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Chakrabarty S, Wang S, Roychowdhury T, Ginsberg SD, Chiosis G. Introducing dysfunctional Protein-Protein Interactome (dfPPI) - A platform for systems-level protein-protein interaction (PPI) dysfunction investigation in disease. Curr Opin Struct Biol 2024; 88:102886. [PMID: 39003916 DOI: 10.1016/j.sbi.2024.102886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024]
Abstract
Protein-protein interactions (PPIs) play a crucial role in cellular function and disease manifestation, with dysfunctions in PPI networks providing a direct link between stressors and phenotype. The dysfunctional Protein-Protein Interactome (dfPPI) platform, formerly known as epichaperomics, is a newly developed chemoproteomic method aimed at detecting dynamic changes at the systems level in PPI networks under stressor-induced cellular perturbations within disease states. This review provides an overview of dfPPIs, emphasizing the novel methodology, data analytics, and applications in disease research. dfPPI has applications in cancer research, where it identifies dysfunctions integral to maintaining malignant phenotypes and discovers strategies to enhance the efficacy of current therapies. In neurodegenerative disorders, dfPPI uncovers critical dysfunctions in cellular processes and stressor-specific vulnerabilities. Challenges, including data complexity and the potential for integration with other omics datasets are discussed. The dfPPI platform is a potent tool for dissecting disease systems biology by directly informing on dysfunctions in PPI networks and holds promise for advancing disease identification and therapeutics.
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Affiliation(s)
- Souparna Chakrabarty
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shujuan Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen D Ginsberg
- Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA; Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA; Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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2
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Mansoor S, Hamid S, Tuan TT, Park JE, Chung YS. Advance computational tools for multiomics data learning. Biotechnol Adv 2024:108447. [PMID: 39251098 DOI: 10.1016/j.biotechadv.2024.108447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 09/01/2024] [Accepted: 09/05/2024] [Indexed: 09/11/2024]
Abstract
The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial roles in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.
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Affiliation(s)
- Sheikh Mansoor
- Department of Plant Resources and Environment, Jeju National University, 63243, Republic of Korea
| | - Saira Hamid
- Watson Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, Pulwama, J&K, India
| | - Thai Thanh Tuan
- Watson Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, Pulwama, J&K, India
| | - Jong Eun Park
- Department of Animal Biotechnology, College of Applied Life Science, Jeju National University, Jeju, Jeju-do, Republic of Korea.
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, 63243, Republic of Korea.
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3
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Lee GB, Mazli WNAB, Hao L. Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons. J Proteome Res 2024; 23:3149-3160. [PMID: 38415376 DOI: 10.1021/acs.jproteome.3c00790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Human induced pluripotent stem cells (iPSCs) can be differentiated into neurons, providing living human neurons to model brain diseases. However, it is unclear how different types of molecules work together to regulate stem cell and neuron biology in healthy and disease states. In this study, we conducted integrated proteomics, lipidomics, and metabolomics analyses with confident identification, accurate quantification, and reproducible measurements to compare the molecular profiles of human iPSCs and iPSC-derived neurons. Proteins, lipids, and metabolites related to mitosis, DNA replication, pluripotency, glycosphingolipids, and energy metabolism were highly enriched in iPSCs, whereas synaptic proteins, neurotransmitters, polyunsaturated fatty acids, cardiolipins, and axon guidance pathways were highly enriched in neurons. Mutations in the GRN gene lead to the deficiency of the progranulin (PGRN) protein, which has been associated with various neurodegenerative diseases. Using this multiomics platform, we evaluated the impact of PGRN deficiency on iPSCs and neurons at the whole-cell level. Proteomics, lipidomics, and metabolomics analyses implicated PGRN's roles in neuroinflammation, purine metabolism, and neurite outgrowth, revealing commonly altered pathways related to neuron projection, synaptic dysfunction, and brain metabolism. Multiomics data sets also pointed toward the same hypothesis that neurons seem to be more susceptible to PGRN loss compared to iPSCs, consistent with the neurological symptoms and cognitive impairment from patients carrying inherited GRN mutations.
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Affiliation(s)
- Gwang Bin Lee
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
| | - Wan Nur Atiqah Binti Mazli
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
| | - Ling Hao
- Department of Chemistry, The George Washington University, 800 22nd St. NW, Washington, D.C. 20052, United States
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Bhat V, Sheehan VA. Can we use biomarkers to identify those at risk of acute pain from sickle cell disease? Expert Rev Hematol 2024; 17:411-418. [PMID: 38949576 DOI: 10.1080/17474086.2024.2372322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/21/2024] [Indexed: 07/02/2024]
Abstract
INTRODUCTION Acute pain episodes, also known as vaso-occlusive crises (VOC), are a major symptom of sickle cell disease (SCD) and lead to frequent hospitalizations. The diagnosis of VOC can be challenging, particularly in adults with SCD, 50% of whom have chronic pain. Several potential biomarkers have been proposed for identifying individuals with VOC, including elevation above the baseline of various vascular growth factors, cytokines, and other markers of inflammation. However, none have been validated to date. AREAS COVERED We summarize prospective biomarkers for the diagnosis of acute pain in SCD, and how they may be involved in the pathophysiology of a VOC. Previous and current strategies for biomarker discovery, including the use of omics techniques, are discussed. EXPERT OPINION Implementing a multi-omics-based approach will facilitate the discovery of objective and validated biomarkers for acute pain.
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Affiliation(s)
- Varsha Bhat
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Vivien A Sheehan
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
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5
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Kafle A, Ojha SC. Advancing vaccine development against Opisthorchis viverrini: A synergistic integration of omics technologies and advanced computational tools. Front Pharmacol 2024; 15:1410453. [PMID: 39076588 PMCID: PMC11284087 DOI: 10.3389/fphar.2024.1410453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/10/2024] [Indexed: 07/31/2024] Open
Abstract
The liver fluke O. viverrini (Opisthorchis viverrini), a neglected tropical disease (NTD), endemic to the Great Mekong Subregion (GMS), mainly afflicts the northeastern region of Thailand. It is a leading cause of cholangiocarcinoma (CCA) in humans. Presently, the treatment modalities for opisthorchiasis incorporate the use of the antihelminthic drug praziquantel, the rapid occurrence of reinfection, and the looming threat of drug resistance highlight the urgent need for vaccine development. Recent advances in "omics" technologies have proven to be a powerful tool for such studies. Utilizing candidate proteins identified through proteomics and refined via immunoproteomics, reverse vaccinology (RV) offers promising prospects for designing vaccines targeting essential antibody responses to eliminate parasite. Machine learning-based computational tools can predict epitopes of candidate protein/antigens exhibiting high binding affinities for B cells, MHC classes I and II, indicating strong potential for triggering both humoral and cell-mediated immune responses. Subsequently, these vaccine designs can undergo population-specific testing and docking/dynamics studies to assess efficacy and synergistic immunogenicity. Hence, refining proteomics data through immunoinformatics and employing computational tools to generate antigen-specific targets for trials offers a targeted and efficient approach to vaccine development that applies to all domains of parasite infections. In this review, we delve into the strategic antigen selection process using omics modalities for the O. viverrini parasite and propose an innovative framework for vaccine design. We harness omics technologies to revolutionize vaccine development, promising accelerated discoveries and streamlined preclinical and clinical evaluations.
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Affiliation(s)
- Alok Kafle
- Department of Tropical Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- WHO Collaborating Centre for Research and Control of Opisthorchiasis, Khon Kaen University, Khon Kaen, Thailand
| | - Suvash Chandra Ojha
- Department of Infectious Diseases, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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6
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Choudhury P, Dasgupta S, Bhattacharyya P, Roychowdhury S, Chaudhury K. Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach. Mol Omics 2024; 20:366-389. [PMID: 38853716 DOI: 10.1039/d3mo00266g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.
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Affiliation(s)
- Priyanka Choudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
| | - Sanjukta Dasgupta
- Department of Biotechnology, Brainware University, Barasat, West Bengal, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
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7
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Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
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Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
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8
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Gutierrez Reyes CD, Alejo-Jacuinde G, Perez Sanchez B, Chavez Reyes J, Onigbinde S, Mogut D, Hernández-Jasso I, Calderón-Vallejo D, Quintanar JL, Mechref Y. Multi Omics Applications in Biological Systems. Curr Issues Mol Biol 2024; 46:5777-5793. [PMID: 38921016 PMCID: PMC11202207 DOI: 10.3390/cimb46060345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.
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Affiliation(s)
| | - Gerardo Alejo-Jacuinde
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance (IGCAST), Texas Tech University, Lubbock, TX 79409, USA; (G.A.-J.); (B.P.S.)
| | - Benjamin Perez Sanchez
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance (IGCAST), Texas Tech University, Lubbock, TX 79409, USA; (G.A.-J.); (B.P.S.)
| | - Jesus Chavez Reyes
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - Sherifdeen Onigbinde
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
| | - Damir Mogut
- Department of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland;
| | - Irma Hernández-Jasso
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - Denisse Calderón-Vallejo
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - J. Luis Quintanar
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
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Lockwood MB, Sung C, Alvernaz SA, Lee JR, Chin JL, Nayebpour M, Bernabé BP, Tussing-Humphreys LM, Li H, Spaggiari M, Martinino A, Park CG, Chlipala GE, Doorenbos AZ, Green SJ. The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities. Biol Res Nurs 2024:10998004241256031. [PMID: 38836469 DOI: 10.1177/10998004241256031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.
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Affiliation(s)
- Mark B Lockwood
- Department of Biobehavioral Nursing Science, University of Illinois Chicago College of Nursing, Chicago, IL, USA
| | - Choa Sung
- Post-Doctoral Fellow, Department of Biobehavioral Nursing Science, University of Illinois Chicago College of Nursing, Chicago, IL, USA
| | - Suzanne A Alvernaz
- Graduate Student, Department of Biomedical Engineering, University of Illinois ChicagoColleges of Engineering and Medicine, Chicago, IL, USA
| | - John R Lee
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jennifer L Chin
- Medical Student, Touro College of Osteopathic Medicine, Middletown, NY, USA
| | - Mehdi Nayebpour
- Virginia BioAnalytics LLC, Washington, District of Columbia, USA
| | - Beatriz Peñalver Bernabé
- Graduate Student, Department of Biomedical Engineering, University of Illinois ChicagoColleges of Engineering and Medicine, Chicago, IL, USA
| | - Lisa M Tussing-Humphreys
- Department of Kinesiology and Nutrition, College of Applied Health Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Hongjin Li
- Department of Biobehavioral Nursing Science, University of Illinois Chicago College of Nursing, Chicago, IL, USA
| | - Mario Spaggiari
- Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Alessandro Martinino
- Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Chang G Park
- Department of Population Health Nursing Science, Office of Research Facilitation, University of Illinois Chicago, Chicago, IL, USA
| | - George E Chlipala
- Research Core Facility, Research Resources Center, University of Illinois Chicago, Chicago, IL, USA
| | - Ardith Z Doorenbos
- Department of Biobehavioral Nursing Science, University of Illinois ChicagoCollege of Nursing, Chicago, IL, USA
| | - Stefan J Green
- Department of Internal Medicine, Division of Infectious Diseases, Rush University Medical Center, Chicago, IL, USA
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10
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Alicehajic A, Duivenvoorden AAM, Lenaerts K. Unveiling the molecular complexity of intestinal ischemia-reperfusion injury through omics technologies. Proteomics 2024; 24:e2300160. [PMID: 38477684 DOI: 10.1002/pmic.202300160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
Intestinal ischemia-reperfusion injury (IR) is implicated in various clinical conditions and causes damage to the intestinal epithelium resulting in intestinal barrier loss. This presents a substantial clinical challenge, emphasizing the importance of gaining a comprehensive understanding of molecular events to aid in the identification of novel therapeutic targets. This review systematically explores the extent to which omics technologies-transcriptomics, proteomics, metabolomics, and metagenomics-have already contributed to deciphering the molecular mechanisms contributing to intestinal IR injury, in in vivo and in vitro animal and human models, and in clinical samples. Recent breakthroughs involve applying omics methodologies on exosomes, organoids, and single cells, shedding light on promising avenues and valuable targets to reduce intestinal IR injury. Future directions aimed at expediting clinical translation are discussed as well and include multi-omics data integration to facilitate the identification of key regulatory nodes driving intestinal IR injury and advancing human organoid models based on the novel insights by single-cell omics technologies, offering hope for clinical application of therapeutic strategies in the years to come.
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Affiliation(s)
- Anja Alicehajic
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Annet Adriana Maria Duivenvoorden
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Kaatje Lenaerts
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
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11
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Procopio N, Bonicelli A. From flesh to bones: Multi-omics approaches in forensic science. Proteomics 2024; 24:e2200335. [PMID: 38683823 DOI: 10.1002/pmic.202200335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 03/12/2024] [Accepted: 03/26/2024] [Indexed: 05/02/2024]
Abstract
Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.
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Affiliation(s)
- Noemi Procopio
- Research Centre for Field Archaeology and Experimental Taphonomy, School of Law and Policing, University of Central Lancashire, Preston, UK
| | - Andrea Bonicelli
- Research Centre for Field Archaeology and Experimental Taphonomy, School of Law and Policing, University of Central Lancashire, Preston, UK
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12
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Matsuoka T, Yashiro M. Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer. Int J Mol Sci 2024; 25:5880. [PMID: 38892067 PMCID: PMC11172243 DOI: 10.3390/ijms25115880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024] Open
Abstract
Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.
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Affiliation(s)
- Tasuku Matsuoka
- Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan;
- Institute of Medical Genetics, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
| | - Masakazu Yashiro
- Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan;
- Institute of Medical Genetics, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
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13
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Zeng IS. Integrating omics atlas in health informatics system design-an opinion article. Front Digit Health 2024; 6:1374359. [PMID: 38784702 PMCID: PMC11111845 DOI: 10.3389/fdgth.2024.1374359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Affiliation(s)
- Irene Suilan Zeng
- Department of Biostatistics and Epidemiology, Auckland University of Technology, Auckland, New Zealand
- School of Clinical Science, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand
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14
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Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
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Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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15
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Pathak RK, Kim JM. Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare. Brief Bioinform 2024; 25:bbae025. [PMID: 38343323 PMCID: PMC10859662 DOI: 10.1093/bib/bbae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/02/2024] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
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16
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Mengelkoch S, Gassen J, Lev-Ari S, Alley JC, Schüssler-Fiorenza Rose SM, Snyder MP, Slavich GM. Multi-omics in stress and health research: study designs that will drive the field forward. Stress 2024; 27:2321610. [PMID: 38425100 PMCID: PMC11216062 DOI: 10.1080/10253890.2024.2321610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Jeffrey Gassen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Shahar Lev-Ari
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Health Promotion, Tel Aviv University, Tel Aviv, Israel
| | - Jenna C. Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M. Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
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17
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Oraki Kohshour M, Heilbronner U. The significance of an integrated multi-omics approach in psychiatric disorders: A pharmacological perspective. Eur Neuropsychopharmacol 2024; 78:64-66. [PMID: 37977087 DOI: 10.1016/j.euroneuro.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 11/19/2023]
Affiliation(s)
- Mojtaba Oraki Kohshour
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, 80336, Munich, Germany; Department of Immunology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, 80336, Munich, Germany.
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18
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Kang CC, Lee TY, Lim WF, Yeo WWY. Opportunities and challenges of 5G network technology toward precision medicine. Clin Transl Sci 2023; 16:2078-2094. [PMID: 37702288 PMCID: PMC10651640 DOI: 10.1111/cts.13640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.
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Affiliation(s)
- Chia Chao Kang
- School of Electrical Engineering and Artificial IntelligenceXiamen University MalaysiaSepangSelangorMalaysia
| | - Tze Yan Lee
- School of Liberal Arts, Science and Technology (PUScLST)Perdana UniversityKuala LumpurMalaysia
| | - Wai Feng Lim
- Sunway Medical CentreSubang JayaSelangor Darul EhsanMalaysia
| | - Wendy Wai Yeng Yeo
- School of PharmacyMonash University MalaysiaBandar SunwaySelangor Darul EhsanMalaysia
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19
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Pfeifer B, Chereda H, Martin R, Saranti A, Clemens S, Hauschild AC, Beißbarth T, Holzinger A, Heider D. Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification. Bioinformatics 2023; 39:btad703. [PMID: 37988152 PMCID: PMC10684359 DOI: 10.1093/bioinformatics/btad703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/06/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023] Open
Abstract
SUMMARY Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).
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Affiliation(s)
- Bastian Pfeifer
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
| | - Hryhorii Chereda
- Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany
| | - Roman Martin
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| | - Anna Saranti
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Sandra Clemens
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| | - Anne-Christin Hauschild
- Institute for Medical Informatics, University Medical Center Göttingen, Göttingen 37075, Germany
| | - Tim Beißbarth
- Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Dominik Heider
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
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20
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Mengelkoch S, Miryam Schüssler-Fiorenza Rose S, Lautman Z, Alley JC, Roos LG, Ehlert B, Moriarity DP, Lancaster S, Snyder MP, Slavich GM. Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations. Brain Behav Immun 2023; 114:475-487. [PMID: 37543247 PMCID: PMC11195542 DOI: 10.1016/j.bbi.2023.07.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/04/2023] [Accepted: 07/30/2023] [Indexed: 08/07/2023] Open
Abstract
The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
| | | | - Ziv Lautman
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jenna C Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Lydia G Roos
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Benjamin Ehlert
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Daniel P Moriarity
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
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21
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Ivanisevic T, Sewduth RN. Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers. Proteomes 2023; 11:34. [PMID: 37873876 PMCID: PMC10594525 DOI: 10.3390/proteomes11040034] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 10/25/2023] Open
Abstract
Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.
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Affiliation(s)
| | - Raj N. Sewduth
- VIB-KU Leuven Center for Cancer Biology (VIB), 3000 Leuven, Belgium;
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22
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Masson HO, Karottki KJLC, Tat J, Hefzi H, Lewis NE. From observational to actionable: rethinking omics in biologics production. Trends Biotechnol 2023; 41:1127-1138. [PMID: 37062598 PMCID: PMC10524802 DOI: 10.1016/j.tibtech.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 04/18/2023]
Abstract
As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.
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Affiliation(s)
- Helen O Masson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Jasmine Tat
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Amgen Inc., Thousand Oaks, CA, USA
| | | | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
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23
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Rusmini M, Lantieri F. Editorial: Elucidation of the causes of human disease by multi-omics integration. Front Genet 2023; 14:1271406. [PMID: 37621705 PMCID: PMC10446758 DOI: 10.3389/fgene.2023.1271406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/26/2023] Open
Affiliation(s)
- Marta Rusmini
- Laboratory of Genetics and Genomics of Rare Diseases, Giannina Gaslini Institute (IRCCS), Genova, Italy
| | - Francesca Lantieri
- Department of Health Sciences, School of Medical and Pharmaceutical Sciences, University of Genoa, Genoa, Italy
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24
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Rakic A, Anicic R, Rakic M, Nejkovic L. Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome. J Pers Med 2023; 13:985. [PMID: 37373974 DOI: 10.3390/jpm13060985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/30/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein-protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy.
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Affiliation(s)
- Aleksandar Rakic
- The Obstetrics and Gynecology Clinic Narodni Front, 11000 Belgrade, Serbia
| | - Radomir Anicic
- The Obstetrics and Gynecology Clinic Narodni Front, 11000 Belgrade, Serbia
- School of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Marija Rakic
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, 6000 Koper, Slovenia
| | - Lazar Nejkovic
- The Obstetrics and Gynecology Clinic Narodni Front, 11000 Belgrade, Serbia
- School of Medicine, University of Belgrade, 11000 Belgrade, Serbia
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