Mining functional subgraphs from cancer protein-protein interaction networks

Background: Protein-protein interaction (PPI) networks carry vital information about proteins’ functions. Analysis of PPI networks associated with specific disease systems including cancer helps us in the understanding of the complex biology of diseases. Specifically, identification of similar and frequently occurring patterns (network motifs) across PPI networks will provide useful clues to better understand the biology of the diseases. Results: In this study, we developed a novel pattern-mining algorithm that detects cancer associated functional subgraphs occurring in multiple cancer PPI networks. We constructed nine cancer PPI networks using differentially expressed genes from the Oncomine dataset. From these networks we discovered frequent patterns that occur in all networks and at different size levels. Patterns are abstracted subgraphs with their nodes replaced by node cluster IDs. By using effective canonical labeling and adopting weighted adjacency matrices, we are able to perform graph isomorphism test in polynomial running time. We use a bottom-up pattern growth approach to search for patterns, which allows us to effectively reduce the search space as pattern sizes grow. Validation of the frequent common patterns using GO semantic similarity showed that the discovered subgraphs scored consistently higher than the randomly generated subgraphs at each size level. We further investigated the cancer relevance of a select set of subgraphs using literature-based evidences. Conclusion: Frequent common patterns exist in cancer PPI networks, which can be found through effective pattern mining algorithms. We believe that this work would allow us to identify functionally relevant and coherent subgraphs in cancer networks, which can be advanced to experimental validation to further our understanding of the complex biology of cancer

Frequency of gaps observed in a structurally aligned protein pair database suggests a simple gap penalty function

Gap penalty is an important component of the scoring scheme that is needed when searching for homologous proteins and for accurate alignment of protein sequences. Most homology search and sequence alignment algorithms employ a heuristic ‘affine gap penalty’ scheme q + r × n, in which q is the penalty for opening a gap, r the penalty for extending it and n the gap length. In order to devise a more rational scoring scheme, we examined the pattern of gaps that occur in a database of structurally aligned protein domain pairs. We find that the logarithm of the frequency of gaps varies linearly with the length of the gap, but with a break at a gap of length 3, and is well approximated by two linear regression lines with R(2) values of 1.0 and 0.99. The bilinear behavior is retained when gaps are categorized by secondary structures of the two residues flanking the gap. Similar results were obtained when another, totally independent, structurally aligned protein pair database was used. These results suggest a modification of the affine gap penalty function

The Case for GNMT as a Biomarker and a Therapeutic Target in Pancreatic Cancer

The high mortality rate for pancreatic cancer (PC) is due to the lack of specific symptoms at early tumor stages and a high biological aggressiveness. Reliable biomarkers and new therapeutic targets would help to improve outlook in PC. In this study, we analyzed the expression of GNMT in a panel of pancreatic cancer cell lines and in early-stage paired patient tissue samples (normal and diseased) by quantitative reverse transcription-PCR (qRT-PCR). We also investigated the effect of 1,2,3,4,6-penta-O-galloyl-β-d-glucopyranoside (PGG) as a therapeutic agent for PC. We find that GNMT is markedly downregulated (p < 0.05), in a majority of PC cell lines. Similar results are observed in early-stage patient tissue samples, where GNMT expression can be reduced by a 100-fold or more. We also show that PGG is a strong inhibitor of PC cell proliferation, with an IC50 value of 12 ng/mL, and PGG upregulates GNMT expression in a dose-dependent manner. In conclusion, our data show that GNMT has promise as a biomarker and as a therapeutic target for PC

Genome Report—A Genome Sequence Analysis of the RB51 Strain of Brucella abortus in the Context of Its Vaccine Properties

The RB51 vaccine strain of Brucella abortus, which confers safe and effective protection of cattle from B. abortus infection, was originally generated via serial passage of B. abortus 2308 to generate spontaneous, attenuating mutations. While some of these mutations have been previously characterized, such as an insertional mutation in the wboA gene that contributes to the rough phenotype of the strain, a comprehensive annotation of genetic differences between RB51 and B. abortus 2308 genomes has not yet been published. Here, the whole genome sequence of the RB51 vaccine strain is compared against two available 2308 parent sequences, with all observed single nucleotide polymorphisms, insertions, and deletions presented. Mutations of interest for future characterization in vaccine development, such as mutations in eipA and narJ genes in RB51, were identified. Additionally, protein homology modeling was utilized to provide in silico support for the hypothesis that the RB51 capD mutation is the second contributing mutation to the rough phenotype of RB51, likely explaining the inability of wboA-complemented strains of RB51 to revert to a smooth phenotype

Pancreatic Cancer Microarray Datasets Included in the Study.

<p>Pancreatic Cancer Microarray Datasets Included in the Study.</p

Top scoring network associated with the upregulated genes in PC.

<p>TGFB1 forms a hub node in the network. IGHG3 (highlighted in blue color) is one of the top twenty-five genes that is potentially important for pancreatic cancer.</p

A Meta Analysis of Pancreatic Microarray Datasets Yields New Targets as Cancer Genes and Biomarkers

<div><p>The lack of specific symptoms at early tumor stages, together with a high biological aggressiveness of the tumor contribute to the high mortality rate for pancreatic cancer (PC), which has a five year survival rate of less than 5%. Improved screening for earlier diagnosis, through the detection of diagnostic and prognostic biomarkers provides the best hope of increasing the rate of curatively resectable carcinomas. Though many serum markers have been reported to be elevated in patients with PC, so far, most of these markers have not been implemented into clinical routine due to low sensitivity or specificity. In this study, we have identified genes that are significantly upregulated in PC, through a meta-analysis of large number of microarray datasets. We demonstrate that the biological functions ascribed to these genes are clearly associated with PC and metastasis, and that that these genes exhibit a strong link to pathways involved with inflammation and the immune response. This investigation has yielded new targets for cancer genes, and potential biomarkers for pancreatic cancer. The candidate list of cancer genes includes protein kinase genes, new members of gene families currently associated with PC, as well as genes not previously linked to PC. In this study, we are also able to move towards developing a signature for hypomethylated genes, which could be useful for early detection of PC. We also show that the significantly upregulated 800+ genes in our analysis can serve as an enriched pool for tissue and serum protein biomarkers in pancreatic cancer.</p></div

A Putative Genetic Signature of Hypomethylated Genes in Pancreatic Cancer.

<p>A Putative Genetic Signature of Hypomethylated Genes in Pancreatic Cancer.</p

A List of the 25 Most Highly Ranked Upregulated Genes in Pancreatic Cancer.

<p>A List of the 25 Most Highly Ranked Upregulated Genes in Pancreatic Cancer.</p

The five most significant pathways associated with genes upregulated in PC are related to inflammation and immune response.

<p>The five most significant pathways associated with genes upregulated in PC are related to inflammation and immune response.</p