RESUMO
Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.
Assuntos
Biologia Computacional , Mutação de Sentido Incorreto , Humanos , Reprodutibilidade dos Testes , Consenso , ProteínasRESUMO
Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.
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Phthalates are a class of known endocrine disrupting chemicals that are found in common everyday products. Several studies associate phthalate exposure with detrimental effects on ovarian function, including growth and development of the follicle and production of steroid hormones. We hypothesized that dysregulation of the ovary by phthalates may be mediated by phthalate toxicity towards granulosa cells, a major cell type in ovarian follicles responsible for key steps of hormone production and nourishing the developing oocyte. To test the hypothesis that phthalates target granulosa cells, we harvested granulosa cells from adult CD-1 mouse ovaries and cultured them for 96 hours in vehicle control, a phthalate mixture, or a phthalate metabolite mixture (0.1-100 µg/mL). After culture, we measured metabolism of the phthalate mixture into monoester metabolites by the granulosa cells, finding that granulosa cells do not significantly contribute to ovarian metabolism of phthalates. Immunohistochemistry of phthalate metabolizing enzymes in whole ovaries confirmed that these enzymes are not strongly expressed in granulosa cells of antral follicles and that ovarian metabolism of phthalates likely occurs primarily in the stroma. RNA sequencing of treated granulosa cells identified 407 differentially expressed genes, with overrepresentation of genes from lipid metabolic processes, cholesterol metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathways. Expression of significantly differentially expressed genes related to these pathways was confirmed using qPCR. Our results agree with previous findings that phthalates and phthalate metabolites have different effects on the ovary, but both interfere with PPAR signaling in granulosa cells.
RESUMO
Phthalates are a class of known endocrine disrupting chemicals that are found in common everyday products. Several studies associate phthalate exposure with detrimental effects on ovarian functions, including growth and development of the follicle and production of steroid hormones. We hypothesized that dysregulation of the ovary by phthalates may be mediated by phthalate toxicity towards granulosa cells, a major cell type in ovarian follicles responsible for key steps of hormone production and nourishing the developing oocyte. To test the hypothesis that phthalates target granulosa cells, we harvested granulosa cells from adult CD-1 mouse ovaries and cultured them for 96 hours in vehicle control, a phthalate mixture, or a phthalate metabolite mixture (0.1-100 µg/mL). After culture, we measured metabolism of the phthalate mixture into monoester metabolites by the granulosa cells, finding that granulosa cells do not significantly contribute to ovarian metabolism of phthalates. Immunohistochemistry of phthalate metabolizing enzymes in whole ovaries confirmed that these enzymes are not strongly expressed in granulosa cells of antral follicles and that ovarian metabolism of phthalates likely occurs primarily in the stroma. RNA sequencing of treated granulosa cells identified 407 differentially expressed genes, with overrepresentation of genes from lipid metabolic processes, cholesterol metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathways. Expression of significantly differentially expressed genes related to these pathways were confirmed using qPCR. Our results agree with previous findings that phthalates and phthalate metabolites have different effects on the ovary and interfere with PPAR signaling in granulosa cells.
RESUMO
Di-2-ethylhexyl terephthalate (DEHTP) is a replacement for its structural isomer di-2-ethylhexyl phthalate (DEHP), a known endocrine disrupting chemical and ovarian toxicant. DEHTP is used as a plasticizer in polyvinyl chloride products and its metabolites are increasingly found in biomonitoring studies at levels similar to phthalates. However, little is known about the effects of DEHTP on the ovary. In this research, we tested the hypothesis that DEHTP is an ovarian toxicant and likely endocrine disrupting chemical like its isomer DEHP. The impact of environmentally relevant exposure to DEHTP and/or its metabolite mono-2-ethylhexyl terephthalate (MEHTP) on the mouse ovary was investigated in vivo and in vitro. For the in vivo studies, young adult CD-1 mice were orally dosed with vehicle, 10 µg/kg, 100 µg/kg, or 100 mg/kg of DEHTP for 10 days. For the in vitro studies, isolated untreated ovarian follicles were exposed to vehicle, 0.1, 1, 10, or 100 µg/mL of DEHTP or MEHTP. Follicle counts, hormone levels, and gene expression of steroidogenic enzymes, cell cycle regulators, and apoptosis factors were analyzed. In vivo, DEHTP exposure increased primordial follicle counts at 100 µg/kg and 100 mg/kg and decreased primary follicle counts at 100 mg/kg compared to control. DEHTP exposure also decreased expression of cell cycle regulators and apoptotic factors compared to control. In vitro, follicle growth was reduced by 1 µg/mL DEHTP and 1, 10, and 100 µg/mL MEHTP compared to controls, and expression of the cell cycle regulator Cdkn2b was increased. Steroid hormone levels and steroidogenic enzyme gene expression trended toward decreases in vivo, whereas progesterone was significantly increased by exposure to 100 µg/mL MEHTP in vitro. Overall, these results suggest that DEHTP and MEHTP may be ovarian toxicants at low doses and should be subjected to further scrutiny for reproductive toxicity due to their similar structures to phthalates.
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Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.