The Genetic Architecture of Dietary Iron Overload and Associated Pathology in Mice

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1. 

   eLife

   Mar 11, 2024

   Author Response

   Reviewer #1 (Public Review):

   In this manuscript, the authors perform a very thorough, extensive characterization of the impact of an iron-rich diet on multiple phenotypes in a wide range of inbred mouse strains. While a work of this type does not offer mechanistic insights, the value of the study lies not only in its immediate results but also in what it can offer to future researchers as they explore the genetic basis of iron levels and other related phenotypes in rodent studies. The creation of a web resource and the offer from the authors to share all available samples is particularly laudable, and helps to increase the accessibility of the work to other scientists. There is one shortcoming to the work however. To induce iron overload in mice in the main study in this work, mice were placed on an iron-rich diet …

   Author Response

   Reviewer #1 (Public Review):

   In this manuscript, the authors perform a very thorough, extensive characterization of the impact of an iron-rich diet on multiple phenotypes in a wide range of inbred mouse strains. While a work of this type does not offer mechanistic insights, the value of the study lies not only in its immediate results but also in what it can offer to future researchers as they explore the genetic basis of iron levels and other related phenotypes in rodent studies. The creation of a web resource and the offer from the authors to share all available samples is particularly laudable, and helps to increase the accessibility of the work to other scientists. There is one shortcoming to the work however. To induce iron overload in mice in the main study in this work, mice were placed on an iron-rich diet that differed in its composition from the baseline diet in more than just iron. This could influence some of the phenotypes observed in this study.

   We thank the reviewer for their comments. We hope that this work can provide insight and/or support for a wide variety of future studies. Regarding the diets, yes, in our initial pilot study with 6 strains, the baseline diet was inadvertently not isocaloric with the high iron diet, and it also used a different source of cellulose and contained individual amino acids in ratios found in casein, instead of casein, which was used as the protein source for the high iron diet. The baseline metal composition however was the same. We included data from the pilot study in this manuscript because it provided some important early insight, but made sure to note this caveat since it could potentially affect some results. We added some additional text to the Methods section to help clarify this further. The other subsequently performed studies in this paper were not affected, for example the Control study performed in C57BL/6J has a baseline diet that matches the high iron diet except for iron. For our HMDP genetic study with 114 strains, we did not have a baseline group, so all mice were on the same high iron diet.

   Reviewer #2 (Public Review):

   Here, the authors tried to identify the genes and biological pathways underlying iron overload and its associated pathologies in mice. Several wet lab experiments and measurements alongside many bioinformatic analyses like GWAS, RNA-seq data analysis (DEG), eQTL analysis, TWAS, and gene-set enrichment analysis have been performed. The study design is good enough and the author tried to validate the results. The data have been submitted (Accession #: GSE230674) but are not public yet.

   Thank you very much for your detailed and thoughtful review and for helping us to improve our manuscript.

   1. The main issue of this manuscript is its length. It's too long, especially the result section. It's hard for readers to follow the paper. Moreover, you added results about other minerals, mostly copper, which seems too much (considering the fact that this study is about iron). The text doesn't have the required Integrity and focus. You should decide where you want to put the focus of this manuscript and I strongly recommend shortening the manuscript, try to be short and sweet as much as you can.

   Thank you for this helpful suggestion. We have moved or removed excess discussion from the Results section. We moved the specific GWAS results for copper and related red cell traits to the Supplementary text file “Supplementary File 24” so that only iron and triglyceride GWAS results are described in the main text. We kept in the discussion about the copper findings in the Discussion section, since we believe the deficiency is an important phenotype induced by the high iron diet that may impact other studies of dietary iron overload. We also believe that the copper and anemia GWAS loci may be of interest to some readers. We considered putting the copper and anemia findings in a separate manuscript, but ultimately decided to include it here, although we do agree it makes the manuscript longer.

   1. Also, the "Methods" section is long, some parts are over-detailed (mostly wet lab procedures) and some parts are not detailed enough. It seems the "Statistical analyses" part doesn't have extra information. I recommend removing the first paragraph and moving some of the information from the second paragraph to the right place in the Method section.

   We reorganized the first part of the statistical analyses section for clarity, and as mentioned further below, added in more detail regarding the GWAS significance thresholds:

   “Analyses were performed using GraphPad Prism (GraphPad Software, La Jolla, CA) and in R. P < 0.05 was considered significant for these tests and for bicor analyses. All reported P values are based on a two-sided hypothesis. The initial number of mice per group in the pilot (N = 6 per group) and Control studies (N = 8 per group) were determined based on previous studies where similar phenotypes were measured. For the HMDP study, permutation and simulation studies were previously used to test the statistical power of the HMDP using parameters including the variance explained by SNPs, genetic background, random errors, and the number of repeated measurements per strain (Bennett, Farber et al. 2010). Appropriate sample sizes to achieve adequate statistical power were determined based on previous analyses. Differences in sample sizes among the HMDP strains were due to differences in strain availability as determined by breeding success and losses. For GWAS, thresholds for significant (P < 4.1e-6; -log10P > 5.387) loci were defined using permutation as previously described (Bennett, Farber et al. 2010). The suggestive locus threshold (P < 4.1e-5; -log10P > 4.387) was based on reducing the significance threshold by one log unit. The cis eQTL GWAS threshold (P < 1e-4) was based on a calculated 1% FDR threshold of 1.73e-3, adjusted to 1e-4 to be slightly more conservative. The trans-eQTL threshold (P < 1e-6) was based on the 4.1e-6 threshold, adjusted to 1e-6 to be more conservative as well.”

   We tried moving the missing values notes in the second paragraph to the various method sections in the paper they apply to, but this led to much repetition and was in some cases not clear, so we decided to keep this information together in the statistical analyses section.

   1. Some part of your discussion section, is retelling the results. Please discuss your results and compare them with previous findings.

   We have revised the discussion to remove several parts that mostly just summarized the results and agree this improves the text. As mentioned above, we moved some discussion that was in the Results section to the Discussion section as well.

   1. Add detail about your GWAS model. As you had repeated samples from each strain, it's good to mention how you considered this. Also, show how you determined the significance threshold.

   Thank you for this suggestion. The GWAS software we used (FaST-LMM) derives a kinship matrix from the genotypes of the individuals considered in the analysis; this kinship matrix is used to correct for population structure including multiple individuals per strain.

   The trait GWAS significance threshold was determined using permutation analysis (Bennett, Farber et al. 2010). The suggestive GWAS threshold was based on reducing the significance threshold by one log unit. The cis eQTL GWAS threshold was based on a calculated 1% FDR threshold of 1.73e-3, adjusted to 1e-4 to be slightly more conservative. The trans-eQTL threshold was based on the 4.1e-6 threshold, adjusted to 1e-6 to be more conservative as well.

   To improve the text, we added to the Methods section under the “Genome-wide association analysis and heritability estimation” header the following:

   “Traits were quantile transformed to normalize the distribution and then GWAS was performed using the FaST-LMM program (Lippert, Listgarten et al. 2011), which corrects for population structure (including multiple samples per strain) by using a kinship matrix derived from the genotypes to be analyzed.”

   We also revised the GWAS threshold text to include more detail:

   “Analyses were performed using GraphPad Prism (GraphPad Software, La Jolla, CA) and in R. P < 0.05 was considered significant for these tests and for bicor analyses. All reported P values are based on a two-sided hypothesis. For GWAS, thresholds for significant (P < 4.1e-6; -log10P > 5.387) loci were defined using permutation as previously described (Bennett, Farber et al. 2010). The suggestive locus threshold (P < 4.1e-5; -log10P > 4.387) was based on reducing the significance threshold by one log unit. The cis eQTL GWAS threshold (P < 1e-4) was based on a calculated 1% FDR threshold of 1.73e-3, adjusted to 1e-4 to be slightly more conservative. The trans-eQTL threshold (P < 1e-6) was based on the 4.1e-6 threshold, adjusted to 1e-6 to be more conservative as well. “

   1. The abstract could be better. It also doesn't have a conclusion.

   We revised the abstract and added in a conclusion:

   “Tissue iron overload is a frequent pathologic finding in multiple disease states including non-alcoholic fatty liver disease (NAFLD), neurodegenerative disorders, cardiomyopathy, diabetes, and some forms of cancer. The role of iron, as a cause or consequence of disease progression and observed phenotypic manifestations, remains controversial. In addition, the impact of genetic variation on iron overload related phenotypes is unclear, and the identification of genetic modifiers is incomplete. Here, we used the Hybrid Mouse Diversity Panel (HMDP), consisting of over 100 genetically distinct mouse strains optimized for genome-wide association studies (GWAS) and systems genetics, to characterize the genetic architecture of dietary iron overload and pathology. Dietary iron overload was induced by feeding male mice (114 strains, 6-7 mice per strain on average) a high iron diet for six weeks, and then tissues were collected at 10-11 weeks of age. Liver metal levels and gene expression were measured by ICP-MS/ICP-AES and RNASeq, and lipids were measured by colorimetric assays. FaST-LMM was used for genetic mapping, and Metascape, WGCNA, and Mergeomics were used for pathway, module, and key driver bioinformatics analyses. Across the HMDP, we identified many traits that exhibited high inter-strain variability on the high iron diet, and we found a substantial contribution of genetics to many traits. Mice on the high iron diet accumulated iron in the liver, with a 6.5 fold difference across strain means. The iron loaded diet also led to a spectrum of copper deficiency and anemia, with liver copper levels highly positively correlated with red blood cell count, hemoglobin, and hematocrit. Hepatic steatosis of various severity was also observed histologically, with 52.5 fold variation in triglyceride levels across the strains. Most clinical traits examined had at least one significant GWAS locus, and notably, liver triglyceride and iron mapped most significantly to an overlapping locus on chromosome 7 that has not been previously associated with either trait. By genetically mapping liver mRNA expression, we identified cis- and trans-eQTL for thousands of genes, and we integrated this with trait correlation data to identify candidate causal genes at many trait loci. Using network modeling, significant key drivers for both iron and triglyceride accumulation were found to be involved in cholesterol biosynthesis and oxidative stress management. To make the full data set accessible and useable by others, we have made our data and analyses available on a resource website. Overall, our study confirms and expands upon the contribution of mouse genetic background to dietary iron overload and associated pathology. The numerous GWAS loci, candidate genes, and biological pathways identified here provide a rich public resource to drive further investigation.”

   1. Page 8, lines 4-7: Please remove these lines or move them to the Method section. The last paragraph of the introduction should clearly explain the goal of the study.

   We removed these lines and revised this paragraph for clarity:

   In order to gain further insight into genetic contributors to iron overload and associated pathology, we measured clinical traits and hepatic mRNA expression in 114 mouse strains fed a high iron diet. The mice are from a genetically diverse cohort known as the Hybrid Mouse Diversity Panel (HMDP), a panel optimized for systems genetics studies that has previously been used to examine numerous complex traits, including obesity, diabetes, atherosclerosis, heart failure, carbon tetrachloride induced liver fibrosis, and fatty liver disease (Lusis, Seldin et al. 2016; Seldin, Yang et al. 2019; Tuominen, Fuqua et al. 2021; Cao, Wang et al. 2022).

   1. Page 68, line 13: Explain the abbreviation (RINe) before use. Also, most probably it is RIN (RNA Integrity Number).

   Thank you for pointing this out. We updated the methods text as follows: “All samples had RNA integrity number equivalents (RINe) values greater than 8 as measured on an Agilent 2200 TapeStation (Agilent, Santa Clara, CA).” We also added RINe to the abbreviations section.

   1. The heritability estimates seem high and the 1% difference between broad- and narrow-sense heritability means there is almost no dominant and epistatic genetic variance between alleles affecting the studied trait (which is hard to accept). I recommend considering a within-group (strain) variance (common environmental effect) component in the model to absorb this source of variation in this component, so the genetic variance and consequently the heritability estimates would be more accurate. You also can consider this source of variance in your GWAS model.

   Thank you for bringing up these points. While we try to minimize environmental effects by keeping these mice and samples in as similar environmental and experimental conditions as feasible, some will remain. Thus, in our analyses, we try to factor in remaining environmental variation by using data from multiple mice per strain. The programs we used for GWAS and heritability calculations take into account within-group (strain) variance. We added the following sentence to the Methods section just after mention of the programs used to calculate heritability:

   “Both of the software packages used for heritability estimation account for environmental variance within strains.”

   We agree that the broad-sense and narrow-sense estimates are close to each other for many traits and that this suggests low levels of dominance and epistasis. A low level of non-additive genetic variance is not uncommon and theoretically predicted for complex traits, as has been reported previously and discussed in the references below:

   Hill WG, Goddard ME, Visscher PM. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 2008 Feb 29;4(2):e1000008. doi: 10.1371/journal.pgen.1000008. PMID: 18454194

   Hivert V, Sidorenko J, Rohart F, Goddard ME, Yang J, Wray NR, Yengo L, Visscher PM. Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals. Am J Hum Genet. 2021 May 6;108(5):786-798. doi: 10.1016/j.ajhg.2021.02.014. Epub 2021 Apr 2. Erratum in: Am J Hum Genet. 2021 May 6;108(5):962. PMID: 33811805

   It has also been argued that many human GWAS studies, as well as studies using populations of mice designed for complex trait analyses, including the HMDP population, inherently lack the statistical power to detect epistasis:

   Buchner DA, Nadeau JH. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res. 2015 Jun;25(6):775-91. doi: 10.1101/gr.187450.114. Epub 2015 May 7. PMID: 25953951

   Taking all this together we would argue that it is not surprising to see the little difference between the narrow and broad heritability estimates for many traits in our study. To provide more context to the reader regarding how to interpret our heritability findings, we added the following text to the discussion section, under limitations:

   “Finally, in our study with the HMDP population, estimated broad and narrow sense heritabilities were similar for many traits, suggesting modest non-additive contributions (e.g dominance and epistasis) to the variance in these traits. While such results are common and theoretically predicted for complex traits (Hill, Goddard et al. 2008; Hivert, Sidorenko et al. 2021), our study population may also not be optimal for detection of these effects (Buchner and Nadeau 2015).”

   Read the original source
2. 

   eLife

   Mar 9, 2024

   eLife assessment

   This manuscript presents a detailed phenotyping of the role of dietary iron in a large number of genetically distinct mouse strains. There are exciting and convincing data that could be valuable in their impact on the fields of nutrition, iron metabolism and anemia.

   Read the original source
3. 

   eLife

   Mar 9, 2024

   Reviewer #1 (Public Review):

   In this manuscript, the authors perform a very thorough, extensive characterization of the impact of an iron-rich diet on multiple phenotypes in a wide range of inbred mouse strains. While a work of this type does not offer mechanistic insights, the value of the study lies not only in its immediate results but also in what it can offer to future researchers as they explore the genetic basis of iron levels and other related phenotypes in rodent studies. The creation of a web resource and the offer from the authors to share all available samples is particularly laudable, and helps to increase the accessibility of the work to other scientists. There is one shortcoming to the work however. To induce iron overload in mice in the main study in this work, mice were placed on an iron-rich diet that differed in its …

   Reviewer #1 (Public Review):

   In this manuscript, the authors perform a very thorough, extensive characterization of the impact of an iron-rich diet on multiple phenotypes in a wide range of inbred mouse strains. While a work of this type does not offer mechanistic insights, the value of the study lies not only in its immediate results but also in what it can offer to future researchers as they explore the genetic basis of iron levels and other related phenotypes in rodent studies. The creation of a web resource and the offer from the authors to share all available samples is particularly laudable, and helps to increase the accessibility of the work to other scientists. There is one shortcoming to the work however. To induce iron overload in mice in the main study in this work, mice were placed on an iron-rich diet that differed in its composition from the baseline diet in more than just iron. This could influence some of the phenotypes observed in this study.

   Read the original source
4. 

   eLife

   Mar 9, 2024

   Reviewer #2 (Public Review):

   Here, the authors tried to identify the genes and biological pathways underlying iron overload and its associated pathologies in mice. Several wet lab experiments and measurements alongside many bioinformatic analyses like GWAS, RNA-seq data analysis (DEG), eQTL analysis, TWAS, and gene-set enrichment analysis have been performed. The study design is good enough and the author tried to validate the results. The data have been submitted (Accession #: GSE230674) but are not public yet.

   (1) The main issue of this manuscript is its length. It's too long, especially the result section. It's hard for readers to follow the paper. Moreover, you added results about other minerals, mostly copper, which seems too much (considering the fact that this study is about iron). The text doesn't have the required Integrity and …

   Reviewer #2 (Public Review):

   Here, the authors tried to identify the genes and biological pathways underlying iron overload and its associated pathologies in mice. Several wet lab experiments and measurements alongside many bioinformatic analyses like GWAS, RNA-seq data analysis (DEG), eQTL analysis, TWAS, and gene-set enrichment analysis have been performed. The study design is good enough and the author tried to validate the results. The data have been submitted (Accession #: GSE230674) but are not public yet.

   (1) The main issue of this manuscript is its length. It's too long, especially the result section. It's hard for readers to follow the paper. Moreover, you added results about other minerals, mostly copper, which seems too much (considering the fact that this study is about iron). The text doesn't have the required Integrity and focus. You should decide where you want to put the focus of this manuscript and I strongly recommend shortening the manuscript, try to be short and sweet as much as you can.
   (2) Also, the "Methods" section is long, some parts are over-detailed (mostly wet lab procedures) and some parts are not detailed enough. It seems the "Statistical analyses" part doesn't have extra information. I recommend removing the first paragraph and moving some of the information from the second paragraph to the right place in the Method section.
   (3) Some part of your discussion section, is retelling the results. Please discuss your results and compare them with previous findings.
   (4) Add detail about your GWAS model. As you had repeated samples from each strain, it's good to mention how you considered this. Also, show how you determined the significance threshold.
   (5) The abstract could be better. It also doesn't have a conclusion.
   (6) Page 8, lines 4-7: Please remove these lines or move them to the Method section. The last paragraph of the introduction should clearly explain the goal of the study.
   (7) Page 68, line 13: Explain the abbreviation (RINe) before use. Also, most probably it is RIN (RNA Integrity Number).
   (8) The heritability estimates seem high and the 1% difference between broad- and narrow-sense heritability means there is almost no dominant and epistatic genetic variance between alleles affecting the studied trait (which is hard to accept). I recommend considering a within-group (strain) variance (common environmental effect) component in the model to absorb this source of variation in this component, so the genetic variance and consequently the heritability estimates would be more accurate. You also can consider this source of variance in your GWAS model.

   Read the original source
5. 

   Version published to 10.1101/2023.06.05.543764v1 on bioRxiv

   Jun 7, 2023