Identification of candidate genes related to pancreatic cancer based on analysis of gene co-expression and protein-protein interaction network

Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated (p < 1.11 × 10−7) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%~1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein–protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At p < 0.005 (~2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.

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A SYSTEMS-BIOLOGY APPROACH TO IDENTIFY CANDIDATE GENES FOR ALZHEIMER'S DISEASE BY INTEGRATING PROTEIN-PROTEIN INTERACTION NETWORK AND SUBSEQUENT IN VIVO VALIDATION OF CANDIDATE GENES USING A C. ELEGANS MODEL OF AB TOXICITY

Alzheimer s & Dementia ◽

10.1016/j.jalz.2014.04.499 ◽

2014 ◽

Vol 10 ◽

pp. P298-P299 ◽

Cited By ~ 4

Author(s):

Shubhabrata Mukherjee ◽

Matt Kaeberlein ◽

John Kauwe ◽

Adam C. Naj ◽

Paul Crane ◽

...

Keyword(s):

Systems Biology ◽

Candidate Genes ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

C Elegans ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network ◽

In Vivo Validation

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

10.21203/rs.3.rs-289913/v1 ◽

2021 ◽

Author(s):

Backiyarani Suthanthiram ◽

Sasikala Rajendran ◽

Sharmiladevi Simeon ◽

Uma Subbaraya

Keyword(s):

Candidate Genes ◽

Signaling Pathway ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Auxin Signaling ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

Abstract Banana, one of the most important staple, delicious 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 exploring the PPI of candidate genes from the putative network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLV-WUSHEL signaling pathway in addition to gibberellin mediated auxin signaling pathway in parthenocarpy. Further validation of candidate genes in seeded and seedless accession of Musa spp using qRT-PCR put forward AGL8, MADS16, IAA (GH3.8), RGA1, EXPA1, GID1C, HK2 and BAM1 as possible target genes in natural parthenocarpy. In contrary, expression profile of ACLB-2 and ZEP is anticipated to highlight the difference in artificially induced and natural 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 protein-protein interaction network.

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