Rewiring Of Transcriptional Networks Research Articles

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation, with smoke exposure being a major risk factor. To define previously unknown biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single-cell RNA-Seq data from the mouse smoke-exposure model to identify significant latent factors (context-specific coexpression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and new biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from patients with COPD, and smoke exposure resulted in enhanced GSN release from airway cells from patients with COPD. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provides a translational analytical platform for other diseases.

Conditional RNA interference in mammalian cells via RNA transactivation

RNA interference (RNAi) is a powerful tool for sequence-specific gene knockdown in therapeutic and research applications. However, spatiotemporal control of RNAi is required to decrease nonspecific targeting, potential toxicity, and allow targeting of essential genes. Herein we describe a class of de-novo-designed RNA switches that enable sequence-specific regulation of RNAi in mammalian cells. Using cis-repressing RNA elements, we engineer RNA devices that only initiate microRNA biogenesis when binding with cognate trigger RNAs. We demonstrate that this conditional RNAi system, termed Orthogonal RNA Interference induced by Trigger RNA (ORIENTR), provides up to 14-fold increases in artificial miRNA biogenesis upon activation in orthogonal libraries. We show that integration of ORIENTR triggers with dCas13d enhances dynamic range to up to 31-fold. We further demonstrate that ORIENTR can be applied to detect endogenous RNA signals and to conditionally knockdown endogenous genes, thus enabling regulatory possibilities including cell-type-specific RNAi and rewiring of transcriptional networks via RNA profile.

Evolution of binding preferences among whole-genome duplicated transcription factors.

Throughout evolution, new transcription factors (TFs) emerge by gene duplication, promoting growth and rewiring of transcriptional networks. How TF duplicates diverge was studied in a few cases only. To provide a genome-scale view, we considered the set of budding yeast TFs classified as whole-genome duplication (WGD)-retained paralogs (~35% of all specific TFs). Using high-resolution profiling, we find that ~60% of paralogs evolved differential binding preferences. We show that this divergence results primarily from variations outside the DNA-binding domains (DBDs), while DBD preferences remain largely conserved. Analysis of non-WGD orthologs revealed uneven splitting of ancestral preferences between duplicates, and the preferential acquiring of new targets by the least conserved paralog (biased neo/sub-functionalization). Interactions between paralogs were rare, and, when present, occurred through weak competition for DNA-binding or dependency between dimer-forming paralogs. We discuss the implications of our findings for the evolutionary design of transcriptional networks.

Abstract 1301: Using the evolutionary ages of genes to understand hallmark features of cancer

Abstract The evolutionary age of a gene is closely tied to its role in cancer. As organisms evolved into multicellular forms, new pathways emerged to control core cellular processes formed in unicellular ancestors such as cell division, DNA replication and energy metabolism. Tumors rely on many of these core processes, at the same time as they suppress key pathways linked to the emergence of multicellularity. This suggests the guiding principles behind many hallmarks of cancer could be better understood in an evolutionary context, by investigating interactions between unicellular and multicellular genes and how they are disrupted in cancer. We have found the evolutionary histories of genes are associated with their expression and regulation (Trigos et al., PNAS 2017), the selection and response to genetic alterations, as well as the rewiring of transcriptional networks across 7 tumor types. Our results reveal different transcriptional and mutational patterns in cancer between genes with different evolutionary ages, with selection for genetic alterations in early metazoan genes leading to the transcriptional rewiring of the regulation between unicellular and multicellular genes, driving carcinogenesis. Examples of how this approach can help prioritize genes for drug treatment and inform novel treatment strategies will be given. Citation Format: David L. Goode, Anna S. Trigos, Anthony T. Papenfuss, Richard B. Pearson. Using the evolutionary ages of genes to understand hallmark features of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1301.

The population genetics of cooperative gene regulation

BackgroundChanges in gene regulatory networks drive the evolution of phenotypic diversity both within and between species. Rewiring of transcriptional networks is achieved either by changes to transcription factor binding sites or by changes to the physical interactions among transcription factor proteins. It has been suggested that the evolution of cooperative binding among factors can facilitate the adaptive rewiring of a regulatory network.ResultsWe use a population-genetic model to explore when cooperative binding of transcription factors is favored by evolution, and what effects cooperativity then has on the adaptive re-writing of regulatory networks. We consider a pair of transcription factors that regulate multiple targets and overlap in the sets of target genes they regulate. We show that, under stabilising selection, cooperative binding between the transcription factors is favoured provided the amount of overlap between their target genes exceeds a threshold. The value of this threshold depends on several population-genetic factors: strength of selection on binding sites, cost of pleiotropy associated with protein-protein interactions, rates of mutation and population size. Once it is established, we find that cooperative binding of transcription factors significantly accelerates the adaptive rewiring of transcriptional networks under positive selection. We compare our qualitative predictions to systematic data on Saccharomyces cerevisiae transcription factors, their binding sites, and their protein-protein interactions.ConclusionsOur study reveals a rich set of evolutionary dynamics driven by a tradeoff between the beneficial effects of cooperative binding at targets shared by a pair of factors, and the detrimental effects of cooperative binding for non-shared targets. We find that cooperative regulation will evolve when transcription factors share a sufficient proportion of their target genes. These findings help to explain empirical pattens in datasets of transcription factors in Saccharomyces cerevisiae and, they suggest that changes to physical interactions between transcription factors can play a critical role in the evolution of gene regulatory networks.