When the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects a human host, it takes control of the host’s signaling pathways by inducing protein-protein interactions (PPIs) between host and viral proteins. While the genetic variations in humans may impact SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) pathology, their role in the signaling networks remains uncharacterized.

Study: Human phospho-signaling networks of SARS-CoV-2 infection are rewired by population genetic variants. Image Credit: Naty.M / Shutterstock.com

Study design

A team of researchers recently studied human single nucleotide variants (SNVs) using machine learning to identify amino acid changes that alter kinase-bound sequence motifs. To this end, they found a total of 2,033 infrequent phosphorylation-associated SNVs (pSNVs) with abundant sequence motif alterations. This demonstrated the evolution of signaling networks regulating host immune defenses.

Almost all the viral life cycle processes and host responses involve proteins with pSNVs or specifically motif-rewiring pSNVs. In fact, pSNVs are also found in core host processes of the viral life cycle, such as ribonucleic acid (RNA) splicing and interferon (IFN) responses, as well as signaling pathways such as glucose homeostasis pathway associated with COVID-19 co-morbidities, and in other human genes involved in viral infections.

Additionally, pSNVs induce structural changes in kinase signaling networks and cross-talk of mitogenic and immune response pathways in some populations. These variants disrupt cyclin-dependent kinases (CDK) and mitogen-associated kinase (MAPK) substrate motifs and replace these with motifs recognized by the Tank Binding Kinase 1 (TBK1) of the IKB kinase (IKK) family, referred to as motif switching. TBK1 is actively involved in innate immune responses and motif switching of pSNVs indicates consistent rewiring of infection signaling networks.

The authors of this review posted on the medRxiv* preprint server employed an array of methods including functional enrichment analysis, PPI network analysis, and analysis of population frequency and disease annotations of pSNVs. These analyses helped highlight the genetic factors contributing to the variation in SARS-CoV-2 infection and suggest pointers for future translational and mechanistic studies.

Key findings

The results of the current study demonstrated that hundreds of pSNVs are capable of altering SARS-CoV-2-driven signaling in host cells through kinase sequence motifs. The researchers highlighted the transcriptional repressor TRIM28 gene as a gene of interest using five pSNVs.

This gene increases IFN beta and pro-inflammatory cytokine production during avian virus infection in lung epithelial cells through the phosphorylation of the S473 amino acid residue. The researchers found that the pSNVs in the TRIM28 phosphosites could alter the signaling of proteins and suppress immune response activation.

BCLAF1, which is a gene with frequent pSNVs, is highly expressed in lung cells and plays a critical role in lung development, thus suggesting that its activity in respiratory tract tissues is affected by SARS-CoV-2 infection. This finding demonstrates the role of top genes with pSNVs in host immune responses and core cellular processes of a viral infection.

A network analysis of 30 physical PPIs of the 77 human proteins and 139 PPIs of SARS-CoV-2 proteins interacting with the top human proteins was performed to understand the functional role of these genes with pSNVs. It was found that the kinase-substrate interactions were limited to phosphosites with at least one pSNV.

The researchers also studied 217 pSNVs with 3,360 motif-rewiring predictions covering 64 kinase families. Sixty pSNVs caused motif switches, whereas 37 pSNVs displayed motif-switching impact. The top proteins with motif-switching pSNVs included transcriptional regulators BCLAF1 and RREB1 and the nuclear matrix protein NUMA.

The allele frequency (AF_all) of pSNVs was studied to evaluate the extent of predicted changes in infection-responsive signaling networks in the 16 human populations derived from the gnomAD dataset. This helped the researchers to study population genetic variation and disease associations of pSNVs.

To this end, the researchers found that most of the pSNVs were relatively infrequent, with a lower median AF_all than the representative population, thereby suggesting the need for a deeper population-based analysis of certain pSNVs. Only 4.2% of pSNVs were relatively common and occurred at over 1% frequency in at least one population.

Limitations of the study

A majority of pSNVs are unmapped in genome-wide association studies, thus making it unfeasible to produce any statistical or clinical evidence of their involvement in COVID-19 risk or co-morbidities. A second limitation is that the current study focused on phosphosites that appear at an early post-infection timepoint in cell culture; therefore, this analysis cannot be considered conclusive to signaling pathways activated further downstream of SARS-CoV-2 infection in human tissues and the immune system.

The pSNV analysis also does not take into account the co-expression and localization of kinases and substrates, thus overestimating the extent of network rewiring. Also, as many motifs bound by kinases and other phospho-enzymes remain unknown, the number of motif-rewiring pSNVs may also be underestimated.

Conclusions

The current study identified many candidate genes, pathways, and kinases that enable functional genomics screens and phenotypic experiments. Taken together, these observations may help in developing an in-depth mechanistic understanding of SARS-CoV-2 infection and COVID-19.

More importantly, the current study highlights human genetic polymorphisms and points out that genes with pSNVs are expressed in diverse human tissues. This explains the broad organotropism of SARS-CoV-2 infection and how pSNVs are likely responsible for multi-organ failure in severely ill COVID-19 patients.

The findings of this integrated proteogenomic analysis of pSNVs could enable further studies focused on viral infections and disease mechanisms. Furthermore, matching the clinical profiles of COVID-19 patients with whole-genome sequencing datasets could help link the functional predictions of infrequent pSNVs with patient risk and comorbidities, as well as enable the development of predictive and prognostic biomarkers for COVID-19.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Written by

Neha Mathur

Neha Mathur has a Master’s degree in Biotechnology and extensive experience in digital marketing. She is passionate about reading and music. When she is not working, Neha likes to cook and travel.