Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis

PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields

MOTIVATION

Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning.

RESULTS

To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena.

AVAILABILITY AND IMPLEMENTATION

All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification

With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.

CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research

BACKGROUND

Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated.

METHODS

To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes.

RESULTS

Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy.

CONCLUSIONS

Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .

Comparative transcriptomic analysis reveals an association of gibel carp fatty liver with ferroptosis pathway

BACKGROUND

Fatty liver has become a main problem that causes huge economic losses in many aquaculture modes. It is a common physiological or pathological phenomenon in aquaculture, but the causes and occurring mechanism are remaining enigmatic.

METHODS

Each three liver samples from the control group of allogynogenetic gibel carp with normal liver and the overfeeding group with fatty liver were collected randomly for the detailed comparison of histological structure, lipid accumulation, transcriptomic profile, latent pathway identification analysis (LPIA), marker gene expression, and hepatocyte mitochondria analyses.

RESULTS

Compared to normal liver, larger hepatocytes and more lipid accumulation were observed in fatty liver. Transcriptomic analysis between fatty liver and normal liver showed a totally different transcriptional trajectory. GO terms and KEGG pathways analyses revealed several enriched pathways in fatty liver, such as lipid biosynthesis, degradation accumulation, peroxidation, or metabolism and redox balance activities. LPIA identified an activated ferroptosis pathway in the fatty liver. qPCR analysis confirmed that gpx4, a negative regulator of ferroptosis, was significantly downregulated while the other three positively regulated marker genes, such as acsl4, tfr1 and gcl, were upregulated in fatty liver. Moreover, the hepatocytes of fatty liver had more condensed mitochondria and some of their outer membranes were almost ruptured.

CONCLUSIONS

We reveal an association between ferroptosis and fish fatty liver for the first time, suggesting that ferroptosis might be activated in liver fatty. Therefore, the current study provides a clue for future studies on fish fatty liver problems.

CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).

Transcription Factor 4 Regulates the Regeneration of Corneal Endothelial Cells

Purpose

Human corneal endothelial cells (hCECs) have limited regenerative capacity in vivo. Reduced hCEC density results in bullous keratopathy requiring corneal transplantation. This study reveals the role of transcription factor 4 (TCF4) in hCEC diseases and suggests that TCF4 may be a molecular target for hCEC regeneration.

Methods

Cell shape, cell proliferation rates, and proliferation-associated proteins were evaluated in normal or senescent hCECs. TCF4 was blocked by siRNA (si-TCF4) or activated using clustered regularly interspaced short palindromic repeats (CRISPR)/dCas9 activation systems (pl-TCF4). The corneal endothelium of six-week-old Sprague-Dawley (SD) rats was transfected by electroporation followed by cryoinjury.

Results

Cell proliferation rates and TCF4 levels were reduced in senescent cells. TCF4 CRISPR activation enhanced corneal endothelial wound healing. TCF4 regulated mitochondrial functions including mitochondrial membrane potential, mitochondrial superoxide levels, and energy production. The percentage of cells in the S-phase was reduced with si-TCF4 and increased with pl-TCF4. Cell proliferation and cell cycle-associated proteins were regulated by TCF4. Autophagy was induced by si-TCF4. In vivo transfection of CRISPR/dCas9 activation systems (a-TCF4) induced regeneration of corneal endothelium.

Conclusions

Corneal endothelial diseases are associated with TCF4 reduction; TCF4 may be a potential target for hCEC diseases. Gene therapy using TCF4 CRISPR/dCas9 may be an effective treatment for hCEC diseases.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia

Background

Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.

Results

We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data.

Conclusions

The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users.

Detection of multiple perturbations in multi-omics biological networks

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA).

A comprehensive analysis of Wnt/β-catenin signaling pathway-related genes and crosstalk pathways in the treatment of As2O3 in renal cancer

We aimed to investigate the effect of As₂O₃ treatment on Wnt/β-catenin signaling pathway-related genes and pathways in renal cancer. Illumina-based RNA-seq of 786-O cells with or without As₂O₃ treatment was performed, and differentially expressed genes (DEGs) were identified using Cuffdiff software. TargetMine was utilized to perform Gene Ontology (GO) pathway and Disease Ontology enrichment analyses. Furthermore, TRANSFAC database and LPIA method were applied to select differentially expressed transcription factors (TFs) and pathways related to Wnt/β-catenin signaling pathway, respectively. Additionally, transcriptional regulatory and pathway crosstalk networks were constructed. In total, 1684 DEGs and 69 TFs were screened out. The 821 up-regulated DEGs were mainly enriched in 67 pathways, 70 GO terms, and 46 disease pathways, while only 1 pathway and 5 GO terms were enriched for 863 down-regulated DEGs. A total of 18 DEGs (4 up-regulated and 14 down-regulated genes) were involved in the Wnt/β-catenin signaling pathway. Among the 18 DEGs, 4 ones were TFs. Furthermore, 211 pathways were predicted to be linked to the Wnt/β-catenin signaling pathway. In conclusion, As₂O₃ may have a significant effect on the Wnt/β-catenin signaling pathway for renal cancer treatment. The potential key DEGs are expected to be used as therapeutic targets.

Dissecting dysfunctional crosstalk pathways regulated by miRNAs during glioma progression

Glioma is a malignant nervous system tumor with a high fatality rate and poor prognosis. MicroRNAs (miRNAs) are important post-transcriptional modulators of glioma initiation and progression. Tumor progression often results from dysfunctional co-operation between pathways regulated by miRNAs. We therefore constructed a glioma progression-related miRNA-pathway crosstalk network that not only revealed some key miRNA-pathway patterns, but also helped characterize the functional roles of miRNAs during glioma progression. Our data indicate that crosstalk between cell cycle and p53 pathways is associated with grade II to grade III progression, while cell communications-related pathways involving regulation of actin cytoskeleton and adherens junctions are associated with grade IV glioblastoma progression. Furthermore, miRNAs and their crosstalk pathways may be useful for stratifying glioma and glioblastoma patients into groups with short or long survival times. Our data indicate that a combination of miRNA and pathway crosstalk information can be used for survival prediction.

Bioinformatics analysis of key genes and latent pathway interactions based on the anaplastic thyroid carcinoma gene expression profile

Anaplastic thyroid carcinoma (ATC) is an aggressive malignant disease in older adults with a high mortality rate. The present study aimed to examine several key genes and pathways, which are associated with ATC. The GSE33630 gene expression profile was downloaded from the Gene Expression Omnibus database, which included 11 ATC and 45 normal thyroid samples. The differentially expressed genes (DEGs) in ATC were identified using the Limma package in R. The Gene Ontology functions and Kyoto Encyclopedia of Genes and Genomes pathways of the selected DEGs were enriched using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the DEGs was constructed to select significant modules. Furthermore, a latent pathway interactive network was constructed to select the significant pathways associated with ATC. A total of 665 DEGs in the ATC samples were screened, and four significant modules were selected from the PPI network. The DEGs in the four modules were enriched in several functions and pathways. In addition, 29 significant pathways associated with ATC were selected, and he Toll-like receptor (TLR) signaling pathway, extracellular matrix (ECM)-receptor interaction and cytokine-cytokine interaction pathway were identified as important pathways. FBJ murine osteosarcoma viral oncogene homolog (FOS), chemokine C-X-C motif ligand 10 (CXCL10), collagen type V α1 (COL5A1) and chemokine (C-C motif) ligand 28 (CCL28) were the key DEGs involved in these significant pathways. The data obtained in the present study revealed that the TLR signaling pathway, ECM-receptor interaction and cytokine-cytokine receptor interaction pathway, and the FOS, CXCL10, COL5A1, COL11A1 and CCL28 genes have different roles in the progression of ATC, and these may be used as therapeutic targets for ATC.

Subpathway-CorSP: Identification of metabolic subpathways via integrating expression correlations and topological features between metabolites and genes of interest within pathways

Metabolic pathway analysis is a popular strategy for comprehensively researching metabolites and genes of interest associated with specific diseases. However, the traditional pathway identification methods do not accurately consider the combined effect of these interesting molecules and neglects expression correlations or topological features embedded in the pathways. In this study, we propose a powerful method, Subpathway-CorSP, for identifying metabolic subpathway regions. This method improved on original pathway identification methods by using a subpathway identification strategy and emphasizing expression correlations between metabolites and genes of interest based on topological features within the metabolic pathways. We analyzed a prostate cancer data set and its metastatic sub-group data set with detailed comparison of Subpathway-CorSP with four traditional pathway identification methods. Subpathway-CorSP was able to identify multiple subpathway regions whose entire corresponding pathways were not detected by traditional pathway identification methods. Further evidences indicated that Subpathway-CorSP provided a robust and efficient way of reliably recalling cancer-related subpathways and locating novel subpathways by the combined effect of metabolites and genes. This was a novel subpathway strategy based on systematically considering expression correlations and topological features between metabolites and genes of interest within given pathways.

Prioritization of rheumatoid arthritis risk subpathways based on global immune subpathway interaction network and random walk strategy

The initiation and development of rheumatoid arthritis (RA) is closely related to mutual dysfunction of multiple pathways. Furthermore, some similar molecular mechanisms are shared between RA and other immune diseases. Therefore it is vital to reveal the molecular mechanism of RA through searching for subpathways of immune diseases and investigating the crosstalk effect among subpathways. Here we exploited an integrated approach combining both construction of a subpathway-subpathway interaction network and a random walk strategy to prioritize RA risk subpathways. Our research can be divided into three parts: (1) acquisition of risk genes and identification of risk subpathways of 85 immune diseases by using subpathway-lenient distance similarity (subpathway-LDS) method; (2) construction of a global immune subpathway interaction (GISI) network with subpathways identified by subpathway-LDS; (3) optimization of RA risk subpathways by random walk strategy based on GISI network. The results showed that our method could effectively identify RA risk subpathways, such as MAPK signaling pathway, prostate cancer pathway and chemokine signaling pathway. The integrated strategy considering crosstalk between immune subpathways significantly improved the effect of risk subpathway identification. With the development of GWAS, our method will provide insight into exploring molecular mechanisms of immune diseases and might be a promising approach for studying other diseases.

Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.

Latent Pathways Identification by Microarray Expression Profiles in Thyroid-Associated Ophthalmopathy Patients

This study aimed to screen potential genes related to thyroid-associated ophthalmopathy (TAO) and get a further understanding about the pathogenesis of this disease. GSE9340 was downloaded from Gene Expression Omnibus, including eight thyroid tissue samples from hyperthyroid patients without TAO and ten ones from hyperthyroid patients with TAO. The differentially expressed genes (DEGs) were identified by the linear models for microarray data package. And their potential functions were predicted by Gene Ontology (GO) and pathway enrichment analyses. Furthermore, protein-protein interaction (PPI) was obtained from the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was visualized with Cytoscape. Then, module analysis was performed by the Molecular Complex Detection analysis. Additionally, the potential pathway interactions were identified by Latent Pathway Identification Analysis. Totally, 511 upregulated and 507 downregulated DEGs in TAO were screened. Some DEGs (e.g., UBE2C) were related to cell cycle, and DEGs encoding proteasome (e.g., PSMA1, PSMC5, PSMC4, and PSMD1) were related to negative regulation of ubiquitin-protein ligase activity. Several upregulated DEGs encoding signal recognition particle (e.g., SRP14, SRP54, and SRP9) were found to be enriched in protein export pathway. Furthermore, some pathways (e.g., ribosome and protein export) had interactions. The DEGs related to cell cycle (e.g., UBE2C), DEGs encoding proteasome (e.g., PSMA1, PSMC5, PSMC4, and PSMD1) and signal recognition particle (e.g., SRP14, SRP54, and SRP9), as well as pathways of ribosome, protein export and retinol metabolism, might play key roles in the development of TAO.

A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI).

The expression profile analysis of NKX2-5 knock-out embryonic mice to explore the pathogenesis of congenital heart disease

BACKGROUND

Mutation of NKX2-5 could lead to the development of congenital heart disease (CHD) which is a common inherited disease. This study aimed to investigate the pathogenesis of CHD in NKX2-5 knock-out embryonic mice.

METHODS

The expression profile in the NKX2-5 knock-out embryonic mice (GSE528) was downloaded from Gene Expression Omnibus. The heart tissues from the null/heterozygous embryonic day 12.5 mice were compared with wild-type mice to identify differentially expressed genes (DEGs), and then DEGs corresponding to the transcriptional factors were filtered out based on the information in the TRANSFAC database. In addition, a transcriptional regulatory network was constructed according to transcription factor binding site information from the University of California Santa Cruz database. A pathway interaction network was constructed by latent pathways identification analysis.

RESULTS

The 42 DEGs corresponding to transcriptional factors from the null and heterozygous embryos were identified. The transcriptional regulatory networks included five down-regulated DEGs (SP1, SRY, JUND, STAT6, and GATA6), and six up-regulated DEGs [POU2F1, NFY (NFYA/NFYB/NFYC), USF2 and MAX]. Latent pathways analysis demonstrated that ribosome, glycolysis/gluconeogenesis, and dilated cardiomyopathy pathways significantly interacted.

CONCLUSION

The identified DEGs and latent pathways could provide new comprehensive view for understanding the pathogenesis of CHD.

Interaction of key pathways in sorafenib-treated hepatocellular carcinoma based on a PCR-array

This study aimed to identify the key pathways and to explore the mechanism of sorafenib in inhibiting hepatocellular carcinoma (HCC). The gene expression profile of GSE33621, including 6 sorafenib treated group and 6 control samples, was downloaded from the GEO (Gene Expression Omnibus) database. The differentially expressed genes (DEGs) in HCC samples were screened using the ΔΔCt method with the homogenized internal GAPDH. Also, the functions and pathways of DEGs were analyzed using the DAVID. Moreover, the significant pathways of DEGs that involved in HCC were analyzed based on the Latent pathway identification analysis (LPIA). A total of 44 down-regulated DEGs were selected in HCC samples. Also, there were 84 biological pathways that these 44 DEGs involved in. Also, LPIA showed that Osteoclast differentiation and hsa04664-Fc epsilon RI signaling pathway was the most significant interaction pathways. Moreover, Apoptosis, Toll-like receptor signaling pathway, Chagas disease, and T cell receptor signaling pathway were the significant pathways that interacted with hsa04664. In addition, DEGs such as AKT1 (v-akt murine thymoma viral oncogene homolog 1), TNF (tumor necrosis factor), SYK (spleen tyrosine kinase), and PIK3R1 (phosphoinositide-3-kinase, regulatory subunit 1 (alpha)) were the common genes that involved in the significant pathways. Several pathway interaction pairs that caused by several downregulated genes such as SYK, PI3K, AKT1, and TNF, were identified play curial role in sorafenib treated HCC. Sorafenib played important inhibition roles in HCC by affecting a complicate pathway interaction network.

A comprehensive analysis of candidate genes and pathways in pancreatic cancer

The study aimed to dissect the molecular mechanism of pancreatic cancer by a range of bioinformatics approaches. Three microarray datasets (GSE32676, GSE21654, and GSE14245) were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) with logarithm of fold change (|logFC|) >0.585 and p value <0.05 were identified between pancreatic cancer samples and normal controls. Transcription factors (TFs) were selected from the DEGs based on TRASFAC database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed for the DEGs using The Database for Annotation, Visualization and Integrated Discovery (p value <0.05), followed by construction of protein-protein interaction (PPI) network using Search Tool for the Retrieval of Interacting Genes software. Latent pathway identification analysis was applied to analyze the DEGs-related pathways crosstalk and the pathways with high weight value were included in the pathway crosstalk network using Cytoscape. Sixty-five DEGs were screened out. CCAAT/enhancer-binding protein delta (CEBPD), FBJ osteosarcoma oncogene B (FOSB), Stratifin (SFN), Krüppel-like factor 5 (KLF5), Pentraxin 3 (PTX3), and nuclear receptor subfamily 4, group A, member 3 (NR4A3) were important TFs. Interleukin-6 (IL-6) was the hub node of the PPI network. DEGs were significantly enriched in NOD-like receptor signaling pathway which was primarily interacted with inflammation and immune related pathways (cytosolic DNA-sensing, hematopoietic cell lineage, intestinal immune network for IgA production and chemokine pathways). The study suggested CEBPD, FOSB, SFN, KLF5, PTX3, NR4A3, IL-6, and NOD-like receptor pathways were involved in pancreatic cancer.

Identification of key genes affecting disease free survival time of pediatric acute lymphoblastic leukemia based on bioinformatic analysis

The poor prognosis of pediatric acute lymphoblastic leukemia (ALL) indicates the existence of key candidate genes that affect pediatric ALL and its prognosis. The limma package in R was applied to screen differentially expressed genes (DEGs), and the Survival package and KMsurv package in R were used to screen disease free survival time related genes (prognosis genes). Then, based on latent pathway identification analysis (LPIA), latent pathways were identified, and pathway-pathway interaction network was constructed and visualized by Cytoscape. Based on the expression values of 8284 genes in 126 chips, 2796 DEGs and 353 prognosis genes were screened out. After overlapping DEGs and prognosis genes, 75 key genes were identified, which were most significantly enriched in 25 GO functions and chronic myeloid leukemia pathway. For the 75 key genes, 27 disease risk sub-pathways were identified, and HK3, HNMT, SULT2B1, KYNU, and PTGS2 were the significant key genes which were enriched in these sub-pathways. Furthermore, based on pathway-pathway interaction analysis, HK3 and PTGS2 were predicted as the most important genes. Through glycolysis and arachidonic acid metabolism, HK3 and PTGS2 might play important roles in pediatric ALL and its prognosis, and thus, might be potential targets for therapeutic intervention to suppress pediatric ALL.

Functional characterization of breast cancer using pathway profiles

BACKGROUND

The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.

METHODS

We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.

RESULTS

Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.

CONCLUSIONS

Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

MPINet: metabolite pathway identification via coupling of global metabolite network structure and metabolomic profile

High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet's effectiveness and reliability for analyzing metabolomic data across multiple different application fields.

Filling out the structural map of the NTF2-like superfamily

Background

The NTF2-like superfamily is a versatile group of protein domains sharing a common fold. The sequences of these domains are very diverse and they share no common sequence motif. These domains serve a range of different functions within the proteins in which they are found, including both catalytic and non-catalytic versions. Clues to the function of protein domains belonging to such a diverse superfamily can be gleaned from analysis of the proteins and organisms in which they are found.

Results

Here we describe three protein domains of unknown function found mainly in bacteria: DUF3828, DUF3887 and DUF4878. Structures of representatives of each of these domains: BT_3511 from Bacteroides thetaiotaomicron (strain VPI-5482) [PDB:3KZT], Cj0202c from Campylobacter jejuni subsp. jejuni serotype O:2 (strain NCTC 11168) [PDB:3K7C], rumgna_01855) and RUMGNA_01855 from Ruminococcus gnavus (strain ATCC 29149) [PDB:4HYZ] have been solved by X-ray crystallography. All three domains are similar in structure and all belong to the NTF2-like superfamily. Although the function of these domains remains unknown at present, our analysis enables us to present a hypothesis concerning their role.

Conclusions

Our analysis of these three protein domains suggests a potential non-catalytic ligand-binding role. This may regulate the activities of domains with which they are combined in the same polypeptide or via operonic linkages, such as signaling domains (e.g. serine/threonine protein kinase), peptidoglycan-processing hydrolases (e.g. NlpC/P60 peptidases) or nucleic acid binding domains (e.g. Zn-ribbons).

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways

Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Annexin A2 modulates radiation-sensitive transcriptional programming and cell fate

We previously established annexin A2 as a radioresponsive protein associated with anchorage independent growth in murine epidermal cells. In this study, we demonstrate annexin A2 nuclear translocation in human skin organotypic culture and murine epidermal cells after exposure to X radiation (10-200 cGy), supporting a conserved nuclear function for annexin A2. Whole genome expression profiling in the presence and absence of annexin A2 [shRNA] identified fundamentally altered transcriptional programming that changes the radioresponsive transcriptome. Bioinformatics predicted that silencing AnxA2 may enhance cell death responses to stress in association with reduced activation of pro-survival signals such as nuclear factor kappa B. This prediction was validated by demonstrating a significant increase in sensitivity toward tumor necrosis factor alpha-induced cell death in annexin A2 silenced cells, relative to vector controls, associated with reduced nuclear translocation of RelA (p65) following tumor necrosis factor alpha treatment. These observations implicate an annexin A2 niche in cell fate regulation such that AnxA2 protects cells from radiation-induced apoptosis to maintain cellular homeostasis at low-dose radiation.

Transcriptional data: a new gateway to drug repositioning?

Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology.

Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug–disease and drug–drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated.

Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.