Identification of sitagliptin binding proteins by affinity purification mass spectrometry.

Type 2 diabetes mellitus (T2DM) is recognized as a serious public health concern with increasing incidence. The dipeptidyl peptidase-4 (DPP-4) inhibitor sitagliptin has been used for the treatment of T2DM worldwide. Although sitagliptin has excellent therapeutic outcome, adverse effects are observed. In addition, previous studies have suggested that sitagliptin may have pleiotropic effects other than treating T2DM. These pieces of evidence point to the importance of further investigation of the molecular mechanisms of sitagliptin, starting from the identification of sitagliptin-binding proteins. In this study, by combining affinity purification mass spectrometry (AP-MS) and stable isotope labeling by amino acids in cell culture (SILAC), we discover seven high-confidence targets that can interact with sitagliptin. Surface plasmon resonance (SPR) assay confirms the binding of sitagliptin to three proteins, i. e., LYPLAL1, TCP1, and CCAR2, with binding affinities (K D) ranging from 50.1 µM to 1490 µM. Molecular docking followed by molecular dynamic (MD) simulation reveals hydrogen binding between sitagliptin and the catalytic triad of LYPLAL1, and also between sitagliptin and the P-loop of ATP-binding pocket of TCP1. Molecular mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis indicates that sitagliptin can stably bind to LYPLAL1 and TCP1 in active sites, which may have an impact on the functions of these proteins. SPR analysis validates the binding affinity of sitagliptin to TCP1 mutant D88A is ~10 times lower than that to the wild-type TCP1. Our findings provide insights into the sitagliptin-targets interplay and demonstrate the potential of sitagliptin in regulating gluconeogenesis and in anti-tumor drug development.

Antibody dynamics to SARS-CoV-2 in asymptomatic COVID-19 infections.

BACKGROUND: The missing asymptomatic COVID-19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic. MEASURE: Utilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty-three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS-CoV-2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset. RESULTS: A combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS-CoV-2. Different from strong and persistent N-specific antibodies, S1-specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months. CONCLUSION: Our findings might have important implications for the definition of asymptomatic COVID-19 infections, diagnosis, serological survey, public health, and immunization strategies.

Nsp2 has the potential to be a drug target revealed by global identification of SARS-CoV-2 Nsp2-interacting proteins.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health threat since December 2019, and there is still no highly effective drug to control the pandemic. To facilitate drug target identification for drug development, studies on molecular mechanisms, such as SARS-CoV-2 protein interactions, are urgently needed. In this study, we focused on Nsp2, a non-structural protein with largely unknown function and mechanism. The interactome of Nsp2 was revealed through the combination of affinity purification mass spectrometry (AP-MS) and stable isotope labeling by amino acids in cell culture (SILAC), and 84 proteins of high-confidence were identified. Gene ontology analysis demonstrated that Nsp2-interacting proteins are involved in several biological processes such as endosome transport and translation. Network analysis generated two clusters, including ribosome assembly and vesicular transport. Bio-layer interferometry (BLI) assay confirmed the bindings between Nsp2- and 4-interacting proteins, i.e. STAU2 (Staufen2), HNRNPLL, ATP6V1B2, and RAP1GDS1 (SmgGDS), which were randomly selected from the list of 84 proteins. Our findings provide insights into the Nsp2-host interplay and indicate that Nsp2 may play important roles in SARS-CoV-2 infection and serve as a potential drug target for anti-SARS-CoV-2 drug development.

Specific pupylation as IDEntity reporter (SPIDER) for the identification of protein-biomolecule interactions.

Protein-biomolecule interactions play pivotal roles in almost all biological processes. For a biomolecule of interest, the identification of the interacting protein(s) is essential. For this need, although many assays are available, highly robust and reliable methods are always desired. By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosis and the streptavidin (SA)-biotin system, we developed the Specific Pupylation as IDEntity Reporter (SPIDER) method for identifying protein-biomolecule interactions. Using SPIDER, we validated the interactions between the known binding proteins of protein, DNA, RNA, and small molecule. We successfully applied SPIDER to construct the global protein interactome for m6A and mRNA, identified a variety of uncharacterized m6A binding proteins, and validated SRSF7 as a potential m6A reader. We globally identified the binding proteins for lenalidomide and CobB. Moreover, we identified SARS-CoV-2-specific receptors on the cell membrane. Overall, SPIDER is powerful and highly accessible for the study of protein-biomolecule interactions.

Anti-SARS-CoV-2 IgG responses are powerful predicting signatures for the outcome of COVID-19 patients.

Introduction: The COVID-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for early predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies evolved with disease progression and severity in COIVD-19 patients. Objectives: We assumed that antibodies may serve as biomarkers for predicting the clinical outcome of hospitalized COVID-19 patients on admission. Methods: By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgG responses against 20 proteins of SARS-CoV-2 in 1034 hospitalized COVID-19 patients on admission and followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. Results: Nonsurvivors (n = 955) induced higher levels of IgG responses against most of non-structural proteins than survivors (n = 79) on admission. In particular, the magnitude of IgG antibodies against 8 non-structural proteins (NSP1, NSP4, NSP7, NSP8, NSP9, NSP10, RdRp, and NSP14) and 2 accessory proteins (ORF3b and ORF9b) possessed significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory biomarkers for disease severity (all with p trend < 0.05). Additionally, IgG responses to all of these 10 non-structural/accessory proteins were also associated with the severity of disease, and differential kinetics and serum positive rate of these IgG responses were confirmed in COVID-19 patients of varying severities within 20 days after symptoms onset. The area under curves (AUCs) for these IgG responses, determined by computational cross-validations, were between 0.62 and 0.71. Conclusions: Our findings might have important implications for improving clinical management of COVID-19 patients.

Antibody landscape against SARS-CoV-2 reveals significant differences between non-structural/accessory and structural proteins.

The immunogenicity of the SARS-CoV-2 proteome is largely unknown, especially for non-structural proteins and accessory proteins. In this study, we collect 2,360 COVID-19 sera and 601 control sera. We analyze these sera on a protein microarray with 20 proteins of SARS-CoV-2, building an antibody response landscape for immunoglobulin (Ig)G and IgM. Non-structural proteins and accessory proteins NSP1, NSP7, NSP8, RdRp, ORF3b, and ORF9b elicit prevalent IgG responses. The IgG patterns and dynamics of non-structural/accessory proteins are different from those of the S and N proteins. The IgG responses against these six proteins are associated with disease severity and clinical outcome, and they decline sharply about 20 days after symptom onset. In non-survivors, a sharp decrease of IgG antibodies against S1 and N proteins before death is observed. The global antibody responses to non-structural/accessory proteins revealed here may facilitate a deeper understanding of SARS-CoV-2 immunology.

Linear epitope landscape of the SARS-CoV-2 Spike protein constructed from 1,051 COVID-19 patients.

To fully decipher the immunogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike protein, it is essential to assess which part is highly immunogenic in a systematic way. We generate a linear epitope landscape of the Spike protein by analyzing the serum immunoglobulin G (IgG) response of 1,051 coronavirus disease 2019 (COVID-19) patients with a peptide microarray. We reveal two regions rich in linear epitopes, i.e., C-terminal domain (CTD) and a region close to the S2' cleavage site and fusion peptide. Unexpectedly, we find that the receptor binding domain (RBD) lacks linear epitope. We reveal that the number of responsive peptides is highly variable among patients and correlates with disease severity. Some peptides are moderately associated with severity and clinical outcome. By immunizing mice, we obtain linear-epitope-specific antibodies; however, no significant neutralizing activity against the authentic virus is observed for these antibodies. This landscape will facilitate our understanding of SARS-CoV-2-specific humoral responses and might be useful for vaccine refinement.

COVID-ONE-hi: The One-stop Database for COVID-19-specific Humoral Immunity and Clinical Parameters.

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, varies with regard to symptoms and mortality rates among populations. Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19. However, differences in immune responses and clinical features among COVID-19 patients remain largely unknown. Here, we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters (named COVID-ONE-hi). COVID-ONE-hi is based on the data that contain the IgG/IgM responses to 24 full-length/truncated proteins corresponding to 20 of 28 known SARS-CoV-2 proteins and 199 spike protein peptides against 2360 serum samples collected from 783 COVID-19 patients. In addition, 96 clinical parameters for the 2360 serum samples and basic information for the 783 patients are integrated into the database. Furthermore, COVID-ONE-hi provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups. A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters. After the "START" button is clicked, one can readily obtain a comprehensive analysis report for further interpretation. COVID-ONE-hi is freely available at www.COVID-ONE.cn.

Systematic evaluation of IgG responses to SARS-CoV-2 spike protein-derived peptides for monitoring COVID-19 patients.

Serological tests play an essential role in monitoring and combating the COVID-19 pandemic. Recombinant spike protein (S protein), especially the S1 protein, is one of the major reagents used for serological tests. However, the high cost of S protein production and possible cross-reactivity with other human coronaviruses pose unavoidable challenges. By taking advantage of a peptide microarray with full spike protein coverage, we analyzed 2,434 sera from 858 COVID-19 patients, 63 asymptomatic patients and 610 controls collected from multiple clinical centers. Based on the results, we identified several S protein-derived 12-mer peptides that have high diagnostic performance. In particular, for monitoring the IgG response, one peptide (aa 1148-1159 or S2-78) exhibited a sensitivity (95.5%, 95% CI 93.7-96.9%) and specificity (96.7%, 95% CI 94.8-98.0%) comparable to those of the S1 protein for the detection of both symptomatic and asymptomatic COVID-19 cases. Furthermore, the diagnostic performance of the S2-78 (aa 1148-1159) IgG was successfully validated by ELISA in an independent sample cohort. A panel of four peptides, S1-93 (aa 553-564), S1-97 (aa 577-588), S1-101 (aa 601-612) and S1-105 (aa 625-636), that likely will avoid potential cross-reactivity with sera from patients infected by other coronaviruses was constructed. The peptides identified in this study may be applied independently or in combination with the S1 protein for accurate, affordable, and accessible COVID-19 diagnosis.