PhenPath identifies biological functions underlying phenotypes

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

Colorectal cancer (CRC) is the most diagnosed cancer with the highest mortality rate each year globally. Although there are treatments for CRC, the development of resistance to therapies decreases the success of treatments. In vitro studies using the Caco-2 cell line have revealed the anticancer properties of silver nanoparticles (AgNPs) as a possible treatment for this disease. This study considered four researches that evaluated the proteomic profiles of cells of the Caco-2 line exposed to AgNPs. We performed a bioinformatics analysis to predict protein-protein interaction, hub genes, Gene Ontology (molecular function, biological process, and cellular components), KEGG pathways, analysis of expression, and immune cell infiltration. For these analyses, the STRING, DAVID, UALCAN, GEPIA2, and TISIDB databases were used. The results in Gene Ontology show that AgNPs cause a deregulation of genes related to cell-cell adhesion, the cytoplasm, the centriole, and carbon metabolism. Hub genes were identified, including GADPH, ENO1, EEF2, and ATP5A1, which showed differential expression in patients with adenocarcinoma of the colon and rectum. Additionally, the expression of the hub genes and immune cells was correlated. It was found that ATP5A1 and ENO1 were positively correlated with the infiltration of CD4+ T lymphocytes in colon adenocarcinoma and a negative correlation between GADPH and PDIA3 with the infiltration of NK cells and CD4+ T lymphocytes in rectal adenocarcinoma, respectively. In conclusion, the administration of AgNPs causes an alteration of biological processes, cellular components, metabolic pathways, deregulation of hub genes, and the activity of immune cells leading to a potential anticancer effect.

Antimicrobial peptides (AMPs) have potential antifungal activities; however, their intracellular protein targets are poorly reported. Proteome microarray is an effective tool with high-throughput and rapid platform that systematically identifies the protein targets. In this study, we have used yeast proteome microarrays for systematical identification of the yeast protein targets of Lactoferricin B (Lfcin B) and Histatin-5. A total of 140 and 137 protein targets were identified from the triplicate yeast proteome microarray assays for Lfcin B and Histatin-5, respectively. The Gene Ontology (GO) enrichment analysis showed that Lfcin B targeted more enrichment categories than Histatin-5 did in all GO biological processes, molecular functions, and cellular components. This might be one of the reasons that Lfcin B has a lower minimum inhibitory concentration (MIC) than Histatin-5. Moreover, pairwise essential proteins that have lethal effects on yeast were analyzed through synthetic lethality. A total of 11 synthetic lethal pairs were identified within the protein targets of Lfcin B. However, only three synthetic lethal pairs were identified within the protein targets of Histatin-5. The higher number of synthetic lethal pairs identified within the protein targets of Lfcin B might also be the reason for Lfcin B to have lower MIC than Histatin-5. Furthermore, two synthetic lethal pairs were identified between the unique protein targets of Lfcin B and Histatin-5. Both the identified synthetic lethal pairs proteins are part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) protein complex that regulates gene expression via histone modification. Identification of synthetic lethal pairs between Lfcin B and Histatin-5 and their involvement in the same protein complex indicated synergistic combination between Lfcin B and Histatin-5. This hypothesis was experimentally confirmed by growth inhibition assay.

Protein sequences are symbols generally different characters representing the 20 amino acids used in human proteins those sequences can range from the very sort to the very long. There are many proteins database for the sequences are known but the function and functional annotation is not. Protein function prediction (PFP) as well as functional annotation (FA) from its structure or sequence is a major field of bioinformatics at the same time how to judge how well perform these algorithms. We proposed the novel method that converts the protein function problem into a language translation problem by a new proposed protein sequence language encoded to the protein function language decoded and build a recurrent neural machine encoding decoding translator (RNNEDT) based on the recurrent neural networks model. The excellent acting on training, testing datasets exhibits the proposed system as an improving direction for PFP. The proposed system alters the PFP matter to a language translation issue as well as applies a recurrent neural network machine version model for PFP, and visualizes the annotation of biological process (BP), molecular function (MF), as well as cellular component (CP).

To explore the deep mechanisms of ursolic acid (UA) for treating atherosclerosis based on network pharmacology and bioinformatics. UA target genes were derived from traditional Chinese medicine system pharmacology, BATMAN-TCM, and SwissTargetPrediction databases. Atherosclerosis-related genes were derived from genecards, NCBI genes, and OMIM databases. The protein interaction network was constructed through the STRING database, and the hub network was extracted by using the Cytoscape software MCODE app. The enrichment analysis of gene ontology and Kyoto encyclopedia of genes and genomes was performed by the R software clusterProfiler package, and the expression and prognostic value of the hub genes were verified on the data set. Screen the genes for expression and prognosis conclusions, conduct methylation analysis, and ceRNA construction. UA had 145 targets in the treatment of atherosclerosis. The top 7 gene ontology (biological process, molecular function, and cellular component) and pathways related to atherosclerosis were screened out. It is principally involved in biological processes, including response to lipopolysaccharide and regulation of inflammatory response. The main signaling pathways incorporated the TNF signaling pathway and the AGE-RAGE signaling pathway. Androgen receptor (AR) and interleukin-1 beta gene (IL1B) were further screened as core target genes. Methylation analysis demonstrated that the AR methylation level was elevated in the atherosclerotic group. On the contrary, the IL1B methylation level was lower in the atherosclerotic group. The results of the ceRNA analysis indicated that there were 43 targeted miRNAs in AR and 3 miRNAs in IL1B. We speculate that the target genes of UA regulating atherosclerosis are AR and IL1B. The mechanism may be that UA regulates the expression of target genes by regulating the methylation of target genes.

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.

BackgroundThe sex hormone estrogen (E2) is pivotal to normal mammary gland growth and differentiation and in breast carcinogenesis. In this in silico study, we examined metabolic differences between ER(+)ve breast cancer cells during E2 deprivation.MethodsPublic repositories of SAGE and MA gene expression data generated from E2 deprived ER(+)ve breast cancer cell lines, MCF-7 and ZR75-1 were compared with normal breast tissue. We analyzed gene ontology (GO), enrichment, clustering, chromosome localization, and pathway profiles and performed multiple comparisons with cell lines and tumors with different ER status.ResultsIn all GO terms, biological process (BP), molecular function (MF), and cellular component (CC), MCF-7 had higher gene utilization than ZR75-1. Various analyses showed a down-regulated immune function, an up-regulated protein (ZR75-1) and glucose metabolism (MCF-7). A greater percentage of 77 common genes localized to the q arm of all chromosomes, but in ZR75-1 chromosomes 11, 16, and 19 harbored more overexpressed genes. Despite differences in gene utilization (electron transport, proteasome, glycolysis/gluconeogenesis) and expression (ribosome) in both cells, there was an overall similarity of ZR75-1 with ER(-)ve cell lines and ER(+)ve/ER(-)ve breast tumors.ConclusionThis study demonstrates integral metabolic differences may exist within the same cell subtype (luminal A) in representative ER(+)ve cell line models. Selectivity of gene and pathway usage for strategies such as energy requirement minimization, sugar utilization by ZR75-1 contrasted with MCF-7 cells, expressing genes whose protein products require ATP utilization. Such characteristics may impart aggressiveness to ZR75-1 and may be prognostic determinants of ER(+)ve breast tumors.

The gene ontology (GO) contains three hierarchies that represent gene function based on categories of molecular function, biological process, and cellular component. However, additional knowledge about the mechanisms underlying gene function is buried in the GO term definitions and is not computationally accessible. We describe a process for adding new links to the GO between molecular function terms and the biological process terms that those molecular functions are involved in. These new links enable inference engines to reason about relationships between genes that have disparate molecular functions but participate in the same biological process. In particular, we demonstrate how these new links enable more effective automated analysis of gene expression data.

AIMTo identify miRNA-regulated proteins differentially expressed between Caco2 and HT-29: two principal cell line models of the intestine.METHODSExponentially growing Caco-2 and HT-29 cells were harvested and prepared for mRNA, miRNA and proteomic profiling. mRNA microarray profiling analysis was carried out using the Affymetrix GeneChip Human Gene 1.0 ST array. miRNA microarray profiling analysis was carried out using the Affymetrix Genechip miRNA 3.0 array. Quantitative Label-free LC-MS/MS proteomic analysis was performed using a Dionex Ultimate 3000 RSLCnano system coupled to a hybrid linear ion trap/Orbitrap mass spectrometer. Peptide identities were validated in Proteome Discoverer 2.1 and were subsequently imported into Progenesis QI software for further analysis. Hierarchical cluster analysis for all three parallel datasets (miRNA, proteomics, mRNA) was conducted in the R software environment using the Euclidean distance measure and Ward’s clustering algorithm. The prediction of miRNA and oppositely correlated protein/mRNA interactions was performed using TargetScan 6.1. GO biological process, molecular function and cellular component enrichment analysis was carried out for the DE miRNA, protein and mRNA lists via the Pathway Studio 11.3 Web interface using their Mammalian database.RESULTSDifferential expression (DE) profiling comparing the intestinal cell lines HT-29 and Caco-2 identified 1795 Genes, 168 Proteins and 160 miRNAs as DE between the two cell lines. At the gene level, 1084 genes were upregulated and 711 were downregulated in the Caco-2 cell line relative to the HT-29 cell line. At the protein level, 57 proteins were found to be upregulated and 111 downregulated in the Caco-2 cell line relative to the HT-29 cell line. Finally, at the miRNAs level, 104 were upregulated and 56 downregulated in the Caco-2 cell line relative to the HT-29 cell line. Gene ontology (GO) analysis of the DE mRNA identified cell adhesion, migration and ECM organization, cellular lipid and cholesterol metabolic processes, small molecule transport and a range of responses to external stimuli, while similar analysis of the DE protein list identified gene expression/transcription, epigenetic mechanisms, DNA replication, differentiation and translation ontology categories. The DE protein and gene lists were found to share 15 biological processes including for example epithelial cell differentiation [P value ≤ 1.81613E-08 (protein list); P ≤ 0.000434311 (gene list)] and actin filament bundle assembly [P value ≤ 0.001582797 (protein list); P ≤ 0.002733714 (gene list)]. Analysis was conducted on the three data streams acquired in parallel to identify targets undergoing potential miRNA translational repression identified 34 proteins, whose respective mRNAs were detected but no change in expression was observed. Of these 34 proteins, 27 proteins downregulated in the Caco-2 cell line relative to the HT-29 cell line and predicted to be targeted by 19 unique anti-correlated/upregulated microRNAs and 7 proteins upregulated in the Caco-2 cell line relative to the HT-29 cell line and predicted to be targeted by 15 unique anti-correlated/downregulated microRNAs.CONCLUSIONThis first study providing “tri-omics” analysis of the principal intestinal cell line models Caco-2 and HT-29 has identified 34 proteins potentially undergoing miRNA translational repression.

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.

Genome-wide search to detect selection signatures harbouring candidate prolificacy genes was carried out in low prolific Attappady Black and high prolific Malabari goat breeds of Kerala. Selective sweep regions were identified by analysing the genotypic data generated by Illumina Goat SNP 50 Bead Chip sequencing of 48 animals (24 each of two breeds) using Out FLANK statistical method based on FST outlier approach. There were 2844 outlier SNPs at genome-wide significance level of P<0.01 and 595 protein coding genes linked to these outlier SNPs. Further analysis showed 16 candidate prolificacy genes inclusive of BMPR, PRLR, INHBB, ESR1, GHR, IGF1, LHCGR, LEPR, SMAD4, BMP2, NR5A2, MTNR1A, GRIA2, MEF2A and MFGE8 located in selection signals. Gene enrichment analysis showed significantly (P<0.05) over-represented 37 gene ontology (GO) biological processes, 14 GO molecular functions and 7 KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathways. The information concerning candidate genes could be further utilised for association analysis for onward utilisation in the genetic improvement of litter size in goats.

Sugars act not only as substrates for plant metabolism, but also have a pivotal role in signaling pathways. Glucose signaling has been widely studied in the vascular plant Arabidopsis thaliana, but it has remained unexplored in non-vascular species such as Physcomitrella patens. To investigate P. patens response to high glucose treatment, we explored the dynamic changes in metabolism and protein population by applying a metabolomic fingerprint analysis (DIESI-MS), carbohydrate and chlorophyll quantification, Fv/Fm determination and label-free untargeted proteomics. Glucose feeding causes specific changes in P. patens metabolomic fingerprint, carbohydrate contents and protein accumulation, which is clearly different from those of osmotically induced responses. The maximal rate of PSII was not affected although chlorophyll decreased in both treatments. The biological process, cellular component, and molecular function gene ontology (GO) classifications of the differentially expressed proteins indicate the translation process is the most represented category in response to glucose, followed by photosynthesis, cellular response to oxidative stress and protein refolding. Importantly, although several proteins have high fold changes, these proteins have no predicted identity. The most significant discovery of our study at the proteome level is that high glucose increase abundance of proteins related to the translation process, which was not previously evidenced in non-vascular plants, indicating that regulation by glucose at the translational level is a partially conserved response in both plant lineages. To our knowledge, this is the first time that metabolome fingerprint and proteomic analyses are performed after a high sugar treatment in non-vascular plants. These findings unravel evolutionarily shared and differential responses between vascular and non-vascular plants.

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).

Crop Brassicas contain monogenomic and digenomic species, with no evidence of a trigenomic Brassica in nature. Through somatic fusion (Sinapis alba + B. juncea), a novel allohexaploid trigenomic Brassica (H1 = AABBSS; 2n = 60) was produced and used for transcriptome analysis to uncover genes for thermotolerance, annotations, and microsatellite markers for future molecular breeding. Illumina Novaseq 6000 generated a total of 76,055,546 paired-end raw reads, which were used for de-novo assembly, resulting in the development of 486,066 transcripts. A total of 133,167 coding sequences (CDSs) were predicted from transcripts with a mean length of 507.12 bp and 46.15% GC content. The BLASTX search of CDSs against public protein databases showed a maximum of 126,131 (94.72%) and a minimum of 29,810 (22.39%) positive hits. Furthermore, 953,773 gene ontology (GO) terms were found in 77,613 (58.28%) CDSs, which were divided into biological processes (49.06%), cellular components (31.67%), and molecular functions (19.27%). CDSs were assigned to 144 pathways by a pathway study using the KEGG database and 1,551 pathways by a similar analysis using the Reactome database. Further investigation led to the discovery of genes encoding over 2,000 heat shock proteins (HSPs). The discovery of a large number of HSPs in allohexaploid Brassica validated our earlier findings for heat tolerance at seed maturity. A total of 15,736 SSRs have been found in 13,595 CDSs, with an average of one SSR per 4.29 kb length and an SSR frequency of 11.82%. The first transcriptome assembly of a meiotically stable allohexaploid Brassica has been given in this article, along with functional annotations and the presence of SSRs, which could aid future genetic and genomic studies.