Considerations for understanding protein measurements: Identification of formation, degradation and more pathological relevant epitopes.

OBJECTIVES: There is a need for precision medicine and an unspoken promise of an optimal approach for identification of the right patients for value-based medicine based on big data. However, there may be a misconception that measurement of proteins is more valuable than measurement of fewer selected biomarkers. In population-based research, variation may be somewhat eliminated by quantity. However, this fascination of numbers may limit the attention to and understanding of the single. This review highlights that protein measurements (with collagens as examples) may mean different things depending on the targeted epitope - formation or degradation of tissues, and even signaling potential of proteins. DESIGN AND METHODS: PubMed was searched for collagen, neo-epitope, biomarkers. RESULTS: Ample examples of assays with specific epitopes, either pathological such as HbA1c, or domain specific such as pro-peptides, which total protein arrays would not have identified were evident. CONCLUSIONS: We suggest that big data may be considered as the funnel of data points, in which most important parameters will be selected. If the technical precision is low or the biological accuracy is limited, and we include suboptimal quality of biomarkers, disguised as big data, we may not be able to fulfill the promise of helping patients searching for the optimal treatment. Alternatively, if the technical precision of the total protein quantification is high, but we miss the functional domains with the most considerable biological meaning, we miss the most important and valuable information of a given protein. This review highlights that measurements of the same protein in different ways may provide completely different meanings. We need to understand the pathological importance of each epitope quantified to maximize protein measurements.

Can biomarkers of extracellular matrix remodelling and wound healing be used to identify high risk patients infected with SARS-CoV-2?: lessons learned from pulmonary fibrosis.

Pulmonary fibrosis has been identified as a main factor leading to pulmonary dysfunction and poor quality of life in post-recovery Severe Acute Respiratory Syndrome (SARS) survivor's consequent to SARS-Cov-2 infection. Thus there is an urgent medical need for identification of readily available biomarkers that in patients with SARS-Cov-2 infection are able to; (1) identify patients in most need of medical care prior to admittance to an intensive care unit (ICU), and; (2) identify patients post-infection at risk of developing persistent fibrosis of lungs with subsequent impaired quality of life and increased morbidity and mortality. An intense amount of research have focused on wound healing and Extracellular Matrix (ECM) remodelling of the lungs related to lung function decline in pulmonary fibrosis (PF). A range of non-invasive serological biomarkers, reflecting tissue remodelling, and fibrosis have been shown to predict risk of acute exacerbations, lung function decline and mortality in PF and other interstitial lung diseases (Sand et al. in Respir Res 19:82, 2018). We suggest that lessons learned from such PF studies of the pathological processes leading to lung function decline could be used to better identify patients infected with SARS-Co-V2 at most risk of acute deterioration or persistent fibrotic damage of the lung and could consequently be used to guide treatment decisions.