Multilayer Omics Research Articles

Abstract 2316: Multilayer omics analyses in CpG island methylator phenotype clear cell renal cell carcinomas

Abstract [Background] We have reported that CpG-island methylator phenotype (CIMP)-positive clear cell renal cell carcinomas (RCCs) are characterized by accumulation of DNA hypermethylation of CpG islands, clinicopathological aggressiveness and poorer patient outcome. We have established the criteria for CIMP diagnosis in clear cell RCCs by quantification of DNA methylation levels of RCC-specific CIMP marker genes. [Purpose] The aim of this study was to reveal the molecular pathways which significantly participate in CIMP-positive renal carcinogenesis and become a therapeutic target of CIMP-positive clear cell RCCs. [Methods] Our previous methylome analysis using Illumina Infinium HumanMethylation27 BeadArray identified CIMP-negative and -positive clear cell RCCs in the present cohort (n=101). In the present study, genome (whole-exome), transcriptome and proteome analyses using Agilent SureSelect All Exon capture followed by sequencing on an Illumina HiSeq 2000 platform, Agilent SurePrint Human Gene Expression microarray and two dimensional image converted analysis of liquid chromatography-mass spectrometry, respectively, were performed in tissue specimens of 87 CIMP-negative and 14 CIMP-positive clear cell RCCs and the corresponding non-cancerous renal cortex tissue specimens. [Results] The number of genes showing genetic aberrations in CIMP-negative clear cell RCCs was quite low, indicating that CIMP-negative RCCs lacked distinct genetic characteristics. On the other hand, many genes, such as genes encoding hisotone modification enzymes and tumor-suppressor genes, showed 10% or more incidence of genetic aberrations in CIMP-positive clear cell RCCs. Based on MetaCore pathway analysis, although mRNA or protein expression alterations in CIMP-negative clear cell RCCs were not significantly accumulated in any molecular pathways, such alterations in CIMP-positive clear cell RCCs were accumulated in the 19 pathways (P<0.05), such as molecular pathways involved in cell cycle and cell adhesion. Quantitative RT-PCR analysis verified expression alterations of genes involved in the most frequently affected pathway in CIMP-positive clear cell RCCs. [Conclusion] The most frequently affected pathway may be a novel therapeutic target of CIMP-positive clear cell RCCs. We are now examining the effectiveness of the inhibitor of the most frequently affected pathway in RCC cell lines showing CIMP. Citation Format: Eri Arai, Hiromi Sakamoto, Masaya Ono, Yoriko Takahashi, Sayaka Miyata, Hiroyuki Fujimoto, Masahiro Gotoh, Tesshi Yamada, Yae Kanai. Multilayer omics analyses in CpG island methylator phenotype clear cell renal cell carcinomas. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2316. doi:10.1158/1538-7445.AM2014-2316

Multilayered Genetic and Omics Dissection of Mitochondrial Activity in a Mouse Reference Population

The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome--a subset of the metabolome--and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPR(mt)). UPR(mt) shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes.

SupraHex: An R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map

Biologists are increasingly confronted with the challenge of quickly understanding genome-wide biological data, which usually involve a large number of genomic coordinates (e.g. genes) but a much smaller number of samples. To meet the need for data of this shape, we present an open-source package called ‘supraHex’ for training, analysing and visualising omics data. This package devises a supra-hexagonal map to self-organise the input data, offers scalable functionalities for post-analysing the map, and more importantly, allows for overlaying additional data for multilayer omics data comparisons. Via applying to DNA replication timing data of mouse embryogenesis, we demonstrate that supraHex is capable of simultaneously carrying out gene clustering and sample correlation, providing intuitive visualisation at each step of the analysis. By overlaying CpG and expression data onto the trained replication-timing map, we also show that supraHex is able to intuitively capture an inherent relationship between late replication, low CpG density promoters and low expression levels. As part of the Bioconductor project, supraHex makes accessible to a wide community in a simple way, what would otherwise be a complex framework for the ultrafast understanding of any tabular omics data, both scientifically and artistically. This package can run on Windows, Mac and Linux, and is freely available together with many tutorials on featuring real examples at http://supfam.org/supraHex.

Polycyclic Aromatic Hydrocarbon Metabolic Network in Mycobacterium vanbaaleniiPYR-1

This study investigated a metabolic network (MN) from Mycobacterium vanbaalenii PYR-1 for polycyclic aromatic hydrocarbons (PAHs) from the perspective of structure, behavior, and evolution, in which multilayer omics data are integrated. Initially, we utilized a high-throughput proteomic analysis to assess the protein expression response of M. vanbaalenii PYR-1 to seven different aromatic compounds. A total of 3,431 proteins (57.38% of the genome-predicted proteins) were identified, which included 160 proteins that seemed to be involved in the degradation of aromatic hydrocarbons. Based on the proteomic data and the previous metabolic, biochemical, physiological, and genomic information, we reconstructed an experiment-based system-level PAH-MN. The structure of PAH-MN, with 183 metabolic compounds and 224 chemical reactions, has a typical scale-free nature. The behavior and evolution of the PAH-MN reveals a hierarchical modularity with funnel effects in structure/function and intimate association with evolutionary modules of the functional modules, which are the ring cleavage process (RCP), side chain process (SCP), and central aromatic process (CAP). The 189 commonly upregulated proteins in all aromatic hydrocarbon treatments provide insights into the global adaptation to facilitate the PAH metabolism. Taken together, the findings of our study provide the hierarchical viewpoint from genes/proteins/metabolites to the network via functional modules of the PAH-MN equipped with the engineering-driven approaches of modularization and rationalization, which may expand our understanding of the metabolic potential of M. vanbaalenii PYR-1 for bioremediation applications.

Comparative integrated omics approach sterically understanding hepatic metabolic dynamics in mouse model

Currently, the general approach of analysis is using a single omics technique , however the combined analysis of data by employing multiple omics analyses can enable a more fundamental understanding of the biological phenomena. Multi-layered combination of multiple omics technologies involves generation of a large amount of data, which leads to increased complexity and makes comprehension of bio-information more difficult. The objective of this study was to investigate the utility of incorporating multiple omics technologies in a multi-layered fashion. Transcriptomic, proteomic, and metabolomic analyses were carried out using a mouse model of diet-induced obesity. The present study reported the comprehensiveness of three omics analyses and the utility of using multiple omics analyses. Uniform changes were observed among changes at all stages but the majority of these specific to the omics approach. This data supports the fact that various molecules progress through the central dogma at differing speeds. Since the time axis differs for each molecule, combining multiple omics analyses makes it possible to investigate the reactions in organisms three-dimensionally. At first glance, it simply appears that combining a number of very large data sets produces even more complexity but, if multi-layered omics data are treated with an awareness of their meaning, benefits, and limitations, then the combination of multiple omics analyses can be extremely useful for research in molecular biology.