Targeted plasma proteomics reveals signatures discriminating COVID-19 from sepsis with pneumonia
Background COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features. Methods We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients. Results We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers. Conclusions This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis..
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2023 |
---|---|
Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
---|---|
Enthalten in: |
Respiratory research - 24(2023), 1 vom: 24. Feb. |
Sprache: |
Englisch |
---|
Links: |
Volltext [kostenfrei] |
---|
Themen: |
COVID-19 |
---|
Anmerkungen: |
© The Author(s) 2023 |
---|
doi: |
10.1186/s12931-023-02364-y |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
OLC213415263X |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC213415263X | ||
003 | DE-627 | ||
005 | 20230506161615.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230506s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s12931-023-02364-y |2 doi | |
035 | |a (DE-627)OLC213415263X | ||
035 | |a (DE-He213)s12931-023-02364-y-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 610 |q VZ |
100 | 1 | |a Palma Medina, Laura M. |e verfasserin |4 aut | |
245 | 1 | 0 | |a Targeted plasma proteomics reveals signatures discriminating COVID-19 from sepsis with pneumonia |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © The Author(s) 2023 | ||
520 | |a Background COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features. Methods We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients. Results We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers. Conclusions This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis. | ||
650 | 4 | |a COVID-19 | |
650 | 4 | |a Community acquired pneumonia | |
650 | 4 | |a Sepsis | |
650 | 4 | |a Septic shock | |
650 | 4 | |a Olink proximity extension assays | |
700 | 1 | |a Babačić, Haris |4 aut | |
700 | 1 | |a Dzidic, Majda |4 aut | |
700 | 1 | |a Parke, Åsa |4 aut | |
700 | 1 | |a Garcia, Marina |4 aut | |
700 | 1 | |a Maleki, Kimia T. |4 aut | |
700 | 1 | |a Unge, Christian |4 aut | |
700 | 1 | |a Lourda, Magda |4 aut | |
700 | 1 | |a Kvedaraite, Egle |4 aut | |
700 | 1 | |a Chen, Puran |4 aut | |
700 | 1 | |a Muvva, Jagadeeswara Rao |4 aut | |
700 | 1 | |a Cornillet, Martin |4 aut | |
700 | 1 | |a Emgård, Johanna |4 aut | |
700 | 1 | |a Moll, Kirsten |4 aut | |
700 | 1 | |a Michaëlsson, Jakob |4 aut | |
700 | 1 | |a Flodström-Tullberg, Malin |4 aut | |
700 | 1 | |a Brighenti, Susanna |4 aut | |
700 | 1 | |a Buggert, Marcus |4 aut | |
700 | 1 | |a Mjösberg, Jenny |4 aut | |
700 | 1 | |a Malmberg, Karl-Johan |4 aut | |
700 | 1 | |a Sandberg, Johan K. |4 aut | |
700 | 1 | |a Gredmark-Russ, Sara |4 aut | |
700 | 1 | |a Rooyackers, Olav |4 aut | |
700 | 1 | |a Svensson, Mattias |4 aut | |
700 | 1 | |a Chambers, Benedict J. |4 aut | |
700 | 1 | |a Eriksson, Lars I. |4 aut | |
700 | 1 | |a Pernemalm, Maria |4 aut | |
700 | 1 | |a Björkström, Niklas K. |4 aut | |
700 | 1 | |a Aleman, Soo |4 aut | |
700 | 1 | |a Ljunggren, Hans-Gustaf |4 aut | |
700 | 1 | |a Klingström, Jonas |4 aut | |
700 | 1 | |a Strålin, Kristoffer |4 aut | |
700 | 1 | |a Norrby-Teglund, Anna |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Respiratory research |d BioMed Central, 2001 |g 24(2023), 1 vom: 24. Feb. |h Online-Ressource |w (DE-627)326646485 |w (DE-600)2041675-1 |w (DE-576)107014823 |x 1465-993X |7 nnns |
773 | 1 | 8 | |g volume:24 |g year:2023 |g number:1 |g day:24 |g month:02 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s12931-023-02364-y |z kostenfrei |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_702 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2007 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2031 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2057 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2113 | ||
912 | |a GBV_ILN_2134 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_2446 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 24 |j 2023 |e 1 |b 24 |c 02 |