Genetic determinants of blood pressure and heart rate identified through ENU-induced mutagenesis with automated meiotic mapping

We used N-ethyl-N-nitrosurea-induced germline mutagenesis combined with automated meiotic mapping to identify specific systolic blood pressure (SBP) and heart rate (HR) determinant loci. We analyzed 43,627 third-generation (G3) mice from 841 pedigrees to assess the effects of 45,378 variant alleles within 15,760 genes, in both heterozygous and homozygous states. We comprehensively tested 23% of all protein-encoding autosomal genes and found 87 SBP and 144 HR (with 7 affecting both) candidates exhibiting detectable hypomorphic characteristics. Unexpectedly, only 18 of the 87 SBP genes were previously known, while 26 of the 144 genes linked to HR were previously identified. Furthermore, we confirmed the influence of two genes on SBP regulation and three genes on HR control through reverse genetics. This underscores the importance of our research in uncovering genes associated with these critical cardiovascular risk factors and illustrate the effectiveness of germline mutagenesis for defining key determinants of polygenic phenotypes that must be studied in an intact organism.

Before and after AlphaFold2: An overview of protein structure prediction

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

Abstract P049: Blood Pressure Polygenic Risk Score Stratifies Individuals With High Risk Of Hypertension In Different Ancestries

The heritability of arterial hypertension ranges from 25 to 68%, but the underlying genetic determinants remain elusive. Thousands of variants with small effects on systolic blood pressure (SBP) from European Ancestry Genome-Wide Association Studies have been used to derive genetic risk scores, whose predictive power in samples from diverse ancestries has yet to be determined. We used the LDPred2 algorithm and 423 thousand genetic variants from UK Biobank that were common to an admixed sample population from Brazil to derive SBP polygenic risk scores (PRS). We selected the best model that displayed a score with hyper-parameters ρ = 0.056, h 2 = 0.0739 and without sparsity, which presented the highest correlation coefficient with observed SBP (0.206) and SBP change per standard deviation increase (4.04mmHg) in a different set of data from the UK Biobank prior to test its predictive power on SBP and hypertension in a third set of the UK Biobank and two admixed Brazilian population samples. All three populations displayed similar normal distribution of PRS, with the UK Biobank PRS distribution slightly shifted to the right, presenting higher PRS average (7.8 vs. 5.6 and 6.4 in Baependi and Pelotas, respectively), but with similar standard deviation (3.3 vs. 3.2 for both Baependi and Pelotas). We used the PRS deciles to stratify the populations and observed a significant difference on SBP average between the top and bottom deciles in all three populations (14.43 mmHg, 9.34 mmHg and 7.51 mmHg for the UK Biobank, Baependi and Pelotas, respectively). We then compared the top 10% PRS versus the remaining 90% of the PRS distribution, and observed that high PRS is associated with 1.8, 2 to 3 and 3 to 4-fold increase in prehypertension, hypertension stage I and hypertension stage II risk, respectively, demonstrating that the increased risk is independently of age and ancestry. Considering the highly admixed nature of the Brazilian population, we provide evidence for the generalization of PRS between populations with different genetic structure.

Uncovering emergent phenotypes in endothelial cells by clustering of surrogates of cardiovascular risk factors

Endothelial dysfunction (ED) is a hallmark of atherosclerosis and is influenced by well-defined risk factors, including hypoxia, dyslipidemia, inflammation, and oscillatory flow. However, the individual and combined contributions to the molecular underpinnings of ED remain elusive. We used global gene expression in human coronary artery endothelial cells to identify gene pathways and cellular processes in response to chemical hypoxia, oxidized lipids, IL-1β induced inflammation, oscillatory flow, and these combined stimuli. We found that clustering of the surrogate risk factors differed from the sum of the individual insults that gave rise to emergent phenotypes such as cell proliferation. We validated these observations in samples of human coronary artery atherosclerotic plaques analyzed using single-cell RNA sequencing. Our findings suggest a hierarchical interaction between surrogates of CV risk factors and the advent of emergent phenotypes in response to combined stimulation in endothelial cells that may influence ED.

Genetics of Resistant Hypertension: the Missing Heritability and Opportunities

PURPOSE OF THE REVIEW

Blood pressure regulation in humans has long been known to be a genetically determined trait. The identification of causal genetic modulators for this trait has been unfulfilling at the least. Despite the recent advances of genome-wide genetic studies, loci associated with hypertension or blood pressure still explain a very low percentage of the overall variation of blood pressure in the general population. This has precluded the translation of discoveries in the genetics of human hypertension to clinical use. Here, we propose the combined use of resistant hypertension as a trait for mapping genetic determinants in humans and the integration of new large-scale technologies to approach in model systems the multidimensional nature of the problem.

RECENT FINDINGS

New large-scale efforts in the genetic and genomic arenas are paving the way for an increased and granular understanding of genetic determinants of hypertension. New technologies for whole genome sequence and large-scale forward genetic screens can help prioritize gene and gene-pathways for downstream characterization and large-scale population studies, and guided pharmacological design can be used to drive discoveries to the translational application through better risk stratification and new therapeutic approaches. Although significant challenges remain in the mapping and identification of genetic determinants of hypertension, new large-scale technological approaches have been proposed to surpass some of the shortcomings that have limited progress in the area for the last three decades. The incorporation of these technologies to hypertension research may significantly help in the understanding of inter-individual blood pressure variation and the deployment of new phenotyping and treatment approaches for the condition.

Using gene-network landscape to dissect genotype effects of TCF7L2 genetic variant on diabetes and cardiovascular risk

The single nucleotide polymorphism (SNP) within the TCF7L2 gene, rs7903146, is, to date, the most significant genetic marker associated with Type 2 diabetes mellitus (T2DM) risk. Nonetheless, its functional role in disease pathology is poorly understood. The aim of the present study was to investigate, in vascular smooth muscle cells from 92 patients undergoing aortocoronary bypass surgery, the contribution of this SNP in T2DM using expression levels and expression correlation comparison approaches, which were visually represented as gene interaction networks. Initially, the expression levels of 41 genes (seven TCF7L2 splice forms and 40 other T2DM relevant genes) were compared between rs7903146 wild-type (CC) and T2DM-risk (CT + TT) genotype groups. Next, we compared the expression correlation patterns of these 41 genes between groups to observe if the relationships between genes were different. Five TCF7L2 splice forms and nine genes showed significant expression differences between groups. RXRα gene was pinpointed as showing the most different expression correlation pattern with other genes. Therefore, T2DM risk alleles appear to be influencing TCF7L2 splice form's expression in vascular smooth muscle cells, and RXRα gene is pointed out as a treatment target candidate for risk reduction in individuals with high risk of developing T2DM, especially individuals harboring TCF7L2 risk genotypes.