351
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Duverdier A, Custovic A, Tanaka RJ. Data-driven research on eczema: Systematic characterization of the field and recommendations for the future. Clin Transl Allergy 2022; 12:e12170. [PMID: 35686200 PMCID: PMC9172212 DOI: 10.1002/clt2.12170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 11/26/2022] Open
Abstract
Background The past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results Five key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were "eczema" papers. Conclusions Research areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.
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Affiliation(s)
- Ariane Duverdier
- Department of ComputingImperial College LondonLondonUK
- Department of BioengineeringImperial College LondonLondonUK
- UKRI Centre for Doctoral Training in AI for HealthcareImperial College LondonLondonUK
| | - Adnan Custovic
- National Heart and Lung InstituteImperial College LondonLondonUK
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Ahmed MM, Tazyeen S, Ali R, Alam A, Imam N, Malik MZ, Ali S, Ishrat R. Network centrality approaches used to uncover and classify most influential nodes with their related miRNAs in cardiovascular diseases. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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353
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Ren Y, Yu G, Shi C, Liu L, Guo Q, Han C, Zhang D, Zhang L, Liu B, Gao H, Zeng J, Zhou Y, Qiu Y, Wei J, Luo Y, Zhu F, Li X, Wu Q, Li B, Fu W, Tong Y, Meng J, Fang Y, Dong J, Feng Y, Xie S, Yang Q, Yang H, Wang Y, Zhang J, Gu H, Xuan H, Zou G, Luo C, Huang L, Yang B, Dong Y, Zhao J, Han J, Zhang X, Huang H. Majorbio Cloud: A one-stop, comprehensive bioinformatic platform for multiomics analyses. IMETA 2022; 1:e12. [PMID: 38868573 PMCID: PMC10989754 DOI: 10.1002/imt2.12] [Citation(s) in RCA: 223] [Impact Index Per Article: 111.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2024]
Abstract
The platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.
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Affiliation(s)
- Yi Ren
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Guo Yu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Caiping Shi
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Linmeng Liu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Quan Guo
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Chang Han
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Dan Zhang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Lei Zhang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Binxu Liu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Hao Gao
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Jing Zeng
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yong Zhou
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yuhan Qiu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Jian Wei
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yanchun Luo
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Fengjuan Zhu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Xiaojie Li
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Qin Wu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Bing Li
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Wenyao Fu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yanli Tong
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Jie Meng
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yahong Fang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Jie Dong
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yitong Feng
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Shichang Xie
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Qianqian Yang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Hui Yang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yan Wang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Junbiao Zhang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Haidong Gu
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Hongdong Xuan
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Guanqing Zou
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Chun Luo
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Long Huang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Bing Yang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Yachen Dong
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Jianhua Zhao
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Jichen Han
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Xianglin Zhang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
| | - Huasheng Huang
- Shanghai Majorbio Bio‐pharm Technology Co., Ltd.ShanghaiChina
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Hasanzad M, Sarhangi N, Naghavi A, Ghavimehr E, Khatami F, Ehsani Chimeh S, Larijani B, Aghaei Meybodi HR. Genomic medicine on the frontier of precision medicine. J Diabetes Metab Disord 2022; 21:853-861. [PMID: 35673457 PMCID: PMC9167337 DOI: 10.1007/s40200-021-00880-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022]
Abstract
Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.
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Affiliation(s)
- Mandana Hasanzad
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, No.10- Jalal -e-Ale-Ahmad Street, Chamran Highway, 1411713119 Tehran, Iran
| | - Negar Sarhangi
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, No.10- Jalal -e-Ale-Ahmad Street, Chamran Highway, 1411713119 Tehran, Iran
| | - Anoosh Naghavi
- Cellular and Molecular Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Ehsan Ghavimehr
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Fatemeh Khatami
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Aghaei Meybodi
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, No.10- Jalal -e-Ale-Ahmad Street, Chamran Highway, 1411713119 Tehran, Iran
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355
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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356
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Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, Papa E, Freeman A, Patel S, Yu W, Huhn M, Sheikh AS, Tan K, Sellman BR, Cohen T, Mangion J, Khan FM, Gusev Y, Shameer K. StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit. Front Genet 2022; 13:868015. [PMID: 35711912 PMCID: PMC9197487 DOI: 10.3389/fgene.2022.868015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/29/2022] [Indexed: 01/26/2023] Open
Abstract
Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.
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Affiliation(s)
- Chiyun Lee
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Junxia Lin
- Georgetown University, Washington, DC, United States
| | | | | | - Richard N. Hanna
- Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Eliseo Papa
- Research Data and Analytics, R&D IT, AstraZeneca, Cambridge, United Kingdom
| | - Adrian Freeman
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Saleha Patel
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Wen Yu
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Monika Huhn
- Biometrics and Information Sciences, BioPharmaceuticals R&D, AstraZeneca, Mölndal, Sweden
| | - Abdul-Saboor Sheikh
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Keith Tan
- Neuroscience, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Bret R. Sellman
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Taylor Cohen
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Jonathan Mangion
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Faisal M. Khan
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Yuriy Gusev
- Georgetown University, Washington, DC, United States
| | - Khader Shameer
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States,*Correspondence: Khader Shameer,
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357
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Yang B, Yang Y, Su X. Deep structure integrative representation of multi-omics data for cancer subtyping. Bioinformatics 2022; 38:3337-3342. [PMID: 35639657 DOI: 10.1093/bioinformatics/btac345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/22/2022] [Accepted: 05/17/2022] [Indexed: 01/01/2023] Open
Abstract
MOTIVATION Cancer is a heterogeneous group of diseases. Cancer subtyping is crucial and critical step to diagnosis, prognosis and treatment. Since high-throughput sequencing technologies provide unprecedented opportunity to rapid collect multi-omics data for the same individuals, an urgent need in current is how to effectively represent and integrate these multi-omics data to achieve clinically meaningful cancer subtyping. RESULTS We propose a novel deep learning model, called Deep Structure Integrative Representation (DSIR), for cancer subtypes dentification by integrating representation and clustering multi-omics data. DSIR simultaneously captures the global structures in sparse subspace and local structures in manifold subspace from multi-omics data and constructs consensus similarity matrix by utilizing deep neural networks. Extensive tests are performed in twelve different cancers on three levels of omics data from The Cancer Genome Atlas. The results demonstrate that DSIR obtains more significant performances than the state-of-the-art integrative methods. AVAILABILITY https://github.com/Polytech-bioinf/Deep-structure-integrative-representation.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bo Yang
- School of Computer Science, Xi'an Polytechnic University, Xi'an, 710048, China.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Yan Yang
- School of Computer Science, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Xueping Su
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, 710048, China
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358
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Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Cassinelli Petersen GI, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers (Basel) 2022; 14:cancers14112623. [PMID: 35681603 PMCID: PMC9179416 DOI: 10.3390/cancers14112623] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 01/27/2023] Open
Abstract
Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.
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Affiliation(s)
- Sara Merkaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Department of Neurosurgery, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ryan C. Bahar
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Visage Imaging, Inc., 12625 High Bluff Dr, Suite 205, San Diego, CA 92130, USA
| | - Ichiro Ikuta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | | | - Gabriel I. Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Lawrence Staib
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - John T. Mongan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA; (J.T.M.); (S.C.)
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA; (J.T.M.); (S.C.)
| | - Mariam S. Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Correspondence: ; Tel.: +650-285-7577
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359
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Lancaster EE, Vladimirov VI, Riley BP, Landry JW, Roberson-Nay R, York TP. LARGE-SCALE INTEGRATION OF DNA METHYLATION AND GENE EXPRESSION ARRAY PLATFORMS IDENTIFIES BOTH cis AND trans RELATIONSHIPS. Epigenetics 2022; 17:1753-1773. [PMID: 35608069 PMCID: PMC9621057 DOI: 10.1080/15592294.2022.2079293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Although epigenome-wide association studies (EWAS) have been successful in identifying DNA methylation (DNAm) patterns associated with disease states, any further characterization of etiologic mechanisms underlying disease remains elusive. This knowledge gap does not originate from a lack of DNAm–trait associations, but rather stems from study design issues that affect the interpretability of EWAS results. Despite known limitations in predicting the function of a particular CpG site, most EWAS maintain the broad assumption that altered DNAm results in a concomitant change of transcription at the most proximal gene. This study integrated DNAm and gene expression (GE) measurements in two cohorts, the Adolescent and Young Adult Twin Study (AYATS) and the Pregnancy, Race, Environment, Genes (PREG) study, to improve the understanding of epigenomic regulatory mechanisms. CpG sites associated with GE in cis were enriched in areas of transcription factor binding and areas of intermediate-to-low CpG density. CpG sites associated with trans GE were also enriched in areas of known regulatory significance, including enhancer regions. These results highlight issues with restricting DNAm-transcript annotations to small genomic intervals and question the validity of assuming a cis DNAm–GE pathway. Based on these findings, the interpretation of EWAS results is limited in studies without multi-omic support and further research should identify genomic regions in which GE-associated DNAm is overrepresented. An in-depth characterization of GE-associated CpG sites could improve predictions of the downstream functional impact of altered DNAm and inform best practices for interpreting DNAm–trait associations generated by EWAS.
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Affiliation(s)
- Eva E Lancaster
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23220
| | | | - Brien P Riley
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23220.,Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23220
| | - Joseph W Landry
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23220
| | - Roxann Roberson-Nay
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23220.,Department of Psychology, Virginia Commonwealth University, Richmond, VA 23220
| | - Timothy P York
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23220.,Department of Obstetrics and Gynecology, Virginia Commonwealth University, Richmond, VA 23220
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360
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Chan YH, Wang C, Soh WK, Rajapakse JC. Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks. Front Neurosci 2022; 16:866666. [PMID: 35677355 PMCID: PMC9168232 DOI: 10.3389/fnins.2022.866666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Both neuroimaging and genomics datasets are often gathered for the detection of neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics data pose tremendous challenge for methods integrating multiple modalities. There are few existing solutions that can combine both multi-modal imaging and multi-omics datasets to derive neurological insights. We propose a deep neural network architecture that combines both structural and functional connectome data with multi-omics data for disease classification. A graph convolution layer is used to model functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data simultaneously to learn compact representations of the connectome. A separate set of graph convolution layers are then used to model multi-omics datasets, expressed in the form of population graphs, and combine them with latent representations of the connectome. An attention mechanism is used to fuse these outputs and provide insights on which omics data contributed most to the model's classification decision. We demonstrate our methods for Parkinson's disease (PD) classification by using datasets from the Parkinson's Progression Markers Initiative (PPMI). PD has been shown to be associated with changes in the human connectome and it is also known to be influenced by genetic factors. We combine DTI and fMRI data with multi-omics data from RNA Expression, Single Nucleotide Polymorphism (SNP), DNA Methylation and non-coding RNA experiments. A Matthew Correlation Coefficient of greater than 0.8 over many combinations of multi-modal imaging data and multi-omics data was achieved with our proposed architecture. To address the paucity of paired multi-modal imaging data and the problem of imbalanced data in the PPMI dataset, we compared the use of oversampling against using CycleGAN on structural and functional connectomes to generate missing imaging modalities. Furthermore, we performed ablation studies that offer insights into the importance of each imaging and omics modality for the prediction of PD. Analysis of the generated attention matrices revealed that DNA Methylation and SNP data were the most important omics modalities out of all the omics datasets considered. Our work motivates further research into imaging genetics and the creation of more multi-modal imaging and multi-omics datasets to study PD and other complex neurodegenerative diseases.
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Affiliation(s)
| | | | | | - Jagath C. Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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361
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Narayana JK, Tsaneva-Atanasova K, Chotirmall SH. Microbiomics Focused Data Integration: A Fresh Solve for the Rubik's Cube of Endophenotyping? Am J Respir Crit Care Med 2022; 206:365-368. [PMID: 35584334 DOI: 10.1164/rccm.202205-0860ed] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Jayanth Kumar Narayana
- Lee Kong Chian School of Medicine, Translational Respiratory Research Laboratory, Singapore, Singapore
| | - Krasimira Tsaneva-Atanasova
- University of Exeter, 3286, Living Systems Institute and Department of Mathematics, Exeter, United Kingdom of Great Britain and Northern Ireland
| | - Sanjay H Chotirmall
- Lee Kong Chian School of Medicine, Translational Respiratory Research Laboratory, Singapore, Singapore;
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362
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Yin C, Cao Y, Sun P, Zhang H, Li Z, Xu Y, Sun H. Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration. Front Genet 2022; 13:884028. [PMID: 35646077 PMCID: PMC9137453 DOI: 10.3389/fgene.2022.884028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.
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Affiliation(s)
- Chaoyi Yin
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Yangkun Cao
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Peishuo Sun
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Hengyuan Zhang
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Zhi Li
- Department of Medical Oncology, the First Hospital of China Medical University, Shenyang, China
- *Correspondence: Zhi Li, ; Huiyan Sun,
| | - Ying Xu
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Huiyan Sun
- School of Artificial Intelligence, Jilin University, Changchun, China
- *Correspondence: Zhi Li, ; Huiyan Sun,
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363
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Abondio P, De Intinis C, da Silva Gonçalves Vianez Júnior JL, Pace L. SINGLE CELL MULTIOMIC APPROACHES TO DISENTANGLE T CELL HETEROGENEITY. Immunol Lett 2022; 246:37-51. [DOI: 10.1016/j.imlet.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/16/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
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364
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Multi-omics approach in tea polyphenol research regarding tea plant growth, development and tea processing: current technologies and perspectives. FOOD SCIENCE AND HUMAN WELLNESS 2022. [DOI: 10.1016/j.fshw.2021.12.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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365
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Pitaloka DAE, Syamsunarno MRAAA, Abdulah R, Chaidir L. Omics Biomarkers for Monitoring Tuberculosis Treatment: A Mini-Review of Recent Insights and Future Approaches. Infect Drug Resist 2022; 15:2703-2711. [PMID: 35664683 PMCID: PMC9160605 DOI: 10.2147/idr.s366580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/20/2022] [Indexed: 11/23/2022] Open
Abstract
Poor sensitivity of sputum conversion for monitoring tuberculosis (TB) treatment that makes identification of a non-sputum-based biomarker is urgently needed. Monitoring biomarkers in TB treatment is used to decide whether critical thresholds have been reached and helps clinicians to conclude the therapeutic success. In this mini review, we highlight recent studies on omics-related contributes to identifying of a novel biomarker as surrogate markers for the cure and predicting future reactivation risk following TB treatment. We catalogue the studies published to seek the progress made in transcriptomics, proteomics, and metabolomics in pulmonary TB. We also discuss how integrative multi-omics data will provide further understanding and effective TB treatment, such as revealing the interrelationships at multiple molecular levels, facilitating the identification of biologically interconnected processes, and accelerating precision medicine in TB treatment. However, proper validation in prospective longitudinal studies with long-term follow-up and outcome assessment must be conducted before the biomarkers are utilized in clinical practice.
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Affiliation(s)
- Dian Ayu Eka Pitaloka
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
- Center for Translational Biomarker Research, Universitas Padjadjaran, Bandung, West Java, 40132, Indonesia
| | - Mas Rizky Anggun A A Syamsunarno
- Center for Translational Biomarker Research, Universitas Padjadjaran, Bandung, West Java, 40132, Indonesia
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
| | - Rizky Abdulah
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
- Center of Excellence in Higher Education for Pharmaceutical Care Innovation, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
| | - Lidya Chaidir
- Center for Translational Biomarker Research, Universitas Padjadjaran, Bandung, West Java, 40132, Indonesia
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
- Correspondence: Lidya Chaidir, Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia, Tel +62-22-84288812, Email
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366
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Shankar A, Sharma KK. Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics. Appl Microbiol Biotechnol 2022; 106:3465-3488. [PMID: 35546367 PMCID: PMC9095418 DOI: 10.1007/s00253-022-11945-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/12/2022] [Accepted: 04/24/2022] [Indexed: 01/16/2023]
Abstract
Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy aging. Their molecular properties are validated through the use of recent high-throughput genomic, transcriptomic, and metabolomic tools and techniques. Together, these updated multi-omic tools have been used to study fungal metabolites structure and their mode of action on biological and cellular processes. Diverse groups of fungi produce different proteins and secondary metabolites, which possess tremendous biotechnological and pharmaceutical applications. Furthermore, its use and acceptability can be accelerated by adopting multi-omics, bioinformatics, and machine learning tools that generate a huge amount of molecular data. The integration of artificial intelligence and machine learning tools in the era of omics and big data has opened up a new outlook in both basic and applied researches in the area of nutraceuticals and functional food and nutrition. KEY POINTS: • Multi-omic tool helps in the identification of novel fungal metabolites • Intra-omic data from genomics to bioinformatics • Novel metabolites and application in human health.
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Affiliation(s)
- Akshay Shankar
- Laboratory of Enzymology and Recombinant DNA Technology, Department of Microbiology, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
| | - Krishna Kant Sharma
- Laboratory of Enzymology and Recombinant DNA Technology, Department of Microbiology, Maharshi Dayanand University, Rohtak, 124001, Haryana, India.
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367
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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368
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Yu CT, Chao BN, Barajas R, Haznadar M, Maruvada P, Nicastro HL, Ross SA, Verma M, Rogers S, Zanetti KA. An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data. Metabolomics 2022; 18:29. [PMID: 35488937 PMCID: PMC9056487 DOI: 10.1007/s11306-022-01878-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/28/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment. AIM OF REVIEW To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data. KEY SCIENTIFIC CONCEPTS OF REVIEW We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.
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Affiliation(s)
- Catherine T Yu
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Brittany N Chao
- Office of Workforce Planning and Development, National Cancer Institute, Rockville, MD, USA
| | - Rolando Barajas
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Majda Haznadar
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Rockville, MD, USA
| | - Padma Maruvada
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Holly L Nicastro
- Office of Nutrition Research, National Institutes of Health, Bethesda, MD, USA
| | - Sharon A Ross
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Mukesh Verma
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Scott Rogers
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Krista A Zanetti
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
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369
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Rhodes CJ, Sweatt AJ, Maron BA. Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension. Circ Res 2022; 130:1423-1444. [PMID: 35482840 PMCID: PMC9070103 DOI: 10.1161/circresaha.121.319969] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.
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Affiliation(s)
- Christopher J Rhodes
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Andrew J Sweatt
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (B.A.M.).,Division of Cardiology, VA Boston Healthcare System, West Roxbury, MA (B.A.M.)
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370
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Liu Z, Xu J, Que S, Geng L, Zhou L, Mardinoglu A, Zheng S. Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation. J Clin Transl Hepatol 2022; 10:363-373. [PMID: 35528975 PMCID: PMC9039708 DOI: 10.14218/jcth.2021.00219] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/14/2021] [Accepted: 10/07/2021] [Indexed: 12/04/2022] Open
Abstract
Omics data address key issues in liver transplantation (LT) as the most effective therapeutic means for end-stage liver disease. The purpose of this study was to review the current application and future direction for omics in LT. We reviewed the use of multiomics to elucidate the pathogenesis leading to LT and prognostication. Future directions with respect to the use of omics in LT are also described based on perspectives of surgeons with experience in omics. Significant molecules were identified and summarized based on omics, with a focus on post-transplant liver fibrosis, early allograft dysfunction, tumor recurrence, and graft failure. We emphasized the importance omics for clinicians who perform LTs and prioritized the directions that should be established. We also outlined the ideal workflow for omics in LT. In step with advances in technology, the quality of omics data can be guaranteed using an improved algorithm at a lower price. Concerns should be addressed on the translational value of omics for better therapeutic effects in patients undergoing LT.
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Affiliation(s)
- Zhengtao Liu
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Key Laboratory of the diagnosis and treatment of organ Transplantation, CAMS, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Organ Transplantation, Zhejiang Province, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jun Xu
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuping Que
- DingXiang Clinics, Hangzhou, Zhejiang, China
| | - Lei Geng
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lin Zhou
- NHC Key Laboratory of Combined Multi-organ Transplantation, Key Laboratory of the diagnosis and treatment of organ Transplantation, CAMS, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Organ Transplantation, Zhejiang Province, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
- Correspondence to: Adil Mardinoglu, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID: https://orcid.org/0000-0002-4254-6090. Tel: +46-31-772-3140, Fax: +46-31-772-3801, E-mail: ; Shusen Zheng, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China. ORCID: https://orcid.org/0000-0003-1459-8261. Tel/Fax: +86-571-87236570, E-mail:
| | - Shusen Zheng
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Key Laboratory of the diagnosis and treatment of organ Transplantation, CAMS, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Organ Transplantation, Zhejiang Province, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Correspondence to: Adil Mardinoglu, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID: https://orcid.org/0000-0002-4254-6090. Tel: +46-31-772-3140, Fax: +46-31-772-3801, E-mail: ; Shusen Zheng, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China. ORCID: https://orcid.org/0000-0003-1459-8261. Tel/Fax: +86-571-87236570, E-mail:
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371
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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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372
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Shaba E, Vantaggiato L, Governini L, Haxhiu A, Sebastiani G, Fignani D, Grieco GE, Bergantini L, Bini L, Landi C. Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone. Proteomes 2022; 10:proteomes10020012. [PMID: 35645370 PMCID: PMC9149947 DOI: 10.3390/proteomes10020012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 02/01/2023] Open
Abstract
In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.
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Affiliation(s)
- Enxhi Shaba
- Functional Proteomics Lab, Department of Life Sciences, University of Siena, 53100 Siena, Italy; (L.V.); (L.B.); (C.L.)
- Correspondence:
| | - Lorenza Vantaggiato
- Functional Proteomics Lab, Department of Life Sciences, University of Siena, 53100 Siena, Italy; (L.V.); (L.B.); (C.L.)
| | - Laura Governini
- Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy; (L.G.); (A.H.)
| | - Alesandro Haxhiu
- Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy; (L.G.); (A.H.)
| | - Guido Sebastiani
- Diabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy; (G.S.); (D.F.); (G.E.G.)
- Fondazione Umberto Di Mario, c/o Toscana Life Sciences, 53100 Siena, Italy
| | - Daniela Fignani
- Diabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy; (G.S.); (D.F.); (G.E.G.)
- Fondazione Umberto Di Mario, c/o Toscana Life Sciences, 53100 Siena, Italy
| | - Giuseppina Emanuela Grieco
- Diabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy; (G.S.); (D.F.); (G.E.G.)
- Fondazione Umberto Di Mario, c/o Toscana Life Sciences, 53100 Siena, Italy
| | - Laura Bergantini
- Respiratory Diseases and Lung Transplant Unit, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy;
| | - Luca Bini
- Functional Proteomics Lab, Department of Life Sciences, University of Siena, 53100 Siena, Italy; (L.V.); (L.B.); (C.L.)
| | - Claudia Landi
- Functional Proteomics Lab, Department of Life Sciences, University of Siena, 53100 Siena, Italy; (L.V.); (L.B.); (C.L.)
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Dobosz P, Stempor PA, Ramírez Moreno M, Bulgakova NA. Transcriptional and post-transcriptional regulation of checkpoint genes on the tumour side of the immunological synapse. Heredity (Edinb) 2022; 129:64-74. [PMID: 35459932 PMCID: PMC9273643 DOI: 10.1038/s41437-022-00533-1] [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: 12/20/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 02/06/2023] Open
Abstract
Cancer is a disease of the genome, therefore, its development has a clear Mendelian component, demonstrated by well-studied genes such as BRCA1 and BRCA2 in breast cancer risk. However, it is known that a single genetic variant is not enough for cancer to develop leading to the theory of multistage carcinogenesis. In many cases, it is a sequence of events, acquired somatic mutations, or simply polygenic components with strong epigenetic effects, such as in the case of brain tumours. The expression of many genes is the product of the complex interplay between several factors, including the organism’s genotype (in most cases Mendelian-inherited), genetic instability, epigenetic factors (non-Mendelian-inherited) as well as the immune response of the host, to name just a few. In recent years the importance of the immune system has been elevated, especially in the light of the immune checkpoint genes discovery and the subsequent development of their inhibitors. As the expression of these genes normally suppresses self-immunoreactivity, their expression by tumour cells prevents the elimination of the tumour by the immune system. These discoveries led to the rapid growth of the field of immuno-oncology that offers new possibilities of long-lasting and effective treatment options. Here we discuss the recent advances in the understanding of the key mechanisms controlling the expression of immune checkpoint genes in tumour cells.
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Affiliation(s)
- Paula Dobosz
- Central Clinical Hospital of the Ministry of Interior Affairs and Administration in Warsaw, Warsaw, Poland
| | | | - Miguel Ramírez Moreno
- School of Biosciences and Bateson Centre, The University of Sheffield, Sheffield, UK
| | - Natalia A Bulgakova
- School of Biosciences and Bateson Centre, The University of Sheffield, Sheffield, UK.
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374
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Integration of Omics and Phenotypic Data for Precision Medicine. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:19-35. [PMID: 35437716 DOI: 10.1007/978-1-0716-2265-0_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Over the past two decades, biomedical research is moving toward a big-data-driven approach. The underlying causes of this transition include the ability to gather genetic or molecular profiles of humans faster, the increasing adoption of electronic health record (EHR) system, and the growing interest in linking omics and phenotypic data for analysis. The integration of individual's biology data (e.g., genomics, proteomics, metabolomics), and health-care data has created unprecedented opportunities for precision medicine, that is, a medical model that uses a patient's unique information, mainly genetic, to prevent, diagnose, or treat disease. This chapter reviewed the research opportunities and applications of integrating omics and phenotypic data for precision medicine, such as understanding the relationship between genotype and phenotype, disease subtyping, and diagnosis or prediction of adverse outcomes. We reviewed the recent advanced methods, particularly the machine learning and deep learning-based approaches used for harnessing and harmonizing the multiomics and phenotypic data to address these applications. We finally discussed the challenges and future directions.
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375
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Grausa K, Mozga I, Pleiko K, Pentjuss A. Integrative Gene Expression and Metabolic Analysis Tool IgemRNA. Biomolecules 2022; 12:biom12040586. [PMID: 35454176 PMCID: PMC9029533 DOI: 10.3390/biom12040586] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/11/2022] [Accepted: 04/14/2022] [Indexed: 01/27/2023] Open
Abstract
Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.
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Affiliation(s)
- Kristina Grausa
- Department of Computer Systems, Latvia University of Life Sciences and Technologies, Liela Street 2, LV-3001 Jelgava, Latvia; (K.G.); (I.M.)
| | - Ivars Mozga
- Department of Computer Systems, Latvia University of Life Sciences and Technologies, Liela Street 2, LV-3001 Jelgava, Latvia; (K.G.); (I.M.)
| | - Karlis Pleiko
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia;
- Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Agris Pentjuss
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia
- Correspondence:
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376
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MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, offering an unprecedented scenario for deriving meaningful biological knowledge through the integration of different layers of information. Methods: We have developed a new software tool, MOMIC, that guides the user through the application of different analysis on a wide range of omic data, from the independent single-omics analysis to the combination of heterogeneous data at different molecular levels. Results: The proposed pipeline is developed as a collection of Jupyter notebooks, easily editable, reproducible and well documented. It can be modified to accommodate new analysis workflows and data types. It is accessible via momic.us.es, and as a docker project available at github that can be locally installed. Conclusions: MOMIC offers a complete analysis environment for analysing and integrating multi-omics data in a single, easy-to-use platform.
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377
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Yadav AK, Banerjee SK, Das B, Chaudhary K. Editorial: Systems Biology and Omics Approaches for Understanding Complex Disease Biology. Front Genet 2022; 13:896818. [PMID: 35495146 PMCID: PMC9039331 DOI: 10.3389/fgene.2022.896818] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Amit Kumar Yadav
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
- *Correspondence: Amit Kumar Yadav,
| | - Sanjay Kumar Banerjee
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research (NIPER), Guwahati, India
| | - Bhabatosh Das
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
| | - Kumardeep Chaudhary
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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378
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Moon S, Lee H. MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification. Bioinformatics 2022; 38:2287-2296. [PMID: 35157023 PMCID: PMC10060719 DOI: 10.1093/bioinformatics/btac080] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 01/01/2022] [Accepted: 02/08/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Accurate diagnostic classification and biological interpretation are important in biology and medicine, which are data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy of clinical phenotypes, and more comprehensive analyses for predicting the prognosis of complex diseases are required. RESULTS Here, we propose a novel multi-task attention learning algorithm for multi-omics data, termed MOMA, which captures important biological processes for high diagnostic performance and interpretability. MOMA vectorizes features and modules using a geometric approach and focuses on important modules in multi-omics data via an attention mechanism. Experiments using public data on Alzheimer's disease and cancer with various classification tasks demonstrated the superior performance of this approach. The utility of MOMA was also verified using a comparison experiment with an attention mechanism that was turned on or off and biological analysis. AVAILABILITY AND IMPLEMENTATION The source codes are available at https://github.com/dmcb-gist/MOMA. SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online.
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Affiliation(s)
- Sehwan Moon
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, South Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, South Korea
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379
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Yue Z, Slominski R, Bharti S, Chen JY. PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics. Front Genet 2022; 13:820361. [PMID: 35495152 PMCID: PMC9039620 DOI: 10.3389/fgene.2022.820361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/17/2022] [Indexed: 12/30/2022] Open
Abstract
Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, "PAGER Web APP", which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP's pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.
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Affiliation(s)
- Zongliang Yue
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Radomir Slominski
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
- Graduate Biomedical Sciences Program, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Samuel Bharti
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jake Y. Chen
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
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380
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Vahabi N, Michailidis G. Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Front Genet 2022; 13:854752. [PMID: 35391796 PMCID: PMC8981526 DOI: 10.3389/fgene.2022.854752] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 02/28/2022] [Indexed: 12/26/2022] Open
Abstract
Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.
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Affiliation(s)
- Nasim Vahabi
- Informatics Institute, University of Florida, Gainesville, FL, United States
| | - George Michailidis
- Informatics Institute, University of Florida, Gainesville, FL, United States
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381
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Iqbal MA, Reyer H, Oster M, Hadlich F, Trakooljul N, Perdomo-Sabogal A, Schmucker S, Stefanski V, Roth C, Camarinha Silva A, Huber K, Sommerfeld V, Rodehutscord M, Wimmers K, Ponsuksili S. Multi-Omics Reveals Different Strategies in the Immune and Metabolic Systems of High-Yielding Strains of Laying Hens. Front Genet 2022; 13:858232. [PMID: 35432452 PMCID: PMC9010826 DOI: 10.3389/fgene.2022.858232] [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: 01/19/2022] [Accepted: 03/10/2022] [Indexed: 01/22/2023] Open
Abstract
Lohmann Brown (LB) and Lohmann Selected Leghorn (LSL) are two commercially important laying hen strains due to their high egg production and excellent commercial suitability. The present study integrated multiple data sets along the genotype-phenotype map to better understand how the genetic background of the two strains influences their molecular pathways. In total, 71 individuals were analyzed (LB, n = 36; LSL, n = 35). Data sets include gut miRNA and mRNA transcriptome data, microbiota composition, immune cells, inositol phosphate metabolites, minerals, and hormones from different organs of the two hen strains. All complex data sets were pre-processed, normalized, and compatible with the mixOmics platform. The most discriminant features between two laying strains included 20 miRNAs, 20 mRNAs, 16 immune cells, 10 microbes, 11 phenotypic traits, and 16 metabolites. The expression of specific miRNAs and the abundance of immune cell types were related to the enrichment of immune pathways in the LSL strain. In contrast, more microbial taxa specific to the LB strain were identified, and the abundance of certain microbes strongly correlated with host gut transcripts enriched in immunological and metabolic pathways. Our findings indicate that both strains employ distinct inherent strategies to acquire and maintain their immune and metabolic systems under high-performance conditions. In addition, the study provides a new perspective on a view of the functional biodiversity that emerges during strain selection and contributes to the understanding of the role of host–gut interaction, including immune phenotype, microbiota, gut transcriptome, and metabolome.
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Affiliation(s)
- Muhammad Arsalan Iqbal
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
| | - Henry Reyer
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
| | - Michael Oster
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
| | - Frieder Hadlich
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
| | - Nares Trakooljul
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
| | - Alvaro Perdomo-Sabogal
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
| | - Sonja Schmucker
- University of Hohenheim, Institute of Animal Science, Stuttgart, Germany
| | - Volker Stefanski
- University of Hohenheim, Institute of Animal Science, Stuttgart, Germany
| | - Christoph Roth
- University of Hohenheim, Institute of Animal Science, Stuttgart, Germany
| | | | - Korinna Huber
- University of Hohenheim, Institute of Animal Science, Stuttgart, Germany
| | - Vera Sommerfeld
- University of Hohenheim, Institute of Animal Science, Stuttgart, Germany
| | | | - Klaus Wimmers
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
- University Rostock, Faculty of Agricultural and Environmental Sciences, Rostock, Germany
| | - Siriluck Ponsuksili
- Research Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, Germany
- *Correspondence: Siriluck Ponsuksili,
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382
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Novel data archival system for multi-omics data of human exposure to harmful substances. Mol Cell Toxicol 2022. [DOI: 10.1007/s13273-022-00226-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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383
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Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases. CARDIOGENETICS 2022. [DOI: 10.3390/cardiogenetics12020015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.
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384
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Liu X, Le Roux X, Salles JF. The legacy of microbial inoculants in agroecosystems and potential for tackling climate change challenges. iScience 2022; 25:103821. [PMID: 35243218 PMCID: PMC8867051 DOI: 10.1016/j.isci.2022.103821] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Microbial inoculations contribute to reducing agricultural systems' environmental footprint by supporting sustainable production and regulating climate change. However, the indirect and cascading effects of microbial inoculants through the reshaping of soil microbiome are largely overlooked. By discussing the underlying mechanisms of plant- and soil-based microbial inoculants, we suggest that a key challenge in microbial inoculation is to understand their legacy on indigenous microbial communities and the corresponding impacts on agroecosystem functions and services relevant to climate change. We explain how these legacy effects on the soil microbiome can be understood by building on the mechanisms driving microbial invasions and placing inoculation into the context of ecological succession and community assembly. Overall, we advocate that generalizing field trials to systematically test inoculants' effectiveness and developing knowledge anchored in the scientific field of biological/microbial invasion are two essential requirements for applying microbial inoculants in agricultural ecosystems to tackle climate change challenges.
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Affiliation(s)
- Xipeng Liu
- Microbial Ecology cluster, Genomics Research in Ecology and Evolution in Nature (GREEN), Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, 9747 AG Groningen, the Netherlands
| | - Xavier Le Roux
- Microbial Ecology Centre LEM, INRAE, CNRS, VetAgroSup, Université Lyon 1, Université de Lyon, UMR 1418 INRAE, UMR 5557 CNRS, 69622 Villeurbanne, France
| | - Joana Falcão Salles
- Microbial Ecology cluster, Genomics Research in Ecology and Evolution in Nature (GREEN), Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, 9747 AG Groningen, the Netherlands
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385
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Wu PY, Stich B, Weisweiler M, Shrestha A, Erban A, Westhoff P, Inghelandt DV. Improvement of prediction ability by integrating multi-omic datasets in barley. BMC Genomics 2022; 23:200. [PMID: 35279073 PMCID: PMC8917753 DOI: 10.1186/s12864-022-08337-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/20/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors. Results The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone. Conclusions The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-022-08337-7).
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386
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Islam Khan MZ, Tam SY, Law HKW. Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer. Cells 2022; 11:973. [PMID: 35326424 PMCID: PMC8946849 DOI: 10.3390/cells11060973] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/05/2022] [Accepted: 03/08/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.
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Affiliation(s)
| | | | - Helen Ka Wai Law
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China; (M.Z.I.K.); (S.Y.T.)
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387
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Sridharan H, Piorino F, Styczynski MP. Systems biology-based analysis of cell-free systems. Curr Opin Biotechnol 2022; 75:102703. [PMID: 35247659 DOI: 10.1016/j.copbio.2022.102703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/07/2021] [Accepted: 02/03/2022] [Indexed: 11/28/2022]
Abstract
Cell-free expression systems are becoming increasingly widely used due to their diverse applications in biotechnology. Despite this rapid expansion in adoption, many aspects of cell-free systems remain surprisingly poorly understood. Systems biology approaches make it possible to characterize cell-free systems deeply and broadly to better understand their underlying complexity. Here, we review recent systems biology studies that have provided insight into cell-free systems. We focus on characterization of the cell-free proteome, including its dependence on preparation protocol and host strain, as well as the cell-free metabolome and the relationship of endogenous metabolism to system performance. We conclude by highlighting promising future research directions.
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Affiliation(s)
- Harini Sridharan
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA, 30332-0100, United States
| | - Fernanda Piorino
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA, 30332-0100, United States
| | - Mark P Styczynski
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA, 30332-0100, United States.
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388
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Wang J, Sidharth S, Zeng S, Jiang Y, Chan YO, Lyu Z, McCubbin T, Mertz R, Sharp RE, Joshi T. Bioinformatics for plant and agricultural discoveries in the age of multiomics: A review and case study of maize nodal root growth under water deficit. PHYSIOLOGIA PLANTARUM 2022; 174:e13672. [PMID: 35297059 DOI: 10.1111/ppl.13672] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
Advances in next-generation sequencing and other high-throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in-house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future.
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Affiliation(s)
- Juexin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Sen Sidharth
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - Shuai Zeng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Yuexu Jiang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Yen On Chan
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Zhen Lyu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Tyler McCubbin
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - Rachel Mertz
- Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
- Division of Biological Sciences, University of Missouri, Columbia, Missouri, USA
| | - Robert E Sharp
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
- Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Department of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA
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389
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Cereceda K, Jorquera R, Villarroel-Espíndola F. Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research. ADVANCES IN LABORATORY MEDICINE 2022; 3:5-29. [PMID: 37359436 PMCID: PMC10197474 DOI: 10.1515/almed-2021-0075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 07/16/2021] [Indexed: 06/28/2023]
Abstract
The development and subsequent adaptation of mass cytometry for the histological analysis of tissue sections has allowed the simultaneous spatial characterization of multiple components. This is useful to find the correlation between the genotypic and phenotypic profile of tumor cells and their environment in clinical-translational studies. In this revision, we provide an overview of the most relevant hallmarks in the development, implementation and application of multiplexed imaging in the study of cancer and other conditions. A special focus is placed on studies based on imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI). The purpose of this review is to help our readers become familiar with the verification techniques employed on this tool and outline the multiple applications reported in the literature. This review will also provide guidance on the use of IMC or MIBI in any field of biomedical research.
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Affiliation(s)
- Karina Cereceda
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Roddy Jorquera
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Franz Villarroel-Espíndola
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
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390
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Zenere A, Rundquist O, Gustafsson M, Altafini C. Multi-omics protein-coding units as massively parallel Bayesian networks: empirical validation of causality structure. iScience 2022; 25:104048. [PMID: 35355520 PMCID: PMC8958332 DOI: 10.1016/j.isci.2022.104048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/17/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022] Open
Abstract
In this article we use high-throughput epigenomics, transcriptomics, and proteomics data to construct fine-graded models of the “protein-coding units” gathering all transcript isoforms and chromatin accessibility peaks associated with more than 4000 genes in humans. Each protein-coding unit has the structure of a directed acyclic graph (DAG) and can be represented as a Bayesian network. The factorization of the joint probability distribution induced by the DAGs imposes a number of conditional independence relationships among the variables forming a protein-coding unit, corresponding to the missing edges in the DAGs. We show that a large fraction of these conditional independencies are indeed verified by the data. Factors driving this verification appear to be the structural and functional annotation of the transcript isoforms, as well as a notion of structural balance (or frustration-free) of the corresponding sample correlation graph, which naturally leads to reduction of correlation (and hence to independence) upon conditioning. Protein coding unit: DAG associated with epigenetic and gene information of a protein DAGs correspond to Bayesian networks Edge absence on a DAG corresponds to conditional independence Multi-omics data (ATAC-seq, RNA-seq and mass-spec) are used for DAG validation
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391
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Michelhaugh SA, Januzzi JL. Using Artificial Intelligence to Better Predict and Develop Biomarkers. Heart Fail Clin 2022; 18:275-285. [PMID: 35341540 DOI: 10.1016/j.hfc.2021.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.
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392
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Shen C, Li H, Li M, Niu Y, Liu J, Zhu L, Gui H, Han W, Wang H, Zhang W, Wang X, Luo X, Sun Y, Yan J, Guan F. DLRAPom: a hybrid pipeline of Optimized XGBoost-guided integrative multiomics analysis for identifying targetable disease-related lncRNA-miRNA-mRNA regulatory axes. Brief Bioinform 2022; 23:6537347. [PMID: 35224615 PMCID: PMC8921741 DOI: 10.1093/bib/bbac046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/13/2022] [Accepted: 01/29/2022] [Indexed: 12/12/2022] Open
Abstract
The lack of a reliable and easy-to-operate screening pipeline for disease-related noncoding RNA regulatory axis is a problem that needs to be solved urgently. To address this, we designed a hybrid pipeline, disease-related lncRNA-miRNA-mRNA regulatory axis prediction from multiomics (DLRAPom), to identify risk biomarkers and disease-related lncRNA-miRNA-mRNA regulatory axes by adding a novel machine learning model on the basis of conventional analysis and combining experimental validation. The pipeline consists of four parts, including selecting hub biomarkers by conventional bioinformatics analysis, discovering the most essential protein-coding biomarkers by a novel machine learning model, extracting the key lncRNA-miRNA-mRNA axis and validating experimentally. Our study is the first one to propose a new pipeline predicting the interactions between lncRNA and miRNA and mRNA by combining WGCNA and XGBoost. Compared with the methods reported previously, we developed an Optimized XGBoost model to reduce the degree of overfitting in multiomics data, thereby improving the generalization ability of the overall model for the integrated analysis of multiomics data. With applications to gestational diabetes mellitus (GDM), we predicted nine risk protein-coding biomarkers and some potential lncRNA-miRNA-mRNA regulatory axes, which all correlated with GDM. In those regulatory axes, the MALAT1/hsa-miR-144-3p/IRS1 axis was predicted to be the key axis and was identified as being associated with GDM for the first time. In short, as a flexible pipeline, DLRAPom can contribute to molecular pathogenesis research of diseases, effectively predicting potential disease-related noncoding RNA regulatory networks and providing promising candidates for functional research on disease pathogenesis.
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Affiliation(s)
- Chen Shen
- Shanghai Key Laboratory of Forensic Medicine, Academy of Forensic Science; Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Huiyu Li
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Miao Li
- Department of Ultrasound, the Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yu Niu
- Department of Endocrinology and Metabolism, Ninth Hospital of Xi'an City, Xi'an, China
| | - Jing Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Zhu
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Hongsheng Gui
- Center for Behavior Health and Psychiatry Research, Henry Ford Health System, Detroit, MI, USA
| | - Wei Han
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Huiying Wang
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Wenpei Zhang
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaochen Wang
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiao Luo
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yu Sun
- Department of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Ji'nan, China
| | - Jiangwei Yan
- Department of Genetics, School of Medicine & Forensics, Shanxi Medical University, Taiyuan, China
| | - Fanglin Guan
- Shanghai Key Laboratory of Forensic Medicine, Academy of Forensic Science; Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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393
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Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med 2022; 14:23. [PMID: 35220969 PMCID: PMC8883622 DOI: 10.1186/s13073-022-01026-w] [Citation(s) in RCA: 105] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/10/2022] [Indexed: 02/07/2023] Open
Abstract
Rare diseases affect 30 million people in the USA and more than 300-400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25-35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.
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Affiliation(s)
- Shruti Marwaha
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.
| | - Joshua W Knowles
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
- Department of Medicine, Diabetes Research Center, Cardiovascular Institute and Prevention Research Center, Stanford, CA, USA
| | - Euan A Ashley
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.
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394
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Taguchi YH, Turki T. Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis. BMC Med Genomics 2022; 15:37. [PMID: 35209912 PMCID: PMC8876179 DOI: 10.1186/s12920-022-01181-4] [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: 10/07/2021] [Accepted: 02/11/2022] [Indexed: 11/27/2022] Open
Abstract
Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately \documentclass[12pt]{minimal}
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\begin{document}$$10^5$$\end{document}105 features. In particular, appropriate methods to weight individual omics datasets are unclear, and the approach adopted has substantial consequences for feature selection. In this study, we extended a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) method to integrate multi-omics datasets obtained from common samples in a weight-free manner. Method KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets. Results The proposed advanced KTD-based unsupervised FE method showed comparative performance to that of the previously proposed KTD method, as well as tensor decomposition-based unsupervised FE, but required reduced memory and central processing unit time. Moreover, this advanced KTD method, specifically designed for multi-omics analysis, attributes P values to features, which is rare for existing multi-omics–oriented methods. Conclusions The sample R code is available at https://github.com/tagtag/MultiR/.
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Affiliation(s)
- Y-H Taguchi
- Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan.
| | - Turki Turki
- Department of Computer Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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395
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Eiger DS, Pham U, Gardner J, Hicks C, Rajagopal S. GPCR Systems Pharmacology: A Different Perspective on the Development of Biased Therapeutics. Am J Physiol Cell Physiol 2022; 322:C887-C895. [PMID: 35196164 PMCID: PMC9037395 DOI: 10.1152/ajpcell.00449.2021] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
G protein-coupled receptors (GPCRs) are the largest family of transmembrane receptors and are the target of approximately one-third of all Food and Drug Administration (FDA)-approved pharmaceutical drugs. GPCRs interact with many transducers, such as heterotrimeric G proteins, GPCR kinases (GRKs), and β-arrestins. Recent experiments have demonstrated that some ligands can activate distinct effector proteins over others, a phenomenon termed biased agonism. These discoveries have raised the potential of developing drugs which preferentially activate therapeutic signaling pathways over those that lead to deleterious side effects. However, to date, only one biased GPCR therapeutic has received FDA approval and many others have either failed to meet their specified primary endpoints and or demonstrate superiority over currently available treatments. Additionally, there is a lack of understanding regarding how biased agonism measured at a GPCR leads to specific downstream physiologic responses. Here, we briefly summarize the history and current status of biased agonism at GPCRs and suggest adoption of a systems pharmacology approach upon which to develop GPCR-targeted drugs that demonstrate heightened therapeutic efficacy with improved side effect profiles.
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Affiliation(s)
- Dylan Scott Eiger
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
| | - Uyen Pham
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
| | - Julia Gardner
- Trinty College, Duke University, Durham, NC, United States
| | - Chloe Hicks
- Trinty College, Duke University, Durham, NC, United States
| | - Sudarshan Rajagopal
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
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396
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Alarcon-Barrera JC, Kostidis S, Ondo-Mendez A, Giera M. Recent advances in metabolomics analysis for early drug development. Drug Discov Today 2022; 27:1763-1773. [PMID: 35218927 DOI: 10.1016/j.drudis.2022.02.018] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/25/2022] [Accepted: 02/21/2022] [Indexed: 12/25/2022]
Abstract
The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.
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Affiliation(s)
- Juan Carlos Alarcon-Barrera
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, the Netherlands; Clinical Research Group, School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 # 63C-69, Bogotá, Colombia
| | - Sarantos Kostidis
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, the Netherlands
| | - Alejandro Ondo-Mendez
- Clinical Research Group, School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 # 63C-69, Bogotá, Colombia
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, the Netherlands.
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397
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Abstract
Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.
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Affiliation(s)
- Zhaoxiang Cai
- ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, 214 Hawkesbury Rd, Westmead, NSW 2145, Australia
| | - Rebecca C. Poulos
- ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, 214 Hawkesbury Rd, Westmead, NSW 2145, Australia
| | - Jia Liu
- ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, 214 Hawkesbury Rd, Westmead, NSW 2145, Australia
- Faculty of Medicine, Western Sydney University, Campbelltown, NSW, Australia
| | - Qing Zhong
- ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, 214 Hawkesbury Rd, Westmead, NSW 2145, Australia
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398
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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399
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Leone M, Galeota E, Masseroli M, Pelizzola M. Identification, semantic annotation and comparison of combinations of functional elements in multiple biological conditions. Bioinformatics 2022; 38:1183-1190. [PMID: 34864898 DOI: 10.1093/bioinformatics/btab815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/12/2021] [Accepted: 11/30/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Approaches such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) represent the standard for the identification of binding sites of DNA-associated proteins, including transcription factors and histone marks. Public repositories of omics data contain a huge number of experimental ChIP-seq data, but their reuse and integrative analysis across multiple conditions remain a daunting task. RESULTS We present the Combinatorial and Semantic Analysis of Functional Elements (CombSAFE), an efficient computational method able to integrate and take advantage of the valuable and numerous, but heterogeneous, ChIP-seq data publicly available in big data repositories. Leveraging natural language processing techniques, it integrates omics data samples with semantic annotations from selected biomedical ontologies; then, using hidden Markov models, it identifies combinations of static and dynamic functional elements throughout the genome for the corresponding samples. CombSAFE allows analyzing the whole genome, by clustering patterns of regions with similar functional elements and through enrichment analyses to discover ontological terms significantly associated with them. Moreover, it allows comparing functional states of a specific genomic region to analyze their different behavior throughout the various semantic annotations. Such findings can provide novel insights by identifying unexpected combinations of functional elements in different biological conditions. AVAILABILITY AND IMPLEMENTATION The Python implementation of the CombSAFE pipeline is freely available for non-commercial use at: https://github.com/DEIB-GECO/CombSAFE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michele Leone
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Eugenia Galeota
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy
| | - Marco Masseroli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Mattia Pelizzola
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy
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400
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Cervantes-Gracia K, Chahwan R, Husi H. Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach. Front Genet 2022; 13:828786. [PMID: 35186042 PMCID: PMC8855827 DOI: 10.3389/fgene.2022.828786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/12/2022] [Indexed: 12/24/2022] Open
Abstract
The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.
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Affiliation(s)
| | - Richard Chahwan
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- *Correspondence: Richard Chahwan, ; Holger Husi,
| | - Holger Husi
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- Division of Biomedical Sciences, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom
- *Correspondence: Richard Chahwan, ; Holger Husi,
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