Extensive age-dependent loss of antibody diversity in naturally short-lived turquoise killifish

William J. Bradshaw, Michael L. Poeschla, Aleksandra Placzek, Samuel Kean, and Dario Riccardo Valenzano

eLife 2022; 11:e65117

DOI: https://doi.org/10.7554/eLife.65117

Abstract

Aging individuals exhibit a pervasive decline in adaptive immune function, with important implications for health and lifespan. Previous studies have found a pervasive loss of immune-repertoire diversity in human peripheral blood during aging; however, little is known about repertoire aging in other immune compartments, or in species other than humans. Here, we perform the first study of immune-repertoire aging in an emerging model of vertebrate aging, the African turquoise killifish (Nothobranchius furzeri). Despite their extremely short lifespans, these killifish exhibit complex and individualized heavy-chain repertoires, with a generative process capable of producing millions of distinct productive sequences. Whole-body killifish repertoires decline rapidly in within-individual diversity with age, while between-individual variability increases. Large, expanded B-cell clones exhibit far greater diversity loss with age than small clones, suggesting important differences in how age affects different B cell populations. The immune repertoires of isolated intestinal samples exhibit especially dramatic age-related diversity loss, related to an elevated prevalence of expanded clones. Lower intestinal repertoire diversity was also associated with transcriptomic signatures of reduced B-cell activity, supporting a functional role for diversity changes in killifish immunosenescence. Our results highlight important differences in systemic vs. organ-specific aging dynamics in the adaptive immune system.

Usage

Pre-processing

The pre-processing pipeline takes raw IgSeq Illumina reads and carries out (among other things) quality filtering, UMI clustering, assignment of VDJ identities, and clonotyping, returning a Change-O database of unique IgH sequences from each sample. For more details, see Supplementary Note 6 from the preprint.

The pre-processing pipeline must be run separately on each dataset to be analysed. Three such datasets were generated for this dataset: the pilot dataset (Fig S1 in the preprint), the ageing dataset (Fig. 2A in the preprint) and the gut dataset (Fig. 4A in the preprint). All three datasets are available via NCBI.

To execute the pre-processing pipeline:

• Clone this repo on your local system.
• Download and extract the raw data from NCBI.
• Create a separate run directory for each dataset.
• Copy the appropriate config file from preprocessing/configs_run (config_pilot.yaml for the pilot dataset, config_ageing.yaml for the ageing dataset, etc) to the corresponding run directory, renaming it to config_preprocess.yaml.
• Edit config_preprocess.yaml to reflect the location of the appropriate raw reads files (in the samples entry).
• Edit the auxiliary_file, primers and tsa entries of config_preprocess.yaml to reflect the location of these files (in the preprocess/source/input_files directory in this repo) on your system.
• Edit the base_path_preprocess entry in config_preprocess.yaml to reflect the absolute path to preprocessing/source/snakefiles/base on your system.
• Copy preprocessing/Snakefile to each run directory.
• Install conda and Snakemake on your system.
• Run Snakemake from within the appropriate run directory: snakemake --use-conda --cores

NB: The preprocessing pipeline is by far the most computationally intensive component of the analysis process. It is recommended to run it on some kind of high-performance computing cluster. Depending on your cluster architecture, you may wish to use a different Snakemake command; see the Snakemake website for more details.

Analysis

The analysis pipeline takes the sequence databases output by the pre-processing pipeline, and infers diversity spectra and (using IGoR) generative models. Unlike the pre-processing pipeline, it can be run on multiple datasets simultaneously.

To generate the outputs required to generate the figures in our preprint, the analysis pipeline can be run on the pre-processed data files included in this repo, or on the outputs of your own pre-processing runs. To do this:

• Clone this repo on your local system.
• Navigate to the analysis/ directory.
• If using your own pre-processed data files, edit the data_changeo entry in config_analysis.yaml to reflect the paths to the output sequence databases for each dataset (within each preprocessing run directory, outfiles/preprocess/changeo/output/seqs-all.tab).
• If not already installed, install Conda and Snakemake on your system as described above.
• Run Snakemake from within the analysis/ directory: snakemake --use-conda --cores

Figure generation

The pipeline to generate the figures used in our preprint can be run on the processed data files included in this repo, or on the corresponding output files of the pre-processing and analysis pipelines. To do this:

• Clone this repo on your local system.
• Navigate to the figures/ directory.
• Edit config_figures.yaml to include paths to the required processed data files; by default it will use the files included in this repository.
• If not already installed, install Conda and Snakemake on your system as described above.
• Run Snakemake from within the figures/ directory: snakemake --use-conda --cores