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Bio Chemistry

The diagnostic accuracy of isothermal nucleic acid point-of-care tests for human coronaviruses: A systematic review and meta-analysis

Republished by Plato



Search results

We identified 2060 articles in total through database searching (Fig. 1). After title and abstract screening, we excluded 1941 articles that were not primary research articles, had no full text available or were unrelated to nucleic acid POCTs for human coronaviruses. 62 non-English articles were found but only two met above eligibility criteria. These two articles were later excluded as the authors did not use clinical samples. After reviewing full text, we found 65 articles with sufficient information to calculate sensitivity, specificity and diagnostic odds ratio (DOR) on clinical samples24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88 (Table S3). Of 65 articles, 13 reported more than one study conducted on different sample groups or using different diagnostic procedures. In total, we included 81 studies in our systematic review.

Figure 1

The preferred reporting items for a systematic review and meta-analysis (PRISMA) flow diagram.

Characteristics of the included studies

Most studies used clinical samples from USA (n = 20 out of 81 studies), followed by China (n = 17) and UK (n = 10). Most studies (n = 76) were COVID-19 diagnostic studies published or uploaded to preprint databases in 2020. Most studies (n = 51) use RT-LAMP as nucleic acid POCTs, followed by CRISPR diagnosis (n = 12), RT-RPA/RAA (n = 7), Abbot ID Now (n = 5), and SAMBA II (n = 2). The rest were iAMP, RT-iiPCR, RT-MDCA, and RCA (n = 1 each). Over a third (n = 28) of all studies attempted to diagnose coronaviruses in crude patient samples, i.e., nasopharyngeal swabs, sputum, saliva, etc. The others (n = 53) used purified RNA from patient samples for viral diagnosis.

Quality of articles

Almost two thirds of all studies (n = 50 out of 81 studies) have high risk of patient selection bias due to non-random patient selection and case–control study design (Fig. 2, Table S2). These studies specifically recruited clinical samples known to be uninfected or infected with coronavirus. Over a third of all studies have unclear risk of patient selection bias because these studies were not case–control but provided insufficient detail about patient inclusion/exclusion criteria. Only four studies38,49,65 has low risk of patient selection bias.

Figure 2

Quality assessment of diagnostic accuracy studies 2 (QUADAS-2) finding per domain for 81 studies included in this systematic review.

Over one fifth of all studies (n = 18 out of 81 studies) have high risk of index test bias because these tests used qualitative fluorescent or colorimetric readout without defined detection thresholds. Only seven studies had low risk of index test bias as these studies had quantitative detection readouts with reported thresholds. These studies also explicitly declared that their index and reference tests were done simultaneously/in parallel to each other or that testing was blinded from each other. For other studies (n = 56), it was either unclear whether index test results were interpreted with knowledge of reference test results or qualitative readout was used for interpreting the results. Thus, index test bias of these studies are unclear.

Only two studies have high risk of reference standard bias as they used RT-PCR (not quantitative, readout result in agarose gel electrophoresis)47 or immunofluorescent assay (IFA)69 as a reference standard test. For the rest of included studies, almost two thirds (n = 52) have unclear risk of reference standard bias because these studies did not provide enough information about whether reference standard results were interpreted without knowledge of the results of the index test.

Most studies have a low risk of flow and timing bias with the following exceptions. One study provided no information on whether the samples for a reference test (IFA) and the index test (RT-LAMP) were taken at the same time69. Another study might have excluded some samples from the workflow87. These two studies were marked as having unknown risk of flow and timing bias. Three studies were designated as having high risk because they used different standard references on different samples80, used different samples test flow on different sample groups65, and excluded some samples from the analysis76 (Table S2).

Our review question did not focus on any particular patient demographics. None of the included studies attempted to exclude patients based on demographics and thus had no ‘concern of patient selection applicability’(Fig. 2, Table S2). Index isothermal tests of all studies have generally been used for POCTs and thus have low concern of index test applicability. Reference standard tests of nearly all studies are RT-qPCR, the current gold standard for RNA virus detection. Thus, we graded these studies as having low concern of standard test applicability. Two studies that used (non-quantitative) RT-PCR47 and IFA69, were marked as having high concern of standard test applicability.

Sensitivity, specificity and diagnostic odd ratio (DOR) of nucleic acid POCTs

Nearly all studies (n = 77 out of 81) reported at least 90% diagnosis specificity while less than two third (n = 48 out of 81) reported 90% sensitivity or above (Fig. 3). Less than a third (n = 14 out of 53) of studies that used purified RNA for diagnosis reported below 90% sensitivity (Fig. 3A). In contrast, over two thirds (n = 19 out of 28) of studies that used crude patient samples for diagnosis reported sensitivities less than 90% (Fig. 3B). Thus, for most studies, diagnostic specificity is of less concern than sensitivity. Moreover, diagnostic sensitivity of purified RNA is generally higher than those of crude patient samples. All studies reported DOR above one.

Figure 3

The forest plot of sensitivity, specificity and diagnostic odds ratio (DOR) of human coronavirus nucleic acid point-of-care tests (POCTs) on purified RNA samples (A) and on crude patient samples (B). Rows shows first author name, and performance (sensitivity, specificity and DOR) of each study. Different studies from the same research articles are labelled with different letter [a], [b], [c], etc. Blue parentheses after first author names indicates the types of coronaviruses diagnosed and publication years of the studies. All rows without blue parentheses show studies on COVID-19 diagnosis published in 2020. Red dots indicate those studies that were only available as pre-prints (not peer-reviewed). The far right blue texts indicate the types of diagnostic assays used in the studies. The far left yellow boxes prefacing the author names denote the studies having no QUADAS-2 domain with high risk bias or high concern of applicability but have unclear bias or concerns in some QUADAS-2 domain. Green boxes denote that the study has low risk of bias and low concern of applicability in all QUADAS-2 domains. All rows without yellow or green box show studies with high risk of bias or high concern of applicability in at least one QUADAS-2 domain.

Among studies that used RT-LAMP on purified RNA samples, the study by Rohaim et al. (2020) is clearly an outlier (Fig. 3A)74. This study used artificial intelligence to interpret the RT-LAMP colorimetric readout. While this approach can reduce assay time and eliminate subjectivity of result interpretation, the false positive rate was high (approximately 50% when using RT-qPCR as a reference test). Osterdahl et al. (2020) is the only study whose both sensitivity and specificity were 80% or below65. This study had high risk of flow and timing bias because some clinical samples were taken on different days for index test and standard reference test. Poon et al. (2004) reported the lowest sensitivity (at 65%) among all studies using RT-LAMP on purified RNA samples69. This study has a high risk of reference test bias and high concern of reference test applicability because immunofluorescent assay (IFA) was used for reference test instead of RT-qPCR. Since the antibody may persist much longer in patients than the virus. As a result, some samples might give a positive result to the antibody test but provide a negative result to the LAMP test. Buck et al. (2020), Thi et al. (2020) and Rodel et al. (2020) are also studies with sensitivity below 80%34,39,72. These studies also reported quantity of viral RNA (as Ct value of RT-qPCR) in purified RNA sample. The authors showed that that samples with low viral RNA (i.e. high Ct value above 30) accounted for a significant portion of coronavirus positive samples used in the studies. Since these samples were more difficult to detect (i.e. more likely to get false negative), this could explain apparently low sensitivities reported by these three studies.

For diagnosis of purified RNA samples using non RT-LAMP assays, all studies using RT-RPA/RAA, CRISPR diagnosis, RT-iiPCR, and RCA as index tests reported sensitivity and specificity at close to 90% or above (Fig. 3A). The Li et al. (2020)59 study is clearly an outlier. The study introduced a new diagnosis assay called reverse transcription multiple cross displacement amplification (RT-MCDA). The authors claimed that this new assay was more sensitive than RT-qPCR. Nonetheless, the result showed that RT-MCDA could only detect viral RNA in 33.8% of COVID-19 confirmed patient samples. Such low sensitivity could result from the performance of RT-MDCA itself or the fact that viral RNA in some samples was degraded as a follow-up RT-qPCR could detect COVID-19 in only 30.7% of the same sample set.

Nearly all studies (n = 12 out of 15) using RT-LAMP on crude patient samples reported less than 90% diagnostic sensitivity. The studies by Thi et al. (2020) and Lamb et al. (2020) reported even less than 50% sensitivity39,57. Such low sensitivity measure could be explained by the fact that these studies used patient samples with low viral load. Excluding positive samples with Ct = 30 or above, the calculated sensitivities from these studies rise above 60% (Table S4). Diagnosis of crude patient samples using non RT-LAMP assays has sensitivity ranging from 74 to 100%, with two exceptions. Basu et al. (2020)29 and Schermer et al. (2020)75 reported 55% and 56% diagnostic sensitivity for ID Now and CRISPR diagnosis, respectively. For the study by Schermer et al., all positive samples had Ct value below 30. This implies that poor sensitivity measure resulted from the performance of the assay itself and not because the samples had low viral load. Basu et al. did not show Ct value of samples used in their study29. Thus, it could still be possible that poor performance partially resulted from positive samples with low viral load. In fact, another study by Smithgall et al. showed that ID Now diagnostic sensitivity is 100% for samples with Ct value not exceeding 30 but only at 34.4% for samples with Ct value above 3077 (Table S4).

Publication bias

Publication bias of all 81 included studies was determined using Deek’s funnel plot test for DOR. The result indicates significant asymmetry in funnel plot (p-value = 3.203 × 10–4).

Meta-analysis of sensitivity, specificity and DOR

We performed subgroup analysis of all studies that used RT-qPCR as reference test and had at least ten positive and ten negative patient samples. The two outlier studies by Rohaim et al.74 and Li et al.59 were excluded from the subgroup analysis. If multiple studies were conducted on the same set of patient samples, only a study with the highest sensitivity and specificity was used. For example, Patchsung et al.67 reported two CRISPR diagnosis studies on the same set of patient samples, one using fluorescent readout and the other using lateral flow assay. In our analysis, we included only the fluorescent readout study, which had higher sensitivity and similar specificity to the lateral flow assay.

In total, 61 studies were used for subgroup analyses (Fig. 4A, Table S5). These studies were divided up further according to the types of samples used (purified RNA vs crude patient samples) and index test assays. We only estimated pooled sensitivity, specificity and diagnosis odds ratio for subgroups that had at least four studies. For the studies subgroup using RT-LAMP on purified RNA samples, we also performed a further subgroup analysis to compare the performance of studies from peer-reviewed and from pre-print articles.

Figure 4

Hierarchical subgrouping of studies for meta-analysis. (A) subgrouping of all qualified studies. (B) subgrouping of only qualified studies that provide Ct values of positive samples. “n” indicates the number of studies in a subgroup. Pooled diagnosis results from subgroups in white boxes were used for calculating pooled sensitivity, specificity and DOR. Subgroups in grey boxes were not used for sensitivity, specificity, nor DOR calculation.

Pooled sensitivity and specificity of all included studies are at 91% and 99%, respectively, indicating overall good performance of isothermal amplification based diagnosis test so far (Table 1). Pooled sensitivity of studies using purified RNA sample at 0.94 (95% CI: 0.92–0.96) is clearly higher than those using crude patient samples at 0.83 (95% CI: 0.74–0.89).

Table 1 Summary of sensitivity, specificity and DOR.

Similarly, pooled sensitivity of studies using RT-LAMP on purified RNA sample is clearly higher than that for studies using RT-LAMP on crude patient samples. Pooled sensitivity of RT-LAMP on crude samples was similar to that of ID Now. Both pooled sensitivities were lower than that of SAMBA II (Fig. 3, not used in subgroup analysis). For diagnosis of purified RNA samples, pooled sensitivities of RT-LAMP, CRISPR diagnosis and RT-RPA/RAA were similar.

Additionally, pooled sensitivity of RT-LAMP studies in peer-reviewed articles was not significantly different from that in pre-print articles.

The distribution of viral load in tested samples is one of the key factors that determine measured sensitivity of an index test. If a large fraction of positive samples used in a study have low viral load (i.e., high Ct value), measured sensitivity will be low. Unfortunately, the majority of our included studies (n = 35 out of 61) do not show Ct values of positive samples. Thus, it was not possible to determine the extent to which viral load in positive samples from these studies affected their measured sensitivity. For this reason, we decide to focus our analysis on only studies that reported Ct values.

In total, 26 studies were used for subgroup analyses (Fig. 4B, Table S5). Again, these studies were divided up further according to types of samples used (purified RNA vs crude patient samples) and index test assays. For each subgroup analysis, we calculated pooled sensitivity, specificity and DOR for the cases when all samples were used and the cases when positive samples with high Ct values were excluded. The ‘high’ Ct cut-off values were not the same in all included studies (depending on data available from the original research articles). Most studies (n = 19 out of 26) had Ct cut-off values of 30–33; the remainder had Ct cut-off values of 34–39 (Table S4). Excluding samples with high Cts from the calculation resulted in substantial increases in sensitivity, particularly for diagnosis of crude samples (Table 1, Table S4). The calculated pooled sensitivity for crude samples increases from 0.76 (95% CI: 0.57–0.88) to 0.95 (95% CI: 0.84–0.99); the calculated pooled sensitivity for RT-LAMP on crude samples increases from 0.73 (95% CI: 0.51–0.88) to 0.91 (95% CI: 0.79–0.97). Diagnostic sensitivity of purified RNA samples remained higher than those of crude patient samples. However, when positive samples with high Ct values were excluded, such difference in sensitivity become smaller.


Bio Chemistry

Structures of the glucocorticoid-bound adhesion receptor GPR97–Go complex

Republished by Plato



No statistical methods were used to predetermine sample size. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.

Cell lines

HEK293 cells were obtained from the Cell Resource Center of Shanghai Institute for Biological Sciences (Chinese Academy of Sciences). Spodoptera frugiperda (Sf9) cells were purchased from Expression Systems (cat. 94-001S). Y-1 cells were originally obtained from the American Type Culture Collection (ATCC). The cells were grown in monolayer culture in RPMI 1640 with 10% FBS (Gibco) at 37 °C in a humidified atmosphere consisting of 5% CO2 and 95% air.

Constructs of GPR97 and miniGo heterotrimer

For protein production in insect cells, the human GPR97 (residues 21–549) with the autoproteolysis motif mutation (H248/A and T250/A) was sub-cloned into the pFastBac1 vector. The native signal peptide was replaced with the haemagglutinin signal peptide (HA) to enhance receptor expression, followed by a Flag tag DYKDDDK (China peptide) to facilitate complex purification. An engineered human Gαo1 with Gαo1 H domain deletion, named miniGαo1 was cloned into pFastBac1 according to published literature29. Human Gβ1 with the C-terminal hexa-histidine tag and human Gγ2 were subcloned into the pFastBacDual vector. scFv16 was cloned into pfastBac1 with the C-terminal hexa-histidine tag and the N-terminal GP67 signal peptide. To examine the activities of GPR97, the GPR97-FL-WT (wild-type full-length GPR97), GPR97-FL-AA (GPR97 GPS site mutation, H248/A and T250/A), GPR97β (GPR97 with the NTF removed, residues 250–549) and GPR97-β-T (GPR97β with the N-terminal tethered Stachel sequence removed, residues 265–549) were sub-cloned into the pcDNA3.1 plasmid. The GPR97 mutations E298A, R299A, F345A, F353A, H362A, L363A, Y364A, V370A, F371A, Y406A, W421A, W490A, A493G, I494A, L498A and N510A were generated using the Quikchange mutagenesis kit (Stratagene). The G protein BRET probes were constructed according to previous publications42,43. Human G protein subunits (Gαq, Gβ1 and Gγ2) were sub-cloned into the pcDNA3.1 expression vectors. The Gαq-RlucII subunit was generated by amplifying and inserting the coding sequence of RlucII into Gαq between residue L97 and K98. The Gqo probe, in which the six amino acids of the C-terminal of Gαq-RlucII were substituted with those from Gαo1, was constructed by PCR amplification using synthesized oligonucleotides encoding swapped C-terminal sequences. The GFP10–Gγ2 plasmid was generated by fusing the GFP10 coding sequence in frame at the N terminus to Gγ2. All of the constructs and mutations were verified by DNA sequencing.

Protein expression

High titre recombinant baculoviruses were generated using Bac-to-Bac Baculovirus Expression System. In brief, 2 μg of recombinant bacmid and 2 μl X-tremGENE HP transfection reagent (Roche) in 100 μl Opti-MEM medium (Gibco) were mixed and incubated for 20 min at room temperature. The transfection solution was added to 2.5 ml Sf9 cells with a density of 1 × 106 per ml in a 24-well plate. The infected cells were cultured in a shaker at 27 °C for 4 days. P0 virus was collected and then amplified to generate P1 virus. The viral titres were determined by flow cytometric analysis of cells stained with gp64-PE antibody (1:200 dilution; 12-6991-82, Thermo Fisher). Then, Sf9 cells were infected with viruses encoding GPR97-FL-AA, miniGαo, Gβγ, and with or without scFv16, respectively, at equal multiplicity of infection. The infected cells were cultured at 27 °C, 110 rpm for 48 h before collection. Cells were finally collected by centrifugation and the cell pellets were stored at −80 °C.

GPR97–Go complex formation and purification

Cell pellets transfected with virus encompassing the GPR97-FL-AA, miniGo trimer and scFv16 (only existed in cell pellets for purifying the cortisol–GPR97-FL-AA–Go–scFv16 complex) were resuspended in 20 mM HEPES, pH 7.4, 100 mM NaCl, 10% glycerol, 10 mM MgCl2 and 5 mM CaCl2 supplemented with Protease Inhibitor Cocktail (B14001, Bimake) and 100 μM TCEP (Thermo Fisher Scientific). The complex was formed for 2 h at room temperature by adding 10 μM BCM (HY-B1540, MedChemExpress) or cortisol (HY-N0583, MedChemExpress), 25 mU/ml apyrase (Sigma), and then solubilized by 0.5% (w/v) lauryl maltose neopentylglycol (LMNG; Anatrace) and 0.1% (w/v) cholesteryl hemisuccinate TRIS salt (CHS; Anatrace) for 2 h at 4 °C. Supernatant was collected by centrifugation at 30,000 rpm for 40 min, and the solubilized complex was incubated with nickel resin for 2 h at 4 °C. The resin was collected and washed with 20 column volumes of 20 mM HEPES, pH 7.4, 100 mM NaCl, 10% glycerol, 2 mM MgCl2, 25 mM imidazole, 0.01% (w/v) LMNG, 0.01% GDN (Anatrace), 0.004% (w/v) CHS, 10 μM BCM (or cortisol) and 100 μM TCEP. The complex was eluted with 20 mM HEPES, pH 7.4, 100 mM NaCl, 10% glycerol, 2 mM MgCl2, 200 mM imidazole, 0.01% (w/v) LMNG, 0.01% GDN, 0.004% (w/v) CHS, 10 μM BCM (or cortisol) and 100 μM TCEP. The elution of nickel resin was applied to M1 anti-Flag resin (Sigma) for 2 h and washed with 20 mM HEPES, pH 7.4, 100 mM NaCl, 10% glycerol, 2 mM MgCl2, 5 mM CaCl2, 0.01% (w/v) LMNG, 0.01% GDN, 0.004% (w/v) CHS, 10 μM BCM (or cortisol) and 100 μM TCEP. The GPR97–Go complex was eluted in buffer containing 20 mM HEPES, pH 7.4, 100 mM NaCl, 10% glycerol, 2 mM MgCl2, 0.01% (w/v) LMNG, 0.01% GDN, 0.004% (w/v) CHS, 10 μM BCM (or cortisol), 100 μM TCEP, 5 mM EGTA and 0.2 mg/ml Flag peptide. The complex was concentrated and then injected onto Superdex 200 increase 10/300 GL column equilibrated in the buffer containing 20 mM HEPES, pH 7.4, 100 mM NaCl, 2 mM MgCl2, 0.00075% (w/v) LMNG, 0.00025% GDN, 0.0002% (w/v) CHS, 10 μM BCM (or cortisol) and 100 μM TCEP. The complex fractions were collected and concentrated individually for EM experiments.

Cryo-EM grid preparation and data collection

For the preparation of cryo-EM grids, 3 μl of purified BCM-bound and cortisol-bound GPR97–Go complex at approximately 20 mg/ml was applied onto a glow-discharged holey carbon grid (Quantifoil R1.2/1.3). Grids were plunge-frozen in liquid ethane cooled by liquid nitrogen using Vitrobot Mark IV (Thermo Fisher Scientific). Cryo-EM imaging was performed on a Titan Krios at 300 kV accelerating voltage in the Center of Cryo-Electron Microscopy, Zhejiang University. Micrographs were recorded using a Gatan K2 Summit direct electron detector in counting mode with a nominal magnification of ×29,000, which corresponds to a pixel size of 1.014 Å. Movies were obtained using serialEM at a dose rate of about 7.8 electrons per Å2 per second with a defocus ranging from −0.5 to −2.5 μm. The total exposure time was 8 s and intermediate frames were recorded in 0.2-s intervals, resulting in an accumulated dose of 62 electrons per Å2 and a total of 40 frames per micrograph. A total of 2,707 and 5,871 movies were collected for the BCM-bound and cortisol-bound GPR97–Go complex, respectively.

Cryo-EM data processing

Dose-fractionated image stacks for the BCM–GPR97–Go complex were subjected to beam-induced motion correction using MotionCor2.144. Contrast transfer function (CTF) parameters for each non-dose-weighted micrograph were determined by Gctf45. Particle selection, 2D and 3D classifications of the BCM–GPR97–Go complex were performed on a binned data set with a pixel size of 2.028 Å using RELION-3.0-beta246.

For the BCM–GPR97–Go complex, semi-automated particle selection yielded 2,026,926 particle projections. The projections were subjected to reference-free 2D classification to discard particles in poorly defined classes, producing 911,519 particle projections for further processing. The map of the 5-HT1BR–miniGo complex (EMDB-4358)47 low-pass filtered to 40 Å was used as a reference model for maximum-likelihood-based 3D classification, resulting in one well-defined subset with 307,700 projections. Further 3D classifications focusing the alignment on the complex produced two good subsets that accounted for 166,116 particles, which were subsequently subjected to 3D refinement, CTF refinement and Bayesian polishing. The final refinement generated a map with an indicated global resolution of 3.1 Å at a Fourier shell correlation of 0.143.

For the cortisol–GPR97–Go complex, particle selection yielded 4,323,518 particle projections for reference-free 2D classification. The well-defined classes with 2,201,933 particle projections were selected for a further two rounds of 3D classification using the map of the BCM-bound complex as reference. One good subset that accounted for 335,552 particle projections was selected for a further two rounds of 3D classifications that focused the alignment on the complex, and produced one high-quality subset with 75,814 particle projections. The final particle projections were subsequently subjected to 3D refinement, CTF refinement and Bayesian polishing, which generates a map with a global resolution of 2.9 Å. Local resolution for both density maps was determined using the Bsoft package with half maps as input maps48.

Model building and refinement

For the structure of the BCM–GPR97–Go complex, the initial template of GPR97 was generated using the module ‘map to model’ in PHENIX44. The coordinate of the 5-HT1BR–Go complex (PDB ID: 6G79) was used to generate the initial models for Go (ref. 44). Models were docked into the EM density map using UCSF Chimera49, followed by iterative manual rebuilding in COOT50 according to side-chain densities. BCM and lipid coordinates and geometry restraints were generated using phenix.elbow. BCM was built to the model using the ‘LigandFit’ module in PHENIX. The placement of BCM shows a correlation coefficient of 0.81, indicating a good ligand fit to the density. The model was further subjected to real-space refinement using Rosetta51 and PHENIX44.

For the structure of the cortisol–GPR97–Go complex, the coordinates of GPR97 and Go from the BCM-bound complex and scFv16 from the human NTSR1–Gi1 complex (PDB ID: 6OS9) were used as initial model. Models were docked into the density map and then were manual rebuilt in COOT. The agonist cortisol was built to the model using the ‘LigandFit’ module as described, showing a good density fit with a correlation coefficient of 0.80. The model was further refined using Rosetta51 and PHENIX44. The final refinement statistics for both structures were validated using the module ‘comprehensive validation (cryo-EM)’ in PHENIX44. The goodness of the fit of the model to the map was performed for both structures using a global model-versus-map FSC (Extended Data Fig. 2). The refinement statistics are provided in Extended Data Table 1. Figures of the structures were generated using UCSF Chimera, UCSF ChimeraX52 and PyMOL53.

Molecular dynamics simulation of the BCM–GPR97 and cortisol–GPR97 complexes

On the basis of the favour binding poses of BCM and cortisol with the receptor GPR97, which was calculated by the LigandFit program of PHENIX, the GPR97–agonist complexes were substrate from the two GPR97–agonist–mGo complexes for molecular dynamics simulation. The orientations of receptors were calculated by the Orientations of Proteins in Membranes (OPM) database. Following this, the whole systems were prepared by the CHARM-GUI and embedded in a bilayer that consisted of 200 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipids by replacement methods. The membrane systems were then solvated into a periodic TIP3P water box supplemented with 0.15 M NaCl. The CHARMM36m Force Filed was used to model protein molecules, CHARMM36 Force Filed for lipids and salt with CHARMM General Force Field (CGenFF) for the agonist molecules BCM and cortisol.

Then, the system was subjected to minimization for 10,000 steps using the conjugated gradient algorithm and then heated and equilibrated at 310.13 K and 1 atm for 200 ps with 10.0 kcal mol−1 Å−2 harmonic restraints in the NAMD 2.13 software. Next followed five cycles of equilibration for 2 ns each at 310.13 K and 1 atm, for which the harmonic restraints were 5.0, 2.5, 1.0, 0.5 and 0.1 kcal mol−1 Å−2 in sequence.

Production simulations were run at 310.13 K and 1 atm in the NPT ensemble using the Langevin thermostat and Nose–Hoover method for 200 ns. Electrostatic interactions were calculated using the particle mesh Ewald (PME) method with a cut-off of 12 Å. Throughout the final stages of equilibration and production, 5.0 kcal mol−1 Å−2 harmonic restraints were placed on the residues of GPR97 that were within 5 Å of Go in the BCM (or cortisol)–GPR97–Go complex to ensure that the receptor remained in the active state in the absence of the G protein. Trajectories were visualized and analysed using Visual Molecular Dynamics (VMD, version 1.9.3)

cAMP ELISA detection in Y-1 cells

Y-1 cells were transfected with Gpr97 siRNA (si-97, GUGCAGGGAAUGUCUUUAA) or control siRNA (si-Con) for 48 h. After starvation for 12 h in serum-free medium, the cells were further stimulated with cortisone (8 nM), forskolin (5 μM) (Sigma-Aldrich) or control vehicle for 10 min. Then, cells were washed three times with pre-cooled PBS and resuspended in pre-cooled 0.1 N HCl containing 500 μM IBMX at a 1:5 ratio (w/v). The samples were neutralized with 1 N NaOH at a 1:10 ratio (v/v) after 10 min. The supernatants were collected after centrifugation of the samples at 600g for 10 min. The supernatants were then prepared for cAMP determination using the cAMP Parameter Assay Kit (R&D Systems) according to the manufacturer’s instruction. The Gpr97 expression level under various conditions were further confirmed using quantitative real-time PCR.

Corticosterone measurements

Mouse adrenocorticotoma cell line Y-1 cells were transfected with Gpr97 siRNA (si-97) or control siRNA (si-Con) for 48 h. Then, the cells were treated with serum-free medium for 12 h. After that, cortisone (16 nM) or ACTH (0.5 μM) were added to cells for 30 min. The supernatants of the cell culture medium were collected for measurements of corticosterone by ELISA according to the manufacturer’s instructions.

Quantitative real-time PCR

Total RNA of cells was extracted using a standard TRIzol RNA isolation method. The reverse transcription and PCR experiments were performed with the Revertra Ace qPCR RT Kit (TOYOBO FSQ-101) using 1.0 μg of each sample, according to the manufacturer’s protocols. The quantitative real-time PCR was conducted in the Light Cycler apparatus (Bio-Rad) using the FastStart Universal SYBR Green Master (Roche). The mRNA level was normalized to GAPDH in the same sample and then compared with the control. The forward and reverse primers for GPR97 used in the experiments were CAGTTTGGGACTGAGGGACC and GCCCACACTTGGTGAAACAC. The mRNA level of GAPDH was used as an internal control. The forward and reverse primers for GAPDH were GCCTTCCGTGTTCCTACC and GCCTGCTTCACCACCTTC.

cAMP inhibition assay

To measure the inhibitory effects on forskolin-induced cAMP accumulation of different GPR97 constructs or mutants in response to different ligands or constitutive activity, the GloSensor cAMP assay (Promega) was performed according to previous publications12,13. HEK293 cells were transiently co-transfected with the GloSensor and various versions of GPR97 or vehicle (pcDNA3.1) plasmids using PEI in six-well plates. After incubation at 37 °C for 24 h, transfected cells were seeded into 96-well plates with serum-free DMEM medium (Gibco) and incubated for another 24 h at 37 °C in a 5% CO2 atmosphere. Different ligands were dissolved in DMSO (Sigma) to a stock concentration of 10 mM and followed by serial dilution using PBS solution immediately before the ligand stimulation. The transfected cells were pre-incubated with 50 μl of serum-free DMEM medium containing GloSensor cAMP reagent (Promega). After incubation at 37 °C for 2 h, varying concentrations of ligands were added into each well and followed by the addition of forskolin to 1 μM. The luminescence intensity was examined on an EnVision multi-label microplate detector (Perkin Elmer).

The Gqo protein activation BRET assay

According to previous publications, the BCM dipropionate-induced GPR97 activity could be measured by chimeric Gqo protein assays25. The Gqo BRET probes were generated by replacing the six amino acids of the C-terminal of Gq-RlucII with those from GoA1, creating a chimeric Gqo-RlucII subunit47. GFP10 was connected to Gγ. The Gqo protein activation BRET assay was performed as previously described54. In brief, HEK293 cells were transiently co-transfected with control D2R and various GPR97 constructs, plasmids encoding the Gqo BRET probes, incubated at 37 °C in a 5% CO2 atmosphere for 48 h. Cells were washed twice with PBS, collected and resuspended in buffer containing 25 mM HEPES, pH 7.4, 140 mM NaCl, 2.7 mM KCl, 1 mM CaCl2, 12 mM NaHCO3, 5.6 mM d-glucose, 0.5 mM MgCl2 and 0.37 mM NaH2PO4. Cells that were dispensed into a 96-well microplate at a density of 5–8 × 104 cells per well were stimulated with different concentrations of ligands. BRET2 between RLucII and GFP10 was measured after the addition of the substrate coelenterazine 400a (5 μM, Interchim) (Cayman) using a Mithras LB940 multimode reader (Berthold Technologies). The BRET2 signal was calculated as the ratio of emission of GFP10 (510 nm) to RLucII (400 nm).

Measurement of receptor cell-surface expression by ELISA

To evaluate the expression level of wild-type GPR97 and its mutants, HEK293 cells were transiently transfected with wild-type and mutant GPR97 or vehicle (pcDNA3.1) using PEI regent at in six-well plates. After incubation at 37 °C for 18 h, transfected cells were plated into 24-well plates at a density of 105 cells per well and further incubated at 37 °C in a 5% CO2 atmosphere for 18 h. Cells were then fixed in 4% (w/v) paraformaldehyde and blocked with 5% (w/v) BSA at room temperature. Each well was incubated with 200 μl of monoclonal anti-FLAG (F1804, Sigma-Aldrich) primary antibody overnight at 4 °C and followed by incubation of a secondary goat anti-mouse antibody (A-21235, Thermo Fisher) conjugated to horseradish peroxide for 1 h at room temperature. After washing, 200 μl of 3,3′,5,5′-tetramethylbenzidine (TMB) solution was added. Reactions were quenched by adding an equal volume of 0.25 M HCl solution and the optical density at 450 nm was measured using the TECAN (Infinite M200 Pro NanoQuant) luminescence counter. For determination of the constitutive activities of different GPR97 constructs or mutants, varying concentrations of desired plasmids were transiently transfected into HEK293 cells and the absorbance at 450 nm was measured.

The FlAsH-BRET assay

HEK293 cells were seeded in six-well plates after transfection with GPR97-FlAsH with Nluc inserted in a specific N-terminal site. Before the BRET assay, HEK293 cells were starved with serum for 1 h. Then cells were digested, centrifuged and resuspended in 500 μl BRET buffer (25 mM HEPES, 1 mM CaCl2, 140 mM NaCl, 2.7 mM KCl, 0.9 mM MgCl2, 0.37 mM NaH2PO4, 5.5 mM d-glucose and 12 mM NaHCO3). The FlAsH-EDT2 was added at a final concentration of 2.5 μM and incubated at 37 °C for 60 min. Subsequently, HEK293 cells were washed with BRET buffer and then distributed into black-wall clear-bottom 96-well plates, with approximately 100,000 cells per well. The cells were treated with a final concentration of BCM and cortisol at 10−5 to 10−11 and then coelenterazinc H was added at a final concentration of 5 μM, followed by checking the luciferase (440–480 nm) and FlAsH (525–585 nm) emissions immediately. The BRET ratio (emission enhanced yellow fluorescent protein/emission Nluc) was calculated using a Berthold Technologies Tristar 3 LB 941 spectrofluorimeter. The procedure was modified from those described previously34,55,56.

Statistical analysis

A one-way ANOVA test was performed to evaluate the statistical significance between various versions of GPR97 and their mutant in terms of expression level, potency or efficacy using GraphPad Prism. For all experiments, the standard error of the mean of the values calculated based on the data sets from three independent experiments is shown in respective figure legends.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.


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Bio Chemistry

A ‘Build and Retrieve’ methodology to simultaneously solve cryo-EM structures of membrane proteins

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    Republished by Plato



    Glycoproteomics is coming of age, thanks to advances in instrumentation, experimental methodologies and computational search algorithms.

    Glycosylation is one of the most common post-translational modifications, and glycoproteins play crucial roles in important biological processes like cell signaling, host–pathogen interaction, immune response and disease, including cancer and even the ongoing COVID-19 pandemic (Science 369, 330–333, 2020). Glycoproteomics aims to determine the positions and identities of the complete repertoire of glycans and glycosylated proteins in a given cell or tissue.

    Glycans are everywhere. High-throughput glycoproteomics approaches offer insights. Credit: Katherine Vicari, Springer Nature

    Mass spectrometry (MS)-based approaches allow large-scale global analysis; however, the structural diversity of glycans and the heterogeneous nature of glycosylation sites make comprehensive analysis particularly challenging. Glycans obstruct complete fragmentation of the protein backbone, and they were traditionally removed for simplicity at the cost of losing glycan information. The MS spectra tend to be complicated due to the presence of isomers, often requiring manual interpretation. Furthermore, database searching for spectral matches can quickly become a combinatorial problem and requires innovative bioinformatics solutions.

    Recent developments in MS instrumentation, fragmentation strategies (J. Proteome Res. 19, 3286–3301, 2020) and high-throughput workflows have made analyzing intact glycoproteins a possibility. Several specific enrichment strategies have made even low-abundance glycans and glycopeptides detectable (Mol. Cell. Proteomics, 2020). A variety of experimental workflows tailored for either N-linked glycans, which are found at consensus sites on the proteins, or O-linked glycans, which have no recognizable consensus sequence, have been developed (Nature 549, 538–542, 2017; Nat. Commun. 11, 5268, 2020; Nat. Methods 16, 902–910, 2019). New software packages based on fragment-ion indexing strategies offer substantial increases in speed for glycopeptide and site assignments (Nat. Methods 17, 1125–1132, 2020; Nat. Methods 17, 1133–1138, 2020).

    With other -omics fields taking the lion’s share of attention in recent years, it is now time for glycoproteomics to shine. Comprehensive understanding of glycosylation at different levels of granularity is bound to serve both basic and translational research.

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    Correspondence to Arunima Singh.

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    Singh, A. Glycoproteomics. Nat Methods 18, 28 (2021).

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