Identification of candidate disease genes in patients with common variable immunodeficiency

Abstract

Background: Common variable immunodeficiency (CVID), the most prevalent form of primary immunodeficiency (PID), is characterized by hypogammaglobulinemia and recurrent infections. Understanding protein-protein interaction (PPI) networks of CVID genes and identifying candidate CVID genes are critical steps in facilitating the early diagnosis of CVID. Here, the aim was to investigate PPI networks of CVID genes and identify candidate CVID genes using computation techniques.

Methods: Network density and biological distance were used to study PPI data for CVID and PID genes obtained from the STRING database. Gene expression data of patients with CVID were obtained from the Gene Expression Omnibus, and then Pearson’s correlation coefficient, a PPI database, and Kyoto Encyclopedia of Genes and Genomes were used to identify candidate CVID genes. We then evaluated our predictions and identified differentially expressed CVID genes.

Results: The majority of CVID genes are characterized by a high network density and small biological distance, whereas most PID genes are characterized by a low network density and large biological distance, indicating that CVID genes are more functionally similar to each other and closely interact with one other compared with PID genes. Subsequently, we identified 172 CVID candidate genes that have similar biological functions to known CVID genes, and eight genes were recently reported as CVID-related genes. MYC, a candidate gene, was down-regulated in CVID duodenal biopsies, but up-regulated in blood samples compared with levels in healthy controls.

Conclusion: Our findings will aid in a better understanding of the complex of CVID genes, possibly further facilitating the early diagnosis of CVID.

Graphical abstract

Keywords

common variable immunodeficiency / primary immunodeficiency / candidate CVID genes / protein-protein interactions / network density / biological distance

References

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/ 10.1007/s40484-019-0174-9.

AUTHOR CONTRIBUTIONS

Guojun Liu performed the computations and analysis; Mikhail A. Bolkov contributed to the preparation and interpretation of the data; Irina A. Tuzankina and Irina G. Danilova conceptualized the study. All authors contributed to the writing of the manuscript and approved the final version of the manuscript.

ACKNOWLEDGEMENTS

This study was funded by the Act 211 Government of the Russian Federation (No. 02.A03.21.0006) and the IIP UB RAS project (No. AAAA-A18-118020590108-7).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Guojun Liu, Mikhail A. Bolkov, Irina A. Tuzankina and Irina G. Danilova declare that they have no conflict of interests.

All procedures performed in studies were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

RIGHTS & PERMISSIONS

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