Plasma Protein and MicroRNA Biomarkers of Insulin Resistance: A Network-Based Integrative -Omics Analysis

被引:27
|
作者
Choi, Hyungwon [1 ,2 ,3 ]
Koh, Hiromi W. L. [1 ,2 ]
Zhou, Lihan [4 ]
Cheng, He [4 ]
Loh, Tze Ping [5 ]
Rizi, Ehsan Parvaresh [1 ]
Toh, Sue Anne [1 ]
Ronnett, Gabriele, V [6 ]
Huang, Bevan E. [7 ]
Khoo, Chin Meng [1 ]
机构
[1] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Med, Singapore, Singapore
[2] Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Singapore, Singapore
[3] Agcy Sci Technol & Res, Inst Mol & Cell Biol, Singapore, Singapore
[4] MiRXES Pte Ltd, Singapore, Singapore
[5] Natl Univ Singapore Hosp, Dept Lab Med, Singapore, Singapore
[6] Janssen Res & Dev US, World Dis Accelerator, Spring House, NJ USA
[7] Janssen Res & Dev US, San Francisco, CA USA
来源
FRONTIERS IN PHYSIOLOGY | 2019年 / 10卷
基金
英国医学研究理事会;
关键词
obesity; insulin resistance; proteomics; microRNAs; network analysis; ADIPOSE-TISSUE; OBESITY; PATHOGENESIS; INFLAMMATION; MUSCLE; LIVER;
D O I
10.3389/fphys.2019.00379
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Computational approaches for network-based integrative multi-omics analysis
    Agamah, Francis E.
    Bayjanov, Jumamurat R.
    Niehues, Anna
    Njoku, Kelechi F.
    Skelton, Michelle
    Mazandu, Gaston K.
    Ederveen, Thomas H. A.
    Mulder, Nicola
    Chimusa, Emile R.
    't Hoen, Peter A. C.
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2022, 9
  • [2] Network-based multi-omics integrative analysis methods in drug discovery: a systematic review
    Jiang, Wei
    Ye, Weicai
    Tan, Xiaoming
    Bao, Yun-Juan
    BIODATA MINING, 2025, 18 (01):
  • [3] Landscape Mapping of Functional Proteins in Insulin Signal Transduction and Insulin Resistance: A Network-Based Protein-Protein Interaction Analysis
    Chakraborty, Chiranjib
    Roy, Sanjiban S.
    Hsu, Minna J.
    Agoramoorthy, Govindasamy
    PLOS ONE, 2011, 6 (01):
  • [4] Insulin Resistance: Development of Omics-Based Biomarkers and Interventions
    Vonk, Roel J.
    ANNALS OF NUTRITION AND METABOLISM, 2013, 62 (02) : 155 - 155
  • [5] Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease
    Lucena-Padros, Helena
    Bravo-Gil, Nereida
    Tous, Cristina
    Rojano, Elena
    Seoane-Zonjic, Pedro
    Fernandez, Raquel Maria
    Ranea, Juan A. G.
    Antinolo, Guillermo
    Borrego, Salud
    BIOMOLECULES, 2024, 14 (02)
  • [6] Network-based analysis of omics data: the LEAN method
    Gwinner, Frederik
    Boulday, Gwenola
    Vandiedonck, Claire
    Arnould, Minh
    Cardoso, Cecile
    Nikolayeva, Iryna
    Guitart-Pla, Oriol
    Denis, Cecile V.
    Christophe, Olivier D.
    Beghain, Johann
    Tournier-Lasserve, Elisabeth
    Schwikowski, Benno
    BIOINFORMATICS, 2017, 33 (05) : 701 - 709
  • [7] MetaBridge: enabling network-based integrative analysis via direct protein interactors of metabolites
    Hinshaw, Samuel J.
    Lee, Amy H. Y.
    Gill, Erin E.
    Hancock, Robert E. W.
    BIOINFORMATICS, 2018, 34 (18) : 3225 - 3227
  • [8] Network-based analysis of omics with multi-objective optimization
    Mosca, Ettore
    Milanesi, Luciano
    MOLECULAR BIOSYSTEMS, 2013, 9 (12) : 2971 - 2980
  • [9] Integrative omics analysis identifies biomarkers of septic cardiomyopathy
    Cai, Kexin
    Luo, Yuqing
    Chen, Hongyin
    Dong, Yanfang
    Su, Yunyun
    Lin, Chen
    Cai, Chuanqi
    Shi, Yikbin
    Lin, Siming
    Lian, Guili
    Lin, Zhihong
    Feng, Shaodan
    PLOS ONE, 2024, 19 (11):
  • [10] Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis
    Eke, Elif Dereli
    Arga, Kazim Yalcin
    Dikicioglu, Duygu
    Eraslan, Serpil
    Erkol, Emir
    Celik, Arzu
    Kirdar, Betul
    Di Camillo, Barbara
    OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, 2019, 23 (05) : 274 - 284