Jiani Sun

Endocrine-disrupting chemicals (EDCs) have been reported to affect populations by disrupting the human endocrine system. Di-(2-ethylhexyl) phthalate (DEHP) is an EDC that is present in various consumer products. Exposure to DEHP could contribute to reproductive system dysfunction, with subsequent adverse female reproductive outcomes. Granulosa cells (GCs) play essential roles in ovarian function and fertility. To further reveal the underlying mechanism by which DEHP impairs female fertility and affects the normal function of GCs, in vivo and in vitro experiments were performed. Transcript sequencing was used to identify genes that were differentially expressed in GCs after DEHP treatment. SLC39A5 was shown to be overexpressed in the DEHP group compared to the normal control group. DEHP treatment and overexpression of SLC39A5 activated NF-κB-related factors, followed by an increase in the transcript expression level of NLRP3. NLRP3 inflammasomes play crucial roles in pyroptosis by acting as sensors. Pyroptosis is a type of inflammation-related cell death associated with various diseases, including ovarian cancer and polycystic ovary syndrome. Activation of NF-κB contributed to the upregulation of pyroptosis in GCs, while pyroptosis factors were downregulated after the inhibition of NF-κB with JSH-23. The same phenomenon was also observed in a mouse model in which DEHP-treated mice had higher expression levels of NF-κB and pyroptosis markers in GCs. Moreover, this phenomenon could be partially reversed by the NF-κB inhibitor JSH-23. DEHP treatment also disrupted the normal expression of ovarian function-related genes and inhibited the proliferation of GCs. Reproductive system impairment was observed in mice exposed to DEHP. DEHP-treated mice had a lower body weight, smaller reproductive organs, fewer healthy follicles, and diminished ovarian reserve. Thus, DEHP contributes to ovarian dysfunction by inducing pyroptosis via the SLC39A5/NF-κB/NLRP3 axis in GCs.

Adjuvant chemotherapy may cause alterations in serum lipids in postoperative breast cancer (BC) patients, but the specific alterations caused by different chemotherapy regimens remain unclear. The aim of this study was to investigate the status of serum lipids pre- and post-chemotherapy and to compare the side effects of different chemotherapy regimens on serum lipid.We retrospectively analysed the lipid profiles of 1934 consecutive postoperative BC patients who received one of the following chemotherapy regimens:The levels of triglycerides (TG), total cholesterols (TC), and low-density lipoprotein (LDL-C) were significantly elevated in patients who received chemotherapy regimens above (P < .001). With respect to different chemotherapy regimens, FEC had less side effects on lipid profiles (TG (P = .006), high-density lipoprotein (HDL-C) (P < .001), and LDL-C (P < .001)) than TC regimen and AC-T and EC-T regimen. Also, the incidence of newly diagnosed dyslipidemia after chemotherapy was lower in FEC group than TC group and AC-T and EC-T group (P < .001). Additionally, the magnitude of the alterations in lipid profiles (TG, TC, HDL-C, and LDL-C) was greater in premenopausal patients than that of the postmenopausal patients (P = .004; P < .001; P = .002; P = .003, respectively). Moreover, after adjusting for multiple baseline covariates, anthracycline-plus-taxane-based regimens (AC-T and EC-T) were still statistically associated with a high level of TG (P = .004) and a low level of HDL-C (P = .033) after chemotherapy compared with FEC regimen. Also, body mass index (BMI) > 24 was associated with abnormal lipid profiles (TG, TC, HDL-C, LDL-C) post-chemotherapy compared with BMI ≤ 24 (P < .001; P = .036; P = .012; P = .048, respectively).BC patients receiving chemotherapy may have elevated lipid profiles, and anthracycline-based regimen had less side effects on lipid profiles compared with regimens containing taxane. Therefore, it is necessary to take lipid metabolism into consideration when making chemotherapy decisions and dyslipidemia prevention and corresponding interventions are indispensable during the whole chemotherapy period.

The management of metabolic disorder associated with polycystic ovary syndrome (PCOS) has been suggested as an effective approach to improve PCOS which is highly involved with gut microbiota, while the underlying mechanism is unclear. Here, we investigated the role of inulin, a gut microbiota regulator, in the alleviation of PCOS. Our findings showed that inulin treatment significantly improved hyperandrogenism and glucolipid metabolism in both PCOS cohort and mice. Consistent with the cohort, inulin increased the abundance of microbial co-abundance group (CAG) 12 in PCOS mice, including Bifidobacterium species and other short-chain fatty acids (SCFAs)-producers. We further verified the enhancement of SCFAs biosynthesis capacity and fecal SCFAs content by inulin. Moreover, inulin decreased lipopolysaccharide-binding protein (LBP) and ameliorated ovarian inflammation in PCOS mice, whereas intraperitoneal lipopolysaccharide (LPS) administration reversed the protective effects of inulin. Furthermore, fecal microbiota transplantation (FMT) from inulin-treated patients with PCOS enhanced insulin sensitivity, improved lipid accumulation and thermogenesis, reduced hyperandrogenism and ovarian inflammatory response in antibiotic-treated mice. Collectively, these findings revealed that gut microbiota mediates the beneficial effects of inulin on metabolic disorder and ovarian dysfunction in PCOS. Therefore, modulating gut microbiota represents a promising therapeutic strategy for PCOS.

Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap in non-human animal disease understanding and breeding research. To address this limitation, we present BioKA (Biomarker Knowledgebase for Animals, https://ngdc.cncb.ac.cn/bioka), a curated and integrated knowledgebase encompassing multiple animal species, diseases/traits, and annotated resources. Currently, BioKA houses 16 296 biomarkers associated with 951 mapped diseases/traits across 31 species from 4747 references, including 11 925 gene/protein biomarkers, 1784 miRNA biomarkers, 1043 mutation biomarkers, 773 metabolic biomarkers, 357 circRNA biomarkers and 127 lncRNA biomarkers. Furthermore, BioKA integrates various annotations such as GOs, protein structures, protein-protein interaction networks, miRNA targets and so on, and constructs an interactive knowledge network of biomarkers including circRNA-miRNA-mRNA associations, lncRNA-miRNA associations and protein-protein associations, which is convenient for efficient data exploration. Moreover, BioKA provides detailed information on 308 breeds/strains of 13 species, and homologous annotations for 8784 biomarkers across 16 species, and offers three online application tools. The comprehensive knowledge provided by BioKA not only advances human disease research but also contributes to a deeper understanding of animal diseases and supports livestock breeding.

Cancer is one of the leading causes of human death. As metabolomics techniques become more and more widely used in cancer research, metabolites are increasingly recognized as crucial factors in both cancer diagnosis and treatment. In this study, we developed MACdb (https://ngdc.cncb.ac.cn/macdb), a curated knowledgebase to recruit the metabolic associations between metabolites and cancers. Unlike conventional data-driven resources, MACdb integrates cancer-metabolic knowledge from extensive publications, providing high quality metabolite associations and tools to support multiple research purposes. In the current implementation, MACdb has integrated 40,710 cancer-metabolite associations, covering 267 traits from 17 categories of cancers with high incidence or mortality, based entirely on manual curation from 1,127 studies reported in 462 publications (screened from 5,153 research papers). MACdb offers intuitive browsing functions to explore associations at multi-dimensions (metabolite, trait, study, and publication), and constructs knowledge graph to provide overall landscape among cancer, trait, and metabolite. Furthermore, NameToCid (map metabolite name to PubChem Cid) and Enrichment tools are developed to help users enrich the association of metabolites with various cancer types and traits. IMPLICATION: MACdb paves an informative and practical way to evaluate cancer-metabolite associations and has a great potential to help researchers identify key predictive metabolic markers in cancers.