Abstract
Influenza genes evolve mostly via point mutations, and so knowing the effect of every amino-acid mutation provides information about evolutionary paths available to the virus. We previously used high-throughput mutagenesis and deep sequencing to estimate the effects of all mutations to an H1 influenza hemagglutinin on viral replication in cell culture (Thyagarajan and Bloom, 2014); however, these measurements suffered from sub-stantial noise. Here we describe advances that greatly improve the accuracy and reproducibility of our measurements. The largest improvements come from using a helper virus to reduce bottlenecks when generating viruses from plasmids. Our measurements confirm that antigenic sites on the globular head of hemagglutinin are highly tolerant of mutations. However, other regions – including stalk epitopes targeted by broadly neutralizing antibodies – have a limited capacity to evolve. The ability to accurately measure the effects of all influenza mutations should enhance efforts to understand and predict viral evolution.
Introduction
Seasonal influenza is a recurrent threat to human health largely because it rapidly accumulates amino-acid mutations in proteins targeted by the immune system (Smith et al., 2004). Measuring the functional impact of every possible amino-acid mutation to influenza can therefore provide useful information about which evolutionary paths are accessible to the virus. Such measurements are now possible using deep mutational scanning (Fowler and Fields, 2014; Boucher et al., 2014). When applied to influenza, this technique involves creating all codon mutants of a viral gene, incorporating these mutant genes into viruses that are subjected to a functional selection, and estimating the functional impact of each mutation by using deep sequencing to quantify its frequency pre- and post-selection. We and others have used deep mutational scanning to estimate the effects of all amino-acid or nucleotide mutations to several influenza genes (Thyagarajan and Bloom, 2014; Bloom, 2014a; Wu et al., 2014; Doud et al., 2015; Wu et al., 2016). However, these studies suffered from substantial noise that degrades the utility of their results. For instance, in every study that reported the results for independent experimental replicates, the replicate-to-replicate correlation was mediocre.
This experimental noise arises primarily from bottlenecking of mutant diversity during the generation of viruses from plasmids. The influenza genome consists of eight negative-sense RNA segments. During viral infection, gene expression from these segments is a highly regulated process (Chua et al., 2013; Robb et al., 2009; Shapiro et al., 1987). Generating influenza from plasmids involves cotransfecting mammalian cells with multiple plasmids that must yield all eight viral gene segments and at least four viral proteins at a stoichiometry that leads to assembly of infectious virions (Hoffmann et al., 2000; Neumann et al., 1999; Fodor et al., 1999). This plasmid-driven process is understandably less efficient than viral infection. A small fraction of transfected cells probably yield most initial viruses, which are then amplified by secondary infection. This bottlenecking severely hampers experiments that require creating a diverse library of viruses from an initial library of plasmids.
Several strategies have been used to overcome problems associated with bottlenecks during the generation of influenza from plasmids. One strategy is to generate and titer each viral variant individually, and then mix them (Varble et al., 2014; Benitez et al., 2015). A second strategy is to reduce the impact of bottlenecks by shrinking the complexity of the libraries, such as by only mutating a small portion of a viral gene (Wu et al., 2015; Jiang et al., 2015). Neither of these strategies scale effectively to the deep mutational scanning of full-length proteins, since there are ~ 104 unique amino-acid mutants of a 500-residue protein.
To overcome these limitations, we have developed a novel approach that uses a “helper virus” to generate virus libraries without strong bottlenecking. We have combined this approach with other technical improvements to repeat our deep mutational scanning of all amino-acid mutations to an H1 hemagglutinin (HA) with much higher accuracy and reproducibility. We use phylogenetic analyses to show that our new measurements better reflect constraints on HA evolution in nature. We confirm that antigenic sites in the globular head of HA are highly tolerant of mutations, and identify other regions of the protein that are more constrained. These advances improve our understanding of HA’s inherent evolutionary capacity, and can help inform evolutionary modeling and guide the development of vaccines targeting sites with a limited capacity for mutational escape.
Results
A helper-virus enables efficient production of mutant virus libraries from plasmids
We reasoned that the process of generating viral libraries carrying HA mutants would be more efficient if transfected cells only needed to produce HA from plasmid, and the other gene segments and proteins were delivered by viral infection (Figure 1A). Marsh et al. (2007) described seven-segmented HA-deficient virus that could be propagated in cells that constitutively express HA protein. We created HA-expressing cells and validated that we could propagate an HA-deficient A/WSN/1933 (H1N1) virus (Figure 1 - figure supplement 1).
We cloned triplicate plasmid libraries of random codon mutants of the A/WSN/1933 HA gene. These libraries contain multi-nucleotide (e.g., GGC→CAT) as well as single-nucleotide (e.g., GGC→GAC) codon mutations. There are 63 × 565 ≈ 3.5 × 104 different codon mutations that can be made to the 565-codon HA gene, corresponding to 19 × 565 ≈ 104 amino-acid mutations. The deep sequencing described below found at least three occurrences of over 97% of these amino-acid mutations in each of the three replicate plasmid mutant libraries. These libraries have a somewhat lower mutation rate than those in Thyagarajan and Bloom (2014), with the number of mutations per clone following a roughly Poisson distribution with a mean of about one (Figure 1 - figure supplement 2). We cloned these HA libraries into both uni-directional and bidirectional reverse-genetics plasmids (Neumann et al., 1999; Hoffmann et al., 2000).
We then transfected cells with one of the HA plasmid mutant libraries along with plasmids expressing the four viral polymerase-related proteins (PB2, PB1, PA, and NP) with the goal of generating preformed viral ribonucleoprotein complexes carrying the HA segment. These transfected cells were then infected with HA-deficient helper virus, and 24 hours later we determined the titer of fully competent virus in the supernatant. The highest titers (∼ 103 TCID50 per µl) were obtained using the uni-directional reverse-genetics plasmid (Figure 1 – figure supplement 3). Overall, this result demonstrates the feasibility of the helper-virus strategy in Figure 1A.
We next used this helper-virus strategy to independently generate three mutant virus libraries, one from each of our triplicate plasmid mutant libraries. Each mutant virus library should sample most of the codon mutations to the A/WSN/1933 HA. We also generated a control virus library from a plasmid encoding the unmutated wild-type HA gene.
Low MOI passage combined with barcoded-subamplicon sequencing reveals strong selection against non-functional HA variants
To select for viruses carrying functional HA variants, we passaged the mutant virus libraries at a low multiplicity of infection (MOI) of 0.0075 TCID50 per cell as outlined in Figure 1A. This MOI is substantially lower than that used in Thyagarajan and Bloom (2014) (0.1), and was chosen with the goal of more effectively purging non-functional HA variants.
To quantify selection on HA, we used deep sequencing to determine the frequency of each mutation pre- and post-selection. Standard Illumina sequencing has an error rate that is too high. In Thyagarajan and Bloom (2014), we reduced this error rate by using overlapping paired-end reads. Here we used an alternative error-correction strategy that involves attaching random barcodes to PCR subamplicons and then clustering reads with the same barcode (Figure 1B). To our knowledge, this basic strategy was first described by Hiatt et al. (2010) and first applied to influenza by Wu et al. (2014). Sequencing of the unmutated plasmid allows us to estimate that the error rate is ∼ 2 × 10-4 per codon, corresponding < 10-4 per nucleotide (Figure 1C). This error rate is substantially lower than we obtained previously using overlapping paired-end reads, consistent with the results of the sequencing-strategy comparison by Zhang et al. (2016). Sequencing of virus generated from the unmutated plasmid shows that the error rates associated with reverse-transcription and viral replication are also tolerably low (Figure 1C).
Figure 1C reveals strong selection against non-functional HA variants. The plasmid mutant libraries contain a mix of synonymous, nonsynonymous, and stop-codon mutations. However, stop-codon mutations are almost completely purged from the passaged mutant virus libraries, as are many nonsynonymous mutations. The selection against the stop codons is stronger than in Thyagarajan and Bloom (2014) (Figure 1 – figure supplement 4). Overall, these results indicate strong selection on HA that can be quantified by accurate deep sequencing.
The mutant virus libraries have reduced bottlenecking and yield reproducible measurements of mutational effects
To evaluate whether the virus libraries were bottlenecked, we examined the distribution of synonymous mutation frequencies in each library. If bottlenecking causes a few mutants to stochastically dominate, we expect that in each library a few sites will have relatively high synonymous mutation frequencies and that these sites will differ among replicates. Figure 2A shows normalized synonymous mutation frequencies across HA for each of the three replicate mutant virus libraries from both Thyagarajan and Bloom (2014) and the current study. In the older study, each replicate has a different handful of sites with greatly elevated synonymous frequencies (green arrows), indicative of stochastic bottlenecking. In contrast, in our new virus libraries, the distribution of synonymous mutation frequencies is much more uniform across the HA gene.
We next evaluated the reproducibility of our measurements of the effects of each amino-acid mutation. We estimated the effect of each mutation from its change in frequency in the mutant viruses relative to the original plasmid libraries, correcting for the site-specific error rates determined by sequencing unmutated virus and plasmid, and performing the analyses using the algorithms described in Bloom (2015) and implemented in the software at http://jbloomlab.github.io/dms_tools/. The results are quantified in terms of the preference of each site for each amino-acid; the set of all 20 preferences at a site can be thought of as representing the expected post-selection frequency of each amino acid at that site if all amino acids are initially present at equal frequencies.
Figure 2B shows the correlation between the amino-acid preferences from each experimental replicate. The replicate-to-replicate reproducibility is dramatically improved in our new experiments relative to those in Thyagarajan and Bloom (2014), with the average Pearson’s R2 increasing from 0.34 to 0.61. The new experiments are also largely free of the most problematic type of noise that plagued the original data, where an amino acid at a site is deemed highly preferred in one replicate but disfavored in another. Overall, these results demonstrate that our new strategies enable more reproducible measurement of the effects of all mutations to HA.
The measurements better reflect the constraints on HA evolution in nature
We next tested whether our new measurements better describe the evolution of HA in nature. The accuracy with which experimental measurements of site-specific amino-acid preferences reflect the constraints shaping a protein’s evolution in nature can be quantified by comparing the phylogenetic fit of experimentally informed substitution models (Bloom, 2014a). We assembled a set of human and swine influenza HA sequences and fit substitution models using phydms (http://jbloomlab.github.io/phydms/; Bloom, 2016), which in turn uses Bio++ (Gue´guen et al., 2013) for the likelihood calculations.
A substitution model informed by our new measurements described the natural evolution of HA better than a model informed by the older measurements from Thyagarajan and Bloom (2014), and vastly better than conventional non-site-specific substitution models (Table 1). Averaging the measurements from both studies improved phylogenetic fit even further, a finding consistent with previous work reporting that combining data from multiple deep mutational scanning studies of the same protein tends to improve substitution models (Doud et al., 2015).
The phylogenetic model fitting optimizes a parameter that accounts for differences in the stringency of selection between the experiments and natural evolution (Bloom, 2014b); a stringency parameter > 1 indicates that natural selection prefers the same amino acids as the experimental selections but with greater strength. The best model in Table 1 has a stringency parameter of 1.8. The site-specific amino-acid preferences for this model scaled by this stringency parameter are displayed in Table 1 – figure supplement 1 and Table 1 – figure supplement 2; text files with unscaled and scaled numerical values are in Supplementary file 1 and Supplementary file 2.
A handful of sites are under very different selection in our experiments than in nature
We next asked whether there are sites in HA that evolve in nature in a way that is highly discordant with our experimental measurements. To do this, we used the approach in Bloom (2016) to identify selection in nature for amino acids that differ from the ones preferred in the deep mutational scanning. At most sites, the magnitude of differential selection is small (Table 1 – figure supplement 3), indicating that the experimentally measured preferences mostly parallel constraints on natural evolution. Sites that are under strong differential selection usually show conservative changes; for example, site 78 prefers isoleucine in nature but leucine in our deep mutational scanning.
One of the most striking exceptions to this general concordance between natural selection and our experiments can be given a clear explanation. At site 342 (328 in H3 numbering), the experimentally measured preference for tyrosine is at odds with nature’s strong preference for serine (Table 1 – figure supplement 3). The labadapted A/WSN/1933 strain used in our experiments differs from naturally occurring influenza in that it uses plasmin to cleave and activate HA (Lazarowitz et al., 1973; Goto and Kawaoka, 1998). Plasmin cleavage is enhanced by tyrosine at site 342 (Sun et al., 2010), so it is unsurprising that our experiments detected a preference at this site unique to the influenza strain we used. This example illustrates how the occasional deviations from the general concordance between deep mutational scanning experiments and natural selection can point to interesting biological mechanisms.
Antigenic sites in HA’s globular head are highly tolerant of mutations, but stalk epitopes targeted by broadly neutralizing antibodies are not
We computed the inherent mutational tolerance of each site using the stringency-scaled amino-acid preferences from the combined datasets (Figure 3A). The mutational tolerance is mapped onto the structure of HA in Figure 3B.
The H1 HA antigenic sites defined by Caton et al. (1982) are significantly more mutationally tolerant than the average site (Figure 3C), even after accounting for relative solvent accessibility (Figure 3 – figure supplement 1A). This high mutational tolerance extends to other solvent-exposed residues in contact with the antigenic sites (Figure 3D, Figure 3 – figure supplement 1B), indicating that the HA molecular surfaces commonly targeted by antibodies have a high inherent capacity for evolutionary change. This high mutational tolerance does not extend to the receptor-binding pocket (Figure 3E, Figure 3 – figure supplement 1C,D), but may be a feature of the sites that make the greatest contributions to the punctuated antigenic evolution of H3N2 and seasonal H1N1 HA (Koel et al., 2013) (Figure 3F), albeit not at a level that is statistically significant after correcting for solvent accessibility (Figure 3 – figure supplement 1E). These results support the findings of Thyagarajan and Bloom (2014) that the sites in HA that are the immunodominant targets of antibodies have a high inherent capacity to tolerate point mutations.
Perhaps in part because of the high mutational tolerance of the antigenic sites in its globular head, HA is adept at escaping antibody-mediated immunity (Smith et al., 2004; Bedford et al., 2014). New vaccines are being developed that aim to elicit immunity against other portions of HA (Krammer and Palese, 2013), most commonly regions in the stalk that are relatively conserved among naturally occurring strains. An important question is whether these stalk regions are conserved because they are inherently intolerant of point mutations, or simply because they are not currently under immune pressure. To answer this question, we examined the inherent mutational tolerance of the largely overlapping epitopes of four broadly neutralizing anti-stalk antibodies: F10 (Sui et al., 2009), CR6261 (Ekiert et al., 2009), FI6v3 (Corti et al., 2011), and CR9114 (Dreyfus et al., 2012). Visual inspection of Figure 3G shows that these stalk epitopes have a low mutational tolerance, a result that is confirmed by statistical analysis (Figure 3 – figure supplement 1F). Therefore, the epitopes that next-generation vaccines aim to target indeed have a reduced capacity for immune escape by point mutations.
We wondered if some of HA’s variation in mutational tolerance is explained by differences in the three ancient domains that compose the protein. HA is the product of a series of ancient insertions that merged a fusion domain, a receptor-binding domain, and a vestigial esterase domain (Rosenthal et al., 1998). We compared the inherent mutational tolerance of these three domains, again correcting for solvent accessibility. We found that sites in the receptor-binding domain have a significantly higher mutational tolerance than all sites in the protein, whereas sites in the fusion domain have a significantly lower mutational tolerance (Figure 3 – figure supplement 2). Therefore, HA’s antigenic evolvability is not just a consequence of the immun-odominant antigenic sites themselves having high mutational tolerance, but also because these sites are found within a protein domain that is intrinsically more mutable than the rest of HA.
Discussion
We have described new techniques that greatly improve the reproducibility of deep mutational scanning of influenza. The largest improvement appears to result from using a helper virus to generate virus mutant libraries without the bottlenecks that plague the creation of viruses purely from plasmids. We have used these techniques to more accurately measure the effects of all amino-acid mutations to HA. These measurements confirm our previous findings that HA’s propensity for immune escape is underpinned by the high inherent mutational tolerance of its antigenic sites. Importantly, our improved data also enable us to show that some regions of HA – including the stalk epitopes targeted by new broadly neutralizing antibodies – have a reduced capacity for evolutionary change. Overall, our work provides insight into how protein-intrinsic mutational tolerance shapes influenza evolution, and will provide a basis for using deep mutational scanning to improve quantitative models of viral evolution and understand virus-immune interactions.
Materials and Methods
Growth of HA-deficient helper virus in HA-expressing cells
MDCK-SIAT1-EF1a-WSN-HA cells were engineered to constitutively express the HA protein of A/WSN/1933 (H1N1) under control of the EF1a promoter by lentiviral transduction. HA surface expression was validated by flow cytometry (Figure 1 – figure supplement 1).
To generate HA-deficient helper virus, we seeded co-cultures of 293T cells (5 × 105 cells per well) and MDCK-SIAT1-EF1a-WSN-HA cells (5×104 cells cells per well) in 6-well dishes in D10 media (DMEM supplemented with 10% heat-inactivated FBS, 2 mM L-glutamine, 100 U of penicillin/ml, and 100 µg of streptomycin/ml). After 24 hours, we transfected these co-cultures with bidirectional reverse-genetics plasmids for the seven non-HA segments of the A/WSN/1933 virus (pHW181-PB2, pHW182-PB1, pHW183-PA, pHW185-NP, pHW186-NA, pHW187-M, and pHW188-NS) (Hoffmann et al., 2000) plus a protein expression plasmid for WSN HA (pHAGE2-CMV-WSNHA, which importantly does not contain non-coding regions of the HA segment or a promoter for the transcription of negativesense viral RNA). Transfection was performed with BioT transfection reagent (Bioland B01-02, Paramount, California) with each well receiving 250 ng of each plasmid. Twenty-two hours after transfection, we changed the media to WSN growth media (Opti-MEM supplemented with 0.5% heat-inactivated FBS, 0.3% BSA, 100 U of penicillin/ml, 100 µg of streptomycin/ml, and 100 µg of calcium chloride/ml). At 96 hours post-transfection, we passaged 400 µl of the transfection supernatant to 15-cm dishes containing 4 × 106 MDCK-SIAT1 cells (as a negative control) or MDCK-SIAT1-EF1a-WSN-HA cells in WSN growth media. HA-deficient helper virus could only be propagated in the HA-expressing cells as expected (Figure 1 – figure supplement 1) We collected the expanded helper virus from these cells after 68 hours, aliquoted, and froze aliquots at −80°C. We titered the helper virus in MDCK-SIAT1-EF1a-WSN-HA cells by TCID50. We obtained titers between 103 and 104 TCID50 per µl when titering in MDCK-SIAT1-EF1a-WSN-HA cells, and no cytopathic effect except with extremely concentrated helper virus in MDCK-SIAT1 cells (Figure 1 – figure supplement 1).
HA plasmid mutant libraries
Codon mutagenesis was performed as described in Thyagarajan and Bloom (2014) except that we performed one overall round of the PCR mutagenesis to yield a lower mutation rate (Figure 1 – figure supplement 2). Ligation and eletroporation were also performed as in Thyagarajan and Bloom (2014), except that we cloned the inserts into both pHW2000 (Hoffmann et al., 2000) and pHH21 (Neumann et al., 1999) plasmid backbones. All steps were performed in triplicate. For each replicate, we pooled over 3 million transformants, cultured in LB for 3 hours in shaking flasks at 37oC, and maxi-prepped plasmid libraries.
Generation of mutant HA virus libraries from mutant plasmids and helper viruses
To generate mutant virus libraries, we transfected 293T cells with a DNA mixture containing one of the three pHH21-MutantHA libraries (or the wild-type pHH21-WSN-HA control) and protein expression plasmids for the four proteins that compose the ribonucleoprotein complex, using plasmids HDM-Nan95-PA, HDM-Nan95-PB1, HDM-Nan95-PB2, and HDM-Aichi68-NP (Gong et al., 2013). Specifically, we plated 293T cells in D10 at a density of 8 × 105 per well in 6-well plates, changed the media to fresh D10 after 16 hours, and then four hours later transfected cells with 500 ng of the HA reverse-genetics plasmid plus 375 ng of each of the PA, PB1, PB2, and NP plasmids using BioT. Twenty-four hours after transfection, we infected the cells with HA-deficient helper virus by making an inoculum of 1.3×103 TCID50 per µl in WSN growth media, aspirating the D10 media from the cells, and adding 2 ml of inoculum to each well. After 3 hours, we removed the inoculum by aspiration and added 2 ml of WSN growth media supplemented with 5% D10. Twenty-four hours after helper virus infection, we collected the supernatants for each replicate, stored aliquots at −80°C, and titered in MDCK-SIAT1 cells. Of note, we found that helper viruses that had been passaged more than once in MDCK-SIAT1-EF1a-WSN-HA cells tended to become less effective at rescuing fully replication competent viruses following infection of transfected cells, so we exclusively used single-passage helper virus in these experiments.
We passaged these transfection supernatants to create a genotype-phenotype link and impose functional selection on HA. We passaged over 9 × 105 TCID50 at an MOI of 0.0075 TCID50 per cell. Specifically, for each library, we plated ten 15-cm dishes with 6 × 106 MDCK-SIAT1 cells per dish and allowed cells to grow for 20 hours, at which point they had reached a density ∼ 1.25 × 107 cells per dish. We then replaced the media in each dish with 25 ml of WSN growth media in each dish containing 3.7 TCID50 of virus per µl. We allowed virus replication to proceed for 40 hours before collecting virus from the supernatant for sequencing.
Barcoded subamplicon sequencing
For each of the three replicate HA virus libraries and the wild-type HA virus, we extracted viral RNA by ultracentrifuging 24 ml of supernatant at 22,000 rpm in a Beckman Coulter SW28 rotor. RNA was extracted using the Qiagen RNeasy kit by resuspending the viral pellet in 400 µl buffer RLT freshly supplemented with β-mercaptoethanol, pipetting 30 times, transferring to an RNase-free microcentrifugefuge tube, adding 600 µl freshly-made 70% ethanol, and con-tinuing with the manufacturer’s recommended protocol, eluting the final RNA product in 40 µl of RNase-free water. HA was then reverse transcribed using AccuScript Reverse Transcriptase (Agilent 200820) with the primers WSNHA-For (5’-AGCAAAAGCAGGGGAAAATAAAAACAAC-3’) and WSNHA-Rev (5’-AGTAGAAACAAGGGTGTTTTTCCTTATATTTCTG-3’).
We generated PCR amplicons of HA for each of the eight samples (three replicate plas-mid DNA libraries, three corresponding virus libraries, one wild-type plasmid DNA, and one wild-type virus) using KOD Hot Start Master Mix (71842, EMD Millipore) with the PCR reac-tion mixture and cycling conditions described in Bloom (2014a) and the primers WSNHA-For and WSNHA-Rev. The templates for these reactions were 2 µl of cDNA (for the virus-derived samples) or 2 µl of plasmid DNA at 10ng/µl. To ensure that the number of molecules used as template did not bottleneck diversity, parallel PCR reactions were run with a standard curve of template molecules and all products were analyzed by band intensity after agarose gel elec-trophoresis; all samples used ≥ 106 molecules as template for PCR. We purified these PCR amplicons using Agencourt AMPure XP beads (bead-to-sample ratio 0.9) (Beckman Coulter).
These PCR amplicons were quantified using Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies) and used as the templates for the barcoded-subamplicon sequencing in Figure 1B. We performed the first round of PCR (“PCR 1”) in six parallel reactions (one for each of the six HA subamplicons) for each of the eight samples. Each reaction contained 12 µl 2X KOD Hot Start Master Mix, 2 µl forward primer diluted to 5µM, 2 µl reverse primer diluted to 5µM, and 8 µl purified amplicon diluted to 0.5ng/µl (primer sequences for PCR 1 and PCR 2 are provided in Supplementary file 4). In addition to containing sequences targeting regions in HA, the forward and reverse primers for PCR 1 each contain an 8-base degenerate barcode and partial Illumina sequencing adaptors. To limit the generation of PCR artifacts, we performed only 9 cycles of PCR for PCR 1 using the following program: 1. 95oC for 2:00, 2. 95oC for 0:20, 3. 70oC for 0:01, 4. 54oC for 0:20, 5. 70oC for 0:20, 6. Go to 2 (8 times), 7. 95oC for 1:00, 8. 4oC hold. The denaturation step after cycling ensures that identical barcode pairs are not annealed at the end, so that most double-stranded molecules entering PCR 2 will contain two unique barcoded mutants. PCR 1 products were purified by Ampure XP (bead-to-sample ratio 1.0), quantified with Quant-iT PicoGreen, and diluted to 0.5 ng/µl.
We then mixed all six subamplicons from each experimental sample at equal concentrations and diluted these subamplicon pools such that the number of template molecules used in PCR 2 was less than the anticipated sequencing depth to ensure multiple reads per barcode. Specifically, we reduced the total amount of DNA for each experimental sample used as template in PCR 2 to 9.24 × 10-4 ng, which corresponds to 1.54 × 10-4 ng of each of the six subamplicons, corresponding to approximately 3.5 × 105 double-stranded DNA molecules (or 7 × 105 uniquely-barcoded single-stranded variants) per subamplicon per sample.
We performed PCR 2 for each sample with the following reaction conditions: 20 ul 2X KOD Hot Start Master Mix, 4 µl forward primer UniversalRnd2for diluted to 5 µM, 4 µl reverse primer indexXXRnd2rev diluted to 5 µM (a different index for each experimental sample), 9.24 × 10-4 ng of the subamplicon pool of PCR 1 products described above, in a total volume of 40 µl. We used the following thermal cycling program: 1. 95oC for 2:00, 2. 95oC for 0:20, 3. 70oC for 0:01, 4. 55oC for 0:20, 5. 70oC for 0:20, 6. Go to 2 (23 times), 7. 4oC hold. PCR 2 products were purified by Ampure XP (bead-to-sample ratio 1.0), quantified with Quant-iT PicoGreen, and equal amounts of each experimental sample were mixed and purified by agarose gel electrophoresis, excising the predominant DNA species at the expected size of approximately 470 bp. Sequencing was performed on one lane of a flow cell of an Illumina HiSeq 2500 using 2×250 bp paired-end reads in rapid-run mode.
Inference of amino-acid preferences from sequencing data
We used dms tools (http://jbloomlab.github.io/dms_tools/), version 1.1.12, to align subamplicon reads to a reference HA sequence, group barcodes to build consensus sequences, quantify mutation counts at every site in the gene for each experimental sample, and infer site-specific amino-acid preferences based on mutation frequencies pre- and post-selection using the algorithm described in Bloom (2015). The code that performs these analyses is in Supplementary file 3.
Phylogenetic modeling using amino-acid preferences
We sub-sampled human and swine H1 sequences (1 sequence per host per year) from the set of sequences from Thyagarajan and Bloom (2014), removed identical sequences, and built a sequence alignment. We then used phydms version 1.1.0 (http://jbloomlab.github.io/phydms/; Bloom, 2016), which in turn uses Bio++ (Gue´guen et al., 2013) for the like-lihood calculations, to compare experimentally informed codon substitution models and other non-site-specific substitution models. The code that performs these analyses is in Supplementary file 3.
Statistical tests
Multiple linear regression of the continuous dependent variable of site entropy as a function of the continuous independent variable of relative solvent accessibility and a binary indicator of a site belonging to a specific classification (e.g. “antigenic sites”) was performed with the same classifications as described in Thyagarajan and Bloom (2014). Additional classifications were obtained from Koel et al. (2013) for sites responsible for antigenic cluster transitions in H3N2 and seasonal H1N1 (sites 158, 168, 169, 171, 172, 202, and 206 in sequential WSN H1 numbering starting with the initiating methionine), and the sites within antibody footprints of broadly-neutralizing antibodies F10, CR6261, FI6v3, and CR9114 (sites 25, 45, 46, 47, 48, 49, 305, 306, 307, 332, 361, 362, 363, 364, 379, 381, 382, 384, 385, 386, 388, 389, 391, 392, 395, 396, 399, and 400 in sequential WSN H1 numbering starting with the initiating methionine) (Sui et al., 2009; Ekiert et al., 2009; Corti et al., 2011; Dreyfus et al., 2012). Definition of the protein domains within HA were from Gamblin et al. (2004) (HA1 fusion domain: 18-72, 291-340; HA1 vestigial esterase domain: 73-125, 279-290; HA1 receptor binding domain: 126-278; HA2 fusion domain: 344-503; all sites in sequential H1 numbering starting with the initiating methionine). The code that performs these analyses is in Supplementary file 3.
Availability of data and computer code
Sequencing data are available from the Sequence Read Archive under accession numbers SRR3113656 (mutant DNA library 1), SRR3113657 (mutant DNA library 2), SRR3113658 (mutant DNA library 3), SRR3113660 (mutant virus library 1), SRR3113661 (mutant virus library 2), SRR3113662 (mutant virus library 3), SRR3113655 (wild-type DNA control), and SRR3113659 (wild-type virus control). An iPython notebook (and a static HTML version of it) for all analyses is in Supplementary file 3. A Python script for visualizing mutational tolerance on the HA structure in PyMol is in Figure 3 – source code 1.
Figure 3 – source code 1: PyMol script for visualization of mutational tolerance on the HA crystal structure.
Supplementary file 1: Text file with the overall merged site-specific amino-acid preferences (average of the new and old data).
Supplementary file 2: Text file with the overall merged site-specific amino-acid preferences (average of the new and old data) scaled by the stringency parameter from Table 1.
Supplementary file 3: A ZIP file containing the data and code for all data analysis and figure generation. The analysis is performed by the iPython notebook in this file.
Supplementary file 4: Text file with primer sequences for the barcoded subamplicon sequencing.
Acknowledgments
We thank Bargavi Thyagarajan for performing the PCR mutagenesis of the HA gene. We thank Anice Lowen for discussions that helped inspire the idea of using a helper virus to generate the mutant virus libraries. This work was supported by the NIGMS of the NIH under grant R01 GM102198. M.B.D. was supported in part by a fellowship from the Seattle Chapter of the Achievement Rewards for College Scientists Foundation.
Footnotes
E-mail: jbloom{at}fredhutch.org