Global RNA editome landscape discovers reduced RNA editing in glioma: loss of editing of gamma-amino butyric acid receptor alpha subunit 3 (GABRA3) favors glioma migration and invasion.

BACKGROUND: Gliomas are the most common and lethal type of intracranial tumors. With the current treatment regime, the median survival of patients with grade IV glioma (glioblastoma/GBM) remains at 14-16 months. RNA editing modifies the function and regulation of transcripts. The development of glial tumors may be caused by altered RNA editing events. METHODS: In this study, we uncover the global RNA editome landscape of glioma patients from RNA-seq data of control, lower grade glioma (LGG) and GBM samples (n = 1,083). RESULTS: A-to-I editing events were found to comprise 80% of the total editing events of which 96% were located in the Alu regions. The total RNA editing events were found to be reduced in glioma compared to control samples. More specifically, we found Gamma-aminobutyric acid type A receptor alpha3 (GABRA3) to be edited (c.1026 A-to-G; pI343M) in 73% (editing ratio 0.8) of control samples compared to LGG (28.96%; 0.47) and GBM (5.2%; 0.53) samples. GABRA3 transcript level was found to be downregulated in glioma compared to control in a grade-specific manner with GBMs having the lowest level of the transcript. Further, GABRA3 transcripts were observed to be higher in edited compared to unedited glioma samples. The transcript and protein levels of exogenously expressed gene were found to be higher for edited compared to unedited GABRA3 in glioma cells. Further, exogenously expressed edited GABRA3 inhibited migration and invasion of glioma cells efficiently but not the unedited GABRA3. CONCLUSION: Collectively, our study discovered a reduction in RNA editing during glioma development. We further demonstrate that elevated RNA editing maintains a high level of GABRA3 RNA and protein in normal glial cells which provides a less migratory environment for the normal functioning of the brain. In contrast, the reduction in GABRA3 protein levels, due to lower stability of unedited RNA, results in the loss of function which confers an aggressive phenotype to GBM tumor.

Comprehensive analysis of Reverse Phase Protein Array data reveals characteristic unique proteomic signatures for glioblastoma subtypes.

The most common and lethal type of intracranial tumors include the astrocytomas. Grade IV astrocytoma or Glioblastoma (GBM) is highly aggressive and treatment-refractory with a median survival of only 14 to 16 months. Molecular profiling of GBMs reveals a high degree of intra- and inter-tumoral heterogeneity, and hence it is important to understand the important signalling axes that get deregulated in different GBM subtypes to provide effective tailor-made therapies. In this study, we have carried out extensive analysis of Reverse Phase Protein Array (RPPA) data from TCGA cohort to develop protein signatures that define glioma grades or subtypes. The protein signatures that distinguished Grade II or III from GBM had largely overlapped, and pathway analysis revealed the positive enrichment of extracellular matrix proteins (ECM), MYC pathway, uPAR pathway and G2/M checkpoint genes in GBM. We also identified protein signatures for GBMs with genetic alterations (IDH mutation, p53 mutation, EGFR amplification or mutation, CDKN2A/CDKN2B deletion, and PTEN mutation) that occur at high frequency. G-CIMP positive GBM-specific protein signature showed a large similarity with IDH1-mutant protein signature, thus signifying the importance of IDH1 mutation driving the G-CIMP. Gene expression subtype analysis revealed an association of specific proteins to classical (EGFR and phosphor variants), mesenchymal (SERPINE1, TAZ, and Myosin-IIa_pS1943), neural (TUBA1B), and proneural (GSK3_pS9) types. Univariate Cox regression analysis identified several proteins showing significant correlation with GBM survival. Multivariate analysis revealed that IGFBP2 and RICTOR_pT1135 are independent predictors of survival. Overall, our analyses reveal that specific proteins are regulated in different glioma subtypes underscoring the importance of diverse signalling axes playing important role in the pathogenesis of glioma tumors.

In silico approach to identify non-synonymous SNPs with highest predicted deleterious effect on protein function in human obesity related gene, neuronal growth regulator 1 (NEGR1).

Neuronal growth regulator 1 (NEGR1) is a candidate gene for human obesity, which encodes the neural cell adhesion and growth molecule. The aim of the current study was to recognize the non-synonymous SNPs (nsSNPs) with the highest predicted deleterious effect on protein function of the NEGR1 gene. We have used five computational tools, namely, PolyPhen, SIFT, PROVEAN, MutPred and M-CAP, to predict the deleterious and pathogenic nsSNPs of the NEGR1 gene. Homology modeling approach was used to model the native and mutant NEGR1 protein models. Furthermore, structural validation was performed by the PROCHECK server to interpret the stability of the predicted models. We have predicted four potential deleterious nsSNPs, i.e., rs145524630 (Ala70Thr), rs267598710 (Pro168Leu), rs373419972 (Arg239Cys) and rs375352213 (Leu158Phe), which might be involved in causing obesity phenotypes. The predicted mutant models showed higher root mean square deviation and free energy values under the PyMoL and SWISS-PDB viewer, respectively. Additionally, the FTSite server predicted one nsSNP, i.e., rs145524630 (Ala70Thr) out of four identified nsSNPs found in the NEGR1 protein-binding site. There were four potential deleterious and pathogenic nsSNPs, i.e., rs145524630, rs267598710, rs373419972 and rs375352213, identified from the above-mentioned tools. In future, further functional in vitro and in vivo analysis could lead to better knowledge about these nsSNPs on the influence of the NEGR1 gene in causing human obesity. Hence, the present computational examination suggest that predicated nsSNPs may feasibly be a drug target and play an important role in contributing to human obesity.

A four-protein expression prognostic signature predicts clinical outcome of lower-grade glioma.

BACKGROUND: Glioma is a wide category of brain tumor originates from glial cells. Lower-Grade Glioma (LGG) consists of World Health Organization (WHO) grade II and grade III gliomas. Since the LGGs can infiltrate into adjacent areas, the complete removal of tumor is difficult and it results in recurrence and malignant progression to high grade glioma. Our study uncovers robust survival indicators in LGG which can be checked by immunohistochemistry to predict the outcome of lower grade. In addition, it unravelled the novel therapeutic targets in order to improve the survival of LGG patients. METHODS: To identify a prognostic signature based on protein expression in LGGs, we analysed Reverse Phase Protein Array data of LGG samples (n = 380) from The Cancer Genome Atlas cohort. We made random stratification of samples into discovery (n = 228) and validation datasets (n = 152). We performed multivariate Cox proportional hazards regression analysis of proteins (n = 219) using discovery dataset with age, WHO grade and IDH mutation status. RESULTS: We identified four-protein prognostic signature that can segregate patients into high- and low-risk. The signature estimates poor overall survival for high-risk patients in both discovery (hazard ratio [HR] = 4.11; 95% confidence interval [CI] = 2.18-7.75; p < 0.0001) and validation datasets (HR = 3.49; 95% CI = 1.52-8.01; p < 0.0001). Among the four markers, CHK2_pT68 was found to be protective, while MSH6, ARID1A and PAXILLIN were associated with poor survival. Additionally, Multivariate Cox proportional hazards regression analysis of this signature with age, WHO grade and IDH mutation status revealed this prognostic signature to be an independent prognosticator in both datasets. CONCLUSIONS: Our finding discovered a set of potential protein biomarkers to predict survival and it will help in the subsequent treatment management of LGG patients.

Genetic landscape of long noncoding RNA (lncRNAs) in glioblastoma: identification of complex lncRNA regulatory networks and clinically relevant lncRNAs in glioblastoma.

The major part of the genome that was previously called junk DNA has been shown to be dynamically transcribed to produce non-coding RNAs. Among them, the long non-coding RNAs (lncRNA) play diverse roles in the cellular context and are therefore involved in various diseases like cancer. LncRNA transcript profiling of glioblastoma (n = 19) and control brain samples (n = 9) identified 2,774 and 5,016 lncRNAs to be upregulated and downregulated in GBMs respectively. Correlation analysis of differentially regulated lncRNAs with mRNA and lncRNA identified several lncRNAs that may potentially regulate many tumor relevant mRNAs and lncRNAs both at nearby locations (cis) and far locations (trans). Integration of our data set with TCGA GBM RNA-Seq data (n = 172) revealed many lncRNAs as a host as well as decoy for many tumor regulated miRNAs. The expression pattern of seven lncRNAs- HOXD-AS2, RP4-792G4.2, CRNDE, ANRIL, RP11-389G6.3, RP11-325122.2 and AC123886.2 was validated by TCGA RNA-Seq data and RT-qPCR. Silencing ANRIL, a GBM upregulated lncRNA, inhibited glioma cell proliferation and colony growth. Cox regression analysis identified several prognostic lncRNAs. An lncRNA risk score derived from five lnRNAs-RP6-99M1.2, SOX21-AS1, CTD-2127H9.1, RP11-375B1.3 and RP3-449M8.9 predicted survival independent of all other markers. Multivariate cox regression analysis involving G-CIMP, IDH1 mutation, MGMT promoter methylation identified lncRNA risk score to be an independent poor predictor of GBM survival. The lncRNA risk score also stratified GBM patients into low and high risk with significant survival difference. Thus our study demonstrates the importance of lncRNA in GBM pathology and underscores the potential possibility of targeting lncRNA for GBM therapy.

In silico approach to identify non-synonymous SNPs in human obesity related gene, MC3R (melanocortin-3-receptor).

The melanocortin-3-receptor (MC3R) is a novel gene candidate for human obesity, which involved in controlling the energy homeostasis and food intake behavior. The main aim behind this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in obesity related gene MC3R by using six computational tools viz., PolyPhen, I-Mutant, PROVEAN, SIFT, PANTHER and PhD-SNP. In our study, we predicted eight nsSNPs i.e., rs74315393 (Ile146Asn), rs368205448 (Asp121Tyr), rs143321797 (Phe45Ser), rs17847261 (Cys274Ser), rs144166442 (Pro257His), rs370533946 (Leu224Pro), rs371354428 (Pro72Leu) and rs373708098 (Gly249Ser) found to be potentially deleterious. The functional impact of three nsSNPs i.e., rs74315393, rs368205448 and rs143321797 have already been validated experimentally in the context of human obesity. Moreover, Homology modeling and structural analysis were carried out for already experimentally validated nsSNPs i.e., rs74315393, rs368205448 and rs143321797 to check the stability of predicted models. The mutant models showed higher energy and RMSD (Root mean square deviation) values. In addition, FTSite server predicted one nsSNP i.e., rs368205448 (Asp121Tyr) out of eight identified nsSNPs found in the MC3R protein binding site. Thus, the present computational study may suggest that predicted nsSNPs possibly be a better drug target and contribute to the treatment and better understanding of human obesity.

Molecular genetics of human obesity: A comprehensive review.

Obesity and its related health complications is a major problem worldwide. Hypothalamus and their signalling molecules play a critical role in the intervening and coordination with energy balance and homeostasis. Genetic factors play a crucial role in determining an individual's predisposition to the weight gain and being obese. In the past few years, several genetic variants were identified as monogenic forms of human obesity having success over common polygenic forms. In the context of molecular genetics, genome-wide association studies (GWAS) approach and their findings signified a number of genetic variants predisposing to obesity. However, the last couple of years, it has also been noticed that alterations in the environmental and epigenetic factors are one of the key causes of obesity. Hence, this review might be helpful in the current scenario of molecular genetics of human obesity, obesity-related health complications (ORHC), and energy homeostasis. Future work based on the clinical discoveries may play a role in the molecular dissection of genetic approaches to find more obesity-susceptible gene loci.

Elucidation of the genetic and epigenetic landscape alterations in RNA binding proteins in glioblastoma.

RNA binding proteins (RBPs) have been implicated in cancer development. An integrated bioinformatics analysis of RBPs (n = 1756) in various datasets (n = 11) revealed several genetic and epigenetically altered events among RBPs in glioblastoma (GBM). We identified 13 mutated and 472 differentially regulated RBPs in GBM samples. Mutations in AHNAK predicted poor prognosis. Copy number variation (CNV), DNA methylation and miRNA targeting contributed to RBP differential regulation. Two sets of differentially regulated RBPs that may be implicated in initial astrocytic transformation and glioma progression were identified. We have also identified a four RBP (NOL3, SUCLG1, HERC5 and AFF3) signature, having a unique expression pattern in glioma stem-like cells (GSCs), to be an independent poor prognostic indicator in GBM. RBP risk score derived from the signature also stratified GBM into low-risk and high-risk groups with significant survival difference. Silencing NOL3, SUCLG1 and HERC5 inhibited GSC maintenance. Gene set enrichment analysis of differentially regulated genes between high-risk and low-risk underscored the importance of inflammation, EMT and hypoxia in high-risk GBM. Thus, we provide a comprehensive overview of genetic and epigenetic regulation of RBPs in glioma development and progression.

Ets-1 expression and gemcitabine chemoresistance in pancreatic cancer cells.

Gemcitabine, a novel pyrimidine nucleoside analog, has become the standard chemotherapeutic agent for pancreatic cancer patients. The clinical impact of gemcitabine remains modest owing to the high degree of inherent and acquired resistance. There are various lines of evidence that confirm the role of Ets-1, a proto-oncoprotein, in tumor invasion, progression, and chemoresistance. This study examines a hypothesis that implicates Ets-1 in the development of gemcitabine-resistance in pancreatic cancer cells. Ets-1 protein expression was assessed in parental pancreatic cancer cells and their gemcitabine-resistant clones. Western blot analysis revealed elevated levels of Ets-1 protein expression in gemcitabine-resistant PANC1(GemRes) (4.8-fold increase; P < 0.05), MIA PaCa2(GemRes) (3.2-fold increase; P < 0.05), and Capan2(GemRes) (2.1-fold increase; P < 0.05) cells as compared to their parental counterparts. A time course analysis was conducted to determine the change in Ets-1 expression in the parental cells after incubation with gemcitabine. Reverse transcriptase quantitative real-time PCR (RT-qPCR) and Western blot analysis revealed a significant increase in Ets-1 expression. All the three parental cells incubated with gemcitabine showed elevated Ets-1 protein expression at 6 h. By 24 h, the expression level had decreased. Using small interfering RNA (siRNA) against Ets-1 in gemcitabine-resistant cells, we demonstrated a reversal in gemcitabine chemosensitivity and also detected a marked reduction in the expression of the Ets-1 target genes MMP1 and uPA. Our novel finding demonstrates the significance of Ets-1 in the development of gemcitabine chemoresistance in pancreatic cancer cells. Based on these results, a new siRNA-based therapeutic strategy targeting the Ets-1 genes can be designed to overcome chemoresistance.