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Sökning: WFRF:(Pfeiffer Peter Benedikt)

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1.
  • Banerjee, Sambhasan, et al. (författare)
  • Amyloid fibril structure from the vascular variant of systemic AA amyloidosis
  • 2022
  • Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Systemic AA amyloidosis is a debilitating protein misfolding disease in humans and animals. In humans, it occurs in two variants that are called 'vascular' and 'glomerular', depending on the main amyloid deposition site in the kidneys. Using cryo electron microscopy, we here show the amyloid fibril structure underlying the vascular disease variant. Fibrils purified from the tissue of such patients are mainly left-hand twisted and contain two non-equal stacks of fibril proteins. They contrast in these properties to the fibrils from the glomerular disease variant which are right-hand twisted and consist of two structurally equal stacks of fibril proteins. Our data demonstrate that the different disease variants in systemic AA amyloidosis are associated with different fibril morphologies.
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2.
  • Demichev, Vadim, et al. (författare)
  • A time-resolved proteomic and prognostic map of COVID-19
  • 2021
  • Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 12:8, s. 780-794.e7
  • Tidskriftsartikel (refereegranskat)abstract
    • COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
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