Transcriptional regulatory network analysis of the over-expressed genes in adipose tissue

Integrated Analysis of lncRNAs, mRNAs, and TFs to Identify Regulatory Networks Underlying MAP Infection in Cattle

Johne’s disease is a chronic infection of ruminants that burdens dairy herds with a significant economic loss. The pathogenesis of the disease has not been revealed clearly due to its complex nature. In order to achieve deeper biological insights into molecular mechanisms involved in MAP infection resulting in Johne’s disease, a system biology approach was used. As far as is known, this is the first study that considers lncRNAs, TFs, and mRNAs, simultaneously, to construct an integrated gene regulatory network involved in MAP infection. Weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis were conducted to explore coexpression modules from which nonpreserved modules had altered connectivity patterns. After identification of hub and hub-hub genes as well as TFs and lncRNAs in the nonpreserved modules, integrated networks of lncRNA-mRNA-TF were constructed, and cis and trans targets of lncRNAs were identified. Both cis and trans targets of lncRNAs were found in eight nonpreserved modules. Twenty-one of 47 nonpreserved modules showed significant biological processes related to the immune system and MAP infection. Some of the MAP infection’s related pathways in the most important nonpreserved modules comprise “positive regulation of cytokine-mediated signaling pathway,” “negative regulation of leukocyte migration,” “T-cell differentiation,” “neutrophil activation,” and “defense response.” Furthermore, several genes were identified in these modules, including SLC11A1, MAPK8IP1, HMGCR, IFNGR1, CMPK2, CORO1A, IRF1, LDLR, BOLA-DMB, and BOLA-DMA, which are potentially associated with MAP pathogenesis. This study not only enhanced our knowledge of molecular mechanisms behind MAP infection but also highlighted several promising hub and hub-hub genes involved in macrophage-pathogen interaction.

Adiponectin DNA methylation in South African women with gestational diabetes mellitus: Effects of HIV infection

DNA methylation is increasingly recognized as a potential biomarker of metabolic disease. However, there is limited information on the impact of human immunodeficiency virus (HIV) infection on the candidacy of DNA methylation to serve as molecular biomarkers. This study investigated the effect of HIV infection on DNA methylation patterns in the peripheral blood of South African women with (n = 95) or without (n = 191) gestational diabetes mellitus (GDM). DNA methylation levels at eight CpG sites in the adiponectin gene (ADIPOQ) promoter were measured using bisulfite conversion and pyrosequencing. Differences between HIV negative (^-) and positive (⁺) women were observed. In HIV^- women, methylation at CpG -3400 was lower in GDM⁺ women compared to those with normoglycemia (8.5-fold; p = 0.004), and was associated with higher fasting glucose (β-co-efficient = 0.973; p = 0.006) and lower adiponectin (β-co-efficient = -0.057; p = 0.014) concentrations. These associations were not observed in HIV⁺ women. In silico analysis showed that Transcription Factor AP2-alpha is able to bind to the altered CpG site, suggesting that CpG -3400 may play a functional role in the regulation of ADIPOQ expression. Our findings show that DNA methylation differs by HIV status, suggesting that HIV infection needs to be taken into consideration in studies exploring DNA methylation as a biomarker of GDM in high HIV prevalence settings.

Mammary tissue transcriptomic analysis for construction of integrated regulatory networks involved in lactogenesis of Ovis aries

The mammary gland experiences vast changes between the onset of lactation and pregnancy. This remodeling involves different functions such as lactation that is controlled by innumerable regulators and various gene networks which are still not completely understood. MicroRNAs (miRNAs) are one of the important non-coding gene regulators which control an extensive range of biological processes. Thus, exploring miRNAs functions is important for solving gene regulation complexity. The main purpose in the present study is to identify the various gene regulative integrated networks involved in lactation progress in mammary gland. We analyzed ovine mammary tissue data sets which included expression profiles of mRNA (genes) and miRNAs related to six ewes in different days of lactation and nutritional treatments. We combined two different types of information: the network that is module inference by mRNAs (RNA-seq data), miRNAs and transcription factors (TFs) expression matrix and prediction of targets via computational methods. To discover the miRNAs regulatory function, 134 modules were predicted by using gene expression data and 14 TFs and 20 miRNAs were allocated to these predicted modules. By applying this integrated computation-based method, 38 miRNA-modules and 35 TF-module interactions were identified from ovine mammary tissue data during lactogenesis. A lot of these modules were involved in lipid and protein metabolism, as well as steroids and vitamin biosynthesis, which would play key roles in mammary tissue and lactation development. These results present new information about the regulatory procedures at the miRNAs and TF levels throughout lactation.

Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis

Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.

Weighted Gene Co-expression Network Analysis of Endometriosis and Identification of Functional Modules Associated With Its Main Hallmarks

Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.

Integrated regulatory network reveals novel candidate regulators in the development of negative energy balance in cattle

Negative energy balance (NEB) is an altered metabolic state in modern high-yielding dairy cows. This metabolic state occurs in the early postpartum period when energy demands for milk production and maintenance exceed that of energy intake. Negative energy balance or poor adaptation to this metabolic state has important effects on the liver and can lead to metabolic disorders and reduced fertility. The roles of regulatory factors, including transcription factors (TFs) and micro RNAs (miRNAs) have often been separately studied for evaluating of NEB. However, adaptive response to NEB is controlled by complex gene networks and still not fully understood. In this study, we aimed to discover the integrated gene regulatory networks involved in NEB development in liver tissue. We downloaded data sets including mRNA and miRNA expression profiles related to three and four cows with severe and moderate NEB, respectively. Our method integrated two independent types of information: module inference network by TFs, miRNAs and mRNA expression profiles (RNA-seq data) and computational target predictions. In total, 176 modules were predicted by using gene expression data and 64 miRNAs and 63 TFs were assigned to these modules. By using our integrated computational approach, we identified 13 TF-module and 19 miRNA-module interactions. Most of these modules were associated with liver metabolic processes as well as immune and stress responses, which might play crucial roles in NEB development. Literature survey results also showed that several regulators and gene targets have already been characterized as important factors in liver metabolic processes. These results provided novel insights into regulatory mechanisms at the TF and miRNA levels during NEB. In addition, the method described in this study seems to be applicable to construct integrated regulatory networks for different diseases or disorders.

Construction of an integrated gene regulatory network link to stress-related immune system in cattle

The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

The Zebrafish Equivalent of Alzheimer's Disease-Associated PRESENILIN Isoform PS2V Regulates Inflammatory and Other Responses to Hypoxic Stress

Dominant mutations in the PRESENILIN genes PSEN1 and PSEN2 cause familial Alzheimer's disease (fAD) that usually shows onset before 65 years of age. In contrast, genetic variation at the PSEN1 and PSEN2 loci does not appear to contribute to risk for the sporadic, late onset form of the disease (sAD), leading to doubts that these genes play a role in the majority of AD cases. However, a truncated isoform of PSEN2, PS2V, is upregulated in sAD brains and is induced by hypoxia and high cholesterol intake. PS2V can increase γ-secretase activity and suppress the unfolded protein response (UPR), but detailed analysis of its function has been hindered by lack of a suitable, genetically manipulable animal model since mice and rats lack this PRESENILIN isoform. We recently showed that zebrafish possess an isoform, PS1IV, that is cognate to human PS2V. Using an antisense morpholino oligonucleotide, we can block specifically the induction of PS1IV that normally occurs under hypoxia. Here, we exploit this ability to identify gene regulatory networks that are modulated by PS1IV. When PS1IV is absent under hypoxia-like conditions, we observe changes in expression of genes controlling inflammation (particularly sAD-associated IL1B and CCR5), vascular development, the UPR, protein synthesis, calcium homeostasis, catecholamine biosynthesis, TOR signaling, and cell proliferation. Our results imply an important role for PS2V in sAD as a component of a pathological mechanism that includes hypoxia/oxidative stress and support investigation of the role of PS2V in other diseases, including schizophrenia, when these are implicated in the pathology.

Unique ability of pandemic influenza to downregulate the genes involved in neuronal disorders

Pandemic influenza remains as a substantial threat to humans with a widespread panic worldwide. In contrast, seasonal (non-pandemic) has a mild non-lethal infection each year. The underlying mechanisms governing the detrimental effects of pandemic influenza are yet to be known. Transcriptomic-based network discovery and gene ontology (GO) analysis of host response to pandemic influenza, compared to seasonal influenza, can shed light on the differential mechanisms which pandemic influenza is employed during evolution. Here, using microarray data of infected ferrets with pandemic and seasonal influenza (as a model), we evaluated the possible link between altered genes after pandemic infection with activation of neuronal disorders. To this end, we utilized novel computational biology techniques including differential transcriptome analysis, network construction, GO enrichment, and GO network to investigate the underlying mechanisms of pandemic influenza infection and host interaction. In comparison to seasonal influenza, pandemic influenza differentially altered the expression of 31 genes with direct involvement in activity of central nervous system (CNS). Network topology highlighted the high interactions of IRF1, NKX2-1 and NR5A1 as well as MIR27A, MIR19A, and MIR17. TGFB2, NCOA3 and SP1 were the central transcription factors in the networks. Pandemic influenza remarkably downregulated GPM6A and GTPase. GO network demonstrated the key roles of GPM6A and GTPase in regulation of important functions such as synapse assembly and neuron projection. For the first time, we showed that besides interference with cytokine/chemokine storm and neuraminidase enzyme, H1N1 pandemic influenza is able to directly affect neuronal gene networks. The possibility of application of some key regulators such as GPM6A protein, MIR128, and MIR367 as candidate therapeutic agents is discussed. The presented approach established a new way to unravel unknown pathways in virus-associated CNS dysfunction by utilizing global transcriptomic data, network and GO analysis.

A novel pairwise comparison method for in silico discovery of statistically significant cis-regulatory elements in eukaryotic promoter regions: application to Arabidopsis

Cis regulatory elements (CREs), located within promoter regions, play a significant role in the blueprint for transcriptional regulation of genes. There is a growing interest to study the combinatorial nature of CREs including presence or absence of CREs, the number of occurrences of each CRE, as well as of their order and location relative to their target genes. Comparative promoter analysis has been shown to be a reliable strategy to test the significance of each component of promoter architecture. However, it remains unclear what level of difference in the number of occurrences of each CRE is of statistical significance in order to explain different expression patterns of two genes. In this study, we present a novel statistical approach for pairwise comparison of promoters of Arabidopsis genes in the context of number of occurrences of each CRE within the promoters. First, using the sample of 1000 Arabidopsis promoters, the results of the goodness of fit test and non-parametric analysis revealed that the number of occurrences of CREs in a promoter sequence is Poisson distributed. As a promoter sequence contained functional and non-functional CREs, we addressed the issue of the statistical distribution of functional CREs by analyzing the ChIP-seq datasets. The results showed that the number of occurrences of functional CREs over the genomic regions was determined as being Poisson distributed. In accordance with the obtained distribution of CREs occurrences, we suggested the Audic and Claverie (AC) test to compare two promoters based on the number of occurrences for the CREs. Superiority of the AC test over Chi-square (2×2) and Fisher's exact tests was also shown, as the AC test was able to detect a higher number of significant CREs. The two case studies on the Arabidopsis genes were performed in order to biologically verify the pairwise test for promoter comparison. Consequently, a number of CREs with significantly different occurrences was identified between the promoters. The results of the pairwise comparative analysis together with the expression data for the studied genes revealed the biological significance of the identified CREs.