• Molecules and Cells
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• v.44 no.7
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• pp.433-443
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• 2021

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1067-1086
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• 2023

With rapid advances in meat science in recent decades, changes in meat quality during the pre-slaughter phase of muscle growth and the post-slaughter process from muscle to meat have been investigated. Commonly used techniques have evolved from early physicochemical indicators such as meat color, tenderness, water holding capacity, flavor, and pH to various omic tools such as genomics, transcriptomics, proteomics, and metabolomics to explore fundamental molecular mechanisms and screen biomarkers related to meat quality and taste characteristics. This review highlights the application of omics and integrated multi-omics in meat quality and taste characteristics studies. It also discusses challenges and future perspectives of multi-omics technology to improve meat quality and taste. Consequently, multi-omics techniques can elucidate the molecular mechanisms responsible for changes of meat quality at transcriptome, proteome, and metabolome levels. In addition, the application of multi-omics technology has great potential for exploring and identifying biomarkers for meat quality and quality control that can make it easier to optimize production processes in the meat industry.

• Genomics & Informatics
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• v.16 no.4
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• pp.17.1-17.6
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• 2018

Tumor heterogeneity, the cellular mosaic of multiple lineages arising from the process of clonal evolution, has continued to thwart multi-omics analyses using traditional bulk sequencing methods. The application of single-cell sequencing, in concert with existing genomics methods, has enabled high-resolution interrogation of the genome, transcriptome, epigenome, and proteome. Applied to cancers, these single-cell multi-omics methods bypass previous limitations on data resolution and have enabled a more nuanced understanding of the evolutionary dynamics of tumor progression, immune evasion, metastasis, and treatment resistance. This review details the growing number of novel single-cell multi-omics methods applied to tumors and further discusses recent discoveries emerging from these approaches, especially in regard to immunotherapy.

• Journal of Microbiology and Biotechnology
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• v.32 no.3
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• pp.387-395
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• 2022

Deciphering the metabolites of human diseases is an important objective of biomedical research. Here, we aimed to capture the core metabolites of Fanconi anemia (FA) using the bioinformatics method of a multi-omics composite network. Based on the assumption that metabolite levels can directly mirror the physiological state of the human body, we used a multi-omics composite network that integrates six types of interactions in humans (gene-gene, disease phenotype-phenotype, disease-related metabolite-metabolite, gene-phenotype, gene-metabolite, and metabolite-phenotype) to procure the core metabolites of FA. This method is applicable in predicting and prioritizing disease candidate metabolites and is effective in a network without known disease metabolites. In this report, we first singled out the differentially expressed genes upon different groups that were related with FA and then constructed the multi-omics composite network of FA by integrating the aforementioned six networks. Ultimately, we utilized random walk with restart (RWR) to screen the prioritized candidate metabolites of FA, and meanwhile the co-expression gene network of FA was also obtained. As a result, the top 5 metabolites of FA were tenormin (TN), guanosine 5'-triphosphate, guanosine 5'-diphosphate, triphosadenine (DCF) and adenosine 5'-diphosphate, all of which were reported to have a direct or indirect relationship with FA. Furthermore, the top 5 co-expressed genes were CASP3, BCL2, HSPD1, RAF1 and MMP9. By prioritizing the metabolites, the multi-omics composite network may provide us with additional indicators closely linked to FA.

• Journal of Genetic Medicine
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• v.20 no.1
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• pp.1-5
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• 2023

Until now, rare disease studies have mainly been carried out by detecting simple variants such as single nucleotide substitutions and short insertions and deletions in protein-coding regions of disease-associated gene panels using diagnostic next-generation sequencing in association with patient phenotypes. However, several recent studies reported that the detection rate hardly exceeds 50% even when whole-exome sequencing is applied. Therefore, the necessity of introducing whole-genome sequencing is emerging to discover more diverse genomic variants and examine their association with rare diseases. When no diagnosis is provided by whole-genome sequencing, additional omics techniques such as RNA-seq also can be considered to further interrogate causal variants. This paper will introduce a description of these multi-omics techniques and their applications in rare disease studies.

• Biomedical Science Letters
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• v.21 no.4
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• pp.181-187
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• 2015

Osteoporosis is one of the diseases caused by accumulation of effects from complex interactions between genetic and environmental factors. Aging is the major cause for osteoporosis, which normally increases skeletal fragility and bone fracture especially among the elder. "Omics" refers to a specialized research field dealing with high-throughput biological data, such as genomics, transcriptomics, proteomics or metabolomics. Integration of data from multi-omics has been approved to be a powerful strategy to colligate biological phenomenon with multiple aspects. Actually, integrative analyses of "omics" datasets were used to present pathogenesis of specific diseases or casual biomarkers including susceptible genes. In this study, we evaluated the proposed relationship of novel susceptible genes (TREML2, HTR1E, and GLO1) with osteoporosis, which genes were obtained using multi-omics integration analyses. To this end, SNPs of the susceptible genes in the Korean female cohort were analyzed. As a result, one SNP of HTR1E and five SNPs of TREML2 were identified to associate with osteoporosis. The highest significant SNP was $rs6938076^*$ of TREML2 (OR=0.63, CI: 0.45~0.89, recessive P=0.009). Consequently, the susceptible genes identified through the multi-omics analyses were confirmed to have association with osteoporosis. Therefore, multi-omics analysis might be a powerful tool to find new genes associated with a disease. We further identified that TREML2 has more associated with osteoporosis in females than did HTR1E.

• Animal Bioscience
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• v.35 no.9
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• pp.1340-1350
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• 2022

Objective: Luxi Black Head sheep (LBH) is the first crossbreed specialized for meat production and was developed by crossbreeding Black Head Dorper sheep (DP) and Small Tailed Han sheep (STH) in the farming areas of northern China. Research on the genomic variations and selection signatures of LBH caused by continuous artificial selection is of great significance for identifying the genetic mechanisms of important traits of sheep and for the continuous breeding of LBH. Methods: We explored the genetic relationships of LBH, DP, and several Mongolian sheep breeds by constructing phylogenetic tree, principal component analysis and linkage disequilibrium analysis. In addition, we analysed 29 whole genomes of sheep. The genome-wide selection signatures have been scanned with four methods: heterozygosity (H_P), fixation index (F_ST), cross-population extended haplotype homozygosity (XP-EHH) and the nucleotide diversity (𝜃_π) ratio. Results: The genetic relationships analysis showed that LBH appeared to be an independent cluster closer to DP. The candidate signatures of positive selection in sheep genome revealed candidate genes for developmental process (HoxA gene cluster, BCL2L11, TSHR), immunity (CXCL6, CXCL1, SKAP2, PTK6, MST1R), growth (PDGFD, FGF18, SRF, SOCS2), and reproduction (BCAS3, TRIM24, ASTL, FNDC3A). Moreover, two signalling pathways closely related to reproduction, the thyroid hormone signalling pathway and the oxytocin signalling pathway, were detected. Conclusion: The selective sweep analysis of LBH genome revealed candidate genes and signalling pathways associated with developmental process, immunity, growth, and reproduction. Our findings provide a valuable resource for sheep breeding and insight into the mechanisms of artificial selection.

• Mass Spectrometry Letters
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• v.11 no.3
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• pp.52-58
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• 2020

Histidine phosphorylation (pHis) is increasingly recognized as an important post translational modification (PTM) in regulating cellular functions in eukaryotes. In order to clarify the role of pHis in mammalian cell signaling system, a global phosphorylation study was performed in human prostate cancer cells, PC-3M, using a TiO₂ affinity chromatography. A total number of 307 pHis sites were identified on the 268 proteins among total identified 9,924 phosphorylation sites on 3,316 proteins. In addition, 22 pHis proteins were classified in enzyme category. This report provides the first database for the study of pHis in prostate cancer cells.

• Mass Spectrometry Letters
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• v.12 no.4
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• pp.172-178
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• 2021

HYIpro-3-1 is an adjuvant for preventing or treating inflammatory growth diseases. In this study, we identified the metabolic pathway of HYIpro-3-1 in human liver microsomes (HLMs) by quadrupole-orbitrap high-resolution mass spectrometry (HR-MS) and characterized the major human cytochrome P450 (CYP). Ten metabolites were identified, including one O-demethylation (M1), two O-demethylation and monohydroxylation (M2 and M3), and seven monohydroxylation metabolites (M4-M10). Based on the HR-MS² spectra, the metabolites are divided into two groups of monohydroxylated metabolites according to the hydroxylation position. We verified that HYIpro-3-1 is metabolized by CYP in HLMs, CYP2B6 is mainly involved in O-demethylation, and various CYPs are involved in the monohydroxylation of HYIpro-3-1.