High-calorie diets uncouple hypothalamic oxytocin neurons from a gut-to-brain satiation pathway via κ-opioid signaling

Oxytocin-expressing paraventricular hypothalamic neurons (PVNOT neurons) integrate afferent signals from the gut, including cholecystokinin (CCK), to adjust whole-body energy homeostasis. However, the molecular underpinnings by which PVNOT neurons orchestrate gut-to-brain feeding control remain unclear. Here, we show that mice undergoing selective ablation of PVNOT neurons fail to reduce food intake in response to CCK and develop hyperphagic obesity on a chow diet. Notably, exposing wild-type mice to a high-fat/high-sugar (HFHS) diet recapitulates this insensitivity toward CCK, which is linked to diet-induced transcriptional and electrophysiological aberrations specifically in PVNOT neurons. Restoring OT pathways in diet-induced obese (DIO) mice via chemogenetics or polypharmacology sufficiently re-establishes CCK's anorexigenic effects. Last, by single-cell profiling, we identify a specialized PVNOT neuronal subpopulation with increased κ-opioid signaling under an HFHS diet, which restrains their CCK-evoked activation. In sum, we document a (patho)mechanism by which PVNOT signaling uncouples a gut-brain satiation pathway under obesogenic conditions.

Gene network analysis of the transcriptome impact of methylated microRNAs on oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck squamous cell tumors. MicroRNAs and DNA methylation, as epigenetic mechanisms, regulate the expression of oncogenes and tumor suppressor genes, contributing to the carcinogenic development. However, the current knowledge on the genetic and epigenetic landscape of OSCC is still limited.

OBJECTIVES

To assess the transcriptomic impact of microRNAs found to be methylated through Infinium genome-wide methylation profiling of archived OSCC tissues, and to analyze their biological role using gene network analysis.

MATERIAL AND METHODS

We used the Infinium array-based methylation assay to assess the genome-wide methylation status at the single-CpG-site level of DNA purified from archived OSCC tissue samples. After quality control, filtering out poorly performing probes and normalization of data, we identified the differentially methylated microRNA loci. We performed a literature-based analysis of OSCC transcriptomic data to identify the predicted target genes for each microRNA, followed by individual network and pathway enrichment analyses.

RESULTS

The analysis of Infinium methylation array data revealed 1469 differentially hypomethylated loci, 4 of which were of interest, namely hsa-microRNA-124-3, hsa-microRNA-24-1, hsa-microRNA-769, and hsa-microRNA-4500. Network and pathway enrichment analyses revealed multiple pathways modulated through DNA methylation-microRNA expression axes.

CONCLUSIONS

We describe the transcriptomic impact of 4 differentially methylated microRNAs in OSCC tissues samples and discuss their role in the pathology of OSCC. These results may contribute to a better understanding of how epigenetic mechanisms such as DNA methylation and microRNAs cooperate to impact the development of OSCC.

Diet triggers specific responses of hypothalamic astrocytes in time and region dependent manner

Hypothalamic astrocytes are particularly affected by energy-dense food consumption. How the anatomical location of these glial cells and their spatial molecular distribution in the arcuate nucleus of the hypothalamus (ARC) determine the cellular response to a high caloric diet remains unclear. In this study, we investigated their distinctive molecular responses following exposure to a high-fat high-sugar (HFHS) diet, specifically in the ARC. Using RNA sequencing and proteomics, we showed that astrocytes have a distinct transcriptomic and proteomic profile dependent on their anatomical location, with a major proteomic reprogramming in hypothalamic astrocytes. By ARC single-cell sequencing, we observed that a HFHS diet dictates time- and cell- specific transcriptomic responses, revealing that astrocytes have the most distinct regulatory pattern compared to other cell types. Lastly, we topographically and molecularly characterized astrocytes expressing glial fibrillary acidic protein and/or aldehyde dehydrogenase 1 family member L1 in the ARC, of which the abundance was significantly increased, as well as the alteration in their spatial and molecular profiles, with a HFHS diet. Together, our results provide a detailed multi-omics view on the spatial and temporal changes of astrocytes particularly in the ARC during different time points of adaptation to a high calorie diet.

Correlation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks

Summary

Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions.

Availability and implementation

The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Identification and characterization of distinct brown adipocyte subtypes in C57BL/6J mice

Using a number of cell biological and statistical methods we identify and characterize EIF5, TCF25 and BIN1 as markers for individual brown adipocyte subtypes in C57BL/6J mice.

Brown adipose tissue (BAT) plays an important role in the regulation of body weight and glucose homeostasis. Although increasing evidence supports white adipose tissue heterogeneity, little is known about heterogeneity within murine BAT. Recently, UCP1 high and low expressing brown adipocytes were identified, but a developmental origin of these subtypes has not been studied. To obtain more insights into brown preadipocyte heterogeneity, we use single-cell RNA sequencing of the BAT stromal vascular fraction of C57/BL6 mice and characterize brown preadipocyte and adipocyte clonal cell lines. Statistical analysis of gene expression profiles from brown preadipocyte and adipocyte clones identify markers distinguishing brown adipocyte subtypes. We confirm the presence of distinct brown adipocyte populations in vivo using the markers EIF5, TCF25, and BIN1. We also demonstrate that loss of Bin1 enhances UCP1 expression and mitochondrial respiration, suggesting that BIN1 marks dormant brown adipocytes. The existence of multiple brown adipocyte subtypes suggests distinct functional properties of BAT depending on its cellular composition, with potentially distinct functions in thermogenesis and the regulation of whole body energy homeostasis.

Identification of Deregulated Pathways, Key Regulators, and Novel miRNA-mRNA Interactions in HPV-Mediated Transformation

Next to a persistent infection with high-risk human papillomavirus (HPV), molecular changes are required for the development of cervical cancer. To identify which molecular alterations drive carcinogenesis, we performed a comprehensive and longitudinal molecular characterization of HPV-transformed keratinocyte cell lines. Comparative genomic hybridization, mRNA, and miRNA expression analysis of four HPV-containing keratinocyte cell lines at eight different time points was performed. Data was analyzed using unsupervised hierarchical clustering, integrated longitudinal expression analysis, and pathway enrichment analysis. Biological relevance of identified key regulatory genes was evaluated in vitro and dual-luciferase assays were used to confirm predicted miRNA-mRNA interactions. We show that the acquisition of anchorage independence of HPV-containing keratinocyte cell lines is particularly associated with copy number alterations. Approximately one third of differentially expressed mRNAs and miRNAs was directly attributable to copy number alterations. Focal adhesion, TGF-beta signaling, and mTOR signaling pathways were enriched among these genes. PITX2 was identified as key regulator of TGF-beta signaling and inhibited cell growth in vitro, most likely by inducing cell cycle arrest and apoptosis. Predicted miRNA-mRNA interactions miR-221-3p_BRWD3, miR-221-3p_FOS, and miR-138-5p_PLXNB2 were confirmed in vitro. Integrated longitudinal analysis of our HPV-induced carcinogenesis model pinpointed relevant interconnected molecular changes and crucial signaling pathways in HPV-mediated transformation.

Ridge estimation of network models from time-course omics data

Time-course omics experiments enable the reconstruction of the dynamics of the cellular regulatory network. Here, we describe the means for this reconstruction and the downstream exploitation of the inferred network. It is assumed that one of the various vector-autoregressive models (VAR) models presented here serves as a reasonably accurate description of the time-course omics data. The models are estimated through ridge penalized likelihood maximization, accompanied by functionality for the determination of optimal penalty paramaters. Prior knowledge on the network topology is accommodated by the estimation procedures. Various routes that translate the fitted models into more tangible implications for the medical researcher are described. The network is inferred from the-nonsparse-ridge estimates through empirical Bayes probabilistic thresholding. The influence of a (trait of a) molecular entity at the current time on those at future time points is assessed by mutual information, impulse response analysis, and path decomposition of the covariance. The presented methodology is applied to the omics data from the p53 signaling pathway during HPV-induced cellular transformation. All methodology is implemented in the ragt2ridges package, freely available from the Comprehensive R Archive Network.

Abstract 5059: Comprehensive molecular profiling of HPV-induced transformation over time

Although persistent infection with high-risk human papillomavirus (HPV) is generally acknowledged as necessary cause for cervical cancer, additional molecular changes are required for the development of cancer. Those changes include chromosomal aberrations that result in deregulated expression of coding and non-coding RNAs. In this study we performed a comprehensive and longitudinal molecular characterization of HPV-transformed keratinocyte cell lines, to identify the sequential order and relevance of these molecular alterations for HPV-induced transformation. Genome-wide chromosomal, mRNA and miRNA expression profiles were generated from 4 HPV-transformed keratinocyte cell lines at 8 different passages representing progressive stages of transformation. Unsupervised hierarchical clustering of DNA copy number, mRNA and miRNA profiles revealed that in all 4 cell lines most pronounced changes were associated with the acquisition of anchorage independence, a hallmark of transformation. Approximately one third of differentially expressed mRNAs and miRNAs was directly attributable to DNA copy number alterations. Focal adhesion, TGF-β signalling and mTOR signalling pathways were enriched among these genes. In addition, longitudinal analysis identified more than 600 potential miRNA-mRNA interactions that were negatively correlated during passaging. Four interactions were also predicted by 3 independent prediction algorithms, and have in part already been functionally validated, thereby demonstrating the validity of our approach. In conclusion, integrated longitudinal analysis of our HPV-induced transformation model enabled us to pinpoint relevant interconnected molecular changes and affected signalling pathways. Increased understanding of the interplay between different molecular alterations and affected pathways will be of importance for the clinical management of patients diagnosed with HPV-induced (pre)cancerous lesions.

Citation Format: Iris Babion, Viktorian Miok, Annelieke Jaspers, Wessel N. van Wieringen, Renske D. Steenbergen, Saskia M. Wilting. Comprehensive molecular profiling of HPV-induced transformation over time [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5059.

Aberrant methylation-mediated silencing of microRNAs contributes to HPV-induced anchorage independence

Cervical cancer and a subset of anogenital and head-and-neck carcinomas are caused by high-risk types of the human papillomavirus (hrHPV). During hrHPV-induced malignant transformation keratinocytes become able to grow anchorage independently, a tumorigenic trait at least partly associated with inactivation of tumor suppressor genes. We used hrHPV-containing keratinocytes to investigate the role of DNA methylation-mediated silencing of microRNAs (miRNAs) in the acquisition of anchorage independence.

Anchorage dependent (n=11) and independent passages (n=19) of 4 hrHPV-immortalized keratinocyte cell lines were treated with 2′-deoxy-5-azacytidine (DAC). Genome-wide miRNA expression profiles before and after treatment were compared to identify miRNAs silenced by methylation. Bisulfite sequencing and methylation-specific PCR showed increased methylation of hsa-mir-129-2/-137/-935/-3663/-3665 and -4281 in anchorage independent HPV-transformed keratinocytes and cervical cancer cell lines. Mature miRNAs derived from hsa-mir-129-2/-137/-3663 and -3665 showed functional relevance as they decreased anchorage independence in cervical cancer cell lines. Cervical (pre)cancerous lesions demonstrated increased methylation of hsa-mir-129-2/-935/-3663/-3665 and -4281, underlining the clinical relevance of our findings.

In conclusion, methylation-mediated silencing of tumor suppressive miRNAs contributes to acquisition of an anchorage independent phenotype. This study further substantiates the importance of miRNAs during early stages of carcinogenesis and underlines their potential as both disease markers and therapeutic targets.

Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data

Omics experiments endowed with a time-course design may enable us to uncover the dynamic interplay among genes of cellular processes. Multivariate techniques (like VAR(1) models describing the temporal and contemporaneous relations among variates) that may facilitate this goal are hampered by the high-dimensionality of the resulting data. This is resolved by the presented ridge regularized maximum likelihood estimation procedure for the VAR(1) model. Information on the absence of temporal and contemporaneous relations may be incorporated in this procedure. Its computational efficient implemention is discussed. The estimation procedure is accompanied with an LOOCV scheme to determine the associated penalty parameters. Downstream exploitation of the estimated VAR(1) model is outlined: an empirical Bayes procedure to identify the interesting temporal and contemporaneous relationships, impulse response analysis, mutual information analysis, and covariance decomposition into the (graphical) relations among variates. In a simulation study the presented ridge estimation procedure outperformed a sparse competitor in terms of Frobenius loss of the estimates, while their selection properties are on par. The proposed machinery is illustrated in the reconstruction of the p53 signaling pathway during HPV-induced cellular transformation. The methodology is implemented in the ragt2ridges R-package available from CRAN.

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Background

To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies.

Results

Here, we present a method that allows for the identification of differential gene expression associated with DNA copy number changes over time. The temporal variation in gene expression is described by a generalized linear mixed model employing low-rank thin-plate splines. Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation. Iteratively, posteriors of hyperparameters and model parameters are estimated. The empirical Bayes procedure shrinks multiple dispersion-related parameters. Shrinkage leads to more stable estimates of the model parameters, better control of false positives and improvement of reproducibility. In addition, to make estimates of the DNA copy number more stable, model parameters are also estimated in a multivariate way using triplets of features, imposing a spatial prior for the copy number effect.

Conclusion

With the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by DNA copy number abnormalities. In particular, in the analysis of an integrative oncogenomics study with a time-course set-up our method finds genes previously reported to be involved in cervical carcinogenesis. Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods. Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-327) contains supplementary material, which is available to authorized users.