Literature DB >> 24683432

How Gene Networks Can Uncover Novel CVD Players.

Laurence D Parnell1, Patricia Casas-Agustench2, Lakshmanan K Iyer3, Jose M Ordovas4.   

Abstract

Cardiovascular diseases (CVD) are complex, involving numerous biological entities from genes and small molecules to organ function. Placing these entities in networks where the functional relationships among the constituents are drawn can aid in our understanding of disease onset, progression and prevention. While networks, or interactomes, are often classified by a general term, say lipids or inflammation, it is a more encompassing class of network that is more informative in showing connections among the active entities and allowing better hypotheses of novel CVD players to be formulated. A range of networks will be presented whereby the potential to bring new objects into the CVD milieu will be exemplified.

Entities:  

Keywords:  cardiovascular disease; genetics; interactions; network; risk

Year:  2014        PMID: 24683432      PMCID: PMC3966201          DOI: 10.1007/s12170-013-0372-3

Source DB:  PubMed          Journal:  Curr Cardiovasc Risk Rep        ISSN: 1932-9520


  57 in total

1.  Genetic associations with expression for genes implicated in GWAS studies for atherosclerotic cardiovascular disease and blood phenotypes.

Authors:  Xiaoling Zhang; Andrew D Johnson; Audrey E Hendricks; Shih-Jen Hwang; Kahraman Tanriverdi; Santhi K Ganesh; Nicholas L Smith; Patricia A Peyser; Jane E Freedman; Christopher J O'Donnell
Journal:  Hum Mol Genet       Date:  2013-09-20       Impact factor: 6.150

Review 2.  Gene-gene and gene-environment interactions defining lipid-related traits.

Authors:  José M Ordovás; Ruairi Robertson; Ellen Ní Cléirigh
Journal:  Curr Opin Lipidol       Date:  2011-04       Impact factor: 4.776

3.  Saturated fatty acids induce c-Src clustering within membrane subdomains, leading to JNK activation.

Authors:  Ryan G Holzer; Eek-Joong Park; Ning Li; Helen Tran; Monica Chen; Crystal Choi; Giovanni Solinas; Michael Karin
Journal:  Cell       Date:  2011-09-30       Impact factor: 41.582

4.  MiR-33 contributes to the regulation of cholesterol homeostasis.

Authors:  Katey J Rayner; Yajaira Suárez; Alberto Dávalos; Saj Parathath; Michael L Fitzgerald; Norimasa Tamehiro; Edward A Fisher; Kathryn J Moore; Carlos Fernández-Hernando
Journal:  Science       Date:  2010-05-13       Impact factor: 47.728

5.  The serine hydrolase ABHD6 Is a critical regulator of the metabolic syndrome.

Authors:  Gwynneth Thomas; Jenna L Betters; Caleb C Lord; Amanda L Brown; Stephanie Marshall; Daniel Ferguson; Janet Sawyer; Matthew A Davis; John T Melchior; Lawrence C Blume; Allyn C Howlett; Pavlina T Ivanova; Stephen B Milne; David S Myers; Irina Mrak; Vera Leber; Christoph Heier; Ulrike Taschler; Jacqueline L Blankman; Benjamin F Cravatt; Richard G Lee; Rosanne M Crooke; Mark J Graham; Robert Zimmermann; H Alex Brown; J Mark Brown
Journal:  Cell Rep       Date:  2013-10-03       Impact factor: 9.423

6.  Plasma lipid profiling in a large population-based cohort.

Authors:  Jacquelyn M Weir; Gerard Wong; Christopher K Barlow; Melissa A Greeve; Adam Kowalczyk; Laura Almasy; Anthony G Comuzzie; Michael C Mahaney; Jeremy B M Jowett; Jonathan Shaw; Joanne E Curran; John Blangero; Peter J Meikle
Journal:  J Lipid Res       Date:  2013-07-18       Impact factor: 5.922

7.  Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.

Authors:  Sekar Kathiresan; Olle Melander; Candace Guiducci; Aarti Surti; Noël P Burtt; Mark J Rieder; Gregory M Cooper; Charlotta Roos; Benjamin F Voight; Aki S Havulinna; Björn Wahlstrand; Thomas Hedner; Dolores Corella; E Shyong Tai; Jose M Ordovas; Göran Berglund; Erkki Vartiainen; Pekka Jousilahti; Bo Hedblad; Marja-Riitta Taskinen; Christopher Newton-Cheh; Veikko Salomaa; Leena Peltonen; Leif Groop; David M Altshuler; Marju Orho-Melander
Journal:  Nat Genet       Date:  2008-01-13       Impact factor: 38.330

8.  Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment.

Authors:  Miles Trupp; Hongjie Zhu; William R Wikoff; Rebecca A Baillie; Zhao-Bang Zeng; Peter D Karp; Oliver Fiehn; Ronald M Krauss; Rima Kaddurah-Daouk
Journal:  PLoS One       Date:  2012-07-09       Impact factor: 3.240

9.  Direct regulation of blood pressure by smooth muscle cell mineralocorticoid receptors.

Authors:  Amy McCurley; Paulo W Pires; Shawn B Bender; Mark Aronovitz; Michelle J Zhao; Daniel Metzger; Pierre Chambon; Michael A Hill; Anne M Dorrance; Michael E Mendelsohn; Iris Z Jaffe
Journal:  Nat Med       Date:  2012-09       Impact factor: 53.440

10.  Genetic variants and their interactions in disease risk prediction - machine learning and network perspectives.

Authors:  Sebastian Okser; Tapio Pahikkala; Tero Aittokallio
Journal:  BioData Min       Date:  2013-03-01       Impact factor: 2.522

View more
  1 in total

1.  Systems genetics analysis defines importance of TMEM43/LUMA for cardiac- and metabolic-related pathways.

Authors:  Qingqing Gu; Fuyi Xu; Buyan-Ochir Orgil; Zaza Khuchua; Undral Munkhsaikhan; Jason N Johnson; Neely R Alberson; Joseph F Pierre; Dennis D Black; Deli Dong; Jaclyn A Brennan; Brianna M Cathey; Igor R Efimov; Jeffrey A Towbin; Enkhsaikhan Purevjav; Lu Lu
Journal:  Physiol Genomics       Date:  2021-11-12       Impact factor: 3.107

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.