Gene expression profiles indicate tissue-specific obesity regulation changes and strong obesity relevant tissues

Phenotypic variance partitioning by transcriptomic gene expression levels and environmental variables for anthropometric traits using GTEx data

Phenotypic variation in human is the results of genetic variation and environmental influences. Understanding the contribution of genetic and environmental components to phenotypic variation is of great interest. The variance explained by genome-wide single nucleotide polymorphisms (SNPs) typically represents a small proportion of the phenotypic variance for complex traits, which may be because the genome is only a part of the whole biological process to shape the phenotypes. In this study, we propose to partition the phenotypic variance of three anthropometric traits, using gene expression levels and environmental variables from GTEx data. We use the gene expression of four tissues that are deemed relevant for the anthropometric traits (two adipose tissues, skeletal muscle tissue and blood tissue). Additionally, we estimate the transcriptome-environment correlation that partly underlies the phenotypes of the anthropometric traits. We found that genetic factors play a significant role in determining body mass index (BMI), with the proportion of phenotypic variance explained by gene expression levels of visceral adipose tissue being 0.68 (SE = 0.06). However, we also observed that environmental factors such as age, sex, ancestry, smoking status, and drinking alcohol status have a small but significant impact (0.005, SE = 0.001). Interestingly, we found a significant negative correlation between the transcriptomic and environmental effects on BMI (transcriptome-environment correlation = -0.54, SE = 0.14), suggesting an antagonistic relationship. This implies that individuals with lower genetic profiles may be more susceptible to the effects of environmental factors on BMI, while those with higher genetic profiles may be less susceptible. We also show that the estimated transcriptomic variance varies across tissues, e.g., the gene expression levels of whole blood tissue and environmental variables explain a lower proportion of BMI phenotypic variance (0.16, SE = 0.05 and 0.04, SE = 0.004 respectively). We observed a significant positive correlation between transcriptomic and environmental effects (1.21, SE = 0.23) for this tissue. In conclusion, phenotypic variance partitioning can be done using gene expression and environmental data even with a small sample size (n = 838 from GTEx data), which can provide insights into how the transcriptomic and environmental effects contribute to the phenotypes of the anthropometric traits.

Transcriptome-wide association study identifies multiple genes associated with childhood body mass index

BACKGROUND

Childhood obesity is one of the most common and costly nutritional problems with high heritability. The genetic mechanism of childhood obesity remains unclear. Here, we conducted a transcriptome-wide association study (TWAS) to identify novel genes for childhood obesity.

METHODS

By integrating the GWAS summary of childhood body mass index (BMI), we conducted TWAS analyses with pre-computed gene expression weights in 39 obesity priority tissues. The GWAS summary statistics of childhood BMI were derived from the early growth genetics consortium with 35,668 children from 20 studies.

RESULTS

We identified 15 candidate genes for childhood BMI after Bonferroni corrections. The most significant gene, ADCY3, was identified in 13 tissues, including adipose, brain, and blood. Interestingly, eight genes were only identified in the specific tissue, such as FAIM2 in the brain (P = 2.04 × 10^-7) and fat mass and obesity-associated gene (FTO) in the muscle (P = 1.93 × 10^-8). Compared with the TWAS results of adult BMI, we found that one gene TUBA1B with predominant influence only on childhood BMI in the muscle (P = 1.12 × 10^-7). We evaluated the candidate genes by querying public databases and identified 12 genes functionally related to obesity phenotypes, including nine differentially expressed genes during the differentiation of human preadipocyte cells. The remaining genes (FAM150B, KNOP1, and LMBR1L) were regarded as novel candidate genes for childhood BMI.

CONCLUSIONS

Our study identified multiple candidate genes for childhood BMI, providing novel clues for understanding the genetic mechanism of childhood obesity.

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.

Variable number tandem repeats mediate the expression of proximal genes

Variable number tandem repeats (VNTRs) account for significant genetic variation in many organisms. In humans, VNTRs have been implicated in both Mendelian and complex disorders, but are largely ignored by genomic pipelines due to the complexity of genotyping and the computational expense. We describe adVNTR-NN, a method that uses shallow neural networks to genotype a VNTR in 18 seconds on 55X whole genome data, while maintaining high accuracy. We use adVNTR-NN to genotype 10,264 VNTRs in 652 GTEx individuals. Associating VNTR length with gene expression in 46 tissues, we identify 163 "eVNTRs". Of the 22 eVNTRs in blood where independent data is available, 21 (95%) are replicated in terms of significance and direction of association. 49% of the eVNTR loci show a strong and likely causal impact on the expression of genes and 80% have maximum effect size at least 0.3. The impacted genes are involved in diseases including Alzheimer's, obesity and familial cancers, highlighting the importance of VNTRs for understanding the genetic basis of complex diseases.

Sex-Specific Transcriptome Differences in Human Adipose Mesenchymal Stem Cells

In humans, sexual dimorphism can manifest in many ways and it is widely studied in several knowledge fields. It is increasing the evidence that also cells differ according to sex, a correlation still little studied and poorly considered when cells are used in scientific research. Specifically, our interest is on the sex-related dimorphism on the human mesenchymal stem cells (hMSCs) transcriptome. A systematic meta-analysis of hMSC microarrays was performed by using the Transcriptome Mapper (TRAM) software. This bioinformatic tool was used to integrate and normalize datasets from multiple sources and allowed us to highlight chromosomal segments and genes differently expressed in hMSCs derived from adipose tissue (hADSCs) of male and female donors. Chromosomal segments and differentially expressed genes in male and female hADSCs resulted to be related to several processes as inflammation, adipogenic and neurogenic differentiation and cell communication. Obtained results lead us to hypothesize that the donor sex of hADSCs is a variable influencing a wide range of stem cell biologic processes. We believe that it should be considered in biologic research and stem cell therapy.

Myokines in skeletal muscle physiology and metabolism: Recent advances and future perspectives

Myokines are molecules produced and secreted by skeletal muscle to act in an auto-, para- and endocrine manner to alter physiological function of target tissues. The growing number of effects of myokines on metabolism of distant tissues provides a compelling case for crosstalk between skeletal muscle and other tissues and organs to regulate metabolic homoeostasis. In this review, we summarize and discuss the current knowledge regarding the impact on metabolism of several canonical and recently identified myokines. We focus specifically on myostatin, β-aminoisobutyric acid, interleukin-15, meteorin-like and myonectin, and discuss how these myokines are induced and regulated as well as their overall function. We also review how these myokines may serve as potential prognostic biomarkers that reflect whole-body metabolism and how they may be attractive therapeutic targets for treating muscle and metabolic diseases.

Comprehensive transcriptomic analysis indicates brain regional specific alterations in type 2 diabetes

Type 2 diabetes (T2D) can result in a number of comorbidities involving various organs including heart, eye, kidney and even the brain. However, little is known about the molecular basis of T2D associated brain disorders. In this study, we performed a comprehensive transcriptomic analysis in a total of 304 T2D samples and 608 matched control samples from thirteen distinct brain regions. We observed prominent difference among transcriptomic profiles of diverse brain regions in T2D. The most striking change was found in caudate with thousands of T2D-associated genes identified, followed by hippocampus, while nearly no transcriptomic change was observed in other brain regions. Functional analysis of T2D-associated genes revealed impaired synaptic functions and an association with neurodegenerative diseases. Co-expression analysis of caudate transcriptomic profiles unveiled a core regional specific module that was disorganized in T2D. Sub-modules consisting of regional markers were enriched in T2D risk single nucleotide polymorphisms (SNPs) and implied a causal link with T2D. Hub genes of this module include NSF and ADD2, the former of which has been associated with T2D and neurogenerative diseases. Thus, our work provides useful information for further studies in T2D associated brain disorders.

Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass Index

Both genetic and environmental factors are suggested to influence overweight and obesity risks. Although individual loci and genes have been frequently shown to be associated with body mass index (BMI), the overall interaction of these genes and their role in BMI remains underexplored. Data were collected in 90 healthy, predominately Caucasian participants (51% female) with a mean age of 26.00 ± 9.02 years. Whole blood samples were assayed by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. We integrated and analyzed the clinical and microarray gene expression data from those individuals to understand various systematic gene expression patterns underlying BMI. Conventional differential expression analysis identified seven genes RBM20, SEPT12, AX748233, SLC30A3, WTIP, CASP10, and OR12D3 associated with BMI. Weight gene co-expression network analysis among 4,647 expressed genes identified two gene modules associated with BMI. These two modules, with different extents of gene connectivity, are enriched for catabolic and muscle system processes respectively, and tend to be regulated by zinc finger transcription factors. A total of 246 hub genes were converted to non-hub genes, and 286 non-hub genes were converted to hub genes between normal and overweight individuals, revealing the network dynamics underlying BMI. A total of 28 three-way gene interactions were identified, suggesting the existence of high-order gene expression patterns underlying BMI. Our study demonstrated a variety of systematic gene expression patterns associated with BMI and thus provided novel understanding regarding the genetic factors for overweight and obesity risks on system levels.

Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine

The global epidemic of obesity is a major public health problem today. Obesity increases the risk of many chronic diseases, such as type 2 diabetes, coronary heart disease, and certain types of cancer, and is associated with lower life expectancy. The body mass index (BMI), which is currently used to classify obesity, is only an imperfect measure of abnormal or excessive body fat accumulation. Studies have shown that waist circumference as a measure of fat distribution may improve disease prediction. More elaborate techniques such as magnetic resonance imaging are increasingly available to assess body fat distribution, but these measures are not readily available in routine clinical practice, and health-relevant cut-offs not yet been established. The measurement of biomarkers that reflect the underlying biological mechanisms for the increased disease risk may be an alternative approach to characterize the relevant obesity phenotype. The insulin/insulin-like growth factor (IGF) axis and chronic low-grade inflammation have been identified as major pathways. In addition, specific adipokines such as leptin, adiponectin and resistin have been related to obesity-associated health outcomes. This biomarker research, which is currently further developed with the application of high throughput methods, gives important insights in obesity-related disease etiology and pathophysiological pathways and may be used to better characterize obese persons at high risk of disease development and target disease-causing biomarkers in personalized prevention strategies.

Prioritizing putative influential genes in cardiovascular disease susceptibility by applying tissue-specific Mendelian randomization

BACKGROUND

The extent to which changes in gene expression can influence cardiovascular disease risk across different tissue types has not yet been systematically explored. We have developed an analysis pipeline that integrates tissue-specific gene expression, Mendelian randomization and multiple-trait colocalization to develop functional mechanistic insight into the causal pathway from a genetic variant to a complex trait.

METHODS

We undertook an expression quantitative trait loci-wide association study to uncover genetic variants associated with both nearby gene expression and cardiovascular traits. Fine-mapping was performed to prioritize possible causal variants for detected associations. Two-sample Mendelian randomization (MR) was then applied using findings from genome-wide association studies (GWAS) to investigate whether changes in gene expression within certain tissue types may influence cardiovascular trait variation. We subsequently used Bayesian multiple-trait colocalization to further interrogate the findings and also gain insight into whether DNA methylation, as well as gene expression, may play a role in disease susceptibility. Finally, we applied our analysis pipeline genome-wide using summary statistics from large-scale GWAS.

RESULTS

Eight genetic loci were associated with changes in gene expression and measures of cardiovascular function. Our MR analysis provided evidence of tissue-specific effects at multiple loci, of which the effects at the ADCY3 and FADS1 loci for body mass index and cholesterol, respectively, were particularly insightful. Multiple-trait colocalization uncovered evidence which suggested that changes in DNA methylation at the promoter region upstream of FADS1/TMEM258 may also affect cardiovascular trait variation along with gene expression. Furthermore, colocalization analyses uncovered evidence of tissue specificity between gene expression in liver tissue and cholesterol levels. Applying our pipeline genome-wide using summary statistics from GWAS uncovered 233 association signals at loci which represent promising candidates for further evaluation.

CONCLUSIONS

Disease susceptibility can be influenced by differential changes in tissue-specific gene expression and DNA methylation. The approach undertaken in our study can be used to elucidate mechanisms in disease, as well as helping prioritize putative causal genes at associated loci where multiple nearby genes may be co-regulated. Future studies which continue to uncover quantitative trait loci for molecular traits across various tissue and cell types will further improve our capability to understand and prevent disease.