From Genotype to Phenotype

PPP3R1 Promoter Polymorphism (Allelic Variation) Affects Tacrolimus Treatment Efficacy by Modulating E2F6 Binding Affinity

BACKGROUND

Tacrolimus is widely used as a first-line immunosuppressant in transplant immunology; however, its clinical application is constrained by the narrow therapeutic index and considerable interindividual variability. In this study, we identified the potential regulatory role of a novel PPP3R1 promoter polymorphism, rs4519508 C > T, in the tacrolimus pharmacodynamic pathway.

METHODS

Dual-luciferase reporter assays and bioinformatic analysis were applied to assess the impact of allelic variation. Electrophoretic mobility shift assays (EMSA) validated the altered binding of transcription factors. Quantitative real-time PCR (qRT-PCR), enzyme-linked immunosorbent assay (ELISA) and Western blots were used to determine the immunosuppressive effect of tacrolimus.

RESULTS

Assays revealed that rs4519508 C > T markedly enhanced PPP3R1 promoter activity. EMSA assays validated the binding of E2F6 to rs4519508 C (wild-type) and the binding was significantly weaker to the rs4519508 T (mutant-type). The overexpression of E2F6 significantly reduced the transcriptional activity and expression of PPP3R1 when the rs4519508 site presented as major C allele, an effect that was not observed with the rs4519508 T allele. Furthermore, the downregulation of E2F6 raises the level of downstream immune cytokines inhibited by TAC.

CONCLUSIONS

This study proposed that E2F6 suppresses the expression of PPP3R1, while rs4519508 C > T impairs the binding of E2F6, and thus elevates the level of PPP3R1, so that the inhibition of the downstream immune cytokines by TAC is attenuated. Our findings reported the potential regulatory role of a novel polymorphism, PPP3R1 rs4519508 C > T, which may serve as pharmacodynamic-associated pharmacogenetic biomarker indicating individual response variability of tacrolimus, and thus aid the clinical management of transplant immunology.

Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications

Physiologically relevant in vitro models are a priority in predictive toxicology to replace and/or reduce animal experiments. The compromised toxicant metabolism of many immortalized human liver cell lines grown as monolayers as compared to in vivo metabolism limits their physiological relevance. However, recent efforts to culture liver cells in a 3D environment, such as spheroids, to better mimic the in vivo conditions, may enhance the toxicant metabolism of human liver cell lines. In this study, we characterized the dynamic changes in the transcriptome of HepG2/C3A hepatocarcinoma cell spheroids maintained in a clinostat system (CelVivo) to gain insight into the metabolic capacity of this model as a function of spheroid size and culture time. We assessed morphological changes (size, necrotic core), cell health, and proliferation rate from initial spheroid seeding to 35 days of continuous culture in conjunction with a time-course (0, 3, 7, 10, 14, 21, 28 days) of the transcriptome (TempO-Seq, BioSpyder). The phenotypic characteristics of HepG2/C3A growing in spheroids were comparable to monolayer growth until ∼Day 12 (Day 10-14) when a significant decrease in cell doubling rate was noted which was concurrent with down-regulation of cell proliferation and cell cycle pathways over this time period. Principal component analysis of the transcriptome data suggests that the Day 3, 7, and 10 spheroids are pronouncedly different from the Day 14, 21, and 28 spheroids in support of a biological transition time point during the long-term 3D spheroid cultures. The expression of genes encoding cellular components involved in toxicant metabolism and transport rapidly increased during the early time points of spheroids to peak at Day 7 or Day 10 as compared to monolayer cultures with a gradual decrease in expression with further culture, suggesting the most metabolically responsive time window for exposure studies. Overall, we provide baseline information on the cellular and molecular characterization, with a particular focus on toxicant metabolic capacity dynamics and cell growth, of HepG2/C3A 3D spheroid cultures over time.

A phenotype-based forward genetic screen identifies Dnajb6 as a sick sinus syndrome gene

Previously we showed the generation of a protein trap library made with the gene-break transposon (GBT) in zebrafish (Danio rerio) that could be used to facilitate novel functional genome annotation towards understanding molecular underpinnings of human diseases (Ichino et al, 2020). Here, we report a significant application of this library for discovering essential genes for heart rhythm disorders such as sick sinus syndrome (SSS). SSS is a group of heart rhythm disorders caused by malfunction of the sinus node, the heart's primary pacemaker. Partially owing to its aging-associated phenotypic manifestation and low expressivity, molecular mechanisms of SSS remain difficult to decipher. From 609 GBT lines screened, we generated a collection of 35 zebrafish insertional cardiac (ZIC) mutants in which each mutant traps a gene with cardiac expression. We further employed electrocardiographic measurements to screen these 35 ZIC lines and identified three GBT mutants with SSS-like phenotypes. More detailed functional studies on one of the arrhythmogenic mutants, GBT411, in both zebrafish and mouse models unveiled Dnajb6 as a novel SSS causative gene with a unique expression pattern within the subpopulation of sinus node pacemaker cells that partially overlaps with the expression of hyperpolarization activated cyclic nucleotide gated channel 4 (HCN4), supporting heterogeneity of the cardiac pacemaker cells.

Patterns of pan-genome occupancy and gene coexpression under water-deficit in Brachypodium distachyon

Natural populations are characterized by abundant genetic diversity driven by a range of different types of mutation. The tractability of sequencing complete genomes has allowed new insights into the variable composition of genomes, summarized as a species pan-genome. These analyses demonstrate that many genes are absent from the first reference genomes, whose analysis dominated the initial years of the genomic era. Our field now turns towards understanding the functional consequence of these highly variable genomes. Here, we analysed weighted gene coexpression networks from leaf transcriptome data for drought response in the purple false brome Brachypodium distachyon and the differential expression of genes putatively involved in adaptation to this stressor. We specifically asked whether genes with variable "occupancy" in the pan-genome - genes which are either present in all studied genotypes or missing in some genotypes - show different distributions among coexpression modules. Coexpression analysis united genes expressed in drought-stressed plants into nine modules covering 72 hub genes (87 hub isoforms), and genes expressed under controlled water conditions into 13 modules, covering 190 hub genes (251 hub isoforms). We find that low occupancy pan-genes are under-represented among several modules, while other modules are over-enriched for low-occupancy pan-genes. We also provide new insight into the regulation of drought response in B. distachyon, specifically identifying one module with an apparent role in primary metabolism that is strongly responsive to drought. Our work shows the power of integrating pan-genomic analysis with transcriptomic data using factorial experiments to understand the functional genomics of environmental response.

Gene polymorphism of leptin and risk for heart disease, obesity, and high BMI: a systematic review and pooled analysis in adult obese subjects

Objectives

Leptin polymorphism (LEP) has been associated with coronary heart disease (CAD), obesity, and high body mass index (BMI). However, we performed a systematic review and meta-analysis to discover the association because previous studies reached different conclusions.

Methods

Review Manager, version 5.3.5, and Stata, version 15.0, were used for statistical analysis. We calculated the effect size of the studies using the OR with the corresponding 95% CI, and two-sided (bilateral) p-values of 0.05 were considered significant. To determine heterogeneity among the selected studies, the Q test and I2 statistics were used. Meta-regression was used to examine the disease (heart disease, obesity, and high BMI) and heterogeneity between these subgroups.

Results

Eleven studies with 18,984 subjects were included in this study. The G-2548A (rs12112075), rs7799039, and A19G (rs2167270) polymorphisms of the leptin gene (but not the Lys656Asn (rs1805094) polymorphism) are associated with an increased risk of cardiovascular disease. Our pooled analysis revealed an association between the G-2548A (rs12112075) polymorphism and heart disease, high BMI, and obesity. This indicates that individuals carrying the AA allele are at an increased risk for heart disease, high BMI, and obesity. People with heart failure and coronary artery disease did not have the rs7799039 polymorphism or its alleles linked to them.

Conclusions

Combined analysis of data from current and published research suggests that the leptin gene polymorphisms G-2548A (rs12112075), rs7799039, and A19G (rs2167270) (but not the Lys656Asn (rs1805094) polymorphism) are associated with an increased risk of cardiovascular disease. Further research is needed to understand this association.

Kidney omics in hypertension: from statistical associations to biological mechanisms and clinical applications

Hypertension is a major cardiovascular disease risk factor and contributor to premature death globally. Family-based investigations confirmed a significant heritable component of blood pressure (BP), whereas genome-wide association studies revealed >1000 common and rare genetic variants associated with BP and/or hypertension. The kidney is not only an organ of key relevance to BP regulation and the development of hypertension, but it also acts as the tissue mediator of genetic predisposition to hypertension. The identity of kidney genes, pathways, and related mechanisms underlying the genetic associations with BP has started to emerge through integration of genomics with kidney transcriptomics, epigenomics, and other omics as well as through applications of causal inference, such as Mendelian randomization. Single-cell methods further enabled mapping of BP-associated kidney genes to cell types, and in conjunction with other omics, started to illuminate the biological mechanisms underpinning associations of BP-associated genetic variants and kidney genes. Polygenic risk scores derived from genome-wide association studies and refined on kidney omics hold the promise of enhanced diagnostic prediction, whereas kidney omics-informed drug discovery is likely to contribute new therapeutic opportunities for hypertension and hypertension-mediated kidney damage.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Detoxifying chemotherapy with genetics-guided stem cell modeling: A personalized affair

Doxorubicin chemotherapy causes cardiotoxicity in some patients and spares others. In this issue of Cell Stem Cell, Magdy et al. (2021) use genome-edited iPSCs to establish a common RARG coding variant as a causal risk factor, pointing to a pharmacogenomic application and to RARG-targeting treatments to protect patients from cardiotoxicity.

Complexities of Understanding Function from CKD-Associated DNA Variants

Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type-specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype-phenotype association.

Transcription Factor KLF14 and Metabolic Syndrome

Metabolic syndrome (MetSyn) is a combination of metabolic abnormalities that lead to the development of cardiovascular disease (CVD) and Type 2 Diabetes (T2D). Although various criteria for defining MetSyn exist, common abnormalities include abdominal obesity, elevated serum triglyceride, insulin resistance, and blood glucose, decreased high-density lipoprotein cholesterol (HDL-C), and hypertension. MetSyn prevalence has been increasing with the rise of obesity worldwide, with significantly higher prevalence in women compared with men and in Hispanics compared with Whites. Affected individuals are at a higher risk of developing T2D (5-fold) and CVD (2-fold). Heritability estimates for individual components of MetSyn vary between 40 and 70%, suggesting a strong contribution of an individual's genetic makeup to disease pathology. The advent of next-generation sequencing technologies has enabled large-scale genome-wide association studies (GWAS) into the genetics underlying MetSyn pathogenesis. Several such studies have implicated the transcription factor KLF14, a member of the Krüpple-like factor family (KLF), in the development of metabolic diseases, including obesity, insulin resistance, and T2D. How KLF14 regulates these metabolic traits and increases the risk of developing T2D, atherosclerosis, and liver dysfunction is still unknown. There have been some debate and controversial results with regards to its expression profile and functionality in various tissues, and a systematic review of current knowledge on KLF14 is lacking. Here, we summarize the research progress made in understanding the function of KLF14 and describe common attributes of its biochemical, physiological, and pathophysiological roles. We also discuss the current challenges in understanding the role of KLF14 in metabolism and provide suggestions for future directions.

2018 George Lyman Duff Memorial Lecture: Genetics and Genomics of Coronary Artery Disease: A Decade of Progress

Recent studies have led to a broader understanding of the genetic architecture of coronary artery disease and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on coronary artery disease risk. The tools applied include genome-wide association studies encompassing over 200 000 individuals complemented by bioinformatic approaches including imputation from whole-genome data sets, expression quantitative trait loci analyses, and interrogation of ENCODE (Encyclopedia of DNA Elements), Roadmap Epigenetic Project, and other data sets. Over 160 genome-wide significant loci associated with coronary artery disease risk have been identified using the genome-wide association studies approach, 90% of which are situated in intergenic regions. Here, I will describe, in part, our research over the last decade performed in collaboration with a series of bright trainees and an extensive number of groups and individuals around the world as it applies to our understanding of the genetic basis of this complex disease. These studies include computational approaches to better understand missing heritability and identify causal pathways, experimental approaches, and progress in understanding at the molecular level the function of the multiple risk loci identified and potential applications of these genomic data in clinical medicine and drug discovery.