles of bacterial genomes and support the notion of transcription factory. CONCLUSIONS: This study revealed the organization features of Escherichia coli genomic functional units in the 3D space and furthered our understanding of the link between the three-dimensional structure of chromosomes and biological function.

ATP selection in a random peptide library consisting of prebiotic amino acids.

Based upon many theoretical findings on protein evolution, we proposed a ligand-selection model for the origin of proteins, in which the most ancient proteins originated from ATP selection in a pool of random peptides. To test this ligand-selection model, we constructed a random peptide library consisting of 15 types of prebiotic amino acids and then used cDNA display to perform six rounds of in vitro selection with ATP. By means of next-generation sequencing, the most prevalent sequence was defined. Biochemical and biophysical characterization of the selected peptide showed that it was stable and foldable and had ATP-hydrolysis activity as well.