Integrative network analysis reveals molecular mechanisms of blood pressure regulation

GWAS for Defining the Pathogenesis of Hypertension: Have They Delivered?

Genome-wide association studies have identified >3500 associated single nucleotide polymorphisms and over 1000 independent loci associated with hypertension. These individually have small effect sizes, and few associated loci have been experimentally tested for causal roles in hypertension using animal models or in humans. Thus, methods to prioritize and maximize the relevance of identified single nucleotide polymorphisms and associated loci are critical to determine their importance in hypertension. We propose several approaches to aid in these efforts, including: (1) integration of genome-wide association study data with multiomic data sets, including proteomics, transcriptomics, and epigenomics, (2) utilizing linked clinical and genetic data sets to determine genetic contributions to hypertension subphenotypes with distinct drivers, and (3) performing whole exome/genome sequencing on cohorts of individuals with severe hypertension to enrich for rare variants with larger effect sizes. Rather than creating longer lists of hypertension-associated single nucleotide polymorphisms, these approaches are needed to identify key mediators of hypertension pathophysiology.

Urinary β2-Microglobulin Predicts the Risk of Hypertension in Populations Chronically Exposed to Environmental Cadmium

Chronic exposure to the pollutant cadmium (Cd) is inevitable for most people because it is present in nearly all food types. Concerningly, the risk of developing hypertension has been linked to dietary Cd exposure lower than 58 µg/day for a 70 kg person. The mechanisms involved are, however, unclear. Since the kidneys play an indispensable role in long-term blood pressure regulation, and they are also the main site of Cd accumulation and toxicity, a retrospective analysis was conducted to examine if kidney damage and malfunction, reflected by urinary β2-microglobulin excretion (Eβ2M), and the estimated glomerular filtration rate (eGFR), are related to Cd excretion (ECd) and blood pressure variation. Data were obtained from 689 Thai Nationals without diabetes or occupational exposure to Cd, of which 32.4% had hypertension and 7.3% had β2-microglobulinuria, defined as an increase in the β2M excretion rate ≥ 300 µg/g creatinine. Respective prevalence odds ratio (POR) and 95% confidence interval (CI) values for β2-microglobulinuria and hypertension were 10.7 (1.36-83.4), p = 0.024 and 2.79 (1.60-4.87) p < 0.001, comparing the top quartile of ECd with the bottom quartile. Only in subjects with eGFR below 90 mL/min/1.73 m2 did systolic blood pressure (SBP) and diastolic blood pressure (DBP) both increase linearly with Eβ2M (respective β = 0.182 and 0.192 for SBP and DBP) after adjustment for age, body mass index, gender, and smoking. The present study confirms the significant impact of Cd on the risk of having hypertension, following GFR loss induced by Cd. A simple mediation model analysis for cause-effect inference has provided, for the first time, evidence that may link rising SBP and DBP in Cd-exposed people to a novel role of β2M as a predictor of blood pressure variability.

Systems genetics analysis of human body fat distribution genes identifies adipocyte processes

Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.

Genetic and Epigenetic Mechanisms Regulating Blood Pressure and Kidney Dysfunction

The pioneering work of Dr Lewis K. Dahl established a relationship between kidney, salt, and high blood pressure (BP), which led to the major genetic-based experimental model of hypertension. BP, a heritable quantitative trait affected by numerous biological and environmental stimuli, is a major cause of morbidity and mortality worldwide and is considered to be a primary modifiable factor in renal, cardiovascular, and cerebrovascular diseases. Genome-wide association studies have identified monogenic and polygenic variants affecting BP in humans. Single nucleotide polymorphisms identified in genome-wide association studies have quantified the heritability of BP and the effect of genetics on hypertensive phenotype. Changes in the transcriptional program of genes may represent consequential determinants of BP, so understanding the mechanisms of the disease process has become a priority in the field. At the molecular level, the onset of hypertension is associated with reprogramming of gene expression influenced by epigenomics. This review highlights the specific genetic variants, mutations, and epigenetic factors associated with high BP and how these mechanisms affect the regulation of hypertension and kidney dysfunction.

Immune and inflammatory mechanisms in hypertension

Hypertension is a global health problem, with >1.3 billion individuals with high blood pressure worldwide. In this Review, we present an inflammatory paradigm for hypertension, emphasizing the crucial roles of immune cells, cytokines and chemokines in disease initiation and progression. T cells, monocytes, macrophages, dendritic cells, B cells and natural killer cells are all implicated in hypertension. Neoantigens, the NLRP3 inflammasome and increased sympathetic outflow, as well as cytokines (including IL-6, IL-7, IL-15, IL-18 and IL-21) and a high-salt environment, can contribute to immune activation in hypertension. The activated immune cells migrate to target organs such as arteries (especially the perivascular fat and adventitia), kidneys, the heart and the brain, where they release effector cytokines that elevate blood pressure and cause vascular remodelling, renal damage, cardiac hypertrophy, cognitive impairment and dementia. IL-17 secreted by CD4+ T helper 17 cells and γδ T cells, and interferon-γ and tumour necrosis factor secreted by immunosenescent CD8+ T cells, exert crucial effector roles in hypertension, whereas IL-10 and regulatory T cells are protective. Effector mediators impair nitric oxide bioavailability, leading to endothelial dysfunction and increased vascular contractility. Inflammatory effector mediators also alter renal sodium and water balance and promote renal fibrosis. These mechanisms link hypertension with obesity, autoimmunity, periodontitis and COVID-19. A comprehensive understanding of the immune and inflammatory mechanisms of hypertension is crucial for safely and effectively translating the findings to clinical practice.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

BACKGROUND

It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.

METHODS

The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.

RESULTS

The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.

CONCLUSIONS

The implemented workflow could be used for other multifactorial diseases.

Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes

BACKGROUND

Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach.

METHODS

We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA.

RESULTS

51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing β-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression.

SUMMARY

ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.

Evidence Linking Cadmium Exposure and β2-Microglobulin to Increased Risk of Hypertension in Diabetes Type 2

The most common causes of chronic kidney disease, diabetes, and hypertension are significant public health issues worldwide. Exposure to the heavy metal pollutant, cadmium (Cd), which is particularly damaging to the kidney, has been associated with both risk factors. Increased levels of urinary β₂-microglobulin (β₂M) have been used to signify Cd-induced kidney damage and circulating levels have been linked to blood pressure control. In this study we investigated the pressor effects of Cd and β₂M in 88 diabetics and 88 non-diabetic controls, matched by age, gender and locality. The overall mean serum β₂M was 5.98 mg/L, while mean blood Cd and Cd excretion normalized to creatinine clearance (C_cr) as E_Cd/C_cr were 0.59 µg/L and 0.0084 µg/L of filtrate (0.95 µg/g creatinine), respectively. The prevalence odds ratio for hypertension rose by 79% per every ten-fold increase in blood Cd concentration. In all subjects, systolic blood pressure (SBP) showed positive associations with age (β = 0.247), serum β₂M (β = 0.230), and E_Cd/C_cr (β = 0.167). In subgroup analysis, SBP showed a strong positive association with E_Cd/C_cr (β = 0.303) only in the diabetic group. The covariate-adjusted mean SBP in the diabetics of the highest E_Cd/C_cr tertile was 13.8 mmHg higher, compared to the lowest tertile (p = 0.027). An increase in SBP associated with Cd exposure was insignificant in non-diabetics. Thus, for the first time, we have demonstrated an independent effect of Cd and β₂M on blood pressure, thereby implicating both Cd exposure and β₂M in the development of hypertension, especially in diabetics.

Cross-Platform Synaptic Network Analysis of Human Entorhinal Cortex Identifies TWF2 as a Modulator of Dendritic Spine Length

Proteomic studies using postmortem human brain tissue samples have yielded robust assessments of the aging and neurodegenerative disease(s) proteomes. While these analyses provide lists of molecular alterations in human conditions, like Alzheimer's disease (AD), identifying individual proteins that affect biological processes remains a challenge. To complicate matters, protein targets may be highly understudied and have limited information on their function. To address these hurdles, we sought to establish a blueprint to aid selection and functional validation of targets from proteomic datasets. A cross-platform pipeline was engineered to focus on synaptic processes in the entorhinal cortex (EC) of human patients, including controls, preclinical AD, and AD cases. Label-free quantification mass spectrometry (MS) data (n = 2260 proteins) was generated on synaptosome fractionated tissue from Brodmann area 28 (BA28; n = 58 samples). In parallel, dendritic spine density and morphology was measured in the same individuals. Weighted gene co-expression network analysis was used to construct a network of protein co-expression modules that were correlated with dendritic spine metrics. Module-trait correlations were used to guide unbiased selection of Twinfilin-2 (TWF2), which was the top hub protein of a module that positively correlated with thin spine length. Using CRISPR-dCas9 activation strategies, we demonstrated that boosting endogenous TWF2 protein levels in primary hippocampal neurons increased thin spine length, thus providing experimental validation for the human network analysis. Collectively, this study describes alterations in dendritic spine density and morphology as well as synaptic proteins and phosphorylated tau from the entorhinal cortex of preclinical and advanced stage AD patients.SIGNIFICANCE STATEMENT Proteomic studies can yield vast lists of molecules that are altered under various experimental or disease conditions. Here, we provide a blueprint to facilitate mechanistic validation of protein targets from human brain proteomic datasets. We conducted a proteomic analysis of human entorhinal cortex (EC) samples spanning cognitively normal and Alzheimer's disease (AD) cases with a comparison of dendritic spine morphology in the same samples. Network integration of proteomics with dendritic spine measurements allowed for unbiased discovery of Twinfilin-2 (TWF2) as a regulator of dendritic spine length. A proof-of-concept experiment in cultured neurons demonstrated that altering Twinfilin-2 protein level induced corresponding changes in dendritic spine length, thus providing experimental validation for the computational framework.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Modulating T Cell Phenotype and Function to Treat Hypertension

Hypertension is the leading modifiable risk factor of worldwide morbidity and mortality because of its effects on cardiovascular and renal end-organ damage. Unfortunately, BP control is not sufficient to fully reduce the risks of hypertension, underscoring the need for novel therapies that address end-organ damage in hypertension. Over the past several decades, the link between immune activation and hypertension has been well established, but there are still no therapies for hypertension that specifically target the immune system. In this review, we describe the critical role played by T cells in hypertension and hypertensive end-organ damage and outline potential therapeutic targets to modulate T-cell phenotype and function in hypertension without causing global immunosuppression.

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

Effect of all-but-one conditional analysis for eQTL isolation in peripheral blood

Expression quantitative trait locus detection has become increasingly important for understanding how noncoding variants contribute to disease susceptibility and complex traits. The major challenges in expression quantitative trait locus fine-mapping and causal variant discovery relate to the impact of linkage disequilibrium on signals due to one or multiple functional variants that lie within a credible set. We perform expression quantitative trait locus fine-mapping using the all-but-one approach, conditioning each signal on all others detected in an interval, on the Consortium for the Architecture of Gene Expression cohorts of microarray-based peripheral blood gene expression in 2,138 European-ancestry human adults. We contrast these results with traditional forward stepwise conditional analysis and a Bayesian localization method. All-but-one conditioning significantly modifies effect-size estimates for 51% of 2,351 expression quantitative trait locus peaks, but only modestly affects credible set size and location. On the other hand, both conditioning approaches result in unexpectedly low overlap with Bayesian credible sets, with just 57% peak concordance and between 50% and 70% SNP sharing, leading us to caution against the assumption that any one localization method is superior to another. We also cross reference our results with ATAC-seq data, cell-type-specific expression quantitative trait locus, and activity-by-contact-enhancers, leading to the proposal of a 5-tier approach to further reduce credible set sizes and prioritize likely causal variants for all known inflammatory bowel disease risk loci active in immune cells.

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

BACKGROUND

Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults.

OBJECTIVE

To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome.

METHODS

Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up).

RESULTS

Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities.

CONCLUSIONS

Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

A Single Nucleotide Polymorphism in SH2B3/LNK Promotes Hypertension Development and Renal Damage

BACKGROUND

SH2B3 (SH2B adaptor protein 3) is an adaptor protein that negatively regulates cytokine signaling and cell proliferation. A common missense single nucleotide polymorphism in SH2B3 (rs3184504) results in substitution of tryptophan (Trp) for arginine (Arg) at amino acid 262 and is a top association signal for hypertension in human genome-wide association studies. Whether this variant is causal for hypertension, and if so, the mechanism by which it impacts pathogenesis is unknown.

METHODS

We used CRISPR-Cas9 technology to create mice homozygous for the major (Arg/Arg) and minor (Trp/Trp) alleles of this SH2B3 polymorphism. Mice underwent angiotensin II (Ang II) infusion to evaluate differences in blood pressure (BP) elevation and end-organ damage including albuminuria and renal fibrosis. Cytokine production and Stat4 phosphorylation was also assessed in Arg/Arg and Trp/Trp T cells.

RESULTS

Trp/Trp mice exhibit 10 mmHg higher systolic BP during chronic Ang II infusion compared to Arg/Arg controls. Renal injury and perivascular fibrosis are exacerbated in Trp/Trp mice compared to Arg/Arg controls following Ang II infusion. Renal and ex vivo stimulated splenic CD8+ T cells from Ang II-infused Trp/Trp mice produce significantly more interferon gamma (IFNg) compared to Arg/Arg controls. Interleukin-12 (IL-12)-induced IFNg production is greater in Trp/Trp compared to Arg/Arg CD8+ T cells. In addition, IL-12 enhances Stat4 phosphorylation to a greater degree in Trp/Trp compared to Arg/Arg CD8+ T cells, suggesting that Trp-encoding SH2B3 exhibits less negative regulation of IL-12 signaling to promote IFNg production. Finally, we demonstrated that a multi-SNP model genetically predicting increased SH2B3 expression in lymphocytes is inversely associated with hypertension and hypertensive chronic kidney disease in humans..

CONCLUSIONS

Taken together, these results suggest that the Trp encoding allele of rs3184504 is causal for BP elevation and renal dysfunction, in part through loss of SH2B3-mediated repression of T cell IL-12 signaling leading to enhanced IFNg production.

Isolevuglandins disrupt PU.1-mediated C1q expression and promote autoimmunity and hypertension in systemic lupus erythematosus

We describe a mechanism responsible for systemic lupus erythematosus (SLE). In humans with SLE and in 2 SLE murine models, there was marked enrichment of isolevuglandin-adducted proteins (isoLG adducts) in monocytes and dendritic cells. We found that antibodies formed against isoLG adducts in both SLE-prone mice and humans with SLE. In addition, isoLG ligation of the transcription factor PU.1 at a critical DNA binding site markedly reduced transcription of all C1q subunits. Treatment of SLE-prone mice with the specific isoLG scavenger 2-hydroxybenzylamine (2-HOBA) ameliorated parameters of autoimmunity, including plasma cell expansion, circulating IgG levels, and anti-dsDNA antibody titers. 2-HOBA also lowered blood pressure, attenuated renal injury, and reduced inflammatory gene expression uniquely in C1q-expressing dendritic cells. Thus, isoLG adducts play an essential role in the genesis and maintenance of systemic autoimmunity and hypertension in SLE.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Network Preservation Analysis Reveals Dysregulated Synaptic Modules and Regulatory Hubs Shared Between Alzheimer’s Disease and Temporal Lobe Epilepsy

Background: There is increased prevalence of epilepsy in patients with Alzheimer’s disease (AD). Although shared pathological and clinical features have been identified, the underlying pathophysiology and cause-effect relationships are poorly understood. We aimed to identify commonly dysregulated groups of genes between these two disorders.

Methods: Using publicly available transcriptomic data from hippocampal tissue of patients with temporal lobe epilepsy (TLE), late onset AD and non-AD controls, we constructed gene coexpression networks representing all three states. We then employed network preservation statistics to compare the density and connectivity-based preservation of functional gene modules between TLE, AD and controls and used the difference in significance scores as a surrogate quantifier of module preservation.

Results: The majority (>90%) of functional gene modules were highly preserved between all coexpression networks, however several modules identified in the TLE network showed various degrees of preservation in the AD network compared to that of control. Of note, two synaptic signalling-associated modules and two metabolic modules showed substantial gain of preservation, while myelination and immune system-associated modules showed significant loss of preservation. The genes SCN3B and EPHA4 were identified as central regulatory hubs of the highly preserved synaptic signalling-associated module. GABRB3 and SCN2A were identified as central regulatory hubs of a smaller neurogenesis-associated module, which was enriched for multiple epileptic activity and seizure-related human phenotype ontologies.

Conclusion: We conclude that these hubs and their downstream signalling pathways are common modulators of synaptic activity in the setting of AD and TLE, and may play a critical role in epileptogenesis in AD.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

IL-18 (Interleukin-18) Produced by Renal Tubular Epithelial Cells Promotes Renal Inflammation and Injury During Deoxycorticosterone/Salt-Induced Hypertension in Mice

IL-18 (interleukin-18) is elevated in hypertensive patients, but its contribution to high blood pressure and end-organ damage is unknown. We examined the role of IL-18 in the development of renal inflammation and injury in a mouse model of low-renin hypertension. Hypertension was induced in male C57BL6/J (WT) and IL-18−/− mice by uninephrectomy, deoxycorticosterone acetate (2.4 mg/d, s.c.) and 0.9% drinking saline (1K/DOCA/salt). Normotensive controls received uninephrectomy and placebo (1K/placebo). Blood pressure was measured via tail cuff or radiotelemetry. After 21 days, kidneys were harvested for (immuno)histochemical, quantitative-PCR and flow cytometric analyses of fibrosis, inflammation, and immune cell infiltration. 1K/DOCA/salt-treated WT mice developed hypertension, renal fibrosis, upregulation of proinflammatory genes, and accumulation of CD3+ T cells in the kidneys. They also displayed increased expression of IL-18 on tubular epithelial cells. IL-18−/− mice were profoundly protected from hypertension, renal fibrosis, and inflammation. Bone marrow transplantation between WT and IL-18−/− mice revealed that IL-18-deficiency in non-bone marrow-derived cells alone afforded equivalent protection against hypertension and renal injury as global IL-18 deficiency. IL-18 receptor subunits—interleukin-18 receptor 1 and IL-18R accessory protein—were upregulated in kidneys of 1K/DOCA/salt-treated WT mice and localized to T cells and tubular epithelial cells. T cells from kidneys of 1K/DOCA/salt-treated mice produced interferon-γ upon ex vivo stimulation with IL-18, whereas those from 1K/placebo mice did not. In conclusion, IL-18 production by tubular epithelial cells contributes to elevated blood pressure, renal inflammation, and fibrosis in 1K/DOCA/salt-treated mice, highlighting it as a promising therapeutic target for hypertension and kidney disease.

Therapeutic targeting of inflammation in hypertension: from novel mechanisms to translational perspective

Both animal models and human observational and genetic studies have shown that immune and inflammatory mechanisms play a key role in hypertension and its complications. We review the effects of immunomodulatory interventions on blood pressure, target organ damage, and cardiovascular risk in humans. In experimental and small clinical studies, both non-specific immunomodulatory approaches, such as mycophenolate mofetil and methotrexate, and medications targeting T and B lymphocytes, such as tacrolimus, cyclosporine, everolimus, and rituximab, lower blood pressure and reduce organ damage. Mechanistically targeted immune interventions include isolevuglandin scavengers to prevent neo-antigen formation, co-stimulation blockade (abatacept, belatacept), and anti-cytokine therapies (e.g. secukinumab, tocilizumab, canakinumab, TNF-α inhibitors). In many studies, trial designs have been complicated by a lack of blood pressure-related endpoints, inclusion of largely normotensive study populations, polypharmacy, and established comorbidities. Among a wide range of interventions reviewed, TNF-α inhibitors have provided the most robust evidence of blood pressure lowering. Treatment of periodontitis also appears to deliver non-pharmacological anti-hypertensive effects. Evidence of immunomodulatory drugs influencing hypertension-mediated organ damage are also discussed. The reviewed animal models, observational studies, and trial data in humans, support the therapeutic potential of immune-targeted therapies in blood pressure lowering and in hypertension-mediated organ damage. Targeted studies are now needed to address their effects on blood pressure in hypertensive individuals.

Serum proteomics reveals disorder of lipoprotein metabolism in sepsis

This study illustrated that lipoprotein and lipid metabolism might play a significant role in patients with sepsis and that complement activation was significantly enriched in patients with sepsis-associated encephalopathy.

Sepsis is defined as an organ dysfunction syndrome and it has high mortality worldwide. This study analysed the proteome of serum from patients with sepsis to characterize the pathological mechanism and pathways involved in sepsis. A total of 59 patients with sepsis were enrolled for quantitative proteomic analysis. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network specific to sepsis. Key regulatory modules that were detected were highly correlated with sepsis patients and related to multiple functional groups, including plasma lipoprotein particle remodeling, inflammatory response, and wound healing. Complement activation was significantly associated with sepsis-associated encephalopathy. Triglyceride/cholesterol homeostasis was found to be related to sepsis-associated acute kidney injury. Twelve hub proteins were identified, which might be predictive biomarkers of sepsis. External validation of the hub proteins showed their significantly differential expression in sepsis patients. This study identified that plasma lipoprotein processes played a crucial role in sepsis patients, that complement activation contributed to sepsis-associated encephalopathy, and that triglyceride/cholesterol homeostasis was associated with sepsis-associated acute kidney injury.

Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength

Genome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes SERTAD4 and GLT8D2. We also perform GWAS in the DO for a wide-range of bone traits and identify Qsox1 as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics.

Osteoporosis GWAS faces two challenges, causal gene discovery and a lack of phenotypic diversity. Here, the authors use the Diversity Outbred mouse population to inform human GWAS using networks and map genetic loci for 55 bone traits, identifying new potential bone strength genes.

EyeDiseases: an integrated resource for dedicating to genetic variants, gene expression and epigenetic factors of human eye diseases

Eye diseases are remarkably common and encompass a large and diverse range of morbidities that affect different components of the visual system and visual function. With advances in omics technology of eye disorders, genome-scale datasets have been rapidly accumulated in genetics and epigenetics field. However, the efficient collection and comprehensive analysis of different kinds of omics data are lacking. Herein, we developed EyeDiseases (https://eyediseases.bio-data.cn/), the first database for multi-omics data integration and interpretation of human eyes diseases. It contains 1344 disease-associated genes with genetic variation, 1774 transcription files of bulk cell expression and single-cell RNA-seq, 105 epigenomics data across 185 kinds of human eye diseases. Using EyeDiseases, we investigated SARS-CoV-2 potential tropism in eye infection and found that the SARS-CoV-2 entry factors, ACE2 and TMPRSS2 are highly correlated with cornea and keratoconus, suggest that ocular surface cells are susceptible to infection by SARS-CoV-2. Additionally, integrating analysis of Age-related macular degeneration (AMD) GWAS loci and co-expression data revealed 9 associated genes involved in HIF-1 signaling pathway and voltage-gate potassium channel complex. The EyeDiseases provides a valuable resource for accelerating the discovery and validation of candidate loci and genes contributed to the molecular diagnosis and therapeutic vulnerabilities with various eyes diseases.

Hypertension: Do Inflammation and Immunity Hold the Key to Solving this Epidemic?

Elevated cardiovascular risk including stroke, heart failure, and heart attack is present even after normalization of blood pressure in patients with hypertension. Underlying immune cell activation is a likely culprit. Although immune cells are important for protection against invading pathogens, their chronic overactivation may lead to tissue damage and high blood pressure. Triggers that may initiate immune activation include viral infections, autoimmunity, and lifestyle factors such as excess dietary salt. These conditions activate the immune system either directly or through their impact on the gut microbiome, which ultimately produces chronic inflammation and hypertension. T cells are central to the immune responses contributing to hypertension. They are activated in part by binding specific antigens that are presented in major histocompatibility complex molecules on professional antigen-presenting cells, and they generate repertoires of rearranged T-cell receptors. Activated T cells infiltrate tissues and produce cytokines including interleukin 17A, which promote renal and vascular dysfunction and end-organ damage leading to hypertension. In this comprehensive review, we highlight environmental, genetic, and microbial associated mechanisms contributing to both innate and adaptive immune cell activation leading to hypertension. Targeting the underlying chronic immune cell activation in hypertension has the potential to mitigate the excess cardiovascular risk associated with this common and deadly disease.

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.

A platform for experimental precision medicine: The extended BXD mouse family

The challenge of precision medicine is to model complex interactions among DNA variants, phenotypes, development, environments, and treatments. We address this challenge by expanding the BXD family of mice to 140 fully isogenic strains, creating a uniquely powerful model for precision medicine. This family segregates for 6 million common DNA variants-a level that exceeds many human populations. Because each member can be replicated, heritable traits can be mapped with high power and precision. Current BXD phenomes are unsurpassed in coverage and include much omics data and thousands of quantitative traits. BXDs can be extended by a single-generation cross to as many as 19,460 isogenic F1 progeny, and this extended BXD family is an effective platform for testing causal modeling and for predictive validation. BXDs are a unique core resource for the field of experimental precision medicine.

Immune Cell Associations with Cancer Risk

Proper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk.

High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways

Supplemental Digital Content is available in the text.

Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease.

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.

Shared Causal Paths underlying Alzheimer's dementia and Type 2 Diabetes

Although Alzheimer's disease (AD) is a central nervous system disease and type 2 diabetes MELLITUS (T2DM) is a metabolic disorder, an increasing number of genetic epidemiological studies show clear link between AD and T2DM. The current approach to uncovering the shared pathways between AD and T2DM involves association analysis; however such analyses lack power to discover the mechanisms of the diseases. As an alternative, we developed novel causal inference methods for genetic studies of AD and T2DM and pipelines for systematic multi-omic casual analysis to infer multilevel omics causal networks for the discovery of common paths from genetic variants to AD and T2DM. The proposed pipelines were applied to 448 individuals from the ROSMAP Project. We identified 13 shared causal genes, 16 shared causal pathways between AD and T2DM, and 754 gene expression and 101 gene methylation nodes that were connected to both AD and T2DM in multi-omics causal networks.

Immune mechanisms of hypertension

Hypertension affects 30% of adults and is the leading risk factor for heart attack and stroke. Traditionally, hypertension has been regarded as a disorder of two systems that are involved in the regulation of salt-water balance and cardiovascular function: the renin-angiotensin-aldosterone system (RAAS) and the sympathetic nervous system (SNS). However, current treatments that aim to limit the influence of the RAAS or SNS on blood pressure fail in ~40% of cases, which suggests that other mechanisms must be involved. This Review summarizes the clinical and experimental evidence supporting a contribution of immune mechanisms to the development of hypertension. In this context, we highlight the immune cell subsets that are postulated to either promote or protect against hypertension through modulation of cardiac output and/or peripheral vascular resistance. We conclude with an appraisal of knowledge gaps still to be addressed before immunomodulatory therapies might be applied to at least a subset of patients with hypertension.

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.

Evidence for a Causal Role of the SH2B3-β2M Axis in Blood Pressure Regulation

Genetic variants at SH2B3 are associated with blood pressure and circulating β₂M (β-2 microglobulin), a well-characterized kidney filtration biomarker. We hypothesize that circulating β₂M is an independent risk predictor of hypertension and may causally contribute to its development. The study sample consisted of 7 065 Framingham Heart Study participants with measurements of plasma β₂M. Generalized estimating equations were used to test the association of β₂M with prevalent and new-onset hypertension. There were 2 145 (30%) cases of prevalent hypertension at baseline and 886 (21%) cases of incident hypertension during 6 years of follow-up. A 1-SD increase in baseline plasma β₂M was associated with a greater risk of prevalent (odds ratio 1.14, 95% CI 1.05-1.24) and new-onset (odds ratio 1.18, 95% CI 1.07-1.32) hypertension. Individuals within the top β₂M quartile had a greater risk than the bottom quartile for prevalent (odds ratio 1.29, 95% CI 1.05-1.57) and new-onset (odds ratio 1.59, 95% CI 1.20-2.11) hypertension. These associations remained essentially unchanged in analyses restricted to participants free of albuminuria and chronic kidney disease. Mendelian randomization demonstrated that lower SH2B3 expression is causal for increased circulating β₂M levels, and in a hypertensive mouse model, knockout of Sh2b3 increased β ₂ M gene expression. In a community-based study of healthy individuals, higher plasma β₂M levels are associated with increased risk of prevalent and incident hypertension independent of chronic kidney disease status. Overlapping genetic signals for hypertension and β₂M, in conjunction with mouse knockout experiments, suggest that the SH2B3-β₂M axis plays a causal role in hypertension.

Systems genetics applications in metabolism research

The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.

Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease

Identifying methylation quantitative trait loci (meQTLs) and integrating them with disease-associated variants from genome-wide association studies (GWAS) may illuminate functional mechanisms underlying genetic variant-disease associations. Here, we perform GWAS of >415 thousand CpG methylation sites in whole blood from 4170 individuals and map 4.7 million cis- and 630 thousand trans-meQTL variants targeting >120 thousand CpGs. Independent replication is performed in 1347 participants from two studies. By linking cis-meQTL variants with GWAS results for cardiovascular disease (CVD) traits, we identify 92 putatively causal CpGs for CVD traits by Mendelian randomization analysis. Further integrating gene expression data reveals evidence of cis CpG-transcript pairs causally linked to CVD. In addition, we identify 22 trans-meQTL hotspots each targeting more than 30 CpGs and find that trans-meQTL hotspots appear to act in cis on expression of nearby transcriptional regulatory genes. Our findings provide a powerful meQTL resource and shed light on DNA methylation involvement in human diseases.

A system view and analysis of essential hypertension

OBJECTIVES

The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution.

METHODS

Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools.

RESULTS

We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network.

CONCLUSION

We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome.

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, "Wnt signaling" and "mammalian target of rapamycin (mTOR) signaling" pathways were found to be unique to SBP, while "cytokine network", and "tryptophan catabolism" to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases.

Benefits and Challenges of Rare Genetic Variation in Alzheimer's Disease

Purpose of Review

It is well established that sporadic Alzheimer's disease (AD) is polygenic with common and rare genetic variation alongside environmental factors contributing to disease. Here, we review our current understanding of the genetic architecture of disease, paying specific attention to rare susceptibility variants, and explore some of the limitations in rare variant detection and analysis.

Recent Findings

Rare variation has been shown to robustly associate with disease. These include potentially damaging and loss of function mutations that are easily modelled in silico, in vitro and in vivo, and represent potentially druggable targets. A number of risk genes, including TREM2, SORL1 and ABCA7 show multiple independent associations suggesting that they may influence disease via multiple mechanisms. With transcriptional regulation, inflammatory response and modification of protein production suggested to be of primary importance.

Summary

We are at the beginning of our journey of rare variant detection in AD. Whole exome sequencing has been the predominant technology of choice. While fruitful, this has introduced a number of challenges with regard to data integration. Ultimately the future of disease-associated rare variant identification lies in whole genome sequencing projects that will allow the testing of the full range of genomic variation.

Mouse Models and Online Resources for Functional Analysis of Osteoporosis Genome-Wide Association Studies

Osteoporosis is a complex genetic disease in which the number of loci associated with the bone mineral density, a clinical risk factor for fracture, has increased at an exponential rate in the last decade. The identification of the causative variants and candidate genes underlying these loci has not been able to keep pace with the rate of locus discovery. A large number of tools and data resources have been built around the use of the mouse as model of human genetic disease. Herein, we describe resources available for functional validation of human Genome Wide Association Study (GWAS) loci using mouse models. We specifically focus on large-scale phenotyping efforts focused on bone relevant phenotypes and repositories of genotype-phenotype data that exist for transgenic and mutant mice, which can be readily mined as a first step toward more targeted efforts designed to deeply characterize the role of a gene in bone biology.

Integrated systems analysis reveals conserved gene networks underlying response to spinal cord injury

Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides systems-level view of the coordinated molecular processes activated in response to SCI.

Integrating genetic, transcriptional, and biological information provides insights into obesity

Objective:

Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks.

Subjects:

We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4,303), as well as implemented an integrative analysis of these data and existing biological information.

Results:

Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist-hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair and degradation of regulatory proteins and are cancer related.

Conclusion:

Our integrative analysis of genetic, transcriptional and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.

Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes global epidemics of debilitating disease worldwide. To gain functional insight into the host cellular genes required for virus infection, we performed whole-blood RNA-seq, 37-plex mass cytometry of peripheral blood mononuclear cells (PBMCs), and serum cytokine measurements of acute- and convalescent-phase samples obtained from 42 children naturally infected with CHIKV Semi-supervised classification and clustering of single-cell events into 57 sub-communities of canonical leukocyte phenotypes revealed a monocyte-driven response to acute infection, with the greatest expansions in "intermediate" CD14++CD16+ monocytes and an activated subpopulation of CD14+ monocytes. Increases in acute-phase CHIKV envelope protein E2 expression were highest for monocytes and dendritic cells. Serum cytokine measurements confirmed significant acute-phase upregulation of monocyte chemoattractants. Distinct transcriptomic signatures were associated with infection timepoint, as well as convalescent-phase anti-CHIKV antibody titer, acute-phase viremia, and symptom severity. We present a multiscale network that summarizes all observed modulations across cellular and transcriptomic levels and their interactions with clinical outcomes, providing a uniquely global view of the biomolecular landscape of human CHIKV infection.

Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our “second genome”—the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine.

New perspectives: systems medicine in cardiovascular disease

Background

Cardiovascular diseases (CVD) represent one of the most important causes of morbidity and mortality worldwide. Innovative approaches to increase the understanding of the underpinnings of CVD promise to enhance CVD risk assessment and might pave the way to tailored therapies. Within the last years, systems medicine has emerged as a novel tool to study the genetic, molecular and physiological interactions.

Conclusion

In this review, we provide an overview of the current molecular-experimental, epidemiological and bioinformatical tools applied in systems medicine in the cardiovascular field. We will discuss the status and challenges in implementing interdisciplinary systems medicine approaches in CVD.

KDiamend: a package for detecting key drivers in a molecular ecological network of disease

Background

Microbial abundance profiles are applied widely to understand diseases from the aspect of microbial communities. By investigating the abundance associations of species or genes, we can construct molecular ecological networks (MENs). The MENs are often constructed by calculating the Pearson correlation coefficient (PCC) between genes. In this work, we also applied multimodal mutual information (MMI) to construct MENs. The members which drive the concerned MENs are referred to as key drivers.

Results

We proposed a novel method to detect the key drivers. First, we partitioned the MEN into subnetworks. Then we identified the most pertinent subnetworks to the disease by measuring the correlation between the abundance pattern and the delegated phenotype—the variable representing the disease phenotypes. Last, for each identified subnetwork, we detected the key driver by PageRank. We developed a package named KDiamend and applied it to the gut and oral microbial data to detect key drivers for Type 2 diabetes (T2D) and Rheumatoid Arthritis (RA). We detected six T2D-relevant subnetworks and three key drivers of them are related to the carbohydrate metabolic process. In addition, we detected nine subnetworks related to RA, a disease caused by compromised immune systems. The extracted subnetworks include InterPro matches (IPRs) concerned with immunoglobulin, Sporulation, biofilm, Flaviviruses, bacteriophage, etc., while the development of biofilms is regarded as one of the drivers of persistent infections.

Conclusion

KDiamend is feasible to detect key drivers and offers insights to uncover the development of diseases. The package is freely available at http://www.deepomics.org/pipelines/3DCD6955FEF2E64A/.