New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling

Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with ‘Candidatus Phytoplasma solani’, and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with ‘Ca. P. solani’. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to ‘Ca. P. solani’ to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.

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High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

Nature Communications ◽

10.1038/s41467-021-23213-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Sarah E. Pierce ◽

Jeffrey M. Granja ◽

William J. Greenleaf

Keyword(s):

Transcription Factor ◽

Single Cell ◽

Cancer Cells ◽

High Throughput ◽

Regulatory Networks ◽

Temporal Dynamics ◽

Chromatin Accessibility ◽

Regulatory Regions ◽

Factor Binding ◽

Genome Wide

AbstractChromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.668448 ◽

2021 ◽

Vol 12 ◽

Author(s):

Maryam Heidari ◽

Abbas Pakdel ◽

Mohammad Reza Bakhtiarizadeh ◽

Fariba Dehghanian

Keyword(s):

Regulatory Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Johne's Disease ◽

Johne’S Disease ◽

Functional Enrichment ◽

Integrated Analysis ◽

Complex Nature ◽

Hub Genes ◽

Cis And Trans

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.

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From static to temporal network theory: Applications to functional brain connectivity

Network Neuroscience ◽

10.1162/netn_a_00011 ◽

2017 ◽

Vol 1 (2) ◽

pp. 69-99 ◽

Cited By ~ 28

Author(s):

William Hedley Thompson ◽

Per Brantefors ◽

Peter Fransson

Keyword(s):

Network Theory ◽

Temporal Dynamics ◽

Brain Connectivity ◽

Resting State Fmri ◽

Network Activity ◽

Temporal Network ◽

Functional Brain ◽

Brain Connectome ◽

The Brain ◽

Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

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Demethylation alters transcriptome profiling of buds and leaves in ‘Kyoho’ grape

BMC Plant Biology ◽

10.1186/s12870-020-02754-0 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Haoran Jia ◽

Zibo Zhang ◽

Ehsan Sadeghnezhad ◽

Qianqian Pang ◽

Shangyun Li ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Epigenetic Modification ◽

Deep Understanding ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Glutathione Metabolism ◽

Endogenous Factors

Abstract Background Grape buds and leaves are directly associated with the physiology and metabolic activities of the plant, which is monitored by epigenetic modifications induced by environment and endogenous factors. Methylation is one of the epigenetic regulators that could be involved in DNA levels and affect gene expression in response to stimuli. Therefore, changes of gene expression profile in leaves and bud through inhibitors of DNA methylation provide a deep understanding of epigenetic effects in regulatory networks. Results In this study, we carried out a transcriptome analysis of ‘Kyoho’ buds and leaves under 5-azacytidine (5-azaC) exposure and screened a large number of differentially expressed genes (DEGs). GO and KEGG annotations showed that they are mainly involved in photosynthesis, flavonoid synthesis, glutathione metabolism, and other metabolic processes. Functional enrichment analysis also provided a holistic perspective on the transcriptome profile when 5-azaC bound to methyltransferase and induced demethylation. Enrichment analysis of transcription factors (TFs) also showed that the MYB, C2H2, and bHLH families are involved in the regulation of responsive genes under epigenetic changes. Furthermore, hormone-related genes have also undergone significant changes, especially gibberellin (GA) and abscisic acid (ABA)-related genes that responded to bud germination. We also used protein-protein interaction network to determine hub proteins in response to demethylation. Conclusions These findings provide new insights into the establishment of molecular regulatory networks according to how methylation as an epigenetic modification alters transcriptome patterns in bud and leaves of grape.

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Explore the Protective Role of Obesity in the Progression of Myocardial Infarction

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.629734 ◽

2021 ◽

Vol 8 ◽

Author(s):

Siyuan Zhao ◽

Rongyuan Cao ◽

Shuhua Zhang ◽

Yan Kang

Keyword(s):

Myocardial Infarction ◽

Blood Pressure ◽

Regulatory Networks ◽

Large Scale ◽

Enrichment Analysis ◽

Protective Role ◽

Gene Set Enrichment Analysis ◽

Shortest Path Analysis ◽

Acute Mi

Obesity has been shown as a risk factor to increase the incidence of myocardial infarction (MI). However, obesity has also been linked to the decreased mortality of acute MI with unknown mechanisms. Here, we firstly used large-scale literature data mining to identify obesity downstream targets and MI upstream regulators with polarity, based on which an obesity-MI regulatory network was constructed. Then, a gene set enrichment analysis was conducted to explore the functional profile of the genes involved in the obesity-MI regulatory networks. After that, a mega-analysis using MI RNA expression datasets was conducted to test the expression of obesity-specific genes in MI patients, followed by a shortest-path analysis to explore any potential gene-MI association. Our results suggested that obesity could inhibit 11 MI promoters, including NPPB, NPPA, IRS1, SMAD3, MIR155, ADRB1, AVP, MAPK14, MC3R, ROCK1, and COL3A1, which were mainly involved in blood pressure-related pathways. Our study suggested that obesity could influence MI progression by driving multiple genes associated with blood pressure regulation. Moreover, PTH could be a novel obesity driven gene associated with the pathogenesis of MI, which needs further validation.

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Identification of Therapeutic Targets and Prognostic Biomarkers Among Integrin Subunits in the Skin Cutaneous Melanoma Microenvironment

Frontiers in Oncology ◽

10.3389/fonc.2021.751875 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yeltai Nurzat ◽

Weijie Su ◽

Peiru Min ◽

Ke Li ◽

Heng Xu ◽

...

Keyword(s):

T Cells ◽

Cutaneous Melanoma ◽

Regulatory Networks ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Expression Levels ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Go Enrichment ◽

Underlying Mechanisms

The roles of different integrin alpha/beta (ITGA/ITGB) subunits in skin cutaneous melanoma (SKCM) and their underlying mechanisms of action remain unclear. Oncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, and Webgestalt analysis tools were used. The expression levels of ITGA3, ITGA4, ITGA6, ITGA10, ITGB1, ITGB2, ITGB3, ITGB4, and ITGB7 were significantly increased in SKCM tissues. The expression levels of ITGA1, ITGA4, ITGA5, ITGA8, ITGA9, ITGA10, ITGB1, ITGB2, ITGB3, ITGB5, ITGB6 and ITGB7 were closely associated with SKCM metastasis. The expression levels of ITGA1, ITGA4, ITGB1, ITGB2, ITGB6, and ITGB7 were closely associated with the pathological stage of SKCM. The expression levels of ITGA6 and ITGB7 were closely associated with disease-free survival time in SKCM, and the expression levels of ITGA6, ITGA10, ITGB2, ITGB3, ITGB6, ITGB7, and ITGB8 were markedly associated with overall survival in SKCM. We also found significant correlations between the expression of integrin subunits and the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells). Finally, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed, and protein-protein interaction (PPI) networks were constructed. We have identified abnormally-expressed genes and gene regulatory networks associated with SKCM, improving understanding of the underlying pathogenesis of SKCM.

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PhenoExam: an R package and Web application for the examination of phenotypes linked to genes and gene sets

10.1101/2021.06.29.450324 ◽

2021 ◽

Author(s):

Alejandro Cisterna García ◽

Aurora González-Vidal ◽

Daniel Ruiz Villa ◽

Jordi Ortiz Murillo ◽

Alicia Gómez-Pascual ◽

...

Keyword(s):

Web Application ◽

Enrichment Analysis ◽

R Package ◽

Web Interface ◽

Gene Set ◽

New Genes ◽

Gene Sets ◽

Phenotype Analysis ◽

New Gene ◽

Early Onset Parkinson’S Disease

Gene set based phenotype enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) can improve the rate of genetic diagnoses amongst other research purposes. To facilitate diverse phenotype analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. PhenoExam achieves these tasks by integrating databases or resources such as the HPO, MGD, CRISPRbrain, CTD, ClinGen, CGI, OrphaNET, UniProt, PsyGeNET, and Genomics England Panel App. PhenoExam accepts both human and mouse genes as input. We developed PhenoExam to assist a variety of users, including clinicians, computational biologists and geneticists. It can be used to support the validation of new gene-to-disease discoveries, and in the detection of differential phenotypes between two gene sets (a phenotype linked to one of the gene set but no to the other) that are useful for differential diagnosis and to improve genetic panels. We validated PhenoExam performance through simulations and its application to real cases. We demonstrate that PhenoExam is effective in distinguishing gene sets or Mendelian diseases with very similar phenotypes through projecting the disease-causing genes into their annotation-based phenotypic spaces. We also tested the tool with early onset Parkinson's disease and dystonia genes, to show phenotype-level similarities but also potentially interesting differences. More specifically, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. Therefore, PhenoExam effectively discovers links between phenotypic terms across annotation databases through effective integration. The R package is available at https://github.com/alexcis95/PhenoExam and the Web tool is accessible at https://snca.atica.um.es/PhenoExamWeb/.

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Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment

10.1101/2020.01.25.919738 ◽

2020 ◽

Author(s):

Jonathan D. Rubin ◽

Jacob T. Stanley ◽

Rutendo F. Sigauke ◽

Cecilia B. Levandowski ◽

Zachary L. Maas ◽

...

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Regulatory Networks ◽

Time Series Data ◽

Effective Means ◽

Enrichment Analysis ◽

Cost Effective ◽

Computational Method ◽

Series Data ◽

Single Experiment

1AbstractDetecting differential activation of transcription factors (TFs) in response to perturbation provides insight into cellular processes. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within a list of ranked regions of interest (ROIs). As ROIs are typically inferred from the data, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent (eg. PRO-Seq), CAGE, ChIP-Seq, and accessibility (e.g. ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

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Identification and analysis of lncRNA, microRNA and mRNA expression profiles and construction of ceRNA network in Talaromyces marneffei-infected THP-1 macrophage

PeerJ ◽

10.7717/peerj.10529 ◽

2021 ◽

Vol 9 ◽

pp. e10529

Author(s):

Yueqi Li ◽

Wudi Wei ◽

Sanqi An ◽

Junjun Jiang ◽

Jinhao He ◽

...

Keyword(s):

Regulatory Networks ◽

Expression Profiles ◽

Enrichment Analysis ◽

R Package ◽

Functional Enrichment ◽

Differentially Expressed ◽

Next Generation Sequencing Technology ◽

Talaromyces Marneffei ◽

Kegg Analysis ◽

Cerna Network

Background Competitive endogenous RNA (ceRNA) reveals new mechanisms for interactions between RNAs, which have been considered to play a significant role in pathogen-host innate immune response. However, knowledge of ceRNA regulatory networks in Talaromyces marneffei (TM)-macrophages is still limited. Methods Next-generation sequencing technology (NGS) was used to obtain mRNA, miRNA and lncRNA expression profiles in TM-infected macrophages. The R package DESeq2 was used to identify differentially expressed lncRNA, miRNA and mRNA. The R package GOseq was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and the ceRNA network of lncRNA–miRNA–mRNA interaction was constructed in Cytoscape. Similarly, functional enrichment analysis on mRNA in the ceRNA network. Finally, two mRNAs and four lncRNAs in the ceRNA network were randomly selected to verify the expression using qRT-PCR. Results In total, 119 lncRNAs, 28 miRNAs and 208 mRNAs were identified as differentially expressed RNAs in TM-infected macrophages. The constructed ceRNA network contains 38 lncRNAs, 10 miRNAs and 45 mRNAs. GO and KEGG analysis of mRNA in the ceRNA network indicated that activated pathways in TM-infected macrophages were related to immunity, inflammation and metabolism. The quantitative validation of the expression of four randomly selected differentially expressed lncRNAs, AC006252.1, AC090197.1, IL6R-AS1, LINC02009 and two mRNAs, CSF1, NR4A3 showed that the expression levels were consistent with those in the RNA-sequencing. Conclusions The ceRNA network related to immunity, inflammation and metabolism plays an important role in TM-macrophage interaction. This study may provide effective and novel insights for further understanding the underlying mechanism of TM infection.

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Disentangling temporal associations in marine microbial networks

10.21203/rs.3.rs-404332/v1 ◽

2021 ◽

Author(s):

Ina Deutschmann ◽

Anders Krabberød ◽

L. Benites ◽

Francisco Latorre ◽

Erwan Delage ◽

...

Keyword(s):

Mediterranean Sea ◽

Annual Cycle ◽

Ecosystem Functioning ◽

Temporal Dynamics ◽

Biogeochemical Cycling ◽

Microbial Interactions ◽

Temporal Network ◽

Microbial Associations

Abstract Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. The omics-based censuses are helpful to predict microbial interactions through the inference of static association networks. However, since microbial interactions are highly dynamic, we have developed an approach to generate a temporal network from a single static network. We applied it to understand the monthly microbial associations’ dynamics occurring over ten years in the Blanes Bay Microbial Observatory (Mediterranean Sea). For the decade, we identified persistent, seasonal, and temporary microbial associations. Moreover, we found that the temporal network appears to follow an annual cycle, collapsing and reassembling when transiting between colder and warmer waters. We observed higher repeatability in colder than warmer months. Altogether, our results indicate that marine microbial networks follow recurrent temporal dynamics, which need to be accounted to better understand the dynamics of the ocean microbiome.

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