Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.

Transcriptome analysis reveals molecular signature and cell-type difference of Homo sapiens endothelial-to-mesenchymal transition

Endothelial-to-mesenchymal transition (EndoMT), a specific form of epithelial-to-mesenchymal transition, drives a growing number of human (Homo sapiens) pathological conditions. This emerging knowledge opens a path to discovering novel therapeutic targets for many EndoMT-associated disorders. Here, we constructed an atlas of the endothelial-cell transcriptome and demonstrated EndoMT-induced global changes in transcriptional gene expression. Our gene ontology analyses showed that EndoMT could be a specific checkpoint for leukocyte chemotaxis, adhesion, and transendothelial migration. We also identified distinct gene expression signatures underlying EndoMT across arterial, venous, and microvascular endothelial cells. We performed protein-protein interaction network analyses, identifying a class of highly connected hub genes in endothelial cells from different vascular beds. Moreover, we found that the short-chain fatty acid acetate strongly inhibits the transcriptional program of EndoMT in endothelial cells from different vascular beds across tissues. Our results reveal the molecular signature and cell-type difference of EndoMT across distinct tissue- and vascular-bed-specific endothelial cells, providing a powerful discovery tool and resource value. These results suggest that therapeutically manipulating the endothelial transcriptome could treat an increasing number of EndoMT-associated pathological conditions.

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with ≥2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

Exploring Lead loci shared between schizophrenia and Cardiometabolic traits

Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs) (rs3865350, rs9860913, rs13307 and rs9614186) and four proximate genes (DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.

Polygenic Risk Score Improves Cataract Prediction in East Asian Population

Cataracts, characterized by crystalline lens opacities in human eyes, is the leading cause of blindness globally. Due to its multifactorial complexity, the molecular mechanisms remain poorly understood. Larger cohorts of genome-wide association studies (GWAS) are needed to investigate cataracts’ genetic basis. In this study, a GWAS was performed on the largest Han population to date, analyzing a total of 7079 patients and 13,256 controls from the Taiwan Biobank (TWB) 2.0 cohort. Two cataract-associated SNPs with an adjustment of p < 1 × 10−7 in the older groups and nine SNPs with an adjustment of p < 1 × 10−6 in the younger group were identified. Except for the reported AGMO in animal models, most variations, including rs74774546 in GJA1 and rs237885 in OXTR, were not identified before this study. Furthermore, a polygenic risk score (PRS) was created for the young and old populations to identify high-risk cataract individuals, with areas under the receiver operating curve (AUROCs) of 0.829 and 0.785, respectively, after covariate adjustments. Younger individuals had 17.45 times the risk while older people had 10.97 times the risk when comparing individuals in the highest and lowest PRS quantiles. Validation analysis on an independent TWB1.0 cohort revealed AUROCs of 0.744 and 0.659.

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19

BACKGROUND

COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain.

SUBJECTS/METHODS

In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry.

RESULTS

Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10-6), obesity status (P = 4.81 × 10-5), higher serum fasting insulin (P = 5.32 × 10-4), BMI (P = 3.94 × 10-4), and lower serum HDL levels (P = 1.92 × 10-7). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10-4) and higher proportion of macrophages (P = 2.74 × 10-5). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression.

CONCLUSIONS

Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.

Cartilage debris and osteoarthritis risk factors influence gene expression in the synovium in end stage osteoarthritis

BACKGROUND

Gene expression in healthy synovium remains poorly characterised. Thus, synovial functional activity changes associated with osteoarthritis (OA) are difficult to define. This study sought to identify differentially expressed genes (DEG) of end-stage OA and assess the influence of OA risk factors on these DEG.

METHODS

Anonymised patient clinical data and x-ray images were analysed. Osteoarthritic and non-osteoarthritic patients with soft tissue or traumatic knee injuries were matched for body mass index (BMI) and sex. Tissue samples were partitioned for immunocytochemistry (IHC) and microarray analysis. Multiple bioinformatics applications were utilised to determine changes in functional and canonical pathway activation.

RESULTS

Age, disease-modifying injections and hypertension were confounding factors between patient groups. Inflammation was present in all tissues. Cartilage debris and inflammatory aggregates were noted in many osteoarthritic patient tissues. IHC and expression analyses revealed upregulation of synoviolin 1 (SYVN1) in osteoarthritic synovium. Significant differential expression was noted in 2084 genes. Osteoarthritic synovium displayed a significant upregulation of 95% of DEG coding for proteins, relative to non-osteoarthritic synovium tissues. Unfolded protein response (UPR)-related genes were upregulated in osteoarthritic synovium; gene expression of molecules within many canonical pathways including protein ubiquitination and UPR pathways was modified by BMI and sex.

CONCLUSIONS

The synovium of all three pathologies exhibited elements of an inflammatory response. Cartilage debris, age, BMI and sex influence DEG of osteoarthritic synovium. UPR pathway is the top deregulated canonical pathway identified in osteoarthritic synovium regardless of BMI and sex, while typical OA-associated inflammatory and matrix gene responses were minimal.

AKR1C2 and AKR1C3 expression in adipose tissue: Association with body fat distribution and regulatory variants

BACKGROUND

Changes in androgen dynamics within adipose tissue have been proposed as modulators of body fat accumulation. In this context, AKR1C2 likely plays a significant role by inactivating 5α-dihydrotestosterone.

AIM

To characterize AKR1C2 expression patterns across adipose depots and cell populations and to provide insight into the link with body fat distribution and genetic regulation.

METHODS

We used RNA sequencing data from severely obese patients to assess patterns of AKR1C2 and AKR1C3 expression in abdominal adipose tissue depots and cell fractions. We additionally used data from 856 women to assess AKR1C2 heritability and to link its expression in adipose tissue with body fat distribution. Further, we used public resources to study AKR1C2 genetic regulation as well as reference epigenome data for regulatory element profiling and functional interpretation of genetic data.

RESULTS

We found that mature adipocytes and adipocyte-committed adipocyte progenitor cells (APCs) had enriched expression of AKR1C2. We found adipose tissue AKR1C2 and AKR1C3 expression to be significantly and positively associated with percentage trunk fat mass in women. We identified strong genetic regulation of AKR1C2 by rs28571848 and rs34477787 located on the binding sites of two nuclear transcription factors, namely retinoid acid-related orphan receptor alpha and the glucocorticoid receptor.

CONCLUSION

We confirm the link between AKR1C2, adipogenic differentiation and adipose tissue distribution. We provide insight into genetic regulation of AKR1C2 by identifying regulatory variants mapping to binding sites for the glucocorticoid receptor and retinoid acid-related orphan receptor alpha which may in part mediate the effect of AKR1C2 expression on body fat distribution.

Further evidence supporting a potential role for ADH1B in obesity

Insulin is an essential hormone that regulates glucose homeostasis and metabolism. Insulin resistance (IR) arises when tissues fail to respond to insulin, and it leads to serious health problems including Type 2 Diabetes (T2D). Obesity is a major contributor to the development of IR and T2D. We previously showed that gene expression of alcohol dehydrogenase 1B (ADH1B) was inversely correlated with obesity and IR in subcutaneous adipose tissue of Mexican Americans. In the current study, a meta-analysis of the relationship between ADH1B expression and BMI in Mexican Americans, African Americans, Europeans, and Pima Indians verified that BMI was increased with decreased ADH1B expression. Using established human subcutaneous pre-adipocyte cell lines derived from lean (BMI < 30 kg m-2) or obese (BMI ≥ 30 kg m-2) donors, we found that ADH1B protein expression increased substantially during differentiation, and overexpression of ADH1B inhibited fatty acid binding protein expression. Mature adipocytes from lean donors expressed ADH1B at higher levels than obese donors. Insulin further induced ADH1B protein expression as well as enzyme activity. Knockdown of ADH1B expression decreased insulin-stimulated glucose uptake. Our findings suggest that ADH1B is involved in the proper development and metabolic activity of adipose tissues and this function is suppressed by obesity.

Parallel Accelerated Evolution in Distant Hibernators Reveals Candidate Cis Elements and Genetic Circuits Regulating Mammalian Obesity

Obesity is a clinical problem and an important adaptation in many species. Hibernating mammals, for example, become obese, insulin resistant, and hyperinsulinemic to store fat. Here, we combine comparative phylogenomics with large-scale human genome data to uncover candidate cis elements and genetic circuits in different cell types. The Fat Mass and Obesity (FTO) locus, the strongest genetic risk factor for human obesity, is an enriched site for hibernator pARs. Our results uncover noncoding cis elements with putative roles in obesity and hibernation.

Obesity is a clinical problem but also an important adaptation in hibernators. By using comparative genomics approaches to analyze the genomes of hibernators from different clades and contrasting the results with human obesity risk loci, Ferris and Gregg found 364 conserved cis elements with putative roles in regulating obesity and hibernation.

ACE2 expression in adipose tissue is associated with COVID-19 cardio-metabolic risk factors and cell type composition

COVID-19 severity has varied widely, with demographic and cardio-metabolic factors increasing risk of severe reactions to SARS-CoV-2 infection, but the underlying mechanisms for this remain uncertain. We investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 ( ACE2 ), which has been shown to act as a receptor for SARS-CoV-2 cellular entry. In a meta-analysis of three independent studies including up to 1,471 participants, lower adipose tissue ACE2 expression was associated with adverse cardio-metabolic health indices including type 2 diabetes (T2D) and obesity status, higher serum fasting insulin and BMI, and lower serum HDL levels (P<5.32x10 -4 ). ACE2 expression levels were also associated with estimated proportions of cell types in adipose tissue; lower ACE2 expression was associated with a lower proportion of microvascular endothelial cells (P=4.25x10 -4 ) and higher macrophage proportion (P=2.74x10 -5 ), suggesting a link to inflammation. Despite an estimated heritability of 32%, we did not identify any proximal or distal genetic variants (eQTLs) associated with adipose tissue ACE2 expression. Our results demonstrate that at-risk individuals have lower background ACE2 levels in this highly relevant tissue. Further studies will be required to establish how this may contribute to increased COVID-19 severity.

Estrogen receptor α controls metabolism in white and brown adipocytes by regulating Polg1 and mitochondrial remodeling

Obesity is heightened during aging, and although the estrogen receptor α (ERα) has been implicated in the prevention of obesity, its molecular actions in adipocytes remain inadequately understood. Here, we show that adipose tissue ESR1/Esr1 expression inversely associated with adiposity and positively associated with genes involved in mitochondrial metabolism and markers of metabolic health in 700 Finnish men and 100 strains of inbred mice from the UCLA Hybrid Mouse Diversity Panel. To determine the anti-obesity actions of ERα in fat, we selectively deleted Esr1 from white and brown adipocytes in mice. In white adipose tissue, Esr1 controlled oxidative metabolism by restraining the targeted elimination of mitochondria via the E3 ubiquitin ligase parkin. mtDNA content was elevated, and adipose tissue mass was reduced in adipose-selective parkin knockout mice. In brown fat centrally involved in body temperature maintenance, Esr1 was requisite for both mitochondrial remodeling by dynamin-related protein 1 (Drp1) and uncoupled respiration thermogenesis by uncoupled protein 1 (Ucp1). In both white and brown fat of female mice and adipocytes in culture, mitochondrial dysfunction in the context of Esr1 deletion was paralleled by a reduction in the expression of the mtDNA polymerase γ subunit Polg1 We identified Polg1 as an ERα target gene by showing that ERα binds the Polg1 promoter to control its expression in 3T3L1 adipocytes. These findings support strategies leveraging ERα action on mitochondrial function in adipocytes to combat obesity and metabolic dysfunction.

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, βsubq = 0.45; Pvisc = 2.5 × 10-55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.

Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs

Adipose tissue is an important endocrine organ with a role in many cardiometabolic diseases. It is comprised of a heterogeneous collection of cell types that can differentially impact disease phenotypes. Cellular heterogeneity can also confound -omic analyses but is rarely taken into account in analysis of solid-tissue transcriptomes. Here, we investigate cell-type heterogeneity in two population-level subcutaneous adipose-tissue RNA-seq datasets (TwinsUK, n = 766 and the Genotype-Tissue Expression project [GTEx], n = 326) by estimating the relative proportions of four distinct cell types (adipocytes, macrophages, CD4+ T cells, and micro-vascular endothelial cells). We find significant cellular heterogeneity within and between the TwinsUK and GTEx adipose datasets. We find that adipose cell-type composition is heritable and confirm the positive association between adipose-resident macrophage proportion and obesity (high BMI), but we find a stronger BMI-independent association with dual-energy X-ray absorptiometry (DXA) derived body-fat distribution traits. We benchmark the impact of adipose-tissue cell composition on a range of standard analyses, including phenotype-gene expression association, co-expression networks, and cis-eQTL discovery. Our results indicate that it is critical to account for cell-type composition when combining adipose transcriptome datasets in co-expression analysis and in differential expression analysis with obesity-related traits. We applied gene expression by cell-type proportion interaction models (G × Cell) to identify 26 cell-type-specific expression quantitative trait loci (eQTLs) in 20 genes, including four autoimmune disease genome-wide association study (GWAS) loci. These results identify cell-specific eQTLs and demonstrate the potential of in silico deconvolution of bulk tissue to identify cell-type-restricted regulatory variants.

Laser capture microdissection of human pancreatic islets reveals novel eQTLs associated with type 2 diabetes

OBJECTIVE

Genome wide association studies (GWAS) for type 2 diabetes (T2D) have identified genetic loci that often localise in non-coding regions of the genome, suggesting gene regulation effects. We combined genetic and transcriptomic analysis from human islets obtained from brain-dead organ donors or surgical patients to detect expression quantitative trait loci (eQTLs) and shed light into the regulatory mechanisms of these genes.

METHODS

Pancreatic islets were isolated either by laser capture microdissection (LCM) from surgical specimens of 103 metabolically phenotyped pancreatectomized patients (PPP) or by collagenase digestion of pancreas from 100 brain-dead organ donors (OD). Genotyping (> 8.7 million single nucleotide polymorphisms) and expression (> 47,000 transcripts and splice variants) analyses were combined to generate cis-eQTLs.

RESULTS

After applying genome-wide false discovery rate significance thresholds, we identified 1,173 and 1,021 eQTLs in samples of OD and PPP, respectively. Among the strongest eQTLs shared between OD and PPP were CHURC1 (OD p-value=1.71 × 10^-24; PPP p-value = 3.64 × 10^-24) and PSPH (OD p-value = 3.92 × 10^-26; PPP p-value = 3.64 × 10^-24). We identified eQTLs in linkage-disequilibrium with GWAS loci T2D and associated traits, including TTLL6, MLX and KIF9 loci, which do not implicate the nearest gene. We found in the PPP datasets 11 eQTL genes, which were differentially expressed in T2D and two genes (CYP4V2 and TSEN2) associated with HbA1c but none in the OD samples.

CONCLUSIONS

eQTL analysis of LCM islets from PPP led us to identify novel genes which had not been previously linked to islet biology and T2D. The understanding gained from eQTL approaches, especially using surgical samples of living patients, provides a more accurate 3-dimensional representation than those from genetic studies alone.

Fasting and time of day independently modulate circadian rhythm relevant gene expression in adipose and skin tissue

BACKGROUND

Intermittent fasting and time-restricted diets are associated with lower risk biomarkers for cardio-metabolic disease. The shared mechanisms underpinning the similar physiological response to these events is not established, but circadian rhythm could be involved. Here we investigated the transcriptional response to fasting in a large cross-sectional study of adipose and skin tissue from healthy volunteers (N = 625) controlling for confounders of circadian rhythm: time of day and season.

RESULTS

We identified 367 genes in adipose and 79 in skin whose expression levels were associated (FDR < 5%) with hours of fasting conditionally independent of time of day and season, with 19 genes common to both tissues. Among these genes, we replicated 38 in human, 157 in non-human studies, and 178 are novel associations. Fasting-responsive genes were enriched for regulation of and response to circadian rhythm. We identified 99 genes in adipose and 54 genes in skin whose expression was associated to time of day; these genes were also enriched for circadian rhythm processes. In genes associated to both exposures the effect of time of day was stronger and in an opposite direction to that of hours fasted. We also investigated the relationship between fasting and genetic regulation of gene expression, including GxE eQTL analysis to identify personal responses to fasting.

CONCLUSION

This study robustly implicates circadian rhythm genes in the response to hours fasting independently of time of day, seasonality, age and BMI. We identified tissue-shared and tissue-specific differences in the transcriptional response to fasting in a large sample of healthy volunteers.

Blood pressure signature genes and blood pressure response to thiazide diuretics: results from the PEAR and PEAR-2 studies

Background

Recently, 34 genes had been associated with differential expression relative to blood pressure (BP)/ hypertension (HTN). We hypothesize that some of the genes associated with BP/HTN are also associated with BP response to antihypertensive treatment with thiazide diuretics.

Methods

We assessed these 34 genes for association with differential expression to BP response to thiazide diuretics with RNA sequencing in whole blood samples from 150 hypertensive participants from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) and PEAR-2 studies. PEAR white and PEAR-2 white and black participants (n = 50 for each group) were selected based on the upper and lower quartile of BP response to hydrochlorothiazide (HCTZ) and to chlorthalidone.

Results

FOS, DUSP1 and PPP1R15A were differentially expressed across all cohorts (meta-analysis p-value < 2.0 × 10^− 6), and responders to HCTZ or chlorthalidone presented up-regulated transcripts. Rs11065987 in chromosome 12, a trans-eQTL for expression of FOS, PPP1R15A and other genes, is also associated with BP response to HCTZ in PEAR whites (SBP: β = − 2.1; p = 1.7 × 10^− 3; DBP: β = − 1.4; p = 2.9 × 10^− 3).

Conclusions

These findings suggest FOS, DUSP1 and PPP1R15A as potential molecular determinants of antihypertensive response to thiazide diuretics.

Trial registration

NCT00246519, NCT01203852www.clinicaltrials.gov

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0370-x) contains supplementary material, which is available to authorized users.

Dietary grape seed proanthocyanidin extract regulates metabolic disturbance in rat liver exposed to lead associated with PPARα signaling pathway

Lead, a pervasive environmental hazard worldwide, causes a wide range of physiological and biochemical destruction, including metabolic dysfunction. Grape seed proanthocyanidin extract (GSPE) is a natural production with potential metabolic regulation in liver. This study was performed to investigate the protective role of GSPE against lead-induced metabolic dysfunction in liver and elucidate the potential molecular mechanism of this event. Wistar rats received GSPE (200 mg/kg) daily with or without lead acetate (PbA, 0.5 g/L) exposure for 56 d. According to biochemical and histopathologic analysis, GSPE attenuated lead-induced metabolic dysfunction, oxidative stress, and liver dysfunction. Liver gene expression profiling was assessed by RNA sequencing and validated by qRT-PCR. Expression of some genes in peroxisome proliferator-activated receptor alpha (PPARα) signaling pathway was significantly suppressed in PbA group and revived in PbA + GSPE group, which was manifested by Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis and validated by western blot analysis. This study supports that dietary GSPE ameliorates lead-induced fatty acids metabolic disturbance in rat liver associated with PPARα signaling pathway, and suggests that dietary GSPE may be a protector against lead-induced metabolic dysfunction and liver injury, providing a novel therapy to protect liver against lead exposure.

Localization of adaptive variants in human genomes using averaged one-dependence estimation

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios.

Integrative analysis of pathway deregulation in obesity

Obesity is a pandemic disease, linked to the onset of type 2 diabetes and cancer. Transcriptomic data provides a picture of the alterations in regulatory and metabolic activities associated with obesity, but its interpretation is typically blurred by noise. Here, we solve this problem by collecting publicly available transcriptomic data from adipocytes and removing batch effects using singular value decomposition. In this way we obtain a gene expression signature of 38 genes associated to obesity and identify the main pathways involved. We then show that similar deregulation patterns can be detected in peripheral markers, in type 2 diabetes and in breast cancer. The integration of different data sets combined with the study of pathway deregulation allows us to obtain a more complete picture of gene-expression patterns associated with obesity, breast cancer, and diabetes.

The worldwide increase in obesity is extremely worrisome, especially because this condition is associated with a higher risk for diseases such as type 2 diabetes and cancer. Identifying alterations in regulatory and metabolic activities associated with obesity is complicated due to the presence of noise. A team lead by Caterina La Porta from the University of Milan addressed the question from the point of view of big data and extracted a signature of 38 genes associated to obesity from the combination of publicly available gene expression data from obese and lean subjects. The results revealed a similarity between the deregulation patterns observed in obesity and those found in breast cancer and diabetes, providing a clearer picture of the role of obesity in these diseases.