Multiple biomarkers of sepsis identified by novel time-lapse proteomics of patient serum

Abstract

Serum components of sepsis patients vary with the severity of infection, the resulting inflammatory response, per individual, and even over time. Tracking these changes is crucial in properly treating sepsis. Hence, several blood-derived biomarkers have been studied for their potential in assessing sepsis severity. However, the classical approach of selecting individual biomarkers is problematic in terms of accuracy and efficiency. We therefore present a novel approach for detecting biomarkers using longitudinal proteomics data. This does not require a predetermined set of proteins and can therefore reveal previously unknown related proteins. Our approach involves examining changes over time of both protein abundance and post-translational modifications in serum, using two-dimensional gel electrophoresis (2D-PAGE). 2D-PAGE was conducted using serum from n = 20 patients, collected at five time points, starting from the onset of sepsis. Changes in protein spots were examined using 49 spots for which the signal intensity changed by at least two-fold over time. These were then screened for significant spikes or dips in intensity that occurred exclusively in patients with adverse outcome. Individual level variation was handled by a mixed effects model. Finally, for each time transition, partial correlations between spots were estimated through a Gaussian graphical model (GGM) based on the ridge penalty. Identifications of spots of interest by tandem mass spectrometry revealed that many were either known biomarkers for inflammation (complement components), or had previously been suggested as biomarkers for kidney failure (haptoglobin) or liver failure (ceruloplasmin). The latter two are common complications in severe sepsis. In the GGM, many of the tightly connected spots shared known biological functions or even belonged to the same protein; including hemoglobin chains and acute phase proteins. Altogether, these results suggest that our screening method can successfully identify biomarkers for disease states and cluster biologically related proteins using longitudinal proteomics data derived from 2D-PAGE.

Severe immunosuppression and not a cytokine storm characterizes COVID-19 infections

“…Flow cytometric analysis of samples was performed in all COVID-19 patients (days 1–3, 4–7, 8–11, and 12–15) and in CINS patients (days 1–3) as previously described ( 33 , 34 ). ALC was profoundly depressed in COVID-19 patients over the entire duration of the study compared with nonseptic patients (first comparison days 1–3; P = 0.01) ( Figure 7A ).…”

“…There have been many efforts to determine patient immune endotype using genomic or proteomic biomarkers of immunity ( 34 , 35 ). While these methods have been helpful in predicting outcomes in sepsis and other disorders ( 36 , 37 ), in general they have either not been able to provide an accurate assessment of the functional state of host immunity, as it varies over time, or have been used to determine response to therapy.…”

Peptidome profiling for the immunological stratification in sepsis: a proof of concept study

“…Sepsis constitutes a highly heterogeneous syndrome of complex pathophysiology. The underlying immunological alterations that include pro-inflammatory, anti-inflammatory and immunosuppressive components 12 are reflected in septic plasma protein composition and varies over time according to disease severity 37 . Thus, plasma represents a favoured biological sample to search for biochemical signals that provide information on the physiological state at a given time.…”

“…However, in the classical approaches the plasma samples are subjected to depletion of abundant proteins 23 and /or fractionation to identify individual biomarkers 37,38 which requires more analysis time, specific equipment and higher costs.…”

New Approaches to Identify Sepsis Biomarkers: The Importance of Model and Sample Source for Mass Spectrometry

“…The latter is increased in the sepsis patient group, indicating a response to inflammation and tissue damage [ 14 ]. Similarly, Hayashi et al, who studied the patient proteome over time, showed a significant decrease in haemoglobin beta 1 and 2 chains in the group of patients who did not survive sepsis [ 36 ]. These molecules could be markers of sepsis severity.…”