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AbstracthPARP14 is a human ADP-ribosyl-transferase (ART) that belongs to the macroPARPs family, together with hPARP9 and hPARP15. It contains a tandem of three macro domains (MD) while each of them has different properties. The first one, namely MD1, has not been reported to exhibit a high binding affinity for ADP-ribose (ADPr) in contrast to the following two (MD2 and MD3). All three MDs exhibit an α/β/α sandwich-like fold as reported by the deposited crystallographic structures. MD2 and MD3 recognize mono-ADP-ribosylated (MARylated) but not poly-ADP-ribosylated (PARylated) substrates and thus they allow hPARP14 to bind its targets, which can be potentially MARylated by its catalytic domain (CD). hPARP14 participates in DNA damage repair process and immune response against viruses like SARS-CoV-2, which also harbors an MD fold. Furthermore, hPARP14 like the other two macroPARPs (hPARP9 and hPARP15), is implicated in numerous types of cancer, such as B-aggressive lymphoma and sarcoma, rendering its MDs as potential important drug targets. Herein, we report the complete NMR backbone and side chain assignment (1H, 13C, 15N) of hPARP14 MD2 in the free and ADPr bound states and the NMR chemical shift-based prediction of its secondary structure elements. This is the first reported NMR study of a hPARP macro domain, paving the way to screen by NMR chemical compounds which may alter the ability of hPARP14 to interact with its substrates affecting its function.

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Previous efforts to estimate the burden of fatigue-related symptoms due to long COVID have a very high threshold for inclusion of cases, relative to the proposed definition from the World Health Organization. In practice, this means that milder cases, that may be occurring very frequently, are not included in estimates of the burden of long COVID, which will underestimate the burden of long COVID.A more comprehensive approach to modelling the disease burden from long COVID, in relation to fatigue, can ensure that we do not only focus on what is easiest to measure, thus losing focus of less severe health states that may be more difficult to measure but are occurring very frequently.Our proposed approach provides a means to better understanding of the scale of challenge from long COVID, for consideration when preventative and mitigative action is being planned.