Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States

Shu, Le; Chan, Kei Hang Katie; Zhang, Guanglin; Huan, Tianxiao; Kurt, Zeyneb; Zhao, Yuqi; Codoni, Veronica; Trégouët, David‐Alexandre; Yang, Jun; Wilson, James G.; Luo, Xi; Levy, Daniel; Lusis, Aldons J.; Liu, Simin; Yang, Xia

doi:10.1371/journal.pgen.1007040

Cited by 79 publications

(78 citation statements)

References 61 publications

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“…Cav1 overlaps with QTL related to body size/growth and was identified as a candidate gene for extreme body size in Gough Island mice [ 45 – 47 ]. Gene network analyses in humans identify CAV1 as a key driver of cardiovascular disease and type 2 diabetes [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

The genomic basis of environmental adaptation in house mice

et al. 2018

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House mice (Mus musculus) arrived in the Americas only recently in association with European colonization (~400–600 generations), but have spread rapidly and show evidence of local adaptation. Here, we take advantage of this genetic model system to investigate the genomic basis of environmental adaptation in house mice. First, we documented clinal patterns of phenotypic variation in 50 wild-caught mice from a latitudinal transect in Eastern North America. Next, we found that progeny of mice from different latitudes, raised in a common laboratory environment, displayed differences in a number of complex traits related to fitness. Consistent with Bergmann’s rule, mice from higher latitudes were larger and fatter than mice from lower latitudes. They also built bigger nests and differed in aspects of blood chemistry related to metabolism. Then, combining exomic, genomic, and transcriptomic data, we identified specific candidate genes underlying adaptive variation. In particular, we defined a short list of genes with cis-eQTL that were identified as candidates in exomic and genomic analyses, all of which have known ties to phenotypes that vary among the studied populations. Thus, wild mice and the newly developed strains represent a valuable resource for future study of the links between genetic variation, phenotypic variation, and climate.

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Section: Resultsmentioning

confidence: 99%

The genomic basis of environmental adaptation in house mice

et al. 2018

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“…Mice with defects in ECM collagen are glucose intolerant, hyperglycemic, and insulin resistant (20). PCOLCE is one of 15 key drivers that collectively account for 22% of GWAS hits for type II diabetes in a recent multiethnic meta-analysis (44). Pcolce expression is significantly increased in 30 week old high fat-fed SM/J brown adipose.…”

Section: Discussionmentioning

confidence: 99%

Brown adipose expansion and remission of glycemic dysfunction in obese SM/J mice

Carson

Macias-Velasco

Gunawardana

et al. 2019

Preprint

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52Disruption of glucose homeostasis increases the risk of type II diabetes, cardiovascular disease, stroke, 53 and cancer. We leverage a novel rodent model, the SM/J mouse, to understand glycemic control in 54 obesity. On a high fat diet, obese SM/J mice initially develop impaired glucose tolerance and elevated 55 fasting glucose. Strikingly, their glycemic dysfunction resolves by 30 weeks of age despite persistence of 56 obesity. A prominent phenotype is that they dramatically expand their brown adipose depots as they 57 resolve glycemic dysfunction. This occurs naturally and spontaneously on a high fat diet, with no 58 temperature or genetic manipulation. When the brown adipose depot is removed from normoglycemic 59 obese mice, fasting blood glucose and glucose tolerance revert to unhealthy levels, and animals become 60 insulin resistant. We identified 267 genes whose expression changes in the brown adipose when the 61 mice resolve their unhealthy glycemic parameters, and find the expanded tissue has a 'healthier' 62 expression profile of cytokines and extracellular matrix genes. We describe morphological, physiological, 63 and transcriptomic changes that occur during the unique brown adipose expansion and remission of 64 glycemic dysfunction in obese SM/J mice. Understanding this phenomenon in mice will open the door for 65 innovative therapies aimed at improving glycemic control in obesity.66 67 Significance Statement 68 Some obese individuals maintain normal glycemic control. Despite being obese, these individuals 69 have low risk for metabolic complications, including type-II diabetes. If we better understood why some 70 obese people maintain normoglycemia then we might develop new approaches for treating metabolic 71 complications associated with obesity. However, the causative factors underlying glycemic control in 72 obesity remain unknown. We discovered that, despite persistence of the obese state, SM/J mice enter 73 into diabetic remission: returning to normoglycemia and reestablishing glucose tolerance and improving 74 insulin sensitivity. A prominent phenotype is that they dramatically expand their brown adipose depots as 75 they resolve glycemic dysfunction. Understanding this phenomenon in mice will open the door for 76 innovative therapies aimed at improving glycemic control in obesity. 78An estimated 10-30% of obese individuals maintain glycemic control and some longitudinal 79 studies suggest their risk of developing type II diabetes is no greater than matched lean individuals (1). 80No causative factors underlying glycemic control in obesity have been discovered, however the strongest 81 predictors of impaired glycemic control in obesity are increased visceral fat mass and adipose tissue 82 dysfunction (2,3). Thus research efforts have focused on understanding the genetic and physiological 83 mechanisms of action of adipose. Recent research reveals that brown adipose activity is associated with 84 anti-diabetic properties. Cold exposure in both obese and lean individuals causes increased uptake...

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“…Recently, multiple more sophisticated statistical and bioinformatics methods have been used to explore shared genetic components across diseases (Fortune et al 2015; Brown et al 2016; Pickrell et al 2016; Shu et al 2017). The various methods can detect genetic sharing at SNP, gene, pathway and network levels.…”

Section: Shared Genetic Factors Between Neurodegenerative Diseasesmentioning

confidence: 99%

Shared mechanisms among neurodegenerative diseases: from genetic factors to gene networks

Arneson

Zhang

Yang

et al. 2018

J Genet

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Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are pressing health concerns in modern societies for which effective therapies are still lacking. Recent high-throughput genomic technologies have enabled genome-scale, multidimensional investigations to facilitate a better understanding of the underlying mechanisms and the identification of novel targets. Here we review the molecular insights gained through such studies, and compare the similarities and differences between neurodegenerative diseases revealed by systems genomics and gene network modelling approaches. We focus specifically on the shared mechanisms at multiple molecular scales ranging from genetic factors to gene expression to network-level features of gene regulation, and whenever possible also point out mechanisms that distinguish one disease from another. Our review sets the stage for similar genome wide inspection in the future on shared/distinct features of neurodegenerative diseases at the levels of cellular, proteomic or epigenomic signatures, and how these features may interact to determine the progression and treatment response of different diseases afflicting the same individual.

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Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States

Cited by 79 publications

References 61 publications

The genomic basis of environmental adaptation in house mice

The genomic basis of environmental adaptation in house mice

Brown adipose expansion and remission of glycemic dysfunction in obese SM/J mice

Shared mechanisms among neurodegenerative diseases: from genetic factors to gene networks

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