The Polygenic Map of Keloid Fibroblasts Reveals Fibrosis-Associated Gene Alterations in Inflammation and Immune Responses

Due to many inconsistencies in differentially expressed genes (DEGs) related to genomic expression changes during keloid formation and a lack of satisfactory prevention and treatment methods for this disease, the critical biomarkers related to inflammation and the immune response affecting keloid formation should be systematically clarified. Normal skin/keloid scar tissue-derived fibroblast genome expression data sets were obtained from the Gene Expression Omnibus (GEO) and ArrayExpress databases. Hub genes have a high degree of connectivity and gene function aggregation in the integration network. The hub DEGs were screened by gene-related protein–protein interactions (PPIs), and their biological processes and signaling pathways were annotated to identify critical biomarkers. Finally, eighty-one hub DEGs were selected for further analysis, and some noteworthy signaling pathways and genes were found to be closely related to keloid fibrosis. For example, IL17RA is involved in IL-17 signal transduction, TIMP2 and MMP14 activate extracellular matrix metalloproteinases, and TNC, ITGB2, and ITGA4 interact with cell surface integrins. Furthermore, changes in local immune cell activity in keloid tissue were detected by DEG expression, immune cell infiltration, and mass CyTOF analyses. The results showed that CD4+ T cells, CD8+ T cells and NK cells were abnormal in keloid tissue compared with normal skin tissue. These findings not only support the key roles of fibrosis-related pathways, immune cells and critical genes in the pathogenesis of keloids but also expand our understanding of targets that may be useful for the treatment of fibrotic diseases.

Changes in Transcriptomic Profiles in Different Reproductive Periods in Yaks

Yak reproductive characteristics have received extensive attention, though the molecular regulation mechanism of its ovarian activity remains to be explored. Therefore, this study initially conducted a comparative analysis of yak ovarian activities in anestrus, estrus, and pregnancy regarding their morphology and histology, followed by implementing RNA sequencing (RNA-seq) technology to detect the overall gene expression and biological mechanism in different reproductive stages. H&E staining showed that there were more growing follicles and mature follicles in ovarian tissue sections during estrus than ovarian tissues during non-estrus. The RNA-seq analysis of yak ovary tissues in three periods showed that DEGs related to follicular development and hormone metabolism were screened in the three comparison groups, such as COL1A2, NR4A1, THBS2, PTGS2, SCARB1, STAR, and WNT2B. Bioinformatics analysis showed that these DEGs are involved in ion binding, cell development, metabolic processes, enriched in ECM–receptor interactions, steroid biosynthesis, together with aldosterone generation/discharge and Wnt/PI3K-Akt signaling pathways. In addition, we speculate alternate splice development events to have important role/s in regulating ovarian functional genomic expression profiles. These results provide essential knowledge aimed at scrutinizing pivotal biomarkers for yak ovarian activity, together with paving the way for enhancing researchers’ focus on improving yak reproductive performance.

Identification of Potential Long Non-Coding RNA Candidates That Contribute to Triple-Negative Breast Cancer in Humans through Computational Approach

Breast cancer (BC) is the most frequent malignancy identified in adult females, resulting in enormous financial losses worldwide. Owing to the heterogeneity as well as various molecular subtypes, the molecular pathways underlying carcinogenesis in various forms of BC are distinct. Therefore, the advancement of alternative therapy is required to combat the ailment. Recent analyses propose that long non-coding RNAs (lncRNAs) perform an essential function in controlling immune response, and therefore, may provide essential information about the disorder. However, their function in patients with triple-negative BC (TNBC) has not been explored in detail. Here, we analyzed the changes in the genomic expression of messenger RNA (mRNA) and lncRNA in standard control in response to cancer metastasis using publicly available single-cell RNA-Seq data. We identified a total of 197 potentially novel lncRNAs in TNBC patients of which 86 were differentially upregulated and 111 were differentially downregulated. In addition, among the 909 candidate lncRNA transcripts, 19 were significantly differentially expressed (DE) of which three were upregulated and 16 were downregulated. On the other hand, 1901 mRNA transcripts were significantly DE of which 1110 were upregulated and 791 were downregulated by TNBCs subtypes. The Gene Ontology (GO) analyses showed that some of the host genes were enriched in various biological, molecular, and cellular functions. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that some of the genes were involved in only one pathway of prostate cancer. The lncRNA-miRNA-gene network analysis showed that the lncRNAs TCONS_00076394 and TCONS_00051377 interacted with breast cancer-related micro RNAs (miRNAs) and the host genes of these lncRNAs were also functionally related to breast cancer. Thus, this study provides novel lncRNAs as potential biomarkers for the therapeutic intervention of this cancer subtype.

Transcriptomic Responses of Rhizobium phaseoli to Root Exudates Reflect Its Capacity to Colonize Maize and Common Bean in an Intercropping System

Corn and common bean have been cultivated together in Mesoamerica for thousands of years in an intercropping system called “milpa,” where the roots are intermingled, favoring the exchange of their microbiota, including symbionts such as rhizobia. In this work, we studied the genomic expression of Rhizobium phaseoli Ch24-10 (by RNA-seq) after a 2-h treatment in the presence of root exudates of maize and bean grown in monoculture and milpa system under hydroponic conditions. In bean exudates, rhizobial genes for nodulation and degradation of aromatic compounds were induced; while in maize, a response of genes for degradation of mucilage and ferulic acid was observed, as well as those for the transport of sugars, dicarboxylic acids and iron. Ch24-10 transcriptomes in milpa resembled those of beans because they both showed high expression of nodulation genes; some genes that were expressed in corn exudates were also induced by the intercropping system, especially those for the degradation of ferulic acid and pectin. Beans grown in milpa system formed nitrogen-fixing nodules similar to monocultured beans; therefore, the presence of maize did not interfere with Rhizobium–bean symbiosis. Genes for the metabolism of sugars and amino acids, flavonoid and phytoalexin tolerance, and a T3SS were expressed in both monocultures and milpa system, which reveals the adaptive capacity of rhizobia to colonize both legumes and cereals. Transcriptional fusions of the putA gene, which participates in proline metabolism, and of a gene encoding a polygalacturonase were used to validate their participation in plant–microbe interactions. We determined the enzymatic activity of carbonic anhydrase whose gene was also overexpressed in response to root exudates.

Two regulatory factors of Vibrio cholerae activating the mannose-sensitive haemagglutinin pilus expression is important for biofilm formation and colonization in mice

Vibrio cholerae the causative agent of cholera, uses a large number of coordinated transcriptional regulatory events to transition from its environmental reservoir to the host intestine, which is its preferred colonization site. Transcription of the mannose-sensitive haemagglutinin pilus (MSHA), which aids the persistence of V. cholerae in aquatic environments, but causes its clearance by host immune defenses, was found to be regulated by a yet unknown mechanism during the infection cycle of V. cholerae . In this study, genomic expression library screening revealed that two regulators, VC1371 and VcRfaH, are able to positively activate the transcription of MSHA operon. VC1371 is localized and active in the cell membrane. Deletion of vc1371 or VcrfaH genes in V. cholerae resulted in less MshA protein production and less efficiency of biofilm formation compared to that in the wild-type strain. An adult mouse model showed that the mutants with vc1371 or VcrfaH deletion colonized less efficiently than the wild-type; the VcrfaH deletion mutant showed less colonization efficiency in the infant mouse model. The findings strongly suggested that the two regulators, namely VC1371 and VcRfaH, which are involved in the regulation of MSHA expression, play an important role in V. cholerae biofilm formation and colonization in mice.

Proficient Mining of Informative Gene from Microarray Gene Expression Dataset Using Machine Intelligence

The quick improvement of DNA microarray innovation empowers analysts to quantify the expression levels of thousands of genomic data and permits scientists effortlessly pick up and understanding the mind-boggling prediction in tumors based on genomic expression levels. The application in malignancy has been demonstrated and extraordinary achievement has been performed in both conclusion and clarification using the neurotic methodologies. In many cases, DNA microarray information about gene contains a large number of qualities and the majority of them are turned out to be uninformative and excess. In the interim, little size of tests of microarray information undermines the determination precision of factual models. In this way, choosing profoundly discriminative qualities from crude quality genetic expression can enhance the execution of genetic prediction and chopped down the cost of medicinal analysis. Pearson Correlation based Feature Selection strategy with machine learning methodologies is effective to locate a conspicuous arrangement of components which can be utilized to anticipate and idealize the blend of quality to analyze the disease. As conflicting to the customary cross approval, filter one cross approval technique is connected for the analyses. As needs be, the proposed blend between the PCBFS and Machine Learning methodology is an effective apparatus for disease grouping and can be actualized as a genuine clinical supportive system.

Comparative transcriptional profiling of canine acanthomatous ameloblastoma and homology with human ameloblastoma

Ameloblastomas are odontogenic tumors that are rare in people but have a relatively high prevalence in dogs. Because canine acanthomatous ameloblastomas (CAA) have clinicopathologic and molecular features in common with human ameloblastomas (AM), spontaneous CAA can serve as a useful translational model of disease. However, the molecular basis of CAA and how it compares to AM are incompletely understood. In this study, we compared the global genomic expression profile of CAA with AM and evaluated its dental origin by using a bulk RNA-seq approach. For these studies, healthy gingiva and canine oral squamous cell carcinoma served as controls. We found that aberrant RAS signaling, and activation of the epithelial-to-mesenchymal transition cellular program are involved in the pathogenesis of CAA, and that CAA is enriched with genes known to be upregulated in AM including those expressed during the early stages of tooth development, suggesting a high level of molecular homology. These results support the model that domestic dogs with spontaneous CAA have potential for pre-clinical assessment of targeted therapeutic modalities against AM.

Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.