DOCSLIB.ORG

Multi-Omics Integration Analysis Robustly Predicts High-Grade Patient

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Multi-Omics Integration Analysis Robustly Predicts High-Grade Patient Author Manuscript Published OnlineFirst on March 7, 2019; DOI: 10.1158/1078-0432.CCR-18-1515 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Multi-omics integration analysis robustly predicts high-grade patient 2 survival and identifies CPT1B effect on fatty acid metabolism in Bladder 3 Cancer 4 Venkatrao Vantaku1^, Jianrong Dong1^, Chandrashekar R Ambati2^, Dimuthu Perera2^, 5 Sri Ramya Donepudi2, Chandra Sekhar Amara1, Vasanta Putluri2, Ravi, Shiva 6 Shankar1, Matthew J. Robertson2, Danthasinghe Waduge Badrajee Piyarathna1, 7 Mariana Villanueva2, Friedrich-Carl von Rundstedt3, Balasubramanyam Karanam4, 8 Leomar Y Ballester5, Martha K Terris6, Roni J Bollag6, Seth P. Lerner7, Apolo B. 9 Andrea8, Hugo Villanueva2, MinJae Lee10, Andrew G. Sikora9, Yair Lotan11, Arun 10 Sreekumar1, 12, Cristian Coarfa1,2,13$, Nagireddy Putluri1# 11 1Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX, USA; 12 2Dan L. Duncan Cancer Center, Advanced Technology Core, Alkek Center for Molecular 13 Discovery, Baylor College of Medicine, Houston, TX, USA; 3Department of Urology, Jena 14 University Hospital, Friedrich-Schiller-University, Jena, Germany; 4Department of Biology and 15 Center for Cancer Research, Tuskegee University, Tuskegee, AL, USA; 5Pathology & 16 Laboratory Medicine, Neurosurgery, University of Texas Health Science center, Houston, 17 6Augusta University, Augusta, GA, USA; 7Scott Department of Urology, Baylor College of 18 Medicine, Houston, TX, USA; 8Center for Cancer Research, National Cancer Institute, Bethesda, 19 MD, USA; 9Otolaryngology-Head & Neck Surgery, Baylor College of Medicine, Houston, TX, 20 USA; 10Division of Clinical and Translational Sciences, Department of Internal Medicine, 21 McGovern Medical School at The University of Texas Health Science Center, Houston, TX, 22 USA; 11Department of Urology, University of Texas Southwestern, Dallas, TX, USA; 12Verna and 1 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 7, 2019; DOI: 10.1158/1078-0432.CCR-18-1515 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 23 Marrs McLean Department of Biochemistry and Molecular Biology, 13Center for Precision 24 Environmental Health, Baylor College of Medicine, Houston, TX, USA; ^authors contributed 25 equally. 26 #Primary Corresponding author: 27 Nagireddy Putluri, Ph.D. 28 Department of Molecular and Cellular Biology 29 Baylor College of Medicine, Houston, TX 77030, Tel.: (713) 798 3139 30 Email: [email protected] 31 32 $Correspondence for Bioinformatics: 33 Cristian Coarfa, Ph.D. 34 Dan L Duncan Comprehensive Cancer Center 35 Center for Precision Environmental Health 36 Department of Molecular and Cellular Biology 37 Baylor College of Medicine, Houston, TX 77030 38 E-mail: [email protected] 39 Conflict of interest: Authors do not have any conflict of interest. 40 Key words: Multi-omics integration analysis, Metabolomics and Lipidomics, CPT1B, 41 Fatty acid oxidation. 42 Running title: OMICS: Identification of CPT1B role in Bladder Cancer 43 Funding support: Supported by American Cancer Society (ACS) Award 127430-RSG- 44 15-105-01-CNE (N.P.), NIH/NCI R01CA220297 (N.P), and NIH/NCI R01CA216426 45 (N.P.). 46 Translational Relevance 47 Cancer metabolism varies depending on the tumor grade. Currently, there is an absence of 48 multi-OMICs data integration to predict bladder cancer (BLCA) survival. To fill in this gap, we 49 performed unbiased metabolomics and lipidomics analyses of matched BLCA tissues and 50 integrated them with BLCA transcriptomics analyses to generate an integrated gene signature 51 that was associated with patient survival in multiple BLCA cohorts. Our results revealed altered 52 metabolic differences between high-grade BLCA and low-grade BLCA and suggest that 53 impaired fatty acid β-oxidation (FAO) due to the downregulation of CPT1B plays a crucial role 54 in the progression of low grade to high grade BLCA. CPT1B overexpression in high-grade 55 BLCA cell lines reduced cell proliferation, epithelial-mesenchymal transition, and metastasis to 56 the liver in vivo by increasing FAO. Our metabolic-centered multi-OMICs based integrative 57 analysis provides a system-level perspective on BLCA and potential targets for novel 58 therapeutics against high-grade BLCA. 2 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 7, 2019; DOI: 10.1158/1078-0432.CCR-18-1515 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 59 Abstract 60 Purpose: The perturbation of metabolic pathways in high-grade BLCA has not been 61 investigated. We aimed to identify a metabolic signature in high-grade BLCA by 62 integrating unbiased metabolomics, lipidomics, and transcriptomics to predict patient 63 survival and to discover novel therapeutic targets. 64 Experimental design: We performed high-resolution liquid chromatography mass 65 spectrometry (LC-MS) and bioinformatic analysis to determine the global metabolome 66 and lipidome in high-grade BLCA. We further investigated the effects of impaired 67 metabolic pathways using in vitro and in vivo models. 68 Results: We identified 519 differential metabolites and 19 lipids that were differentially 69 expressed between low-grade and high-grade BLCA using the NIST MS metabolomics 70 compendium and lipidblast MS/MS libraries, respectively. Pathway analysis revealed a 71 unique set of biochemical pathways that are highly deregulated in high-grade BLCA. 72 Integromics analysis identified a molecular gene signature associated with poor patient 73 survival in BLCA. Low expression of CPT1B in high-grade tumors was associated with 74 low FAO and low acyl carnitine levels in high-grade BLCA which were confirmed using 75 tissue microarrays. Ectopic expression of the CPT1B in high-grade BLCA cells led to 76 reduced EMT in in vitro, and reduced cell proliferation, EMT, and metastasis in vivo. 77 Conclusions: Our study demonstrates a novel approach for the integration of 78 metabolomics, lipidomics, and transcriptomics data, and identifies a common gene 79 signature associated with poor survival in BLCA patients. Our data also suggest that 80 impairment of FAO due to down-regulation of CPT1B plays an important role in the 81 progression towards high-grade BLCA and provide potential targets for therapeutic 82 intervention. 83 84 85 86 87 88 89 3 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 7, 2019; DOI: 10.1158/1078-0432.CCR-18-1515 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 90 Introduction 91 Bladder cancer (BLCA) causes significant morbidity and mortality worldwide. In 92 the United States, there were about 79,000 cases and 17,000 deaths due to BLCA in 93 20171. In the past few decades advances in cancer genomics, transcriptomics, 94 proteomics, and metabolomics resulted in the discovery of potential biomarkers for 95 cancer2,3. Unfortunately, most of the biomarkers have failed to demonstrate superior 96 performance characteristics compared with existing clinical tests. Recent advancements 97 in OMICs technologies have ushered in a new era of targeted cancer therapies by 98 improving our understanding of molecular carcinogenesis. OMICs studies have 99 identified therapeutic targets and resulted in the successful development of new drugs 100 to treat various solid tumors like breast cancer and lung cancer4,5. For BLCA, 101 prognostication and treatment still depend mainly on pathological and clinical 102 characteristics6. Patients diagnosed with high-grade invasive disease are difficult to 103 treat effectively and have a relatively low life-expectancy despite available multimodal 104 therapies. Therefore, novel prognostic and therapeutic targets against BLCA are 105 needed. 106 Tumor progression relies on the reprogramming of cellular metabolism7. Our 107 previous metabolomic and lipidomic studies highlighted the significance of altered 108 xenobiotic and fatty acid metabolism in BLCA development8-10. However, we still know 109 little about the altered metabolic pathways during the transition from low grade to high 110 grade BLCA. The integration of OMICS facilitates the study of interactions among all 111 classes of biomolecules that can occur in cell, which in turn, determines the cellular 112 physiology and behavior. Treatment for high-grade muscle invasive bladder cancer 113 (MIBC) has not advanced beyond cisplatin-based combination chemotherapy in the last 114 three decades and very few drugs for the disease have been approved recently11. The 115 identification of markers to predict poor outcomes among patients with BLCA will 116 improve our ability to identify patients who might benefit from adjuvant therapy. 117 Furthermore, a greater understanding of the metabolic molecular mechanisms of BLCA 118 progression and the identification of novel therapeutic targets will improve outcomes for 119 patients with high-grade BLCA. 4 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 7, 2019; DOI: 10.1158/1078-0432.CCR-18-1515
Load more

Recommended publications
  • The 'Omics Revolution: How an Obsession with Compiling Lists Is
    The ‘omics revolution: how an obsession with compiling lists is threatening the ancient art of experimental design. Claudio D Stern Department of Cell & Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK. [email protected] The last quarter of a century ushered in a transformation in Biology following the huge successes in sequencing entire genomes of organisms (including humans) with increasing precision and speed and rapidly decreasing cost: genomics. Later other “omics” approaches followed: proteomics, metabolomics, epigenomics, and many more. Huge quantities of data were generated and new computational methods to find hidden correlations emerged. Almost imperceptibly, however, it seems that this has led to a crisis in our ability to design elegant experiments to establish causality. Imagine an extra-terrestrial excursion to planet Earth to discover about life on this planet. A spaceship with alien scientists lands in a park with many statues on display, representing important people from British history. The aliens unload their sophisticated analytical equipment and start to investigate. The first one uses a mass spectrometer – s/he approaches each statue and analyse it: one statue contains 5% copper, 10% iron, 17% aluminium, 63% lead. Another statue seems to consist mainly of carbon and a little water. A third object is made of granite. And so on. Another scientist follows: their analysis with a powerful super-resolution electron microscope reveals different sets of atomic structures. A third scientist uses a balance and lists the mass of each statue. A fourth measures the dimensions – some statues are very small, others quite large, and they have different overall shapes: some are tall, some are rather rounded.
    [Show full text]
  • From Proteomics to Systems Biology Outline and Learning Objectives
    From Proteomics to Systems Biology Outline and learning objectives “Omics” science provides global analysis tools to study entire systems • How to obtain omics - data • What can we learn? Limitations? • Integration of omics - data • In-class practice: Omics-data visualization Integration of “omics”- information Omics - data provide systems-level information Omics - data provide systems-level information Whole-genome sequencing Microarrays 2D-electrophoresis, mass spectrometry Joyce & Palsson, 2006 Joyce & Palsson, 2006 Transcriptomics (indirectly) tells about Proteomics aims to detect and quantify RNA-transcript abundances a system’s entire protein content ! primary genomic readout Strengths: - very good genome-wide coverage - variety of commercial products Drawback: No protein-level info!! -> RNA degradation Strengths: -> Post-translational modifications -> info about post-translational modifications => validation by e.g. RT-PCR -> high throughput possible due to increasing quality and cycle speed of mass spec instrumentation Limitations: - coverage dependent on sample, preparation & separation method - bias towards most highly abundant proteins Omics - data provide systems-level information Metabolomics and Lipidomics Detector Metabolites GC-MS extracted NMR from cell lysate Glycan arrays, Lipids Glyco-gene chips, mass spec / NMR of carbohydrates ESI-MS/MS Joyce & Palsson, 2006 Metabolomics and Lipidomics Metabolomics and Lipidomics Metabolomics: Metabolomics: Large-scale measurement of cellular metabolites Large-scale measurement of
    [Show full text]
  • Impacts of Genomics and Other 'Omics' for the Crop, Forestry, Livestock
    Impacts of genomics and other ‘omics’ for the crop, forestry, livestock, fishery and agro-industry sectors in developing countries 1. Introduction Advances in genomics, the study of all the genetic material (i.e. the genome) of an organism, have been remarkable in recent years. Publication of the first draft of the human genome in 2001 was a milestone, quickly followed by that of the first crop (rice) in 2002 and the first farm animal (chicken) in 2004. Huge technological advancements have meant that sequencing has become dramatically quicker and cheaper over time, so the genomes of many of the important crops, livestock, forest trees, aquatic animals and agricultural pests are now already sequenced or soon will be. The FAO Biotechnology Forum (http://www.fao.org/biotech/biotech-forum/) is hosting this e-mail conference to look at the impacts that genomics, and the other related 'omics', have had so far on food and agriculture in developing countries as well as their potential impacts in the near future. Before looking at genomics in more detail, a quick overview of some basic genetic concepts can be provided [for more technical details, see FAO (2011a) or the FAO biotechnology glossary (FAO, 2001)]. All living things are made up of cells that contain genetic material called DNA, a molecule made up of a long chain of nitrogen-containing bases (of four kinds: A, C, G and T). DNA is organized as a double helix, where two DNA chains are held together through bonding of the bases, where A bonds with T and C bonds with G.
    [Show full text]
  • New Technologies and Their Impact on 'Omics' Research
    Available online at www.sciencedirect.com New technologies and their impact on ‘omics’ research Editorial overview Matthew Bogyo and Pauline M Rudd Current Opinion in Chemical Biology 2013, 17:1–3 For a complete overview see the Issue 1367-5931/$ – see front matter, Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.cbpa.2013.01.005 Matthew Bogyo The use of the suffix ‘ome’ is defined in the Oxford English dictionary as ‘being used in cellular and molecular biology to form nouns with the sense Department of Pathology, Stanford University ‘‘all constituents considered collectively’’’. Interestingly, the use of the School of Medicine, Stanford, CA, USA e-mail: [email protected] ‘ome’ suffix in biology dates back to the early 1900s when it was first used to describe a ‘biome’ and genome (although originally in German as genom). Matthew Bogyo received his BSc degree in Over the past few decades, the use of the omics suffix has rapidly increased, Chemistry from Bates College in 1993 and a due in a large part, to the rapid growth in technologies that allow global PhD in Biochemistry from the Massachusetts analysis of samples on a systems level. The familiarity of the ‘omics’ term Institute of Technology in 1997. He became a also was rapidly advanced by the completion of the human genome in the Faculty Fellow at the University of California, early 2000s. Since that time, we have seen the term ‘omics’ move beyond use San Francisco in 1998. In 2001, he moved to for genomics and proteomics and gain acceptance for a diverse array of Celera Genomics to head the Department of Chemical Proteomics until 2003 when he systems biology applications.
    [Show full text]
  • Expanding the Molecular Landscape of Exercise Biology
    H OH metabolites OH Review Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology Mehdi R. Belhaj 1, Nathan G. Lawler 2,3 and Nolan J. Hoffman 1,* 1 Exercise and Nutrition Research Program, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne 3000, Australia; [email protected] 2 Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Murdoch, Perth 6150, Australia; [email protected] 3 School of Health and Medical Sciences, Edith Cowan University, Joondalup 6027, Australia * Correspondence: [email protected]; Tel.: +61-3-9230-8277 Abstract: Dynamic changes in circulating and tissue metabolites and lipids occur in response to exercise-induced cellular and whole-body energy demands to maintain metabolic homeostasis. The metabolome and lipidome in a given biological system provides a molecular snapshot of these rapid and complex metabolic perturbations. The application of metabolomics and lipidomics to map the metabolic responses to an acute bout of aerobic/endurance or resistance exercise has dramatically expanded over the past decade thanks to major analytical advancements, with most exercise-related studies to date focused on analyzing human biofluids and tissues. Experimental and analytical considerations, as well as complementary studies using animal model systems, are warranted to help overcome challenges associated with large human interindividual variability and decipher the breadth of molecular mechanisms underlying the metabolic health-promoting effects of exercise. In this review, we provide a guide for exercise researchers regarding analytical techniques and experimental workflows commonly used in metabolomics and lipidomics. Furthermore, we discuss Citation: Belhaj, M.R.; Lawler, N.G.; advancements in human and mammalian exercise research utilizing metabolomic and lipidomic Hoffman, N.J.
    [Show full text]
  • Multi-Omics: an Opportunity to Dive Into Systems Biology
    Brazilian Journal of Analytical Chemistry 2020, Volume 7, Issue 29, pp 18-44 doi: 10.30744/brjac.2179-3425.RV-03-2020 REVIEW Multi-omics: An Opportunity to Dive into Systems Biology Emerson Andrade Ferreira dos Santos1 Elisa Castañeda Santa Cruz1 Henrique Caracho Ribeiro1 Luidy Darllan Barbosa1 Flávia da Silva Zandonadi1 Alessandra Sussulini1,2,* 1Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), P.O. Box 6154, 13083-970, Campinas, SP, Brazil 2National Institute of Science and Technology for Bioanalytics – INCTBio, Institute of Chemistry, University of Campinas (UNICAMP), P.O. Box 6154, 13083-970, Campinas, SP, Brazil Omics data integration employing multi-omics approach is an outstanding opportunity to design a reliable picture of the biochemistry and dynamics of biological systems, as well as prioritize strategies for biomarker discovery. Biological functions are characterized by complex interaction networks, in which the dynamics of biomolecules manages physical and biochemical processes. However, since the emergence of omic sciences, researchers are still looking for the most accurate method to classify and determine the identity and function of biomarkers that describe a system and the ongoing biological processes. Thus, according to the strategies unveiled in the multi-omics literature, this is considered a challenging science field. Therefore, this review describes a workflow example regarding multi-omics data integration, indicating mathematical and computational tools in analysis pipelines that use various methods to perform a sequence of tasks, which would be able to describe biological processes within the systems biology context. Keywords: multi-omics, data integration tools, omics INTRODUCTION The application of computational and mathematical modeling towards a deeper and broader understanding of biological systems is called systems biology.
    [Show full text]
  • MOBN: an Interactive Database of Multi-Omics Biological Networks Cheng Zhang1,#,*, Muhammad Arif1,#, Xiangyu Li1, Sunjae Lee1, Abdellah Tebani1, Wenyu Zhou2, Brian D
    bioRxiv preprint doi: https://doi.org/10.1101/662502; this version posted June 8, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. MOBN: an interactive database of multi-omics biological networks Cheng Zhang1,#,*, Muhammad Arif1,#, Xiangyu Li1, Sunjae Lee1, Abdellah Tebani1, Wenyu Zhou2, Brian D. Piening3, Linn Fagerberg1, Nathan Price4, Leroy Hood4, Michael P. Snyder2, Jens Nielsen5, Mathias Uhlen1, Adil Mardinoglu1,6,* 1Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm, SE-171 21, Sweden 2Department of Genetics, Stanford University, Stanford, CA 94305, USA 3Earle A Chiles Research Institute and Providence Cancer Institute, Portland, OR 97213, USA 4Institute of Systems Biology, Seattle, USA 5Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden 6Centre for Host–Microbiome Interactions, Dental Institute, King's College London, London, SE1 9RT, United Kingdom #These authors contributed equally. *To whom correspondence should be addressed. Abstract Summary: The associations among different omics are essential to understand human wellness and disease. However, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present an interactive database of multi-omics biological networks (MOBN) and describe associations between clinical chemistry, anthropometrics, plasma proteome, plasma metabolome and gut microbiome obtained from the same individuals. MOBN allows the user to interactively explore the association of a single feature with other omics data and customize its specific context (e.g.
    [Show full text]
  • Lipidomics and Metabolomics Service Gain Deeper Insights Into Exosomes
    EXOSOMES LIPIDOMICS AND METABOLOMICS SERVICE GAIN DEEPER INSIGHTS INTO EXOSOMES SYSTEMBIO.COM/LIPIDOMICS HIGHLIGHTS What can lipidomics of exosomes tell you? n Discover novel circulating biomarkers Lipids are an important part of cellular physiology, and are increasingly being recognized for their importance in exosome biology as well. Exosomes were recently shown to have the highest lipid- n Learn more about exosome biology to-protein ratio of all classes of extracellular vesicles (1), with lipid content that both differs from n Send us your sample and receive data the parent cell the vesicles are shed from (2) and also changes as exosomes undergo a variety of in 4 - 6 weeks physiological processes (3). These unique lipid profiles can serve as novel circulating biomarkers, and recent evidence suggests that specific lipid species carried by exosomes can also modulate Service Overview the function of recipient cells (4). Whether you’re interested in With so much information revealed by lipid content, lipidomics studies of exosomes are a great way to identify lipid-based biomarkers and for understanding vesicle biogenesis and function (5). circulating biomarker discovery, basic exosome research, or other TUMOR MICROENVIRONMENT exosome-related studies, SBI’s CANCER CELLS CAFs EXOSOMES Exosome Lipidomics & Metabolomics Service helps you quickly and efficiently get the most information from your exosomes. Simply send Exosomes affect metabolism of cancer us your sample or purified exosomes cells A recent study by Zhao, et al, (6) and we’ll send back a report with demonstrated that exosomes from putative identifications, mass/charge patient-derived cancer-associated fibroblasts (CAFs) can reprogram EXOSOME ratios, and differential analysis (if UPTAKE the cellular machinery in cancer requested).
    [Show full text]
  • Lipidomics at the Interface of Structure and Function in Systems Biology
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Chemistry & Biology Crosstalk Lipidomics at the Interface of Structure and Function in Systems Biology Richard W. Gross1,2,3,4,* and Xianlin Han1,2 1Division of Bioorganic Chemistry and Molecular Pharmacology 2Departments of Medicine 3Developmental Biology Washington University School of Medicine, St. Louis, MO 63110, USA 4Department of Chemistry, Washington University, St. Louis, MO 63105, USA *Correspondence: [email protected] DOI 10.1016/j.chembiol.2011.01.014 Cells, tissues, and biological fluids contain a diverse repertoire of many tens of thousands of structurally distinct lipids that play multiple roles in cellular signaling, bioenergetics, and membrane structure and func- tion. In an era where lipid-related disease states predominate, lipidomics has assumed a prominent role in systems biology through its unique ability to directly identify functional alterations in multiple lipid metabolic and signaling networks. The development of shotgun lipidomics has led to the facile accrual of high density information on alterations in the lipidome mediating physiologic cellular adaptation during health and path- ologic alterations during disease. Through both targeted and nontargeted investigations, lipidomics has already revealed the chemical mechanisms underlying many lipid-related disease states. The Multiple Roles of Lipids (Zhang et al., 2002; Breen et al., 2005). Cantley, 2006; Chiang et al., 2006; Simon in Cellular
    [Show full text]
  • Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales
    life Review Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales Jayan D. M. Senevirathna 1,2,* and Shuichi Asakawa 1 1 Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan; [email protected] 2 Department of Animal Science, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka * Correspondence: [email protected] Abstract: Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identify- ing unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challeng- ing. As an environmental stress, the acoustic radiation has a significant effect on the alteration of Citation: Senevirathna, J.D.M.; Asakawa, S. Multi-Omics Approaches lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics and Radiation on Lipid Metabolism applications.
    [Show full text]
  • From Omics to Systems Biology: Towards a More Complete Description and Understanding of Biology
    First Words From omics to systems biology: towards a more complete description and understanding of biology reductionist approaches (for example, focusing on single genes and/or proteins) to generate data that is difficult to accommodate in the context of the biology of the organism being studied. And the reason for this is not that the science is flawed, it’s just that biological systems are complex and reductionist approaches, on Paul Chambers their own, do not deal well with complexity. Research Manager, The Australian Wine Research Institute What is meant by complexity in this context? The components PO Box 197, Glen Osmond SA 5064 of a biological system (for example, genes, proteins, metabolites www.awri.com.au and so on) function in networks and these networks interact [email protected] with each other. For example, a gene might be knocked out with little or no impact on a phenotype that, in other experiments, it has been shown to affect. This might be because other genes “If you try and take a cat apart to see how it works, the have compensated; there is often more than one route to a first thing you have on your hands is a non-working cat.” particular phenotype and the same gene can produce a different – Douglas Adams phenotype in different genetic backgrounds. Technology sets limits on what can be achieved in New technologies, the names of which are suffixed –omics research. The advent of genetic engineering accompanied (meaning a totality of some sort), are, however, enabling the by the development of monoclonal antibody technology development of holistic approaches in biological experiments.
    [Show full text]
  • Translating Cancer 'Omics' to Improved Outcomes
    Downloaded from genome.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Perspective Translating cancer ‘omics’ to improved outcomes Emily A. Vucic,1,2,6,7 Kelsie L. Thu,1,6 Keith Robison,3 Leszek A. Rybaczyk,4 Raj Chari,1,2,5 Carlos E. Alvarez,4 and Wan L. Lam1,2 1British Columbia Cancer Research Centre, Vancouver V5Z 1L3, Canada; 2Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver V6T 1Z4, Canada; 3Warp Drive Bio, Cambridge, Massachusetts 02142, USA; 4Center for Molecular and Human Genetics, The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio 43205, USA; 5Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA The genomics era has yielded great advances in the understanding of cancer biology. At the same time, the immense complexity of the cancer genome has been revealed, as well as a striking heterogeneity at the whole-genome (or omics) level that exists between even histologically similar tumors. The vast accrual and public availability of multi-omics da- tabases with associated clinical annotation including tumor histology, patient response, and outcome are a rich resource that has the potential to lead to rapid translation of high-throughput omics to improved overall survival. We focus on the unique advantages of a multidimensional approach to genomic analysis in this new high-throughput omics age and discuss the implications of the changing cancer demographic to translational omics research. The remarkable technological breakthroughs of the last 10 yr have disruptions—is critical for the translation of omics findings (Hudson reshaped how we view the cancer genome; therefore, so must our et al.
    [Show full text]