Genome-Wide Analysis of Protein-Protein Interactions and Involvement of Viral Proteins in SARS-CoV Replication

Analyses of viral protein-protein interactions are an important step to understand viral protein functions and their underlying molecular mechanisms. In this study, we adopted a mammalian two-hybrid system to screen the genome-wide intraviral protein-protein interactions of SARS coronavirus (SARS-CoV) and therefrom revealed a number of novel interactions which could be partly confirmed by in vitro biochemical assays. Three pairs of the interactions identified were detected in both directions: non-structural protein (nsp) 10 and nsp14, nsp10 and nsp16, and nsp7 and nsp8. The interactions between the multifunctional nsp10 and nsp14 or nsp16, which are the unique proteins found in the members of Nidovirales with large RNA genomes including coronaviruses and toroviruses, may have important implication for the mechanisms of replication/transcription complex assembly and functions of these viruses. Using a SARS-CoV replicon expressing a luciferase reporter under the control of a transcription regulating sequence, it has been shown that several viral proteins (N, X and SUD domains of nsp3, and nsp12) provided in trans stimulated the replicon reporter activity, indicating that these proteins may regulate coronavirus replication and transcription. Collectively, our findings provide a basis and platform for further characterization of the functions and mechanisms of coronavirus proteins

Confirmation of the novel interactions by pull-down assays.

<p>The two test proteins were fused with glutathione S-transferase (GST) and maltose-binding protein (MBP), respectively, and mixed for binding in PBS buffer as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#s4" target="_blank">Materials and Methods</a> section. The protein mixture was pulled down with glutathione-Sepharose that binds GST and GST fusion proteins. Proteins bound by glutathione-Sepharose were resolved in SDS-PAGE, transferred to PVDF membrane and then was detected by anti-MBP rabbit serum. For every assay, GST protein was used as a negative control. For example, to examine the interaction between nsp10 and nsp14, the mixtures of GST-nsp10/MBP-nsp14 and GST/MBP-nsp14 were incubated with glutathione-Sepharose and the proteins pulled down by glutathione-Sepharose were identified by anti-MBP rabbit serum, respectively. The proteins indicated on the left side of the vertical line were MBP fusions and that on the right are GST fusions with “-” indicating non-fused GST as negative control. The star signs indicate the expected bands for MBP-fusion proteins. The smaller bands observed are probably premature proteins or degradation products of the same proteins.</p

The sequences of SARS-CoV used for interaction analysis.

*<p>The coordinate of the sequence is based on the genome of SARS-CoV WHU (GenBank accession number: AY394850). n/a: not applied.</p>**<p>Abbreviations: ADRP, adenosine diphosphate-ribose 1″-phosphatase; SUD, SARS Unique Domain; OGB, oligo(G)-binding; PLpro, papain-like cysteine proteinase; DU, deubiquitinating activity; TM, transmembrane domain; 3CLpro, 3C-like cysteine proteinase; RdRp, RNA-dependent RNA polymerase; Hel, 5′ to 3′ RNA helicase; NTPase, NTP and RNA 5′ triphosphatase; ExoN, 3′ to 5′ exonuclease; XendoU, endoribonuclease; 2′-O-MT, S-adenosylmethionine-dependent ribose 2′-O-methyltransferase; IFN, interferon.</p

An aboveground biomass partitioning coefficient model for rapeseed (Brassica napus L.)

Biomass partitioning is a pivotal part of the function-structure feedback mechanism. To improve the simulation of aboveground biomass partitioning in grBiomass partitioning is a pivotal part of the function-structure feedback mechanism. To improve the simulation of aboveground biomass partitioning in growth models for rapeseed (Brassica napus\ua0L.), we developed an aboveground biomass partitioning coefficient model for main stem and primary branches, and the stems, leaves, and pods on them, by quantifying the relationships between the biomass partitioning coefficient of major organs aboveground and physiological day of development (DPD). To realize this goal, dry matter data of organs were derived from an outdoor experiment with rapeseed cultivars Ningyou18 and Ningza19 under different fertilizer and transplanting density treatments in the 2012–2015 growing seasons. The model was fitted by calculating the partitioning coefficients of different organs as the ratio of the biomass of organs and their superior organs and normalizing\ua0DPD\ua0into the [0, 1] interval. Various model variables were parameterized to explain the effects of cultivar and environmental conditions on biomass partitioning coefficients for different organs. Our descriptive models were validated with independent experimental data, the correlation (r) of simulation and observation values all had significant level at\ua0P\ua0< 0.001, the absolute values of the average absolute difference (da) are all less than 0.062, except for the main-stem pods, primary branch, primary-branch leaves model, the ratio of\ua0da\ua0to the average observation (dap) are all less than 6.263%, and\ua0r\ua0are all greater than 0.9 except primary-branch leaves and primary-branch stems model. The results showed that most models have good performance and reliability for predicting biomass partitioning coefficient of the main stem, the primary branch, and the organs on them. This sets the stage for linking a growth model with the biomass-based morphological model, for the development of a functional-structural rapeseed model.owth models for rapeseed (Brassica napus L.), we developed an aboveground biomass partitioning coefficient model for main stem and primary branches, and the stems, leaves, and pods on them, by quantifying the relationships between the biomass partitioning coefficient of major organs aboveground and physiological day of development (DPD). To realize this goal, dry matter data of organs were derived from an outdoor experiment with rapeseed cultivars Ningyou18 and Ningza19 under different fertilizer and transplanting density treatments in the 2012–2015 growing seasons. The model was fitted by calculating the partitioning coefficients of different organs as the ratio of the biomass of organs and their superior organs and normalizing DPD into the [0, 1] interval. Various model variables were parameterized to explain the effects of cultivar and environmental conditions on biomass partitioning coefficients for different organs. Our descriptive models were validated with independent experimental data, the correlation (r) of simulation and observation values all had significant level at P < 0.001, the absolute values of the average absolute difference (d) are all less than 0.062, except for the main-stem pods, primary branch, primary-branch leaves model, the ratio of d to the average observation (d) are all less than 6.263%, and r are all greater than 0.9 except primary-branch leaves and primary-branch stems model. The results showed that most models have good performance and reliability for predicting biomass partitioning coefficient of the main stem, the primary branch, and the organs on them. This sets the stage for linking a growth model with the biomass-based morphological model, for the development of a functional-structural rapeseed model

Protein interactions of SARS-CoV detected by mammalian two-hybrid assays.

<p>(A) Interaction matrix of SARS-CoV proteins. The grey squares indicate the novel interactions detected in this work. The black squares represent the interactions which have also been reported previously, including nsp5–nsp5<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Anand1" target="_blank">[19]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Shi1" target="_blank">[68]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Yang1" target="_blank">[69]</a>, nsp5–nsp7 and nsp8<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-vonBrunn1" target="_blank">[9]</a>, nsp7–nsp7<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Zhai1" target="_blank">[21]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Peti1" target="_blank">[70]</a>, nsp7–nsp8<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Zhai1" target="_blank">[21]</a>, nsp7–nsp9<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-vonBrunn1" target="_blank">[9]</a>, nsp8–9b<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-vonBrunn1" target="_blank">[9]</a>, nsp10–nsp14 and nsp10–nsp16<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Imbert1" target="_blank">[10]</a>, nsp15–nsp15 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Guarino1" target="_blank">[54]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Joseph2" target="_blank">[56]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Renzi1" target="_blank">[71]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Ricagno1" target="_blank">[72]</a>, nsp7-E and 7a-M<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Fielding1" target="_blank">[73]</a>, N-N<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-He1" target="_blank">[55]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Surjit1" target="_blank">[57]</a> and N-N<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-He1" target="_blank">[55]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#pone.0003299-Surjit1" target="_blank">[57]</a>. (B) A typical result for a positive interaction with the example of nsp10–nsp14. The combination of pM-53 and pVP16-T represents a positive control. (C) A typical interaction inhibition assay performed to confirm that the interaction was not resulted from self-activation. Error bars represent standard deviations from three independent experiments.</p

Impacts of viral proteins provided in <i>trans</i> on the replication/ transcription of SARS-CoV replicon.

<p>(A) Nonstructural proteins; (B) Structural and accessory proteins. 2×10⁵ BHK21 cells were transfected with pBAC- Rep-SCV-luc/neo (0.25 µg), the viral protein expression plasmids (0.2 µg each) and pRL-TK (0.05 µg) as an internal control. The corresponding empty vectors (0.2 µg each) were used as the negative controls. The luciferase activities were measured 24 h post transfection. Error bars represent standard deviations of the mean of three experiments.</p

Structure and activity assay of the reporter replicon construct pBAC-Rep-SCV-luc/neo.

<p>(A) Schematic structure of the replicon. The coding sequence of luciferase-neomycin fusion under the control of M gene TRS was inserted into the basic replicon construct pBAC-SARS-CoV-REP between AscI and BamHI sites (For details, see the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003299#s4" target="_blank">Materials and Methods</a>). (B) Luciferase assays of the reporter replicons. 2×10⁵ BHK21 cells were transfected with the three kinds of replicon plasmids (0.4 µg each), respectively, and pRL-TK plasmid (0.1 µg) as an internal control. The luciferase activity assays were performed 24 h post transfection. Error bars represent standard deviations of the mean of three experiments.</p