Interactome Networks and Human Disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

Formal representations of Waddington's epigenetic landscape represent cell fate decisions as smooth, continuous events in which cells follow predetermined trajectories. Here, the authors provide an alternative interpretation and posit that fate decisions are discontinuous, stochastic events within cell lineages. Waddington's epigenetic landscape is an abstract metaphor frequently used to represent the relationship between gene activity and cell fates during development. Over the past few years, it has become a useful framework for interpreting results from single-cell transcriptomics experiments. It has led to the proposal that, during fate transitions, cells experience smooth, continuous progressions of global transcriptional activity, which can be captured by (pseudo)temporal dynamics. Here, focusing strictly on the fate decision events, we suggest an alternative view: that fate transitions occur in a discontinuous, stochastic manner whereby signals modulate the probability of the transition events.

/pdf/transition-states-and-cell-fate-decisions-in-epigenetic-1enkjbnv3w.pdf

Transition states and cell fate decisions in epigenetic landscapes

In any given cell, thousands of genes are expressed and work in concert to ensure the cell's function, fitness, and survival. Each gene, in turn, must be expressed at the proper time and in the proper amounts to ensure the appropriate functional outcome. The regulation and expression of some genes are highly robust; their expression is controlled by invariable expression programs. For instance, developmental gene expression is extremely similar in a given cell type from one individual to another. The expression of other genes is more variable: Their levels are noisy and are different from cell to cell and from individual to individual. This can be highly beneficial in physiological responses to outside cues and stresses. Recent advances have enabled the analysis of differential gene expression at a systems level. Gene regulatory networks (GRNs) involving interactions between large numbers of genes and their regulators have been mapped onto graphic diagrams that are used to visualize the regulatory relationships. The further characterization of GRNs has already uncovered global principles of gene regulation. Together with synthetic network biology, such studies are starting to provide insights into the transcriptional mechanisms that cause robust versus stochastic gene expression and their relationships to phenotypic robustness and variability. Here, we discuss GRNs and their topological properties in relation to transcriptional and phenotypic outputs in development and organismal physiology.

/pdf/gene-regulatory-networks-and-the-role-of-robustness-and-57q4bq9spr.pdf

Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression

https://www.cell.com/cell/pdf/S0092-8674(09)00519-4.pdf

A Multiparameter Network Reveals Extensive Divergence between C. elegans bHLH Transcription Factors

Transcription regulatory networks play a pivotal role in the development, function, and pathology of metazoan organisms Such networks are comprised of protein–DNA interactions between transcription factors (TFs) and their target genes An important question pertains to how the architecture of such networks relates to network functionality Here, we show that a Caenorhabditis elegans core neuronal protein–DNA interaction network is organized into two TF modules These modules contain TFs that bind to a relatively small number of target genes and are more systems specific than the TF hubs that connect the modules Each module relates to different functional aspects of the network One module contains TFs involved in reproduction and target genes that are expressed in neurons as well as in other tissues The second module is enriched for paired homeodomain TFs and connects to target genes that are often exclusively neuronal We find that paired homeodomain TFs are specifically expressed in C elegans and mouse neurons, indicating that the neuronal function of paired homeodomains is evolutionarily conserved Taken together, we show that a core neuronal C elegans protein–DNA interaction network possesses TF modules that relate to different functional aspects of the complete network

/pdf/transcription-factor-modularity-in-a-gene-centered-c-elegans-oxxfx7ykif.pdf

Jenny Aurielle B. Babon

Papers

Transcription factor modularity in a gene-centered C. elegans core neuronal protein–DNA interaction network