Gene Ontology
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2021 ◽  
Krzysztof Kotlarz ◽  
Magda Mielczarek ◽  
Yachun Wang ◽  
Jinhuan Dou ◽  
Tomasz Suchocki ◽  

Abstract Since global temperature is expected to rise by 2℃ in 2050 heat stress may become the most severe environmental factor. In the study, we illustrate the application of mixed linear models for the analysis of whole transcriptome expression in livers and adrenal tissues of Sprague-Dawley rats obtained by a heat stress experiment. By applying those models, we considered four sources of variation in transcript expression, comprising transcripts (1), genes (2), Gene Ontology terms (3), and Reactome pathways (4) and focussed on accounting for the similarity within each source, which was expressed as a covariance matrix. Models based on transcripts or genes levels explained a larger proportion of log2 fold change than models fitting the functional components of Gene Ontology terms or Reactome pathways. In the liver, among the most significant genes were PNKD and TRIP12. In the adrenal tissue, one transcript of the SUCO gene was expressed more strongly in the control group than in the heat-stress group. PLEC had two transcripts, which were significantly overexpressed in the heat-stress group. PER3 was significant only on gene level. Moving to the functional scale, five Gene Ontologies and one Reactome pathway were significant in the liver. They can be grouped into ontologies related to DNA repair, histone ubiquitination, the regulation of embryonic development and cytoplasmic translation. Linear mixed models are valuable tools for the analysis of high-throughput biological data. Their main advantages are the possibility to incorporate information on covariance between observations and circumventing the problem of multiple testing.

2021 ◽  
Vol 22 (1) ◽  
Rasmus Magnusson ◽  
Zelmina Lubovac-Pilav

Abstract Background Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes. Results We present the TFTenricher, a Python toolbox that focuses specifically at identifying gene ontology terms, cellular pathways, and diseases that are over-represented among genes downstream of user-defined sets of human TFs. We evaluated the inference of downstream gene targets with respect to false positive annotations, and found an inference based on co-expression to best predict downstream processes. Based on these downstream genes, the TFTenricher uses some of the most common databases for gene functionalities, including GO, KEGG and Reactome, to calculate functional enrichments. By applying the TFTenricher to differential expression of TFs in 21 diseases, we found significant terms associated with disease mechanism, while the gene set enrichment analysis on the same dataset predominantly identified processes related to transcription. Conclusions and availability The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes. The TFTenricher is available as a Python 3 toolbox at, under a GNU GPL license and with minimal dependencies.

2021 ◽  
Mehrdad Shahbazi ◽  
Nikta Ziaei

Abstract This research is aimed to explore the molecular response of the skin cells to Hidradenitis Suppurativa (HS). Microarray transcriptome data for patients with HS, merged and employed to identify the differentially expressed genes (DEGs), gene ontology (GO), and long non-coding RNAs (lncRNAs). A protein-protein interaction network, and consequently, a co-expression network, was constructed for the essential genes. The key genes' clinical relevance was also established by survival analysis, relative expression level, immunohistochemistry, and immune infiltration correlation analysis. Finally, potential herbal ingredients with inhibitory effects on cancer inducer proteins were characterized using the Traditional Chinese Medicine (TCMD) database. The chemokine signaling pathway was the gene ontology of the most significant cluster found by clusterONE. Myocardial Infarction Associated Transcript (MIAT) and RNA Polymerase II Subunit J4, Pseudogene (POLR2J4) are the predicted lncRNAs for up-and down-regulated genes. Interleukin 6 (IL6), Formyl Peptide Receptor 2 (FPR2), C-X-C Motif Chemokine Ligand 10 (CXCL10), C-C Motif Chemokine Receptor 7 (CCR7), and C-C Motif Chemokine Ligand 5 (CCL5) genes were predicted as hub-genes. Tryptophan 2,3-Dioxygenase (TDO2), Serpin Family B Member 4 (SERPINB4), and Matrix Metallopeptidase 3 (MMP3) were demonstrated to be potential cancer inducers due to their high expression level in HS disease. These genes also were positively correlated with dendritic cells, T cell CD4+, monocytes, and neutrophils in the skin cancer microenvironment. Piceatannol, Salicylic acid, and magnolol herbal ingredients were predicted as potential compounds with inhibitory effects on SERPIN B4, MMP3, and TDO2, respectively. The output of the present study will aid in a better understanding of the HS disease and consequent cancer induction mechanism.

2021 ◽  
Vol 12 ◽  
Ahmed H. El-Sappah ◽  
Rania G. Elbaiomy ◽  
Ahmed S. Elrys ◽  
Yu Wang ◽  
Yumin Zhu ◽  

Metal tolerance proteins (MTPs) encompass plant membrane divalent cation transporters to specifically participate in heavy metal stress resistance and mineral acquisition. However, the molecular behaviors and biological functions of this family in Medicago truncatula are scarcely known. A total of 12 potential MTP candidate genes in the M. truncatula genome were successfully identified and analyzed for a phylogenetic relationship, chromosomal distributions, gene structures, docking analysis, gene ontology, and previous gene expression. M. truncatula MTPs (MtMTPs) were further classified into three major cation diffusion facilitator (CDFs) groups: Mn-CDFs, Zn-CDFs, and Fe/Zn-CDFs. The structural analysis of MtMTPs displayed high gene similarity within the same group where all of them have cation_efflux domain or ZT_dimer. Cis-acting element analysis suggested that various abiotic stresses and phytohormones could induce the most MtMTP gene transcripts. Among all MTPs, PF16916 is the specific domain, whereas GLY, ILE, LEU, MET, ALA, SER, THR, VAL, ASN, and PHE amino acids were predicted to be the binding residues in the ligand-binding site of all these proteins. RNA-seq and gene ontology analysis revealed the significant role of MTP genes in the growth and development of M. truncatula. MtMTP genes displayed differential responses in plant leaves, stems, and roots under five divalent heavy metals (Cd2+, Co2+, Mn2+, Zn2+, and Fe2+). Ten, seven, and nine MtMTPs responded to at least one metal ion treatment in the leaves, stems, and roots, respectively. Additionally, MtMTP1.1, MtMTP1.2, and MtMTP4 exhibited the highest expression responses in most heavy metal treatments. Our results presented a standpoint on the evolution of MTPs in M. truncatula. Overall, our study provides a novel insight into the evolution of the MTP gene family in M. truncatula and paves the way for additional functional characterization of this gene family.

2021 ◽  
Vol 22 (17) ◽  
pp. 9527
Laura Ravazzolo ◽  
Sara Trevisan ◽  
Silvia Iori ◽  
Cristian Forestan ◽  
Mario Malagoli ◽  

Maize root responds to nitrate by modulating its development through the coordinated action of many interacting players. Nitric oxide is produced in primary root early after the nitrate provision, thus inducing root elongation. In this study, RNA sequencing was applied to discover the main molecular signatures distinguishing the response of maize root to nitrate according to their dependency on, or independency of, nitric oxide, thus discriminating the signaling pathways regulated by nitrate through nitric oxide from those regulated by nitrate itself of by further downstream factors. A set of subsequent detailed functional annotation tools (Gene Ontology enrichment, MapMan, KEGG reconstruction pathway, transcription factors detection) were used to gain further information and the lateral root density was measured both in the presence of nitrate and in the presence of nitrate plus cPTIO, a specific NO scavenger, and compared to that observed for N-depleted roots. Our results led us to identify six clusters of transcripts according to their responsiveness to nitric oxide and to their regulation by nitrate provision. In general, shared and specific features for the six clusters were identified, allowing us to determine the overall root response to nitrate according to its dependency on nitric oxide.

2021 ◽  
Chiara E. Cotroneo ◽  
Isobel Claire Gormley ◽  
Denis C. Shields ◽  
Michael Salter-Townshend

Abstract Background: In bacteria, genes with related functions - such as those involved in the metabolism of the same compound or in infection processes - are often physically close on the genome and form groups called clusters. The enrichment of such clusters over various distantly related bacteria can be used to predict the roles of genes of unknown function that cluster with characterised genes. There is no obvious rule to define a cluster, given their variability in size and intergenic distances, and the definition of what comprises a “gene”, since genes can gain and lose domains over time. Protein domains can cluster within a gene, or in adjacent genes of related function, and in both cases these are chromosomally clustered. Here, we model the distances between pairs of protein domain coding regions across a wide range of bacteria and archaea via a probabilistic two component mixture model, without imposing arbitrary thresholds in terms of gene numbers or distances. Results: We trained our model using matched Gene Ontology terms to label functionally related pairs and assess the stability of the parameters of the model across 14, 178 archaeal and bacterial strains. We found that the parameters of our mixture model are remarkably stable across bacteria and archaea, except for endosymbionts and obligate intracellular pathogens. Obligate pathogens have smaller genomes, and although they vary, on average do not show noticeably different clustering distances; the main difference in the parameter estimates is that a far greater proportion of the genes sharing ontology terms are clustered. This may reflect that these genomes are enriched for complexes encoded by clustered core housekeeping genes, as a proportion of the total genes. Given the overall stability of the parameter estimates, we then used the mean parameter estimates across the entire dataset to investigate which gene ontology terms are most frequently associated with clustered genes. Conclusions: Given the stability of the mixture model across species, it may be used to predict bacterial gene clusters that are shared across multiple species, in addition to giving insights into the evolutionary pressures on the chromosomal locations of genes in different species.

2021 ◽  
Vol 11 ◽  
Daniel Escuin ◽  
Laura López-Vilaró ◽  
Josefina Mora ◽  
Olga Bell ◽  
Antonio Moral ◽  

MicroRNAs have emerged as important regulators of the metastatic process. In addition, circulating miRNAs appear to be surprisingly stable in peripheral blood making them ideal noninvasive biomarkers for disease diagnosis. Here, we performed a proof-of-principle study to investigate the expression profile of circulating miRNAs and their association with the metastatic lymph node status in early breast cancer patients. Sentinel lymph node status was detected by one-step nucleic acid (OSNA) analysis. We performed RNA-sequencing in 16 plasma samples and validated the results by qPCR. Gene Ontology term enrichment and KEGG pathway analyses were carried out using DAVID tools. We found16 differentially expressed miRNAs (q < 0.01) in patients with positive SLNs. Fourteen miRNAs were down-regulated (miR-339-5p, miR-133a-3p, miR-326, miR-331-3p, miR-369-3p, miR-328-3p, miR-26a-3p, miR-139-3p, miR-493-3p, miR-664a-5p, miR-146a-5p, miR-323b-3p, miR-1307-3p and miR-423-3p) and 2 were up-regulated (miR-101-3pand miR-144-3p). Hierarchical clustering using differentially expressed miRNAs clearly distinguished patients according to their lymph node status. Gene ontology analysis showed a significant enrichment of biological processes associated with the regulation of the epithelial mesenchymal transition, cell proliferation and transcriptional regulation. Our results suggest the potential role of several circulating miRNAs as surrogate markers of lymph node metastases in early breast cancer patients. Further validation in a larger cohort of patients will be necessary to confirm our results.

2021 ◽  
Vol 12 ◽  
Lun Hu ◽  
Xiaojuan Wang ◽  
Yu-An Huang ◽  
Pengwei Hu ◽  
Zhu-Hong You

Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.

2021 ◽  
Ke-Na Sun ◽  
Fei Huang ◽  
Ming-Yi Wang ◽  
Jing Wu ◽  
Cheng-Jin Hu ◽  

Abstract We previously reported that the Vibrio vulnificus hemolysin A (VvhA) protein elicited good immune protection and could effectively control V. vulnificus infection in mice. However, its molecular mechanism remains unknown. In this study, we found that IL-21 enhances immune protection by inducing a Tfh-cell and germinal center (GC) B-cell response. We used RNA-seq and identified 10 upregulated and 30 downregulated genes that were involved in IL-21-upregulated protection. We also performed Gene Ontology (GO) analysis and pathway analysis of these differentially expressed genes. Our findings indicate that IL-21 can enhance the immune protection effect of VvhA protein and may serve as a novel strategy for enhancing the immune protection effect of protein vaccines.

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Dong Zhang ◽  
Lang Guo ◽  
Xiaoting Wu ◽  
Meng Luo ◽  
Xinyi Liang ◽  

The Chinese medicine Qigesan can be used to treat esophageal adenocarcinoma in the Chinese mainland widely, but its mechanism is unclear. In order to investigate the mechanism of Qigesan in the treatment of esophageal adenocarcinoma, the concept of network pharmacology was used in this study. The database named TCMSP was used to identify the active therapeutic components as well as targets of Qigesan. The TTD, OMIM, CTD, DrugBank, and GeneCards database were used to identify genes related to esophageal adenocarcinoma. In STRING database, the potential targets were imported to obtain a PPI network, and then Cytoscape software has been used to analyse the results. Subsequently, important components and targets were simulated by molecular docking. Finally, experiments on the cell have been done to verify well docking targets. A total of 124 effective compounds and 646 corresponding targets were filtered. 1478 genes were found to be related to esophageal adenocarcinoma. 68 genes were identified as potential targets for esophageal adenocarcinoma. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the 68 potential targets indicated that the genes were mainly involved in cell transcription, translation, and apoptosis and mostly expressed in cancer-related pathways. The molecular docking analysis of the hub targets with their corresponding compounds indicated that the well docking targets were AR, ERBB2, and VEGFA. The cell experiments showed that Qigesan can reduce the expression of AR, ERBB2, and VEGFA at transcription and translation level. This network pharmacology study described that the possible targets of Qigesan in treatment of esophageal adenocarcinoma were AR, ERBB2, and VEGFA.

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