Sexually-dimorphic targeting of functionally-related genes in COPD

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma

Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD.

New role for cardiomyocyte Bmal1 in the regulation of sex-specific heart transcriptomes

It has been well established that cardiovascular diseases exhibit significant differences between sexes in both preclinical models and humans. In addition, there is growing recognition that disrupted circadian rhythms can contribute to the onset and progression of cardiovascular diseases. However little is known about sex differences between the cardiac circadian clock and circadian transcriptomes in mice. Here, we show that the the core clock genes are expressed in common in both sexes but the circadian transcriptome of the mouse heart is very sex-specific. Hearts from female mice expressed significantly more rhythmically expressed genes (REGs) than male hearts and the temporal pattern of REGs was distinctly different between sexes. We next used a cardiomyocyte-specific knock out of the core clock gene, Bmal1, to investigate its role in sex-specific gene expression in the heart. All sex differences in the circadian transcriptomes were significantly diminished with cardiomyocyte-specific loss of Bmal1. Surprisingly, loss of cardiomyocyte Bmal1 also resulted in a roughly 8-fold reduction in the number of all the differentially expressed genes between male and female hearts. We conclude that cardiomyocyte-specific Bmal1, and potentially the core clock mechanism, is vital in conferring sex-specific gene expression in the adult mouse heart.

Alterations in the molecular control of mitochondrial turnover in COPD lung and airway epithelial cells

Abnormal mitochondria have been observed in bronchial- and alveolar epithelial cells of patients with chronic obstructive pulmonary disease (COPD). However, it is unknown if alterations in the molecular pathways regulating mitochondrial turnover (mitochondrial biogenesis vs mitophagy) are involved. Therefore, in this study, the abundance of key molecules controlling mitochondrial turnover were assessed in peripheral lung tissue from non-COPD patients (n = 6) and COPD patients (n = 11; GOLDII n = 4/11; GOLDIV n = 7/11) and in both undifferentiated and differentiated human primary bronchial epithelial cells (PBEC) from non-COPD patients and COPD patients (n = 4-7 patients/group). We observed significantly decreased transcript levels of key molecules controlling mitochondrial biogenesis (PPARGC1B, PPRC1, PPARD) in peripheral lung tissue from severe COPD patients. Interestingly, mRNA levels of the transcription factor TFAM (mitochondrial biogenesis) and BNIP3L (mitophagy) were increased in these patients. In general, these alterations were not recapitulated in undifferentiated and differentiated PBECs with the exception of decreased PPARGC1B expression in both PBEC models. Although these findings provide valuable insight in these pathways in bronchial epithelial cells and peripheral lung tissue of COPD patients, whether or not these alterations contribute to COPD pathogenesis, underlie changes in mitochondrial function or may represent compensatory mechanisms remains to be established.

Precision Approaches to Chronic Obstructive Pulmonary Disease Management

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.

Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

Recent evidence from omic analysis for redox signalling and mitochondrial oxidative stress in COPD

COPD is driven by exogenous and endogenous oxidative stress derived from inhaled cigarette smoke, air pollution and reactive oxygen species from dysregulated mitochondria in activated inflammatory cells within the airway and lung. This is compounded by the loss in antioxidant defences including FOXO and NRF2 and other antioxidant transcription factors together with various key enzymes that attenuate oxidant effects. Oxidative stress enhances inflammation; airway remodelling including fibrosis and emphysema; post-translational protein modifications leading to autoantibody generation; DNA damage and cellular senescence. Recent studies using various omics technologies in the airways, lungs and blood of COPD patients has emphasised the importance of oxidative stress, particularly that derived from dysfunctional mitochondria in COPD and its role in immunity, inflammation, mucosal barrier function and infection. Therapeutic interventions targeting oxidative stress should overcome the deleterious pathologic effects of COPD if targeted to the lung. We require novel, more efficacious antioxidant COPD treatments among which mitochondria-targeted antioxidants and Nrf2 activators are promising.

Connectivity in eQTL networks dictates reproducibility and genomic properties

Expression quantitative trait locus (eQTL) analysis associates SNPs with gene expression; these relationships can be represented as a bipartite network with association strength as "edge weights" between SNPs and genes. However, most eQTL networks use binary edge weights based on thresholded FDR estimates: definitions that influence reproducibility and downstream analyses. We constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated a comprehensive set of network specifications based on false discovery rates, test statistics, and p values, focusing on the degree centrality-a metric of an SNP or gene node's potential network influence. We found a thresholded Benjamini-Hochberg q value weighted by the Z-statistic balances metric reproducibility and computational efficiency. Our estimated gene degrees positively correlate with gene degrees in gene regulatory networks, demonstrating that these networks are complementary in understanding regulation. Gene degrees also correlate with genetic diversity, and heritability analyses show that highly connected nodes are enriched for tissue-relevant traits.

A novel network based linear model for prioritization of synergistic drug combinations

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.

Constructing gene regulatory networks using epigenetic data

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

Sex-specific associations with DNA methylation in lung tissue demonstrate smoking interactions

Cigarette smoking impacts DNA methylation, but the investigation of sex-specific features of lung tissue DNA methylation in smokers has been limited. Women appear more susceptible to cigarette smoke, and often develop more severe lung disease at an earlier age with less smoke exposure. We aimed to analyse whether there are sex differences in DNA methylation in lung tissue and whether these DNA methylation marks interact with smoking. We collected lung tissue samples from former smokers who underwent lung tissue resection. One hundred thirty samples from white subjects were included for this analysis. Regression models for sex as a predictor of methylation were adjusted for age, presence of COPD, smoking variables and technical batch variables revealed 710 associated sites. 294 sites demonstrated robust sex-specific methylation associations in foetal lung tissue. Pathway analysis identified 6 nominally significant pathways including the mitophagy pathway. Three CpG sites demonstrated a suggested interaction between sex and pack-years of smoking: GPR132, ANKRD44 and C19orf60. All of them were nominally significant in both male- and female-specific models, and the effect estimates were in opposite directions for male and female; GPR132 demonstrated significant association between DNA methylation and gene expression in lung tissue (P < 0.05). Sex-specific associations with DNA methylation in lung tissue are wide-spread and may reveal genes and pathways relevant to sex differences for lung damaging effects of cigarette smoking.

Molecular omics resources should require sex annotation: a call for action

The most commonly-used omics databases are a compilation of results from primarily male-only and sex-agnostic studies. The pervasive use of these databases critically hinders progress towards fully accounting for the biology of sex differences.

Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues

Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

Gene Targeting in Disease Networks

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.

Sex and Gender Omic Biomarkers in Men and Women With COPD: Considerations for Precision Medicine

Sex and gender differences in lung health and disease are imperative to consider and study if precision pulmonary medicine is to be achieved. The development of reliable COPD biomarkers has been elusive, and the translation of biomarkers to clinical care has been limited. Useful and effective biomarkers must be developed with attention to clinical heterogeneity of COPD; inherent heterogeneity exists related to grouping women and men together in the studies of COPD. Considering sex and gender differences and influences related to -omics may represent progress in susceptibility, diagnostic, prognostic, and therapeutic biomarker development and clinical innovation to improve the lung health of men and women.

PUMA: PANDA Using MicroRNA Associations

Motivation

Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue.

Availability and implementation

PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/).

Supplementary information

Supplementary data are available at Bioinformatics online.

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types

BACKGROUND

It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level.

METHODS

Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism.

RESULTS

For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF.

CONCLUSIONS

We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Chronic Obstructive Pulmonary Disease in Women: A Biologically Focused Review with a Systematic Search Strategy

Purpose

Evidence suggests that chronic obstructive pulmonary disease (COPD) symptoms and progression may differ between men and women. However, limited information is currently available on the pathophysiological and biological factors that may underlie these sex-related differences. The objective of this review is to systematically evaluate reports of potential sex-related differences, including genetic, pathophysiological, structural, and other biological factors, that may influence COPD development, manifestation, and progression in women.

Patients and Methods

A PubMed literature search was conducted from inception until January 2020. Original reports of genetic, hormonal, and physiological differences, and biological influences that could contribute to COPD development, manifestation, and progression in women were included.

Results

Overall, 491 articles were screened; 29 articles met the inclusion criteria. Results from this analysis demonstrated between-sex differences in inflammatory, immune, genetic, structural, and physiological factors in patients with COPD.

Conclusion

Various biological differences are observed between men and women with COPD including differences in inflammatory and metabolic pathways related to obesity and fat distribution, immune cell function and autophagy, extent and distribution of emphysema and airway wall remodeling. An enhanced understanding of these differences has the potential to broaden our understanding of how COPD develops and progresses, thereby providing an opportunity to ultimately improve diagnosis, treatment, and monitoring of COPD in both men and women.

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.

Pharmacological Management of Chronic Obstructive Lung Disease (COPD). Focus on Mutations - Part 1

BACKGROUND

We report a comprehensive overview of current Chronic Obstructive Lung Disease (COPD) therapies and discuss the development of possible new pharmacological approaches based on "new" knowledge. Specifically, sensitivity/resistance to corticosteroids is evaluated with a special focus on the role of gene mutations in drug response.

OBJECTIVE

Critically review the opportunities and the challenges occurring in the treatment of COPD.

CONCLUSION

Findings from "omics" trials should be used to learn more about biological targeted drugs, and to select more specific drugs matching patient's distinctive molecular profile. Specific markers of inflammation such as the percentage of eosinophils are important in determining sensitivity/resistance to corticosteroids. Specific gene variations (Single nucleotide polymorphisms: SNPs) may influence drug sensitivity or resistance. Clinicians working in a real-world need to have a suitable interpretation of molecular results together with a guideline for the treatment and recommendations. Far more translational research is required before new results from omics techniques can be applied in personalized medicine in realworld settings.

Estimating Sample-Specific Regulatory Networks

Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or "edge," in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78-100] survival compared with 61% (95% CI, 45-82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538-47. ©2018 AACR.

The protein phosphatase 2A regulatory subunit PR70 is a gonosomal melanoma tumor suppressor gene

Male gender is independently and significantly associated with poor prognosis in melanoma of all clinical stages. The biological underpinnings of this sex difference remain largely unknown, but we hypothesized that gene expression from gonosomes (sex chromosomes) might play an important role. We demonstrate that loss of the inactivated X chromosome in melanomas arising in females is strongly associated with poor distant metastasis-free survival, suggesting a dosage benefit from two X chromosomes. The gonosomal protein phosphatase 2 regulatory subunit B, beta (PPP2R3B) gene is located on the pseudoautosomal region (PAR) of the X chromosome in females and the Y chromosome in males. We observed that, despite its location on the PAR that predicts equal dosage across genders, PPP2R3B expression was lower in males than in females and was independently correlated with poor clinical outcome. PPP2R3B codes for the PR70 protein, a regulatory substrate-recognizing subunit of protein phosphatase 2A. PR70 decreased melanoma growth by negatively interfering with DNA replication and cell cycle progression through its role in stabilizing the cell division cycle 6 (CDC6)-chromatin licensing and DNA replication factor 1 (CDT1) interaction, which delays the firing of origins of DNA replication. Hence, PR70 functionally behaves as an X-linked tumor suppressor gene.

Estimating drivers of cell state transitions using gene regulatory network models

Background

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks.

Results

Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state.

Conclusions

We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.

Understanding Tissue-Specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Gene regulatory pattern analysis reveals essential role of core transcriptional factors' activation in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. Genome-scale molecular characteristics and regulatory mechanisms that distinguish TNBC from other subtypes remain incompletely characterized.

Results

By combining gene expression analysis and PANDA network, we defined three different TF regulatory patterns. A core TNBC-Specific TF Activation Driven Pattern (TNBCac) was specifically identified in TNBC by computational analysis. The essentialness of core TFs (ZEB1, MZF1, SOX10) in TNBC was highlighted and validated by cell proliferation analysis. Furthermore, 13 out of 35 co-targeted genes were also validated to be targeted by ZEB1, MZF1 and SOX10 in TNBC cell lines by real-time quantitative PCR. In three breast cancer cohorts, non-TNBC patients could be stratified into two subgroups by the 35 co-targeted genes along with 5 TFs, and the subgroup that more resembled TNBC had a worse prognosis.

Methods

We constructed gene regulatory networks in breast cancer by Passing Attributes between Networks for Data Assimilation (PANDA). Co-regulatory modules were specifically identified in TNBC by computational analysis, while the essentialness of core translational factors (TF) in TNBC was highlighted and validated by in vitro experiments. Prognostic effects of different factors were measured by Log-rank test and displayed by Kaplan-Meier plots.

Conclusions

We identified a core co-regulatory module specifically existing in TNBC, which enabled subtype re-classification and provided a biologically feasible view of breast cancer.

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

BACKGROUND

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis.

RESULTS

We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project.

CONCLUSIONS

An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .

Regulatory network changes between cell lines and their tissues of origin

BACKGROUND

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin.

RESULTS

We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE.

CONCLUSIONS

Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Differential connectivity of gene regulatory networks distinguishes corticosteroid response in asthma

BACKGROUND

Variations in drug response between individuals have prevented us from achieving high drug efficacy in treating many complex diseases, including asthma. Genetics plays an important role in accounting for such interindividual variations in drug response. However, systematic approaches for addressing how genetic factors and their regulators determine variations in drug response in asthma treatment are lacking.

OBJECTIVE

We sought to identify key transcriptional regulators of corticosteroid response in asthma using a novel systems biology approach.

METHODS

We used Passing Attributes between Networks for Data Assimilations (PANDA) to construct the gene regulatory networks associated with good responders and poor responders to inhaled corticosteroids based on a subset of 145 white children with asthma who participated in the Childhood Asthma Management Cohort. PANDA uses gene expression profiles and published relationships among genes, transcription factors (TFs), and proteins to construct the directed networks of TFs and genes. We assessed the differential connectivity between the gene regulatory network of good responders versus that of poor responders.

RESULTS

When compared with poor responders, the network of good responders has differential connectivity and distinct ontologies (eg, proapoptosis enriched in network of good responders and antiapoptosis enriched in network of poor responders). Many of the key hubs identified in conjunction with clinical response are also cellular response hubs. Functional validation demonstrated abrogation of differences in corticosteroid-treated cell viability following siRNA knockdown of 2 TFs and differential downstream expression between good responders and poor responders.

CONCLUSIONS

We have identified and validated multiple TFs influencing asthma treatment response. Our results show that differential connectivity analysis can provide new insights into the heterogeneity of drug treatment effects.

Estimating gene regulatory networks with pandaR

CONTACT

johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu.

AVAILABILITY AND IMPLEMENTATION

PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

Sex-Based Genetic Association Study Identifies CELSR1 as a Possible Chronic Obstructive Pulmonary Disease Risk Locus among Women

Chronic obstructive pulmonary disease (COPD) is a complex disease with strong environmental and genetic influences and sexually dimorphic features. Although genetic risk factors for COPD have been identified, much of the heritability remains unexplained. Sex-based genetic association studies may uncover additional COPD genetic risk factors. We studied current and former smokers from COPD case-control cohorts (COPDGene non-Hispanic whites and African Americans, Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-Points, and Genetics of Chronic Obstructive Lung Disease). COPD was defined as post-bronchodilator forced expiratory volume in 1 second/forced vital capacity less than 0.70 and forced expiratory volume in 1 second percent predicted less than 80. Testing was performed across all cohorts and combined in a meta-analysis adjusted for age, pack-years, and genetic ancestry. We first performed genome-wide single-nucleotide polymorphism (SNP)-by-sex interaction testing on the outcome of COPD affection status. We performed sex-stratified association testing for SNPs with interaction P less than 10^-6. We examined over 8 million SNPs in four populations, including 6,260 subjects with COPD (40.6% female) and 5,269 smoking control subjects (47.3% female). The SNP rs9615358 in the cadherin gene CELSR1 approached genome-wide significance for an interaction with sex (P = 1.24 × 10^-7). In the sex-stratified meta-analysis, this SNP was associated with COPD among females (odds ratio, 1.37 [95% confidence interval, 1.25-1.49]; P = 3.32 × 10^-7) but not males (odds ratio, 0.90 [95% confidence interval, 0.79-1.01]; P = 0.06). CELSR1 is involved in fetal lung development. In a human fetal lung tissue dataset, we observed greater CELSR1 expression in female compared with male samples. This SNP-by-sex genome-wide association analysis identified the fetal lung development gene, CELSR1, as a potential sex-specific risk factor for COPD. Identifying sex-specific genetic risk factors may reveal new insights into sexually dimorphic features of COPD.

Construction of disease-specific transcriptional regulatory networks identifies co-activation of four gene in esophageal squamous cell carcinoma

Even though various molecules may serve as biomarkers, little is known concerning the mechanisms underlying the carcinogenesis of ESCC, particularly the transcriptional regulatory network. Thus, in the present study, paired ESCC and non-cancerous (NC) tissues were assayed by Affymetrix microarray assays. Passing Attributes between Networks for Data Assimilation (PANDA) was used to construct networks between transcription factors (TFs) and their targets. AnaPANDA program was applied to compare the regulatory networks. A hypergeometric distribution model-based target profile similarity analysis was utilized to find co-activation effects using both TF-target networks and differential expression data. There were 1,116 genes upregulated and 1,301 genes downregulated in ESCC compared with NC tissues. In TF-target networks, 16,970 ESCC-specific edges and 9,307 NC-specific edges were identified. Edge enrichment analysis by AnaPANDA indicated 17 transcription factors (NFE2L2, ELK4, PAX6, TLX1, ESR1, ZNF143, TP53, REL, ELF5, STAT1, TBP, NHLH1, FOXL1, SOX9, STAT3, ELK1, and HOXA5) suppressed in ESCC and 5 (SPIB, BRCA1, MZF1, MAFG and NFE2L1) activated in ESCC. For SPIB, MZF1, MAFG and NFE2L1, a strong and significant co-activation effect among them was detected in ESCC. In conclusion, the construction of transcriptional regulatory networks found SPIB, MZF1, MAFG and NFE2L1 co-activated in ESCC, which provides distinctive insight into the carcinogenesis mechanism of ESCC.

The draft genome of Ruellia speciosa (Beautiful Wild Petunia: Acanthaceae)

The genus Ruellia (Wild Petunias; Acanthaceae) is characterized by an enormous diversity of floral shapes and colours manifested among closely related species. Using Illumina platform, we reconstructed the draft genome of Ruellia speciosa, with a scaffold size of 1,021 Mb (or ∼1.02 Gb) and an N50 size of 17,908 bp, spanning ∼93% of the estimated genome (∼1.1 Gb). The draft assembly predicted 40,124 gene models and phylogenetic analyses of four key enzymes involved in anthocyanin colour production [flavanone 3-hydroxylase (F3H), flavonoid 3'-hydroxylase (F3'H), flavonoid 3',5'-hydroxylase (F3'5'H), and dihydroflavonol 4-reductase (DFR)] found that most angiosperms here sampled harboured at least one copy of F3H, F3'H, and DFR. In contrast, fewer than one-half (but including R. speciosa) harboured a copy of F3'5'H, supporting observations that blue flowers and/or fruits, which this enzyme is required for, are less common among flowering plants. Ka/Ks analyses of duplicated copies of F3'H and DFR in R. speciosa suggested purifying selection in the former but detected evidence of positive selection in the latter. The genome sequence and annotation of R. speciosa represents only one of only four families sequenced in the large and important Asterid clade of flowering plants and, as such, will facilitate extensive future research on this diverse group, particularly with respect to floral evolution.

Computational analysis of the mesenchymal signature landscape in gliomas

BACKGROUND

Epithelial to mesenchymal transition, and mimicking processes, contribute to cancer invasion and metastasis, and are known to be responsible for resistance to various therapeutic agents in many cancers. While a number of studies have proposed molecular signatures that characterize the spectrum of such transition, more work is needed to understand how the mesenchymal signature (MS) is regulated in non-epithelial cancers like gliomas, to identify markers with the most prognostic significance, and potential for therapeutic targeting.

RESULTS

Computational analysis of 275 glioma samples from "The Cancer Genome Atlas" was used to identify the regulatory changes between low grade gliomas with little expression of MS, and high grade glioblastomas with high expression of MS. TF (transcription factor)-gene regulatory networks were constructed for each of the cohorts, and 5 major pathways and 118 transcription factors were identified as involved in the differential regulation of the networks. The most significant pathway - Extracellular matrix organization - was further analyzed for prognostic relevance. A 20-gene signature was identified as having prognostic significance (HR (hazard ratio) 3.2, 95% CI (confidence interval) = 1.53-8.33), after controlling for known prognostic factors (age, and glioma grade). The signature's significance was validated in an independent data set. The putative stem cell marker CD44 was biologically validated in glioma cell lines and brain tissue samples.

CONCLUSIONS

Our results suggest that the differences between low grade gliomas and high grade glioblastoma are associated with differential expression of the signature genes, raising the possibility that targeting these genes might prolong survival in glioma patients.

PyPanda: a Python package for gene regulatory network reconstruction

PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of 'omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis.

AVAILABILITY AND IMPLEMENTATION

The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda CONTACT: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl.

Diet-induced weight loss leads to a switch in gene regulatory network control in the rectal mucosa

BACKGROUND

Weight loss may decrease risk of colorectal cancer in obese individuals, yet its effect in the colorectum is not well understood. We used integrative network modeling, Passing Attributes between Networks for Data Assimilation, to estimate transcriptional regulatory network models from mRNA expression levels from rectal mucosa biopsies measured pre- and post-weight loss in 10 obese, pre-menopausal women.

RESULTS

We identified significantly greater regulatory targeting of glucose transport pathways in the post-weight loss regulatory network, including "regulation of glucose transport" (FDR=0.02), "hexose transport" (FDR=0.06), "glucose transport" (FDR=0.06) and "monosaccharide transport" (FDR=0.08). These findings were not evident by gene expression analysis alone. Network analysis also suggested a regulatory switch from NFΚB1 to MAX control of MYC post-weight loss.

CONCLUSIONS

These network-based results expand upon standard gene expression analysis by providing evidence for a potential mechanistic alteration caused by weight loss.

Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

BACKGROUND

Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes.

RESULTS

Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses.

CONCLUSIONS

There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets.