Optimized Bioinformatic Strategy for the Analysis of Clinical Proteomic Data of the Endometrium in Chronic Endometritis

Key words: chronic endometritis; tandem mass spectrometry; functional clustering; tissue-specific expression.

2019, volume 11, issue 2, page 50.

DOI: https://doi.org/10.17691/stm2019.11.2.07

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The aim of the study is to analyze the entire set of proteins (proteome) expressed in the endometrial tissue and to identify protein markers specific for carcinogenesis.

Materials and Methods. Tissue samples were obtained using endometrial pipelle biopsy in women with chronic endometritis. After homogenization the samples were subjected to protein electrophoresis in polyacrylamide gel in the presence of sodium dodecyl sulfate according to the Lamley method. The proteins separated according to their molecular weights were digested by modified trypsin using the standard method. Obtained tryptic peptides were analyzed and identified by high-performance liquid chromatography coupled with tandem mass spectrometry.

The Human Protein Atlas and Tissue-Specific Gene Expression and Regulation databases were used to analyze the tissue-specific protein expression.

Functional protein annotation and gene set enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery bioinformatics resource.

Results. In the obtained endometrial tissue samples, 103 proteins were identified by tandem mass spectrometry. Analysis of tissue specificity showed that 83 proteins were expressed in the tissues of the female reproductive system. Functional annotation followed by clustering revealed that 51 proteins (49.5% of the identified ones) were encoded by the genes differentially expressed in cell cultures of the female reproductive organs. Along with that, 4 groups of proteins were expressed both in tumors (serous ovarian adenocarcinoma, immortalized ovarian cystadenoma, ovarian carcinoma) and in the immortalized normal ovarian surface epithelium.

Conclusion. Endometrial tissue proteins were identified using a clinical proteomic analysis. The bioinformatic approach allowed us to annotate the functional clusters of the identified proteins based on their potential involvement in carcinogenesis. The obtained data can serve as the starting point for further in-depth studies of the endometrium using the proteomic approach, as well as other OMICS technologies. Subsequent application of bioinformatic tools will allow revealing of molecular mechanisms of relationship between inflammation and endometrium tissue malignant transformation.

Cicinelli E., Matteo M., Tinelli R., Lepera A., Alfonso R., Indraccolo U., Marrocchella S., Greco P., Resta L. Prevalence of chronic endometritis in repeated unexplained implantation failure and the IVF success rate after antibiotic therapy. Hum Reprod 2015; 30(2): 323–330, https://doi.org/10.1093/humrep/deu292.
Kitaya K., Matsubayashi H., Yamaguchi K., Nishiyama R., Takaya Y., Ishikawa T., Yasuo T., Yamada H. Chronic endometritis: potential cause of infertility and obstetric and neonatal complications. Am J Reprod Immunol 2016; 75(1): 13–22, https://doi.org/10.1111/aji.12438.
Kasius J.C., Fatemi H.M., Bourgain C., Sie-Go D.M., Eijkemans R.J., Fauser B.C., Devroey P., Broekmans F.J. The impact of chronic endometritis on reproductive outcome. Fertil Steril 2011; 96(6): 1451–1456, https://doi.org/10.1016/j.fertnstert.2011.09.039.
Tortorella C., Piazzolla G., Matteo M., Pinto V., Tinelli R., Sabbà C., Fanelli M., Cicinelli E. Interleukin-6, interleukin-1β, and tumor necrosis factor in menstrual effluents as biomarkers of chronic endometritis. Fertil Steril 2014; 101(1): 242–247, https://doi.org/10.1016/j.fertnstert.2013.09.041.
Viana G.A., Cela V., Ruggiero M., Pluchino N., Genazzani A.R., Tantini C. Endometritis in infertile couples: the role of hysteroscopy and bacterial endotoxin. JBRA Assist Reprod 2015; 19(1): 21–23, https://doi.org/10.5935/1518-0557.20150006.
Arlas T.R., Wolf C.A., Petrucci B.P., Estanislau J.F., Gregory R.M., Jobim M.I., Mattos R.C. Proteomics of endometrial fluid after dexamethasone treatment in mares susceptible to endometritis. Theriogenology 2015; 84: 617–623, https://doi.org/10.1016/j.theriogenology.2015.04.019.
Diakos C.I., Charles K.A., McMillan D.C., Clarke S.J. Cancer-related inflammation and treatment effectiveness. Lancet Oncol 2014; 15(11): 493–503, https://doi.org/10.1016/s1470-2045(14)70263-3.
Lax S.F. Pathology of endometrial carcinoma. Adv Exp Med Biol 2017; 943: 75–96, https://doi.org/10.1007/978-3-319-43139-0_3.
Nakamura K., Smyth M.J. Targeting cancer-related inflammation in the era of immunotherapy. Immunol Cell Biol 2017; 95(4): 325–332, https://doi.org/10.1038/icb.2016.126.
Zhang X., Meng X., Chen Y., Leng S.X., Zhang H. The biology of aging and cancer: frailty, inflammation, and immunity. Cancer J 2017; 23(4): 201–205, https://doi.org/10.1097/00130404-201707000-00002.
Adamyan L.V., Starodubtseva N., Borisova A., Stepanian A.A., Chagovets V., Salimova D., Wang Z., Kononikhin A., Popov I., Bugrova A., Chingin K., Kozachenko A., Chen H., Frankevich V. Direct mass spectrometry differentiation of ectopic and eutopic endometrium in patients with endometriosis. J Minim Invasive Gynecol 2017; 25(3): 426–433, https://doi.org/10.1016/j.jmig.2017.08.658.
Martinez-Garcia E., Lesur A., Devis L., Cabrera S., Matias-Guiu X., Hirschfeld M., Asberger J., van Oostrum J., Casares de Cal M.L.Á., Gómez-Tato A., Reventos J., Domon B., Colas E., Gil-Moreno A. Targeted proteomics identifies proteomic signatures in liquid biopsies of the endometrium to diagnose endometrial cancer and assist in the prediction of the optimal surgical treatment. Clin Cancer Res 2017; 23(21): 6458–6467, https://doi.org/10.1158/1078-0432.ccr-17-0474.
Kosteria I., Anagnostopoulos A.K., Kanaka-Gantenbein C., Chrousos G.P., Tsangaris G.T. The use of proteomics in assisted reproduction. In Vivo 2017; 31(3): 267–283, https://doi.org/10.21873/invivo.11056.
Moza Jalali B., Likszo P., Skarzynski D.J. Proteomic and network analysis of pregnancy-induced changes in the porcine endometrium on day 12 of gestation. Mol Reprod Dev 2016; 83: 827–841, https://doi.org/10.1002/mrd.22733.
Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., Paulovich A., Pomeroy S.L., Golub T.R., Lander E.S., Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102(43): 15545–15550, https://doi.org/10.1073/pnas.0506580102.
The Human Protein Atlas. URL: https://www.proteinatlas.org/.
Tissue-Specific Gene Expression and Regulation (TiGER). URL: http://bioinfo.wilmer.jhu.edu/tiger/.
DAVID 6.8. URL: https://david.ncifcrf.gov/.
Huang da W., Sherman B.T., Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4(1): 44–57, https://doi.org/10.1038/nprot.2008.211.

Gainullin M.R., Yazykova A.B., Motovilova T.M., Klemente Apumaita H.M., Khodosova T.G., Gagaeva Y.A., Kolomina E.S., Kovaleva M.M., Militskaya A.A., Shcherina A.N., Boyko E.L., Zgoda V.G., Grechkanev G.O. Optimized Bioinformatic Strategy for the Analysis of Clinical Proteomic Data of the Endometrium in Chronic Endometritis. Sovremennye tehnologii v medicine 2019; 11(2): 50, https://doi.org/10.17691/stm2019.11.2.07