Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

The transcriptomic analysis of gene expression profiles associated with fungal keratitis in mice based on RNA-Seq

ABSTRACT: Fungal keratitis (FK) is a blinding eye disease with a high incidence in the world. At present, there is no effective treatment for this disease. At the same time, we found that animal organisms have inherent immune response mechanisms to fungal infections such as C-type lectin receptors (CLRs), which can identify fungal invaders and trigger signal transduction pathways and cellular responses to eliminate pathogens. However, previous studies mostly focused on single receptor factors, lacking a systematic analysis of the genetic factors underlying the pathogenesis of FK. Herein,we performed a transcriptomic study of FK in mice model using RNA sequencing to obtain the relevant gene expression profiles and screen the differentially expressed gene sets, signaling pathways and regulatory networks of key genetic factors in the pathogenesis of FK in mice. As a result, several marker genes of FK were identified such as Il1b and Wnt5a. Wnt signaling pathway, cGMP-PKG signaling pathway and Hippo signaling pathway were significantly enriched during fungal infection in mice cornea. In conclusion, our study may provide a novel insights into the pathogenesis of FK as well as more target sites for its drug treatment.

INSTRUMENT(S): Illumina HiSeq 3000

ORGANISM(S): Mus musculus

SUBMITTER: mengting gong

PROVIDER: E-MTAB-7907 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Microbial keratitis is a major cause of blindness worldwide. An excessive host inflammatory response can occur even after adequate antimicrobial treatment. This results in tissue damage with corneal thinning and even perforation, which may require corneal transplantation. In this study we investigated the pathways involved in the pathophysiology of this disease by comparing the human transcriptome profile of tissue from culture-proven bacterial and fungal keratitis (n=7 and n=8 respectively) with normal non-infected cadaveric corneal tissue (C, n=12) using Illumina HT12 v4 microarrays. The causative organisms were Streptococcus pneumoniae (n=6) and Pseudomonas aeruginosa (n=1) for bacterial keratitis (BK). Fungal keratitis (FK) was caused by Fusarium sp. (n=5), Aspergillus sp. (n=2, A. flavus and terreus) and Lasiodiplodia sp. (n=1). Differential expression (DE) analysis revealed 2310 significantly altered probes in the BK v C comparison, and 1813 probes for FK v C. The most highly upregulated gene in both comparisons was MMP9 with fold changes (FC) of 64 (fdr-adjusted p<6 x10-11) for FK v C and 89 for BK v C (fdr-adjusted p<4 x10-11) respectively. Network co-expression analyses revealed the defense response, inflammatory response and extracellular matrix mechanisms to be the main functional pathways involved. Microarray results were validated by performing real-time quantitative PCR (RTqPCR) for 46 DE genes using RNA extracted from the same samples. There was a high correlation between log2 FC values from microarray and RTqPCR. Further studies are needed to evaluate the most highly differentially expressed genes as possible biomarkers of disease progression or therapeutic targets. Case - control study design. Corneal ulcer tissue from 8 bacterial and 9 fungal ulcers was excised at the time of corneal transplantation surgery and immediately preserved in RNALater. Non-infected corneal tissue from 13 cadaver corneas were the control tissue. Transcriptome profile generated using Illumina HT12 v4 beadchips. Differential expression analysis was performed with pairwise comparisons: bacterial ulcers versus controls, fungal ulcers versus controls and bacterial versus fungal ulcers. Microarray results validated with RTqPCR.

Dataset Information

The transcriptomic analysis of gene expression profiles associated with fungal keratitis in mice based on RNA-Seq

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets