Skip to main content

Mass spectrometry-based proteomics in basic and translational research of SARS-CoV-2 coronavirus and its emerging mutants

Abstract

Molecular diagnostics of the coronavirus disease of 2019 (COVID-19) now mainly relies on the measurements of viral RNA by RT-PCR, or detection of anti-viral antibodies by immunoassays. In this review, we discussed the perspectives of mass spectrometry-based proteomics as an analytical technique to identify and quantify proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to enable basic research and clinical studies on COVID-19. While RT-PCR and RNA sequencing are indisputably powerful techniques for the detection of SARS-CoV-2 and identification of the emerging mutations, proteomics may provide confirmatory diagnostic information and complimentary biological knowledge on protein abundance, post-translational modifications, protein–protein interactions, and the functional impact of the emerging mutations. Pending advances in sensitivity and throughput of mass spectrometry and liquid chromatography, shotgun and targeted proteomic assays may find their niche for the differential quantification of viral proteins in clinical and environmental samples. Targeted proteomic assays in combination with immunoaffinity enrichments also provide orthogonal tools to evaluate cross-reactivity of serology tests and facilitate development of tests with the nearly perfect diagnostic specificity, this enabling reliable testing of broader populations for the acquired immunity. The coronavirus pandemic of 2019–2021 is another reminder that the future global pandemics may be inevitable, but their impact could be mitigated with the novel tools and assays, such as mass spectrometry-based proteomics, to enable continuous monitoring of emerging viruses, and to facilitate rapid response to novel infectious diseases.

Background

The coronavirus disease 2019 (COVID-19) pandemic hit the world in November 2019 and quickly spread to all continents and nearly all countries. The COVID-19 disease was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has resulted in over 128 million confirmed cases and 2.8 million deaths worldwide as of March 2021. Prior to the SARS-CoV-2, the SARS-CoV-1 (year 2002) and MERS-CoV (year 2012) epidemics claimed together about 1,600 lives. Enormous efforts were devoted worldwide to limit the spread of SARS-CoV-2, develop rapid diagnostics, mitigate severe and deadly clinical complications, and produce prophylactic vaccines. Immense resources were allocated to investigate the biology of SARS-CoV-2 and discover emergency therapeutics to alleviate viral pneumonia, cytokine storm syndromes, thromboembolism, and neurological complications.

Similar to other RNA viruses, SARS-CoV-2 is continuously mutating which challenges initial diagnostic and therapeutic efforts. While the next generation sequencing is indispensable for tracking evolution of SARS-CoV-2 strains across countries and regions, orthogonal -omics approaches, such as mass-spectrometry-based proteomics and metabolomics, offer powerful tools to investigate the functional and clinical impact of the emerging mutations. In this perspective review, we will discuss mass spectrometry-based proteomics as an analytical approach for identification and quantification of SARS-CoV-2 proteins, emerging mutant proteins, homologous proteins of related alpha- and betacoronaviruses, and anti-viral immunoglobulins. Differential quantification of the nearly identical and low-abundance viral proteins is a recognized analytical challenge, and mass spectrometry may emerge as a viable tool to facilitate basic research, environmental monitoring, and clinical diagnostics of SARS-CoV-2.

Main text

Human and zoonotic coronaviruses

Orthocoronavirinae is a subfamily of 45 single-stranded RNA viruses which infect mammals and birds [1]. Zoonotic alpha- and betacoronaviruses are found in bats, rodents, and rabbits, with bats being the major hosts [2]. The presence of multiple coronaviruses within the related genera of mammals drives the emergence of novel coronavirus strains by shifting between the host species [2]. Extensive global deforestation has been promoting close interactions and cohabitation of species previously residing within the discrete ecological niches. Examples include emerging interactions of horseshoe bats and palm civets (intermediate hosts of SARS-CoV-1), or horseshoe bats and dromedary camels (intermediate hosts of MERS-CoV) [3]. Early evidences indicated that SARS-CoV-2 gained its ability to infect humans through an intermediate mammalian host, such as a pangolin [4]. On a global scale, however, our societal organization, interactions with the wildlife, and economic activities were the actual triggers for MERS and SARS epidemics and pandemics [5].

Alpha- and betacoronavirus genera of Orthocoronavirinae subfamily include seven species which can infect humans. Two alphacoronaviruses (HCoV-229E and HCoV-NL63) and two betacoronaviruses (HCoV-OC43 and HCoV-HKU1) are the most common species of the human coronaviruses (HCoVs). These species are associated with “common cold” upper respiratory infections and mild symptoms, and rarely with lower respiratory infections in the elderly or immunocompromised patients [6]. Infections with the remaining three species of betacoronaviruses (SARS-CoV-1, SARS-CoV-2 and MERS-CoV) result in severe clinical manifestations including intense respiratory distress syndromes, systemic hyperinflammation, pulmonary complications, thrombosis, encephalitis, diarrhea, lymphopenia, and multiorgan failure [7], with the case fatality rates of 10%, 6.6%, and 35%, respectively [8, 9]. While several SARS-CoV-2 vaccines have recently been approved [10], no therapies or vaccines against the remaining coronaviruses are available [11].

Overview of SARS-CoV-2 genome and proteome

Structurally, coronaviruses are the spherical enveloped virions of ~ 100 nm diameter (Fig. 1). The viral genetic material, a positive-sense single-stranded RNA, is associated with ~ 1,000 copies of a nucleoprotein (NCAP_SARS2), and is additionally protected by an envelope small membrane protein (VEMP_SARS2) [12]. Spike glycoprotein (SPIKE_SARS2) homotrimers embedded into the lipid bilayer (~ 100 copies per virion) are essential for the cell invasion and appear as a characteristic crown (from Latin “corona”) under electron microscopy. Coronaviruses have the largest known genomes for RNA viruses (26–32 kilobases) [13]. RNA genome of coronaviruses mutates at a relatively low rate of 10−4 nucleotides per site per year [14]. Phylogenetic and mutational analysis of SARS-CoV-2 sequences obtained from 1086 patients revealed its very recent origin from a single predecessor [14].

Fig. 1
figure 1

Genome and proteome organization of SARS-CoV-2. Positive-sense single-stranded RNA genome of SARS-CoV-2 (29,903 nucleotides) is composed of two non-structural open reading frames (replicase polyprotein 1a, R1A_SARS2, and replicase polyprotein 1ab, R1AB_SARS2), four structural (spike glycoprotein, SPIKE_SARS2; envelope small membrane protein, VEMP_SARS2; membrane protein, VME1_SARS2; and nucleoprotein, NCAP_SARS2), six accessory non-overlapping open reading frames (ORF3a protein, AP3A_SARS2; ORF6 protein, NS6_SARS2; ORF7a protein, NS7A_SARS2; ORF7b protein, NS7B_SARS2; ORF8 protein, NS8_SARS2; and ORF10 protein, A0A663DJA2_SARS2), and five accessory overlapping open reading frames (ORF3b protein, ORF3B_SARS2; ORF3c protein, ORF3C_SARS2; ORF3d protein, ORF3D_SARS2; ORF9b protein, ORF9B_SARS2; and ORF9c protein, ORF9C_SARS2). Due to a ribosomal frameshift, ORF1ab gene is translated into two replicase polyproteins. Viral proteases PLpro1/PLpro2 (non-structural protein 3; nsp3) and 3CLpro (3C-like proteinase; nsp5) are auto-cleaved from polyproteins and complete the cleavage of the remaining 14 non-structural proteins

The entry of coronavirus into host cells is orchestrated by the homotrimer spike glycoprotein. The S1 subunit of spike glycoprotein binds to the cellular receptor ACE2_HUMAN of the host cell [11, 15], and the sequence between S1 and S2 subunits is post-translationally cleaved by two human proteases, furin and TMPRSS2. Such cleavage generates a fusogenic subunit S2 and triggers the virion-cell membrane fusion [11, 16]. It should be noted that TMPRSS2 protease inhibitors efficiently block SARS-CoV-2 cell entry and infection [17]. Recent studies revealed that the structure of the receptor binding domain of SARS-CoV-1 and SARS-CoV-2 were very similar, with the difference of only five amino acids. The S2 subunit was found to be the most conserved region with 93% sequence identity, while S1 subunit revealed only ~ 70% sequence identity to the bat-derived viruses [14].

Proteomic studies on SARS-CoV-2

Identification of SARS-CoV-2 proteins by mass spectrometry

Several studies identified and quantified SARS-CoV-2 proteome by mass spectrometry, in order to reveal expression of the open reading frames, elucidate the viral host response, and identify potential drug targets for therapeutic interventions [18,19,20]. Using a human colon cancer cell line Caco-2 infected with SARS-CoV-2, Bojkova et al. identified and quantified temporal expression of nine SARS-CoV-2 proteins: replicase polyprotein 1ab (R1AB_SARS2), spike glycoprotein (SPIKE_SARS2), ORF3a protein (AP3A_SARS2), membrane protein (VME1_SARS2), ORF6 protein (NS6_SARS2), ORF7a protein (NS7A_SARS2), ORF8 protein (NS8_SARS2), nucleoprotein (NCAP_SARS2), and ORF9b protein (ORF9B_SARS2). The proteome of Caco-2 cells revealed considerable changes after 24 h past SARS-CoV-2 infection, with the distinct impact on splicing, translation, and carbon and nucleic acid metabolism [19]. Gordon et al. employed 26 recombinant viral proteins and affinity purification-mass spectrometry to identify 332 high confidence protein–protein interactions in Vero E6 cells [18]. Results included identification of interactions of pp1ab (nsp1) protein with the DNA replication protein PRIM1, formation of a complex between ORF3a protein (AP3A_SARS2) and vesicle trafficking protein VSP11, and binding of ORF9b protein (ORF9B_SARS2) to mitochondrial receptor TOMM70, which could result in suppression of the host cell interferon response. Interaction of ORF6 protein with an interferon-inducible mRNA nuclear export complex NUP98-RAE1 was suggested inhibiting the nuclear transport system. Interestingly, a hypothetical protein ORF10 (TrEMBL UniProt entry A0A663DJA2_SARS2; no evidence of expression in humans [21]) was found associating with Cullin 2 E3 ligase and potentially altering the ubiquitination and protein degradation machinery. Identified protein–protein interactions revealed potential drug targets and small molecule inhibitors of SARS-CoV-2 replication [18].

Recently, Davidson et al. investigated SARS-CoV-2 in vitro and in vivo through combination of transcriptomics and proteomics [20]. The study found that SARS-CoV-2 replication in cell culture resulted in a 9-amino acid deletion and removal of a furin-like cleavage site RRAR of spike glycoprotein. Since the furin cleavage site was essential for viral entry, its loss during replication in cell culture supported the hypothesis of zoonotic, rather than laboratory, origin of SARS-COV-2 [22]. A recently discovered bat-derived virus RmYN02 comprising insertions in its spike protein similar to those in SPIKE_SARS2 also suggested natural evolution of SARS-CoV-2 [23].

Proteomic studies also identified post-translational modifications of SARS-CoV-2 proteins. Thus, SARS-CoV-2 spike glycoprotein revealed 22 N-linked glycosylation sites [24], similar to 19 sites previously identified for SARS-CoV-1 spike glycoprotein [11, 25]. Since glycosylation may facilitate escape from the host immune system, the diversity of spike glycoprotein glycosylation sites could explain the higher mortality rate of SARS-CoV-2 over SARS-CoV-1 [11]. In addition, 49 phosphorylation sites were identified in SARS-CoV-2 proteins (including nucleoprotein, nsp9, and membrane proteins), and were suggested modulating the host cellular kinase activity and pathways regulating viral replication [26]. It should be noted that one of the first proteomic studies on SARS-related proteins was conducted in 2003 and revealed NCAP_SARS protein as a major immunogen [25]. Interestingly, a caspase cleavage motif (responsible for the virus proteolysis and elimination by the host cells caspases) was present in all previous coronavirus nucleoproteins [27], but not in SARS-COV-1 [25]. Dysregulation of PI3K/AKT pathway was identified in both SARS-CoV-2 and SARS-CoV-1 [28].

Quantification of SARS-CoV-2 proteins by targeted mass spectrometry

Quantitative proteomic studies on SARS-CoV-2 proteins revealed that nucleoprotein (NCAP_SARS2) was the most abundant protein contributing up to 90% of all the total proteome [29]. Limit of detection of NCAP_SARS2 parallel reaction monitoring (PRM) assay was in the attomole range (~ 0.9 pg) corresponding to ~ 10,000 SARS-CoV-2 particles. Among all SARS-CoV-2-derived tryptic peptides, GFYAEGSR and ADETQALPQR of nucleoprotein and EITVATSR of membrane protein emerged as peptides with the highest intensities and responsiveness for quantification by mass spectrometry [29]. Likewise, PRM assay of Vero E6 cell supernatant quantified 57 endogenous tryptic peptides corresponding to five viral proteins (SPIKE_SARS2, NCAP_SARS2, VME1_SARS2, ORF9B_SARS2, and NS8_SARS2). Since sequences of some tryptic peptides were identical to those of common cold coronaviruses (229E, HKU1, NL63 and OC43), only 23 peptides were selected for an optimized PRM assay. Some high-intensity peptides (VAGDSGFAAYSR of VME1_SARS2, and LQSLQTYVTQQLIR, FQTLLALHR, and HTPINLVR of SPIKE_SARS2) were well-conserved among human coronaviruses [30]. Since several independent studies revealed identical high-intensity and unique tryptic peptides of NCAP_SARS2 (AYNVTQAFGR, GFYAEGSR, ADETQALPQR) and VME1_SARS2 (EITVATSR, VAGDSGFAAYSR) proteins [29,30,31], these peptides could be considered as proteotypic peptides for SARS-CoV-2 quantification in biological, clinical, or environmental samples.

Phenotypic changes of SARS-CoV-2

Identification of the emerging mutations

SARS-CoV-2 is continuously mutating, as compared to the original strain isolated in Wuhan, China in January 2020 (GenBank accession MN988668). Early phylogenetic analysis of 160 SARS-CoV-2 genomes revealed three major variants named A, B, and C, with A being the ancestral type originated from the bat coronavirus from Wuhan (GenBank accession MG772933). The B type was derived from A type through the nonsynonymous mutation L84S of ORF8 protein (NS8_SARS2) and adapted to populations outside of East Asia. Both A and C types were found in Europe and the United States, and the C type (harboring G250V mutation in AP3A_SARS2) was the major variant spreading across Europe [32]. Numerous SARS-CoV-2 variants have emerged since then and were rapidly spreading worldwide. The most concerning variants included 20B/501Y.V1 (United Kingdom), 20C/501Y.V2 (South Africa), and 20J/501Y.V3 (Brazil) [33]. These novel variants were harboring missense mutations within S1 domain and receptor-binding domain (RBD) of SPIKE_SARS2 protein, and facilitated stronger binding to the ACE2 receptor [34, 35]. The COVID-CG database is now continuously tracking the real-time evolution and spread of the mutated strains of SARS-CoV-2 across different regions [36]. More than 770,000 SARS-CoV-2 genomes were already summarized, and the most frequent mutations were identified.

Perspectives of mass spectrometry to measure SARS-CoV-2 mutations

Since standard bottom-up proteomic approaches rely on measurements of relatively short tryptic peptides (which may not harbor nonsynonymous mutations of interest), proteomic approaches utilizing non-tryptic proteases present viable alternatives. Examples include Lys-C (cleaves exclusively after K), Arg-C (cleaves after R), Asp-N (cleaves before D), neprosin (cleaves after P), Glu-C (cleaves after E, or E and D depending on conditions), and others [37,38,39]. Analysis of the COVID-CG database revealed that the most frequent nonsynonymous mutations within the trypsin-, Glu-C or neprosin-derived peptides could be measurable by liquid chromatography–mass spectrometry. Figure 2 presents the most common nonsynonymous mutations and their frequencies calculated based on ~ 770,000 SARS-CoV-2 genomes [36]. D614G (SPIKE_SARS2) emerged as the dominant nonsynonymous mutation with a frequency > 95% (Fig. 2). For the 37 most frequent nonsynonymous mutations, deletions, and terminations (global frequency > 3%), 16 resided within 6–31 aa tryptic peptides potentially measurable by standard bottom-up proteomic assays (Fig. 2). Additional 12 and 14 mutations could be independently covered with Glu-C- and neprosin-derived peptides, thus providing detection of ~ 89% of the most frequent mutations. While the most frequent mutation D614G could not be detected within short trypsin- or Glu-C-derived peptides (Fig. 2), SPIKE_SARS2 proteolysis by neprosin could enable D614G identification within a 21 aa peptide GTNTSNQVAVLYQDVNCTEVP. Likewise, relatively short neprosin-derived peptides could facilitate measurements of the emerging variants of concern 20B/501Y.V1 (United Kingdom), 20C/501Y.V2 (South Africa), and 20J/501Y.V3 (Brazil) [40,41,42]. Continuous monitoring of the emerging mutations and their impact on structure and immunogenicity of viral proteins will support studies on SARS-CoV-2 re-infections and future seasonal infections.

Fig. 2
figure 2

Most frequent nonsynonymous mutations of SARS-CoV-2 proteins and their corresponding trypsin-, Glu-C-, and neprosin-derived peptides. For the 37 most frequent nonsynonymous mutations, deletions (−) and terminations (*) (global frequency > 3%; [36]), 16 reside within the relatively short tryptic peptides (6–31 amino acids). Some of these tryptic peptides were experimentally detected by mass spectrometry [20, 29]. Additional 12 and 14 mutations could be independently detected within Glu-C- and neprosin-derived peptides, thus enabling quantification of ~ 89% of the most frequent SARS-CoV-2 mutations. The most frequent mutation D614G (~ 94%), as well as emerging variants of concern 20B/501Y.V1 (United Kingdom; S N501Y, S A570D, S HV69-, S D614G, ORF8 Q27*, N R203K, N G204R) and 20C/501Y.V2 (South Africa; S N501Y, S D614G) could be detected within the relatively short neprosin-derived peptides [38]

Functional impact of mutations

Even though viral epidemics are often weakened by emerging mutations interfering with the function of viral proteins [43], there is a possibility that certain mutations may increase viral fitness and drive evolution of more infectious or more deadly strains. The latter could have been a case behind infamous 1918 H1N1 influenza A pandemic which claimed as many as 50 million lives [44].

Accumulation of mutations in the SARS-CoV-2 genome may result in phenotypical changes and functional impact on the cell attachment, membrane fusion, viral replication, and exocytosis. Recent studies identified not only mutations favoring infections, such as a furin cleavage site RRAR of SARS-CoV-2 spike glycoprotein [45], but also mutations leading to mild infections, such as ∆382 of NS8_SARS2 protein [46]. Recent X-ray crystallography study revealed that SPIKE_SARS2 residues D936 (as compared to E918 of SPIKE_SARS) and S943 (as compared to T925) facilitated formation of salt bridges and hydrogen bonds, resulting in higher receptor binding affinities and higher infectivity [47]. Likewise, M58R mutation impaired the binding of NS6_SARS2 protein to the Nup98-Rae1 complex and abolished interferon antagonist function of the wildtype NS6_SARS2 protein [48]. Some mutations of AP3A_SARS2 provided alternative B-cell epitopes within the polyproline regions and promoted immune evasion [49]; investigation of AP3A_SARS2 mutants also confirmed their potential to induce apoptosis [50]. Interestingly, preliminary studies suggested that the most frequent mutation D614G was neutral to the function of SPIKE_SARS2; additional studies, however, will be required to assess D614G impact on protein conformation after its interaction with ACE2 [51]. D614G mutation retained within the emerging variants of concern 20B/501Y.V1 (United Kingdom), 20C/501Y.V2 (South Africa), and 20J/501Y.V3 (Brazil), with all three variants having an additional mutation N501Y and resulting in a higher affinity of spike glycoprotein for human ACE2 [33, 40]. E484K mutation shared between the South African and Brazilian variants raised serious concerns due to its increased resistance to antibody neutralization, potentially making some vaccines less effective [41, 52, 53]. Occurrence of both E484K and N501Y mutations in novel variants suggested active and rapid evolution of SARS-CoV-2 [54, 55].

Investigation of SARS-CoV-2 mutations revealed that non-structural proteins 3CLpro (nsp5), PLpro1/PLpro2 (nsp3), and RdRp (nsp12) were highly conserved between SARS-CoV-1 and SARS-CoV-2 and could emerge as promising therapeutic targets [56]. Host translation inhibitor nsp1, a well characterized protein with a conserved biological function [57] among SARS-CoVs and bat CoVs, was found promoting degradation of host mRNA and inhibiting the host innate immune system, while mutations of nsp1 resulted in high levels of interferon beta [58, 59]. Function and enzymatic activities of other non-structural proteins were predicted to be conserved and essential for viral replication.

It should be emphasized that proteogenomic approaches combining mass spectrometry and RNA sequencing data facilitate detailed characterization of novel nonsynonymous mutations and their impact on protein expression, post-translational modifications, or phenotypic variations [60,61,62,63]. For example, mass spectrometry was previously used to annotate unknown regions of the bovine herpes virus BoHV-1 genome and identify a novel protein-coding gene ORF-A [64]. Another mass spectrometry study utilizing de novo peptide sequencing detected three amino acid substitutions within nucleocapsid protein of influenza A virus [65]. It is apparent that such knowledge on structure and function of viral proteins could not be obtained by RT-PCR or RNA sequencing alone.

Perspectives of clinical proteomics and mass spectrometry for COVID-19 diagnostics

Serology diagnostics and immunogenicity of mutant strains

Detection of specific anti-viral antibodies in blood is indispensable to confirm past infections, identify past asymptomatic or mild symptomatic cases, and reveal immune status of the recovered patients or vaccinated populations. Laboratory or point-of-care tests detecting anti-SARS-CoV-2 antibodies are now mainly based on simple and affordable indirect immunoassays (Fig. 3A). A number of COVID-19 serology assays were recently reported [66,67,68].

Fig. 3
figure 3

Approaches for quantification of anti-SARS-CoV-2 immunoglobulins. A The setup of common serology tests using indirect ELISA allows for quantification of total IgG immunoglobulins, with no differentiation of distinct subclasses. Alternative assays by IP-SRM BD facilitate comprehensive investigation of serological response. B IP-SRM assays allow for the direct, multiplex and differential quantification of immunoglobulin isotypes (IgG, IgM, IgA) and subclasses (IgG1-4, and IgA1-2) binding to protein antigens or linear epitopes coated onto microwell plates. “Absolute” quantification (pmol/mL or ng/mL) allows for the inter-laboratory standardization of serology assays using stable isotope-labeled peptide internal standards, and for investigation of cross-reactivity of the indirect ELISA. C Indirect IP-SRM assays provide additional tools for investigation of cross-reactivity of serology tests. D Surrogate neutralization assays by IP-SRM allow for quantification of immunoglobulins disrupting ACE2-spike glycoprotein interactions, and investigation of the impact of mutations emerging within the neutralizing linear epitopes

Recent serological studies suggested that 13 days was an average time for seroconversion for IgG and IgM immunoglobulins, even though some SARS-CoV-2 patients plateaued for IgG and IgM within the first week [69]. Serum and plasma also exhibited strong reactivity for IgG3, IgM, and IgA [68]. Another study focused on anti-NCAP_SARS2 antibodies identified IgM and IgA antibodies within 5 days after symptom onset, with the positive detection rates of 85% and 93%, respectively; IgG antibodies were identified 14 days after symptom onset, with the positive detection rate of 78% [70]. The IgG antibodies in serum and saliva were still detectable in 3 months after symptom onset, while IgM and IgA levels rapidly decreased [71]. A combination of RBD and NCAP_SARS2 antigens was the most powerful and facilitated detection of recent mild cases using IgG (AUC ≥ 0.99), IgA (AUC ≥ 0.92), and IgM (AUC ≥ 0.79) antibodies [72]. Detection of IgG antibodies using a combination of RBD and NCAP_SARS2 antigens was suggested as the most informative assay for serosurveillance of all severe and mild cases, including older mild infections (> 5 months; AUC ≥ 0.99). Tan et al. detected IgG antibodies in convalescent COVID-19 serum samples that specifically recognized S1 domain [73]. Those studies suggested that serum and saliva were suitable clinical samples for detection of COVID-19 seroconversion based on IgG measurements.

Recombinant RBD protein has been reported as an antigen with ~ 98% diagnostic sensitivity and ~ 100% diagnostic specificity for detection of anti-viral IgG and IgM antibodies as early as 9 days after of symptoms onset [74]. RBD also distinguished anti-SARS-CoV-2 antibodies from the “common cold” HCoVs infections. Furthermore, anti-NCAP_SARS2 antibodies were produced earlier than anti-SPIKE_SARS2 antibodies. NCAP_SARS2 was the most sensitive (100%) and specific (100%) antigen for the early detection of SARS-CoV-2 antibodies [75]. These results were in agreement with previous findings for SARS-CoV-1 [76, 77]. It should be emphasized  that not fully validated serology tests may generate false-positive results due to cross-reactivity with antibodies developed against the “common cold” HCoV-229E or HCoV-OC43 infections.

It has previously been established that patients recovered from SARS-CoV-1 developed antibody-mediated immunity for at least two years [78]; the similar outcome could be expected for SARS-CoV-2 infections. For instance, recent studies demonstrated that Moderna mRNA-1273 vaccine induced a high titter of binding and neutralizing antibodies which remained elevated in all participants for at least 3 months after the second vaccination [79]. However, some mutated strains may evade immune response , thus resulting in re-infections, similar to the seasonal influenza infections.

Spike glycoprotein was traditionally considered as a key immunogen for vaccine development [80]. Recent studies revealed that some novel mutations were accumulating within the antibody “binding” epitopes (48 novel nonsynonymous mutations) [81] and “neutralizing” epitopes of spike glycoprotein (13 novel mutations) [81, 82] (Fig. 4). Our cross-search of known epitopes and nonsynonymous mutations revealed some concerning mutations of spike glycoprotein (D614G), membrane protein (D3G), and nucleoprotein (G204R) [83, 84]. A neutralizing epitope PSKPSKRSFIEDLLFNKV of spike glycoprotein [81] alone revealed seven mutations. Such mutated epitopes should be evaluated during development of serology tests and vaccines. Interestingly, due to its relatively conserved sequence and high immunogenicity, nucleoprotein was suggested as an alternative target for vaccine development [80].

Fig. 4
figure 4

Correlation between the abundance of SARS-CoV-2 proteins and IgG/IgM binding epitopes. A Re-analysis of Davidson et al. dataset [20] using the label-free intensity-based absolute quantification (iBAQ) approach revealed the relative abundance of SARS-CoV-2 proteins. Interestingly, the relative abundance of SARS-CoV-2 proteins correlated with the number of IgG and IgM binding epitopes identified by Wang et al.[81]. B Emerging mutations within the “binding” and confirmed “neutralizing” epitopes of SARS-CoV-2 structural and accessory proteins, according to the GISAID hCoV-19 database [153]. Fourteen mutations within the confirmed “neutralizing” epitopes of S protein (red, underlined, bold) and 35 mutations within the antibody “binding” epitopes (bold, underlined) are presented

Viral and human proteins as prognostic biomarkers

In the 2000s, some SARS-CoV-1 proteins (R1A_SARS, AP3A_SARS, NS6_SARS and ORF9B_SARS) were suggested as prognostic serum biomarkers to predict the severity of SARS-CoV-1 infections [85,86,87], cytokine storm syndromes, or pneumonia complications [88]. Validation of SARS-CoV-2 proteins, including accessory proteins, as prognostic biomarkers is still pending due to the lack of high-quality paired antibodies and high-quality immunoassays. Since rational development of protein biomarkers involves numerous phases of discovery, verification and validation, mass spectrometry-based proteomic assays would be particularly useful at the early phases of biomarker development [89, 90].

While mass spectrometry may not be sensitive enough for the direct measurements of SARS-CoV-2 non-structural and accessory proteins in serum, immunoprecipitation (IP)-targeted proteomic assays (such as selected reaction monitoring, SRM, or parallel reaction monitoring, PRM) present viable alternatives for the preliminary validation of these proteins as prognostic biomarkers in clinical samples (Fig. 5B). Since SRM quantification provides the required analytical selectivity [91, 92], IP-SRM assays may utilize even the low quality polyclonal antibodies, or polyclonal antibodies previously developed against highly homologous SARS-CoV-1 or MERS-CoV proteins.

Fig. 5
figure 5

Development of IP-SRM assays. A Selection of proteotypic peptides of SARS-COV-2 nucleoprotein based on experimental shotgun mass spectrometry data. Peptide identification by shotgun mass spectrometry provided tryptic peptides with the highest MS1 intensities (our unpublished data), and three peptides AYNVTQAFGR, DQVILLNK and GFYAEGSR were selected. MaxQuant search revealed the most intense y-ion transitions of peptide AYNVTQAFGR. Finally, optimization of the nanoflow liquid chromatography and targeted mass spectrometry parameters generated a quantitative SRM assay. B IP-SRM included an antibody-mediated enrichment of NCAP_SARS2 followed by proteomic sample preparation and SRM quantification

In addition to SARS-CoV-2 proteins, some circulating human proteins may also emerge as prognostic biomarkers of COVID-19 severity or complications [93, 94]. For example, patients in the severe group revealed significantly higher levels of C-reactive protein and interleukin-6 in serum [95]. Likewise, d-dimers > 1 µg/mL (by-products of the blood clotting and fibrin degradation) facilitated identification of patients with poor prognosis and risk of ischemia and thrombosis [96]. Overall, elevated D-dimer levels suggesting abnormal blood coagulation were common among non-survivor COVID-19 patients [97] and served as biomarkers to predict mortality rates of COVID-19 patients [98, 99].

To design a large-scale proteomic study on prognostic biomarkers, Messner et al. developed an ultra-high throughput clinical platform (~ 180 samples/day on a single mass spectrometer) to analyze blood serum and plasma collected from patients with the different WHO severity grades of COVID-19 [100]. As a result, 27 potential prognostic biomarkers (including complement factors, coagulation proteins, inflammation modulators, and pro-inflammatory factors induced by IL-6) were identified. Another study on serum proteome of patients with the severe and mild infections revealed differential expression of 91 proteins, of which 77 proteins were associated with neutrophil degranulation, blood coagulation, and neutrophil mediated immunity, and were proposed as prognostic biomarkers [101].

Detection of SARS-CoV-2 in clinical samples by mass spectrometry

While RT-PCR remains the most sensitive and specific assay to diagnose COVID-19 through detection of SARS-CoV-2 mRNA in various clinical samples (pharyngeal swabs, nasal samples, saliva, bronchoalveolar lavage, serum, urine, and feces [102,103,104]), alternative approaches for quantification of SARS-CoV-2 proteins by mass spectrometry are emerging.

Earlier proteomic studies demonstrated that PRM quantification of SARS-CoV-2 proteins in clinical respiratory specimens was confined to individuals with very high viral loads. Only 11 of 54 RT-PCR-confirmed respiratory specimens (nasopharyngeal swabs and bronchoalveolar lavage) were tested positive for nucleoprotein by PRM assay [31]. Interestingly, potential cross-reactivity of RT-PCR with other coronaviruses was found in two specimens, as revealed by unambiguous identification of peptides shared among common human coronaviruses, while no unique SARS-CoV-2 peptides were detected. Independent mass spectrometry studies detected SARS-CoV-2 proteins in nasopharyngeal swabs and gargle samples, and confirmed that nucleoprotein was the most abundant protein and the best candidate for mass spectrometry detection of SARS-CoV-2 infection [105, 106].

MALDI-TOF has previously been suggested as a technique for unambiguous identification of HCoVs, and for monitoring of emerging pathogens [107]. Nachtigall et al. reported MALDI-TOF detection of SARS-COV-2 in nasal swab samples with an accuracy of 94% [108]. A recent study has also utilized analysis of blood serum samples by MALDI-TOF to distinguish COVID-19 patients from negative controls [109]. Relative to RT-PCR analysis of nasal swabs, MALDI-TOF analysis of serum was suggested as a convenient assay due to the direct use of serum after sterilization, low volumes (5 μL), rapid measurements (< 1 min), and lower cross-contamination. Comparison of RT-PCR and MALDI-TOF revealed an acceptable conformity rate (> 80%), and MALDI-TOF was suggested as a surrogate technique for the routine SARS-CoV-2 diagnostics [108]. Analysis of saliva and gargle samples by MALDI-TOF revealed ~ 100% sensitivity to detect SARS-CoV2, and the spike protein fragment S1 was proposed as the marker for SARS-CoV2 screening. SARS-CoV-2 positive samples also revealed elevated levels of IgA immunoglobulins, which could not be measured by RT-PCR assays [110].

Differential diagnosis of viral co-infections by mass spectrometry

While RT-PCR is a “gold standard” assay for clinical diagnostics of viral infections and seasonal co-infections [111], proteomics by mass spectrometry may emerge as an alternative approach. Mass spectrometry may find a niche in clinical diagnostics in case of increased diagnostic capacity needs, shortage of RT-PCR reagents, or as an alternative screening tool to complement RT-PCR or immunoassays [112,113,114].

MALDI-TOF mass spectrometry with its high specificity, rapid turnaround time, automation, and lower costs has recently revolutionized bacterial subtyping [113]. Pending assay development, mass spectrometry could provide differential diagnosis of simultaneous seasonal viral infections. For instance, MALDI-TOF was demonstrated as a promising technique to characterize nonsynonymous mutations of influenza A [65]. In addition, shotgun mass spectrometry allowed for discrimination between two strains of influenza A, H3N2 and H1N1, in the throat swabs [115]. Measurements of signature peptides of neuraminidase of the 2009 H1N1 influenza pandemic by mass spectrometry enabled differentiation of H1N1 from all other strains, including H5N1 [116]. Likewise, measurement of influenza A nucleocapsid protein differentiated between type A H1N1, H3N2, and type B strains [117]. MALDI-TOF proved itself as a tool for the rapid identification of three poliovirus serotypes [118] and differentiation between six known HCoVs (229E, OC43, NL63, HKU1, SARS-CoV, and MERS-CoV) [107], as well as diagnosis of co-infected patients [119].

It should be emphasized that early “common cold” symptoms are not informative to differentiate any of the seven coronavirus infections in human. Furthermore, respiratory tract infections could be associated with the viral and bacterial co-infections in significant number of patients. For instance, some patients were found simultaneously infected with HCoV-OC43 and influenza type A [120], or SARS-CoV-2 and influenza A [121]. Co-infections with influenza type A and B, respiratory syncytial virus type A and B, and parainfluenza type 3 were also known [120].

While in this review we focused on mass spectrometry-based proteomics, it should be noted that mass spectrometry is being increasingly utilized for diagnosis of respiratory viruses through the detection of RT-PCR products [122]. Mass spectrometry enables accurate, rapid and high-throughput (1500 samples/day) measurements of PCR products including single nucleotide polymorphisms, thus permitting the distinction between the nearly identical viral strains. PCR or RT-PCR followed by electrospray ionization in the negative ion-mode and TOF mass spectrometry is emerging as a technology for identification of nearly all known human pathogens directly from clinical samples [123]. Examples include HCoVs differentiation from influenza and respiratory viruses [124, 125]. In addition, RT-PCR/MALDI-TOF assays reveal high agreement with the conventional real-time RT-PCR and equivalent diagnostic sensitivity (97%) and specificity (100%), albeit four times higher limit of detection in saliva (1,563 versus 391 copies/mL) [126].

Detection of high-abundance coronavirus proteins, such as nucleoprotein, may facilitate rapid differentiation of the common strains, diagnosis of coronavirus co-infections, and detection of the emerging strains. Table 1 presents two most intense (our unpublished data), relatively conserved, but unique tryptic peptides of NCAP_SARS2 protein. Quantification of these two variant peptides may facilitate unambiguous differentiation of present and future coronavirus infections and co-infections [116].

Table 1 Tryptic peptides suitable for the differential subtyping of seven human coronaviruses

Perspectives of immunoprecipitation-mass spectrometry for quantification of viral proteins and serology diagnostics

Immunoprecipitation-mass spectrometry assays combine advantages of two worlds: immunoassays with their high analytical sensitivity, and mass spectrometry with its near-absolute analytical specificity [127, 128]. Targeted proteomic assays were demonstrated as robust tools to quantify proteins in human cell lines [129, 130], primary cells [131,132,133], tissues [134], various biological fluids [135,136,137,138,139], and blood serum [140]. Steps of IP-SRM development for nucleoprotein (NCAP_SARS2) are presented at Fig. 5A (our unpublished data). Immunoprecipitation provides 100- to 1000-fold gain in sensitivity and enables quantification of low-abundance proteins (LOD ~ 0.1 ng/mL) in clinical samples. Internal standards based on synthetic heavy isotope-labeled peptides with trypsin-cleavable tags facilitate “absolute quantification” (pmol/mL or ng/mL) of proteins in clinical samples [140]. All steps of immunoprecipitation and proteomic sample preparation could be implemented on multiwell microtiter plates and provide high reproducibility and sufficient throughput (~ 20 samples per day). Various types of hybrid mass spectrometers (triple quadrupoles, quadrupole-ion traps, quadrupole-Orbitraps, and etc.) support targeted quantification of unique tryptic peptides. When combined with immunoprecipitation and liquid chromatography, IP-LC-SRM assays provide four stages of analyte enrichment and separation, and emerge as highly selective assays for protein quantification [141].

IP-MS and IP-SRM has previously been used to analyze Ebola, influenza, and hepatitis C proteins [142,143,144]. IP-MS confirmed interaction of ICP27 protein of herpes simplex virus type 1 (HSV-1) with the cellular translation initiation factors [145], identified proteins interacting with ICP28 protein [146], and confirmed association of a single-stranded DNA-binding protein ICP8 with numerous host proteins involved in the cellular DNA replication, DNA damage repair, chromatin remodeling, and mRNA splicing [146]. Likewise, novel interactions of influenza A ribonucleoprotein with the nuclear transport protein importin-β3 and the regulatory protein PARP-1 were discovered by IP-MS [147]. Sensitivity of IP-MS assay was sufficient to quantify influenza hemagglutinins in purified virus and commercial vaccines [148]. IP-SRM has also been suggested as a rapid response assay for future influenza pandemics [148, 149]. With respect to IP-MS for human coronaviruse studies, ACE2 and dipeptidyl peptidase 4 (DPP4) were identified as receptors for SARS-CoV-1 and MERS-CoV, respectively [150, 151].

IP-MS and IP-SRM assays could find applications as alternative tools for highly specific and sensitive quantification of SARS-CoV-2 proteins, and for independent evaluation of serology tests (Fig. 3). Thus, the complete antibody‐mediated immune response could be measured with a multiplex IP-SRM assay targeting all human antibody isotypes and subclasses (IgG1-4, IgM, IgA1-2, IgD, and IgE). Similar to validation of the conserved and immunogenic antigens of parasitic microorganisms [152], evaluation of the numerous wildtype and mutated linear epitopes by IP-MS might also reveal the most conservative antigens and promising epitopes for development of monoclonal antibody therapies or universal vaccines.

Conclusions and future perspectives

In this review, we described perspectives of mass spectrometry as an analytical technique to identify and quantify SARS-CoV-2 proteins and enable basic research and clinical studies on COVID-19. While RT-PCR and RNA sequencing are indisputably powerful techniques for detection of SARS-CoV-2 and identification of emerging mutations, proteomics by mass spectrometry may generate novel knowledge which cannot be derived from RNA sequences. Examples include the abundance of viral proteins, post-translational modifications, protein–protein interactions, and the impact of emerging mutations on protein structure and function.

LC-MS, MALDI-TOF and RT-PCR/MS present promising mass spectrometry approaches for COVID-19 diagnostics in clinical laboratories. Among those, MALDI-TOF is a well-established technique available in the large microbiology laboratories for identification of bacterial and fungal infections, and has already been demonstrated for diagnosis of SARS-CoV-2 infections [107,108,109,110]. Although the throughput of IP-MS assays (as compared to MALDI-TOF and RT-PCR/MALDI-TOF) is not sufficient for the routine diagnostic testing, IP-MS will find certain applications in biomedical research, including studies on protein–protein interactions, measurement of the low-abundance mutant proteins, assessment of the functional impact of novel mutations, and investigation of cross-reactivity of serology tests.

The COVID-19 pandemic is another reminder that the future global pandemics are inevitable due to the increased interaction between wild animals, livestock, and humans. The threat of future epidemics, however, could be mitigated through the continuous monitoring of emerging mutations of zoonotic and human viruses and predicting the risk of animal-to-human or human-to-human transmission of the emerging strains.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

aa:

Amino acid

ACE2:

Angiotensin converting enzyme 2

ELISA:

Enzyme linked immunosorbent assay

MALDI:

Matrix-assisted desorption ionization

nsp:

Non-structural protein

Ig:

Immunoglobulin

IP-MS:

Immunoprecipitation-mass spectrometry

PRM:

Parallel reaction monitoring

SARS:

Sever acute respiratory syndrome

SRM:

Selected reaction monitoring

References

  1. ICTV taxonomy. https://talk.ictvonline.org/taxonomy. Accessed 11 Jan 2021.

  2. Hu B, Ge X, Wang LF, Shi Z. Bat origin of human coronaviruses. Virol J. 2015;12:221.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Reusken CB, Raj VS, Koopmans MP, Haagmans BL. Cross host transmission in the emergence of MERS coronavirus. Curr Opin Virol. 2016;16:55–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Li X, Giorgi EE, Marichann MH, Foley B, Xiao C, Kong XP, et al. Emergence of SARS-CoV-2 through recombination and strong purifying selection. Sci Adv. 2020;6(27):eabb9153.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Frutos R, Serra-Cobo J, Chen T, Devaux CA. COVID-19: Time to exonerate the pangolin from the transmission of SARS-CoV-2 to humans. Infect Genet Evol. 2020;84:104493.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pyrc K, Berkhout B, van der Hoek L. The novel human coronaviruses NL63 and HKU1. J Virol. 2007;81(7):3051–7.

    Article  CAS  PubMed  Google Scholar 

  7. Zhou Z, Kang H, Li S, Zhao X. Understanding the neurotropic characteristics of SARS-CoV-2: from neurological manifestations of COVID-19 to potential neurotropic mechanisms. J Neurol. 2020;267:2179–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Petersen E, Koopmans M, Go U, Hamer DH, Petrosillo N, Castelli F, et al. Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. Lancet Infect Dis. 2020;20(9):E238–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Abdollahi E, Champredon D, Langley JM, Galvani AP, Moghadas SM. Temporal estimates of case-fatality rate for COVID-19 outbreaks in Canada and the United States. CMAJ. 2020;192(25):E666–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ledford H. Moderna COVID vaccine becomes second to get green light in United States. Nature. 2020. https://doi.org/10.1038/d41586-020-03593-7.

    Article  PubMed  Google Scholar 

  11. Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D. Structure, function, and antigenicity of the SARS-CoV-2 Spike glycoprotein. Cell. 2020;181(2):281–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bar-On YM, Flamholz A, Phillips R, Milo R. SARS-CoV-2 (COVID-19) by the numbers. eLife. 2020;9:e57309.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gorbalenya AE, Enjuanes L, Ziebuhr J, Snijder EJ. Nidovirales: evolving the largest RNA virus genome. Virus Res. 2006;117(1):17–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020;395(10224):565–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lan J, Ge J, Yu J, Shan S, Zhou H, Fan S, et al. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature. 2020;581(7807):215–20.

    Article  CAS  PubMed  Google Scholar 

  16. Ying W, Hao Y, Zhang Y, Peng W, Qin E, Cai Y, et al. Proteomic analysis on structural proteins of Severe Acute Respiratory Syndrome coronavirus. Proteomics. 2004;4(2):492–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hoffmann M, Kleine-Weber H, Schroeder S, Kruger N, Herrler T, Erichsen S, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181(2):271-80.e8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, White KM, et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. 2020;583:459–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bojkova D, Klann K, Koch B, Widera M, Krause D, Ciesek S, et al. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature. 2020;583:469–72.

    Article  CAS  PubMed  Google Scholar 

  20. Davidson AD, Williamson MK, Lewis S, Shoemark D, Carroll MW, Heesom KJ, et al. Characterisation of the transcriptome and proteome of SARS-CoV-2 reveals a cell passage induced in-frame deletion of the furin-like cleavage site from the spike glycoprotein. Genome Med. 2020;12(1):68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pancer K, Milewska A, Owczarek K, Dabrowska A, Kowalski M, Labaj PP, et al. The SARS-CoV-2 ORF10 is not essential in vitro or in vivo in humans. PLoS Pathog. 2020;16(12):e1008959.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Andersen KG, Rambaut A, Lipkin WI, Holmes EC, Garry RF. The proximal origin of SARS-CoV-2. Nat Med. 2020;26(4):450–2.

    Article  CAS  PubMed  Google Scholar 

  23. Zhou H, Chen X, Hu T, Li J, Song H, Liu Y, et al. A novel bat coronavirus closely related to SARS-CoV-2 contains natural insertions at the S1/S2 cleavage site of the spike protein. Curr Biol. 2020;30(19):3896.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Watanabe Y, Allen JD, Wrapp D, McLellan JS, Crispin M. Site-specific glycan analysis of the SARS-CoV-2 spike. Science. 2020;369(6501):330–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Krokhin O, Li Y, Andonov A, Feldmann H, Flick R, Jones S, et al. Mass spectrometric characterization of proteins from the SARS virus: a preliminary report. Mol Cell Proteomics. 2003;2(5):346–56.

    Article  CAS  Google Scholar 

  26. Bouhaddou M, Memon D, Meyer B, White KM, Rezelj VV, Correa Marrero M, et al. The global phosphorylation landscape of SARS-CoV-2 infection. Cell. 2020;182:685–712.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sperk M, Van Domselaar R, Rodriguez JE, Mikaeloff F, Sá Vinhas B, Saccon E, et al. Utility of proteomics in emerging and re-emerging infectious diseases caused by RNA viruses. J Proteome Res. 2020;19(11):4259–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Appelberg S, Gupta S, Svensson Akusjarvi S, Ambikan AT, Mikaeloff F, Saccon E, et al. Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells. Emerg Microbes Infect. 2020;9(1):1748–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bezstarosti K, Lamers MM, van Kampen JJA, Haagmans BL, Demmers JAA. Targeted proteomics as a tool to detect SARS-CoV-2 proteins in clinical specimens. BioRxiv. 2020. https://doi.org/10.1101/2020.04.23.057810.

    Article  Google Scholar 

  30. Gouveia D, Grenga L, Gaillard JC, Gallais F, Bellanger L, Pible O, et al. Shortlisting SARS-CoV-2 peptides for targeted studies from experimental data-dependent acquisition tandem mass spectrometry data. Proteomics. 2020;20(14):e2000107.

    Article  PubMed  CAS  Google Scholar 

  31. Zecha J, Lee CY, Bayer FP, Meng C, Grass V, Zerweck J, et al. Data, reagents, assays and merits of proteomics for SARS-CoV-2 research and testing. Mol Cell Proteomics. 2020;19(9):1503–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Forster P, Forster L, Renfrew C, Forster M. Phylogenetic network analysis of SARS-CoV-2 genomes. Proc Natl Acad Sci U S A. 2020;117(17):9241–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fontanet A, Autran B, Lina B, Kieny MP, Karim SSA, Sridhar D. SARS-CoV-2 variants and ending the COVID-19 pandemic. Lancet. 2021;397:952–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Grubaugh ND, Hodcroft EB, Fauver JR, Phelan AL, Cevik M. Public health actions to control new SARS-CoV-2 variants. Cell. 2021;184(5):1127–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Laffeber C, de Koning K, Kanaar R, Lebbink JH. Experimental evidence for enhanced receptor binding by rapidly spreading SARS-CoV-2 variants. J Mol Biol. 2021;433(15):167058.

    Article  CAS  Google Scholar 

  36. Chen AT, Altschuler K, Zhan SH, Chan YA, Deverman BE. COVID-19 CG enables SARS-CoV-2 mutation and lineage tracking by locations and dates of interest. eLife. 2021;10:e63409.

    Article  Google Scholar 

  37. Tsiatsiani L, Heck AJ. Proteomics beyond trypsin. FEBS J. 2015;282(14):2612–26.

    Article  CAS  PubMed  Google Scholar 

  38. Schrader CU, Lee L, Rey M, Sarpe V, Man P, Sharma S, et al. Neprosin, a selective prolyl endoprotease for bottom-up proteomics and histone mapping. Mol Cell Proteomics. 2017;16(6):1162–71.

    Article  CAS  Google Scholar 

  39. Schiza C, Korbakis D, Jarvi K, Diamandis EP, Drabovich AP. Identification of TEX101-associated proteins through proteomic measurement of human spermatozoa homozygous for the missense variant rs35033974. Mol Cell Proteomics. 2019;18(2):338–51.

    Article  CAS  Google Scholar 

  40. Lauring AS, Hodcroft EB. Genetic variants of SARS-CoV-2—what do they mean? JAMA. 2021;325:529–31.

    Article  CAS  PubMed  Google Scholar 

  41. Wibmer CK, Ayres F, Hermanus T, Madzivhandila M, Kgagudi P, Oosthuysen B, et al. SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID-19 donor plasma. Nat Med. 2021;27:622–5.

    Article  CAS  PubMed  Google Scholar 

  42. Sabino EC, Buss LF, Carvalho MPS, Prete CA Jr, Crispim MAE, Fraiji NA, et al. Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence. Lancet. 2021;397(10273):452–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Callaway E, Cyranoski D, Mallapaty S, Stoye E, Tollefson J. The coronavirus pandemic in five powerful charts. Nature. 2020;579(7800):482–3.

    Article  CAS  PubMed  Google Scholar 

  44. Neumann G, Noda T, Kawaoka Y. Emergence and pandemic potential of swine-origin H1N1 influenza virus. Nature. 2009;459(7249):931–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Xia S, Lan Q, Su S, Wang X, Xu W, Liu Z, et al. The role of furin cleavage site in SARS-CoV-2 spike protein-mediated membrane fusion in the presence or absence of trypsin. Signal Transduct Target Ther. 2020;5(1):92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Young BE, Fong SW, Chan YH, Mak TM, Ang LW, Anderson DE, et al. Effects of a major deletion in the SARS-CoV-2 genome on the severity of infection and the inflammatory response: an observational cohort study. Lancet. 2020;396(10251):603–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Xia S, Liu M, Wang C, Xu W, Lan Q, Feng S, et al. Inhibition of SARS-CoV-2 (previously 2019-nCoV) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion. Cell Res. 2020;30(4):343–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Miorin L, Kehrer T, Sanchez-Aparicio MT, Zhang K, Cohen P, Patel RS, et al. SARS-CoV-2 Orf6 hijacks Nup98 to block STAT nuclear import and antagonize interferon signaling. Proc Natl Acad Sci U S A. 2020;117(45):28344–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Issa E, Merhi G, Panossian B, Salloum T, Tokajian S. SARS-CoV-2 and ORF3a: nonsynonymous mutations, functional domains, and viral pathogenesis. mSystems. 2020;5(3):e00266-20.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ren Y, Shu T, Wu D, Mu J, Wang C, Huang M, et al. The ORF3a protein of SARS-CoV-2 induces apoptosis in cells. Cell Mol Immunol. 2020;17(8):881–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Isabel S, Grana-Miraglia L, Gutierrez JM, Bundalovic-Torma C, Groves HE, Isabel MR, et al. Evolutionary and structural analyses of SARS-CoV-2 D614G spike protein mutation now documented worldwide. Sci Rep. 2020;10(1):14031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wise J. Covid-19: The E484K mutation and the risks it poses. BMJ. 2021;372:n359.

    Article  PubMed  Google Scholar 

  53. Wang P, Nair MS, Liu L, Iketani S, Luo Y, Guo Y, et al. Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7. Nature. 2021;593:130–213.

    Article  CAS  PubMed  Google Scholar 

  54. Nelson G, Buzko O, Spilman P, Niazi K, Rabizadeh S, Soon-Shiong P. Molecular dynamic simulation reveals E484K mutation enhances spike RBD-ACE2 affinity and the combination of E484K, K417N and N501Y mutations (501Y.V2 variant) induces conformational change greater than N501Y mutant alone, potentially resulting in an escape mutant. BioRxiv. 2021. https://doi.org/10.1101/2021.01.13.426558.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ferrareze PAG, Franceschi VB, de Menezes MA, Caldana GD, Zimerman RA, Thompson CE. E484K as an innovative phylogenetic event for viral evolution: genomic analysis of the E484K spike mutation in SARS-CoV-2 lineages from Brazil. BioRxiv. 2021. https://doi.org/10.1101/2021.01.27.426895.

    Article  Google Scholar 

  56. Wu CLY, Yang Y, Zhang P, Zhong W, Wang Y, Wang Q, Xu Y, Li M, Li X, Zheng M, Chen L, Li H. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B. 2020;10(5):766–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Narayanan K, Ramirez SI, Lokugamage KG, Makino S. Coronavirus nonstructural protein 1: common and distinct functions in the regulation of host and viral gene expression. Virus Res. 2015;202:89–100.

    Article  CAS  PubMed  Google Scholar 

  58. Kamitani W, Narayanan K, Huang C, Lokugamage K, Ikegami T, Ito N, et al. Severe acute respiratory syndrome coronavirus nsp1 protein suppresses host gene expression by promoting host mRNA degradation. Proc Natl Acad Sci U S A. 2006;103(34):12885–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wathelet MG, Orr M, Frieman MB, Baric RS. Severe acute respiratory syndrome coronavirus evades antiviral signaling: role of nsp1 and rational design of an attenuated strain. J Virol. 2007;81(21):11620–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jean Beltran PM, Federspiel JD, Sheng X, Cristea IM. Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases. Mol Syst Biol. 2017;13(3):922.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Lewis JK, Bendahmane M, Smith TJ, Beachy RN, Siuzdak G. Identification of viral mutants by mass spectrometry. Proc Natl Acad Sci U S A. 1998;95(15):8596–601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Dimitrakopoulos L, Prassas I, Diamandis EP, Nesvizhskii A, Kislinger T, Jaffe J, et al. Proteogenomics: opportunities and caveats. Clin Chem. 2016;62(4):551–7.

    Article  CAS  PubMed  Google Scholar 

  63. Drabovich AP, Saraon P, Drabovich M, Karakosta TD, Dimitromanolakis A, Hyndman ME, et al. Multi-omics biomarker pipeline reveals elevated levels of protein-glutamine gamma-glutamyltransferase 4 in seminal plasma of prostate cancer patients. Mol Cell Proteomics. 2019;18(9):1807–23.

    Article  CAS  Google Scholar 

  64. Jefferson VA, Barber KA, El-Mayet FS, Jones C, Nanduri B, Meyer F. Proteogenomic Identification of a novel protein-encoding gene in bovine herpesvirus 1 that is expressed during productive infection. Viruses. 2018;10(9):499.

    Article  PubMed Central  CAS  Google Scholar 

  65. Li Z, Sun W, Wu D, Gao X, Sun N, Liu N. Mass spectrometry analysis coupled with de novo sequencing reveals amino acid substitutions in nucleocapsid protein from influenza A virus. Int J Mol Sci. 2014;15(2):2465–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Brochot E, Demey B, Handala L, Francois C, Duverlie G, Castelain S. Comparison of different serological assays for SARS-CoV-2 in real life. J Clin Virol. 2020;130:104569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Theel ES, Slev P, Wheeler S, Couturier MR, Wong SJ, Kadkhoda K. The role of antibody testing for SARS-CoV-2: is there one? J Clin Microbiol. 2020;58(8):e00797-20.

    PubMed  PubMed Central  Google Scholar 

  68. Amanat F, Stadlbauer D, Strohmeier S, Nguyen THO, Chromikova V, McMahon M, et al. A serological assay to detect SARS-CoV-2 seroconversion in humans. Nat Med. 2020;26(7):1033–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Long QX, Liu BZ, Deng HJ, Wu GC, Deng K, Chen YK, et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med. 2020;26:845–8.

    Article  CAS  PubMed  Google Scholar 

  70. Guo L, Ren L, Yang S, Xiao M, Chang, Yang F, et al. Profiling early humoral response to diagnose novel coronavirus disease (COVID-19). Clin Infect Dis. 2020;71(15):778–85.

    Article  CAS  PubMed  Google Scholar 

  71. Isho B, Abe KT, Zuo M, Jamal AJ, Rathod B, Wang JH, et al. Persistence of serum and saliva antibody responses to SARS-CoV-2 spike antigens in COVID-19 patients. Sci Immunol. 2020. https://doi.org/10.1126/sciimmunol.abe5511.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Marien J, Ceulemans A, Michiels J, Heyndrickx L, Kerkhof K, Foque N, et al. Evaluating SARS-CoV-2 spike and nucleocapsid proteins as targets for antibody detection in severe and mild COVID-19 cases using a Luminex bead-based assay. J Virol Methods. 2021;288:114025.

    Article  CAS  PubMed  Google Scholar 

  73. Tan X, Krel M, Dolgov E, Park S, Li X, Wu W, et al. Rapid and quantitative detection of SARS-CoV-2 specific IgG for convalescent serum evaluation. Biosens Bioelectron. 2020;169:11257.

    Article  CAS  Google Scholar 

  74. Premkumar L, Segovia-Chumbez B, Jadi R, Martinez DR, Raut R, Markmann A, et al. The receptor binding domain of the viral spike protein is an immunodominant and highly specific target of antibodies in SARS-CoV-2 patients. Sci Immunol. 2020;5(48):eabc8413.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Burbelo PD, Riedo FX, Morishima C, Rawlings S, Smith D, Das S, et al. Sensitivity in detection of antibodies to nucleocapsid and spike proteins of severe acute respiratory syndrome coronavirus 2 in patients with coronavirus disease 2019. J Infect Dis. 2020;222(2):206–13.

    Article  CAS  PubMed  Google Scholar 

  76. Leung DT, Tam FC, Ma CH, Chan PK, Cheung JL, Niu H, et al. Antibody response of patients with severe acute respiratory syndrome (SARS) targets the viral nucleocapsid. J Infect Dis. 2004;190(2):379–86.

    Article  CAS  PubMed  Google Scholar 

  77. Zhu H, Hu S, Jona G, Zhu X, Kreiswirth N, Willey BM, et al. Severe acute respiratory syndrome diagnostics using a coronavirus protein microarray. Proc Natl Acad Sci U S A. 2006;103(11):4011–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Wu LP, Wang NC, Chang YH, Tian XY, Na DY, Zhang LY, et al. Duration of antibody responses after severe acute respiratory syndrome. Emerg Infect Dis. 2007;13(10):1562–4.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Widge AT, Rouphael NG, Jackson LA, Anderson EJ, Roberts PC, Makhene M, et al. Durability of responses after SARS-CoV-2 mRNA-1273 vaccination. N Engl J Med. 2021;384(1):80–2.

    Article  CAS  PubMed  Google Scholar 

  80. Zhao P, Cao J, Zhao LJ, Qin ZL, Ke JS, Pan W, et al. Immune responses against SARS-coronavirus nucleocapsid protein induced by DNA vaccine. Virology. 2005;331(1):128–35.

    Article  CAS  PubMed  Google Scholar 

  81. Wang H, Wu X, Zhang X, Hou X, Liang T, Wang D, et al. SARS-CoV-2 proteome microarray for mapping COVID-19 antibody interactions at amino acid resolution. ACS Cent Sci. 2020;6(12):2238–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Poh CM, Carissimo G, Wang B, Amrun SN, Lee CY, Chee RS, et al. Two linear epitopes on the SARS-CoV-2 spike protein that elicit neutralising antibodies in COVID-19 patients. Nat Commun. 2020;11(1):2806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Dahlke CHJ, Kobbe R, Santer R, Koch T, Fathi A, Ly ML. Distinct early IgA profile may determine severity of COVID-19 symptoms: an immunological case series. MedRxiv. 2020. https://doi.org/10.1101/2020.04.14.20059733.

    Article  Google Scholar 

  84. Li MJR, Peng Y, Wang C, Ren W, Lv F, Gong S, Fang F, Wang Q, Li J, Shen T, Sun H, Zhou L, Cui Y, Song H, Sun L. Generation of antibodies against COVID-19 virus for development of diagnostic tools. MedRxiv. 2020. https://doi.org/10.1101/2020.02.20.20025999.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Ng EK, Hui DS, Chan KC, Hung EC, Chiu RW, Lee N, et al. Quantitative analysis and prognostic implication of SARS coronavirus RNA in the plasma and serum of patients with severe acute respiratory syndrome. Clin Chem. 2003;49(12):1976–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Chow SC, Ho CY, Tam TT, Wu C, Cheung T, Chan PK, et al. Specific epitopes of the structural and hypothetical proteins elicit variable humoral responses in SARS patients. J Clin Pathol. 2006;59(5):468–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Qiu M, Shi Y, Guo Z, Chen Z, He R, Chen R, et al. Antibody responses to individual proteins of SARS coronavirus and their neutralization activities. Microbes Infect. 2005;7(5–6):882–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Channappanavar R, Perlman S. Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology. Semin Immunopathol. 2017;39(5):529–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Drabovich AP, Martinez-Morillo E, Diamandis EP. Toward an integrated pipeline for protein biomarker development. Biochim Biophys Acta. 2015;1854(6):677–86.

    Article  CAS  PubMed  Google Scholar 

  90. Drabovich AP, Pavlou MP, Batruch I, Diamandis EP. Proteomic and mass spectrometry technologies for biomarker discovery. In: Issaq HJ, Veenstra TD, editors. Proteomic and metabolomic approaches to biomarker discovery. Waltham: Academic Press (Elsevier); 2013. p. 17–37.

    Google Scholar 

  91. Drabovich AP, Dimitromanolakis A, Saraon P, Soosaipillai A, Batruch I, Mullen B, et al. Differential diagnosis of azoospermia with proteomic biomarkers ECM1 and TEX101 quantified in seminal plasma. Sci Transl Med. 2013;5(212):212ra160.

    Article  PubMed  CAS  Google Scholar 

  92. Drabovich AP, Diamandis EP. Combinatorial peptide libraries facilitate development of multiple reaction monitoring assays for low-abundance proteins. J Proteome Res. 2010;9(3):1236–45.

    Article  CAS  PubMed  Google Scholar 

  93. Pieri M, Ciotti M, Nuccetelli M, Perrone MA, Calio MT, Lia MS, et al. Serum Amyloid A Protein as a useful biomarker to predict COVID-19 patients severity and prognosis. Int Immunopharmacol. 2021;95:107512.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Shu T, Ning W, Wu D, Xu J, Han Q, Huang M, et al. Plasma proteomics identify biomarkers and pathogenesis of COVID-19. Immunity. 2020;53(5):1108-22 e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Liu F, Li L, Xu M, Wu J, Luo D, Zhu Y, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127:104370.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. He X, Yao F, Chen J, Wang Y, Fang X, Lin X, et al. The poor prognosis and influencing factors of high D-dimer levels for COVID-19 patients. Sci Rep. 2021;11(1):1830.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Yao Y, Cao J, Wang Q, Shi Q, Liu K, Luo Z, et al. D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study. J Intensive Care. 2020;8:49.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Messner CB, Demichev V, Wendisch D, Michalick L, White M, Freiwald A, et al. Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst. 2020;11(1):11–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Park J, Kim H, Kim SY, Kim Y, Lee JS, Dan K, et al. In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients. Sci Rep. 2020;10(1):22418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA. 2020;323:1843–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Iwasaki S, Fujisawa S, Nakakubo S, Kamada K, Yamashita Y, Fukumoto T, et al. Comparison of SARS-CoV-2 detection in nasopharyngeal swab and saliva. J Infect. 2020;81(2):e145–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Feng W, Newbigging AM, Le C, Pang B, Peng H, Cao Y, et al. Molecular diagnosis of COVID-19: challenges and research needs. Anal Chem. 2020;92(15):10196–209.

    Article  CAS  PubMed  Google Scholar 

  105. Nikolaev ENIM, Brzhozovskiy AG, Bugrova AE, Kononikhin AS, Starodubtseva NL, Petrotchenko EV, Kovalev GI, Borchers CH, Sukhikh GT. Mass-spectrometric detection of SARS-CoV-2 virus in scrapings of the epithelium of the nasopharynx of infected patients via nucleocapsid N protein. J Proteome Res. 2020;19:4393–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Ihling C, Tanzler D, Hagemann S, Kehlen A, Huttelmaier S, Arlt C, et al. Mass Spectrometric identification of SARS-CoV-2 proteins from gargle solution samples of COVID-19 patients. J Proteome Res. 2020;19:4389–92.

    Article  CAS  PubMed  Google Scholar 

  107. Xiu L, Zhang C, Wu Z, Peng J. Establishment and application of a universal coronavirus screening method using MALDI-TOF mass spectrometry. Front Microbiol. 2017;8:1510.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Nachtigall FM, Pereira A, Trofymchuk OS, Santos LS. Detection of SARS-CoV-2 in nasal swabs using MALDI-MS. Nat Biotechnol. 2020;38(10):1168–73.

    Article  CAS  PubMed  Google Scholar 

  109. Yan L, Yi J, Huang C, Zhang J, Fu S, Li Z, et al. Rapid detection of COVID-19 using MALDI-TOF-based serum peptidome profiling. Anal Chem. 2021;93:4782–7.

    Article  CAS  PubMed  Google Scholar 

  110. Iles RK, Zmuidinaite R, Iles JK, Carnell G, Sampson A, Heeney JL. Development of a clinical MALDI-ToF mass spectrometry assay for SARS-CoV-2: rational design and multi-disciplinary team work. Diagnostics (Basel). 2020;10(10):746.

    Article  CAS  Google Scholar 

  111. Coiras MT, Perez-Brena P, Garcia ML, Casas I. Simultaneous detection of influenza A, B, and C viruses, respiratory syncytial virus, and adenoviruses in clinical samples by multiplex reverse transcription nested-PCR assay. J Med Virol. 2003;69(1):132–44.

    Article  CAS  PubMed  Google Scholar 

  112. Maxmen A. Thousands of coronavirus tests are going unused in US labs. Nature. 2020;580(7803):312–3.

    Article  CAS  PubMed  Google Scholar 

  113. Mahmud I, Garrett TJ. Mass spectrometry techniques in emerging pathogens studies: COVID-19 perspectives. J Am Soc Mass Spectrom. 2020;31(10):2013–24.

    Article  CAS  PubMed  Google Scholar 

  114. Cardozo KHM, Lebkuchen A, Okai GG, Schuch RA, Viana LG, Olive AN, et al. Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts. Nat Commun. 2020;11(1):6201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Majchrzykiewicz-Koehorst JA, Heikens E, Trip H, Hulst AG, de Jong AL, Viveen MC, et al. Rapid and generic identification of influenza A and other respiratory viruses with mass spectrometry. J Virol Methods. 2015;213:75–83.

    Article  CAS  PubMed  Google Scholar 

  116. Schwahn AB, Wong JW, Downard KM. Rapid differentiation of seasonal and pandemic H1N1 influenza through proteotyping of viral neuraminidase with mass spectrometry. Anal Chem. 2010;82(11):4584–90.

    Article  CAS  PubMed  Google Scholar 

  117. Schwahn AB, Wong JW, Downard KM. Signature peptides of influenza nucleoprotein for the typing and subtyping of the virus by high resolution mass spectrometry. Analyst. 2009;134(11):2253–61.

    Article  CAS  PubMed  Google Scholar 

  118. Calderaro A, Arcangeletti MC, Rodighiero I, Buttrini M, Gorrini C, Motta F, et al. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry applied to virus identification. Sci Rep. 2014;4:6803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Liu N, Wang L, Cai G, Zhang D, Lin J. Establishment of a simultaneous detection method for ten duck viruses using MALDI-TOF mass spectrometry. J Virol Methods. 2019;273:113723.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Stefanska I, Romanowska M, Donevski S, Gawryluk D, Brydak LB. Co-infections with influenza and other respiratory viruses. Adv Exp Med Biol. 2013;756:291–301.

    Article  CAS  PubMed  Google Scholar 

  121. Wu XCY, Huang X, Yu X, Zhao L, Wang F, Li Q, Gu S, Xu T, Li Y, Lu B, Zhan Q. Co-infection with SARS-CoV-2 and influenza A virus in patient with pneumonia, China. Emerg Infect Dis. 2020;26(6):1324–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Sampath R, Hall TA, Massire C, Li F, Blyn LB, Eshoo MW, et al. Rapid identification of emerging infectious agents using PCR and electrospray ionization mass spectrometry. Ann N Y Acad Sci. 2007;1102:109–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Wolk DM, Kaleta EJ, Wysocki VH. PCR-electrospray ionization mass spectrometry: the potential to change infectious disease diagnostics in clinical and public health laboratories. J Mol Diagn. 2012;14(4):295–304.

    Article  CAS  PubMed  Google Scholar 

  124. Sampath R, Hofstadler SA, Blyn LB, Eshoo MW, Hall TA, Massire C, et al. Rapid identification of emerging pathogens: coronavirus. Emerg Infect Dis. 2005;11(3):373–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Chen KF, Rothman RE, Ramachandran P, Blyn L, Sampath R, Ecker DJ, et al. Rapid identification viruses from nasal pharyngeal aspirates in acute viral respiratory infections by RT-PCR and electrospray ionization mass spectrometry. J Virol Methods. 2011;173(1):60–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Hernandez MM, Banu R, Shrestha P, Patel A, Chen F, Cao L, et al. Comparison of SARS-CoV-2 detection in Saliva by real-time RT-PCR and RT-PCR/MALDI-TOF Methods. MedRxiv. 2021. https://doi.org/10.1101/2021.03.11.21253234.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Korbakis D, Brinc D, Schiza C, Soosaipillai A, Jarvi K, Drabovich AP, et al. Immunocapture-selected reaction monitoring screening facilitates the development of ELISA for the measurement of native TEX101 in biological fluids. Mol Cell Proteomics. 2015;14(6):1517–26.

    Article  CAS  Google Scholar 

  128. Schiza C, Korbakis D, Panteleli E, Jarvi K, Drabovich AP, Diamandis EP. Discovery of a human testis-specific protein complex TEX101-DPEP3 and selection of its disrupting antibodies. Mol Cell Proteomics. 2018;17(12):2480–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Drabovich AP, Pavlou MP, Dimitromanolakis A, Diamandis EP. Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay. Mol Cell Proteomics. 2012;11(8):422–34.

    Article  CAS  Google Scholar 

  130. Drabovich AP, Pavlou MP, Schiza C, Diamandis EP. Dynamics of Protein expression reveals primary targets and secondary messengers of estrogen receptor alpha signaling in MCF-7 breast cancer cells. Mol Cell Proteomics. 2016;15(6):2093–107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Konvalinka A, Zhou J, Dimitromanolakis A, Drabovich AP, Fang F, Gurley S, et al. Determination of an angiotensin II-regulated proteome in primary human kidney cells by stable isotope labeling of amino acids in cell culture (SILAC). J Biol Chem. 2013;288(34):24834–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Korbakis D, Schiza C, Brinc D, Soosaipillai A, Karakosta TD, Legare C, et al. Preclinical evaluation of a TEX101 protein ELISA test for the differential diagnosis of male infertility. BMC Med. 2017;15(1):60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Cho CK, Drabovich AP, Karagiannis GS, Martinez-Morillo E, Dason S, Dimitromanolakis A, et al. Quantitative proteomic analysis of amniocytes reveals potentially dysregulated molecular networks in Down syndrome. Clin Proteom. 2013;10(1):2.

    Article  CAS  Google Scholar 

  134. Fu Z, Rais Y, Bismar TA, Hyndman ME, Le XC, Drabovich AP. Mapping isoform abundance and interactome of the endogenous TMPRSS2-ERG fusion protein by orthogonal immunoprecipitation-mass spectrometry assays. Mol Cell Proteomics. 2021;20:100075.

    Article  CAS  Google Scholar 

  135. Begcevic I, Brinc D, Dukic L, Simundic AM, Zavoreo I, Basic Kes V, et al. Targeted mass spectrometry-based assays for relative quantification of 30 brain-related proteins and their clinical applications. J Proteome Res. 2018;17(7):2282–92.

    Article  CAS  PubMed  Google Scholar 

  136. Martinez-Morillo E, Cho CK, Drabovich AP, Shaw JL, Soosaipillai A, Diamandis EP. Development of a multiplex selected reaction monitoring assay for quantification of biochemical markers of down syndrome in amniotic fluid samples. J Proteome Res. 2012;11(7):3880–7.

    Article  CAS  PubMed  Google Scholar 

  137. Drabovich AP, Jarvi K, Diamandis EP. Verification of male infertility biomarkers in seminal plasma by multiplex selected reaction monitoring assay. Mol Cell Proteomics. 2011;10(12):M110 004127.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Cho CK, Drabovich AP, Batruch I, Diamandis EP. Verification of a biomarker discovery approach for detection of Down syndrome in amniotic fluid via multiplex selected reaction monitoring (SRM) assay. J Proteomics. 2011;74(10):2052–9.

    Article  CAS  Google Scholar 

  139. Konvalinka A, Batruch I, Tokar T, Dimitromanolakis A, Reid S, Song X, et al. Quantification of angiotensin II-regulated proteins in urine of patients with polycystic and other chronic kidney diseases by selected reaction monitoring. Clin Proteomics. 2016;13:16.

    Article  CAS  Google Scholar 

  140. Karakosta TD, Soosaipillai A, Diamandis EP, Batruch I, Drabovich AP. Quantification of human kallikrein-related peptidases in biological fluids by multiplatform targeted mass spectrometry assays. Mol Cell Proteomics. 2016;15(9):2863–76.

    Article  CAS  Google Scholar 

  141. Shi T, Su D, Liu T, Tang K, Camp DG 2nd, Qian WJ, et al. Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics. Proteomics. 2012;12(8):1074–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Johnson RF, McCarthy SE, Godlewski PJ, Harty RN. Ebola virus VP35-VP40 interaction is sufficient for packaging 3E–5E minigenome RNA into virus-like particles. J Virol. 2006;80(11):5135–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Bier K, York A, Fodor E. Cellular cap-binding proteins associate with influenza virus mRNAs. J Gen Virol. 2011;92(Pt 7):1627–34.

    Article  CAS  PubMed  Google Scholar 

  144. Waxman L, Whitney M, Pollok BA, Kuo LC, Darke PL. Host cell factor requirement for hepatitis C virus enzyme maturation. Proc Natl Acad Sci U S A. 2001;98(24):13931–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Fontaine-Rodriguez EC, Taylor TJ, Olesky M, Knipe DM. Proteomics of herpes simplex virus infected cell protein 27: association with translation initiation factors. Virology. 2004;330(2):487–92.

    Article  CAS  PubMed  Google Scholar 

  146. Taylor TJ, Knipe DM. Proteomics of herpes simplex virus replication compartments: association of cellular DNA replication, repair, recombination, and chromatin remodeling proteins with ICP8. J Virol. 2004;78(11):5856–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Mayer D, Molawi K, Martinez-Sobrido L, Ghanem A, Thomas S, Baginsky S, et al. Identification of cellular interaction partners of the influenza virus ribonucleoprotein complex and polymerase complex using proteomic-based approaches. J Proteome Res. 2007;6(2):672–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Pierce CL, Williams TL, Moura H, Pirkle JL, Cox NJ, Stevens J, et al. Quantification of immunoreactive viral influenza proteins by immunoaffinity capture and isotope-dilution liquid chromatography-tandem mass spectrometry. Anal Chem. 2011;83(12):4729–37.

    Article  CAS  PubMed  Google Scholar 

  149. Pierce CL, Williams TL, Santana WI, Levine M, Chen LM, Cooper HC, et al. Immunocapture isotope dilution mass spectrometry in response to a pandemic influenza threat. Vaccine. 2017;35(37):5011–8.

    Article  CAS  PubMed  Google Scholar 

  150. Li W, Moore MJ, Vasilieva N, Sui J, Wong SK, Berne MA, et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature. 2003;426(6965):450–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Raj VS, Mou H, Smits SL, Dekkers DH, Muller MA, Dijkman R, et al. Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC. Nature. 2013;495(7440):251–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Davids BJLC, Hanson EM, Le CHYAJ, Hanevik K, Fischer M, Radunovic M, Langeland NFM, Svärd SG, Ghassemian MMY, Eckmann L. Identification of conserved candidate vaccine antigens in the surface proteome of Giardia lamblia. Infect Immun. 2019;87(6):e00219-e319.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. GISAID Next-hcov-19 database. https://www.gisaid.org. Accessed 11 Jan 2021.

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Canadian Institutes of Health Research COVID-19 Immunity Task Force grant (VR2-173207) and Alberta Innovates Health Programs grant to A.P.D.

Author information

Authors and Affiliations

Authors

Contributions

YR and AD wrote the manuscript; ZF contributed to manuscript revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Andrei P. Drabovich.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rais, Y., Fu, Z. & Drabovich, A.P. Mass spectrometry-based proteomics in basic and translational research of SARS-CoV-2 coronavirus and its emerging mutants. Clin Proteom 18, 19 (2021). https://doi.org/10.1186/s12014-021-09325-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12014-021-09325-x

Keywords