Clinical significance of spatiotemporal transcriptional bursting and control.

The rapid development of technologies provides the potential to perform real-time visualization of transcriptional bursting patterns, superenhancer formation and sensitivity to perturbation, and interactions between enhancers, promoters, and regulators during the burst. The transcriptional bursting-induced fluctuation can modify cell capacities, cell-cell communications, cell responses to microenvironmental changes, and forms of cell death. A large number of clinical and translational studies describe the existence of heterogeneity among cells, tissues, and organs but mechanism-based understanding of how and why the heterogeneity exists and how it is formed. The transcriptional bursting, fluctuation, and control determine the development of heterogeneity and optimize cell functions in the cell development and differentiation, contribute to the initiation of cell dysfunction and tumorigenesis in response to environments, and development/evolvement of hyper/hyposensitivity to drugs. Spatiotemporal monitoring of transcriptional bursting and control provides a new insight and deeper understanding of spatiotemporal molecular medicine by integrating the transcriptional positioning and function with cell phenotypes, cell-cell communication, and clinical phenomes.

Off-target genome editing: A new discipline of gene science and a new class of medicine.

With an increasing growth of genome editing, off-target effects such as non-specific genetic modifications resulting from the designed process of genome editing become a new discipline of gene science and new class medicine. The degree of short-term and long-term side effects and toxicity or dynamics of the primary and secondary off-target genome editing varies with the application of different methodologies of gene editing and measuring, readouts of genetic modifications, or comparison reference. Measurements of dynamic off-target effects caused directly or indirectly by genome editing are critical in clinical application of gene editing. The quality of genome editing methods is one of the decisive factors in the occurrence of off-target effects. Mechanisms by which off-target effects of genome editing occurs are more complex and comprehensive than we expected. The heterogeneity of off-target effects of gene-edited cells at single-cell levels should be defined during the development and formation of cell clusters. In addition to off-target effects on gene-edited cells per se, alterations of gene sequence, structure, dimension, and function of related regulators caused by off-target effects may also influence intercellular communications and interactions between gene-edited cells, between gene-edited cells and non-edited cells, or between non-edited cells. Thus, controlling, measuring, defining, categorizing, and predicting off-target genome editing need to be standardized and prioritized before clinical application of gene editing.

Systems heterogeneity: An integrative way to understand cancer heterogeneity.

The concept of systems heterogeneity was firstly coined and explained in the Special Issue, as a new alternative to understand the importance and complexity of heterogeneity in cancer. Systems heterogeneity can offer a full image of heterogeneity at multi-dimensional functions and multi-omics by integrating gene or protein expression, epigenetics, sequencing, phosphorylation, transcription, pathway, or interaction. The Special Issue starts with the roles of epigenetics in the initiation and development of cancer heterogeneity through the interaction between permanent genetic mutations and dynamic epigenetic alterations. Cell heterogeneity was defined as the difference in biological function and phenotypes between cells in the same organ/tissue or in different organs, as well as various challenges, as exampled in telocytes. The single cell heterogeneity has the value of identifying diagnostic biomarkers and therapeutic targets and clinical potential of single cell systems heterogeneity in clinical oncology. A number of signaling pathways and factors contribute to the development of systems heterogeneity. Proteomic heterogeneity can change the strategy and thinking of drug discovery and development by understanding the interactions between proteins or proteins with drugs in order to optimize drug efficacy and safety. The association of cancer heterogeneity with cancer cell evolution and metastasis was also overviewed as a new alternative for diagnostic biomarkers and therapeutic targets in clinical application.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling.

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.