| 1. |
- Demichev, Vadim, et al.
(author)
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A proteomic survival predictor for COVID-19 patients in intensive care
- 2022
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In: PLOS Digital Health. - : Public Library of Science (PLoS). - 2767-3170. ; 1:1 January
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Journal article (peer-reviewed)abstract
- Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.
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| 2. |
- Demichev, Vadim, et al.
(author)
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A time-resolved proteomic and prognostic map of COVID-19
- 2021
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In: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 12:8, s. 780-794.e7
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Journal article (peer-reviewed)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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| 3. |
- Gattinger, Pia, et al.
(author)
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Neutralization of SARS-CoV-2 requires antibodies against conformational receptor-binding domain epitopes
- 2022
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In: Allergy. European Journal of Allergy and Clinical Immunology. - : Wiley. - 0105-4538 .- 1398-9995. ; 77:1, s. 230-242
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Journal article (peer-reviewed)abstract
- Background: The determinants of successful humoral immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of critical importance for the design of effective vaccines and the evaluation of the degree of protective immunity conferred by exposure to the virus. As novel variants emerge, understanding their likelihood of suppression by population antibody repertoires has become increasingly important.Methods: In this study, we analyzed the SARS-CoV-2 polyclonal antibody response in a large population of clinically well-characterized patients after mild and severe COVID-19 using a panel of microarrayed structurally folded and unfolded SARS-CoV-2 proteins, as well as sequential peptides, spanning the surface spike protein (S) and the receptor-binding domain (RBD) of the virus.Results: S- and RBD-specific antibody responses were dominated by immunoglobulin G (IgG), mainly IgG1, and directed against structurally folded S and RBD and three distinct peptide epitopes in S2. The virus neutralization activity of patients´ sera was highly correlated with IgG antibodies specific for conformational but not sequential RBD epitopes and their ability to prevent RBD binding to its human receptor angiotensin-converting enzyme 2 (ACE2). Twenty percent of patients selectively lacked RBD-specific IgG. Only immunization with folded, but not with unfolded RBD, induced antibodies against conformational epitopes with high virus-neutralizing activity. Conformational RBD epitopes required for protection do not seem to be altered in the currently emerging virus variants.Conclusion: These results are fundamental for estimating the protective activity of antibody responses after natural infection or vaccination and for the design of vaccines, which can induce high levels of SARS-CoV-2–neutralizing antibodies conferring sterilizing immunity.
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| 4. |
- Sarneel, Judith M., et al.
(author)
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Reading tea leaves worldwide : decoupled drivers of initial litter decomposition mass-loss rate and stabilization
- 2024
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In: Ecology Letters. - : John Wiley & Sons. - 1461-023X .- 1461-0248. ; 27:5
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Journal article (peer-reviewed)abstract
- The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.
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| 5. |
- Sarneel, Judith M., et al.
(author)
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Reading tea leaves worldwide: Decoupled drivers of initial litter decomposition mass-loss rate and stabilization
- 2024
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In: ECOLOGY LETTERS. - : John Wiley & Sons. - 1461-023X .- 1461-0248. ; 27:5
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Journal article (peer-reviewed)abstract
- The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.
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