Dataset Information

Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek's disease using next generation sequencing.

ABSTRACT:

Background

Marek's disease (MD) is a highly neoplastic disease primarily affecting chickens, and remains as a chronic infectious disease that threatens the poultry industry. Copy number variation (CNV) has been examined in many species and is recognized as a major source of genetic variation that directly contributes to phenotypic variation such as resistance to infectious diseases. Two highly inbred chicken lines, 6₃ (MD-resistant) and 7₂ (MD-susceptible), as well as their F₁ generation and six recombinant congenic strains (RCSs) with varied susceptibility to MD, are considered as ideal models to identify the complex mechanisms of genetic and molecular resistance to MD.

Results

In the present study, to unravel the potential genetic mechanisms underlying resistance to MD, we performed a genome-wide CNV detection using next generation sequencing on the inbred chicken lines with the assistance of CNVnator. As a result, a total of 1649 CNV regions (CNVRs) were successfully identified after merging all the nine datasets, of which 90 CNVRs were overlapped across all the chicken lines. Within these shared regions, 1360 harbored genes were identified. In addition, 55 and 44 CNVRs with 62 and 57 harbored genes were specifically identified in line 6₃ and 7₂, respectively. Bioinformatics analysis showed that the nearby genes were significantly enriched in 36 GO terms and 6 KEGG pathways including JAK/STAT signaling pathway. Ten CNVRs (nine deletions and one duplication) involved in 10 disease-related genes were selected for validation by using quantitative real-time PCR (qPCR), all of which were successfully confirmed. Finally, qPCR was also used to validate two deletion events in line 7₂ that were definitely normal in line 6₃. One high-confidence gene, IRF2 was identified as the most promising candidate gene underlying resistance and susceptibility to MD in view of its function and overlaps with data from previous study.

Conclusions

Our findings provide valuable insights for understanding the genetic mechanism of resistance to MD and the identified gene and pathway could be considered as the subject of further functional characterization.

SUBMITTER: Bai H

PROVIDER: S-EPMC7364486 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek's disease using next generation sequencing.

Bai Hao H He Yanghua Y Ding Yi Y Chu Qin Q Lian Ling L Heifetz Eliyahu M EM Yang Ning N Cheng Hans H HH Zhang Huanmin H Chen Jilan J Song Jiuzhou J

BMC genetics 20200716 1

<h4>Background</h4>Marek's disease (MD) is a highly neoplastic disease primarily affecting chickens, and remains as a chronic infectious disease that threatens the poultry industry. Copy number variation (CNV) has been examined in many species and is recognized as a major source of genetic variation that directly contributes to phenotypic variation such as resistance to infectious diseases. Two highly inbred chicken lines, 6<sub>3</sub> (MD-resistant) and 7<sub>2</sub> (MD-susceptible), as well ...[more]

PMID: 32677890

Similar Datasets

Project description:Genomic instability is a hallmark of cancer often associated with poor patient outcome and resistance to targeted therapy. Assessment of genomic instability in bulk tumor or biopsy can be complicated due to sample availability, surrounding tissue contamination, or tumor heterogeneity. The Epic Sciences circulating tumor cell (CTC) platform utilizes a non-enrichment based approach for the detection and characterization of rare tumor cells in clinical blood samples. Genomic profiling of individual CTCs could provide a portrait of cancer heterogeneity, identify clonal and sub-clonal drivers, and monitor disease progression. To that end, we developed a single cell Copy Number Variation (CNV) Assay to evaluate genomic instability and CNVs in patient CTCs. For proof of concept, prostate cancer cell lines, LNCaP, PC3 and VCaP, were spiked into healthy donor blood to create mock patient-like samples for downstream single cell genomic analysis. In addition, samples from seven metastatic castration resistant prostate cancer (mCRPC) patients were included to evaluate clinical feasibility. CTCs were enumerated and characterized using the Epic Sciences CTC Platform. Identified single CTCs were recovered, whole genome amplified, and sequenced using an Illumina NextSeq 500. CTCs were then analyzed for genome-wide copy number variations, followed by genomic instability analyses. Large-scale state transitions (LSTs) were measured as surrogates of genomic instability. Genomic instability scores were determined reproducibly for LNCaP, PC3, and VCaP, and were higher than white blood cell (WBC) controls from healthy donors. A wide range of LST scores were observed within and among the seven mCRPC patient samples. On the gene level, loss of the PTEN tumor suppressor was observed in PC3 and 5/7 (71%) patients. Amplification of the androgen receptor (AR) gene was observed in VCaP cells and 5/7 (71%) mCRPC patients. Using an in silico down-sampling approach, we determined that DNA copy number and genomic instability can be detected with as few as 350K sequencing reads. The data shown here demonstrate the feasibility of detecting genomic instabilities at the single cell level using the Epic Sciences CTC Platform. Understanding CTC heterogeneity has great potential for patient stratification prior to treatment with targeted therapies and for monitoring disease evolution during treatment.

Dataset Information

Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek's disease using next generation sequencing.

Background

Results

Conclusions

Publications

Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek's disease using next generation sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets