Abstract
Infectious diseases are among the strongest selective pressures driving human evolution1,2. This includes the single greatest mortality event in recorded history, the first outbreak of the second pandemic of plague, commonly called the Black Death, which was caused by the bacterium Yersinia pestis3. This pandemic devastated Afro-Eurasia, killing up to 30–50% of the population4. To identify loci that may have been under selection during the Black Death, we characterized genetic variation around immune-related genes from 206 ancient DNA extracts, stemming from two different European populations before, during and after the Black Death. Immune loci are strongly enriched for highly differentiated sites relative to a set of non-immune loci, suggesting positive selection. We identify 201 variants that are highly differentiated within the London dataset. Combining evidence from during the Black Death, our replicate population in Denmark, and function evidence, rs2549794 near ERAP2 emerges as the strongest candidate for positive selection. The selected allele at rs2549794 is associated with the production of a full-length (versus truncated) ERAP2 transcript, variation in cytokine response to Y. pestis and increased ability to control intracellular Y. pestis in macrophages. Finally, we show that protective variants overlap with alleles that are today associated with increased susceptibility to autoimmune diseases, providing empirical evidence for the role played by past pandemics in shaping present-day susceptibility to disease.
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Data availability
Hybridization capture data from the ancient individuals have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject PRJNA798381. Expression data have been deposited into the NCBI Gene Expression Omnibus (GEO) under project GSE194118 (for macrophages) and the NCBI SRA under project accession PRJNA871128 (for PBMCs). Cytokine data is available in Supplementary Table 8 and CFU data in Supplementary Table 9.
Code availability
Scripts for all data analyses are available at github.com/TaurVil/VilgalysKlunk_yersinia_pestis/.
Change history
15 January 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41586-024-08522-6
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Acknowledgements
We thank all members of the Barreiro laboratory and the Poinar laboratory for their constructive comments and feedback. We thank J. Tung for her comments and edits to the manuscript. Computational resources were provided by the University of Chicago Research Computing Center. Sequencing was performed at the Farncombe Sequencing Facility McMaster University. We thank the Cytometry and Biomarkers platform at the Institut Pasteur for support in conducting this study, with a special thanks to C. Petitdemange for help running the Luminex assay. We thank X. Zhang for assistance in simulating allele frequency changes under neutral evolution. This work was supported by grant R01-GM134376 to L.B.B., H.P. and J.P.-C., a grant from the Wenner-Gren Foundation to J.F.B. (8702), and the UChicago DDRCC, Center for Interdisciplinary Study of Inflammatory Intestinal Disorders (C-IID) (NIDDK P30 DK042086). The SSHRC Insight Development Grant supported the collection of the Danish samples (430-2017-01193). H.N.P. was supported by an Insight Grant no. 20008499 from the Social Sciences and Humanities Research Council of Canada (SSHRC) and The Canadian Institute for Advanced Research under the Humans and the Microbiome programme. T.P.V. was supported by NIH F32GM140568. X.C. and M. Steinrücken were supported by grant R01GM146051. We also thank the University of Chicago Genomics Facility (RRID:SCR_019196), especially P. Faber, for their assistance with RNA sequencing. H.P. thanks D. Poinar for continued support and manuscript suggestions and editing.
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Authors and Affiliations
Contributions
L.B.B. and H.N.P. directed the study. J.K. designed the enrichment assays and generated ancient genomic data. T.P.V. led all data and computational analyses, with contributions from J.-C.G. X.C. performed all analyses to estimate selection coefficients under the supervision of M. Steinrücken. J.F.B., R.S. and V.Y. performed challenge experiments with macrophages and heat-killed Y. pestis. M. Shiratori and A. Dumaine performed the infection experiments on PBMCs and generated the single-cell RNA-sequencing data, with assistance from D.E. and D.M. C.E.D. and J.P.-C. performed and designed the infection experiments with live Y. pestis on macrophages and generated both cytokine and CFU data, with assistance from J.M. and R.B. C.J.Y. and M.B. designed the probes to quantify the isoform encoding the short version of ERAP2 and M.I.P. performed the experiments. R.R., S.N.D., J.A.G. and J.L.B. provided access to samples, archaeological information, including dating, and other relevant information. A.C. and N.V. provided insights into historical context. K.E. and G.B.G. provided additional sampling and bioinformatic processing and cluster maintenance. A. Devault and J.-M.R. provided insight on targeted enrichment and modified versions of baits used for immune enrichment. G.A.R. provided genomic input on loci and contributed financially to the sequencing of targets. T.P.V., J.K., H.N.P. and L.B.B. wrote the manuscript, with input from all authors.
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J.K., A. Devault and J.-M.R. declare financial interest in Daicel Arbor Biosciences, which provided the myBaits hybridization capture kits for this work. All other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Differences in recombination rate and background selection are insufficient to explain the marked enrichment of high FST values among immune loci.
Comparison of recombination rate (A) and background selection levels (B) between neutral loci and our candidate regions. Candidate regions were stratified into those which were tested and those which were candidates for positive selection based on high differentiation in London pre- vs post-BD. (C) Forward simulations matched for the rates of recombination and background selection of the regions targeted in our study show a slight enrichment of highly differentiated sites in candidate regions, but far from the level of enrichment observed in our collected data (D), replicated from Fig. 2a for comparison. For example, whereas our data differentiation at immune loci exceeded the 99th percentile of neutral variants at 4.3x the rate expected by chance, the same enrichment is less than 1.4x in the simulated data.
Extended Data Fig. 2 Estimates of the selection coefficients for the four SNPs of interest and power of the inference procedure.
(A) Distributions of \({\hat{s}}_{{\rm{MLE}}}\) for rs2549794, the strongest candidate for positive selection, when replicates are simulated based on the bootstrapped allele frequency distributions as initial conditions and bootstrap-corrected estimates \({\widetilde{s}}_{{\rm{MLE}}}\). Whiskers on the violin plots label the 2.5-, 50-, and 97.5-percentiles of their respective distributions. (B) ROC and (C) Precision-Recall curves for the estimation procedure to distinguish replicates under selection from those under neutrality.
Extended Data Fig. 3 Principal components of gene expression for macrophages stimulated with heat-killed Y. pestis.
The first principal component clearly separates stimulated samples from matched controls.
Extended Data Fig. 4 Response to Y. pestis is similar between macrophages stimulated with heat-killed and live Y. pestis.
Effect size of Y. pestis stimulation compared between heat-killed Y. pestis (x-axis, n = 33 individuals) and live Y. pestis (y-axis, n = 8 individuals). (A) shows all genes, with a blue line representing the best fit line (r = 0.88). (B) compares effect sizes at genes near candidates for positive selection profiled in both expression datasets (red: heat-killed; purple: live bacteria). Error bars represent the standard error in estimating the effect size. Asterisks placed near the point estimate of each value represent the significance: *** p < 0.001; ** p < 0.01; * p < 0.05.
Extended Data Fig. 5 Transcriptional changes of genes nearby candidate loci in response to bacterial and viral stimuli.
Data are derived from Nedelec et al27. and Quach et al28 Nedelec et al. measured the gene expression response of monocyte-derived macrophages to infection with two live intracellular bacteria: Listeria monocytogenes (a Gram-positive bacterium) and Salmonella typhimurium (a Gram-negative bacterium). Quach et al. characterized the transcriptional response at 6 h of primary monocytes to bacterial and viral stimuli ligands activating Toll-like receptor pathways (TLR1/2, TLR4, and TLR7/8) and live influenza virus. The data for Y. pestis are the fold change responses observed in response to heat-killed bacteria. A negative estimate in plot (purple) indicates that the gene is downregulated and a positive value (red) indicates that the gene is up-regulated. The statistical support for the reported changes is given by the associated p values. Larger circle sizes represent smaller p values and empty circles refer to cases where the gene was not expressed in that dataset.
Extended Data Fig. 7 UMAP project of single cell data.
UMAP projection of single-cell RNA sequencing data of non-infected cells and cells infected with live Y. pestis for five hours, after integrating samples. Major immune cell types cluster separately and cells are colored by the cell type to which they were assigned.
Extended Data Fig. 8 Transcriptional changes of genes nearby candidate loci in response to Y. pestis infection across cell types.
For each cell type profiled using single-cell RNA sequencing, we show the effect of Y. pestis infection upon gene expression. A negative estimate (purple) indicates that the gene is downregulated and a positive value (red) indicates that the gene is up-regulated. The statistical support for the reported changes is given by the associated p values. Larger circle sizes represent smaller p values and empty circles refer to cases where the gene was not expressed in that cell type.
Extended Data Fig. 9 Expression of ERAP2 isoforms.
Bioanalyzer traces showing the results of the PCR amplification of cDNA across the exon 10 splice junction from macrophages of 10 individuals with different genotypes for the slice variant rs2248374. The genotype of each individual is shown on top. A negative PCR control was also performed using water. The G allele at rs2248374 is predicted to produce an elongated exon 10 containing two premature stop codons (red rectangles), leading to nonsense mediated decay.
Extended Data Fig. 10 Schematics for estimating selection coefficients.
The time axis serves as an approximate reference of the relative sampling times for the empirical samples. Dashed vertical lines indicate the relative sampling time for each group of samples considered in the analysis, and the floating boxes with orange and blue dots represent pools of samples from a bi-allelic locus. Above and below the time axis are sketches that respectively correspond to the simulation scheme and the likelihood computations. The shaded red horizontal tree represents the population-continuity along approximate time (x-axis), with the Black Death pandemic occurring in the dark shaded period. The shortened branch with a skull at the end represents people who died of the disease. In each simulated replicate, ∆p and −∆p mark the respective changes of allele frequency during the pandemic in the mid-pandemic and post-pandemic sample pools. In the inference schematics, each horizontal straight line represents a sampling scheme from which a likelihood was computed. Lightning bolts labeled with s or −s represent the selection coefficients.
Supplementary information
Supplementary Information
Supplementary Methods, References, Fig. 1 (Correcting for ancient genomic damage), Fig. 2 (Power to identify protective alleles against Black Death), Fig. 3 (Log-likelihood as a function of the selection coefficient s for each target SNP) and Fig. 4 (Distributions of \({\hat{s}}_{{\rm{MLE}}}\) for simulated data starting with the pre-pandemic frequencies of the four target SNPs).
Supplementary Tables 1–10
Supplementary Table 1 (Individuals sampled for ancient DNA analysis), Table 2 (Loci used for Exon, GWAS and neutral bait design), Table 3 (hg18 positions targeted in Exon captures), Table 4 (Highly differentiated variants), Table 5 (Estimates of selection coefficients), Table 6 (Changes in gene expression following Y. pestis stimulation), Table 7 (Cytokine results), Table 8 (Cytokine data), Table 9 (Colony-forming unit data) and Table 10 (Interaction effects between ERAP2 genotype and Y. pestis stimulation).
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Klunk, J., Vilgalys, T.P., Demeure, C.E. et al. Evolution of immune genes is associated with the Black Death. Nature 611, 312–319 (2022). https://doi.org/10.1038/s41586-022-05349-x
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DOI: https://doi.org/10.1038/s41586-022-05349-x
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Alan Frier
Very interesting. My wife has a blood disease homozygous EE haemoglobin. Some SE Asians have the single E genome along with the dominant A genome that most of us have, however the double EE in haemoglobin is very rare.
I read a study ( link added below) from a team including the current Cambodian government along with a university in BC Canada and the very same university my wife's samples were being sent to - Oxford University Hospital, United Kingdom. The study evolves samples being taken from women in just a few communes in rural Prey Veng Province about twenty years ago.
Many years before that Pol Pot was scared of people from this province as they weren’t starving or dying from overwork or whatever at the same rate as other provinces during the war. He considered some people from Prey Veng to be ‘superhuman’. Point being that a mutation or disease can have unusual benefits to the genetic code as well as the obvious defects.
https://academic.oup.com/jn...