Journal Staff

MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions

Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles

Молекуларно-дијагностички методи, ризици и превенција на фамилијарната хиперхолестеролемија

методи, ризици и превенција на фамилијарната хиперхолестеролемија Апстракт Фамилијарната хиперхолестеролемија (FH – Familiar Hypercholesterolemia) претставува генетичко заболување на организмот предизвикана најчесто од мутација во генот за рецепторот за липопротеини со мала густина (LDLR) и аполипопротеин Б генот (apoB). Претставува наследно заболување и како такво се карактеризира со покачени количества на вкупен холестерол и LDL – холестерол, како резултат на присуство на дисфункционални рецептори за LDL – холестерол или недостаток од рецептори за LDL- холестерол во црниот дроб со кои организмот, поточно црниот дроб, би го чистел LDL – холестеролот од циркулацијата во организмот. Пациентите со оваа болест се со голем ризик на многу млада возраст да развијат кардиоваскуларна, цереброваскуларна или периферна васкуларна болест како резултат на атероматозни промени во крвните садови, а со тоа и голем ризик од фатален исход доколку состојбата не се открие и третира соодветно. Во овој истражувачки труд се користени резултатите од лабораториски испитувања од амбулантскиот дневник на ПЗУ ДЛ „Павлина” – Виница, во временски период од две години 2010-2011 година. Трудот има за цел да ја прикаже преваленцата и ризикот од појава на можна хиперхолестеролемија, дефинирана по пол и возраст. Според анализираните резултати, заклучивме дека кај машката популација најмногу пациенти со високи концентрации на холестерол има во возрасната група од 40 до 60 години, додека кај женскиот пол во возрасната групата на жени над 60 години има најмногу пациенти со покачени концентрации на холестерол. Утврдено е и генерациска врска, т.е. покачени концентрации на холестерол во две генерации во една фамилија. Фамилијарната хиперхолестеролемија е болест која е проценето дека е присутна кај најмалку 250 милиони луѓе во светот и е од особена важност нејзината брза и рана детекција. Тоа се прави со помош на клиничко лабораториски методи и критериуми и молекуларно-дијагностички методи за откривање на генетската причина за појава на оваа болест

Combining Network Modeling and Gene Expression Microarray Analysis to Explore the Dynamics of Th1 and Th2 Cell Regulation

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease

Bioinformatic identification of disease associated pathways by network based analysis

Many common diseases are complex, meaning that they are caused by many interacting genes. This makes them difficult to study; to determine disease mechanisms, disease-associated genes must be analyzed in combination. Disease-associated genes can be detected using high-throughput methods, such as mRNA expression microarrays, DNA methylation microarrays and genome-wide association studies (GWAS), but determining how they interact to cause disease is an intricate challenge. One approach is to organize disease-associated genes into networks using protein-protein interactions (PPIs) and dissect them to identify disease causing pathways. Studies of complex disease can also be greatly facilitated by using an appropriate model system. In this dissertation, seasonal allergic rhinitis (SAR) served as a model disease. SAR is a common disease that is relatively easy to study. Also, the key disease cell types, like the CD4+ T cell, are known and can be cultured and activated in vitro by the disease causing pollen. The aim of this dissertation was to determine network properties of disease-associated genes, and develop methods to identify and validate networks of disease-associated genes. First, we showed that disease-associated genes have distinguishing network properties, one being that they co-localize in the human PPI network. This supported the existence of disease modules within the PPI network. We then identified network modules of genes whose mRNA expression was perturbed in human disease, and showed that the most central genes in those network modules were enriched for disease-associated polymorphisms identified by GWAS. As a case study, we identified disease modules using mRNA expression data from allergen-challenged CD4+ cells from patients with SAR. The case study identified and validated a novel disease-associated gene, FGF2 using GWAS data and RNAi mediated knockdown. Lastly, we examined how DNA methylation caused disease-associated mRNA expression changes in SAR. DNA methylation, but not mRNA expression profiles, could accurately distinguish allergic patients from healthy controls. Also, we found that disease-associated mRNA expression changes were associated with a low DNA methylation content and absence of CpG islands. Specifically within this group, we found a correlation between disease-associated mRNA expression changes and DNA methylation changes. Using ChIP-chip analysis, we found that targets of a known disease relevant transcription factor, IRF4, were also enriched among non CpG island genes with low methylation levels. Taken together, in this dissertation the network properties of disease-associated genes were examined, and then used to validate disease networks defined by mRNA expression data. We then examined regulatory mechanisms underlying disease-associated mRNA expression changes in a model disease. These studies support network-based analyses as a method to understand disease mechanisms and identify important disease causing genes, such as treatment targets or markers for personalized medication

Bioinformatic identification of disease associated pathways by network based analysis

Many common diseases are complex, meaning that they are caused by many interacting genes. This makes them difficult to study; to determine disease mechanisms, disease-associated genes must be analyzed in combination. Disease-associated genes can be detected using high-throughput methods, such as mRNA expression microarrays, DNA methylation microarrays and genome-wide association studies (GWAS), but determining how they interact to cause disease is an intricate challenge. One approach is to organize disease-associated genes into networks using protein-protein interactions (PPIs) and dissect them to identify disease causing pathways. Studies of complex disease can also be greatly facilitated by using an appropriate model system. In this dissertation, seasonal allergic rhinitis (SAR) served as a model disease. SAR is a common disease that is relatively easy to study. Also, the key disease cell types, like the CD4+ T cell, are known and can be cultured and activated in vitro by the disease causing pollen. The aim of this dissertation was to determine network properties of disease-associated genes, and develop methods to identify and validate networks of disease-associated genes. First, we showed that disease-associated genes have distinguishing network properties, one being that they co-localize in the human PPI network. This supported the existence of disease modules within the PPI network. We then identified network modules of genes whose mRNA expression was perturbed in human disease, and showed that the most central genes in those network modules were enriched for disease-associated polymorphisms identified by GWAS. As a case study, we identified disease modules using mRNA expression data from allergen-challenged CD4+ cells from patients with SAR. The case study identified and validated a novel disease-associated gene, FGF2 using GWAS data and RNAi mediated knockdown. Lastly, we examined how DNA methylation caused disease-associated mRNA expression changes in SAR. DNA methylation, but not mRNA expression profiles, could accurately distinguish allergic patients from healthy controls. Also, we found that disease-associated mRNA expression changes were associated with a low DNA methylation content and absence of CpG islands. Specifically within this group, we found a correlation between disease-associated mRNA expression changes and DNA methylation changes. Using ChIP-chip analysis, we found that targets of a known disease relevant transcription factor, IRF4, were also enriched among non CpG island genes with low methylation levels. Taken together, in this dissertation the network properties of disease-associated genes were examined, and then used to validate disease networks defined by mRNA expression data. We then examined regulatory mechanisms underlying disease-associated mRNA expression changes in a model disease. These studies support network-based analyses as a method to understand disease mechanisms and identify important disease causing genes, such as treatment targets or markers for personalized medication

Network properties of human disease genes with pleiotropic effects

BACKGROUND: The ability of a gene to cause a disease is known to be associated with the topological position of its protein product in the molecular interaction network. Pleiotropy, in human genetic diseases, refers to the ability of different mutations within the same gene to cause different pathological effects. Here, we hypothesized that the ability of human disease genes to cause pleiotropic effects would be associated with their network properties. RESULTS: Shared genes, with pleiotropic effects, were more central than specific genes that were associated with one disease, in the protein interaction network. Furthermore, shared genes associated with phenotypically divergent diseases (phenodiv genes) were more central than those associated with phenotypically similar diseases. Shared genes had a higher number of disease gene interactors compared to specific genes, implying higher likelihood of finding a novel disease gene in their network neighborhood. Shared genes had a relatively restricted tissue co-expression with interactors, contrary to specific genes. This could be a function of shared genes leading to pleiotropy. Essential and phenodiv genes had comparable connectivities and hence we investigated for differences in network attributes conferring lethality and pleiotropy, respectively. Essential and phenodiv genes were found to be intra-modular and inter-modular hubs with the former being highly co-expressed with their interactors contrary to the latter. Essential genes were predominantly nuclear proteins with transcriptional regulation activities while phenodiv genes were cytoplasmic proteins involved in signal transduction. CONCLUSION: The properties of a disease gene in molecular interaction network determine its role in manifesting different and divergent diseases

Network properties of human disease genes with pleiotropic effects

BACKGROUND: The ability of a gene to cause a disease is known to be associated with the topological position of its protein product in the molecular interaction network. Pleiotropy, in human genetic diseases, refers to the ability of different mutations within the same gene to cause different pathological effects. Here, we hypothesized that the ability of human disease genes to cause pleiotropic effects would be associated with their network properties. RESULTS: Shared genes, with pleiotropic effects, were more central than specific genes that were associated with one disease, in the protein interaction network. Furthermore, shared genes associated with phenotypically divergent diseases (phenodiv genes) were more central than those associated with phenotypically similar diseases. Shared genes had a higher number of disease gene interactors compared to specific genes, implying higher likelihood of finding a novel disease gene in their network neighborhood. Shared genes had a relatively restricted tissue co-expression with interactors, contrary to specific genes. This could be a function of shared genes leading to pleiotropy. Essential and phenodiv genes had comparable connectivities and hence we investigated for differences in network attributes conferring lethality and pleiotropy, respectively. Essential and phenodiv genes were found to be intra-modular and inter-modular hubs with the former being highly co-expressed with their interactors contrary to the latter. Essential genes were predominantly nuclear proteins with transcriptional regulation activities while phenodiv genes were cytoplasmic proteins involved in signal transduction. CONCLUSION: The properties of a disease gene in molecular interaction network determine its role in manifesting different and divergent diseases