Prediction of human disease genes by human-mouse conserved coexpression analysis

The functional evolution of collembolan Ubx on the regulation of abdominal appendage formation

Folsomia candida is a tiny soil-living arthropod belonging to the Collembola, which is an outgroup to Insecta. It resembles insects as having a pair of antennae and three pairs of thorax legs, while it also possesses three abdominal appendages: a ventral tube located in the first abdominal segment (A1), a retinaculum in A3, and a furca in A4. Collembolan Ubx and AbdA specify abdominal appendages, but they are unable to repress appendage marker gene Dll. The genetic basis of collembolan appendage formation and the mechanisms by which Ubx and AbdA regulate Dll transcription and appendage development remains unknown. In this study, we analysed the developmental transcriptomes of F. candida and identified candidate appendage formation genes, including Ubx (FcUbx). The expression data revealed the dominance of Dll over Ubx during the embryonic 3.5 and 4.5 days, suggesting that Ubx is deficient in suppressing Dll at early appendage formation stages. Furthermore, via electrophoretic mobility shift assays and dual luciferase assays, we found that the binding and repression capacity of FcUbx on Drosophila Dll resembles those of the longest isoform of Drosophila Ubx (DmUbx_Ib), while the regulatory mechanism of the C-terminus of FcUbx on Dll repression is similar to that of the crustacean Artemia franciscana Ubx (AfUbx), demonstrating that the function of collembolan Ubx is intermediate between that of Insecta and Crustacea. In summary, our study provides novel insights into collembolan appendage formation and sheds light on the functional evolution of Ubx. Additionally, we propose a model that collembolan Ubx regulates abdominal segments in a context-specific manner.

Navigating the microalgal maze: a comprehensive review of recent advances and future perspectives in biological networks

MAIN CONCLUSION

PPI analysis deepens our knowledge in critical processes like carbon fixation and nutrient sensing. Moreover, signaling networks, including pathways like MAPK/ERK and TOR, provide valuable information in how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. This review examines recent advancements in the study of biological networks within microalgae, with a focus on the intricate interactions that define these organisms. It emphasizes how network biology, an interdisciplinary field, offers valuable insights into microalgae functions through various methodologies. Crucial approaches, such as protein-protein interaction (PPI) mapping utilizing yeast two-hybrid screening and mass spectrometry, are essential for comprehending cellular processes and optimizing functions, such as photosynthesis and fatty acid biosynthesis. The application of advanced computational methods and information mining has significantly improved PPI analysis, revealing networks involved in critical processes like carbon fixation and nutrient sensing. The review also encompasses transcriptional networks, which play a role in gene regulation and stress responses, as well as metabolic networks represented by genome-scale metabolic models (GEMs), which aid in strain optimization and the prediction of metabolic outcomes. Furthermore, signaling networks, including pathways like MAPK/ERK and TOR, are crucial for understanding how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. The integration of these network biology approaches has deepened our understanding of microalgal interactions, paving the way for more efficient cultivation and new industrial applications.

A Comparative Study of Gene Co-Expression Thresholding Algorithms

The thresholding problem is studied in the context of graph theoretical analysis of gene co-expression data. A number of thresholding methodologies are described, implemented, and tested over a large collection of graphs derived from real high-throughput biological data. Comparative results are presented and discussed.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices

Background

Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging.

Results

Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains.

Conclusions

By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.

Circular RNA- and microRNA-Mediated Post-Transcriptional Regulation of Preadipocyte Differentiation in Adipogenesis: From Expression Profiling to Signaling Pathway

Adipogenesis is an indispensable cellular process that involves preadipocyte differentiation into mature adipocyte. Dysregulated adipogenesis contributes to obesity, diabetes, vascular conditions and cancer-associated cachexia. This review aims to elucidate the mechanistic details on how circular RNA (circRNA) and microRNA (miRNA) modulate post-transcriptional expression of targeted mRNA and the impacted downstream signaling and biochemical pathways in adipogenesis. Twelve adipocyte circRNA profiling and comparative datasets from seven species are analyzed using bioinformatics tools and interrogations of public circRNA databases. Twenty-three circRNAs are identified in the literature that are common to two or more of the adipose tissue datasets in different species; these are novel circRNAs that have not been reported in the literature in relation to adipogenesis. Four complete circRNA-miRNA-mediated modulatory pathways are constructed via integration of experimentally validated circRNA-miRNA-mRNA interactions and the downstream signaling and biochemical pathways involved in preadipocyte differentiation via the PPARγ/C/EBPα gateway. Despite the diverse mode of modulation, bioinformatics analysis shows that the circRNA-miRNA-mRNA interacting seed sequences are conserved across species, supporting mandatory regulatory functions in adipogenesis. Understanding the diverse modes of post-transcriptional regulation of adipogenesis may contribute to the development of novel diagnostic and therapeutic strategies for adipogenesis-associated diseases and in improving meat quality in the livestock industries.

Gene Differential Co-Expression Networks Based on RNA-Seq: Construction and Its Applications

Gene co-expression network (GCN) becomes an increasingly important tool in omics data analysis. A great challenge for GCN construction is that the sample size is far lower than the number of genes. Traditional methods rely on considerable samples. Moreover, association signals are likely weak, nonlinear and stochastic, which are difficult to be identified among thousands of candidates. In this paper, the gray correlation coefficient (GCC) is introduced, and a novel method to construct gene differential co-expression networks (GDCNs) is proposed. Based on the GDCNs, three measures are proposed to explore informative genes. The proposed method can make full use of the information provided by a handful of samples and overcome the shortages of GCNs, which can evaluate the changes of co-expression relationships that are possibly triggered by treatments. Based on RNA-seq data of Brassica napus, GDCNs under multiple experimental conditions are constructed and investigated. It is found that the GCC-based method is very robust to data processing. The GDCNs facilitate the inference of gene functions and the identification of informative genes that are responsible for stress responsiveness. The GDCN-based approaches integrate the 'guilt by association' and the 'guilt by rewiring' rules, which provide alternative tools for omics data analysis.

Approaches in Gene Coexpression Analysis in Eukaryotes

Simple Summary

Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it.

Abstract

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning

A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool-Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.

α/β-Hydrolase Domain (ABHD) Inhibitors as New Potential Therapeutic Options against Lipid-Related Diseases

Much of the experimental evidence in the literature has linked altered lipid metabolism to severe diseases such as cancer, obesity, cardiovascular pathologies, diabetes, and neurodegenerative diseases. Therefore, targeting key effectors of the dysregulated lipid metabolism may represent an effective strategy to counteract these pathological conditions. In this context, α/β-hydrolase domain (ABHD) enzymes represent an important and diversified family of proteins, which are involved in the complex environment of lipid signaling, metabolism, and regulation. Moreover, some members of the ABHD family play an important role in the endocannabinoid system, being designated to terminate the signaling of the key endocannabinoid regulator 2-arachidonoylglycerol. This Perspective summarizes the research progress in the development of ABHD inhibitors and modulators: design strategies, structure-activity relationships, action mechanisms, and biological studies of the main ABHD ligands will be highlighted.

A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data

Background

Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data.

Results

In this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization.

Conclusions

Simulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-07790-0).

Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome

The 3q29 deletion (3q29Del) confers high risk for schizophrenia and other neurodevelopmental and psychiatric disorders. However, no single gene in this interval is definitively associated with disease, prompting the hypothesis that neuropsychiatric sequelae emerge upon loss of multiple functionally-connected genes. 3q29 genes are unevenly annotated and the impact of 3q29Del on the human neural transcriptome is unknown. To systematically formulate unbiased hypotheses about molecular mechanisms linking 3q29Del to neuropsychiatric illness, we conducted a systems-level network analysis of the non-pathological adult human cortical transcriptome and generated evidence-based predictions that relate 3q29 genes to novel functions and disease associations. The 21 protein-coding genes located in the interval segregated into seven clusters of highly co-expressed genes, demonstrating both convergent and distributed effects of 3q29Del across the interrogated transcriptomic landscape. Pathway analysis of these clusters indicated involvement in nervous-system functions, including synaptic signaling and organization, as well as core cellular functions, including transcriptional regulation, posttranslational modifications, chromatin remodeling, and mitochondrial metabolism. Top network-neighbors of 3q29 genes showed significant overlap with known schizophrenia, autism, and intellectual disability-risk genes, suggesting that 3q29Del biology is relevant to idiopathic disease. Leveraging "guilt by association", we propose nine 3q29 genes, including one hub gene, as prioritized drivers of neuropsychiatric risk. These results provide testable hypotheses for experimental analysis on causal drivers and mechanisms of the largest known genetic risk factor for schizophrenia and highlight the study of normal function in non-pathological postmortem tissue to further our understanding of psychiatric genetics, especially for rare syndromes like 3q29Del, where access to neural tissue from carriers is unavailable or limited.

Landscape of toll-like receptors expression in tumor microenvironment of triple negative breast cancer (TNBC): Distinct roles of TLR4 and TLR8

TNBC is the most aggressive and hormone receptor-negative subtype of breast cancer with molecular heterogeneity in bulk tumors hindering effective treatment. Toll-like receptors (TLRs) have the potential to ignite diverse immune responses in the tumor microenvironment (TME). This encouraged us to screen their transcript expression in the publically available TCGA datasets. Reported molecular subtypes of TNBC may represent different TMEs and we observed differentially expressed TLRs (DETs) i.e. TLR3/4/6/8/9 have unique expression pattern in the TNBC subtypes, particularly in Immunomodulatory (IM) TNBC subtype. We then dissected expression of the DETs in immune and other components of the TME. TLR4 and TLR8 showed significant (p-value ≤ 0.05) negative partial correlation with tumor purity compared to other DETs. Interestingly, TLR4 and TLR8 expression showed a significant (adjusted p-value ≤ 0.05) correlation with different subsets of immune infiltrating cells having the highest correlation with monocytes/macrophage/dendritic cell populations mediating both innate and adaptive response in TNBC. The co-expression network identified genes correlated with these immune cells. Further, GSEA analysis of co-expressed genes showed a significant association of TLR8 partners with 'Peptide ligand binding', 'Gά-signaling', and 'Cytokine-cytokine interaction' while TLR4 associated genes correlated with 'Adaptive immune system' and 'Systemic lupus erythematosus' interactome. Finally, the expression of TLR4 protein was validated in a panel of TNBC cell lines. TLR4 expression in chemoresponsive TNBC was also validated in TNBC cell lines upon Paclitaxel (PTX) treatment. Collectively, the present study identified specific DETs in TNBC and discovered a prospective role of TLR4 and TLR8 in the maintenance of tumor-immune-microenvironment.

N-semble-based method for identifying Parkinson’s disease genes

Parkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.

Epoxide hydrolase 3 (Ephx3) gene disruption reduces ceramide linoleate epoxide hydrolysis and impairs skin barrier function

The mammalian epoxide hydrolase (EPHX)3 is known from in vitro experiments to efficiently hydrolyze the linoleate epoxides 9,10-epoxyoctadecamonoenoic acid (EpOME) and epoxyalcohol 9R,10R-trans-epoxy-11E-13R-hydroxy-octadecenoate to corresponding diols and triols, respectively. Herein we examined the physiological relevance of EPHX3 to hydrolysis of both substrates in vivo. Ephx3^−/− mice show no deficiency in EpOME-derived plasma diols, discounting a role for EPHX3 in their formation, whereas epoxyalcohol-derived triols esterified in acylceramides of the epidermal 12R-lipoxygenase pathway are reduced. Although the Ephx3^−/− pups appear normal, measurements of transepidermal water loss detected a modest and statistically significant increase compared with the wild-type or heterozygote mice, reflecting a skin barrier impairment that was not evident in the knockouts of mouse microsomal (EPHX1/microsomal epoxide hydrolase) or soluble (EPHX2/sEH). This barrier phenotype in the Ephx3^−/− pups was associated with a significant decrease in the covalently bound ceramides in the epidermis (40% reduction, p < 0.05), indicating a corresponding structural impairment in the integrity of the water barrier. Quantitative LC-MS analysis of the esterified linoleate-derived triols in the murine epidermis revealed a marked and isomer-specific reduction (∼85%) in the Ephx3^−/− epidermis of the major trihydroxy isomer 9R,10S,13R-trihydroxy-11E-octadecenoate. We conclude that EPHX3 (and not EPHX1 or EPHX2) catalyzes hydrolysis of the 12R-LOX/eLOX3-derived epoxyalcohol esterified in acylceramide and may function to control flux through the alternative and crucial route of metabolism via the dehydrogenation pathway of SDR9C7. Importantly, our findings also identify a functional role for EPHX3 in transformation of a naturally esterified epoxide substrate, pointing to its potential contribution in other tissues.

Exploring chromatin conformation and gene co-expression through graph embedding

MOTIVATION

The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships.

RESULTS

To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis.

AVAILABILITY AND IMPLEMENTATION

The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Interference with the Cannabinoid Receptor CB1R Results in Miswiring of GnRH3 and AgRP1 Axons in Zebrafish Embryos

The G protein-coupled cannabinoid receptors type 1 (CB1R) and type 2 (CB2R), and their endocannabinoid (eCBs) ligands, have been implicated in several aspects of brain wiring during development. Here we aim to assess whether interfering with CB1R affects development, neuritogenesis and pathfinding of GnRH and AgRP neurons, forebrain neurons that control respectively reproduction and appetite. We pharmacologically and genetically interfered with CB1R in zebrafish strains with fluorescently labeled GnRH3 and the AgRP1 neurons. By applying CB1R antagonists we observed a reduced number of GnRH3 neurons, fiber misrouting and altered fasciculation. Similar phenotypes were observed by CB1R knockdown. Interfering with CB1R also resulted in a reduced number, misrouting and poor fasciculation of the AgRP1 neuron’s axonal projections. Using a bioinformatic approach followed by qPCR validation, we have attempted to link CB1R functions with known guidance and fasciculation proteins. The search identified stathmin-2, a protein controlling microtubule dynamics, previously demonstrated to be coexpressed with CB1R and now shown to be downregulated upon interference with CB1R in zebrafish. Together, these results raise the likely possibility that embryonic exposure to low doses of CB1R-interfering compounds could impact on the development of the neuroendocrine systems controlling sexual maturation, reproduction and food intake.

TarGo: network based target gene selection system for human disease related mouse models

Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background

High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects.

Results

In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors.

Conclusions

Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named “SCBN”, which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2745-1) contains supplementary material, which is available to authorized users.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Catalytic activities of mammalian epoxide hydrolases with cis and trans fatty acid epoxides relevant to skin barrier function

Lipoxygenase (LOX)-catalyzed oxidation of the essential fatty acid, linoleate, represents a vital step in construction of the mammalian epidermal permeability barrier. Analysis of epidermal lipids indicates that linoleate is converted to a trihydroxy derivative by hydrolysis of an epoxy-hydroxy precursor. We evaluated different epoxide hydrolase (EH) enzymes in the hydrolysis of skin-relevant fatty acid epoxides and compared the products to those of acid-catalyzed hydrolysis. In the absence of enzyme, exposure to pH 5 or pH 6 at 37°C for 30 min hydrolyzed fatty acid allylic epoxyalcohols to four trihydroxy products. By contrast, human soluble EH [sEH (EPHX2)] and human or murine epoxide hydrolase-3 [EH3 (EPHX3)] hydrolyzed cis or trans allylic epoxides to single diastereomers, identical to the major isomers detected in epidermis. Microsomal EH [mEH (EPHX1)] was inactive with these substrates. At low substrate concentrations (<10 μM), EPHX2 hydrolyzed 14,15-epoxyeicosatrienoic acid (EET) at twice the rate of the epidermal epoxyalcohol, 9R,10R-trans-epoxy-11E-13R-hydroxy-octadecenoic acid, whereas human or murine EPHX3 hydrolyzed the allylic epoxyalcohol at 31-fold and 39-fold higher rates, respectively. These data implicate the activities of EPHX2 and EPHX3 in production of the linoleate triols detected as end products of the 12R-LOX pathway in the epidermis and implicate their functioning in formation of the mammalian water permeability barrier.

Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network

Background

Polygenic diseases are usually caused by the dysfunction of multiple genes. Unravelling such disease genes is crucial to fully understand the genetic landscape of diseases on molecular level. With the advent of ‘omic’ data era, network-based methods have prominently boosted disease gene discovery. However, how to make better use of different types of data for the prediction of disease genes remains a challenge.

Results

In this study, we improved the performance of disease gene prediction by integrating the similarity of disease phenotype, biological function and network topology. First, for each phenotype, a phenotype-specific network was specially constructed by mapping phenotype similarity information of given phenotype onto the protein-protein interaction (PPI) network. Then, we developed a gene gravity-like algorithm, to score candidate genes based on not only topological similarity but also functional similarity. We tested the proposed network and algorithm by conducting leave-one-out and leave-10%-out cross validation and compared them with state-of-art algorithms. The results showed a preference to phenotype-specific network as well as gene gravity-like algorithm. At last, we tested the predicting capacity of proposed algorithms by test gene set derived from the DisGeNET database. Also, potential disease genes of three polygenic diseases, obesity, prostate cancer and lung cancer, were predicted by proposed methods. We found that the predicted disease genes are highly consistent with literature and database evidence.

Conclusions

The good performance of phenotype-specific networks indicates that phenotype similarity information has positive effect on the prediction of disease genes. The proposed gene gravity-like algorithm outperforms the algorithm of Random Walk with Restart (RWR), implicating its predicting capacity by combing topological similarity with functional similarity. Our work will give an insight to the discovery of disease genes by fusing multiple similarities of genes and diseases.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0519-9) contains supplementary material, which is available to authorized users.

Comparative analysis of weighted gene co-expression networks in human and mouse

The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally central genes in the human and mouse networks.

Computational prediction of human disease-related microRNAs by path-based random walk

MicroRNAs (miRNAs) are a class of small, endogenous RNAs that are 21–25 nucleotides in length. In animals and plants, miRNAs target specific genes for degradation or translation repression. Discovering disease-related miRNA is fundamental for understanding the pathogenesis of diseases. The association between miRNA and a disease is mainly determined via biological investigation, which is complicated by increased biological information due to big data from different databases. Researchers have utilized different computational methods to harmonize experimental approaches to discover miRNA that articulates restrictively in specific environmental situations. In this work, we present a prediction model that is based on the theory of path features and random walk to obtain a relevancy score of miRNA-related disease. In this model, highly ranked scores are potential miRNA-disease associations. Features were extracted from positive and negative samples of miRNA-disease association. Then, we compared our method with other presented models using the five-fold cross-validation method, which obtained an area under the receiver operating characteristic curve of 88.6%. This indicated that our method has a better performance compared to previous methods and will help future biological investigations.

Generation and characterization of epoxide hydrolase 3 (EPHX3)-deficient mice

Cytochrome P450 (CYP) epoxygenases metabolize arachidonic acid into epoxyeicosatrienoic acids (EETs), which play an important role in blood pressure regulation, protection against ischemia-reperfusion injury, angiogenesis, and inflammation. Epoxide hydrolases metabolize EETs to their corresponding diols (dihydroxyeicosatrienoic acids; DHETs) which are biologically less active. Microsomal epoxide hydrolase (EPHX1, mEH) and soluble epoxide hydrolase (EPHX2, sEH) were identified >30 years ago and are capable of hydrolyzing EETs to DHETs. A novel epoxide hydrolase, EPHX3, was recently identified by sequence homology and also exhibits epoxide hydrolase activity in vitro with a substrate preference for 9,10-epoxyoctadecamonoenoic acid (EpOME) and 11,12-EET. EPHX3 is highly expressed in the skin, lung, stomach, esophagus, and tongue; however, its endogenous function is unknown. Therefore, we investigated the impact of genetic disruption of Ephx3 on fatty acid epoxide hydrolysis and EET-related physiology in mice. Ephx3-/- mice were generated by excising the promoter and first four exons of the Ephx3 gene using Cre-LoxP methodology. LC-MS/MS analysis of Ephx3-/- heart, lung, and skin lysates revealed no differences in endogenous epoxide:diol ratios compared to wild type (WT). Ephx3-/- mice also exhibited no change in plasma levels of fatty acid epoxides and diols relative to WT. Incubations of cytosolic and microsomal fractions prepared from Ephx3-/- and WT stomach, lung, and skin with synthetic 8,9-EET, 11,12-EET, and 9,10-EpOME revealed no significant differences in rates of fatty acid diol formation between the genotypes. Ephx3-/- hearts had similar functional recovery compared to WT hearts following ischemia/reperfusion injury. Following intranasal lipopolysaccharide (LPS) exposure, Ephx3-/- mice were not different from WT in terms of lung histology, bronchoalveolar lavage fluid cell counts, or fatty acid epoxide and diol levels. We conclude that genetic disruption of Ephx3 does not result in an overt phenotype and has no significant effects on the metabolism of EETs or EpOMEs in vivo.

Predicting disease-related genes using integrated biomedical networks

Background

Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.

Results

We propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.

Conclusions

The experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3263-4) contains supplementary material, which is available to authorized users.

The Precise Structures and Stereochemistry of Trihydroxy-linoleates Esterified in Human and Porcine Epidermis and Their Significance in Skin Barrier Function: IMPLICATION OF AN EPOXIDE HYDROLASE IN THE TRANSFORMATIONS OF LINOLEATE

Creation of an intact skin water barrier, a prerequisite for life on dry land, requires the lipoxygenase-catalyzed oxidation of the essential fatty acid linoleate, which is esterified to the ω-hydroxyl of an epidermis-specific ceramide. Oxidation of the linoleate moiety by lipoxygenases is proposed to facilitate enzymatic cleavage of the ester bond, releasing free ω-hydroxyceramide for covalent binding to protein, thus forming the corneocyte lipid envelope, a key component of the epidermal barrier. Herein, we report the transformations of esterified linoleate proceed beyond the initial steps of oxidation and epoxyalcohol synthesis catalyzed by the consecutive actions of 12R-LOX and epidermal LOX3. The major end product in human and porcine epidermis is a trihydroxy derivative, formed with a specificity that implicates participation of an epoxide hydrolase in converting epoxyalcohol to triol. Of the 16 possible triols arising from hydrolysis of 9,10-epoxy-13-hydroxy-octadecenoates, using LC-MS and chiral analyses, we identify and quantify specifically 9R,10S,13R-trihydroxy-11E-octadecenoate as the single major triol esterified in porcine epidermis and the same isomer with lesser amounts of its 10R diastereomer in human epidermis. The 9R,10S,13R-triol is formed by S_N2 hydrolysis of the 9R,10R-epoxy-13R-hydroxy-octadecenoate product of the LOX enzymes, a reaction specificity characteristic of epoxide hydrolase. The high polarity of triol over the primary linoleate products enhances the concept that the oxidations disrupt corneocyte membrane lipids, promoting release of free ω-hydroxyceramide for covalent binding to protein and sealing of the waterproof barrier.

Taking Bioinformatics to Systems Medicine

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels

Background

A deeper understanding of differences and similarities in transcriptional regulation between species can uncover important information about gene functions and the role of genes in disease. Deciphering such patterns between mice and humans is especially important since mice play an essential role in biomedical research.

Results

Here, in order to characterize evolutionary changes between humans and mice, we compared gene co-expression maps to evaluate the conservation of co-expression. We show that the conservation of co-expression connectivity of homologous genes is negatively correlated with molecular evolution rates, as expected. Then we investigated evolutionary aspects of gene sets related to functions, tissues, pathways and diseases. Genes expressed in the testis, eye and skin, and those associated with regulation of transcription, olfaction, PI3K signalling, response to virus and bacteria were more divergent between mice and humans in terms of co-expression connectivity. Surprisingly, a deeper investigation of the PI3K signalling cascade revealed that its divergence is caused by the most crucial genes of this pathway, such as mTOR and AKT2. On the other hand, our analysis revealed that genes expressed in the brain and in the bone, and those associated with cell adhesion, cell cycle, DNA replication and DNA repair are most strongly conserved in terms of co-expression network connectivity as well as having a lower rate of duplication events. Genes involved in lipid metabolism and genes specific to blood showed a signature of increased co-expression connectivity in the mouse. In terms of diseases, co-expression connectivity of genes related to metabolic disorders is the most strongly conserved between mice and humans and tumor-related genes the most divergent.

Conclusions

This work contributes to discerning evolutionary patterns between mice and humans in terms of gene interactions. Conservation of co-expression is a powerful approach to identify gene targets and processes with potential similarity and divergence between mice and humans, which has implications for drug testing and other studies employing the mouse as a model organism.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0534-7) contains supplementary material, which is available to authorized users.

A computational framework for the prioritization of disease-gene candidates

Background

The identification of genes and uncovering the role they play in diseases is an important and complex challenge. Genome-wide linkage and association studies have made advancements in identifying genetic variants that underpin human disease. An important challenge now is to identify meaningful disease-associated genes from a long list of candidate genes implicated by these analyses. The application of gene prioritization can enhance our understanding of disease mechanisms and aid in the discovery of drug targets. The integration of protein-protein interaction networks along with disease datasets and contextual information is an important tool in unraveling the molecular basis of diseases.

Results

In this paper we propose a computational pipeline for the prioritization of disease-gene candidates. Diverse heterogeneous data including: gene-expression, protein-protein interaction network, ontology-based similarity and topological measures and tissue-specific are integrated. The pipeline was applied to prioritize Alzheimer's Disease (AD) genes, whereby a list of 32 prioritized genes was generated. This approach correctly identified key AD susceptible genes: PSEN1 and TRAF1. Biological process enrichment analysis revealed the prioritized genes are modulated in AD pathogenesis including: regulation of neurogenesis and generation of neurons. Relatively high predictive performance (AUC: 0.70) was observed when classifying AD and normal gene expression profiles from individuals using leave-one-out cross validation.

Conclusions

This work provides a foundation for future investigation of diverse heterogeneous data integration for disease-gene prioritization.

Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

Transcriptomic analysis of mononuclear phagocyte differentiation and activation

Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the sets of genes that are associated with specific functions and the mechanisms by which thousands of genes are regulated in response to pathogen challenge. In large datasets, it is possible to identify large sets of genes that are coregulated with the transcription factors that regulate them. They include macrophage-specific genes, interferon-responsive genes, early inflammatory genes, and those associated with endocytosis. Such analyses can also extract macrophage-associated signatures from large cancer tissue datasets. However, cluster analysis provides no support for a signature that distinguishes macrophages from antigen-presenting dendritic cells, nor the classification of macrophage activation states as classical versus alternative, or M1 versus M2. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.

Comparative genomic analysis of slc39a12/ZIP12: insight into a zinc transporter required for vertebrate nervous system development

The zinc transporter ZIP12, which is encoded by the gene slc39a12, has previously been shown to be important for neuronal differentiation in mouse Neuro-2a neuroblastoma cells and primary mouse neurons and necessary for neurulation during Xenopus tropicalis embryogenesis. However, relatively little is known about the biochemical properties, cellular regulation, or the physiological role of this gene. The hypothesis that ZIP12 is a zinc transporter important for nervous system function and development guided a comparative genetics approach to uncover the presence of ZIP12 in various genomes and identify conserved sequences and expression patterns associated with ZIP12. Ortholog detection of slc39a12 was conducted with reciprocal BLAST hits with the amino acid sequence of human ZIP12 in comparison to the human paralog ZIP4 and conserved local synteny between genomes. ZIP12 is present in the genomes of almost all vertebrates examined, from humans and other mammals to most teleost fish. However, ZIP12 appears to be absent from the zebrafish genome. The discrimination of ZIP12 compared to ZIP4 was unsuccessful or inconclusive in other invertebrate chordates and deuterostomes. Splice variation, due to the inclusion or exclusion of a conserved exon, is present in humans, rats, and cows and likely has biological significance. ZIP12 also possesses many putative di-leucine and tyrosine motifs often associated with intracellular trafficking, which may control cellular zinc uptake activity through the localization of ZIP12 within the cell. These findings highlight multiple aspects of ZIP12 at the biochemical, cellular, and physiological levels with likely biological significance. ZIP12 appears to have conserved function as a zinc uptake transporter in vertebrate nervous system development. Consequently, the role of ZIP12 may be an important link to reported congenital malformations in numerous animal models and humans that are caused by zinc deficiency.

Construction and comparison of gene co-expression networks shows complex plant immune responses

Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

Ensemble positive unlabeled learning for disease gene identification

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links

BACKGROUND

Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes.

RESULTS

We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone.

CONCLUSIONS

The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.

Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs)

Background

The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets.

Results

By analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence.

Conclusions

Our results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation.

An expression atlas of human primary cells: inference of gene function from coexpression networks

BACKGROUND

The specialisation of mammalian cells in time and space requires genes associated with specific pathways and functions to be co-ordinately expressed. Here we have combined a large number of publically available microarray datasets derived from human primary cells and analysed large correlation graphs of these data.

RESULTS

Using the network analysis tool BioLayout Express3D we identify robust co-associations of genes expressed in a wide variety of cell lineages. We discuss the biological significance of a number of these associations, in particular the coexpression of key transcription factors with the genes that they are likely to control.

CONCLUSIONS

We consider the regulation of genes in human primary cells and specifically in the human mononuclear phagocyte system. Of particular note is the fact that these data do not support the identity of putative markers of antigen-presenting dendritic cells, nor classification of M1 and M2 activation states, a current subject of debate within immunological field. We have provided this data resource on the BioGPS web site (http://biogps.org/dataset/2429/primary-cell-atlas/) and on macrophages.com (http://www.macrophages.com/hu-cell-atlas).

Pathprinting: An integrative approach to understand the functional basis of disease

New strategies to combat complex human disease require systems approaches to biology that integrate experiments from cell lines, primary tissues and model organisms. We have developed Pathprint, a functional approach that compares gene expression profiles in a set of pathways, networks and transcriptionally regulated targets. It can be applied universally to gene expression profiles across species. Integration of large-scale profiling methods and curation of the public repository overcomes platform, species and batch effects to yield a standard measure of functional distance between experiments. We show that pathprints combine mouse and human blood developmental lineage, and can be used to identify new prognostic indicators in acute myeloid leukemia. The code and resources are available at http://compbio.sph.harvard.edu/hidelab/pathprint

Neurulation and neurite extension require the zinc transporter ZIP12 (slc39a12)

Zn(2+) is required for many aspects of neuronal structure and function. However, the regulation of Zn(2+) in the nervous system remains poorly understood. Systematic analysis of tissue-profiling microarray data showed that the zinc transporter ZIP12 (slc39a12) is highly expressed in the human brain. In the work reported here, we confirmed that ZIP12 is a Zn(2+) uptake transporter with a conserved pattern of high expression in the mouse and Xenopus nervous system. Mouse neurons and Neuro-2a cells produce fewer and shorter neurites after ZIP12 knockdown without affecting cell viability. Zn(2+) chelation or loading in cells to alter Zn(2+) availability respectively mimicked or reduced the effects of ZIP12 knockdown on neurite outgrowth. ZIP12 knockdown reduces cAMP response element-binding protein activation and phosphorylation at serine 133, which is a critical pathway for neuronal differentiation. Constitutive cAMP response element-binding protein activation restores impairments in neurite outgrowth caused by Zn(2+) chelation or ZIP12 knockdown. ZIP12 knockdown also reduces tubulin polymerization and increases sensitivity to nocodazole following neurite outgrowth. We find that ZIP12 is expressed during neurulation and early nervous system development in Xenopus tropicalis, where ZIP12 antisense morpholino knockdown impairs neural tube closure and arrests development during neurulation with concomitant reduction in tubulin polymerization in the neural plate. This study identifies a Zn(2+) transporter that is specifically required for nervous system development and provides tangible links between Zn(2+), neurulation, and neuronal differentiation.

Age-specific gene expression signatures for breast tumors and cross-species conserved potential cancer progression markers in young women

Breast cancer in young women is more aggressive with a poorer prognosis and overall survival compared to older women diagnosed with the disease. Despite recent research, the underlying biology and molecular alterations that drive the aggressive nature of breast tumors associated with breast cancer in young women have yet to be elucidated. In this study, we performed transcriptomic profile and network analyses of breast tumors arising in Middle Eastern women to identify age-specific gene signatures. Moreover, we studied molecular alterations associated with cancer progression in young women using cross-species comparative genomics approach coupled with copy number alterations (CNA) associated with breast cancers from independent studies. We identified 63 genes specific to tumors in young women that showed alterations distinct from two age cohorts of older women. The network analyses revealed potential critical regulatory roles for Myc, PI3K/Akt, NF-κB, and IL-1 in disease characteristics of breast tumors arising in young women. Cross-species comparative genomics analysis of progression from pre-invasive ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) revealed 16 genes with concomitant genomic alterations, CCNB2, UBE2C, TOP2A, CEP55, TPX2, BIRC5, KIAA0101, SHCBP1, UBE2T, PTTG1, NUSAP1, DEPDC1, HELLS, CCNB1, KIF4A, and RRM2, that may be involved in tumorigenesis and in the processes of invasion and progression of disease. Array findings were validated using qRT-PCR, immunohistochemistry, and extensive in silico analyses of independently performed microarray datasets. To our knowledge, this study provides the first comprehensive genomic analysis of breast cancer in Middle Eastern women in age-specific cohorts and potential markers for cancer progression in young women. Our data demonstrate that cancer appearing in young women contain distinct biological characteristics and deregulated signaling pathways. Moreover, our integrative genomic and cross-species analysis may provide robust biomarkers for the detection of disease progression in young women, and lead to more effective treatment strategies.

Chapter 15: disease gene prioritization

Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.

Similarity-based methods for potential human microRNA-disease association prediction

BACKGROUND

The identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases. However, experimental determination of associations between microRNAs and diseases remains challenging. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gained a lot of research interest.

METHODS

Herein, based on the assumption that functionally related microRNAs tend to be associated with phenotypically similar diseases, three inference methods were presented for microRNA-disease association prediction, namely MBSI (microRNA-based similarity inference), PBSI (phenotype-based similarity inference) and NetCBI (network-consistency-based inference). Global network similarity measure was used in the three methods to predict new microRNA-disease associations.

RESULTS

We tested the three methods on 242 known microRNA-disease associations by leave-one-out cross-validation for prediction evaluation, and achieved AUC values of 74.83%, 54.02% and 80.66%, respectively. The best-performed method NetCBI was then chosen for novel microRNA-disease association prediction. Some associations strongly predicted by NetCBI were confirmed by the publicly accessible databases, which indicated the usefulness of this method. The newly predicted associations were publicly released to facilitate future studies. Moreover, NetCBI was especially applicable to predicting target diseases for microRNAs whose target association information was not available.

CONCLUSIONS

The encouraging results suggest that our method NetCBI can not only provide help in identifying novel microRNA-disease associations but also guide biological experiments for scientific research.

Prediction of associations between OMIM diseases and microRNAs by random walk on OMIM disease similarity network

Increasing evidence has revealed that microRNAs (miRNAs) play important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA. Therefore, there is a strong incentive to develop computational methods to detect potential OMIM disease-miRNA associations. In this paper, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases. Our method makes full use of global disease similarity values. We tested our method on 1226 known OMIM disease-miRNA associations in the framework of leave-one-out cross-validation and achieved an area under the ROC curve of 71.42%. Excellent performance enables us to predict a number of new potential OMIM disease-miRNA associations and the newly predicted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manually checked and were confirmed from the publicly available databases, which was a strong evidence for the practical relevance of our method.

A novel method for cross-species gene expression analysis

Background

Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.

Results

In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.

Conclusions

The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.