SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Guadecitabine plus ipilimumab in unresectable melanoma: five-year follow-up and integrated multi-omic analysis in the phase 1b NIBIT-M4 trial

Association with hypomethylating agents is a promising strategy to improve the efficacy of immune checkpoint inhibitors-based therapy. The NIBIT-M4 was a phase Ib, dose-escalation trial in patients with advanced melanoma of the hypomethylating agent guadecitabine combined with the anti-CTLA-4 antibody ipilimumab that followed a traditional 3 + 3 design (NCT02608437). Patients received guadecitabine 30, 45 or 60 mg/m2/day subcutaneously on days 1 to 5 every 3 weeks starting on week 0 for a total of four cycles, and ipilimumab 3 mg/kg intravenously starting on day 1 of week 1 every 3 weeks for a total of four cycles. Primary outcomes of safety, tolerability, and maximum tolerated dose of treatment were previously reported. Here we report the 5-year clinical outcome for the secondary endpoints of overall survival, progression free survival, and duration of response, and an exploratory integrated multi-omics analysis on pre- and on-treatment tumor biopsies. With a minimum follow-up of 45 months, the 5-year overall survival rate was 28.9% and the median duration of response was 20.6 months. Re-expression of immuno-modulatory endogenous retroviruses and of other repetitive elements, and a mechanistic signature of guadecitabine are associated with response. Integration of a genetic immunoediting index with an adaptive immunity signature stratifies patients/lesions into four distinct subsets and discriminates 5-year overall survival and progression free survival. These results suggest that coupling genetic immunoediting with activation of adaptive immunity is a relevant requisite for achieving long term clinical benefit by epigenetic immunomodulation in advanced melanoma patients.

Integrative Co-methylation Network Analysis Identifies Novel DNA Methylation Signatures and Their Target Genes in Alzheimer's Disease

BACKGROUND

DNA methylation is a key epigenetic marker, and its alternations may be involved in Alzheimer's disease (AD). CpGs sharing similar biological functions or pathways tend to be co-methylated.

METHODS

We performed an integrative network-based DNA methylation analysis on 2 independent cohorts (N = 941) using brain DNA methylation profiles and RNA-sequencing as well as AD pathology data.

RESULTS

Weighted co-methylation network analysis identified 6 modules as significantly associated with neuritic plaque burden. In total, 15 hub CpGs including 3 novel CpGs were identified and replicated as being significantly associated with AD pathology. Furthermore, we identified and replicated 4 target genes (ATP6V1G2, VCP, RAD52, and LST1) as significantly regulated by DNA methylation at hub CpGs. In particular, VCP gene expression was also associated with AD pathology in both cohorts.

CONCLUSIONS

This integrative network-based multiomics study provides compelling evidence for a potential role of DNA methylation alternations and their target genes in AD.

Direct Comparison of HPV16 Viral Genomic Integration, Copy Loss, and Structural Variants in Oropharyngeal and Uterine Cervical Cancers Reveal Distinct Relationships to E2 Disruption and Somatic Alteration

Squamous cell carcinoma of the oropharynx caused by HPV type 16 (HPV16+ OPSCC) is the most common HPV-associated malignancy in the USA and has many molecular differences from uterine cervical squamous cell carcinoma (UCSCC). Our understanding of HPV oncogenesis relied on studies of UCSCC revealing a consensus model reliant on HPV integration with a loss of E2. Here, we compare patterns of HPV integration in UCSCC and OPSCC by analysis of affinity capture sequencing of the HPV16 genome in 104 OPSCC and 44 UCSCC tumors. These cohorts were contemporaneously sequenced using an identical strategy. Integration was identified using discordant read pair clustering and assembly-based approaches. Viral integration sites, structural variants, and copy losses were examined. While large-scale deep losses of HPV16 genes were common in UCSCC and were associated with E2 loss, deep copy losses of the HPV16 genome were infrequent in HPV16+ OPSCC. Similarly, structural variants within HPV16 favored E2 loss in UCSCC but not OPSCC. HPV16 integration sites were non-random, with recurrent integration hot-spots identified. OPSCC tumors had many more integration sites per tumor when compared to UCSCC and had more integration sites in genomic regions with high gene density. These data show that viral integration and E2 disruption are distinct in UCSCC and OPSCC. Our findings also add to growing literature suggesting that HPV tumorigenesis in OPSCC does not follow the model developed based on UCSCC.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

Alpha-synuclein overexpression induces epigenomic dysregulation of glutamate signaling and locomotor pathways

Parkinson’s disease (PD) is a neurological disorder with complex interindividual etiology that is becoming increasingly prevalent worldwide. Elevated alpha-synuclein levels can increase risk of PD and may influence epigenetic regulation of PD pathways. Here, we report genome-wide DNA methylation and hydroxymethylation alterations associated with overexpression of two PD-linked alpha-synuclein variants (wild-type and A30P) in LUHMES cells differentiated to dopaminergic neurons. Alpha-synuclein altered DNA methylation at thousands of CpGs and DNA hydroxymethylation at hundreds of CpGs in both genotypes, primarily in locomotor behavior and glutamate signaling pathway genes. In some cases, epigenetic changes were associated with transcription. SMITE network analysis incorporating H3K4me1 ChIP-seq to score DNA methylation and hydroxymethylation changes across promoters, enhancers, and gene bodies confirmed epigenetic and transcriptional deregulation of glutamate signaling modules in both genotypes. Our results identify distinct and shared impacts of alpha-synuclein variants on the epigenome, and associate alpha-synuclein with the epigenetic etiology of PD.

Multi-walled carbon nanotubes elicit concordant changes in DNA methylation and gene expression following long-term pulmonary exposure in mice

Pulmonary exposure to multi-walled carbon nanotubes (MWCNTs) causes inflammation and fibrosis. Our previous work has shown that industrially produced MWCNTs trigger specific changes in gene expression in the lungs of exposed animals. To elucidate whether epigenetic effects play a role for these gene expression changes, we performed whole genome bisulphite sequencing to assess DNA methylation patterns in the lungs 56 days after exposure to MWCNTs. Lung tissues were also evaluated with respect to histopathological changes and cytokine profiling of bronchoalveolar lavage (BAL) fluid was conducted using a multi-plex array. Integrated analysis of transcriptomics data and DNA methylation data revealed concordant changes in gene expression. Functional analysis showed that the muscle contraction, immune system/inflammation, and extracellular matrix pathways were the most affected pathways. Taken together, the present study revealed that MWCNTs exert epigenetic effects in the lungs of exposed animals, potentially driving the subsequent gene expression changes.

An Integrated Genomic Approach Identifies HOXC8 as an Upstream Regulator in Ovarian Endometrioma

PURPOSE

To identify the upstream regulators (URs) involved in the onset and pathogenesis of ovarian endometrioma.

METHODS

Recently, a method called Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) that uses transcriptome data in combination with publicly available data for identifying URs of cellular processes has been developed. Here, we used SMITE with transcriptome data from ovarian endometrioma stromal cells (ovESCs) and eutopic endometrium stromal cells (euESCs) in combination with publicly available gene regulatory network data. To confirm the URs identified by SMITE, we developed a Boolean network simulation to see if correcting aberrant expressions of the identified genes could restore the entire gene expression profile of ovESCs to a profile similar to that of euESCs. We then established euESCs overexpressing the identified gene and characterized them by cell function assays and transcriptome analysis.

RESULTS

SMITE identified 12 potential URs in ovarian endometrioma that were confirmed by the Boolean simulation. One of the URs, HOXC8, was confirmed to be overexpressed in ovESCs. HOXC8 overexpression significantly enhanced cell proliferation, migration, adhesion, and fibrotic activities, and altered expression statuses of the genes involved in transforming growth factor (TGF)-β signaling. HOXC8 overexpression also increased the expression levels of phosphorylated SMAD2/SMAD3. The increased adhesion and fibrosis activities by HOXC8 were significantly inhibited by E-616452, a selective inhibitor of TGF-β receptor type I kinases.

MAIN CONCLUSIONS

Integrated genomic approaches identified HOXC8 as an UR in ovarian endometrioma. The pathological features of ovarian endometrioma including cell proliferation, adhesion, and fibrosis were induced by HOXC8 and its subsequent activation of TGF-β signaling.

Identification of DNA hydroxymethylation associated genes in osteoarthritis by combined analysis of hydroxymethylation and gene expression

BACKGROUND

Our study aimed to explore more mechanistic insights into the epigenetic regulation of osteoarthritis (OA).

METHODS

The expression profiles (accession number: GSE64393 and GSE64394) were downloaded from the Gene Expression Omnibus database. The differentially hydroxymethylated regions (DhMRs) and differentially expressed genes (DEGs) between OA and control groups were identified. The distribution of DhMRs in the whole genome and the correlation between DhMRs and DEGs were analyzed. Functional module mining for the DEGs and DhMRs was conducted, followed by protein-protein interaction (PPI) analysis. The transcriptional factor (TF) was predicted.

RESULTS

Total 52,282 DhMRs were obtained, among which 31,452 ones were annotated to 9726 genes. Additionally, 1806 DEGs were selected. Hydroxymethylation mainly occurred in gene body region. Correlation analysis revealed that more than 70% of DhMRs were uncorrelated with DEGs expression. Functional module mining for the DEGs and DhMRs identified 2 functional modules, which were involved in pathways of regulation of actin cytoskeleton, and TGF-β signaling pathway. A PPI network was constructed, and ITGB3 had the highest degree. Furthermore, 7 TFs were predicted, which regulated 12 candidate genes, such as HES1-PTEN.

CONCLUSIONS

The onset and progression of OA may be associated with the upregulated hydroxymethylation in gene body region of PTEN. HES1 may be important TF in the pathogenesis of OA. Additionally, pathways of regulation of actin cytoskeleton, and TGF-beta signaling pathway may also play important roles in OA progression.

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.

Nucleoporins in cardiovascular disease

Cardiovascular disease is a pressing health problem with significant global health, societal, and financial burdens. Understanding the molecular basis of polygenic cardiac pathology is thus essential to devising novel approaches for management and treatment. Recent identification of uncharacterized regulatory functions for a class of nuclear envelope proteins called nucleoporins offers the opportunity to understand novel putative mechanisms of cardiac disease development and progression. Consistent reports of nucleoporin deregulation associated with ischemic and dilated cardiomyopathies, arrhythmias and valvular disorders suggests that nucleoporin impairment may be a significant but understudied variable in cardiopathologic disorders. This review discusses and converges existing literature regarding nuclear pore complex proteins and their association with cardiac pathologies, and proposes a role for nucleoporins as facilitators of cardiac disease.

Multiomics of azacitidine-treated AML cells reveals variable and convergent targets that remodel the cell-surface proteome

Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are diseases of abnormal hematopoietic differentiation with aberrant epigenetic alterations. Azacitidine (AZA) is a DNA methyltransferase inhibitor widely used to treat MDS and AML, yet the impact of AZA on the cell-surface proteome has not been defined. To identify potential therapeutic targets for use in combination with AZA in AML patients, we investigated the effects of AZA treatment on four AML cell lines representing different stages of differentiation. The effect of AZA treatment on these cell lines was characterized at three levels: the DNA methylome, the transcriptome, and the cell-surface proteome. Untreated AML cell lines showed substantial overlap at all three omics levels; however, while AZA treatment globally reduced DNA methylation in all cell lines, changes in the transcriptome and surface proteome were subtle and differed among the cell lines. Transcriptome analysis identified five commonly up-regulated coding genes upon AZA treatment in all four cell lines, TRPM4 being the only gene encoding a surface protein, and surface proteome analysis found no commonly regulated proteins. Gene set enrichment analysis of differentially regulated RNA and surface proteins showed a decrease in metabolic pathways and an increase in immune defense response pathways. As such, AZA treatment led to diverse effects at the individual gene and protein levels but converged to common responses at the pathway level. Given the heterogeneous responses in the four cell lines, we discuss potential therapeutic strategies for AML in combination with AZA.

Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures

Background

In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential.

Methods and findings

Treatment by DAC demethylation with 5-Aza-2’-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In light of the stronger treatment effects observed in non-metastatic conditions, some limitations likely refer to in silico data integration tools and resources available for the analysis of tumor antigens.

Conclusion

Demethylation treatment strongly affects early melanoma progression by re-activating many genes. This evidence suggests that the efficacy of this type of therapeutic intervention is potentially high at the pre-metastatic stages. The biomarkers that can be assessed through antigens seem informative depending on the metastatic conditions, and networks help to elucidate the assessment of possible targets actionability.

Integration of genome-wide mRNA and miRNA expression, and DNA methylation data of three cell lines exposed to ten carbon nanomaterials

We present data derived from an exposure experiment in which three cell-lines representative of cell types of the respiratory tissue (epithelial type-I A549, epithelial type-II BEAS-2B, and macrophage THP-1) have been exposed to ten different carbon-based nanomaterials for 48 h. In particular, we provide: genome-wide mRNA and miRNA expression, and DNA methylation; gene tables, containing information on the aberrations induced in these three genomic data layers at the gene level; mechanism of action (MOA) maps representing the comparative functional alteration induced in each cell line and each exposure.

Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation

New strategies to characterize the effects of engineered nanomaterials (ENMs) based on omics technologies are emerging. However, given the intricate interplay of multiple regulatory layers, the study of a single molecular species in exposed biological systems might not allow the needed granularity to successfully identify the pathways of toxicity (PoT) and, hence, portraying adverse outcome pathways (AOPs). Moreover, the intrinsic diversity of different cell types composing the exposed organs and tissues in living organisms poses a problem when transferring in vivo experimentation into cell-based in vitro systems. To overcome these limitations, we have profiled genome-wide DNA methylation, mRNA and microRNA expression in three human cell lines representative of relevant cell types of the respiratory system, A549, BEAS-2B and THP-1, exposed to a low dose of ten carbon nanomaterials (CNMs) for 48 h. We applied advanced data integration and modelling techniques in order to build comprehensive regulatory and functional maps of the CNM effects in each cell type. We observed that different cell types respond differently to the same CNM exposure even at concentrations exerting similar phenotypic effects. Furthermore, we linked patterns of genomic and epigenomic regulation to intrinsic properties of CNM. Interestingly, DNA methylation and microRNA expression only partially explain the mechanism of action (MOA) of CNMs. Taken together, our results strongly support the implementation of approaches based on multi-omics screenings on multiple tissues/cell types, along with systems biology-based multi-variate data modelling, in order to build more accurate AOPs.

Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

Background

Preeclampsia (PE) is a heterogeneous, hypertensive disorder of pregnancy, with no robust biomarkers or effective treatments. We hypothesized that this heterogeneity is due to the existence of multiple subtypes of PE and, in support of this hypothesis, we recently identified five clusters of placentas within a large gene expression microarray dataset (N = 330), of which four (clusters 1, 2, 3, and 5) contained a substantial number of PE samples. However, while transcriptional analysis of placentas can subtype patients, we propose that the addition of epigenetic information could discern gene regulatory mechanisms behind the distinct PE pathologies, as well as identify clinically useful potential biomarkers.

Results

We subjected 48 of our samples from transcriptional clusters 1, 2, 3, and 5 to Infinium HumanMethylation450 arrays. Samples belonging to transcriptional clusters 1–3 still showed visible relationships to each other by methylation, but cluster 5, with known chromosomal abnormalities, no longer formed a cohesive group. Within transcriptional clusters 2 and 3, controlling for fetal sex and gestational age in the identification of differentially methylated sites, compared to the healthier cluster 1, dramatically reduced the number of significant sites, but increased the percentage that demonstrated a strong linear correlation with gene expression (from 5% and 2% to 9% and 8%, respectively). Locations exhibiting a positive relationship between methylation and gene expression were most frequently found in CpG open sea enhancer regions within the gene body, while those with a significant negative correlation were often annotated to the promoter in a CpG shore region. Integrated transcriptome and epigenome analysis revealed modifications in TGF-beta signaling, cell adhesion, oxidative phosphorylation, and metabolism pathways in cluster 2 placentas, and aberrations in antigen presentation, allograft rejection, and cytokine-cytokine receptor interaction in cluster 3 samples.

Conclusions

Overall, we have established DNA methylation alterations underlying a portion of the transcriptional development of “canonical” PE in cluster 2 and “immunological” PE in cluster 3. However, a significant number of the observed methylation changes were not associated with corresponding changes in gene expression, and vice versa, indicating that alternate methods of gene regulation will need to be explored to fully comprehend these PE subtypes.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0463-6) contains supplementary material, which is available to authorized users.

ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data

BACKGROUND

ATAC-seq (Assays for Transposase-Accessible Chromatin using sequencing) is a recently developed technique for genome-wide analysis of chromatin accessibility. Compared to earlier methods for assaying chromatin accessibility, ATAC-seq is faster and easier to perform, does not require cross-linking, has higher signal to noise ratio, and can be performed on small cell numbers. However, to ensure a successful ATAC-seq experiment, step-by-step quality assurance processes, including both wet lab quality control and in silico quality assessment, are essential. While several tools have been developed or adopted for assessing read quality, identifying nucleosome occupancy and accessible regions from ATAC-seq data, none of the tools provide a comprehensive set of functionalities for preprocessing and quality assessment of aligned ATAC-seq datasets.

RESULTS

We have developed a Bioconductor package, ATACseqQC, for easily generating various diagnostic plots to help researchers quickly assess the quality of their ATAC-seq data. In addition, this package contains functions to preprocess aligned ATAC-seq data for subsequent peak calling. Here we demonstrate the utilities of our package using 25 publicly available ATAC-seq datasets from four studies. We also provide guidelines on what the diagnostic plots should look like for an ideal ATAC-seq dataset.

CONCLUSIONS

This software package has been used successfully for preprocessing and assessing several in-house and public ATAC-seq datasets. Diagnostic plots generated by this package will facilitate the quality assessment of ATAC-seq data, and help researchers to evaluate their own ATAC-seq experiments as well as select high-quality ATAC-seq datasets from public repositories such as GEO to avoid generating hypotheses or drawing conclusions from low-quality ATAC-seq experiments. The software, source code, and documentation are freely available as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/ATACseqQC.html .

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.