Objective. To evaluate the role of Pentraxin 3 level in PCOS-related infertility and its correlation with the disease's hormonal profile. Patients and Methods.A case-control study involves a total of 90 women for one year, 30 women diagnosed with PCOS who are fertile, 30 women diagnosed with PCOS who are infertile, and 30 healthy controls, all women in the early follicular phase were sent for baseline investigations: FSH, LH, AMH, Fasting blood sugar, Insulin, Testosterone, TSH, and Pentraxin 3 that were measured by sandwich electrochemiluminescence immunoassay.Results. There were statistically significant variations in LH, LH/FSH ratio, Testosterone levels, and AMH across the groups. The mean of Pentraxin for the fertile PCOS group (4.14 ± 1.97) ng/mL was significantly higher than for the infertile PCOS group (1.39 ± 1.10 ng/Ml) and control (1.99 ± 1.66 ng/mL). For the infertile PCOS group, the correlation of Pentraxin was significantly positive with age and negative with AMH. ROC Curve analysis showed a cutoff value of 1.05 ng/ml with a sensitivity of 46.67% and specificity of 83.33% for the infertile group.Conclusions. Pentraxin 3 level is significantly higher in fertile PCOS patients and lower in the infertile PCOS group in comparison to the control group suggesting its possible role in PCOSrelated infertility.

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Introduction: Polycystic ovary syndrome (POCS) is a mystery disorder with mysterious multiple players characterized by their mystic effects on disease pathophysiology resulting in various phenotypic pictures among the PCOS population. The Luteinizing hormone beta subunit (LH-B) (protein ID P01229) is a gonadotropin hormone secreted from the anterior pituitary belongs to the glycoprotein family, mapped on (chr19p13.3) and consists of three exons. It has a central role in promoting ovulation via stimulation of ovarian steroidogenesis. Objectives: This is a prospective laboratory-based cross-sectional study to determine genetic mutations associate with polycystic ovary syndrome (PCOS) among (30) Sudanese families ((cases n=35 families, 90 females, and (controls n= 11 families, 30 females) in Khartoum State, Sudan. Methods: Quantitative Enzyme-Linked Immuno-Sorbent Assay (ELISA), enzymatic methods, and polymerase chain reaction (PCR) used to analyze both the biochemical parameters and polymorphism detection followed by Sanger sequencing for genotyping in addition to bioinformatics software for protein structure and function. Results: All the biochemical parameters levels of (FBG, LH, Testosterone, Insulin, and lipid profile) elevated from the control group were statistically significant except for the serum FSH (cases=5.4±4.6, controls=5.3±2.8) and PRL ((cases=12.4±8.2, controls=8.0±6.1)) which were statistically insignificant p=0.94 and p=0.06. After Sanger sequencing; (5) single nucleotide polymorphisms ((rs5030775, A18T), rs746167425, R22K), (rs1800447, W28R), (rs35270001, H30R) and (rs34349826, I35T)) located on (exon 2) of the LH beta gene was statistically significant with serum LH, Testosterone, and insulin levels among PCOS families. Conclusion: This is the first molecular identification of a family-based study in Sudan exploring the genetic of the LHB gene and interrelated its serum level with PCOS manifestation. The revelation of these mutations will give a clue to the genetic inheritance mode links and might explain the abnormal poor response of controlled ovarian stimulation tests in some PCOS women.

Background: Insulin gene mapped on chromosome 11p15.5 consists of 3 exons, which translated to 51 residues of small globular protein secreted from the secretory granules in β-cells of the pancreatic gland. In this study, we used various computational approaches to identify nsSNPs, which probably be deleterious to the structure and/or function of insulin protein that might be associated with different disease pathophysiology. Methods: The data on human insulin gene retrieved from dbSNP/NCBI. Three functional analysis tools SIFT, Polyphen-2, Provean used to predict whether the effect of retrieved SNPs on the biological function of Insulin based on sequence homology, position-specific independent count scores and the impact of amino acid substitution on protein 3D structure. Then the shortlisted SNPs tested by three disease-associated SNPs predicting tools; Predictor of human Deleterious Single Nucleotide Polymorphisms, Pathogenic mutation prediction and SNPS&GO. Then the disease-causing SNPs analyzed to check protein stability analysis using I-mutant 2.0, and we used RaptorX server for homology modeling and its visualization analysis applied by Chimera through entering the native wild type protein structure and the mutant protein residues as input inquiry. Results: After retrieval of SNPs from the NCBI database, 130 SNPs classified as missense SNPs. From functional analysis software, 60 SNPs were predicted to be deleterious then they analyzed by disease – related software resulted on 28 SNPs, which checked for protein stability, and the final analysis revealed seven novel SNPs that decreased protein stability. Conclusion: These seven novel mutations within the proinsulin gene will give a clue for their effect on the biological function of insulin and might contribute on developing new biomarkers that will used on therapeutic and diagnostic area of different diseases.

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