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

Adjuvant EGFR tyrosine kinase inhibitors for patients with resected <i>EGFR</i>-mutated non-small-cell lung cancer: a network meta-analysis

Aim: To investigate the efficacy and safety of adjuvant EGFR tyrosine kinase inhibitors for resected EGFR-mutated non-small-cell lung cancer. Materials &amp; methods: Eligible phase II/III randomized controlled trials were included for the network meta-analyses (PROSPERO CRD42021275150). Results: Nine records and 831 patients were involved. Adjuvant chemotherapy followed with osimertinib significantly prolonged disease-free survival compared with chemotherapy (hazard ratio [HR]: 0.2; 95% CI: 0.14–0.29), chemotherapy followed with erlotinib (HR: 0.33; 95% CI: 0.18–0.6), chemotherapy followed with gefitinib (HR: 0.36; 95% CI: 0.16–0.82), gefitinib (HR: 0.26; 95% CI: 0.17–0.41) and icotinib (HR: 0.56; 95% CI: 0.3–0.98). Icotinib was the least likely to cause grade ≥3 adverse events. Conclusion: Chemotherapy followed with osimertinib brings about the best disease-free survival. Icotinib monotherapy shows the best safety. </jats:p

Catalysis of 5-methyltetrahydrofolate to MeFox facilitates folate biofortification in crops

Abstract Folate deficiency is a global health problem. Biofortification has been considered a cost-effective means to tackle this problem. Here, we describe the genetic cloning and functional identification of a previously uncharacterised plant protein, designated as CTM, which functions as an enzyme in folate metabolism. Plant CTMs are capable of catalysing 5-methyl-tetrahydrofolate to MeFox, a pyrazino-s-triazine derivative of 4α-hydroxy-5-methyl-tetrahydrofolate. The natural asparagine-to-glycine substitution caused by an A-to-G single nucleotide variation in maize CTM enhances its enzymatic activity, as demonstrated by in vitro enzymatic assays and in silico analyses using a maize CTM structure model based on a monomeric sorghum CTM crystal. Loss of the CTM function led to accumulation of 5-methyl-tetrahydrofolate, and overexpression of the maize CTM carrying the G-allele boosted the metabolic flow towards MeFox, and showed no negative impacts on plant growth. Our results suggest that CTM, which has evolved 5-methyl-tetrahydrofolate-to-MeFox converting activity in plants, could be valuable for developing folate-biofortified crops to provide an alternative to the challenge presented by the global folate deficiency.</jats:p

The effect of additional N protein on viral genome replication and transcription.

<p>(A) Kinetics of luciferase activity of the reporter replicon. 2×10⁵ BHK21 cells were transfected with pRL-TK plasmid (0.1 µg) and pBAC-Rep-SCV-luc/neo (0.4 µg). After transfection, the cells were collected for luciferase assays at different time points (6 h to 72 h). (B) Reporter gene activity in presence or absence of additional N protein provided in <i>trans</i>. 2×10⁵ BHK21 cells were transfected with pRL-TK plasmid (0.05 µg), pBAC-Rep-SCV-luc/neo (0.25 µg) and pcDNA3.0-N (0.2 µg) or pcDNA3.0 (0.2 µg). After transfection, the cells were harvested for luciferase assays at different time points (6 h to 38 h). (C) The ratios of luciferase activities of Rep-SCV-luc/neo in presence of N protein related to that in absence of N protein at different time points (6 h to 38 h). Error bars represent standard deviations of the mean of three experiments.</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

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

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

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