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.

Neonatal outcomes of extremely preterm infants from the NICHD Neonatal Research Network.

OBJECTIVE: This report presents data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network on care of and morbidity and mortality rates for very low birth weight infants, according to gestational age (GA). METHODS: Perinatal/neonatal data were collected for 9575 infants of extremely low GA (22-28 weeks) and very low birth weight (401-1500 g) who were born at network centers between January 1, 2003, and December 31, 2007. RESULTS: Rates of survival to discharge increased with increasing GA (6% at 22 weeks and 92% at 28 weeks); 1060 infants died at CONCLUSION: Although the majority of infants with GAs of >or=24 weeks survive, high rates of morbidity among survivors continue to be observed.

B cell memory and idiotype network regulation

This investigation examined the clonal dynamics of B-cell expression and evaluated the role of idiotype network interactions in shaping the expressed secondary B-cell repertoire. Three interrelated experimental approaches were applied. The first approach was designed to distinguish between regulatory influences controlled by the major histocompatibility complex (MHC) and regulatory influences controlled by non-MHC factors including the idiotype network. This approach consisted of studies on the clonal dynamics and heterogeneity of the expressed IgG antibody repertoire of BALB/c mice. The second approach involved the analysis of the clonal dynamics of antibody responses of outbred rabbits. This analysis was coupled with studies to detect the occurrence and activity of constituents of the idiotype network. In the third approach the transfer of rabbit lymphocytes from immunized donors to MHC matched naive recipients was used to examine the effects of recipient non-MHC immunoregulatory influences on the expression of donor memory B-cells. Although many memory B cells were unaffected by non-MHC influences, these data show that non-MHC immunoregulatory influences can affect the expression of B-cells in the secondary response of inbred mice and outbred rabbits. The results also indicate that most IgG antibody responses are heterogeneous and are characterized by a stable group of dominant clonotypes. Clonal dominance and B-cell memory were found to be established early in an immune response. The expression of B memory clones appeared to be favored over the expression of virgin B cells. The injection of anti-tetanus antibody induced the antigen independent production of anti-tetanus antibody, probably through idiotypic mechanisms. These results demonstrate that both antibody and antigen can affect the expressed B-ceIl repertoire. Thus, idiotypic interactions are capable of influencing the expression of B-cells and these findings support the existence and function of an idiotype network with strong immunoregulatory potential. ^