In a recent study published in the journal eBioMedicine, researchers used proteomics to investigate differentially expressed proteins (DEPs) associated with long-COVID. Identified up- or down-regulated proteins were characterized via ingenuity pathway analyses to elucidate their downstream pathological and physiological effects. This 2-year-long longitudinal study was thereby able to reveal protein biomarkers useful in long-COVID diagnosis and some potential mechanisms by which the condition debilitates survivors.

Study: Probing long COVID through a proteomic lens: a comprehensive two-year longitudinal cohort study of hospitalised survivors

Long COVID and the challenges associated with its treatment

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused coronavirus disease 2019 (COVID-19) has hitherto infected more than 771 million people, leaving almost 7 million dead worldwide. Its impacts extend beyond its lethality, with a growing number of survivors reporting symptoms that persist or, in some cases, develop long after primary disease termination. This colloquially termed ‘long-COVID’ has been confirmed to afflict 65 million people, with research estimating that between 14-55% of all COVID-19 survivors suffer from the condition.

Long-COVID has had severe and widespread impacts on COVID-19 survivors’ quality of life. The condition has further resulted in national-scale economic loss, as seen in the United Kingdom (UK) and Europe, wherein its prevalence has been directly responsible for early retirement and shortages in the employment market. This is mainly due to the physical and neurological debilitation brought about by long-COVID, which may persist for two years or more.

Unfortunately, given the novelty of the disease, it remains poorly understood, with a clinically defined definition of long-COVID hitherto lacking. The currently accepted working definition for long COVID is that of the World Health Organization (WHO), which establishes the affliction as the persistence or development of COVID-19-like symptoms two months following hospital discharge. Given the vagueness surrounding long-COVID, research into its mechanisms and prevalence, though growing by the day, remains insufficient.

Recent research has identified race, sex, age, and severity of COVID-19 infections as possible risk factors associated with long-COVID development, with hypotheses suggesting the cross-reactivity of SARS-CoV-2 antibodies and host immune proteins as the mechanistic underpinning for the adverse effects of the condition. However, targeted extensive cohort studies are required to test and verify these hypotheses, failing which management and clinical interventions to counter long-COVID cannot be implemented. Alarmingly, clinicians still do not have any diagnostic test for the condition, with the current diagnosis being based on patient-reported symptoms.

About the study

In the present study, researchers investigated the proteomic landscape surrounding long-COVID to identify protein biomarkers that may serve as future diagnostic determinants of the condition and provide insights into the pathophysiological impacts of severe SARS-CoV-2 infections on survivors. They conducted a 2-year-long profiling of survivor plasma samples to identify significantly up- or down-regulated proteins in long-COVID patients versus COVID-19 survivors who did not suffer from the condition. Identified proteins were then integrated with existing proteomic knowledge databases to elucidate the consequences of their altered concentrations on host physiology.

Participants were recruited from hospitals between 7 January and 29 May 2020. For inclusion in the study, participants needed to have had clinically confirmed COVID-19-associated hospital admissions. Patients who died in the six months following hospital discharge had dementia or other severe neurophysiological disorders or who were immobile were excluded from the study.

Following the screening of inclusion and exclusion criteria, 516 patients were enlisted into the study, of which 181 provided data across both years of the study. They were thus included in the data analyses. Follow-up data collection was conducted at six months, one year, and two years following hospital discharge and comprised functional tests of the pulmonary system, kidneys, and lungs. Plasma samples were collected alongside the functional tests and used for proteomic investigations.

To elucidate the pathophysiological changes in COVID-19 survivors, 181 age- and sex-matched health controls were recruited from Wuhan, China, and underwent the same battery of tests as the case-cohort (survivors). Additionally, demographic, clinical, disease severity (for cases), and medical intervention data was collected from hospital records (for cases) or self-reported for controls.

Plasma tests for differentially expressed proteins (DEPs) identification and quantification comprised of liquid chromatography-mass spectrometry (LC/MS) in combination with data-independent acquisition (DIA) tandem mass spectrometry for the generation of a spectral library. The data was enriched using differential enrichment analysis of proteomics data (DEP) analysis. Finally, resultant data was compared against the UniProt database for human proteins, and principal component analysis (PCA) was used to identify proteins that significantly up-or down-regulated in cases.

A random forest (RF) machine learning (ML) model was used to compare identified proteins in cases versus controls.

Study findings

Demographic and clinical data revealed significant long-term symptoms in COVID-19 cases, including physical (reduced exercise capacity, mobility, and quality of life) and clinical (increased healthcare usage following hospital discharge, reduced immune response, and reduced lung function). Proteomics analysis in combination with PCAs revealed distinct stratification of COVID-19 survivors into three cohorts, corroborated between all three follow-up visits.

Proteomics and DIA analyses identified 1.370 proteins, of which DEP analyses revealed 249 proteins that were significantly different between cases and controls. Numbers of DEP proteins varied between follow-up studies, with some returning to baseline (control) values while others remained distinct.

“Our data showed majority of DEPs enriched in immune response pathways were immunoglobulins. These immunoglobulins were involved in several pathways including regulation of B cell and lymphocyte activation, Fc receptor signaling pathway, and immunoglobulin mediated immune response.”

Comparisons with the UniProt database revealed four major recovery modes of biological processes comprising focal adhesion, regulation of action cytoskeleton (cellular biology associated), ECM-receptor interactions, and hyper- and dilated-cardiomyopathy pathways (cardiovascular system associated). Immune response, complement cascade, and coagulation cascade pathways returned to baseline two years following discharge, but the Fc receptor signaling pathway were not observed to recover even at the two-year follow-up.

Conclusions

The present study identified four different biological processes detrimentally impacted by SARS-CoV-2 infection, thereby providing molecular insights into the mechanistic processes involved in the long-COVID condition. Multiple potential protein biomarkers of long-COVID were identified, which could lead to the future development of diagnostic tests for long-COVID identification.

Written by

Hugo Francisco de Souza

Hugo Francisco de Souza is a scientific writer based in Bangalore, Karnataka, India. His academic passions lie in biogeography, evolutionary biology, and herpetology. He is currently pursuing his Ph.D. from the Centre for Ecological Sciences, Indian Institute of Science, where he studies the origins, dispersal, and speciation of wetland-associated snakes. Hugo has received, amongst others, the DST-INSPIRE fellowship for his doctoral research and the Gold Medal from Pondicherry University for academic excellence during his Masters. His research has been published in high-impact peer-reviewed journals, including PLOS Neglected Tropical Diseases and Systematic Biology. When not working or writing, Hugo can be found consuming copious amounts of anime and manga, composing and making music with his bass guitar, shredding trails on his MTB, playing video games (he prefers the term ‘gaming’), or tinkering with all things tech.