Premature ovarian insufficiency is associated with global alterations in the regulatory landscape and gene expression in balanced X-autosome translocations

Genetic insights into the complexity of premature ovarian insufficiency

Premature Ovarian Insufficiency (POI) is a highly heterogeneous condition characterized by ovarian dysfunction in women occurring before the age of 40, representing a significant cause of female infertility. It manifests through primary or secondary amenorrhea. While more than half of POI cases are idiopathic, genetic factors play a pivotal role in all instances with known causes, contributing to approximately 20-25% of cases. This article comprehensively reviews the genetic factors associated with POI, delineating the primary candidate genes. The discussion delves into the intricate relationship between these genes and ovarian development, elucidating the functional consequences of diverse mutations to underscore the fundamental impact of genetic effects on POI. The identified genetic factors, encompassing gene mutations and chromosomal abnormalities, are systematically classified based on whether the resulting POI is syndromic or non-syndromic. Furthermore, this paper explores the genetic interplay between mitochondrial genes, such as Required for Meiotic Nuclear Division 1 homolog Gene (RMND1), Mitochondrial Ribosomal Protein S22 Gene (MRPS22), Leucine-rich Pentapeptide Repeat Gene (LRPPRC), and non-coding RNAs, including both microRNAs and Long non-coding RNAs, with POI. The insights provided serve to consolidate and enhance our understanding of the etiology of POI, contributing to establishing a theoretical foundation for diagnosing and treating POI patients, as well as for exploring the mechanisms underlying the disease.

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock

A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock species can provide critical insights into the distribution and characteristics of OCRs for disease treatment in livestock, thereby improving animal welfare. However, most current machine-learning methods for OCR prediction were originally designed for a limited number of model organisms, such as humans and some model organisms, and thus their performance on non-model organisms, specifically livestock, is often unsatisfactory. To bridge this gap, we propose DeepOCR, a lightweight depth-separable residual network model for predicting OCRs in livestock, including chicken, cattle, and sheep. DeepOCR integrates a single convolution layer and two improved residue structure blocks to extract and learn important features from the input DNA sequences. A fully connected layer was also employed to further process the extracted features and improve the robustness of the entire network. Our benchmarking experiments demonstrated superior prediction performance of DeepOCR compared to state-of-the-art approaches on testing datasets of the three species. The source code of DeepOCR is freely available for academic purposes at https://github.com/jasonzhao371/DeepOCR/. We anticipate DeepOCR servers as a practical and reliable computational tool for OCR-related studies in livestock species.

Transcriptomic profile of premature ovarian insufficiency with RNA-sequencing

Introduction

This study aimed to explore the transcriptomic profile of premature ovarian insufficiency (POI) by investigating alterations in gene expression.

Methods

A total of sixty-one women, comprising 31 individuals with POI in the POI group and 30 healthy women in the control group (HC group), aged between 24 and 40 years, were recruited for this study. The transcriptomic profiles of peripheral blood samples from all study subjects were analyzed using RNA-sequencing.

Results

The results revealed 39 differentially expressed genes in individuals with POI compared to healthy controls, with 10 upregulated and 29 downregulated genes. Correlation analysis highlighted the relationship between the expression of SLC25A39, CNIH3, and PDZK1IP1 and hormone levels. Additionally, an effective classification model was developed using SLC25A39, CNIH3, PDZK1IP1, SHISA4, and LOC389834. Functional enrichment analysis demonstrated the involvement of these differentially expressed genes in the "haptoglobin-hemoglobin complex," while KEGG pathway analysis indicated their participation in the "Proteoglycans in cancer" pathway.

Conclusion

The identified genes could play a crucial role in characterizing the genetic foundation of POI, potentially serving as valuable biomarkers for enhancing disease classification accuracy.

Characteristics and clinical evaluation of X chromosome translocations

BACKGROUND

Individuals with X chromosomal translocations, variable phenotypes, and a high risk of live birth defects are of interest for scientific study. These characteristics are related to differential breakpoints and various types of chromosomal abnormalities. To investigate the effects of X chromosome translocation on clinical phenotype, a retrospective analysis of clinical data for patients with X chromosome translocation was conducted. Karyotype analysis plus endocrine evaluation was utilized for all the patients. Additional semen analysis and Y chromosome microdeletions were assessed in male patients.

RESULTS

X chromosome translocations were detected in ten cases, including seven females and three males. Infantile uterus and no ovaries were detected in case 1 (FSH: 114 IU/L, LH: 30.90 mIU/mL, E2: < 5.00 pg/ml), and the karyotype was confirmed as 46,X,t(X;22)(q25;q11.2) in case 1. Infantile uterus and small ovaries were both visible in two cases (FSH: 34.80 IU/L, LH: 17.06 mIU/mL, E2: 15.37 pg/ml in case 2; FISH: 6.60 IU/L, LH: 1.69 mIU/mL, E2: 23.70 pg/ml in case 3). The karyotype was detected as 46,X,t(X;8)(q13;q11.2) in case 2 and 46,X,der(X)t(X;5)(q21;q31) in case 3. Normal reproductive hormone levels and fertility abilities were found for cases 4, 6 and 7. The karyotype were detected as 46,X,t(X;5)(p22.3;q22) in case 4 and 46,X,der(X)t(X;Y)(p22.3;q11.2) in cases 6 and 7. These patients exhibited unremarkable clinical manifestations but experienced a history of abnormal chromosomal pregnancy. Normal phenotype and a complex reciprocal translocation as 46,X,t(X;14;4)(q24;q22;q33) were observed in case 5 with a history of spontaneous abortions. In the three male patients, multiple semen analyses confirmed the absence of sperm. Y chromosome microdeletion and hormonal analyses were normal. The karyotypes were detected as 46,Y,t(X;8)(q26;q22), 46,Y,t(X;1)(q26;q23), 46,Y,t(X;3)(q26;p24), respectively.

CONCLUSIONS

Our study provides insights into individuals with X chromosome translocations. The clinical phenotypes are variable and unpredictable due to differences in breakpoints and X chromosome inactivation (XCI) patterns. Our results suggest that physicians should focus on the characteristics of the X chromosome translocations and provide personalized clinical evaluations in genetic counselling.