DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

Identification of differentially expressed subpathways via a bilevel consensus scoring of network topology and gene expression

Identifying differentially expressed subpathways connected to the emergence of a disease that can be considered as candidates for pharmacological intervention, with minimal off-target effects, is a daunting task. In this direction, we present a bilevel subpathway analysis method to identify differentially expressed subpathways that are connected with an experimental condition, while taking into account potential crosstalks between subpathways which arise due to their connectivity in a combined multi-pathway network. The efficacy of the method is demonstrated on a hematopoietic stem cell aging dataset, with findings corroborated using recent literature.

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Background

Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting.

Results

We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories.

We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets.

Conclusions

This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Detecting Common Pathways and Key Molecules of Neurodegenerative Diseases from the Topology of Molecular Networks

MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.

psSubpathway: a software package for flexible identification of phenotype-specific subpathways in cancer progression

Summary

Subpathways, which are defined as local gene subregions within a biological pathway, have been reported to be associated with the occurrence and development of cancer. The recent subpathway identification tools generally identify differentially expressed subpathways between normal and cancer samples. psSubpathway is a novel systems biology R-based software package that enables flexible identification of phenotype-specific subpathways in a cancer dataset with multiple categories (such as multiple subtypes and developmental stages of cancer). The operation modes include extraction of subpathways from pathway networks, inference with subpathway activities in the context of gene expression data, identification of subtype-specific subpathways, identification of dynamic-changed subpathways associated with the cancer developmental stage and visualization of subpathway activities of samples in different phenotypes. Its capabilities enable psSubpathway to find specific abnormal subpathways in the datasets with multi-phenotype categories and to fill the gaps in the recent tools. psSubpathway may identify more specific biomarkers to facilitate the development of tailored treatment for patients with cancer.

Availability and implementation

The package is implemented in R and available under GPL-2 license from the CRAN website (https://cran.r-project.org/web/packages/psSubpathway/).

Supplementary information

Supplementary data are available at Bioinformatics online.

SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways

Topological gene-set analysis has emerged as a powerful means for omic data interpretation. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. Here, we propose a new method, called SourceSet, able to distinguish between the primary and the secondary dysregulation within a Gaussian graphical model context. The proposed method compares gene expression profiles in the control and in the perturbed condition and detects the differences in both the mean and the covariance parameters with a series of likelihood ratio tests. The resulting evidence is used to infer the primary and the secondary set, i.e. the genes responsible for the primary dysregulation, and the genes affected by the perturbation through network propagation. The proposed method demonstrates high specificity and sensitivity in different simulated scenarios and on several real biological case studies. In order to fit into the more traditional pathway analysis framework, SourceSet R package also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results.

The rapid increase in omic studies has created a need to understand the biological implications of their results. Gene-set analysis has emerged as a powerful means for gaining such understanding, evolving in the last decade from the classical enrichment analysis to the more powerful topological approaches. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. This distinction is crucial for network medicine, where the prioritization of the effect of biological perturbations may help in the molecular understanding of drug treatments and diseases. Here we propose a new method, called SourceSet, able to distinguish between primary and secondary dysregulation within a graphical model context, demonstrating a high specificity and sensitivity in different simulated scenarios and on real biological case studies.

Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

Investigating the aetiology of adverse events following HPV vaccination with systems vaccinology

In contrast to the insidious and poorly immunogenic human papillomavirus (HPV) infections, vaccination with the HPV virus-like particles (vlps) is non-infectious and stimulates a strong neutralizing-antibody response that protects HPV-naïve vaccinees from viral infection and associated cancers. However, controversy about alleged adverse events following immunization (AEFI) with the vlps have led to extensive reductions in vaccine acceptance, with countries like Japan dropping it altogether. The AEFIs are grouped into chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). In this review, we present a hypothesis that the AEFIs might arise from malfunctions within the immune system when confronted with the unusual antigen. In addition, we outline how the pathophysiology of the AEFIs can be cost-effectively investigated with the holistic principles of systems vaccinology in a two-step process. First, comprehensive immunological profiles of HPV vaccinees exhibiting the AEFIs are generated by integrating the data derived from serological profiling for prominent HPV antibodies and serum cytokines, with data from serum metabolomics, peripheral white blood cells transcriptomics and gut microbiome profiling. Next, the immunological profiles are compared with corresponding profiles generated for matched (a) HPV vaccinees without AEFIs; (b) non-HPV-vaccinated individuals with CFS/ME-like symptoms; and (c) non-HPV-vaccinated individuals without CFS/ME. In these comparisons, any causal links between HPV vaccine and the AEFIs, as well as the underlying molecular basis for the links will be revealed. Such a study should provide an objective basis for evaluating HPV vaccine safety and for identifying biomarkers for individuals at risk of developing AEFI with HPV vaccination.

Computational Methods to Investigate the Impact of miRNAs on Pathways

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

Recent Advances in the Systems Biology of Aging

SIGNIFICANCE

Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging.

CRITICAL ISSUES

Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology.

FUTURE DIRECTIONS

Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach

In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states.