Mapping of anti-flu serum against the Perth/2009 H3 HA

Mutational antigenic profiling of Perth/2009 H3 HA codon mutant libraries against ferret and human sera.

This is the computer code and raw data for the study Mapping person-to-person variation in viral mutations that escape polyclonal serum targeting influenza hemagglutinin, eLife, 2019.

Study led by Juhye Lee and Jesse Bloom.

Quick summary

• Analysis of mutational antigenic profiling data: results/notebooks/analyze_map.md

• Analysis of neutralization curves: results/notebooks/analyze_neut.md

• Interactive protein structures showing the immune selection can be viewed via the folowing mybinder link. Specifically, use the below links to open web apps for each notebook showing the selection from:

  • The antibodies targeting antigenic region B

  • The antibodies targeting lower on the HA head

  • The human sera from 2009-2010 (the "VIDD sera")

  • The human sera from vaccinees in 2015 (the "Hensley sera")

  • The ferret sera

• A brief examination of changes in amino-acid frequencies at strongly selected sites: results/notebooks/analyze_natseqs.md

Running the analysis

Automated steps

The main analysis is performed primarily by a series of Jupyter notebooks and Python scripts:

1. analyze_map.ipynb: analyzes mutational antigenic profiling

2. analyze_neut.ipynb: analyzes neutralization assays

3. analyze_natseqs.ipynb: analyzes changes in amino-acid frequencies among natural sequences

4. parameterize_map_on_struct.py: parameterizes the template Jupyter notebook map_on_struct_template.ipynb to show structures for each type of sera.

To run the three steps above, execute the bash script run.bash with:

./run.bash

On the Hutch cluster, you can also submit this script using slurm with:

sbatch -p largenode -c 16 --mem=100000 run.bash

Manual steps

The following steps to must be performed manually to finalize the paper figures:

5. The automated steps above create Jupyter notebooks that map the immune selection onto the structure using dms_struct (which is a wrapper around nglview). These notebooks are in results/notebooks with names matching map_on_struct_*.ipynb. To open them interactively with mybinder, click here. You can also directly open each notebook as an interactive app in appmode by clicking on the links in the Quick summary section at the top of this README. To generate static protein structure images for the final figures, you also need to run each notebook locally and interactively cell-by-cell (giving time for each structure to render).

6. The Jupyter notebook make_final_figs.ipynb generates the final figures for the paper, which are placed in .results/figures/final. You need to run this notebook to generate the figures.

Configuring the analysis

The configuration for the analysis is in a separate file, config.yaml. This file defines key variables for the analysis, and should be self-explanatory. The config.yaml file points to several files in the ./data/ subdirectory that specify essential data for the analysis:

• data/serum_info.yaml: YAML file that gives information on all of the serum samples used for selections. For each serum there is an entry with the label used in the experiments, then:

  • name: a more informative name used when displaying results
  • description: description of the serum
  • group: group of samples to which serum belong
  • species: species from which serum is derived (if relevant)
  • vaccination: information of vaccination status (if relevant)

• data/sample_list.csv: CSV file giving each sample that was deep sequenced. Columns are:

  • sample: sample label used in experiments
  • serum: serum used for selection
  • library: viral library, using simple 1, 2, 3 naming rather than the more confusing library codes used to label experiments
  • date: day when sequencing was done
  • serum_dilution: dilution of serum used; this includes the 1:4 dilution used during the RDE treatment of the serum. For antibodies, it is the concentration in ug/ml.
  • percent_infectivity: percent of viral library retaining infectivity
  • R1: glob pattern to R1 FASTQ files on Hutch server; the R2 file names are guessed from the R1 names. If config.yaml sets seq_data_source to R1 then there must be a valid R1 file glob for all samples; otherwise this column is ignored.
  • SRA_accession: the accession number on the Sequence Read Archive (SRA) for the sequencing for this sample. If config.yaml sets seq_data_source to SRA_accession then there must be a valid accession for all samples; otherwise this column is ignored.

• data/Perth09_HA_reference.fa: FASTA file giving the sequence of the wildtype Perth/2009 HA used in the experiments.

• data/H3renumbering_scheme.csv: A CSV file that maps sequential 1, 2, ... numbering of the Perth/2009 HA protein sequence (original column) to the standard H3 HA numbering scheme (new column).

• data/H3_site_to_PDB_4o5n.csv: A CSV file that matches the H3 HA numbering to the site numbers and PDB chains in PDB structure 4o5n.

• data/human_H3N2_HA_2007-2018.fasta.gz: A gzipped FASTA file that contains all human H3N2 influenza HA coding sequences collected between 2007 and 2018 as downloaded from the Influenza Virus Resource on June-2-2019.

• data/neut_assays: Data from neutralization assays:

  • data/neut_assays/neut_config.yaml: Details on neutralization assays for each serum, with Excel path relative to top-level analysis directory.
  • data/plate_reader_data/: The plate reader data (Excel format).

• data/figure_config.yaml: YAML file giving specifications for fine-tuned figures showing logo plot zooms and neutralization curves.

Results

Results are placed in the ./results/ subdirectory. Many of the results files are not tracked in this GitHub repo since they are very large. However, the following results are tracked:

• results/notebooks/analyze_map.md: Markdown rendering of the notebook analyzing the mutational antigenic profiling

• results/notebooks/analyze_neut.md: Markdown rendering of the notebook analyzing the neutralization assays

• results/notebooks/analyze_natseqs.md: Markdown rendering of the notebook analyzing the amino-acid frequencies in nature

• results/notebooks: Jupyter notebooks with names like map_on_struct_*.ipynb that render the immune selection onto interactive structure widgets.

• results/avgdiffsel/avg_sel_tidy.csv: the across-replicate average (median) differential selection for each mutation for each serum or antibody that was analyzed by mutational antigenic profiling. This is where you should look if you want the numerical results shown in the plots.

• results/avgdiffsel/full_logo_plots/: PDF files with full-gene logo plots of the replicate average (median) positive differential selection for each serum / antibody that was analyzed by mutational antigenic profiling.

• results/selection_tables/percent_infectivity_table.csv: the percent infectivity remaining for the mutant virus library for each replicate of mutational antigenic profiling.

• results/selection_tables/serum_dilution_table.csv: the concentration of serum or antibody used in each replicate of mutational antigenic profiling. For sera, the values indicate the dilution of serum. For antibodies, they are the concentration in ug/ml. For serum / antibody mixes, they are the dilution of serum followed by the antibody concentration in ug/ml.

• results/diffsel/: CSV files giving the mutation and site differential selection for each replicate (library) of mutational antigenic profiling. The file names also indicate the percent infectivity remaining. These are the values that are averaged across replicates (by median) to give the values shown in the paper and provided in results/avgdiffsel/avg_sel_tidy.csv.

• results/renumbered_codoncounts: the counts of each mutation in each sample, after renumbering to H3 numbering.

• results/figures/final: the final figures for the paper.

• results/neutralization_assays/neut_assay_figs_fit_params.csv: the curve-fit parameters for all neutralization curves plotted in the figures.

Other subdirectories

Other subdirectories in the repo are:

• ./SRA_upload/ has information on how the sequencing data were uploaded to the NIH Sequence Read Archive. See ./SRA_upload/README.md for details.

• ./binder has the configuration for visualizing the protein structure notebooks on mybinder.