Combined Transcriptomics and Metabolomics Analysis Reveals the Effect of Selenium Fertilization on Lycium barbarum Fruit

As a beneficial nutrient and essential trace element, selenium plays a significant role in plant growth functions and human protein biosynthesis. Plant selenium enrichment is mainly obtained from both natural soil and exogenous selenium supplementation, while human beings consume selenium-enriched foods for the purposes of selenium supplementation. In this study, different types of selenium fertilizers were sprayed onto Lycium barbarum in Ningxia, and transcriptomics and metabolomics techniques were used to explore the effects of selenium on the fruit differentials and differential genes in Lycium barbarum. Taking the "Ning Qiyi No.1" wolfberry as the research object, sodium selenite, nano-selenium, and organic selenium were sprayed at a concentration of 100 mg·L-1 three times from the first fruiting period to the harvesting period, with a control treatment comprising the spraying of clear water. We determined the major metabolites and differential genes of the amino acids and derivatives, flavonoids, and alkaloids in ripe wolfberries. We found that spraying selenium significantly enhanced the Lycium barbarum metabolic differentiators; the most effective spray was the organic selenium, with 129 major metabolic differentiators and 10 common metabolic pathways screened after spraying. Nano-selenium was the next best fertilizer we screened, with 111 major metabolic differentiators, the same number as organic selenium in terms of differential genes and common metabolite pathways. Sodium selenite was the least effective of the three, with only 59 of its major metabolic differentials screened, but its differential genes and metabolites were enriched for five common pathways.

Transcriptome Identification and Analysis of Fatty Acid Desaturase Gene Expression at Different Temperatures in Tausonia pullulans 6A7

Tausonia pullulans 6A7 is a low-temperature yeast strain that can produce lipases. Yeast, which is made up of chassis cells, is an important part of synthetic biology, and the use of the lipase-producing properties of T. pullulans 6A7 for the production of fatty acids provides a new pathway for targeted synthesis in yeast cell factories. In this study, we performed RNA-seq on lipase-producing T. pullulans 6A7 at different temperatures (15 °C, 20 °C, 20 °C without corn oil, and 25 °C). Therefore, a total of 8455 differentially expressed genes were screened, and 16 of them were FAD candidate genes. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of group A (15 °C) vs. group D (25 °C) showed that the pathways of fatty acid biosynthesis (map00061) and the biosynthesis of unsaturated fatty acids (map01040) were significantly enriched. In the proposed temporal analysis of differentially expressed genes among the four temperature modulations, we found differentially expressed genes in nine clusters that had the same expression trends; these genes may be jointly involved in multiple biological processes in T. pullulans 6A7. In addition, we found 16 FAD candidate genes involved in fatty acid biosynthesis, and the expression of these genes had similar expression in the transcriptome trends with the different temperature treatments. These findings will help in future in-depth studies of the function and molecular mechanisms of these important FAD genes involved in fatty acid metabolism in yeast, and they could also be conducive to the establishment of a cellular factory for targeted fatty acid production by using yeast.

Integrative analysis of metabolome, proteome, and transcriptome for identifying genes influencing total lignin content in Populus trichocarpa

Lignin, a component of plant cell walls, possesses significant research potential as a renewable energy source to replace carbon-based products and as a notable pollutant in papermaking processes. The monolignol biosynthetic pathway has been elucidated and it is known that not all monolignol genes influence the total lignin content. However, it remains unclear which monolignol genes are more closely related to the total lignin content and which potential genes influence the total lignin content. In this study, we present a combination of t-test, differential gene expression analysis, correlation analysis, and weighted gene co-expression network analysis to identify genes that regulate the total lignin content by utilizing multi-omics data from transgenic knockdowns of the monolignol genes that includes data related to the transcriptome, proteome, and total lignin content. Firstly, it was discovered that enzymes from the PtrPAL, Ptr4CL, PtrC3H, and PtrC4H gene families are more strongly correlated with the total lignin content. Additionally, the co-downregulation of three genes, PtrC3H3, PtrC4H1, and PtrC4H2, had the greatest impact on the total lignin content. Secondly, GO and KEGG analysis of lignin-related modules revealed that the total lignin content is not only influenced by monolignol genes, but also closely related to genes involved in the "glutathione metabolic process", "cellular modified amino acid metabolic process" and "carbohydrate catabolic process" pathways. Finally, the cinnamyl alcohol dehydrogenase genes CAD1, CADL3, and CADL8 emerged as potential contributors to total lignin content. The genes HYR1 (UDP-glycosyltransferase superfamily protein) and UGT71B1 (UDP-glucosyltransferase), exhibiting a close relationship with coumarin, have the potential to influence total lignin content by regulating coumarin metabolism. Additionally, the monolignol genes PtrC3H3, PtrC4H1, and PtrC4H2, which belong to the cytochrome P450 genes, may have a significant impact on the total lignin content. Overall, this study establishes connections between gene expression levels and total lignin content, effectively identifying genes that have a significant impact on total lignin content and offering novel perspectives for future lignin research endeavours.

Transcriptomic and metabolomic analyses reveal the antifungal mechanism of the compound phenazine-1-carboxamide on Rhizoctonia solani AG1IA

To explore the molecular mechanisms of the antifungal compound phenazine-1-carboxamide (PCN) inhibits Rhizoctonia solani and discover potential targets of action, we performed an integrated analysis of transcriptome and metabolome in R. solani mycelium by whether PCN treating or not. A total of 511 differentially expressed genes (DEGs) were identified between the PCN treatment and control groups. The fluorescence-based quantitative PCR (qPCR) got the accordant results of the gene expression trends for ten randomly selected DEGs. The Gene Ontology (GO) enrichment analysis revealed that fatty acid metabolic process, fatty acid oxidation, and lipid oxidation were among the most enriched in the biological process category, while integral component of membrane, plasma membrane, and extracellular region were among the most enriched in the cellular component category and oxidoreductase activity, cofactor binding, and coenzyme binding were among the most enriched in the molecular function category. KEGG enrichment analysis revealed the most prominently enriched metabolic pathways included ATP-binding cassette (ABC) transporters, nitrogen metabolism, aminobenzoate degradation. The DEGs related functions of cellular structures, cell membrane functions, cellular nutrition, vacuole-mitochondrion membrane contact site and ATPase activity, pH, anti-oxidation, were downregulated. A total of 466 differential metabolites were found between the PCN treatment and control groups after PCN treatment. KEGG enrichment found purine, arachidonic acid, and phenylpropanoid biosynthesis pathways were mainly affected. Further results proved PCN decreased the mycelial biomass and protein content of R. solani, and superoxide dismutase (SOD) activity reduced while peroxidase (POD) and cytochrome P450 activities increased. The molecule docking indicted that NADPH nitrite reductase, ATP-binding cassette transporter, alpha/beta hydrolase family domain-containing protein, and NADPH-cytochrome P450 reductase maybe the particular target of PCN. In conclusion, the mechanisms via which PCN inhibits R. solani AG1IA may be related to cell wall damage, cell membrane impairment, intracellular nutrient imbalance, disturbed antioxidant system, and altered intracellular pH, which laid foundation for the further new compound designing to improve antifungal efficacy.

Screening and Identification of Muscle-Specific Candidate Genes via Mouse Microarray Data Analysis

Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, and the movement cannot be separated from the participation of skeletal muscle. However, the process of muscle development and the regulatory mechanisms of muscle development are not clear at present. In this study, we used bioinformatics techniques to identify differentially expressed genes specifically expressed in multiple muscle tissues of mice as potential candidate genes for studying the regulatory mechanisms of muscle development. Mouse tissue microarray data from 18 tissue samples was selected from the GEO database for analysis. Muscle tissue as the treatment group, and the other 17 tissues as the control group. Genes expressed in the muscle tissue were different to those in the other 17 tissues and identified 272 differential genes with highly specific expression in muscle tissue, including 260 up-regulated genes and 12 down regulated genes. is the genes were associated with the myofibril, contractile fibers, and sarcomere, cytoskeletal protein binding, and actin binding. KEGG pathway analysis showed that the differentially expressed genes in muscle tissue were mainly concentrated in pathways for AMPK signaling, cGMP PKG signaling calcium signaling, glycolysis, and, arginine and proline metabolism. A PPI protein interaction network was constructed for the selected differential genes, and the MCODE module used for modular analysis. Five modules with Score > 3.0 are selected. Then the Cytoscape software was used to analyze the tissue specificity of differential genes, and the genes with high degree scores collected, and some common genes selected for quantitative PCR verification. The conclusion is that we have screened the differentially expressed gene set specific to mouse muscle to provide potential candidate genes for the study of the important mechanisms of muscle development.

Molecular Mechanism of Slow Vegetative Growth in Populus Tetraploid

Tetraploid plants often have altered rates of vegetative growth relative to their diploid progenitors. However, the molecular basis for altered growth rates remains a mystery. This study reports microRNA (miRNA) and gene expression differences in Populus tetraploids and counterpart diploids using RNA and miRNA sequencing. The results showed that there was no significant difference between young leaves in the expression of vegetative growth-related miRNAs. However, as leaves aged, the expression of auxin- and gibberellin-related miRNAs was significantly upregulated, while the expression of senescence-related miRNAs was significantly downregulated. The dose effect enhanced the negative regulation of the target genes with ARFs, GA20ox, GA3ox, and GAMYB being downregulated, and TCP and NAC being upregulated. As a result, the chloroplast degradation of tetraploid leaves was accelerated, the photosynthetic rate was decreased, and the synthesis and decomposition ability of carbohydrate was decreased.

Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy

A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.

[Transcriptome analysis of seed embryo in dormancy and dormancy release state of Thesium chinense]

We used exogenous GA_3 to break the seed dormancy of Thesium chinense. We used high-throughput sequencing technology was used to sequence the transcriptome of dormant seed embryos and dormancy breaking seed embryos of Th. chinense, and the data was analyzed bioinformatically and systematically. The results showed that exogenous GA_3 could effectively break the seed dormancy of Th. chinense; 73 794 up-regulated genes and 42 776 down regulated genes were obtained by transcriptome sequencing; 116 570 diffe-rential genes were annotated by GO function to GO items such as metabolism process, cell process, cell, cell component, binding and catalytic activity. A total of 133 metabolic pathways were found by Pathway analysis of 26 508 differentially expressed genes. In the process of dormancy release, DEGs were mainly enriched in translation, carbohydrate metabolism, folding, classification, degradation and amino acid metabolism. Based on the annotation results in KEGG database, 20 metabolic pathways related to dormancy release were found. Dormancy release of Th. chinense seeds is a complex biological process, including cell morphology construction, secondary metabolite synthesis, sugar metabolism and plant signal transduction, among which plant hormone signal transduction is one of the key factors to regulate dormancy release. The results of qRT-PCR showed that the sequencing results were consistent with the actual results.

chinensis seeds by transcriptome sequencing]

The study aims at exploring the expression of differential genes and related metabolic pathways in the process of seed dormancy release. The dormant embryo and the dormant released embryo of Paris polyphylla var. chinensis were used as the test materials, a new generation high-throughput sequencing methods to sequence the transcriptome of the samples was used to carry out systematic bioinformatics analysis. We obtained 62 882 650 and 62 263 366 clean reads from the DNA libraries of the samples before and after dormancy breaking. A total of 69 248 differentially expressed genes(DEGs) were obtained, 56 426 up-regulated genes and 12 822 down-regulated genes. There are 138 267 differentially expressed genes in the process of embryo dormancy release, which were annotated by GO function to 58 subclasses of biological processes, molecular functions and cell components. The annotated differentially expressed genes were closely related to metabolic processes, biological regulation, cell component synthesis and enzyme catalytic activity. We found 139 metabolic pathways through pathway analysis of 58 722 differentially expressed genes. Before and after dormancy, DEGs were mainly enriched in carbon metabolism, secondary metabolite biosynthesis and polysaccharide metabolism. Based on the annotation results in KEGG database, we found 16 metabolic pathways related to the dormancy release of P. polyhoylla var. chinensis. A large number of differentially expressed genes were involved in embryo morphogenesis, polysaccharide decomposition and protein synthesis during seed development and dormancy release. It involves the interaction of multiple metabolic pathways and constitutes a complex regulation network for dormancy relief.

Altered Expression of Differential Genes in Thoracic Spinal Cord Involved in Experimental Cholestatic Itch Mouse Model

The spinal origin of cholestatic itch in experimental obstructive jaundice mouse model remains poorly understood. In this study, the jaundice model was established by bile duct ligation (BDL) in mice, and differential gene expression patterns were analyzed in the lower thoracic spinal cord involved in cholestatic pruritus after BDL operation using high-throughput RNA sequencing. At 21st day after BDL, the expression levels of ENSRNOG00000060523, ENSRNOG00000058405 and ENSRNOG00000055193 mRNA were significantly up-regulated, and those of ENSRNOG00000042197, ENSRNOG00000008478, ENSRNOGOOOOOO19607, ENSRNOG00000020647, ENSRNOG00000046289, Gemin8, Serpina3n and Trim63 mRNA were significantly down-regulated in BDL group. The RNAseq data of selected mRNAs were validated by RT-qPCR. The expression levels of ENSRNOG00000042197, ENSRNOG00000008478, ENSRNOGOOOOOO 19607, ENSRNOG00000020647, ENSRNOG00000046289 and Serpina3n mRNA were significantly down-regulated in BDL group. This study suggested that cholestatic pruritus in experimental obstructive jaundice mouse model is related with in the changes of gene expression profiles in spinal cord.

Exploring prognostic genes in ovarian cancer stage-related coexpression network modules

Identification of meaningful cluster modules of differential genes or representative biomarkers related to the stages of ovarian cancer (OC) is pivotal, which may help to detect mechanisms of OC progression and evaluate OC patients' prognosis.We downloaded gene expression data and the corresponding clinical information of OC patients from The Cancer Genome Atlas (TCGA) database, which included 379 ovarian cancer patients. Differentially expressed genes (DEGs) of OC patients between stages were picked out using R. There were 731 differential genes between ovarian cancer stage II and stage III (DEGs II-III) and 563 differential genes between ovarian cancer stage III and stage IV (DEGs III-IV), then we performed GO analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, CytoHubba was used to detect the top 20 hub genes in DEGs II-III and DEGs III-IV, followed Cytoscape with search tool for the retrieval of interacting genes (STRING) and MCODE plug-in was utilized to construct protein-protein interaction (PPI) modules of these genes. Three important coexpression modules of DEGs II-III and 3 more meaningful modules of DEGs III-IV were detected from PPI network using molecular complex detection (MCODE) tool. In addition, 5 hub genes in these stage-related DEGs modules with worse overall survival were selected, including COL3A1, COL1A1, COL1A2, KRAS, NRAS. This bioinformatics analysis demonstrated that stage-related prognostic DEGs, such as COL3A1, COL1A1, COL1A2, KRAS, and NRAS might play an unfavorable role in the development as well as metastasis of ovarian cancer. Furthermore, they need to be experimentally verified as a new biomarker to predict OC patient prognosis.

Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database

The aim of the present study was to identify the differentially expressed genes between cervical intraepithelial neoplasias (CIN) and adjacent normal tissue, and to construct a protein-protein interaction (PPI) network. A CIN dataset was obtained from Gene Expression Omnibus, and data of gene expression in CIN and adjacent normal tissue were extracted from GSE64217. The differentially expressed genes were selected using software package and heat map was drawn using the ‘pheatmap’ package. The selected differentially expressed genes were subjected to PPI, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Cytoscape, Database for Annotation, Visualization and Integrated Discovery, STRING and KOBAS. In the present study, 287 genes were differentially expressed between CIN and adjacent normal tissue, of which 170 were significantly upregulated and 118 genes were significantly downregulated (P<0.00001, fold-change >6). A differential gene expression network map was constructed to show the interactions of 30 protein products encoded by differentially expressed genes using STRING software. In particular, the key gene, EGR1, was identified using Cytoscape software. The KEGG pathway analysis revealed that the differential genes were mainly involved in several pathways, including ‘glutathione metabolism’, ‘arachidonic acid metabolism’, and ‘pentose phosphate pathway’. Results of the GO analysis showed that differential genes were enriched in different subsets. Specifically, small proline-rich protein 2E and 3, distal-less homeobox 5, epithelial membrane protein 1, cornifelin, periplakin, homeobox protein Hox-A13, estrogen receptor α, transglutaminase 1, small proline-rich protein 2A, Rh C glycoprotein, tumor protein p63, TGM3, homeobox B5 and small proline-rich protein 2D were enriched in ‘epithelial cell differentiation’, which affected the differentiation of epithelial cells. In conclusion, 287 differentially expressed genes were identified successfully. The key gene was identified based on the results of PPI, GO and KEGG analyses, and functional annotation and pathway analysis were also performed. Our study provides the basis for further studies on the interaction among differentially expressed genes.