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.

Corrigendum: Effects of Yulin Tong Bu formula on modulating gut microbiota and fecal metabolite interactions in mice with polycystic ovary syndrome.

[This corrects the article DOI: 10.3389/fendo.2023.1122709.].

Effects of Yulin Tong Bu formula on modulating gut microbiota and fecal metabolite interactions in mice with polycystic ovary syndrome.

Background: Polycystic ovarian syndrome (PCOS) is a common endocrine disorder characterized by hyperandrogenism, ovarian dysfunction and polycystic ovarian morphology. Gut microbiota dysbiosis and metabolite are associated with PCOS clinical parameters. Yulin Tong Bu formula (YLTB), a traditional Chinese medicine formula, has been recently indicated to be capable of ameliorating polycystic ovary symptoms and correcting abnormal glucose metabolism. However, the therapeutic mechanism of YLTB on PCOS has not been fully elucidated. Methods: A pseudo sterile mouse model was established during this four-day acclimatization phase by giving the animals an antibiotic cocktail to remove the gut microbiota. Here, the therapeutic effects of YLTB on PCOS were investigated using dehydroepiandrosterone plus high-fat diet-induced PCOS mice model. Female prepuberal mice were randomly divided into three groups; namely, the control group, PCOS group and YLTB (38.68 g·kg-1·day-1) group. To test whether this effect is associated with the gut microbiota, we performed 16S rRNA sequencing studies to analyze the fecal microbiota of mice. The relationships among metabolites, gut microbiota, and PCOS phenotypes were further explored by using Spearman correlation analysis. Then, the effect of metabolite ferulic acid was then validated in PCOS mice. Results: Our results showed that YLTB treatment ameliorated PCOS features (ovarian dysfunction, delayed glucose clearance, decreased insulin sensitivity, deregulation of glucolipid metabolism and hormones, etc.) and significantly attenuated PCOS gut microbiota dysbiosis. Spearman correlation analysis showed that metabolites such as ferulic acid and folic acid are negatively correlated with PCOS clinical parameters. The effect of ferulic acid was similar to that of YLTB. In addition, the bacterial species such as Bacteroides dorei and Bacteroides fragilis were found to be positively related to PCOS clinical parameters, using the association study analysis. Conclusion: These results suggest that YLTB treatment systematically regulates the interaction between the gut microbiota and the associated metabolites to ameliorate PCOS, providing a solid theoretical basis for further validation of YLTB effect on human PCOS trials.

MIAOME: Human microbiome affect the host epigenome.

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/.

LncRNA functional annotation with improved false discovery rate achieved by disease associations.

The long non-coding RNAs (lncRNAs) play critical roles in various biological processes and are associated with many diseases. Functional annotation of lncRNAs in diseases attracts great attention in understanding their etiology. However, the traditional co-expression-based analysis usually produces a significant number of false positive function assignments. It is thus crucial to develop a new approach to obtain lower false discovery rate for functional annotation of lncRNAs. Here, a novel strategy termed DAnet which combining disease associations with cis-regulatory network between lncRNAs and neighboring protein-coding genes was developed, and the performance of DAnet was systematically compared with that of the traditional differential expression-based approach. Based on a gold standard analysis of the experimentally validated lncRNAs, the proposed strategy was found to perform better in identifying the experimentally validated lncRNAs compared with the other method. Moreover, the majority of biological pathways (40%∼100%) identified by DAnet were reported to be associated with the studied diseases. In sum, the DAnet is expected to be used to identify the function of specific lncRNAs in a particular disease or multiple diseases.

GIMICA: host genetic and immune factors shaping human microbiota.

Besides the environmental factors having tremendous impacts on the composition of microbial community, the host factors have recently gained extensive attentions on their roles in shaping human microbiota. There are two major types of host factors: host genetic factors (HGFs) and host immune factors (HIFs). These factors of each type are essential for defining the chemical and physical landscapes inhabited by microbiota, and the collective consideration of both types have great implication to serve comprehensive health management. However, no database was available to provide the comprehensive factors of both types. Herein, a database entitled 'Host Genetic and Immune Factors Shaping Human Microbiota (GIMICA)' was constructed. Based on the 4257 microbes confirmed to inhabit nine sites of human body, 2851 HGFs (1368 single nucleotide polymorphisms (SNPs), 186 copy number variations (CNVs), and 1297 non-coding ribonucleic acids (RNAs)) modulating the expression of 370 microbes were collected, and 549 HIFs (126 lymphocytes and phagocytes, 387 immune proteins, and 36 immune pathways) regulating the abundance of 455 microbes were also provided. All in all, GIMICA enabled the collective consideration not only between different types of host factor but also between the host and environmental ones, which is freely accessible without login requirement at: https://idrblab.org/gimica/.