Testicular content of metabolites related to insulin resistance, cell membrane remodeling, nutritional support and antioxidative stress  were altered in sons and grandsons of mice fed with HFD, comparing to descendants of chow-fed mice. Sperm counts were lower in the grandsons of mice fed with HFD, even if transient. Sperm quality was correlated to testicular metabolite content in all generations. Principal Component Analysis of sperm parameters and testicular metabolites revealed an HFD-related phenotype, especially in the diet-challenged generation and their grandsons. Ancestral HFD, even if transient, causes transgenerational “inherited metabolic memory” in the testicular tissue, characterized by changes in testicular metabolome and function.The consumption of energy-dense diets has contributed to an increase in the prevalence of obesity and its comorbidities worldwide. The adoption of unhealthy feeding habits often occurs at early age, prompting the early onset of metabolic disease with unknown consequences for reproductive function later in life. Recently, evidence has emerged regarding the intergenerational and transgenerational effects of high-fat diets (HFD) on sperm parameters and testicular metabolism. Hereby, we study the impact of high-fat feeding male mice (F Obesity-related comorbidities have increased proportionally, including non-communicable diseases, such as type 2 diabetes (T2D)2. Of interest, male infertility has been also correlated to this increase in obesity prevalence. Several studies report a coincident temporal trend in sperm quality decline4. Others report how male obesity is associated with infertility6 and lower success of Assisted Reproduction Techniques (ART)8. Recently, evidence has emerged concerning the intergenerational and transgenerational effects of male obesity on the health of their progeny11, particularly on sexual health13. HFD cause acute changes in human sperm14, and permanent changes in murine testicular metabolite content15. The age of onset of obesity and its comorbidities are occurring at an ever younger age16. Therefore, it is of utmost relevance to understand the metabolic factors transmitted via paternal lineage to the progeny.Modern societies promote a fast-paced daily life that often leads to poor lifestyle choices. Notably, readily available and palatable fast food contributes to a high-fat diet (HFD) that has led to a marked increase in the prevalence of overweight and obesity worldwide18, including studies restricted to paternal inheritance. Although the male gamete can only carry a minimal amount of small non-coding RNAs (sncRNAs) besides the DNA content17, these studies show metabolic phenotypic changes inherited by direct progeny (sons) and subsequent descendants (grandsons)21. Moreover, seminal fluid composition potentially affects early phases of embryo development22. Epigenetic mechanisms respond to metabolic inputs, such as metabolite availability at cellular level, which is affected by diet22. “Metabolic memory” has been coined with metabolic adaptations in somatic cells, in the context of glycemic control and T2D complications23. Previous studies demonstrate that both somatic and germline cells adapt in response to metabolic, environmental cues, particularly HFD, and these adaptations are potentially inherited by the offspring via the paternal lineage21. Therefore, we advocate that “metabolic memory” could be adapted to the context of transgenerational epigenetic inheritance. This “inherited metabolic memory” refers to the inheritance of acquired adaptations to environmental variables in ancestry, such as diet, by progeny which has not been exposed to the same stimuli, for several generations. In this study we adopted a multivariate analysis of untargeted 1H-NMR metabolomics, to assess the effects of lifelong or temporary paternal HFD upon testicular metabolome and sperm parameters of the direct offspring (sons) and second-generation offspring (grandsons) in mice. Biometric and glucose homeostasis data was also recorded individually across generations. Based on these data, we demonstrate “inherited metabolic memory” caused by ancestral exposure to HFD in the paternal lineage.Rodent models have been widely applied in transgenerational studies involving ancestral exposure to HFD1) were significantly heavier than sons of CTRL mice (19 ± 2 g vs 15 ± 4 g). In generation F2, no differences were found between groups , 60 days after weaning, HFDt mice start losing weight, and thereafter HFD mice became heavier than both CTRL and HFDt mice. In F1 Generation no differences were found between groups at any period, except at weaning (as previously mentioned). Similarly, no differences were found between the grandsons of the different groups (F2 Generation). The weight of various organs, fat mass and gonadosomatic index (GSI) were also assessed . Comparing to mice from CTRL, mice from the HFDt generated F1 mice with smaller testes , less epididymal (0.55 ± 0.14 g vs 0.76 ± 0.20 g) and perirenal (0.21 ± 0.07 g vs 0.29 ± 0.10 g) fat, lower fat mass (4.19 ± 0.96% vs 5.32 ± 1.01%) and GSI (0.74 ± 0.09% vs 0.85 ± 0.10%).The experimental design of this study is detailed in “0 Generation) characterized by increased insulin resistance   and the intraperitoneal Insulin Tolerance Test (ipITT)  and lower proportion of decapitated sperm (9 ± 2%) than those from HFD (32 ± 5% and 11 ± 6%) and CTRL (33 ± 5% and 13 ± 6%). The offspring of HFD mice also had a lower proportion of decapitated sperm (11 ± 6%) than CTRL (13 ± 6%), but higher proportions of pin head (9 ± 2%) and bent neck (8 ± 4%) defects. Interestingly, the F2 generation from HFD and HFDt mice had decreased sperm counts compared to CTRL (36.42 ± 8.62 M/mL and 33.60 ± 6.12 M/mL respectively). Moreover, although no changes in sperm viability were found between groups in F2 generation, it is interesting to note that F2 from HFD and HFDt mice had lower values of sperm viability (32 ± 8% and 36 ± 6% respectively) than their F1 progenitors (43 ± 4% and 32 ± 8%). Regarding sperm morphology in generation F2, those mice obtained from HFD mice had significantly lower proportion of normal sperm (34 ± 5%) than those F2 obtained from the other groups. Mice of the F1 generation had a significantly higher proportion of head defects  than their offspring in F2 generation . The proportion of head defects in the HFD mice, generation F2, explains the lower proportion of normal sperm in this group. Reproductive parameters  were assessed in Generations F0 and F1  to CTRL of the corresponding generation. The full list of metabolites identified by 1H-NMR is available as Supplementary Table 2 FC) than in sons of CTRL mice (F1 generation). The grandsons of mice fed HFD (F2 generation) had lower levels of testicular leucine (0.03 ± 0.21 log2 FC) than their F1 progenitors. Testicular glutamine levels . No changes were found in the sons of the diet-challenged mice (F1 generation). However, the grandsons of HFDt mice had decreased levels of testicular glutamine, comparing to grandsons of CTRL (-0.18 ± 0.07 log2 FC). HFD-fed mice, even transiently, decreased testicular acetate content more than 1.5-fold, compared to CTRL, in generation F0  overcompensated their progenitors’ defect in testicular acetate, particularly the sons of HFDt (0.48 ± 0.35 log2 FC). This effect persisted in the grandsons of HFD-fed mice (F2 generation), notably in the grandsons of HFD (1.33 ± 0.16 log2 FC), which had more than 2.5-fold more testicular acetate than the grandsons of CTRL. Despite the mean testicular acetate content of the grandsons of HFDt being 1.2-fold increased comparing to CTRL, this difference was not significant due to the large standard deviation of the results (0.27 ± 0.78 log2 FC). Concerning inosine  compared to CTRL (0.00 ± 0.31 log2 FC) in generation F0. Again, no differences were found for the testicular content of inosine in the sons of the diet-challenged mice (F1 generation). Interestingly, the grandsons of HFDt mice had lower inosine levels in testes (− 0.68 ± 0.40 log2 FC) than the grandsons of CTRL (0.00 ± 0.15 log2 FC). Detailed values are shown in Table Testicular polar metabolites were extracted and analyzed by els Fig. b, in F0  F0 Fig. c. The so0: 31 correlations; F1: 17 correlations; F2: 9 correlations), especially when the p-value is adjusted for multiple testing . Before FDR p-adjustment, acetate, glutamine and myo-inositol showed significant correlations with sperm parameters in all generations, whereas glutamine had the highest number of significant correlations with sperm parameters (9), most of them in generation F0 (4). After FDR p-adjustment, acetate and succinate had the highest number of significant correlations with sperm parameters (2), all in F0 generation.Sperm parameters and testicular metabolites were correlated using Pearson’s method Fig. . Correla24  and all tested testicular metabolites was performed using Principal Component Analysis (PCA). Two principal components were extracted to represent each sample in a two-dimensional Euclidean space Fig. . A spati24 Table .Figure 60 clustering and well-separated groups are evident  , the discrimination between groups was still significant   were better separated than their F1 progenitors  . There is also a significant difference between HFD and HFDt in F2 Generation (p = 0.089). When analyzing all the groups at once . Also, regardless of the generation, all CTRL samples are clustered near the origin. In this model, all the factors are significant predictors, although generation is the most relevant predictor . It was not possible to calculate multiple comparisons in this scenario.In generation F2. Our data show that there are paternally-mediated intergenerational and transgenerational effects on testicular metabolome and sperm quality caused by a HFD, even if transient  and grandsons  of the diet challenged fathers.Metabolic diseases have reached pandemic proportions worldwide. Excess energy intake is considered the primary driver for the development of metabolic diseases. However, it is now clear that there are hereditable latent signals that trigger metabolic dysfunction. We used a multivariate analysis approach based on untargeted testicular metabolomics and analyzed sperm quality in fathers exposed to different diets and their offspring up to F28 and the metabolic health of the father has been overlooked. However, an increasing number of studies show that the metabolic profile of the father at conception also influences the metabolic profile of the offspring. For instance, paternal obesity has an impact on the offspring metabolic health30 as fathers with obesity are more likely to have children with obesity31. Interestingly, we have found limited evidence on this phenomenon. No differences were found in HOMA2-IR, fasting glucose and fasting insulinemia in the sons and grandsons of mice fed with HFD. Yet, the sons of HFD mice had increased body weight at weaning than the sons of CTRL and HFDt, whereas the sons of HFDt had lower fat mass than the other groups, which elicits an effect of the father’s diet in the body composition of the progeny. Also, the sons of HFD mice had increased serum glucose levels at 90 and 120 min during insulin tolerance test than the sons of CTRL and HFDt mice, reflecting an abnormal insulin response. These intergenerational effects of HFD in body adiposity and glucose homeostasis have been associated with altered DNA methylation patterns32. Particularly, it has been shown that paternal exposure to HFD is associated with aberrant DNA methylation of adipogenic genes and genes related to increased diabetic risk34, even in humans35. We have found that sons of mice fed with HFDt had lower GSI than sons of CTRL and HFD mice, suggesting potential fertility problems. Infertility has been described as one of the most striking silent co-morbidities caused by metabolic diseases36. We have previously showed that consumption of HFD causes irreversible changes in testicular metabolism even after switching to normal diet, using the same experimental model used here for the progenitors15. Sperm quality is the first line assessment of male fertility potential and a central indicator of testicular function. Our data show a transgenerational effect on sperm quality, particularly on sperm counts. The grandsons of individuals fed with HFD, even if transient, had a decreased sperm counts when compared with grandsons of individuals fed with a normal diet. A recent systematic review and meta-regression analysis showed that sperm counts are decreasing worldwide to dangerous levels3. Concomitantly, metabolic diseases incidence and prevalence are also rising41. Thus, a relationship between these two factors has been proposed and discussed42. Our data show that HFD, even if transient, has a transgenerational effect on sperm counts. We also detected inter- and transgenerational effects of diet on sperm morphology, particularly on head defects. Thus, our data suggest that unhealthy dietary habits induce inter- and transgenerational effects on sperm quality.Maternal metabolic dysfunction has been associated with a reprogramming that promotes diabetes or obesity in the offspring43. Decreased glycine levels have been associated with decreased cell permeability, mitochondrial membrane potential and the expression of blood-testis-barrier factors in the TM4 cell line44, which derives from murine Sertoli Cells. The increase in testicular glycine content could be due to an overcompensation mechanism to overcome the damage caused by HFD in the ancestors. Indeed, despite the increased body weight at weaning of the sons of HFD mice, there were no differences in sperm parameters between mice in this generation (F1), and glycine positively correlated with sperm concentration. The sons of mice fed with HFD until early adulthood displayed changes in the testicular content of leucine, a branched-chain amino acid, compared to sons of CTRL. Branched-chain amino acids have been implicated in the onset of metabolic syndrome and insulin resistance45. Increased serum branched-chain amino acid levels are associated with obesity and diabetes in humans, in response to insulin resistance47. There are several testis-specific or abundant leucine-rich proteins49. Though the role for these proteins is not well characterized, accumulation of leucine may impact upon the assembly and function of these testicular leucine-rich proteins. Testicular acetate levels were increased in the sons of HFDt mice and in the grandsons of HFD mice, compared to CTRL mice of the corresponding generation. Acetate is exported by Sertoli Cells to developing germ cells, where it is used as a membrane lipid precursor50. It was previously described that leptin promotes the excretion of acetate in isolated human Sertoli Cells51. Considering the reduced sperm counts of the grandsons of mice fed HFD, compared to the grandsons of CTRL, our data suggests that the turnover of germ cells is increased in these mice. Testicular glutamine levels were decreased in the grandsons of mice temporarily exposed to HFD. Glutamine is a crucial energy substrate for Sertoli Cells, whose metabolism is similar to the Warburg effect described in cancer cells52. Grandsons of HFDt mice also had a decrease in testicular inosine, compared to grandsons of CTRL and grandsons of HFD. Inosine is a precursor of purines. In rat testis, high-energy diets decrease inosine levels, with concomitant increase in hypoxanthine, which promotes a pro-inflammatory environment53, due to increased xanthine oxidase activity. Indeed, testicular ascorbate levels are decreased in the grandsons of HFD mice, compared to the grandsons of HFDt mice. Therefore, the decrease in glutamine may impair Sertoli Cell ability to provide nutritional support to developing germ cells. The inhibition of the rate-limiting reaction converting inosine to guanylyl metabolites has been used to arrest meiosis in mice oocytes54. Thus, lower testicular inosine availability may inhibit the differentiation of germ cells, notably by inhibiting meiosis. This is reflected by our data that shows that the grandsons of HFDt mice had lower sperm counts than the grandsons of CTRL. However, the association between transgenerational epigenetic and metabolomic fingerprints is scarce in the literature. Yet, a study in rats has demonstrated that early life exposure to di-n-butyl phthalate, an endocrine disruptor, caused an increase in testicular betaine levels for three generations, with concomitant decrease in sperm counts and DNA hypomethylation55. Although the ancestral exposure in this study was an indirect in utero exposure (embryonic exposure), it illustrates that metabolic fingerprints are likely the result of epigenetic factors that persist in the paternal lineage for several generations.To further determine how diet-mediated inter- and transgenerational effects on sperm are mediated in the testis, we performed an exploratory analysis based on untargeted metabolomics from testicular extracts and multivariate analysis. Testicular metabolome reflected inter- and transgenerational effects of HFD consumption even when this occurred only until early adulthood. We found that the sons of mice fed with HFD, even temporarily, have higher levels of testicular glycine than sons of mice fed with standard chow. Glycine supplementation has been reported to improve sperm parameters in hypercholesterolemic ratst mice display an intermediate phenotype between the sons of CTRL and the sons of HFD mice. However, the grandsons of HFD and HFDt present specific testicular metabolome traits compared to CTRL grandsons, which are related to their poorer sperm parameters. Taken together, these results suggest that HFD, even transiently, causes a significant impact on sperm parameters and testicular metabolism in the diet-challenged generation (F0), but also has intergenerational (F1) and transgenerational (F2) effects on the offspring. Our findings corroborate previous studies implicating deleterious effects caused by ancestral HFD on sperm parameters in rodents57. Our data also support the theory that testicular metabolome is involved in this phenomenon, though the mechanism underlying the inheritance of this phenotype is still unknown. The epigenetic signatures carried by spermatozoa is one of the most plausible mechanisms involved in this inheritance. They have been recently associated to the inheritance of the acquired phenotypes, notably in the male lineage and in response to ancestral exposure to HFD58. The DNA methylation pattern is one of the mechanisms previously implicated in the epigenetic transmission of traits. Previous studies have demonstrated that dietary and environmental exposures can alter the extent and the loci of methylation of the DNA carried by spermatozoa, thus conditioning gene expression as early as during embryo development60. Yet, the extent to which DNA imprinting due to methylation can significantly affect development and health outcomes in the offspring is still debatable61. Nevertheless, the influence of sperm DNA methylation and sperm sncRNA content in the inheritance of testicular metabolomic traits should be considered, and it must be a focus of further studies.PCA was used in conjugation with permANOVA to evaluate the discriminant power of testicular metabolite content and sperm parameters as a function of diet and the male ancestor’s diet Fig. . Indeed,15. Hence, spermatozoa may carry signatures sensitive to HFD that encode the “metabolic memory” of testicular cells in the offspring, which drive the inter- and transgenerational phenotypes of sperm parameters. “Metabolic memory” was coined with the metabolic adaptations occurring at the cellular level in progressive stages of metabolic syndrome, and as a response to hyperglycemic states23. This term is, however, too narrow in the context of intergenerational and transgenerational inheritance. “Inherited Metabolic Memory” refers to the inheritance of acquired metabolic signatures in response to environmental variables by the progeny which has not been exposed to the original stimuli. In summary, we suggest that an ancestral exposure to HFD, even if transiently, causes “inherited metabolic memory” in testicular tissue of the offspring up to two generation. This “inherited metabolic memory” is likely transmitted by epigenetic markers carried by the sperm, and is characterized by changes in the testicular metabolome, testicular function, and sperm parameters of the non-exposed progeny. Further research must focus on the epigenetic mechanisms underlying the “inherited metabolic memory”.Here we show that sperm parameters are correlated with testicular metabolome, which corroborates our earlier findingsMus musculus C57BL6/J mice  was reared as previously described62. Mice were originated from normoponderal progenitors , fed with a standard chow  and water ad libitum. After weaning (21–23 days), F0 mice (n = 36) were randomly divided in three groups: control (CTRL) (n = 12), HFD (n = 12) and HFDt (n = 12). Mice from the CTRL group were fed with a standard chow. Mice from the HFD group received a HFD . The mice from HFDt group were fed with a HFD for 60 days  and then switched to standard chow . F0 mice were mated starting at 120 days of age with normoponderal, chow-fed, randomly selected females of same age to generate the F1 generation. Mating lasted for 8 days and involved placing a male and a female in the same cage for 6 h each day, without water or food supply. After weaning, F1 mice were assigned to the same experimental group as their fathers: CTRL_F1—offspring of CTRL (n = 12); HFD_F1—offspring of HFD (n = 12); HFDt_F1—offspring of HFDt (n = 12). In this generation (F1), all mice were fed with standard chow. Food and water were supplied without restrictions. The mating of F1 mice was performed under the same conditions as their progenitors (Generation F0). Mice from the resulting generation F2 were assigned to experimental groups in the same way as their fathers . All mice of the generation F2 were fed with standard chow after weaning. Food and water were supplied without restrictions. All mice in all generations were kept in 12 h dark:light cycles, 4 mice per cage. Mice from all generations were euthanized by cervical dislocation 200 days after weaning, and tissues were collected for further analysis. Total body weight, water and food intake were monitored weekly from weaning to sacrifice. The animal model is compliant with the ARRIVE guidelines, was internally reviewed by the Organization for Animal Welfare (ORBEA) and approved and licensed by the Portuguese Veterinarian and Food Department (DGAV) with the registration number 0421/000/000/2016. All animal experiments were performed according to the “Guide for the Care and Use of Laboratory Animals” published by the US National Institutes of Health  and European rules for the care and handling of laboratory animals (Directive 86/609/EEC).This study was performed in three generations of 63, at 196 and 198 days post-weaning age. ipGTT was performed after overnight fast, whereas ipITT was performed in the morning after a 4 h fast. Fasting glycemia was measured before sacrifice (200 days post-weaning) using a glucometer  by collecting a drop of blood from the tail vein. Blood was then collected by cardiac puncture and centrifuged at 1500×g, 4 ºC, for 10 min to collect the serum. Insulin was quantified in serum using a Rat/Mouse Insulin ELISA assay . Glucose homeostasis was evaluated according to the HOMA2 scores64, using the HOMA2 calculator .Glucose tolerance and insulin resistance were assessed experimentally using the intraperitoneal glucose tolerance test (ipGTT) and intraperitoneal insulin tolerance test (ipITT) protocols65. Sperm motility was calculated as the average proportion of motile sperm in 10 random microscope fields, observing a drop of sperm suspension on a warmed slide (37 °C) using an optical microscope (× 100 magnification). Epididymal sperm concentration was determined using a Neubauer counting chamber and an optical microscope (× 400 magnification), diluting the sperm suspension 1:50 in HBSS. Sperm viability and morphology were assessed in differently stained epididymal sperm smears, counting 333 spermatozoa in random fields using an optical microscope (× 400 magnification). Sperm viability smears were stained with eosin‐nigrosin66, as membrane-compromised spermatozoa stain pink. Sperm morphology smears were stained with Diff‐Quick . Sperm morphology categories were mutually exclusive, i.e., spermatozoa displaying more than one defect were assigned according to the most serious defect category (decapitated > pinhead > flattened head > bent neck > coiled tail)66.Epididymides were isolated immediately after sacrifice (200 days post-weaning age) and placed in pre-warmed (37 °C) Hank’s Balanced Salt Solution (HBSS) pH 7.4, minced with a scalpel blade and the suspension was incubated for 5 min (37 °C). Sperm parameters were evaluated as previously described0 and F1 generations, we assessed the number of pregnant females to calculate the fertility rate of mice in different groups. Matings were performed until a sufficient number of pups to continue the experiment was achieved (12 pups per group in each generation). The total number of matings performed in each group and generation is available in the Supplementary Table After the mating of animals from F67. The aqueous phase containing polar water-soluble metabolites was lyophilized and analyzed by NMR spectroscopy, following a semi-quantitative methodology as described previously68. Metabolites were identified by comparing recorded spectra with reference spectra and the Human Metabolome Database69 and according to Metabolomics Standards Initiative guidelines for metabolite identification70. Identification levels are indicated in the Supplementary Table 1H spectra were processed as described previously and well-resolved metabolite multiplets were integrated 68. Obtained areas were normalized to total spectral area, and then represented as the log2 fold variation (FC) to the CTRL group of the corresponding generation. Univariate and multivariate statistical analysis was conducted based on these values . Tabular data was tested for independence by χ2 test, and column proportions were tested using z-test with Bonferroni’s correction. Correlations between variables coding for sperm parameters and testicular metabolites were calculated using Pearson’s method. Correlation strength was classified according to ranks72, and adjusted p-values for FDR were calculated using Benjamini–Hochberg method. PCA based on testicular metabolites and sperm parameters was performed to assess sample clustering according to diet and generation. Sample adequacy was assessed by the Kaiser–Meyer–Olkin Test and by the Bartlett’s Test of Sphericity. Forced factor extraction was performed to extract two principal components, using Varimax with Keiser’s Normalization as Rotation Method. These methods were performed using IBM SPSS Statistics v26 . Euclidean distances between samples were obtained from the rotated PCA solution. Groups were compared according to distances in the Euclidean space using permANOVA24. Pairwise comparisons were corrected by controlling the FDR using the Benjamini–Hochberg method. These methods were performed on R 4.0.173, running the R-packages vegan 2.5-674 and RVAideMemoire, 0.9-7775. The significance level for pairwise corrections  was set to p < 0.05. The significance level for FDR-controlling methods was considered when adjusted p < 0.1.Data normality and homoscedasticity were tested using Kolmogorov-Smirnoff test with Lilliefors’s correction and Levene’s test. Parametric statistics were adopted if one of the above assumptions was met. Pairwise comparisons were tested using univariate ANOVA corrected by Tukey’s HSD. Testicular metabolites were further corrected for multiple hypothesis testing, controlling the False Discovery Rate (FDR), using Benjamini–Hochberg methodSupplementary Information.


In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids. Heritable traits can be affected by additive, dominance, and epistatic effects at genetic loci. Here, the authors use chromosomally-encoded barcodes to perform linkage mapping in diploid cross progeny in budding yeast, finding that epistasis in diploids frequently modifies both additivity and dominance. How alleles at these loci collectively produce the heritable trait variation in genetically diverse populations remains unresolved3. While additive loci play a major role in most traits, non-additive genetic effects are also likely important9. However, loci with non-additive genetic effects are often difficult to detect, limiting knowledge of their properties11.Most complex traits, including many phenotypes of agricultural, clinical, and evolutionary significance, are specified by multiple loci13. Most empirical studies of non-additive genetic effects have focused on haploid or inbred individuals16, which provide higher statistical power to detect loci due to their minimal levels of heterozygosity. However, by design, these populations cannot furnish insight into dominance or its relationship with epistasis. This is a problem because many eukaryotic species that matter to humans, including our species itself, exist predominantly as diploids that outbreed and have high levels of heterozygosity19. Dominance may be an important contributor to traits in these species.The two purely genetic sources of non-additivity are dominance among alleles of individual loci and epistasis between alleles at different loci (or genetic interactions)22. Such interplay has implications for efforts to genetically dissect phenotypes, predict heritable traits from genotypes, and understand the evolutionary trajectories of beneficial and deleterious alleles. Yet, exploration of the relationship between additivity, dominance, and epistasis has mainly been limited to theory because of technical challenges in identifying non-additive loci.When epistasis occurs in diploids, a locus may influence only the additive effects, only the dominance effects, or both the additive and dominance effects of its interactor(s)Saccharomyces cerevisiae is a potentially powerful system for studying non-additive genetics in diploids. Haploid yeast segregants with known genotypes can be mated to produce diploid strains that also have known genotypes23. This strategy facilitates the generation of diploid mapping populations that are roughly the square of the number of haploid progenitors. However, phenotyping large diploid populations of more than ~10,000 individuals has been technically difficult24, limiting the use of this strategy.The budding yeast Here, we develop a chromosomally-encoded barcoding system that enables phenotyping of large yeast diploid mapping populations. We fuse two genomic barcodes, one from each haploid parent, in vivo to create a unique double barcode for each diploid strain Fig. . This syS. cerevisiae isolates, the commonly used lab strain BY4716 (BY) and a haploid derivative of the clinical isolate 322134S (3S)  Fig. . These sMATa and MATα strains (2 barcodes per segregant) were mated as pairs and grown on media that induced site-directed recombination between the MATa barcode and MATα barcode on homologous chromosomes . BY/3S and 3S/BY segregants were more fit than the BY/BY or 3S/3S diploids in all environments but one , with 40% 19–53%) being additive and 21% (19–26%) being non-additive 38 to identify an average of 18 loci per environment (10–26).Using quantile normalized fitness estimates from barcode sequencing, we mapped loci that contribute to growth , >2.5-fold more loci than detected by FaST-LMM Fig.  and b. AMany of the loci identified by FaST-LMM overlapped with the conservative set of enriched family-level loci, but there were also a number of differences between the methods Fig. . 29% of 23. Using these values accounts for family structure and enables mapping of non-additive loci in the full segregant panel with fixed effects linear models. Regarding dominance effects, we identified an average of 18 loci showing dominance per environment (12–30). 49% of these loci were also identified by FaST-LMM, while 72% of these loci were detected in the conservative set of loci from the family-level scans. Among loci with dominance effects, the average degree of dominance was ~51% , with 82% of the loci showing incomplete dominance  of all two- and three-locus interactions involved at least one hub. Fine-mapping localized the Chromosome VI, VIII, X, and XII hubs to genes involved in amino acid sensing (PTR3), copper resistance (CUP1), vacuolar protein sorting (VPS70), and a gene of unknown function (YLR257W), respectively.We next analyzed the relationship between individual loci and their genetic interactions. We found a strong positive relationship between the effect of a locus and its involvement in two- and three-locus interactions Fig. . This su22. To better characterize how loci are modified, each two-locus interaction was partitioned into additive and dominance components. We found that changes in dominance account for ~44% of the average epistatic effect .Although not a hub, the Chromosome III locus nevertheless had a prominent impact on phenotype by modifying the dominance effects of multiple variable effect loci. Interactions with the Chromosome III locus had greater impacts on dominance than additivity at all focal loci. For example, in hydrogen peroxide, dominance at the Chromosome X variable effect locus depended on the Chromosome III locus, ranging from complete to nearly absent in a genotype-dependent manner Fig. . We deliMATa, MATα, and diploid transcriptional programs40. This region of the genome is unique because four genotype classes segregate , and as a result, the two heterozygotes are not identical  and a haploid derivative of the clinical isolate 322134S (3S). To generate counterselectable markers in each strain, we first introduced clean deletions of FCY1 and URA3. Each gene was deleted using a two step approach: first, genes were replaced with a KanMX cassette44 via lithium acetate transformation45. Next, the cassette was targeted using CRISPR/Cas9 and a gRNA specific to the pTEF promoter region in each cassette46. A repair template homologous to the upstream and downstream region of the target gene was co-transformed with the CRISPR system to generate a clean deletion. The MATa BY and 3S strains were then mating-type-switched by transforming a plasmid containing galactose-inducible HO and URA3 using the lithium acetate protocol47. Strains with the plasmid were selected on SCM-Ura plates and inoculated into SCM-Ura + 2% galactose media overnight. Individual colonies were then obtained by plating out 102 cells onto YPD plates. Colonies were tested for their mating type using the yeast mating halo assay48. Successfully mating-type-switched BY and 3S MATα clones were cured of the HO plasmid by growing the cells on YPD + 5-FOA plates. The BY MATa and MATα cross parents were preserved at −80 °C with the unique identifiers IEY1176 and IEY1177, respectively. The 3S MATa and MATα cross parents were preserved at −80 °C with the unique identifiers IEY1178 and IEY1179, respectively.All haploid segregants and diploid segregants described in this paper were generated from a cross using two isolates of FLO8, a transcriptional activator of many flocculins49, and FLO11, the flocculin responsible for many aggregation phenotypes in S. cerevisiae50, were first knocked out in both mating types of the BY fcy1∆ ura3∆ and 3 S fcy1∆ ura3∆ strains. Each parental strain was then engineered to have a genomic ‘landing pad’29 containing two partially crippled LoxP sites51, Lox71 and Lox2272/71, and a galactose-inducible Cre recombinase52 at the YBR209W locus via CRISPR/Cas9-mediated homologous recombination  to maximize the number of unique recombination breakpoints. To avoid segregants carrying aneuploidies, only tetrads that produced 4 spores were utilized.Opposite mating types of BY MATα segregants were barcoded by integrating a randomly barcoded plasmid via Cre-mediated homologous recombination at lox2272/71  to determine where crossover events occured. A sequencing library was prepared using the Illumina Nextera Kit with custom multiplexing barcodes26. Libraries from different segregants were pooled in equimolar fractions and these multiplex pools were size selected using the Qiagen Gel Extraction Kit. Multiplexed samples were then sequenced on an Illumina HiSeq 2500 using 150 bp × 150 bp paired-end reads. For each strain, reads were mapped against the S288c genome using BWA with default settings55. Alignments were converted to a bam format and sorted using SAMTOOLS (default settings)56. Read duplicates were then removed and bam files were converted to pileups using SAMTOOLS (default settings). Base calls and coverage values were obtained from the pileup files for 43,865 high-confidence SNPs that segregate in the BYx3S cross. Any segregants that showed signs of cross-contamination, where the allele frequency at the SNP markers significantly deviated from the expected 0% or 100% 3S allele, were excluded from further analysis. To avoid segregants with aneuploidy, all segregants whose average coverage of each individual chromosome or segments of the chromosome significantly deviated from the overall average coverage were also removed. Additionally, all segregants with a mean coverage of less than 2 were removed. In total, 76 MATα and 11 MATa segregants were removed after filtering for quality. For the remaining 927 MATa and 489 MATα segregants, a vector containing the fraction of 3S calls at each SNP was generated and used to make initial genotype calls with sites above and below 50% classified as 3S and BY, respectively. This vector of initial genotype calls was then corrected with a Hidden Markov Model (HMM), implemented using the HMM package version 1.057 in R58. We used the following transition and emission probability matrices: transProbs = matrix, emissionProbs = matrix. All SNP markers within the first and last 30 kb of each chromosome were omitted because we observed higher sequencing error rate and/or lower read mapping quality for these specific regions. Adjacent SNPs in the HMM-corrected genotype calls that lack recombination in the segregants were collapsed into a single SNP, reducing the number of SNP markers in subsequent analysis from 43,865 to 7742.Clones containing different barcodes for 1003 MATa segregants, the genomic region containing the barcode was PCR amplified using primer pairs where one primer was located on the partial TruSeq read 1 adapter sequence and the other primer was located downstream of the barcode. Similarly, barcodes for MATα segregants were PCR amplified using primer pairs where one primer was located on the partial TruSeq read 2 adapter sequence and the other primer was located upstream of the barcode. PCR products were purified using a Qiagen MinElute PCR purification kit, amplified using Illumina P1 and P2 primers, and then size selected via gel extraction prior to sequencing. The 20-mer barcode sequences for each segregant were then extracted from the sequencing reads and barcodes within a Hamming distance of 2 were clustered with Bartender30. Only barcode clusters comprising >5% of the total reads for each sample were considered true barcodes. One possible concern with using random barcodes is that sequencing errors from an abundant barcode could erroneously contribute to read counts of a barcode with a similar sequence. To prevent this, we determined the number of mismatches away from a nearest neighbor for each barcode. All barcodes were at least 4 mismatches away from each other. Thus, a sequencing read was unlikely to be assigned to the wrong barcode cluster unless it contained 3 or more errors. Overall, we recovered 403 MATa segregants and 679 MATα segregants with good genotype calls and at least 3 barcodes that are unique from all others.For all segregants, the associated barcodes were determined by pooling all clones and sequencing the genomic region containing the barcode using Novogene Illumina HiSeq 2500 150 bp × 150 bp paired-end reads at ~2000× coverage per barcode. For the MATa and 600 MATα segregants were mated to create a panel of ~240,000 diploid segregants, each labeled with 4 unique pairs of barcodes . To minimize skews in the initial frequency distribution of genotypes, each MATa segregant was individually mated to each MATα segregant to generate the panel of diploid segregants. Specifically, two barcoded versions of each segregant were first grown to saturation and mixed in equal proportion. Each MATa segregant mixture was then systematically mated with a MATα segregant mixture using a LabCyte Echo 550 acoustic liquid handling robot. For each pairwise mating, the Echo transferred 100 nl of MATa and MATα segregants onto overlapping positions on a new YPD plate, resulting in the formation of diploid segregants labeled with ~4 double barcodes. After growing the cells overnight at 30°C, successfully mated diploids were isolated from unmated haploids by pinning the colonies onto YPD + 200 μg/ml G418 + 300 μg/ml Hygromycin B agar plates using Singer ROTOR pinning robot and grown for 2 days. Haploid strains carried either KanMX or HphMX, therefore only mated diploids that carry both selection markers should grow. In addition to the diploid segregants, different barcoded versions of the homozygous and heterozygous parental diploid strains were made as controls, using the same approaches described for segregants .400 URA3 marker onto the same chromosome, resulting in the reconstitution of a functional URA3 marker. Diploids that have undergone successful recombination were selected by pinning the diploid colonies onto SCM -Ura agar plates and growing for 2 days. To minimize bias due to differences in growth rate between diploids, the colonies were then pinned onto fresh SCM -Ura plates and pooled immediately with SC -Ura media. The pooled sample of ~960,000 double barcoded segregants was then spun down, re-suspended in SC -Ura media at a concentration of 2 × 109 cells/ml, and frozen in 25% glycerol for later use. In parallel, the BY/BY, 3S/3S, BY/3S, and 3S/BY parental diploids were generated in the same manner and stored at the same concentration as the segregants.After mating, the two barcodes are located on different chromosomes  was inoculated in 198 ml of SC -Ura media and then grown in a 500 ml flask at 30 °C with 300 rpm shaking for 48 h. At saturation, the cell concentration was 1.5 × 108 cells/ml for a total of 3 × 1010 cells, or ~3 × 104 cells per double barcode. In parallel, 10 μl of the parental frozen stocks were inoculated in 990 μl of SCM-Ura media and grown at 30 °C with 300 rpm for 48 h. The parental diploids were then spiked into the segregant culture at a concentration of 10−5. This mixed culture served as the seed culture (time point 0 or T0) for subsequent growth time points.The panel of diploid segregants was evolved for 15 generations by serial batch culture under carbon limitation in 100 ml of SC -Ura media with 4% ammonium sulfate and 2% dextrose. First, 2 ml of the segregant frozen stock (2 × 103 cells per double barcode at bottleneck) was transferred into eight 500 ml flasks, each containing 87.5 ml of SC -Ura media supplemented with different drugs or chemicals . The sample was again mixed at 30 rpm for 10 min, centrifuged at 16,000 × g for 10 min, and the upper aqueous layer was transferred to a new tube. Finally, to remove residual chloroform, the DNA sample was ethanol precipitated using one-tenth volume of 3 M sodium acetate (pH 5.5) and 2.5 volume of 100% ethanol. The resulting DNA pellet was washed with ice-cold 70% ethanol twice, and dissolved in 10 mM Tris-HCl pH 8.0. After the DNA was extracted and cleaned, a two-step PCR was used to amplify the double barcodes for sequencing. Because only a small fraction of the total genome contains relevant information (~250 bp amplicon out of a 12 Mb genome size), we amplified 30 µg of template per time point sample, which corresponds to ~2 × 109 genomes or ~2000 copies per double barcode.Frozen cultures were thawed and DNA was extracted for library preparation using the MasterPure Yeast DNA Purification kit. Any remaining RNA was removed by adding RNaseA (10 mg/ml) and incubating the sample at 37 °C for one hour. DNA was then cleaned by adding one volume of phenol:chloroform:isoamyl alcohol (25:24:1). The sample was gently mixed using a tube rotator at 30 rpm for 10 min and then centrifuged at 16,000 × 2, 3% DMSO, and 125 µL total volume per well. Primers for this reaction were: AATGATACGGCGACCACCGAGATCTACACNXXXXXNNACACTCTTTCCCTACACGACGCTCTT and CAAGCAGAAGACGGCATACGAGATNXXXXXNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT.First, a 4-cycle PCR with OneTaq polymerase (New England Biolabs) was performed in 60 wells of 96-well PCR plates, with ~500 ng of DNA template, 4.5 mM MgClThe Ns in these sequences correspond to any random nucleotide and are used as unique molecular identifiers (UMIs) in downstream analysis to identify PCR duplicates. The Xs correspond to one of several multiplexing tags, which were used to distinguish different samples when loaded on the same sequencing flow cell. Multiplexing tags were designed to have a Levenshtein distance of 3 from each other, such that reads with 1 or less sequencing errors in the multiplexing tag can be assigned to the correct sample.After amplification, 16 wells of PCR products were pooled , run through a NucleoSpin PCR cleanup column, and eluted in 120 μL of elution buffer. The pooled PCR products were then cleaned for the second time to remove any residual primers using a NucleoSpin column and eluted in 120 μL of elution buffer. A second 22-cycle PCR was performed with PrimeStar HS DNA polymerase (Takara) in 16 reaction tubes, with 30 μl of cleaned product from the first PCR as template and 125 μL total volume per tube. Illumina primers P1 and P2 were used for this reaction.PCR products from all reaction tubes were concentrated into 100 μl using ethanol precipitation. The appropriate PCR band (264 bp) was isolated by agarose gel electrophoresis and quantified by Bioanalyzer (Agilent) and Qubit fluorometer (Life Technologies).MATa and MATα barcodes were extracted from the sequencing reads and fused into one double barcode.Sequencing (150 bp paired-end) was performed on an Illumina HiSeq 4000 or NovaSeq 6000. Each flow cell contained at least 2 multiplexed time points and 25% genomic DNA or PhiX. Genomic DNA/PhiX was included to increase the read complexity for proper calibration of the instrument since most of the bases in each barcode read are fixed. Sequencing reads were analyzed using custom written code in Python and R, which are available on GitHub. Reads were sorted by their multiplexing tags (the Xs in the primers above) and removed if they failed to pass either of two quality filters: 1) The average Illumina quality score for the double barcode insert region must be greater than 30, and 2) the double barcode region must contain the fixed sequence ATAACTTCGTATAATGTATGCTAT with less than 2 mismatches. After filtering, 30. One source of bias in these counts that we wanted to avoid is PCR duplicates or other non-linearities between the amount of template for a double barcode and the number of sequences observed for that double barcode. We removed these errors by using UMIs (the Ns in the primers above). Specifically, 2 random 3-mers were attached to each template DNA molecule in the first few rounds of PCR (see section ‘Library preparation’ for more detail). Because the total sequence space of 2 random 3-mers  is much larger than the target coverage for each double barcode (~100×), it is unlikely that any two template DNA molecules from the same time point that contain the same double barcode will be attached to identical pairs of 3-mers. Thus, sequence reads for a double barcode that contained the same pair of 3-mers were counted as PCR duplicates and removed from our final counts.We next used Bartender to cluster similar sequences into consensus double barcodes, with the maximum allowable sequence distance (-d) set to 2MATa and MATα segregant and parental barcodes were extracted. All double barcodes with an average read count of <5 across the 4–5 time points were removed, as accurately estimating fitness for samples with very low read counts is difficult  separated by a fixed region can lead to formation of undesired chimeric molecules due to template switching29. This can result in erroneous double barcode counts, leading to errors in fitness estimation.Previous studies have shown that PCR amplification of DNA sequences containing two variable regions   double barcodes per environment were used for the remaining downstream analysis  with a spectrally decomposed genetic similarity matrix (GSM).Our experimental design resulted in genotypes that are not equally related to each other. For example, two diploid segregants that share a parental haploid segregant are more related to each other than two diploids that do not share a parental haploid segregant. We found that differences in relatedness had a large effect on the fitness of the diploid segregants. Thus, failing to account for family structure could inflate type I error and result in false positives. To account for these potential errors, we detected one-locus effect loci using FaST-LMM61. To avoid proximal contamination, all SNP markers that are located on the same chromosome as the SNP marker that is being tested were omitted from the calculation of the GSM by setting the leave_out_one_chrom function as TRUE. To determine appropriate significance thresholds, 1000 permutations were conducted with the correspondence between genotypes and phenotypes randomly shuffled each time. Among the minimum p values obtained in the permutations, the fifth quantile was identified and used as the threshold for determining significant loci.We used FaST-LMM’s single_snp function to perform a genome-wide linear mixed effect analysis. The genotype table was reformatted as a binary biallelic genotype table (BED) prior to running FaST-LMM using PLINK version 1.07p-value) of the maximal significance observed at the peak position (hereafter referred to as “3-LOD drop”).To detect loci that are closely linked to a nearby locus, we ran multiple rounds of stepwise forward regression in FaST-LMM. For the second round of forward regression, we included the most significant SNP marker from each chromosome that was above the significance threshold from the initial genome-wide scan as a covariate in the mixed effect linear model. In the third round, all SNP markers detected in the first two rounds were added as covariates in the mixed effect model. This process was repeated until no significant SNP markers were detected. The confidence interval of a locus was defined as the region surrounding a peak marker in which the significance of detection was within three −log10 had a huge impact on the statistical power to detect loci. Because all genetic differences between diploids within a family are random, they do not need to be corrected for family structure prior to mapping. Genome-wide mapping was conducted in each family using a fixed effects linear model in R using the lm function: lm(fitness ~ locus). The fitness term corresponded to the vector of quantile normalized fitness values of individuals within a family and the locus term corresponded to the vector of these individuals’ genotypes at a given marker. Because diploids within a family are derived from the same MATa haploid, they possess only two possible genotype states: BY/BY and BY/3S or BY/3S and 3S/3S, depending on the allele present in the MATa progenitor. Although the locus term was encoded as a categorical variable here, the coding of the locus term should not matter. The categorical coding should be equivalent to a count of an allele because either defines two categories. The p-value of the locus term was obtained using the summary.aov in R. Among markers exceeding a significance threshold, the most significant marker from each chromosome was identified and utilized in subsequent rounds of forward regression. Specifically, identified loci were included in the fixed effects linear model as a covariate, e.g. fitness ~ known_locus1 + known_locus2 + … known_locusN + locus. This process was repeated until no additional locus terms were discovered within an environment. The significance threshold for these forward scans was established by pooling the p values from 1000 permutations of 10 randomly selected families. In each permutation, the correspondence between genotype and phenotype was shuffled within a family. From the resulting distribution of minimum p-values obtained in each permutation, the fifth quantile was identified and used as the significance threshold. After calling peaks, loci detected in multiple families were consolidated using 3-LOD drops within each family.We also performed scans for one-locus effects within families generated from the same 39. We divided the genome into nonoverlapping 20 kb bins and determined the maximum number of detections expected by chance at a Bonferroni-corrected p = 0.05/(the number of 20 kb nonoverlapping bins). The threshold was then obtained from a Poisson distribution with λ = /(the number of 20 kb nonoverlapping bins tiling the genome) using the qpois function in R, with lower.tail set to FALSE.We identified distinct loci that were enriched for detections across families23. Specifically, we first estimated the fitnesses of each parental haploid segregant by calculating the mean fitness of all diploids that originated from each parental strain (additive genetic background contribution to fitness). Next, the expected midparent value for each diploid was obtained by taking the average of the 2 parental fitnesses. If phenotypic variation is due to only additive effects, then we expect the fitness of a diploid to be the same as its midparent value. Deviations from the midparent value were then determined by fitting a linear model between fitness and the midparent value, fitness ~ midparent value, and taking the residuals . Not only does this correct out the additive portion of each diploid’s phenotype, it also effectively accounts for family structure , we ran multiple rounds of forward regression. In the first round, a genome-wide scan was conducted using the following fixed effects linear model: fitness value for each genotype state was calculated. Additive effect sizes of the 3S allele were calculated by subtracting the mean fitness of diploids that are BY/BY at the focal locus from the mean fitness of diploids that are 3 S/3 S at the focal locus and dividing by two. Dominance effect sizes for each focal locus were calculated by subtracting the mean fitness of diploids that are BY/3S at the focal locus from the average of the mean fitness of diploids that are BY/BY and 3S/3S at the focal locus . Dominance effect sizes were then normalized based on the absolute additive effect sizes of the 3S allele relative to the BY allele at the same loci. Normalized dominance values ranging from −0.9 to 0 were classified as incomplete dominance for the deleterious allele, while values ranging from 0 to 0.9 were classified as incomplete dominance for the allele conferring higher fitness. Values that were between −0.9 to −1.1 or 0.9 to 1.1 were classified as complete dominance for the deleterious and fit allele, respectively. Values < −1.1 or >1.1 were classified as underdominant or overdominant, respectively.For each locus detected in the scan for dominance effects, the magnitude of dominance was calculated in the following manner. First, the data was subsetted based on the genotype state at the focal locus . Then, the mean residuals ~ locus1 + locus2 + locus1:locus2. Here, the significance of the locus1:locus2 interaction term was tested. Simpler terms were included in each model to ensure that variances due to one-locus non-additive effects  were not erroneously attributed to more complex terms. All locus terms were treated as categorical values, including the locus1:locus2 interaction term, such that all 9 genotype classes  are treated as independent categories rather than continuous numerical values. The significance threshold was determined by conducting 1,000 permutations with the correspondence between genotype and residuals shuffled each time. Among the minimum p-values obtained in each permutation, the fifth quantile was identified and used as the significance threshold for our comprehensive scan of all two-loci interactions. To reduce computational time, 10,000 random pairs of loci were randomly selected for each permutation rather than all possible pairs of loci. All significant pairs of loci where both loci were within 3-LOD drop of each other were consolidated. For each set of overlapping loci, the SNP marker with the most significant p-value was used for downstream analysis while less significant markers were recorded but treated as the same genetic effect and not used downstream.We conducted a genome-wide scan for pairwise interactions using the following linear fixed effect model: locus1 fixed for a hub locus, e.g., residuals ~ hub + locus2 + hub:locus2. The significance of the hub:locus2 interaction term was tested. Significance thresholds were determined using the same permutation strategy as the comprehensive scan. The most significant interacting locus from each chromosome that was above the significance threshold was identified as a significant interactor of a hub locus. For subsequent rounds of forward regression, all identified interactions were included in the fixed effects linear model as covariates, including the simpler terms, e.g. residuals ~ hub + locus2 + known_locus1 + known_locus2 + … known_locusN + hub:known_locus1 + hub:known_locus2 + … hub:known_locusN + hub:locus2. The most significant interacting locus from each chromosome that was above the significance threshold was again identified as a significant interactor with a hub locus. This process was repeated until no additional hub:locus2 terms were discovered for each environment. To avoid double counting interactions, all pairwise interactions identified in the comprehensive scan that involved a locus within 50 kb of a hub were removed.During our comprehensive scan for pairwise interactions, we detected several hubs that were involved in large numbers of interactions. These interactions often spanned the entire length of the genome, making it difficult to differentiate sites interacting with these hubs. In such cases, a forward regression approach was conducted. Specifically, we first ran the same fixed effects linear model as the comprehensive scan for pairwise interactions, but with residuals ~ locus1 + locus2 + locus3 + locus1:locus2 + locus1:locus3 + locus2:locus3 + locus1:locus2:locus3. Similar to the scan for pairwise interactions, all locus and interaction terms were treated as categorical values. Here, the significance of the locus1:locus2:locus3 interaction term was tested. Simpler terms were included in each model to ensure that variances due to one-locus non-additive effects  and pairwise interactions were not erroneously attributed to more complex terms. As before, the significance threshold was determined by conducting 1000 permutations with the correspondence between genotype and residuals shuffled each time. Among the minimum p-values obtained in each permutation, the fifth quantile was identified and used as the significance threshold for our comprehensive scan of all two-loci interactions. Similar to the pairwise interaction scan, for each permutation, 10,000 trios of loci were randomly selected for each permutation rather than all possible trios of loci.A comprehensive scan for three-locus effects using all SNP markers is computationally expensive. Instead, we scanned for three-locus effects using a smaller subset of 579 SNP markers, where each SNP marker was at least 5 cM apart. The following fixed effects linear model was used: MATa families. The peak position of each hub was defined as the most significant marker for that locus across all families. Family-level detections with confidence intervals overlapping the peak position of a hub were identified. The first and last coordinates of all confidence intervals overlapping a peak position were compared, and the coordinates closest to each side of a peak position were recorded as the minimum bounds for a given hub.Hub loci were resolved using data from family-level scans in each of the additive-additive, dominance-additive, additive-dominance, and dominance-dominance62. The following linear fixed effect model was used: residuals ~ locus1 + locus2 + additive_locus1:additive_locus2(a1:a2) + dominance_locus1:additive_locus2(d1:a2) + additive_locus1:dominance_locus2(a1:d2) + dominance_locus1:dominance_locus2(d1:d2). Additive_locus terms were treated as numerical values, while locus and dominance_locus terms were treated as categorical values. The percent variance explained (PVE) by each of the four epistatic types was then estimated by taking the sum of squares of each interaction term and dividing it by the total sum of squares. Using these PVE values for each interaction term, the fraction of epistatic effect involving dominance was then calculated. To estimate the fraction in which locus1’s modifying effect on locus2 acts on dominance, the following equation was used: (d1:a2 + d1:d2)/(a1:a2 + d1:a2 + a1:d2 + d1:d2). Conversely, to estimate the fraction in which locus2’s modifying effect on locus1 acts on dominance, the following equation was used: (a1:d2 + d1:d2)/(a1:a2 + d1:a2 + a1:d2 + d1:d2).To understand how a locus modifies the effect of an interacting locus in a pairwise interaction, each pairwise interaction was partitioned into four epistasis types: a1:a2 and a1:d2 interaction terms, where locus1 is the hub. The magnitude in which the hub modifier affects the dominance component of the hub (‘dom_mod’) was determined by adding the PVE of d1:a2 and d1:d2 interaction terms. Significantly large outliers in add_mod or dom_mod, defined as values more than 1.5 interquartile range above the third quartile, were identified as major effect modifiers.Using the PVE values for the four epistatic types (see section ‘Estimating the fraction of epistatic effect that involves dominance’), we examined how loci that interact with a hub contribute to additive and dominance effect size variance across different genetic backgrounds. For each two-locus interaction involving a hub, the magnitude in which the hub modifier affects the additive component of the hub (‘add_mod’) was determined by adding the PVE of fitness of diploids that are BY/BY at the focal hub from the mean fitness of diploids that are 3S/3S at the focal hub. Dominance effect sizes for each genotype class were calculated by subtracting the mean fitness of diploids that are heterozygous at the focal hub from the average of the mean fitness of diploids that are BY/BY and 3S/3S at the focal hub.To examine how modifiers collectively change the effect size of hubs, four modifiers with the largest modifying effects on additivity and dominance were chosen for each hub. Data was then subsetted into 81 genotype classes based on the genotype state at the four modifiers. For each genotype class, the data were further subsetted based on the genotype state of the focal hub locus. Additive effect sizes for each genotype class were calculated by subtracting the mean Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


The AMP-activated protein kinase (AMPK) is a major regulator of cellular energetics which plays key role in acute metabolic response and in long-term adaptation to stress. Recent works have also suggested non-metabolic effects.Ampkα2) in mice. This allowed us to avoid the eventual impact of AMPK-KO in peripheral organs.To decipher AMPK roles in the heart, we generated a cardio-specific inducible model of gene deletion of the main cardiac catalytic subunit of AMPK (Ampkα2 deficiency led to a progressive left ventricular systolic dysfunction and the development of cardiac fibrosis in males. We observed a reduction in complex I-driven respiration without change in mitochondrial mass or in vitro complex I activity, associated with a rearrangement of the cardiolipins and reduced integration of complex I into the electron transport chain supercomplexes. Strikingly, none of these defects were present in females. Interestingly, suppression of estradiol signaling by ovariectomy partially mimicked the male sensitivity to AMPK loss, notably the cardiac fibrosis and the rearrangement of cardiolipins, but not the cardiac function that remained protected.Cardio-specific Our results confirm the close link between AMPK and cardiac mitochondrial function, but also highlight links with cardiac fibrosis. Importantly, we show that AMPK is differently involved in these processes in males and females, which may have clinical implications for the use of AMPK activators in the treatment of heart failure. AMPK is a metabolic sensor of cellular energy which regulates energy homeostasis.We generated a cardiac-specific inducible deletion of Ampkα2 and demonstrated that this deletion induces mild cardiac dysfunction in male only.Cardiac dysfunction observed in males was associated with cardiac fibrosis and cardiac cardiolipin remodeling that are not seen in females.Although no significant cardiac function alteration was noticed in ovariectomized female Ampkα2ciKO mice, these latter exhibited cardiac fibrosis and mild cardiolipins remodeling.Our results show a higher dependence on AMPK signaling fibrosis and cardiolipin biosynthesis/maturation in males, either due to the absence of female hormones protection or/and to the action of male hormones. This may contribute to the known difference in cardiovascular risk and outcome between sexes. Measurements aiming at determining mitochondrial parameters were expressed per gram of dry fiber weight.Fibers prepared from the left ventricle were permeabilized with saponin as previously described  and keptl-glycerol-3-phosphate (4 mM), palmitoyl-CoA and carnitine (100 µM and 2 mM), pyruvate (1 mM), glutamate (10 mM), succinate (15 mM), amytal  and the complex IV substrates N,N´,N´-tetramethyl-phenylenediamine dihydrochloride (TMPD)-ascorbate (0.5 mM–2 mM) (activator of complex IV) to a solution containing in mM: 2.77 CaK2 ethyleneglycol tetraacetic acid (EGTA), 7.23 K2EGTA [100 nM free Ca2+], 1.38 MgCl2, 3 K2HPO4, 20 taurine, 0.5 dithiothreitol (DTT), 90 K-methane sulfonate and 10 Na-methane sulfonate, 20 imidazole, pH 7.1 at 23 °C. Rates of respiration are given in µmoles O2/min/g dry weight.Mitochondrial respiratory function was studied in situ in saponin-permeabilized cardiac muscle fibers using a Clarke electrode as previously described . A protoFrozen tissue samples were weighed, homogenized (Bertin Precellys 24) in ice-cold buffer (50 mg/ml) containing 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) 5 mM (pH 8.7), EGTA 1 mM, DTT 1 mM and 0.1% Triton X-100. Activities of citrate synthase (CS), cytochrome c oxidase (COX) and complex I were determined using standard spectrophotometric assays , 31.Frozen tissue samples were homogenized (Bertin Precellys 24) in ice-cold buffer containing HEPES 50 mM, KCl 50 mM, ethylenediaminetetraacetic acid (EDTA) 1 mM, β-glycerophosphate 5 mM, Triton X-100 0.1%, orthovanadate 1 mM, dithiothreitol 1 mM, sodium fluoride 50 mM, Na pyrophosphate 5 mM, phenylmethylsulfonyl fluoride 0.2 mM and antiprotease cocktail set . Protein extracts were separated on SDS–polyacrylamide gel (8 to 12%) and then transferred to polyvinylidene difluoride membranes for Western blot. After an hour of blocking in PBS containing Tween 20 (0.1%) and non-fat milk (5%), the membranes were incubated overnight at 4 °C with primary antibody  . Fifty mt method. The average Ct obtained in CT group was used as a calibrator and the geometric mean of the 5 housekeeping genes was used as the reference for normalization. The amplification efficiency of each probe is estimated maximal as described by the manufacturer.Frozen tissue samples were weighed and homogenized (Bertin Precellys 24) in ice-cold buffer. Total ventricular RNA was extracted using standard procedure with Trizol reagent (Invitrogen). cDNAs were synthesized from 2 μg total RNA according to the protocol provided with the High Capacity cDNA Reverse Transcription Kit . Real-time PCR was performed using TaqMan Low Density Array (TLDA) technology as previously described . Pre-desCol1a1 , and Col3a1  mRNA were assessed using the SYBR®Green method on a LightCycler rapid thermal cycler (Roche Diagnostics) as previously described [Quantification of escribed . For eacLeft ventricular papillary muscles were isolated from three control and three KO mouse hearts, fixed with 2% glutaraldehyde in cacodylate buffer  for 1 h, post-fixated by 1% osmium tetroxide in cacodylate buffer for 30 min and stained with 1% aqueous solution of uranyl acetate. After dehydration in graded ethanol series and acetone, the tissue was embedded in Durcupan (ACM Fluka). Ultrathin (58–60 nm) longitudinal sections were cut using an ultramicro-tome . The sections were mounted on formvar-coated copper grids, contrasted with lead citrate and examined with a JEM 1200 electron microscope  at 80 kV. Random images of cardiomyocytes were recorded with a CCD camera  at a magnification of 15,000 and analyzed using Graphic Cell Analyzer14.. [Cardiolipin analysis was performed by liquid chromatography using corona-CAD detector as previously described . As card. . The floAll results are expressed as mean ± SEM. To assess significance, we performed Student’s t test when the experimental design compared only 2 groups or two-way ANOVA for independent factors when appropriate for the experimental design; Tukey post hoc tests were used to identify significant differences between means. Results were considered statistically different when p-value was strictly lower than 0.05. Symbols referring to statistics are defined in each figure legend.Ampkα2f/f mice were given tamoxifen to induce exon 6 excision from the floxed Ampkα2 alleles. Sixteen weeks after Ampkα2 deletion, AMPKα2 protein level in left ventricle (LV) homogenates exhibited reductions of 95 ± 1% in Ampkα2ciKO males and 90 ± 3% in females when compared with the respective control Ampkα2f/f mice , the active form of AMPK. The antibody recognizing both phosphorylated AMPKα1 (Thr 183), and AMPKα2 (Thr 172) showed a 30% reduction of signal. Thus, the remnant 70% phospho-AMPK signal comes most probably mostly from phosphorylated AMPKα1. Even though this set of data could suggest a decrease in AMPK activity, phosphorylation of acetyl-CoA carboxylase (ACC), a target of AMPKα1/AMPKα2, was not significantly different between all groups while the total amount of ACC was unchanged in Ampkα2ciKO mice  was significantly increased after Ampkα2 deletion and was significantly lower in females than in males  and fractional shortening (FS) as well as a significant increase in end-systolic left ventricular internal diameter (LVIDs) were observed starting 7 weeks after tamoxifen treatment  as well as around the cardiomyocytes   Fig. A. No carAmpkα2ciKO male mice  raised questions. We thus sought to determine whether the microenvironment surrounding complex I within the inner mitochondrial membrane could be different in mutant mice and could explain its lack of efficiency within the ETC. Sixteen weeks after tamoxifen injection, Ampkα2ciKO male mice clearly showed a marked rearrangement in the mitochondrial membrane cardiolipin (CL) profile  chains and a slight increase in CL containing eicosatrienoic acid (20:3) or eicosatetraenoic acid (20:4) were observed  did not induce cardiac hypertrophy; (2) led to a mild left ventricular dysfunction; (3) resulted in the development of cardiac fibrosis; (4) reduced complex I-driven respiration without changes in mitochondrial mass or in in vitro complex I activity; (5) was associated with a rearrangement of the CL species and a reduced integration of complex I into the ETC supercomplexes. Importantly, the effects of the cardiac-specific Ampkα2 deletion observed in males were not found in females at basal state, suggesting that alternative pathways to AMPK are active in female mice. Ovariectomy of female KO mice suggest that their lower sensitivity to cardiac Ampkα2 deletion could partly be explained by the blunting effects of female hormones on the development of cardiac fibrosis and by a role in the maturation of cardiolipins; however alterations in these females remained quite mild since they displayed normal mitochondrial function and were asymptomatic as regard to systolic function.Although the role of AMPK has largely been studied for many years, its role in cardiac physiology and pathophysiology is still not completely understood. With the aim of clarifying the place of this kinase in cardiomyocyte homeostasis, we generated an original inducible cardiac-specific Mer mice . Using tAmpk null mice have already been generated using KO or dominant negative technologies targeting one or the other subunit of AMPK and gave variable results on AMPK activity and compensatory mechanisms between the diverse isoforms [Ampkα2 deletion, the AMPKα2 protein content in LV was drastically decreased and was associated with a reduction in phosphorylated AMPK level. Yet, phosphorylation of ACC, a direct target of AMPK, was not modified. This result which can be surprising at first sight, can actually be explained by the fact that the experiments have been done in non-stressing conditions which are not expected to induce strong AMPK activity and consequently a high ACC phosphorylation level. Furthermore, this indicates that the tiny residual amount of AMPKα2 together with AMPKα1, the expression of which was not changed in the present model, could maintain the amount of phosphorylated ACC at basal level. Indeed, ACC is a common target of both AMPKα isoforms and a small amount of AMPKα1/2 could be sufficient to ensure phosphorylation status of this enzyme when the animal is not subjected to stress [Ampkα2ciKO males may impact the signaling pathways downstream of AMPK because the residual AMPKα2 could not ensure phosphorylation of all the targets, some of which would not be phosphorylated by AMPKα1 as efficiently as by AMPKα2.Owing to the AMPK structure based on the assembly of several subunits, suppression of AMPK activity in genetically modified animal models can result from different strategies. Many isoforms , 38–40. o stress . Nevertho stress , the draAmpkα2ciKO mice, the modest alterations of several cardiac contractile parameters  suggest a mild left ventricular systolic dysfunction which is in accordance with the cardiac impairment described in global and constitutive Ampkα2 deletion [Ampkα2 got older, but was not associated with important anatomical changes when compared to control. Several studies showed narrow links between AMPK and protein synthesis regulation [Ampkα2ciKO male nor in Ampkα2ciKO female mice when the animals were not subjected to any stress. This result, which incidentally recalls what was observed in other dominant negative or global Ampkα2 KO mice [Ampkα2 deletion in females probably does not mean that females are totally insensible to AMPKα2 loss. Indeed, although not statistically significant the slight increase in Bnp expression in Ampkα2ciKO females after 16 weeks of deletion could suggest that females were not fully protected from this deletion but rather more resistant. It cannot be excluded that these females would develop significant cardiac dysfunction at a later age and this point will require further investigations.In male deletion , 25, 39.gulation , 43 and gulation , 45. How KO mice , 39, sugAmpkα2ciKO male and in KO-OVX female mice could suggest that AMPKα2 is linked to fibrosis. It is generally thought that the cardiac fibrotic response is essentially mediated by cardiac fibroblasts; yet in this cardiac-specific Ampkα2 KO model, Ampkα2 was only deleted in cardiomyocytes and it could be suggested that this deletion would stimulate cardiac fibrotic response by cardiac fibroblasts through communication between both cell types. Even though the link between AMPK and cardiac fibrosis has already been suggested since the activation of AMPK can suppress fibrosis induced by various stresses , regulaview see . From anview see , 47 and Ampkα2 deletion provoked mitochondrial complex I-driven respiration dysfunction only in male LV. This was not associated with any significant changes in maximal mitochondrial oxidative capacity and mitochondrial mass, even though gene expression of several markers of mitochondrial biogenesis were significantly reduced (Nrf-2 and Cox1) or tended to be lower  in KO. This is in accordance with the observation made in the mouse model of global and constitutive deletion of Ampkα2 [Ampkα2 led to complex I-driven respiration impairment without any decrease in complex I enzymatic activity measured in total heart extract, thereby suggesting an alteration of in situ regulation of this complex.In this study, cardiac f Ampkα2  and suggAmpkα2 KO mice, the alteration of complex I-driven respiration was associated with a clear reduction in LV content of CL [Ampkα2 deletion in males induced a decrease in the expression of several key enzymes of CL biosynthesis  and substantial CL rearrangement even though the total CL content was similar to control. The difference between global constitutive Ampkα2 KO mice and the present model could come from the different timing and duration of the deletion, from the beginning of in utero life in the constitutive KO versus a loss of AMPK starting in the adult life in Ampkα2ciKO mice, from an established pool of CL in mitochondria. Nevertheless, our present study and a previous one by our team on doxorubicin-induced cardiomyopathy further bring evidence that AMPK loss has an impact on mitochondrial CL profile [It is known that the in situ activity of ETC complexes can be affected by the phospholipid content of inner mitochondrial membrane and that complex I is particularly sensitive to its microenvironment . Cardiolnt of CL . Given tnt of CL , this su profile , 21. Rec profile . The inc profile , thereby profile . Given t profile , the CL Cds1 expression in Ampkα2ciKO mice suggests a role for AMPK signaling in the regulation of genes involved in CL biosynthesis. Interestingly, the expression of this enzyme has been shown to be under the control of PGC-1α/ERRα axis [Errα expression in Ampkα2ciKO males in comparison with Ampkα2f/f males is in favor of this hypothesis. The present study shows that this alteration of mitochondrial membrane CL composition was not observed in female KO mice in which complex I-driven respiration was similar to control. This once again highlights the connection between complex I activity and mitochondrial membrane CL composition [Ampkα2ciKO mice did not affect the mitochondrial membrane CL composition as strongly as in males. Nevertheless, cardiac mitochondrial CL profile in ovariectomized females was modified according to a pattern reminding the rearrangements observed in Ampkα2ciKO males, strengthening the idea that female hormones normally participate in the maintenance of inner mitochondrial CL composition and compensate for the lack of Ampkα2 in KO-sham females. Even so, the CL alterations in KO-OVX mice are quite mild and appear insufficient to impact mitochondrial function as in the males, suggesting that the duration of female hormone deficiency was not sufficient to totally suppress the higher resistance of the Ampkα2ciKO females. This could also be explained by the fact that OVX mice had surgery at the age of 7 weeks and a first impregnation with female hormones cannot be excluded as 6-week-old mice may be able to reproduce. These mice could have been exposed to female hormones during one or two weeks and it could interfere with the development of the phenotype of Ampkα2ciKO mice and explain the milder effect of Ampkα2 deletion in OVX mice than in males. In addition, even after ovariectomy, females continue to display a different global morphology than males and are not exposed to high level of male hormones. The fact that Ampkα2ciKO OVX females did not perfectly reproduced the CL rearrangement observed in males may not be that surprising since male hormones could also differently regulate CL biosynthesis [Ampkα2 deletion-induced alterations in OVX females by 17-β estradiol treatment to better understand the underlying phenomena, the present study clearly confirms that AMPK is involved in CL biosynthesis and remodeling in a sex-dependent manner.The reduction in RRα axis . As a maposition , 56 and position . Surprisynthesis . In the ynthesis . TogetheAmpkα2 KO mice, the proportion of complex I not enclosed in supercomplexes was increased and these mice displayed less cardiac complex-I-containing supercomplexes. When considering the fact that neither mitochondrial respiration nor CLs profile nor the amount of complex-I-containing supercomplexes were significantly modulated in female KO mice, one can suggest that the concomitant alterations of ETC functioning, mitochondrial membrane CL composition and supercomplex disorganization in males, together with modest systolic dysfunction are in no way coincidental. In X-linked Barth syndrome induced by a mutation in the gene encoding tafazzin, an enzyme responsible for CL maturation, mature CLs are lost and this leads to an instability of respiratory chain supercomplexes which affects complex I activity [Ampkα2 KO mice, the alterations of CL profile and complex I-containing supercomplexes formation undeniably affects the ETC functioning and this at least explains in part the alteration of complex-I-driven respiration observed in this model.CLs play many roles in mitochondrial membrane organization and are crucial for mitochondrial supercomplex formation . In maleactivity , therebyAmpkα2 deletion is induced by injections of tamoxifen which interacts with estrogen receptor could be a limitation of this study that was focused on sex differences. However, the tamoxifen dose used was chosen after a long period of development and study of the literature to determine the lowest dose of tamoxifen to achieve an optimal level of deletion of Ampkα2 and limit side-effects. Thus, each mouse was injected with only two 40 mg/kg doses which correspond to 2 doses of 1 mg tamoxifen for a 25 g mouse. A recent study testing the clearance of tamoxifen in mice [The fact that  in mice  showed tAmpkα2ciKO mice was the result of a set of perturbations. We did not extensively study AMPK roles which are particularly intricate and there were undoubtedly many other disturbances that had not been uncovered in this work. Nevertheless, this study has the merit of highlighting a poorly understood role for AMPK, which partly explains the disturbances in energy metabolism observed in this model and which certainly played its part in the phenotype resulting from the deletion of Ampkα2 in the heart.Naturally, AMPK is involved in many signaling pathways and the phenotype of Ampkα2 deletion model proved helpful in deciphering the role of AMPK in basal conditions. In this model, Ampkα2 deletion induced a progressive cardiac dysfunction associated with cardiac fibrosis and a mitochondrial dysfunction associated with cardiolipin remodeling in males only, thereby suggesting a role for AMPKα2 in these processes. Although the involvement of AMPKα2 in the regulation of myocardial fibrosis and cardiolipin biosynthesis and maturation requires further studies, our results highlight a higher dependence on AMPK signaling for these processes in males, either due to the absence of female hormones protection or to counteract the action of male hormones, and that may contribute to the known difference in cardiovascular risk and outcome between sexes.Cardiac-specific inducible