RESULTS: The study described the transcriptomes of salivary glands from three swiftlet species (28 samples) generated by RNASeq. A total of 14,835 annotated genes and 428 unmapped genes were cataloged. The current study investigated the genes and pathways that are associated with the development of salivary gland and EBN composition. Differential expression and pathway enrichment analysis indicated that the expression of CREB3L2 and several signaling pathways involved in salivary gland development, namely, the EGFR, BMP, and MAPK signaling pathways, were up-regulated in swiftlets producing white EBN (Aerodramus fuciphagus) and black EBN (Aerodramus maximus) compared with non-EBN-producing swiftlets (Apus affinis). Furthermore, MGAT, an essential gene for the biosynthesis of N-acetylneuraminic acid (sialic acid), was highly expressed in both white- and black-nest swiftlets compared to non-EBN-producing swiftlets. Interspecies comparison between Aerodramus fuciphagus and Aerodramus maximus indicated that the genes involved in N-acetylneuraminic and fatty acid synthesis were up-regulated in Aerodramus fuciphagus, while alanine and aspartate synthesis pathways were up-regulated in Aerodramus maximus. Furthermore, gender-based analysis revealed that N-glycan trimming pathway was significantly up-regulated in male Aerodramus fuciphagus from its natural habitat (cave) compared to their female counterpart.
CONCLUSIONS: Transcriptomic analysis of salivary glands of different swiftlet species reveal differential expressions of candidate genes that are involved in salivary gland development and in the biosynthesis of various bioactive compounds found in EBN.
METHODS: In this study, the metabolic responses of C. glabrata under acetate growth condition was explored using high-throughput transcriptomic and proteomic approaches.
RESULTS: Collectively, a total of 1482 transcripts (26.96%) and 242 proteins (24.69%) were significantly up- or down-regulated. Both transcriptome and proteome data revealed that the regulation of alternative carbon metabolism in C. glabrata resembled other fungal pathogens such as Candida albicans and Cryptococcus neoformans, with up-regulation of many proteins and transcripts from the glyoxylate cycle and gluconeogenesis, namely isocitrate lyase (ICL1), malate synthase (MLS1), phosphoenolpyruvate carboxykinase (PCK1) and fructose 1,6-biphosphatase (FBP1). In the absence of glucose, C. glabrata shifted its metabolism from glucose catabolism to anabolism of glucose intermediates from the available carbon source. This observation essentially suggests that the glyoxylate cycle and gluconeogenesis are potentially critical for the survival of phagocytosed C. glabrata within the glucose-deficient macrophages.
CONCLUSION: Here, we presented the first global metabolic responses of C. glabrata to alternative carbon source using transcriptomic and proteomic approaches. These findings implicated that reprogramming of the alternative carbon metabolism during glucose deprivation could enhance the survival and persistence of C. glabrata within the host.
METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.
RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.
DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.
OBJECTIVES: To screen hypermethylated genes with a microarray approach and to validate selected hypermethylated genes with the methylation-specific polymerase chain reaction (MSPCR).
MATERIALS AND METHODS: Genome-wide analysis of normal oral mucosa and OSCC tissues was conducted using the Illumina methylation microarray. The specified differential genes were selected and hypermethylation status was further verified with an independent cohort sample of OSCC samples. Candidate genes were screened using microarray assay and run by MSPCR analysis.
RESULTS: TP73, PIK3R5, and CELSR3 demonstrated high percentages of differential hypermethylation status.
CONCLUSIONS: Our microarray screening and MSPCR approaches revealed that the signature candidates of differentially hypermethylated genes may possibly become potential biomarkers which would be useful for diagnostic, prognostic and therapeutic targets of OSCC in the near future.