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Coilia nasus is an anadromous fish that has been successfully domesticated in the last decade due to its high economic value. The fish exhibits a delayed ovary development during the reproductive season, despite breeding and selection for five to six offspring. The molecular mechanism of the delayed ovary development is still unknown, so the obstacles have not been removed in the large-scale breeding program. This study aims to investigate the key genes regulating ovarian development by comparing the transcriptomes of ovarian-stage IV and stage II brain/pituitary of Coilia nasus. Ovarian stages were validated by histological sections. A total of 75,097,641 and 66,735,592 high-quality reads were obtained from brain and pituitary transcriptomes, respectively, and alternatively spliced transcripts associated with gonadal development were detected. Compared to ovarian Ⅱ- brain, 515 differentially expressed genes (DEGs) were upregulated and 535 DEGs were downregulated in ovarian Ⅳ- brain, whereas 470 DEGs were upregulated and 483 DEGs were downregulated in ovarian Ⅳ- pituitary compared to ovarian Ⅱ- pituitary. DEGs involved in hormone synthesis and secretion and in the GnRH signaling pathway were screened. Weighted gene co-expression network analysis identified gene co-expression modules that were positively correlated with ovarian phenotypic traits. The hub genes Smad4 and TRPC4 in the modules were co-expressed with DEGs including Kiss1 receptor and JUNB, suggesting that ovarian development is controlled by a hypothalamic-pituitary-gonadal axis. Our results have provided new insights that advance our understanding of the molecular mechanism of C. nasus reproductive functions and will be useful for future breeding.

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Mung bean is a dual-use crop widely cultivated in Southeast Asia as a food and medicine resource. The development of new functional mung bean varieties demands identifying new genes regulating anthocyanidin synthesis and investigating their molecular mechanism. In this study, we used high-throughput sequencing technology to generate transcriptome sequence of leaves, petioles, and hypocotyls for investigating the anthocyanins accumulation in common mung bean variety as well as anthocyanidin rich mung bean variety, and to elucidate their molecular mechanisms. 29 kinds of anthocyanin compounds were identified. Most of the anthocyanin components contents were significantly higher in ZL23 compare with AL12. Transcriptome analysis suggested that a total of 93 structural genes encoding the anthocyanin biosynthetic pathway and 273 regulatory genes encoding the ternary complex of MYB-bHLH-WD40 were identified, of which 26 and 78 were differentially expressed in the two varieties. Weighted gene co-expression network analysis revealed that VrMYB3 and VrMYB90 might have enhanced mung bean anthocyanin content by inducing the expression of structural genes such as PAL, 4CL, F3'5'H, LDOX, and F3'H, which was consistent with qRT-PCR results. These findings are envisaged to provide a reference for studying the molecular mechanism of anthocyanin accumulation in mung beans.

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Diet influences the pathogenesis and clinical course of inflammatory bowel disease (IBD). The Mediterranean diet (MD) is linked to reductions in inflammatory biomarkers and alterations in microbial taxa and metabolites associated with health. We aimed to identify features of the gut microbiome that mediate the relationship between the MD and fecal calprotectin (FCP) in ulcerative colitis (UC). Weighted gene co-expression network analysis (WGCNA) was used to identify modules of co-abundant microbial taxa and metabolites correlated with the MD and FCP. The features considered were gut microbial taxa, serum metabolites, dietary components, short-chain fatty acid and bile acid profiles in participants that experienced an increase (n = 13) or decrease in FCP (n = 16) over eight weeks. WGCNA revealed ten modules containing sixteen key features that acted as key mediators between the MD and FCP. Three taxa (Faecalibacterium prausnitzii, Dorea longicatena, Roseburia inulinivorans) and a cluster of four metabolites (benzyl alcohol, 3-hydroxyphenylacetate, 3-4-hydroxyphenylacetate and phenylacetate) demonstrated a strong mediating effect (ACME: -1.23, p = 0.004). This study identified a novel association between diet, inflammation and the gut microbiome, providing new insights into the underlying mechanisms of how a MD may influence IBD. See clinicaltrials.gov (NCT04474561).

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Heat stress (HS) causes imbalance of cellular homeostasis, growth impairment and extensively yield loss in crop production. In the present study, the tropic maize inbred CIMBL55 showed more thermotolerance than the maize temperate inbred B73, with less leaf damage rate and ROS accumulation. Transcriptome profiling of CIMBL55 and B73 upon (exposing at 45 â„ƒ for 0, 1, and 6 h) and post (recovering at 28 â„ƒ for 1 and 6 h) HS were further assessed and a total of 20204 DEGs were identified. Functional annotation revealed that HS activated unfolded protein response in endoplasmic reticulum in both two inbreds. Moreover, in CIMBL55, far more primary and secondary metabolism pathways were transcriptional altered. Afterwards, weighted gene co-expression analysis grouped all expressed genes into eighteen co-expressed modules. Four HS responsive and four CIMBL55 recovery-related modules were subsequently identified. Highly connected genes (hub genes) in these modules were characterized as transcription factors, heat shock proteins, Ca2+ signaling related genes and various enzymes. Moreover, one hub gene, ZmHsftf13 was verified to positively regulate thermotolerance by heterologous expressing in Arabidopsis and its Mu insertion mutant. The present research provides promising genes related to HS response in maize and is of great significance for breeding.

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Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disease characterized by progressive memory loss. The main pathological features of the disease are extracellular deposition of amyloid ß (Aß) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. Understanding factors contributing to AD progression, the number of molecular signatures, and the development of therapeutic agents played a significant role in the discovery of disease-modifying drugs to treat the disease. Bioinformatics has established its significance in many areas of biology. The role of bioinformatics in drug discovery, is emerging significantly and will continue to evolve. In recent years, different bioinformatics methodologies, viz. protein signaling pathway, molecular signature differences between different classes of drugs, interacting profiles of drugs and their potential therapeutic mechanisms, have been applied to identify potential therapeutic targets of AD. Bioinformatics tools were also found to contribute to the discovery of novel drugs, omics-based biomarkers, and drug repurposing for AD. The review aims to explore the applications of various advanced bioinformatics tools in the identification of targets, biomarkers, pathways, and potential therapeutics for the treatment of the disease.

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As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine.

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A large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features - gene size, mRNA abundance, and guanine-cytosine content - affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

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A large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features - gene size, mRNA abundance, and guanine-cytosine content - affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

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Hickory (Carya cathayensis Sarg.) is an extraordinary nut-bearing deciduous arbor with high content of oil in its embryo. However, the molecular mechanism underlying high oil accumulation is mostly unknown. Here, we reported that the lipid droplets and oil accumulation gradually increased with the embryo development and the oil content was up to ~76% at maturity. Furthermore, transcriptome and proteome analysis of developing hickory embryo identified 32,907 genes and 9857 proteins. Time-series analysis of gene expressions showed that these genes were divided into 12 clusters and lipid metabolism-related genes were enriched in Cluster 3, with the highest expression levels at 95 days after pollination (S2). Differentially expressed genes and proteins indicated high correlation, and both were enriched in the lipid metabolism. Notably, the genes involved in biosynthesis, transport of fatty acid/lipid and lipid droplets formation had high expression levels at S2, while the expression levels of other genes required for suberin/wax/cutin biosynthesis and lipid degradation were very low at all the sampling time points, ultimately promoting the accumulation of oil. Quantitative reverse-transcription PCR analysis also verified the results of RNA-seq. The co-regulatory networks of lipid metabolism were further constructed and WRINKLED1 (WRI1) was a core transcriptional factor located in the nucleus. Of note, CcWRI1A/B could directly activate the expression of some genes (CcBCCP2A, CcBCCP2B, CcFATA and CcFAD3) required for fatty acid synthesis. These results provided in-depth evidence for revealing the molecular mechanism of high oil accumulation in hickory embryo.

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BACKGROUND: Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted. RESULTS: In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered. CONCLUSIONS: All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

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Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.

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Solanum melongena L. (eggplant) bacterial wilt is a severe soil borne disease. Here, this study aimed to explore the regulation mechanism of eggplant bacterial wilt-resistance by transcriptomics with weighted gene co-expression analysis network (WGCNA). The different expression genes (DEGs) of roots and stems were divided into 21 modules. The module of interest (root: indianred4, stem: coral3) with the highest correlation with the target traits was selected to elucidate resistance genes and pathways. The selected module of roots and stems co-enriched the pathways of MAPK signalling pathway, plant pathogen interaction, and glutathione metabolism. Each top 30 hub genes of the roots and stems co-enriched a large number of receptor kinase genes. A total of 14 interesting resistance-related genes were selected and verified with quantitative polymerase chain reaction (qPCR). The qPCR results were consistent with those of WGCNA. The hub gene of EGP00814 (namely SmRPP13L4) was further functionally verified; SmRPP13L4 positively regulated the resistance of eggplant to bacterial wilt by qPCR and virus-induced gene silencing (VIGS). Our study provides a reference for the interaction between eggplants and bacterial wilt and the breeding of broad-spectrum and specific eggplant varieties that are bacterial wilt-resistant.

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Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein-protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

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BACKGROUND: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas. METHOD: The "CIBERSORT" algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the "ESTIMATE" algorithm was used to assess the "Estimate," "Immune," and "Stromal" scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the "clusterProfiler" package in R for annotation and visualization. RESULTS: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. CONCLUSION: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

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Rhabdomyosarcoma is subclassified by the presence or absence of a recurrent chromosome translocation that fuses the FOXO1 and PAX3 or PAX7 genes. The fusion protein (FOXO1-PAX3/7) retains both binding domains and becomes a novel and potent transcriptional regulator in rhabdomyosarcoma subtypes. Many studies have characterized and integrated genomic, transcriptomic, and epigenomic differences among rhabdomyosarcoma subtypes that contain the FOXO1-PAX3/7 gene fusion and those that do not; however, few investigations have investigated how gene co-expression networks are altered by FOXO1-PAX3/7. Although transcriptional data offer insight into one level of functional regulation, gene co-expression networks have the potential to identify biological interactions and pathways that underpin oncogenesis and tumorigenicity. Thus, we examined gene co-expression networks for rhabdomyosarcoma that were FOXO1-PAX3 positive, FOXO1-PAX7 positive, or fusion negative. Gene co-expression networks were mined using local maximum Quasi-Clique Merger (lmQCM) and analyzed for co-expression differences among rhabdomyosarcoma subtypes. This analysis observed 41 co-expression modules that were shared between fusion negative and positive samples, of which 17/41 showed significant up- or down-regulation in respect to fusion status. Fusion positive and negative rhabdomyosarcoma showed differing modularity of co-expression networks with fusion negative (n = 109) having significantly more individual modules than fusion positive (n = 53). Subsequent analysis of gene co-expression networks for PAX3 and PAX7 type fusions observed 17/53 were differentially expressed between the two subtypes. Gene list enrichment analysis found that gene ontology terms were poorly matched with biological processes and molecular function for most co-expression modules identified in this study; however, co-expressed modules were frequently localized to cytobands on chromosomes 8 and 11. Overall, we observed substantial restructuring of co-expression networks relative to fusion status and fusion type in rhabdomyosarcoma and identified previously overlooked genes and pathways that may be targeted in this pernicious disease.

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[This corrects the article DOI: 10.3389/fpls.2019.00156.].

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Viola is a large genus with worldwide distribution and many traits not currently exemplified in model plants including unique breeding systems and the production of cyclotides. Here we report de novo genome assembly and transcriptomic analyses of the non-model species Viola pubescens using short-read DNA sequencing data and RNA-Seq from eight diverse tissues. First, V. pubescens genome size was estimated through flow cytometry, resulting in an approximate haploid genome of 455 Mbp. Next, the draft V. pubescens genome was sequenced and assembled resulting in 264,035,065 read pairs and 161,038 contigs with an N50 length of 3,455 base pairs (bp). RNA-Seq data were then assembled into tissue-specific transcripts. Together, the DNA and transcript data generated 38,081 ab initio gene models which were functionally annotated based on homology to Arabidopsis thaliana genes and Pfam domains. Gene expression was visualized for each tissue via principal component analysis and hierarchical clustering, and gene co-expression analysis identified 20 modules of tissue-specific transcriptional networks. Some of these modules highlight genetic differences between chasmogamous and cleistogamous flowers and may provide insight into V. pubescens' mixed breeding system. Orthologous clustering with the proteomes of A. thaliana and Populus trichocarpa revealed 8,531 sequences unique to V. pubescens, including 81 novel cyclotide precursor sequences. Cyclotides are plant peptides characterized by a stable, cyclic cystine knot motif, making them strong candidates for drug scaffolding and protein engineering. Analysis of the RNA-Seq data for these cyclotide transcripts revealed diverse expression patterns both between transcripts and tissues. The diversity of these cyclotides was also highlighted in a maximum likelihood protein cladogram containing V. pubescens cyclotides and published cyclotide sequences from other Violaceae and Rubiaceae species. Collectively, this work provides the most comprehensive sequence resource for Viola, offers valuable transcriptomic insight into V. pubescens, and will facilitate future functional genomics research in Viola and other diverse plant groups.

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Background: Soft tissue sarcomas (STS) are heterogeneous tumors derived from mesenchymal cells that differentiate into soft tissues. The prognosis of patients who present with an STS is influenced by the regulation of a complex gene network. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with STS (Samples = 156). Results: Among the 11 modules identified, the black and blue modules were highly correlated with STS. However, using preservation analysis, the black module demonstrated low preservation, therefore the blue module was chosen as the module of interest. Furthermore, a total of 20 network hub genes were identified in the blue module, 12 of which were also hub nodes in the protein-protein interaction network of the module genes. Following additional verification, 4 of 12 genes (RRM2, BUB1B, CENPF, and KIF20A) demonstrated poorer overall survival and disease-free survival rate in the test datasets. In addition, gene set enrichment analysis (GSEA) demonstrated that samples with a high level of blue module eigengene (ME) were enriched in cell cycle and metabolism associated signaling pathways. Conclusion: In summary, co-expression network analysis identified four hub genes associated with prognosis for STS, which may diminish the prognosis by influencing cell cycle and metabolism associated signaling pathways.

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Sulfur is an essential nutrient for plant growth and development. Sulfur is a constituent of proteins, the plasma membrane and cell walls, among other important cellular components. To obtain new insights into the gene regulatory networks underlying the sulfate response, we performed an integrative meta-analysis of transcriptomic data from five different sulfate experiments available in public databases. This bioinformatic approach allowed us to identify a robust set of genes whose expression depends only on sulfate availability, indicating that those genes play an important role in the sulfate response. In relation to sulfate metabolism, the biological function of approximately 45% of these genes is currently unknown. Moreover, we found several consistent Gene Ontology terms related to biological processes that have not been extensively studied in the context of the sulfate response; these processes include cell wall organization, carbohydrate metabolism, nitrogen compound transport, and the regulation of proteolysis. Gene co-expression network analyses revealed relationships between the sulfate-responsive genes that were distributed among seven function-specific co-expression modules. The most connected genes in the sulfate co-expression network belong to a module related to the carbon response, suggesting that this biological function plays an important role in the control of the sulfate response. Temporal analyses of the network suggest that sulfate starvation generates a biphasic response, which involves that major changes in gene expression occur during both the early and late responses. Network analyses predicted that the sulfate response is regulated by a limited number of transcription factors, including MYBs, bZIPs, and NF-YAs. In conclusion, our analysis identified new candidate genes and provided new hypotheses to advance our understanding of the transcriptional regulation of sulfate metabolism in plants.