The ConsensusPathDB interaction database: 2013 update

Genotype effects contribute to variation in longitudinal methylome patterns in older people

BACKGROUND

DNA methylation levels change along with age, but few studies have examined the variation in the rate of such changes between individuals.

METHODS

We performed a longitudinal analysis to quantify the variation in the rate of change of DNA methylation between individuals using whole blood DNA methylation array profiles collected at 2-4 time points (N = 2894) in 954 individuals (67-90 years).

RESULTS

After stringent quality control, we identified 1507 DNA methylation CpG sites (rsCpGs) with statistically significant variation in the rate of change (random slope) of DNA methylation among individuals in a mixed linear model analysis. Genes in the vicinity of these rsCpGs were found to be enriched in Homeobox transcription factors and the Wnt signalling pathway, both of which are related to ageing processes. Furthermore, we investigated the SNP effect on the random slope. We found that 4 out of 1507 rsCpGs had one significant (P < 5 × 10-8/1507) SNP effect and 343 rsCpGs had at least one SNP effect (436 SNP-probe pairs) reaching genome-wide significance (P < 5 × 10-8). Ninety-five percent of the significant (P < 5 × 10-8) SNPs are on different chromosomes from their corresponding probes.

CONCLUSIONS

We identified CpG sites that have variability in the rate of change of DNA methylation between individuals, and our results suggest a genetic basis of this variation. Genes around these CpG sites have been reported to be involved in the ageing process.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Metabolic Remodeling during Liver Regeneration

Liver disease is linked to a decreased capacity of hepatocytes to divide. In addition, cellular metabolism is important for tissue homeostasis and regeneration. Since metabolic changes are a hallmark of liver disease, we investigated the connections between metabolism and cell division. We determined global metabolic changes at different stages of liver regeneration using a combination of integrated transcriptomic and metabolomic analyses with advanced functional redox in vivo imaging. Our data indicate that blocking hepatocyte division during regeneration leads to mitochondrial dysfunction and downregulation of oxidative pathways. This resulted in an increased redox ratio and hyperactivity of alanine transaminase allowing the production of alanine and α-ketoglutarate from pyruvate when mitochondrial functions are impaired. Our data suggests that during liver regeneration, cell division leads to hepatic metabolic remodeling. Moreover, we demonstrate that hepatocytes are equipped with a flexible metabolic machinery able to adapt dynamically to changes during tissue regeneration.

Variants in the 14q32 miRNA cluster are associated with osteosarcoma risk in the Spanish population

Association studies in osteosarcoma risk found significant results in intergenic regions, suggesting that regions which do not codify for proteins could play an important role. The deregulation of microRNAs (miRNAs) has been already associated with osteosarcoma. Consequently, genetic variants affecting miRNA function could be associated with risk. This study aimed to evaluate the involvement of all genetic variants in pre-miRNAs described so far in relationship to the risk of osteosarcoma. We analyzed a total of 213 genetic variants in 206 pre-miRNAs in two cohorts of osteosarcoma patients (n = 100) and their corresponding controls (n = 256) from Spanish and Slovenian populations, using Goldengate Veracode technology (Illumina). Four polymorphisms in pre-miRNAs at 14q32 miRNA cluster were associated with osteosarcoma risk in the Spanish population (rs12894467, rs61992671, rs58834075 and rs12879262). Pathway enrichment analysis including target genes of these miRNAs pointed out the WNT signaling pathways overrepresented. Moreover, different single nucleotide polymorphism (SNP) effects between the two populations included were observed, suggesting the existence of population differences. In conclusion, 14q32 miRNA cluster seems to be a hotspot for osteosarcoma susceptibility in the Spanish population, but not in the Slovenian, which supports the idea of the existence of population differences in developing this disease.

Differential responses of endothelial integrity upon the intake of microencapsulated garlic, tomato extract or a mixture: a single-intake, randomized, double-blind, placebo-controlled crossover trial

This study investigated the effect of microencapsulated garlic and/or tomato on endothelial dysfunction induced by the PhenFlex test (PFT) in healthy male smokers. In a randomized, double-blind, placebo-controlled crossover trial, 41 healthy male smokers were randomly assigned to one of four groups to receive the test groups (in microencapsulated garlic powder, tomato extract and a mixture thereof) or the placebo group. Proteomic biomarkers related to endothelial integrity were measured in plasma. Microencapsulated garlic, tomato extract and the mixture affected endothelial integrity biomarkers differently. Garlic consumption increased prothrombin time and decreased SAA and IL-12. Tomato extract intake increased activated partial thrombin time and decreased d-dimer, SAA, sVCAM-1, IL-13 and MCP-3 levels. Consumption of the mixture increased sE-selectin and lowered D-dimer, SAA, IL-13 and IL-10 responses after PFT challenge for 6 h. The different responses became clearer under high compliance in the dietary restriction groups. This single-intake clinical trial addressed the different responses of biomarkers related to endothelial integrity.

Adoptive T Cell Therapy: New Avenues Leading to Safe Targets and Powerful Allies

Adoptive transfer of TCR-engineered T cells is a potent therapy, able to induce clinical responses in different human malignancies. Nevertheless, treatment toxicities may occur and, in particular for solid tumors, responses may be variable and often not durable. To address these challenges, it is imperative to carefully select target antigens and to immunologically interrogate the corresponding tumors when designing optimal T cell therapies. Here, we review recent advances, covering both omics- and laboratory tools that can enable the selection of optimal T cell epitopes and TCRs as well as the identification of dominant immune evasive mechanisms within tumor tissues. Furthermore, we discuss how these techniques may aid in a rational design of effective combinatorial adoptive T cell therapies.

Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module

The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (C_AB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the C_AB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Genome-wide DNA methylation profiling of primary colorectal laterally spreading tumors identifies disease-specific epimutations on common pathways

Colorectal laterally spreading tumors (LSTs) grow to extremely large size while rarely invade deeply. Also, there is a low tendency to become cancerous. We used the Illumina Human Methylation 450K array to query the main epigenetic difference of LSTs. We built a discovery cohort with 10 matched cases, and a validation cohort with 9 additional matched cases. Our results suggest that LST displays significant decrease in DNA methylation, highlighted by the discovery of 1,018 hypomethylated intergenic regions (IGRs). Comparing to classic differentially methylated probes and regions that overlap transcription starting site and CpG island, IGR-regions were associated more closely with genes involved in functional biological processes and correlated with specific histone modifications. Hypomethylated IGR regions were often annotated as tissue-specific regulatory elements for noncolon tissues and were typically epigenetically silenced in normal colon mucosa. By integration of public data, we defined the commonality and specific epigenetic signatures for adenomas, LSTs and colon adenocarcinomas. Only 435 hypermethylated differentially methylated probes (DMPs) and differentially methylated regions (DMRs) and 517 hypomethylated DMPs and DMRs were shared by the three diseases. However, our pathway-level analysis discovered that genes in four pathways were common target of epimutations in LSTs, adenomas and CRCs. More interestingly, different diseases seem to employ distinct epigenetic insult to disturb specific pathways. Between LST and adenoma, we found eight pathways including Ras signaling and Rap1 signaling pathway were commonly targeted but the epimutation patterns were opposite. Comparison between precancerous conditions and invasive states revealed the key pathways governing the progression to malignancy, including PI3K-Akt pathways.

Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease

We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39 × 10-08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 × 10-04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritizing candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease.

Sex-specific patterns of gene expression following influenza vaccination

Sex-based variations in the immune response to the influenza vaccines was reported, however, the genetic basis responsible for the sex variations in the immune response toward the influenza vaccines remains unclear. Here, the genes responsible for sex-specific responses after vaccination with trivalent inactivated influenza virus were identified. These genes were enriched in virus response pathways, especially interferon signaling. A list of genes showing different responses to the vaccine between females and males were obtained next. Our results demonstrated that females generate stronger immune responses to seasonal influenza vaccines within 24 hours than males. However, most of these genes with variability between sexes had the opposite expression levels after three days, suggesting that males retained the immune responses longer than female. To summary, our study identified genes responsible for the sex variations toward influenza vaccination. Our findings might provide insights into the development of the sex-dependent influenza vaccines.

Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease

It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.

Identification of early biological changes in palmitate-treated isolated human islets

Background

Long-term exposure to elevated levels of free fatty acids (FFAs) is deleterious for beta-cell function and may contribute to development of type 2 diabetes mellitus (T2DM). Whereas mechanisms of impaired glucose-stimulated insulin secretion (GSIS) in FFA-treated beta-cells have been intensively studied, biological events preceding the secretory failure, when GSIS is accentuated, are poorly investigated. To identify these early events, we performed genome-wide analysis of gene expression in isolated human islets exposed to fatty acid palmitate for different time periods.

Results

Palmitate-treated human islets showed decline in beta-cell function starting from day two. Affymetrix Human Transcriptome Array 2.0 identified 903 differentially expressed genes (DEGs). Mapping of the genes onto pathways using KEGG pathway enrichment analysis predicted four islet biology-related pathways enriched prior but not after the decline of islet function and three pathways enriched both prior and after the decline of islet function. DEGs from these pathways were analyzed at the transcript level. The results propose that in palmitate-treated human islets, at early time points, protective events, including up-regulation of metallothioneins, tRNA synthetases and fatty acid-metabolising proteins, dominate over deleterious events, including inhibition of fatty acid detoxification enzymes, which contributes to the enhanced GSIS. After prolonged exposure of islets to palmitate, the protective events are outweighed by the deleterious events, which leads to impaired GSIS.

Conclusions

The study identifies temporal order between different cellular events, which either promote or protect from beta-cell failure. The sequence of these events should be considered when developing strategies for prevention and treatment of the disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5008-z) contains supplementary material, which is available to authorized users.

Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships. Differential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive. Here, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample set. First, we use one of the variable importance measures generated by the random forests classification algorithm as a metric to rank genes. Second, we define the EPS (extreme pseudo-samples) pipeline, making use of VAEs (Variational Autoencoders) and regressors to extract a ranking of genes while leveraging the feature space of both virtual and comparable samples. We show that, on 12 cancer RNA-Seq data sets ranging from 323 to 1,210 samples, using either a random forests-based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival. These results demonstrate the potential of supervised learning-based gene selection methods in RNA-Seq studies and highlight the need to use such multivariate gene selection methods alongside the widely used differential expression analysis.

Epigenetic Aging in Major Depressive Disorder

OBJECTIVE

Major depressive disorder is associated with an increased risk of mortality and aging-related diseases. The authors examined whether major depression is associated with higher epigenetic aging in blood as measured by DNA methylation (DNAm) patterns, whether clinical characteristics of major depression have a further impact on these patterns, and whether the findings replicate in brain tissue.

METHOD

DNAm age was estimated using all methylation sites in blood of 811 depressed patients and 319 control subjects with no lifetime psychiatric disorders and low depressive symptoms from the Netherlands Study of Depression and Anxiety. The residuals of the DNAm age estimates regressed on chronological age were calculated to indicate epigenetic aging. Major depression diagnosis and clinical characteristics were assessed with questionnaires and psychiatric interviews. Analyses were adjusted for sociodemographic characteristics, lifestyle, and health status. Postmortem brain samples of 74 depressed patients and 64 control subjects were used for replication. Pathway enrichment analysis was conducted using ConsensusPathDB to gain insight into the biological processes underlying epigenetic aging in blood and brain.

RESULTS

Significantly higher epigenetic aging was observed in patients with major depression compared with control subjects (Cohen's d=0.18), with a significant dose effect with increasing symptom severity in the overall sample. In the depression group, epigenetic aging was positively and significantly associated with childhood trauma score. The case-control difference was replicated in an independent data set of postmortem brain samples. The top significantly enriched Gene Ontology terms included neuronal processes.

CONCLUSIONS

As compared with control subjects, patients with major depression exhibited higher epigenetic aging in blood and brain tissue, suggesting that they are biologically older than their corresponding chronological age. This effect was even more profound in the presence of childhood trauma.

Longitudinal expression profiling of CD4+ and CD8+ cells in patients with active to quiescent giant cell arteritis

Background

Giant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies but symptoms and signs are variable thereby making it a challenging disease to diagnose. A temporal artery biopsy is the gold standard to confirm GCA, but there are currently no specific biochemical markers to aid diagnosis. We aimed to identify a less invasive method to confirm the diagnosis of GCA, as well as to ascertain clinically relevant predictive biomarkers by studying the transcriptome of purified peripheral CD4+ and CD8+ T lymphocytes in patients with GCA.

Methods

We recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital, Melbourne, Australia, and aimed to collect blood samples at six time points: acute phase, 2–3 weeks, 6–8 weeks, 3 months, 6 months and 12 months after clinical diagnosis. CD4+ and CD8+ T-cells were positively selected at each time point through magnetic-assisted cell sorting. RNA was extracted from all 195 collected samples for subsequent RNA sequencing. The expression profiles of patients were compared to those of 16 age-matched controls.

Results

Over the 12-month study period, polynomial modelling analyses identified 179 and 4 statistically significant transcripts with altered expression profiles (FDR < 0.05) between cases and controls in CD4+ and CD8+ populations, respectively. In CD8+ cells, two transcripts remained differentially expressed after 12 months; SGTB, associated with neuronal apoptosis, and FCGR3A, associatied with Takayasu arteritis. We detected genes that correlate with both symptoms and biochemical markers used for predicting long-term prognosis. 15 genes were shared across 3 phenotypes in CD4 and 16 across CD8 cells. In CD8, IL32 was common to 5 phenotypes including Polymyalgia Rheumatica, bilateral blindness and death within 12 months.

Conclusions

This is the first longitudinal gene expression study undertaken to identify robust transcriptomic biomarkers of GCA. Our results show cell type-specific transcript expression profiles, novel gene-phenotype associations, and uncover important biological pathways for this disease. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide in initiating appropriate patient management.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0376-4) contains supplementary material, which is available to authorized users.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

BACKGROUND

Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download.

RESULTS

We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis.

CONCLUSION

The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.

Integrative analysis of gene expression and methylation data for breast cancer cell lines

Background

The deadly costs of cancer and necessity for an accurate method of early cancer detection have demanded the identification of genetic and epigenetic factors associated with cancer. DNA methylation, an epigenetic event, plays an important role in cancer susceptibility. In this paper, we use DNA methylation and gene expression data integration and pathway analysis to further explore and understand the complex relationship between methylation and gene expression.

Results

Through linear modeling and analysis of variance, we obtain genes that show a significant correlation between methylation and gene expression. We then examine the functions and relationships of these genes using bioinformatic tools and databases. In particular, using ConsensusPathDB, we analyze the networks of statistically significant genes to identify hub genes, genes with a large number of links to other genes. We identify eight major hub genes, all in strong association with cancer susceptibility. Through further analysis of the function, gene expression level, and methylation level of these hub genes, we conclude that they are novel potential biomarkers for breast cancer.

Conclusions

Our findings have various implications for cancer screening, early detection methods, and potential novel treatments for cancer. Researchers can also use our results to develop more effective methods for cancer study.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0174-8) contains supplementary material, which is available to authorized users.

Changes in methylation within the STK32B promoter are associated with an increased risk for generalized anxiety disorder in adolescents

Generalized anxiety disorder (GAD) is highly prevalent among adolescents. An early detection of individuals at risk may prevent later psychiatric condition. Genome-wide studies investigating single nucleotide polymorphisms (SNPs) concluded that a focus on epigenetic mechanisms, which mediate the impact of environmental factors, could more efficiently help the understanding of GAD pathogenesis. We investigated the relationship between epigenetic shifts in blood and the risk to develop GAD, evaluated by the Development and Well-Being Assessment (DAWBA) score, in 221 otherwise healthy adolescents. Our analysis focused specifically on methylation sites showing high inter-individual variation but low tissue-specific variation, in order to infer a potential correlation between results obtained in blood and brain. Two statistical methods were applied, 1) a linear model with limma and 2) a likelihood test followed by Bonferroni correction. Methylation findings were validated in a cohort of 160 adults applying logistic models against the outcome variable "anxiety treatment obtained in the past" and studied in a third cohort with regards to associated expression changes measured in monocytes. One CpG site showed 1% increased methylation in adolescents at high risk of GAD (cg16333992, p_adj. = 0.028, estimate = 3.22), as confirmed in the second cohort (p = 0.031, estimate = 1.32). The identified and validated CpG site is located within the STK32B promoter region and its methylation level was positively associated with gene expression. Gene ontology analysis revealed that STK32B is involved in stress response and defense response. Our results provide evidence that shifts in DNA methylation are associated with a modulated risk profile for GAD in adolescence.

Transcriptomics analysis reveals new insights in E171-induced molecular alterations in a mouse model of colon cancer

Titanium dioxide as a food additive (E171) has been demonstrated to facilitate growth of chemically induced colorectal tumours in vivo and induce transcriptomic changes suggestive of an immune system impairment and cancer development. The present study aimed to investigate the molecular mechanisms behind the tumour stimulatory effects of E171 in combination with azoxymethane (AOM)/dextran sodium sulphate (DSS) and compare these results to a recent study performed under the same conditions with E171 only. BALB/c mice underwent exposure to 5 mg/kg_bw/day of E171 by gavage for 2, 7, 14, and 21 days. Whole genome mRNA microarray analyses on the distal colon were performed. The results show that E171 induced a downregulation of genes involved in the innate and adaptive immune system, suggesting impairment of this system. In addition, over time, signalling genes involved in colorectal cancer and other types of cancers were modulated. In relation to cancer development, effects potentially associated with oxidative stress were observed through modulation of genes related to antioxidant production. E171 affected genes involved in biotransformation of xenobiotics which can form reactive intermediates resulting in toxicological effects. These transcriptomics data reflect the early biological responses induced by E171 which precede tumour formation in an AOM/DSS mouse model.

Microglia activation in Niemann-Pick disease, type C1 is amendable to therapeutic intervention

Niemann-Pick disease, type C1 (NPC1) is a neurodegenerative disorder with limited treatment options. NPC1 is associated with neuroinflammation; however, attempts to therapeutically target neuroinflammation in NPC1 have had mixed success. We show here that NPC1 neuroinflammation is characterized by an atypical microglia activation phenotype. Specifically, Npc1-/- microglia demonstrated altered morphology, reduced levels of lineage markers and a shift toward glycolytic metabolism. Treatment with 2-hydroxypropyl-β-cyclodextrin (HPβCD), a drug currently being studied in a phase 2b/3 clinical trial, reversed all microglia-associated defects in Npc1-/- animals. In addition, impairing microglia mediated neuroinflammation by genetic deletion of IRF8 led to decreased symptoms and increased lifespan. We identified CD22 as a marker of dysregulated microglia in Npc1 mutant mice and subsequently demonstrated that elevated cerebrospinal fluid levels of CD22 in NPC1 patients responds to HPβCD administration. Collectively, these data provide the first in-depth analysis of microglia function in NPC1 and suggest possible new therapeutic approaches.

Mapping biological process relationships and disease perturbations within a pathway network

Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signaling, immunity, and the cell cycle. We mapped a range of diseases onto this network and find that pathways associated with diseases tend to be functionally connected, highlighting the perturbed functions that result in disease phenotypes. We demonstrated that disease pathways cluster within the network. We then examined the distribution of cancer pathways and showed that cancer pathways tend to localize within the signaling, DNA processes and immune modules, although some cancer-associated nodes are found in other network regions. Altogether, we generated a high-confidence functional network, which avoids some of the shortcomings faced by conventional molecular models. Our representation provides an intuitive functional interpretation of cellular organization, which relies only on high-quality pathway and Gene Ontology data. The network is available at https://data.mendeley.com/datasets/3pbwkxjxg9/1.

Somatic mutations in benign breast disease tissue and risk of subsequent invasive breast cancer

BACKGROUND

Insights into the molecular pathogenesis of breast cancer might come from molecular analysis of tissue from early stages of the disease.

METHODS

We conducted a case-control study nested in a cohort of women who had biopsy-confirmed benign breast disease (BBD) diagnosed between 1971 and 2006 at Kaiser Permanente Northwest and who were followed to mid-2015 to ascertain subsequent invasive breast cancer (IBC); cases (n = 218) were women with BBD who developed subsequent IBC and controls, individually matched (1:1) to cases, were women with BBD who did not develop IBC in the same follow-up interval as that for the corresponding case. Targeted sequence capture and sequencing were performed for 83 genes of importance in breast cancer.

RESULTS

There were no significant case-control differences in mutation burden overall, for non-silent mutations, for individual genes, or with respect either to the nature of the gene mutations or to mutational enrichment at the pathway level. For seven subjects with DNA from the BBD and ipsilateral IBC, virtually no mutations were shared.

CONCLUSIONS

This study, the first to use a targeted multi-gene sequencing approach on early breast cancer precursor lesions to investigate the genomic basis of the disease, showed that somatic mutations detected in BBD tissue were not associated with breast cancer risk.

Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome

Background

Natural selection shapes cancer genomes. Previous studies used signatures of positive selection to identify genes driving malignant transformation. However, the contribution of negative selection against somatic mutations that affect essential tumor functions or specific domains remains a controversial topic.

Results

Here, we analyze 7546 individual exomes from 26 tumor types from TCGA data to explore the portion of the cancer exome under negative selection. Although we find most of the genes neutrally evolving in a pan-cancer framework, we identify essential cancer genes and immune-exposed protein regions under significant negative selection. Moreover, our simulations suggest that the amount of negative selection is underestimated. We therefore choose an empirical approach to identify genes, functions, and protein regions under negative selection. We find that expression and mutation status of negatively selected genes is indicative of patient survival. Processes that are most strongly conserved are those that play fundamental cellular roles such as protein synthesis, glucose metabolism, and molecular transport. Intriguingly, we observe strong signals of selection in the immunopeptidome and proteins controlling peptide exposition, highlighting the importance of immune surveillance evasion. Additionally, tumor type-specific immune activity correlates with the strength of negative selection on human epitopes.

Conclusions

In summary, our results show that negative selection is a hallmark of cell essentiality and immune response in cancer. The functional domains identified could be exploited therapeutically, ultimately allowing for the development of novel cancer treatments.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1434-0) contains supplementary material, which is available to authorized users.

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Application of pressure cycling technology and Sequential Windowed Acquisition of all THeoretical mass spectrometry allows quantifying the degree of intra-tumor heterogeneity of protein expression in prostate tumors. The data show that protein intra-tumor heterogeneity, if not characterized, may distort protein biomarker suitability in tumor tissues.

It remains unclear to what extent tumor heterogeneity impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of prostate tissues using pressure cycling technology and Sequential Windowed Acquisition of all THeoretical fragment ion mass spectrometry. We quantified 6,873 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied depending on proteins and tissue types. Benign tissues exhibited more complex ITH patterns than malignant tissues. Spatial variability of 10 prostate biomarkers was validated by immunohistochemistry in an independent cohort (n = 83) using tissue microarrays. Prostate-specific antigen was preferentially variable in benign prostatic hyperplasia, whereas growth/differentiation factor 15 substantially varied in prostate adenocarcinomas. Furthermore, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery: it suggests that recent technological progress should be exploited to quantify and account for spatial proteome variation to complement biomarker identification and utilization.

Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci

RATIONALE

As the third leading cause of death in the United States, the impact of chronic obstructive pulmonary disease (COPD) makes identification of its molecular mechanisms of great importance. Genome-wide association studies (GWASs) have identified multiple genomic regions associated with COPD. However, genetic variation only explains a small fraction of the susceptibility to COPD, and sub-genome-wide significant loci may play a role in pathogenesis.

OBJECTIVES

Regulatory annotation with epigenetic evidence may give priority for further investigation, particularly for GWAS associations in noncoding regions. We performed integrative genomics analyses using DNA methylation profiling and genome-wide SNP genotyping from lung tissue samples from 90 subjects with COPD and 36 control subjects.

METHODS

We performed methylation quantitative trait loci (mQTL) analyses, testing for SNPs associated with percent DNA methylation and assessed the colocalization of these results with previous COPD GWAS findings using Bayesian methods in the R package coloc to highlight potential regulatory features of the loci.

MEASUREMENTS AND MAIN RESULTS

We identified 942,068 unique SNPs and 33,996 unique CpG sites among the significant (5% false discovery rate) cis-mQTL results. The genome-wide significant and subthreshold (P < 10-4) GWAS SNPs were enriched in the significant mQTL SNPs (hypergeometric test P < 0.00001). We observed enrichment for sites located in CpG shores and shelves, but not CpG islands. Using Bayesian colocalization, we identified loci in regions near KCNK3, EEFSEC, PIK3CD, DCDC2C, TCERG1L, FRMD4B, and IL27.

CONCLUSIONS

Colocalization of mQTL and GWAS loci provides regulatory characterization of significant and subthreshold GWAS findings, supporting a role for genetic control of methylation in COPD pathogenesis.

Integrated multi-omics data analysis identifying novel drug sensitivity-associated molecular targets of hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the most common type of liver cancer and the third-leading cause of malignancy-associated mortality worldwide. HCC cells are highly resistant to chemotherapeutic agents. Therefore, there are currently only two US Food and Drug Administration-approved drugs available for the treatment of HCC. The objective of the present study was to analyze the results of previously published high-throughput drug screening, and in vitro genomic and transcriptomic data from HCC cell lines, and to integrate the obtained results to define the underlying molecular mechanisms of drug sensitivity and resistance in HCC cells. The results of treatment with 225 different small molecules on 14 different HCC cell lines were retrieved from the Genomics of Drug Sensitivity in Cancer database and analyzed. Cluster analysis using the treatment results determined that HCC cell lines consist of two groups, according to their drug response profiles. Continued analyses of these two groups with Gene Set Enrichment Analysis method revealed 6 treatment-sensitive molecular targets (epidermal growth factor receptor, mechanistic target of rapamycin, deoxyribonucleic acid-dependent protein kinase, the Aurora kinases, Bruton's tyrosine kinase and phosphoinositide 3-kinase; all P<0.05) and partially effective drugs. Genetic and genome-wide gene expression data analyses of the determined targets and their known biological partners revealed 2 somatically mutated and 13 differentially expressed genes, which differed between drug-resistant and drug-sensitive HCC cells. Integration of the obtained data into a short molecular pathway revealed a drug treatment-sensitive signaling axis in HCC cells. In conclusion, the results of the present study provide novel drug sensitivity-associated molecular targets for the development of novel personalized and targeted molecular therapies against HCC.

Involvement of SNPs in miR-3117 and miR-3689d2 in childhood acute lymphoblastic leukemia risk

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Numerous studies have shown that microRNAs (miRNAs) could play a role in this disease. Nowadays, more than 2500 miRNAs have been described, that regulate more than 50% of genes, including those involved in B-cell maturation, differentiation and proliferation. Genetic variants in miRNAs can alter their own levels or function, affecting their target gene expression, and then, may affect ALL risk. Therefore, the aim of this study was to determine the role of miRNA genetic variants in B-ALL susceptibility. We analyzed all variants in pre-miRNAs (MAF > 1%) in two independent cohorts from Spain and Slovenia and inferred their functional effect by in silico analysis. SNPs rs12402181 in miR-3117 and rs62571442 in miR-3689d2 were associated with ALL risk in both cohorts, possibly through their effect on MAPK signalling pathway. These SNPs could be novel markers for ALL susceptibility.

Deconvolution of Human Brain Cell Type Transcriptomes Unraveled Microglia-Specific Potential Biomarkers

Microglial cells form a context-dependent network of brain immunoeffector cells. Despite their indispensable roles, unresolved questions exist around biomarker discovery relevant to their cellular localization, self-renewing potential, and brain developmental dynamics. To resolve the existent gap in the annotation of candidate biomarkers, we conducted a meta-analysis of brain cells using available high-throughput data sets for deciphering microglia-specific expression profiles. We have identified 3,290 significant genes specific to microglia and further selected the top 20 dysregulated genes on the basis of p-value and log₂FC. To this list, we added 7 known microglia-specific markers making the candidate list comprising 27 genes for further downstream analyses. Next, we established a connectome of these potential markers with their putative protein partners, which demonstrated strong associations of upregulated genes like Dedicator of cytokinesis 2 (DOCK2) with early/mature microglial markers such as Sphingosine kinase 1 (SPHK1), CD68, and CD45. To elucidate their respective brain anatomical location, we deconvoluted the BrainSpan Atlas expression data. This analysis showed high expression of the majority of candidate genes in microglia-dense regions (Amygdala, Hippocampus, Striatum) in the postnatal brain. Furthermore, to decipher their localized expression across brain ages, we constructed a developmental dynamics map (DDM) comprising extensive gene expression profiles throughout prenatal to postnatal stages, which resulted in the discovery of novel microglia-specific gene signatures. One of the interesting readout from DDM is that all the microglia-dense regions exhibit dynamic regulation of few genes at 37 post conception week (pcw), the transition period between pre- and postnatal stages. To validate these findings and correlate them as potential biomarkers, we analyzed the expression of corresponding proteins in hESC-derived human microglia precursors. The cultured microglial precursors showed expression of Pentraxin 3 (PTX3) and SPHK1 as well as several known markers like CD68, Allograft inflammatory factor 1 (AIF1/IBA1). In summary, this study has furnished critical insights into microglia dynamics across human brain ages and cataloged potential transcriptomic fingerprints that can be further exploited for designing novel neurotherapeutics.

Blocking negative effects of senescence in human skin fibroblasts with a plant extract

There is increasing evidence that senescent cells are a driving force behind many age-related pathologies and that their selective elimination increases the life- and healthspan of mice. Senescent cells negatively affect their surrounding tissue by losing their cell specific functionality and by secreting a pro-tumorigenic and pro-inflammatory mixture of growth hormones, chemokines, cytokines and proteases, termed the senescence-associated secretory phenotype (SASP). Here we identified an extract from the plant Solidago virgaurea subsp. alpestris, which exhibited weak senolytic activity, delayed the acquisition of a senescent phenotype and induced a papillary phenotype with improved functionality in human dermal fibroblasts. When administered to stress-induced premature senescent fibroblasts, this extract changed their global mRNA expression profile and particularly reduced the expression of various SASP components, thereby ameliorating the negative influence on nearby cells. Thus, the investigated plant extract represents a promising possibility to block age-related loss of tissue functionality.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.

Systematic characterization of pan-cancer mutation clusters

Cancer genome sequencing has shown that driver genes can often be distinguished not only by the elevated mutation frequency but also by specific nucleotide positions that accumulate changes at a high rate. However, properties associated with a residue's potential to drive tumorigenesis when mutated have not yet been systematically investigated. Here, using a novel methodological approach, we identify and characterize a compendium of 180 hotspot residues within 160 human proteins which occur with a significant frequency and are likely to have functionally relevant impact. We find that such mutations (i) are more prominent in proteins that can exist in the on and off state, (ii) reflect the identity of a tumor of origin, and (iii) often localize within interfaces which mediate interactions with other proteins or ligands. Following, we further examine structural data for human protein complexes and identify a number of additional protein interfaces that accumulate cancer mutations at a high rate. Jointly, these analyses suggest that disruption and dysregulation of protein interactions can be instrumental in switching functions of cancer proteins and activating downstream changes.

Molecular hydrogen upregulates heat shock response and collagen biosynthesis, and downregulates cell cycles: meta-analyses of gene expression profiles

Molecular hydrogen exerts its effect on multiple pathologies, including oxidative stress, inflammation, and apoptosis. However, its molecular mechanisms have not been fully elucidated. In order to explore the effects of molecular hydrogen, we meta-analysed gene expression profiles modulated by molecular hydrogen. We performed microarray analysis of the mouse liver with or without drinking hydrogen water. We also integrated two previously reported microarray datasets of the rat liver into meta-analyses. We used two categories of meta-analysis methods: the cross-platform method and the conventional meta-analysis method (Fisher's method). For each method, hydrogen-modulated pathways were analysed by (i) the hypergeometric test (HGT) in the class of over-representation analysis (ORA), (ii) the gene set enrichment analysis (GSEA) in the class of functional class scoring (FCS), and (iii) the signalling pathway impact analysis (SPIA), pathway regulation score (PRS), and others in the class of pathway topology-based approach (PTA). Pathways in the collagen biosynthesis and the heat-shock response were up-regulated according to (a) HGT with the cross-platform method, (b) GSEA with the cross-platform method, and (c) PRS with the cross-platform method. Pathways in cell cycles were down-regulated according to (a) HGT with the cross-platform method, (b) GSEA with the cross-platform method, and (d) GSEA with the conventional meta-analysis method. Because the heat-shock response leads to up-regulation of collagen biosynthesis and a transient arrest of cell cycles, induction of the heat-shock response is likely to be a primary event induced by molecular hydrogen in the liver of wild-type rodents.

Proteomic characterization of high-density lipoprotein particles in patients with non-alcoholic fatty liver disease

Background

Metabolic diseases such as obesity and diabetes are associated with changes in high-density lipoprotein (HDL) particles, including changes in particle size and protein composition, often resulting in abnormal function. Recent studies suggested that patients with non-alcoholic fatty liver disease (NAFLD), including individuals with non-alcoholic steatohepatitis (NASH), have smaller HDL particles when compared to individuals without liver pathologies. However, no studies have investigated potential changes in HDL particle protein composition in patients with NAFLD, in addition to changes related to obesity, to explore putative functional changes of HDL which may increase the risk of cardiovascular complications.

Methods

From a cohort of morbidly obese females who were diagnosed with simple steatosis (SS), NASH, or normal liver histology, we selected five matched individuals from each condition for a preliminary pilot HDL proteome analysis. HDL particles were enriched using size-exclusion chromatography, and the proteome of the resulting fraction was analyzed by liquid chromatography tandem mass spectrometry. Differences in the proteomes between the three conditions (normal, SS, NASH) were assessed using label-free quantitative analysis. Gene ontology term analysis was performed to assess the potential impact of proteomic changes on specific functions of HDL particles.

Results

Of the 95 proteins identified, 12 proteins showed nominally significant differences between the three conditions. Gene ontology term analysis revealed that severity of the liver pathology may significantly impact the anti-thrombotic functions of HDL particles, as suggested by changes in the abundance of HDL-associated proteins such as antithrombin III and plasminogen.

Conclusions

The pilot data from this study suggest that changes in the HDL proteome may impact the functionality of HDL particles in NAFLD and NASH patients. These proteome changes may alter cardio-protective properties of HDL, potentially contributing to the increased cardiovascular disease risk in affected individuals. Further validation of these protein changes by orthogonal approaches is key to confirming the role of alterations in the HDL proteome in NAFLD and NASH. This will help elucidate the mechanistic effects of the altered HDL proteome on cardioprotective properties of HDL particles.

Sorafenib promotes graft-versus-leukemia activity in mice and humans through IL-15 production in FLT3-ITD-mutant leukemia cells

Individuals with acute myeloid leukemia (AML) harboring an internal tandem duplication (ITD) in the gene encoding Fms-related tyrosine kinase 3 (FLT3) who relapse after allogeneic hematopoietic cell transplantation (allo-HCT) have a 1-year survival rate below 20%. We observed that sorafenib, a multitargeted tyrosine kinase inhibitor, increased IL-15 production by FLT3-ITD+ leukemia cells. This synergized with the allogeneic CD8+ T cell response, leading to long-term survival in six mouse models of FLT3-ITD+ AML. Sorafenib-related IL-15 production caused an increase in CD8+CD107a+IFN-γ+ T cells with features of longevity (high levels of Bcl-2 and reduced PD-1 levels), which eradicated leukemia in secondary recipients. Mechanistically, sorafenib reduced expression of the transcription factor ATF4, thereby blocking negative regulation of interferon regulatory factor 7 (IRF7) activation, which enhanced IL-15 transcription. Both IRF7 knockdown and ATF4 overexpression in leukemia cells antagonized sorafenib-induced IL-15 production in vitro. Human FLT3-ITD+ AML cells obtained from sorafenib responders following sorafenib therapy showed increased levels of IL-15, phosphorylated IRF7, and a transcriptionally active IRF7 chromatin state. The mitochondrial spare respiratory capacity and glycolytic capacity of CD8+ T cells increased upon sorafenib treatment in sorafenib responders but not in nonresponders. Our findings indicate that the synergism of T cells and sorafenib is mediated via reduced ATF4 expression, causing activation of the IRF7-IL-15 axis in leukemia cells and thereby leading to metabolic reprogramming of leukemia-reactive T cells in humans. Therefore, sorafenib treatment has the potential to contribute to an immune-mediated cure of FLT3-ITD-mutant AML relapse, an otherwise fatal complication after allo-HCT.

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

Toward an integrated map of genetic interactions in cancer cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.

Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks (“modules”). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene–protein interactions directly affected by genetic variance in CAD risk loci.

MiRNA-513a-5p inhibits progesterone receptor expression and constitutes a risk factor for breast cancer: the hOrmone and Diet in the ETiology of breast cancer prospective study

MicroRNAs (miRNAs) might be considered both predictors and players of cancer development. The aim of the present report was to investigate whether many years before the diagnosis of breast cancer miRNA expression is already disregulated. In order to test this hypothesis, we compared miRNAs extracted from leukocytes in healthy women who later developed breast cancer and in women who remain healthy during the whole 15-year follow-up time. Accordantly, we used a case-control study design nested in the hOrmone and Diet in the ETiology of breast cancer (ORDET) prospective cohort study addressing the possibility that miRNAs can serve as both early biomarkers and components of the hormonal etiological pathways leading to breast cancer development in premenopausal women. We compared leukocyte miRNA profiles of 191 incident premenopausal breast cancer cases and profiles of 191 women who remained healthy over a follow-up period of 20 years. The analysis identified 20 differentially expressed miRNAs in women candidate to develop breast cancer versus control women. The upregulated miRNAs, miR-513-a-5p, miR-513b-5p and miR-513c-5p were among the most significantly deregulated miRNAs. In multivariate analysis, miR-513a-5p upregulation was directly and statistically significant associated with breast cancer risk (OR = 1.69; 95% CI 1.08-2.64; P = 0.0293). In addition, the upregulation of miR-513-a-5p displayed the strongest direct association with serum progesterone and testosterone levels. The experimental data corroborated the inhibitory function of miR-513a-5p on progesterone receptor expression confirming that progesterone receptor is a target of miR-513a-5p. The identification of upregulated miR-513a-5p with its oncogenic potential further validates the use of miRNAs as long-term biomarker of breast cancer risk.

Differential gene expression induced by anti-cancer agent plumbagin is mediated by androgen receptor in prostate cancer cells

Treatment of mice harboring PTEN-P2 tumors in the prostate or on prostate tissue in vivo with 5-hydroxy-2-methyl-1,4-naphthoquinone, also known as plumbagin, results in tumor regression in castrated mice, but not in intact mice. This suggested that dihydrotestosterone (DHT) production in the testes may prevent cell death due to plumbagin treatment, but the underlying mechanism is not understood. We performed RNA-seq analysis on cells treated with combinations of plumbagin and DHT, and analyzed differential gene expression, to gain insight into the interactions between androgen and plumbgin. DHT and plumbagin synergize to alter the expression of many genes that are not differentially regulated by either single agent when used alone. These experiments revealed that, for many genes, increases in mRNAs caused by DHT are sharply down-regulated by plumbagin, and that many transcripts change in response to plumbagin in a DHT-dependent manner. This suggests that androgen receptor mediates some of the effects of plumbagin on gene expression.

Time course gene expression data in colon of mice after exposure to food-grade E171

We investigated gene expression responses in BALB/c mice exposed by gavage to 5 mg/kg bw/day of E171 for 2, 7, 14 and 21 days. Food additive E171 (titanium dioxide) has been shown to induce oxidative stress and DNA damage in vitro as well as facilitating growth of colorectal tumours in vivo. Full genome expression changes of the colon of mice were investigated by using Agilent SurePrint G3 mouse Gene exp 60kv2 microarrays slides. The data presented in this DiB include all differentially expressed for each time point with EntrezGeneID, gene symbols, gene names and Log2FC as well as genes included in pathways after over-representation analysis in ConsensusPathDataBase. The functions of these genes in relation to the colon were described in our associated article (Proquin et al., 2017 in press) [1]. Raw and normalized gene expression data are available through NCBI GEO (GEO accession: GSE92563).

Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia

Finding correlations across multiple data sets in imaging and (epi)genomics is a common challenge. Sparse multiple canonical correlation analysis (SMCCA) is a multivariate model widely used to extract contributing features from each data while maximizing the cross-modality correlation. The model is achieved by using the combination of pairwise covariances between any two data sets. However, the scales of different pairwise covariances could be quite different and the direct combination of pairwise covariances in SMCCA is unfair. The problem of "unfair combination of pairwise covariances" restricts the power of SMCCA for feature selection. In this paper, we propose a novel formulation of SMCCA, called adaptive SMCCA, to overcome the problem by introducing adaptive weights when combining pairwise covariances. Both simulation and real-data analysis show the outperformance of adaptive SMCCA in terms of feature selection over conventional SMCCA and SMCCA with fixed weights. Large-scale numerical experiments show that adaptive SMCCA converges as fast as conventional SMCCA. When applying it to imaging (epi)genetics study of schizophrenia subjects, we can detect significant (epi)genetic variants and brain regions, which are consistent with other existing reports. In addition, several significant brain-development related pathways, e.g., neural tube development, are detected by our model, demonstrating imaging epigenetic association may be overlooked by conventional SMCCA. All these results demonstrate that adaptive SMCCA are well suited for detecting three-way or multiway correlations and thus can find widespread applications in multiple omics and imaging data integration.

Genetic variant in CACNA1C is associated with PTSD in traumatized police officers

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder that may develop after a traumatic event. Here we aimed to identify epigenetic and genetic loci associated with PTSD. We included 73 traumatized police officers with extreme phenotypes regarding symptom severity despite similar trauma history: n = 34 had PTSD and n = 39 had minimal PTSD symptoms. Epigenetic and genetic profiles were based on the Illumina HumanMethylation450 BeadChip. We searched for differentially methylated probes (DMPs) and differentially methylated regions (DMRs). For genetic associations we analyzed the CpG-SNPs present on the array. We detected no genome-wide significant DMPs and we did not replicate previously reported DMPs associated with PTSD. However, GSE analysis of the top 100 DMPs showed enrichment of three genes involved in the dopaminergic neurogenesis pathway. Furthermore, we observed a suggestive association of one relatively large DMR between patients and controls, which was located at the PAX8 gene and previously associated with other psychiatric disorders. Finally, we validated five PTSD-associated CpG-SNPs identified with the array using sanger sequencing. We subsequently replicated the association of one common SNP (rs1990322) in the CACNA1C locus with PTSD in an independent cohort of traumatized children. The CACNA1C locus was previously associated with other psychiatric disorders, but not yet with PTSD. Thus, despite the small sample size, inclusion of extreme symptom severity phenotypes in a highly homogenous traumatized cohort enabled detection of epigenetic and genetic loci associated with PTSD. Moreover, here we showed that genetically confounded 450K probes are informative for genetic association analysis.

Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease

BACKGROUND

Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic obstructive pulmonary disease (COPD). However, many genetic variants show suggestive evidence for association but do not meet the strict threshold for genome-wide significance. Integrative analysis of multiple omics datasets has the potential to identify novel genes involved in disease pathogenesis by leveraging these variants in a functional, regulatory context.

RESULTS

We performed expression quantitative trait locus (eQTL) analysis using genome-wide SNP genotyping and gene expression profiling of lung tissue samples from 86 COPD cases and 31 controls, testing for SNPs associated with gene expression levels. These results were integrated with a prior COPD GWAS using an ensemble statistical and network methods approach to identify relevant genes and observe them in the context of overall genetic control of gene expression to highlight co-regulated genes and disease pathways. We identified 250,312 unique SNPs and 4997 genes in the cis(local)-eQTL analysis (5% false discovery rate). The top gene from the integrative analysis was MAPT, a gene recently identified in an independent GWAS of lung function. The genes HNRNPAB and PCBP2 with RNA binding activity and the gene ACVR1B were identified in network communities with validated disease relevance.

CONCLUSIONS

The integration of lung tissue gene expression with genome-wide SNP genotyping and subsequent intersection with prior GWAS and omics studies highlighted candidate genes within COPD loci and in communities harboring known COPD genes. This integration also identified novel disease genes in sub-threshold regions that would otherwise have been missed through GWAS.

Trichlorobenzene-substituted azaaryl compounds as novel FGFR inhibitors exhibiting potent antitumor activity in bladder cancer cells in vitro and in vivo

In the present study, we examined the antitumor activity of a series of trichlorobenzene-substituted azaaryl compounds and identified MPT0L145 as a novel FGFR inhibitor with better selectivity for FGFR1, 2 and 3. It was preferentially effective in FGFR-activated cancer cells, including bladder cancer cell lines expressing FGFR3-TACC3 fusion proteins (RT-112, RT-4). MPT0L145 decreased the phosphorylation of FGFR1, FGFR3 and their downstream proteins (FRS2, ERK and Akt). Mechanistically, cDNA microarray analysis revealed that MPT0L145 decreased genes associated cell cycle progression, and increased genes associated with autophagy pathway. Accordingly, the data revealed that MPT0L145 induced G₀/G₁ cell cycle arrest and decreased protein levels of cyclin E. Moreover, we provided the evidence that autophagy contributes to FGFR inhibitor-related cell death. Finally, MPT0L145 exhibited comparable antitumor activity to cisplatin with better safety in a RT-112 xenograft model. Taken together, these findings support the utility of MPT0L145 as a novel FGFR inhibitor, providing a strong rationale for further evaluation of this compound as a therapeutic agent for bladder cancers.