1. Genetics and Genomics
  2. Immunology and Inflammation
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Evolution of cytokine production capacity in ancient and modern European populations

  1. Jorge Domínguez-Andrés
  2. Yunus Kuijpers
  3. Olivier B Bakker
  4. Martin Jaeger
  5. Cheng-Jian Xu
  6. Jos WM Van der Meer
  7. Mattias Jakobsson
  8. Jaume Bertranpetit
  9. Leo AB Joosten
  10. Yang Li  Is a corresponding author
  11. Mihai G Netea  Is a corresponding author
  1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Netherlands
  2. Radboud Institute for Molecular Life Sciences (RIMLS), RadboudUniversity Medical Center, Netherlands
  3. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Germany
  4. TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Germany
  5. Department of Genetics, University Medical Centre Groningen, Netherlands
  6. Human Evolution, Department of Organismal Biology, Uppsala University, Sweden
  7. Centre for Anthropological Research, Department of Anthropology and Development Studies, University of Johannesburg, South Africa
  8. Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Spain
  9. Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Germany
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Cite this article as: eLife 2021;10:e64971 doi: 10.7554/eLife.64971

Abstract

As our ancestors migrated throughout different continents, natural selection increased the presence of alleles advantageous in the new environments. Heritable variations that alter the susceptibility to diseases vary with the historical period, the virulence of the infections, and their geographical spread. In this study we built polygenic scores for heritable traits that influence the genetic adaptation in the production of cytokines and immune-mediated disorders, including infectious, inflammatory, and autoimmune diseases, and applied them to the genomes of several ancient European populations. We observed that the advent of the Neolithic was a turning point for immune-mediated traits in Europeans, favoring those alleles linked with the development of tolerance against intracellular pathogens and promoting inflammatory responses against extracellular microbes. These evolutionary patterns are also associated with an increased presence of traits related to inflammatory and auto-immune diseases.

Introduction

Human history has been shaped by infectious diseases. Human genes, especially host defense genes, have been constantly influenced by the pathogens encountered (Fumagalli and Sironi, 2014; Karlsson et al., 2014; Quintana-Murci and Clark, 2013). Pathogens drive the selection of genetic variants affecting resistance or tolerance to the infection, and heritable variations that increase survival to diseases with high morbidity and mortality will be naturally selected in people before reproductive age (Karlsson et al., 2014). These selection signatures vary with historical period, virulence of the pathogen, and the geographical spread.

Here we investigated the historical evolutionary patterns leading to genetic adaptation in cytokine production and immune-mediated diseases, including infectious, inflammatory, and autoimmune diseases. Cytokine production capacity is a key component of the host defense mechanisms: it induces inflammation, activates phagocytes to eliminate the pathogens and present antigens, and controls induction of T-helper (Th) adaptive immune responses. We have therefore chosen to investigate the evolutionary trajectories of cytokine production capacity in modern human populations during history. To determine the difference in polygenic regulation of diseases and cytokine production capacity, we used data derived from the 500 Functional Genomics (500FG) cohort of the Human Functional Genomics Project (HFGP; http://www.humanfunctionalgenomics.org). The HFGP is an international collaboration aiming to identify the host and environmental factors responsible for the variability of human immune responses in health and disease (Netea et al., 2016). Within the HFGP project, the 500FG study generated a large database of immunological, phenotypic, and multi-omics data from a cohort of 534 individuals of Western-European ancestry, which has been used to integrate the impact of genetic and environmental factors on cytokine production and immune parameters. We subsequently deciphered the factors that influence inter-individual variations in the immune responses against different stimuli (Bakker et al., 2018; Li et al., 2016; Schirmer et al., 2016; Ter Horst et al., 2016).

Results and discussion

Peripheral blood mononuclear cells from these individuals were challenged with bacterial, fungal, viral, and non-microbial stimuli, and six cytokines (tumor necrosis factor (TNF)α, interleukin (IL)-1β, IL-6, IL-17, IL-22, and interferon (IFN)γ) were measured at 24 hr or 7 days after stimulation, generating 105 cytokine-stimulation pairs (Figure 1—figure supplement 1 and Supplementary file 1a). The stimulation time intervals were chosen based on extensive studies that showed that the time points used were best suited for assessing monocyte-derived and lymphocyte-derived cytokines per stimulus. Not all the stimuli induce the production of all cytokines; so the selection of the cytokine-stimulus pairs was performed for those pairs for which cytokine production was measurable (Li et al., 2016; Ruschen et al., 1992; Schirmer et al., 2016; Ter Horst et al., 2016; van de Veerdonk et al., 2009). We correlated cytokine production with genetic variant data to obtain cytokine quantitative trait loci (QTLs), which were employed to compute and compare the polygenic risk score (PRS) of the genomes of 827 individuals from different human historical eras (early upper Paleolithic, late upper Paleolithic, Mesolithic, Neolithic, post-Neolithic), which were downloaded from version 37.2 of the compiled dataset containing unimputed published ancient genotypes (https://reich.hms.harvard.edu/downloadable-genotypes-present-day-and-ancient-dna-data-compiled-published-papers) and 250 modern Europeans randomly selected from the European 1000G cohort. The individuals in this cohort present a similar genetic background (Western-European ancestry) and balanced characteristics in terms of age, sex, body mass index (BMI), and habits (Ter Horst et al., 2016), allowing us to represent the natural variability in the immune responses and minimizing the impact of the inter-individual variability in our results. In line with this, cytokines were measured in batches in which a certain cytokine was measured the same day for all the samples, in order to decrease the potential influence of technical variations. Intra-individual variation of cytokine production capacity was found to be limited (Ter Horst et al., 2021,) while inter-individual variability was largely depedent on genetic variants (Bakker et al., 2018; Li et al., 2016).

We investigated the PRS changes over time through constructing linear models and correlation analysis. In order to account for the ancient DNA (aDNA) samples being pseudo-haploid, ambiguous single-nucleotide polymorphisms (SNPs) (A/T and C/G) were excluded when computing PRS to prevent errors due to strand flips. PRS was computed using the most significant QTLs that had a p-value lower than our predetermined threshold for each given trait and removing all variants within a 250-kb window around these variants. Additional PRS models were calculated at varying window sizes (250, 500, 1000 kb) in order to show consistency in the direction of the trends (Figure 1—figure supplement 2). The dosage of these variants was multiplied by their effect size while the dosage of missing variants in a sample was supplemented with the average dosage. Although replacing missing values with the population average does not skew the observed trends over time, it does affect the scale of the data. We opted for this approach, however, due to the scarcity of variants shared by all samples; we focused instead on consistent observations after excluding samples with higher missing genotype rates. Finally, we scaled the PRS to a range of −1 and 1 and correlated the scores of the samples with their respective carbon-dated ages. In order to verify the robustness of our results, we repeated the analysis at multiple threshold combinations for variant missingness and QTL thresholds.

We plotted locally estimated scatterplot smoothing (LOESS) regression models to show the variations in PRS across time for each trait and highlight the time periods around which they drastically change. After plotting the changes before and after the Neolithic revolution at various thresholds for both the missing genotype rate in samples as well as QTL inclusion threshold, only traits that showed consistently significant trends in the same direction were chosen. Additional boxplots and t-tests show the variations in PRS between different pairings besides pre- and post-Neolithic samples. A schematic representation of the steps performed is shown in Figure 1—figure supplement 3. Lastly, we used a separate test to determine whether traits were under selective pressure between any two broad time periods using the trait-associated Wright’s fixation index (Fst). This test shows an increase in the number of traits under selective pressure after the start of the Neolithic and highlights the traits and points in time for which the observed trends in PRS can be partially attributed to selective pressure as opposed to drift (Supplementary file 1b).

Applying the methodology described above, several patterns were apparent (Figure 1). The first overall observation is that the estimation of cytokine production capacity based on PRS shows significant differences between populations in various historical periods, and the strength of evolutionary pressure on cytokine responses was different before and after the Neolithic revolution. We did not observe significant changes in cytokine production capacity between individuals who lived in different historical periods before the Neolithic, whereas strong pressure is apparent after adoption of agriculture and animal domestication in Europe. This different pattern may have resulted from the more limited number of samples available for the older time periods, resulting in lower statistical power, but the presence of some evolutionary pressure also before the Neolithic argues that this is most likely not the full explanation. The development of agriculture and domestication of animals in the Neolithic increased population densities on the one hand and the contact between humans and domesticated animals as a source of pathogens on the other hand. The number of zoonoses increased dramatically (examples being tuberculosis, brucellosis, Q-fever, and influenza), which strongly increased the selective pressure and caused significant adaptations of immunity at the genetic level (Flandroy et al., 2018). Most of the genetic adaptations to pathogens took place in the period since modern humans abandoned their hunting-gathering lifestyle and developed agriculture (Deschamps et al., 2016). In this respect, the strongest changes leading to tolerance (decreased cytokine production) were exerted in the cytokine responses to intracellular zoonotic infections (tuberculosis and Coxiella). In contrast, responses to the extracellular pathogens Staphylococcus aureus and Candida albicans indicate increased resistance, with high production of IL-22 and TNFα, respectively. The increased response to the important fungal pathogen C. albicans after the Neolithic period is validated also at the transcriptional level. Overall, these patterns are reminiscent of the studies showing that human immune responses need to adapt to a new landscape of infectious agents depending on the geographical location and types of microbe encountered (Ferwerda et al., 2007). Such different patterns were most likely encountered also through history.

Figure 1 with 3 supplements see all
Distribution of PRS of cytokine response QTLs across time.

(A–C) Polygenic risk score (PRS) models based on quantitative trait loci (QTLs) at a p-value threshold of 1e-5. Gray area around regression lines represents the 95% confidence interval. (A) LOESS regression models showing the changing differences in PRS across time. (B) Dual linear models showing the difference in trends before and after the Neolithic revolution. (C) Boxplots showing the difference in mean PRS between adjacent broad time periods using a t-test and the overall difference in means using analysis of variance (ANOVA). (D) Heatmap showing the consistency in regression trends before and after the Neolithic revolution using different QTL inclusion thresholds (1e-3 to 1e-6). Intracellular organisms that can cause zoonotic disease: Mycobacterium tuberculosis, Coxiella burnetti. Extracellular organisms: Staphylococcus aureus, Candida albicans. MSUC: monosodium urate crystals. PolyIC: polyinosinic:polycytidylic acid.

Importantly, our results also show significant patterns in changes in the production of specific cytokines during history. The resistance against intracellular pathogens increased after Neolithic with higher IFNγ responses (see Figure 1—figure supplement 1): indeed, it is known that Th1-IFNγ responses are crucial for the host defense against intracellular pathogens such as mycobacteria or Coxiella (Thakur et al., 2019). In addition, the resistance to the extracellular pathogens C. albicans and S. aureus is also increased after this Neolithic era, with TNFα and IFNγ production increasing steadily after. These two cytokines are very well known to be important for anti-Candida and anti-Staphylococcus host defense (MRSA Systems Immunobiology Group et al., 2018; Domínguez-Andrés et al., 2017). On the other hand, a different pattern emerges in relation with the IL1β-IL6-IL17 axis: the production of these cytokines is seen decreasing after Neolithic (see Figure 1a and b). In this context, the decrease through time of poly I:C induction of cytokines, as a model of viral stimulation, is intriguing but potentially very important: many important viruses such as influenza and coronaviruses (severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and SARS coronavirus 2 (SARS-CoV-2)) exert life-threatening effects through induction of cytokine-mediated hyperinflammation (also termed ‘cytokine storm’) (Tay et al., 2020); evolutionary processes to curtail these exaggerated responses are thus likely to be protective, and tolerance against viruses becomes a host defense mechanism (Diard and Hardt, 2017).

These evolutionary genetic adaptations to pathogens throughout human history greatly influence the way we respond to multiple diseases in modern times as well. To assess these effects, we calculated the PRS associated with the risk of several highly prevalent immune-mediated diseases. The first focus was on common infectious diseases such as malaria, human immunodeficiency virus-acquired immunodeficiency syndrome (HIV-AIDS), tuberculosis, and chronic viral hepatitis; we calculated the changes in susceptibility to these diseases in the last 50,000 years of human history, based on summary statistics from genome-wide association studies (GWAS) databases available from the literature (Figure 2—figure supplement 1). Our results show that humans are becoming more resistant to these diseases, with the notable exception of tuberculosis, whose risk score remained stable along the period studied (Figure 2). Of note, the QTLs that passed our thresholds were scarce, resulting in a PRS model using a limited number of SNPs, making them sensitive to changes in the p-value cutoff, especially in traits related with infectious diseases. Our results suggest that humans have built up a genetic makeup that made them more resistant to a variety of microbes. The pattern of this adaptation is very interesting as well, with a suggested decrease of susceptibility to malaria especially in the last 10,000 years. The reason for this accelerated resistance after Neolithic might be linked to a higher disease prevalence due to increased population density, as otherwise Plasmodium parasites are known to have circulated in Africa since at least the Paleogene 30 million years ago (Poinar, 2005), and we have likely inherited it from gorillas (Liu et al., 2010). Intriguingly, we also observed a strong decrease in susceptibility to HIV: this is a contemporary pathogen, therefore this signal could be due to common genetic and immune pathways with other infections that were present in human populations. The increased resistance to HIV in Europeans may be derived from selective pressures induced by other pathogens such as Yersinia pestis (Duncan et al., 2005). Our data suggest, on the other hand, that the source of this increased resistance is even older.

Figure 2 with 1 supplement see all
Distribution of PRS of infectious diseases across time.

(A–C) Polygenic risk score (PRS) models based on a p-value threshold of 1e-5. Gray area around regression lines represents the 95% confidence interval. (A) LOESS regression models showing the changing differences in PRS across time. (B) Dual linear models showing the difference in trends before and after the Neolithic revolution. (C) Boxplots showing the difference in mean PRS between adjacent broad time periods using a t-test and the overall difference in means using analysis of variance (ANOVA). (D) Heatmap showing the consistency in regression trends before and after the Neolithic revolution using different quantitative trait locus (QTL) inclusion thresholds (1e-5 to 1e-8).

In contrast, the lack of genetic adaptation in susceptibility to tuberculosis is intriguing. This surprising finding may be explained by a concept in which Mycobacterium tuberculosis is at the same time a pathogen and a symbiont, in which latent infection enhances the resistance against other pathogens, and this is why our immune system tolerates mycobacterial presence (Pai et al., 2016). In this regard, individuals with latent tuberculosis exhibit enhanced macrophage functions that may protect against other pathogens through the induction of trained immunity (Joosten et al., 2018). In this context, humanity may not be adapting to tuberculosis because increased resistance against mycobacteria is not evolutionarily advantageous. All in all, these results suggest that the risk of suffering infectious diseases has steadily decreased at least for the last 50,000 years as a result of the selection of genetic variants that confer resistance to infections.

It has been proposed that the increased prevalence of inflammatory and autoimmune diseases is associated with the immune-related alleles that have been positively selected through evolutionary processes to protect against infection; hence, the contrasting differences in the prevalence of autoimmune diseases between populations result from diverse selective pressures (Ramos et al., 2015). In line with this, it has been hypothesized that genetic variants associated with protection against infectious agents are behind the increased prevalence of autoimmune diseases in populations with low pathogen exposure, such as Europeans (Fumagalli et al., 2011; Raj et al., 2013). To study the changing patterns of susceptibility to autoimmune and inflammatory diseases during history, we used publicly available summary statistics from GWAS of digestive tract-related autoimmune and inflammatory diseases and arthritis-related diseases (see Figure 2—figure supplement 1) and calculated the PRS for each of the samples under study. Interestingly, we observed a robust increase of the genetic variants related with the development of inflammatory diseases in the digestive tract after the Neolithic revolution (Figure 3). PRS scores associated with celiac disease, Crohn’s disease, ulcerative colitis, and inflammatory bowel disease were strongly associated with the age of the samples, regardless of the p-value thresholds or the missing genotype rates used for PRS calculation, showing the robustness of these results (see Figure 1—figure supplement 2). The fact that especially intestinal inflammatory pathology is increased after a historical event that fundamentally modified human diet is unlikely to be an accident. Our results are in line with earlier research demonstrating that variants in genes important for immune responses and involved in celiac disease pathophysiology (such as IL-12, IL-18RAP, SH2B3) are under strong positive selection (Zhernakova et al., 2010). The reasons for the selection pressure on these genes are not completely understood, but an advantage for host defense has been suggested (Zhernakova et al., 2010).

Distribution of PRS of inflammatory diseases across time.

(A–C) Polygenic risk score (PRS) models based on a p-value threshold of 1e-5. Gray area around regression lines represents the 95% confidence interval. (A) LOESS regression models showing the changing differences in PRS across time. (B) Dual linear models showing the difference in trends before and after the Neolithic revolution. (C) Boxplots showing the difference in mean PRS between adjacent broad time periods using a t-test and the overall difference in means using analysis of variance (ANOVA). (D) Heatmap showing the consistency in regression trends before and after the Neolithic revolution using different quantitative trait locus (QTL) inclusion thresholds (1e-5 to 1e-8).

In contrast to intestinal inflammation, the PRS of traits linked with juvenile-idiopathic arthritis, rheumatoid arthritis, and multiple sclerosis shows a decrease in genetic susceptibility with the age of the sample after the Neolithic revolution. For pre-Neolithic periods, these patterns had little impact with decreasing PRS for digestive tract diseases and increasing PRS for ankylosing spondylitis and juvenile idiopathic arthritis. A strong decrease in susceptibility to juvenile idiopathic arthritis, rheumatoid arthritis, and multiple sclerosis is seen after the Neolithic period (see Figure 3). This is likely linked to the decreased production of the IL-1/IL-6/IL-17 axis described in Figure 2, which is particularly important in the pathophysiology of these disorders (Akioka, 2019; Mei et al., 2011).

The significant changes in cytokine production and disease susceptibility in European populations after the Neolithic can be due to selective processes on the one hand (as described above), but also due to important demographic changes due to migrations of human communities such as the Anatolians (in Neolithic) or the Yamnaya populations from the Pontic steppe (during the Bronze Age) (Racimo et al., 2020). In this regard, several loci associated with inflammatory diseases displayed a group of alleles linked with Crohn’s disease, celiac disease, and ulcerative colitis in Neolithic Aegeans, the community that spread farming across Europe (Hofmanová et al., 2016), with several of these alleles showing signs of positive selection in modern Europeans (Raj et al., 2013). In addition, the gene expression PRS of several cytokines based on the cis- and trans- [gene] expression quantitative trait locus (eQTLs) from the eQTLGen Consortium (https://www.eqtlgen.org/) displayed a very strong association with time for TNFα after the Neolithic revolution (Figure 4).

Figure 4 with 1 supplement see all
Changes in cytokine gene expression PRS across time before and after the Neolithic revolution using different QTL thresholds (1e-5 to 1e-8).

Missing genotype rates ranged through 0.96, 0.9, 0.8, and 0.7. Quantitative trait locus (QTL) p-values for variants included in our polygenic risk score (PRS) models ranged through 10−3, 10−4, 10−5, 10−6, 10−7, and 10−8. The color key indicates the range of -log10 p-values of the Pearson correlation between PRS and time. Red and blue indicate positive and negative association, respectively.

Of note, the availability of samples in the pre-Neolithic dataset is limited compared to the post-Neolithic era. To test the robustness of the trends observed, we recalculated the correlation coefficients and p-values to show the consistency of the results from the down-sampled data of the post-Neolithic samples to match the sample size of the pre-Neolithic samples. This analysis showed that the trajectories observed in our results are consistent regardless of the sample size (Figure 4—figure supplement 1).

In this study we focus on the effects of different pathogens on cytokine production and, through that, on the response to different infections. Human evolution has been influenced by multiple factors such as migration, urbanization, climate, and diet, which also influence the responses to pathogens. The advent of the Neolithic lifestyle was a milestone for human societies: after the Neolithic, the density of human populations and the rate of contact with domesticated animals increased significantly, which increased the emergence of pathogens and the ease with which those could spread. Our data show that while various events and conditions throughout human history have influenced our immune responses to pathogens, the advent of the Neolithic lifestyle was an important turning point for our capacity to respond to pathogens.

Collectively, our results show that the advent of the Neolithic era was a turning point for the evolution of immune-mediated traits in European populations, driving the expansion of alleles that favor the development of tolerance against intracellular pathogens and promote inflammatory responses against extracellular microbes. This is associated with a higher presence of genetic traits related with inflammatory and auto-immune diseases of the digestive tract and a lower number of alleles linked with the development of arthritis. It is important to underline that our results are obtained using European aDNA samples and GWAS summary statistics computed using European cohorts. Therefore, our results would be specific to European populations and should be interpreted with caution for populations from other geographical locations. Further research should compare the trends in different populations that have been exposed to different environments across the planet and clarify the influence of ancestry, time, and rural vs urban lifestyle to shed light on the influence of the infectious environment on genetics and human evolution.

Materials and methods

Cohort selection

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Ancient DNA genotype data were downloaded from version 37.2 of the published aDNA genotype database, compiled by and available on the David Reich Lab website (https://reich.hms.harvard.edu/downloadable-genotypes-present-day-and-ancient-dna-data-compiled-published-papers). The aDNA samples consisted of pseudo-haploid genotype data. This was due to the low genotyping coverage. Samples with variant missingness above 96% were filtered out using Plink (Purcell et al., 2007). This was done in order to remove outliers with extremely low coverage. Only samples within Europe were used for this study, and these samples were selected based on their geographic location, that is latitude (within 35 and 70 degrees north) and longitude (within 10 degrees west and 40 degrees east). Samples without a carbon-dated age were also filtered out. We also selected 250 European samples from the 1000 genomes project phase 3. Only variants present in both the ancient samples and the modern samples were retained. This resulted in a dataset of 827 ancient samples and 250 modern samples containing 1,233,013 variants.

Carbon-dated sample origin and geographical location

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Both carbon-dated age of origin and latitudinal and longitudinal data were available for these 827 ancient European samples. Broad time periods were assigned to these samples with the early upper Paleolithic era for all samples originating from before 25,000 years before the common era standardized to 1950 (BCE). The late upper Paleolithic era follows until 11,000 BCE. The Mesolithic era ranges from 11,000 to 5500 BCE. The Neolithic era ranges from 8500 to 3900 BCE, and the post-Neolithic era ranges from 5000 BCE and more recent ages. Using the geographical data in combination with archeological clues and the genetic data, the broad time period of origin was also available for samples that were dated to a point in time with overlapping broad time periods. This allowed the samples to be classified as either early upper Paleolithic, late upper Paleolithic, Mesolithic, Neolithic, or post-Neolithic. The sample age of the 250 modern European samples was set to 0.

Summary statistics of GWAS and cytokine QTLs

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Summary statistics for complex traits were obtained from the UK Biobank (Bycroft et al., 2018) and the GWAS catalog (MacArthur et al., 2017) last accessed on March 29, 2020. The stimulated cytokine response summary statistics from the 500FG cohort of the HFGP were used (Li et al., 2016). Some complex traits had multiple different sets of summary statistics available. In these cases, the data, which were more recent and used bigger cohorts that were either of European or mixed (European and Asian) ancestry, were selected. The variants of these summary statistics were then filtered by only keeping bi-allelic variants. Most aDNA genotypes available are pseudo-haploid as a consequence of their lower sample quality. We excluded ambiguous SNPs (A/T and C/G) in order to prevent errors due to strand flips present in these pseudo-haploid samples.

Polygenic risk score calculation

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Polygenic risk scores were then calculated by first intersecting the filtered variants from the summary statistics with the variants present in the DNA samples. Starting at the most significant variant, all variants within a 250-kb window around that variant were excluded until no variants remained. We then multiplied the dosage of these variants with the effect size and these values were summed. If a variant is missing in a sample, the dosage is substituted with the average genotyped dosage for that variant within the entire dataset. This way the PRS is not skewed in any specific direction. The formula for this is described below with the score S being the weighted sum of a variant’s dosage Xn multiplied by its associated weight or βn calculated using m variants.

S=n=1mXnβn

Relation between PRS and carbon-dated sample age

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We constructed piecewise linear models for each trait by separating the samples into two groups. These two groups consisted of all samples preceding the Neolithic era and those of the Neolithic era and later, respectively. We correlated PRS with the carbon-dated age of our samples. We then multiplied the -log10 of the Pearson correlation p-values with the sign of the correlation coefficients.

In addition, we plotted LOESS regression models to highlight the change in PRS at each point in time independent of any predefined breakpoint between historical periods. We also performed a group-based comparison using Student’s t-test. We compared pre-Neolithic, Neolithic, post-Neolithic, and Mesolithic samples with their respective adjacent historical periods to show the difference between other historical transitions besides the pre- and post-Neolithic.

Selection test

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We tested whether traits were under selection or if observed changes were due to the genetic drift between adjacent time periods. We performed a two-tailed test using the mean Fst calculated with trait-specific SNPs between two adjacent periods and a reference distribution of 10,000 random linkage disequilibrium (LD)- and minor allele frequency (MAF)-matched mean Fst scores calculated using an equal amount of SNPs (Schirmer et al., 2016). Bonferroni correction was performed to account for multiple testing.

Robustness of results

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In order to test the robustness of our results we calculated PRS using multiple different p-value thresholds for QTL inclusion. We used p-value thresholds from 10−3 to 10−8 for the complex traits obtained through GWAS catalog and the UK Biobank. The thresholds used for the stimulated cytokine responses ranged from 10−3 to 10−6. We also calculated PRS using different variant missingness thresholds. This means we removed samples with a variant missingness rate higher than 96, 90, 80, or 70%. All of the results from the piecewise linear models were then used to create a heatmap depicting the consistency and robustness of our observed correlations.

Additionally, various window sizes were used for clumping the QTLs, and LD-based clumping was also performed excluding variants with an LD greater than 0.2 compared to our lead SNP within a window. In order to see whether our observations were due to sample imbalances between the pre-Neolithic period and the later periods, samples originating from the Neolithic period and later were randomly down-sampled to the same number of samples as the pre-Neolithic samples. Correlation coefficients between PRS and sample age were then recalculated for the Neolithic and younger samples and compared to the coefficients obtained using all Neolithic and younger samples.

Data availability

All the data employed in this manuscript have been obtained from publicly available databases.

The following previously published data sets were used
    1. Liu JZ
    2. Huang H
    3. Ng SC
    4. Alberts R
    5. Takahashi A
    6. Ripke S
    7. Lee JC
    8. Jostins L
    9. Shah T
    10. Abedian S
    11. Cheon JH
    12. Cho J
    13. Dayani NE
    14. Franke L
    15. Fuyuno Y
    16. Hart A
    17. Juyal RC
    18. Juyal G
    (2015) NHGRI-EBI GWAS Catalog
    ID 26192919. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations.
    1. Bakker OB
    (2018) UK Biobank
    ID uk-biobank. GWAS round 2.

References

Decision letter

  1. Jameel Iqbal
    Reviewing Editor; Icahn School of Medicine at Mount Sinai, United States
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States
  3. Fernando Racimo
    Reviewer; University of Copenhagen, Denmark

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

Dominguez-Andrés et al., collect a large amount of immune-related trait association data from a cohort is made up of 534 individuals of Western European ancestry. The goal was to track the evolutionary trajectories of cytokine production capacity over time in a number of patients with different exposure to infectious organisms, infectious disease, autoimmune and inflammatory diseases using the 500 Functional Genomics cohort of the Human Functional Genomics Project. From this analysis it was hypothesized that the Neolithic transition was characterized by strong changes in the adaptive response to pathogens in human biology.

Decision letter after peer review:

Thank you for submitting your article "Evolution of cytokine production capacity in ancient and modern European populations" for consideration by eLife. Your article has been sent to 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors and the evaluation has been overseen by Mone Zaidi as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Fernando Racimo (Reviewer #3).

The Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions:

Overall, the main concern of both reviewers focused on statistical methodology followed by the authors and its limitations. There was a feeling that it was not strong enough to support the manuscript's conclusions as stated. These reviewer comments, which were numerous, centered on what was felt to be inadequate models and partitioning. Fully addressing the reviewer's concerns and revising the model and data analysis as appropriate will be needed to strengthen the paper.

1. The authors should comment on the impact and limitations on using the "average genotyped dosage" to be substituted for missing variants and data interpretation. It was described that the dosage of these variants was multiplied by their effect size while the dosage of missing variants in a sample were supplemented with the average dosage. Although agreed that this method doesn't skew the PRS preferentially one way or the other, but it could dilute or strengthen the scale of the data without knowing what the "true" values of the missing data points are.

2. Peripheral blood mononuclear cells were challenged with bacterial, fungal, viral, and non-microbial stimuli while measuring 6 cytokines (TNF α, IL-1 β, IL-6, IL-17, IL-22, and IFN γ) at 24 hrs and 7 days post-stimulation yielding 105 unique cytokine-stimulation pairs. Although a large number of such pairings were provided, likely due to data availability, the data set is not complete i.e. for the same organism not all 6 cytokines were followed and not all pairings had both a 24 hour and a 7 day time points. Some had a 48 hour time point. The authors should try to provide a subset of data in which there is completeness of data to allow the readers to more clearly be able to compare trends appropriately (i.e. across the same cytokine markers or the same time point post stimulus etc …).

3. In Figure 1, how was this particular subset of cytokines-stimulants pairings chosen from the larger set in Figure S1? The authors should show representative examples that highlights their discussion in to the text and to clearly label as such intracellular vs. extracellular organisms, zoonotic organisms vs not, etc. For example, the text makes reference to data on the IFN γ responses which are not presented in Figure 1.

4. The authors should also comment on the impact of inter-patient or inter-measurement variabilities of the cytokine responses to stimuli could impact their current results. They should describe any potential limitations or biases that this might induce in their data set.

5. The authors estimated cytokine production capacity based on PRS which authors claim to have significant differences between populations in various historical periods with the largest strength difference reported before and after the Neolithic revolution. In Figure 1, the split was done to look at before and after the Neolithic era and the linear regression correspond to those two eras. However, the authors do not comment or show the data to demonstrate why they choose that specific break point as opposed to looking at every historical era transition i.e. from early upper paleolithic to late upper paleolithic to Mesolithic to Neolithic to post-Neolithic to modern. The authors should justify why grouping together all of the historical eras to being before and after the Neolithic revolution is acceptable and demonstrate from the data that the linear regression lines would follow the same pattern in each distinct historical period vs in the pre- and post-Neolithic grouping. This should be addressed in the main figures or as a supplemental figure.

6. In Figure 1-3 and as briefly discussed in the text, there are very few data points in the pre-Neolithic in comparison to post-era which would make it challenging for the reader to draw robust conclusions about the trends in the pre-Neolithic era. This should be addressed in the main text to prevent any overinterpretation of the main figures. In the supplemental figure S2, the authors do make claims about robustness of the trend when comparing to downsampled numbers in the post-Neolithic era and do comment that the power decreases with lower number of samples.

7. Throughout Figures 1-4, the authors state that the correlation pattern is the same across a number of thresholds 0.96, 0.9, 0.8, and 0.7. It would be best for the only one threshold range to be presented in these main figures and to move the complete data across multiple threshold ranges into a supplemental figure as the added ranges do not add anything new to the data results and interpretation.

8. Although the authors picked a very homogeneous population, the authors should discuss in terms of addressing the limitations of the current study, what the impact of other factors or events throughout history that could have a role here such as the development of vaccines the cross reactivity of antigens that may be shared between organisms, the development of medications (antimicrobials, antivirals, anti-inflammatory, etc …)?

9. Although the authors had a robust discussion on the prevalence of inflammatory and autoimmune diseases within the European population over different historical eras. A similar discussion around factors such as population density, migration, living conditions, living environments, diet/nutrition, sanitation/hygiene, etc … is lacking in discussing the evolution of cytokine production to specific pathogens.

10. The authors should highlight additional limitations of this current study in terms of the generalizability to other populations or to clearly state that this is limited to the European population at the specified latitude and longitudes used. The authors did demonstrate for the most part agreement with prior published studies which adds strength to their current results.

11. A concern is that the statistical methodology followed by the authors is not appropriate to support their conclusions – in particular, the fact that data imbalance and their effect on the P-values has not been properly accounted for, and that the choice of pre-partitioned pair of linear models is not the correct way to detect a shift in scores (or to make the much stronger claim of adaptation). I would recommend changing the model being used so as not to rely on a preparation of the data, and to properly account for genetic drift.

https://doi.org/10.7554/eLife.64971.sa1

Author response

1. The authors should comment on the impact and limitations on using the "average genotyped dosage" to be substituted for missing variants and data interpretation. It was described that the dosage of these variants was multiplied by their effect size while the dosage of missing variants in a sample were supplemented with the average dosage. Although agreed that this method doesn't skew the PRS preferentially one way or the other, but it could dilute or strengthen the scale of the data without knowing what the "true" values of the missing data points are.

We thank the reviewer for this important remark. Replacing missing values by the average dosage would indeed dilute the observed effects or artificially increase the power of the observed trends. The alternative would be using only SNPs present in all samples. This would also bias our analysis since older samples have a higher error rate during genotype calling compared to our more modern samples. This is due to the sparsity of data and cytosine deamination which affects older samples to a greater extent. In our analysis, we tried to alleviate potential bias by using multiple thresholds for average missing genotype rate. By focusing on the consistent pattern across different thresholds, we were able to put our findings in a better context and show that the trends we observed hold true even when filtering out samples with high rates of substitute values. We have included text in the Discussion part of revised manuscript (lines 115 to 119).

2. Peripheral blood mononuclear cells were challenged with bacterial, fungal, viral, and non-microbial stimuli while measuring 6 cytokines (TNF α, IL-1 β, IL-6, IL-17, IL-22, and IFN γ) at 24 hrs and 7 days post-stimulation yielding 105 unique cytokine-stimulation pairs. Although a large number of such pairings were provided, likely due to data availability, the data set is not complete i.e. for the same organism not all 6 cytokines were followed and not all pairings had both a 24 hour and a 7 day time points. Some had a 48 hour time point. The authors should try to provide a subset of data in which there is completeness of data to allow the readers to more clearly be able to compare trends appropriately (i.e. across the same cytokine markers or the same time point post stimulus etc …).

We thank the reviewer for these remarks. The stimulation periods were chosen based on extensive studies that showed that the timepoints used were best suited for assessing monocyte-derived and lymphocyte-derived cytokines per stimulus. Not all the stimuli induce the production of all cytokines, so the selection of the cytokine-stimulus pairs was performed for those pairs in which a cytokine production could be measured (PMID: 1385767; PMID: 19380112; PMID: 27814509; PMID: 27814508; PMID: 27814507). The missing stimulus-cytokine pairs are simply due to the fact that some stimuli did not induce the production of some cytokines. The differences in the cytokine availability and time points are adjusted to the optimal time of production per stimuli. Monocyte-derived cytokines (IL-1b, IL-6 and TNFa) are early response cytokines, produced by innate immune cells shortly after stimulation (6 to 24h, the latter being used standard). IFNγ, IL-17 and IL-22 are lymphocyte-derived cytokines, produced by adaptive immune cells, in this case T helper cells upon stimulation by antigen-presenting cells. These cells need to differentiate for 2-7 days before they start to produce these cytokines, this is the reason why the time point of the measurements of these cytokines is 7 days. In the case of IFNγ, the kinetics of its production is more rapid, and we therefore measured it 48h after stimulation in whole blood samples.

We have included these considerations and new references in the new version of the text (lines 82 to 87).

3. In Figure 1, how was this particular subset of cytokines-stimulants pairings chosen from the larger set in Figure S1? The authors should show representative examples that highlights their discussion in to the text and to clearly label as such intracellular vs. extracellular organisms, zoonotic organisms vs not, etc. For example, the text makes reference to data on the IFN γ responses which are not presented in Figure 1.

The selected cQTL traits were chosen based on whether their effects were consistently significant and in the same direction across different threshold combinations. In the new version of the text and the figures we have highlighted in the figure legends which of the organisms are intracellular/extracellular and which organisms cause zoonotic diseases (lines 419-421).

4. The authors should also comment on the impact of inter-patient or inter-measurement variabilities of the cytokine responses to stimuli could impact their current results. They should describe any potential limitations or biases that this might induce in their data set.

The interindividual variability in the responses is something that happens naturally and is part of the analysis and the representations performed. Therefore, the variability of the cytokine responses between different individuals caused by the intrinsic gene expression variation and the environmental factors cannot be avoided. In this study we have used a cohort of individuals with a similar genetic background (Western-European ancestry) but with balanced characteristics in terms of age, sex, BMI and habits (PMID: 27814508), which allowed us to represent the natural variability in the responses. Cytokines were measured in batches in which a certain cytokine was measured the same day for all the samples. This was done to decrease the potential technical variations. Moreover, in a recent study we evaluated the intra-individual variation of cytokine production capacity, which was found limited (ter Horst et al., J Immunol, in press). On the other hand, inter-individual variability of cytokine production was largely depedent on genetic variants (PMID: 27814507, PMID: 29784908) We have included these considerations in the new version of the manuscript (lines 96 to 105).

5. The authors estimated cytokine production capacity based on PRS which authors claim to have significant differences between populations in various historical periods with the largest strength difference reported before and after the Neolithic revolution. In Figure 1, the split was done to look at before and after the Neolithic era and the linear regression correspond to those two eras. However, the authors do not comment or show the data to demonstrate why they choose that specific break point as opposed to looking at every historical era transition i.e. from early upper paleolithic to late upper paleolithic to Mesolithic to Neolithic to post-Neolithic to modern. The authors should justify why grouping together all of the historical eras to being before and after the Neolithic revolution is acceptable and demonstrate from the data that the linear regression lines would follow the same pattern in each distinct historical period vs in the pre- and post-Neolithic grouping. This should be addressed in the main figures or as a supplemental figure.

We thank the reviewer for this helpful suggestion. We have followed it and made the following changes:

– The original figures showed only models using two separate linear regression lines and the different thresholds for missing genotype rates showed consistent results.

– In the new figures we depict LOESS regression models to better show the difference in mean PRS at every point in time and we additionally show boxplots with the different major age periods pooling the paleolithic and mesolithic samples together as pre-neolithic samples in order to account for the lower sample number in the earlier historical periods. To highlight this we have added a new section in lines 123 to 129.

– In the revised figure 2 we add LOESS regression models for which we do not bias our analysis into defining a break at a certain time period. We furthermore show boxplots with pairwise comparisons (student’s T-test) for broader time periods highlighting the changes in PRS that would correspond with major changes in human lifestyle such as the shift from a hunter-gatherer to a neolithic lifestyle or the later urbanization of modern human societies.

– In the new version of Figure 3 we confirm that the various traits showing a clear change in PRS start at the advent of the Neolithic or post-Neolithic era using both the LOESS regression and pairwise comparisons (student T-test).

– Similarly the heatmap in our original figure 4 has also been revised to only show the large sample set.

6. In Figure 1-3 and as briefly discussed in the text, there are very few data points in the pre-Neolithic in comparison to post-era which would make it challenging for the reader to draw robust conclusions about the trends in the pre-Neolithic era. This should be addressed in the main text to prevent any overinterpretation of the main figures. In the supplemental figure S2, the authors do make claims about robustness of the trend when comparing to downsampled numbers in the post-Neolithic era and do comment that the power decreases with lower number of samples.

We fully agree with the reviewer that the power decreases with lower number of samples for the pre-Neolithic period. We have carefully checked whether our conclusion was robust by using a down-sampling method. Previously we plotted the correlation coefficients of the full dataset compared to the downsampled dataset. In the new Figure 4—figure supplement 1 we plot the -log10 P values times the sign of the correlation coefficient of the traits in the main figures using both the full dataset as well as the downsampled dataset to better show the consistency in direction of the effects. We have also discussed this more in depth on lines 267-272.

7. Throughout Figures 1-4, the authors state that the correlation pattern is the same across a number of thresholds 0.96, 0.9, 0.8, and 0.7. It would be best for the only one threshold range to be presented in these main figures and to move the complete data across multiple threshold ranges into a supplemental figure as the added ranges do not add anything new to the data results and interpretation.

We followed this suggestion and revised the figures accordingly. Please see details described in our response to point number 5.

8. Although the authors picked a very homogeneous population, the authors should discuss in terms of addressing the limitations of the current study, what the impact of other factors or events throughout history that could have a role here such as the development of vaccines the cross reactivity of antigens that may be shared between organisms, the development of medications (antimicrobials, antivirals, anti-inflammatory, etc …)?

We thank the reviewer for raising such an important point. We have included a Discussion about these topics in the new version of the manuscript (lines 273 to 282). We have to mention though that the analyses in the 500FG were performed in individuals who did not use any medication.

9. Although the authors had a robust discussion on the prevalence of inflammatory and autoimmune diseases within the European population over different historical eras. A similar discussion around factors such as population density, migration, living conditions, living environments, diet/nutrition, sanitation/hygiene, etc … is lacking in discussing the evolution of cytokine production to specific pathogens.

We agree that this is a very good point raised by the reviewer, and we added that in the revised manuscript. As mentioned also in our response to the previous reviewer, in this study we focus on the effects of different pathogens in cytokine production and therefore in the response to different infections. Human evolution has been influenced by multiple factors, such as migrations, urbanization, climate, diet, which also influence the responses to pathogens. The advent of the Neolithic era was a milestone for human civilization and human lifestyle, with higher density of human populations and rate of contact with animals which increased the emergence of pathogens. Our data show that despite the fact that other events and conditions throughout human history have influenced our immune responses to pathogens, the advent of the Neolithic was a turning point for our capacity to respond to pathogens. The evolution of cytokine production to specific pathogens is also a very important factor to take into account. The more contact humans have had with a pathogen, the greater the likelihood that the interaction between host and pathogen will lead to changes in the cytokine production and also the adaptation of the pathogen to the human host. We have included a Discussion about these topics in the new version of the manuscript (lines 273 to 282).

10. The authors should highlight additional limitations of this current study in terms of the generalizability to other populations or to clearly state that this is limited to the European population at the specified latitude and longitudes used. The authors did demonstrate for the most part agreement with prior published studies which adds strength to their current results.

Indeed, this is a very valid point. In our study we focus on summary statistics obtained from European populations and only employ European aDNA samples, so our results should not be extrapolated to other populations from other geographical areas. We have included this in the Discussion of the new version of the manuscript (lines 289 to 292). However, as the reviewer mentions, our findings are mostly in agreement with previous studies in other populations, which adds robustness to the results of our study.

11. A concern is that the statistical methodology followed by the authors is not appropriate to support their conclusions – in particular, the fact that data imbalance and their effect on the P-values has not been properly accounted for, and that the choice of pre-partitioned pair of linear models is not the correct way to detect a shift in scores (or to make the much stronger claim of adaptation). I would recommend changing the model being used so as not to rely on a preparation of the data, and to properly account for genetic drift.

We thank the reviewer for this important remark. In order to account for the data imbalance in terms of samples per historical age period we pooled all samples prior to the neolithic age together before testing the difference in average PRS between neighbouring periods using a T test, we also show LOESS regression models to not depend on a pre-partitioning of our data. In addition we aim to show how consistent our post-Neolithic results are by downsampling the post-Neolithic samples and reperforming linear regression (please see our replies to points 5 and 6).

In addition we performed a seperate test for adaption as opposed to genetic drift using Wright’s fixation index (Fst) to test for significant differences in genetic differentiation of trait specific SNPs compared to background SNPs in order to determine whether the changes we see in PRS over time are due to selection or due to genetic drift. This analysis shows that changes between the pre-Neolithic to Neolithic are not significantly different from drift, whereas after the onset of the Neolithic we do see significant amount of selection. We have explained this further in the manuscript line 130 to 135 and included the new Table S2.

https://doi.org/10.7554/eLife.64971.sa2

Article and author information

Author details

  1. Jorge Domínguez-Andrés

    1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Radboud Institute for Molecular Life Sciences (RIMLS), RadboudUniversity Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Investigation, Writing - original draft
    Contributed equally with
    Yunus Kuijpers
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9091-1961
  2. Yunus Kuijpers

    1. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
    2. TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft
    Contributed equally with
    Jorge Domínguez-Andrés
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5075-3970
  3. Olivier B Bakker

    Department of Genetics, University Medical Centre Groningen, Nijmegen, Netherlands
    Contribution
    Resources, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Martin Jaeger

    1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Radboud Institute for Molecular Life Sciences (RIMLS), RadboudUniversity Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Cheng-Jian Xu

    1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
    3. TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
    Contribution
    Resources, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1586-4672
  6. Jos WM Van der Meer

    Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    Senior editor, eLife
  7. Mattias Jakobsson

    1. Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, Sweden
    2. Centre for Anthropological Research, Department of Anthropology and Development Studies, University of Johannesburg, Auckland Park, South Africa
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  8. Jaume Bertranpetit

    Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
    Contribution
    Resources, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Leo AB Joosten

    1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Radboud Institute for Molecular Life Sciences (RIMLS), RadboudUniversity Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Supervision
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6166-9830
  10. Yang Li

    1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Radboud Institute for Molecular Life Sciences (RIMLS), RadboudUniversity Medical Center, Nijmegen, Netherlands
    3. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
    4. TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing
    Contributed equally with
    Mihai G Netea
    For correspondence
    yang.li@helmholtz-hzi.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4022-7341
  11. Mihai G Netea

    1. Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Radboud Institute for Molecular Life Sciences (RIMLS), RadboudUniversity Medical Center, Nijmegen, Netherlands
    3. Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing
    Contributed equally with
    Yang Li
    For correspondence
    Mihai.Netea@radboudumc.nl
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2421-6052

Funding

European Commission (833247)

  • Mihai G Netea

European Commission (948207)

  • Yang Li

ZonMw (09150161910024)

  • Jorge Domínguez-Andrés

Netherlands Organisation for Scientific Research (Spinoza)

  • Mihai G Netea

Radboud University Medical Center (Hypatia Grant (2018))

  • Yang Li

Netherlands Organisation for Scientific Research (09150161910024)

  • Jorge Domínguez-Andrés

MINECO (PID2019-110933GB-I00 (AEI/FEDER UE))

  • Jaume Bertranpetit

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

MGN was supported by an ERC Advanced Grant (833247) and a Spinoza Grant of the Netherlands Organization for Scientific Research. YL was supported by an ERC Starting Grant (948207) and the Radboud University Medical Centre Hypatia Grant (2018) for Scientific Research. JD-A is supported by The Netherlands Organization for Scientific Research (VENI grant 09150161910024). JB was supported by PID2019-110933GB-I00 (AEI/FEDER, UE) MINECO, Spain.

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Jameel Iqbal, Icahn School of Medicine at Mount Sinai, United States

Reviewer

  1. Fernando Racimo, University of Copenhagen, Denmark

Publication history

  1. Received: November 17, 2020
  2. Preprint posted: January 17, 2021 (view preprint)
  3. Accepted: June 30, 2021
  4. Version of Record published: September 7, 2021 (version 1)

Copyright

© 2021, Domínguez-Andrés et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

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    Longevity is often associated with stress resistance, but whether they are causally linked is incompletely understood. Here we investigate chemosensory-defective Caenorhabditis elegans mutants that are long-lived and stress resistant. We find that mutants in the intraflagellar transport protein gene osm-3 were significantly protected from tunicamycin-induced ER stress. While osm-3 lifespan extension is dependent on the key longevity factor DAF-16/FOXO, tunicamycin resistance was not. osm-3 mutants are protected from bacterial pathogens, which is pmk-1 p38 MAP kinase dependent, while TM resistance was pmk-1 independent. Expression of P-glycoprotein (PGP) xenobiotic detoxification genes was elevated in osm-3 mutants and their knockdown or inhibition with verapamil suppressed tunicamycin resistance. The nuclear hormone receptor nhr-8 was necessary to regulate a subset of PGPs. We thus identify a cell-nonautonomous regulation of xenobiotic detoxification and show that separate pathways are engaged to mediate longevity, pathogen resistance, and xenobiotic detoxification in osm-3 mutants.

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    Our analytic approach identified several known targets (e.g. ABO, OAS1), but also nominated new proteins such as soluble Fas (colocalisation probability >0.9, p=1 × 10-4), implicating Fas-mediated apoptosis as a potential target for COVID-19 risk. The polygenic (pan) and cis-Mendelian randomisation analyses showed consistent associations of genetically predicted ABO protein with several COVID-19 phenotypes. The ABO signal is highly pleiotropic, and a look-up of proteins associated with the ABO signal revealed that the strongest association was with soluble CD209. We demonstrated experimentally that CD209 directly interacts with the spike protein of SARS-CoV-2, suggesting a mechanism that could explain the ABO association with COVID-19.

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    Our work provides a prioritised list of host targets potentially exploited by SARS-CoV-2 and is a precursor for further research on CD209 and FAS as therapeutically tractable targets for COVID-19.

    Funding:

    MAK, JSc, JH, AB, DO, MC, EMM, MG, ID were funded by Open Targets. J.Z. and T.R.G were funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/4). JSh and GJW were funded by the Wellcome Trust Grant 206194. This research was funded in part by the Wellcome Trust [Grant 206194]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.