Cloning, distribution, and effects of growth regulation of MC3R and MC4R in red crucian carp (Carassius auratus red var.)

BackgroundMelanocortin-3 and -4 receptors (MC3R and MC4R), G protein-coupled receptors, play vital roles in the regulation of energy homeostasis. To understand the functions of mc3r and mc4r in the energy homeostasis of red crucian carp (Carassius auratus red var., RCC), we cloned mc3r and mc4r, analyzed the tissue expression and localization of the genes, and investigated the effects of knockout of mc3r (mc3r+/-) and mc4r (mc4r+/-) in RCC. ResultsThe full-length cDNAs of RCC mc3r and mc4r were 1459 base pairs (bp) and 1894 bp, respectively. qRT-PCR indicated that mc3r and mc4r were profusely expressed in the brain, but lower expressed in the periphery tissues. ISH revealed that mc3r and mc4r were located in NPP, NPO, NAPv, NSC, NAT, NRL, NLTl, and NLTp of the brain, suggesting that mc3r and mc4r might regulate many physiological and behavioral aspects in RCC. To further verify the roles of mc3r and mc4r in energy homeostasis, the mc3r+/- and mc4r+/- fish were obtained by the CRISPR/Cas9 system. The average body weights, total lengths, body depths, and food intake of mc4r+/- fish were significantly higher than those of mc3r+/- and the normal wild-type (WT) fish, but there was no difference between the mc3r+/- and WT fish, indicating that the RCC phenotype and food intake were mainly influenced by mc4r but not mc3r. Interestingly, mc4r+/- fish displayed more visceral fat mass than mc3r+/- and WT fish, and mc3r+/- fish also exhibited slightly more visceral fat mass compared to WT. RNA-seq of the liver and muscle revealed that a large number of differentially expressed genes (DEGs) differed in WT vs. mc3r+/-, WT vs. mc4r+/-, and mc3r+/- vs. mc4r+/-, mainly related to lipid, glucose, and energy metabolism. The KEGG enrichment analysis revealed that DEGs were mainly enriched in pathways such as steroid biosynthesis, fatty acid metabolism, fatty acid biosynthesis, glycolysis/gluconeogenesis, wnt signaling pathway, PPAR signaling pathway, and MAPK signaling pathway, thereby affecting lipid accumulation and growth. ConclusionIn conclusion, these results will assist in the further investigation of the molecular mechanisms in which MC3R and MC4R were involved in the regulation of energy homeostasis in fish

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

Correlation between Molecular Structure and Interfacial Properties of Edge or Basal Plane Modified Graphene Oxide

Although graphene oxide (GO) has been reported to be able to be edge functionalized or basal-plane functionalized separately, no research has been done on comparing both the molecular structure and interfacial properties of them. In this study, an alkyl amine was grafted to the epoxy group on the basal planes of GO (b-GO) and carboxyl group at the edges of GO (e-GO) separately by using different synthetic approach. With the combination of various molecular structure and morphology characterization methodologies, we proved that the reaction site for e-GO was only with the carboxyl group at the edge of GO and that for b-GO was epoxy group on the basal plane of GO, indicating that GO could be controllably functionalized (fGOs), and the structure of fGOs could be tuned. Study of the interfacial behavior of fGOs at liquid–liquid interface showed that the interfacial tension reducing capability of e-GO was broader than that of b-GO, and for alkyl oil phase, b-GO was slightly better than e-GO, and both were better than traditional nonionic surfactant. Study of the interfacial behavior of fGOs at liquid–solid interface demonstrated that, after absorption, b-GO arranged vertically on the metal surface, forming dense, compact, and strong film, while e-GO aligned horizontally to form loosely assembled film, resulting in higher interfacial shear strength than that of b-GO. Our results indicate the possibilities for tuning the interfacial properties of GO at both liquid–liquid and liquid–solid interfaces, which may be promising in the potential applications in controlled drug delivery, surface protection, absorption and separation, lubrication, nanocomposite, and catalyst fields

Image_2_Cloning, distribution, and effects of growth regulation of MC3R and MC4R in red crucian carp (Carassius auratus red var.).jpeg

BackgroundMelanocortin-3 and -4 receptors (MC3R and MC4R), G protein-coupled receptors, play vital roles in the regulation of energy homeostasis. To understand the functions of mc3r and mc4r in the energy homeostasis of red crucian carp (Carassius auratus red var., RCC), we cloned mc3r and mc4r, analyzed the tissue expression and localization of the genes, and investigated the effects of knockout of mc3r (mc3r+/-) and mc4r (mc4r+/-) in RCC. ResultsThe full-length cDNAs of RCC mc3r and mc4r were 1459 base pairs (bp) and 1894 bp, respectively. qRT-PCR indicated that mc3r and mc4r were profusely expressed in the brain, but lower expressed in the periphery tissues. ISH revealed that mc3r and mc4r were located in NPP, NPO, NAPv, NSC, NAT, NRL, NLTl, and NLTp of the brain, suggesting that mc3r and mc4r might regulate many physiological and behavioral aspects in RCC. To further verify the roles of mc3r and mc4r in energy homeostasis, the mc3r+/- and mc4r+/- fish were obtained by the CRISPR/Cas9 system. The average body weights, total lengths, body depths, and food intake of mc4r+/- fish were significantly higher than those of mc3r+/- and the normal wild-type (WT) fish, but there was no difference between the mc3r+/- and WT fish, indicating that the RCC phenotype and food intake were mainly influenced by mc4r but not mc3r. Interestingly, mc4r+/- fish displayed more visceral fat mass than mc3r+/- and WT fish, and mc3r+/- fish also exhibited slightly more visceral fat mass compared to WT. RNA-seq of the liver and muscle revealed that a large number of differentially expressed genes (DEGs) differed in WT vs. mc3r+/-, WT vs. mc4r+/-, and mc3r+/- vs. mc4r+/-, mainly related to lipid, glucose, and energy metabolism. The KEGG enrichment analysis revealed that DEGs were mainly enriched in pathways such as steroid biosynthesis, fatty acid metabolism, fatty acid biosynthesis, glycolysis/gluconeogenesis, wnt signaling pathway, PPAR signaling pathway, and MAPK signaling pathway, thereby affecting lipid accumulation and growth. ConclusionIn conclusion, these results will assist in the further investigation of the molecular mechanisms in which MC3R and MC4R were involved in the regulation of energy homeostasis in fish.</p

Table_1_Cloning, distribution, and effects of growth regulation of MC3R and MC4R in red crucian carp (Carassius auratus red var.).docx

BackgroundMelanocortin-3 and -4 receptors (MC3R and MC4R), G protein-coupled receptors, play vital roles in the regulation of energy homeostasis. To understand the functions of mc3r and mc4r in the energy homeostasis of red crucian carp (Carassius auratus red var., RCC), we cloned mc3r and mc4r, analyzed the tissue expression and localization of the genes, and investigated the effects of knockout of mc3r (mc3r+/-) and mc4r (mc4r+/-) in RCC. ResultsThe full-length cDNAs of RCC mc3r and mc4r were 1459 base pairs (bp) and 1894 bp, respectively. qRT-PCR indicated that mc3r and mc4r were profusely expressed in the brain, but lower expressed in the periphery tissues. ISH revealed that mc3r and mc4r were located in NPP, NPO, NAPv, NSC, NAT, NRL, NLTl, and NLTp of the brain, suggesting that mc3r and mc4r might regulate many physiological and behavioral aspects in RCC. To further verify the roles of mc3r and mc4r in energy homeostasis, the mc3r+/- and mc4r+/- fish were obtained by the CRISPR/Cas9 system. The average body weights, total lengths, body depths, and food intake of mc4r+/- fish were significantly higher than those of mc3r+/- and the normal wild-type (WT) fish, but there was no difference between the mc3r+/- and WT fish, indicating that the RCC phenotype and food intake were mainly influenced by mc4r but not mc3r. Interestingly, mc4r+/- fish displayed more visceral fat mass than mc3r+/- and WT fish, and mc3r+/- fish also exhibited slightly more visceral fat mass compared to WT. RNA-seq of the liver and muscle revealed that a large number of differentially expressed genes (DEGs) differed in WT vs. mc3r+/-, WT vs. mc4r+/-, and mc3r+/- vs. mc4r+/-, mainly related to lipid, glucose, and energy metabolism. The KEGG enrichment analysis revealed that DEGs were mainly enriched in pathways such as steroid biosynthesis, fatty acid metabolism, fatty acid biosynthesis, glycolysis/gluconeogenesis, wnt signaling pathway, PPAR signaling pathway, and MAPK signaling pathway, thereby affecting lipid accumulation and growth. ConclusionIn conclusion, these results will assist in the further investigation of the molecular mechanisms in which MC3R and MC4R were involved in the regulation of energy homeostasis in fish.</p

Image_1_Cloning, distribution, and effects of growth regulation of MC3R and MC4R in red crucian carp (Carassius auratus red var.).jpeg

BackgroundMelanocortin-3 and -4 receptors (MC3R and MC4R), G protein-coupled receptors, play vital roles in the regulation of energy homeostasis. To understand the functions of mc3r and mc4r in the energy homeostasis of red crucian carp (Carassius auratus red var., RCC), we cloned mc3r and mc4r, analyzed the tissue expression and localization of the genes, and investigated the effects of knockout of mc3r (mc3r+/-) and mc4r (mc4r+/-) in RCC. ResultsThe full-length cDNAs of RCC mc3r and mc4r were 1459 base pairs (bp) and 1894 bp, respectively. qRT-PCR indicated that mc3r and mc4r were profusely expressed in the brain, but lower expressed in the periphery tissues. ISH revealed that mc3r and mc4r were located in NPP, NPO, NAPv, NSC, NAT, NRL, NLTl, and NLTp of the brain, suggesting that mc3r and mc4r might regulate many physiological and behavioral aspects in RCC. To further verify the roles of mc3r and mc4r in energy homeostasis, the mc3r+/- and mc4r+/- fish were obtained by the CRISPR/Cas9 system. The average body weights, total lengths, body depths, and food intake of mc4r+/- fish were significantly higher than those of mc3r+/- and the normal wild-type (WT) fish, but there was no difference between the mc3r+/- and WT fish, indicating that the RCC phenotype and food intake were mainly influenced by mc4r but not mc3r. Interestingly, mc4r+/- fish displayed more visceral fat mass than mc3r+/- and WT fish, and mc3r+/- fish also exhibited slightly more visceral fat mass compared to WT. RNA-seq of the liver and muscle revealed that a large number of differentially expressed genes (DEGs) differed in WT vs. mc3r+/-, WT vs. mc4r+/-, and mc3r+/- vs. mc4r+/-, mainly related to lipid, glucose, and energy metabolism. The KEGG enrichment analysis revealed that DEGs were mainly enriched in pathways such as steroid biosynthesis, fatty acid metabolism, fatty acid biosynthesis, glycolysis/gluconeogenesis, wnt signaling pathway, PPAR signaling pathway, and MAPK signaling pathway, thereby affecting lipid accumulation and growth. ConclusionIn conclusion, these results will assist in the further investigation of the molecular mechanisms in which MC3R and MC4R were involved in the regulation of energy homeostasis in fish.</p

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis