Pro-Q aerosol foam

The His-MoSep1, GST-MoMkk1, GST-MoMkk1^S19A, GST-MoMkk1^T24A, GST-MoMkk1^S125A, GST-MoMkk1^S136A, GST-MoMkk1^T139A, GST-MoMkk1^T207A, GST-MoMkk1^6A were expressed in Escherichia coli BL21-CodonPlus (DE3) cells and purified. A rapid Fluorescence Detection in Tube (FDIT) method using Pro-Q Diamond Phosphorylation Gel Stain was used to analyze protein phosphorylation in vitro. First, 2 mg MoMkk1 (or the unphosphorylated mutations) was mixed with MoSep1 in a kinase reaction buffer (100 mM PBS, 1 mM ascorbic acid, pH 7.5, 10 mM MgCl₂), with 50 mM ATP at 25°C for 1 h. 10 folds of cold acetone was then added to terminate the reaction. Casein was homogenized and suspended in Mili-Q water at a concentration of 0.2 mg/ml to stain the proteins. Briefly, 100 ml of Pro-Q Diamond was mixed with 10 ml of casein and the mixture was kept in dark 1 h. 10 folds of cold acetone was then added and the mixture was allowed to incubate overnight at -20°C. Proteins were precipitated by centrifugation at 14,000 rpm for 1 h at 4°C. Discard the supernatant. The pellet (proteins) was washed twice using 500 μl cold acetone. The pellet was dissolved in 200 μl of Mili-Q water and transferred to a 96 well plate. Fluorescence signal at 590 nm (exited at 530 nm) was measured using a Cytation3 microplate reader.

Bacteria were cultured to A₆₀₀ ~0.5. One milliltre was harvested by centrifugation, washed with PBS, then repelleted and resuspended in a volume of 1× Novex Tricine SDS sample buffer (Invitrogen) normalized for cell density (50 µl per 1 ml A₆₀₀ 0.5). Samples were boiled for 15 min and either cooled on ice (no proteinase K) or incubated with proteinase K (NEB) at 55 °C for 1 h. Samples were re-boiled and electrophoresed using the tricine buffer system with Novex tricine 16%-acrylamide gels (Invitrogen). Spectra Multicolor Low Range Protein Ladder (Thermo) was included to indicate approximate molecular weights. Gels were fixed, washed, stained using Pro-Q Emerald 300 (Invitrogen), and imaged using UV transillumination (Biorad Chemidoc MP). Gels were subsequently stained with Coomassie Brilliant Blue for detection of total protein. Image lab software (Biorad) was used to quantify LOS or total protein intensity levels. Samples were normalized by dividing the LOS intensity level of each band region by the total protein level from Coomassie staining. Relative values were calculated by dividing each normalized LOS value by the total normalized LOS levels in WT.

For the determination of phosphorylated serines, preliminary experiments tested phosphorylation at a wide range of PKA concentrations (0.02, 0.1, 0.5, 2.5, 13, 65, and 250 ng/μg C0–C2). Phosphorylation was monitored by in-gel staining of proteins with Pro-Q Diamond (Thermo Fisher) and staining total protein with SYPRO-Ruby (Thermo Fisher), according to the supplier's instructions. Maximal phosphorylation plateaued at 2.5 ng PKA/μg C0–C2. To ensure that all potential serines were phosphorylated, we used the sample treated with 65 ng/μg C0–C2. We excised the C0–C2 band as we described for LC-MS/MS (21 (link)) and utilized a probability-based approach for high-throughput protein phosphorylation analysis and site localization (28 (link)). The supporting information contains additional details of LC-MS/MS data processing and annotated spectra for all identified phosphopeptides (Table S1 and Fig. S2, A–E).
In separate preparations, C0–C2 was treated with GSK3β (G4296, Sigma) at 37 °C, for 2 h, in 10 mm imidazole, 145 mm KCl, 1 mm MgCl₂, 2 mm EGTA, 4 mm ATP, pH 7.0. Pro-Q Diamond staining of treated C0–C2 detected no phosphorylation at any concentration up to 100×. 1× treatment is the amount required to completely phosphorylate a GSK3β substrate, based on the specific activity of 175 pmol (of a standard substrate)/min/μg GSK3β. This was repeated with separate batches of GSK3β and C0–C2. As not all phosphorylation sites are equally detected by Pro-Q Diamond, we excised the C0–C2 band for LC-MS/MS analysis.

The phosphorylated and total proteins in gels were stained with the Pro-Q Diamond phosphoprotein stain or SYPRO-Ruby protein stain, respectively, following the protocol provided by the manufacturer (Thermo Fisher Scientific) [27 (link)]. Protein samples prepared from the control or platelets incubated with glucose for 2 h were separated by 2DE (n = 3 per group). Since the gel staining method for detecting phosphorylated proteins requires a lot of time and effort, only three samples each in the control and loaded-glucose for 2 h were examined. The gels were first stained with Pro-Q Diamond phosphoprotein gel stain to detect phosphoproteins, and visualized using the Typhoon 9400 imager. The gels were then stained with SYPRO-Ruby protein gel stain to reveal the total proteome before being visualized. The analysis process was carried out by matching all gels from the control or glucose-incubated platelets to determine quantitatively the effect of glucose using PDQuest™ 8.0 Advanced 2D Analysis software (Bio-Rad Laboratories, Hercules, CA, USA). The relative abundance of phosphoproteins was calculated in the Pro-Q Diamond images and the SYPRO Ruby images, as described previously [27 (link)]. The Mann–Whitney U test was used for statistical analysis when comparing the values of the two groups using the JMP 16 software.
To determine the position of phosphoproteins in the total protein, phosphoprotein spots were matched in gels stained by the Pro-Q Diamond and SYPRO Ruby stains. The matched spots were further matched with the spots in 2D-DIGE gels. Subsequently, phosphoprotein spots detected qualitatively by Pro-Q Diamond staining, combined with SYPRO Ruby staining and also detected quantitatively by 2D-DIGE with a statistically significant difference (p < 0.05), were selected. Selected spots were analyzed using MALDI-TOF/TOF MS to identify the proteins, as described above.

Among the 848 non-redundant virophage genomes, several categories of sequences were considered as likely representing complete and near-complete genomes: (i) reference genomes from isolates (n = 4), (ii) sequences identified as integrated with upstream and downstream host regions ≥ 2 kb (n = 7), (iii) sequences with direct or inverted terminal repeats (n = 118 and n = 8, respectively), (iv) sequences predicted to be ≥90% complete based on CheckV (AAI-based prediction, n = 59), and (v) linear contigs ≥ 25 kb (n = 61). This latter category was based on the median length of predicted complete and near-complete genomes from all other categories (25,168 bp). Overall, 257 sequences were considered complete or near-complete virophage genomes.
These complete and near-complete genomes were used as input for phylogenetic trees and genome-wide clustering to establish groups and potential taxa within the virophages. For phylogenetic trees, the sequences of the four morphogenesis genes detected in the 257 complete and near-complete genomes using the new HMM profiles (see above) were used after excluding all sequences that covered <60% of the HMM profile to remove partial gene predictions. Multiple alignments were then built for each gene using an iterative clustering-alignment-phylogeny procedure specifically adapted for aligning highly diverging sequences [54 (link)]. The alignments were then automatically trimmed using clipkit v1.3.0 [55 (link)] using the kpi-smart-gap mode to remove uninformative positions, and the trimmed alignments were used as input for tree building with IQ-Tree v2.2.0.3 [56 (link)] with automatic detection of the most appropriate substitution matrix, and 1000 replicates of ultra-fast bootstraps. The best-fit model was Q.pfam+F+R7 for PRO, Q.yeast+F+R8 for ATPase, and Q.pfam+F+R8 for both MCP and penton. For the larger MCP phylogeny, including both complete and partial virophage genomes (Figure S6), multiple alignments were computed with MAFFT v7.490 based on the curated multiple alignment including MCP from complete and near-complete genomes only (options “–add” and “–keeplength”) [51 (link)], and the phylogeny was built with tree IQ-Tree v2.2.0.3 [56 (link)] with similar parameters as described above.
Genome-wide amino acid identity (AAI) clustering was performed as in [57 (link)]. Briefly, predicted protein sequences from the 257 complete and near-complete virophages were compared all-vs-all using diamond v0.9.24.125 [58 (link)] and the following options: “--evalue 1e-5 --max-target-seqs 10,000–query-cover 50–subject-cover 50”. The resulting file was used as input for the script “amino_acid_identity.py” to calculate the average AAI for all pairs of genomes. The script “filter_aai.py” was then used to select only pairs of genomes with a minimum normalized cumulative bit score of 0.05. Finally, these selected pairwise AAI values were used as input for an MCL clustering using MCL 14-137 (inflation parameter = 1.1) [50 ].