Identification of COVID-19 Disease Biomarkers Using Proteomics and Informatics
It has become urgently necessary to create new diagnostic and therapeutic approaches as a result of the advent of novel coronavirus illness 2019 (COVID-19), which is brought on by the SARS-CoV-2 coronavirus. Assays for detecting SARS-CoV-2 RNA and host immunoglobulins have already been developed through rapid research and development on a global scale. However, due to the COVID-19's complexity, more comprehensive definitions of the patient's state, trajectory, sequelae, and therapeutic responses are now necessary. Studies on COVID-19 and the linked illness SARS are developing evidence that these protein biomarkers may aid to define the disease. As potential indicators of the severity or mortality of COVID-19, proteins linked to blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (C-reactive protein) have already been found.
These preliminary findings are now being expanded upon by proteomics methods, which can identify several proteins in a single examination. Proteomics tactics must incorporate informatics tools to extract useful information from massive amounts of data in addition to mass spectrometry technologies for comprehensive data collecting. Here, we examine proteomics' applications to COVID-19 and SARS.
Currently, there are two basic methods for diagnosing COVID-19: immunoassays that look for antibodies to particular viruses SARS-CoV-2 in patients and assays utilising real-time reverse-transcription polymerase chain reaction (RT-PCR), which can be performed using a variety of clinical specimens, including blood or bronchoalveolar lavage fluid, sputum, nasal or pharyngeal swabs, bronchoscopy, or nasal or pharyngeal swabs. However, as this disease progresses, a phalanx of additional issues and a corresponding requirement for biomarker assays materialise.
Artificial intelligence techniques work best when used to extract information from the vast amount of data gathered by DIA methods, particularly when there is also a sizable amount of multimodal clinical data available, such as comorbidities, imaging data, respiratory function, age, sex, and clinical biochemistry laboratory measurement of proteins like troponin and D dimer.