GOMA: functional enrichment analysis tool based on GO modules

Effect of morroniside on the transcriptome profiles of rat in injured spinal cords

We have previously reported that morroniside promoted motor activity after spinal cord injury (SCI) in rats. However, the mechanism by which morroniside induces recovery of injured spinal cord (SC) remains unknown. In the current study, RNA sequencing (RNA-seq) was employed to evaluate changes of gene expressions at the transcriptional level of the injured spinal cords in morroniside-administrated rats. Principal component analysis, analysis of enriched Gene Ontology (GO), enrichment analyses Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and other bioinformatics analyses were executed to distinguish differentially expressed genes (DEGs). The results of RNA-seq confirmed the anti-inflammatory and anti-apoptotic effects of morroniside on injured SC tissues, and provided the basis for additional research of the mechanisms involving the protective effects of morroniside on SCI.

miR-30a-3p can inhibit the proliferation and promote the differentiation of chicken primary myoblasts

1. Chicken muscle is an important factor in meat quality and its development is controlled by a complex regulatory network.2. The following study examined the expression of miR-30a-3p in Gushi chicken breast muscle tissue and found that it was differentially expressed at different embryonic stages, reaching a peak in the 14-day-old embryo (E14).3. The effect of miR-30a-3p on chicken primary myoblasts (CPMs) was explored. Results from both cell counting kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) showed that this can inhibit the proliferation of myoblasts, and through cell cycle experiments, the inhibition of myoblast proliferation was found, which may be due to G0/G1 arrest in the cell cycle.4. The effect of miR-30a-3p on the differentiation of myoblasts was studied. The results showed that miR-30a-3p can promote the expression of MYOD, myogenin (MYOG), and myosin heavy chain (MYHC) genes to promote the differentiation of myoblasts. Through MYHC protein immunofluorescence experiments, it was found that miR-30a-3p can effectively increase the area of myotubes.5. Finally, mRNA transcriptome data was analysed, which showed that miR-30a-3p has 51 potential target genes. Among them, forkhead box O3 (FOXO3), ankyrin repeat domain 1 (ANKRD1), and insulin-induced 1 (INSIG1) genes were differentially expressed at different developmental stages and were enriched in Gene Ontology (GO) terms, such as cell differentiation and cellular developmental process. The data showed that tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma (YWHAG), BUB1 mitotic checkpoint serine/threonine kinase (BUB1), and growth arrest and DNA damage-inducible 45 (GADD45) genes were enriched in the cell cycle pathway.6. It can be speculated that miR-30a-3p plays roles through these genes in myoblast development. This research provides information for further improving knowledge of the chicken muscle development regulation network.

Identification of key genes and pathways in mild and severe nonalcoholic fatty liver disease by integrative analysis

Background

The global prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing. The pathogenesis of NAFLD is multifaceted, and the underlying mechanisms are elusive. We conducted data mining analysis to gain a better insight into the disease and to identify the hub genes associated with the progression of NAFLD.

Methods

The dataset GSE49541, containing the profile of 40 samples representing mild stages of NAFLD and 32 samples representing advanced stages of NAFLD, was acquired from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the R programming language. The Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database were used to perform the enrichment analysis and construct protein–protein interaction (PPI) networks, respectively. Subsequently, transcription factor networks and key modules were identified. The hub genes were validated in a mice model of high fat diet (HFD)-induced NAFLD and in cultured HepG2 cells by real-time quantitative PCR.

Results

Based on the GSE49541 dataset, 57 DEGs were selected and enriched in chemokine activity and cellular component, including the extracellular region. Twelve transcription factors associated with DEGs were indicated from PPI analysis. Upregulated expression of five hub genes (SOX9, CCL20, CXCL1, CD24, and CHST4), which were identified from the dataset, was also observed in the livers of HFD-induced NAFLD mice and in HepG2 cells exposed to palmitic acid or advanced glycation end products.

Conclusion

The hub genes SOX9, CCL20, CXCL1, CD24, and CHST4 are involved in the aggravation of NAFLD. Our results offer new insights into the underlying mechanism of NAFLD progression.

Integration of gene expression data identifies key genes and pathways in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignant tumor and prevalent cause of cancer-related death worldwide. In this study, we analyzed the gene expression profiles of patients with CRC with the aim of better understanding the molecular mechanism and key genes in CRC. Four gene expression profiles including, GSE9348, GSE41328, GSE41657, and GSE113513 were downloaded from GEO database. The data were processed using R programming language, in which 319 common differentially expressed genes including 94 up-regulated and 225 down-regulated were identified. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were conducted to find the most significant enriched pathways in CRC. Based on the GO and KEGG pathway analysis, the most important dysregulated pathways were regulation of cell proliferation, biocarbonate transport, Wnt, and IL-17 signaling pathways, and nitrogen metabolism. The protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software and hub genes including MYC, CXCL1, CD44, MMP1, and CXCL12 were identified as the most critical hub genes. The present study enhances our understanding of the molecular mechanisms of the CRC, which might potentially be applied in the treatment strategies of CRC as molecular targets and diagnostic biomarkers.

Talon Cusp Associates With MMP2 in a Cohort of Individuals Born With Oral Clefts

OBJECTIVE

The aim of this study was to use dental development as a tool to subphenotype oral clefts and investigate the association of MMP2 with dentin-pulp complex anomalies, in order to identify dental anomalies that are a part of a "cleft syndrome."

DESIGN

Two hundred and ninety individuals born with cleft lip and palate were evaluated and several clinical features, such as cleft completeness or incompleteness, laterality, and presence of dental anomalies were used to assess each individual's cleft status. We tested for overrepresentation of MMP2 single nucleotide polymorphism rs9923304 alleles depending on individuals having certain dental anomalies. Chi-square and Fisher exact tests were used in all comparisons (α = .05).

RESULTS

All individuals studied had at least one dental anomaly outside the cleft area. Significant differences between individuals born with clefts with and without talon cusp (P = .04) were observed for the frequency of the MMP2 less common allele.

CONCLUSION

All individuals born with cleft lip and palate had alterations of the dentition, and a quarter to half of the individuals had alterations of the internal anatomy of their teeth, which further indicates that dental anomalies can be considered as an extended phenotype for clefts. MMP2 was associated with talon cusp in individuals born with oral clefts.

Effect of VX‑765 on the transcriptome profile of mice spinal cords with acute injury

Previous studies have shown that caspase-1 plays an important role in the acute inflammatory response of spinal cord injury (SCI). VX‑765, a novel and irreversible caspase‑1 inhibitor, has been reported to effectively intervene in inflammation. However, the effect of VX‑765 on genome‑wide transcription in acutely injured spinal cords remains unknown. Therefore, in the present study, RNA‑sequencing (RNA‑Seq) was used to analyze the effect of VX‑765 on the local expression of gene transcription 8 h following injury. The differentially expressed genes (DEGs) underwent enrichment analysis of functions and pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively. Parallel analysis of western blot confirmed that VX‑765 can effectively inhibit the expression and activation of caspase‑1. RNA‑Seq showed that VX‑765 treatment resulted in 1,137 upregulated and 1,762 downregulated DEGs. These downregulated DEGs and their associated signaling pathways, such as focal adhesion, cytokine‑cytokine receptor interaction, leukocyte transendothelial migration, extracellular matrix‑receptor interaction, phosphatidylinositol 3‑kinase‑protein kinase B, Rap1 and hypoxia inducible factor‑1 signaling pathway, are mainly associated with inflammatory response, local hypoxia, macrophage differentiation, adhesion migration and apoptosis of local cells. This suggests that the application of VX‑765 in the acute phase can improve the local microenvironment of SCI by inhibiting caspase‑1. However, whether VX‑765 can be used as a therapeutic drug for SCI requires further exploration. The sequence data have been deposited into the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA548970).

A transcriptomic study of probenecid on injured spinal cords in mice

Background

Recent studies have found that probenecid has neuroprotective and reparative effects on central nervous system injuries. However, its effect on genome-wide transcription in acute spinal cord injury (SCI) remains unknown. In the present study, RNA sequencing (RNA-Seq) is used to analyze the effect of probenecid on the local expression of gene transcription 8 h after spinal injury.

Methods

An Infinite Horizon impactor was used to perform contusive SCI in mice. The SCI model was made by using a rod (1.3 mm diameter) with a force of 50 Kdynes. Sham-operated mice only received a laminectomy without contusive injury. The injured mice were randomly assigned into either the control (SCI_C) or probenecid injection (SCI_P) group. In the latter group, the probenecid drug was intraperitoneally injected (0.5 mg/kg) immediately following injury. Eight hours after the injury or laminectomy, the spinal cords were removed from the mice in both groups. The total RNAs were extracted and purified for library preparation and transcriptome sequencing. Differential gene expressions (DEGs) of the three groups-sham, SCI_C and SCI_P-were analyzed using a DESeq software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs were performed using a GOseq R package and KOBAS software. Real-time quantitative reverse-transcriptase polymerase chain reaction was used to validate RNA-Seq results.

Results

RNA-Seq showed that, compared to the SCI_C group, the number of DEGs was 641 in the SCI_P group (286 upregulated and 355 downregulated). According to GO analysis, DEGs were most enriched in extracellular matrix (ECM), collagen trimer, protein bounding and sequence specific DNA binding. KEGG analysis showed that the most enriched pathways included: cell adhesion molecules, Leukocyte transendothelial migration, ECM-receptor interactions, PI3K-Akt signaling pathways, hematopoietic cell lineages, focal adhesions, the Rap1 signaling pathway, etc. The sequence data have been deposited into the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA554464).

Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis

Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas. DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were significantly linked with poor survival in pancreatic adenocarcinoma. Conclusions: These hub genes may be used as potential targets for pancreatic cancer diagnosis and treatment.

Effect of M2 macrophage adoptive transfer on transcriptome profile of injured spinal cords in rats

The previous studies showed that alternatively activated anti-inflammatory macrophage (M2) adoptive immunity can improve the proportion of local M2 cells and play the neuroprotective effect after spinal cord injury (SCI). Its molecular mechanism is not yet very clear. Therefore, this study aims to analyze the effect of the M2 adoptive transfer on the local expression of gene transcription. Sprague-Dawley (SD) rats were used for culture of macrophages and establishment of SCI models. After SCI, the polarized M2 macrophages were transferred to the injured rats by tail vein injection. Seven days after operation, the differentially expressed genes (DEGs) in the spinal cords were analyzed by RNA-sequencing (RNA-Seq). Then, the functional enrichment analysis and pathways were performed by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), respectively. RNA-Seq showed that M2 adoptive immunity can down-regulate many well-studied gene expressions associated with signaling pathways of inflammatory, such as antigen processing and presentation, phagosome, cell adhesion molecules, natural killer cell-mediated cytotoxicity, endocytosis, proteasome, and Toll-like receptor signaling pathway. These may explain the mechanism of our previous adoptive immunization of M2 cells to provide neuroprotection for SCI. In addition, a novel pathway, retinoic acid-inducible gene-1 (RIG-I)-like receptor signaling pathway was found to be involved in the pathological process of SCI and the response to M2 adoptive immunity as well. This will provide a new explanation for the pathological mechanism of SCI and a new theoretical and experimental basis for its clinical treatment. The raw Illumina data are available at http://www.ncbi.nlm.nih.gov/sra (accession number PRJNA517238).

Impact statement

This research aimed to analyze the effect of M2 macrophage adoptive transfer on the local expression of gene transcription after SCI by RNA-Seq. The results showed that M2 adoptive immunity can down-regulate many well-studied gene expressions associated with signaling pathways of inflammatory. These may explain the mechanism of our previous adoptive immunization of M2 cells to provide neuroprotection for SCI. In addition, a novel pathway, RIG-I-like receptor signaling pathway was also found to involve in the pathological process of SCI and the response to M2 adoptive immunity. This will provide a new explanation for the pathological mechanism of SCI and a new theoretical and experimental basis for its clinical treatment.

CEA: Combination-based gene set functional enrichment analysis

Functional enrichment analysis is a fundamental and challenging task in bioinformatics. Most of the current enrichment analysis approaches individually evaluate functional terms and often output a list of enriched terms with high similarity and redundancy, which makes it difficult for downstream studies to extract the underlying biological interpretation. In this paper, we proposed a novel framework to assess the performance of combination-based enrichment analysis. Using this framework, we formulated the enrichment analysis as a multi-objective combinatorial optimization problem and developed the CEA (Combination-based Enrichment Analysis) method. CEA provides the whole landscape of term combinations; therefore, it is a good benchmark for evaluating the current state-of-the-art combination-based functional enrichment methods in a comprehensive manner. We tested the effectiveness of CEA on four published microarray datasets. Enriched functional terms identified by CEA not only involve crucial biological processes of related diseases, but also have much less redundancy and can serve as a preferable representation for the enriched terms found by traditional single-term-based methods. CEA has been implemented in the R package CopTea and is available at http://github.com/wulingyun/CopTea/.

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

CoCiter: an efficient tool to infer gene function by assessing the significance of literature co-citation

A routine approach to inferring functions for a gene set is by using function enrichment analysis based on GO, KEGG or other curated terms and pathways. However, such analysis requires the existence of overlapping genes between the query gene set and those annotated by GO/KEGG. Furthermore, GO/KEGG databases only maintain a very restricted vocabulary. Here, we have developed a tool called "CoCiter" based on literature co-citations to address the limitations in conventional function enrichment analysis. Co-citation analysis is widely used in ranking articles and predicting protein-protein interactions (PPIs). Our algorithm can further assess the co-citation significance of a gene set with any other user-defined gene sets, or with free terms. We show that compared with the traditional approaches, CoCiter is a more accurate and flexible function enrichment analysis method. CoCiter is freely available at www.picb.ac.cn/hanlab/cociter/.

Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules

In this editorial preface, I briefly review cancer bioinformatics and introduce the four articles in this special issue highlighting important applications of the field: detection of chromatin states; detection of SNP-containing motifs and association with transcription factor-binding sites; improvements in functional enrichment modules; and gene association studies on aging and cancer. We expect this issue to provide bioinformatics scientists, cancer biologists, and clinical doctors with a better understanding of how cancer bioinformatics can be used to identify candidate biomarkers and targets and to conduct functional analysis.