Influence of PCOS in Obese vs. Non-Obese women from Mesenchymal Progenitors Stem Cells and Other Endometrial Cells: An in silico biomarker discovery

Candidate genes for infertility: an in-silico study based on cytogenetic analysis

Background

The cause of infertility remains unclear in a significant proportion of reproductive-age couples who fail to conceive naturally. Chromosomal aberrations have been identified as one of the main genetic causes of male and female infertility. Structural chromosomal aberrations may disrupt the functioning of various genes, some of which may be important for fertility. The present study aims to identify candidate genes and putative functional interaction networks involved in male and female infertility using cytogenetic data from cultured peripheral blood lymphocytes of infertile patients.

Methods

Karyotypic analyses was done in 201 infertile patients (100 males and 101 females) and 201 age and gender matched healthy controls (100 males and 101 females) after 72 h peripheral lymphocyte culturing and GTG banding, followed by bioinformatic analysis using Cytoscape v3.8.2 and Metascape.

Results

Several chromosomal regions with a significantly higher frequency of structural aberrations were identified in the infertile males (5q2, 10q2, and 17q2) and females (6q2, 16q2, and Xq2). Segregation of the patients based on type of infertility (primary v/s secondary infertility) led to the identification of chromosomal regions with a significantly higher frequency of structural aberrations exclusively within the infertile males (5q2, 17q2) and females (16q2) with primary infertility. Cytoscape identified two networks specific to these regions: a male specific network with 99 genes and a female specific network with 109 genes. The top enriched GO terms within the male and female infertility networks were “skeletal system morphogenesis” and “mRNA transport” respectively. PSME3, PSMD3, and CDC27 were the top 3 hub genes identified within the male infertility network. Similarly, UPF3B, IRF8, and PSMB1 were the top 3 hub genes identified with the female infertility network. Among the hub genes identified in the male- and female-specific networks, PSMB1, PSMD3, and PSME3 are functional components of the proteasome complex. These hub genes have a limited number of reports related to their respective roles in maintenance of fertility in mice model and humans and require validation in further studies.

Conclusion

The candidate genes predicted in the present study can serve as targets for future research on infertility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01320-x.

The Endometrial Transcriptome of Metabolic and Inflammatory Pathways During the Window of Implantation Is Deranged in Infertile Obese Polycystic Ovarian Syndrome Women

Introduction and Aim: Obese women with polycystic ovarian syndrome (PCOS) have a reduced rate of spontaneous conception even when their cycles are ovulatory. Endometrial receptivity is an important factor for poor implantation and increased miscarriage rates. Mechanisms in which both pathologies modify the endometrium are not fully clarified. The aim of our study was to compare the endometrial transcriptomic profiles between infertile obese PCOS (O-PCOS) women and infertile normal weight subjects during the window of implantation in ovulatory menstrual cycles. Methods: We conducted a prospective transcriptomic analysis of the endometrium using RNA sequencing. In this way, potential endometrial mechanisms leading to the poor reproductive outcome in O-PCOS patients could be characterized. Endometrial samples during days 21-23 of the menstrual cycle were collected from infertile O-PCOS women (n = 11) and normal weight controls (n = 10). Subgroups were defined according to the ovulatory/anovulatory status in the natural cycles, and O-PCOS women were grouped into the O-PCOS ovulatory (O-PCOS-ovul) subgroup. RNA isolation, sequencing with library reparation, and subsequent RNAseq data analysis were performed. Results: Infertile O-PCOS patients had 610 differentially expressed genes (DEGs), after adjustment for multiple comparisons with normal weight infertile controls, related to obesity (MXRA5 and ECM1), PCOS (ADAMTS19 and SLC18A2), and metabolism (VNN1 and PC). In the ovulatory subgroup, no DEGs were found, but significant differences in canonical pathways and the upstream regulator were revealed. According to functional and upstream analyses of ovulatory subgroup comparisons, the most important biological processes were related to inflammation (TNFR1 signaling), insulin signaling (insulin receptor signaling and PI3/AKT), fatty acid metabolism (stearate biosynthesis I and palmitate biosynthesis I), and lipotoxicity (unfolded protein response pathway). Conclusions: We demonstrated that endometrial transcription in ovulatory O-PCOS patients is deranged in comparison with the control ovulatory endometrium. The most important pathways of differentiation include metabolism and inflammation. These processes could also represent potential mechanisms for poor embryo implantation, which prevent the development of a successful pregnancy. ClinicalTrials.gov ID: NCT03353948.

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening

Background

Feature screening plays a critical role in handling ultrahigh dimensional data analyses when the number of features exponentially exceeds the number of observations. It is increasingly common in biomedical research to have case-control (binary) response and an extremely large-scale categorical features. However, the approach considering such data types is limited in extant literature. In this article, we propose a new feature screening approach based on the iterative trend correlation (ITC-SIS, for short) to detect important susceptibility loci that are associated with the polycystic ovary syndrome (PCOS) affection status by screening 731,442 SNP features that were collected from the genome-wide association studies.

Results

We prove that the trend correlation based screening approach satisfies the theoretical strong screening consistency property under a set of reasonable conditions, which provides an appealing theoretical support for its outperformance. We demonstrate that the finite sample performance of ITC-SIS is accurate and fast through various simulation designs.

Conclusion

ITC-SIS serves as a good alternative method to detect disease susceptibility loci for clinic genomic data.

Obesity does not alter endometrial gene expression in women with endometriosis

RESEARCH QUESTION

Does obesity affect endometrial gene expression in women with endometriosis, specifically women with stage I disease?

DESIGN

Differential gene expression analysis was conducted on endometrium from women with and without endometriosis (n = 169). Women were diagnosed after surgical visualization and staged according to the revised American Society for Reproductive Medicine (stage I-IV). Women were grouped by body mass index (BMI) (kg/m²) as underweight, normal, pre-obese or obese. After accounting for menstrual cycle stage, endometrial gene expression was analysed by BMI (continuous and grouped) in women with endometriosis, and in non-endometriosis controls.

RESULTS

No significant interaction effect was found between BMI and endometriosis status on endometrial gene expression. We have previously reported that obese women with endometriosis have a reduced incidence of stage I disease; however, stratifying our analysis into stage I endometriosis versus combined II, III and IV endometriosis failed to reveal any differentially expressed endometrial genes between normal, pre-obese and obese patients.

CONCLUSIONS

Despite obesity having deleterious effects on endometrial gene expression in other gynaecological pathologies, e.g. endometrial cancer and polycystic ovary syndrome, our results do not support an association between BMI and altered endometrial gene expression in women with or without endometriosis.

Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.