Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Dynamics of Co-Transcriptional Pre-mRNA Folding Influences the Induction of Dystrophin Exon Skipping by Antisense Oligonucleotides

Antisense oligonucleotides (AONs) mediated exon skipping offers potential therapy for Duchenne muscular dystrophy. However, the identification of effective AON target sites remains unsatisfactory for lack of a precise method to predict their binding accessibility. This study demonstrates the importance of co-transcriptional pre-mRNA folding in determining the accessibility of AON target sites for AON induction of selective exon skipping in DMD. Because transcription and splicing occur in tandem, AONs must bind to their target sites before splicing factors. Furthermore, co-transcriptional pre-mRNA folding forms transient secondary structures, which redistributes accessible binding sites. In our analysis, to approximate transcription elongation, a “window of analysis” that included the entire targeted exon was shifted one nucleotide at a time along the pre-mRNA. Possible co-transcriptional secondary structures were predicted using the sequence in each step of transcriptional analysis. A nucleotide was considered “engaged” if it formed a complementary base pairing in all predicted secondary structures of a particular step. Correlation of frequency and localisation of engaged nucleotides in AON target sites accounted for the performance (efficacy and efficiency) of 94% of 176 previously reported AONs. Four novel insights are inferred: (1) the lowest frequencies of engaged nucleotides are associated with the most efficient AONs; (2) engaged nucleotides at 3′ or 5′ ends of the target site attenuate AON performance more than at other sites; (3) the performance of longer AONs is less attenuated by engaged nucleotides at 3′ or 5′ ends of the target site compared to shorter AONs; (4) engaged nucleotides at 3′ end of a short target site attenuates AON efficiency more than at 5′ end

A Review of HIV/AIDS System-Level Interventions

The escalating HIV/AIDS epidemic worldwide demands that on-going prevention efforts be strengthened, disseminated, and scaled-up. System-level interventions refer to programs aiming to improve the functioning of an agency as well as the delivery of its services to the community. System-level interventions are a promising approach to HIV/AIDS prevention because they focus on (a) improving the agency's ability to adopt evidence-based HIV prevention and care programs; (b) develop and establish policies and procedures that maximize the sustainability of on-going prevention and care efforts; and (c) improve decision-making processes such as incorporating the needs of communities into their tailored services. We reviewed studies focusing on system-level interventions by searching multiple electronic abstracting indices, including PsycInfo, PubMed, and ProQuest. Twenty-three studies out of 624 peer-reviewed studies (published from January 1985 to February 2007) met study criteria. Most of the studies focused on strengthening agency infrastructure, while other studies included collaborative partnerships and technical assistance programs. Our findings suggest that system-level interventions are promising in strengthening HIV/AIDS prevention and treatment efforts. Based on our findings, we propose recommendations for future work in developing and evaluating system-level interventions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85204/1/Bauermeister_AIBEReview_08.pd