Confirmation and dissection of QTL controlling resistanceto malaria in mice

Maria Hernandez-Valladares; Jan Naessens; John P. Gibson; Anthony J. Musoke; Sonal Nagda; Pascal Rihet; Onesmo K. ole-MoiYoi; Fuad A. Iraqi

doi:10.1007/s00335-004-3042-4

Mammalian Genome

May 2004, Volume 15, Issue 5, pp 390–398 | Cite as

Confirmation and dissection of QTL controlling resistanceto malaria in mice

Authors
Authors and affiliations

Maria Hernandez-Valladares
Jan Naessens
John P. Gibson
Anthony J. Musoke
Sonal Nagda
Pascal Rihet
Onesmo K. ole-MoiYoi
Fuad A. IraqiEmail author

Original Contributions

Received: 07 October 2003

Accepted: 17 December 2003

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31 Citations

Abstract

We developed an F₁₁ AIL population from an F₁ cross of A/J (susceptible) and C57BL/6J (resistant) mouse strains. One thousand F₁₁ mice were challenged with P.c. chabaudi 54X, and 340 mice selected from the phenotypic extremes for susceptibility and resistance were genotyped for microsatellite markers on Chromosomes (Chrs) 5, 8, and 17. QTL originally detected in backcross and F₂ populations were confirmed on the three chromosomes within narrower genomic regions, by maximum likelihood and regression analyses. Each of the previously mapped QTL on Chrs 5 and 17 resolved into two linked QTLs. The distal and proximal QTLs on Chrs 5 and 17, respectively, map to the previously reported QTL.

Keywords

Quantitative Trait Locus Malaria Quantitative Trait Locus Analysis Resistance Quantitative Trait Locus Advanced Intercross Line

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Notes

Acknowledgments

The authors thank Bob King for the production of the F₁₁ AIL population, and Moses Ogugo, Timothy Njoroge, Thomas Njoroge, Joseph Nthale, John Wambugu, and Daniel Mwangi for excellent technical support from ILRI. We also thank Dr. John Rowlands for statistical support and comments on the writing of the manuscript. This work has benefitted from the financial support of the French Ministry of Research (PAL+Program) and IMCB-A, and from logistical and technical support from ILRI. Dr. Hernandez-Valladares was supported by the Spanish Agency for International Cooperation (AECI), IMCB-A and the International Centre of Insect Physiology and Ecology (ICIPE).

Supplementary information on the Mammalian Genome web site

Materials and methods

Generation of F₁₁ AIL of A × B6 mouse population

Thirty-five F₁ litters were obtained from the parental strains. The F₂ was generated by crossing F₁ males and females (50 non-related pairs mated). From F₂ up to F₁₁ generation, a minimum of 60 randomized non-related pairs from individually caged litters was selected to produce the next generation. Multiple litters from the 60 F₁₀ pairs produced the 1000 mice of the F₁₁ generation.

Phenotypic data analysis

Thirty-four F₁₁ infected mice died before the end of the experiment. The transformed parasitemia data (y) from the succumbed mice were also included in the analysis of the phenotype. Correlations of transformed parasitemia scores of the 1000 (A × B6) F₁₁ mice were positive among days 2 to 7 pi. The transformed traits among days 2 to 7 pi were generally negatively correlated with those among days 11 to 25 pi. To describe the variation among mice over the infection, it appeared appropriate just to use the data up to day 7 pi. Mean of the transformed parasitemia scores of the (A × B6) F₁₁ mice over the first week of infection varied among the groups and between females vs males. Twenty-nine of the 34 F₁₁ mice that died before the end of the experiment were male. This sex-parasitemia-related behavior has also been observed in a population of B10 mice (Wunderlich et al. 1988; Benten et al. 1991) and in recombinant congenic strains derived from A and B6 mice (Fortin et al. 2001).

ANOVA was done on transformed parasitemia scores for days 2 to 7 pi by fitting sex and group effects. From the ANOVA, group was significantly different at P < 0.001 for days 2 to 7 pi. Supplementary Table 3 shows the average variation among individuals within each group. Sex was significantly different at P < 0.001 from day 3 to 7 pi. Therefore, the data were adjusted for group and sex effects at each time point among days 2 to 7 pi. Least squares (LS) means obtained for each group were subtracted from the overall average and the deviation for each group subtracted from each individual transformed parasitemia score corresponding to the group. The same procedure was done to adjust for sex. Supplementary Table 4 shows the correlation matrix of the adjusted data for both effects at days 2, 3, 4, 5 and 7 pi.

Supplementary Table 3. Average variation among individuals within each of the five groups (A–E) of 200 F₁₁ mice infected with P.c. chabaudi 54X

Groups →Day pi ↓	A	B	C	D	E
2	0.05	0.18	0.39	0.64	0.49
3	0.04	0.11	0.32	0.59	0.32
4	0.08	0.30	0.66	1.09	0.59
5	0.07	0.25	0.58	0.76	0.49
7	0.05	0.26	0.58	0.73	0.79

Supplementary Table 4. Correlation matrix of the transformed parasitemia scores from the 1000 F₁₁ mouse population adjusted for group and sex effects over the first week of infection by P.c. chabaudi 54X

Day pi	2	3	4	5	7
2	1.00	0.51	0.49	0.44	0.30
3		1.00	0.73	0.66	0.47
4			1.00	0.75	0.56
5				1.00	0.70
7					1.00

Principal component analysis was performed with the adjusted data for days 2 to 7 pi to define two traits for QTL mapping. These adjusted data were also used to define daily traits D2-GS (D standing for day and GS for group and sex), D3-GS, D4-GS, D5-GS and D7-GS, which were used to study the time dependency of the putative QTL over a period of 7 days pi.

DNA genotyping

The following markers spanning the previously mapped QTL on Chr 5, 8, and 17 were used in the genotyping: D5Mit201, D5Mit10, D5Mit157, D5Mit240, D5Mit24, D5Mit188, D5Mit242, D5Mit139, D5Mit161, D5Mit215, D5Mit372, D5Mit165, D5Mit168, D5Mit99, D5Mit375, D5Mit223, D5Mit122, D5Mit169, D8Mit4, D8Mit64, D8Mit191, D8Mit339, D8Mit46, D8Mit205, D8Mit25, D8Mit73, D8Mit249, D8Mit195, D8Mit78, D8Mit41, D8Mit266, D8Mit241, D8Mit85, D8Mit211, D8Mit112, D17Mit198, D17Mit45, D17Mit81, D17Mit191, D17Mit175, D17Mit214, D17Mit83, D17MitTnfa, D17Mit148, D17Mit233, D17Mit176, D17Mit11, D17Mit52, D17Mit177, D17Mit68, D17Mit85, D17Mit117, D17Mit139, D17Mit7, D17Mit87, D17Mit119, D17Mit53, D17Mit184, D17Mit185, D17Mit93, D17Mit127, D17Mit72, and D17Mit155.

Sequence length polymorphisms were separated on denaturing 4.25% polyacrylamide gels on an ABI 377 DNA sequencer and were analyzed using the GENESCAN^TM (Version 3.1.2) and the GENOTYPER^TM (Version 2.0) software (Applied Biosystems).

Linkage analysis and QTL mapping

The most likely genetic map of each chromosome obtained with MapMaker software was compared with the marker order obtained from the recent B6 genome sequence assembly v3 (http://www.ensembl.org or http://genome.ucsc.edu or http://www.ncbi.nlm.nih.gov).

QTL Express software was also used to perform chromosome-wise permutation tests (1000 iterations per chromosome) to calculate the QTL significance thresholds. Also, composite interval mapping (CIM) analysis was performed, where each QTL was reanalyzed while fitting all other significant QTLs as co-factors. An iterative process was employed until estimates of QTL locations and effects converged.

The selective genotyping strategy is expected to create a substantial bias in estimates of QTL effects (Lander and Botstein 1989; Ronin et al. 1998). Therefore, ML interval mapping was performed with using the phenotypes of all 1000 F₁₁ mice to obtain unbiased estimates of QTL effects.

The QTL Cartographer software performs QTL mapping analysis for multiple QTL in multiple intervals for a single trait. For m putative QTLs, the model is:

Open image in new window

where y _i is the trait, μ is the mean and e _i the residual for individual i. The parameters α_r and δ_r are the additive and dominance effects of QTL r. The β’s are epistatic interactions and the superscripts on them are for the type of interaction (AA for additive by additive, AD for additive by dominance, DA for dominance by additive, and DD for dominance by dominance interactions). The x* and z*-are coded variables denoting the genotype of the putative QTL. The sum over r ≠ s means over all unordered QTL pairs taking the convention r > s.

The 95% CI of single QTL location was calculated by bootstrapping the data set before and after CIM analysis, as well as by the use of the formula designed for AIL: CI = [530/(Nv)]/(t/2), where N is the population size, v is the proportion of variance explained, and t is the generation of the AIL (Darvasi and Soller 1995, 1997). None of the software used in this study was capable of calculating the 95% CI for linked QTLs.

Discussion

Study of candidate genes in Chrs 5, 8 and 17

Cora1 (correlation in cytokine production 1), Ncf1 (NADPH oxidase subunit), and the membrane protein Act1 (actin related gene 1) could be suggested as potential candidate genes on Chr 5. It is interesting to remark that the relationship between the level of interleukin-4 and interleukin-10 depends on the genotype at the murine Cora1 locus (Kosarova et al. 1999), NADPH oxidases are the major source of reactive oxygen intermediates in macrophages (Sacks and Sher 2002), and PfEMP1 (P. falciparum erythrocyte membrane protein 1) is anchored to the actin-spectrin junction and knob-associated histidine-rich protein in the erythrocyte skeleton (Oh et al. 2000).

Membrane protein GypA (glycophorin A), Ea1 (erythrocyte antigen 1), IL15 (interleukin-15), Scyr (class-A scavenger receptor), and Hp (haptoglobin) have been previously suggested as candidate genes contained in char2 (Foote et al. 1997; Fortin et al. 1997). Our studies also support the three first candidates. However, Scyr locus is out of our narrower 95% CI. This could explain why functional analysis of the SR-AI and SR-AII proteins encoded by the A and B6 haplotypes failed to detect significant differences that might affect the susceptibility to P.c. chabaudi AS infection (Fortin et al. 2000). A recent study with SR-AI/II knockout mice and wild-type B6 mice has confirmed that these receptors are not critically required for the control of blood-stage malaria and for the phagocytosis of pRBCs and merozoites (Su et al. 2002).

The latter candidate Hp could not be also suggested by our study, since we did not scan on that chromosomal region. In addition, another membrane protein Cspg3 (chondroitin sulfate proteoglycan 3) and Il12rb1 (interleukin-12 receptor beta1) could also be presented as attractive candidate genes. Chondroitin sulfate proteoglycans have been described to mediate the adherence of P. falciparum-infected erythrocytes to the human placenta (Achur et al. 2000; Alkhalil et al. 2000). High interleukin-12 production, accompanied by an early and sustained up-regulation of both IL-12 receptor beta1 and beta2 mRNA levels in the spleen, as occurs in resistant B6 mice, appears to preferentially induce protective Th1 responses against blood-stage P.c. chabaudi AS malaria (Sam and Stevenson 1999).

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Authors and Affiliations

Maria Hernandez-Valladares
- 1
- 2
Jan Naessens
- 1
John P. Gibson
- 1
Anthony J. Musoke
- 1
Sonal Nagda
- 1
Pascal Rihet
- 3
Onesmo K. ole-MoiYoi
- 2
Fuad A. Iraqi
- 1
Email author

1.International Livestock Research Institute (ILRI)NairobiKenya
2.Institute of Molecular and Cell Biology-Africa (IMCB-A)NairobiKenya
3.Faculte de Pharmacie, Université de la MéditerranéeLaboratoire d’Immunogénétique et de Pharmacologie du PaludismeMarseille Cedex 5France

Confirmation and dissection of QTL controlling resistanceto malaria in mice

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