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H NMR Metabolic Profiling of Plasma Reveals Additional Phenotypes in Knockout Mouse Models

Fay Probert,†,‡ Paul Rice,† Cheryl L. Scudamore,† Sara Wells,† Roger Williams,‡ Tertius A. Hough,*,†,§ and I. Jane Cox*,‡,§ †

Mary Lyon Centre, MRC Harwell, Oxfordshire OX11 0RD, United Kingdom Institute of Hepatology, Foundation for Liver Research, 69-75 Chenies Mews, London WC1E 6HX, United Kingdom

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ABSTRACT: The International Mouse Phenotyping Consortium program has been established to ascribe biological functions to systematically knocked-out (KO) genes by in vivo and ex vivo phenotyping. The plasma clinical chemistry screen includes an assessment of liver, kidney, and bone function and provides a basic lipid profile and histopathology reports on 32 tissues. We report on the inclusion of plasma analysis by proton nuclear magnetic resonance (1H NMR) spectroscopy. 1 H NMR spectroscopy data are summarized from 116 running baseline controls with 18 homozygous and 2 heterozygous KO mouse lines along with wild-type controls (typically n = 7 per gender). For the baseline group, the intersample variation of 1H NMR glucose measurement was 12%, and the 1H NMR spectroscopy data were influenced by gender and feeding status. There were good correlations between the clinical chemistry and the 1H NMR spectroscopy measurements for glucose, triglycerides, and HDL cholesterol. Significant differences were observed in two KO lines, Agl (MGI: 1924809) and Bbs5 (MGI: 1919819), by 1 H NMR spectroscopy, clinical chemistry, and histopathology. In a further two KO lines, Elmod1 (MGI: 3583900) and Emc10 (MGI: 1916933), 1H NMR metabolic differences were observed, but no other ex vivo changes were detected. In the remaining 16 lines, no ex vivo abnormal phenotypes were observed. Plasma 1H NMR spectroscopy can therefore provide a novel perspective on the function of knocked-out genes. KEYWORDS: liver, proton nuclear magnetic resonance spectroscopy, plasma, knockout mouse, phenotyping screen, metabonomics, metabolomics

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INTRODUCTION The International Mouse Phenotyping Consortium (IMPC) program is an international consortium for the large-scale generation and primary phenotyping of genetically altered mice.1 The IMPC plans to knock out (KO) 20 000 known and predicted mouse genes and pass each KO line through a broadbased primary phenotyping pipeline. The program aims to discover and ascribe biological functions to each KO gene and to show how these might impact various areas of human disease. Phenotyping data from all participating centers are being deposited in a centralized public database for unrestricted access by the wider scientific community (http://www. mousephenotype.org). Current challenges in mouse phenotyping include the absence of phenotypic annotations for a large proportion of knockout mouse lines as well as the exploration of pleiotropy. These challenges can be addressed by improving the utility of phenotyping pipelines. 1 Tests that make no a priori assumptions concerning gene function, such as metabolomics by mass spectrometry or nuclear magnetic resonance (NMR) spectroscopy, are therefore of particular interest for enriching the existing IMPC pipeline.2 The IMPC pipeline is expected to evolve with time as new tests are assessed for either their suitability for inclusion in the primary phenotyping pipeline or © 2015 American Chemical Society

their utility in in-depth secondary phenotyping at specialist laboratories.1 Metabolic changes in murine body fluids can be assessed by various methods, including mass spectrometry (in combination with high-performance or ultraperformance liquid chromatography,3 capillary electrophoresis,4 or gas chromatography5) and proton (1H) NMR spectroscopy.6 This study specifically investigates the use of plasma 1H NMR spectroscopy.7−10 Methodologies for obtaining robust and reproducible plasma NMR metabolic profiling results under automation from both clinical11−15 and experimental16 samples continue to be addressed. 1H NMR spectroscopy techniques for obtaining data from small volume blood samples (for example, samples upward of 3 μL extracted from fruit flies17 and 2 μL samples of mouse plasma) have been described.18 Recently, there has been increased interest toward the use of high-throughput metabolomics of samples from clinical cohorts to identify the biomarkers and metabolic fingerprints associated with specific disease conditions,19−23 a practice which is facilitating overall risk assessment24 and is likely to play an important role in personalized medicine.25,26 The application of 1H NMR Received: October 7, 2014 Published: April 7, 2015 2036

DOI: 10.1021/pr501039k J. Proteome Res. 2015, 14, 2036−2045

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Journal of Proteome Research

The samples used in this study were obtained from mice that were cared for and used humanely according to the MRC Harwell project licenses approved by the U.K. Home Office in accordance with the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 (SI 4 2012/3039). The work was performed in the Mary Lyon Centre under the Establishment License for MRC Harwell. All efforts were made to minimize suffering with considerate housing and husbandry. Cages were checked on a daily basis, and any welfare issues were dealt with as a matter of highest priority. Adult mice were killed by terminal anesthesia followed by exsanguination and cervical dislocation.

spectroscopy changes were expected on the basis of the IMPC clinical chemistry results already produced at MRC Harwell (data presented in Table 1 in the Results section). The other lines were selected according to whether there was sufficient plasma available for 1H NMR spectroscopy analysis and, in some cases, for having a known in vivo phenotype (http:// www.mousephenotype.org). In effect, most of the lines were randomly selected for this 1H NMR spectroscopy study, and the genes knocked out were expected to be of interest to a diverse range of clinical studies. The selected lines each had one of the following 20 genes knocked out: Adh5 (MGI: 87929), Af mid (MGI: 2448704), Agl (MGI: 1924809), Bbs5 (MGI: 1919819), Btrc (MGI: 1338871), Ccdc111 (MGI: 3603756), Clstn3 (MGI: 2178323), Cyb5r2 (MGI: 2444415), Eaf1 (MGI: 1921677, homozygous lethal, heterozygotes phenotyped), Elmod1 (MGI: 3583900), Emc10 (MGI: 1916933), Fam134c (MGI: 1915248), Fam63a (MGI: 1922257), Gpr33 (MGI: 1277106), Itga2 (MGI: 96600), Jmjd5 (MGI: 1924285, homozygous lethal, heterozygotes phenotyped), Kcnj9 (MGI: 108007), Mxra7 (MGI: 1914872), Nptn (MGI: 108077), and Usp38 (MGI: 1922091). The tm1b (reporter-tagged deletion, post-Cre) allele was phenotyped for each of the selected KO lines.36 In accordance with the IMPC protocol at MRC Harwell, n = 7 male and n = 7 female homozygous null mice, if viable, were scheduled to be phenotyped from each line, along with n = 7 male and n = 7 female WT controls. This was the case for the majority of knockout lines except for Eaf1, Jmdj5, and Agl. Both Eaf1 and Jmdj5 were homozygous lethal, and phenotypes were assessed in mice that were heterozygous for these knockout alleles. Because no wild-type control samples were available for the Agl line, the significance was assessed by comparing the results from the homozygous null mice to those from mice that were heterozygous for this knockout allele.

Animal Housing Conditions

Plasma Collection

Mice were maintained in the Mary Lyon Centre in a specific pathogen-free unit on a 12 h light:12 h dark cycle. They were housed using a stocking density of up to 4−5 mice per cage in individually ventilated caging (Tecniplast, Sealsafe, H × L × W dimensions of 187 × 398 × 215 mm). In addition to pine shavings for bedding material, a cardboard tunnel and shredded paper were provided as environmental enrichment. Cages received 65−75 air changes per hour. The ambient temperature was 19−21 °C, and the humidity was 45−65%. Mice were given water and fed ad libitum with RM3 Diet (Special Diet Services) containing 55% carbohydrate, 4% fat, and 20% protein.

A total of 800 μL of blood was collected between 8 a.m. and 11 a.m. from the retro-orbital sinus of mice aged 16 weeks at the end of the in vivo phenotyping pipeline. Collection was performed under terminal anesthesia and into pediatric lithium heparin tubes. Blood samples were kept on wet ice for a maximum of 1 h before being centrifuged at 5000g for 10 min in a refrigerated centrifuge set at 4 °C for plasma removal.12,13 Routine plasma clinical chemistry was performed on the fresh samples, and 75 μL aliquots of plasma were stored in 96 well plates at −70 °C for retrospective analysis by 1H NMR spectroscopy.

IMPC Mouse Models

The terminal plasma samples were analyzed on a Beckman Coulter AU680 semiautomated clinical chemistry analyzer for a routine panel consisting of 21 standard tests that examine, among others, the kidney, bone, and liver functions as well as a basic lipid profile (https://www.mousephenotype.org/impress/ protocol/151/7).

spectroscopy as a phenotyping tool in functional genetics, however, relies on the ability to interpret alterations in metabolite concentrations by linking them to specific metabolic pathways.27 Although there is some overlap between the parameters measured by clinical chemistry and those measured by 1H NMR spectroscopy,28 including glucose and creatinine, the majority of clinical chemistry parameters fluctuate in response to longer-term metabolic changes, and plasma 1H NMR spectroscopy findings relate to a nonselective snapshot assessment of a range of metabolic processes. This study involved the parallel analysis of plasma samples produced as part of the primary phenotyping pipeline of the IMPC program at MRC Harwell. We report the findings from a proof-of-principle study using small volumes of plasma from 20 of the first 62 IMPC KO mouse lines phenotyped at MRC Harwell. Using the experience gained, we discuss the feasibility and utility of incorporating 1H NMR spectroscopy into broad-based phenotyping pipelines.

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METHODS

Ethics

IMPC Clinical Chemistry Screen

All KO lines in the IMPC pipeline are nominated by the wider scientific community (http://www.mousephenotype.org) and are produced on a C57BL/6NTac background. A total of 116 C57BL/6NTac mice (59 female and 57 male) formed a running baseline control group and passed through the broadbased phenotyping pipeline, which included clinical chemistry, histopathology, and 1H NMR spectroscopy. These baseline control mice were phenotyped at the same age (16 weeks) as those in the KO lines studied, and the data were collected from these small batches of C57BL/6NTac mice throughout the life of the project to monitor analytical performance over time. Archived plasma from 20 lines was selected from the 62 lines that had passed through the IMPC phenotyping pipeline at MRC Harwell at the time of study. A total of two of the 20 KO lines chosen, Agl and Bbs5, were included, as plasma 1H NMR

IMPC Histopathology

The histopathology screen involved the collection of 32 tissues at the end of the in vivo pipeline that were then fixed, wax embedded, sectioned, H&E stained (https://www. mousephenotype.org/impress/protocol/99/7), and reviewed by a pathologist (C.L.S.). Plasma 1H NMR Spectroscopy Studies

For most lines, we studied n = 3−10 mutants and n = 3−8 wildtype controls per gender; the exception was the Agl KO line, 2037


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variation was defined by the coefficient of variation calculated from the plasma 1H NMR spectroscopy results obtained from all male C57BL/6Ntac mice in the control group. The clinical chemistry glucose concentrations from the same samples were used to compare the CV between the clinical chemistry and the 1 H NMR spectroscopy data. There is some overlap between the parameters measured by the 1H NMR spectroscopy and those measured by the clinical chemistry. For example, both glucose and creatinine are measured directly by both techniques, although the 1H NMR-defined region for creatinine also included creatine. In addition, the mobile lipid−CH2 1H NMR peak can be correlated to triglycerides,34 and the 1H NMR peak attributable to choline-containing metabolites can be dominated by high-density lipoproteins (HDL);11,35 we investigated whether these resonances could be correlated with the triglyceride and HDL cholesterol concentrations measured by clinical chemistry, respectively. To compare the 1H NMR results to the clinical chemistry findings, we fit data from the same animals to a linear equation, and the coefficient of determination (R2) was used to assess the degree of correlation between the two techniques. These correlations are presented as scatter plots for the 1H NMR spectroscopy of the glucose region versus the glucose concentration, the 1H NMR spectroscopy of the lipid CH2 region versus the triglyceride concentration, and the 1H NMR spectroscopy of the cholinecontaining compounds versus the HDL cholesterol concentration and are also presented as a heat map for a comparison of all the clinical chemistry and 1H NMR spectroscopy measurements. According to the IMPC protocol, mice were free-fed prior to terminal blood sampling. To confirm the extent of the variation between male and female mice that could be attributable to differences in feeding, we obtained plasma 1H NMR spectra from 4 male and 4 female C57BL/6NTac mice fasted overnight and compared them to the 1H NMR spectra of the running baseline control group of 59 female and 57 male freefed mice.

which was found to be subviable (defined as producing 0.40 for a number of 1H NMR spectroscopy regions, including acetoacetate/mobile lipid CH2CO, glycoprotein, mobile lipid CH2CH2CO, lactate, and mobile lipid CH3 as well as mobile lipid CH2. Normalized 1H NMR signal intensities of glucose, BCAA, choline-containing metabolites, and 3-hydroxybutyrate from the female mice of the 20 KO lines studied as well as their WT controls are illustrated in Figure 4. A summary of the plasma 1H NMR spectroscopy, clinical chemistry, and histopathology findings

Figure 1. Representative 1H NMR spectrum of a control C57BL/6NTac plasma sample with the 21 most prominent metabolites assigned: (1) glucose, (2) β-glucose, (3) choline-containing metabolites, (4) free choline, (5) creatinine, (6) creatine, (7) citrate, (8) glutamine, (9) pyruvate, (10) acetoacetate/mobile lipid CH2CO, (11) glycoprotein, (12) acetate, (13) arginine, (14) mobile lipid CH2CH2CO, (15) alanine, (16) lactate, (17) mobile lipid CH2, (18) 3-hydroxybutyrate, (19) 2,3-butanediol, (20) branched chain amino acids [leucine, isoleucine, and valine], and (21) mobile lipid CH3. 2039


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Figure 2. (A) Representative 1H NMR spectra of male (black) and female (red) C57BL/6NTac mice scaled according to the lactate doublet at 1.33 ppm. The normalized glucose 1H NMR signal intensity of female plasma is higher than that of male plasma. (B) PCA scores plot of 116 C57BL/ 6NTac samples showing gender separation. Comparison of the normalized intensities of (C) glucose, (D) lipid−CH2, and (E) 3-hydroxybutyrate peaks for both the fed (filled circles/squares) and the fasted (unfilled circles/squares) controls, C57BL/6NTac mice. Variation in glucose and mobile lipid intensities is still evident between females (square) and males (circle) in the fasted samples, although the variation is not as pronounced as in free-fed samples. Fasted (both male and female) samples show elevated 3-hydroxybutyrate peaks when compared to those of free-fed mice. (F) PCA scores plot of fasted male and female samples.

in the 20 KO lines compared with those of their WT controls is presented in Table 1. A total of two KO lines, Agl and Bbs5, showed differences in all three ex vivo phenotyping methodologies. A further two KO lines, Elmod1 and Emc10, showed changes only in plasma 1H NMR spectroscopy parameters. The remaining 16 KO lines, Adh5, Afmid, Btrc, Ccdc111, Clstn33, Cyb5r2, Eaf1, Fam134c, Fam63a, Gpr33, Itga2, Jmjd5, Kcnj9, Mxra7, Nptn, and Usp38 did not show any phenotypes in the three ex vivo tests, even though the in vivo pipeline showed no evidence of any abnormal phenotype in only four (Ccdc111, Cyb5r2, Itga2, and Kcnj9) (http://www.mousephenotype.org). Both Agl and Bbs5 KO mice showed abnormal glucose clearance and alterations in lean body mass

(http://www.mousephenotype.org). Clinical chemistry highlighted some similarities among Agl (females only studied) and male and female Bbs5 KO mice, including increased alpha amylase, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total cholesterol. However, glucose and fructosamine were decreased in Agl KO mice and increased in Bbs5 KO mice. The histopathology results showed liver microvesicular vacuolation in both KO models, and it additionally showed skeletal and cardiac muscle vacuolation in Agl KO mice and reduced reproductive tract activity in Bbs5 KO mice. Clinical chemistry and histopathology therefore suggest liver pathology together with abnormalities in glucose metabolism. 1H NMR findings in female Agl KO mice included

Table 1. Summary of All KO Mouse Lines Studieda KO line

1

H NMR spectroscopy significant changes

clinical chemistry significant changes

[F only studied] ↑α-amylase, ↑ALP, ↑ALT, [F only studied] ↑arginine, ↑BCAA, ↑creatinine and creatine, ↓glucose, ↑glutamine, ↑AST, ↓fructosamine, ↓glucose, ↑iron, ↑total cholesterol, ↑total protein ↑glycoproteins, ↑lipid−CH2, ↑lipid−CH3, ↑choline-containing metabolites Bbs5 [F] ↓BCAA, ↓glutamine, ↓lactate [M] ↑α-amylase, ↑ALP, ↑ALT, ↑AST, ↑creatinine, ↑fructosamine, ↑glucose, ↑HDL cholesterol, ↑total cholesterol [F] ↑α-amylase, ↑ALT, ↑AST, ↑calcium, ↓chloride, ↑glucose, ↑HDL cholesterol, ↑iron, ↑total cholesterol, ↑total protein Elmod1 [M] ↑3-hydroxybutyrate, ↑arginine, ↑BCAA, none ↑free choline, ↑glutamine, ↑choline-containing metabolites Emc10 [F] ↑2,3-butanediol, ↑3-hydroxybutyrate none none Adh5, Afmid, Btrc, Ccdc111, Clstn3, none Cyb5r2, Eaf1, Fam34cj, Fam63a, Gpr33,Itga2,Jmjd5,Kcnj9,Mxra7, Nptn, Usp38 Agl

histopathological observations [M,F] liver microvesicular vacuolation, skeletal and cardiac muscle vacuolation

[M,F] liver microvesicular vacuolation, reproductive tract reduced activity

none

none none

Significant changes in the plasma 1H-NMR spectral findings, clinical chemistry, and histopathology are listed. The gender in which significant changes were observed is stated in square brackets. a

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Figure 3. Correlation between the clinical chemistry and the 1H NMR spectroscopy data for (A) glucose, (B) triglyceride, (C) HDL cholesterol, and (D) all parameters for all plasma samples studied from baseline, WT controls, and KO mouse groups. Additional correlations are observed between a number of the 1H NMR spectroscopy metabolite regions, including acetoacetate (AcAc)/mobile lipid CH2CO, glycoprotein, mobile lipid CH2CH2CO, lactate and lipid CH3, and clinical chemistry triglyceride.

binning strategies such as narrower, fixed width buckets or intelligent bucketing may provide more information by sampling more regions of the NMR spectrum and may resolve some of the issues with peak overlap. However, in regions of considerable peak overlap (for example, the glucose region), narrower buckets did not offer any improvement in the correlation coefficients. Also, for this study, an increase in the number of variables measured with narrower bins would increase the likelihood of overfitting, given the small number of samples per KO line. Although only previously identified metabolites were observed in this study, if unusual metabolites were identified, spiking with the relevant metabolite or 2D total correlation spectroscopy (TOCSY) experiments would be required for assignment. For further studies, the analysis approach may also be improved by the use of statistical total correlation spectroscopy methods that could aid in both compound identification and metabolic pathway analysis.40 Alternatively, automated metabolite deconvolution and quantification may be achieved by using a Bayesian model (for example, by using the BATMAN package).41 This would be of particular use in regions with overlap from broad, mobile lipoprotein resonances. We also recognize that the free-fed samples used in this study may have introduced variation. To eliminate such variation, we would need to collect samples from fasted mice, which was not an option within the current IMPC protocol. As gender differences were observed in some of the 1H NMR spectral parameters in the baseline group, 1H NMR spectroscopy data from male and female KO lines were analyzed separately, and 1H NMR spectral differences attributable to the KO gene were sometimes of similar magnitude to those of differences in

increased glycoproteins, lipid−CH2, lipid−CH3, cholinecontaining metabolites, creatinine, creatine, arginine, BCAA, and glutamine and decreased glucose. The plasma 1H NMR spectral changes were different in Bbs5 KO mice and comprised a significant reduction in BCAA, glutamine, and lactate in females only. In the Elmod1 and Emc10 KO lines, in which only plasma 1H NMR spectroscopy differences were observed ex vivo, changes were also noted in the in vivo phenotyping pipeline (http:// www.mousephenotype.org). Plasma 1H NMR spectroscopy changes were detected in Elmod1 males and included increases in choline-containing metabolites, free choline, glutamine, arginine, 3-hydroxybutyrate, and BCAA. The plasma 1H NMR spectroscopy data in Emc10 showed significant increases in 3-hydroxybutyrate and 2,3-butanediol in females only.

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DISCUSSION This study has shown that the 1H NMR spectroscopy analysis of small volumes of plasma can be included in routine mouse phenotyping screens. The semiautomated NMR spectral analysis approach was chosen as a straightforward method of incorporating plasma NMR into the existing IMPC pipeline. We show that this method of integrating specific metabolite regions can identify potential phenotypes that elude the existing pipeline. Our analysis approach can be automated and is accessible to a nonexpert, although there are limitations in the analysis of low-intensity resonances and in regions with multiple peak overlap. This is highlighted, for example, by the weak correlation of the creatinine and creatine bin with the clinical chemistry measurements of creatinine. The use of alternative 2041


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Figure 4. Normalized 1H NMR signal intensities of (A) glucose, (B) branched chain amino acids, (C) choline-containing metabolites, and (D) 3-hydroxybutyrate for all female knockout lines studied. Results for mutant samples (white triangle) are compared to those of wild-type controls (black triangles). Lines with a significant difference between mutant and wild-type are marked with an asterisk.

a more in-depth analysis of lipoprotein subclasses, but it requires detailed information about the 1H NMR spectroscopy characteristics of murine lipoprotein subclasses. Cross-correlation of the 1H NMR spectroscopy measurements with clinical chemistry showed that there was no overlap for the majority of parameters, which was to be expected. A total of three clinical chemistry measurements

gender, as illustrated by a comparison of Figures 2 and 4. By analyzing the 1H NMR spectroscopy data acquired by spinecho techniques, we have emphasized the low-molecular-weight metabolite changes while including information about the lipoprotein composition. The analysis of 1H NMR spectroscopy data sets acquired using pulse-collect techniques35 can provide 2042


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Hochberg adjustment. Bbs5 KO mice displayed reduced levels of BCAA, lactate, and glutamine compared to the levels in WT mice. These changes may reflect systemic changes in cell signaling resulting from the underlying dysfunction of primary cilia. Further understanding of the downstream implications of the Bbs5 KO is required because the changes observed in plasma could, in principle, be linked to the regulation of the mammalian target of rapamycin (mTOR) signaling, which has been implicated in cilia biology.47 Plasma 1H NMR changes were seen in both the Elmod1 and the Emc10 KO models in the absence of differences in either clinical chemistry or histopathology. Mammalian ELMO family proteins are involved in complexes that activate small GTPases to regulate the actin cytoskeleton during phagocytosis and cell migration and have been shown to function as GTPase-activating proteins (GAPs) for the Arf family of small G proteins.48 Plasma 1H NMR spectroscopy changes in glutamine, in particular, may be consistent with the systemic role of GTPases in regulating membrane traffic, cell division, and energy metabolism.48,49 Changes in choline and choline-containing metabolites suggest a more subtle alteration in lipid metabolism than can be detected by clinical chemistry and, therefore, it may be appropriate to additionally look at plasma lipoprotein subclass analysis in this KO model and also to analyze tissue extracts to achieve a more targeted and mechanistic understanding of the alterations attributable to the KO gene. Emc10 codes for the ER membrane protein complex subunit 10, a protein associated with insulinoma. Insulinoma, also known as islet cell adenoma, is a pancreatic tumor that is derived from β cells and secretes insulin. Plasma 3-hydroxybutyrate has been found to be raised in patients with insulinoma.50,51 The 1H NMR spectroscopy changes seen would therefore be consistent with decreased insulin levels and diabetic ketosis. A total of 16 of the KO lines showed no changes in clinical chemistry, histopathology, or plasma 1H NMR spectroscopy, although 12 of these displayed at least one in vivo phenotype. Only four of the 20 KO lines showed no abnormalities at all. The sensitivity of any phenotyping pipeline when detecting abnormalities is directly linked to the breadth and depth of the phenotyping tests included in it. Some knockout lines might not display any detectable phenotypes as a result of systemic compensation.52 The 20 KO lines in this study passed through the IMPC pipeline, which bears similarities to the pipelines used for the preceding EUMODIC and Wellcome Trust Sanger Institute Mouse Genetics Projects. We observed at least one in vivo or ex vivo phenotype in 16 of the 20 (80%) of the KO lines studied, and the same overall hit rate was also observed in the EUMODIC and Wellcome Trust Sanger Institute Mouse Genetic Projects.53 As a wider range of KO lines is studied, it will become clearer whether the sensitivity of ex vivo 1H NMR phenotyping we observed (hit rate of 20%) is representative. Our findings suggest that plasma 1H NMR metabolic profiling can be useful as a screening tool in mice to help uncover novel phenotypes attributable to knocked out genes. The nonselective assessment of multiple metabolites is ideal for uncovering metabolic changes that would not necessarily be detected by conventional primary phenotyping tools such as clinical chemistry and histology. The large data sets generated by 1H NMR spectroscopy do, however, present a challenge in the environment of a fast-paced primary phenotyping screen. Spectral changes that occur due to metabolic alterations were easy to detect and could be fully automated. The interpretation of how altered metabolites relate to one or more disease pathways is not straightforward and would likely represent the most difficult

(glucose, HDL cholesterol, and triglycerides) showed a correlation of R2 > 0.40 with at least one 1H NMR spectroscopy measurement. In this study, the measurement of clinical chemistry triglycerides was of limited value in identifying KO mice models with dyslipidemias because the mice were free-fed. Two of the KO lines studied, Agl and Bbs5, had been expected to show metabolic changes in the plasma 1H NMR spectrum, an expectation formed on the basis of previously determined abnormal clinical chemistry findings and on knowledge of the functions of the homologous human genes. The Agl gene on mouse chromosome 3 is a homolog of the AGL on human chromosome 1 and codes for the glycogen debranching enzyme that is involved in glycogen metabolic processes, polysaccharide binding, and polyubiquitin binding. AGL is clinically associated with glycogen storage disease type III (also known as Cori’s disease or Forbes disease).42 Clinical characteristics include variable liver, cardiac muscle, and skeletal involvement. Liver involvement presents as hypoglycemia, hepatomegaly, elevated hepatic transaminases, and hyperlipidemia.43 In the untreated clinical state, serum levels of triglycerides and cholesterol are elevated.43 In the female KO mice studied, we observed clinical chemistry changes suggestive of liver dysfunction, hypoglycemia, and elevated cholesterol. Histopathology revealed liver microvesicular vacuolation as well as skeletal and cardiac muscle vacuolation, with the morpholicla appearance being consistent with the accumulation of abnormally short branched glycogen. The most prominent plasma 1 H NMR spectroscopy findings were the hyperlipidemia and reduced glucose levels in agreement with the clinical chemistry findings. The phenotypes observed by clinical chemistry, 1H NMR spectroscopy, and histopathology are therefore all consistent with the phenotypes associated with human glycogen storage disease type III. The reduced plasma glucose levels observed by both the 1H NMR spectroscopy and the clinical chemistry in free-fed mice were not observed in a recent study of an Agl mouse model.44 This hypoglycemia may result in a compensatory increase in the β-oxidation of fats and contribute to the observed elevation in cholesterol,44 as hyperlipidemia can be caused by a decreased rate of fat oxidation.45 Plasma 1H NMR spectroscopy also detected increases in creatinine and creatine, arginine, BCAA, and glutamine, all of which might be linked to the observed muscle pathology. Bbs5 is the mouse homolog of human BBS5, one of a family of genes that underlie Bardet−Biedl syndrome (BBS), a rare ciliopathy disorder. To date, 16 genes involved in BBS have been reported in humans, seven of which (BBS1, 2, 4, 5, 7, 8, and 9) code for proteins that form a complex known as the BBSome. The function of the BBSome involves ciliary membrane biogenesis. The predominant organs affected by BBS include the kidney, eye, liver, and brain, although diabetes and obesity are also common features of ciliopathies.46 The clinical chemistry findings in Bbs5 KO mice were concomitant with some of these known clinical observations and included raised ALT, AST, and ALP (potentially resulting from liver dysfunction), raised creatinine (males only, indicative of compromised kidney function), raised blood glucose (and fructosamine in males only), and increased HDL and total cholesterol. Histopathology findings confirmed liver involvement and also showed reproductive tract abnormalities consistent with reduced function. Plasma 1H NMR spectroscopy data from female Bbs5 KO mice did show trends of increased glucose and choline-containing metabolites, although these did not reach significance after applying the Benjamini− 2043


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Journal of Proteome Research and time-consuming aspect of the incorporation of plasma 1H NMR spectroscopy into a primary phenotyping pipeline. Until our ability to interpret large 1H NMR data sets improves, it may be more appropriate to use 1H NMR spectroscopy as a secondary phenotyping tool when it can be applied to answer more targeted questions based on the primary phenotyping data. In conclusion, plasma 1H NMR spectroscopy analysis of multiple metabolites is helpful for uncovering metabolic changes that are not necessarily detected by clinical chemistry or histopathology, thereby suggesting a scope for using plasma 1H NMR spectroscopy in identifying abnormal clinical phenotypes.

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(7) Kim, I. Y.; Jung, J.; Jang, M.; Ahn, Y. G.; Shin, J. H.; Choi, J. W.; Sohn, M. R.; Shin, S. M.; Kang, D. G.; Lee, H. S.; Bae, Y. S.; Ryu, d. H.; Seong, J. K.; Hwang, G. S. 1H NMR-based metabolomic study on resistance to diet-induced obesity in AHNAK knock-out mice. Biochem. Biophys. Res. Commun. 2010, 403 (3−4), 428−434. (8) Martin, F. P.; Rezzi, S.; Philippe, D.; Tornier, L.; Messlik, A.; Holzlwimmer, G.; Baur, P.; Quintanilla-Fend, L.; Loh, G.; Blaut, M.; Blum, S.; Kochhar, S.; Haller, D. Metabolic assessment of gradual development of moderate experimental colitis in IL-10 deficient mice. J. Proteome Res. 2009, 8 (5), 2376−2387. (9) Dumas, M. E.; Barton, R. H.; Toye, A.; Cloarec, O.; Blancher, C.; Rothwell, A.; Fearnside, J.; Tatoud, R.; Blanc, V.; Lindon, J. C.; Mitchell, S. C.; Holmes, E.; McCarthy, M. I.; Scott, J.; Gauguier, D.; Nicholson, J. K. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. U. S. A 2006, 103 (33), 12511−12516. (10) Jiang, N.; Yan, X.; Zhou, W.; Zhang, Q.; Chen, H.; Zhang, Y.; Zhang, X. NMR-based metabonomic investigations into the metabolic profile of the senescence-accelerated mouse. J. Proteome Res. 2008, 7 (9), 3678−3686. (11) Bell, J. D.; Sadler, P. J.; MacLeod, A. F.; Turner, P. R.; La, V. A. 1H NMR studies of human blood plasma. Assignment of resonances for lipoproteins. FEBS Lett. 1987, 219 (1), 239−243. (12) Beckonert, O.; Keun, H. C.; Ebbels, T. M.; Bundy, J.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2007, 2 (11), 2692−2703. (13) Pinto, J.; Domingues, M. R.; Galhano, E.; Pita, C.; Almeida, M. C.; Carreira, I. M.; Gil, A. M. Human plasma stability during handling and storage: impact on NMR metabolomics. Analyst (Cambridge, U. K.) 2014, 139 (5), 1168−1177. (14) Maher, A. D.; Crockford, D.; Toft, H.; Malmodin, D.; Faber, J. H.; McCarthy, M. I.; Barrett, A.; Allen, M.; Walker, M.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Optimization of human plasma 1H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes. Anal. Chem. 2008, 80 (19), 7354−7362. (15) Dona, A. C.; Jimenez, B.; Schafer, H.; Humpfer, E.; Spraul, M.; Lewis, M. R.; Pearce, J. T.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping. Anal. Chem. 2014, DOI: 10.1021/ac5025039. (16) Li, J. V.; Saric, J.; Yap, I. K.; Utzinger, J.; Holmes, E. Metabonomic investigations of age- and batch-related variations in female NMRI mice using proton nuclear magnetic resonance spectroscopy. Mol. BioSyst. 2013, 9 (12), 3155−3165. (17) Ragan, T. J.; Bailey, A. P.; Gould, A. P.; Driscoll, P. C. Volume determination with two standards allows absolute quantification and improved chemometric analysis of metabolites by NMR from submicroliter samples. Anal. Chem. 2013, 85 (24), 12046−12054. (18) Griffin, J. L.; Nicholls, A. W.; Keun, H. C.; Mortishire-Smith, R. J.; Nicholson, J. K.; Kuehn, T. Metabolic profiling of rodent biological fluids via 1H NMR spectroscopy using a 1 mm microlitre probe. Analyst (Cambridge, U. K.) 2002, 127 (5), 582−584. (19) Ala-Korpela, M.; Kangas, A. J.; Soininen, P. Quantitative highthroughput metabolomics: a new era in epidemiology and genetics. Genome Med. 2012, 4 (4), 36. (20) Raitoharju, E.; Seppala, I.; Oksala, N.; Lyytikainen, L. P.; Raitakari, O.; Viikari, J.; Ala-Korpela, M.; Soininen, P.; Kangas, A. J.; Waldenberger, M.; Klopp, N.; Illig, T.; Leiviska, J.; Loo, B. M.; HutriKahonen, N.; Kahonen, M.; Laaksonen, R.; Lehtimaki, T. Blood microRNA profile associates with the levels of serum lipids and metabolites associated with glucose metabolism and insulin resistance and pinpoints pathways underlying metabolic syndrome: The cardiovascular risk in Young Finns Study. Mol. Cell. Endocrinol. 2014, 391 (1−2), 41−49. (21) Peng, J.; Zeng, J.; Cai, B.; Yang, H.; Cohen, M. J.; Chen, W.; Sun, M. W.; Lu, C. D.; Jiang, H. Establishment of quantitative severity

AUTHOR INFORMATION

Corresponding Authors

*T.A.H. E-mail: [email protected]. Tel: +44(0)1235 841188. Fax: +44(0)1235 841200. *I.J.C. E-mail: [email protected]. Tel: +44(0)20 7255 9836. Fax: +44(0)20 7380 0405. Author Contributions §

T.A.H. and I.J.C. contributed equally to the project.

Funding

This work was funded by a Medical Research Council research grant under the high-throughput “omic” science and imaging funding scheme [MC_PC_13045]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful for support from the Foundation for Liver Research. We gratefully acknowledge the National Institute of Health and Medical Research Council for supporting the dayto-day running of the IMPC. We thank the Mary Lyon Centre husbandry, in vivo phenotyping, clinical pathology, and anatomic pathology teams for their contributions to this study.

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REFERENCES

(1) Brown, S. D.; Moore, M. W. The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm. Genome 2012, 23 (9−10), 632−640. (2) Robinette, S. L.; Holmes, E.; Nicholson, J. K.; Dumas, M. E. Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations. Genome Med. 2012, 4 (4), 30. (3) Sarafian, M. H.; Gaudin, M.; Lewis, M. R.; Martin, F. P.; Holmes, E.; Nicholson, J. K.; Dumas, M. E. Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography-mass spectrometry. Anal. Chem. 2014, 86 (12), 5766−5774. (4) Saric, J.; Want, E. J.; Duthaler, U.; Lewis, M.; Keiser, J.; Shockcor, J. P.; Ross, G. A.; Nicholson, J. K.; Holmes, E.; Tavares, M. F. Systematic evaluation of extraction methods for multiplatform-based metabotyping: application to the Fasciola hepatica metabolome. Anal. Chem. 2012, 84 (16), 6963−6972. (5) Snelling, C.; Tanchuck-Nipper, M. A.; Ford, M. M.; Jensen, J. P.; Cozzoli, D. K.; Ramaker, M. J.; Helms, M.; Crabbe, J. C.; Rossi, D. J.; Finn, D. A. Quantification of ten neuroactive steroids in plasma in Withdrawal Seizure-Prone and -Resistant mice during chronic ethanol withdrawal. Psychopharmacology 2014, 231 (17), 3401−3414. (6) Mestdagh, R.; Dumas, M. E.; Rezzi, S.; Kochhar, S.; Holmes, E.; Claus, S. P.; Nicholson, J. K. Gut microbiota modulate the metabolism of brown adipose tissue in mice. J. Proteome Res. 2012, 11 (2), 620− 630. 2044


Article

Journal of Proteome Research evaluation model for spinal cord injury by metabolomic fingerprinting. PLoS One 2014, 9 (4), e93736. (22) Sanz-Cortes, M.; Carbajo, R. J.; Crispi, F.; Figueras, F.; PinedaLucena, A.; Gratacos, E. Metabolomic profile of umbilical cord blood plasma from early and late intrauterine growth restricted (IUGR) neonates with and without signs of brain vasodilation. PLoS One 2013, 8 (12), e80121. (23) Wang, J.; Li, Z.; Chen, J.; Zhao, H.; Luo, L.; Chen, C.; Xu, X.; Zhang, W.; Gao, K.; Li, B.; Zhang, J.; Wang, W. Metabolomic identification of diagnostic plasma biomarkers in humans with chronic heart failure. Mol. BioSyst. 2013, 9 (11), 2618−2626. (24) Amathieu, R.; Triba, M. N.; Nahon, P.; Bouchemal, N.; Kamoun, W.; Haouache, H.; Trinchet, J. C.; Savarin, P.; Le, M. L.; Dhonneur, G. Serum 1H-NMR metabolomic fingerprints of acute-onchronic liver failure in intensive care unit patients with alcoholic cirrhosis. PLoS One 2014, 9 (2), e89230. (25) Lindon, J. C.; Nicholson, J. K. The emergent role of metabolic phenotyping in dynamic patient stratification. Expert Opin. Drug Metab. Toxicol. 2014, 10 (7), 915−919. (26) Nicholson, G.; Rantalainen, M.; Maher, A. D.; Li, J. V.; Malmodin, D.; Ahmadi, K. R.; Faber, J. H.; Hallgrimsdottir, I. B.; Barrett, A.; Toft, H.; Krestyaninova, M.; Viksna, J.; Neogi, S. G.; Dumas, M. E.; Sarkans, U.; The Molpage Consortium; Silverman, B. W.; Donnelly, P.; Nicholson, J. K.; Allen, M.; Zondervan, K. T.; Lindon, J. C.; Spector, T. D.; McCarthy, M. I.; Holmes, E.; Baunsgaard, D.; Holmes, C. C. Human metabolic profiles are stably controlled by genetic and environmental variation. Mol. Syst. Biol. 2011, 7, 525. (27) Ellinger, J. J.; Chylla, R. A.; Ulrich, E. L.; Markley, J. L. Databases and Software for NMR-Based Metabolomics. Curr. Metabolomics 2013, DOI: 10.2174/2213235X11301010028. (28) Bock, J. L. NMR in clinical chemistrywhere do we stand? Clin. Chem. 1994, 40 (7Pt 1), 1215−1217. (29) Williams, H. R.; Willsmore, J. D.; Cox, I. J.; Walker, D. G.; Cobbold, J. F.; Taylor-Robinson, S. D.; Orchard, T. R. Serum metabolic profiling in inflammatory bowel disease. Dig. Dis. Sci. 2012, 57 (8), 2157−2165. (30) Merrifield, C. A.; Lewis, M.; Claus, S. P.; Beckonert, O. P.; Dumas, M. E.; Duncker, S.; Kochhar, S.; Rezzi, S.; Lindon, J. C.; Bailey, M.; Holmes, E.; Nicholson, J. K. A metabolic system-wide characterisation of the pig: a model for human physiology. Mol. BioSyst. 2011, 7 (9), 2577−2588. (31) Soininen, P.; Kangas, A. J.; Wurtz, P.; Tukiainen, T.; Tynkkynen, T.; Laatikainen, R.; Jarvelin, M. R.; Kahonen, M.; Lehtimaki, T.; Viikari, J.; Raitakari, O. T.; Savolainen, M. J.; AlaKorpela, M. High-throughput serum NMR metabonomics for costeffective holistic studies on systemic metabolism. Analyst 2009, 134 (9), 1781−1785. (32) Psychogios, N.; Hau, D. D.; Peng, J.; Guo, A. C.; Mandal, R.; Bouatra, S.; Sinelnikov, I.; Krishnamurthy, R.; Eisner, R.; Gautam, B.; Young, N.; Xia, J.; Knox, C.; Dong, E.; Huang, P.; Hollander, Z.; Pedersen, T. L.; Smith, S. R.; Bamforth, F.; Greiner, R.; McManus, B.; Newman, J. W.; Goodfriend, T.; Wishart, D. S. The human serum metabolome. PLoS One 2011, 6 (2), 16957. (33) Li, J. V.; Saric, J.; Wang, Y.; Keiser, J.; Utzinger, J.; Holmes, E. Chemometric analysis of biofluids from mice experimentally infected with Schistosoma mansoni. Parasites Vectors 2011, 4, 179. (34) De, M. T.; Sinnaeve, D.; Van, G. B.; Rietzschel, E. R.; De Buyzere, M. L.; Langlois, M. R.; Bekaert, S.; Martins, J. C.; Van, C. W. Evaluation of standard and advanced preprocessing methods for the univariate analysis of blood serum 1H-NMR spectra. Anal. Bioanal. Chem. 2010, 398 (4), 1781−1790. (35) Suna, T.; Salminen, A.; Soininen, P.; Laatikainen, R.; Ingman, P.; Makela, S.; Savolainen, M. J.; Hannuksela, M. L.; Jauhiainen, M.; Taskinen, M. R.; Kaski, K.; Ala-Korpela, M. 1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organising maps. NMR Biomed. 2007, 20 (7), 658−672. (36) Stechman, M. J.; Ahmad, B. N.; Loh, N. Y.; Reed, A. A.; Stewart, M.; Wells, S.; Hough, T.; Bentley, L.; Cox, R. D.; Brown, S. D.;

Thakker, R. V. Establishing normal plasma and 24-h urinary biochemistry ranges in C3H, BALB/c and C57BL/6J mice following acclimatization in metabolic cages. Lab. Anim. 2010, 44 (3), 218−225. (37) Fukao, T.; Mitchell, G.; Sass, J. O.; Hori, T.; Orii, K.; Aoyama, Y. Ketone body metabolism and its defects. J. Inherited Metab. Dis. 2014, DOI: 10.1007/10545-014-9704-9. (38) Rui, L. Energy metabolism in the liver. Compr. Physiol. 2014, 4 (1), 177−197. (39) Fox, J. G.; Davisson, M. T.; Quimby, F. W.; Barthold, S. W.; Newcomer, C. E.; Smith, A. L.The Mouse in Biomedical Research, 2nd ed.; Normative Biology, Husbandry, and Models, Vol. 3; Academic Press: New York, 2007. (40) Robinette, S. L.; Lindon, J. C.; Nicholson, J. K. Statistical spectroscopic tools for biomarker discovery and systems medicine. Anal. Chem. 2013, 85 (11), 5297−5303. (41) Hao, J.; Liebeke, M.; Astle, W.; De, I. M.; Bundy, J. G.; Ebbels, T. M. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat. Protoc. 2014, 9 (6), 1416−1427. (42) Kapoor, S.; Kumar, P. S.; Mathur, N. B.; Gondal, R.; Khalil, A. Glycogen storage disease type III. Indian Pediatr. 1994, 31 (10), 1288−1291. (43) Dagli, A.; Sentner, C. P.; Weinstein, D. A. Glycogen Storage Disease Type III. In GeneReviews [Online]; Pagon, R. A., Adam, M. P., Ardinger, H. H., Bird, T. D., Dolan, C. R., Fong, C. T., Smith, R. J. H., Stephens, K., Eds.; University of Washington: Seattle, 2012. (44) Liu, K. M.; Wu, J. Y.; Chen, Y. T. Mouse model of glycogen storage disease type III. Mol. Genet. Metab. 2014, 111 (4), 467−476. (45) Duarte, I. F.; Goodfellow, B. J.; Barros, A.; Jones, J. G.; Barosa, C.; Diogo, L.; Garcia, P.; Gil, A. M. Metabolic characterisation of plasma in juveniles with glycogen storage disease type 1a (GSD1a) by high-resolution (1)H NMR spectroscopy. NMR Biomed. 2007, 20 (4), 401−412. (46) Waters, A. M.; Beales, P. L. Ciliopathies: an expanding disease spectrum. Pediatr. Nephrol. 2011, 26 (7), 1039−1056. (47) Cardenas-Rodriguez, M.; Irigoin, F.; Osborn, D. P.; Gascue, C.; Katsanis, N.; Beales, P. L.; Badano, J. L. The Bardet−Biedl syndromerelated protein CCDC28B modulates mTORC2 function and interacts with SIN1 to control cilia length independently of the mTOR complex. Hum. Mol. Genet. 2013, 22 (20), 4031−4042. (48) Johnson, K. R.; Longo-Guess, C. M.; Gagnon, L. H. Mutations of the mouse ELMO domain containing 1 gene (Elmod1) link small GTPase signaling to actin cytoskeleton dynamics in hair cell stereocilia. PLoS One 2012, 7 (4), e36074. (49) Ivanova, A. A.; East, M. P.; Yi, S. L.; Kahn, R. A. Characterization of recombinant ELMOD (cell engulfment and motility domain) proteins as GTPase-activating proteins (GAPs) for ARF family GTPases. J. Biol. Chem. 2014, 289 (16), 11111−11121. (50) Hale, P. J.; Nattrass, M. Metabolic profiles in patients with insulinoma. Clin. Endocrinol. (Oxford, U. K.) 1989, 30 (1), 29−38. (51) Berger, M.; Zimmermann-Telschow, H.; Berchtold, P.; Drost, H.; Muller, W. A.; Gries, F. A.; Zimmermann, H. Blood amine acid levels in patients with insulin excess (functioning insulinoma) and insulin deficiency (diabetic ketosis). Metabolism 1978, 27 (7), 793− 799. (52) Barbaric, I.; Miller, G.; Dear, T. N. Appearances can be deceiving: phenotypes of knockout mice. Briefings Funct. Genomics Proteomics 2007, 6 (2), 91−103. (53) Ayadi, A.; Birling, M. C.; Bottomley, J.; Bussell, J.; Fuchs, H.; Fray, M.; Gailus-Durner, V.; Greenaway, S.; Houghton, R.; Karp, N.; Leblanc, S.; Lengger, C.; Maier, H.; Mallon, A. M.; Marschall, S.; Melvin, D.; Morgan, H.; Pavlovic, G.; Ryder, E.; Skarnes, W. C.; Selloum, M.; Ramirez-Solis, R.; Sorg, T.; Teboul, L.; Vasseur, L.; Walling, A.; Weaver, T.; Wells, S.; White, J. K.; Bradley, A.; Adams, D. J.; Steel, K. P.; Hrabe de, A. M.; Brown, S. D.; Herault, Y. Mouse largescale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project. Mamm. Genome 2012, 23 (9−10), 600−610.

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