OBJECTIVES: The current study investigated the gene expression profile of hepatocellular carcinoma, HepG2, cells after treatment with Limonene.
METHODS: The concentration that killed 50% of HepG2 cells was used to elucidate the genetic mechanisms of limonene anticancer activity. The apoptotic induction was detected by flow cytometry and confocal fluorescence microscope. Two of the pro-apoptotic events, caspase-3 activation and phosphatidylserine translocation were manifested by confocal fluorescence microscopy. Highthroughput real-time PCR was used to profile 1023 cancer-related genes in 16 different gene families related to the cancer development.
RESULTS: In comparison to untreated cells, limonene increased the percentage of apoptotic cells up to 89.61%, by flow cytometry, and 48.2% by fluorescence microscopy. There was a significant limonene- driven differential gene expression of HepG2 cells in 15 different gene families. Limonene was shown to significantly (>2log) up-regulate and down-regulate 14 and 59 genes, respectively. The affected gene families, from the most to the least affected, were apoptosis induction, signal transduction, cancer genes augmentation, alteration in kinases expression, inflammation, DNA damage repair, and cell cycle proteins.
CONCLUSION: The current study reveals that limonene could be a promising, cheap, and effective anticancer compound. The broad spectrum of limonene anticancer activity is interesting for anticancer drug development. Further research is needed to confirm the current findings and to examine the anticancer potential of limonene along with underlying mechanisms on different cell lines.
METHODS: Thirty-nine subjects were enrolled from various health clinics in Kelantan, Malaysia, and divided into two groups: patients with chronic HCV infection (HP) and healthy control (HS). The serum cytokines IL-6, TNF-a-were measured using Luminex assay, and serum TGF-β1 was measured by ELISA. The mRNA gene expression for IL-6, TNF-α and TGF-β1 was measured by real-time reverse transcriptase polymerase chain reaction (RT-PCR).
RESULTS: There were statistically significant differences in the mean serum levels of IL-6, and TGF-β1 in HP compared to HS group (p = 0.0180 and p = 0.0005, respectively). There was no significant difference in the mean serum level of TNF-α in HP compared to HS group. The gene expression for the studied cytokines showed no significant differences in HP compared to HS group.
CONCLUSION: Serum IL-6 was significantly associated with chronic HCV infection.
Methods: In the current study, a transcriptome investigation was performed to explore the mechanism underlying the biofilm dispersal of P. aeruginosa after the exposure to Trigona honey.
Results: Microarray analysis of the Pseudomonas biofilm treated by 20% Trigona honey has revealed a down-regulation of 3478 genes among the 6085 screened genes. Specifically, around 13.5% of the down-regulated genes were biofilm-associated genes. The mapping of the biofilm-associated pathways has shown an ultimate decrease in the expression levels of the D-GMP signaling pathway and diguanylate cyclases (DGCs) genes responsible for c-di-GMP formation.
Conclusion: We predominantly report the lowering of c-di-GMP through the down-regulation of DGC genes as the main mechanism of biofilm inhibition by Trigona honey.
METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).
RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.
CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.
Methods: First, studies of codon usage in monocots were reviewed. The current information was then extended regarding codon usage, as well as codon-pair context bias, using four completely sequenced non-grass monocot genomes (Musa acuminata, Musa balbisiana, Phoenix dactylifera and Spirodela polyrhiza) for which comparable transcriptome datasets are available. Measurements were taken regarding relative synonymous codon usage, effective number of codons, derived optimal codon and GC content and then the relationships investigated to infer the underlying evolutionary forces.
Key Results: The research identified optimal codons, rare codons and preferred codon-pair context in the non-grass monocot species studied. In contrast to the bimodal distribution of GC3 (GC content in third codon position) in grasses, non-grass monocots showed a unimodal distribution. Disproportionate use of G and C (and of A and T) in two- and four-codon amino acids detected in the analysis rules out the mutational bias hypothesis as an explanation of genomic variation in GC content. There was found to be a positive relationship between CAI (codon adaptation index; predicts the level of expression of a gene) and GC3. In addition, a strong correlation was observed between coding and genomic GC content and negative correlation of GC3 with gene length, indicating a strong impact of GC-biased gene conversion (gBGC) in shaping codon usage and nucleotide composition in non-grass monocots.
Conclusion: Optimal codons in these non-grass monocots show a preference for G/C in the third codon position. These results support the concept that codon usage and nucleotide composition in non-grass monocots are mainly driven by gBGC.