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

Biomolecules ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1379
Author(s):  
Su Yon Jung ◽  
Jeanette C. Papp ◽  
Matteo Pellegrini ◽  
Herbert Yu ◽  
Eric M. Sobel
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Full Range ◽  
Gene Interaction ◽  
Interferon Γ ◽  
Inflammatory Biomarkers ◽  
Genome Wide Association Studies ◽  
Tissue Specific

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.

Abstract 056: Integrative Genomic Analysis Unravels Novel Pathways And Key Regulatory Networks In Blood Pressure

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
Yuqi Zhao ◽  
Xingyi Shi ◽  
Ville-Petteri Mäkinen ◽  
Qingying Meng ◽  
Xia Yang
Keyword(s):  
Blood Pressure ◽  
Endothelial Cells ◽  
Cardiovascular Diseases ◽  
Regulatory Networks ◽  
Association Studies ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Aortic Endothelial Cells ◽  
Aortic Endothelial

Blood pressure (BP) is a highly heritable trait and hypertension is a major risk factor for cardiovascular diseases. Recent genome-wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, these loci together only explain 1% of the BP variability and the underlying mechanisms remain elusive. In this study, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including 1) GWAS from CHARGE (The Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium for DBP and SBP, 2) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to hypertension (such as peripheral blood, whole blood, human aortic endothelial cells, and adipose tissue), 3) knowledge-driven biological pathways, and 4) data-driven regulatory gene networks. The integration of these diverse data sources enabled tissue-specific investigations on whether the genetic variants associated with BP concentrated on specific parts of gene regulatory networks, termed as “subnetworks”, and whether novel key regulators in the subnetworks could be identified based on data-driven network topology. We identified 10 and 8 subnetworks for DBP and SBP respectively. Among these, subnetworks associated with ion homeostasis, ALK in cardiac myocytes, B cell receptor signaling, and Regulation of Insulin Secretion, were shared between DBP and SBP. More interestingly, we detected tissue-specific pathways, for example, L1CAM interactions in aortic endothelial cells and Leukocyte transendothelial migration in adipose. Among the trait-specific subnetworks, those involved in megakaryocyte development and cytoskeleton remodeling were found to be SBP-specific while GAB1 signalosome subnetwork was DBP-specific. Finally, by utilizing the gene-gene relationships revealed by the network architecture, we detected key regulator genes, both known (e.g. COL1A1, KL, and OSR1) and novel (e.g. EFEMP1, and CRABP2), in these blood pressure subnetworks. Our results shed lights on the complex mechanisms underlying blood pressure and highlight potential novel targets for hypertension and cardiovascular diseases.

MicroRNAs as Biomarker for Breast Cancer

2020 ◽  
Vol 20 (10) ◽  
pp. 1597-1610 ◽  
Author(s):  
Taru Aggarwal ◽  
Ridhima Wadhwa ◽  
Riya Gupta ◽  
Keshav Raj Paudel ◽  
Trudi Collet ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Association Studies ◽  
Large Family ◽  
Breast Cancer Diagnosis ◽  
Cell Multiplication ◽  
Cell Physiology ◽  
Gene Transcript ◽  
Genome Wide Association Studies ◽  
Non Coding Rnas

Regardless of advances in detection and treatment, breast cancer affects about 1.5 million women all over the world. Since the last decade, genome-wide association studies (GWAS) have been extensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosis and therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation of key cellular processes such as cell multiplication, differentiation, and death. They cause a disturbance in the cell physiology by interfering directly with the translation and stability of a targeted gene transcript. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate target gene expression and protein levels that affect several human diseases and are suggested as the novel markers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not only associated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis or prognosis. These are explained in detail in the following review. This review will also provide an impetus to study the role of microRNAs in breast cancer.

scholarly journals Molecular Mechanisms of ZC3H12C/Reg-3 Biological Activity and Its Involvement in Psoriasis Pathology

2021 ◽  
Vol 22 (14) ◽  
pp. 7311
Author(s):  
Mateusz Wawro ◽  
Jakub Kochan ◽  
Weronika Sowinska ◽  
Aleksandra Solecka ◽  
Karolina Wawro ◽  
...  
Keyword(s):  
Family Member ◽  
Cellular Localization ◽  
Molecular Mechanisms ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Psoriasis Patient ◽  
Genome Wide Association Studies ◽  
Mrna Levels ◽  
Transcript Levels

The members of the ZC3H12/MCPIP/Regnase family of RNases have emerged as important regulators of inflammation. In contrast to Regnase-1, -2 and -4, a thorough characterization of Regnase-3 (Reg-3) has not yet been explored. Here we demonstrate that Reg-3 differs from other family members in terms of NYN/PIN domain features, cellular localization pattern and substrate specificity. Together with Reg-1, the most comprehensively characterized family member, Reg-3 shared IL-6, IER-3 and Reg-1 mRNAs, but not IL-1β mRNA, as substrates. In addition, Reg-3 was found to be the only family member which regulates transcript levels of TNF, a cytokine implicated in chronic inflammatory diseases including psoriasis. Previous meta-analysis of genome-wide association studies revealed Reg-3 to be among new psoriasis susceptibility loci. Here we demonstrate that Reg-3 transcript levels are increased in psoriasis patient skin tissue and in an experimental model of psoriasis, supporting the immunomodulatory role of Reg-3 in psoriasis, possibly through degradation of mRNA for TNF and other factors such as Reg-1. On the other hand, Reg-1 was found to destabilize Reg-3 transcripts, suggesting reciprocal regulation between Reg-3 and Reg-1 in the skin. We found that either Reg-1 or Reg-3 were expressed in human keratinocytes in vitro. However, in contrast to robustly upregulated Reg-1 mRNA levels, Reg-3 expression was not affected in the epidermis of psoriasis patients. Taken together, these data suggest that epidermal levels of Reg-3 are negatively regulated by Reg-1 in psoriasis, and that Reg-1 and Reg-3 are both involved in psoriasis pathophysiology through controlling, at least in part different transcripts.

scholarly journals Genetics and Epigenetics of Atrial Fibrillation

2020 ◽  
Vol 21 (16) ◽  
pp. 5717 ◽  
Author(s):  
Estefanía Lozano-Velasco ◽  
Diego Franco ◽  
Amelia Aranega ◽  
Houria Daimi
Keyword(s):  
Atrial Fibrillation ◽  
Regulatory Networks ◽  
Association Studies ◽  
Functional Characterization ◽  
The Elderly ◽  
Supraventricular Arrhythmia ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms

Atrial fibrillation (AF) is known to be the most common supraventricular arrhythmia affecting up to 1% of the general population. Its prevalence exponentially increases with age and could reach up to 8% in the elderly population. The management of AF is a complex issue that is addressed by extensive ongoing basic and clinical research. AF centers around different types of disturbances, including ion channel dysfunction, Ca2+-handling abnormalities, and structural remodeling. Genome-wide association studies (GWAS) have uncovered over 100 genetic loci associated with AF. Most of these loci point to ion channels, distinct cardiac-enriched transcription factors, as well as to other regulatory genes. Recently, the discovery of post-transcriptional regulatory mechanisms, involving non-coding RNAs (especially microRNAs), DNA methylation, and histone modification, has allowed to decipher how a normal heart develops and which modifications are involved in reshaping the processes leading to arrhythmias. This review aims to provide a current state of the field regarding the identification and functional characterization of AF-related epigenetic regulatory networks

scholarly journals Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach

2021 ◽  
Vol 10 (8) ◽  
pp. 1666
Author(s):  
Micaela F. Beckman ◽  
Farah Bahrani Mougeot ◽  
Jean-Luc C. Mougeot
Keyword(s):  
Protein Interactions ◽  
Association Studies ◽  
Meta Analysis ◽  
Holistic Approach ◽  
Gene Interaction ◽  
Snp Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Data Mining Approach

The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (n = 22) from six disease categories were found to have significant associated pathways, validated by Q–Q plots (p < 0.05). Protein–protein interactions of significant (p < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.

scholarly journals Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer

2021 ◽  
Vol 17 (3) ◽  
pp. e1008819
Author(s):  
Héctor Climente-González ◽  
Christine Lonjou ◽  
Fabienne Lesueur ◽  
Dominique Stoppa-Lyonnet ◽  
Nadine Andrieu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Biological Networks ◽  
Familial Breast Cancer ◽  
Association Studies ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Functional Relationships ◽  
Network Methods ◽  
The Impact

Genome-wide association studies (GWAS) explore the genetic causes of complex diseases. However, classical approaches ignore the biological context of the genetic variants and genes under study. To address this shortcoming, one can use biological networks, which model functional relationships, to search for functionally related susceptibility loci. Many such network methods exist, each arising from different mathematical frameworks, pre-processing steps, and assumptions about the network properties of the susceptibility mechanism. Unsurprisingly, this results in disparate solutions. To explore how to exploit these heterogeneous approaches, we selected six network methods and applied them to GENESIS, a nationwide French study on familial breast cancer. First, we verified that network methods recovered more interpretable results than a standard GWAS. We addressed the heterogeneity of their solutions by studying their overlap, computing what we called the consensus. The key gene in this consensus solution was COPS5, a gene related to multiple cancer hallmarks. Another issue we observed was that network methods were unstable, selecting very different genes on different subsamples of GENESIS. Therefore, we proposed a stable consensus solution formed by the 68 genes most consistently selected across multiple subsamples. This solution was also enriched in genes known to be associated with breast cancer susceptibility (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10−4). The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. In general, network methods preferred highly connected genes, even after random rewirings that stripped the connections of any biological meaning. In conclusion, we present the advantages of network-guided GWAS, characterize their shortcomings, and provide strategies to address them. To compute the consensus networks, implementations of all six methods are available at https://github.com/hclimente/gwas-tools.

scholarly journals Identification of Novel Genome-Wide Associations for Oral Inflammatory Traits

2020 ◽  
Author(s):  
Yanjiao Jin ◽  
Jie Yang ◽  
Shuyue Zhang ◽  
Jin Li ◽  
Songlin Wang
Keyword(s):  
Candidate Genes ◽  
Immune Regulation ◽  
Functional Annotation ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Causal Variants

Abstract Background: Oral diseases impact the majority of the world’s population. The following traits are common in oral inflammatory diseases: mouth ulcers, painful gums, bleeding gums, loose teeth, and toothache. Despite the prevalence of genome-wide association studies, the associations between these traits and common genomic variants, and whether pleiotropic loci are shared by some of these traits remain poorly understood. Methods: In this work, we conducted multi-trait joint analyses based on the summary statistics of genome-wide association studies of these five oral inflammatory traits from the UK Biobank, each of which is comprised of over 10,000 cases and over 300,000 controls. We estimated the genetic correlations between the five traits. We conducted fine-mapping and functional annotation based on multi-omics data to better understand the biological functions of the potential causal variants at each locus. To identify the pathways in which the candidate genes were mainly involved, we applied gene-set enrichment analysis, and further performed protein-protein interaction (PPI) analyses.Results: We identified 39 association signals that surpassed genome-wide significance, including three that were shared between two or more oral inflammatory traits, consistent with a strong correlation. Among these genome-wide significant loci, two were novel for both painful gums and toothache. We performed fine-mapping and identified causal variants at each novel locus. Further functional annotation based on multi-omics data suggested IL10 and IL12A/TRIM59 as potential candidate genes at the novel pleiotropic loci, respectively. Subsequent analyses of pathway enrichment and protein-protein interaction networks suggested the involvement of candidate genes at genome-wide significant loci in immune regulation.Conclusions: Our results highlighted the importance of immune regulation in the pathogenesis of oral inflammatory diseases. Some common immune-related pleiotropic loci or genetic variants are shared by multiple oral inflammatory traits. These findings will be beneficial for risk prediction, prevention, and therapy of oral inflammatory diseases.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

Sign in / Sign up

Export Citation Format

Share Document