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Objective Menopause at a young age is associated with many health problems in women, including osteoporosis, depressive symptoms, coronary disease, and stroke. Many traditional observational studies have reported some potential risk factors for early menopause but have drawn different conclusions. This inconsistency can be attributed mainly to unmodified confounding factors. Identifying the factors causally associated with age at menopause is important for early intervention in women with abnormal menopause timing, and for improving the quality of life for postmenopausal women. This study aims to appraise whether the previously reported risk factors are causally associated with early age at natural menopause (ANM) susceptibility. Methods We used Mendelian randomization, a statistical method wherein genetic variants are used to determine whether an observational association between a risk factor and an outcome is consistent with a causal effect. Results Women with earlier age at menarche (β = 0.34, se = 0.16, p = 0.035), lower education level (β = 1.19, se = 0.41, p = 0.004) and higher body mass index (β = -0.05, se = 0.02, p = 0.027) had greater risk for early ANM. The causal link between early age at menarche and early ANM was replicated using ReproGen consortium data (β = 0.23, se = 0.07, p = 0.001). However, a current smoking habit, one of previously reported risk factors, was less likely to be correlated causally with early ANM, suggesting that previous observational studies may not have sufficiently adjusted for confounders. Conclusion Our results help to identify the risk factors of ANM via a genetics approach and future research into the biological mechanism could further help with targeted prevention for early menopause.Pathogen infection triggers extensive reprogramming of the plant transcriptome, including numerous genes the function of which is unknown. Due to their wide taxonomic distribution, genes encoding proteins with Domains of Unknown Function (DUFs) activated upon pathogen challenge likely play important roles in disease. In Arabidopsis thaliana, we identified thirteen genes harboring a DUF4228 domain in the top 10% most induced genes after infection by the fungal pathogen Sclerotinia sclerotiorum. Based on functional information collected through homology and contextual searches, we propose to refer to this domain as the pathogen and abiotic stress response, cadmium tolerance, disordered region-containing (PADRE) domain. Genome-wide and phylogenetic analyses indicated that PADRE is specific to plants and diversified into 10 subfamilies early in the evolution of Angiosperms. PADRE typically occurs in small single-domain proteins with a bipartite architecture. PADRE N-terminus harbors conserved sequence motifs, while its C-terminus includes an intrinsically disordered region with multiple phosphorylation sites. A pangenomic survey of PADRE genes expression upon S. sclerotiorum inoculation in Arabidopsis, castor bean, and tomato indicated consistent expression across species within phylogenetic groups. Multi-stress expression profiling and co-expression network analyses associated AtPADRE genes with the induction of anthocyanin biosynthesis and responses to chitin and to hypoxia. Our analyses reveal patterns of sequence and expression diversification consistent with the evolution of a role in disease resistance for an uncharacterized family of plant genes. These findings highlight PADRE genes as prime candidates for the functional dissection of mechanisms underlying plant disease resistance to fungi.Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.Background Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC). Methods The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events. Result A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC. learn more Conclusion This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.Globally, two billion people suffer from micronutrient deficiencies. Cereal grains provide more than 50% of the daily requirement of calories in human diets, but they often fail to provide adequate essential minerals and vitamins. Cereal crop production in developing countries achieved remarkable yield gains through the efforts of the Green Revolution (117% in rice, 30% in wheat, 530% in maize, and 188% in pearl millet). However, modern varieties are often deficient in essential micronutrients compared to traditional varieties and land races. Breeding for nutritional quality in staple cereals is a challenging task; however, biofortification initiatives combined with genomic tools increase the feasibility. Current biofortification breeding activities include improving rice (for zinc), wheat (for zinc), maize (for provitamin A), and pearl millet (for iron and zinc). Biofortification is a sustainable approach to enrich staple cereals with provitamin A, carotenoids, and folates. Significant genetic variation has been found for provitamin A (96-850 μg and 12-1780 μg in 100 g in wheat and maize, respectively), carotenoids (558-6730 μg in maize), and folates in rice (11-51 μg) and wheat (32.
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