• Title/Summary/Keyword: -omics

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Single-Cell Sequencing in Cancer: Recent Applications to Immunogenomics and Multi-omics Tools

  • Sierant, Michael C.;Choi, Jungmin
    • 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.

Multi-omics techniques for the genetic and epigenetic analysis of rare diseases

  • Yeonsong Choi;David Whee-Young Choi;Semin Lee
    • 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.

Association of the TREML2 and HTR1E Genetic Polymorphisms with Osteoporosis

  • Jung, Dongju;Jin, Hyun-Seok
    • 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.

Integration of metabolomics and transcriptomics in nanotoxicity studies

  • Shin, Tae Hwan;Lee, Da Yeon;Lee, Hyeon-Seong;Park, Hyung Jin;Jin, Moon Suk;Paik, Man-Jeong;Manavalan, Balachandran;Mo, Jung-Soon;Lee, Gwang
    • BMB Reports
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    • v.51 no.1
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    • pp.14-20
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    • 2018
  • Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics."

Deciphering the Core Metabolites of Fanconi Anemia by Using a Multi-Omics Composite Network

  • Xie, Xiaobin;Chen, Xiaowei
    • 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.

Whole genome sequencing of Luxi Black Head sheep for screening selection signatures associated with important traits

  • Liu, Zhaohua;Tan, Xiuwen;Wang, Jianying;Jin, Qing;Meng, Xianfeng;Cai, Zhongfeng;Cui, Xukui;Wang, Ke
    • 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 (HP), fixation index (FST), cross-population extended haplotype homozygosity (XP-EHH) and the nucleotide diversity (𝜃π) 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.

Global Histidine Phosphoproteomics in Human Prostate Cancer Cells

  • Gao, Yan;Kim, Doeun;Sung, Eunji;Tan, Minjia;Kwon, Tae Gyun;Lee, Jun Nyung;Lee, Sangkyu
    • 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.

Classification of Colon Cancer Patients Based on the Methylation Patterns of Promoters

  • Choi, Wonyoung;Lee, Jungwoo;Lee, Jin-Young;Lee, Sun-Min;Kim, Da-Won;Kim, Young-Joon
    • Genomics & Informatics
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    • v.14 no.2
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    • pp.46-52
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    • 2016
  • Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.

Identification of HYIpro-3-1 Metabolites, a Novel Anti-Inflammatory Compound, in Human Liver Microsomes by Quadrupole-Orbitrap High-Resolution Mass Spectrometry

  • Bai, Honghao;Kim, Younah;Paudel, Sanjita;Lee, Eung-Seok;Lee, Sangkyu
    • 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.