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Editorial

Integrative Multi-Omics in Biomedical Research

by
Michelle M. Hill
1,2,* and
Christopher Gerner
3
1
QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia
2
UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Herston, Brisbane, QLD 4006, Australia
3
Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria
*
Author to whom correspondence should be addressed.
Biomolecules 2021, 11(10), 1527; https://doi.org/10.3390/biom11101527
Submission received: 6 October 2021 / Accepted: 8 October 2021 / Published: 16 October 2021
(This article belongs to the Special Issue Integrative Multi-Omics in Biomedical Research)
Genome technologies have revolutionized biomedicine, but the complexity of biological systems cannot be explained by genomics alone. Advances in sequencing and mass spectrometry technologies coupled with methodological and computational innovations are essential in driving multidimensional omics applications.
This Special Issue covers the latest methods and novel findings from integrative analysis of multiple omics datasets to address diverse questions in biology and pathology.
The scene is set with a review article by Lancaster et al. [1], which introduces six players (genome, epigenome, transcriptome, metagenome, proteome and metabolome) that use two different technologies (sequencing and mass spectrometry). After characterizing individual omics data and analytical approaches, considerations for multi-omic study design and data integration methods are discussed.
The contributed research papers span a broad range of studies from clinical cohorts and mouse models to cell-based investigations, thus illustrating the diverse applications of multi-omics.
Two papers applied multi-omics to investigate physiological interventions.
Odenkirk et al. [2] compared the blood lipidome and metabolome in two cohorts of patients undergoing exercise and planned myocardial infarction, respectively, to gain insight on the metabolic pathways underlying the disease and its prevention.
Molendijk et al. [3] applied lipidomic and metagenomic profiling in a dietary model of gastro-esophageal reflux disease and associated esophageal pathology in mice, revealing increased microbiome diversity and a lipidomics signature associated with esophageal inflammation and metaplasia.
Five papers applied multi-omics to diverse cell models, with a study by Niederstaetter et al. [4] highlighting the variability and influence of fetal calf serum (used in culture media)-contained eicosanoids on cellular function, evaluated via proteomics and lipidomics. Neuditschko et al. [5] investigated endometrial pain mechanisms by applying proteomics, metabolomics and eicosanoid profiling to cells derived from endometriotic lesions.
Gillen et al. [6] applied metabolic measurements with secretome profiling to assess the impact of endotoxin (LPS) on macrophages, while Novikova et al. [7] combined transcriptome and proteomic profiling to investigate granulocyte differentiation and discovered HIC1, CEBPB, LYN and PARP1 as potential therapeutic targets in acute myeloid leukemia.
Finally, the paper by Kim et al. [8] illustrates the standardized application of combining drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) to facilitate target protein identification for other existing natural therapeutic compounds.
To wrap up this Special Issue, the comprehensive review article by Howard and Cristea [9] highlights the role of integrative multi-omics in deciphering system-level mechanisms of DNA sensing during viral infections. Following viral infection, protein–protein interactome and protein post-translational modifications drive the remodeling of the cellular transcriptome, proteome and secretome; hence, multi-omic investigations should also include interactome and modification analyses such as phosphoproteome.
In conclusion, multi-omic investigation has become a central technique for deciphering complex biological systems. Continued innovations in technologies, methodologies and applications will enable and support further expansion and integration of multi-omics in future biomedical research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lancaster, S.M.; Sanghi, A.; Wu, S.; Snyder, M.P. A Customizable Analysis Flow in Integrative Multi-Omics. Biomolecules 2020, 10, 1606. [Google Scholar] [CrossRef] [PubMed]
  2. Odenkirk, M.T.; Stratton, K.G.; Bramer, L.M.; Webb-Robertson, B.M.; Bloodsworth, K.J.; Monroe, M.E.; Burnum-Johnson, K.E.; Baker, E.S. From Prevention to Disease Perturbations: A Multi-Omic Assessment of Exercise and Myocardial Infarctions. Biomolecules 2021, 11, 40. [Google Scholar] [CrossRef] [PubMed]
  3. Molendijk, J.; Nguyen, T.M.; Brown, I.; Mohamed, A.; Lim, Y.; Barclay, J.; Hodson, M.P.; Hennessy, T.P.; Krause, L.; Morrison, M.; et al. Chronic High-Fat Diet Induces Early Barrett’s Esophagus in Mice through Lipidome Remodeling. Biomolecules 2020, 10, 776. [Google Scholar] [CrossRef] [PubMed]
  4. Niederstaetter, L.; Neuditschko, B.; Brunmair, J.; Janker, L.; Bileck, A.; Del Favero, G.; Gerner, C. Eicosanoid Content in Fetal Calf Serum Accounts for Reproducibility Challenges in Cell Culture. Biomolecules 2021, 11, 113. [Google Scholar] [CrossRef] [PubMed]
  5. Neuditschko, B.; Leibetseder, M.; Brunmair, J.; Hagn, G.; Skos, L.; Gerner, M.C.; Meier-Menches, S.M.; Yotova, I.; Gerner, C. Epithelial Cell Line Derived from Endometriotic Lesion Mimics Macrophage Nervous Mechanism of Pain Generation on Proteome and Metabolome Levels. Biomolecules 2021, 11, 1230. [Google Scholar] [CrossRef] [PubMed]
  6. Gillen, J.; Ondee, T.; Gurusamy, D.; Issara-Amphorn, J.; Manes, N.P.; Yoon, S.H.; Leelahavanichkul, A.; Nita-Lazar, A. LPS Tolerance Inhibits Cellular Respiration and Induces Global Changes in the Macrophage Secretome. Biomolecules 2021, 11, 164. [Google Scholar] [CrossRef] [PubMed]
  7. Novikova, S.; Tikhonova, O.; Kurbatov, L.; Farafonova, T.; Vakhrushev, I.; Lupatov, A.; Yarygin, K.; Zgoda, V. Omics Technologies to Decipher Regulatory Networks in Granulocytic Cell Differentiation. Biomolecules 2021, 11, 907. [Google Scholar] [CrossRef] [PubMed]
  8. Kim, Y.; Sugihara, Y.; Kim, T.Y.; Cho, S.M.; Kim, J.Y.; Lee, J.Y.; Yoo, J.S.; Song, D.; Han, G.; Rezeli, M.; et al. Identification and Validation of VEGFR2 Kinase as a Target of Voacangine by a Systematic Combination of DARTS and MSI. Biomolecules 2020, 10, 508. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Howard, T.R.; Cristea, I.M. Interrogating Host Antiviral Environments Driven by Nuclear DNA Sensing: A Multiomic Perspective. Biomolecules 2020, 10, 1591. [Google Scholar] [CrossRef] [PubMed]
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Hill, M.M.; Gerner, C. Integrative Multi-Omics in Biomedical Research. Biomolecules 2021, 11, 1527. https://doi.org/10.3390/biom11101527

AMA Style

Hill MM, Gerner C. Integrative Multi-Omics in Biomedical Research. Biomolecules. 2021; 11(10):1527. https://doi.org/10.3390/biom11101527

Chicago/Turabian Style

Hill, Michelle M., and Christopher Gerner. 2021. "Integrative Multi-Omics in Biomedical Research" Biomolecules 11, no. 10: 1527. https://doi.org/10.3390/biom11101527

APA Style

Hill, M. M., & Gerner, C. (2021). Integrative Multi-Omics in Biomedical Research. Biomolecules, 11(10), 1527. https://doi.org/10.3390/biom11101527

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