Ancient DNA study provides clues to leprosy susceptibility in medieval Europe

doi:10.21203/rs.3.rs-3879251/v1

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Ancient DNA study provides clues to leprosy susceptibility in medieval Europe

https://doi.org/10.21203/rs.3.rs-3879251/v1

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Background

Leprosy is a chronic infectious disease caused by Mycobacterium leprae (M. leprae) that reached an epidemic scale in the Middle Ages. Nowadays, the disease is absent in Europe and host genetic influences have been considered as a contributing factor to leprosy disappearance. In this study, a case-control association analysis between multiple human leukocyte antigen (HLA) alleles and leprosy was performed in a medieval European population for the first time. The sample comprised 293 medieval individuals from 18 archaeological sites in Denmark (N = 16) and Germany (N = 2).

Results

Our results indicate that HLA-B*38 was associated with leprosy risk. Furthermore, we detected three novel variants that were possibly involved in leprosy susceptibility (HLA-A*23, DRB1*13 and DPB1*452). Interestingly, we noted a subtle temporal change in frequency for several alleles previously associated with infectious diseases, inflammatory disorders and cancer in present-day populations.

Conclusions

This study demonstrates the potential of ancient DNA in the identification of genetic variants involved in predisposition to diseases that are no longer present in Europe but remain endemic elsewhere. Although it is difficult to pinpoint the reason behind the temporal frequency shift, past epidemics of infectious diseases have likely influenced the HLA pool in present-day Europe.

Evolutionary Biology

Molecular Genetics

Infectious Diseases

ancient DNA

ancient genomics

Human Leukocyte Antigens

case-control association study

human immunity

leprosy

Mycobacterium leprae

pathogen-driven selection

Middle Ages

epidemics

Based on historical documentation, archaeological sources and molecular data, it is evident that medieval Europeans struggled with a high disease burden and the ever-present threat of infections [e.g., 1–4]. One of the diseases that reached an epidemic scale in the Middle Ages was leprosy (Hansen’s disease) [5–7] caused by a Mycobacterium leprae (M. leprae) infection. Leprosy mainly influences the superficial structures, such as skin, mucous membranes, eyes, testes and peripheral nerves [8]. Blindness and sensory loss often lead to injuries and secondary infections, which can result in complete loss of extremities, making leprosy highly debilitating. Deformities of the face and hands that are clear signs of disease have led to ostracism and consequent isolation of leprosy-affected individuals from society. For this purpose, specialized institutions called leprosaria were established in the Middle Ages. As leprosy is a lifelong disease, leprosaria became a new home for the sick who were eventually buried at the associated cemeteries [4, 9, 10]. Leprosy virtually disappeared from Europe in the 16th century, with only a few cases being reported in Norway until the 19th century [11–13]. The reason behind leprosy’s disappearance from Europe is unclear. Considering the high stability of the M. leprae genome since the early Middle Ages [6, 14–17], it is likely that environmental and host genetic factors played a major role in the disappearance of the disease from the European continent [16].

Although the clinical outcome of an infection may depend on various intrinsic and extrinsic factors [18–21], leprosy is an epitome of the substantial influence of the host’s immune response on disease manifestation. Depending on the degree of the immunological reaction, M. leprae infection can lead to severe disability, mild disfigurement, painful chronic inflammation or a complete lack of symptoms [22–24]. Several association studies have identified genetic variants conferring susceptibility to or protection against the disease for populations in which it is endemic today, such as India, China, Brazil and Mexico [e.g., 25–29]. Many associations were reported for variants in the human leukocyte antigen (HLA) region (reviewed in [30, 31]. As Europe has been free of leprosy for centuries, all modern association studies have been carried out in non-European populations. So far, only one ancient DNA (aDNA) leprosy case-control association study on medieval Europeans has been performed [16]. The study focused on testing the association between HLA-DRB1*15:01 and leprosy risk that had been reported previously in present-day non-Europeans [32–36]. The authors replicated the association [16], but did not investigate other susceptibility loci in their samples. Therefore, the effect of additional HLA variation in leprosy risk of Europeans remains unexplored. Here, we performed a case-control association study between six classical class I (A, B and C) and II (DRB1, DQB1 and DPB1) HLA loci and leprosy in a large sample of 293 medieval Europeans (Danish and German) to identify genetic variants involved in leprosy susceptibility. This is the first study that examined multiple HLA loci in a historical population in the context of leprosy. Additionally, we investigated whether any temporal changes in the HLA allele frequencies have occurred in the population since the Middle Ages.

Data collection

To investigate the link between HLA alleles and leprosy susceptibility in Europeans, it was necessary to examine individuals who lived in medieval Europe, where the leprosy epidemic was estimated to affect a large proportion of the population [5, 10, 11, 37]. To this end, skeletal remains of over 350 individuals excavated from 16 Danish and 2 German archaeological sites (10th -19th century AD) (Table 1; Additional file 0: Skeletal_collections-literature) were analysed using a combination of molecular and osteological methods (see Material and Methods). Following the application of stringent selection criteria and inclusion of previously published data [38] (see below), 83 cases and 210 controls were used for the association study (Table 1).

Table 1

**Number of examined individuals per archaeological site.**
Site			N	N2
Skt. Jørgensgården^†	Odense	1270–1550	68	60^#
Skt. Knuds Plads		1086–1800	2	2
Albani Torv		1000–1540	8	7
Klosterbakken		1086–1800	1	1
Skt. Trinitatis/Drotten	Viborg	1000–1529	13	12
Skt. Morten		1000–1529	4	4
Skt. Mathias		1000–1529	2	2
Skt. Mikkel		1000–1800	11	9
Gråbrødrekloster		1529–1812	2	2
Faldborg Kirkegård		1100-mid 16th c.	3	3
Klosterkirke	Horsens	1600–1800	50	49
Ole Worms Gade		1050–1536	24	21
Ødekirkegård	Sejet	1250–1575	25	24
Tirup kirke	Tirup	1150–1350	29	25
Gråbrødrekloster	Ribe	1250–1536	15	14
Ribe Domkirke		900–1738	9	5
St. Jürgen	Lübeck	1270–1550	30	30
Gut Melaten	Aachen	1230–1550	56	23
Total	-	900–1812	352	293^#

N – number of analysed individuals; N2 – number of individuals included in the case-control study; ^† - also known as St. Jørgen; # - includes individuals with HLA genotypes reported in a previous study by Pierini et al., 2020.

Metagenomic screening for M. leprae

Screening for the presence of M. leprae DNA revealed nine (16%) individuals from Gut Melaten and 28 (41.2%) individuals from the Skt. Jørgensgården cemetery to be positive for infection (Additional file 1: Tab. S1). M. leprae was not detected at any other site. Six M. leprae genomes (five from Gut Melaten and one from Skt. Jørgensgården) were successfully reconstructed with over 50% of the genome covered by at least one read (Table 2). The strains were subsequently subjected to phylogenetic analysis together with 38 previously published medieval and 139 modern M. leprae genomes (Fig. 1, Additional file 2: Fig. S1 and Additional file 3: Tab. S2). M. lepromatosis was used as an outgroup. All new genomes clustered within the diversity of phylogenetic branch 3. None of the Melaten strains clustered together, showing a considerable degree of diversity among them.

Table 2

**Basic statistics of the sequence reads from six samples mapping against the** **M. leprae** **reference.**
Site	Grave ID	N of reads^#	Mean coverage	Coverage 1x [%]	Coverage 2x [%]	Coverage 3x [%]
Gut Melaten	G19	152884	4.22x	85.63	68.73	52.86
Gut Melaten	G119	111253	3x	83.66	63.13	43.68
Gut Melaten	G43	376821	11.71x	97.04	94.8	91.18
Gut Melaten	G91	103455	2.38x	66.19	38.36	20.55
Gut Melaten	G35	68098	1.47x	51.24	21.18	7.87
Skt. Jørgensgården	G33	232312	5.03x	95.96	90.42	79.76

^#The BAM files were filtered for a mapping quality of > 30.

Figure 1. A maximum-likelihood tree illustrating the phylogenetic position of the M. leprae strains. The strains from Gut Melaten and Skt. Jørgensgården (shown in red). Previously published medieval strains are marked in blue and modern M. leprae are shown in black. Circles at each node represent bootstrap support over 500 replications. Only branches with bootstrap support of at least 60 are marked. The tree includes 183 strains (139 modern and 44 medieval) (Additional file 3: Tab. S2). M. lepromatosis was used as an outgroup. Country codes can be found in the Additional file 4: Tab. S3. Dates are provided for ancient strains. The tree was visualized with iTOL [39].

Determination of cases and controls under strict selection criteria

All individuals were evaluated for kinship to avoid introducing bias stemming from close relatedness. Kinship was detected for four pairs of individuals (Additional file 5: Tab. S4) and therefore, one individual from each pair (n = 4) was excluded from further analysis. Based on molecular and osteological evidence of leprosy, 72 individuals were considered leprosy-affected “cases”, while 222 individuals were assigned to the “control” group (Additional file 1: Tab. S1).

After the HLA-targeted capture and HLA typing of the six classical loci (class I: HLA-A, B, C and class II: DRB1, DQB1 and DPB1), HLA profiles were successfully produced for 266 individuals (90.5%) (56 cases and 210 controls) (Additional file 1: Tab. S1). Additionally, HLA genotypes generated in a previous study [38] were incorporated, which resulted in a final dataset of 293 individuals (83 cases and 210 controls) (Table 1, Fig. 2 and Additional file 1: Tab. S1).

Figure 2. Map of archaeological sites from which skeletal remains included in the case-control study were excavated. Archaeological dating and the number of individuals are presented in brackets. The leprosy status of individuals at each site included in the association study is color-coded: red for cases and blue for controls. The location of all sites is shown in panel A. Panel B presents the names and locations of all Danish sites.

Evaluation of heterogeneity within the medieval sample and temporal genetic continuity in Europeans

Due to the inter-population variation in HLA allele frequencies [40], the sample was evaluated for genome-wide heterogeneity. Based on a set of informative SNPs, principal component analysis (PCA) was performed. The PCA showed that all individuals grouped together within the diversity of present-day Europeans with the majority of individuals clustering with the populations of western and northern Europe (Fig. 3A, Additional file 2: Fig. S2). When cases and controls were considered separately, the groups overlapped on the PCA plot along both axes (Additional file 2: Fig. S3). These results indicate that there is no substantial genome-wide diversity between the groups and a comparison of allelic frequencies in a case-control association study is warranted. Furthermore, population differentiation was measured with the fixation index (F_ST) of the HLA alleles. The F_ST between medieval cases and controls was low (< 0.004), suggesting that the allele pools of the two groups were highly similar. All medieval individuals pooled together exhibited very low levels of differentiation from present-day northern and central Europeans (Fig. 3B), which points to population continuity since the Middle Ages.

Figure 3. Analysis of differentiation between medieval and modern populations.

A - PCA of 185 medieval individuals, based on SNPs from the 2140k SNP panel, projected onto the first two principal components calculated from 66 present-day West-Eurasian populations.

B – PCoA of 168 medieval individuals based on the pairwise F_ST values for HLA genotypes. Variance accounted for each principal coordinate is shown within axis labels.

Association analysis – primary analysis

In comparison to studies conducted in present-day populations, our sample size was relatively small. Therefore, testing of all observed alleles would drastically reduce the power of detecting significant association signals (Additional file 6: Tab. S5). To overcome this limitation, a targeted approach was taken, i.e., 13 alleles previously linked with leprosy in modern non-European populations were selected for the association analysis (primary analysis) (Additional file 7: Tab. S6). The alleles were chosen based on a strong association (odds ratio (OR) of at least 2 or less than 0.5) reported in studies with a robust sample size of at least 200 cases and 200 controls. As a large proportion of the HLA calls in this study were made at the first-field resolution, which groups alleles of highly similar sequence and function, the association analysis was performed at this level.

Across the 13 selected alleles, the frequency of HLA-B*38 differed statistically significantly between cases (4.5%) and controls (0.3%) (Table 3). Furthermore, although not statistically significant, trends were observed for HLA-B*44 and DRB1*15 which were more frequent in cases in comparison to controls: 14.3% vs. 8.6% and 25.2% vs. 17.1%, respectively. Statistical power analysis indicated that this study is underpowered (achieved power < 0.5) to detect the observed frequency shifts for these alleles (Additional file 8: Tab. S7). Allele frequencies for the remaining 10 alleles were comparable between cases and controls.

Table 3

Results of the case-control association analysis between 13 selected HLA alleles and leprosy.
HLA allele	CASES (n = 83)		CONTROLS (n = 210)		p-value	pc	OR	CI low	CI up
HLA allele	N	Fr	N	Fr	p-value	pc	OR	CI low	CI up
*A29**	2	0.016	5	0.014	1.000	1.000	1.158	0.109	7.184
*B07**	20	0.179	54	0.150	0.459	1.000	1.235	0.665	2.227
*B14**	0	0.000	1	0.003	1.000	1.000	0.000	0.000	125.461
*B38**	5	0.045	1	0.003	0.003	0.044^*	16.701	1.841	794.261
*B44**	16	0.143	31	0.086	0.102	1.000	1.772	0.866	3.506
*B49**	0	0.000	2	0.006	1.000	1.000	0.000	0.000	17.200
*C02**	5	0.041	17	0.048	1.000	1.000	0.842	0.238	2.448
*C05**	9	0.074	23	0.065	0.834	1.000	1.139	0.450	2.648
*C08**	0	0.000	6	0.017	0.346	1.000	0.000	0.000	2.447
*C15**	5	0.041	12	0.034	0.778	1.000	1.210	0.327	3.788
*DRB110**	1	0.008	1	0.003	0.457	1.000	2.812	0.036	221.567
*DRB115**	31	0.252	59	0.171	0.062	0.802	1.631	0.959	2.742
*DQB104**	4	0.032	18	0.052	0.464	1.000	0.602	0.145	1.876
N – number of alleles; Fr – frequency; pc – p-value corrected for multiple testing using Bonferroni correction (number of tests = 13); ^* – p-value statistically significant after correction for multiple testing; CI low – confidence interval lower bound of OR; CI up – confidence interval upper bound of OR

Association analysis – explorative analysis

To investigate the medieval HLA dataset for potentially novel associations, an explorative association study was performed for all the remaining alleles observed across the six classical HLA loci (HLA-A, B, C, DRB1, DQB1 and DPB1) (Additional file 9: Tab. S8). Nominally significant differences in allele frequencies were noted for HLA-A*23, DRB1*13 and DPB1*452 (Table 4). Whilst A*23 was more frequent among cases (5.5% vs. 0.3%, p = 0.0004, OR = 21.207), the frequency of DRB1*13 was over two times higher in controls relative to cases (16.8% vs. 8.1%, p = 0.017, OR = 0.439). The two instances of DPB1*452 were noted exclusively in the case group (3.9% vs. 0%, p = 0.039). In addition to the nominally significant associations, nonsignificant trends were observed for one class I and three class II alleles (Table 4). Similar to the trends noted in the primary analysis, the achieved statistical power was low (below or equal to 0.05) (Additional file 8: Tab. S7).

Table 4

**Exploratory case-control association analysis: nominally significant results and non-significant trends**.
HLA allele	CASES (n = 83)		CONTROLS (n = 210)		p-value	OR	CI low	CI up
HLA allele	N	Fr	N	Fr	p-value	OR	CI low	CI up
*A23**	7	0.055	1	0.003	0.0004^*	21.207	2.681	959.693
*C06**	5	0.041	32	0.091	0.081	0.428	0.127	1.143
*DRB103**	10	0.081	51	0.148	0.063	0.511	0.223	1.062
*DRB104**	27	0.220	55	0.159	0.167	1.482	0.848	2.544
*DRB113**	10	0.081	58	0.168	0.017^*	0.439	0.193	0.904
*DPB104**	22	0.431	118	0.578	0.083	0.554	0.282	1.075
*DPB1452**	2	0.039	0	0.000	0.039^*	Inf	0.758	Inf
N – number of alleles; Fr – frequency; ^* – p-value statistically significant; CI low – confidence interval lower bound of OR; CI up – confidence interval upper bound of OR; Inf – infinity due to the frequency of 0 in one of the groups

Heterozygosity and allelic richness in cases and controls

To further explore differences between cases and controls, heterozygosity and allelic richness were calculated at first-field resolution and compared between the groups (Table 5). Heterozygosity was calculated as the proportion of heterozygous individuals within each group (cases or controls). Allelic richness was calculated using the rarefaction method that accounts for the differences in sample sizes. Interestingly, while the allelic richness values were similar in both groups, heterozygosity was consistently lower in controls relative to cases for all class I and II alleles, except for the HLA-DRB1 locus. The difference, however, was statistically significant only for HLA-A.

Table 5

Diversity parameters for each HLA loci at the first-field resolution.
Locus	Group	Sample size	Htzg	pc	N of alleles	Allelic Richness
A	control	176	0.76	0.038^*	17	13.8
	case	56	0.93	0.038^*	13	13
B	control	170	0.81	0.5	23	17.8
	case	48	0.92	0.5	18	18
C	control	167	0.73	1	16	12.5
	case	53	0.81	1	11	11
DRB1	control	165	0.87	1	14	11.5
	case	56	0.84	1	12	12
DQB1	control	166	0.69	1	5	5
	case	58	0.76	1	5	5
DPB1	control	88	0.53	1	19	7.4
	case	13	0.69	1	11	11
Htzg – heterozygosity; pc - p-value corrected for testing on six HLA loci; ^* – p-value statistically significant after correction for multiple testing (n = 6)

Furthermore, zygosity analysis (Additional file 10: Tab. S9) showed a significant association between the heterozygous genotype of DRB1*13 (i.e., allele 1: DRB1*13 and allele 2: DRB1*X, where X is any other DRB1 allele) and absence of leprosy (p = 0.0097, OR = 3.03). On the contrary, the heterozygous genotypes of A*23 and B*38 were linked to leprosy presence (p = 0.001, OR = 0.07 and p = 0.0004, OR = 0.02).

Binding prediction of M. leprae proteins

The presentation of specific peptides on the cell surface, which is the major function of HLA molecules, is commonly assumed to be the causal factor for HLA-mediated resistance or susceptibility to diseases [41, 42].To investigate the peptide binding properties of the HLA alleles associated with leprosy in this study, a computational binding prediction was performed for all HLA alleles observed in the relevant loci (HLA-A, B and DRB1) and 46 antigenic M. leprae proteins (Additional file 11: Tab. S10, Fig. 4).

Figure 4. The number of peptides predicted to be bound by HLA-A (A-B), HLA-B (C-D) and HLA-DRB1 (E-F). Bound peptides were determined based on %RankEL scores either by using a strong binding threshold giving the strongly bound peptides or a weak binding threshold giving both strongly and weakly bound peptides.

Interestingly, A*23:01 is one of the HLA-A alleles that bind the lowest number of peptides (Fig. 4A and B). Within the HLA-B locus, B*38:01 binds the average number of peptides relative to other alleles (Fig. 4C and D). However, B*44 alleles are placed on the lower end of the spectrum. For the class II locus (Fig. 4E and F), DRB1*08:01, DRB1*11:01 and DRB1*13 alleles bind the lowest number of peptides. On the contrary, DRB1*09:01, DRB1*01:01 and DRB1*04 alleles are on the higher end of the spectrum.

Temporal shifts in HLA allele frequencies

Genetic continuity between medieval individuals and modern Germans and Danish (Fig. 3A and B, Additional file 2: Figures S2 and S3) allowed an investigation of potential shifts in HLA diversity over time. HLA allele frequencies were compared between the medieval controls and a large cohort of modern Germans (Allele Frequency Net Database (AFND): Germany DMKS (n = 3,456,066)) (Additional file 12: Tab. S11). The German population was used as the modern Danish cohort available in AFND included only 55 individuals [43]. Overall, the allele frequencies are similar between the two groups. Interestingly, the proportions of C*04, DRB1*11, DQB1*05 and DPB1*02 seem to have increased since the Middle Ages. On the contrary, C*03 and DQB1*06 are less common in present-day Germans relative to medieval individuals (Table 6). The shift is particularly pronounced for C*03, with a difference of over 10%.

Table 6

HLA alleles which exhibit a temporal shift (> 5%) in frequency.
	Frequency [%]
Allele	Medieval controls	Modern Germans
C*03	25	14.24
C*04	6.25	11.5
DRB1*11	4.93	11.96
DQB1*05	12.07	17.12
DQB1*06	33.05	25.7
DPB1*02	8.33	14.52

In this study, HLA profiles of 293 medieval individuals were produced and examined to uncover links between human genetic factors and susceptibility to leprosy in the Middle Ages. To date, it is the first ancient DNA association study in which variation at six HLA loci were examined in the context of leprosy susceptibility in the medieval European population. It is also one of very few disease association studies performed in past populations [16, 44].

Primary analysis: HLA-B*38 involved in leprosy susceptibility in medieval Europe

In the primary analysis of the study, we replicated the association between B*38 and leprosy risk, which was previously reported for present-day Brazilians and Vietnamese [45, 46] in our sample of medieval Europeans. HLA-B*38 is considered a risk factor for several autoimmune conditions, such as psoriatic arthritis [47], pemphigus vulgaris [48, 49] and ankylosing spondylitis (AS) [50]. It is also thought to contribute to the development of various adverse reactions to certain medications [51–53]. Although based on a relatively few studies linking B*38 with disease susceptibility, the overall picture portrays the B*38 allele as a potentially detrimental allele.

Primary analysis: frequency trends for HLA-B44 and DRB115

The frequency of B*44 was higher in cases, showing a trend that was opposite to the report previously made for the Vietnamese population [46]. This discrepancy in the association direction (protection vs. risk) likely reflects differences at the second-field resolution (protein level), meaning that the disease susceptibility could be linked to different B*44 alleles, e.g., B*44:03 in the Vietnamese [46] and B*44:02 in medieval Europeans (B*44:02 was by far the most frequent B*44 allele in our sample (n = 17)). HLA-B*44 allele was also associated with an increased risk of multiple sclerosis (MS) [54]. On the other hand, B*44 was associated with protection against viral diseases, such as HIV infection [55, 56] and Dengue fever [57].

A strong trend was also noted for DRB1*15 (25.2% vs 17.1%). In our ancient dataset DRB1*15:01 was the most common DRB*15 allele at the two-field resolution (n = 44). Also in modern Europeans DRB1*15:01 is present at a high frequencies of 13.3% followed by DRB*15:02 with 0.7% (AFND): Germany DMKS (n = 3,456,066) [43]. HLA-DRB1*15:01 is an allele that is most consistently associated with leprosy across various populations [16, 32–36]. This allele was also identified to significantly increase the likelihood of developing MS [58]. On the contrary, carriers of DRB1*15:01 displayed an increased protection against type I diabetes [59] and rheumatoid arthritis (RA) [60]. HLA-DRB1*15:01 was shown to be introduced into the European gene pool via admixture of the Steppe populations at the end of the Neolithic period [61], highlighting the role of ancient migrations in the interplay between host genetic factors and disease susceptibility. Although the previous association of this allele with leprosy risk for medieval Europeans [16] could not be confirmed in this study, this is likely due to the insufficient sample size (achieved power = 0.5) (Additional file 8: Tab. S7). Due to the strict allele calling, it is possible that homozygous calls are underrepresented, which would result in a lower allele frequency.

Exploratory analysis: Identification of potential novel risk variants in leprosy

In the exploratory analysis, we detected three potentially novel risk variants, A*23, DPB1*452 and DRB1*13. Interestingly, computational binding prediction of antigenic M. leprae peptides for all observed HLA-A alleles revealed that A*23 is among the variants exhibiting the smallest peptide repertoire (Fig. 4A and B). This could potentially affect the efficiency of antigen recognition and contribute to the association between A*23 and leprosy susceptibility. The same trend, however, was not observed for other associated alleles. HLA-DRB1*13 was nominally associated with leprosy protection in our sample of medieval Europeans In addition, substantial differences in frequency between cases and controls were noted for four other HLA alleles (Table 4). The differences were, however, insignificant likely due to the limited sample size and the achieved power (Additional file 8: Tab. S7). Among the alleles were HLA-C*06 and DPB1*04 that were more frequent in controls. These alleles were previously associated with leprosy protection in present-day India and China as well as Brazil, respectively [29, 62, 63]. On the contrary, substantially more frequent in leprosy-affected individuals was DRB1*04. Interestingly, this allele was identified as a protective factor in Argentinians, Brazilians, Vietnamese, Japanese (DRB1*04:01) and Taiwanese (DRB1*04:05) [33, 34, 64–66]. Like B*44, this discrepancy in association direction may be a result of inter-population variation in HLA frequencies and leprosy susceptibility.

High heterozygosity at the HLA-A locus among leprosy-affected individuals

Heterozygosity was higher in leprosy cases in comparison to controls for all class I loci and two class II loci. The difference, however, was only significant for the HLA-A locus (Table 5). This observation stands somewhat in contrast to the hypothesis of population heterozygote advantage, which states that individuals who are heterozygous at particular loci might be less susceptible to infectious diseases, as they can recognize and present a wider repertoire of antigens [67–69]. Based on this hypothesis, we would expect heterozygosity to be higher in controls. This study, however, focuses on one specific disease (leprosy) and does not evaluate the general health status of individuals. Furthermore, it was shown that in particular cases, for instance during an HIV infection, homozygosity at certain HLA loci is advantageous [70].

Allele frequency differences between medieval and modern controls as an indicator of the selection character of infectious diseases since the Middle Ages

Comparison of HLA allele frequencies between medieval controls and modern Germans showed similar values across most observed alleles (Additional file 12: Tab. S11), including those associated with leprosy in this study (Tables 3 and 4). This is likely due to genetic continuity between the populations and possibly balancing selection, which is well-documented for the HLA region [71]. Nevertheless, we noted substantial differences in frequencies (> 5%) of six alleles between medieval and modern populations (Table 6). Interestingly, all the alleles were previously shown to influence susceptibility to numerous diseases, including infections, autoimmune and inflammatory conditions (Fig. 5, Additional file 13: Tab. S12). The same alleles that are linked to infection susceptibility were also shown to play a role in inflammatory disorders. It was previously suggested that an evolutionary trade-off exists between infection resistance and autoimmunity/autoinflammation [72–74]. Whilst a large repertoire of recognized antigens provides a more efficient protection against infections, it also raises the risk of reacting to self-antigens [75]. In reality, as shown in Fig. 5 and the Additional file 13: Tab. S12, the pattern of associations is highly complex and obscured by the intricacy of the pathophysiological mechanisms underlying different pathological conditions. For instance, HLA-DRB1*15:01 is a known risk factor for leprosy in different populations [16, 32–36] and a protective factor for type I diabetes [59]. At the same time, DRB1*15:01 largely contributes to the risk of developing MS [58]. Similarly, HLA-DRB1*13, which was nominally associated with leprosy protection in this study, is considered a generally advantageous variant against various infectious diseases [76–78] and autoimmune conditions [78]. The allele was also previously associated with an increased risk of chronic and autoimmune hepatitis C [80, 81], Parkinson’s disease [82] and certain types of cancer [83, 84]. Therefore, there seems to be no simple directional link between protection against infections and increased autoimmunity reflected in the HLA associations. Nevertheless, ancient epidemics of infectious diseases, together with population admixture [61, 85], likely shaped the present-day diversity of HLA alleles in the European population and influenced the prevalence of chronic inflammation and autoimmunity [16, 44].

Figure 5. Six HLA alleles that exhibited a temporal shift in frequency are associated with increased risk to and protection against various diseases across different populations (Additional file 13: Tab. S12). meta – meta-analysis

Addressing the limitations and potential of case-control association studies in past populations

The study is not without limitations, which must be considered when interpreting the results. First, as the sample examined here is of relatively small size in comparison to genome-wide association studies (GWAS) [29, 36], variants of small effect size were likely undetected. The statistical power could be potentially further reduced by the presence of leprosy-affected or susceptible individuals in the control group. Whilst stringent selection criteria were applied for cases, it is possible that early-stage infections were undetected in the control group or that susceptible individuals among the controls simply had not been exposed to M. leprae. Nevertheless, unlike mass graves that are often a result of epidemics, the cemeteries that were sampled for this study should reflect an average picture of the population at the time. Second, errors in genotyping could have theoretically influenced the HLA frequencies. The highly degraded nature of aDNA makes the HLA typing challenging. The semi-automated TARGT pipeline is currently the most suitable method for this kind of data, Optitype [86] was used to support or reject a call for class I alleles where the manual call was ambiguous. Lastly, this study was mostly explorative. Whilst such analysis can detect potentially novel associations, as we did here, confirmation of the findings in an independent ancient sample from Europe is necessary.

Our case-control association study between HLA alleles and leprosy in medieval Europeans revealed a link between HLA-B*38 and susceptibility to M. leprae infection. Furthermore, we detected three novel variants that were possibly involved in leprosy susceptibility (HLA-A*23, DRB1*13 and DPB1*452). Nowadays, Europe is free of many infectious diseases which used to be common in the Middle Ages. Therefore, the genetic predisposition to these diseases in present-day Europeans cannot be investigated. However, the development of the aDNA technology allows for such analyses to be performed in past populations [16, 44, 87, 88]. This study demonstrates the tremendous potential of this methodology in the identification of genetic variants involved in predisposition to infectious diseases that are no longer present in Europe but remain endemic in other parts of the world. Identification of such variants can improve our understanding concerning the contribution of specific HLA alleles to the disease under study and potentially allow us to harness this knowledge in a medical setting.

The sample analysed in this study comprised a total of 526 human skeletal elements belonging to 388 individuals. Based on the amount of endogenous DNA, duplicate rates and the evaluation of samples for contamination (see Methods), 484 samples (279 petrous bones, 192 teeth and 13 postcranial elements) from 352 individuals were included in the analyses (Additional file 1: Tab S1). The individuals were excavated from 16 Danish and 2 German archaeological sites dating between the 10th and 19th century AD (Table 1). The cemeteries of Skt. Jørgensgården in Odense (Denmark) and Gut Melaten in Aachen (Germany) were associated with institutions that housed leprosy-affected individuals in the Middle Ages [89–91]. The remaining sites are considered ordinary parish/monastery cemeteries (except for a multiple burial at Albani Torv) (Additional file 0: Skeletal_collections-literature). HLA genotypes for individuals from Skt. Jørgensgården, Odense published previously were also incorporated in the study (Tab. S7 in Pierini et al., 2020 [38]).

Methods

DNA extraction, library preparation and sequencing

All DNA samples were extracted and processed in a dedicated ancient DNA facility at the University of Kiel following the guidelines on contamination control in ancient DNA [92–94], according to a previously published protocol for the non-UGD treated samples [16]. Shotgun sequencing was performed on the Illumina HiSeq 6000 (2x100) platform of the Institute of Clinical Molecular Biology (IKMB) in Kiel.

Read preprocessing, mapping and evaluation for authenticity and contamination

Adapter sequences were removed and paired-end reads were merged with ClipAndMerge v1.7.7. [95]. Shotgun sequence data was mapped to the human genome (build hg19) using BWA v0.7.12 [96] with a reduced mapping stringency parameter “-n 0.01” to account for mismatches in aDNA. Duplicated reads were removed with DeDup v0.12.2 [95].

To confirm the ancient origin of the sequences, terminal damage of the reads (C to T substitutions) was assessed with DamageProfiler [97]. After the validation, the first two positions from the 5’ and 3’-ends of the reads were trimmed. Furthermore, X-chromosome and mitochondrial DNA contamination were assessed with ANGSD and Schmutzi, respectively [98, 99].

Pathogen screening and phylogenetic analysis of M. leprae

All clipped and merged metagenomic datasets were screened for the presence of M. leprae DNA with the Megan Alignment Tool 0.3.0 (MALT) [100] (SemiGlobal alignment mode, identity threshold = 90%), using a custom database containing bacterial genomes available at the NCBI platform (24.01.2019). Output alignments were inspected visually in MEGAN 6 [101]. M. leprae-positive samples were then subjected to targeted in-solution capture using myBaits® from Arbor Biosciences [102] to obtain more data for analysis. Subsequently, the samples were mapped against the M. leprae TN reference genome (NC_002677) with Burrows-Wheeler Aligner (BWA) v0.7.12 [96]. Duplicated reads were removed with DeDup v0.12.2 [95] and the BAM was filtered for a mapping quality of at least 30. Mapping statistics were obtained with Qualimap v2.2.1 [103] using bamqc option. M. leprae strains with at least 50% of the genome covered with 1x were included in subsequent phylogenetic analysis. UnifiedGenotyper module of the Genome Analysis Toolkit (GATK) v3.6 [104] was used to generate a VCF file. Phylogenetically informative SNPs [14] were extracted from the VCF (min. coverage 3x, min quality 30, majority call 90%). The phylogeny was reconstructed with RAxML [105] using a GTR-GAMMA model and a bootstrap of 500 replicates. A total of 44 medieval and 139 modern strains was included in the analysis (Additional file 3: Tab. S2). M. lepromatosis was used as an outgroup.

Determination of the “case vs. control” status

Determination of the disease status was performed based on molecular screening of metagenomic sequences for M. leprae DNA and paleopathological analysis of skeletal remains. The association of Gut Melaten and Skt. Jørgensgården cemeteries with medieval leprosaria was also considered.

Based on the work of Møller-Christensen and his successors [e.g., 5,11,37,89,90], it has been widely accepted that a combination of characteristic facial skeleton deformities (facies leprosa, rhinomaxillary syndrome) together with lesions of the extremities (atrophy of phalanges in hands and feet, periostitis of tibia and/or fibula) is considered pathognomonic of severe leprosy (lepromatous leprosy, LL). Therefore, all skeletal remains were assessed for the presence of such lesions. Osteological analysis of skeletons from Gut Melaten, St Jürgen and, partially, Skt. Jørgensgården were performed as part of a previous studies [16, 91, 106].

For the leprosaria-associated cemeteries (Skt. Jørgensgården and Gut Melaten), individuals who exhibited skeletal lesions pathognomonic for leprosy and/or molecular evidence of infection were treated as “cases”. As both leprosaria were eventually transformed into either a general infirmary (Gut Melaten) [91, 107] and a cemetery for the poor (Skt. Jørgensgården) [108], it is possible that leprosy-unaffected individuals were among the people buried at the associated cemeteries. Therefore, the disease status for the individuals who showed neither molecular nor osteological signs of leprosy was considered uncertain, and they were excluded from further analysis.

Similarly, individuals from ordinary cemeteries were assigned to the “case” status based on the presence of molecular and/or osteological evidence of leprosy. Individuals who did not show signs of leprosy were considered as controls and those who showed skeletal lesions possibly suggestive of the disease were excluded from the analysis due to the uncertain disease status.

Population genetic analysis

Pseudo-haploid genotypes were generated for a pool of 1,233,013 SNPs, commonly used for population genetic analyses, with SequenceTools 1.2.2 [109–111]. Here, an intermediate threshold of 20,000 SNPs was set and samples with at least 20,000 typed SNPs were included in the principal component analysis (PCA). In total, genotype data for 185 medieval individuals was merged with a dataset of previously published 1138 individuals from 66 contemporary West Eurasian populations (in the Allen Ancient DNA Resource (AADR) dataset v50.0.p1) [112] using mergeit from the EIGENSOFT package. The PCA was performed with smartpca and the ‘lsqproject’ option [113].

Kinship analysis

Kinship analysis was performed using READ [114] which infers kinship based on the proportion of non-matching autosomal genotypes. A minimum of 2500 SNPs is required to produce reliable results [114]. Therefore, individuals were examined for relatedness only in instances when the number of SNPs used for the calculations was > 2500.

HLA targeted capture and typing

One sample per individual was subjected to targeted enrichment of the HLA exonic regions. A sample was a good candidate for capture when the endogenous DNA content in the shotgun data exceeded one percent and the proportion of duplicated reads mapping to the human genome (hg19) was below 40 percent.

Individuals for which the HLA capture was successful were then typed with the semi-automated HLA-typing pipeline TARGT (Targeted Analysis of sequencing Reads for GenoTyping), designed for the analysis of low-coverage sequences typical of aDNA [38]. Six classical HLA loci were typed: class I (HLA-A, B and C) as well as class II (HLA-DRB1, DQB1 and DPB1). Class I HLA alleles were also called using the OptiType software (available only for class I HLA alleles) [86] with default parameters. Results obtained with OptiType were used in instances where the call made with TARGT was ambiguous (two possible options). In such cases, the allele supported by Optitype was chosen. When OptiType did not support either of the two TARGT options, the allele was not called.

HLA allele frequency calculation and statistical analysis

In addition to individuals that were successfully typed in this study, HLA calls for 51 individuals from Odense reported in a previous publication were included (Tab. S7 in Pierini et al., 2020 [38]). The sample sets from this and the previous analysis overlapped for 35 individuals out of which HLA calls were available for 24 samples in both studies. The alleles were called using the same typing pipeline TARGT. They were, however, based on sequences from independent DNA extracts. For the overlapping individuals (n = 24), the calls were merged in a way that would increase the resolution of the calls. In instances of discrepancy, the allele supported by OptiType results generated in this study was preferred (class I HLA). For class II alleles, the call of a higher resolution was selected. Moreover, heterozygous calls were chosen over homozygous calls.

Association analysis was performed with the Fisher’s exact test (two-tailed) using R v. 4.3.1 [115]. Correction for multiple testing was done in R v. 4.3.1 using the mt.rawp2adjp function from the multtest package [116]. Statistical power was calculated with G*Power v3.1.9.4 [117].

F_ST calculation

HLA genotype data of five European populations from the 1000 genomes project were incorporated in the analysis of genetic differentiation between medieval cases and controls as well as between the medieval sample and the present-day populations [118]. The modern populations used were Utah residents (CEPH) with Northern and Western European ancestry (CEU), the Iberian populations from Spain (IBS), the Toscani from Italy (TSI), populations of Finland (FIN) as well as England and Scotland (GBR). Pairwise Weir-Cockerham F_ST estimations based on individuls with complete HLA-A, B, C, DRB1 and DQB1 genotypes at the first field resolution were obtained with the adegenet and hierfstat packages in R v. 4.3.1 [119].

Allele zygosity test

The software pyHLA was used to perform the zygosity test at the first-field resolution with default parameters [120]. Fisher’s exact test was used to identify associations of the disease with homozygous and heterozygous genotypes for the formerly identified disease-associated alleles.

Estimation of heterozygosity and allelic richness

Heterozygosity and allelic richness (i.e., rarefied allele counts) were calculated separately for cases and controls using the hierfstat package in R v. 4.3.1 [121]. Prior to any calculations, individuals with missing genotypes were removed. The analysis was performed for all six HLA loci (HLA-A, B, C, DRB1, DQB1 and DPB1). Differences in heterozygosity between cases and controls were evaluated with Fisher’s exact test (two-tailed) using R v. 4.3.1 [115].

Computational binding prediction of M. leprae proteins

Computational prediction of peptide binding was performed for all HLA alleles observed at loci that show association with leprosy in this study (HLA-A, B and DRB1) and M. leprae proteins that exhibit antigenic potential (Additional file 11: Tab. S10). Although one allele within the DPB1 locus (DPB1*452) was associated with leprosy risk in the explorative phase of this study, predictions for the HLA-DPB1 were not generated. This would require the complementary genotypes of the DPA1 locus which exhibit significantly less polymorphisms relative to the DPB1 locus [122]. It was therefore omitted in this study. For the HLA class I loci, HLA-A and B, peptide binding predictions were generated with netMHCpan (4.1). NetMHCIIpan (4.0) was used for the class II locus (HLA-DRB1) [123]. The number of bound 9-mer peptides for class I HLA alleles and 15-mer peptides for class II alleles were calculated based on the %RankEL scores. Default thresholds of strong and weak binding were used for HLA class I (0.5% and 2%, respectively) and HLA class II (2% and 10%, respectively).

Ethics approval and consent to participate

Due to the nature of the samples analyzed, ethical approval was not needed.

Consent for publication

Not applicable

Availability of data and materials

Analyzed sequences are available through the European Nucleotide Archive under Accession Number XXXX: http://www.ebi.ac.uk/ena/browser/view/<accession>

Competing interests

The authors declare no competing interests.

Funding

The study was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Excellence Cluster Precision Medicine in Chronic Inflammation (PMI, EXC 2167 – project number 390884018) and the Collaborative Research Centre 1266 Scales of Transformation - Human-Environmental Interaction in Prehistoric and Archaic Societies (CRC 1266 – project number 290391021). J.H.B. was funded by the International Max Planck Research School for Evolutionary Biology and PMI, O.Ö. by the DFG Excellence Cluster ROOTS (EXC 2150 - project number 390870439), N.dS. by the CRC 1266 and N.M.M. by the Research Training Group for Translational Evolutionary Research (RTG 2501 – project number 400993799).

Author contributions

J.H.B. – research design, data generation and analysis, osteological analysis, results interpretation, visualization; A.C. – research design and methodology, results interpretation; O.O. and N.dS. – data analysis and visualization; N.M. – data analysis; D.D.P. and J.B.– osteological analysis and data curation; L.A.L., L.S., M.S., K.H., D.R. and A.P. – provided skeletal samples; A.N. and B. K-K. – research design, results interpretation, resources and supervision; J.H.B. – wrote the manuscript with input from all other co-authors

Acknowledgements

We want to thank the museums in Viborg, Horsens and Odense as well as the Museum of Southwest Jutland in Denmark for the permission to sample the skeletal remains.

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Ancient DNA study provides clues to leprosy susceptibility in medieval Europe

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Version 1

Abstract

Background

Results

Conclusions

Figures

Introduction

Results

Data collection

Determination of cases and controls under strict selection criteria

Evaluation of heterogeneity within the medieval sample and temporal genetic continuity in Europeans

Association analysis – primary analysis

Association analysis – explorative analysis

Heterozygosity and allelic richness in cases and controls

Temporal shifts in HLA allele frequencies

Discussion

Primary analysis: HLA-B*38 involved in leprosy susceptibility in medieval Europe

Primary analysis: frequency trends for HLA-B*44 and DRB1*15

Exploratory analysis: Identification of potential novel risk variants in leprosy

High heterozygosity at the HLA-A locus among leprosy-affected individuals

Addressing the limitations and potential of case-control association studies in past populations

Conclusions

Materials

Methods

DNA extraction, library preparation and sequencing

Read preprocessing, mapping and evaluation for authenticity and contamination

Determination of the “case vs. control” status

Population genetic analysis

Kinship analysis

HLA targeted capture and typing

HLA allele frequency calculation and statistical analysis

FST calculation

Allele zygosity test

Estimation of heterozygosity and allelic richness

Declarations

References

Additional files

Additional Declarations

Status:

Version 1

Primary analysis: frequency trends for HLA-B44 and DRB115

F_ST calculation