Physiological Genomics

Wfs1 gene deletion causes growth retardation in mice and interferes with the growth hormone pathway

S. Kõks, U. Soomets, J. L. Paya-Cano, C. Fernandes, H. Luuk, M. Plaas, A. Terasmaa, V. Tillmann, K. Noormets, E. Vasar, L. C. Schalkwyk


The aim of present study was to describe changes in gene expression in the temporal lobe of mice induced by deletion of the Wfs1 gene. Temporal lobes samples were analyzed using Affymetrix Mouse Genome 420 2 GeneChips and expression profiles were functionally annotated with GSEA and Ingenuity Pathway Analysis. We found that Wfs1 mutant mice are significantly smaller (20.9 ± 1.6 g) than their wild-type counterparts (31.0 ± 0.6 g, P < 0.0001). This difference existed in 129S6 and C57B6 backgrounds. Interestingly, microarray analysis identified upregulation of growth hormone (GH) transcripts and functional analysis revealed activation of GH pathways. In line with microarray data, the level of IGF-1 in the plasma of Wfs1 mutant mice was significantly increased (P < 0.05). Thus, Wfs1 deletion induces growth retardation, whereas the GH pathway is activated. To test the interaction between the Wfs1 deletion and genomic background, mutant mice were backcrossed to two different genetic backgrounds. In line with previous studies, an interaction between a gene knockout and genetic background was found in gene expression profiles in the congenic region. However, genetic background did not alter the effect of the Wfs1 mutation on either body weight or GH pathway activation. Further studies are needed to describe biochemical and molecular changes of the growth hormone axis as well as in other hormones to clarify their role in growth retardation in the Wfs1 mutant mice.

  • wolframin protein
  • knockout mice
  • oligonucleotide microarrays
  • congenic footprint

wolfram syndrome (WS, MIM222300) is an autosomal recessive disorder most frequently characterized by diabetes insipidus, diabetes mellitus, optic atrophy, and deafness (26), first described by Wolfram and Wagener (31) as a juvenile diabetes mellitus with optic atrophy. Only insulin-dependent diabetes mellitus and progressive optic atrophy are necessary to confirm WS, and both of these syndromes may be present in childhood or adolescence (1). In addition to these diagnostic syndromes, most WS patients have highly variable clinical symptoms including several neurological abnormalities such as nystagmus, mental retardation, and seizures (1). Moreover, several studies have shown diffuse and widespread atrophy in the brain (19, 22). Central respiratory failure due to brainstem atrophy has described as a common cause of death, indicating the significance of neurodegeneration in WS (1, 22). In addition to the neurological manifestations, psychiatric illnesses have often found in WS patients. The most prominent psychiatric manifestations in WS homozygous individuals are depression, violent or assaultive behavior, and organic brain syndromes (28).

WS is caused by mutations in the WFS1 gene, but the molecular function of WFS1 protein is not fully known. It is membrane bound (9–11 transmembrane segments) and located in endoplasmic reticulum (ER) (8). There is evidence that this protein play a role in the regulation of ER Ca2+ levels (18, 29). WFS1 is involved in the unfolded protein response (UPR), which is an adaptive response that counteracts ER stress (7). ER stress is defined as an imbalance between the actual folding capacity of the ER and the demand (16). Induction of ER stress with thapsigargin and tunicamucin causes significant upregulation of WFS1 expression (7). WFS1 seems to act as a survival factor: it is upregulated when ER stress is present and its deficiency leads to more pronounced apoptosis (11).

Mutant mice lacking the Wfs1 gene have been generated in three independent laboratories (11, 17, 20). The aim of the present study is to analyze gene expression profiles in the temporal lobe of the most recent Wfs1 mutant mice, which were generated in our lab. The temporal lobe, containing amygdala and parts of the extended amygdala, was chosen for gene expression profiling as it is the region with the highest levels of Wfs1 expression and, therefore, the most likely location for expression changes induced by the mutation (2). Moreover, the temporal lobe is part of the limbic system that plays a role in the regulation of emotional behavior, and this regulation is disturbed in WS patients. Namely, WS patients have psychiatric abnormalities with heterozygous carriers having an increased risk for mood disorders. Moreover, WS patients have been shown to have severe gliosis in the temporal lobes. Therefore, the temporal lobe seems to be a functionally relevant target for gene expression profiling in a model of WS. A recent study measured hippocampal gene expression profiles in Wfs1-deficient mice (12). However, in this paper most dramatic changes in gene expression were found to be associated with a congenic footprint effect of the gene knockout as we have described elsewhere (21). Therefore, the aim of our study was twofold: to perform gene expression analysis of Wfs1-deficient mice so that we can avoid congenic footprint (or flanking allele) effect and to give a preliminary physiological description of the phenotype of the Wfs1 mutant mice.




Mice were housed under standard laboratory conditions on a 12-h light/dark cycle (lights on at 07:00 AM) with free access to food and water. All animal experiments in this study were performed in accordance with the European Communities Directive (86/609/EEC) and permit (no. 39, October 7, 2005) from the Estonian National Board of Animal Experiments, which reviewed and approved all procedures on animals.

Generation of Wfs1-targeted chimeras.

We generated a targeting construct to replace most of the coding region of Wfs1 gene (Fig. 1). In brief, an 8.8 kb BamHI restriction fragment from the PAC clone 391-J24 (RPCI21 library, MRC UK HGMP Resource Centre, UK) was subcloned into pGem11 cloning plasmid (Promega, Madison, WI). We replaced a 3.7-kb NcoI fragment with an in-frame NLSLacZNeo cassette. This resulted in the deletion of amino acids 360–890 in the Wfs1 protein and fusion between Wfs1 residues 1–360 and LacZ. The construct was inserted into W4/129S6 embryonic stem (ES) cells (Taconic, Hudson, NY) in the Biocenter of the University of Oulu ( Colonies resistant to G418 and gancyclovir were screened for homologous recombination using the recombination-specific polymerase chain reaction (PCR) primer pair NeoR1 5′GACCGCTATCAGGACA TAGCG3′ and Wfs1_WTR1 5′AGGACTCAGGTTCTGCCTCA3′. We sequenced the PCR product to verify homologous recombination, and ES clone 8A2 was injected into C57BL/6 blastocysts.

Fig. 1.

Targeting vector and the strategy for homologous recombination to generate the mice used in our study.

Breeding scheme for Wfs1 mutant mice.

The aim of the breeding program was to examine the effect of genetic background on the mutation (21). To achieve this, we generated mutant mice using two different genetic backgrounds: heterogenic to the ES cells (C57BL/6/Bkl) and identical to the ES cells (129S6/SvEvTac) (Fig. 2). To produce two different founder animals, male chimeras were mated with C57BL/6 and with 129S6 female mice. F2 generation mice homozygous for Wfs1 mutation were obtained by mating heterozygous founder animals. Mice were genotyped by multiplex PCR for both alleles using the following primers: WfsKO_wtF2 5′TTGGCTTGTATTTGTCGGCC3′, NeoR1 5′GACCGCTATCAG GACATAGCG3′ and WfsKO_uniR2 5′CCCATCCTGCTCTCTGAACC3′. Homozygous knockout (KO) and wild-type (WT) mice on the different genetic backgrounds were used in GeneChip analysis. These mice were designated as follows: 129WFSKO (mutation in 129S6 background), 129WT (WT, 129S6), C57WFSKO (mutation in C57BL/6 background), and C57WT (WT, C57BL/6).

Fig. 2.

Depiction of the breeding strategy of the mice we used in present study. Four groups of mice (129WFSKO, 129WT, C57WT, C57WFSKO) were used.

Confirmation of the footprint effect.

To verify the footprint effect, or the effect of mixing two different background strains, on gene expression levels, we generated a hybrid mouse strain using C57BL/6Bkl and 129S6/SvEvTac. We genotyped two single nucleotide polymorphisms (SNPs) flanking the Wfs1 locus (rs29529523 and rs33457565) by PCR and sequencing and generated a congenic mouse strain with a Wfs1 locus derived from the 129S6 strain in a background of C57BL/6. In this mouse strain we performed gene expression analysis with QRT-PCR and measured the mRNA levels of genes selected from the microarray study with the most significant differential expression level. DNA sequences were analyzed and aligned using 4Peaks software (Mekentosj) and CLC Main Workbench (CLC Bio).

Tissue Collection and RNA Preparation

Mice were killed by cervical dislocation, brains were removed, and the temporal lobe was dissected and snap-frozen in liquid nitrogen. Total RNA was extracted using the guanidium thiocyanate method with TRIzol reagent (Invitrogen Life Technologies, UK) (4). For array hybridization 10 male mice were used in each group (129WT, 129WFSKO, C57WT, C57WFSKO). The mean ages of mice in the groups were 107, 110, 118, and 121 days, respectively. For confirmation of the footprint effect, we used 9 mice with a Wfs1 locus originating from 129S6 on a C57BL/6 background and 12 C57BL/6 mice out of 45 mice from the breeding. The remaining mice from this breeding were heterozygous for the Wfs1 locus. Tissue samples were collected as described above.

Microarray hybridization and analysis.

Double-stranded cDNA was synthesized from 1.2 μg of total RNA by reverse transcription using T7-Oligo(dT) promoter primer and then biotin-labeled cRNA was made from the cDNA template by in vitro transcription (One-Cycle Target Labeling kit; Affymetrix, Santa Clara, CA). cRNA was fragmented and hybridized to the Mouse 430 2.0 Gene Expression Array (Affymetrix). The arrays were subsequently washed, stained with phycoerythrin streptavidin, and scanned according to standard Affymetrix protocol. Images were processed using the Affymetrix Microarray Suite 5.0, the processed data (cel files) were further analyzed using Bioconductor software based on R programming language.

Statistical analysis.

The normalized, background subtracted, and modeled expression (robust microarray analysis) data were further analyzed using linear modeling in the statistical software package R ( (10) without further transformation. There were 10 animals for each of genotype, and ANOVA was used to detect significant mean differences between the KO and WT strains. To address multiple testing q value package was used (25). For the analysis we applied two different R commands. To evaluate the effect of background strain over the mutation and their dependence on the age of animals we used: “summary (aov(signal∼genotype*strain))”. As we saw a very high influence of the background strain, we decided to perform further analysis on the 129S6 background mice only (129WFSKO vs. 129WT). Welch's t-test was used to evaluate the effect of mutation in 129S6 background, as well as for analysis of the data of ELISA tests and QRT-PCR.

Functional annotation of differentially expressed genes.

For functional analysis and interpretation of expression data we used Gene Set Enrichment Analysis (GSEA) tool (27). This is a recently developed tool to interpret genome-wide expression profiles. GSEA determines whether the members of the previously defined set of genes (genes encoding products in a metabolic pathway, etc.) are randomly distributed throughout the ranked list of genes or primarily found at the top or bottom of the list. The sets of genes that are related to the phenotype should show a nonrandom distribution. Based on the distribution of the genes from the lists, an enrichment score is calculated, for estimation of significance level, phenotype-based permutation test procedure is used (27). Again, nominal P values were adjusted for multiple hypotheses testing by calculating false discovery rate (FDR).

Pathway and network analysis.

To define the functional networks of the differentially expressed genes, data were analyzed through the use of Ingenuity Pathway Analysis (IPA, Ingenuity Systems, A data set containing Affymetrix probe set identifiers and corresponding expression values (ratios >1.2 and <0.8) was uploaded into application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base to generate the list of focus genes. Networks of these focus genes were then algorithmically generated based on their connectivity. IPA calculates a significance score for each network. The score is generated using a P value calculation and is displayed as the negative logarithm of that P value. This score indicates the likelihood that the assembly of a set of focus genes in a network could be explained by random chance alone. A score of 2 indicates that there is a 1 in 100 chance that the focus genes are together in a network due to random chance.

A network is a graphical representation of the molecular relationships between genes/gene products. Genes or gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature or from canonical information stored in the Ingenuity Pathways Knowledge Base. Human, mouse, and rat orthologs of a gene are stored as separate objects in the Ingenuity Pathways Knowledge Base but are represented as a single node in the network. The intensity of the node color indicates the degree of up (red)- or down (green)-regulation. Nodes are displayed using various shapes that represent the functional class of the gene product. Edges are displayed with various labels that describe the nature of the relationship between the nodes (e.g., P for phosphorylation, T for transcription).

Quantitative real-time PCR analysis.

The RNA samples from the congenic mice were analyzed by quantitative real-time PCR. Selection of genes was based on the ANOVA (contained mutant mice with different backgrounds) of the microarray data. The genes with significantly different expression that are located in the same locus of Wfs1 were chosen for RT-PCR analysis. These genes were Jakmip1, Janus kinase and microtubule interacting protein 1 (assay ID Mm00617809_m1); Cacna2d1, calcium channel, voltage-dependent, alpha2/delta subunit 1 (assay ID Mm00486607_m1); Ppp1cb, protein phosphatase 1, catalytic subunit, beta isoform (assay ID Mm00554690_m1); Abhd1, abhydrolase domain containing 1 (assay ID). Mouse Hprt1 was used as a housekeeping control. Raw PCR data were processed with the Q-Gene application allowing data normalization based on PCR efficiency, suitable for duplex RT-PCR reactions (23). Sample comparisons were made using Welch's t-test.

IGF-1 plasma measurements with ELISA.

To measure the plasma levels of IGF-1 we used Quantikine Mouse IGF-1 Immunoassay (R&D Systems). In brief, mice were decapitated, blood was collected in heparin-coated tubes and centrifuged for 20 min at 2,000 g, plasma was removed within 30 min of collection, and plasma samples were stored at −80°C until analysis. Plasma samples were diluted 500-fold into Calibration Diluent RD5–38. All samples and the standard curve were processed in duplicate. Optical density was measured at 450 nm with correction at 540 nm within 30 min after addition of the stop solution. Duplicate readings for each standard, control, and sample were averaged, and mean zero standard optical density was subtracted. A standard curve was created by four-parameter logistic curve-fit. IGF-1 differences between genotypes were statistically analyzed by means of Welch's t-test.


Wfs1-deficient Mice Have Growth Retardation

We generated another mouse model for the WS, and these mice develop glucose intolerance (data not shown). In addition to glucose intolerance, we found another remarkable phenotype of reduced body weight in this mutant strain. Homozygous Wfs1 mutant mice (KO) were much smaller compared with age-matched WT mice (Fig. 3), This difference was evident in mutant mice in both genetic backgrounds and was independent of the sex of the mice. Moreover, mice heterozygous for Wfs1 mutation had body weight values between WT and KO genotypes. To illustrate the growth rates of the mice we performed growth curve analysis (Fig. 4).

Fig. 3.

Body weight differences between homozygous Wfs1 mutant mice in 129S6 background (KO) compared with wild-type (WT) mice (***P < 0.001).

Fig. 4.

Growth curves of the mice with Wfs1 mutation in 129S6 background (KO) and their WT counterparts.

Gene Expression Profiling

Analysis of the gene expression data was divided into different stages. First we applied multifactorial ANOVA to each probe set in the sample containing all 40 microarrays. Three microarrays were removed from further analysis due to a genotyping error (Wfs1 expression level was intermediate between the WT and KO mice as these were subsequently found to be heterozygous for the Wfs1 deletion). As most of the variance underlying the statistical significance came from the parental (129S6) flanking alleles inside the foreign (C57BL/6) genomic background, we then analyzed mice with the Wfs1 mutation in the homogenous 129S6 background (19 mice) only.

At first we analyzed all 37 microarrays and we found several genes to be differently expressed in mutant (KO) vs. WT mice (see Table 1). Eleven genes were statistically significant even after the correction for multiple testing (FDR q-value <0.05). All of these genes are located on chromosome 5, close to Wfs1. This finding most probably illustrates the flanking allele effect, where the genes surrounding the targeted locus in the mutant mice are in a foreign background and the gene expression difference is not caused by a direct functional interaction with the mutated gene. We therefore decided to look for transcriptional changes only in 129S6 background mice to get a clearer view of the effects of the Wfs1 knockout itself.

View this table:
Table 1.

Gene expression profile analysis of the mutant mice in two different genetic backgrounds revealed very strong genotype effect

Next we analyzed the effects of the Wfs1 mutation in a homogenous 129S6 background using a t-test to compare WT and KO. 160 genes were found to have differential expression at a P value <0.001. However, following FDR correction only the expression change in the Wfs1 gene remained statistically significant (q-value = 5.01E-08); see Table 2. To get information on the magnitude of changes, we compared with signal intensities from different genotypes. Based on fold change, lists of the 20 most up- and downregulated genes are shown in Tables 3 and 4. The most upregulated genes were growth hormone [Gh (1.87)], pro-opiomelanocortin-alpha [Pomc (1.62)], Golgi-specific brefeldin A-resistance factor[Gbf1 (1.51)], and arylacetamide deacetylase-like 1 [Aadacl1 (1.50)]. The most remarkably downregulated genes were Wfs1 (0.01) and transthyretin (Ttr, different probe sets, 0.28–0.46).

View this table:
Table 2.

Gene expression profiling of the mice lacking the Wfs1 gene in the isogenic 129S6 background abolishes the effect of a flanking allele

View this table:
Table 3.

Fold change (S6KO/S6WT) analysis of differential gene expression suggests upregulation of some genes related to peptides and ER stress

View this table:
Table 4.

Fold change (S6KO/S6WT) analysis of differential gene expression suggests downregulation of some genes related to neurodegeneration

Functional Analysis of Gene Expression Data

To find the genetic pathways activated after the deletion of Wfs1 gene in the temporal lobe of mice, we applied GSEA (Table 5). The most significantly (P < 0.0001, q < 0.0001) enriched gene set was MARCINIAK-CHOP-DIFF (this is the gene set characteristic to the deletion of CHOP gene). This pathway is upregulated during ER stress, and its activation reflects ER stress in the brain of Wfs1 mutant mice. The second pathway is NING_COPD_UP (P < 0.0001, q < 0.001), these are genes upregulated in the lung tissue of smokers with chronic obstructive pulmonary disease. Next two pathways are similar: GH_EXOGENOUS_ANY_DN (P < 0.0001, q = 0.026) and GH_EXOGENOUS_ LATE_DN (P < 0.0001, q = 0.04). These pathways contain genes that are downregulated after exogenous human growth hormone treatment of mammary carcinoma cells. Another gene set we found significantly enriched in our sample is the KERATIOCYTEPATHWAY (P < 0.0001, q = 0.034). This pathway contains genes related to the differentiation of keratinocytes. Further gene sets had low nominal P values and FDR q-values >0.05, so we considered these nonsignificant. To support the GSEA results, IPA was also performed. The most significant enrichment scores were found for the three pathways (Table 6): nervous system development (score 48, focus 26), metabolic disease (endocrine system disorder, score 30, focus 22), and cellular assembly and organization (score 29, focus 18). Figure 5 illustrates the metabolic disease pathway and the relationship of all the mapped genes.

Fig. 5.

Functional analysis of the gene expression profile in the mice with Wfs1 mutation in 129S6 background indicated changes in the metabolic disease pathway. This figure illustrates the relations and the changes of the different genes in this pathway.

View this table:
Table 5.

Results from the Gene Set Enrichment Analysis of the gene expression data

View this table:
Table 6.

IPA results

Plasma Levels of IGF-1

To confirm the upregulation of growth hormone, we measured the plasma levels of IGF-1 in both genotypes of mice. Interestingly, mutant mice had significantly higher IGF-1 plasma levels compared with WT animals (Fig. 6, 36.2 ± 13.7 vs. 6.3 ± 3.6 ng/ml, respectively). This difference was statistically significant (t = 2.276, df = 11, P < 0.05) and indicates an endogenously activated growth hormone pathway in Wfs1 mutant mice.

Fig. 6.

Plasma IGF-1 levels in the Wfs1 mutant mice in 129S6 background. *P < 0.05.

Analysis of Selected Genes in the Mice With Mixed Genomic Background Without Wfs1 Mutation

To verify the existence of a congenic footprint effect, the effect of foreign genomic context on the gene expression level without any mutation in this locus, we crossed S6 and B6 mice and then intercrossed the heterozygous litters once. We analyzed genotype of these mice with two SNPs (one from each side of Wfs1 gene) and obtained animals with homozygous S6 (9) or B6 (12) loci in this region. We were able to see very clear and significant dependence of the gene expression level from the background genomic context (Fig. 7). Namely, Abhd1 (P value 1.43E-10), Cacna2d1 (P value 0.01), and Ppp1cb (P value 0.001) were significantly overexpressed in the mice with S6 locus inside B6 background. For Jakmip1 we only detected a nonsignificant trend (P value 0.1) toward downregulation of the gene inside the B6 context. As Ttr indicated very strong downregulation in Wfs1 mutant mice and this gene is outside the Wfs1 locus (in chromosome 18), we analyzed expression of this gene in these mice to verify the lack of flanking allele effect. Indeed, the difference was not even close to the statistical significance.

Fig. 7.

The effect of genetic background on the gene expression levels without the Wfs1 mutation, as determined by RT-PCR. The expression level of genes located near to the Wfs1 locus (Abhd1, A; Cacna2d1, B; Ppp1cb, C; Jakmip1, D) is dependent on the origin of Wfs1 locus. (Strain b6, C57BL/6; s6, 129S6 genetic origin of the Wfs1 flanking region).


This study is a description of the mutant mice lacking the Wfs1 protein. The aim of our study was to give a general characterization of the phenotype of these mice and to combine the data with gene expression analysis. These mice are models for WS, and they express glucose intolerance (data not shown). In addition to glucose intolerance, we found smaller body weight in this mutant strain. Although smaller weight or short stature is not usually described in WS patients, there are some data showing short stature is quite common in patients with WS (24).

One of the main findings of the present study is the growth retardation of Wfs1 mutant mice. At the age we studied the mice (110 days), the weight difference was as high as 30%. This is not the first study describing the phenotype for a Wfs1 mutant. There are two other Wfs1 mouse mutant mice (11, 20). One of them is a conditional mutant with specific targeting of the Wfs1 gene in pancreatic islet beta cells and describes reduced body weight at the age of 24 wk (20). This is at a later age than in our study, but differences in the severity of phenotype of mice could be explained by tissue specificity. The other mutant mouse was generated in a similar way to ours: a neomycin-resistance cassette was inserted into coding region of the gene (11). Only the precise targeted region and the strain background are different. Ishihara et al. (11) targeted exon 2 of the gene, we targeted exon 8; they inserted a Neo-cassette without removing any coding sequence (Neo was inserted into Sma1 site), whereas we replaced 3,700 base pairs (NcoI/NcoI fragment) of coding sequence in exon 8. This replacement removed amino acids 360–890 from the Wfs1 protein. In our case we have a strain expressing truncated Wfs1 protein 1–359 aa fused to the LacZ enzyme. LacZ itself can induce nonspecific changes that we can't exclude at the moment, but to our knowledge no growth effects of LacZ have been reported. We also weighted heterozygous animals and found their body weight to be between the weight of the WT and KO mice.

There is one additional detail that supports the idea that different approaches used in different mutants could actually generate different phenotypes. Ishihara et al. (11) found that their mice do express truncated Wfs1 mRNA. They tested that there is no NH2-terminal Wfs1 protein, but they did not exclude the existence of COOH-terminal portion of the protein. We tested our mice and found that they express truncated NH2-terminal part of Wfs1 protein (1–329 aa), but it is fused to LacZ protein and cannot function properly. However, in both mutant strains diabetes as the main feature of WS exists. Potentially, the differences in the phenotypes may arise from the slightly different molecular approach used.

Gene expression analysis revealed growth hormone transcripts were upregulated in mutant mice. This difference was evident in case of all the three probe sets (1437522_at, 1460613_at, 1456595_at) hybridizing with the growth hormone mRNA. Moreover, functional annotation and analysis with the GSEA tool also indicated activation of the growth hormone pathway. This pathway analysis was also performed with another tool (IPA). The results was almost the same: Wfs1 mutant mice have an activated pathway characteristic for metabolic diseases. To get more direct evidence, we measured the plasma concentration of IGF-1 hormone. Indeed, Wfs1 mutant mice have a significantly higher plasma concentration of IGF-I. However, as Wfs1 mutant mice were smaller but had higher plasma levels of IGF-1, detailed analysis of the growth hormone axis (growth hormone stimulation tests, levels of IGF binding proteins) as well as other hormones (insulin, leptin), are required to determine the nature of the disturbances in the growth hormone pathway and growth in Wfs1 mutants.

We also need to stress the limitation of the gene expression profiling in the response to the disruption of the Wfs1 gene. Wfs1 participates in the secretory pathway and functions in the ER stress response. Therefore, it is very likely that deletion of this gene will primarily affect posttranscriptional processes. Therefore, changes in the transcriptional profile are mostly induced by indirect consequences of Wfs1 disruption.

Another finding of our study is related to the methodology of the analysis of mutant mice. Namely, we tested the effect of mutation in two different genomic backgrounds and found a congenic footprint similar to that described in two recent papers (21, 32). In general, the idea behind this finding is that genetic background of mice has a much stronger effect on gene expression profile than the mutation itself. In our study, we tested this effect on the gene expression profile in F2 mice generated in two different backgrounds: one is parentally isogenic 129S6, whereas the second is conventional backcrossing into the C57BL/6 background. As a result (Table 1), all most significant differences derived from chromosome 5 and the genes located closely around the Wfs1 gene. The important point is that this flanking effect will not disappear even after extensive backcrossing (21). This fact is often ignored in studies of KO mice. A recent study on another Wfs1 mutant mouse is an example of this problem (12). In that paper, authors used Wfs1 mutant mice backcrossed for eight generations to analyze hippocampal gene expression profiles using the same microarrays (Mouse Genome 430 2.0). The authors find many significant differences in gene expression from the footprint region (region around the Wfs1 gene). Therefore, this is additional support for the existence of a footprint effect at the level of the transcriptome. Interestingly, the genes with differential expression values and outside the Wfs1 locus are similar in our study and the study of Kato et al. (12). Namely, Kato et al. (12) found differential expression for the Rho GTPase, we found rhophilin; they found histocompatibility 2, class II locus Mb2, we found histocompatibility 2, M region locus 3. Thus, these two studies describe similar “footprint”-induced and true-positive differences which indicates quite good correlation between these studies.

To further explore the footprint issue, we generated mice with mixed genomic background and checked the origin of the Wfs1 locus to verify whether the origin of Wfs1 locus from 129S6 strain is a strong enough factor to generate these differences in gene expression level. For this purpose we crossed two parental lines once and then intercrossed the offspring and genotyped offspring using two SNPs surrounding Wfs1 locus. We got 12 mice where this locus was from C57BL/6 and 9 mice with Wfs1 locus of 129S6 origin. Gene expression analysis with quantitative real-time PCR indicated highly significant differences in the gene expression levels of selected genes. This experiment is quite illustrative of the real situation; having KO mice and analyzing the gene expression with quantitative real-time PCR in the locus of targeted allele, we find a very high probability that differential expression will be seen in genes located in the footprint. This issue should be kept in mind when working with mutant mice.

In addition to growth hormone, we found the Pomc gene transcript upregulated in the Wfs1 mutant mice. This could indicate possible functional deficiency in the opioid system. Another interesting finding is the upregulation of Golgi-specific brefeldin A-resistance factor 1 or Gbf1. Brefeldin A is a fungal fatty acid metabolite; it is an antibiotic that interferes with anterograde protein transport from the ER into the Golgi apparatus (14). It inhibits Golgi transport and induces accumulation of proteins in the ER and UPR. Gbf1 is a guanine nucleotide-exchange factor, and its overexpression allows transfected cells to live in the presence of brefeldin A (6). Deletion of Gbf1 induces UPR (5). Thus, deletion of the Wfs1 gene induces upregulation of the gene that is known to enhance the survival of cells under stressful conditions. This finding indicates a potential link between Wfs1 and brefeldin A sensitivity and guanine nucleotide-exchange factors. However, this link needs further analysis.

Analysis of the list of genes downregulated in the Wfs1 mutant mice gives some support to the link between degeneration and Wfs1. Namely, a gene with 0.69-fold downregulation, heat shock protein 8 (also known as Hsc70), is a constitutively expressed heat shock protein. This protein binds to nascent polypeptides to facilitate their correct folding (15). Widespread atrophy in WS patients suggests this finding to be true. Namely, there is a report describing accumulation of Hsc70 glial cytoplasmic inclusions in patients with multiple system atrophy (13). In addition to the most suppressed gene (Wfs1) we found transthyretin (Ttr) to be 0.3 times downregulated in mutant mice. Ttr is a major amyloid fibril protein in certain systemic forms of amyloidosis (30). Interestingly, its expression occurs also in endocrine cells in the islets of Langerhans, and its mutations are related to familial forms of neurodegenerative disorders (9, 30). Moreover, Ttr has been shown to be neuroprotective, and it can protect from amyloidogenic toxicity (3). Therefore, even if we cannot exclude the contamination possibility, Ttr seems to potentially interact with Wfs1.

In conclusion, we described growth retardation of Wfs1 mutant mice and performed detailed gene expression analysis. This confirms extensive degeneration also in the nervous system. Moreover, despite the small size Wfs1 mutant mice have elevated IGF-1 levels and an activated growth hormone pathway. More detailed molecular analysis is needed to understand the functional and molecular background of these findings.


This study was supported by University of Tartu Grants PARBK06906, PARFS07915, and PARFS07904; by Estonian Science Foundation Grants GARFS7479, GARLA7295, and GARFS5688; by the European Union through the European Regional Development Fund; and by the Archimedes Foundation.


  • Address for reprint requests and other correspondence: S. Kõks, Dept. of Physiology, Univ. of Tartu, 19 Ravila St., Tartu 50411, Estonia (e-mail: Sulev.Koks{at}


View Abstract