Linking molecular function and biological process terms in the ontology for gene expression data analysis

In chromosome 11, 71 out of its 1254 proteins remain functionally uncharacterized on the basis of their existence evidence (uPE1s) following the latest version of neXtProt (release 2020-01-17). Because in vivo and in vitro experimental strategies are often time-consuming and labor-intensive, there is a need for a bioinformatics tool to predict the function annotation. Here, we used I-TASSER/COFACTOR provided on the neXtProt web site, which predicts gene ontology (GO) terms based on the 3D structure of the protein. I-TASSER/COFACTOR predicted 2413 GO terms with a benchmark dataset of the 22 proteins belonging to PE1 of chromosome 11. In this study, we developed a filtering algorithm in order to select specific GO terms using the GO map generated by I-TASSER/COFACTOR. As a result, 187 specific GO terms showed a higher average precision-recall score at the least cellular component term compared to 2413 predicted GO terms. Next, we applied 65 proteins belonging to uPE1s of chromosome 11, and then 409 out of 6684 GO terms survived, where 103 and 142 GO terms of molecular function and biological process, respectively, were included. Representatively, the cellular component GO terms of CCDC90B, C11orf52, and the SMAP were predicted and validated using the overexpression system into 293T cells and immunofluorescence staining. We will further study their biological and molecular functions toward the goal of the neXt-CP50 project as a part of C-HPP. We shared all results and programs in Github (https://github.com/heeyounh/I-TASSER-COFACTOR-filtering.git).

The aim of the present study was to identify the differentially expressed genes (DEGs) in esophageal squamous-cellcarcinoma (ESCC) and provide potential therapeutic targets. The microarray dataset GSE20347 was downloaded from the Gene Expression Omnibus (GEO) database, and included 17 tissue samples and 13 normal adjacent tissue samples from patients with ESCC. A total of 22,277 DEGs were identified. A heat map for the DEGs was constructed with the Morpheus online tool and the top 200 genes (100 upregulated and 100 downregulated) were selected for further bioinformatics analysis, including analysis of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, protein-protein interaction networks and Spearman's correlation tests. The results of the GO analysis indicated that the upregulated DEGs were most significantly enriched in membrane-bounded vesicles in the cellular component (CC) category, but were not significantly enriched in any GO terms of the categories biological process (BP) or molecular function (MF); furthermore, the downregulated DEGs were most significantly enriched in regulation of DNA metabolic processes, nucleotide binding and chromosomes in the categories BP, MF and CC, respectively. The KEGG analysis indicated that the downregulated DEGs were enriched in the regulation of cell cycle pathways. The top 10 hub proteins in the protein-protein interaction network were cyclin-dependent kinase 4, budding uninhibited by benzimidazoles 1, cyclin B2, heat shock protein 90AA1, aurora kinase A, H2A histone family member Z, replication factor C subunit 4, and minichromosome maintenance complex component 2, −4 and −7. These proteins are mainly involved in regulating tumor progression. The genes in the four top modules were mainly implicated in regulating cell cycle pathways. Secreted Ly-6/uPAR-related protein (SLURP) was the hub gene, and SLURP and its interacting genes were most enriched in the chromosomal part in the CC category, organelle organization in the BP category and protein binding in the MF category, and were involved in pathways including DNA replication, cell cycle and P53 signaling. The expression of SLURP-1 in fifteen patients with esophageal carcinoma was detected using quantitative polymerase chain reaction analysis, and the results indicated that SLURP-1 expression was significantly decreased in the tumor samples relative to that in normal adjacent tissues. These results suggest that several hub proteins and the hub gene SLURP-1 may serve as potential therapeutic targets, and that gene dysfunction may be involved in the tumorigenesis of ESCC.

Objective To investigate the regulation of emodin on microRNA expressions in mouse model with sepsis by GeneChip microRNA array. Methods Forty two c57 mice were randomly (random number) divided into 3 groups: sham operation group (sham group, n=14),sepsis group(n=14) and emodin group (n=14). The sepsis model of the mouse was subjected to cecal ligation and puncture (CLP). Mice in emodin group received intraperitoneal injection of emodin (40 mg/kg) half an hour before operation and every 12 hours after CLP. All mice were sacrificed 48 h after surgery and part of the ileum were removed for intestine tissue stained with hematoxylin eosin. The levels of tumor necrosis factor-α(TNF-α), interleukin-6 (IL-6) and intestinal fatty acid binding protein (I-FABP) in peripheral blood were measured by enzyme-linked immunosorbent assay (ELISA). Total RNA of ileum tissues was extracted from the sepsis group and emodin group, and then subjected to miRNA microarray. The results of microarray were further verified by quantitative real-time PCR (qRT-PCR). Target genes of differentially expressed miRNAs were predicted and subjected to gene ontology (GO) and KEGG pathway enrichment analysis. Data of multi-groups were analyzed by one way variance (ANOVA) and inter-group comparisons were made by SNK-q tests. Mann-Whitney U test was used when homogeneity of variance were not met. The value of P<0.05 was considered statistically significant. Results Compared to the sepsis group, the levels of serum TNF-α, IL-6 and I-FABP in the emodin group were decreased significantly. MiRNA microarray showed that 23 miRNAs were differentially expressed in the emodin group as compared to the sepsis group, among which 17 miRNAs were up-regulated and 6 miRNAs were down-regulated. qRT-PCR results were consistent with miRNA array data. Target genes of 10 selected miRNAs were predicted and subjected to bioinformatic analysis. A total of 3 410 target genes were significantly enriched in 2 072 GO biological process terms, 246 GO cellular component items and 277 GO molecular function terms. Moreover, KEGG pathway analysis showed that these differential miRNAs were involved in the regulation of PI3K-Akt signaling pathway, MAPK signaling pathway, and TNF signaling pathway. Conclusions Emodin can regulate the expression of multiple microRNAs, and play an important role in protecting the intestinal mucosal barrier via a variety of targets and pathways. Key words: Emodin; Sepsis; Intestinal mucosa; MicroRNA; Expression profiles; Bioinformatics

Aspergillus ochraceus causes food spoilage and produces mycotoxin ochratoxin A (OTA) during storage of agricultural commodities. In this study, citral was used to inhibit A. ochraceus growth and OTA accumulation, proteomic analysis was employed to verify the mechanism of citral. Citral was found to significantly inhibit fungal growth and mycotoxin production in A. ochraceus. Specifically, 75, 125, 150 and 200 μL L-1 citral suppressed mycelial growth by 33%, 46%, 50% and 100%, respectively. Additionally, 75 μL L-1 citral inhibited OTA accumulation by 25%. Proteomic analysis was performed to elucidate the inhibitory mechanism of citral on mycelial growth and OTA production at subinhibitory concentrations (75 μL L-1 ). Proteomics analysis identified 2646 proteins in A. ochraceus fc-1, of which 218 were differentially expressed between control and 75 μL L-1 citral treatment samples. Differentially expressed proteins were identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of biological process, cellular component and molecular function terms. Potential factors affecting mycelial growth and OTA production were analysed, and OTA production was revealed to be a complex process involving many associated factors related to various processes including nutrient intake, sterol biosynthesis, ribosome biogenesis, energy metabolism, oxidative stress and amino acid metabolism. In addition, citral at 75 μL L-1 down-regulated OTA biosynthetic genes including pks and nrps, but slightly up-regulated the global regulatory factors veA, velB and laeA. The findings further demonstrate the potential of citral for the preservation of grains and other agricultural products, and provide new insight into its antifungal mechanisms at subinhibitory concentrations. © 2021 Society of Chemical Industry.

The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins.

The Gene Ontology (GO) is a widely used terminology for gene product characterization in, for example, interpretation of biology underlying microarray experiments. The current GO defines term relationships within each of the independent subontologies: molecular function, biological process and cellular component. However, it is evident that there also exist biological relationships between terms of different subontologies. Our aim was to connect the three subontologies to enable GO to cover more biological knowledge, enable a more consistent use of GO and provide new opportunities for biological reasoning. We propose a new structure, the Second Gene Ontology Layer, capturing biological relations not directly reflected in the present ontology structure. Given molecular functions, these paths identify biological processes where the molecular functions are involved and cellular components where they are active. The current Second Layer contains 6271 validated paths, covering 54% of the molecular functions of GO and can be used to render existing gene annotation sets more complete and consistent. Applying Second Layer paths to a set of 4223 human genes, increased biological process annotations by 24% compared to publicly available annotations and reproduced 30% of them. The Second GO is publicly available through the GO Annotation Toolbox (GOAT.no): http://www.goat.no.

De novo RNA-Seq based transcriptome analysis of Papiliotrema laurentii strain RY1 under nitrogen starvation

Taxanes are natural compounds for the treatment of lung cancer, but the molecular mechanism behind the effects is unclear. In the present study, through network pharmacology and molecular docking, the mechanism of the target and pathway of taxanes in the treatment of lung cancer was studied. The taxanes targets were determined by PubChem database, and an effective compounds-targets network was constructed. The GeneCards database was used to determine the disease targets of lung cancer, and the intersection of compound targets and disease targets was obtained. The Protein-Protein Interaction (PPI) network of the intersection targets was analyzed, and the PPI network was constructed by Cytoscape 3.6.0 software. The hub targets were screened according to the degree value, and the binding activity between taxanes and hub targets was verified by molecular docking. The results showed that eight taxane-active compounds and 444 corresponding targets were screened out, and 131 intersection targets were obtained after mapping with lung cancer disease targets. The hub targets obtained by PPI analysis were TP53, EGFR, and AKT1. Gene Ontology (GO) biological function enrichment analysis obtained 1795 biological process (BP) terms, 101 cellular component (CC) terms, and 164 molecular function (MF) terms. There were 179 signaling pathways obtained by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Twenty signaling pathways were screened out, mainly pathways in cancer, proteoglycans in cancer pathway, microRNAs in cancer pathway, and so on. Molecular docking shows that the binding energies of eight taxanes with TP53, EGFR, and AKT1 targets were less than -8.8 kcal/mol, taxanes acts on TP53, EGFR, and AKT1 targets through pathways in cancer, proteoglycans in cancer pathway and microRNAs in cancer pathway, and plays a role in treating lung cancer in biological functions such as protein binding, enzyme binding, and identical protein binding.

A protein's function is closely linked to its subcellular localization. Use of Gene Ontology (GO) molecular function terms to extend sequence-based subcellular localization prediction has been previously shown to improve predictive performance. Here, we explore directly the relationship between GO function annotations and localization information, identifying both highly predictive single terms, and terms with large information gain with respect to location. The results identify a number of predictive and informative GO terms with respect to subcellular location, particularly nucleus, extracellular space, membrane, mitochondrion, endoplasmic reticulum and Golgi. There are several clear examples illustrating why the addition of function information provides additional predictive power over sequence alone. Other interesting phenomena can also be seen in the results. Most predictive or informative terms are imperfect, and incorrect prediction may often call out significant biological phenomena. Finally, these results may be useful in the GO annotation process.

Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis.

The ERK1/2 MAP kinase pathway is an evolutionarily conserved signaling module that controls many fundamental physiological processes. Deregulated activity of ERK1/2 MAP kinases is associated with developmental syndromes and several human diseases. Despite the importance of this pathway, a comprehensive picture of the natural substrate repertoire and biochemical mechanisms regulated by ERK1/2 is still lacking. In this study, we used large-scale quantitative phosphoproteomics and bioinformatics analyses to identify novel candidate ERK1/2 substrates based on their phosphorylation signature and kinetic profiles in epithelial cells. We identified a total of 7936 phosphorylation sites within 1861 proteins, of which 155 classify as candidate ERK1/2 substrates, including 128 new targets. Candidate ERK1/2 substrates are involved in diverse cellular processes including transcriptional regulation, chromatin remodeling, RNA splicing, cytoskeleton dynamics, cellular junctions and cell signaling. Detailed characterization of one newly identified substrate, the transcriptional regulator JunB, revealed that ERK1/2 phosphorylate JunB on a serine adjacent to the DNA-binding domain, resulting in increased DNA-binding affinity and transcriptional activity. Our study expands the spectrum of cellular functions controlled by ERK1/2 kinases.

Small RNA and degradome deep sequencing reveals important roles of microRNAs in cotton (Gossypium hirsutum L.) response to root-knot nematode Meloidogyne incognita infection

The growing volume and complexity of gene expression data necessitate biologically meaningful and statistically robust methods for feature selection to enhance the effectiveness of disease diagnosis systems. The present study addresses this challenge by proposing a pipeline that integrates RNA-seq data preprocessing, differential gene expression analysis, Gene Ontology (GO) enrichment, and ensemble-based machine learning. The pipeline employs the non-parametric Kruskal–Wallis test to identify differentially expressed genes, followed by dual enrichment analysis using both Fisher’s exact test and the Kolmogorov–Smirnov test across three GO categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Genes associated with GO terms found significant by both tests were used to construct multiple gene subsets, including subsets based on individual categories, their union, and their intersection. Classification experiments using a random forest model, validated via 5-fold cross-validation, demonstrated that gene subsets derived from the CC category and the union of all categories achieved the highest accuracy and weighted F1-scores, exceeding 0.97 across 14 cancer types. In contrast, subsets derived from BP, MF, and especially their intersection exhibited lower performance. These results confirm the discriminative power of spatially localized gene annotations and underscore the value of integrating statistical and functional information into gene selection. The proposed approach improves the reliability of biomarker discovery and supports downstream analyses such as clustering and biclustering, providing a strong foundation for developing precise diagnostic tools in personalized medicine.

Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development. Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development.

The process of functional annotation of human genetic variants is important for some significant biomedical tasks like understanding functional genomics and gene-disease associations. Moreover, genetic variants that lead to pathogenic mutations can have harmful consequences which may lead to certain medical conditions or diseases. This paper investigates and presents two methodologies utilizing the Gene Ontology (GO) for functional annotation of genetic variants (and human gene mutations): (1) using the concept of term enrichment from the biological process taxonomy in the gene ontology, and (2) using the concept of least common subsumer (LCS) within the gene ontology tree. The second methodology induces the most significant and accurate functions for a given set of variants having one common aspect, e.g. specific disease. This method is based on the structure of the directed acyclic graph of GO to induce and identify the most significant LCS’s to be used for functionally annotating the variants under investigation. We applied the methods on a large sets of genetic variants causing mutations. Our method can determine important functions and with significance level of p<0.02, the outcomes and results of the method are biologically significant. We found that certain ontology annotations are more related to mutations through certain genes compared with normal bp functions (biological process terms) in GO; for example, one of these bp functions is {GO:0031325; positive regulation of cellular metabolic process}. The results are basically important and suggest that a mutation can be annotated with functions from the gene ontology, e.g. biological process aspect, just like the genes. This outcome may contribute into a more complete understanding of mutation-gene-disease relationships, mutation pathogenicity, and understanding disease mechanisms..