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Lehman, H.

Publications and source records attributed to Lehman, H..

2 recordsLinked to original sources

Infection Tunes the Dynamics of Adenoviral E1A Disordered Regions

Intrinsically Disordered Proteins and protein regions (IDPs) are abundant in many viral proteomes and play diverse roles in the viral infectious cycles. The adenovirus Early Protein 1A (E1A) is one such viral IDP. E1A acts as a molecular hub that regulates viral infection by mediating interactions between viral and multiple host proteins. Like other IDPs, E1A exists in a flexible ensemble of conformations. Despite a demonstrated link between ensemble structure and function in E1A, no real-time measurement of its ensemble has been performed. Here, we use live cell FRET microscopy to measure the local ensemble structure of E1A in human cells, both in healthy cells and in cells infected with adenovirus. We found specific disordered regions undergo significant changes to their ensemble in infected cells. Furthermore, infection also alters the propensity of these regions to partition between the cytoplasm and nucleus, a hallmark of E1A function during infection. Our results showcase that the structural ensembles of viral IDPs are responsive to infection, and suggest that these may play a role in regulating infection progression. SignificanceIntegral to many viral proteomes are intrinsically disordered proteins and protein regions (IDPs), which target and rewire cellular pathways to ensure infection progression. During this process, the physical and chemical composition of the cell changes dramatically: metabolism is rewired, viral proteins are produced en masse, and as a result, the chemical composition of the host proteome is significantly altered. IDPs are known to be structurally sensitive to even mild changes in their environment, and their structural changes can result in a change to function. Here, using live cell FRET microscopy, we show that the structure and spatial localization of an adenovirus IDP, E1A, is altered in cells infected by the virus. Beyond a possible functional role for structural sensitivity in viral IDP function, our findings suggest that host IDPs may also be structurally altered by infection, with downstream functional consequences.

biophysics↗

Machine Learning to Summarize and Provide Context for Sleep and Eating Schedules.

The relative timing of sleep and of eating within the circadian day is important for human health. Despite much data on sleep, and a growing data set for eating, there remains a need for an interpretative framework for the understanding of this data for health decisions. This study provides a new statistical and machine learning analysis of more than 500 participants in the Daily24 project. From their data, and the analysis, we propose a framework for determining the classification of participants into different chronotypes and with that the ability to realize the potential impact of daily circadian habits on health. We propose that our resulting distribution curves could be used, similar to NHANES (National Health and Nutrition Examination Survey) data for pediatric growth, as a measure for circadian misalignment and used to help guide re-entrainment schedules. Author summaryDaily habits can be positive, negative or neutral for human health. Generally sleep and eating schedules are assumed without thought for their potential to help or interfere with health. In this study we propose a framework, based on data from more than 500 participants, for evaluating the relative timing of meals and sleep schedules. This evaluation, similar to pediatric growth charts, can guide clinical suggestions for those at the extremes, while helping others to realize that they are unusual relative to the population average

bioinformatics↗