Multiomics Analysis of the PHLDA Gene Family in Different Cancers and Their Clinical Prognostic Value.

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein-protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data.

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

G-Protein-Coupled Receptors Mediate Modulations of Cell Viability and Drug Sensitivity by Aberrantly Expressed Recoverin 3 within A549 Cells.

To elucidate the currently unknown molecular mechanisms responsible for the aberrant expression of recoverin (Rec) within cancerous cells, we examined two-dimensional (2D) and three-dimensional (3D) cultures of Rec-negative lung adenocarcinoma A549 cells which had been transfected with a plasmid containing human recoverin cDNA (A549 Rec) or an empty plasmid as a mock control (A549 MOCK). Using these cells, we measured cytotoxicity by several anti-tumor agents (2D), cellular metabolism including mitochondrial and glycolytic functions by a Seahorse bio-analyzer (2D), the physical properties, size and stiffness of the 3D spheroids, trypsin sensitivities (2D and 3D), and RNA sequencing analysis (2D). Compared with the A549 MOCK, the A549 Rec cells showed (1) more sensitivity toward anti-tumor agents (2D) and a 0.25% solution of trypsin (3D); (2) a metabolic shift from glycolysis to oxidative phosphorylation; and (3) the formation of larger and stiffer 3D spheroids. RNA sequencing analysis and bioinformatic analyses of the differentially expressed genes (DEGs) using Gene Ontology (GO) enrichment analysis suggested that aberrantly expressed Rec is most likely associated with several canonical pathways including G-protein-coupled receptor (GPCR)-mediated signaling and signaling by the cAMP response element binding protein (CREB). The findings reported here indicate that the aberrantly expressed Rec-induced modulation of the cell viability and drug sensitivity may be GPCR mediated.

Comparative Transcriptomic Analyses for the Optimization of Thawing Regimes during Conventional Cryopreservation of Mature and Immature Human Testicular Tissue.

Cryopreservation of human testicular tissue, as a key element of anticancer therapy, includes the following stages: saturation with cryoprotectants, freezing, thawing, and removal of cryoprotectants. According to the point of view existing in "classical" cryobiology, the thawing mode is the most important consideration in the entire process of cryopreservation of any type of cells, including cells of testicular tissue. The existing postulate in cryobiology states that any frozen types of cells must be thawed as quickly as possible. The technologically maximum possible thawing temperature is 100 °C, which is used in our technology for the cryopreservation of testicular tissue. However, there are other points of view on the rate of cell thawing, according to how thawing should be carried out at physiological temperatures. In fact, there are morphological and functional differences between immature (from prepubertal patients) and mature testicular tissue. Accordingly, the question of the influence of thawing temperature on both types of tissues is relevant. The purpose of this study is to explore the transcriptomic differences of cryopreserved mature and immature testicular tissue subjected to different thawing methods by RNA sequencing. Collected and frozen testicular tissue samples were divided into four groups: quickly (in boiling water at 100 °C) thawed cryopreserved mature testicular tissue (group 1), slowly (by a physiological temperature of 37 °C) thawed mature testicular tissue (group 2), quickly thawed immature testicular tissue (group 3), and slowly thawed immature testicular tissue (group 4). Transcriptomic differences were assessed using differentially expressed genes (DEG), the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), and protein-protein interaction (PPI) analyses. No fundamental differences in the quality of cells of mature and immature testicular tissue after cryopreservation were found. Generally, thawing of mature and immature testicular tissue was more effective at 100 °C. The greatest difference in the intensity of gene expression was observed in ribosomes of cells thawed at 100 °C in comparison with cells thawed at 37 °C. In conclusion, an elevated speed of thawing is beneficial for frozen testicular tissue.

Structural property, molecular regulation, and functional diversity of glutamine synthetase in higher plants: a data-mining bioinformatics approach.

Glutamine synthetase (GS; E.C.6.3.1.2) is a key enzyme in higher plants with two isozymes, cytosolic GS1 and plastidic GS2, and involves in the assimilation and recycling of NH4+ ions and maintenance of complex traits such as crop nitrogen-use efficiency and yield. Our present understanding of crop nitrogen-use efficiency and its correlation with the functional role of the GS family genes is inadequate, which delays harnessing the benefit of this key enzyme in crop improvement. In this report, we performed a comprehensive investigation on the phylogenetic relationship, structural properties, complex multilevel gene regulation, and expression patterns of the GS genes to enrich present understanding about the enzyme. Our Gene Ontology and protein-protein interactions analysis revealed the functional aspects of GS isozymes in stress mitigation, aging, nucleotide biosynthesis/transport, DNA repair and response to metals. The insight gained here contributes to the future research strategies in developing climate-smart crops for global sustainability.

MDV-induced differential microRNA expression in the primary lymphoid organ of thymus.

Marek's disease virus (MDV), a highly contagious cell associated virus, is the etiological agent of Marek's disease (MD), a lymphoproliferative and neuropathic disease of domestic chickens. Clinical signs of MD include transient paralysis, bursal/thymic atrophy, and T cell lymphomas. MicroRNAs (miRNAs) are short single-stranded non-coding RNAs that regulate gene expression by transcriptional suppression or mRNA degradation. Herpesviruses, including MDV, encode for miRNAs that are known to play essential roles in viral pathogenicity, oncogenesis, and evasion of immune responses. In this study, we performed miRNA sequencing in thymuses of control and MDV-infected chickens of MD-resistant (63) and susceptible (72) lines at 21 days post infection (dpi). The thymus is a lymphoid organ that undergoes severe atrophy due to MDV-induced apoptotic mediated destruction of T cells. Sequence analysis identified 658 total chicken miRNAs in the thymuses of control and MDV-infected birds of both lines. Of these, 453 were novel and 205 were known microRNAs. All novel miRNAs mapped to chicken genome with no sequence homology to existing miRNAs in the chicken miRbase. Comparative analysis between the thymuses of control and infected birds of resistant and susceptible lines identified 78 differentially expressed microRNAs that might provide insights into mechanisms of thymus atrophy.

MAP kinase and mammalian target of rapamycin are main pathways of gallbladder carcinogenesis: results from bioinformatic analysis of next generation sequencing data from a hospital-based cohort (NCT05404347).

BACKGROUND: Gallbladder Cancer (GBC) is one of the most common cancers of the biliary tract and the third commonest gastrointestinal (GI) malignancy worldwide. The disease is characterized by the late presentation and poor outcome despite treatment, and hence, newer therapies and targets need to be identified. METHODS: The current study investigated various functionally enriched pathways in GBC pathogenesis involving the genes identified through Next Generation Sequencing (NGS) in a hospital-based cohort. The Pathway enrichment analysis and Gene Ontology (GO) were carried out after NGS, followed by the construction of the protein-protein interaction (PPI) network to discover associations among the genes. RESULTS: Of the thirty-three patients with GBC who were screened through next-generation sequencing (NGS), 27somatic mutations were identified. These mutations involved a total of 14 genes. The p53 and KRAS were commonly found to be mutated, while mutations in other genes were seen in one case each, the mean number of mutations were 1.2, and maximum mutation in a single case (eight) was seen in one case. The bioinformatics analysis identified MAP kinase, PI3K-AKT, EGF/EGFR, and Focal Adhesion PI3K-AKT-mTOR signaling pathways and cross-talk between these. CONCLUSION: The results suggest that the complex crosstalk between the mTOR, MAPK, and multiple interacting cell signaling cascades can promote GBC progression, and hence, mTOR-MAPK targeted treatment will be an attractive option.

MicroRNA profiling in the bursae of Marek's disease virus-infected resistant and susceptible chicken lines.

Marek's disease (MD) is a lymphoproliferative disease of domestic chickens caused by a cell-associated oncogenic alpha-herpesvirus, Marek's disease virus (MDV). Clinical signs of MD include bursal/thymic atrophy, neurologic disorders, and T cell lymphomas. MiRNAs play key roles in regulation of gene expression by targeting translational suppression or mRNA degradation. MDV encodes miRNAs that are associated with viral pathogenicity and oncogenesis. In this study, we performed miRNA sequencing in the bursal tissues, non-tumorous but viral-induced atrophied lymphoid organ, from control and infected MD-resistant and susceptible chickens at 21 days post infection. In addition to some known miRNAs, a minimum of 300 novel miRNAs were identified in each group that mapped to the chicken genome with no sequence homology to existing miRNAs in chicken miRbase. Comparative analysis identified 54 deferentially expressed miRNAs between the chicken lines that might shed light on underlying mechanism of bursal atrophy and resistance or susceptibility to MD.

Comparative Transcriptome and Expression Profiling of Resistant and Susceptible Banana Cultivars during Infection by Fusarium oxysporum.

Fusarium wilt caused by Fusarium oxysporum f. sp. cubense (Foc) is one of the most destructive diseases of banana. Methods to control the disease are still inadequate. The present investigation targeted expression of defense-related genes in tissue cultured banana plantlets of Fusarium resistant and susceptible cultivars after infection with biological control agents (BCAs) and Fusarium (Foc race 1). In total 3034 differentially expressed genes were identified which annotated to 58 transcriptional families (TF). TF families such as MYB, bHLH and NAC TFs were mostly up-regulated in response to pathogen stress, whereas AP2/EREBP were mostly down-regulated. Most genes were associated with plant-pathogen response, plant hormone signal transduction, starch and sucrose metabolism, cysteine and methionine metabolism, flavonoid biosynthesis, selenocompound metabolism, phenylpropanoid biosynthesis, mRNA surveillance pathway, mannose type O-glycan biosynthesis, amino acid and nucleotide sugar metabolism, cyanoamino acid metabolism, and hormone signal transduction. Our results showed that the defense mechanisms of resistant and susceptible banana cultivars treated with BCAs, were regulated by differentially expressed genes in various categories of defense pathways. Furthermore, the association with different resistant levels might serve as a strong foundation for the control of Fusarium wilt of banana.

Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics.

The pathogenesis and molecular mechanisms of ovarian low malignant potential (LMP) tumors or borderline ovarian tumors (BOTs) have not been fully elucidated to date. Surgery remains the cornerstone of treatment for this disease, and diagnosis is mainly made by histopathology to date. However, there is no integrated analysis investigating the tumorigenesis of BOTs with open experimental data. Therefore, we first utilized a functionome-based speculative model from the aggregated obtainable datasets to explore the expression profiling data among all BOTs and two major subtypes of BOTs, serous BOTs (SBOTs) and mucinous BOTs (MBOTs), by analyzing the functional regularity patterns and clustering the separate gene sets. We next prospected and assembled the association between these targeted biomolecular functions and their related genes. Our research found that BOTs can be accurately recognized by gene expression profiles by means of integrative polygenic analytics among all BOTs, SBOTs, and MBOTs; the results exhibited the top 41 common dysregulated biomolecular functions, which were sorted into four major categories: immune and inflammatory response-related functions, cell membrane- and transporter-related functions, cell cycle- and signaling-related functions, and cell metabolism-related functions, which were the key elements involved in its pathogenesis. In contrast to previous research, we identified 19 representative genes from the above classified categories (IL6, CCR2 for immune and inflammatory response-related functions; IFNG, ATP1B1, GAS6, and PSEN1 for cell membrane- and transporter-related functions; CTNNB1, GATA3, and IL1B for cell cycle- and signaling-related functions; and AKT1, SIRT1, IL4, PDGFB, MAPK3, SRC, TWIST1, TGFB1, ADIPOQ, and PPARGC1A for cell metabolism-related functions) that were relevant in the cause and development of BOTs. We also noticed that a dysfunctional pathway of galactose catabolism had taken place among all BOTs, SBOTs, and MBOTs from the analyzed gene set databases of canonical pathways. With the help of immunostaining, we verified significantly higher performance of interleukin 6 (IL6) and galactose-1-phosphate uridylyltransferase (GALT) among BOTs than the controls. In conclusion, a bioinformatic platform of gene-set integrative molecular functionomes and biophysiological pathways was constructed in this study to interpret the complicated pathogenic pathways of BOTs, and these important findings demonstrated the dysregulated immunological functionome and dysfunctional metabolic pathway as potential roles during the tumorigenesis of BOTs and may be helpful for the diagnosis and therapy of BOTs in the future.

A consensus multi-view multi-objective gene selection approach for improved sample classification.

BACKGROUND: In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different 'omics' resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency. RESULTS: In this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification. CONCLUSIONS: The experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach.

BACKGROUND: In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. RESULTS: In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. CONCLUSION: A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC. METHODS: We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba. RESULTS: We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network. CONCLUSIONS: Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.

Comparative Transcriptome Analysis of Two Contrasting Soybean Varieties in Response to Aluminum Toxicity.

: Aluminum (Al) toxicity is a major factor limiting crop productivity on acid soils. Soybean (Glycine max) is an important oil crop and there is great variation in Al tolerance in soybean germplasms. However, only a few Al-tolerance genes have been reported in soybean. Therefore, the purpose of this study was to identify candidate Al tolerance genes by comparative transcriptome analysis of two contrasting soybean varieties in response to Al stress. Two soybean varieties, M90-24 (M) and Pella (P), which showed significant difference in Al tolerance, were used for RNA-seq analysis. We identified a total of 354 Al-tolerance related genes, which showed up-regulated expression by Al in the Al-tolerant soybean variety M and higher transcript levels in M than P under Al stress. These genes were enriched in the Gene Ontology (GO) terms of cellular glucan metabolic process and regulation of transcription. Five out of 11 genes in the enriched GO term of cellular glucan metabolic process encode cellulose synthases, and one cellulose synthase gene (Glyma.02G205800) was identified as the key hub gene by co-expression network analysis. Furthermore, treatment of soybean roots with a cellulose biosynthesis inhibitor decreased the Al tolerance, indicating an important role of cellulose production in soybean tolerance to Al toxicity. This study provides a list of candidate genes for further investigation on Al tolerance mechanisms in soybean.

The Potential Role of Complement System in the Progression of Ovarian Clear Cell Carcinoma Inferred from the Gene Ontology-Based Immunofunctionome Analysis.

Ovarian clear cell carcinoma (OCCC) is the second most common epithelial ovarian carcinoma (EOC). It is refractory to chemotherapy with a worse prognosis after the preliminary optimal debulking operation, such that the treatment of OCCC remains a challenge. OCCC is believed to evolve from endometriosis, a chronic immune/inflammation-related disease, so that immunotherapy may be a potential alternative treatment. Here, gene set-based analysis was used to investigate the immunofunctionomes of OCCC in early and advanced stages. Quantified biological functions defined by 5917 Gene Ontology (GO) terms downloaded from the Gene Expression Omnibus (GEO) database were used. DNA microarray gene expression profiles were used to convert 85 OCCCs and 136 normal controls into to the functionome. Relevant offspring were as extracted and the immunofunctionomes were rebuilt at different stages by machine learning. Several dysregulated pathogenic functions were found to coexist in the immunopathogenesis of early and advanced OCCC, wherein the complement-activation-alternative-pathway may be the headmost dysfunctional immunological pathway in duality for carcinogenesis at all OCCC stages. Several immunological genes involved in the complement system had dual influences on patients' survival, and immunohistochemistrical analysis implied the higher expression of C3a receptor (C3aR) and C5a receptor (C5aR) levels in OCCC than in controls.

The 15q11.2 BP1-BP2 Microdeletion (Burnside-Butler) Syndrome: In Silico Analyses of the Four Coding Genes Reveal Functional Associations with Neurodevelopmental Phenotypes.

The 15q11.2 BP1-BP2 microdeletion (Burnside-Butler) syndrome is emerging as the most frequent pathogenic copy number variation (CNV) in humans associated with neurodevelopmental disorders with changes in brain morphology, behavior, and cognition. In this study, we explored functions and interactions of the four protein-coding genes in this region, namely NIPA1, NIPA2, CYFIP1, and TUBGCP5, and elucidate their role, in solo and in concert, in the causation of neurodevelopmental disorders. First, we investigated the STRING protein-protein interactions encompassing all four genes and ascertained their predicted Gene Ontology (GO) functions, such as biological processes involved in their interactions, pathways and molecular functions. These include magnesium ion transport molecular function, regulation of axonogenesis and axon extension, regulation and production of bone morphogenetic protein and regulation of cellular growth and development. We gathered a list of significantly associated cardinal maladies for each gene from searchable genomic disease websites, namely MalaCards.org: HGMD, OMIM, ClinVar, GTR, Orphanet, DISEASES, Novoseek, and GeneCards.org. Through tabulations of such disease data, we ascertained the cardinal disease association of each gene, as well as their expanded putative disease associations. This enabled further tabulation of disease data to ascertain the role of each gene in the top ten overlapping significant neurodevelopmental disorders among the disease association data sets: (1) Prader-Willi Syndrome (PWS); (2) Angelman Syndrome (AS); (3) 15q11.2 Deletion Syndrome with Attention Deficit Hyperactive Disorder & Learning Disability; (4) Autism Spectrum Disorder (ASD); (5) Schizophrenia; (6) Epilepsy; (7) Down Syndrome; (8) Microcephaly; (9) Developmental Disorder, and (10) Peripheral Nervous System Disease. The cardinal disease associations for each of the four contiguous 15q11.2 BP1-BP2 genes are NIPA1- Spastic Paraplegia 6; NIPA2-Angelman Syndrome and Prader-Willi Syndrome; CYFIP1-Fragile X Syndrome and Autism; TUBGCP5-Prader-Willi Syndrome. The four genes are individually associated with PWS, ASD, schizophrenia, epilepsy, and Down syndrome. Except for TUBGCP5, the other three genes are associated with AS. Unlike the other genes, TUBGCP5 is also not associated with attention deficit hyperactivity disorder and learning disability, developmental disorder, or peripheral nervous system disease. CYFIP1 was the only gene not associated with microcephaly but was the only gene associated with developmental disorders. Collectively, all four genes were associated with up to three-fourths of the ten overlapping neurodevelopmental disorders and are deleted in this most prevalent known pathogenic copy number variation now recognized among humans with these clinical findings.

[Bioinformatic analysis of differentially expressed genes and Chinese medicine prediction for ulcerative colitis].

The aim of this paper was to analyze the microarray data between ulcerative colitis(UC) patients and healthy people by bioinformatics technology, screen the differentially expressed genes of UC, and predict the potential Chinese medicines for UC. The GSE36807 gene expression profile was downloaded from the gene expression database(GEO) and the differentially expressed(both up-regulated and down-regulated) genes(DEGs) were analyzed by using R language software. The core genes in the DEGs were obtained by using String database, Cytoscape software and its plug-in analysis, and the gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) were used to analyze the core genes. Moreover, the core genes and the medical ontology information retrieval platform(Coremine Medical) were mapped to each other to screen the traditional Chinese medicines and its active ingredients for treating UC. A total of 648 DEGs were screened, including 397 up-regulated genes and 251 down-regulated genes. Up-regulation of DEGs yielded 15 core genes including CXCL8, IL1 B, MMP9, CXCL1, CXCL10, CXCL9, CXCL2, CXCL5, TIMP1, CXCL11, STAT1,LCN2, IL1 RN, MMP1 and IDO1. Their biological processes and pathways were mainly enriched in interleukins, chemokine ligands and cytokines, chemokine-mediated signaling pathways, and were closely related to inflammatory responses, defense responses, cell chemotaxis, secretory granules, IL17 signaling pathways, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Potential Chinese medicines for the treatment of UC include Curcumae Longae Rhizoma, Coptidis Rhizoma, Scutellariae Radix, Dendrobii Caulis, Sanguisorbae Radix, Phellodendri Chinensis Cortex, Bletillae Rhizoma and Atractylodis Rhizoma. The analysis of DEGs and core genes could promote our understanding on pathogenesis of UC. This study provides potential gene targets and research ideas for the development of new drugs of Chinese medicine intervention for UC.

Identification of dysregulated miRNAs in triple negative breast cancer: A meta-analysis approach.

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor clinical outcomes and lack of approved targeted therapy. Dysregulated microRNAs (miRNAs) have been considered a promising biomarker, which plays an important role in the tumorigenesis of human cancer. Due to the increase in miRNA profiling datasets of TNBC, a proper analysis is required for studying. Therefore, this study used meta-analysis to amalgamate ten miRNA profiling studies of TNBC. By the robust rank aggregation method, metasignatures of six miRNAs (4 upregulated: hsa-miR-135b-5p, hsa-miR-18a-5p, hsa-miR-9-5p and hsa-miR-522-3p; 2 downregulated: hsa-miR-190b and hsa-miR-449a) were obtained. The gene ontology analysis revealed that target genes regulated by miRNAs were associated with processes like the regulation of transcription, DNA dependent, and signal transduction. Also, it is noted from the pathway analysis that signaling and cancer pathways were associated with the progression of TNBC. A Naïve Bayes-based classifier built with miRNA signatures discriminates TNBC and non-TNBC samples in test data set with high diagnostic sensitivity and specificity. From the analysis carried out by the study, it is suggested that the identified miRNAs are of great importance in improving the diagnostics and therapeutics for TNBC.

Identification of key genes and pathways associated with osteogenic differentiation of adipose stem cells.

Adipose stem cells (ASCs) are considered a great alternative source of mesenchymal stem cells (MSCs) and have shown great promise on tissue engineering and regenerative medicine applications, including bone repair. However, the underlying mechanisms regulating the osteogenic differentiation of ASCs remain poorly known. Gene expression profiles of GSE63754 and GSE37329 were downloaded from gene expression omnibus database. R software and Bioconductor packages were used to compare and identify the differentially expressed genes (DEGs) before and after ASC osteogenic differentiation. The common significant DEGs between GSE63754 and GSE37329 were then subjected to gene ontology (GO) enrichment analysis, ingenuity pathway analysis (IPA), and protein-protein interactions (PPIs) networks analysis. One of the central node genes FOXO1 was selected for further investigation. A total of 142 up- and 69 downregulated genes were aberrantly expressed in both GSE63754 and GSE37329. GO analysis revealed that these DEGs were associated with extracellular matrix organization, proteinaceous extracellular matrix, and Wnt-protein binding. IPA analysis showed that canonical pathways, such as FXR/RXR activation, adipogenesis pathway, and LXR/RXR activation, were involved in regulating osteogenic differentiation of ASCs. A total of three subnetworks and 39 nodes were identified with PPI network and MCODE plugin. Moreover, suppression of one central node gene FOXO1 inhibited the osteogenic differentiation of ASCs. Our study provides a registry of genes and pathways that play important roles in regulating osteogenic differentiation of ASCs, which might have potential therapeutic applications in bone regeneration and bone tissue engineering.

De novo transcriptome assembly, annotation and comparison of four ecological and evolutionary model salmonid fish species.

BACKGROUND: Salmonid fishes exhibit high levels of phenotypic and ecological variation and are thus ideal model systems for studying evolutionary processes of adaptive divergence and speciation. Furthermore, salmonids are of major interest in fisheries, aquaculture, and conservation research. Improving understanding of the genetic mechanisms underlying traits in these species would significantly progress research in these fields. Here we generate high quality de novo transcriptomes for four salmonid species: Atlantic salmon (Salmo salar), brown trout (Salmo trutta), Arctic charr (Salvelinus alpinus), and European whitefish (Coregonus lavaretus). All species except Atlantic salmon have no reference genome publicly available and few if any genomic studies to date. RESULTS: We used paired-end RNA-seq on Illumina to generate high coverage sequencing of multiple individuals, yielding between 180 and 210 M reads per species. After initial assembly, strict filtering was used to remove duplicated, redundant, and low confidence transcripts. The final assemblies consisted of 36,505 protein-coding transcripts for Atlantic salmon, 35,736 for brown trout, 33,126 for Arctic charr, and 33,697 for European whitefish and are made publicly available. Assembly completeness was assessed using three approaches, all of which supported high quality of the assemblies: 1) ~78% of Actinopterygian single-copy orthologs were successfully captured in our assemblies, 2) orthogroup inference identified high overlap in the protein sequences present across all four species (40% shared across all four and 84% shared by at least two), and 3) comparison with the published Atlantic salmon genome suggests that our assemblies represent well covered (~98%) protein-coding transcriptomes. Thorough comparison of the generated assemblies found that 84-90% of transcripts in each assembly were orthologous with at least one of the other three species. We also identified 34-37% of transcripts in each assembly as paralogs. We further compare completeness and annotation statistics of our new assemblies to available related species. CONCLUSION: New, high-confidence protein-coding transcriptomes were generated for four ecologically and economically important species of salmonids. This offers a high quality pipeline for such complex genomes, represents a valuable contribution to the existing genomic resources for these species and provides robust tools for future investigation of gene expression and sequence evolution in these and other salmonid species.