Venom proteomics

To generate a genotype-phenotype map and verify toxin expression proteomically, we performed quantitative mass spectrometry (qMS) on whole venom samples following Rokyta & Ward⁶¹ and Ward et al.⁶². We first quantified venom protein samples using the Qubit Protein Assay kit with a Qubit 1.0 Fluorometer (Thermo Fisher Scientific). For each sample, we then digested approximately 5 μg of whole venom using the Calbiochem ProteoExtract All-in-One Trypsin Digestion Kit (Merck, Darmstadt, Germany) according to the manufacturer’s instructions and using LC/MS grade solvents, leaving an overall yield of approximately 4.3 μg of digested venom protein after digestion. Samples were dried using a SpeedVac at 25 °C for 1 hour and stored at −20 °C until use. To initiate the mass spectrometry run, the resulting dried and digested tryptic peptides were resuspended in 0.1% formic acid at a final concentration of 250 ng/μL. Three digested Escherichia coli proteins-purchased from Abcam at known concentrations and mixed in the specified proportions (1000×) prior to digestion-were used as internal standards: 25 fmol of {"type":"entrez-protein","attrs":{"text":"P00811","term_id":"113726","term_text":"P00811"}}P00811 (Beta-lactamase ampC), 250 fmol of {"type":"entrez-protein","attrs":{"text":"P31658","term_id":"2506683","term_text":"P31658"}}P31658 (Protein deglycase 1), and 2500 fmol of {"type":"entrez-protein","attrs":{"text":"P31697","term_id":"729490","term_text":"P31697"}}P31697 (Chaperone protein FimC) per injection. Final sample concentrations were achieved by infusing the internal standard peptide mix into samples. For the LCMS/MS run, a 2 μL aliquot was analyzed using an externally calibrated Thermo Q Exactive HF (high-resolution electrospray tandem mass spectrometer) in conjunction with Dionex UltiMate3000 RSLCnano System. A 2 μL sample was aspirated into a 50 μL loop and loaded onto the trap column (Thermo μ-Precolumn 5 mm, with nanoViper tubing 30 μm i.d. ×10 cm). For separation on the analytical column (Acclaim pepmap RSLC 75 μMx 15 cm nanoviper), the flow rate was set to 300 nl/min. Mobile phase A was composed of 99.9% H2O (EMD Omni Solvent) and 0.1% formic acid, and mobile phase B was composed of 99.9% ACN and 0.1% formic acid. We performed a 60 min linear gradient from 3% to 45% B. The LC eluent was directly nanosprayed into the Q Exactive HF mass spectrometer (Thermo Scientific), and during the chromatographic separation, the Q Exactive HF was operated in a data-dependent mode and under direct control of the Thermo Excalibur 3.1.66 (Thermo Scientific). Resulting MS data were acquired using a data-dependent top-20 method for the Q Exactive HF platform, dynamically choosing the most abundant not-yet-sequenced precursor ions from the survey scans (350–1700).

Sequencing was performed via higher energy collisional dissociation fragmentation with a target value of 10⁵ ions determined with predictive automatic gain control. Full scans (350–1700 m/z) were performed at 60,000 resolution in profile mode. MS2 were acquired in centroid mode at 15,000 resolution. We excluded ions with a single charge, charges more than seven, or unassigned charge. A 15-s dynamic exclusion window was used. All measurements were performed at room temperature, and done with three technical replicates to account for machine-related variability and to facilitate label-free quantification. We searched the resulting raw files with Proteome Discoverer 1.4 using SequestHT as the search engine with custom-generated FASTA databases and percolator as peptide validator. The SequestHT search parameters used were: enzyme name = Trypsin, maximum missed cleavage = 2, minimum peptide length = 6, maximum peptide length = 144, maximum delta Cn = 0.05, precursor mass tolerance = 10 ppm, fragment mass tolerance = 0.2 Da, dynamic modifications, carbamidomethyl + 57.021 Da(C) and oxidation + 15.995 Da(M). Protein identities were validated using Scaffold (version 4.3.4, Proteome Software Inc., Portland, OR, USA) software. We accepted protein identities based on a 1.0% false discovery rate (FDR) using the Scaffold Local FDR algorithm and a minimum of one recognized peptide. We considered a transcript proteomically detected if it was found in at least one of the three replicates per individual. Any protein not detected in all three replicates was excluded from further analyses. From all individuals we also excluded Bradykinin-potentiation peptides due to the potential for extensive proteolytic cleavage⁴⁹, as well as myotoxins due to low quality assignments. Finally, individual toxins that appeared to be ambiguously assigned in all three replicates of a sample were omitted.

To estimate proteomic abundances, we followed Rokyta & Ward⁶¹ and Ward et al.⁶² and calculated separate conversion factors for each of three replicates based on the known concentrations of the Escherichia coli control proteins and their observed quantitative values (normalized spectral counts) determined by Scaffold. These conversion factors were calculated by finding the slope of the best fit line of the known control concentrations and the observed normalized spectral counts, with an intercept at the origin. The conversions factors were then used to convert the normalized spectral counts for each venom protein in each replicate to concentrations; final concentrations for each sample were then averaged across each individual’s corresponding replicates. The centered log-ratio (clr) transformation⁶³ was applied to all our transcriptome and proteome abundances for these analyses; this transformation preserves rank and is equivalent to a log transformation for linear relationships⁴⁹.

The methods used here are designed to provide information on relative protein abundances, primarily to validate transcriptomic findings. In snakes, the internal standard approach has previously been shown to provide a strong agreement between venom proteins and transcripts across multiple species⁴⁹. Using this approach to determine absolute protein abundances has limitations, however, due to protein-specific properties which can affect downstream abundance calculations. For this reason, all analyses are done in a compositional framework. Prior work has shown that different approaches to quantification do not affect correlation between mRNA and protein levels using the internal standard approach⁴⁹. Although additional proteomic and/or analytical methods for identification and quantification may afford greater detectability of specific toxins (e.g. BPPs, myotoxins), these methods introduce other potential biases to quantification. Rather than potentially biasing our results by using different parameters for different proteins, we chose instead to treat all proteins equally using the same parameters across all samples.