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Ceyhan AB, Ozcan M, Kim W, Li X, Altay O, Zhang C, Mardinoglu A. Novel drug targets and molecular mechanisms for sarcopenia based on systems biology. Biomed Pharmacother 2024; 176:116920. [PMID: 38876054 DOI: 10.1016/j.biopha.2024.116920] [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: 04/24/2024] [Revised: 06/05/2024] [Accepted: 06/09/2024] [Indexed: 06/16/2024] Open
Abstract
Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.
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Affiliation(s)
- Atakan Burak Ceyhan
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| | - Mehmet Ozcan
- Department of Medical Biochemistry, Faculty of Medicine, Zonguldak Bulent Ecevit University, Zonguldak, Turkiye
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Xiangyu Li
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Ozlem Altay
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK; Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
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2
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Paquette A, Ahuna K, Hwang YM, Pearl J, Liao H, Shannon P, Kadam L, Lapehn S, Bucher M, Roper R, Funk C, MacDonald J, Bammler T, Baloni P, Brockway H, Mason WA, Bush N, Lewinn KZ, Karr CJ, Stamatoyannopoulos J, Muglia LJ, Jones H, Sadovsky Y, Myatt L, Sathyanarayana S, Price ND. A genome scale transcriptional regulatory model of the human placenta. SCIENCE ADVANCES 2024; 10:eadf3411. [PMID: 38941464 PMCID: PMC11212735 DOI: 10.1126/sciadv.adf3411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/28/2024] [Indexed: 06/30/2024]
Abstract
Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.
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Affiliation(s)
- Alison Paquette
- University of Washington, Seattle, WA, USA
- Seattle Children’s Research Institute, Seattle, WA, USA
| | - Kylia Ahuna
- Oregon Health and Sciences University, Portland, OR, USA
| | | | | | - Hanna Liao
- University of Washington, Seattle, WA, USA
| | | | - Leena Kadam
- Oregon Health and Sciences University, Portland, OR, USA
| | | | - Matthew Bucher
- Oregon Health and Sciences University, Portland, OR, USA
| | - Ryan Roper
- Institute for Systems Biology, Seattle, WA, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | | | - Heather Brockway
- Department of Physiology and Aging, University of Florida, Gainesville, FL, USA
| | - W. Alex Mason
- University of Tennessee Health Sciences Center, Memphis, TN, USA
| | - Nicole Bush
- University of California San Francisco, San Francisco, CA, USA
| | - Kaja Z. Lewinn
- University of California San Francisco, San Francisco, CA, USA
| | | | | | - Louis J. Muglia
- The Burroughs Wellcome Fund, Research Triangle Park, NC, USA
- Cincinnati Children’s Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Yoel Sadovsky
- Magee Womens Research Institute, Pittsburgh, PA, USA
- University of Pittsburgh, Pittsburgh, PA, USA
| | - Leslie Myatt
- Oregon Health and Sciences University, Portland, OR, USA
| | - Sheela Sathyanarayana
- University of Washington, Seattle, WA, USA
- Seattle Children’s Research Institute, Seattle, WA, USA
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York City, NY, USA
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3
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Matulevičius A, Žukauskaitė K, Gineikaitė R, Dasevičius D, Trakymas M, Naruševičiūtė I, Ušinskienė J, Ulys A, Jankevičius F, Jarmalaitė S. Combination of DNA methylation biomarkers with multiparametric magnetic resonance and ultrasound imaging fusion biopsy to detect the local spread of prostate cancer. Prostate 2023; 83:1572-1583. [PMID: 37614027 DOI: 10.1002/pros.24615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/04/2023] [Accepted: 08/12/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND This study aimed to investigate the extent of field cancerization adjacent to index lesions in prostate cancer (PCa) by measuring DNA methylation of selected tumor suppressor genes in the perifocal tissue of PCa not visible on multiparametric magnetic resonanse imaging (mpMRI) for the safe zone of focal therapy identification. METHODS A total of 272 patients were enrolled in this study, 44 patients' tissue biosamples were included in the field cancerization research, and 272 urine samples were included in the urine-based test development. Targeted biopsies were performed using the mpMRI/ultrasoundimage fusion system. RESULTS Quantitative analysis revealed significantly higher DNA methylation levels of RARB, RASSF1, GSTP1 & APC genes in the index lesion compared with perifocal tissue samples 10 mm away from it (p < 0.0001). Notably, the RARB, GSTP1 & APC and RARB, RASSF1, GSTP1 & APC biomarker combinations exhibited the highest sensitivity and specificity comparing the extent of DNA methylation in index lesions and noncancerous prostate tissues 20 mm away (both area under the curve [AUC] = 0.98; p < 0.0001). The analysis of the potential urinary biomarkers showed that the combination of all four DNA methylation biomarkers with prostate-specific antigen (PSA) or PSA density (PSAD) in the blood significantly improves the detection of clinically significant PCa (csPCa). The combination of the four-biomarker test with PSAD allowed the identification of csPCa with ≥90% sensitivity and specificity. CONCLUSION Thus, this study suggests that for focal therapy by region target hemi-ablation, the safe distance from the index lesion is no less than 10 mm. Noninvasive urine DNA methylation tests in combination with PSAD could be used for further follow-up of the patients, but larger prospective studies with external validation are needed.
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Affiliation(s)
- Augustinas Matulevičius
- Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, Lithuania
- National Cancer Institute, Vilnius, Lithuania
- Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Kristina Žukauskaitė
- Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, Lithuania
- National Cancer Institute, Vilnius, Lithuania
| | | | - Darius Dasevičius
- National Centre of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | | | | | | | | | - Feliksas Jankevičius
- Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Sonata Jarmalaitė
- Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, Lithuania
- National Cancer Institute, Vilnius, Lithuania
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4
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Li Y, Xie G, Zha Y, Ning K. GAN-GMHI: a generative adversarial network with high discriminative power for microbiome-based disease prediction. J Genet Genomics 2023; 50:1026-1028. [PMID: 36972797 DOI: 10.1016/j.jgg.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/29/2023]
Affiliation(s)
- Yuxue Li
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Gang Xie
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yuguo Zha
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Kang Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
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5
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Iqbal MA, Siddiqui S, Smith K, Singh P, Kumar B, Chouaib S, Chandrasekaran S. Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance. iScience 2023; 26:108059. [PMID: 37854701 PMCID: PMC10579441 DOI: 10.1016/j.isci.2023.108059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 07/17/2023] [Accepted: 09/22/2023] [Indexed: 10/20/2023] Open
Abstract
Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.
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Affiliation(s)
- Mohammad A. Iqbal
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates
| | | | - Kirk Smith
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi, India
| | - Bhupender Kumar
- Department of Microbiology, Swami Shraddhanand College, University of Delhi, New Delhi, Delhi, India
| | - Salem Chouaib
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates
- INSERM UMR 1186, Gustave Roussy, EPHE, Faculty of Medicine, University of Paris-Saclay, Villejuif, France
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA
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6
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Chang D, Gupta VK, Hur B, Cobo-López S, Cunningham KY, Han NS, Lee I, Kronzer VL, Teigen LM, Karnatovskaia LV, Longbrake EE, Davis JM, Nelson H, Sung J. Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560294. [PMID: 37873265 PMCID: PMC10592848 DOI: 10.1101/2023.09.30.560294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual's unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.
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Affiliation(s)
- Daniel Chang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Vinod K Gupta
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Benjamin Hur
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Sergio Cobo-López
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Kevin Y Cunningham
- Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nam Soo Han
- Brain Korea 21 Center for Bio-Health Industry, Department of Food Science and Biotechnology, Chungbuk National University, Cheongju, South Korea
| | - Insuk Lee
- Department of Biotechnology, Yonsei University, Seoul 03722, South Korea
| | - Vanessa L Kronzer
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Levi M Teigen
- Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, USA
| | | | - Erin E Longbrake
- Department of Neurology, Yale University, New Haven, CT 06510, USA
| | - John M Davis
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Heidi Nelson
- Emeritus, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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7
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Lim PJ, Marcionelli G, Srikanthan P, Ndarugendamwo T, Pinner J, Rohrbach M, Giunta C. Perturbations in fatty acid metabolism and collagen production infer pathogenicity of a novel MBTPS2 variant in Osteogenesis imperfecta. Front Endocrinol (Lausanne) 2023; 14:1195704. [PMID: 37305034 PMCID: PMC10248412 DOI: 10.3389/fendo.2023.1195704] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Osteogenesis imperfecta (OI) is a heritable and chronically debilitating skeletal dysplasia. Patients with OI typically present with reduced bone mass, tendency for recurrent fractures, short stature and bowing deformities of the long bones. Mutations causative of OI have been identified in over 20 genes involved in collagen folding, posttranslational modification and processing, and in bone mineralization and osteoblast development. In 2016, we described the first X-linked recessive form of OI caused by MBTPS2 missense variants in patients with moderate to severe phenotypes. MBTPS2 encodes site-2 protease, a Golgi transmembrane protein that activates membrane-tethered transcription factors. These transcription factors regulate genes involved in lipid metabolism, bone and cartilage development, and ER stress response. The interpretation of genetic variants in MBTPS2 is complicated by the gene's pleiotropic properties; MBTPS2 variants can also cause the dermatological conditions Ichthyosis Follicularis, Atrichia and Photophobia (IFAP), Keratosis Follicularis Spinulosa Decalvans (KFSD) and Olmsted syndrome (OS) without skeletal abnormalities typical of OI. Using control and patient-derived fibroblasts, we previously identified gene expression signatures that distinguish MBTPS2-OI from MBTPS2-IFAP/KFSD and observed stronger suppression of genes involved in fatty acid metabolism in MBTPS2-OI than in MBTPS2-IFAP/KFSD; this was coupled with alterations in the relative abundance of fatty acids in MBTPS2-OI. Furthermore, we observed a reduction in collagen deposition in the extracellular matrix by MBTPS2-OI fibroblasts. Here, we extrapolate our observations in the molecular signature unique to MBTPS2-OI to infer the pathogenicity of a novel MBTPS2 c.516A>C (p.Glu172Asp) variant of unknown significance in a male proband. The pregnancy was terminated at gestational week 21 after ultrasound scans showed bowing of femurs and tibiae and shortening of long bones particularly of the lower extremity; these were further confirmed by autopsy. By performing transcriptional analyses, gas chromatography-tandem mass spectrometry-based quantification of fatty acids and immunocytochemistry on fibroblasts derived from the umbilical cord of the proband, we observed perturbations in fatty acid metabolism and collagen production similar to what we previously described in MBTPS2-OI. These findings support pathogenicity of the MBTPS2 variant p.Glu172Asp as OI-causative and highlights the value of extrapolating molecular signatures identified in multiomics studies to characterize novel genetic variants.
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Affiliation(s)
- Pei Jin Lim
- Connective Tissue Unit, Division of Metabolism and Children’s Research Center, University Children’s Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Giulio Marcionelli
- Connective Tissue Unit, Division of Metabolism and Children’s Research Center, University Children’s Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Pakeerathan Srikanthan
- Department of Clinical Chemistry and Biochemistry, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Timothée Ndarugendamwo
- Connective Tissue Unit, Division of Metabolism and Children’s Research Center, University Children’s Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Jason Pinner
- Centre for Clinical Genetics, Sydney Children’s Hospital, Sydney, Australia
- UNSW Medicine and Health, University of New South Wales, Sydney, Australia
| | - Marianne Rohrbach
- Connective Tissue Unit, Division of Metabolism and Children’s Research Center, University Children’s Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Cecilia Giunta
- Connective Tissue Unit, Division of Metabolism and Children’s Research Center, University Children’s Hospital Zurich and University of Zurich, Zurich, Switzerland
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8
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Cunningham KY, Hur B, Gupta VK, Arment CA, Wright KA, Mason TG, Peterson LS, Bekele DI, Schaffer DE, Bailey ML, Delger KE, Crowson CS, Myasoedova E, Zeng H, Rodriguez M, Weyand CM, Davis JM, Sung J. Patients with ACPA-positive and ACPA-negative rheumatoid arthritis show different serological autoantibody repertoires and autoantibody associations with disease activity. Sci Rep 2023; 13:5360. [PMID: 37005480 PMCID: PMC10066987 DOI: 10.1038/s41598-023-32428-4] [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: 11/18/2022] [Accepted: 03/27/2023] [Indexed: 04/04/2023] Open
Abstract
Patients with rheumatoid arthritis (RA) can test either positive or negative for circulating anti-citrullinated protein antibodies (ACPA) and are thereby categorized as ACPA-positive (ACPA+) or ACPA-negative (ACPA-), respectively. In this study, we aimed to elucidate a broader range of serological autoantibodies that could further explain immunological differences between patients with ACPA+ RA and ACPA- RA. On serum collected from adult patients with ACPA+ RA (n = 32), ACPA- RA (n = 30), and matched healthy controls (n = 30), we used a highly multiplex autoantibody profiling assay to screen for over 1600 IgG autoantibodies that target full-length, correctly folded, native human proteins. We identified differences in serum autoantibodies between patients with ACPA+ RA and ACPA- RA compared with healthy controls. Specifically, we found 22 and 19 autoantibodies with significantly higher abundances in ACPA+ RA patients and ACPA- RA patients, respectively. Among these two sets of autoantibodies, only one autoantibody (anti-GTF2A2) was common in both comparisons; this provides further evidence of immunological differences between these two RA subgroups despite sharing similar symptoms. On the other hand, we identified 30 and 25 autoantibodies with lower abundances in ACPA+ RA and ACPA- RA, respectively, of which 8 autoantibodies were common in both comparisons; we report for the first time that the depletion of certain autoantibodies may be linked to this autoimmune disease. Functional enrichment analysis of the protein antigens targeted by these autoantibodies showed an over-representation of a range of essential biological processes, including programmed cell death, metabolism, and signal transduction. Lastly, we found that autoantibodies correlate with Clinical Disease Activity Index, but associate differently depending on patients' ACPA status. In all, we present candidate autoantibody biomarker signatures associated with ACPA status and disease activity in RA, providing a promising avenue for patient stratification and diagnostics.
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Affiliation(s)
- Kevin Y Cunningham
- Bioinformatics and Computational Biology Program, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Benjamin Hur
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Vinod K Gupta
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Courtney A Arment
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kerry A Wright
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Thomas G Mason
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Lynne S Peterson
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Delamo I Bekele
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Daniel E Schaffer
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Marissa L Bailey
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kara E Delger
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Cynthia S Crowson
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Elena Myasoedova
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Hu Zeng
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Immunology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Moses Rodriguez
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Cornelia M Weyand
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Immunology, Mayo Clinic, Rochester, MN, 55905, USA
| | - John M Davis
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
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9
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Chalikiopoulou C, Gómez-Tamayo JC, Katsila T. Untargeted Metabolomics for Disease-Specific Signatures. Methods Mol Biol 2023; 2571:71-81. [PMID: 36152151 DOI: 10.1007/978-1-0716-2699-3_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Human diseases account for complex traits that usually exhibit markedly diverse clinical manifestations coming from a series of pathogenic processes that shape heterogeneous phenotypes. Considering that correlation does not imply causation as well as population differences and/or inter-individual variability, disease-specific signatures are becoming critical for biomarker discovery. Untargeted metabolomics is deemed to be a powerful approach to delineate molecular pathways of prime interest. Metabotypes capture the interplay of genomics and environmental influences per se. Untargeted metabolomics share the charm of being not only hypothesis-driven but also hypothesis-generating. Notwithstanding, the applicability of untargeted metabolomics toward clinically relevant outcomes depend on wet- and dry-lab procedures in the context of elegant study designs with clear rationale. As ideal may be far from feasible, herein we provide recommendations to combat sample mishandling that adversely affect data outcomes and if so, deal with imbalanced datasets toward data integrity.
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Affiliation(s)
| | | | - Theodora Katsila
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.
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10
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Hur B, Koster MJ, Jang JS, Weyand CM, Warrington KJ, Sung J. Global Transcriptomic Profiling Identifies Differential Gene Expression Signatures Between Inflammatory and Noninflammatory Aortic Aneurysms. Arthritis Rheumatol 2022; 74:1376-1386. [PMID: 35403833 PMCID: PMC9902298 DOI: 10.1002/art.42138] [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: 01/05/2022] [Revised: 02/17/2022] [Accepted: 03/08/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To identify hallmark genes and biomolecular processes in aortitis using high-throughput gene expression profiling, and to provide a range of potentially new drug targets (genes) and therapeutics from a pharmacogenomic network analysis. METHODS Bulk RNA sequencing was performed on surgically resected ascending aortic tissues from inflammatory aneurysms (giant cell arteritis [GCA] with or without polymyalgia rheumatica, n = 8; clinically isolated aortitis [CIA], n = 17) and noninflammatory aneurysms (n = 25) undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the 2 patient groups were identified while controlling for clinical covariates. A protein-protein interaction model, drug-gene target information, and the DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis. RESULTS Overall, tissue gene expression patterns were the most associated with disease state than with any other clinical characteristic. We identified 159 and 93 genes that were significantly up-regulated and down-regulated, respectively, in inflammatory aortic aneurysms compared to noninflammatory aortic aneurysms. We found that the up-regulated genes were enriched in immune-related functions, whereas the down-regulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA were no different than those from patients with CIA. Finally, our pharmacogenomic network analysis identified genes that could potentially be targeted by immunosuppressive drugs currently approved for other inflammatory diseases. CONCLUSION We performed the first global transcriptomics analysis in inflammatory aortic aneurysms from surgically resected aortic tissues. We identified signature genes and biomolecular processes, while finding that CIA may be a limited presentation of GCA. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions and suggests future biomarker discovery directions for the precise diagnosis and treatment of aortitis.
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Affiliation(s)
- Benjamin Hur
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Matthew J. Koster
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jin Sung Jang
- Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Cornelia M. Weyand
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, USA
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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11
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Methods for Stratification and Validation Cohorts: A Scoping Review. J Pers Med 2022; 12:jpm12050688. [PMID: 35629113 PMCID: PMC9144352 DOI: 10.3390/jpm12050688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/31/2022] [Accepted: 04/15/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.
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12
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Loric S, Conti M. Versatile Functional Energy Metabolism Platform Working From Research to Patient: An Integrated View of Cell Bioenergetics. FRONTIERS IN TOXICOLOGY 2022; 3:750431. [PMID: 35295105 PMCID: PMC8915814 DOI: 10.3389/ftox.2021.750431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/08/2021] [Indexed: 12/06/2022] Open
Abstract
Mitochondrial dysfunctions that were not discovered during preclinical and clinical testing have been responsible for at least restriction of use as far as withdrawal of many drugs. To solve mitochondrial machinery complexity, integrative methodologies combining different data, coupled or not to mathematic modelling into systems biology, could represent a strategic way but are still very hard to implement. These technologies should be accurate and precise to avoid accumulation of errors that can lead to misinterpretations, and then alter prediction efficiency. To address such issue, we have developed a versatile functional energy metabolism platform that can measure quantitatively, in parallel, with a very high precision and accuracy, a high number of biological parameters like substrates or enzyme cascade activities in essential metabolism units (glycolysis, respiratory chain ATP production, oxidative stress...) Its versatility (our platform works on either cell lines or small animals and human samples) allows cell metabolism pathways fine tuning comparison from preclinical to clinical studies. Applied here to OXPHOS and/or oxidative stress as an example, it allows discriminating compounds with acute toxic effects but, most importantly, those inducing low noise chronic ones.
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13
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Rebuzzi SE, Banna GL, Murianni V, Damassi A, Giunta EF, Fraggetta F, De Giorgi U, Cathomas R, Rescigno P, Brunelli M, Fornarini G. Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence. Cancers (Basel) 2021; 13:5517. [PMID: 34771680 PMCID: PMC8583566 DOI: 10.3390/cancers13215517] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 12/13/2022] Open
Abstract
In recent years, the treatment landscape of urothelial carcinoma has significantly changed due to the introduction of immune checkpoint inhibitors (ICIs), which are the standard of care for second-line treatment and first-line platinum-ineligible patients with advanced disease. Despite the overall survival improvement, only a minority of patients benefit from this immunotherapy. Therefore, there is an unmet need to identify prognostic and predictive biomarkers or models to select patients who will benefit from ICIs, especially in view of novel therapeutic agents. This review describes the prognostic and predictive role, and clinical readiness, of clinical and tumour factors, including new molecular classes, tumour mutational burden, mutational signatures, circulating tumour DNA, programmed death-ligand 1, inflammatory indices and clinical characteristics for patients with urothelial cancer treated with ICIs. A classification of these factors according to the levels of evidence and grades of recommendation currently indicates both a prognostic and predictive value for ctDNA and a prognostic relevance only for concomitant medications and patients' characteristics.
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Affiliation(s)
- Sara Elena Rebuzzi
- Medical Oncology, Ospedale San Paolo, 17100 Savona, Italy
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genova, 16132 Genova, Italy
| | | | - Veronica Murianni
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (V.M.); (G.F.)
| | - Alessandra Damassi
- Academic Unit of Medical Oncology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy;
| | - Emilio Francesco Giunta
- Department of Precision Medicine, Università Degli Studi della Campania Luigi Vanvitelli, 80131 Naples, Italy;
| | | | - Ugo De Giorgi
- Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy;
| | - Richard Cathomas
- Division of Oncology/Hematology, Kantonsspital Graubünden, 7000 Chur, Switzerland;
| | - Pasquale Rescigno
- Interdisciplinary Group for Translational Research and Clinical Trials, Urogenital Cancers GIRT-Uro, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, 10060 Turin, Italy;
| | - Matteo Brunelli
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, 37134 Verona, Italy;
| | - Giuseppe Fornarini
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (V.M.); (G.F.)
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14
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Naithani N, Sinha S, Misra P, Vasudevan B, Sahu R. Precision medicine: Concept and tools. Med J Armed Forces India 2021; 77:249-257. [PMID: 34305276 PMCID: PMC8282508 DOI: 10.1016/j.mjafi.2021.06.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Precision medicine is the new age medicine and refers to tailoring treatments to a subpopulation who have a common susceptibility to a particular disease or similar response to a particular drug. Although the concept existed even during the times of Sir William Osler, it was given a shot in the arm with the Precision Medicine Initiative launched by Barack Obama in 2015. The main tools of precision medicine are Big data, artificial intelligence, the various omics, pharmaco-omics, environmental and social factors and the integration of these with preventive and population medicine. Big data can be acquired from electronic health records of patients and includes various biomarkers (clinical and omics based), laboratory and radiological investigations and these can be analysed through machine learning by various complex flowcharts setting up an algorithm for the management of specific subpopulations. So, there is a move away from the traditional "one size fits all" treatment to precision-based medicine. Research in "omics" has increased in leaps and bounds and advancements have included the fields of genomics, epigenomics, proteomics, transcriptomics, metabolomics and microbiomics. Pharmaco-omics has also come to the forefront with development of new drugs and suiting a particular drug to a particular subpopulation, thus avoiding their prescription to non-responders, preventing unwanted adverse effects and proving economical in the long run. Environmental, social and behavioural factors are as important or in fact more important than genetic factors in most complex diseases and managing these factors form an important part of precision medicine. Finally integrating precision with preventive and public health makes "precision medicine" a complete final product which will change the way medicine will be practised in future.
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Affiliation(s)
- Nardeep Naithani
- Director & Commandant, Armed Forces Medical College, Pune, India
| | - Sharmila Sinha
- Professor & Head, Department of Pharmacology, Armed Forces Medical College, Pune, India
| | - Pratibha Misra
- Professor & Head, Department of Biochemistry, Armed Forces Medical College, Pune, India
| | - Biju Vasudevan
- Professor & Head, Department of Dermatology, Armed Forces Medical College, Pune, India
| | - Rajesh Sahu
- Associate Professor, Department of Community Medicine, Armed Forces Medical College, Pune, India
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15
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Hur B, Gupta VK, Huang H, Wright KA, Warrington KJ, Taneja V, Davis JM, Sung J. Plasma metabolomic profiling in patients with rheumatoid arthritis identifies biochemical features predictive of quantitative disease activity. Arthritis Res Ther 2021; 23:164. [PMID: 34103083 PMCID: PMC8185925 DOI: 10.1186/s13075-021-02537-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 05/19/2021] [Indexed: 02/06/2023] Open
Abstract
Background Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA. Methods Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups (“lower”, DAS28-CRP ≤ 3.2; and “higher”, DAS28-CRP > 3.2) and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups (“low-CRP”, CRP ≤ 3.0 mg/L; “high-CRP”, CRP > 3.0 mg/L) were investigated. Results We identified 33 metabolites differentially abundant between the lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE] ± SD: 1.51 ± 1.77) compared to a GLM without feature selection (MAE ± SD: 2.02 ± 2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted and actual DAS28-CRP (with feature selection: Spearman’s ρ = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearman’s ρ = 0.18, 95% CI: [−0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with the CRP patient groups while controlling for potential confounders (P < 0.05). Conclusions We demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients. Supplementary Information The online version contains supplementary material available at 10.1186/s13075-021-02537-4.
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Affiliation(s)
- Benjamin Hur
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Vinod K Gupta
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Harvey Huang
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA
| | - Kerry A Wright
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kenneth J Warrington
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Veena Taneja
- Department of Immunology, Mayo Clinic, Rochester, MN, USA
| | - John M Davis
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. .,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, USA. .,Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
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16
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Letieri ADS, Freitas-Fernandes LB, Albarello LL, Fontes GP, de Souza IPR, Valente AP, Fidalgo TKDS. Analysis of Salivary Metabolites by Nuclear Magnetic Resonance Before and After Oral Mucosa Cleaning of Infants in the Pre-dental Period. FRONTIERS IN DENTAL MEDICINE 2021. [DOI: 10.3389/fdmed.2021.667365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The aim of the present study was to verify if a protocol for cleaning the oral cavity of infants in the pre-dental period can reduce extrinsic salivary metabolites observed through Nuclear Magnetic Resonance (NMR). A cross-sectional clinical study with a convenience sample was conducted, and infants were recruited at the UFRJ Pediatric Dentistry Clinic. Participants who had used antibiotics and/or antifungals up to 3 months before and whose legal guardians did not consent or sign the Informed Consent Form were excluded. An anamnesis was performed with the guardians and the participants' intraoral clinical examination. Initial collection of unstimulated total saliva was performed using an automatic pipette with sterile plastic tips in the buccal floor region, at least 1 h after the last feeding. Subsequently, the infants' oral mucosa was cleaned with gauze moistened with filtered water, and after 5 min, a new collection was performed, using the same methodology. The obtained samples were immediately transferred on ice to the laboratory, centrifuged (10,000 g), and stored at −80°C. The NMR analyses were performed using a 500-MHz spectrometer Bruker, Germany); evaluations were done via the 1H and 1H-1H TOCSY spectra for metabolite signaling. Eleven pre-dental infants were evaluated, with a mean age of 3.8 months, including six girls (55%). Of these, nine participants (82%) were exclusively breastfed. The higher presence of components such as lactose, glucose, sugars, acetate, alanine, and lactate were observed in the samples before oral mucosa cleaning. Regarding the type of diet, more lactose was observed in the saliva of patients who were exclusively breastfed than those that received mixed feeding. We conclude that the oral mucosa cleaning of infants in the pre-dental period tends to reduce the concentration of extrinsic components from the diet, such as lactose, in the salivary metabolomic profile analyzed by NMR.
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17
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Chandrasekaran S, Danos N, George UZ, Han JP, Quon G, Müller R, Tsang Y, Wolgemuth C. The Axes of Life: A roadmap for understanding dynamic multiscale systems. Integr Comp Biol 2021; 61:2011-2019. [PMID: 34048574 DOI: 10.1093/icb/icab114] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast data sets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.
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Affiliation(s)
| | - Nicole Danos
- Department of Biology, University of San Diego, San Diego, CA, USA
| | - Uduak Z George
- Department of Mathematics & Statistics, San Diego State University, San Diego, CA, USA
| | - Jin-Ping Han
- IBM TJ Watson Research Center, Ossining, NY, USA
| | - Gerald Quon
- Department of Molecular and Cellular Biology, University of California-Davis, Davis, CA,USA
| | - Rolf Müller
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, VI, USA
| | - Yinphan Tsang
- Department of Natural Resources and Environmental Management, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Charles Wolgemuth
- Departments of Physics and Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
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18
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Abstract
A clinically reliable non-invasive test for endometriosis is expected to reduce the diagnostic delay. Although varieties of biomarkers have been investigated for decades, and cancer antigen-125, cancer antigen-199, interleukin-6, and urocortin were the most studied ones among hundreds of biomarkers, no clinically reliable biomarkers have been confirmed so far. Some emerging technologies including “omics” technologies, molecular imaging techniques, and microRNAs are promising in solving these challenges, but their utility to detect endometriosis has yet to be verified. New combinations of researched indicators or other non-invasive methods and further exploration of the emerging technologies may be new targets and future research hotspots for non-invasive diagnosis of endometriosis. In conclusion, researches of biomarkers for the detection of endometriosis are still ongoing and may benefit from novel molecular biology, bioinformatics methods and a combination of more diverse monitoring methods. Though it will be a daunting task, the identification of a specific set of diagnostic biomarkers will undoubtedly improve the status of endometriosis.
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19
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Wu J, Xia X, Hu Y, Fang X, Orsulic S. Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis. Front Genet 2021; 12:580190. [PMID: 33613630 PMCID: PMC7887323 DOI: 10.3389/fgene.2021.580190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 01/04/2021] [Indexed: 12/19/2022] Open
Abstract
Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.
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Affiliation(s)
- Jingni Wu
- Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, China.,Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Xiaomeng Xia
- Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ye Hu
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Xiaoling Fang
- Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Sandra Orsulic
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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20
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Gaia-Oltean AI, Braicu C, Gulei D, Ciortea R, Mihu D, Roman H, Irimie A, Berindan-Neagoe I. Ovarian endometriosis, a precursor of ovarian cancer: Histological aspects, gene expression and microRNA alterations (Review). Exp Ther Med 2021; 21:243. [PMID: 33603851 PMCID: PMC7851621 DOI: 10.3892/etm.2021.9674] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 12/17/2020] [Indexed: 12/24/2022] Open
Abstract
Ovarian endometriosis is a frequent chronic gynecological disease with an uncertain evolution regarding its progression or association with ovarian malignant lesions. The present review summarized the histological aspects, gene expression and microRNA (miRNA/miR) alterations associated with ovarian endometriosis and cancer and their possible interaction. The endometriosis-ovarian cancer interaction has been proposed by certain researchers as a single entity. Histological results indicated that endometriosis has been in different circumstances coexisting with ovarian cancer, with reference to endometrioid and clear cell carcinoma. Endometriosis with moderate and severe atypia can influence cell proliferation and architecture, resulting in a possible malignant transformation. Gene expression analysis indicated that the pathologies of both endometriosis and ovarian cancer are characterized by genetic instability from a molecular point of view, as several important genetic mutations, including ARID1A, PI3KCA, PTEN, BRCA1, BRCA2, TP53 and KRAS genes, were identified. miRNA alterations have been implicated in the regulation of gene expression. Common dysregulated miRNAs, such as miR-331, miR-335, miR-891, miR-548, miR-124, miR-148, miR-215, miR-192, miR-337, miR-153, miR-155, miR-144, miR-221 and miR-3688 were extensively investigated in understanding endometriosis and ovarian cancer evolution. From a combined viewpoint including histological aspects, gene expression and miRNA alterations, it is reasonable to speculate that endometriosis is associated with ovarian cancer. Ovarian endometriosis lesions may present a risk for ovarian malignant lesions, which supports a model of endometriosis as a malignant precursor. However, the endometriosis-ovarian cancer association is not widely accepted in the literature and additional studies are required to validate this association.
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Affiliation(s)
- Adriana Ioana Gaia-Oltean
- Department of Oncological Surgery, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Cornelia Braicu
- Research Center for Functional Genomics and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania
| | - Diana Gulei
- MedFuture-Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania
| | - Razvan Ciortea
- Second Department of Obstetrics and Gynecology, Iuliu Hatieganu University of Medicine and Pharmacy, 400124 Cluj-Napoca, Romania
| | - Dan Mihu
- Second Department of Obstetrics and Gynecology, Iuliu Hatieganu University of Medicine and Pharmacy, 400124 Cluj-Napoca, Romania
| | - Horace Roman
- Center of Endometriosis, Clinique Tivoli-Ducos, 33000 Bordeaux, France
| | - Alexandru Irimie
- Department of Oncological Surgery, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.,Research Center for Functional Genomics and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania
| | - Ioana Berindan-Neagoe
- Research Center for Functional Genomics and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania.,MedFuture-Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 40015 Cluj-Napoca, Romania.,Department of Functional Genomics and Experimental Pathology, Oncology Institute 'Prof. Dr. Ion Chiricuta', 400015 Cluj-Napoca, Romania
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21
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Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1294:167-186. [PMID: 33079369 DOI: 10.1007/978-3-030-57616-5_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.
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22
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Simonaggio A, Epaillard N, Pobel C, Moreira M, Oudard S, Vano YA. Tumor Microenvironment Features as Predictive Biomarkers of Response to Immune Checkpoint Inhibitors (ICI) in Metastatic Clear Cell Renal Cell Carcinoma (mccRCC). Cancers (Basel) 2021; 13:E231. [PMID: 33435262 PMCID: PMC7827724 DOI: 10.3390/cancers13020231] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/18/2020] [Accepted: 12/29/2020] [Indexed: 12/11/2022] Open
Abstract
Renal cell carcinoma (RCC) is the seventh most frequently diagnosed malignancy with an increasing incidence in developed countries. Despite a greater understanding of the cancer biology, which has led to an increase of therapeutic options, metastatic clear cell renal cell carcinoma (mccRCC) still have a poor prognosis with a median five-years survival rate lower than 10%. The standard of care for mccRCC has changed dramatically over the past decades with the emergence of new treatments: anti-VEGFR tyrosine kinase inhibitors, mTOR Inhibitors and immune checkpoint inhibitors (ICI) such as anti-Programmed cell-Death 1 (PD-1) and anti-anti-Programmed Death Ligand-1 (PD-L1) used as monotherapy or as a combination with anti CTLA-4 or anti angiogenic therapies. In the face of these rising therapeutic options, the question of the therapeutic sequences is crucial. Predictive biomarkers are urgently required to provide a personalized treatment for each patient. Disappointingly, the usual ICI biomarkers, PD-L1 expression and Tumor Mutational Burden, approved in melanoma or non-small cell lung cancer (NSCLC) have failed to distinguish good and poor mccRCC responders to ICI. The tumor microenvironment is known to be involved in ICI response. Innovative technologies can be used to explore the immune contexture of tumors and to find predictive and prognostic biomarkers. Recent comprehensive molecular characterization of RCC has led to the development of robust genomic signatures, which could be used as predictive biomarkers. This review will provide an overview of the components of the RCC tumor microenvironment and discuss their role in disease progression and resistance to ICI. We will then highlight the current and future ICI predictive biomarkers assessed in mccRCC with a major focus on immunohistochemistry markers and genomic signatures.
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Affiliation(s)
- Audrey Simonaggio
- Medical Oncology, Hôpital Européen Georges Pompidou, APHP Centre–Université de Paris, 75015 Paris, France; (A.S.); (N.E.); (C.P.); (S.O.)
| | - Nicolas Epaillard
- Medical Oncology, Hôpital Européen Georges Pompidou, APHP Centre–Université de Paris, 75015 Paris, France; (A.S.); (N.E.); (C.P.); (S.O.)
| | - Cédric Pobel
- Medical Oncology, Hôpital Européen Georges Pompidou, APHP Centre–Université de Paris, 75015 Paris, France; (A.S.); (N.E.); (C.P.); (S.O.)
| | - Marco Moreira
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Team “Cancer, Immune Control and Escape”, University Paris Descartes Paris 5, Sorbonne Paris Cite, 75006 Paris, France;
| | - Stéphane Oudard
- Medical Oncology, Hôpital Européen Georges Pompidou, APHP Centre–Université de Paris, 75015 Paris, France; (A.S.); (N.E.); (C.P.); (S.O.)
- INSERM UMR-S1147, Université de Paris, Sorbonne Université, 75006 Paris, France
| | - Yann-Alexandre Vano
- Medical Oncology, Hôpital Européen Georges Pompidou, APHP Centre–Université de Paris, 75015 Paris, France; (A.S.); (N.E.); (C.P.); (S.O.)
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Team “Cancer, Immune Control and Escape”, University Paris Descartes Paris 5, Sorbonne Paris Cite, 75006 Paris, France;
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23
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Kim SY, Song HK, Lee SK, Kim SG, Woo HG, Yang J, Noh HJ, Kim YS, Moon A. Sex-Biased Molecular Signature for Overall Survival of Liver Cancer Patients. Biomol Ther (Seoul) 2020; 28:491-502. [PMID: 33077700 PMCID: PMC7585639 DOI: 10.4062/biomolther.2020.157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 09/18/2020] [Accepted: 09/18/2020] [Indexed: 12/31/2022] Open
Abstract
Sex/gender disparity has been shown in the incidence and prognosis of many types of diseases, probably due to differences in genes, physiological conditions such as hormones, and lifestyle between the sexes. The mortality and survival rates of many cancers, especially liver cancer, differ between men and women. Due to the pronounced sex/gender disparity, considering sex/gender may be necessary for the diagnosis and treatment of liver cancer. By analyzing research articles through a PubMed literature search, the present review identified 12 genes which showed practical relevance to cancer and sex disparities. Among the 12 sex-specific genes, 7 genes (BAP1, CTNNB1, FOXA1, GSTO1, GSTP1, IL6, and SRPK1) showed sex-biased function in liver cancer. Here we summarized previous findings of cancer molecular signature including our own analysis, and showed that sex-biased molecular signature CTNNB1High, IL6High, RHOAHigh and GLIPR1Low may serve as a female-specific index for prediction and evaluation of OS in liver cancer patients. This review suggests a potential implication of sex-biased molecular signature in liver cancer, providing a useful information on diagnosis and prediction of disease progression based on gender.
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Affiliation(s)
- Sun Young Kim
- Department of Chemistry, College of Natural Sciences, Duksung Women's University, Seoul 01369, Republic of Korea
| | - Hye Kyung Song
- Department of Chemistry, College of Natural Sciences, Duksung Women's University, Seoul 01369, Republic of Korea
| | - Suk Kyeong Lee
- Department of Medical Life Sciences, Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06649, Republic of Korea
| | - Sang Geon Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University_Seoul, Goyang 10326, Republic of Korea
| | - Hyun Goo Woo
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea.,Department of Biomedical Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea
| | - Jieun Yang
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea.,Department of Biomedical Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea
| | - Hyun-Jin Noh
- Department of Biomedical Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea.,Department of Biochemistry, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - You-Sun Kim
- Department of Biomedical Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea.,Department of Biochemistry, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - Aree Moon
- Duksung Innovative Drug Center, College of Pharmacy, Duksung Women's University, Seoul 01369, Republic of Korea
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24
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Lam D, Clark S, Stirzaker C, Pidsley R. Advances in Prognostic Methylation Biomarkers for Prostate Cancer. Cancers (Basel) 2020; 12:E2993. [PMID: 33076494 PMCID: PMC7602626 DOI: 10.3390/cancers12102993] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/12/2020] [Accepted: 10/13/2020] [Indexed: 12/24/2022] Open
Abstract
There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.
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Affiliation(s)
- Dilys Lam
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia; (D.L.); (S.C.); (C.S.)
| | - Susan Clark
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia; (D.L.); (S.C.); (C.S.)
- St. Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales 2010, Australia
| | - Clare Stirzaker
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia; (D.L.); (S.C.); (C.S.)
- St. Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales 2010, Australia
| | - Ruth Pidsley
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia; (D.L.); (S.C.); (C.S.)
- St. Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales 2010, Australia
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25
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Gupta VK, Kim M, Bakshi U, Cunningham KY, Davis JM, Lazaridis KN, Nelson H, Chia N, Sung J. A predictive index for health status using species-level gut microbiome profiling. Nat Commun 2020; 11:4635. [PMID: 32934239 PMCID: PMC7492273 DOI: 10.1038/s41467-020-18476-8] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 08/19/2020] [Indexed: 12/26/2022] Open
Abstract
Providing insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independent of the clinical diagnosis. GMHI is formulated upon 50 microbial species associated with healthy gut ecosystems. These species are identified through a multi-study, integrative analysis on 4347 human stool metagenomes from 34 published studies across healthy and 12 different nonhealthy conditions, i.e., disease or abnormal bodyweight. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of disease presence (or absence) compared to α-diversity indices. Validation on 679 samples from 9 additional studies results in a balanced accuracy of 73.7% in distinguishing healthy from non-healthy groups. Our findings suggest that gut taxonomic signatures can predict health status, and highlight how data sharing efforts can provide broadly applicable discoveries. A biologically-interpretable and robust metric that provides insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, the authors introduce a species-level index that predicts the likelihood of having a disease.
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Affiliation(s)
- Vinod K Gupta
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Minsuk Kim
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Utpal Bakshi
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kevin Y Cunningham
- Graduate Research Education Program (GREP), Mayo Clinic, Rochester, MN, 55905, USA.,Department of Computer Science and Engineering, University of Minnesota Twin-Cities, Minneapolis, MN, 55455, USA
| | - John M Davis
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Konstantinos N Lazaridis
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Heidi Nelson
- Emeritus Chair, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA. .,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA. .,Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
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26
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Sadovsky Y, Mesiano S, Burton GJ, Lampl M, Murray JC, Freathy RM, Mahadevan-Jansen A, Moffett A, Price ND, Wise PH, Wildman DE, Snyderman R, Paneth N, Capra JA, Nobrega MA, Barak Y, Muglia LJ. Advancing human health in the decade ahead: pregnancy as a key window for discovery: A Burroughs Wellcome Fund Pregnancy Think Tank. Am J Obstet Gynecol 2020; 223:312-321. [PMID: 32565236 PMCID: PMC7303037 DOI: 10.1016/j.ajog.2020.06.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/07/2020] [Accepted: 06/10/2020] [Indexed: 12/16/2022]
Abstract
Recent revolutionary advances at the intersection of medicine, omics, data sciences, computing, epidemiology, and related technologies inspire us to ponder their impact on health. Their potential impact is particularly germane to the biology of pregnancy and perinatal medicine, where limited improvement in health outcomes for women and children has remained a global challenge. We assembled a group of experts to establish a Pregnancy Think Tank to discuss a broad spectrum of major gestational disorders and adverse pregnancy outcomes that affect maternal-infant lifelong health and should serve as targets for leveraging the many recent advances. This report reflects avenues for future effects that hold great potential in 3 major areas: developmental genomics, including the application of methodologies designed to bridge genotypes, physiology, and diseases, addressing vexing questions in early human development; gestational physiology, from immune tolerance to growth and the timing of parturition; and personalized and population medicine, focusing on amalgamating health record data and deep phenotypes to create broad knowledge that can be integrated into healthcare systems and drive discovery to address pregnancy-related disease and promote general health. We propose a series of questions reflecting development, systems biology, diseases, clinical approaches and tools, and population health, and a call for scientific action. Clearly, transdisciplinary science must advance and accelerate to address adverse pregnancy outcomes. Disciplines not traditionally involved in the reproductive sciences, such as computer science, engineering, mathematics, and pharmacology, should be engaged at the study design phase to optimize the information gathered and to identify and further evaluate potentially actionable therapeutic targets. Information sources should include noninvasive personalized sensors and monitors, alongside instructive "liquid biopsies" for noninvasive pregnancy assessment. Future research should also address the diversity of human cohorts in terms of geography, racial and ethnic distributions, and social and health disparities. Modern technologies, for both data-gathering and data-analyzing, make this possible at a scale that was previously unachievable. Finally, the psychosocial and economic environment in which pregnancy takes place must be considered to promote the health and wellness of communities worldwide.
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Affiliation(s)
- Yoel Sadovsky
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, PA
| | - Sam Mesiano
- Department of Reproductive Biology, Case Western Reserve University, and Department of Obstetrics and Gynecology, University Hospitals of Cleveland, Cleveland, OH
| | - Graham J Burton
- Centre for Trophoblast Research; University of Cambridge, Cambridge, United Kingdom
| | - Michelle Lampl
- Center for the Study of Human Health, Emory University, Atlanta, GA
| | | | | | - Anita Mahadevan-Jansen
- Vanderbilt Biophotonics Center and Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Ashley Moffett
- Department of Pathology; Centre for Trophoblast Research
| | | | - Paul H Wise
- Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA
| | - Derek E Wildman
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL
| | - Ralph Snyderman
- Duke Center for Personalized Health Care, Duke University Medical Center, Durham, NC
| | - Nigel Paneth
- Departments of Epidemiology and Biostatistics and of Pediatrics and Human Development, College of Human Medicine, Michigan State University, East Lansing, MI
| | | | | | - Yaacov Barak
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, PA
| | - Louis J Muglia
- Office of the President, Burroughs Wellcome Fund, Research Triangle Park, NC.
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27
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Dekker LJM, Kannegieter NM, Haerkens F, Toth E, Kros JM, Steenhoff Hov DA, Fillebeen J, Verschuren L, Leenstra S, Ressa A, Luider TM. Multiomics profiling of paired primary and recurrent glioblastoma patient tissues. Neurooncol Adv 2020; 2:vdaa083. [PMID: 32793885 PMCID: PMC7415260 DOI: 10.1093/noajnl/vdaa083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Despite maximal therapy with surgery, chemotherapy, and radiotherapy, glioblastoma (GBM) patients have a median survival of only 15 months. Almost all patients inevitably experience symptomatic tumor recurrence. A hallmark of this tumor type is the large heterogeneity between patients and within tumors itself which relates to the failure of standardized tumor treatment. In this study, tissue samples of paired primary and recurrent GBM tumors were investigated to identify individual factors related to tumor progression. Methods Paired primary and recurrent GBM tumor tissues from 8 patients were investigated with a multiomics approach using transcriptomics, proteomics, and phosphoproteomics. Results In the studied patient cohort, large variations between and within patients are observed for all omics analyses. A few pathways affected at the different omics levels partly overlapped if patients are analyzed at the individual level, such as synaptogenesis (containing the SNARE complex) and cholesterol metabolism. Phosphoproteomics revealed increased STMN1(S38) phosphorylation as part of ERBB4 signaling. A pathway tool has been developed to visualize and compare different omics datasets per patient and showed potential therapeutic drugs, such as abobotulinumtoxinA (synaptogenesis) and afatinib (ERBB4 signaling). Afatinib is currently in clinical trials for GBM. Conclusions A large variation on all omics levels exists between and within GBM patients. Therefore, it will be rather unlikely to find a drug treatment that would fit all patients. Instead, a multiomics approach offers the potential to identify affected pathways on the individual patient level and select treatment options.
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Affiliation(s)
- Lennard J M Dekker
- Department of Neurology, Erasmus University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | | | | | - Emma Toth
- Department of Neurology, Erasmus University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Johan M Kros
- Department of Pathology, Erasmus University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | | | | | - Lars Verschuren
- Department of Microbiology and Systems Biology, The Netherlands Organization for Applied Scientific Research (TNO), Zeist, The Netherlands
| | - Sieger Leenstra
- Department of Neurosurgery, Erasmus University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | | | - Theo M Luider
- Department of Neurology, Erasmus University Medical Centre Rotterdam, Rotterdam, The Netherlands
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28
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Vinga S. Structured sparsity regularization for analyzing high-dimensional omics data. Brief Bioinform 2020; 22:77-87. [PMID: 32597465 DOI: 10.1093/bib/bbaa122] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/18/2022] Open
Abstract
The development of new molecular and cell technologies is having a significant impact on the quantity of data generated nowadays. The growth of omics databases is creating a considerable potential for knowledge discovery and, concomitantly, is bringing new challenges to statistical learning and computational biology for health applications. Indeed, the high dimensionality of these data may hamper the use of traditional regression methods and parameter estimation algorithms due to the intrinsic non-identifiability of the inherent optimization problem. Regularized optimization has been rising as a promising and useful strategy to solve these ill-posed problems by imposing additional constraints in the solution parameter space. In particular, the field of statistical learning with sparsity has been significantly contributing to building accurate models that also bring interpretability to biological observations and phenomena. Beyond the now-classic elastic net, one of the best-known methods that combine lasso with ridge penalizations, we briefly overview recent literature on structured regularizers and penalty functions that have been applied in biomedical data to build parsimonious models in a variety of underlying contexts, from survival to generalized linear models. These methods include functions of $\ell _k$-norms and network-based penalties that take into account the inherent relationships between the features. The successful application to omics data illustrates the potential of sparse structured regularization for identifying disease's molecular signatures and for creating high-performance clinical decision support systems towards more personalized healthcare. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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29
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Sala C, Di Lena P, Fernandes Durso D, Prodi A, Castellani G, Nardini C. Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform. PLoS One 2020; 15:e0229763. [PMID: 32155174 PMCID: PMC7064179 DOI: 10.1371/journal.pone.0229763] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 02/13/2020] [Indexed: 01/06/2023] Open
Abstract
Introduction Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability. Material and methods To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 × 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis). Results and conclusion The pre-processing of DNA methylation data affects both the between-sample variability and the loci identified as differentially methylated, and the effects of pre-processing are strongly dataset-dependent. By contrast, application of our renormalization algorithm regRCPqn: (i) reduces variability and (ii) increases agreement between meta-analysed datasets, both critical components of data harmonization.
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Affiliation(s)
- Claudia Sala
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
- * E-mail: (CS); (CN)
| | - Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Danielle Fernandes Durso
- Division of Infectious Diseases and Immunology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Andrea Prodi
- Smart Cities Living Lab, Institute of Organic Synthesis and Photoreactivity, CNR, Bologna, Italy
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
- Interdepartmental Center “L. Galvani”, University of Bologna, Bologna, Italy
| | - Christine Nardini
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- CNR IAC “Mauro Picone”, Roma, Italy
- Sol Group, Monza, Italy
- * E-mail: (CS); (CN)
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30
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Salvucci M, Zakaria Z, Carberry S, Tivnan A, Seifert V, Kögel D, Murphy BM, Prehn JHM. System-based approaches as prognostic tools for glioblastoma. BMC Cancer 2019; 19:1092. [PMID: 31718568 PMCID: PMC6852738 DOI: 10.1186/s12885-019-6280-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/09/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The evasion of apoptosis is a hallmark of cancer. Understanding this process holistically and overcoming apoptosis resistance is a goal of many research teams in order to develop better treatment options for cancer patients. Efforts are also ongoing to personalize the treatment of patients. Strategies to confirm the therapeutic efficacy of current treatments or indeed to identify potential novel additional options would be extremely beneficial to both clinicians and patients. In the past few years, system medicine approaches have been developed that model the biochemical pathways of apoptosis. These systems tools incorporate and analyse the complex biological networks involved. For their successful integration into clinical practice, it is mandatory to integrate systems approaches with routine clinical and histopathological practice to deliver personalized care for patients. RESULTS We review here the development of system medicine approaches that model apoptosis for the treatment of cancer with a specific emphasis on the aggressive brain cancer, glioblastoma. CONCLUSIONS We discuss the current understanding in the field and present new approaches that highlight the potential of system medicine approaches to influence how glioblastoma is diagnosed and treated in the future.
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Affiliation(s)
- Manuela Salvucci
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Zaitun Zakaria
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Steven Carberry
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Amanda Tivnan
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Volker Seifert
- Department of Neurosurgery, Frankfurt University Hospital, Frankfurt am Main, Germany
| | - Donat Kögel
- Department of Neurosurgery, Frankfurt University Hospital, Frankfurt am Main, Germany
| | - Brona M. Murphy
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Jochen H. M. Prehn
- Centre for Systems Medicine, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland
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Abstract
The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining-inspired, and "allochthonous" knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.
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Affiliation(s)
- Eberhard O. Voit
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America
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32
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Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
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Paquette AG, Brockway HM, Price ND, Muglia LJ. Comparative transcriptomic analysis of human placentae at term and preterm delivery. Biol Reprod 2019; 98:89-101. [PMID: 29228154 PMCID: PMC5803773 DOI: 10.1093/biolre/iox163] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 11/30/2017] [Indexed: 12/11/2022] Open
Abstract
Preterm birth affects 1 out of every 10 infants in the United States, resulting in substantial neonatal morbidity and mortality. Currently, there are few predictive markers and few treatment options to prevent preterm birth. A healthy, functioning placenta is essential to positive pregnancy outcomes. Previous studies have suggested that placental pathology may play a role in preterm birth etiology. Therefore, we tested the hypothesis that preterm placentae may exhibit unique transcriptomic signatures compared to term samples reflective of their abnormal biology leading to this adverse outcome. We aggregated publicly available placental villous microarray data to generate a preterm and term sample dataset (n = 133, 55 preterm placentae and 78 normal term placentae). We identified differentially expressed genes using the linear regression for microarray (LIMMA) package and identified perturbations in known biological networks using Differential Rank Conservation (DIRAC). We identified 129 significantly differentially expressed genes between term and preterm placenta with 96 genes upregulated and 33 genes downregulated (P-value <0.05). Significant changes in gene expression in molecular networks related to Tumor Protein 53 and phosphatidylinositol signaling were identified using DIRAC. We have aggregated a uniformly normalized transcriptomic dataset and have identified novel and established genes and pathways associated with developmental regulation of the placenta and potential preterm birth pathology. These analyses provide a community resource to integrate with other high-dimensional datasets for additional insights in normal placental development and its disruption.
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Affiliation(s)
| | - Heather M Brockway
- Division of Human Genetics, Center for Prevention of Preterm Birth, Cincinnati Children's, Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Louis J Muglia
- Division of Human Genetics, Center for Prevention of Preterm Birth, Cincinnati Children's, Hospital Medical Center, Cincinnati, Ohio, USA
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Balafas E, Katsila T, Melissa P, Doulou A, Moltsanidou E, Agapaki A, Patrinos GP, Kostomitsopoulos N. A Noninvasive Ocular (Tear) Sampling Method for Genetic Ascertainment of Transgenic Mice and Research Ethics Innovation. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 23:312-317. [PMID: 31099704 DOI: 10.1089/omi.2019.0057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Animal models, animal welfare and research ethics are both facilitators and gatekeepers for Big Data generation in genomics and multi-omics R&D. Safeguarding animal welfare is also a research ethics issue that can benefit from technical innovations in biosample collection in particular. Animal welfare draws from the guiding principles of 3R, namely, "Replacement" (methods avoiding the use of animals in research), "Reduction" (methods using fewer animals or derive more information from the same number of animals), and "Refinement" (methods removing or minimizing pain or distress). We report here that noninvasive ocular (tear) sampling for genetic ascertainment of transgenic mice can serve as an innovative ethical safeguard for animal welfare, and as a veritable alternative to the surgical tail biopsies, ear puncture, or blood sampling from the weanling transgenic mice. We compared ocular versus tail biopsy sampling in regard to ascertainment, by genotyping, of apolipoprotein E-deficient (ApoE-/-) transgenic weanling mice (n = 60) by one-round polymerase chain reaction analysis. We found that ocular sampling compares to the results obtained by tail sampling with the obvious benefit of being noninvasive and improving the 3R, especially for the Refinement principle of animal welfare. To place the importance of this new biosample collection approach into further context, transgenic mice research and animal models are at the epicenter of Big Data translation to health innovation. We suggest that ocular sampling is considered and evaluated further in transgenic mice models, not to mention warrant exploration for applications in other types of animal models that require noninvasive biosample collection.
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Affiliation(s)
- Evangelos Balafas
- 1 Laboratory Animal Facilities, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Theodora Katsila
- 2 Institute of Biology, Medicinal Chemistry & Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Pelagia Melissa
- 3 Center of Basic Research, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Athanasia Doulou
- 1 Laboratory Animal Facilities, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Eleni Moltsanidou
- 1 Laboratory Animal Facilities, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Anna Agapaki
- 4 Histochemistry Unit, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - George P Patrinos
- 5 Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.,6 Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Nikolaos Kostomitsopoulos
- 1 Laboratory Animal Facilities, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation, Academy of Athens, Athens, Greece
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35
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Jamshidi N, Mantri N, Cohen MM. Acute effects of dietary plant nutrients on transcriptome profiles: evidence from human studies. Crit Rev Food Sci Nutr 2019; 60:1869-1880. [PMID: 31032630 DOI: 10.1080/10408398.2019.1608154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The health benefits of long-term dietary plant ingestion are well-established. However, literature on acute nutritional challenges is very limited. This study aimed to identify available evidence on transcriptomics responses to acute ingestion of plants or plant extracts and identify signature gene profiles that may serve as biomarkers of health status. We systematically searched electronic databases and extracted information based-on inclusion criteria such as human clinical studies, single plant-based nutrients and outcomes reported on acute transcriptome responses. A total of 11 studies reported on acute intake of plant dietary interventions. Four studies investigating natural oil extracts with three reporting on whole plants and two studies on natural plant-derived extracts. Gene expression was found to be associated with immune response (7 studies), inflammation (9 studies), metabolism (8 studies), cellular processes and cancer. The finding of this systematic review suggests that acute ingestion may significantly impact diverse physiological and pathological pathways including inflammatory, immune, cancer and oxidative stress pathways. Transcriptomics approach is proven to be an effective strategy in discovery of these anticipated mechanisms. Further studies are now required to validate and continue exploring the short-term health impact of dietary plants and their bioactive phytochemicals on gene expression and function.
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Affiliation(s)
- Negar Jamshidi
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
| | - Nitin Mantri
- School of Science, RMIT University, Bundoora, Victoria, Australia
| | - Marc M Cohen
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
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Abstract
Endometriosis is a benign gynecological disorder which presents significant challenges in terms of diagnosis and management. Despite decades of research, there are no sufficiently sensitive and specific signs and symptoms nor blood tests for the clinical confirmation of endometriosis, which hampers prompt diagnosis and treatment. The huge majority of potential biomarkers has been discarded in research stage and very few have been translated to clinical practice. Serum CA-125 is the most studied and used one, but studies have shown its poor diagnostic performance. Several factors involved in the chronic inflammatory process of endometriosis, such as hormones, cytokines, chemokines, angiogenic factors, oxidative stress markers and others, have been implicated in the disease's pathogenesis and have been extensively studied, but not a single one has successfully been able to accurately identify the disease. MicroRNAs have emerged more recently but their utility to detect endometriosis remains uncertain. The search for a biomarker or a set of biomarkers is still open and may benefit from novel molecular biology and bioinformatics approaches to mine and uncover molecular signatures specifically associated with the disease.
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Affiliation(s)
- Larissa M Coutinho
- Department of Maternal and Child Health, Universidade Federal de Juiz de Fora, Belo Horizonte, Brazil; Department of Obstetrics and Gynecology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Márcia C Ferreira
- Department of Obstetrics and Gynecology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ana Luiza L Rocha
- Department of Obstetrics and Gynecology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Márcia M Carneiro
- Department of Obstetrics and Gynecology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Fernando M Reis
- Department of Obstetrics and Gynecology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
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Chujan S, Suriyo T, Ungtrakul T, Pomyen Y, Satayavivad J. Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis. Biomed Rep 2018; 9:42-52. [PMID: 29930804 PMCID: PMC6007048 DOI: 10.3892/br.2018.1101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 05/18/2018] [Indexed: 12/14/2022] Open
Abstract
Cholangiocarcinoma (CCA) remains to be a major health problem in several Asian countries including Thailand. The molecular mechanism of CCA is poorly understood. Early diagnosis is difficult, and at present, no effective therapeutic drug is available. The present study aimed to identify the molecular mechanism of CCA by gene expression profile analysis and to search for current approved drugs which may interact with the upregulated genes in CCA. Gene Expression Omnibus (GEO) was used to analyze the gene expression profiles of CCA patients and normal subjects. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology enrichment analysis was also performed, with the KEGG pathway analysis indicating that pancreatic secretion, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism may serve important roles in CCA oncogenesis. The drug signature database (DsigDB) was used to search for US Food and Drug Administration (FDA)-approved drugs potentially capable of reversing the effects of the upregulated gene expression in CCA. A total of 61 antineoplastic and 86 non-antineoplastic drugs were identified. Checkpoint kinase 1 was the most interacting with drug signatures. Many of the targeted protein inhibitors that were identified have been approved by the US-FDA as therapeutic agents for non-antineoplastic diseases, including cimetidine, valproic acid and lovastatin. The current study demonstrated an application for bioinformatics analysis in assessing the potential efficacy of currently approved drugs for novel use. The present results suggest novel indications regarding existing drugs useful for CCA treatment. However, further in vitro and in vivo studies are required to support the current predictions.
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Affiliation(s)
- Suthipong Chujan
- Applied Biological Sciences Program, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Tawit Suriyo
- Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok 10210, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Education, Bangkok 10400, Thailand
| | - Teerapat Ungtrakul
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Yotsawat Pomyen
- Translational Research Unit, Chulabhorn Research Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Jutamaad Satayavivad
- Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok 10210, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Education, Bangkok 10400, Thailand.,Environmental Toxicology Program, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
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38
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Kearney P, Boniface JJ, Price ND, Hood L. The building blocks of successful translation of proteomics to the clinic. Curr Opin Biotechnol 2018; 51:123-129. [PMID: 29427919 PMCID: PMC6091638 DOI: 10.1016/j.copbio.2017.12.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Accepted: 12/11/2017] [Indexed: 11/28/2022]
Abstract
Recently, the first two multiplexed tests using selective reaction monitoring (SRM-MS) mass spectrometry have entered clinical practice. Despite different areas of indication, risk stratification in lung cancer and preterm birth, they share multiple steps in their development strategies. Here we review these strategies and their implications for successful translation of biomarkers to clinical practice. We believe that the identification of blood protein panels for the identification of disease phenotypes is now a reproducible and standard (albeit complex) process.
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Affiliation(s)
- Paul Kearney
- Integrated Diagnostics, Seattle, WA, United States
| | | | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, United States
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, United States.
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39
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De Meulder B, Lefaudeux D, Bansal AT, Mazein A, Chaiboonchoe A, Ahmed H, Balaur I, Saqi M, Pellet J, Ballereau S, Lemonnier N, Sun K, Pandis I, Yang X, Batuwitage M, Kretsos K, van Eyll J, Bedding A, Davison T, Dodson P, Larminie C, Postle A, Corfield J, Djukanovic R, Chung KF, Adcock IM, Guo YK, Sterk PJ, Manta A, Rowe A, Baribaud F, Auffray C. A computational framework for complex disease stratification from multiple large-scale datasets. BMC SYSTEMS BIOLOGY 2018; 12:60. [PMID: 29843806 PMCID: PMC5975674 DOI: 10.1186/s12918-018-0556-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 02/21/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
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Affiliation(s)
- Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
| | - Diane Lefaudeux
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Aruna T Bansal
- Acclarogen Ltd, St John's Innovation Centre, Cambridge, CB4 OWS, UK
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Amphun Chaiboonchoe
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Hassan Ahmed
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Irina Balaur
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Nathanaël Lemonnier
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Kai Sun
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Ioannis Pandis
- Data Science Institute, Imperial College, London, SW7 2AZ, UK.,Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Xian Yang
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | | | | | | | | | - Timothy Davison
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Paul Dodson
- AstraZeneca Ltd, Alderley Park, Macclesfield, SK10 4TG, UK
| | | | - Anthony Postle
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Julie Corfield
- AstraZeneca R & D, 43150, Mölndal, Sweden.,Arateva R & D Ltd, Nottingham, NG1 1GF, UK
| | - Ratko Djukanovic
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Kian Fan Chung
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Ian M Adcock
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Yi-Ke Guo
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Peter J Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, AZ1105, The Netherlands
| | - Alexander Manta
- Research Informatics, Roche Diagnostics GmbH, 82008, Unterhaching, Germany
| | - Anthony Rowe
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | | | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
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40
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Chaousis S, Leusch FDL, van de Merwe JP. Charting a path towards non-destructive biomarkers in threatened wildlife: A systematic quantitative literature review. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 234:59-70. [PMID: 29156442 DOI: 10.1016/j.envpol.2017.11.044] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 11/07/2017] [Accepted: 11/10/2017] [Indexed: 06/07/2023]
Abstract
Threatened species are susceptible to irreversible population decline caused by adverse sub-lethal effects of chemical contaminant exposure. It is therefore vital to develop the necessary tools to predict and detect these effects as early as possible. Biomarkers of contaminant exposure and effect are widely applied to this end, and a significant amount of research has focused on development and validation of sensitive and diagnostic biomarkers. However, progress in the use biomarkers that can be measured using non-destructive techniques has been relatively slow and there are still many difficulties to overcome in the development of sound methods. This paper systematically quantifies and reviews studies that have aimed to develop or validate non-destructive biomarkers in wildlife, and provides an analysis of the successes of these methods based on the invasiveness of the methods, the potential for universal application, cost, and the potential for new biomarker discovery. These data are then used to infer what methods and approaches appear the most effective for successful development of non-destructive biomarkers of contaminant exposure in wildlife. This review highlights that research on non-destructive biomarkers in wildlife is severely lacking, and suggests further exploration of in vitro methods in future studies.
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Affiliation(s)
- Stephanie Chaousis
- Griffith School of Environment, Australian Rivers Institute, Griffith University, Qld, 4222 Australia.
| | - Frederic D L Leusch
- Griffith School of Environment, Australian Rivers Institute, Griffith University, Qld, 4222 Australia
| | - Jason P van de Merwe
- Griffith School of Environment, Australian Rivers Institute, Griffith University, Qld, 4222 Australia
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41
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Adeola HA, Van Wyk JC, Arowolo A, Ngwanya RM, Mkentane K, Khumalo NP. Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics. Proteomics Clin Appl 2017; 12. [PMID: 28960873 DOI: 10.1002/prca.201700048] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/09/2017] [Indexed: 01/22/2023]
Abstract
The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.
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Affiliation(s)
- Henry A Adeola
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Jennifer C Van Wyk
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Afolake Arowolo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Reginald M Ngwanya
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Khwezikazi Mkentane
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Nonhlanhla P Khumalo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
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42
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König IR, Fuchs O, Hansen G, von Mutius E, Kopp MV. What is precision medicine? Eur Respir J 2017; 50:50/4/1700391. [DOI: 10.1183/13993003.00391-2017] [Citation(s) in RCA: 175] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 07/15/2017] [Indexed: 01/06/2023]
Abstract
The term “precision medicine” has become very popular over recent years, fuelled by scientific as well as political perspectives. Despite its popularity, its exact meaning, and how it is different from other popular terms such as “stratified medicine”, “targeted therapy” or “deep phenotyping” remains unclear. Commonly applied definitions focus on the stratification of patients, sometimes referred to as a novel taxonomy, and this is derived using large-scale data including clinical, lifestyle, genetic and further biomarker information, thus going beyond the classical “signs-and-symptoms” approach.While these aspects are relevant, this description leaves open a number of questions. For example, when does precision medicine begin? In which way does the stratification of patients translate into better healthcare? And can precision medicine be viewed as the end-point of a novel stratification of patients, as implied, or is it rather a greater whole?To clarify this, the aim of this paper is to provide a more comprehensive definition that focuses on precision medicine as a process. It will be shown that this proposed framework incorporates the derivation of novel taxonomies and their role in healthcare as part of the cycle, but also covers related terms.
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43
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Knebel B, Goeddeke S, Poschmann G, Markgraf DF, Jacob S, Nitzgen U, Passlack W, Preuss C, Dicken HD, Stühler K, Hartwig S, Lehr S, Kotzka J. Novel Insights into the Adipokinome of Obese and Obese/Diabetic Mouse Models. Int J Mol Sci 2017; 18:ijms18091928. [PMID: 28885548 PMCID: PMC5618577 DOI: 10.3390/ijms18091928] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 08/19/2017] [Accepted: 08/21/2017] [Indexed: 12/16/2022] Open
Abstract
The group of adipokines comprises hundreds of biological active proteins and peptides released from adipose tissue. Alterations of those complex protein signatures are suggested to play a crucial role in the pathophysiology of multifactorial, metabolic diseases. We hypothesized that also the pathophysiology of type-2-diabetes is linked to the dysregulation of the adipocyte secretome. To test this, we investigated mouse models with monogenic defects in leptin signaling which are susceptible to adipositas (C57BL/6 Cg-Lepob (obob)) or adipositas with diabetes (C57BL/KS Cg-Leprdb (dbdb)) according to their genetic background. At the age of 17 weeks, visceral fat was obtained and primary murine adipocytes were isolated to harvest secretomes. Quantitative proteome analyses (LC-ESI-MS/MS) identified more than 800 potential secreted proteins. The secretome patterns revealed significant differences connected to the pathophysiology of obese mice. Pathway analyses indicated that these differences focus on exosome modelling, but failed to provide more precise specifications. To investigate the relationship of secretome data to insulin sensitivity, we examined the content of diabetogenic lipids, i.e., diacylglycerols (DAGs), identified as key players in lipid-induced insulin resistance. In contrast to obob mice, fat tissue of dbdb mice showed elevated DAG content, especially of DAG species with saturated fatty acid C16:0 and C18:0, while unsaturated fatty acid C16:1 were only changed in obob. Furthermore, DAG signatures of the models specifically correlate to secreted regulated adipokines indicating specific pathways. In conclusion, our data further support the concept that the fat tissue is an endocrine organ that releases bioactive factors corresponding to adipose tissue health status.
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Affiliation(s)
- Birgit Knebel
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Simon Goeddeke
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Gereon Poschmann
- Molecular Proteomics Laboratory, Biomedizinisches Forschungszentrum (BMFZ), Heinrich-Heine-University Duesseldorf, 40225 Duesseldorf, Germany.
| | - Daniel F Markgraf
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
- Institute of Clinical Diabetology, German Diabetes Center at the Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, 40225 Duesseldorf, Germany.
| | - Sylvia Jacob
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Ulrike Nitzgen
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Waltraud Passlack
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Christina Preuss
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
- Institute of Clinical Diabetology, German Diabetes Center at the Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, 40225 Duesseldorf, Germany.
| | - Hans-Dieter Dicken
- Multimedia Center, Heinrich-Heine-University Duesseldorf, 40225 Duesseldorf, Germany.
| | - Kai Stühler
- Molecular Proteomics Laboratory, Biomedizinisches Forschungszentrum (BMFZ), Heinrich-Heine-University Duesseldorf, 40225 Duesseldorf, Germany.
- Institute for Molecular Medicine, University Hospital Duesseldorf, Heinrich-Heine-University Duesseldorf, 40225 Duesseldorf, Germany.
| | - Sonja Hartwig
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Stefan Lehr
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
| | - Jorg Kotzka
- Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Heinrich-Heine-University Duesseldorf, Leibniz Center for Diabetes Research, Aufm Hennekamp 65, 40225 Dusseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Duesseldorf, 40225 Duesseldorf, Germany.
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Thamrin C, Frey U, Kaminsky DA, Reddel HK, Seely AJE, Suki B, Sterk PJ. Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet. Am J Respir Crit Care Med 2017; 194:1053-1061. [PMID: 27556336 DOI: 10.1164/rccm.201511-2288pp] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.
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Affiliation(s)
- Cindy Thamrin
- 1 Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Urs Frey
- 2 University Children's Hospital Basel, Basel, Switzerland
| | - David A Kaminsky
- 3 University of Vermont College of Medicine, Burlington, Vermont
| | - Helen K Reddel
- 1 Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Andrew J E Seely
- 4 Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Béla Suki
- 5 Department of Biomedical Engineering, Boston University, Boston, Massachusetts; and
| | - Peter J Sterk
- 6 Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
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45
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Adeola HA, Soyele OO, Adefuye AO, Jimoh SA, Butali A. Omics-based molecular techniques in oral pathology centred cancer: prospect and challenges in Africa. Cancer Cell Int 2017; 17:61. [PMID: 28592923 PMCID: PMC5460491 DOI: 10.1186/s12935-017-0432-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 05/29/2017] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The completion of the human genome project and the accomplished milestones in the human proteome project; as well as the progress made so far in computational bioinformatics and "big data" processing have contributed immensely to individualized/personalized medicine in the developed world. MAIN BODY At the dawn of precision medicine, various omics-based therapies and bioengineering can now be applied accurately for the diagnosis, prognosis, treatment, and risk stratification of cancer in a manner that was hitherto not thought possible. The widespread introduction of genomics and other omics-based approaches into the postgraduate training curriculum of diverse medical and dental specialties, including pathology has improved the proficiency of practitioners in the use of novel molecular signatures in patient management. In addition, intricate details about disease disparity among different human populations are beginning to emerge. This would facilitate the use of tailor-made novel theranostic methods based on emerging molecular evidences. CONCLUSION In this review, we examined the challenges and prospects of using currently available omics-based technologies vis-à-vis oral pathology as well as prompt cancer diagnosis and treatment in a resource limited setting.
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Affiliation(s)
- Henry A. Adeola
- Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, University of the Western Cape and Tygerberg Hospital, Cape Town, South Africa
- International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Olujide O. Soyele
- Department of Oral Maxillo-facial Surgery and Oral Pathology, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Anthonio O. Adefuye
- Division of Health Sciences Education, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Sikiru A. Jimoh
- Department of Anatomical Sciences, Faculty of Health Sciences, Walter Sisulu University, Mthatha, Eastern Cape South Africa
| | - Azeez Butali
- Department of Oral Pathology, Radiology and Medicine, University of Iowa, Iowa City, IA USA
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Horváth I, Barnes PJ, Loukides S, Sterk PJ, Högman M, Olin AC, Amann A, Antus B, Baraldi E, Bikov A, Boots AW, Bos LD, Brinkman P, Bucca C, Carpagnano GE, Corradi M, Cristescu S, de Jongste JC, Dinh-Xuan AT, Dompeling E, Fens N, Fowler S, Hohlfeld JM, Holz O, Jöbsis Q, Van De Kant K, Knobel HH, Kostikas K, Lehtimäki L, Lundberg J, Montuschi P, Van Muylem A, Pennazza G, Reinhold P, Ricciardolo FLM, Rosias P, Santonico M, van der Schee MP, van Schooten FJ, Spanevello A, Tonia T, Vink TJ. A European Respiratory Society technical standard: exhaled biomarkers in lung disease. Eur Respir J 2017; 49:49/4/1600965. [PMID: 28446552 DOI: 10.1183/13993003.00965-2016] [Citation(s) in RCA: 369] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 01/09/2017] [Indexed: 12/19/2022]
Abstract
Breath tests cover the fraction of nitric oxide in expired gas (FeNO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for FeNO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and FeNO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management.Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members.Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised.Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.
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Affiliation(s)
- Ildiko Horváth
- Dept of Pulmonology, National Korányi Institute of Pulmonology, Budapest, Hungary
| | - Peter J Barnes
- National Heart and Lung Institute, Imperial College London, Royal Brompton Hospital, London, UK
| | | | - Peter J Sterk
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Marieann Högman
- Centre for Research & Development, Uppsala University/Gävleborg County Council, Gävle, Sweden
| | - Anna-Carin Olin
- Occupational and Environmental Medicine, Sahlgrenska Academy and University Hospital, Goteborg, Sweden
| | - Anton Amann
- Innsbruck Medical University, Innsbruck, Austria
| | - Balazs Antus
- Dept of Pathophysiology, National Korányi Institute of Pulmonology, Budapest, Hungary
| | | | - Andras Bikov
- Dept of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Agnes W Boots
- Dept of Pharmacology and Toxicology, University of Maastricht, Maastricht, The Netherlands
| | - Lieuwe D Bos
- Intensive Care, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Paul Brinkman
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Caterina Bucca
- Biomedical Sciences and Human Oncology, Universita' di Torino, Turin, Italy
| | | | | | - Simona Cristescu
- Dept of Molecular and Laser Physics, Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Johan C de Jongste
- Dept of Pediatrics/Respiratory Medicine, Erasmus MC-Sophia Childrens' Hospital, Rotterdam, The Netherlands
| | | | - Edward Dompeling
- Dept of Paediatrics/Family Medicine Research School CAPHRI, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Niki Fens
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Stephen Fowler
- Respiratory Research Group, University of Manchester Wythenshawe Hospital, Manchester, UK
| | - Jens M Hohlfeld
- Clinical Airway Research, Fraunhofer Institute of Toxicology and Experimental Medicine (ITEM), Hannover, Germany.,Medizinische Hochschule Hannover, Hannover, Germany
| | - Olaf Holz
- Clinical Airway Research, Fraunhofer Institute of Toxicology and Experimental Medicine (ITEM), Hannover, Germany
| | - Quirijn Jöbsis
- Department of Paediatric Respiratory Medicine, Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands
| | - Kim Van De Kant
- Dept of Paediatrics/Family Medicine Research School CAPHRI, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Hugo H Knobel
- Philips Research, High Tech Campus 11, Eindhoven, The Netherlands
| | | | | | - Jon Lundberg
- Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Paolo Montuschi
- Pharmacology, Catholic University of the Sacred Heart, Rome, Italy
| | - Alain Van Muylem
- Hopital Erasme Cliniques Universitaires de Bruxelles, Bruxelles, Belgium
| | - Giorgio Pennazza
- Faculty of Engineering, University Campus Bio-Medico, Rome, Italy
| | - Petra Reinhold
- Institute of Molecular Pathogenesis, Friedrich Loeffler Institut, Jena, Germany
| | - Fabio L M Ricciardolo
- Clinic of Respiratory Disease, Dept of Clinical and Biological Sciences, University of Torino, Torino, Italy
| | - Philippe Rosias
- Dept of Paediatrics/Family Medicine Research School CAPHRI, Maastricht University Medical Centre, Maastricht, The Netherlands.,Dept of Pediatrics, Maasland Hospital, Sittard, The Netherlands
| | - Marco Santonico
- Faculty of Engineering, University Campus Bio-Medico, Rome, Italy
| | - Marc P van der Schee
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | | | | | - Thomy Tonia
- European Respiratory Society, Lausanne, Switzerland
| | - Teunis J Vink
- Philips Research, High Tech Campus 11, Eindhoven, The Netherlands
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Ghosh D, Funk CC, Caballero J, Shah N, Rouleau K, Earls JC, Soroceanu L, Foltz G, Cobbs CS, Price ND, Hood L. A Cell-Surface Membrane Protein Signature for Glioblastoma. Cell Syst 2017; 4:516-529.e7. [PMID: 28365151 DOI: 10.1016/j.cels.2017.03.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 09/08/2016] [Accepted: 03/03/2017] [Indexed: 02/08/2023]
Abstract
We present a systems strategy that facilitated the development of a molecular signature for glioblastoma (GBM), composed of 33 cell-surface transmembrane proteins. This molecular signature, GBMSig, was developed through the integration of cell-surface proteomics and transcriptomics from patient tumors in the REMBRANDT (n = 228) and TCGA datasets (n = 547) and can separate GBM patients from control individuals with a Matthew's correlation coefficient value of 0.87 in a lock-down test. Functionally, 17/33 GBMSig proteins are associated with transforming growth factor β signaling pathways, including CD47, SLC16A1, HMOX1, and MRC2. Knockdown of these genes impaired GBM invasion, reflecting their role in disease-perturbed changes in GBM. ELISA assays for a subset of GBMSig (CD44, VCAM1, HMOX1, and BIGH3) on 84 plasma specimens from multiple clinical sites revealed a high degree of separation of GBM patients from healthy control individuals (area under the curve is 0.98 in receiver operating characteristic). In addition, a classifier based on these four proteins differentiated the blood of pre- and post-tumor resections, demonstrating potential clinical value as biomarkers.
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Affiliation(s)
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | - Nameeta Shah
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | | | - John C Earls
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Liliana Soroceanu
- California Pacific Medical Center Research Institute, San Francisco, CA 94107, USA
| | - Greg Foltz
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Charles S Cobbs
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA 98109, USA.
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48
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Lokhov PG, Maslov DL, Kharibin ON, Balashova EE, Archakov AI. Label-free data standardization for clinical metabolomics. BioData Min 2017; 10:10. [PMID: 28261328 PMCID: PMC5329969 DOI: 10.1186/s13040-017-0132-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 02/21/2017] [Indexed: 11/24/2022] Open
Abstract
Background In metabolomics, thousands of substances can be detected in a single assay. This capacity motivates the development of metabolomics testing, which is currently a very promising option for improving laboratory diagnostics. However, the simultaneous measurement of an enormous number of substances leads to metabolomics data often representing concentrations only in conditional units, while laboratory diagnostics generally require actual concentrations. To convert metabolomics data to actual concentrations, calibration curves need to be generated for each substance, and this process represents a significant challenge due to the number of substances that are present in the metabolomics data. To overcome this limitation, a label-free standardization algorithm for metabolomics data is required. Results It was discovered that blood plasma has a set of stable internal standards. The appropriate usage of these newly discovered internal standards provides a background for the label-free standardization of metabolomics data that underlies the SantaOmics (Standardization algorithm for nonlinearly transformed arrays in Omics) algorithm. In this study, using the knee point, it was shown that the metabolomics data can be converted by SantaOmics into a standardized scale that can substitute actual concentration measurements, thus making the metabolomics data directly comparable with each other as well as with reference data presented in the same scale. Conclusion The developed algorithm sufficiently facilitates the usage of metabolomics data in laboratory diagnostics. Electronic supplementary material The online version of this article (doi:10.1186/s13040-017-0132-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Petr G Lokhov
- Institute of Biomedical Chemistry, Pogodinskaya st.10, 119121 Moscow, Russia
| | - Dmitri L Maslov
- Institute of Biomedical Chemistry, Pogodinskaya st.10, 119121 Moscow, Russia
| | - Oleg N Kharibin
- Institute of Biomedical Chemistry, Pogodinskaya st.10, 119121 Moscow, Russia
| | - Elena E Balashova
- Institute of Biomedical Chemistry, Pogodinskaya st.10, 119121 Moscow, Russia
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Abstract
Precision medicine is an emerging approach for prevention and treatment of diseases considering individuals’ uniqueness. Omics provide one step forward toward advanced precision medicine and include technologies such as genomics, proteomics and metabolomics generating valuable data through characterization of entire biological systems. With the aid of omics, a major shift has been started to occur in understanding of diseases followed by potential fundamental changes in medical care strategies. This short review aims at providing some examples of current omics that are applied in the field of pain in terms of new biomarkers for diagnosis of different pain types, stratification of patients and new therapeutic targets. Implementation of omics would most likely offer breakthrough in the future of pain management.
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Affiliation(s)
- Parisa Gazerani
- Department of Health Science & Technology, Faculty of Medicine, Aalborg University, Frederik Bajers Vej 7A2-A2-208, 9220 Aalborg East, Denmark
| | - Hye Sook Han Vinterhøj
- Department of Health Science & Technology, Faculty of Medicine, Aalborg University, Frederik Bajers Vej 7A2-A2-208, 9220 Aalborg East, Denmark
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50
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Affiliation(s)
- Peter J Sterk
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
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