Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis

Cell adhesion molecule CD44 is dispensable for reactive astrocyte activation during prion disease

Prion diseases are fatal, infectious, neurodegenerative disorders resulting from accumulation of misfolded cellular prion protein in the brain. Early pathological changes during CNS prion disease also include reactive astrocyte activation with increased CD44 expression, microgliosis, as well as loss of dendritic spines and synapses. CD44 is a multifunctional cell surface adhesion and signalling molecule which is considered to play roles in astrocyte morphology and the maintenance of dendritic spine integrity and synaptic plasticity. However, the role of CD44 in prion disease was unknown. Here we used mice deficient in CD44 to determine the role of CD44 during prion disease. We show that CD44-deficient mice displayed no difference in their response to CNS prion infection when compared to wild type mice. Furthermore, the reactive astrocyte activation and microgliosis that accompanies CNS prion infection was unimpaired in the absence of CD44. Together, our data show that although CD44 expression is upregulated in reactive astrocytes during CNS prion disease, it is dispensable for astrocyte and microglial activation and the development of prion neuropathogenesis.

Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation

Gene expression (GE)data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. Because proteins function by interacting with each other, and because biological networks (BNs)capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP)to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related)subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP. Our data and code are available at https://nd.edu/~cone/dynetinf.

Improving Analysis and Annotation of Microarray Data with Protein Interactions

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.

Core transcriptional regulatory circuits in prion diseases

Complex diseases involve dynamic perturbations of pathophysiological processes during disease progression. Transcriptional programs underlying such perturbations are unknown in many diseases. Here, we present core transcriptional regulatory circuits underlying early and late perturbations in prion disease. We first identified cellular processes perturbed early and late using time-course gene expression data from three prion-infected mouse strains. We then built a transcriptional regulatory network (TRN) describing regulation of early and late processes. We found over-represented feed-forward loops (FFLs) comprising transcription factor (TF) pairs and target genes in the TRN. Using gene expression data of brain cell types, we further selected active FFLs where TF pairs and target genes were expressed in the same cell type and showed correlated temporal expression changes in the brain. We finally determined core transcriptional regulatory circuits by combining these active FFLs. These circuits provide insights into transcriptional programs for early and late pathophysiological processes in prion disease.

Modeling tumor progression via the comparison of stage-specific graphs

Can we use graph mining algorithms to find patterns in tumor molecular mechanisms? Can we model disease progression with multiple time-specific graph comparison algorithms? In this paper, we will focus on this area. Our main contributions are 1) we proposed the Temporal-Omics (Temp-O) workflow to model tumor progression in non-small cell lung cancer (NSCLC) using graph comparisons between multiple stage-specific graphs, and 2) we showed that temporal structures are meaningful in the tumor progression of NSCLC. Other identified temporal structures that were not highlighted in this paper may also be used to gain insights to possible novel mechanisms. Importantly, the Temp-O workflow is generic; while we applied it on NSCLC, it can be applied in other cancers and diseases. We used gene expression data from tumor samples across disease stages to model lung cancer progression, creating stage-specific tumor graphs. Validating our findings in independent datasets showed that differences in temporal network structures capture diverse mechanisms in NSCLC. Furthermore, results showed that structures are consistent and potentially biologically important as we observed that genes with similar protein names were captured in the same cliques for all cliques in all datasets. Importantly, the identified temporal structures are meaningful in the tumor progression of NSCLC as they agree with the molecular mechanism in the tumor progression or carcinogenesis of NSCLC. In particular, the identified major histocompatibility complex of class II temporal structures capture mechanisms concerning carcinogenesis; the proteasome temporal structures capture mechanisms that are in early or late stages of lung cancer; the ribosomal cliques capture the role of ribosome biosynthesis in cancer development and sustainment. Further, on a large independent dataset we validated that temporal network structures identified proteins that are prognostic for overall survival in NSCLC adenocarcinoma.

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.

Reelin Expression in Creutzfeldt-Jakob Disease and Experimental Models of Transmissible Spongiform Encephalopathies

Reelin is an extracellular glycoprotein involved in key cellular processes in developing and adult nervous system, including regulation of neuronal migration, synapse formation, and plasticity. Most of these roles are mediated by the intracellular phosphorylation of disabled-1 (Dab1), an intracellular adaptor molecule, in turn mediated by binding Reelin to its receptors. Altered expression and glycosylation patterns of Reelin in cerebrospinal and cortical extracts have been reported in Alzheimer's disease. However, putative changes in Reelin are not described in natural prionopathies or experimental models of prion infection or toxicity. With this is mind, in the present study, we determined that Reelin protein and mRNA levels increased in CJD human samples and in mouse models of human prion disease in contrast to murine models of prion infection. However, changes in Reelin expression appeared only at late terminal stages of the disease, which prevent their use as an efficient diagnostic biomarker. In addition, increased Reelin in CJD and in in vitro models does not correlate with Dab1 phosphorylation, indicating failure in its intracellular signaling. Overall, these findings widen our understanding of the putative changes of Reelin in neurodegeneration.