Robustness in phenotypic plasticity and heterogeneity patterns enabled by EMT networks

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BACKGROUND

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.

RESULTS

We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.

CONCLUSIONS

Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Cancer plasticity: Investigating the causes for this agility

Cancer is not a hard-wired phenomenon but an evolutionary disease. From the onset of carcinogenesis, cancer cells continuously adapt and evolve to satiate their ever-growing proliferation demands. This results in the formation of multiple subtypes of cancer cells with different phenotypes, cellular compositions, and consequently displaying varying degrees of tumorigenic identity and function. This phenomenon is referred to as cancer plasticity, during which the cancer cells exist in a plethora of cellular states having distinct phenotypes. With the advent of modern technologies equipped with enhanced resolution and depth, for example, single-cell RNA-sequencing and advanced computational tools, unbiased cancer profiling at a single-cell resolution are leading the way in understanding cancer cell rewiring both spatially and temporally. In this review, the processes and mechanisms that give rise to cancer plasticity include both intrinsic genetic factors such as epigenetic changes, differential expression due to changes in DNA, RNA, or protein content within the cancer cell, as well as extrinsic environmental factors such as tissue perfusion, extracellular milieu are detailed and their influence on key cancer plasticity hallmarks such as epithelial-mesenchymal transition (EMT) and cancer cell stemness (CSCs) are discussed. Due to therapy evasion and drug resistance, tumor heterogeneity caused by cancer plasticity has major therapeutic ramifications. Hence, it is crucial to comprehend all the cellular and molecular mechanisms that control cellular plasticity. How this process evades therapy, and the therapeutic avenue of targeting cancer plasticity must be diligently investigated.