CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis

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.

Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation

Gene expression (GE)data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. Because proteins function by interacting with each other, and because biological networks (BNs)capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP)to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related)subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP. Our data and code are available at https://nd.edu/~cone/dynetinf.

Whole-genome sequencing reveals forgotten lineages and recurrent hybridizations within the kelp genus Alaria (Phaeophyceae)

The genomic era continues to revolutionize our understanding of the evolution of biodiversity. In phycology, emphasis remains on assembling nuclear and organellar genomes, leaving the full potential of genomic datasets to answer long-standing questions about the evolution of biodiversity largely unexplored. Here, we used whole-genome sequencing (WGS) datasets to survey species diversity in the kelp genus Alaria, compare phylogenetic signals across organellar and nuclear genomes, and specifically test whether phylogenies behave like trees or networks. Genomes were sequenced from across the global distribution of Alaria (including Alaria crassifolia, A. praelonga, A. crispa, A. marginata, and A. esculenta), representing over 550 GB of data and over 2.2 billion paired reads. Genomic datasets retrieved 3,814 and 4,536 single-nucleotide polymorphisms (SNPs) for mitochondrial and chloroplast genomes, respectively, and upwards of 148,542 high-quality nuclear SNPs. WGS revealed an Arctic lineage of Alaria, which we hypothesize represents the synonymized taxon A. grandifolia. The SNP datasets also revealed inconsistent topologies across genomic compartments, and hybridization (i.e., phylogenetic networks) between Pacific A. praelonga, A. crispa, and putative A. grandifolia, and between some lineages of the A. marginata complex. Our analysis demonstrates the potential for WGS data to advance our understanding of evolution and biodiversity beyond amplicon sequencing, and that hybridization is potentially an important mechanism contributing to novel lineages within Alaria. We also emphasize the importance of surveying phylogenetic signals across organellar and nuclear genomes, such that models of mixed ancestry become integrated into our evolutionary and taxonomic understanding.

MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer

Background

Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients.

Methods

We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules.

Results

We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets.

Conclusions

Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.

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.

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.

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.

mirTime: identifying condition-specific targets of microRNA in time-series transcript data using Gaussian process model and spherical vector clustering

Background

MicroRNAs, small noncoding RNAs, are conserved in many species, and they are key regulators that mediate post-transcriptional gene silencing. Since biologists cannot perform experiments for each of target genes of thousands of microRNAs in numerous specific conditions, prediction on microRNA target genes has been extensively investigated. A general framework is a two-step process of selecting target candidates based on sequence and binding energy features and then predicting targets based on negative correlation of microRNAs and their targets. However, there are few methods that are designed for target predictions using time-series gene expression data.

Results

In this article, we propose a new pipeline, mirTime, that predicts microRNA targets by integrating sequence features and time-series expression profiles in a specific experimental condition. The most important feature of mirTime is that it uses the Gaussian process regression model to measure data at unobserved or unpaired time points. In experiments with two datasets in different experimental conditions and cell types, condition-specific target modules reported in the original papers were successfully predicted with our pipeline. The context specificity of target modules was assessed with three (correlation-based, target gene-based and network-based) evaluation criteria. mirTime showed better performance than existing expression-based microRNA target prediction methods in all three criteria.

Availability and implementation

mirTime is available at https://github.com/mirTime/mirtime.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples

BACKGROUND

Towards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of 'partial' computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of 'complete' approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes.

RESULTS

We present Deblender, a flexible complete deconvolution tool operating in semi-/unsupervised mode based on the user's access to known marker gene lists and information about cell/tissue composition. In case of no prior knowledge, global gene expression variability is used in clustering the mixed data to substitute marker sets with cluster sets. In addition, we integrate a model selection criterion to predict the number of constituent cell/tissue types. Moreover, we provide a tailored algorithmic scheme to estimate mixture proportions for realistic experimental cases where the number of involved cell/tissue types exceeds the number of mixed samples. We assess the performance of Deblender and a set of state-of-the-art existing tools on a comprehensive set of benchmark and patient cancer mixture expression datasets (including TCGA).

CONCLUSION

Our results corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization. Deblender is implemented in MATLAB and is available from ( https://github.com/kondim1983/Deblender/ ).

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

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.

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

DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases.

AVAILABILITY AND IMPLEMENTATION

DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: tassos.bezerianos@nus.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online.

Colorectal cancer characterization and therapeutic target prediction based on microRNA expression profile

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a major cause of cancer death. However, the molecular mechanisms underlying CRC initiation, growth and metastasis are poorly understood. In this study, based on our previous work for comprehensively analyzing miRNA sequencing data, we examined a series of colorectal cancer microRNAs expression profiles data. Results show that all these CRC samples share the same four pathways including TGF-beta signaling pathway, which is important in colorectal carcinogenesis. Twenty-one microRNAs that evolved in the four overlapped pathways were then discovered. Further analysis selected miR-21 as an important regulator for CRC through TGF-beta pathways. This study develops methods for discovering tumor specific miRNA cluster as biomarker and for screening new cancer therapy targets based on miRNA sequencing.