Vitamin B12 and folate concentrations during pregnancy and insulin resistance in the offspring: the Pune Maternal Nutrition Study

Aims/hypothesis Raised maternal plasma total homocysteine (tHcy) concentrations predict small size at birth, which is a risk factor for type 2 diabetes mellitus. We studied the association between maternal vitamin B12, folate and tHcy status during pregnancy, and offspring adiposity and insulin resistance at 6 years.
Introduction
The ‘thrifty phenotype’ hypothesis [1] introduced a new paradigm in the aetiology of chronic disease and stimulated research into the relationship between maternal nutrition, fetal growth and offspring risk of type 2 diabetes mellitus. India has the largest number of diabetic patients in the world [2] and this may be related to the fact that Indian babies are amongst the smallest in the world [3]. We were the first to demonstrate that low birthweight of Indian babies predicts insulin resistance and adiposity in childhood [4]. Subsequently we demonstrated that the apparently small Indian newborn babies are relatively adipose, hyperinsulinaemic and hyperleptinaemic compared with white babies [5, 6]. These babies grow into shorter, thinner (low BMI) but more adipose (higher body fat per cent and higher central fat) adults compared with whites, and have a many times higher risk of type 2 diabetes [7]. Thus, in addition to the role of putative genetic and modern day lifestyle factors, the intrauterine environment may also influence the risk of type 2 diabetes mellitus. The Pune Maternal Nutrition Study (PMNS) is the first study in India to investigate the relationship between maternal nutrition and offspring risk of type 2 diabetes and cardiovascular disease. Data were collected on the mother’s size, diet, micronutrient status and physical workload during pregnancy [6, 8, 9], and the newborns were measured in detail at birth and every 6 months thereafter. At 6 years of age, the offspring were investigated for risk of type 2 diabetes and cardiovascular disease.
We have reported that a higher frequency of maternal intake of green leafy vegetables (GLVs), milk and fruit and higher erythrocyte folate concentrations are associated with larger newborn size [8]. In a small sub-sample we found that higher maternal circulating total homocysteine (tHcy) predicted low offspring birthweight [10]. Other studies in Pune have shown that vitamin B12 deficiency is common and contributes to hyperhomocysteinaemia, while folate deficiency is rare [11, 12].
We therefore hypothesised that vitamin B12 and folate deficiency in the mother during pregnancy would predict greater adiposity and insulin resistance in the offspring.
Methods
The recruitment of mothers and follow-up of the children in the PMNS (Fig. 1) has been described [6, 8, 9]. The study started in 1993. Non-pregnant married women living in six villages near Pune had detailed anthropometric measurements every 3 months and menstrual dates were recorded monthly. Women who became pregnant were assessed at 18 ± 2 and 28 ± 2 weeks of gestation for anthropometry, physical workload, dietary intake (24 h recall and food frequency questionnaire [FFQ]) and measurement of circulating nutrients. According to the National Nutritional Anemia Control Programme, women were given 100 tablets of iron (60 mg per tablet) and folic acid (500 μg per tablet) from 18 weeks of gestation. Newborn babies were measured within 72 h of birth (1994–1996), and the children were followed up post-natally with repeat anthropometry every 6 months.
Fig. 1The PMNS: a flow diagram to describe data collection and exclusions from the study. LMP Last menstrual period
At 6 years of age (December 2000–February 2003) the children were investigated for body size, body composition and risk factors for type 2 diabetes mellitus and cardiovascular disease. Families were instructed to continue their usual diet and activity prior to the study, and were transported to the Research Unit the previous evening. They were provided with a standard dinner and then water only until the next morning. A fasting blood sample was collected, followed by a 1.75 g/kg body weight oral anhydrous glucose load and a further blood sample 120 min later.
The family’s socioeconomic status (SES) was assessed using the Standard of Living Index devised by the National Family Health Survey [13]. Weight and height were measured using standardised protocols. Whole-body dual-energy X-ray absorptiometry (DEXA) scans (Lunar DPX-IQ 240; Lunar Corporation, Madison, WI, USA; paediatric software) were carried out to measure total, truncal and leg fat mass, percentage body fat and lean mass. Standard quality assurance tests were performed every day. The DEXA machine was installed 3 months after the study started, and therefore 59 children had DEXA measurements later than the other investigations.
Plasma glucose was measured using an Hitachi 911 automated analyser (Hitachi, Tokyo, Japan) by the glucose oxidase peroxidase method. Plasma insulin was measured using a Delfia technique (Victor 2; Wallac, Turku, Finland) [14]. Insulin resistance was calculated using the fasting insulin and glucose concentrations (homeostatic model assessment of insulin resistance [HOMA-R]) [15]. As previously described, maternal erythrocyte folate concentrations were measured at the time of the original study (1993–1996), taking all necessary precautions in the collection, transport and storage of samples [8]. Based on the findings of a preliminary study [10], we measured plasma vitamin B12, tHcy and methylmalonic acid (MMA) concentrations in all the stored maternal fasting samples (−80°C) in February 2004, as described [11].
Ethical permission for the study was granted by the KEM Hospital Ethical Committee, and by the local village leaders. Parents gave informed written consent. None of these volunteers were paid for participating in the study.
Statistics Skewed variables were transformed to normality using the following transformations: log to the base e (ln) (plasma vitamin B12 [18 weeks], tHcy and MMA and erythrocyte folate concentrations, maternal fat intake, and the child’s trunk and leg fat mass), square root (maternal energy, protein, and carbohydrate intakes, and the child’s total fat mass) and reciprocal of square root (maternal vitamin B12 concentrations at 28 weeks). Maternal pre-pregnant fat mass was calculated from the sum of four skinfold thickness measurements [16]. Maternal intakes of specific foods, based on the FFQ, were analysed as categorical variables. Low vitamin B12 and erythrocyte folate concentrations were defined as <150 pmol/l and <283 nmol/l, respectively. Elevated tHcy and MMA concentrations were defined as >10 and >0.26 μmol/l [17]. Correlations between different nutritional measures in the mothers were tested using Pearson correlation coefficients. Relationships between maternal nutritional variables and outcomes in the children were analysed using multiple linear regression.
As a final stage of the analysis, we summarised the inter-relationships between maternal factors and outcomes in the children using principal components analysis (PCA) and conditional independence analysis. We first performed a PCA to condense the information contained in large groups of variables into a small number of ‘component’ variables. These components are independent of each other and therefore can be used in regression analysis without the problem of colinearity [18]. The groups of variables were: (1) maternal pre-pregnant size (height, skinfold thicknesses, head circumference, mid-upper-arm circumference [MUAC] and waist and hip circumferences); (2) maternal macronutrient intakes (energy, protein, carbohydrate and fat intakes at 18 and 28 weeks); (3) maternal micronutrient-rich foods in pregnancy (frequency of intake of dairy products, GLV, fruit and non-vegetarian items [meat, fish and eggs] at 18 and 28 weeks of gestation); (4) maternal micronutrient status (vitamin B12, folate, tHcy and MMA concentrations at 18 and 28 weeks); (5) newborn size (birthweight, length, skinfold thicknesses, MUAC, head and abdominal circumferences); and (6) 6 year body composition (height and DEXA measurements of lean mass, and total, truncal and limb fat). Other key variables (maternal SES and physical workload score [mean of 18 and 28 week scores], and HOMA-R in the child at 6 years) remained as single variables. These, and the relevant principal components were included simultaneously in a conditional independence analysis, which is a method of displaying ‘pathways’ of association between a pre-specified set of variables (in this case, the groups of variables described above). The partial correlation (that is, the correlation while holding the remaining variables constant in the set) of each pair of variables was calculated [19]. A ‘path’ diagram was then drawn, connecting pairs of variables that were significantly correlated (p < 0.01). Two-tailed significance was calculated at 5% level. Analyses were performed using SPSS 11.0 for windows (SPSS, Chicago, IL, USA) and STATA 7.0 (STATA, College Station, TX, USA).
Results
Many of the maternal size and nutritional measurements and their relationship to neonatal size have been described before [8]. The age (median [25th, 75th centiles]) of the mothers when they became pregnant was 21 (19, 23) years. They were short (152.0 [148.5, 155.4] cm) and had a low pre-pregnant BMI (17.8 [16.7, 19.1] kg/m2) but a relatively high percentage body fat (20% [18, 24]). Their dietary intakes and micronutrient status during pregnancy are shown in Table 1. The women’s energy and protein intakes were low compared with the recommended dietary allowances of 10,565 kJ (2,525 kcal) and 65 g/day, respectively [20]. At 18 and 28 weeks, their energy intakes were 1.57 and 1.43 times their calculated basal metabolic rate [21]. One-third of women were lacto-vegetarian, and only 15% of women ate non-vegetarian foods more frequently than once every alternate day. The portion sizes of non-vegetarian foods were small (<120 g/day for chicken, fish and meat dishes and ~60 g/day for eggs).
Table 1Maternal nutrition data during pregnancy (median and 25th and 75th centiles, unless otherwise stated) Number18 weeks of gestationNumber28 weeks of gestationMacronutrient intakes Energy (kJ)6927,293 (5,858, 8,774)6706,803 (5,523, 8,268) Energy (kcal)6921,743 (1,400, 2,090)6701,626 (1,320, 1,976) Carbohydrate (g)692313 (257, 376)670296 (236, 360) Protein (g)69244.9 (35.4, 54.8)67041.7 (33.8, 51.3) Fat (g)69232.7 (23.9, 42.9)67029.6 (22.4, 39.2)FFQ, n (%) GLVs Never69215 (2)67169 (10) <1 per week692139 (20)671197 (29) >1 per week692277 (40)671243 (36) >Alternate day692261 (38)671162 (24)Dairy products, n (%) Never692107 (15)67196 (14) <1 per week692141 (20)671137 (20) >1 per week692129 (19)671144 (21) >Alternate day692315 (46)671294 (44)Non-vegetarian foods, n (%) Never692228 (33)671254 (38) <1 per week692182 (26)671181 (27) >1 per week692182 (26)671149 (22) >Alternate day692100 (14)67187 (13)Circulating micronutrients Vitamin B12 (pmol/l)638135 (103, 175)594122 (94, 160)  <150 pmol/l, n (%)380 (60)423 (71) Erythrocyte folate (nmol/l)618874 (687, 1,106)562961 (736, 1,269)  <283 nmol/l, n (%)1 (0.2)1 (0.2) MMA (μmol/l)6360.80 (0.50, 1.34)5940.73 (0.44, 1.18)  >0.26 μmol/l, n (%)586 (94)533 (90) tHcy (μmol/l)6398.1 (6.8, 10.3)5938.6 (6.7, 10.8)  >10 μmol/l, n (%)177 (28)193 (33)Dairy products refers to whole milk plus milk products (milk in tea and other beverages, yoghurt, buttermilk, ghee, ice cream and other milk-based preparations). Non-vegetarian foods are meat, fish and eggs
Maternal vitamin B12 and folate status Women in whom vitamin B12, folate and related measurements were not available had similar pre-pregnant weight, SES and weight gain at 28 weeks of pregnancy compared with those who were studied. The majority of women (>60%) had low plasma vitamin B12 concentrations (Table 1), over 90% had elevated MMA concentrations, and one-third were hyperhomocysteinaemic. On the other hand only one woman had a low erythrocyte folate concentration. Between 18 and 28 weeks of gestation, plasma vitamin B12, MMA and tHcy concentrations remained similar (p ~ 0.11, for all), while erythrocyte folate concentrations increased (p < 0.001). Plasma tHcy and MMA concentrations were inversely related to plasma vitamin B12 (p < 0.001 for both at 18 and 28 weeks of gestation). Low vitamin B12 concentration contributed 41 and 24% to the population attributable risk of hyperhomocysteinaemia, and 40 and 12% to high MMA at 18 and 28 weeks of gestation, respectively; the contribution of low folate concentration could not be calculated because of small numbers. Higher frequency of intake of dairy products and non-vegetarian foods was associated with higher plasma vitamin B12 concentrations (p = 0.005 and 0.04, respectively) and lower tHcy (p = 0.1 and p = 0.04) and MMA concentrations (p = 0.01 and p = 0.003). Plasma vitamin B12 concentrations were also related to protein intakes (28 weeks, p = 0.03) but not to energy intake. Higher frequency of GLV intake predicted higher erythrocyte folate concentrations (p = 0.001). All these associations were independent of the energy intake and SES.
Maternal nutrition during pregnancy and newborn size Maternal vitamin B12 and MMA concentrations were unrelated to neonatal measurements. As previously described [8], lower maternal folate concentrations were associated with smaller newborn weight, MUAC and abdominal circumference (p = 0.003, 0.008, 0.008, respectively). Higher tHcy concentrations at 18 weeks were associated with smaller newborn size (MUAC, p = 0.02; abdominal circumference, p = 0.02; and subscapular and triceps skinfold thicknesses, p = 0.01 and p = 0.007).
Maternal nutrition during pregnancy and offspring size, body composition and HOMA-R at 6 years At 6 years, the children were light, short and had a low BMI compared with an international (UK) reference [22] (Table 2); none were overweight or obese as defined by International Obesity Task Force [23] criteria. However, skinfold thickness measurements showed that the children were relatively truncally adipose; the mean SD score for subscapular skinfold thickness was −0.42 compared with the UK growth standards [24], in contrast with −2.23 for weight and −1.86 for BMI. Higher fat mass and higher body fat per cent were associated with higher fasting insulin concentrations, higher HOMA-R and higher 120 min plasma glucose concentrations (p < 0.05 for all).
Table 2Characteristics of the children at 6 years (n = 653) Median (IQR)Mean SD scorea (SD)Age (years)6.1 (6.1, 6.2)Weight (kg)16.0 (14.8, 17.3)−2.23 (1.00)Height (cm)109.7 (106.7, 112.9)−1.39 (0.89)BMI (kg/m2)13.3 (12.8, 14.0)−1.86 (0.90)Subscapular skinfold thickness (mm)5.0 (4.4, 5.6)−0.42 (0.68)Triceps skinfold thickness (mm)6.2 (5.4, 7.1)−1.41 (0.79)Fat mass (kg)3.1 (2.4, 3.7)Fat (%)18.8 (15.5, 22.2)Leg fat mass (kg)1.3 (1.0, 1.5)Trunk fat mass (kg)0.9 (0.7, 1.2)Lean mass (kg)12.7 (11.7, 13.7)Insulin resistance (HOMA-R)0.70 (0.37, 1.06)120 min glucose (mmol/l)5.49 (4.82, 6.21)Vitamin B12 (pmol/l)224 (167, 311)aSD scores at 6 years were derived from the UK growth standards [26, 28]IQR Interquartile range
Higher frequency of maternal intake of GLVs and higher erythrocyte folate concentrations (28 weeks) were associated with higher fat mass and higher per cent body fat in the children (GLVs: p = 0.02, 0.08; and folate: p = 0.01, 0.002; Table 3). None of the maternal nutritional variables were related to lean mass in the children.
Table 3Outcomes in the children at 6 years (body composition and insulin resistance) according to maternal vitamin B12 concentrations at 18 weeks, erythrocyte folate at 28 weeks and GLV and dairy product intake at 28 weeks NumberHeight (cm)Fat mass (kg)Lean mass (kg)Insulin resistance (HOMA-R)Vitamin B12 at 18 weeks (pmol/l) <103146110.2 (4.3)3.3 (0.9)12.8 (1.6)0.78 (0.42, 1.34) 103–134150109.3 (4.6)3.0 (1.0)12.7 (1.5)0.68 (0.35, 0.99) 135–174145109.6 (4.9)3.1 (1.0)12.9 (1.7)0.69 (0.30, 0.95) ≥175151110.0 (4.9)3.2 (1.0)12.8 (1.7)0.61 (0.35, 1.00) p valuea0.80.50.30.03 p valueb0.60.40.30.04Erythrocyte folate at 28 weeks (nmol/l) <734129109.2 (4.6)3.0 (1.0)12.8 (1.7)0.52 (0.28, 0.81) 734–691136109.8 (4.5)3.1 (0.9)12.9 (1.6)0.65 (0.36, 0.92) 962–1,268131109.9 (4.7)3.2 (1.2)12.7 (1.8)0.71 (0.39, 1.12) ≥1,269127110.7 (4.8)3.4 (1.1)13.0 (1.6)0.85 (0.51, 1.27) p valuea0.020.0010.5<0.001 p valueb0.70.010.4<0.001GLVs at 28 weeks Never63109.1 (5.5)3.0 (0.9)12.5 (1.7)0.83 (0.25, 1.23) <Once per week183109.5 (4.7)3.0 (1.0)12.7 (1.7)0.73 (0.36, 1.05) >Once per week231109.8 (4.6)3.2 (1.0)12.8 (1.7)0.69 (0.34, 0.99) ≥Alternate days146110.5 (4.1)3.3 (1.2)13.0 (1.6)0.66 (0.41, 1.00) p valuea0.0020.0010.010.99 p valueb0.40.020.40.98Dairy products at 28 weeks Never87108.4 (4.9)2.9 (0.9)12.6 (1.8)0.60 (0.32, 0.95) <Once per week124110.2 (4.1)3.2 (1.0)12.8 (1.6)0.62 (0.37, 0.99) >Once per week134109.4 (4.6)3.2 (0.9)12.7 (1.7)0.73 (0.40, 1.06) ≥Alternate days278110.3 (4.6)3.2 (1.2)12.9 (1.7)0.74 (0.37, 1.11) p valuea0.0050.030.30.02 p valueb0.20.20.80.006Values are mean (SD) or median (interquartile range)ap value adjusted for the child’s age and sexbp value adjusted for the above, plus: SES, the mother’s pre-pregnant height and fat mass, the child’s birthweight, skinfold thicknesses and gestation at delivery and the mother’s protein intake at the time of measurement
Higher frequency of maternal intake of dairy products at 28 weeks, and lower plasma vitamin B12 (18 weeks) and higher erythrocyte folate concentrations (28 weeks) were associated with higher HOMA-R in the children (Table 3). The highest HOMA-R was in children whose mothers had the lowest vitamin B12 and highest folate concentrations (Fig. 2). There was no statistically significant interaction, however, between vitamin B12 and folate concentrations in relation to HOMA-R (Table 4). The associations remained significant after further adjustment for the child’s own fat mass and plasma vitamin B12 concentrations. None of the other maternal dietary or macronutrient variables were related to HOMA-R in the children.
Fig. 2Insulin resistance (HOMA-R) in the children at 6 years in relation to maternal vitamin B12 (18 weeks) and erythrocyte folate (28 weeks)Table 4Multiple linear regression models for child’s insulin resistance (HOMA-R) at 6 years as the dependent variableIndependent variableModel 1Model 2Model 3Model 4βp valueβp valueβp valueβp valuePlasma vitamin B12 at 18 weeks (pmol/l)−0.160.03−0.160.04Erythrocyte folate at 28 weeks (nmol/l)0.38<0.0010.38<0.001Interaction term (vitamin B12 × folate)−0.080.7The independent variable included maternal plasma vitamin B12 concentration at 18 weeks gestation, erythrocyte folate at 28 weeks gestation, child’s age, sex, fat mass and standard of living index. The models progressively included vitamin B12, erythrocyte folate, both, and an interaction term (models 1 to 4, respectively)
Maternal vitamin B12 concentrations were not related to the child’s insulinogenic index (a measure of beta cell function) but 28 week erythrocyte folate concentration was directly related (p < 0.01).
PCA and conditional independence analysis PCA and conditional independence analyses are summarised in Fig. 3 and the Electronic supplementary material (ESM) Table 1. Mothers of higher SES had higher intakes of the micronutrient-rich foods, except non-vegetarian foods (correlation A). Larger maternal body size (correlation B) and larger size of the offspring at birth (correlation C) were associated with larger 6 year size. None of the maternal nutritional variables were related to 6 year body composition in the children, but the contrast between folate and vitamin B12 concentrations (micronutrient status, factor 3: higher maternal folate and MMA and lower vitamin B12) was associated with higher HOMA-R in the child at 6 years (correlation D).
Fig. 3The groups of variables used in the PCAs are described in Statistical methods. The notation F1–F3 indicates the first to third principal components, or factors (F), derived from each group of variables. Lines connecting boxes indicate significant positive correlations (p < 0.01), and bold lines denote correlations significant at p < 0.001. Correlations labelled A–D are explained in the Results (PCA and conditional independence analysis). Veg Vegetables
Discussion
We have demonstrated for the first time in a purposeful, community-based prospective study an association between maternal nutritional measurements in pregnancy and two major risk factors for type 2 diabetes in the offspring. Among rural mothers in Pune, energy and protein intakes are lower than the recommended daily allowance, vitamin B12 status is poor but folate status is adequate. Maternal macronutrient intakes were unrelated to adiposity and insulin resistance in the offspring. However, higher maternal folate concentrations predicted greater adiposity (fat mass and body fat per cent) and higher insulin resistance, and lower vitamin B12 concentrations predicted higher insulin resistance. Children born to mothers with low vitamin B12 concentrations but high folate concentrations were the most insulin resistant. The 18 week vitamin B12 concentration was more strongly associated with insulin resistance than the 28 week value, while the reverse was true for erythrocyte folate. The lifespan of erythrocytes (4 months) means that 28 week erythrocyte folate reflects nutritional status earlier in pregnancy. Thus, early-mid pregnancy may be a critical period for the programming of adiposity and insulin resistance.
The PMNS children are short and thin but relatively adipose compared with white children, similar to the situation in Indian newborns [5, 6] and adults [25]. There were no excessively adipose or insulin-resistant children in this cohort, but adiposity measures were strongly related to metabolic risk factors (glycaemia and insulin resistance) in the normal range. Other studies have shown that higher levels of risk factors in the normal range in childhood predict an increased risk of disease in later life [26, 27].
We have demonstrated that the ‘thin-fat’ phenotype of Indians is associated with a higher risk of type 2 diabetes [7]. This phenotype reflects the simultaneous involvement of two major body compartments (less lean but more adipose), which contribute to the pathogenesis of type 2 diabetes [28]. Our study suggests that an intrauterine imbalance between two related micronutrients (vitamin B12 and folate) may be responsible. A related concept is that of sarcopenic obesity, which increases risk of metabolic and skeletal adverse outcomes [29].
We did not find an association between maternal vitamin B12 concentrations and size at birth, possibly because so many women had values in the deficient range. However, higher maternal tHcy concentrations (which were related to low vitamin B12 concentrations) predicted smaller newborn size. A study in Bangalore, South India, where mothers had higher vitamin B12 concentrations, found that low maternal vitamin B12 concentrations predicted fetal growth retardation [30]. These findings are consistent with the ‘thrifty phenotype hypothesis,’ which proposed that ‘poor’ maternal nutritional status increases the risk of type 2 diabetes in the offspring [1]. On the other hand, the positive associations of maternal folate and intakes of GLVs and dairy products with adiposity or insulin resistance in the children, are contrary to the hypothesis because they predicted larger offspring size at birth. Our results suggest a need for caution in designing nutritional strategies to improve fetal growth and future health based on relationships with birth size alone.
Previous studies linking maternal nutrition in pregnancy to cardiovascular risk factors in the offspring did not measure vitamin B12 or folate [31]. These two vitamins play a crucial role in nucleic acid synthesis and one-carbon metabolism. Low maternal folate status has been implicated in the aetiology of neural tube defects [32] and high tHcy levels have been associated with poor pregnancy outcomes [10, 33, 34]. This has led to a policy of supplementation with folic acid before and during pregnancy, and fortification of flour with folic acid in some countries. The Indian policy is to provide iron and folic acid (60 mg and 500 μg per day) to all pregnant mothers [35]. Despite the evidence of widespread deficiency [11] vitamin B12 supplementation is not a consideration in pregnant Indian women. Vegetarianism and low milk intakes contribute to low vitamin B12 status in Indians [36, 37]. Vegetarianism is multigenerational, and influenced by religious and socioeconomic factors. King Samrat Ashok (273 bc) first banned the killing of animals for food and this was institutionalised by three religions: Jainism, Hinduism and Buddhism. The role of socioeconomic factors is evident in the small portion sizes of non-vegetarian foods habitually eaten by our mothers. Adequate folate status in the PMNS indicates adequate dietary intake even before supplementation was started (18 weeks of gestation), which further increased the levels. In addition, it is our experience that many Indian obstetricians routinely prescribe high doses of folic acid (5 mg or more) in ‘early’ pregnancy with the intention of preventing neural tube defects, even though the majority of pregnant women approach the doctor after 12 weeks of gestation, when the neural tube is already closed. Thus religious and socioeconomic factors, and medical practices, contribute to create an imbalance in these two related vitamins. The association of higher maternal intake of dairy products with insulin resistance in the offspring requires further exploration. It is intriguing that higher milk consumption is associated with higher insulin resistance in European children [38].
We can only speculate about the possible mechanisms for our findings (Fig. 1). Vitamin B12 deficiency will trap folate as 5-methyltetrahydrofolate [39], prevent the generation of methionine from homocysteine and therefore reduce protein synthesis and lean tissue deposition. Elevated methylmalonyl-CoA could contribute to increased lipogenesis by inhibiting carnitine palmitoyltransferase [40] and thereby inhibit β-oxidation [41]. An analogous clinical situation is high-dose folic acid treatment of severely vitamin B12-deficient pernicious anaemia patients: anaemia improves but neurological damage worsens, possibly because of accumulation of odd-chain carbon fatty acids [42]. Epigenetic regulation, involving DNA methylation, may be another mechanism of nutritional programming, as demonstrated in animal models [43–45]. It would be interesting to study these aspects further.
The PMNS has many strengths including a population-based design and high participation and follow-up rates. The nutritional assessment methods were developed specifically for the population. The rural setting is representative of over 70% of the Indian population, in whom the prevalence of diabetes is rapidly rising [46]. The lacto-vegetarian food habits of our women are similar to ~40% of Indian households and ~10% of the world population [47]. Although vitamin B12 was measured in stored frozen samples, it is stable during long-term storage [48]. In the absence of accepted guidelines for interpreting vitamin B12 status in pregnancy [49], we cannot define the true extent of vitamin B12 deficiency, but high levels of MMA and tHcy suggested its presence in a majority of the women in our study [17]. Associations with folate and vitamin B12 concentrations but not with tHcy and MMA could be due to dependence of the latter two on non-nutritional factors that are altered in pregnancy (haemodilution and elevated glomerular filtration rate), although we did find an association between high maternal MMA and insulin resistance in the children in the conditional independence analysis. A limitation of the study is that it is observational, and therefore causality cannot be ascertained.
In conclusion, our data raise the important possibility that high folate intakes in vitamin B12-deficient mothers could increase the risk of type 2 diabetes in the offspring. This is the first report in humans to suggest that defects in one-carbon metabolism might be at the heart of intra-uterine programming of adult disease. There is a need for further studies to test our findings and to determine the correct management of low vitamin B12 concentrations in Indian mothers.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Figure S1 Suggested metabolic mechanisms for adiposity, insulin resistance and altered gene expression in a situation of dietary vitamin B12 deficiency combined with adequate folate status (PDF 35.7 kb)
Table S1
Unrotated factor loadings from the PCA (PDF 75.3 kb)

vitamin b12
folate
pregnancy
insulin resistance
offspring
maternal nutrition
adiposity


Polymorphisms of Human Leukocyte Antigen Genes in Korean Children with Kawasaki Disease

Background Kawasaki disease is a leading cause of acquired heart disease in children. The prevalence rate varies in different ethnic groups. Recently, with the clinical application of molecular genetic technology, human leukocyte antigen (HLA) polymorphisms associated with several diseases have been identified by DNA analysis. This study aimed to assess the association of HLA alleles with susceptibility and complications of Kawasaki disease in Korean children.
Kawasaki disease is a systemic vasculitis that currently is a leading cause of acquired heart disease in children. The etiology of Kawasaki disease continues to be unknown, although it was described 4 decades ago in 1967 by Kawasaki [15]. Diagnosis continues to depend primarily on clinical manifestations.
It appears that the pathogenesis of Kawasaki disease is related to infection of a susceptible host coupled with epidemiologic and ethnic factors. In a study examining the epidemiologic characteristics of Kawasaki disease in San Diego, CA, USA, Asians/Pacific Islanders were 2.7 times more likely and Hispanics one-third as likely to be hospitalized for Kawasaki disease compared with the children of other ethnic groups combined [4]. The sibling risk ratio is also known to be higher, than the risk of being affected with Kawasaki disease without sibling history, and cases of Kawasaki disease in parents and children have been reported [10, 21].
For these reasons, many reports have suggested involvement of the human leukocyte antigen (HLA) system. The results of such studies vary depending on the ethnic group studied. Several reports show the association of HLA genotypes with Kawasaki disease including HLA-Bw22 (now referred to as HLA-B54) in Japanese [14, 20], HLA-B51 in white populations [13, 16, 18, 19], and the major histocompatibility complex class I chain-related gene A (MICA) genes and others in southern Chinese population [6, 12].
The major histocompatibility complex (MHC) class II genes in Kawasaki disease show variable correlations [2, 8, 27]. To date, there have been no reports supporting a consistent relationship between Kawasaki disease and HLA genes in Korean patients. Therefore, the current study aimed to assess the association of HLA-A,-B,-C, and -DRB1 alleles with Kawasaki disease in Korean children.
Materials and Methods
Subjects
From March 2004 to December 2004, 74 patients with Kawasaki disease (44 boys and 30 girls) attending the pediatric outpatient clinic or admitted to St. Vincent’s Hospital in Korea were recruited for this study. The mean age of the patients at diagnosis was 2.7 ± 1.9 years. For comparison, genetically unrelated healthy Korean adults (n = 159) who had no history of Kawasaki disease were studied as a healthy control group. Blood samples were obtained after informed consent in compliance with the institutional review board of St. Vincent’s hospital, College of Medicine, The Catholic University of Korea.
The diagnosis of Kawasaki disease was established by clinical manifestations meeting the defined criteria according to the report of the Research Committee on Kawasaki Disease of the Japanese Ministry of Health and Welfare in addition to exclusion of any other possible illnesses [25]. Coronary complications included the following lesions detected by echocardiography: an internal diameter exceeding 3 mm in patients younger than 5 years and exceeding 4 mm in patients age 5 years or older, dilation of a segmental luminal diameter to at least 1.5 times that of an adjacent segment, or a definitely irregular internal lumen of the coronary arteries. The clinical characteristics of the patients are shown in Table 1.
Table 1Profiles of subjects with Kawasaki diseasen (%)Male/female44/30 (60/40)Typical KD/atypical KD65/9 (88/12)Family history of KD3 (4.1)Recurrence history of KD4 (5.4)Coronary complications21 (28)Other complications of KDGallbladder hydrops14 (19)Pyuria25 (34)Hepatopathy38 (51)Arthritis10 (14)KD, Kawasaki disease
Genomic DNA was extracted by standard methods using the AccuPrep DNA extraction kit (Bioneer, Daejeon, Korea) from peripheral blood collected (4 ml) with ethylenediaminetetraacetic acid (EDTA) and kept at −20°C.
HLA-A, -B, and -C Genotyping
The genotyping of HLA-A, -B, and -C was performed by the amplification refractory mutation system (ARMS)–polymerase chain reaction (PCR) method. Each reaction contained a primer mix consisting of the allele- or group-specific primer pairs as well as internal control primers matching nonallelic sequences. Specific amplifications of the HLA-A, -B, and -C genes were performed using forward and reverse primers (44 for HLA-A, 47 for HLA-B, and 33 for HLA-C) designed according to the published nucleotide sequences [5, 17, 26].
The PCR procedure was carried out in a reaction (13 μl), containing 100 to 200 ng genomic DNA, 0.8 X buffer (40 mmol/l KCl, 1.2 mmol/l MgCl2, 8 mmol/l Tris-HCl pH 8.8, 0.08% Triton X-100), 5% dimethylsulphoxide (DMSO), 200 μmol/l of each dNTP, 0.25 U Taq DNA polymerase (Boehringer, Mannheim, Germany), 1 μmol/l of each sequence-specific primer, and 0.2 μmol/l of internal control primers.
The amplifications were performed in a My Cycler thermocycler (Bio-Rad, Hercules, USA). For amplification, 30 cycles were performed using the following steps: heating to 96°C for 1 min to denature the DNA; denaturation at 96°C for 25 s and at 70°C for 45 s; annealing and extension at 72°C for 30 s (for the first 5 cycles), 96°C for 25 s, 65°C 45 s, 72°C for 30 s (for the next 21 cycles); 96°C for 25 s, 55°C for 60 s, 72°C for 120 s (for the last 4 cycles); and a final 1 min extension at 72°C. The presence or absence of PCR products was determined after separation of the samples on a 1.5% agarose gel containing 0.5 μg/ml of ethidium bromide.
HLA-DRB1 Genotyping
The PCR sequence-specific primer (SSP) method was used for HLA-DRB1 genotyping. Each reaction contained a primer mix consisting of the allele- or group-specific primer pairs as well as the internal control primers that matched the nonallelic sequences. Specific amplification of the HLA-DRB1 gene was performed using 47 forward and reverse primers for HLA-DRB1 designed on the basis of the published nucleotide sequences [23].
The PCR procedure was carried out in a reaction (10 μl) containing 100 ng genomic DNA, 1 X buffer (60 mmol/l Tris-HCl pH 9.0, 15 mmol/l ammonium sulphate, 2.5 mmol/l MgCl2), 200 μmol/l of each dNTP, 0.25 U Taq DNA polymerase (Boehringer Mannheim, Germany), 1 μmol/l of each sequence-specific primer, and 0.2 μmol/l of the internal control primers.
The amplifications performed in a My Cycler thermocycler (Bio-Rad, Hercules, USA). For amplification, 27 cycles were performed using the following steps: heating to 96°C for 3 min to denature the DNA; denaturation at 96°C for 20 s; annealing and extension at 66°C for 60 s (for the first 10 cycles), 62°C for 80 s (for the next 10 cycles), and 61°C for 120 s (for the last 7 cycles); and a final 10 min extension at 72°C. The presence or absence of PCR products was determined after separation of samples on a 1.5% agarose gel containing 0.5 μg/ml of ethidium bromide.
Statistical Analysis
Statistical differences between Kawasaki disease patients and healthy control subjects and between subgroups of Kawasaki disease patients were tested with chi-square and Fisher’s exact test. The quantification of the relationship of the frequencies studied, in cases with Kawasaki disease, was performed by calculating the relative risks using the method of Woolf. Haldane’s modification was applied for cases in which the variables included zero. A p value less than 0.05 was considered to be significant.
In this study, the data were not adjusted for multiple comparisons because the sample size was not large enough for multiple-comparison analysis correction.
Results
The Genotype Distribution and Allele Frequencies of HLA-A for the Patients With Kawasaki Disease and the Healthy Control Group
In the analysis of the polymorphisms of HLA-A alleles, no statistical difference was found in the frequency of alleles between the patients with Kawasaki disease and the control group. However, the frequency of HLA-A26 alleles was significantly decreased in the Kawasaki disease patients without coronary complications (CC) compared with the healthy control group (p < 0.04; relative risk (RR) = 0.2). In a comparison between subgroups, with and without CC, no significant difference was identified (Table 2).
Table 2The allele frequencies of human leukocyte antigen-A (HLA-A) in the patients with Kawasaki disease and the control subjectsHLA allelesHealthy controls (n =159) n (%)KD patients (n = 74) n (%)Coronary complicationsPresent (n = 21) n (%)Absent (n = 53 n (%)HLA-A015 (3.1)5 (6.8)2 (9.5)3 (5.7)0285 (53.5)43 (58.1)12 (57.1)31 (58.5)035 (3.1)3 (4.1)0 (0.0)3 (5.7)1131 (19.5)21 (28.4)6 (28.6)15 (28.3)2457 (35.8)33 (44.6)12 (57.1)21 (39.6)2625 (15.7)6 (8.1)4 (19.0)2 (3.8)a3020 (12.6)4 (5.4)1 (4.8)3 (5.7)3114 (8.8)6 (8.1)0 (0.0)6 (11.3)323 (1.9)0 (0.0)0 (0.0)0 (0.0)3338 (23.9)15 (20.3)2 (9.5)13 (24.5)681 (0.6)0 (0.0)0 (0.0)0 (0.0)KD, Kawasaki disease; RR, relative riskap < 0.04; RR = 0.2 (95% confidence interval [CI], 0.05–0.90) vs healthy control subjects
The Genotype Distribution and Allele Frequencies of HLA-B for the Patients With Kawasaki Disease and the Healthy Control Group
In analysis of the polymorphisms of HLA-B alleles, there was a significant increase in the frequency of HLA-B35 and -B75 alleles in the patients with Kawasaki disease compared with the control group (p < 0.006; RR = 3.1 vs p < 0.02; RR = 8.2). When the patients with Kawasaki disease were divided into two subgroups, with or without CC, the Kawasaki disease patients without CC showed a significantly increased frequency of HLA-B35 and -B75 alleles compared with the healthy control group (p < 0.02; RR = 3.1 vs p < 0.003; RR = 11) (Table 3). Although the frequency of the HLA-B35 alleles were similar in the groups with and without CC (19% vs 18.9%), the HLA-B75 allele was found only in the Kawasaki disease patients without CC (0.0% vs. 13.2%). Therefore, the HLA-B35 allele was implicated in susceptibility to Kawasaki disease and the HLA-B75 allele in susceptibility to Kawasaki disease without CC. In a comparison between the subgroups, with and without CC, there was no overall significant difference identified between the two groups.
Table 3The allele frequencies of human leukocyte antigen-B (HLA-B) in the patients with Kawasaki disease and the control subjectsHLA allelesHealthy controls (n = 159) n (%)KD patients (n = 74) n (%)Coronary complicationsPresent (n = 21) n (%)Absent (n = 53) n (%)HLA-B0719 (11.9)9 (12.2)3 (14.3)6 (11.3)082 (1.3)0 (0.0)0 (0.0)0 (0.0)1320 (12.6)5 (6.8)2 (9.5)3 (5.7)146 (3.8)0 (0.0)0 (0.0)0 (0.0)2711 (6.9)3 (4.1)1 (4.8)2 (3.8)3511 (6.9)14 (18.9)a4 (19.0)10 (18.9)b375 (3.1)4 (5.4)2 (9.5)2 (3.8)387 (4.4)3 (4.1)0 (0.0)3 (5.7)392 (1.3)3 (4.1)2 (9.5)1 (1.9)4429 (18.2)10 (13.5)3 (14.3)7 (13.2)4618 (11.3)7 (9.5)2 (9.5)5 (9.4)470 (0.0)1 (1.4)0 (0.0)1 (1.9)4813 (8.2)2 (2.7)0 (0.0)2 (3.8)5129 (18.2)13 (17.6)5 (23.8)8 (15.1)529 (5.7)3 (4.1)2 (9.5)1 (1.9)5423 (14.5)10 (13.5)3 (14.3)7 (13.2)553 (1.9)4 (5.4)1 (4.8)3 (5.7)562 (1.3)0 (0.0)0 (0.0)0 (0.0)572 (1.3)1 (1.4)0 (0.0)1 (1.9)5815 (9.4)5 (6.8)0 (0.0)5 (9.4)594 (2.5)3 (4.1)0 (0.0)3 (5.7)6011 (6.9)7 (9.5)2 (9.5)5 (9.4)6123 (14.5)13 (17.6)6 (28.6)7 (13.2)6229 (18.2)14 (18.9)1 (4.8)13 (24.5)674 (2.5)2 (2.7)1 (4.8)1 (1.9)717 (4.4)1 (1.4)0 (0.0)1 (1.9)752 (1.3)7 (9.5)c0 (0.0)7 (13.2)dKD, Kawasaki disease; RR, relative riskap < 0.006; RR = 3.1 (95% confidence interval [CI], 1.29–7.62) vs healthy control subjectsbp < 0.02; RR = 3.1 (95% CI, 1.16–8.43) vs healthy control subjectscp < 0.02; RR = 8.2 (95% CI, 1.74–38.68) vs healthy control subjectsdp < 0.003; RR = 11 (95% CI, 2.79–51.13) vs healthy control subjects
The Genotype Distribution and Allele Frequency of HLA-C for the Patients With Kawasaki Disease and the Healthy Control Group
In the analysis of the polymorphisms of HLA-C alleles, there was a significant increase in the frequency of the HLA-Cw09 allele in the patients with Kawasaki disease compared with the healthy control group (p < 0.04; RR = 2.0). When the Kawasaki disease patients were divided into two subgroups, with or without CC, the Kawasaki disease patients without CC showed a significantly increased frequency of the HLA-Cw09 allele compared with the healthy control group (p < 0.05; RR = 2.1). However, no significant difference was found between the subgroups with and without CC (Table 4).
Table 4The allele frequencies of human leukocyte antigen-C (HLA-C) in the patients with Kawasaki disease and the control subjectsHLA allelesHealthy controls (n = 159) n (%)KD patients (n = 74) n (%)Coronary complicationsPresent (n = 21) n (%)Absent (n = 53) n (%)HLA-Cw0159 (37.1)22 (29.7)6 (28.6)16 (30.2)020 (0.0)1 (1.4)0 (0.0)1 (1.9)0421 (13.2)11 (14.9)2 (9.5)9 (17.0)056 (3.8)1 (1.4)0 (0.0)1 (1.9)0625 (15.7)10 (13.5)5 (23.8)5 (9.4)0744 (27.7)20 (27.0)6 (28.6)14 (26.4)0829 (18.2)10 (13.5)3 (14.3)7 (13.2)0923 (14.5)19 (25.7)a5 (23.8)14 (26.4)b1039 (24.5)24 (32.4)5 (23.8)19 (35.8)1211 (6.9)4 (5.4)2 (9.5)2 (3.8)1434 (21.4)18 (24.3)7 (33.3)11 (20.8)158 (5.0)1 (1.4)1 (4.8)0 (0.0)KD, Kawasaki disease; RR, relative riskap < 0.04; RR = 2.0 (95% confidence interval [CI], 0.97–4.29) vs healthy control subjectsbp < 0.05; RR = 2.1 (95% CI, 0.92–4.87) vs healthy control subjects
The Genotype Distribution and the Allele Frequencies of HLA-DRB1 for the Patients With Kawasaki Disease and the Healthy Control Group
Among the HLA-DRB1 alleles, there was no increase in the frequency of alleles in the Kawasaki disease patients compared with the healthy control group. However, after subgrouping of the Kawasaki disease patients, with or without CC, the frequency of HLA-DRB1*11 was significantly increased in the Kawasaki disease patients with CC compared with the healthy control group (p < 0.04; RR = 4.7). In a comparison between the subgroups, with and without CC, the frequency of the HLA-DRB1*09 allele was increased in the Kawasaki disease patients with CC compared with the Kawasaki disease patients without CC (33.3% vs 9.4%; p < 0.04). However, there was no significant difference in the frequency of these alleles when each was compared with the healthy control group. The frequency of the HLA-DRB1*04 allele was slightly increased in the Kawasaki disease patients without CC compared with the healthy control group (p < 0.05; RR = 1.9) (Table 5).
Table 5The allele frequencies of human leukocyte antigen-DRB1 (HLA-DRB1) in the patients with Kawasaki disease and the control subjectsHLA allelesHealthy controls (n = 159) n (%)KD patients (n = 74) n (%)Coronary complicationsPresent (n = 21) n (%)Absent (n = 53) n (%)HLA-DRB10129 (18.2)8 (10.8)1 (4.8)7 (13.2)037 (4.4)1 (1.4)0 (0.0)1 (1.9)0448 (30.2)30 (40.5)6 (28.6)24 (45.3)a0726 (16.4)8 (10.8)1 (4.8)7 (13.2)0832 (20.1)14 (18.9)3 (14.3)11 (20.8)0928 (17.6)12 (16.2)7 (33.3)b5 (9.4)104 (2.5)4 (5.4)2 (9.5)2 (3.8)1110 (6.3)8 (10.8)5 (23.8)c3 (5.7)1218 (11.3)11 (14.9)2 (9.5)9 (17.0)1332 (20.1)10 (13.5)3 (14.3)7 (13.2)1421 (13.2)10 (13.5)3 (14.3)7 (13.2)1535 (22.0)20 (27.0)5 (23.8)15(28.3)164 (2.5)2 (2.7)1 (4.8)1 (1.9)KD, Kawasaki disease; RR, relative riskap < 0.05; RR = 1.9 (95% confidence interval [CI], 0.96–3.82) vs healthy control subjectsbp < 0.04 between KD patients with and without coronary complicationcp < 0.04; RR = 4.7 (95% CI, 1.26–17.17) vs healthy control subjects
Discussion
The prevalence of CC among the patients with Kawasaki disease in this study was higher than previously reported (28.4% vs 5%) [22]. This is likely because the patients included had a previous diagnosis of Kawasaki disease and were attending our clinic for this reason. Patients with more severe Kawasaki disease, including those with CC, present for medical care more frequently than patients without CC, and this may explain the higher proportion of CC in our study. Three patients (4%) had a positive family history, and four patients (5.4%) had a history of Kawasaki disease recurrence. Prior studies reported the familial occurrence and recurrence rates for Kawasaki disease to be 1% and 3%, respectively [11, 24, 28].
Our data were confined to Korean children and focused on patients who had Kawasaki disease with CC. Therefore, further research on the effect of the family history and recurrence of Kawasaki disease in association with HLA polymorphisms is needed.
Studies have associated HLA alleles with many different human diseases, such as HLA-B27 with ankylosing spondylitis and Reiter syndrome, HLA-B35 with subacute thyroiditis, and HLA-DR8 with juvenile rheumatoid arthritis. Most of the early studies on the genetics of Kawasaki disease were conducted using serologic testing that measured HLA antigens. The association of the HLA-Bw22 allele with Kawasaki disease in Japanese studies was evaluated with the microcytotoxicity test used for tissue typing of 205 patients with Kawasaki disease and 500 control samples in the late 1970s [14]. On basis of these studies, a gene was thought to control susceptibility to Kawasaki disease and was linked to the Japanese-specific HLA antigen.
Since the clinical application of genetic technology became available in the 1980s, polymorphisms at many HLA loci have been identified. In addition, there were changes in nomenclature, and the alleles were reclassified. For example, the previously labeled HLA-Bw22 allele was changed to the HLA B54 allele [1, 3].
Our study on the polymorphisms of HLA genes was conducted using the PCR-ARMS method for the HLA class I genes and the PCR-SSP method for the HLA class II genes. There was a significant increase in the frequencies of HLA-B35, -B75, and -Cw09 alleles in the patients with Kawasaki disease compared with the healthy control group. As previously mentioned, this study showed that the HLA-B35 allele was related to susceptibility to Kawasaki disease and the HLA-B75 allele to Kawasaki disease without CC in Korean children. These results suggest that polymorphisms (in some of the B and C loci) in HLA class I genes are associated with Kawasaki disease in Korean children; consistent with prior reports on other ethnic groups [12, 14, 16, 19].
Review of the medical literature on Kawasaki disease and HLA shows that there is a trend toward an association between the HLA-B loci and Kawasaki disease. However, to date, there is no confirmed relationship to a particular locus. It is likely that the HLA-B locus is not the only locus associated with Kawasaki disease. This can be the case because of its functional variation or the potential effects from other related genes around the HLA-B locus. The HLA-B locus and other associated genes might be useful genetic markers for Kawasaki disease.
The HLA-B35 gene is known to play a major role in a variety of infections. For example, in cases with HIV infection, the patients with an increased frequency of HLA-B35 show rapid progression of HIV [9]. Infection has been implicated in the etiology of Kawasaki disease. The finding in our study that the frequency of the HLA-B35 allele was increased in Korean children with Kawasaki disease suggests a role for infection in Kawasaki disease.
The HLA-DRB1 alleles showed no increased frequency in the Kawasaki disease patients compared with the healthy control group. However, when the patients were compared by subgroups, with and without CC, the frequency of HLA-DRB1*11 was significantly increased in the Kawasaki disease patients with CC (p < 0.04; RR = 4.7), and HLA-DRB1*04 was increased in the Kawasaki disease patients without CC (p < 0.05; RR =1.9) compared with the healthy control group. Comparison of the subgroups with and without CC showed that the frequency of the HLA-DRB1*09 allele was increased in Kawasaki disease patients with CC compared with the Kawasaki disease patients who had no CC (33.3% vs 9.4%; p < 0.04), and that there was no significant difference in comparison with the healthy control group. For the HLA-DRB1*11 allele, the finding of a higher allele frequency compared with the healthy control group was amplified by the subgrouping of Kawasaki disease patients with and without CC. Future studies with a larger sample size are needed to confirm these findings.
Several reports describe the presence of HLA-DRB1 and the progression of infections [7]. However, most of the reports on the association of HLA-DRB1 with Kawasaki disease concluded that despite a regional association, their data failed to support a consistent role for HLA class II alleles in Kawasaki disease. If there was any role, it was predicted to be minor [2, 8].
Interpopulation discrepancies of the frequencies of HLA alleles make generalization of results difficult. Matched population profiles are needed for disease association studies because ethnic differences can provide altered disease associations.
In this study, we compared HLA alleles of Kawasaki disease patients with known polymorphic loci of HLA genes in healthy Korean adults. The results we report on the polymorphisms of HLA genes in Kawasaki disease patients may be limited to the Korean population. However, this information may be helpful in future studies on HLA genetic polymorphisms in Kawasaki disease. Further studies with a larger sample size are needed for confirmation of our findings.


MHC class I A region diversity and polymorphism in macaque species

The HLA-A locus represents a single copy gene that displays abundant allelic polymorphism in the human population, whereas, in contrast, a nonhuman primate species such as the rhesus macaque (Macaca mulatta) possesses multiple HLA-A-like (Mamu-A) genes, which parade varying degrees of polymorphism. The number and combination of transcribed Mamu-A genes present per chromosome display diversity in a population of Indian animals. At present, it is not clearly understood whether these different A region configurations are evolutionarily stable entities. To shed light on this issue, rhesus macaques from a Chinese population and a panel of cynomolgus monkeys (Macaca fascicularis) were screened for various A region-linked variations. Comparisons demonstrated that most A region configurations are old entities predating macaque speciation, whereas most allelic variation (>95%) is of more recent origin. The latter situation contrasts the observations of the major histocompatibility complex class II genes in rhesus and cynomolgus macaques, which share a high number of identical alleles (>30%) as defined by exon 2 sequencing.
Introduction
Major histocompatibility complex (MHC) represents a multigene family that plays a crucial role in the generation of adaptive immune responses in vertebrate species. A key feature of the system is that some of its genes display abundant polymorphism at the population level. In addition, the number of Mhc class I or II genes may differ significantly between species, as well as between individuals of a species (Kelley et al. 2005). MHC polymorphisms may have a profound impact on several features such as disease susceptibility, organ transplantation, and reproductive success (Lagaaij et al. 1989; Goulder and Watkins 2004; Bontrop and Watkins 2005; Ziegler et al. 2005; Smith et al. 2006). The MHC systems of various primate species, including humans, have been studied extensively (Watkins 1995; Antunes et al. 1998; Bontrop et al. 1999; Adams and Parham 2001; de Groot et al. 2002; Lafont et al. 2004; Middleton et al. 2004; Marsh et al. 2005; Penedo et al. 2005; Abbott et al. 2006; Huchard et al. 2006). For example, the MHC of the rhesus macaque (MhcMamu), an Old World primate species, has been shown to share many similarities with the human leukocyte antigen (HLA) system in humans. The evolutionary orthologs of the HLA-DP, -DQ, and -DR genes are also present in rhesus monkeys, and as in humans, these loci are polymorphic (Bontrop et al. 1999). The number of Mamu-DRB region configurations appears to be expanded in comparison with humans, and some of these regions seem to harbor an extended number of genes (Doxiadis et al. 2000). Subsequent cDNA studies have illustrated, however, that in humans and rhesus macaques, comparable numbers of DRB genes are transcribed (de Groot et al. 2004).
Apart from Mamu-E (Knapp et al. 1998), -F (Otting and Bontrop 1993), -G (Boyson et al. 1996a; Castro et al. 1996), and -AG (Boyson et al. 1997), rhesus macaques also possess a B-like sequence, designated Mamu-I, which appears to be present in each haplotype (Urvater et al. 2000). As observed in humans, these nonclassical genes display low levels of polymorphism. An ortholog of the HLA-C gene appears to be absent in the rhesus macaque, whereas evolutionary equivalents of the classical HLA-A and -B genes have been described (Boyson et al. 1996b). The Mamu-A and -B genes have been subjected to several rounds of duplication as was shown by genomic sequencing (Daza-Vamenta et al. 2004; Kulski et al. 2004). Analysis of a panel of rhesus macaques, mainly originating from the Indian subcontinent, illustrated that the number and combination of Mamu-A and -B genes that are expressed per haplotype may differ at the population level (Otting et al. 2005). In the same study, marked differences in expression levels were shown. At this stage it is not known whether rhesus macaques originating from other geographic areas have unique Mhc class I alleles and/or region configurations. Comparative studies have illustrated that many Mhc loci and lineages predate speciation events. The sharing of Mhc alleles between two primate species seems to be a rare event, and only a few cases have been documented (Cooper et al. 1998; Evans et al. 1998). An exception is provided by rhesus and cynomolgus macaques, which seem to share a high number of Mhc class II alleles as was defined by exon 2 sequencing (Blancher et al. 2006; Doxiadis et al. 2006). Whether this sharing is because of introgression or purifying selection remains to be elucidated. Thus far, about 50 Mafa-A sequences have been published for the cynomolgus macaque (Uda et al. 2004; Krebs et al. 2005). The absence of pedigreed material did not allow us to define Mafa-A loci or region configurations. For that reason, a panel of pedigreed cynomolgus macaques was incorporated in the present study. Comparison with rhesus macaque Mhc class I sequences obtained from different populations enabled us to draw conclusions on the evolutionary stability of A region variations.
Materials and methods
Animals and cell lines
The Biomedical Primate Research Centre houses a self-sustaining colony of approximately 1,000 rhesus macaques, mainly of Indian origin, that have been pedigreed based on the segregation of serologically defined MHC allotypes (Bontrop et al. 1999; Penedo et al. 2005; Doxiadis et al. 2006). Furthermore, a large collection of DNA samples as well as B-cell lines is available. In this study, B-cells derived from rhesus macaques of Chinese origin and from cynomolgus monkeys were used to isolate RNA. Most cynomolgus monkey samples originate from a pedigreed group housed at the campus of the University of Utrecht.
cDNA cloning and sequencing
RNA was isolated from B cells (Rneasy kit, Qiagen) and subjected to One-step reverse transcriptase polymerase chain reaction (RT-PCR), as recommended by the supplier (Qiagen). The primers (5′MAS) AATTCATGGCGCCCCGAACCCTCCTCCTGG, and (3′MAS) CTAGACCACACAAGGCGGCTGTCTCAC were used, which are specific for class I A transcripts in macaques. The final elongation step was extended to 30 min to generate a 3′dA overhang. The RT-PCR products were cloned using the InsT/Aclone kit (Fermentas). After transformation, 16 to 32 colonies were picked for plasmid isolations. The separate Mamu-A loci show differences in expression levels, as indicated by the numbers of clones, picked from each animal. The alleles with high expression levels (majors) may be associated with serotypes and thus involved in classical antigen presentation, whereas those with low expression levels (minors) are considered nonclassicals and may exhibit more specialized types of functions (Otting et al. 2005).
Sequencing reactions were performed using the BigDye terminator cycle sequencing kit, and samples were run on an automated capillary sequencing system (ABI Genetic Analyzer 3100) as has been described previously (Otting et al. 2005).
Locus-specific PCR reactions
To test the presence of Mamu- and Mafa-A4*14 alleles, 1 μl of the RT-PCR samples was taken for a single-specific-primer PCR (SSP-PCR) reaction in advance of the cloning step. The relevant primers, (5′A*14) GGGACCCGACGGGCGCCTCCAA and (3′A*14) GGCCCTCCAGGTAGACTCTGTC have annealing sites in exon 3. Amplifications were carried out starting with 2 min at 94°C, followed by 25 cycles at 94°C, 65°C, and 72°C for 1 min each. The PCR products were subjected to direct sequencing, and the reactions were performed as described above.
Phylogenetic analysis and nomenclature
The sequences were analyzed with the Sequence Navigator Software version 1.0.1 (Applied Biosystems), and alleles are based on at least three clones with identical full-length sequences. To define loci and lineages, alignments of the sequences were made using the MacVector™ version 8.1.1 (Oxford Molecular Group), followed by manual adjustments. Phylogenetic analyses on the full-length (1,065 bp) sequences were also performed with the MacVector software. Neighbor-joining trees were constructed with the Kimura 2 parameter method. Bootstrap analyses were performed based on 1,000 replications.
In total, 130 unreported Mamu-A and Mafa-A sequences were submitted to the European Bioinformatics Institute and European Molecular Biology Laboratory (EBI-EMBL) database. Relevant information such as accession number and a reference cell-line are provided (Table 1). Moreover, all novel Mamu-A and Mafa-A sequences are named in accordance with a generally accepted nomenclature proposal (Klein et al. 1990; Robinson et al. 2003; Ellis et al. 2006). For example, Mamu-A1*0101 defines a Mhc allele in the rhesus macaque, which is encoded by one of the class I loci: namely, A1. The first two digits after the asterisk define the lineage, whereas the third and fourth digits define the allele number. These allele numbers are arbitrary, as they reflect the order in which the alleles were discovered. A fifth and sixth digit are used to mark a synonymous basepair difference between two sequences.
Table 1Summary of Mamu-A and Mafa-A alleles detected in this studyAlleleAccessionAnimalsAlleleAccessionAnimalsMamu-A1Mafa-A1A1*0102AM295885EKKA1*0101AM295828YabaaA1*0103AM295886Ri056A1*0301AM295829HoebaA1*030102AM295887Ri185A1*0302AM29583077A1*0302AM295888r300A1*1002AM295831k71A1*0303AM2958898822A1*1003AM295832TrufoA1*0304AM295890Ri301A1*1702AM295833BufoA1*0305AM295891Ri037A1*1901AM29583477A1*040102AM295892Ri165A1*2202AM295835k94A1*0402AM295893Ri366A1*3101AM295836GayoA1*1001AM295894Ri002A1*3102AM295823BilboaA1*1002AM295895Ri142A1*3201AM29582480A1*1102AM295896Ri136A1*3801AM295825k66A1*1701AM295897Ri145A1*4002AM295826PedroA1*1801AM295898Ri011A1*4102AM295827k65A1*1802AM295899Ri018A1*4302AM295837k65A1*1803AM295900Ri260A1*5201AM29583888A1*1902AM295901Ri081A1*5301AM295839k390A1*1903AM295902BB10A1*5501AM295840k127A1*1904AM295903Ri197A1*5601AM295841k766A1*2201AM295904Ri009A1*5701AM295842k2,A1*2502AM295905Ri088A1*5801AM295843RastafaA1*2602AM2959068765A1*5901AM295844RozaA1*2802AM295907Ri082A1*6001AM295845VivaaA1*3201AM295908Ri094A1*6101AM295846k135A1*3301AM295909Ri253A1*6201AM29584781A1*4001AM295910Ri165A1*6301AM295848HippoA1*4101AM295911Ri284A1*6401AM295849BufoA1*4701AM295912Ri189A1*6402AM295850k95A1*4801AM2959138822A1*650101AM29585181A1*4901AM295914Ri088A1*650102AM295852k390A1*5001AM2959154053A1*6601AM295853k73A1*5101AM295916Ri281A1*6602AM295854MilvaA1*5201AM295917Ri136A1*6701AM29585583A1*5301AM295918Ri056A1*6801AM295856JuanitaA1*5401AM295919Ri260A1*6901AM295857ZazaaA1*5402AM295920Ri011A1*7001AM295858FrikoA1*5601AM295921Ri146A1*7101AM295859ZolaA1*5602AM295922Ri228A1*7201AM295860CanadaA1*5701AM295923Ri006A1*5801AM295924Ri026Mamu-A2Mafa-A2A2*050301AM295925Ri056A2*0501AM295861ClintA2*050302AM295930Ri226A2*0502AM29586284A2*050402AM295926Ri009A2*0503AM295863k95A2*0513AM295927Ri159A2*0504AM295864ClintA2*0514AM295928Ri095A2*0505AM295865BufoA2*0515AM295929Ri009A2*0506AM295866TrufoA2*0517AM295931Ri284A2*0507AM29586780,A2*0518AM295932Ri094A2*0508AM295868ZebraA2*0519AM295933Ri137A2*0509AM29586981A2*0520AM295934Ri197A2*0510AM295870MilvaA2*0521AM295935Ri126A2*0511AM295871HippoA2*0522AM295936Ri026A2*051201AM295872k73A2*0523AM295937Ri028A2*051202AM295876DojoA2*0524AM295938Ri301A2*0513AM29587381A2*0525AM295939Ri137A2*0514AM295874k95A2*0526AM295940Ri002A2*0515AM295875JuanitaA2*2402AM295941BB67A2*0516AM295878JawaA2*2403AM295942Ri366A2*0517AM295877LabaMamu-A3Mafa-A3A3*1307AM295943Ri136A3*1301AM295879k2,Mamu-A4Mafa-A4A4*1404AM295944Ri137A4*1401AM29588077A4*1402AM295881BufoMamu-A5Mafa-A5A5*300101AM295945BB67A5*3001AM295882GayoA5*300102AM295946Ri081A5*3002AM295883k766A5*3002AM295948Ri088A5*3003AM295947Ri366Mamu-A6Mafa-A6A6*0101AM295949Ri145A6*0101AM295884k73Mamu-A7A7*0101AM295950Ri082A7*0102AM295951Ri315A7*0103AM295952Ri009The Chinese rhesus macaques are indicated by Ri numbers. Nine alleles found in rhesus macaques of mixed breeding are also reported.
Results and discussion
Polymorphism and diversity of the Mamu-A region: comparison of Chinese and Indian rhesus macaques
In a previous communication, five different Mamu-A region configurations were defined in a population of Indian rhesus macaques (Otting et al. 2005). These configurations display diversity with regard to the number and combination of distinct Mamu-A genes present per chromosome. The loci have been designated Mamu-A1, -A2, -A3, and -A4, respectively (Fig. 1). As can be seen, each region configuration comprises a Mamu-A1 gene characterized by high transcription levels (major) combined with one or two other Mamu-A genes characterized by lower transcription levels (minors). The Mamu-A1 gene is probably responsible for executing the classical antigen presentation function (Evans et al. 1999; Sidney et al. 2000; Sette et al. 2005), whereas the others can be considered as nonclassicals and may interact, for instance, with the KIR gene family present on NK cells. Two complete MHC haplotypes in the rhesus macaque are sequenced (Daza-Vamenta et al. 2004; Kulski et al. 2004), and they represent two-region configurations. One haplotype contains the -A1/-A2 combination, whereas in the other, the -A1/-A4 pair is observed. Both region configurations are present in the animals of Indian origin (Fig. 1).
Fig. 1Schematic representation of different Mamu-A region configurations observed in Indian rhesus macaques. The exact order and physical distances of the loci on the genome are still unknown. The relative levels of polymorphism and transcription of the loci are indicated. Two Mamu-A region configurations (1 and 5) are confirmed by the sequencing of the complete rhesus MHC region (Daza-Vamenta et al. 2004; Kulski et al. 2004)
Within the present panel of 42 Chinese animals, 59 different full-length Mamu-A cDNAs were detected that can be grouped into various loci and lineages (Fig. 2). A complete listing of the alleles detected in the Chinese macaques is provided as electronic supplementary material (Table 5). Because of codominant expression, rhesus macaques can be heterozygous for the Mamu-A1 gene and thus can express up to two allotypes. Nine animals were found to express three different Mamu-A1-like sequences. These animals shared one of these sequences in separated clades in phylogenetic tree (Fig. 2; -A5, -A6, and -A7). The triplets of the three animals are indicated by asterisks. The clustering of the three alleles, obtained from one animal, in clades distinct from -A2, -A3, and -A4 indicates that one allele belongs to a separate locus. The newly detected loci are named Mamu-A5, -A6, and-A7, according to the generally accepted nomenclature system (Klein et al. 1990; Robinson et al. 2003; Ellis et al. 2006). Representatives of two loci, A5 and A6, are also observed in cynomolgus macaques that express more than two A1-like sequences.
Fig. 2Phylogenetic tree of Mamu-A and Mafa-A gene/alleles detected in this study. The -A2 locus is represented by four alleles only. The tree is based on full-length sequences, although analyses on only exon 2 and 3 showed no significant differences. The nomenclature of the new alleles is based on a more extended tree (not shown) containing all the Mamu-A and Mafa-A alleles now available. Asterisk, three alleles of animal Ri081, of which one represents the locus Mamu-A5. Double asterisks, three alleles of animal Ri145, of which one represents the locus Mamu-A6. Triple asterisks, three alleles of animal Ri078, of which one represents the locus Mamu-A7
Thirty-three new Mamu-A1 alleles were detected in the Chinese rhesus macaques. Although some of the alleles in this population group into lineages that are also present in Indian animals, sharing of alleles for the Mamu-A1 locus is rare and was observed only once. The Mamu-A1*26 allele is detected in both populations, whereas the Mamu-A1*03 and for -A1*04, alleles are highly similar and differ for one synonymous basepair substitution. Hence, it is concluded that most allelic polymorphism observed for the Mamu-A1 locus was probably generated after the rhesus macaque populations were separated. Sequence comparisons illustrated that most of the variations map at the contact residues of the peptide-binding site (data not shown). Thus, polymorphism at the highly divergent Mamu-A1 locus, characterized by high expression levels, must have resulted from positive Darwinian selection (Hughes and Nei 1988; Borghans et al. 2004).
The Mamu-A2 gene displays differential haplotype distribution in the Indian population of rhesus macaques (Fig. 1). This also appears to be the case for Chinese animals, as Mamu-A2 cDNAs were detected in 39 out of 42 of them. In this panel, 16 alleles that differ from the ones earlier detected in Indian animals were defined. In the Indian population, low expression levels characterize the Mamu-A2 gene, which appears also to be the case in Chinese animals.
Five Chinese macaques possess the Mamu-A3*1307 sequence, which is always observed in combination with Mamu-A1*1102. In concordance with the Indian animals, the number of Mamu-A3 clones detected also reflects low expression levels. A full-length Mamu-A4*1404 cDNA was detected in only three animals. In contrast to the Mamu-A4 allele in the Indian macaque, the Chinese allele has a stopcodon at the last triplet of exon 5 encoding the transmembrane part of the class I protein. This suggests that the corresponding gene product may settle in the membrane, but the signal transduction is impaired. As was observed earlier in Indian animals, this locus is characterized by extremely low expression levels (Fig. 1). For that reason, the presence of Mamu-A4 cDNAs in the other animals was tested with SSP on the RT-PCR samples, and 17 out of 42 Chinese animals appeared to be positive for this locus. The Mamu-A5, -A6, and -A7 genes are also characterized by low expression levels and are considered to represent minors.
In contrast to the previously studied Indian population, the Chinese animals were selected randomly from a large population. Because of the lack of pedigree data and segregation profiles, it was not possible to firmly establish the segregation of different Mamu-A genes present per chromosome. Haplotypes can only be deduced based on sharing of sequences between animals, and such haplotypes are listed in Table 2. At this stage, it is impossible to determine in which region configurations the Mamu-A5,-A6, and -A7 genes are present.
Table 2Chinese Mamu-A haplotypes deduced by sharing of alleles grouped into two-region configurations Mamu-A1Mamu-A2Mamu-A3Number1*1801*15163*2201*05156*5201*051332*1102*13075
Mafa-A region polymorphism and diversity in cynomolgus monkeys
Sixty-two alleles were detected in the 96 cynomolgus macaques analyzed. As observed in the rhesus macaques, most animals had two polymorphic Mafa-A1 sequences, in combination with orthologs of the Mamu-A2, -A3, -A4, -A5, and/or -A6 genes. In total, 38 Mafa-A1 alleles were detected, illustrating the high level of polymorphism of this locus. These alleles were compared with sequences reported earlier by two other research groups (Uda et al. 2004; Krebs et al. 2005) that have used different nomenclature systems. Only four alleles -A1*3101, -A1*3201, -A1*3801, and -A5*3001 detected in our panel are identical to the earlier described sequences A*310101, A*320101, A*380101, and A*300101.
A total of 79 out of 96 animals have at least one Mafa-A2 sequence, and 18 alleles could be distinguished (Table 1). The oligomorphic A2 locus is also present in the pigtailed macaque (Macaca nemestrina) as was recently described (Pratt et al. 2006; Lafont et al. 2007). In these studies, 90% of the animals possess the gene, and lower transcription levels were also observed in comparison to the other Mane-A alleles. The Mafa-A3 gene is observed in only two animals, whereas the Mafa-A4 locus is detected in 41 individuals. Two Mafa-A4 alleles were found that differ in only one basepair (nonsynonymous) substitution (Fig. 2). As in the Chinese rhesus macaques, the Mafa-A4 alleles have a stopcodon at the end of exon 5. The Mafa-A5 and -A6 alleles were detected in, respectively, eight and four animals and display low levels of polymorphism. Phylogenetic analyses illustrate that apart from loci rhesus and cynomolgus monkeys also share lineages (Fig. 2). As found in rhesus macaques, the Mafa-A1 alleles are characterized by high transcription levels, whereas the other loci display moderate or low expression levels.
Because most of the cynomolgus macaques are pedigreed, the combination of sequences that are inherited on one chromosome could be defined. As an example, the kinship tree of one breeding group is provided (Fig. 3). The haplotypes are grouped based on the combination of loci (Table 3). Seven combinations of loci or region configurations are recognized (Fig. 4), and four of them (two to five) are also observed in the Indian rhesus macaques (Fig. 1), which indicates that they predate speciation. Mafa-A5*3001 segregates in combination with Mafa-A1*6001 and -A1*7201 but not exclusively. The exact region configurations of Mafa-A6 containing haplotypes are not yet known.
Fig. 3Pedigree of a cynomolgus macaque family showing segregation of Mafa-A alleles. The animals analyzed are indicated by shading. A question mark indicates that the sire has not been identifiedTable 3Mafa-A haplotypes as defined by segregation grouped into seven-region configurations Mafa-A1Mafa-A2Mafa-A3Mafa-A4Mafa-A5Number1A1*40026A1*58014A1*700132A1*0101A2*05144A1*1003A2*050610A1*1901A2*05133A1*3101A2*05044A1*5901A2*050812A1*6301A2*05017A1*6301A2*05113A1*6401A2*05059A1*6801A2*05153A1*6601A2*0512013A1*6602A2*051010A1*6801A2*051533A1*5701A3*130124A1*1702A4*140211A1*7101A4*1465A1*650102A2*05A4*1436A2*051202A4*1497A1*6001A5*30012A1*7201A5*30013One region configuration appears to lack the Mafa-A1 gene. At this stage, it is unknown whether the locus is really absent or was missed because of PCR failure, for example, because of a mutation in a primer site.Fig. 4Schematic representation of different Mafa-A region configurations in cynomolgus macaques. Four configurations (2–5) are shared with the Indian rhesus macaques (Fig. 1). One region configuration lacks the Mafa-A1 gene. It is not yet known whether the locus is really absent or missed because of PCR failure. Should a Mafa-A1 gene be found in extended studies than this region, configuration has to be deleted from the list
Sharing of -A1 and -A2 sequences between different macaque species
A comparison of all available Mamu-A to Mafa-A sequences showed that six full-length cDNA transcripts are shared between both species of macaque. This is the case for five -A1 pairs and one -A2 pair (Table 4). One of these cynomolgus monkey alleles is identical to an Indian rhesus allele, whereas the other five are shared with animals of Chinese origin. This observation was unexpected as the two rhesus macaque populations investigated share only one allele. The fact that cynomolgus macaques share more alleles with Chinese rhesus macaques than with Indian individuals may be explained by the fact that there is an overlap in the geographic areas inhabited by both species in Indochina (eastern Asia). It is known that rhesus macaques and cynomolgus monkeys can interbreed and produce offspring (Tosi et al. 2002). Sharing of alleles was not observed for the minors controlled by the A3–A7 loci.
Table 4Identical Mhc class I sequences detected in the two species of macaqueMacaca mulattaMacaca fascicularisMamu-A1*0103Mafa-A*260101aMamu-A1*4001Mafa-A*400101aMamu-A1*1001Mafa-A1*1002Mamu-A*21bMafa-A1*0302Mamu-A1*5301Mafa-A1*5301Mamu-A2*0519Mafa-A2*051202aPublished by Krebs et al. 2005.bPublished by Otting et al. 2005.
Different modes of selection operating on the Mhc class I and II genes in macaques
Although Mamu-A and Mafa-A sequences are interspersed in the phylogenetic tree, the vast majority of the alleles are species unique, and for rhesus macaque, most of them appear to be population specific. This is in sharp contrast to the situation observed for the macaque class II region, where sharing for different exon 2 sequences at the Mhc-DR, but especially the -DQ and -DP genes, is far more common (Blancher et al. 2006; Doxiadis et al. 2006). About half of the Mafa-DPB1, -DQA1, and -DQB1 and one third of the Mafa-DRB sequences are identical to rhesus orthologs. As animals of the same populations have been used to study Mhc class I and II sequences, the present results exclude the possibility that this high sharing of Mhc class II sequences is because of introgression. A more likely explanation is that in macaques, the exon 2 sequences of the Mhc class II genes, which encode the peptide-binding site, have been subjected to purifying selection. As a consequence, many Mhc class II alleles in these two macaque species predate speciation processes. A databank search indicated that the phenomenon also extends to other macaque species (Robinson et al. 2003).
The Mhc class I alleles in macaques are largely unique, illustrating that selection has favored diversity. Mhc class I proteins are involved in the presentation of intracellular pathogens such as viruses and parasites that are known to evolve at high mutation rates. Recognition of Mhc class I molecules combined with a foreign peptide may result in the lysis of an infected cell. The Mhc class II molecules select peptides, originating from extracellular pathogens such as bacteria and fungi, for binding. Mhc class II-mediated activation may result in antibody production and/or providing help to cytotoxic T cells. The present results illustrate that, probably because of coevolution with intracellular pathogens, macaques have generated a highly complex and divergent Mhc class I repertoire. The fact that Mhc class I alleles evolve quickly, even within a species, has been documented in humans for instance (Belich et al. 1992; Watkins et al. 1992). The high degree of sharing of Mhc class II sequences seems to be unique for macaques and has not been observed in any other group of vertebrate species.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Table 5
Overview of Mamu-A genes/alleles in the panel of Chinese rhesus macaques. Sequences represented by two ciphers (for example A*05) are not confirmed and are not submitted to the to the EBI-EMBL database (PDF 127 KB).

nonhuman primates
rhesus macaque
cynomolgus macaque
evolution