Multi-omics integration reveals molecular networks and regulators of psoriasis

Mass-spectrometric analysis of APOB polymorphism rs1042031 (G/T) and its influence on serum proteome of coronary artery disease patients: genetic-derived proteomics consequences

Genetic polymorphisms of apolipoprotein B gene (APOB) may result into serum proteomic perturbance in Coronary Artery Disease (CAD). The current case-control cohort of Pakistani subjects was designed to analyze the genetic influence of APOB rs1042031, (G/T) genotype on serum proteome. Subjects were categorized into two groups: CAD patients (n = 480) and healthy individuals (n = 220). For genotyping, tetra ARMS-PCR was carried out and validated through sequencing, whereas LC/MS-based proteomic analysis of serum samples was performed through label-free quantification. In initial step of genotyping, the frequencies of each genotype GG, GT, and TT were 70%, 27%, and 30% in CAD patients, while in control group, the subjects were 52%, 43%, and 5%, respectively, in CAD patients. The genotypic frequencies in patients vs. control groups found significantly different (p = 0.004), and a strong association of dominant alleles GG with the CAD was observed in both dominant (OR: 2.4 (1.71-3.34), p = 0.001) and allelic genetic models (OR: 2.0 (1.45-2.86), p = 0.001). In second step of label-free quantitation, a total of 40 significant proteins were found with altered expression in CAD patients. The enriched Gene Ontology (GO) terms of molecular functions and pathways of these protein showed upregulated pathways as follows: chylomicron remodeling and assembly, complement cascade activation, plasma lipoprotein assembly, apolipoprotein-A receptor binding, and metabolism of fat-soluble vitamins in G allele carrier of rs1042031 (G > T) vs. mutant T-allele carriers. This study provides better understanding of CAD pathobiology by proteogenomics of APOB. It evidences the influence of APOB rs1042031-dominant (GG) genotype with CAD patients.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Disentangling the complexity of psoriasis in the post-genome-wide association era

In recent years, genome-wide association studies (GWAS) have been instrumental in unraveling the genetic architecture of complex diseases, including psoriasis. The application of large-scale GWA studies in psoriasis has illustrated several associated loci that participate in the cutaneous inflammation, however explaining a fraction of the disease heritability. With the advent of high-throughput sequencing technologies and functional genomics approaches, the post-GWAS era aims to unravel the functional mechanisms underlying the inter-individual variability in psoriasis patients. In this review, we present the key advances of psoriasis GWAS in under-represented populations, rare, non-coding and structural variants and epistatic phenomena that orchestrate the interplay between different cell types. We further review the gene-gene and gene-environment interactions contributing to the disease predisposition and development of comorbidities through Mendelian randomization studies and pleiotropic effects of psoriasis-associated loci. We finally examine the holistic approaches conducted in psoriasis through system genetics and state-of-the-art transcriptomic analyses, discussing their potential implication in the expanding field of precision medicine and characterization of comorbidities.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

Psoriasis immunometabolism: progress on metabolic biomarkers and targeted therapy

Psoriasis is a common inflammatory disease that affects mainly the skin. However, the moderate to severe forms have been associated with several comorbidities, such as psoriatic arthritis, Crohn's disease, metabolic syndrome and cardiovascular disease. Keratinocytes and T helper cells are the dominant cell types involved in psoriasis development via a complex crosstalk between epithelial cells, peripheral immune cells and immune cells residing in the skin. Immunometabolism has emerged as a potent mechanism elucidating the aetiopathogenesis of psoriasis, offering novel specific targets to diagnose and treat psoriasis early. The present article discusses the metabolic reprogramming of activated T cells, tissue-resident memory T cells and keratinocytes in psoriatic skin, presenting associated metabolic biomarkers and therapeutic targets. In psoriatic phenotype, keratinocytes and activated T cells are glycolysis dependent and are characterized by disruptions in the TCA cycle, the amino acid metabolism and the fatty acid metabolism. Upregulation of the mammalian target of rapamycin (mTOR) results in hyperproliferation and cytokine secretion by immune cells and keratinocytes. Metabolic reprogramming through the inhibition of affected metabolic pathways and the dietary restoration of metabolic imbalances may thus present a potent therapeutic opportunity to achieve long-term management of psoriasis and improved quality of life with minimum adverse effects.

Multi-Omics Research Strategies for Psoriasis and Atopic Dermatitis

Psoriasis and atopic dermatitis (AD) are multifactorial and heterogeneous inflammatory skin diseases, while years of research have yielded no cure, and the costs associated with caring for people suffering from psoriasis and AD are a huge burden on society. Integrating several omics datasets will enable coordinate-based simultaneous analysis of hundreds of genes, RNAs, chromatins, proteins, and metabolites in particular cells, revealing networks of links between various molecular levels. In this review, we discuss the latest developments in the fields of genomes, transcriptomics, proteomics, and metabolomics and discuss how they were used to identify biomarkers and understand the main pathogenic mechanisms underlying these diseases. Finally, we outline strategies for achieving multi-omics integration and how integrative omics and systems biology can advance our knowledge of, and ability to treat, psoriasis and AD.

Identification immune response genes in psoriasis after treatment with secukinumab

BACKGROUND

Secukinumab is a fully human IgG1κ MoAb that selectively binds to IL-17A with high affinity, and it has been proven effective for the treatment of psoriasis. However, the immune response pathways and mechanisms during the treatment are still masked. Therefore, the current study was designed to investigate the potential immune response genes via bioinformatics approaches.

METHODS

Gene expression data of severe plaque-type psoriasis was retrieved from the GEO database. Quantification of immune infiltration by ssGSEA and identification of differentially infiltrated immune cells were conducted to validate the treatment effect of secukinumab. After data processing, differentially expressed genes were identified between the treatment and untreated group. TC-seq was employed to analyze the trend of gene expression and clustering analysis. IL-17 therapeutic immune response genes were selected by taking the intersection of the genes inside the key cluster set and the MAD3-PSO geneset. Based on these therapeutic response genes, protein-protein interaction networks were built for key hub gene selection. These hub genes would work as potential immune response genes, and be validated via an external dataset.

RESULTS

Enrichment scores calculated by ssGSEA illustrated that the immune infiltration level of T cells had a strong difference before and after medication, which validated the treatment effect of Secukinumab. 1525 genes that have significantly different expression patterns before and after treatment were extracted for further analysis, and the enrichment result shows that these genes have the function related to epidermal development, differentiation, and keratinocytes differentiation. After overlapping candidate genes with MAD3-PSO gene set, 695 genes were defined as anti-IL7A treatment immune response genes, which were mainly enriched in receptor signaling and IL-17 signaling pathways. Hub gene were pinpointed from the PPI network constructed by anti-IL7A treatment immune response genes, their expression pattern fits TC-seq gene expression pattern.

CONCLUSION

Our study revealed the potential anti-IL7A treatment immune response genes, and the central hub genes, which may act critical roles in Secukinumab, induced immune response. This would open up a novel and effective avenue for the treatment of psoriasis.

Biological skin regeneration using epigenetic targets

Epigenetics targets are the newest branches for building a novel platform of drugs for preventive and regenerative skin health care. Epigenetic regions [vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), transforming growth factor beta (TGFβ), DNA methyltransferases (DNMTs), histone deacetylase 1/2 (HDAC1/2), and miRNA) are innovative druggable targets. As we discuss here, a series of epigenetic-based small molecules are undergoing both clinical and preclinical trials for skin regeneration. Epigenetic writers, eraser targets, and epigenetic readers will become the key therapeutic windows for skin regenerative in the near future.

Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data

It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes were gleaned using the protein-protein interaction Network in the STRING database followed by CytoHubba analysis in Cytoscope. Gene ontology, annotation and pathway enrichment was completed using ShinyGO, David tool and QTL analysis. These analysis shows that 21 genes are linked with the secretion of milk.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Effect of APOB polymorphism rs562338 (G/A) on serum proteome of coronary artery disease patients: a "proteogenomic" approach

In the current study, APOB (rs1052031) genotype-guided proteomic analysis was performed in a cohort of Pakistani population. A total of 700 study subjects, including Coronary Artery Disease (CAD) patients (n = 480) and healthy individuals (n = 220) as a control group were included in the study. Genotyping was carried out by using tetra primer-amplification refractory mutation system-based polymerase chain reaction (T-ARMS-PCR) whereas mass spectrometry (Orbitrap MS) was used for label free quantification of serum samples. Genotypic frequency of GG genotype was found to be 90.1%, while 6.4% was for GA genotype and 3.5% was for AA genotypes in CAD patients. In the control group, 87.2% healthy subjects were found to have GG genotype, 11.8% had GA genotype, and 0.9% were with AA genotypes. Significant (p = 0.007) difference was observed between genotypic frequencies in the patients and the control group. The rare allele AA was found to be strongly associated with the CAD [OR: 4 (1.9-16.7)], as compared to the control group in recessive genetic model (p = 0.04). Using label free proteomics, altered expression of 60 significant proteins was observed. Enrichment analysis of these protein showed higher number of up-regulated pathways, including phosphatidylcholine-sterol O-acyltransferase activator activity, cholesterol transfer activity, and sterol transfer activity in AA genotype of rs562338 (G>A) as compared to the wild type GG genotype. This study provides a deeper insight into CAD pathobiology with reference to proteogenomics, and proving this approach as a good platform for identifying the novel proteins and signaling pathways in relation to cardiovascular diseases.

Analytical approaches to assess metabolic changes in psoriasis

Psoriasis is one of the most common human skin diseases, although its development is not limited to one tissue, but is associated with autoimmune reactions throughout the body. Overproduction of pro-inflammatory cytokines and growth factors systemically stimulates the proliferation of skin cells, which manifests as excessive exfoliation of the epidermis, and/or arthritis, as well as other comorbidities such as insulin resistance, metabolic syndrome, hypertension, and depression. Thus, there is a great need for a thorough analysis of the pathophysiology of psoriatic patients, including classical methods, such as spectrophotometry, chromatography, or Western blot, and also novel omics approaches such as lipidomics and proteomics. Moreover, the extensive pathophysiology forces increased research examining biological changes in both skin cells, and systemically. A wide range of techniques involved in lipidomic research based on a combination of mass spectrometry and different types of chromatography (RP-LC-QTOF-MS/MS, HILIC-QTOF-MS/MS or RP-LC-QTRAP-MS/MS), have allowed comprehensive assessment of lipid modification in psoriatic skin and provided new insight into the role of lipids and their mechanism of action in psoriasis. Moreover, proteomic analysis using gel-nanoLC-OrbiTrap-MS/MS, as well as MALDI-TOF/TOF techniques facilitates the description of panels of enzymes involved in lipidome modifications, and the response of the endocannabinoid system to metabolic changes. Psoriasis is known to alter the expression of proteins that are involved in the inflammatory and antioxidant response, as well as protein biosynthesis, degradation, as well as cell proliferation and apoptosis. Knowledge of changes in the lipidomic and proteomic profile will not only allow the understanding of psoriasis pathophysiology, but also facilitate proper and early diagnosis and effective pharmacotherapy.

Multiscale Biology of Cardiovascular Risk in Psoriasis: Protocol for a Case-Control Study

Background

Patients with psoriasis have increased risk of cardiovascular disease (CVD) independent of traditional risk factors. The molecular mechanisms underlying the psoriasis-CVD connection are not fully understood. Advances in high-throughput molecular profiling technologies and computational analysis techniques offer new opportunities to improve the understanding of disease connections.

Objective

We aim to characterize the complexity of cardiovascular risk in patients with psoriasis by integrating deep phenotypic data with systems biology techniques to perform comprehensive multiomic analyses and construct network models of the two interacting diseases.

Methods

The study aims to include 120 adult patients with psoriasis (60 with prior atherosclerotic CVD and 60 without CVD). Half of the patients are already receiving systemic antipsoriatic treatment. All patients complete a questionnaire, and a medical interview is conducted to collect medical history and information on, for example, socioeconomics, mental health, diet, and physical exercise. Participants are examined clinically with assessment of the Psoriasis Area and Severity Index and undergo imaging by transthoracic echocardiography, ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT), and carotid artery ultrasonography. Skin swabs are collected for analysis of microbiome metagenomics; skin biopsies and blood samples are collected for transcriptomic profiling by RNA sequencing; skin biopsies are collected for immunohistochemistry; plasma samples are collected for analyses of proteomics, lipidomics, and metabolomics; blood samples are collected for high-dimensional mass cytometry; and feces samples are collected for gut microbiome metagenomics. Bioinformatics and systems biology techniques are utilized to analyze the multiomic data and to integrate data into a network model of CVD in patients with psoriasis.

Results

Recruitment was completed in September 2020. Preliminary results of ¹⁸F-FDG-PET/CT data have recently been published, where vascular inflammation was reduced in the ascending aorta (P=.046) and aortic arch (P=.04) in patients treated with statins and was positively associated with inflammation in the visceral adipose tissue (P<.001), subcutaneous adipose tissue (P=.007), pericardial adipose tissue (P<.001), spleen (P=.001), and bone marrow (P<.001).

Conclusions

This systems biology approach with integration of multiomics and clinical data in patients with psoriasis with or without CVD is likely to provide novel insights into the biological mechanisms underlying these diseases and their interplay that can impact future treatment.

International Registered Report Identifier (IRRID)

DERR1-10.2196/28669

Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis

As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon γ, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene–gene interaction networks enriched by those topmost pathways, we identified KDs—both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)—in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

Network analysis of potential risk genes for psoriasis

Background

Psoriasis is a complex chronic inflammatory skin disease. The aim of this study was to analyze potential risk genes and molecular mechanisms associated with psoriasis.

Methods

GSE54456, GSE114286, and GSE121212 were collected from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between psoriasis and controls were screened respectively in three datasets and common DEGs were obtained. The biological role of common DEGs were identified by enrichment analysis. Hub genes were identified using protein–protein interaction (PPI) networks and their risk for psoriasis was evaluated through logistic regression analysis. Moreover, differentially methylated positions (DMPs) between psoriasis and controls were obtained in the GSE115797 dataset. Methylation markers were identified after comparison with the common genes.

Results

A total of 118 common DEGs were identified, which were mainly involved in keratinocyte differentiation and IL-17 signaling pathway. Through PPI network, we identified top 10 degrees as hub genes. Among them, high expression of CXCL9 and SPRR1B may be risk factors for psoriasis. In addition, we selected 10 methylation-modified genes with the higher area under receiver operating characteristic curve (AUC) value as methylation markers. Nomogram showed that TGM6 and S100A9 may be associated with an increased risk of psoriasis.

Conclusion

This suggests that immune and inflammatory responses are active in keratinocytes of psoriatic skin. CXCL9, SPRR1B, TGM6 and S100A9 may be potential targets for the diagnosis and treatment of psoriasis.

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Multiomics Integration in Skin Diseases with Alterations in Notch Signaling Pathway: PlatOMICs Phase 1 Deployment

The high volume of information produced in the age of omics was and still is an important step to understanding several pathological processes, providing the enlightenment of complex molecular networks and the identification of molecular targets associated with many diseases. Despite these remarkable scientific advances, the majority of the results are disconnected and divergent, making their use limited. Skin diseases with alterations in the Notch signaling pathway were extensively studied during the omics era. In the GWAS Catalog, considering only studies on genomics association (GWAS), several works were deposited, some of which with divergent results. In addition, there are thousands of scientific articles available about these skin diseases. In our study, we focused our attention on skin diseases characterized by the impairment of Notch signaling, this pathway being of pivotal importance in the context of epithelial disorders. We considered the pathologies of five human skin diseases, Hidradenitis Suppurativa, Dowling Degos Disease, Adams-Oliver Syndrome, Psoriasis, and Atopic Dermatitis, in which the molecular alterations in the Notch signaling pathway have been reported. To this end, we started developing a new multiomics platform, PlatOMICs, to integrate and re-analyze omics information, searching for the molecular interactions involved in the pathogenesis of skin diseases with alterations in the Notch signaling pathway.

Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Research progress and perspective in metabolism and metabolomics of psoriasis

ABSTRACT

Psoriasis is considered a systemic disease associated with metabolic abnormalities, and it is important to understand the mechanisms by which metabolism affects pathophysiological processes both holistically and systematically. Metabolites are closely related to disease phenotypes, especially in systemic diseases under multifactorial modulation. The emergence of metabolomics has provided information regarding metabolite changes in lesions and circulation and deepened our understanding of the association between metabolic reprogramming and psoriasis. Metabolomics has great potential for the development of effective biomarkers for clinical diagnosis, therapeutic monitoring, prediction of the efficacy of psoriasis management, and further discovery of new metabolism-based therapeutic targets.

Anti-tumor necrosis factor drug responses and skin-blood DNA methylation age: Relationships in moderate-to-severe psoriasis

Studies have examined the utility of DNA methylation as a biomarker of psoriasis treatment responses, but investigations of treatment responses with Skin-Blood DNA methylation age (SkinBloodAge)-a methylation-based measure of health designed using skin tissues-are lacking. Using a HumanMethylation450 BeadChip blood DNA methylation data set from 70 white patients who presented with moderate-to-severe plaque psoriasis and were treated with anti-tumor necrosis factor (TNF) agents in Madrid, Spain, we examined the cross-sectional relationships of SkinBloodAge with anti-TNF treatment responses. Partial responders had a 7.2-year higher mean SkinBloodAge than excellent responders (P = .03). In linear regression models adjusted for chronological age, sex and anti-TNF agents - on average - partial responders had a 2.65-year higher SkinBloodAge than excellent responders (95%CI: 0.44, 4.86, P = .02). This relationship was attenuated in a sensitivity analysis adjusting for white blood cells including known T-cell mediators of psoriasis pathophysiology (β = 1.91-years, 95%CI: -0.50, 4.32, P = .12). Overall, our study suggests that partial responders to anti-TNF therapy have higher SkinBloodAges when compared to excellent responders. Although these findings still need to be confirmed more broadly, they further suggest that SkinBloodAge may be a useful treatment response biomarker that can be incorporated with other blood tests before anti-TNF therapy initiation in moderate-to-severe psoriasis patients.

Multitissue Multiomics Systems Biology to Dissect Complex Diseases

Most complex diseases involve genetic and environmental risk factors, engage multiple cells and tissues, and follow a polygenic or omnigenic model depicting numerous genes contributing to pathophysiology. These multidimensional complexities pose challenges to traditional approaches that examine individual factors. In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate within- and cross-tissue molecular networks underlying gene-by-environment interactions and contributing to complex diseases. The power of systems biology in retrieving novel insights and formulating new hypotheses has been well documented. However, the field faces various challenges that call for debate and action. In this opinion article, I discuss the concepts, benefits, current state, and challenges of the field and point to the next steps toward network-based systems medicine.

Re-evaluating the relationship between missing heritability and the microbiome

Human genome-wide association studies (GWASs) have recurrently estimated lower heritability estimates than familial studies. Many explanations have been suggested to explain these lower estimates, including that a substantial proportion of genetic variation and gene-by-environment interactions are unmeasured in typical GWASs. The human microbiome is potentially related to both of these explanations, but it has been more commonly considered as a source of unmeasured genetic variation. In particular, it has recently been argued that the genetic variation within the human microbiome should be included when estimating trait heritability. We outline issues with this argument, which in its strictest form depends on the holobiont model of human-microbiome interactions. Instead, we argue that the microbiome could be leveraged to help control for environmental variation across a population, although that remains to be determined. We discuss potential approaches that could be explored to determine whether integrating microbiome sequencing data into GWASs is useful. Video abstract.

Environmental temperature and human epigenetic modifications: A systematic review

The knowledge about the effects of environmental temperature on human epigenome is a potential key to understand the health impacts of temperature and to guide acclimation under climate change. We performed a systematic review on the epidemiological studies that have evaluated the association between environmental temperature and human epigenetic modifications. We identified seven original articles on this topic published between 2009 and 2019, including six cohort studies and one cross-sectional study. They focused on DNA methylation in elderly people (blood sample) or infants (placenta sample), with sample size ranging from 306 to 1798. These studies were conducted in relatively low temperature setting (median/mean temperature: 0.8-13 °C), and linear models were used to evaluate temperature-DNA methylation association over short period (≤28 days). It has been reported that short-term ambient temperature could affect global human DNA methylation. A total of 15 candidate genes (ICAM-1, CRAT, F3, TLR-2, iNOS, ZKSCAN4, ZNF227, ZNF595, ZNF597, ZNF668, CACNA1H, AIRE, MYEOV2, NKX1-2 and CCDC15) with methylation status associated with ambient temperature have been identified. DNA methylation on ZKSCAN4, ICAM-1 partly mediated the effect of short-term cold temperature on high blood pressure and ICAM-1 protein (related to cardiovascular events), respectively. In summary, epidemiological evidence about the impacts of environment temperature on human epigenetics remains scarce and limited to short-term linear effect of cold temperature on DNA methylation in elderly people and infants. More studies are needed to broaden our understanding of temperature related epigenetic changes, especially under a changing climate.

The Brain-Skin Connection and the Pathogenesis of Psoriasis: A Review with a Focus on the Serotonergic System

Psoriasis is a common non-communicable chronic immune-mediated skin disease, affecting approximately 125 million people in the world. Its pathogenesis results from a combination of genetic and environmental factors. The pathogenesis of psoriasis seems to be driven by the interaction between innate immune cells, adaptive immune cells and keratinocytes, in a process mediated by cytokines (including interleukins (IL)-6, IL-17 and IL-22, interferon and tumor necrosis factor) and other signaling molecules. This leads to an inflammatory process with increased proliferation of epidermal cells, neo-angiogenesis and infiltration of dendritic cells in the skin. Dysfunctional de novo glucocorticoid synthesis in psoriatic keratinocytes and the skin microbiome have also been suggested as mediators in the pathogenesis of this disease. To understand psoriasis, it is essential to comprehend the processes underlying the skin immunity and neuroendocrinology. This review paper focuses on the skin as a neuroendocrine organ and summarizes what is known about the skin immune system, the brain-skin connection and the role played by the serotonergic system in skin. Subsequently, the alterations of neuroimmune processes and of the serotonergic system in psoriatic skin are discussed, as well as, briefly, the genetic basis of psoriasis.

Review-Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis

During the last decades, high-throughput assessment of gene expression in patient tissues using microarray technology or RNA-Seq took center stage in clinical research. Insights into the diversity and frequency of transcripts in healthy and diseased conditions provide valuable information on the cellular status in the respective tissues. Growing with the technique, the bioinformatic analysis toolkit reveals biologically relevant pathways which assist in understanding basic pathophysiological mechanisms. Conventional classification systems of inflammatory skin diseases rely on descriptive assessments by pathologists. In contrast to this, molecular profiling may uncover previously unknown disease classifying features. Thereby, treatments and prognostics of patients may be improved. Furthermore, disease models in basic research in comparison to the human disease can be directly validated. The aim of this article is not only to provide the reader with information on the opportunities of these techniques, but to outline potential pitfalls and technical limitations as well. Major published findings are briefly discussed to provide a broad overview on the current findings in transcriptomics in inflammatory skin diseases.

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.

Evaluation of expression pattern of selected genes associated with IL12/23 signaling paths in psoriatic patients during cyclosporine A therapy

Molecular analysis is key to a better understanding of drug resistance during therapy. The aim of this study was to evaluate changes in the expression of tumor necrosis factor α (TNF-α), interleukin (IL)-IL12A, IL12B, IL23A, interferon gamma (IFN-γ) in psoriatic patients during 84 days of treatment and TNF-α on the protein level. The study group consisted of 32 psoriatic patients during cyclosporine A therapy. The molecular analysis was made by using real-time reverse transcription polymerase chain assay (RTqPCR) and MALDI ToF mass spectroscopy three times: after 0, 42, 84 days of treatment. Statistically significant differences (p < .05) in transcriptional activity were observed for genes: TNF-α (0 vs. 42nd days p = .006; 0 vs. 84th days p = .005), IL23A (0 vs. 42nd days p = .041), IFN-γ (0 vs. 42th days p = .040; 0 vs. 84th days p = .041), IL17 (0 vs. 42nd p = .000003 0 vs. 84th p = .001650), IL12A (0 vs. 42nd p = .0047 vs. 84th p = .0063). The expression of TNF-α was downregulated during therapy, IL23A was upregulated during CsA treatment, while the expression of IFN-γ and IL17 were higher after 42 days and lower after 84 days compared to 0 days of CsA treatment. It seems that TNF-α, IL12A, IL23A, IFN-γ, and IL17 can be useful complementary molecular markers to assess the efficacy of psoriasis treatment.

Network modeling of single-cell omics data: challenges, opportunities, and progresses

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

Pathologic gene network rewiring implicates PPP1R3A as a central regulator in pressure overload heart failure

Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure.

The genetic and pathogenetic basis of heart failure is incompletely understood. Here, the authors present a high-fidelity tissue collection from rapidly preserved failing and non-failing control hearts which are used for eQTL mapping and network analysis, resulting in the prioritization of PPP1R3A as a heart failure gene.