The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

BACKGROUND: Reference genome and transcriptome assemblies of helminths have reached a level of completion whereby secondary analyses that rely on accurate gene estimation or syntenic relationships can be now conducted with a high level of confidence. Recent public release of the v.3 assembly of the mouse bile-duct tapeworm, Hymenolepis microstoma, provides chromosome-level characterisation of the genome and a stabilised set of protein coding gene models underpinned by bioinformatic and empirical data. However, interactome data have not been produced. Conserved protein-protein interactions in other organisms, termed interologs, can be used to transfer interactions between species, allowing systems-level analysis in non-model organisms. RESULTS: Here, we describe a probabilistic, integrated network of interologs for the H. microstoma proteome, based on conserved protein interactions found in eukaryote model species. Almost a third of the 10,139 gene models in the v.3 assembly could be assigned interaction data and assessment of the resulting network indicates that topologically-important proteins are related to essential cellular pathways, and that the network clusters into biologically meaningful components. Moreover, network parameters are similar to those of single-species interaction networks that we constructed in the same way for S. cerevisiae, C. elegans and H. sapiens, demonstrating that information-rich, system-level analyses can be conducted even on species separated by a large phylogenetic distance from the major model organisms from which most protein interaction evidence is based. Using the interolog network, we then focused on sub-networks of interactions assigned to discrete suites of genes of interest, including signalling components and transcription factors, germline multipotency genes, and genes differentially-expressed between larval and adult worms. Results show not only an expected bias toward highly-conserved proteins, such as components of intracellular signal transduction, but in some cases predicted interactions with transcription factors that aid in identifying their target genes. CONCLUSIONS: With key helminth genomes now complete, systems-level analyses can provide an important predictive framework to guide basic and applied research on helminths and will become increasingly informative as new protein-protein interaction data accumulate

Iteration Method for Predicting Essential Proteins Based on Orthology and Protein-protein Interaction Networks

Background: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. Results: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12. Conclusions: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks

A Novel Algorithm for Detecting Protein Complexes with the Breadth First Search

Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction (PPI) data can compromise the utility of the datasets for complexes reconstruction. In order to reduce the impact of such discrepancies, a number of data integration and affinity scoring schemes have been devised. The methods encode the reliabilities (confidence) of physical interactions between pairs of proteins. The challenge now is to identify novel and meaningful protein complexes fromthe weighted PPI network. To address this problem, a novel protein complex mining algorithm ClusterBFS (Cluster with Breadth-First Search) is proposed. Based on the weighted density, ClusterBFS detects protein complexes of the weighted network by the breadth first search algorithm, which originates from a given seed protein used as starting-point. The experimental results show that ClusterBFS performs significantly better than the other computational approaches in terms of the identification of protein complexes

Identifying therapeutic weak spots in cancer using network analysis

Mathematical network analysis has been proven to be a useful and powerful tool for biological networks including networks of protein interactions, gene similarity and metabolic interactions. Here I use network analysis to model human cancer and predict which genes or reactions are essential for cancer to allow it to grow or recovery from stress. A general assumption for biological networks is that the centrality of a node is in some way reflective of its biological importance. So I evaluated a wide range of weighted and unweighted node centralities and measures derived from centralities to predict reaction essentiality in metabolic networks. The metabolic networks are Mass Flow Graphs (MFGs), based on the Recon2 reconstruction of human metabolism and constrains on reaction fluxes from the PRIME algorithm. The edge weights in the networks are computed from Flux Balance Analysis (FBA) results in a selection of human cancer cell lines from NCI-60. I could not detect a direct connection between node essentiality and any centrality, but there is a correlation between the overall change of the centrality distribution in the inhibited condition compared to wild type and the inhibited reaction essentiality. With this I have found a promising network measure that can be used to predict possible drug targets. With MFGs a wide range of cellular conditions can be modelled, but only when we know what the cellular objective for FBA is. When cancer cells are put under stress through treatment, they adapt their metabolism to react to the stress. This dynamic process with fluctuating gene expression is difficult to capture in a metabolic network. A better way to analyse the recovery process is to extract which genes are active during which phase. I evaluated seven time points of gene expression data for multiple myeloma cells that were treated with a proteasome inhibitor (PI), which disrupts the protein recycling process. From the pairwise gene expression similarity I constructed network to cluster the genes into groups that are active at the same time. The networks were clustered with a random walk algorithm called Markov Stability and evaluated with gene enrichment analysis. From the resulting clusters, collaborators were able to extract tRNAs that activate a protein called GCN2 that is essential for recovery. Followup experiments showed that a combination of PI and GCN2 is lethal for multiple myeloma as well as a few other cancer cells.Open Acces

CO-expression Analysis of RNA-sequence Data from Parkinson's Disease Patients

Parkinson’s disease is known as a progressive neurological disease characterized by motor symptoms. The motor symptoms are caused by neurodegeneration that causes dysfunctionalities in molecular functions crucial for movement. Network analysis contributes to identifying new biomarkers of diseases by considering the interactions between the disease-specific genes and proteins. This study focuses on a differential weighted gene co-expression network analysis of transcriptomics data, comparing data from healthy persons with Parkinson’s diseased patients. This analysis method constructs networks and identifies modules that can be compared with different evaluation and analysis methods, to identify dysregulated pathways and causative genes of Parkinson’s disease. This disease is a complex disease by multiple variations of symptoms with each individual. This study contributes to the predictive part of personalized medicine that enables improved treatments.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO