Identification of candidate genes for reproductive traits in cattle using a functional interaction network approach

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

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A Systems Level Comparison of Mycobacterium tuberculosis, Mycobacterium leprae and Mycobacterium smegmatis Based on Functional Interaction Network Analysis

Journal of Bacteriology & Parasitology ◽

10.4172/2155-9597.1000173 ◽

2013 ◽

Vol 04 (04) ◽

Cited By ~ 5

Author(s):

Richard O Akinola

Keyword(s):

Mycobacterium Tuberculosis ◽

Network Analysis ◽

Mycobacterium Smegmatis ◽

Interaction Network ◽

Mycobacterium Leprae ◽

Functional Interaction ◽

Functional Interaction Network

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Genetic and functional interaction network analysis reveals global enrichment of regulatory T cell genes influencing basal cell carcinoma susceptibility

Genome Medicine ◽

10.1186/s13073-021-00827-9 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Christelle Adolphe ◽

Angli Xue ◽

Atefeh Taherian Fard ◽

Laura A. Genovesi ◽

Jian Yang ◽

...

Keyword(s):

Cell Cycle ◽

Network Analysis ◽

Basal Cell Carcinoma ◽

Regulatory Network ◽

Interaction Network ◽

Susceptibility Genes ◽

Genome Wide Association ◽

Functional Interaction ◽

Functional Interaction Network ◽

Genome Wide

Abstract Background Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. Methods We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. Results A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. Conclusions Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

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A network-based method for predicting disease-associated enhancers

PLoS ONE ◽

10.1371/journal.pone.0260432 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260432

Author(s):

Duc-Hau Le

Keyword(s):

Random Walk ◽

Heterogeneous Network ◽

Target Genes ◽

Interaction Network ◽

Potential Method ◽

Diffusion Method ◽

Random Walk With Restart ◽

Functional Interaction ◽

Functional Interaction Network ◽

Network Diffusion

Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

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Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

Scientific Reports ◽

10.1038/s41598-021-93661-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Suthanthiram Backiyarani ◽

Rajendran Sasikala ◽

Simeon Sharmiladevi ◽

Subbaraya Uma

Keyword(s):

Candidate Genes ◽

Protein Interaction ◽

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Auxin Signaling ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

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Transcriptomic Analysis Reveals Candidate Genes Responsive to Sclerotinia scleroterum and Cloning of the Ss-Inducible Chitinase Genes in Morus laevigata

International Journal of Molecular Sciences ◽

10.3390/ijms21218358 ◽

2020 ◽

Vol 21 (21) ◽

pp. 8358

Author(s):

Huanhuan Jiang ◽

Xiaoyun Jin ◽

Xiaofeng Shi ◽

Yufei Xue ◽

Jiayi Jiang ◽

...

Keyword(s):

Candidate Genes ◽

Molecular Basis ◽

Expression Patterns ◽

Interaction Network ◽

Sclerotinia Stem Rot ◽

Response Characteristics ◽

Kegg Pathways ◽

Cdna Sequences ◽

Chitinase Genes ◽

Morus Laevigata

Sclerotinia sclerotiorum (Ss) is a devastating fungal pathogen that causes Sclerotinia stem rot in rapeseed (Brassica napus), and is also detrimental to mulberry and many other crops. A wild mulberry germplasm, Morus laevigata, showed high resistance to Ss, but the molecular basis for the resistance is largely unknown. Here, the transcriptome response characteristics of M. laevigata to Ss infection were revealed by RNA-seq. A total of 833 differentially expressed genes (DEGs) were detected after the Ss inoculation in the leaf of M. laevigata. After the GO terms and KEGG pathways enrichment analyses, 42 resistance-related genes were selected as core candidates from the upregulated DEGs. Their expression patterns were detected in the roots, stems, leaves, flowers, and fruits of M. laevigata. Most of them (30/42) were specifically or mainly expressed in flowers, which was consistent with the fact that Ss mainly infects plants through floral organs, and indicated that Ss-resistance genes could be induced by pathogen inoculation on ectopic organs. After the Ss inoculation, these candidate genes were also induced in the two susceptible varieties of mulberry, but the responses of most of them were much slower with lower extents. Based on the expression patterns and functional annotation of the 42 candidate genes, we cloned the full-length gDNA and cDNA sequences of the Ss-inducible chitinase gene set (MlChi family). Phylogenetic tree construction, protein interaction network prediction, and gene expression analysis revealed their special roles in response to Ss infection. In prokaryotic expression, their protein products were all in the form of an inclusion body. Our results will help in the understanding of the molecular basis of Ss-resistance in M. laevigata, and the isolated MlChi genes are candidates for the improvement in plant Ss-resistance via biotechnology.

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