Reactome knowledgebase of human biological pathways and processes

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

Integrative analysis of neuroblastoma by single-cell RNA sequencing identifies the NECTIN2-TIGIT axis as a target for immunotherapy

Pediatric patients with high-risk neuroblastoma have poor survival rates and urgently need more effective treatment options with less side effects. Since novel and improved immunotherapies may fill this need, we dissect the immunoregulatory interactions in neuroblastoma by single-cell RNA-sequencing of 24 tumors (10 pre- and 14 post-chemotherapy, including 5 pairs) to identify strategies for optimizing immunotherapy efficacy. Neuroblastomas are infiltrated by natural killer (NK), T and B cells, and immunosuppressive myeloid populations. NK cells show reduced cytotoxicity and T cells have a dysfunctional profile. Interaction analysis reveals a vast immunoregulatory network and identifies NECTIN2-TIGIT as a crucial immune checkpoint. Combined blockade of TIGIT and PD-L1 significantly reduces neuroblastoma growth, with complete responses (CR) in vivo. Moreover, addition of TIGIT+PD-L1 blockade to standard relapse treatment in a chemotherapy-resistant Th-ALKF1174L/MYCN 129/SvJ syngeneic model induces CR. In conclusion, our integrative analysis provides promising targets and a rationale for immunotherapeutic combination strategies.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Sample-specific perturbation of gene interactions identifies breast cancer subtypes

Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.

Regulation of hepatic microRNAs in response to early stage Echinococcus multilocularis egg infection in C57BL/6 mice

We present a comprehensive analysis of the hepatic miRNA transcriptome at one month post-infection of experimental primary alveolar echinococcosis (AE), a parasitic infection caused upon ingestion of E. multilocularis eggs. Liver tissues were collected from infected and non-infected C57BL/6 mice, then small RNA libraries were prepared for next-generation sequencing (NGS). We conducted a Stem-loop RT-qPCR for validation of most dysregulated miRNAs. In infected mice, the expression levels of 28 miRNAs were significantly altered. Of these, 9 were up-regulated (fold change (FC) ≥ 1.5) and 19 were down-regulated (FC ≤ 0.66) as compared to the non-infected controls. In infected livers, mmu-miR-148a-3p and mmu-miR-101b-3p were 8- and 6-fold down-regulated, respectively, and the expression of mmu-miR-22-3p was reduced by 50%, compared to non-infected liver tissue. Conversely, significantly higher hepatic levels were noted for Mus musculus (mmu)-miR-21a-5p (FC = 2.3) and mmu-miR-122-5p (FC = 1.8). In addition, the relative mRNA expression levels of five genes (vegfa, mtor, hif1-α, fasn and acsl1) that were identified as targets of down-regulated miRNAs were significantly enhanced. All the five genes exhibited a higher expression level in livers of E. multilocularis infected mice compared to non-infected mice. Finally, we studied the issue related to functionally mature arm selection preference (5p and/or 3p) from the miRNA precursor and showed that 9 pre-miRNAs exhibited different arm selection preferences in normal versus infected liver tissues. In conclusion, this study provides first evidence that miRNAs are regulated early in primary murine AE. Our findings raise intriguing questions such as (i) how E. multilocularis affects hepatic miRNA expression;(ii) what are the alterations in miRNA expression patterns in more advanced AE-stages; and (iii) which hepatic cellular, metabolic and/or immunologic processes are modulated through altered miRNAs in AE. Thus, further research on the regulation of miRNAs during AE is needed, since miRNAs constitute an attractive potential option for development of novel therapeutic approaches against AE.

Various infectious diseases in humans have been associated with altered expression patterns of microRNAs (miRNAs), a class of small non-coding RNAs involved in negative regulation of gene expression. Herein, we revealed that significant alteration of miRNAs expression occurred in murine liver subsequently to experimental infection with E. multilocularis eggs when compared to non-infected controls. At the early stage of murine AE, hepatic miRNAs were mainly down-regulated. Respective target genes of the most extensively down-regulated miRNAs were involved in angiogenesis and fatty acid synthesis. Furthermore, we found higher mRNA levels of three angiogenic and two lipogenic genes in E. multilocularis infected livers compared to non-infected controls. Angiogenesis and fatty acid biosynthesis may be beneficial for development of the E. multilocularis metacestodes. In fact the formation of new blood vessels in the periparasitic area may ensure that parasites are supplied with oxygen and nutrients and get rid of waste products. Additionally, E. multilocularis is not able to undertake de novo fatty acid synthesis, thus lipids must be scavenged from its host. More research on the regulation of the hepatic miRNA transcriptome at more advanced stages of AE is needed.

Identification of potential pathogenic candidates or diagnostic biomarkers in papillary thyroid carcinoma using expression and methylation profiles

The mechanisms underlying the pathogenesis of papillary thyroid carcinoma (PTC) have not yet been elucidated. The aim of the current study was to identify potential pathogenic biomarkers in PTC by comprehensively analyzing gene expression and methylation profiles, and to increase the understanding of PTC pathogenesis. The gene expression profiles of the GSE97001 and GSE83520 datasets, the miRNA expression profiles of the GSE73182 dataset, and the DNA methylation profiles of the GSE86961 and GSE97466 datasets were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and the differentially expressed microRNAs (DEMs) were identified using the limma package in R, and the differentially methylated sites (DMSs) were identified using the β distribution and two-sample t-tests. The Database for Annotation, Visualization and Integrated Discovery, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome were subsequently used to perform functional and pathway enrichment analysis. The miRNA target genes were predicted using the online databases miRWalk. The protein-protein interactions (PPI) were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins. The regulatory network was constructed, and the gene expression and methylation levels of the key nodes were detected using reverse-transcription quantitative-polymerase chain reaction (PCR) and methylation-specific PCR. A total of 155 overlapping DEGs were identified between the GSE97001 and GSE83520 datasets, and 19 DEMs between PTC tissue and normal tissue samples were identified in the GSE73182 set. In the GSE86961 and GSE97466 datasets, 2,910 overlapping DMSs that were associated with 38 downregulated methylated genes were identified. The overlapping DEGs were enriched in 46 Gene Ontology terms and one KEGG pathway. A total of 60 PPI pairs were identified for the overlapping DEGs and 12 negative miRNA-gene pairs were identified for the DEMs. The expression levels of hsa-miR-199a-5p and decorin (DCN) were decreased in patients with PTC. C-X-C motif chemokine ligand 12 (CXCL12) was hypermethylated and had a decreased expression level in PTC tissues. LDL receptor related protein 4 (LRP4) and carbonic anhydrase 12 (CA12) were hypomethylated and had an increased expression level. The present study revealed that hsa-miR-199a-5p, DCN, CXCL12, LRP4 and CA12 may serve important roles in the pathogenesis of PTC.

Identification of atherosclerosis-related prioritizing metabolites based on a multi-omics composite network

Metabolites are the final products of cellular regulation processes, their level is the ultimate response of biological systems to environmental and genetic changes. Therefore, the identification of key metabolites is required for the diagnosis and therapy of diseases. In this study, atherosclerosis-related gene expression profile information was extracted from ArrayExpress database (GEOD-57691), and analyzed with limma package. Furthermore, we constructed an intricate multi-omics network involved in genes, phenotypes, metabolites and their associations. To identify the prioritization of atherosclerosis-related metabolites, the relation score of each metabolite in the composite network was computed with the random walk with restart (RWR) method. The top 50 metabolites and top 100 genes were chosen based on the score in the weighted composite network. Consequently, several key metabolites that were ranked in the top 5 of relation score or degree greater than 70 were confirmed. Particularly, metabolites Tretinoin and Estraderm not only have high relation scores, but also contain more degrees. Moreover, we obtained 24 co-expression genes that may be regarded as the targets of atherosclerosis therapy. Therefore, identification of metabolite prioritizations by the composite network integrated the information of genes, phenotypes and metabolites may be available to diagnose atherosclerosis, and can provide the potential therapeutic strategies for atherosclerosis.

Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

p.E95K mutation in Indian hedgehog causing brachydactyly type A1 impairs IHH/Gli1 downstream transcriptional regulation

Background

Brachydactyly type A1 (BDA1, OMIM 112500) is a rare inherited malformation characterized primarily by shortness or absence of middle bones of fingers and toes. It is the first recorded disorder of the autosomal dominant Mendelian trait. Indian hedgehog (IHH) gene is closely associated with BDA1, which was firstly mapped and identified in Chinese families in 2000. Previous studies have demonstrated that BDA1-related mutant IHH proteins affected interactions with its receptors and impaired IHH signaling. However, how the altered signaling pathway affects downstream transcriptional regulation remains unclear.

Results

Based on the mouse C3H10T1/2 cell model for IHH signaling activation, two recombinant human IHH-N proteins, including a wild type protein (WT, amino acid residues 28–202) and a mutant protein (MT, p.E95k), were analyzed. We identified 347, 47 and 4 Gli1 binding sites in the corresponding WT, MT and control group by chromatin immunoprecipitation and the overlapping of these three sets was poor. The putative cis regulated genes in WT group were enriched in sensory perception and G-protein coupled receptor-signaling pathway. On the other hand, putative cis regulated genes were enriched in Runx2-related pathways in MT group. Differentially expressed genes in WT and MT groups indicated that the alteration of mutant IHH signaling involved cell-cell signaling and cellular migration. Cellular assay of migration and proliferation validated that the mutant IHH signaling impaired these two cellular functions.

Conclusions

In this study, we performed integrated genome-wide analyses to characterize differences of IHH/Gli1 downstream regulation between wild type IHH signaling and the E95K mutant signaling. Based on the cell model, our results demonstrated that the E95K mutant signaling altered Gli1-DNA binding pattern, impaired downstream gene expressions, and leaded to weakened cellular proliferation and migration. This study may help to deepen the understanding of pathogenesis of BDA1 and the role of IHH signaling in chondrogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12863-018-0697-5) contains supplementary material, which is available to authorized users.

Sex-specific gene and pathway modeling of inherited glioma risk

Background

To date, genome-wide association studies (GWAS) have identified 25 risk variants for glioma, explaining 30% of heritable risk. Most histologies occur with significantly higher incidence in males, and this difference is not explained by currently known risk factors. A previous GWAS identified sex-specific glioma risk variants, and this analysis aims to further elucidate risk variation by sex using gene- and pathway-based approaches.

Methods

Results from the Glioma International Case-Control Study were used as a testing set, and results from 3 GWAS were combined via meta-analysis and used as a validation set. Using summary statistics for nominally significant autosomal SNPs (P < 0.01 in a previous meta-analysis) and nominally significant X-chromosome SNPs (P < 0.01), 3 algorithms (Pascal, BimBam, and GATES) were used to generate gene scores, and Pascal was used to generate pathway scores. Results were considered statistically significant in the discovery set when P < 3.3 × 10-6 and in the validation set when P < 0.001 in 2 of 3 algorithms.

Results

Twenty-five genes within 5 regions and 19 genes within 6 regions reached statistical significance in at least 2 of 3 algorithms in males and females, respectively. EGFR was significantly associated with all glioma and glioblastoma in males only and a female-specific association in TERT, all of which remained nominally significant after conditioning on known risk loci. There were nominal associations with the BioCarta telomeres pathway in both males and females.

Conclusions

These results provide additional evidence that there may be differences by sex in genetic risk for glioma. Additional analyses may further elucidate the biological processes through which this risk is conferred.

Reactome diagram viewer: data structures and strategies to boost performance

Motivation

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community.

Results

The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources.

Availability and implementation

Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram.

Supplementary information

Supplementary data are available at Bioinformatics online.

Network pharmacology-based strategy to investigate pharmacological mechanisms of Zuojinwan for treatment of gastritis

BACKGROUND

Zuojinwan (ZJW), a classic herbal formula, has been extensively used to treat gastric symptoms in clinical practice in China for centuries. However, the pharmacological mechanisms of ZJW still remain vague to date.

METHODS

In the present work, a network pharmacology-based strategy was proposed to elucidate its underlying multi-component, multi-target, and multi-pathway mode of action against gastritis. First we collected putative targets of ZJW based on TCMSP and STITCH databases, and a network containing the interactions between the putative targets of ZJW and known therapeutic targets of gastritis was built. Then four topological parameters, "degree", "betweenness", "closeness", and "coreness" were calculated to identify the major targets in the network. Furthermore, the major hubs were imported to the Metacore database to perform a pathway enrichment analysis.

RESULTS

A total of 118 nodes including 59 putative targets of ZJW were picked out as major hubs in terms of their topological importance. The results of pathway enrichment analysis indicated that putative targets of ZJW mostly participated in various pathways associated with anti-inflammation response, growth and development promotion and G-protein-coupled receptor signaling. More importantly, five putative targets of ZJW (EGFR, IL-6, IL-1β, TNF-α and MCP-1) and two known therapeutic targets of gastritis (CCKBR and IL-12β) and a link target NF-κB were recognized as active factors involved in the main biological functions of treatment, implying the underlying mechanisms of ZJW acting on gastritis.

CONCLUSION

ZJW could alleviate gastritis through the molecular mechanisms predicted by network pharmacology, and this research demonstrates that the network pharmacology approach can be an effective tool to reveal the mechanisms of traditional Chinese medicine (TCM) from a holistic perspective.

A dose-dependent perturbation in cardiac energy metabolism is linked to radiation-induced ischemic heart disease in Mayak nuclear workers

Epidemiological studies show a significant increase in ischemic heart disease (IHD) incidence associated with total external gamma-ray dose among Mayak plutonium enrichment plant workers. Our previous studies using mouse models suggest that persistent alteration of heart metabolism due to the inhibition of peroxisome proliferator-activated receptor (PPAR) alpha accompanies cardiac damage after high doses of ionising radiation. The aim of the present study was to elucidate the mechanism of radiation-induced IHD in humans. The cardiac proteome response to irradiation was analysed in Mayak workers who were exposed only to external doses of gamma rays. All participants were diagnosed during their lifetime with IHD that also was the cause of death. Label-free quantitative proteomics analysis was performed on tissue samples from the cardiac left ventricles of individuals stratified into four radiation dose groups (0 Gy, < 100 mGy, 100–500 mGy, and > 500 mGy). The groups could be separated using principal component analysis based on all proteomics features. Proteome profiling showed a dose-dependent increase in the number of downregulated mitochondrial and structural proteins. Both proteomics and immunoblotting showed decreased expression of several oxidative stress responsive proteins in the irradiated hearts. The phosphorylation of transcription factor PPAR alpha was increased in a dose-dependent manner, which is indicative of a reduction in transcriptional activity with increased radiation dose. These data suggest that chronic external radiation enhances the risk for IHD by inhibiting PPAR alpha and altering the expression of mitochondrial, structural, and antioxidant components of the heart.

Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets

Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH’s efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu

Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis

OBJECTIVE

Breast cancer is a severe risk to public health and has adequately convoluted pathogenesis. Therefore, the description of key molecular markers and pathways is of much importance for clarifying the molecular mechanism of breast cancer-associated fibroblasts initiation and progression. Breast cancer-associated fibroblasts gene expression dataset was downloaded from Gene Expression Omnibus database.

METHODS

A total of nine samples, including three normal fibroblasts, three granulin-stimulated fibroblasts and three cancer-associated fibroblasts samples, were used to identify differentially expressed genes (DEGs) between normal fibroblasts, granulin-stimulated fibroblasts and cancer-associated fibroblasts samples. The gene ontology (GO) and pathway enrichment analysis was performed, and protein-protein interaction (PPI) network of the DEGs was constructed by NetworkAnalyst software.

RESULTS

Totally, 190 DEGs were identified, including 66 up-regulated and 124 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell-cell signalling and negative regulation of cell proliferation; molecular function (MF), including insulin-like growth factor II binding and insulin-like growth factor I binding; cellular component (CC), including insulin-like growth factor binding protein complex and integral component of plasma membrane; the down-regulated DEGs were significantly enriched in BP, including cell adhesion and extracellular matrix organization; MF, including N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase activity and calcium ion binding; CC, including extracellular space and extracellular matrix. WIKIPATHWAYS analysis showed the up-regulated DEGs were enriched in myometrial relaxation and contraction pathways. WIKIPATHWAYS, REACTOME, PID_NCI and KEGG pathway analysis showed the down-regulated DEGs were enriched endochondral ossification, TGF beta signalling pathway, integrin cell surface interactions, beta1 integrin cell surface interactions, malaria and glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulphate. The top 5 up-regulated hub genes, CDKN2A, MME, PBX1, IGFBP3, and TFAP2C and top 5 down-regulated hub genes VCAM1, KRT18, TGM2, ACTA2, and STAMBP were identified from the PPI network, and subnetworks revealed these genes were involved in significant pathways, including myometrial relaxation and contraction pathways, integrin cell surface interactions, beta1 integrin cell surface interaction. Besides, the target hsa-mirs for DEGs were identified. hsa-mir-759, hsa-mir-4446-5p, hsa-mir-219a-1-3p and hsa-mir-26a-5p were important miRNAs in this study.

CONCLUSIONS

We pinpoint important key genes and pathways closely related with breast cancer-associated fibroblasts initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying breast cancer-associated fibroblasts occurrence and progression, holding promise for acting as molecular markers and probable therapeutic targets.

Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins

Traditional Protein-Protein Interaction (PPI) networks, which use a node and edge representation, lack some valuable information about the mechanistic details of biological processes. Mapping protein structures to these PPI networks not only provides structural details of each interaction but also helps us to find the mutual exclusive interactions. Yet it is not a comprehensive representation as it neglects the conformational changes of proteins which may lead to different interactions, functions, and downstream signalling. In this study, we proposed a new representation for structural PPI networks inspecting the alternative conformations of proteins. We performed a large-scale study by creating breast cancer metastasis network and equipped it with different conformers of proteins. Our results showed that although 88% of proteins in our network has at least two structures in Protein Data Bank (PDB), only 22% of them have alternative conformations and the remaining proteins have different regions saved in PDB. However, using even this small set of alternative conformations we observed a considerable increase in our protein docking predictions. Our protein-protein interaction predictions increased from 54% to 76% using the alternative conformations. We also showed the benefits of investigating structural data and alternative conformations of proteins through three case studies.

PCID: A Novel Approach for Predicting Disease Comorbidity by Integrating Multi-Scale Data

Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.

Expression Quantitative Trait loci (QTL) in tumor adjacent normal breast tissue and breast tumor tissue

We investigate 71 single nucleotide polymorphisms (SNPs) identified in meta-analytic studies of genome-wide association studies (GWAS) of breast cancer, the majority of which are located in intergenic or intronic regions. To explore regulatory impacts of these variants we conducted expression quantitative loci (eQTL) analyses on tissue samples from 376 invasive postmenopausal breast cancer cases in the Nurses' Health Study (NHS) diagnosed from 1990-2004. Expression analysis was conducted on all formalin-fixed paraffin-embedded (FFPE) tissue samples (and on 264 adjacent normal samples) using the Affymetrix Human Transcriptome Array. Significance and ranking of associations between tumor receptor status and expression variation was preserved between NHS FFPE and TCGA fresh-frozen sample sets (Spearman r = 0.85, p<10^-10 for 17 of the 21 Oncotype DX recurrence signature genes). At an FDR threshold of 10%, we identified 27 trans-eQTLs associated with expression variation in 217 distinct genes. SNP-gene associations can be explored using an open-source interactive browser distributed in a Bioconductor package. Using a new a procedure for testing hypotheses relating SNP content to expression patterns in gene sets, defined as molecular function pathways, we find that loci on 6q14 and 6q25 affect various gene sets and molecular pathways (FDR < 10%). Although the ultimate biological interpretation of the GWAS-identified variants remains to be uncovered, this study validates the utility of expression analysis of this FFPE expression set for more detailed integrative analyses.

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

Protein function in precision medicine: deep understanding with machine learning

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

HPIDB 2.0: a curated database for host-pathogen interactions

Identification and analysis of host–pathogen interactions (HPI) is essential to study infectious diseases. However, HPI data are sparse in existing molecular interaction databases, especially for agricultural host–pathogen systems. Therefore, resources that annotate, predict and display the HPI that underpin infectious diseases are critical for developing novel intervention strategies. HPIDB 2.0 (http://www.agbase.msstate.edu/hpi/main.html) is a resource for HPI data, and contains 45, 238 manually curated entries in the current release. Since the first description of the database in 2010, multiple enhancements to HPIDB data and interface services were made that are described here. Notably, HPIDB 2.0 now provides targeted biocuration of molecular interaction data. As a member of the International Molecular Exchange consortium, annotations provided by HPIDB 2.0 curators meet community standards to provide detailed contextual experimental information and facilitate data sharing. Moreover, HPIDB 2.0 provides access to rapidly available community annotations that capture minimum molecular interaction information to address immediate researcher needs for HPI network analysis. In addition to curation, HPIDB 2.0 integrates HPI from existing external sources and contains tools to infer additional HPI where annotated data are scarce. Compared to other interaction databases, our data collection approach ensures HPIDB 2.0 users access the most comprehensive HPI data from a wide range of pathogens and their hosts (594 pathogen and 70 host species, as of February 2016). Improvements also include enhanced search capacity, addition of Gene Ontology functional information, and implementation of network visualization. The changes made to HPIDB 2.0 content and interface ensure that users, especially agricultural researchers, are able to easily access and analyse high quality, comprehensive HPI data. All HPIDB 2.0 data are updated regularly, are publically available for direct download, and are disseminated to other molecular interaction resources.

Database URL:http://www.agbase.msstate.edu/hpi/main.html

Network analysis and juvenile idiopathic arthritis (JIA): a new horizon for the understanding of disease pathogenesis and therapeutic target identification

Juvenile idiopathic arthritis (JIA) is a clinically diverse and genetically complex autoimmune disease. Currently, there is very limited understanding of the potential underlying mechanisms that result in the range of phenotypes which constitute JIA.The elucidation of the functional relevance of genetic associations with phenotypic traits is a fundamental problem that hampers the translation of genetic observations to plausible medical interventions. Genome wide association studies, and subsequent fine-mapping studies in JIA patients, have identified many genetic variants associated with disease. Such approaches rely on 'tag' single nucleotide polymorphisms (SNPs). The associated SNPs are rarely functional variants, so the extrapolation of genetic association data to the identification of biologically meaningful findings can be a protracted undertaking. Integrative genomics aims to bridge the gap between genotype and phenotype.Systems biology, principally through network analysis, is emerging as a valuable way to identify biological pathways of relevance to complex genetic diseases. This review aims to highlight recent findings in systems biology related to JIA in an attempt to assist in the understanding of JIA pathogenesis and therapeutic target identification.

In Silico Modeling of Liver Metabolism in a Human Disease Reveals a Key Enzyme for Histidine and Histamine Homeostasis

Primary hyperoxaluria type I (PH1) is an autosomal-recessive inborn error of liver metabolism caused by alanine:glyoxylate aminotransferase (AGT) deficiency. In silico modeling of liver metabolism in PH1 recapitulated accumulation of known biomarkers as well as alteration of histidine and histamine levels, which we confirmed in vitro, in vivo, and in PH1 patients. AGT-deficient mice showed decreased vascular permeability, a readout of in vivo histamine activity. Histamine reduction is most likely caused by increased catabolism of the histamine precursor histidine, triggered by rerouting of alanine flux from AGT to the glutamic-pyruvate transaminase (GPT, also known as the alanine-transaminase ALT). Alanine administration reduces histamine levels in wild-type mice, while overexpression of GPT in PH1 mice increases plasma histidine, normalizes histamine levels, restores vascular permeability, and decreases urinary oxalate levels. Our work demonstrates that genome-scale metabolic models are clinically relevant and can link genotype to phenotype in metabolic disorders.

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In silico model of liver metabolism reveals global metabolic alterations in PH1

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Changes in amino acid metabolism in PH1 result in a reduction of histidine and histamine

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GPT overexpression normalizes histamine levels and reduces oxalate in PH1 mice

Candidate genes and pathogenesis investigation for sepsis-related acute respiratory distress syndrome based on gene expression profile

Background

Acute respiratory distress syndrome (ARDS) is a potentially devastating form of acute inflammatory lung injury as well as a major cause of acute respiratory failure. Although researchers have made significant progresses in elucidating the pathophysiology of this complex syndrome over the years, the absence of a universal detail disease mechanism up until now has led to a series of practical problems for a definitive treatment. This study aimed to predict some genes or pathways associated with sepsis-related ARDS based on a public microarray dataset and to further explore the molecular mechanism of ARDS.

Results

A total of 122 up-regulated DEGs and 91 down-regulated differentially expressed genes (DEGs) were obtained. The up- and down-regulated DEGs were mainly involved in functions like mitotic cell cycle and pathway like cell cycle. Protein–protein interaction network of ARDS analysis revealed 20 hub genes including cyclin B1 (CCNB1), cyclin B2 (CCNB2) and topoisomerase II alpha (TOP2A). A total of seven transcription factors including forkhead box protein M1 (FOXM1) and 30 target genes were revealed in the transcription factor-target gene regulation network. Furthermore, co-cited genes including CCNB2-CCNB1 were revealed in literature mining for the relations ARDS related genes.

Conclusions

Pathways like mitotic cell cycle were closed related with the development of ARDS. Genes including CCNB1, CCNB2 and TOP2A, as well as transcription factors like FOXM1 might be used as the novel gene therapy targets for sepsis related ARDS.

Network stratification analysis for identifying function-specific network layers

A major challenge of systems biology is to capture the rewiring of biological functions (e.g. signaling pathways) in a molecular network. To address this problem, we proposed a novel computational framework, namely network stratification analysis (NetSA), to stratify the whole biological network into various function-specific network layers corresponding to particular functions (e.g. KEGG pathways), which transform the network analysis from the gene level to the functional level by integrating expression data, the gene/protein network and gene ontology information altogether. The application of NetSA in yeast and its comparison with a traditional network-partition both suggest that NetSA can more effectively reveal functional implications of network rewiring and extract significant phenotype-related biological processes. Furthermore, for time-series or stage-wise data, the function-specific network layer obtained by NetSA is also shown to be able to characterize the disease progression in a dynamic manner. In particular, when applying NetSA to hepatocellular carcinoma and type 1 diabetes, we can derive functional spectra regarding the progression of the disease, and capture active biological functions (i.e. active pathways) in different disease stages. The additional comparison between NetSA and SPIA illustrates again that NetSA could discover more complete biological functions during disease progression. Overall, NetSA provides a general framework to stratify a network into various layers of function-specific sub-networks, which can not only analyze a biological network on the functional level but also investigate gene rewiring patterns in biological processes.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network

The identification of disease-related metabolites is important for a better understanding of metabolite pathological processes in order to improve human medicine. Metabolites, which are the terminal products of cellular regulatory process, can be affected by multi-omic processes. In this work, we propose a powerful method, MetPriCNet, to predict and prioritize disease candidate metabolites based on integrated multi-omics information. MetPriCNet prioritized candidate metabolites based on their global distance similarity with seed nodes in a composite network, which integrated multi-omics information from the genome, phenome, metabolome and interactome. After performing cross-validation on 87 phenotypes with a total of 602 metabolites, MetPriCNet achieved a high AUC value of up to 0.918. We also assessed the performance of MetPriCNet on 18 disease classes and found that 4 disease classes achieved an AUC value over 0.95. Notably, MetPriCNet can also predict disease metabolites without known disease metabolite knowledge. Some new high-risk metabolites of breast cancer were predicted, although there is a lack of known disease metabolite information. A predicted disease metabolic landscape was constructed and analyzed based on the results of MetPriCNet for 87 phenotypes to help us understand the genetic and metabolic mechanism of disease from a global view.

SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets

Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.

Identification of genes associated with methotrexate resistance in methotrexate-resistant osteosarcoma cell lines

BACKGROUND

This study aimed to better understand the mechanisms underlying methotrexate (MTX)-resistance in osteosarcoma.

METHODS

The raw transcription microarray data GSE16089 collected from three MTX-sensitive osteosarcoma (Saos-2) cell samples and three MTX-resistant osteosarcoma (Saos-2) cell samples were downloaded from Gene Expression Omnibus. After data processing, the differentially expressed genes (DEGs) were identified. Next, DEGs were submitted to DAVID for functional annotation based on the GO (Gene Ontology) database, as well as pathway enrichment analysis based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Transcription factors (TFs) and tumor-associated genes (TAGs) were identified with reference to TRANSFAC and TAG, and TSGene databases, respectively. The protein-protein interaction (PPI) network of the gene-encoded products was constructed, and the subnetwork with the highest score was also detected using Search Tool for the Retrieval of Interacting Genes and BioNet package.

RESULTS

A total of 690 up-regulated genes and down-regulated 626 genes were identified. Up-regulated DEGs (including AARS and PARS2) were associated to transfer RNA (tRNA) aminoacylation while down-regulated DEGs (including AURKA, CCNB1, CCNE2, CDK1, and CENPA) were correlated with mitotic cell cycle. Totally, 13 TFs (including HMGB2), 13 oncogenes (including CCNA2 and AURKA), and 19 tumor suppressor genes (TSGs) (including CDKN2C) were identified from the down-regulated DEGs. Ten DEGs, including nine down-regulated genes (such as AURKA, CDK1, CCNE2, and CENPA) and one up-regulated gene (GADD45A), were involved in the highest score subnetwork.

CONCLUSION

AARS, AURKA, AURKB, CENPA, CCNB1, CCNE2, and CDK may contribute to MTX resistance via aminoacyl-tRNA biosynthesis pathway, cell cycle pathway, or p53 signaling pathway.

Unique proteomic signature for radiation sensitive patients; a comparative study between normo-sensitive and radiation sensitive breast cancer patients

Radiation therapy is a cornerstone of modern cancer treatment. Understanding the mechanisms behind normal tissue sensitivity is essential in order to minimize adverse side effects and yet to prevent local cancer reoccurrence. The aim of this study was to identify biomarkers of radiation sensitivity to enable personalized cancer treatment. To investigate the mechanisms behind radiation sensitivity a pilot study was made where eight radiation-sensitive and nine normo-sensitive patients were selected from a cohort of 2914 breast cancer patients, based on acute tissue reactions after radiation therapy. Whole blood was sampled and irradiated in vitro with 0, 1, or 150 mGy followed by 3 h incubation at 37°C. The leukocytes of the two groups were isolated, pooled and protein expression profiles were investigated using isotope-coded protein labeling method (ICPL). First, leukocytes from the in vitro irradiated whole blood from normo-sensitive and extremely sensitive patients were compared to the non-irradiated controls. To validate this first study a second ICPL analysis comparing only the non-irradiated samples was conducted. Both approaches showed unique proteomic signatures separating the two groups at the basal level and after doses of 1 and 150 mGy. Pathway analyses of both proteomic approaches suggest that oxidative stress response, coagulation properties and acute phase response are hallmarks of radiation sensitivity supporting our previous study on oxidative stress response. This investigation provides unique characteristics of radiation sensitivity essential for individualized radiation therapy.

Lithium protects against paraquat neurotoxicity by NRF2 activation and miR-34a inhibition in SH-SY5Y cells

Lithium is a mood stabilizing agent commonly used for the treatment of bipolar disorder. Here, we investigated the potential neuroprotective effect of lithium against paraquat toxicity and its underlying mechanisms in vitro. SH-SY5Y human neuroblastoma cells were treated with paraquat (PQ) 0.5 mM concentration after lithium pretreatment to test lithium's capability in preventing cell toxicity. Cell death was evaluated by LDH, WST-8, and tryphan blue assays. Apoptosis was analyzed using DNA fragmentation, Annexin V immunostaining, Sub G1 cell cycle analysis, and caspase-3 activity assays. BCL2, BAX, and NRF2 protein expression were evaluated by Western-blotting and the BDNF protein level was determined with ELISA. mRNA levels of BCL2, BAX, BDNF, and NRF2 target genes (HO-1, GCS, NQO1), as well as miR-34a expression were analyzed by qPCR assay. Functional experiments were done via transfection with NRF2 siRNA and miR-34a mimic. Lithium treatment prevented paraquat induced cell death and apoptosis. Lithium treated cells showed increased anti-apoptotic protein BCL2 and decreased pro-apoptotic protein BAX expression. Lithium exerted a neurotrophic effect by increasing BDNF protein expression. It also diminished reactive oxygen species production and activated the redox sensitive transcription factor NRF2 and increased its target genes expression. Knockdown of NRF2 abolished neuroprotective, anti-apoptotic, and anti-oxidant effects of lithium. Furthermore, lithium significantly decreased both basal and PQ-induced expression of miR-34a. Transfection of miR-34a specific mimic reversed neuroprotective, anti-apoptotic, and anti-oxidant effects of lithium against PQ-toxicity. Our results revealed two novel mechanisms of lithium neuroprotection, namely NRF2 activation and miR-34a suppression.

A Systems Biology Perspective on the Molecular Mechanisms Underlying the Therapeutic Effects of Buyang Huanwu Decoction on Ischemic Stroke

Ischemic stroke is the leading cause of adult disability worldwide. The outcome is worse in older patients, especially in terms of disability. Buyang Huanwu decoction (BHD), a famous traditional Chinese medicine formula, has been used extensively in the treatment of ischemic stroke for centuries. However, its pharmacological mechanisms have not been fully elucidated. In this study, 82 putative targets for 411 composite compounds contained in BHD were predicted on the basis of our previously developed target prediction system. On the basis of large-scale molecular docking, more than 80% compound-putative target pairs had medium to strong binding efficiency. The pharmacological networks of BHD were built according to relationships among herbs, putative targets, and known therapeutic targets for ischemic stroke, and 121 major nodes were identified by calculating three topological features-degree, node betweenness, and closeness. Importantly, the pathway enrichment analysis identified several signaling pathways involved with major putative targets of BHD, such as the calcium signaling pathway, vascular smooth muscle contraction, and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway, which have not hitherto been reported. These data are expected to help find new therapeutic effects of BHD and optimize clinical use of this formula. Collectively, our study developed a comprehensive systems approach integrating drug target prediction and network and functional analyses to reveal the relationships of the herbs in BHD with their putative targets, and for the first time with ischemic stroke-related pathway systems. This is a pilot study based on bioinformatics analysis; thus, further experimental studies are required to validate our findings.

Reduced keratin expression in colorectal neoplasia and associated fields is reversible by diet and resection

BACKGROUND

Patients with adenomatous colonic polyps are at increased risk of developing further polyps suggesting field-wide alterations in cancer predisposition. The current study aimed to identify molecular alterations in the normal mucosa in the proximity of adenomatous polyps and to assess the modulating effect of butyrate, a chemopreventive compound produced by fermentation of dietary residues.

METHODS

A cross-sectional study was undertaken in patients with adenomatous polyps: biopsy samples were taken from the adenoma, and from macroscopically normal mucosa on the contralateral wall to the adenoma and from the mid-sigmoid colon. In normal subjects biopsies were taken from the mid-sigmoid colon. Biopsies were frozen for proteomic analysis or formalin-fixed for immunohistochemistry. Proteomic analysis was undertaken using iTRAQ workflows followed by bioinformatics analyses. A second dietary fibre intervention study arm used the same endpoints and sampling strategy at the beginning and end of a high-fibre intervention.

RESULTS

Key findings were that keratins 8, 18 and 19 were reduced in expression level with progressive proximity to the lesion. Lesional tissue exhibited multiple K8 immunoreactive bands and overall reduced levels of keratin. Biopsies from normal subjects with low faecal butyrate also showed depressed keratin expression. Resection of the lesion and elevation of dietary fibre intake both appeared to restore keratin expression level.

CONCLUSION

Changes in keratin expression associate with progression towards neoplasia, but remain modifiable risk factors. Dietary strategies may improve secondary chemoprevention.

TRIAL REGISTRATION NUMBER

ISRCTN90852168.

Integrative proteomics and targeted transcriptomics analyses in cardiac endothelial cells unravel mechanisms of long-term radiation-induced vascular dysfunction

Epidemiological data from radiotherapy patients show the damaging effect of ionizing radiation on heart and vasculature. The endothelium is the main target of radiation damage and contributes essentially to the development of cardiac injury. However, the molecular mechanisms behind the radiation-induced endothelial dysfunction are not fully understood. In the present study, 10-week-old C57Bl/6 mice received local X-ray heart doses of 8 or 16 Gy and were sacrificed after 16 weeks; the controls were sham-irradiated. The cardiac microvascular endothelial cells were isolated from the heart tissue using streptavidin-CD31-coated microbeads. The cells were lysed and proteins were labeled with duplex isotope-coded protein label methodology for quantification. All samples were analyzed by LC-ESI-MS/MS and Proteome Discoverer software. The proteomics data were further studied by bioinformatics tools and validated by targeted transcriptomics, immunoblotting, immunohistochemistry, and serum profiling. Radiation-induced endothelial dysfunction was characterized by impaired energy metabolism and perturbation of the insulin/IGF-PI3K-Akt signaling pathway. The data also strongly suggested premature endothelial senescence, increased oxidative stress, decreased NO availability, and enhanced inflammation as main causes of radiation-induced long-term vascular dysfunction. Detailed data on molecular mechanisms of radiation-induced vascular injury as compiled here are essential in developing radiotherapy strategies that minimize cardiovascular complications.

Pharmacogenomics related to growth disorders

Growth disorders resulting in short stature are caused by a wide range of underlying pathophysiological processes. To improve height many of these conditions are treated with recombinant human growth hormone (rhGH). However, substantial inter-individual variability in growth response both in the short and long-term is recognised. Over the last decade, disease-specific growth prediction models have been developed that the clinician can use to define a child's potential response to rhGH and to optimise starting and maintenance doses of rhGH. These models, however, are not able to predict all the variations in treatment response. There has, therefore, been recent interest in using genetic information to contribute to the evaluation of responses to rhGH, including high-throughput technologies for assessing DNA markers (genome) and mRNA transcripts (transcriptome) as pharmacogenomic tools. This review will focus on how these pharmacogenomic approaches are being applied to growth disorders.

EPO Mediates Neurotrophic, Neuroprotective, Anti-Oxidant, and Anti-Apoptotic Effects via Downregulation of miR-451 and miR-885-5p in SH-SY5Y Neuron-Like Cells

Erythropoietin (EPO) is a neuroprotective cytokine, which has been applied in several animal models presenting neurological disorders. One of the proposed modes of action resulting in neuroprotection is post-transcriptional gene expression regulation. This directly brings to mind microRNAs (miRNAs), which are small non-coding RNAs that regulate gene expression at the post-transcriptional level. It has not yet been evaluated whether miRNAs participate in the biological effects of EPO or whether it, inversely, modulates specific miRNAs in neuronal cells. In this study, we employed miRNA and mRNA arrays to identify how EPO exerts its biological function. Notably, miR-451 and miR-885-5p are downregulated in EPO-treated SH-SY5Y neuronal-like cells. Accordingly, target prediction and transcriptome analysis of cells treated with EPO revealed an alteration of the expression of genes involved in apoptosis, cell survival, proliferation, and migration. Low expression of miRNAs in SH-SY5Y was correlated with high expression of their target genes, vascular endothelial growth factor A, matrix metallo peptidase 9 (MMP9), cyclin-dependent kinase 2 (CDK2), erythropoietin receptor, Mini chromosome maintenance complex 5 (MCM5), B-cell lymphoma 2 (BCL2), and Galanin (GAL). Cell viability, apoptosis, proliferation, and migration assays were carried out for functional analysis after transfection with miRNA mimics, which inhibited some biological actions of EPO such as neuroprotection, anti-oxidation, anti-apoptosis, and migratory effects. In this study, we report for the first time that EPO downregulates the expression of miRNAs (miR-451 and miR-885-5p) in SH-SY5Y neuronal-like cells. The correlation between the over-expression of miRNAs and the decrease in EPO-mediated biological effects suggests that miR-451 and miR-885-5p may play a key role in the mediation of biological function.

Challenges for the implementation of high-throughput testing and liquid biopsies in personalized medicine cancer trials

During recent decades, major advances in the comprehension of biology and in biotechnologies have paved the way for what is commonly named personalized medicine. For cancer therapy, personalized medicine represents a paradigm shift in which patient treatment is based on biology in addition to histology and tumor location. Here, we report the major personalized medicine trials in oncology that are either based on molecular alterations from tumor tissue or from circulating blood markers. We next review important challenges facing the implementation of personalized medicine in daily clinical practice, including tumor heterogeneity, reliability of high-throughput technologies, the key role of bioinformatics and the assessment of biomarkers and synthetic models, in order to use big data in actual cancer biology.

Integrated enrichment analysis and pathway-centered visualization of metabolomics, proteomics, transcriptomics, and genomics data by using the InCroMAP software

In systems biology, the combination of multiple types of omics data, such as metabolomics, proteomics, transcriptomics, and genomics, yields more information on a biological process than the analysis of a single type of data. Thus, data from different omics platforms is usually combined in one experimental setup to obtain insight into a biological process or a disease state. Particularly high accuracy metabolomics data from modern mass spectrometry instruments is currently more and more integrated into biological studies. Reflecting this trend, we extended InCroMAP, a data integration, analysis and visualization tool for genomics, transcriptomics, and proteomics data. Now, the tool is able to perform an integrated enrichment analysis and pathway-based visualization of multi-omics data and thus, it is suitable for the evaluation of comprehensive systems biology studies.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

Staphylococcal enterotoxin B influences the DNA methylation pattern in nasal polyp tissue: a preliminary study

Staphylococcal enterotoxins may influence the pro-inflammatory pattern of chronic sinus diseases via epigenetic events. This work intended to investigate the potential of staphylococcal enterotoxin B (SEB) to induce changes in the DNA methylation pattern. Nasal polyp tissue explants were cultured in the presence and absence of SEB; genomic DNA was then isolated and used for whole genome methylation analysis. Results showed that SEB stimulation altered the methylation pattern of gene regions when compared with non stimulated tissue. Data enrichment analysis highlighted two genes: the IKBKB and STAT-5B, both playing a crucial role in T- cell maturation/activation and immune response.

Moving H5N1 studies into the era of systems biology

The dynamics of H5N1 influenza virus pathogenesis are multifaceted and can be seen as an emergent property that cannot be comprehended without looking at the system as a whole. In past years, most of the high-throughput studies on H5N1-host interactions have focused on the host transcriptomic response, at the cellular or the lung tissue level. These studies pointed out that the dynamics and magnitude of the innate immune response and immune cell infiltration is critical to H5N1 pathogenesis. However, viral-host interactions are multidimensional and advances in technologies are creating new possibilities to systematically measure additional levels of 'omic data (e.g. proteomic, metabolomic, and RNA profiling) at each temporal and spatial scale (from the single cell to the organism) of the host response. Natural host genetic variation represents another dimension of the host response that determines pathogenesis. Systems biology models of H5N1 disease aim at understanding and predicting pathogenesis through integration of these different dimensions by using intensive computational modeling. In this review, we describe the importance of 'omic studies for providing a more comprehensive view of infection and mathematical models that are being developed to integrate these data. This review provides a roadmap for what needs to be done in the future and what computational strategies should be used to build a global model of H5N1 pathogenesis. It is time for systems biology of H5N1 pathogenesis to take center stage as the field moves toward a more comprehensive view of virus-host interactions.

Human growth is associated with distinct patterns of gene expression in evolutionarily conserved networks

Background

A co-ordinated tissue-independent gene expression profile associated with growth is present in rodent models and this is hypothesised to extend to all mammals. Growth in humans has similarities to other mammals but the return to active long bone growth in the pubertal growth spurt is a distinctly human growth event. The aim of this study was to describe gene expression and biological pathways associated with stages of growth in children and to assess tissue-independent expression patterns in relation to human growth.

Results

We conducted gene expression analysis on a library of datasets from normal children with age annotation, collated from the NCBI Gene Expression Omnibus (GEO) and EBI Arrayexpress databases. A primary data set was generated using cells of lymphoid origin from normal children; the expression of 688 genes (ANOVA false discovery rate modified p-value, q < 0.1) was associated with age, and subsets of these genes formed clusters that correlated with the phases of growth – infancy, childhood, puberty and final height. Network analysis on these clusters identified evolutionarily conserved growth pathways (NOTCH, VEGF, TGFB, WNT and glucocorticoid receptor – Hyper-geometric test, q < 0.05). The greatest degree of network ‘connectivity’ and hence functional significance was present in infancy (Wilcoxon test, p < 0.05), which then decreased through to adulthood. These observations were confirmed in a separate validation data set from lymphoid tissue. Similar biological pathways were observed to be associated with development-related gene expression in other tissues (conjunctival epithelia, temporal lobe brain tissue and bone marrow) suggesting the existence of a tissue-independent genetic program for human growth and maturation.

Conclusions

Similar evolutionarily conserved pathways have been associated with gene expression and child growth in multiple tissues. These expression profiles associate with the developmental phases of growth including the return to active long bone growth in puberty, a distinctly human event. These observations also have direct medical relevance to pathological changes that induce disease in children. Taking into account development-dependent gene expression profiles for normal children will be key to the appropriate selection of genes and pathways as potential biomarkers of disease or as drug targets.

Pathway analysis using genome-wide association study data for coronary restenosis--a potential role for the PARVB gene

Background

Coronary restenosis after percutaneous coronary intervention (PCI) still remains a significant limitation of the procedure. The causative mechanisms of restenosis have not yet been fully identified. The goal of the current study was to perform gene-set analysis of biological pathways related to inflammation, proliferation, vascular function and transcriptional regulation on coronary restenosis to identify novel genes and pathways related to this condition.

Methods

The GENetic DEterminants of Restenosis (GENDER) databank contains genotypic data of 556,099SNPs of 295 cases with restenosis and 571 matched controls. Fifty-four pathways, related to known restenosis-related processes, were selected. Gene-set analysis was performed using PLINK, GRASS and ALIGATOR software. Pathways with a p<0.01 were fine-mapped and significantly associated SNPs were analyzed in an independent replication cohort.

Results

Six pathways (cell-extracellular matrix (ECM) interactions pathway, IL2 signaling pathway, IL6 signaling pathway, platelet derived growth factor pathway, vitamin D receptor pathway and the mitochondria pathway) were significantly associated in one or two of the software packages. Two SNPs in the cell-ECM interactions pathway were replicated in an independent restenosis cohort. No replication was obtained for the other pathways.

Conclusion

With these results we demonstrate a potential role of the cell-ECM interactions pathway in the development of coronary restenosis. These findings contribute to the increasing knowledge of the genetic etiology of restenosis formation and could serve as a hypothesis-generating effort for further functional studies.

Cancer networks and beyond: interpreting mutations using the human interactome and protein structure

Over recent years, with the advances in next-generation sequencing, a large number of cancer mutations have been identified and accumulated in public repositories. Coupled to this is our increased ability to generate detailed interactome maps that help to enrich our knowledge of the biological implications of cancer mutations. As a result, network analysis approaches have become an invaluable tool to predict and interpret mutations that are associated with tumour survival and progression. Our understanding of cancer mechanisms is further enhanced by mapping protein structure information to such networks. Here we review the current methodologies for annotating the functional impacts of cancer mutations, which range from analysis of protein structures to protein-protein interaction network studies.

Bioinformatics for spermatogenesis: annotation of male reproduction based on proteomics

Proteomics strategies have been widely used in the field of male reproduction, both in basic and clinical research. Bioinformatics methods are indispensable in proteomics-based studies and are used for data presentation, database construction and functional annotation. In the present review, we focus on the functional annotation of gene lists obtained through qualitative or quantitative methods, summarizing the common and male reproduction specialized proteomics databases. We introduce several integrated tools used to find the hidden biological significance from the data obtained. We further describe in detail the information on male reproduction derived from Gene Ontology analyses, pathway analyses and biomedical analyses. We provide an overview of bioinformatics annotations in spermatogenesis, from gene function to biological function and from biological function to clinical application. On the basis of recently published proteomics studies and associated data, we show that bioinformatics methods help us to discover drug targets for sperm motility and to scan for cancer-testis genes. In addition, we summarize the online resources relevant to male reproduction research for the exploration of the regulation of spermatogenesis.

Microarray analysis of microRNA expression in peripheral blood mononuclear cells of critically ill patients with influenza A (H1N1)

BACKGROUND

With concerns about the disastrous health and economic consequences caused by the influenza pandemic, comprehensively understanding the global host response to influenza virus infection is urgent. The role of microRNA (miRNA) has recently been highlighted in pathogen-host interactions. However, the precise role of miRNAs in the pathogenesis of influenza virus infection in humans, especially in critically ill patients is still unclear.

METHODS

We identified cellular miRNAs involved in the host response to influenza virus infection by performing comprehensive miRNA profiling in peripheral blood mononuclear cells (PBMCs) from critically ill patients with swine-origin influenza pandemic H1N1 (2009) virus infection via miRNA microarray and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) assays. Receiver operator characteristic (ROC) curve analysis was conducted and area under the ROC curve (AUC) was calculated to evaluate the diagnostic accuracy of severe H1N1 influenza virus infection. Furthermore, an integrative network of miRNA-mediated host-influenza virus protein interactions was constructed by integrating the predicted and validated miRNA-gene interaction data with influenza virus and host-protein-protein interaction information using Cytoscape software. Moreover, several hub genes in the network were selected and validated by qRT-PCR.

RESULTS

Forty-one significantly differentially expressed miRNAs were found by miRNA microarray; nine were selected and validated by qRT-PCR. QRT-PCR assay and ROC curve analyses revealed that miR-31, miR-29a and miR-148a all had significant potential diagnostic value for critically ill patients infected with H1N1 influenza virus, which yielded AUC of 0.9510, 0.8951 and 0.8811, respectively. We subsequently constructed an integrative network of miRNA-mediated host-influenza virus protein interactions, wherein we found that miRNAs are involved in regulating important pathways, such as mitogen-activated protein kinase signaling pathway, epidermal growth factor receptor signaling pathway, and Toll-like receptor signaling pathway, during influenza virus infection. Some of differentially expressed miRNAs via in silico analysis targeted mRNAs of several key genes in these pathways. The mRNA expression level of tumor protein T53 and transforming growth factor beta receptor 1 were found significantly reduced in critically ill patients, whereas the expression of Janus kinase 2, caspase 3 apoptosis-related cysteine peptidase, interleukin 10, and myxovirus resistance 1 were extremely increased in critically ill patients.

CONCLUSIONS

Our data suggest that the dysregulation of miRNAs in the PBMCs of H1N1 critically ill patients can regulate a number of key genes in the major signaling pathways associated with influenza virus infection. These differentially expressed miRNAs could be potential therapeutic targets or biomarkers for severe influenza virus infection.