Identification of Novel MiR-122 Targets with Potential Role in Hepatocellular Cancer

MicroRNA 122 (MiR-122) is an abundant liver-specific miRNA which serves to regulate hepatic metabolism, and functions as a tumor suppressor. We are determining how a tiny, twenty-two nucleotide microRNA maintains homeostatic liver function. Loss of hepatic phenotype, metastasis, and poor prognosis are characteristics of down regulation of miR-122 expression. Spontaneous Hepatocellular Carcinoma (HCC) development has been observed in liver-specific (LKO) and germ-line (KO) knockout miR-122 mice. Results of Ago-HITS-CLIP analysis conducted in livers of 6 week-old wild type and KO mice confirmed ~1800 miR-122 binding sites present in wild-type, but absent in KO livers. MiR-122 binding sites were identified on the 3’ untranslated regions (3’-UTR) of the snail family zinc finger 2 (Snai2) gene, TIMP metallopeptidase inhibitor 2 (Timp2) gene, LIM domain kinase 1 (Limk1) gene, Hepatocyte Nuclear Factor 4 alpha (Hnf4a) and Pygopus Family PHD Finger 2 (Pygo2). The encoded protein of Snai2 is involved in epithelial mesenchymal transitions, and has the ability to enable the cell to resist apoptotic signals. Timp2 functions as a metastasis suppressor. Limk1 regulates actin polymerization through phosphorylation and subsequent inactivation of cofilin, while Hnf4a regulates liver development. Pygo2 is involved in signal transduction of the Wnt and GPCR pathways. The presence of these discovered binding sites highly suggests targeting and regulation by miR-122. These genes are potentially involved in liver pathogenesis, and could be crucial with respect to determining HCC progression. To explore the possibility of targeting and regulation by miR-122, real-time reverse-transcription polymerase chain reaction (qRT-PCR) was conducted.  We hypothesized that in the absence of miR-122, expression levels of these genes would be increased. These five targets were found to be highly expressed in miR-122 knockout mice. RNA-sequencing analysis suggests that these genes are undergoing regulation by miR-122. Snai2 has been validated as a direct miR-122 target using luciferase assay technology with hepatoma (Hepa) cells. Snai2 has been further validated using site-directed mutagenesis in Hepa cells. Broader implications of this study include miR-122 itself or Snai2 being targeted for HCC therapy in human patients. .Second-year Transformational Experience Program (STEP) FundingA three-year embargo was granted for this item.Academic Major: Molecular Genetic

Localization of ligand binding site in proteins identified in silico

Knowledge-based models for protein folding assume that the early-stage structural form of a polypeptide is determined by the backbone conformation, followed by hydrophobic collapse. Side chain-side chain interactions, mostly of hydrophobic character, lead to the formation of the hydrophobic core, which seems to stabilize the structure of the protein in its natural environment. The fuzzy-oil-drop model is employed to represent the idealized hydrophobicity distribution in the protein molecule. Comparing it with the one empirically observed in the protein molecule reveals that they are not in agreement. It is shown in this study that the irregularity of hydrophobic distributions is aim-oriented. The character and strength of these irregularities in the organization of the hydrophobic core point to the specificity of a particular protein\u27s structure/function. When the location of these irregularities is determined versus the idealized fuzzy-oil-drop, function-related areas in the protein molecule can be identified. The presented model can also be used to identify ways in which protein-protein complexes can possibly be created. Active sites can be predicted for any protein structure according to the presented model with the free prediction server at http://www.bioinformatics.cm-uj. krakow.pl/activesite. The implication based on the model presented in this work suggests the necessity of active presence of ligand during the protein folding process simulation. © Springer-Verlag 2007

Towards ontology-driven navigation of the lipid bibliosphere

10.1186/1471-2105-9-S1-S5BMC Bioinformatics9SUPPL. 1BBMI

Bioinformatic Analysis for the Validation of Novel Biomarkers for Cancer Diagnosis and Drug Sensitivity

Background: The genetic control of tumour progression presents the opportunity for bioinformatics and gene expression data to be used as a basis for tumour grading. The development of a genetic signature based on microarray data allows for the development of personalised chemotherapeutic regimes. Method: ONCOMINE was utilised to create a genetic signature for ovarian serous adenocarcinoma and to compare the expression of genes between normal ovarian and cancerous cells. Ingenuity Pathways Analysis was also utilised to develop molecular pathways and observe interactions with exogenous molecules. Results: The gene signature demonstrated 98.6% predictive capability for the differentiation between borderline ovarian serous neoplasm and ovarian serous adenocarcinoma. The data demonstrated that many genes were related to angiogenesis. Thymidylate synthase, GLUT-3 and HSP90AA1 were related to tanespimycin sensitivity (p=0.005). Conclusions: Genetic profiling with the gene signature demonstrated potential for clinical use. The use of tanespimycin alongside overexpression of thymidylate synthase, GLUT-3 and HSP90AA1 is a novel consideration for ovarian cancer treatment

Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment

Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships.In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0.The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research

A Bayesian system for modeling promoter structure: A case study of histone promoters

Ph.DDOCTOR OF PHILOSOPH

Developing semantic pathway comparison methods for systems biology

Systems biology is an emerging multi-disciplinary field in which the behaviour of complex biological systems is studied by considering the interaction of many cellular and molecular constituents rather than using a “traditional” reductionist approach where constituents are studied individually. Systems are often studied over time with the ultimate goal of developing models which can be used to understand and predict complex biological processes, such as human diseases. To support systems biology, a large number of biological pathways are being derived for many different organisms, and these are stored in various databases. This pathway collection presents an opportunity to compare and contrast pathways, and to utilise the knowledge they represent. This thesis presents some of the first algorithms that are designed to explore this opportunity. It is argued that the methods will be useful to biologists in order to assess the biological plausibility of derived pathways, compare different biological pathways for semantic similarities, and to derive putative pathways that are semantically similar to documented biological pathways. The methods will therefore extend the systems biology toolbox that biologists can use to make new biological discoveries.Knowledge Foundation. Grant No. 2003/0215Information Fusion Research Program (University of Skovde, Sweden) Grant No 2003/010