Promoter/enhancer-based controllability of regulatory networks.

Understanding the mechanisms of tissue-specific transcriptional regulation is crucial as mis-regulation can cause a broad range of diseases. Here, we investigated transcription factors (TF) that are indispensable for the topological control of tissue specific and cell-type specific regulatory networks as a function of their binding to regulatory elements on promoters and enhancers of corresponding target genes. In particular, we found that promoter-binding TFs that were indispensable for regulatory network control regulate genes that are tissue-specifically expressed and overexpressed in corresponding cancer types. In turn, indispensable, enhancer-binding TFs were enriched with disease and signaling genes as they control an increasing number of cell-type specific regulatory networks. Their target genes were cell-type specific for blood and immune-related cell-types and over-expressed in blood-related cancers. Notably, target genes of indispensable enhancer-binding TFs in cell-type specific regulatory networks were enriched with cancer drug targets, while target genes of indispensable promoter-binding TFs were bona-fide targets of cancer drugs in corresponding tissues. Our results emphasize the significant role control analysis of regulatory networks plays in our understanding of transcriptional regulation, demonstrating potential therapeutic implications in tissue-specific drug discovery research.

Single-cell transcriptomic reveals molecular diversity and developmental heterogeneity of human stem cell-derived oligodendrocyte lineage cells.

Injury and loss of oligodendrocytes can cause demyelinating diseases such as multiple sclerosis. To improve our understanding of human oligodendrocyte development, which could facilitate development of remyelination-based treatment strategies, here we describe time-course single-cell-transcriptomic analysis of developing human stem cell-derived oligodendrocyte-lineage-cells (hOLLCs). The study includes hOLLCs derived from both genome engineered embryonic stem cell (ESC) reporter cells containing an Identification-and-Purification tag driven by the endogenous PDGFRα promoter and from unmodified induced pluripotent (iPS) cells. Our analysis uncovers substantial transcriptional heterogeneity of PDGFRα-lineage hOLLCs. We discover sub-populations of human oligodendrocyte progenitor cells (hOPCs) including a potential cytokine-responsive hOPC subset, and identify candidate regulatory genes/networks that define the identity of these sub-populations. Pseudotime trajectory analysis defines developmental pathways of oligodendrocytes vs astrocytes from PDGFRα-expressing hOPCs and predicts differentially expressed genes between the two lineages. In addition, pathway enrichment analysis followed by pharmacological intervention of these pathways confirm that mTOR and cholesterol biosynthesis signaling pathways are involved in maturation of oligodendrocytes from hOPCs.

Computational Identification of Kinases That Control Axon Growth in Mouse.

The determination of signaling pathways and transcriptional networks that control various biological processes is a major challenge from both basic science and translational medicine perspectives. Because such analysis can point to critical disease driver nodes to target for therapeutic purposes, we combined data from phenotypic screening experiments and gene expression studies of mouse neurons to determine information flow through a molecular interaction network using a network propagation approach. We hypothesized that differences in information flow between control and injured conditions prioritize relevant driver nodes that cause this state change. Identifying paths likely taken from potential source nodes to a set of transcription factors (TFs), called sinks, we found that kinases are enriched among source genes sending significantly different amounts of information to TFs in an axonal injury model. Additionally, TFs found to be differentially active during axon growth were enriched in the set of sink genes that received significantly altered amounts of information from source genes. Notably, such enrichment levels hold even when restricting the set of source genes to only those kinases observed to support or hamper neurite growth. That way, we found a set of 71 source genes that send significantly different levels of information to axon growth-relevant TFs. We analyzed their information flow changes in response to axonal injury and their influences on TFs predicted to facilitate or antagonize axon growth. Finally, we drew a network diagram of the interactions and changes in information flow between these source genes and their axon growth-relevant sink TFs.

Controllability analysis of molecular pathways points to proteins that control the entire interaction network.

Inputs to molecular pathways that are the backbone of cellular activity drive the cell to certain outcomes and phenotypes. Here, we investigated proteins that topologically controlled different human pathways represented as independent molecular interaction networks, suggesting that a minority of proteins control a high number of pathways and vice versa. Transcending different topological levels, proteins that controlled a large number of pathways also controlled a network of interactions when all pathways were combined. Furthermore, control proteins that were robust when interactions were rewired or inverted also increasingly controlled an increasing number of pathways. As for functional characteristics, such control proteins were enriched with regulatory and signaling genes, disease genes and drug targets. Focusing on evolutionary characteristics, proteins that controlled different pathways had a penchant to be evolutionarily conserved as equal counterparts in other organisms, indicating the fundamental role that control analysis of pathways plays for our understanding of regulation, disease and evolution.

Protein-protein interactions of human viruses.

Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.

Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets.

The availability of large-scale screens of host-virus interaction interfaces enabled the topological analysis of viral protein targets of the host. In particular, host proteins that bind viral proteins are generally hubs and proteins with high betweenness centrality. Recently, other topological measures were introduced that a virus may tap to infect a host cell. Utilizing experimentally determined sets of human protein targets from Herpes, Hepatitis, HIV and Influenza, we pooled molecular interactions between proteins from different pathway databases. Apart from a protein's degree and betweenness centrality, we considered a protein's pathway participation, ability to topologically control a network and protein PageRank index. In particular, we found that proteins with increasing values of such measures tend to accumulate viral targets and distinguish viral targets from non-targets. Furthermore, all such topological measures strongly correlate with the occurrence of a given protein in different pathways. Building a random forest classifier that is based on such topological measures, we found that protein PageRank index had the highest impact on the classification of viral (non-)targets while proteins' ability to topologically control an interaction network played the least important role.