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Science Papers Examine Feedback Mechanism Affecting Xist, Continuous Health Monitoring for Precision Medicine

X-inactive specific transcript (Xist) is a noncoding RNA that directs the process of X chromosome inactivation in mammals by spreading in cis along the chromosome from which it is transcribed and recruiting chromatin modifiers to silence gene transcription. The basis for cis-confinement to a single chromosome territory, however, is poorly understood. To understand the mechanisms underlying Xist RNA cis-confinement, a University of Oxford team developed a sequential dual-color labeling, super-resolution imaging approach, called RNA-SPLIT, to trace individual Xist RNA molecules over time. As they report in this week's Science, they uncover a feedback mechanism linking Xist RNA synthesis and degradation, as well as an unexpected physical coupling between preceding and newly synthesized Xist RNA molecules. They find a previously unidentified role for the protein SPEN, which is involved in Xist-mediated gene silencing, in Xist RNA localization, stability, and coupling behaviors.

An overview of the state of continuous health monitoring, its role in precision medicine, and challenges facing the use of new technologies in this field is presented in this week's Science Translational Medicine by a team of Stanford University scientists. Precision medicine, they say, can only be realized by integrating health monitoring and diagnostics into everyday life, rather than by basing healthcare decisions on annual checkups and standardized health evaluations. Already, advances in wearable health sensors and genomic screening are having an impact on risk assessment and early intervention, they write, and new technologies are under development to track, transmit, and analyze an individual's health data. They envision the precision medicine field leading to the development of a "digital twin," a personalized disease risk profile set with base parameters and continuously updated with data from monitoring devices to forecast disease, recommend timely checkups, and continuously refine its prediction. "The path to fulfill this vision will be difficult," they warn, and will require advances in basic biology, knowledge of which biomarkers to monitor, and an understanding of "the healthy state as thoroughly as the disease state to define the threshold in between." Managing a massive and growing amount of healthcare data will also be a challenge, requiring the optimization of data analytics to arrive at actionable conclusions.