Patrik Wernhoff

Identification of epistasis through a partial advanced intercross reveals three arthritis loci within the Cia5 QTL in mice

Identification of genes controlling complex diseases has proven to be difficult; however, animal models may pave the way to determine how low penetrant genes interact to promote disease development. We have dissected the Cia5/Eae3 susceptibility locus on mouse chromosome 3 previously identified to control disease in experimental models of multiple sclerosis and rheumatoid arthritis. Congenic strains showed significant but small effects on severity of both diseases. To improve the penetrance, we have now used a new strategy that defines the genetic interactions. The QTL interacted with another locus on chromosome 15 and a partial advanced intercross breeding of the two congenic strains for eight generations accumulated enough statistical power to identify interactions with several loci on chromosome 15. Thereby, three separate loci within the original QTL could be identified; Cia5 affected the onset of arthritis by an additive interaction with Cia31 on chromosome 15, whereas the Cia21 and Cia22 affected severity during the chronic phase of the disease through an epistatic interaction with Cia32 on chromosome 15. The definition of genetic interactions was a prerequisite to dissect the Cia5 QTL and we suggest the partial advanced intercross strategy to be helpful also for dissecting other QTL controlling complex phenotypes.

Genetic Interactions in Eae2 Control Collagen-Induced Arthritis and the CD4+/CD8+ T Cell Ratio

The Eae2 locus on mouse chromosome 15 controls the development of experimental autoimmune encephalomyelitis (EAE); however, in this study we show that it also controls collagen-induced arthritis (CIA). To find the smallest disease-controlling locus/loci within Eae2, we have studied development of CIA in 676 mice from a partially advanced intercross. Eae2 congenic mice were bred with mice congenic for the Eae3/Cia5 locus on chromosome 3, previously shown to interact with Eae2. To create a large number of genetic recombinations within the congenic fragments, the offspring were intercrossed, and the eight subsequent generations were analyzed for CIA. We found that Eae2 consists of four Cia subloci (Cia26, Cia30, Cia31, and Cia32), of which two interacted with each other, conferring severe CIA. Genes within the other two loci independently interacted with genes in Eae3/Cia5. Investigation of the CD4/CD8 T cell ratio in mice from the partially advanced intercross shows that this trait is linked to one of the Eae2 subloci through interactions with Eae3/Cia5. Furthermore, the expression of CD86 on stimulated macrophages is linked to Eae2.

Cartilage‐specific autoimmunity in rheumatoid arthritis: characterization of a triple helical B cell epitope in the integrin‐binding‐domain of collagen type II

Cartilage‐specific proteins are considered potential autoantigens that could continuously fuel autoimmune responses directed to the joints in rheumatoid arthritis (RA). Using recombinant chimeric collagen type II we have identified one major type II collagen (CII) epitope (denoted U1) recognized by RA sera. The U1 epitope is a triple helical structure formed by 11 amino acids (triple helical position 494–504) and colocalizes with the recently described α1β1/α2β1 integrin binding site. It is a major epitope, found in 14/22 RA sera positive for antibodies to CII. One individual could be followed for a long time and the results showed that IgG antibodies specific for the U1 epitope were maintained along the chronic disease course but suppressed during periods of cyclosporin A and anti‐CD4 treatment. We also found that the U1 epitope was recognized in rats susceptible to collagen‐induced arthritis. A monoclonal autoantibody (mAb 126.30) was raised from DA rats, which bound the same epitope. The antibodies bound the cartilage in vivo showing that the epitope is exposed to the immune system for immune complex formation in the intact joint.

Fine Mapping of Collagen-Induced Arthritis Quantitative Trait Loci in an Advanced Intercross Line

The generation of advanced intercross lines (AIL) is a powerful approach for high-resolution fine mapping of quantitative trait loci (QTLs), because they accumulate much more recombination events compared with conventional F2 intercross and N2 backcross. However, the application of this approach is severely hampered by the requirements of excessive resources to maintain such crosses, i.e., in terms of animal care, space, and time. Therefore, in this study, we produced an AIL to fine map collagen-induced arthritis (CIA) QTLs using comparatively limited resources. We used only 308 (DBA/1 × FVB/N)F11/12 AIL mice to refine QTLs controlling the severity and onset of arthritis as well as the Ab response and T cell subset in CIA, namely Cia2, Cia27, and Trmq3. These QTLs were originally identified in (DBA/1 × FVB/N)F2 progeny. The confidence intervals of the three QTLs were refined from 40, 43, and 48 Mb to 12, 4.1, and 12 Mb, respectively. The data were complemented by the use of another QTL fine-mapping approach, haplotype analysis, to further refine Cia2 into a 2-Mb genomic region. To aid in the search for candidate genes for the QTLs, genome-wide expression profiling was performed to identify strain-specific differentially expressed genes within the confidence intervals. Of the 1396 strain-specific differentially expressed genes, 3, 3, and 12 genes were within the support intervals of the Cia2, Cia27, and Trmq3, respectively. In addition, this study revealed that Cia27 and Trmq3 controlling anti-CII IgG2a Ab and CD4:CD8 T cell ratio, respectively, also regulated CIA clinical phenotypes.

A comparative genetic analysis between collagen-induced arthritis and pristane-induced arthritis.

Abstract Objective To compare the genetic regulation of collagen‐induced arthritis (CIA) with that of pristane‐induced arthritis (PIA) in rats. Methods A genome‐wide linkage analysis of an (E3 × DA)DA backcross of rats with CIA (n = 364 male rats; the same strain combinations as previously used to determine the genetic control of PIA) was performed. The strongest loci in both CIA and PIA (i.e., Cia12/Pia4 and Cia13/Pia7) were isolated in congenic strains. Susceptibility in both congenic strains was tested in rats with CIA and in rats with PIA. Results We found a striking, although not complete, similarity of the arthritis‐controlling loci in CIA and in PIA, as well as the previously defined loci associated with cartilage destruction, antibody production, and the acute‐phase response. All major PIA quantitative trait loci (QTLs) identified in early severe arthritis were also strong regulators of CIA. The 2 strongest QTLs, Cia12/Pia4 on chromosome 12 and Cia13/Pia7 on chromosome 4, were also analyzed in congenic strains with DA or E3 as the background genome. Consistent with the results of linkage analysis, the congenic strain experiments showed that the chromosome 4 locus was more penetrant in CIA than in PIA, while the chromosome 12 locus almost completely dominated the control of PIA severity. Conclusion The underlying genetic control of CIA was found to have many, but not all, pathogenic mechanisms in common with PIA, despite the use of a cartilage‐specific antigen (type II collagen) to induce CIA but not PIA.

Identification of genetic regions of importance for reproductive performance in female mice.

Both environmental and genetic factors can dramatically affect reproductive performance in mice. In this study we have focused on the identification of genetic regions, quantitative trait loci (QTL), which affect the breeding capacity of female mice. We have identified polymorphic microsatellite markers for the mouse strains used and performed a genomewide scan on 237 females from a gene-segregating backcross between a high breeder and a relatively poor breeder. The high-breeder mouse strain we used is the inbred NFR/N mouse (MHC haplotype H-2q), which has extraordinary good breeding properties. The moderate breeder chosen for F1 and N2 progeny was B10.Q, which is a genetically well-characterized MHC-congenic mouse of the H-2q haplotype. Each of the 237 females of the N2 generation was allowed to mate twice with MHC-congenic B10.RIII (H-2r) males and twice with B10.Q males. A predetermined number of phenotypes related to reproductive performance were recorded, and these included litter size, neonatal growth, and pregnancy rate. Loci controlling litter size were detected on chromosomes 1 (Fecq3) and 9 (Fecq4). The neonatal growth phenotype was affected by Fecq3 and a locus on chromosome 9 (Neogq1). On chromosome 11 two loci affecting the pregnancy rate (Pregq1 and Pregq2) were identified. Furthermore, on chromosomes 13 and 17 we found loci (Pregq3 and Pregq4) influencing the outcome of allogeneic pregnancy (allogeneic by means of MHC disparity between mother and fetuses). A locus on chromosome 1 affecting maternal body weight was also identified and has been denoted Bwq7. It is well known that reproductive performance is polygenically controlled, and the identification of the major loci in this complex process opens the possibility of investigating the natural genetic control of reproduction.

Quantitative Proteomic Analysis Reveals Metabolic Alterations, Calcium Dysregulation, and Increased Expression of Extracellular Matrix Proteins in Laminin α2 Chain–deficient Muscle

Congenital muscular dystrophy with laminin α2 chain deficiency (MDC1A) is one of the most severe forms of muscular disease and is characterized by severe muscle weakness and delayed motor milestones. The genetic basis of MDC1A is well known, yet the secondary mechanisms ultimately leading to muscle degeneration and subsequent connective tissue infiltration are not fully understood. In order to obtain new insights into the molecular mechanisms underlying MDC1A, we performed a comparative proteomic analysis of affected muscles (diaphragm and gastrocnemius) from laminin α2 chain–deficient dy3K/dy3K mice, using multidimensional protein identification technology combined with tandem mass tags. Out of the approximately 700 identified proteins, 113 and 101 proteins, respectively, were differentially expressed in the diseased gastrocnemius and diaphragm muscles compared with normal muscles. A large portion of these proteins are involved in different metabolic processes, bind calcium, or are expressed in the extracellular matrix. Our findings suggest that metabolic alterations and calcium dysregulation could be novel mechanisms that underlie MDC1A and might be targets that should be explored for therapy. Also, detailed knowledge of the composition of fibrotic tissue, rich in extracellular matrix proteins, in laminin α2 chain–deficient muscle might help in the design of future anti-fibrotic treatments. All MS data have been deposited in the ProteomeXchange with identifier PXD000978 (http://proteomecentral.proteomexchange.org/dataset/PXD000978).

Two-loci interaction confirms arthritis-regulating quantitative trait locus on rat chromosome 6.

A form of genetic interaction, or epistasis, occurs when one gene interferes with the phenotypic effect of another nonallelic gene. In pristane-induced arthritis (PIA) in rats we have previously identified Pia3, on chromosome 6, to be a locus that regulates onset of disease. In a single congenic strain containing Pia3 on the arthritis-susceptible DA background, DA.Pia3, no difference in onset of disease or early disease severity could be detected. After a two-loci interaction analysis of (E3 x DA)F2 intercross data, Pia3 was found to interact with Pia4 (chromosome 12). Subsequently, the DA.Pia3 congenic strain was combined with the DA.Pia4 congenic strain so that an effect of Pia3 could be observed. The effect of heterozygosity in Pia4 results in lower severity and thus in combination with Pia3 made it possible to observe that Pia3 alleles from the arthritis-resistant E3 strain rendered more severe arthritis into the otherwise 100% susceptible DA strain. As the introduction of Pia4 heterozygosity results in a lower level of arthritis severity we regard this as an additive interaction with a severity threshold-lowering effect.

Mutation spectrum in South American Lynch syndrome families

BackgroundGenetic counselling and testing for Lynch syndrome have recently been introduced in several South American countries, though yet not available in the public health care system.MethodsWe compiled data from publications and hereditary cancer registries to characterize the Lynch syndrome mutation spectrum in South America. In total, data from 267 families that fulfilled the Amsterdam criteria and/or the Bethesda guidelines from Argentina, Brazil, Chile, Colombia and Uruguay were included.ResultsDisease-predisposing mutations were identified in 37% of the families and affected MLH1 in 60% and MSH2 in 40%. Half of the mutations have not previously been reported and potential founder effects were identified in Brazil and in Colombia.ConclusionThe South American Lynch syndrome mutation spectrum includes multiple new mutations, identifies potential founder effects and is useful for future development of genetic testing in this continent.

Using an advanced intercross line to identify quantitative trait loci controlling immune response during collagen-induced arthritis

Advanced intercross line (AIL) is a powerful tool for high-resolution mapping of quantitative trait loci (QTLs). Several AILs have been generated to refine QTLs since the method was proposed about a decade ago. However, no AIL has been used for identifying novel QTLs. Here we used an AIL to test this possibility. We genotyped 308 (DBA/1 × FVB/N) F11/12 AIL mice with 109 informative markers covering four chromosomes, with an average intermarker distance of 5.5 Mb. Several normally distributed quantitative traits involved in the immune response during the course of collagen-induced arthritis (CIA), such as anti-collagen II antibodies, T-cell subset proportions and reactive oxygen species (ROS) production were taken as phenotypes. Four QTLs, namely Ciaa1, Lctlp1, Lctlp2 and Rosq1, controlling anti-collagen II IgG2a levels, lymph nodes CD8+ T cell proportion and ROS production were identified with support intervals of 15, 14, 8 and 8 Mb, respectively. Alleles of Lctlp1 and Lctlp2 suppressing CD8+ T cell proportion as well as the Rosq1 allele enhancing ROS production were correlated with higher CIA severity scores. Taken together, we successfully used an AIL to identify novel QTLs controlling immune responses during CIA with relatively small support intervals.