PCOSKB: A KnowledgeBase on genes, diseases, ontology terms and biochemical pathways associated with PolyCystic Ovary Syndrome

Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques

Background

To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods.

Methods

We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the "limma" R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks.

Results

We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes.

Conclusion

Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.

Network pharmacology-based strategic prediction and target identification of apocarotenoids and carotenoids from standardized Kashmir saffron (Crocus sativus L.) extract against polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a hormonal disorder that affects women of reproductive age, characterized by a range of symptoms, including irregular menstrual cycles, excess male hormones (androgens), metabolic abnormalities such as hyperinsulinemia, hyperlipidemia, and metabolic disturbances like glucose imbalance. Botanical supplements are perceived first and safe choice over available regimens to regulate PCOS. There are several reports available stating that apocarotenoids, carotenoids, and whole extracts of Crocus sativus were identified to have a potential role in the management of women health. This study aimed to propose a network pharmacology-based method to determine the potential therapeutic pathways of phytoconstituents (apocarotenoids and carotenoids) of UHPLC-PDA standardized stigma-based Crocus sativus extract (CSE) for the management of PCOS. Furthermore, to validate the potential targets and signaling pathways, these apocarotenoids, and carotenoids were screened for molecular docking and in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. The information regarding PCOS-related genes was retrieved from the PCOS knowledge database (PCOSKB), resulting in an established network between putative targets of PCOS and Crocus sativus extract phytochemicals to prevail the mechanism of action. Based on the screening conditions, 4 prominent targets namely, serine/threonine kinase 1 (AKT1), signal transducer and activator of transcription (STAT3), mitogen-activated protein kinase 3 (MAPK3), and mitogen-activated protein kinase 1 (MAPK1), were identified through network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that MAP kinase and serine-threonine pathways were found prominent targets in PCOS. Further, a molecular docking study shows that crocetin, picrocrocin, and safranal had the best binding affinity for the identified targets. In silico ADMET results revealed that carotenoids and apocarotenoids were found to have the maximum bioavailability and were able to cross the blood-brain barrier without any toxic effects. The combined results revealed that the apocarotenoids and carotenoids of Crocus sativus extract could act on various targets to regulate multiple pathways related to PCOS.

Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network

Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.

Transcriptomic screening to identify hub genes and drug signatures for PCOS based on RNA-Seq data in granulosa cells

BACKGROUND

Polycystic ovary syndrome (PCOS) is one of the most incident reproductive diseases, and remains the main cause of female infertility. Granulosa cells play a critical role in normal follicle development and steroid hormones synthesis. In spite of extensive research, no sole medication has been approved by FDA to treat PCOS. This study aimed to investigate the novel therapeutics targets in PCOS, focusing on granulosa cells transcriptome functional analysis with a drug repositioning approach.

METHODS

PCOS microarray and RNA-Seq datasets in granulosa cells were screened and reanalyzed. KEGG pathway enrichment and interaction network analyses were performed and followed by a set of drug signature screening and Poly-pharmacology survey.

RESULTS

545 deregulated genes were identified via filters including p_adj < 0.05 and |log2FC| > 1. Amongst the top 15 KEGG pathways significantly enriched, metabolism of xenobiotics by cytochrome P450, steroid hormone biosynthesis and ovarian steroidogenesis were observed. The Protein-Protein Interaction network identified 18 hub genes amongst this set. Interestingly, most candidate drug signatures have been introduced by databases are either FDA approved or entered into clinical trials, including melatonin, resveratrol and raloxifene. Investigational or experimental introduced drugs obey rules of drug-likeness with almost safe and acceptable ADMET properties. Notably, 21 top target genes of the final drug set were also included in the granulosa significant differentially expressed genes.

CONCLUSION

Results of the current study represent approved, investigational and experimental drug signatures according to the differentially expressed genes in granulosa cells with supported literature reviews. This data might be useful for researchers and clinicians to pave the way for better management of PCOS.

ADPG: Biomedical entity recognition based on Automatic Dependency Parsing Graph

Named entity recognition is a key task in text mining. In the biomedical field, entity recognition focuses on extracting key information from large-scale biomedical texts for the downstream information extraction task. Biomedical literature contains a large amount of long-dependent text, and previous studies use external syntactic parsing tools to capture word dependencies in sentences to achieve nested biomedical entity recognition. However, the addition of external parsing tools often introduces unnecessary noise to the current auxiliary task and cannot improve the performance of entity recognition in an end-to-end way. Therefore, we propose a novel automatic dependency parsing approach, namely the ADPG model, to fuse syntactic structure information in an end-to-end way to recognize biomedical entities. Specifically, the method is based on a multilayer Tree-Transformer structure to automatically extract the semantic representation and syntactic structure in long-dependent sentences, and then combines a multilayer graph attention neural network (GAT) to extract the dependency paths between words in the syntactic structure to improve the performance of biomedical entity recognition. We evaluated our ADPG model on three biomedical domain and one news domain datasets, and the experimental results demonstrate that our model achieves state-of-the-art results on these four datasets with certain generalization performance. Our model is released on GitHub: https://github.com/Yumeng-Y/ADPG.

Advancements in lead therapeutic phytochemicals polycystic ovary syndrome: A review

Polycystic ovary syndrome (PCOS) is one of the most common endocrine diseases in women of reproductive age and features complex pathological symptoms and mechanisms. Existing medical treatments have, to some extent, alleviated the deterioration of PCOS. However, these strategies only temporarily control symptoms, with a few side effects and no preventive effect. Phytochemicals extracted from medicinal herbs and plants are vital for discovering novel drugs. In recent years, many kinds of research have proven that phytochemicals isolated from traditional Chinese medicine (TCM) and medicinal plants show significant potential in preventing, alleviating, and treating PCOS. Nevertheless, compared to the abundance of experimental literature and minimal specific-topic reviews related to PCOS, there is a lack of systematic reviews to summarize these advancements in this promising field. Under this background, we systematically document the progress of bioactive phytochemicals from TCM and medicinal plants in treating PCOS, including flavonoids, polyphenols, and alkaloids. According to the literature, these valuable phytochemicals demonstrated therapeutic effects on PCOS supported by in vivo and in vitro experiments, mainly depending on anti-inflammatory, antioxidation, improvement of hormone disorder and insulin resistance (IR), and alleviation of hyperinsulinemia. Based on the current progress, future research directions should emphasize 1) exploring bioactive phytochemicals that potentially mediate bone metabolism for the treatment of PCOS; 2) improving unsatisfactory bioavailability by using advanced drug delivery systems such as nanoparticles and antibody-conjugated drugs, as well as a chemical modification; 3) conducting in-depth research on the pathogenesis of PCOS to potentially impact the gut microbiota and its metabolites in the evolution of PCOS; 4) revealing the pharmacological effects of these bioactive phytochemicals on PCOS at the genetic level; and 5) exploring the hypothetical and unprecedented functions in regulating PCOS by serving as proteolysis-targeting chimeras and molecular glues compared with traditional small molecule drugs. In brief, this review aims to provide detailed mechanisms of these bioactive phytochemicals and hopefully practical and reliable insight into clinical applications concerning PCOS.

Noise Reduction Learning Based on XLNet-CRF for Biomedical Named Entity Recognition

In recent years, Biomedical Named Entity Recognition (BioNER) systems have mainly been based on deep neural networks, which are used to extract information from the rapidly expanding biomedical literature. Long-distance context autoencoding language models based on transformers have recently been employed for BioNER with great success. However, noise interference exists in the process of pre-training and fine-tuning, and there is no effective decoder for label dependency. Current models have many aspects in need of improvement for better performance. We propose two kinds of noise reduction models, Shared Labels and Dynamic Splicing, based on XLNet encoding which is a permutation language pre-training model and decoding by Conditional Random Field (CRF). By testing 15 biomedical named entity recognition datasets, the two models improved the average F1-score by 1.504 and 1.48, respectively, and state-of-the-art performance was achieved on 7 of them. Further analysis proves the effectiveness of the two models and the improvement of the recognition effect of CRF, and suggests the applicable scope of the models according to different data characteristics.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

Further delineation of familial polycystic ovary syndrome (PCOS) via whole-exome sequencing: PCOS-related rare FBN3 and FN1 gene variants are identified

AIM

To identify pathogenic rare coding Mendelian/high-effect size variant(s) by whole-exome sequencing in familial polycystic ovary syndrome (PCOS) patients to elucidate PCOS-related pathways.

METHODS

Twenty women and their affected available relatives diagnosed with PCOS according to Rotterdam criteria were recruited. Whole-exome sequencing on germ-line DNA from 31 PCOS probands and their affected relatives was performed. Whole-exome sequencing data were further evaluated by pathway and chemogenomics analyses. In-slico analysis of candidate variants were done by VarCards for functional predictions and VarSite for impact on three-dimensional (3D) structures in the candidate proteins.

RESULTS

Two heterozygous rare FBN3 missense variants in three patients, and one FN1 missense variant in one patient from three different PCOS families were identified.

CONCLUSION

We identified three novel FBN3 and FN1 variants for the first time in the literature and linked with PCOS. Further functional studies may identify causality of these newly discovered PCOS-related variants, and their role yet remains to be investigated. Our findings may improve our understanding of the biological pathways affected and identify new drug targets.

The PNA mouse may be the best animal model of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) exerts negative effects on females of childbearing age. It is important to identify more suitable models for fundamental research on PCOS. We evaluated animal models from a novel perspective with the aim of helping researchers select the best model for PCOS. RNA sequencing was performed to investigate the mRNA expression profiles in the ovarian tissues of mice with dehydroepiandrosterone (DHEA) plus high-fat diet (HFD)-induced PCOS. Meanwhile, 14 datasets were obtained from the Gene Expression Omnibus (GEO), including eight studies on humans, three on rats and three on mice, and genes associated with PCOS were obtained from the PCOSKB website. We compared the consistency of each animal model and human PCOS in terms of DEGs and pathway enrichment analysis results. There were 239 DEGs shared between prenatally androgenized (PNA) mice and PCOS patients. Moreover, 1113 genes associated with PCOS from the PCOSKB website were identified among the DEGs of PNA mice. A total of 134 GO and KEGG pathways were shared between PNA mice and PCOS patients. These findings suggest that the PNA mouse model is the best animal model to simulate PCOS.

Posttranslational modifications in pathogenesis of PCOS

Polycystic ovary syndrome (PCOS) is a lifelong reproductive, metabolic, and psychiatric disorder that affects 5-18% of women, which is associated with a significantly increased lifetime risk of concomitant diseases, including type 2 diabetes, psychiatric disorders, and gynecological cancers. Posttranslational modifications (PTMs) play an important role in changes in protein function and are necessary to maintain cellular viability and biological processes, thus their maladjustment can lead to disease. Growing evidence suggests the association between PCOS and posttranslational modifications. This article mainly reviews the research status of phosphorylation, methylation, acetylation, and ubiquitination, as well as their roles and molecular mechanisms in the development of PCOS. In addition, we briefly summarize research and clinical trials of PCOS therapy to advance our understanding of agents that can be used to target phosphorylated, methylated, acetylated, and ubiquitinated PTM types. It provides not only ideas for future research on the mechanism of PCOS but also ideas for PCOS treatments with therapeutic potential.

An update on polycystic ovary syndrome: A review of the current state of knowledge in diagnosis, genetic etiology, and emerging treatment options

Polycystic ovary syndrome is the most common endocrine disorder in women of reproductive age, which is still incurable. However, the symptoms can be successfully managed with proper medication and lifestyle interventions. Despite its prevalence, little is known about its etiology. In this review article, the up-to-date diagnostic features and parameters recommended on the grounds of evidence-based data and different guidelines are explored. The ambiguity and insufficiency of data when diagnosing adolescent women have been put under special focus. We look at some of the most recent research done to establish relationships between different gene polymorphisms with polycystic ovary syndrome in various populations along with the underestimated impact of environmental factors like endocrine-disrupting chemicals on the reproductive health of these women. Furthermore, the article concludes with existing treatments options and the scopes for advancement in the near future. Various therapies have been considered as potential treatment through multiple randomized controlled studies, and clinical trials conducted over the years are described in this article. Standard therapies ranging from metformin to newly found alternatives based on vitamin D and gut microbiota could shine some light and guidance toward a permanent cure for this female reproductive health issue in the future.

DES-Tcell is a knowledgebase for exploring immunology-related literature

T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave’s disease (GD) and Hashimoto’s thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.

High fructose-enriched diet synergistically exacerbates endocrine but not metabolic changes in letrozole-induced polycystic ovarian syndrome in Wistar rats

Background

Polycystic Ovarian Syndrome (PCOS) is a multifactorial endocrine-metabolic disorder that highly contributes to the prevalence of infertility globally. The increased consumption of refined carbohydrate, particularly fructose has been associated with pandemic metabolic disorders, including in women of reproductive age. However, the effects of high fructose consumption (FRD) on endocrine and metabolic disorders associated with PCOS are not clear. Therefore, this study investigated the effects of FRD on endocrine/metabolic changes in letrozole-induced PCOS in Wistar rats.

Materials and methods

Twenty-eight adult female Wistar rats were randomly allotted into 4 groups and treated with vehicle, letrozole (LET; 0.5 mg/kg), FRD (D-fructose chow pellet mixture) and LET + FRD. The treatment lasted for 21days.

Results

Data showed a significant increase in ovarian weight, liver weight, luteinising hormone (LH), testosterone and decrease in follicle stimulating hormone as well as moderate histopathological changes in the fallopian tube, uterus and liver of animals with PCOS. FRD-treated group showed a significant increase in ovarian weight and liver weight but no significant alteration in hormonal profile or histopathological changes in uterus and fallopian tube. However, FRD significantly altered hormonal profile with consequent histopathological changes in fallopian tube and uterus but FRD did not alter ovarian/liver weight or blood glucose in animals with PCOS when compared with animals without PCOS.

Conclusion

The present results demonstrate that FRD synergistically aggravates endocrine but not metabolic changes in PCOS, suggesting that FRD might deteriorate endocrine-related phenotypes in PCOS.

Deciphering the multi-scale mechanisms of Tephrosia purpurea against polycystic ovarian syndrome (PCOS) and its major psychiatric comorbidities: Studies from network pharmacological perspective

Tephrosia purpurea (T. purpurea), a plant belonging to Fabaceae (pea) family, is a well-known Ayurvedic herb and commonly known as Sarapunkha in traditional Indian medicinal system. Described as "Sarwa wranvishapaka", i.e. having a capability to heal all types of wounds, it is particularly recognized for its usage in splenomegaly. Towards exploring the comprehensive effects of T. purpurea against polycystic ovarian syndrome (PCOS) and three comorbid neuropsychiatric diseases (anxiety, depression, and bipolar disorder), its constituent phytochemicals (PCs) were extensively reviewed and their network pharmacology evaluation was carried out in this study. The complex regulatory potential of its 76 PCs against PCOS is enquired by developing and analyzing high confidence tripartite networks of protein targets of each phytochemical at both pathway and disease association scales. We also developed a high-confidence human Protein-Protein Interaction (PPI) sub-network specific to PCOS, explored its modular architecture, and probed 30 drug-like phytochemicals (DPCs) having multi-module regulatory potential. The phytochemicals showing good binding affinity towards their protein targets were also evaluated for similarity against currently available approved drugs present in DrugBank. Multi-targeting and synergistic capacities of 12 DPCs against 10 protein targets were identified and evaluated using molecular docking and interaction analyses. Eight DPCs as a potential source of PCOS and its comorbidity regulators are reported in T. purpurea. The results of network-pharmacology study highlight the therapeutic relevance of T. purpurea as PCOS-regulator and demonstrate the effectiveness of the approach in revealing action-mechanism of Ayurvedic herbs from holistic perspective.

Prenatal Androgenization Alters the Development of GnRH Neuron and Preoptic Area RNA Transcripts in Female Mice

Polycystic ovary syndrome (PCOS) is the most common form of infertility in women. The causes of PCOS are not yet understood and both genetics and early-life exposure have been considered as candidates. With regard to the latter, circulating androgens are elevated in mid-late gestation in women with PCOS, potentially exposing offspring to elevated androgens in utero; daughters of women with PCOS are at increased risk for developing this disorder. Consistent with these clinical observations, prenatal androgenization (PNA) of several species recapitulates many phenotypes observed in PCOS. There is increasing evidence that symptoms associated with PCOS, including elevated luteinizing hormone (LH) (and presumably gonadotropin-releasing hormone [GnRH]) pulse frequency emerge during the pubertal transition. We utilized translating ribosome affinity purification coupled with ribonucleic acid (RNA) sequencing to examine GnRH neuron messenger RNAs from prepubertal (3 weeks) and adult female control and PNA mice. Prominent in GnRH neurons were transcripts associated with protein synthesis and cellular energetics, in particular oxidative phosphorylation. The GnRH neuron transcript profile was affected more by the transition from prepuberty to adulthood than by PNA treatment; however, PNA did change the developmental trajectory of GnRH neurons. This included families of transcripts related to both protein synthesis and oxidative phosphorylation, which were more prevalent in adults than in prepubertal mice but were blunted in PNA adults. These findings suggest that prenatal androgen exposure can program alterations in the translatome of GnRH neurons, providing a mechanism independent of changes in the genetic code for altered expression.

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. Epidemiological findings revealed that women with PCOS are prone to develop certain cancer types due to their shared metabolic and endocrine abnormalities. However, the mechanism that relates PCOS and oncogenesis has not been addressed. Herein, in this review article the genomic status, transcriptional and protein profiles of 264 strongly PCOS related genes (PRG) were evaluated in endometrial cancer (EC), ovarian cancer (OV) and breast cancer (BC) exploring oncogenic databases. The genomic alterations of PRG were significantly higher when compared with a set of non-diseases genes in all cancer types. PTEN had the highest number of mutations in EC, TP53, in OC, and FSHR, in BC. Based on clinical data, women older than 50 years and Black or African American females carried the highest ratio of genomic alterations among all cancer types. The most altered signaling pathways were p53 in EC and OC, while Fc epsilon RI in BC. After evaluating PRG in normal and cancer tissue, downregulation of the differentially expressed genes was a common feature. Less than 30 proteins were up and downregulated in all cancer contexts. We identified 36 highly altered genes, among them 10 were shared between the three cancer types analyzed, which are involved in the cell proliferation regulation, response to hormone and to endogenous stimulus. Despite limited PCOS pharmacogenomics studies, 10 SNPs are reported to be associated with drug response. All were missense mutations, except for rs8111699, an intronic variant characterized as a regulatory element and presumably binding site for transcription factors. In conclusion, in silico analysis revealed key genes that might participate in PCOS and oncogenesis, which could aid in early cancer diagnosis. Pharmacogenomics efforts have implicated SNPs in drug response, yet still remain to be found.

Genetic construction between polycystic ovarian syndrome and type 2 diabetes

Polycystic ovarian syndrome (PCOS) in reproductive-aged women is identified to be one of the endocrine disorders. This heterogeneous disorder is categorized through oligo-anovulation and hyperandrogenemia. National institutes of health and Rotterdam criterions were used to diagnose PCOS women. Type 2 Diabetes (T2D) is one of the complications in PCOS which is connected through insulin resistance (IR), which is a condition in which liver, muscles and fat infrequently respond to the hormones, and this leads to extreme IR and consequently leads to T2D disease. PCOS is inherited by the autosomal dominant mode of inheritance and may also with the different intricate patterns. Till now, many studies have been performed in PCOS with the genes identified by T2D and till now no studies have shown the similar genetic association and pathophysiology between both the diseases. So, the current review aims to investigate the genetic relation between PCOS and T2D and why both the diseases cannot be reverted. In this review, published data were screened with the T2D related genes and single nucleotide polymorphisms in PCOS women. The case-control, hospital-based and meta-analysis molecular studies disclosed both positive and negative connotations. Genetically, no relationship has been established between PCOS and T2D. Maximum studies have shown as PCOS women had developed T2D later in life because as a risk-factor, but none of the studies documented T2D women having developed PCOS as a risk factor. Apart from this, the disease PCOS is developed in women with reproductive age and T2D develops in both the men and women during adulthood. This review concludes as there is a genetic relation only in between PCOS and T2D, but not with T2D to PCOS and further it cannot be explicitly reverted from T2D to PCOS.

PCOSKBR2: a database of genes, diseases, pathways, and networks associated with polycystic ovary syndrome

PolyCystic Ovary Syndrome KnowledgeBase (PCOSKBR2) is a manually curated database with information on 533 genes, 145 SNPs, 29 miRNAs, 1,150 pathways, and 1,237 diseases associated with PCOS. This data has been retrieved based on evidence gleaned by critically reviewing literature and related records available for PCOS in databases such as KEGG, DisGeNET, OMIM, GO, Reactome, STRING, and dbSNP. Since PCOS is associated with multiple genes and comorbidities, data mining algorithms for comorbidity prediction and identification of enriched pathways and hub genes are integrated in PCOSKBR2, making it an ideal research platform for PCOS. PCOSKBR2 is freely accessible at http://www.pcoskb.bicnirrh.res.in/ .

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.

Directly Transmitted 12.3-Mb Deletion with a Consistent Phenotype in the Variable 11q21q22.3 Region

A phenotype is emerging for the proximal pair of G-dark bands in 11q (11q14.1 and q14.3) but not yet for the distal pair (11q22.1 and q22.3). A mother and daughter with the same directly transmitted 12.3-Mb interstitial deletion of 11q21q22.3 (GRCh37: 93,551,765-105,817,723) both had initial feeding difficulties and failure to thrive, speech delay, learning difficulties, and mild dysmorphism. Among 17 patients with overlapping deletions, developmental or speech delay, dysmorphism, hypotonia, intellectual disability or learning difficulties, short stature, and coloboma were each found in 2 or more. These results may provide the basis for a consistent phenotype for this region. Among the 53 deleted and additional breakpoint genes, CNTN5, YAP1, and GRI4 were the most likely candidates. Non-penetrance of haploinsufficient genes and dosage compensation among related genes may account for the normal cognition in the mother and variable phenotypes that can extend into the normal range.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

Genome-wide methylation profiling in granulosa lutein cells of women with polycystic ovary syndrome (PCOS)

Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder amongst women of reproductive age, whose aetiology remains unclear. To improve our understanding of the molecular mechanisms underlying the disease, we conducted a genome-wide DNA methylation profiling in granulosa lutein cells collected from 16 women suffering from PCOS, in comparison to 16 healthy controls. Samples were collected by follicular aspiration during routine egg collection for IVF treatment. Study groups were matched for age and BMI, did not suffer from other disease and were not taking confounding medication. Comparing women with polycystic versus normal ovarian morphology, after correcting for multiple comparisons, we identified 106 differentially methylated CpG sites with p-values <5.8 × 10^-8 that were associated with 88 genes, several of which are known to relate either to PCOS or to ovarian function. Replication and validation of the experiment was done using pyrosequencing to analyse six of the identified differentially methylated sites. Pathway analysis indicated potential disruption in canonical pathways and gene networks that are, amongst other, associated with cancer, cardiogenesis, Hedgehog signalling and immune response. In conclusion, these novel findings indicate that women with PCOS display epigenetic changes in ovarian granulosa cells that may be associated with the heterogeneity of the disorder.

Recent advances in mammalian reproductive biology

Reproductive biology is a uniquely important topic since it is about germ cells, which are central for transmitting genetic information from generation to generation. In this review, we discuss recent advances in mammalian germ cell development, including preimplantation development, fetal germ cell development and postnatal development of oocytes and sperm. We also discuss the etiologies of female and male infertility and describe the emerging technologies for studying reproductive biology such as gene editing and single-cell technologies.

Immunological reactions by T cell and regulation of crucial genes in treated celiac disease patients

AIM

To assess the immunological reactions and gene expression level in the celiac disease (CD) patients under a gluten-free diet (GFD).

BACKGROUND

CD is an autoimmune disorder in genetic susceptible individuals and lifelong gluten free diet is the effective treatment method. It seems that treated patients will experience a normal life style though there are documents about some potential damages.

METHODS

Gene expression profiles of treated CD patients and healthy samples were obtained from Gene Expression Omnibus (GEO) and compared to find the differentially expressed genes (DEGs). The identified DEGs were introduced in the network and gene ontology (GO) analysis.

RESULTS

Ten differentially expressed genes (DEGs) including CCR2, IRF4, FASLG, CCR4, ICOS, TNFSF18, BACH2, LTF, PRM1, and PRM2 were investigated via network analysis. Seven clusters of biological processes (BP) were determined as the affected BP. PThe finding led to introduction of CCR2, IRF4, FASLG, CCR4, and ICOS as the potential immunological markers that are still active despite GFD in the treated CD patients.

CONCLUSION

The results of this study indicated that the immune system is already active in treated CD patients despite GFD treatment and exposure to gluten causes potential immunological reactions in these patients.

Polycystic ovary syndrome (PCOS) and genetic predisposition: A review article

Polycystic ovary syndrome (PCOS) is a heterogeneous condition which is related to an endocrine reproductive disorder of females. It affects females of 18-44 age. The persistent hormonal disbalance leads to the complexities such as numerous cysts, an irregular menstrual cycle that ultimately leads to infertility among females. Many candidate genes have been identified to be one of the causes of PCOS. Different studies have been carried out to find the genetic correlation of PCOS. It is essential to carry out such studies that identify the clear cause of PCOS and its genetic association and hormonal disbalance. This review has highlighted different genes and their correlation with PCOS that leads to hormonal disbalance. Yet not in-depth but an attempt to study the genetic predisposition of PCOS.

Computational characterization and identification of human polycystic ovary syndrome genes

Human polycystic ovary syndrome (PCOS) is a highly heritable disease regulated by genetic and environmental factors. Identifying PCOS genes is time consuming and costly in wet-lab. Developing an algorithm to predict PCOS candidates will be helpful. In this study, for the first time, we systematically analyzed properties of human PCOS genes. Compared with genes not yet known to be involved in PCOS regulation, known PCOS genes display distinguishing characteristics: (i) they tend to be located at network center; (ii) they tend to interact with each other; (iii) they tend to enrich in certain biological processes. Based on these features, we developed a machine-learning algorithm to predict new PCOS genes. 233 PCOS candidates were predicted with a posterior probability >0.9. Evidence supporting 7 of the top 10 predictions has been found.

A Census of Disease Ontologies

For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.

A transition-based joint model for disease named entity recognition and normalization

Motivation

Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this.

Methods

We propose a transition-based model to jointly perform disease named entity recognition and normalization, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs. Beam search and online structured learning are used, with learning being designed to guide search. Compared with the only existing method for joint DEN and DER, our method allows non-local features to be used, which significantly improves the accuracies.

Results

We evaluate our model on two corpora: the BioCreative V Chemical Disease Relation (CDR) corpus and the NCBI disease corpus. Experiments show that our joint framework achieves significantly higher performances compared to competitive pipeline baselines. Our method compares favourably to other state-of-the-art approaches.

Availability and Implementation

Data and code are available at https://github.com/louyinxia/jointRN.

Contact

dhji@whu.edu.cn.

PCOSBase: a manually curated database of polycystic ovarian syndrome

Polycystic ovarian syndrome (PCOS) is one of the main causes of infertility and affects 5–20% women of reproductive age. Despite the increased prevalence of PCOS, the mechanisms involved in its pathogenesis and pathophysiology remains unclear. The expansion of omics on studying the mechanisms of PCOS has lead into vast amounts of proteins related to PCOS resulting to a challenge in collating and depositing this deluge of data into one place. A knowledge-based repository named as PCOSBase was developed to systematically store all proteins related to PCOS. These proteins were compiled from various online databases and published expression studies. Rigorous criteria were developed to identify those that were highly related to PCOS. They were manually curated and analysed to provide additional information on gene ontologies, pathways, domains, tissue localizations and diseases that associate with PCOS. Other proteins that might interact with PCOS-related proteins identified from this study were also included. Currently, 8185 PCOS-related proteins were identified and assigned to 13 237 gene ontology vocabulary, 1004 pathways, 7936 domains, 29 disease classes, 1928 diseases, 91 tissues and 320 472 interactions. All publications related to PCOS are also indexed in PCOSBase. Data entries are searchable in the main page, search, browse and datasets tabs. Protein advanced search is provided to search for specific proteins. To date, PCOSBase has the largest collection of PCOS-related proteins. PCOSBase aims to become a self-contained database that can be used to further understand the PCOS pathogenesis and towards the identification of potential PCOS biomarkers.

Database URL: http://pcosbase.org

PCOSDB: PolyCystic Ovary Syndrome Database for manually curated disease associated genes

Polycystic ovary syndrome (PCOS) is a complex disorder affecting approximately 5-10 percent of all women of reproductive age. It is a multi-factorial endocrine disorder, which demonstrates menstrual disturbance, infertility, anovulation, hirsutism, hyper androgenism and others. It has been indicated that differential expression of genes, genetic level variations, and other molecular alterations interplay in PCOS and are the target sites for clinical applications. Therefore, integrating the PCOS-associated genes along with its alteration and underpinning the underlying mechanism might definitely provide valuable information to understand the disease mechanism. We manually curated the information from 234 published literatures, including gene, molecular alteration, details of association, significance of association, ethnicity, age, drug, and other annotated summaries. PCOSDB is an online resource that brings comprehensive information about the disease, and the implication of various genes and its mechanism. We present the curated information from peer reviewed literatures, and organized the information at various levels including differentially expressed genes in PCOS, genetic variations such as polymorphisms, mutations causing PCOS across various ethnicities. We have covered both significant and non-significant associations along with conflicting studies. PCOSDB v1.0 contains 208 gene reports, 427 molecular alterations, and 46 phenotypes associated with PCOS.