Progranulin signaling in sepsis, community-acquired bacterial pneumonia and COVID-19: a comparative, observational study

Florian Brandes, Melanie Borrmann, Dominik Buschmann, Agnes S Meidert, Marlene Reithmair, Markus Langkamp, Lutz Pridzun, Benedikt Kirchner, Jean-Noël Billaud, Nirav M Amin, Joseph C Pearson, Matthias Klein, Daniela Hauer, Clarissa Gevargez Zoubalan, Anja Lindemann, Alexander Choukér, Thomas W Felbinger, Ortrud K Steinlein, Michael W Pfaffl, Ines Kaufmann, Gustav Schelling, Florian Brandes, Melanie Borrmann, Dominik Buschmann, Agnes S Meidert, Marlene Reithmair, Markus Langkamp, Lutz Pridzun, Benedikt Kirchner, Jean-Noël Billaud, Nirav M Amin, Joseph C Pearson, Matthias Klein, Daniela Hauer, Clarissa Gevargez Zoubalan, Anja Lindemann, Alexander Choukér, Thomas W Felbinger, Ortrud K Steinlein, Michael W Pfaffl, Ines Kaufmann, Gustav Schelling

Abstract

Background: Progranulin is a widely expressed pleiotropic growth factor with a central regulatory effect during the early immune response in sepsis. Progranulin signaling has not been systematically studied and compared between sepsis, community-acquired pneumonia (CAP), COVID-19 pneumonia and a sterile systemic inflammatory response (SIRS). We delineated molecular networks of progranulin signaling by next-generation sequencing (NGS), determined progranulin plasma concentrations and quantified the diagnostic performance of progranulin to differentiate between the above-mentioned disorders using the established biomarkers procalcitonin (PCT), interleukin-6 (IL-6) and C-reactive protein (CRP) for comparison.

Methods: The diagnostic performance of progranulin was operationalized by calculating AUC and ROC statistics for progranulin and established biomarkers in 241 patients with sepsis, 182 patients with SIRS, 53 patients with CAP, 22 patients with COVID-19 pneumonia and 53 healthy volunteers. miRNAs and mRNAs in blood cells from sepsis patients (n = 7) were characterized by NGS and validated by RT-qPCR in an independent cohort (n = 39) to identify canonical gene networks associated with upregulated progranulin at sepsis onset.

Results: Plasma concentrations of progranulin (ELISA) in patients with sepsis were 57.5 (42.8-84.9, Q25-Q75) ng/ml and significantly higher than in CAP (38.0, 33.5-41.0 ng/ml, p < 0.001), SIRS (29.0, 25.0-35.0 ng/ml, p < 0.001) and the healthy state (28.7, 25.5-31.7 ng/ml, p < 0.001). Patients with COVID-19 had significantly higher progranulin concentrations than patients with CAP (67.6, 56.6-96.0 vs. 38.0, 33.5-41.0 ng/ml, p < 0.001). The diagnostic performance of progranulin for the differentiation between sepsis vs. SIRS (n = 423) was comparable to that of procalcitonin. AUC was 0.90 (95% CI = 0.87-0.93) for progranulin and 0.92 (CI = 0.88-0.96, p = 0.323) for procalcitonin. Progranulin showed high discriminative power to differentiate bacterial CAP from COVID-19 (sensitivity 0.91, specificity 0.94, AUC 0.91 (CI = 0.8-1.0) and performed significantly better than PCT, IL-6 and CRP. NGS and partial RT-qPCR confirmation revealed a transcriptomic network of immune cells with upregulated progranulin and sortilin transcripts as well as toll-like-receptor 4 and tumor-protein 53, regulated by miR-16 and others.

Conclusions: Progranulin signaling is elevated during the early antimicrobial response in sepsis and differs significantly between sepsis, CAP, COVID-19 and SIRS. This suggests that progranulin may serve as a novel indicator for the differentiation between these disorders.

Trial registration: Clinicaltrials.gov registration number NCT03280576 Registered November 19, 2015.

Keywords: COVID-19; Gene networks; Pneumonia; Procalcitonin; Progranulin; Sensitivity; Sepsis; Specificity.

Conflict of interest statement

FB, MB, DB, GS, MR, BK, MK, DH, CGZ, ASM, AL, AC, OS, GS, MP, TF have no conflict of interest; LP and ML are employees of MEDIAGNOST, Reutlingen, Germany which developed and markets the progranulin assay used in the study, and hold a patent on progranulin together with IK. JNB, NMA, JCP are employees of Qiagen Digital Insights, which markets the Ingenuity Pathway Analysis software package used for data analysis in the study.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Flowchart describing the origin of the study population, divided by exploratory and confirmatory study. Overall, the exploratory study population consists of 260 individuals (including 114 patients with sepsis, 90 patients with SIRS, 24 patients with localized infection and 32 volunteers) and the confirmatory sample consists of 317 individuals (including 127 patients with sepsis, 92 patients with SIRS, 24 patients with localized infection, 22 patients with a COVID-19 CAP, 31 patients with non-COVID-19 CAP as well as 21 volunteers). The community-acquired pneumonia (COVID and non-COVID) groups are only represented in the confirmatory group
Fig. 2
Fig. 2
Comparison of progranulin (PGRN, A) and procalcitonin (PCT, B) plasma concentrations between healthy controls and patients with either a severe localized infection (e.g., a large peripheral abscess at high risk for sepsis), non-COVID-19 community-acquired pneumonia (CAP), sepsis or septic shock. Data are presented separately for the exploratory (blue boxplots) and the confirmatory study (orange boxplots). All symbols indicate p < 0.001. Significant p-values ≥ 0.001 are given as numbers in addition to symbols. # Indicates a significant difference between patients with sepsis, septic shock or CAP when compared to healthy controls. $ Indicates a significant difference between patients with sepsis, septic shock or healthy controls when compared to patients with CAP. + Indicates a significant difference between patients with sepsis, CAP or healthy controls when compared to septic shock patients
Fig. 3
Fig. 3
Comparison of progranulin (PGRN) (A) and procalcitonin (PCT) (B) plasma concentrations in sepsis of pulmonary, abdominal or other origin. Data are presented separately for the exploratory (blue boxplots) and the confirmatory study (orange boxplots). Significant differences between groups are marked with bars and p-values. * Indicates a p value < 0.001
Fig. 4
Fig. 4
Kinetics of progranulin (PGRN, A) in comparison to procalcitonin (PCT, B) over the course of ICU therapy from day 0 (ICU admittance and study inclusion) until day 21 in surviving patients (n = 191). Both the exploratory and the confirmatory group were pooled for this illustration. * Indicates a p < 0.001 (ANOVA on ranks with Dunn’s post hoc test)
Fig. 5
Fig. 5
Comparison of progranulin (PGRN, A) and procalcitonin (PCT, B) plasma concentrations in healthy volunteers, patients with SIRS and patients with either sepsis or septic shock ("Sepsis") at study inclusion. Data are presented separately for the exploratory (blue boxplots) and the confirmatory study (orange boxplots). *Indicates a p < 0.001, significant p values ≥ 0.001 are given as numbers
Fig. 6
Fig. 6
ROC curves for the differentiation between patients with SIRS and sepsis for procalcitonin (PCT, A), C-reactive protein (CRP, B) and interleukin-6 (IL-6, C) in comparison to progranulin. Patients with sepsis and septic shock were combined for this analysis. The curves for progranulin are printed in red and the respective lines of the compared reference marker are outlined in blue. Lines printed in lighter colors represent measurements in the exploratory cohort and dashed lines the corresponding measurements in the confirmatory (validation) sample. Dark lines show the summary values from both samples. See text for AUC values and statistical comparison and Additional file 1: Tables S8 and S9 for further details
Fig. 7
Fig. 7
ROC curves for the differentiation between patients with community-acquired pneumonia (CAP) and patients with sepsis in the validation cohort. Red lines indicate ROC curves for progranulin in all graphs and blue lines the corresponding values for procalcitonin (PCT, A), C-reactive protein (CRP, B) and interleukin-6 (IL-6, C). Cut-off values and data for sensitivity/specificity for the reference biomarkers in comparison to progranulin are given in Additional file 1: Table S9
Fig. 8
Fig. 8
ROC analysis for the differentiation between patients with viral COVID-19 or non-COVID-19 pneumonia (CAP) for progranulin vs. procalcitonin (PCT), interleukin-6 (IL-6) or C-reactive protein (CRP). Procalcitonin and C-reactive protein act as a negative predictor for the detection of a SARS-COV-2 pneumonia and have therefore been inverted for this ROC analysis
Fig. 9
Fig. 9
Illustration of the role of progranulin (GRN gene) in the molecular network activated during the early antimicrobial response in patients with septic shock at admittance to the ICU. The network was constructed using data from high-throughput sequencing followed by RT-qPCR confirmation. Red color indicates upregulation in comparison to healthy volunteers, green indicates downregulation. Color shading corresponds to log2FC values, which are reported below the molecules along with FDR-adjusted p-values. The effect of sepsis-associated upregulation of calcitonin precursors (CALC1) and its gene product procalcitonin on sortilin (SORT1), the principal binding partner of progranulin, is illustrated on the right side of the graph
Fig. 10
Fig. 10
Molecular network constructed from high-throughput sequencing data showing progranulin activation during the early inflammatory response of immune cells during sepsis. Red color indicates upregulation in comparison to healthy volunteers, green indicates downregulation. Color shading corresponds to log2FC values. These values are indicated below the molecules along with FDR-adjusted p-values. Molecules are shown as symbols in the network, and the molecule type is given in the legend

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