NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Is obesity a possible risk factor for Clostridium difficile infection

Aims: Obesity is a state of chronic inflammation characterized by depressed Th2 immune response. Animal studies have shown decreased IgA levels in obese rats and Leptin an adipose cell origin cytokine have been shown to enhance the activity of Clostridium difficile Toxin A. Hence we hypothesized that obesity is a risk factor for C. difficile infection (CDI) ^ Methods: 33 cases of CDI and 131 controls matched by age and HORNS index were identified from an IRB approved observational study at St. Luke's Episcopal Hospital in Houston. Variables like age, gender, height, weight, chronic antibiotic use, proton pump inhibitor use, diabetes mellitus, myocardial infarction, inflammatory bowel disease, diverticulitis, transfer from nursing home, hospital or home, nasogastric tube use and use of hemodialysis were provided in the dataset. Height and weight of the patient were used to calculate the BMI, based on which the study subjects were classified as obese and non-obese. Using STATA these variables were analyzed using test, chi square test followed by conditional logistic regression. ^ Results: On univariate analysis and conditional logistic regression, no significant increase in risk was associated with obesity (OR: 1.24; 95% CI: 0.46 - 3.36; p = 0.67) or BMI (OR: 0.98; CI: 0.92 - 1.04; p = 0.92). Hence, we cannot reject our hypothesis and conclude that "obesity is a risk factor associated with higher incidence of CDI in hospitalized patients. On univariate analysis using hemodialysis, nursing home transfer, home transfer, PPI and chronic antibiotics were found to be significantly different (p<0.05) in the cases and controls. On conditional logistic regression home (OR: 3.4; 95% CI: 1.15 - 9.61) and hemodialysis (OR: 4.1; 95% CI: 1.14 - 15.57) were found to be significantly different (p<0.05) between the case and control groups. ^ Conclusion: Our results show that obesity is not a significant risk factor for CDI. Our sample size was small and hence this may need conformation with a larger study. Patients transferred from home to the hospital and patients on hemodialysis had significantly higher incidence of CDI.^