Bioinformatic Analysis of the Wound Peptidome Reveals Potential Biomarkers and Antimicrobial Peptides

Abstract

Wound infection is a common and serious medical condition with an unmet need for improved diagnostic tools. A peptidomic approach, aided by mass spectrometry and bioinformatics, could provide novel means of identifying new peptide biomarkers for wound healing and infection assessment. Wound fluid is suitable for peptidomic analysis since it is both intimately tied to the wound environment and is readily available. In this study we investigate the peptidomes of wound fluids derived from surgical drainages following mastectomy and from wound dressings following facial skin grafting. By applying sorting algorithms and open source third party software to peptidomic label free tandem mass spectrometry data we provide an unbiased general methodology for analyzing and differentiating between peptidomes. We show that the wound fluid peptidomes of patients are highly individualized. However, differences emerge when grouping the patients depending on wound type. Furthermore, the abundance of peptides originating from documented antimicrobial regions of hemoglobin in infected wounds may contribute to an antimicrobial wound environment, as determined by in silico analysis. We validate our findings by compiling literature on peptide biomarkers and peptides of physiological significance and cross checking the results against our dataset, demonstrating that well-documented peptides of immunological significance are abundant in infected wounds, and originate from certain distinct regions in proteins such as hemoglobin and fibrinogen. Ultimately, we have demonstrated the power using sorting algorithms and open source software to help yield insights and visualize peptidomic data.

Method development and characterisation of the low-molecular-weight peptidome of human wound fluids

“…Together, the work defines a workflow for analysis of peptides derived from human wound fluids and demonstrate a proof-of-concept that such wound fluids can be used for analysis of subtle qualitative differences in peptide patterns derived from individual patient samples. Moreover, as recently also demonstrated ( Hartman et al, 2021 ), the uploaded data sets derived from the present report were further mined and processed using global bioinformatic approaches exploring quantitative differences in the identified peptidomes, demonstrating the applicability of the peptidome data generated in this study.…”

“…It is therefore notable that corresponding observations on the above proteins were indeed made in this study at the peptidome level as well, lending further support for the diagnostic potential of the here described methodology. The significance of the methodology is further underscored by recent bioinformatics applications based on the present datasets ( Hartman et al, 2021 ). Thus, in a global approach, algorithms in Python as well as open source softwares such Deep-AmPEP30 ( Yan et al, 2020 ) and Proteasix ( Klein et al, 2013 ) were employed.…”

“…Moreover, specific sequences like HKYH, LERM, and SKYR were especially prevalent at the C-terminal in infected wounds. In silico prediction using Proteasix showed that cleavages by proteases such as MMP 7, MMP 9, and neutrophil elastase were most prevalent, and in particular neutrophil elastase was particularly up regulated in infected wound samples ( Hartman et al, 2021 ), serving as a possible indicator of wound infection. DeepAMP identified novel antimicrobial peptides in hemoglobin, and moreover, peptides derived from LPS-binding regions of hemoglobin were identified in infected surgical wounds.…”

“…As demonstrated in this report, apart from the cytokines IL-1β, IL-6, IL-8, and TNF-α, a vast number of potential new peptide sequences could be detected in these wounds, using a qualitative approach focusing on major protein families involved in coagulation, complement activation, and protease control. Furthermore, subsequent mining of the peptidome data sets with bioinformatics methods identified additional subtle sequence differences ( Hartman et al, 2021 ). Together, these results provide a proof-of-principle for utilisation of wound fluid peptidomics in future biomarker-oriented studies on larger well-defined patient groups.…”

Peptimetric: Quantifying and visualizing differences in peptidomic data

“…In previous work, computation in combination with tools such as Peptigram was utilized to explore the presence of biomarkers and endogenous antimicrobial peptides in infected wounds, further showcasing the applicability and usefulness of algorithms on peptidomic data [9]. In this paper, we have continued to explore and develop algorithms allowing for massive screening and visualization of peptides in large cohorts of samples.…”