scholarly journals Differential Gene Expression and Functional Enrichment Analysis of Smoothed PDAC Spatial Transcriptomics Slides

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ben Duggan ◽  
Yusong Liu ◽  
Tongxin Wang ◽  
Jie Zhang ◽  
Travis S. Johnson ◽  
...  
Keyword(s):  
Tumor Heterogeneity ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cellular Level ◽  
Functional Enrichment ◽  
Cancer Heterogeneity ◽  
Smoothing Methods ◽  
Single Cell Rna Sequencing ◽  
Differential Gene ◽  
Low Expression

Background: Cancer heterogeneity can impact diagnosis and therapeutics. This has been studied at the cellular level using single cell RNA sequencing (scRNA-seq) but not spatially. Spatial transcriptomics (ST) has recently enabled measuring transcriptomes at hundreds of locations in a tissue, revolutionizing our understanding of tumor heterogeneity. Low RNA quantities in scRNA-seq and ST introduce missed reads and noise. Multiple methods have been developed to impute and smooth scRNA-seq data including MAGIC and SAVER. To date, only our newly developed spatial and pattern combined smoothing (SPCS) method has been developed specifically for ST. We compared the biological interpretability of these smoothing methods to determine which method best informs tumor heterogeneity. Methods: Ten ST slides from six patients with PDAC were computationally smoothed using SAVER, MAGIC, and SPCS. ST spots from unsmoothed and smoothed slide were split into TM4SF1 high or low expression groups and differentially expressed genes (DEG) found. Significant up- and down-regulated DEGs were used for functional enrichment analysis (EA) to compare the biological insights each method provides. Results: DEGs were found in eight samples, with SPCS finding the most DEGs in six of the eight samples. The number of EA terms generally correlated with the numbers DEGs. SPCS had the most up-regulated enriched terms for every ST slide. Top ten up- and down-regulated EA terms were presented from one ST slide to demonstrate that SPCS gives more biologically interpretable results. Lastly, we present SPCS terms previously been reported in PDAC or involved in the TM4SF1 cancer pathway. Conclusion: Smoothing ST data using SPCS provided more DEGs and more useful EA terms. The EA terms found using SPCS are more consistent with our current understanding of PDAC. Not smoothing, MAGIC, and SAVER missed many important terms which SPCS found. Compared to other methods, SPCS smoothed data is more interpretable.

scholarly journals Cistrome-GO: a web server for functional enrichment analysis of transcription factor ChIP-seq peaks

2019 ◽  
Vol 47 (W1) ◽  
pp. W206-W211 ◽  
Author(s):  
Shaojuan Li ◽  
Changxin Wan ◽  
Rongbin Zheng ◽  
Jingyu Fan ◽  
Xin Dong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Transcription Factor ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differential Gene ◽  
Direct Target ◽  
Human And Mouse

AbstractCharacterizing the ontologies of genes directly regulated by a transcription factor (TF), can help to elucidate the TF’s biological role. Previously, we developed a widely used method, BETA, to integrate TF ChIP-seq peaks with differential gene expression (DGE) data to infer direct target genes. Here, we provide Cistrome-GO, a website implementation of this method with enhanced features to conduct ontology analyses of gene regulation by TFs in human and mouse. Cistrome-GO has two working modes: solo mode for ChIP-seq peak analysis; and ensemble mode, which integrates ChIP-seq peaks with DGE data. Cistrome-GO is freely available at http://go.cistrome.org/.

Single-cell RNA-sequencing Unravels Heterogeneity of Cardiomyocytes and Signaling Pathways in Pressure Overload

2020 ◽  
Author(s):  
Rongjun Zou ◽  
Wanting Shi ◽  
Minghui Zou ◽  
Weidan Chen ◽  
Wenlei Li ◽  
...  
Keyword(s):  
Cardiac Hypertrophy ◽  
Mouse Models ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing

Abstract Background This study aimed to unravel the heterogeneity of cardiomyocytes and probed out hub genes and hub pathways for cardiac hypertrophy based on transverse aortic constriction (TAC) mouse models using single-cell RNA sequencing (scRNA-seq). Methods scRNA-seq data of TAC mouse models were retrieved from the GSE95140 dataset. After filtering, cell clusters were detected using scRNA-seq data, followed by identification of differentially expressed genes (DEGs). Then, functional enrichment analysis of DEGs was presented. GSVA scores of hub pathways were calculated. After that, hub genes were detected by protein-protein interaction (PPI) network and expression association analysis. Cell subtypes were clustered using UMAP and the expression patterns of hub genes across different cell subtypes and different stages of cardiac hypertrophy were visualized. Finally, hub genes and hub pathways were verified using the GSE76 and GSE36074 datasets. Results Following data filtering and normalization, 3408 DEGs were identified between TAC and sham operation. As shown functional enrichment analysis, hub pathways were identified including cardiac hypertrophy, ion transport, myocardial remodeling, apoptosis, HIF pathway and metabolise. Eight hub genes (Vldlr, Ugp2, Tgm2, Pygm, Flnc, Ctsd, Clu and Atp1b1) with the highest degree in the PPI network and the strongest correlation with GSVA calculated score of hub pathways were identified for cardiac hypertrophy. Six cell subtypes were clustered, composed of fibroblast, CM-A, CM-V, trabecular CM and endothelial cell. There was a distinct heterogeneity in the expression patterns of hub genes and the GSVA scores of hub pathways across different cell clusters and different stages of cardiac hypertrophy. The hub genes and hub pathways were externally verified by the two independent datasets. Conclusion Our findings identified hub genes and hub pathways for cardiac hypertrophy, which had a distinct heterogeneity across different cell clusters and different stages of cardiac hypertrophy.

scholarly journals Low expression or hypermethylation of PLK2 might predict favorable prognosis for patients with glioblastoma multiforme

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7974 ◽  
Author(s):  
Xiangping Xia ◽  
Fang Cao ◽  
Xiaolu Yuan ◽  
Qiang Zhang ◽  
Wei Chen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Regression Analysis ◽  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Favorable Prognosis ◽  
Low Expression

Background As the most aggressive brain tumor, patients with glioblastoma multiforme (GBM) have a poor prognosis. Our purpose was to explore prognostic value of Polo-like kinase 2 (PLK2) in GBM, a member of the PLKs family. Methods The expression profile of PLK2 in GBM was obtained from The Cancer Genome Atlas database. The PLK2 expression in GBM was tested. Kaplan–Meier curves were generated to assess the association between PLK2 expression and overall survival (OS) in patients with GBM. Furthermore, to assess its prognostic significance in patients with primary GBM, we constructed univariate and multivariate Cox regression models. The association between PLK2 expression and its methylation was then performed. Differentially expressed genes correlated with PLK2 were identified by Pearson test and functional enrichment analysis was performed. Results Overall survival results showed that low PLK2 expression had a favorable prognosis of patients with GBM (P-value = 0.0022). Furthermore, PLK2 (HR = 0.449, 95% CI [0.243–0.830], P-value = 0.011) was positively associated with OS by multivariate Cox regression analysis. In cluster 5, DNA methylated PLK2 had the lowest expression, which implied that PLK2 expression might be affected by its DNA methylation status in GBM. PLK2 in CpG island methylation phenotype (G-CIMP) had lower expression than non G-CIMP group (P = 0.0077). Regression analysis showed that PLK2 expression was negatively correlated with its DNA methylation (P = 0.0062, Pearson r = −0.3855). Among all differentially expressed genes of GBM, CYGB (r = 0.5551; P < 0.0001), ISLR2 (r = 0.5126; P < 0.0001), RPP25 (r = 0.5333; P < 0.0001) and SOX2 (r = −0.4838; P < 0.0001) were strongly correlated with PLK2. Functional enrichment analysis results showed that these genes were enriched several biological processes or pathways that were associated with GBM. Conclusion Polo-like kinase 2 expression is regulated by DNA methylation in GBM, and its low expression or hypermethylation could be considered to predict a favorable prognosis for patients with GBM.

scholarly journals Bioinformatic Analysis of Key Genes Related to Tumor Microenvironment in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Jingxu Zhang ◽  
Hao Liu ◽  
Keyi Zhao ◽  
Zhiye Bao ◽  
Zhishuo Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Expression Data ◽  
Hub Genes ◽  
Oncomine Database ◽  
Key Genes ◽  
Low Expression

Abstract Background: Tumor microenvironment (TME) plays important roles in the development of different types of cancer. However, the critical regulatory members of TME related to hepatocellular carcinoma (HCC) remain unclear. In this study, a bioinformatic analysis based on Cancer Genome Atlas (TCGA) and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) datasets was conducted to predict the key genes affecting TME in HCC.Material and Methods: First, 340 patients and 20531 genes’ expression data with ESTIMATE scores were filtered and combined to identify differentially expressed genes. Next, protein-protein interaction (PPI) network and functional enrichment analysis were conducted to find hub genes. Then, log-rank test and functional enrichment analysis were conducted on the consensus genes and hub genes. Finally, Kaplan-Meier curves of the hub genes were drawn. As verification, those genes were searched on Oncomine database.Results: Among all differentially expressed genes, 916 genes were expressed in both the immune and stromal groups. The Gene Ontology (GO) terms they enriched were T cell activation, leukocyte migration, collagen-containing matrix, external side of plasma membrane, receptor ligand and activator activity. Cytokine-cytokine receptor interaction was the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) term. Furthermore, cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 hub genes were low expressed in 304 patients, participating in a variety of responses including immune response−activating cell surface receptor signaling, immune response−activating signal transduction, clathrin−coated vesicle membrane, immune receptor activity， peptide binding and amide binding pathways. Their low expression was also verified on Oncomine database.Conclusion: cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 participated in many aspects related to TME, and their low expression constructs a signature, may predict a poor 5 years’ survival in hepatocellular carcinoma.

Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

2019 ◽  
Vol 14 (7) ◽  
pp. 591-601 ◽  
Author(s):  
Aravind K. Konda ◽  
Parasappa R. Sabale ◽  
Khela R. Soren ◽  
Shanmugavadivel P. Subramaniam ◽  
Pallavi Singh ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Wrky Transcription Factor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Callose Deposition ◽  
Significant Probability

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

scholarly journals Structural variations in papaya genomes

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhenyang Liao ◽  
Xunxiao Zhang ◽  
Shengcheng Zhang ◽  
Zhicong Lin ◽  
Xingtan Zhang ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Enrichment Analysis ◽  
Copy Number Variant ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Structural Variations ◽  
Reproduction Traits ◽  
Depth Study ◽  
Environmental Adaptability ◽  
The Impact

Abstract Background Structural variations (SVs) are a type of mutations that have not been widely detected in plant genomes and studies in animals have shown their role in the process of domestication. An in-depth study of SVs will help us to further understand the impact of SVs on the phenotype and environmental adaptability during papaya domestication and provide genomic resources for the development of molecular markers. Results We detected a total of 8083 SVs, including 5260 deletions, 552 tandem duplications and 2271 insertions with deletion being the predominant, indicating the universality of deletion in the evolution of papaya genome. The distribution of these SVs is non-random in each chromosome. A total of 1794 genes overlaps with SV, of which 1350 genes are expressed in at least one tissue. The weighted correlation network analysis (WGCNA) of these expressed genes reveals co-expression relationship between SVs-genes and different tissues, and functional enrichment analysis shows their role in biological growth and environmental responses. We also identified some domesticated SVs genes related to environmental adaptability, sexual reproduction, and important agronomic traits during the domestication of papaya. Analysis of artificially selected copy number variant genes (CNV-genes) also revealed genes associated with plant growth and environmental stress. Conclusions SVs played an indispensable role in the process of papaya domestication, especially in the reproduction traits of hermaphrodite plants. The detection of genome-wide SVs and CNV-genes between cultivated gynodioecious populations and wild dioecious populations provides a reference for further understanding of the evolution process from male to hermaphrodite in papaya.

scholarly journals CCTs as new biomarkers for the prognosis of head and neck squamous cancer

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 672-688
Author(s):  
Yanbo Dong ◽  
Siyu Lu ◽  
Zhenxiao Wang ◽  
Liangfa Liu
Keyword(s):  
Head And Neck ◽  
Survival Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Advanced Tumor Stage ◽  
Expression Levels ◽  
T Complex ◽  
Advanced Tumor ◽  
Squamous Cancer

AbstractThe chaperonin-containing T-complex protein 1 (CCT) subunits participate in diverse diseases. However, little is known about their expression and prognostic values in human head and neck squamous cancer (HNSC). This article aims to evaluate the effects of CCT subunits regarding their prognostic values for HNSC. We mined the transcriptional and survival data of CCTs in HNSC patients from online databases. A protein–protein interaction network was constructed and a functional enrichment analysis of target genes was performed. We observed that the mRNA expression levels of CCT1/2/3/4/5/6/7/8 were higher in HNSC tissues than in normal tissues. Survival analysis revealed that the high mRNA transcriptional levels of CCT3/4/5/6/7/8 were associated with a low overall survival. The expression levels of CCT4/7 were correlated with advanced tumor stage. And the overexpression of CCT4 was associated with higher N stage of patients. Validation of CCTs’ differential expression and prognostic values was achieved by the Human Protein Atlas and GEO datasets. Mechanistic exploration of CCT subunits by the functional enrichment analysis suggests that these genes may influence the HNSC prognosis by regulating PI3K-Akt and other pathways. This study implies that CCT3/4/6/7/8 are promising biomarkers for the prognosis of HNSC.

scholarly journals Investigation and Functional Enrichment Analysis of the Human Host Interaction Network with Common Gram-Negative Respiratory Pathogens Predicts Possible Association with Lung Adenocarcinoma

2021 ◽  
Vol 28 (1) ◽  
pp. 20-33
Author(s):  
Lydia-Eirini Giannakou ◽  
Athanasios-Stefanos Giannopoulos ◽  
Chrissi Hatzoglou ◽  
Konstantinos I. Gourgoulianis ◽  
Erasmia Rouka ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Junctions ◽  
Respiratory Pathogens ◽  
Gram Negative ◽  
Human Proteins ◽  
Apoptotic Pathways

Haemophilus influenzae (Hi), Moraxella catarrhalis (MorCa) and Pseudomonas aeruginosa (Psa) are three of the most common gram-negative bacteria responsible for human respiratory diseases. In this study, we aimed to identify, using the functional enrichment analysis (FEA), the human gene interaction network with the aforementioned bacteria in order to elucidate the full spectrum of induced pathogenicity. The Human Pathogen Interaction Database (HPIDB 3.0) was used to identify the human proteins that interact with the three pathogens. FEA was performed via the ToppFun tool of the ToppGene Suite and the GeneCodis database so as to identify enriched gene ontologies (GO) of biological processes (BP), cellular components (CC) and diseases. In total, 11 human proteins were found to interact with the bacterial pathogens. FEA of BP GOs revealed associations with mitochondrial membrane permeability relative to apoptotic pathways. FEA of CC GOs revealed associations with focal adhesion, cell junctions and exosomes. The most significantly enriched annotations in diseases and pathways were lung adenocarcinoma and cell cycle, respectively. Our results suggest that the Hi, MorCa and Psa pathogens could be related to the pathogenesis and/or progression of lung adenocarcinoma via the targeting of the epithelial cellular junctions and the subsequent deregulation of the cell adhesion and apoptotic pathways. These hypotheses should be experimentally validated.

scholarly journals Isolation and identification of a novel bacterium, Pseudomonas sp. ZyL-01, involved in the biodegradation of CL-20

AMB Express ◽  
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhiyong Liu ◽  
Kai Dang ◽  
Cunzhi Li ◽  
Junhong Gao ◽  
Hong Wang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Environmental Fate ◽  
Draft Genome ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Community Diversity ◽  
Functional Enrichment ◽  
Metagenomic Sequencing ◽  
Isolation And Identification ◽  
Novel Bacterium

Abstract Hexanitrohexaazaisowurtzitane (CL-20) is a compound with a polycyclic cage and an N-nitro group that has been shown to play an unfavorable role in environmental fate, biosafety, and physical health. The aim of this study was to isolate the microbial community and to identify a single microbial strain that can degrade CL-20 with desirable efficiency. Metagenomic sequencing methods were performed to investigate the dynamic changes in the composition of the community diversity. The most varied genus among the microbial community was Pseudomonas, which increased from 1.46% to 44.63% during the period of incubation (MC0–MC4). Furthermore, the new strain was isolated and identified from the activated sludge by bacterial morphological and 16s rRNA sequencing analyses. The CL-20 concentrations decreased by 75.21 μg/mL and 74.02 μg/mL in 48 h by MC4 and Pseudomonas sp. ZyL-01, respectively. Moreover, ZyL-01 could decompose 98% CL-20 of the real effluent in 14 day’s incubation with the glucose as carbon source. Finally, a draft genome sequence was obtained to predict possible degrading enzymes involved in the biodegradation of CL-20. Specifically, 330 genes that are involved in energy production and conversion were annotated by Gene Ontology functional enrichment analysis, and some of these candidates may encode enzymes that are responsible for CL-20 degradation. In summary, our studies indicate that microbes might be a valuable biological resource for the treatment of environmental contamination caused by CL-20 and that Pseudomonas sp. ZyL-01 might be a promising candidate for eradicating CL-20 to achieve a more biosafe environment and improve public health.

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