1
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Fisher DW, Dunn JT, Keszycki R, Rodriguez G, Bennett DA, Wilson RS, Dong H. Unique transcriptional signatures correlate with behavioral and psychological symptom domains in Alzheimer's disease. Transl Psychiatry 2024; 14:178. [PMID: 38575567 PMCID: PMC10995139 DOI: 10.1038/s41398-024-02878-z] [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: 06/08/2023] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Despite the significant burden, cost, and worse prognosis of Alzheimer's disease (AD) with behavioral and psychological symptoms of dementia (BPSD), little is known about the molecular causes of these symptoms. Using antemortem assessments of BPSD in AD, we demonstrate that individual BPSD can be grouped into 4 domain factors in our cohort: affective, apathy, agitation, and psychosis. Then, we performed a transcriptome-wide analysis for each domain utilizing bulk RNA-seq of post-mortem anterior cingulate cortex (ACC) tissues. Though all 4 domains are associated with a predominantly downregulated pattern of hundreds of differentially expressed genes (DEGs), most DEGs are unique to each domain, with only 22 DEGs being common to all BPSD domains, including TIMP1. Weighted gene co-expression network analysis (WGCNA) yielded multiple transcriptional modules that were shared between BPSD domains or unique to each domain, and NetDecoder was used to analyze context-dependent information flow through the biological network. For the agitation domain, we found that all DEGs and a highly associated transcriptional module were functionally enriched for ECM-related genes including TIMP1, TAGLN, and FLNA. Another unique transcriptional module also associated with the agitation domain was enriched with genes involved in post-synaptic signaling, including DRD1, PDE1B, CAMK4, and GABRA4. By comparing context-dependent changes in DEGs between cases and control networks, ESR1 and PARK2 were implicated as two high-impact genes associated with agitation that mediated significant information flow through the biological network. Overall, our work establishes unique targets for future study of the biological mechanisms of BPSD and resultant drug development.
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Affiliation(s)
- Daniel W Fisher
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Jeffrey T Dunn
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Rachel Keszycki
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Guadalupe Rodriguez
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Rush University Medical Center, Chicago, IL, 60611, USA
| | - Robert S Wilson
- Rush Alzheimer's Disease Center, Rush University Medical Center, Rush University Medical Center, Chicago, IL, 60611, USA
| | - Hongxin Dong
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
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2
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Xianyu Z, Correia C, Ung CY, Zhu S, Billadeau DD, Li H. The Rise of Hypothesis-Driven Artificial Intelligence in Oncology. Cancers (Basel) 2024; 16:822. [PMID: 38398213 PMCID: PMC10886811 DOI: 10.3390/cancers16040822] [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: 12/13/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.
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Affiliation(s)
- Zilin Xianyu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Shizhen Zhu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Daniel D. Billadeau
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
- Department of Immunology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
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3
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Yu GT, Monie DD, Khosla S, Tchkonia T, Kirkland JL, Wyles SP. Mapping cellular senescence networks in human diabetic foot ulcers. GeroScience 2024; 46:1071-1082. [PMID: 37380899 PMCID: PMC10828272 DOI: 10.1007/s11357-023-00854-x] [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: 12/17/2022] [Accepted: 06/14/2023] [Indexed: 06/30/2023] Open
Abstract
Cellular senescence, a cell fate defined by irreversible cell cycle arrest, has been observed to contribute to chronic age-related conditions including non-healing wounds, such as diabetic foot ulcers. However, the role of cellular senescence in the pathogenesis of diabetic foot ulcers remains unclear. To examine the contribution of senescent phenotypes to these chronic wounds, differential gene and network analyses were performed on publicly available bulk RNA sequencing of whole skin biopsies of wound edge diabetic foot ulcers and uninvolved diabetic foot skin. Wald tests with Benjamini-Hochberg correction were used to evaluate differential gene expression. Results showed that cellular senescence markers, CDKN1A, CXCL8, IGFBP2, IL1A, MMP10, SERPINE1, and TGFA, were upregulated, while TP53 was downregulated in diabetic foot ulcers compared to uninvolved diabetic foot skin. NetDecoder was then used to identify and compare context-specific protein-protein interaction networks using known cellular senescence markers as pathway sources. The diabetic foot ulcer protein-protein interaction network demonstrated significant perturbations with decreased inhibitory interactions and increased senescence markers compared to uninvolved diabetic foot skin. Indeed, TP53 (p53) and CDKN1A (p21) appeared to be key regulators in diabetic foot ulcer formation. These findings suggest that cellular senescence is an important mediator of diabetic foot ulcer pathogenesis.
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Affiliation(s)
- Grace T Yu
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic Alix School of Medicine, Rochester, MN, USA
| | - Dileep D Monie
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic Alix School of Medicine, Rochester, MN, USA
| | - Sundeep Khosla
- Division of Endocrinology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center On Aging, Mayo Clinic, Rochester, MN, USA
| | - Tamar Tchkonia
- Robert and Arlene Kogod Center On Aging, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - James L Kirkland
- Robert and Arlene Kogod Center On Aging, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Saranya P Wyles
- Robert and Arlene Kogod Center On Aging, Mayo Clinic, Rochester, MN, USA.
- Department of Dermatology, Mayo Clinic, Rochester, MN, USA.
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4
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Ung CY, Correia C, Li H, Adams CM, Westendorf JJ, Zhu S. Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities. Drug Discov Today 2024; 29:103825. [PMID: 37967790 PMCID: PMC11109989 DOI: 10.1016/j.drudis.2023.103825] [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: 07/17/2023] [Revised: 10/29/2023] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.
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Affiliation(s)
- Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Christopher M Adams
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Jennifer J Westendorf
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA; Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Shizhen Zhu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA.
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5
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Fisher DW, Dunn JT, Keszycki R, Rodriguez G, Bennett DA, Wilson RS, Dong H. Unique Transcriptional Signatures Correlate with Behavioral and Psychological Symptom Domains in Alzheimer's Disease. RESEARCH SQUARE 2023:rs.3.rs-2444391. [PMID: 36711772 PMCID: PMC9882691 DOI: 10.21203/rs.3.rs-2444391/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Despite the significant burden, cost, and worse prognosis of Alzheimer's disease (AD) with behavioral and psychological symptoms of dementia (BPSD), little is known about the molecular causes of these symptoms. Using antemortem assessments of BPSD in AD, we demonstrate that individual BPSD can be grouped into 4 domain factors in our sample: affective, apathy, agitation, and psychosis. Then, we performed a transcriptome-wide analysis for each domain utilizing bulk RNA-seq of post-mortem anterior cingulate cortex (ACC) tissue. Though all 4 domains are associated with a predominantly downregulated pattern of hundreds of differentially expressed genes (DEGs), most DEGs are unique to each domain, with only 22 DEGs being common to all BPSD domains, including TIMP1. Weighted gene co-expression network analysis (WGCNA) yielded multiple transcriptional modules that were shared between BPSD domains or unique to each domain, and NetDecoder was used to analyze context-dependent information flow through the biological network. For the agitation domain, we found that all DEGs and a highly correlated transcriptional module were functionally enriched for ECM-related genes including TIMP1, TAGLN, and FLNA. Another unique transcriptional module also associated with the agitation domain was enriched with genes involved in post-synaptic signaling, including DRD1, PDE1B, CAMK4, and GABRA4. By comparing context-dependent changes in DEGs between cases and control networks, ESR1 and PARK2 were implicated as two high impact genes associated with agitation that mediated significant information flow through the biological network. Overall, our work establishes unique targets for future study of the biological mechanisms of BPSD and resultant drug development.
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Affiliation(s)
- Daniel W. Fisher
- Department of Psychiatry and Behavioral Sciences,
Northwestern University Feinberg School of Medicine
- Department of Psychiatry and Behavioral Sciences,
University of Washington School of Medicine
| | - Jeffrey T. Dunn
- Department of Psychiatry and Behavioral Sciences,
Northwestern University Feinberg School of Medicine
| | - Rachel Keszycki
- Department of Psychiatry and Behavioral Sciences,
Northwestern University Feinberg School of Medicine
- Mesulam Center for Cognitive Neurology and
Alzheimer’s Disease, Northwestern University Feinberg School of
Medicine
| | - Guadalupe Rodriguez
- Department of Psychiatry and Behavioral Sciences,
Northwestern University Feinberg School of Medicine
| | | | | | - Hongxin Dong
- Department of Psychiatry and Behavioral Sciences,
Northwestern University Feinberg School of Medicine
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6
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Tian Y, Zhang C, Ma W, Huang A, Tian M, Zhao J, Dang Q, Sun Y. A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix. Aging (Albany NY) 2022; 14:3155-3174. [PMID: 35398839 PMCID: PMC9037255 DOI: 10.18632/aging.204004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/28/2022] [Indexed: 11/25/2022]
Abstract
The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).
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Affiliation(s)
- Yuan Tian
- Somatic Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong Provincial ENT Hospital, Jinan, Shandong 250023, PR China
| | - Caiqing Zhang
- Department of Respiratory and Critical Care Medicine, Shandong Second Provincial General Hospital, Shandong Provincial ENT Hospital, Shandong University, Jinan, Shandong 250023, PR China
| | - Wanru Ma
- Department of Blood Transfusion, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Alan Huang
- Department of Oncology, Jinan Central Hospital, The Hospital Affiliated with Shandong First Medical University, Jinan, Shandong 250013, PR China
| | - Mei Tian
- Respiratory Department, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, PR China
| | - Junyan Zhao
- Nursing Department, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong 250014, PR China
| | - Qi Dang
- Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250012, PR China
| | - Yuping Sun
- Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250012, PR China
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7
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Correia C, Weiskittel TM, Ung CY, Villasboas Bisneto JC, Billadeau DD, Kaufmann SH, Li H. Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View. Front Cell Dev Biol 2022; 10:752326. [PMID: 35359437 PMCID: PMC8962639 DOI: 10.3389/fcell.2022.752326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 02/10/2022] [Indexed: 12/14/2022] Open
Abstract
Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.
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Affiliation(s)
- Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Taylor M Weiskittel
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | | | - Daniel D Billadeau
- Department of Immunology, Mayo Clinic, Rochester, MN, United States,Division of Oncology Research, Mayo Clinic, Rochester, MN, United States
| | - Scott H Kaufmann
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States,Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, United States,Division of Oncology Research, Mayo Clinic, Rochester, MN, United States
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States,*Correspondence: Hu Li,
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8
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Ung CY, Levee TM, Zhang C, Correia C, Yeo KS, Li H, Zhu S. Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma. Comput Struct Biotechnol J 2022; 20:3291-3303. [PMID: 35832612 PMCID: PMC9251784 DOI: 10.1016/j.csbj.2022.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/11/2022] [Indexed: 11/03/2022] Open
Abstract
Neuroblastoma (NB) is the most common extracranial solid tumor in children. Although only a few recurrent somatic mutations have been identified, chromosomal abnormalities, including the loss of heterozygosity (LOH) at the chromosome 1p and gains of chromosome 17q, are often seen in the high-risk cases. The biological basis and evolutionary forces that drive such genetic abnormalities remain enigmatic. Here, we conceptualize the Gene Utility Model (GUM) that seeks to identify genes driving biological signaling via their collective gene utilities and apply it to understand the impact of those differentially utilized genes on constraining the evolution of NB karyotypes. By employing a computational process-guided flow algorithm to model gene utility in protein–protein networks that built based on transcriptomic data, we conducted several pairwise comparative analyses to uncover genes with differential utilities in stage 4 NBs with distinct classification. We then constructed a utility karyotype by mapping these differentially utilized genes to their respective chromosomal loci. Intriguingly, hotspots of the utility karyotype, to certain extent, can consistently recapitulate the major chromosomal abnormalities of NBs and also provides clues to yet identified predisposition sites. Hence, our study not only provides a new look, from a gene utility perspective, into the known chromosomal abnormalities detected by integrative genomic sequencing efforts, but also offers new insights into the etiology of NB and provides a framework to facilitate the identification of novel therapeutic targets for this devastating childhood cancer.
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Weiskittel TM, Ung CY, Correia C, Zhang C, Li H. De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens. Genome Res 2021; 32:124-134. [PMID: 34876496 PMCID: PMC8744682 DOI: 10.1101/gr.275889.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 11/30/2021] [Indexed: 12/04/2022]
Abstract
Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.
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Affiliation(s)
- Taylor M Weiskittel
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Choong Y Ung
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Cristina Correia
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Cheng Zhang
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
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10
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Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet. NPJ Genom Med 2021; 6:50. [PMID: 34131148 PMCID: PMC8206141 DOI: 10.1038/s41525-021-00216-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/25/2021] [Indexed: 11/09/2022] Open
Abstract
Many of genes mediating Known Drug-Disease Association (KDDA) are escaped from experimental detection. Identifying of these genes (hidden genes) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we presented a novel computational tool, called KDDANet, for systematic and accurate uncovering the hidden genes mediating KDDA from the perspective of genome-wide functional gene interaction network. KDDANet demonstrated the competitive performances in both sensitivity and specificity of identifying genes in mediating KDDA in comparison to the existing state-of-the-art methods. Case studies on Alzheimer's disease (AD) and obesity uncovered the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulated known genes mediating KDDAs related to cancer, but also revealed novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet .
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11
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Monie DD, Correia C, Zhang C, Ung CY, Vile RG, Li H. Modular network mechanism of CCN1-associated resistance to HSV-1-derived oncolytic immunovirotherapies for glioblastomas. Sci Rep 2021; 11:11198. [PMID: 34045642 PMCID: PMC8159930 DOI: 10.1038/s41598-021-90718-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022] Open
Abstract
Glioblastomas (GBMs) are the most common and lethal primary brain malignancy in adults. Oncolytic virus (OV) immunotherapies selectively kill GBM cells in a manner that elicits antitumor immunity. Cellular communication network factor 1 (CCN1), a protein found in most GBM microenvironments, expression predicts resistance to OVs, particularly herpes simplex virus type 1 (HSV-1). This study aims to understand how extracellular CCN1 alters the GBM intracellular state to confer OV resistance. Protein-protein interaction network information flow analyses of LN229 human GBM transcriptomes identified 39 novel nodes and 12 binary edges dominating flow in CCN1high cells versus controls. Virus response programs, notably against HSV-1, and cytokine-mediated signaling pathways are highly enriched. Our results suggest that CCN1high states exploit IDH1 and TP53, and increase dependency on RPL6, HUWE1, and COPS5. To validate, we reproduce our findings in 65 other GBM cell line (CCLE) and 174 clinical GBM patient sample (TCGA) datasets. We conclude through our generalized network modeling and system level analysis that CCN1 signals via several innate immune pathways in GBM to inhibit HSV-1 OVs before transduction. Interventions disrupting this network may overcome immunovirotherapy resistance.
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Affiliation(s)
- Dileep D Monie
- Medical Scientist Training Program, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Immunology, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Center for Regenerative Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Center for Individualized Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Cheng Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Center for Individualized Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Center for Individualized Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Richard G Vile
- Department of Immunology, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
- Center for Individualized Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
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12
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Monie DD, Bhandarkar AR, Parney IF, Correia C, Sarkaria JN, Vile RG, Li H. Synthetic and systems biology principles in the design of programmable oncolytic virus immunotherapies for glioblastoma. Neurosurg Focus 2021; 50:E10. [PMID: 33524942 DOI: 10.3171/2020.12.focus20855] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/04/2020] [Indexed: 12/11/2022]
Abstract
Oncolytic viruses (OVs) are a class of immunotherapeutic agents with promising preclinical results for the treatment of glioblastoma (GBM) but have shown limited success in recent clinical trials. Advanced bioengineering principles from disciplines such as synthetic and systems biology are needed to overcome the current challenges faced in developing effective OV-based immunotherapies for GBMs, including off-target effects and poor clinical responses. Synthetic biology is an emerging field that focuses on the development of synthetic DNA constructs that encode networks of genes and proteins (synthetic genetic circuits) to perform novel functions, whereas systems biology is an analytical framework that enables the study of complex interactions between host pathways and these synthetic genetic circuits. In this review, the authors summarize synthetic and systems biology concepts for developing programmable, logic-based OVs to treat GBMs. Programmable OVs can increase selectivity for tumor cells and enhance the local immunological response using synthetic genetic circuits. The authors discuss key principles for developing programmable OV-based immunotherapies, including how to 1) select an appropriate chassis, a vector that carries a synthetic genetic circuit, and 2) design a synthetic genetic circuit that can be programmed to sense key signals in the GBM microenvironment and trigger release of a therapeutic payload. To illustrate these principles, some original laboratory data are included, highlighting the need for systems biology studies, as well as some preliminary network analyses in preparation for synthetic biology applications. Examples from the literature of state-of-the-art synthetic genetic circuits that can be packaged into leading candidate OV chassis are also surveyed and discussed.
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Affiliation(s)
- Dileep D Monie
- Departments of1Immunology.,6Mayo Clinic Alix School of Medicine.,7Mayo Clinic Graduate School of Biomedical Sciences; and Mayo Clinic College of Medicine and Science, Rochester, Minnesota
| | | | | | - Cristina Correia
- 5Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic
| | | | | | - Hu Li
- 5Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic
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13
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Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells. JOURNAL OF BIOINFORMATICS AND SYSTEMS BIOLOGY : OPEN ACCESS 2021; 4:13-32. [PMID: 33842927 PMCID: PMC8031731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/29/2022]
Abstract
Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.
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14
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Apicco DJ, Zhang C, Maziuk B, Jiang L, Ballance HI, Boudeau S, Ung C, Li H, Wolozin B. Dysregulation of RNA Splicing in Tauopathies. Cell Rep 2019; 29:4377-4388.e4. [PMID: 31875547 PMCID: PMC6941411 DOI: 10.1016/j.celrep.2019.11.093] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 05/28/2019] [Accepted: 11/22/2019] [Indexed: 12/13/2022] Open
Abstract
Pathological aggregation of RNA binding proteins (RBPs) is associated with dysregulation of RNA splicing in PS19 P301S tau transgenic mice and in Alzheimer's disease brain tissues. The dysregulated splicing particularly affects genes involved in synaptic transmission. The effects of neuroprotective TIA1 reduction on PS19 mice are also examined. TIA1 reduction reduces disease-linked alternative splicing events for the major synaptic mRNA transcripts examined, suggesting that normalization of RBP functions is associated with the neuroprotection. Use of the NetDecoder informatics algorithm identifies key upstream biological targets, including MYC and EGFR, underlying the transcriptional and splicing changes in the protected compared to tauopathy mice. Pharmacological inhibition of MYC and EGFR activity in neuronal cultures tau recapitulates the neuroprotective effects of TIA1 reduction. These results demonstrate that dysfunction of RBPs and RNA splicing processes are major elements of the pathophysiology of tauopathies, as well as potential therapeutic targets for tauopathies.
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Affiliation(s)
- Daniel J Apicco
- Boston University School of Medicine, Department of Pharmacology and Experimental Therapeutics, Boston, MA, USA
| | | | - Brandon Maziuk
- Boston University School of Medicine, Department of Pharmacology and Experimental Therapeutics, Boston, MA, USA
| | - Lulu Jiang
- Boston University School of Medicine, Department of Pharmacology and Experimental Therapeutics, Boston, MA, USA
| | - Heather I Ballance
- Boston University School of Medicine, Department of Pharmacology and Experimental Therapeutics, Boston, MA, USA
| | - Samantha Boudeau
- Boston University School of Medicine, Department of Pharmacology and Experimental Therapeutics, Boston, MA, USA
| | | | - Hu Li
- Mayo Clinic, Rochester, MN, USA.
| | - Benjamin Wolozin
- Boston University School of Medicine, Department of Pharmacology and Experimental Therapeutics, Boston, MA, USA; Boston University School of Medicine, Department of Neurology, Boston, MA, USA.
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15
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Zhu L, Hofestadt R, Ester M. Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1471-1482. [PMID: 30736003 DOI: 10.1109/tcbb.2019.2897683] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.
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16
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Srivastava M, Bencurova E, Gupta SK, Weiss E, Löffler J, Dandekar T. Aspergillus fumigatus Challenged by Human Dendritic Cells: Metabolic and Regulatory Pathway Responses Testify a Tight Battle. Front Cell Infect Microbiol 2019; 9:168. [PMID: 31192161 PMCID: PMC6540932 DOI: 10.3389/fcimb.2019.00168] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 05/06/2019] [Indexed: 12/18/2022] Open
Abstract
Dendritic cells (DCs) are antigen presenting cells which serve as a passage between the innate and the acquired immunity. Aspergillosis is a major lethal condition in immunocompromised patients caused by the adaptable saprophytic fungus Aspergillus fumigatus. The healthy human immune system is capable to ward off A. fumigatus infections however immune-deficient patients are highly vulnerable to invasive aspergillosis. A. fumigatus can persist during infection due to its ability to survive the immune response of human DCs. Therefore, the study of the metabolism specific to the context of infection may allow us to gain insight into the adaptation strategies of both the pathogen and the immune cells. We established a metabolic model of A. fumigatus central metabolism during infection of DCs and calculated the metabolic pathway (elementary modes; EMs). Transcriptome data were used to identify pathways activated when A. fumigatus is challenged with DCs. In particular, amino acid metabolic pathways, alternative carbon metabolic pathways and stress regulating enzymes were found to be active. Metabolic flux modeling identified further active enzymes such as alcohol dehydrogenase, inositol oxygenase and GTP cyclohydrolase participating in different stress responses in A. fumigatus. These were further validated by qRT-PCR from RNA extracted under these different conditions. For DCs, we outlined the activation of metabolic pathways in response to the confrontation with A. fumigatus. We found the fatty acid metabolism plays a crucial role, along with other metabolic changes. The gene expression data and their analysis illuminate additional regulatory pathways activated in the DCs apart from interleukin regulation. In particular, Toll-like receptor signaling, NOD-like receptor signaling and RIG-I-like receptor signaling were active pathways. Moreover, we identified subnetworks and several novel key regulators such as UBC, EGFR, and CUL3 of DCs to be activated in response to A. fumigatus. In conclusion, we analyze the metabolic and regulatory responses of A. fumigatus and DCs when confronted with each other.
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Affiliation(s)
- Mugdha Srivastava
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Shishir K Gupta
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Esther Weiss
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | - Jürgen Löffler
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.,EMBL Heidelberg, Structural and Computational Biology, Heidelberg, Germany
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17
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Oulas A, Minadakis G, Zachariou M, Sokratous K, Bourdakou MM, Spyrou GM. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform 2019; 20:806-824. [PMID: 29186305 PMCID: PMC6585387 DOI: 10.1093/bib/bbx151] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/17/2017] [Indexed: 02/01/2023] Open
Abstract
Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kleitos Sokratous
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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18
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Reconstruction of complex single-cell trajectories using CellRouter. Nat Commun 2018; 9:892. [PMID: 29497036 PMCID: PMC5832860 DOI: 10.1038/s41467-018-03214-y] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 01/25/2018] [Indexed: 12/11/2022] Open
Abstract
A better understanding of the cell-fate transitions that occur in complex cellular ecosystems in normal development and disease could inform cell engineering efforts and lead to improved therapies. However, a major challenge is to simultaneously identify new cell states, and their transitions, to elucidate the gene expression dynamics governing cell-type diversification. Here, we present CellRouter, a multifaceted single-cell analysis platform that identifies complex cell-state transition trajectories by using flow networks to explore the subpopulation structure of multi-dimensional, single-cell omics data. We demonstrate its versatility by applying CellRouter to single-cell RNA sequencing data sets to reconstruct cell-state transition trajectories during hematopoietic stem and progenitor cell (HSPC) differentiation to the erythroid, myeloid and lymphoid lineages, as well as during re-specification of cell identity by cellular reprogramming of monocytes and B-cells to HSPCs. CellRouter opens previously undescribed paths for in-depth characterization of complex cellular ecosystems and establishment of enhanced cell engineering approaches. Single cell analysis provides insight into cell states and transitions, but to interpret the data, improved algorithms are needed. Here, the authors present CellRouter as a method to analyse single-cell trajectories from RNA-sequencing data, and provide insight into erythroid, myeloid and lymphoid differentiation.
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19
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Monie DD, DeLoughery EP. Pathogenesis of thrombosis: cellular and pharmacogenetic contributions. Cardiovasc Diagn Ther 2017; 7:S291-S298. [PMID: 29399533 DOI: 10.21037/cdt.2017.09.11] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Our understanding of thrombosis formation has evolved significantly ever since physician Rudolf Virchow proposed his "triad" theory in 1856. Modern science has elucidated the mechanisms of stasis, hypercoagulability, and endothelial dysfunction. Today, we have a firm understanding of the key molecular factors involved in the coagulation cascade and fibrinolytic system, as well as the underlying genetic influences. This knowledge of cellular and genetic contributors has been translated into diverse pharmaceutical interventions. Here, we examine the molecular and cellular mechanisms of thrombosis and its associated pathologies. We also review the current state of pharmacologic interventions, including pro- and anti-thrombotics, direct oral anticoagulants, and anti-platelet therapies. The pharmacogenetic factors that guide clinical decision making and prognosis are described in detail. Finally, we explore new approaches to thrombosis drug discovery, repurposing, and diagnostics. We argue that network biology tools will enable a systems pharmacology revolution in the next generation of interventions, facilitating precision medicine applications and ultimately leading to improved patient outcomes.
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Affiliation(s)
- Dileep D Monie
- School of Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.,Graduate School of Biomedical Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.,Medical Scientist Training Program, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emma P DeLoughery
- School of Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
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20
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Ghanat Bari M, Ung CY, Zhang C, Zhu S, Li H. Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks. Sci Rep 2017; 7:6993. [PMID: 28765560 PMCID: PMC5539301 DOI: 10.1038/s41598-017-07481-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/27/2017] [Indexed: 12/25/2022] Open
Abstract
Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.
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Affiliation(s)
- Mehrab Ghanat Bari
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Cheng Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA.
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21
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Salazar BM, Balczewski EA, Ung CY, Zhu S. Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology. Int J Mol Sci 2016; 18:E37. [PMID: 28035989 PMCID: PMC5297672 DOI: 10.3390/ijms18010037] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/14/2016] [Accepted: 12/17/2016] [Indexed: 12/13/2022] Open
Abstract
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.
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Affiliation(s)
- Brittany M Salazar
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA.
| | - Emily A Balczewski
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA.
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
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