Switch-like compaction of poly(ADP-ribose) upon cation binding.

Poly(ADP-ribose) (PAR) is a homopolymer of adenosine diphosphate ribose that is added to proteins as a posttranslational modification to regulate numerous cellular processes. PAR also serves as a scaffold for protein binding in macromolecular complexes, including biomolecular condensates. It remains unclear how PAR achieves specific molecular recognition. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) to evaluate PAR flexibility under different cation conditions. We demonstrate that, compared to RNA and DNA, PAR has a longer persistence length and undergoes a sharper transition from extended to compact states in physiologically relevant concentrations of various cations (Na+, Mg2+, Ca2+, and spermine4+). We show that the degree of PAR compaction depends on the concentration and valency of cations. Furthermore, the intrinsically disordered protein FUS also served as a macromolecular cation to compact PAR. Taken together, our study reveals the inherent stiffness of PAR molecules, which undergo switch-like compaction in response to cation binding. This study indicates that a cationic environment may drive recognition specificity of PAR.

Switch-like Compaction of Poly(ADP-ribose) Upon Cation Binding.

Poly(ADP-ribose) (PAR) is a homopolymer of adenosine diphosphate ribose that is added to proteins as a post-translational modification to regulate numerous cellular processes. PAR also serves as a scaffold for protein binding in macromolecular complexes, including biomolecular condensates. It remains unclear how PAR achieves specific molecular recognition. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) to evaluate PAR flexibility under different cation conditions. We demonstrate that, compared to RNA and DNA, PAR has a longer persistence length and undergoes a sharper transition from extended to compact states in physiologically relevant concentrations of various cations (Na + , Mg 2+ , Ca 2+ , and spermine). We show that the degree of PAR compaction depends on the concentration and valency of cations. Furthermore, the intrinsically disordered protein FUS also served as a macromolecular cation to compact PAR. Taken together, our study reveals the inherent stiffness of PAR molecules, which undergo switch-like compaction in response to cation binding. This study indicates that a cationic environment may drive recognition specificity of PAR. Significance: Poly(ADP-ribose) (PAR) is an RNA-like homopolymer that regulates DNA repair, RNA metabolism, and biomolecular condensate formation. Dysregulation of PAR results in cancer and neurodegeneration. Although discovered in 1963, fundamental properties of this therapeutically important polymer remain largely unknown. Biophysical and structural analyses of PAR have been exceptionally challenging due to the dynamic and repetitive nature. Here, we present the first single-molecule biophysical characterization of PAR. We show that PAR is stiffer than DNA and RNA per unit length. Unlike DNA and RNA which undergoes gradual compaction, PAR exhibits an abrupt switch-like bending as a function of salt concentration and by protein binding. Our findings points to unique physical properties of PAR that may drive recognition specificity for its function.

A pilot study to predict cardiac arrest in the pediatric intensive care unit.

BACKGROUND: Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance. METHODS: Our data consists of 240 Hz ECG waveform data, 0.5 Hz physiological time series data, medications, and demographics from 1,145 patients in the pediatric intensive care unit at the Johns Hopkins Hospital, 15 of whom experienced a cardiac arrest. The data were divided into training, validating, and testing sets, and features were generated every five minutes. 23 heart rate variability (HRV) metrics were determined from ECG waveforms. 96 summary statistics were calculated for 12 vital signs, such as respiratory rate and blood pressure. Medications were classified into 42 therapeutic drug classes. Binary features were generated to indicate the administration of these different drugs. Next, six machine learning models were evaluated: logistic regression, support vector machine, random forest, XGBoost, LightGBM, and a soft voting ensemble. RESULTS: XGBoost performed the best, with 0.971 auROC, 0.797 auPRC, 99.5% sensitivity, and 69.6% specificity on an independent test set. CONCLUSION: We have created high-performing models that identify signatures of in-hospital cardiac arrest (IHCA) that may not be evident to clinicians. These signatures include a combination of heart rate variability metrics, vital signs data, and therapeutic drug classes. These machine learning models can predict IHCA up to three hours prior to onset with high performance, allowing clinicians to intervene earlier, improving patient outcomes.