Early life diet conditions the molecular response to post-weaning protein restriction in the mouse
© Rakyan et al. 2018
Received: 15 February 2018
Accepted: 9 April 2018
Published: 2 May 2018
Environmental influences fluctuate throughout the life course of an organism. It is therefore important to understand how the timing of exposure impacts molecular responses. Herein, we examine the responses of two key molecular markers of dietary stress, namely variant-specific methylation at ribosomal DNA (rDNA) and small RNA distribution, including tRNA fragments, in a mouse model of protein restriction (PR) with exposure at pre- and/or post-weaning.
We first confirm that pre-weaning PR exposure modulates the methylation state of rDNA in a genotype-dependent manner, whereas post-weaning PR exposure has no such effect. Conversely, post-weaning PR induces a shift in small RNA distribution, but there is no effect in the pre-weaning PR model. Intriguingly, mice exposed to PR throughout their lives show neither of these two dietary stress markers, similar to controls.
The results show that the timing of the insult affects the nature of the molecular response but also, critically, that ‘matching’ diet exposure either side of weaning eliminates the stress response at the level of rDNA methylation and small RNA in sperm.
Various environmental factors, such as levels of physical activity or poor diet, can potentially influence health and disease states in mammals. As environmental stressors can operate at any point during the life course , it is necessary to understand how the timing of exposure to these factors influences molecular responses. Within this context, understanding the dynamics of molecular markers of the stress response would greatly enhance the ability to monitor the impact of environmental stressors in mammals and, ultimately, to gain mechanistic insight into the stress pathways involved.
On the other hand, Shea et al.  reported a mouse model in which they exposed mice to a PR diet from weaning onwards, and although they observed substantial genetic and epigenetic heterogeneity at rDNA, there were no observable diet-specific effects. Both our group  and Shea et al.  examined inbred C57BL/6 J mice and used similar PR exposures. Although part of the reason for the discrepant observations between these two studies could be that Shea et al.  did not discriminate between the A or C genetic variants, another potential explanation lies in the differences in the timing of PR exposure. Interestingly, using a similar post-weaning PR exposure mouse model (albeit in a different mouse strain), the authors subsequently reported a marked increase in transfer RNA (tRNA) fragments that result from the cleavage of mature tRNAs at specific sites  (Fig. 1b). Separately, Chen et al.  reported tRNA fragments to be increased in the sperm of male mice after being fed a high-fat diet post-weaning.
Although valuable insights have been gained by previous genomic and epigenomic analyses performed in the context of dietary models in rodents [9–13], what is noteworthy about the findings related to rDNA and tRNA fragments is that they have been reported by independent groups in different dietary exposure models [2, 5, 7, 8, 14]. Indeed, both rDNA and tRNA fragments have been implicated in cellular stress response mechanisms that are conserved amongst species [15–20]. Given the potential of rDNA and tRNA fragments to be robust ‘molecular barometers’ of dietary stress in different experimental models, including rodents, we set out to address three key inter-related questions raised by the recent studies described above. Firstly, is the difference in rDNA responses between our previous model  and that of Shea et al.  due to the timing of the PR exposure? Secondly, are tRNA fragments upregulated when the environmental challenge is experienced during early life? And finally, what happens when the animal is exposed to poor nutrition throughout the life-course? Answers to these questions would, in a more general sense, provide an enhanced understanding of how the timing of environmental exposures impacts the dynamic molecular responses of a mammalian genome.
Pregnant inbred C57BL/6 J mice were fed either a control (CT, 20% protein, 11 litters) diet or a PR (8% protein, 10 litters) diet throughout pregnancy and lactation (Additional file 1: Table S1). All litters were derived from independent females, i.e., no females were used to generate more than one litter. At weaning (3 weeks), the male offspring from each litter were assigned to either the CT or PR diet until they were killed at 11–13 weeks of age (Fig. 1c). Four diet combinations were therefore studied, namely (1) CT throughout life (CTCT, n Litters = 10), (2) CT pre-weaning followed by PR post-weaning (CTPR, as in Shea et al. , n Litters = 11), (3) PR pre-weaning followed by CT post-weaning (PRCT, as in Holland et al. , 2016, n Litters = 9) and (4) PR throughout life (PRPR, n Litters = 10).
Mice were killed at 11–13 weeks of age, after overnight fasting. The death weights of the four diet groups were significantly different from each other (Fig. 2c; CTPR P = 0.0003, PRCT P = 4.65 × 10− 6, PRPR P = 7.5 × 10− 12) and this was because CTPR mice lost less weight than CTCT mice (Fig. 2b; P = 0.048), whilst weight loss in PRCT and PRPR mice was similar to the controls (P = 0.26 and P = 0.37, respectively). Interestingly, despite their differences in size, relative organ and fat deposit weights were the same between the CTCT and the PRPR groups (Additional file 1: Figures S2 and S3), except for the relative kidney weight, which was lower in the PRPR group (Additional file 1: Figure S2e).
Overall, the PR model reported here replicates the phenotypes seen previously by our group  and others in the PRCT [2, 21] and CTPR branches [22, 23]. We have extended the model to include the PRPR group, which, despite being the lightest in body mass, appears to differ little from the CTCT group in terms of phenotype, at least as measured between 11 and 13 weeks.
To study the molecular responses to differences in timing of PR exposure, we focussed on mature sperm, as this was the common tissue between the previous models from our group  and the model from Sharma et al. , thus permitting direct comparison. High sperm purity was consistently obtained (Additional file 1: Figure S4). Extracted DNA was sequenced by multiplex bisulfite PCR sequencing (‘BisPCR-Seq’), as in Holland et al. . This method allowed quantification of the A/C genetic variant frequency at position −104 bp in the promoter region of rDNA and of the frequency of methylation at the functional CpG site at −133 bp.
Genetic–epigenetic interactions at rDNA and an increase in tRNA fragments have been shown to represent molecular markers of dietary stress (such as PR) in different mouse models [2, 5, 7, 24]. The model used herein includes a re-examination of previously published pre- or post-weaning only PR exposures (PRCT and CTPR, respectively), and reveals the following key observations. Firstly, we confirm that rDNA variant-specific methylation effects are induced when PR exposure occurs pre-weaning only but not when it occurs post-weaning only and, secondly, that the CTPR group was the only one of the three different exposure groups that displayed a significant redistribution of the relative proportion of mapped tRNA fragments, small nucleolar RNAs and piRNAs. We were unable to reproduce the relative increase in specific tRNA fragments in the CTPR group as reported by Sharma et al. , but this may simply reflect natural experimental variation between our iteration of the model versus theirs, or subtle but relevant differences between our C57BL/6 J mice and the FVB/NJ mice used therein. However, the data do support the broader conclusion that post-weaning dietary stress induces perturbation of small RNAs (at least in sperm), whereas this is not observed in the pre-weaning PR exposure. Collectively, these results address the first two aims of our study, and further underline the robustness of rDNA and tRNA as ‘molecular barometers’ of dietary stress in mouse models.
There are some limitations to our work that can be addressed in future studies. First, it is well established that the birth-to-weaning time window is another critical period in the life-course of the mouse , with growth and development of many neuroendocrine systems occurring in this period in mice as would occur in utero in humans . No single model can capture the nuances of mismatch between every critical developmental stage so the birth-to-weaning period will be an important model to address in the future, and this may also shed light on why the relationship between %A and Ameth% is not observed in PRPR mice. Second, it will be important to analyse the role that rDNA and small RNA perturbations play in the development of disease phenotypes using dedicated models in which one would specifically modulate, for example, specific small RNAs in vivo or alter the methylation of specific rDNA copies. These experiments would also need to be performed in tissues potentially more relevant to the phenotype under consideration, for example, in liver or adipose tissues, to investigate the potential downstream metabolic outcomes. We focused on sperm in this study allowing us to directly compare our previous model  and that from Sharma et al. , and because sperm can be isolated to very high degrees of purity, thus reducing the differential cell composition biases that can potentially arise when using more complex tissues (although it should be noted that we also observed the %A vs. Ameth% relationship in livers of PRCT mice ). Indeed, the mechanisms by which rDNA and small RNAs act as stress responses may be interconnected – it was recently found that certain tRNA fragments can modulate the expression of ribosomal proteins and therefore ribosome biogenesis . In this case, we suggest that different molecular mechanisms could operate to bring about the same outcome (changes to ribosome biogenesis) when the stressor occurs at different times in the life-course. It will be interesting to investigate whether there are other genomic perturbations conserved across different dietary mouse models that may differ in their nature depending on the timing of the stress exposure . Finally, it has been shown that tRNA fragments in sperm can cause gene expression changes in the livers of the offspring of sires exposed to PR during adulthood only [7, 8], which raises the question of whether inter-generational effects would be seen in the offspring of the PRPR group where no tRNA or other small RNA response is seen. The result supports the idea that small RNA in sperm are reflective of the paternal state , which in this case is the absence of an acute stress response due to long-term exposure to the stressor.
To conclude, we show that the nature of the molecular response to PR is different depending on the timing of the exposure and that ‘matching’ diets either side of weaning eliminates responses measured at rDNA and small RNA. It is important to emphasise that both rDNA and small RNA stress responses are, broadly speaking, conserved amongst different species [15–20] and in different nutritional stress models [2, 8, 32, 33]. Our work supports the idea that genetic–epigenetic interactions at rDNA and small RNA could have utility as biomarkers to study key aspects of human biology and disease and how environmental pressures during the entire life-course could impact outcomes.
Breeding and housing conditions
All animal procedures were conducted in accordance with the Home Office Animals (Scientific Procedures) Act 1986 (Project License number: 70/6693). Female and male C57BL/6 J mice were obtained from Charles Rivers UK, aged 6–8 weeks and 10 weeks, respectively. Mice were maintained on a 12 h light/dark cycle (07:00–19:00) and housed at a constant temperature and humidity. After 1 week of acclimatisation in the mouse facility on standard chow (control diet, 20% protein), matings were set up by transferring one, or sometimes two, females into a male’s cage in the late afternoon. On the discovery of a vaginal plug the next morning (designated 0.5 days post coitum), pregnant females were singly housed and given ad libitum access to either a PR diet (8% protein) or maintained on the CT diet. Breeding males were housed individually for the duration of the breeding period. Females were maintained on the respective diet until offspring were weaned. Whole litters were weighed at 7 and 14 days. Upon weaning at 21 days, male offspring from each litter were put on either a CT or PR diet until death. Only litters with 5–10 pups were included. Litter sizes had no impact on the conclusions reported here (Additional file 1: Figures S8 and S9). Male offspring were housed in cages containing 3–5 mice from weaning and weighed individually every week from weaning until they were killed at 11–13 weeks of age.
The CT diet was PicoLab® Mouse Diet 20 Extruded (5R58*), consisting of a standard chow containing roughly 20% of calories from protein. The PR diet was a custom diet obtained from Special Diet Services and was isocaloric with the control diet but contained only 8% of calories from protein (code: 829277, name: RB 8% CP ISO E (P)) (Diet compositions outlined in Additional file 1: Table S1).
Adult male dissection and phenotyping
Mice were fasted for 16 h before being killed by CO2 asphyxiation. After weighing the whole animal and measuring its length from nose to base of the tail, cardiac puncture was performed using a 23 G needle and 1 mL syringe. Between 100 and 500 μL of blood was collected. A drop of blood from the syringe (0.6 μL) was placed on a glucose measurement strip and blood glucose concentration was measured using a Bayer NEXT Contour Glucose meter. The remaining blood was decanted into a 1.5 mL Eppendorf tube and allowed to clot at room temperature before being placed on ice. In male mice, the epididymis was next dissected from the base of the testes and transferred to a 2 mL Eppendorf tube containing pre-made sperm motility medium warmed to 37 °C in a water bath (sperm motility medium: 1 M NaCl, 100 mM KCl, 25 mM KH2PO4, 20 mM MgSO4, 0.6% sodium lactate, 500 mM NaHCO3, 25 mM sodium pyruvate, 25 mM CaCl, 500 mM HEPES, 34.5 mg/mL of BSA). Epididymides were homogenised using a fine pair of scissors in the tube for 5 min and placed in a water bath for 30 min at 37 °C, with regular inversion, to allow the sperm to swim out. After incubation, the tube was briefly spun down using a nanofuge to collect debris, then the supernatant containing free-swimming sperm was removed and placed in a new 1.5 mL Eppendorf tube and stored on ice for the rest of the dissection.
Liver, kidneys and visceral gonadal white adipose tissue deposits were dissected out, weighed and flash frozen in liquid nitrogen. A small amount of pancreas and small intestine were also removed and flash frozen. Next, the subcutaneous inguinal white adipose tissue deposits on each side of the mouse and the interscapular brown adipose tissue deposits on the back of the mouse were removed, weighed and flash frozen. Finally, a small section of ear was flash frozen.
Phenol:chloroform DNA extraction
For DNA extraction, one-quarter of the extracted sperm was incubated overnight in 600 μL of PK buffer (10 mM Tris-HCl, 100 mM NaCl, 25 mM EDTA, 1% SDS) with 2 μL of Proteinase K enzyme (19 mg/mL) and 0.1 M DTT at 55 °C with slow rotation. Phenol (750 μL) was added to the samples and agitated for several minutes before spinning at 17,000x g for 5 min at 4 °C. The upper aqueous phase was transferred to a new tube and the process repeated with phenol:chloroform, then chloroform alone. After the final spin, 5 μL of Rnase was added and samples were incubated at 37 °C for 60 min. Then, 0.1 volumes of 3 M sodium acetate (pH 5.2) and 2.5 volumes of 100% EtOH were added and samples incubated at −20 °C for 1 h. Samples were spun at 17,000x g for 10 min at 4 °C and the pellet was washed with 75% EtOH. Finally, the pellet was air-dried at 37 °C, resuspended in 200 μL of TE buffer and incubated at 50 °C for 3 h before storage at 4 °C. DNA was quantified using the High Sensitivity Qubit® kit (Thermo Fish Scientific, Cat. Q32851) as per the protocol. Sperm purity was confirmed by Bis-PCR-Seq of imprinting control regions associated with MEST, MCTS2, NESP and IGF2/H19.
RNA extraction and small RNA library preparation
For RNA extraction, three-quarters of the extracted sperm were incubated at 60 °C for 15 min with slow rotation in 33.3 μL of sperm lysis buffer (6.4 M Guanidine HCl, 5% Tween 20, 5% Triton, 120 mM EDTA, 120 mM Tris; pH 8.0) with 3.3 μL of Proteinase K (19 mg/mL) and 3.3 μL of 0.1 M DTT. After the incubation, one volume (100 μL) of ultra-pure water was added, followed by 700 μL of Qiazol Lysis reagent (QIAGEN, Cat. 79,306) and samples were vortexed for 5 min. Chloroform (140 μL) was added and samples were shaken vigorously for 30 s before 3 min incubation at room temperature. Samples were centrifuged at 12,000x g at 4 °C for 15 min then the upper aqueous phase was transferred to a new reaction tube. One volume of 70% EtOH was added and mixed thoroughly. Samples were transferred to a RNeasy mini spin column and the protocol from the miRNeasy Mini Kit (Qiagen, Cat. 217,004) was then followed, including the separation of the small RNA and large RNA fractions using an RNA MinElute spin column (Qiagen, Cat. 74,204). The small RNA fraction was eluted in 14 μL of RNase-free water and quantified using the microRNA kit from Qubit® (Cat. Q32880). Them, 6 μL of the small RNA fraction was used for small RNA library preparation using the NEBNext® Small RNA library prep set for Illumina (Cat. E7330S) as per the protocol. Each sample was uniquely barcoded using one of the NEBNext® Index Primers for Illumina (Cat. E7300S, E7580S, E7710S, E7730S). For PCR amplification, 15 cycles were used. Libraries were purified using the QIAQuick PCR purification kit (Qiagen, Cat. 28,104) and DNA was eluted into 32 μL of nuclease-free water. An aliquot of each library was diluted and 1 μL was run on an Agilent 2100 Bioanalyzer using the Agilent High Sensitivity DNA kit (Cat. 5067-4626) to assess the size distribution of the library. The libraries were pooled using equal volumes and size selected for between 140 and 200 bp (corresponding to an insert size of 13–73 bp) using a BluePippin machine (Sage Science) with 3% agarose cassettes (Sage Science, Cat. BDF3010).
Sequencing and data analysis
DNA from sperm was diluted to a concentration of 11 ng/μL and 45 μL of each sample was sent for sequencing to the Genome Centre Facility at Charterhouse Square, QMUL. Bis-PCR-Seq was performed using the 48.48 layout on the Fluidigm® C1 system (Fluidigm®, USA), coupled with Illumina MiSeq sequencing using version2 chemistry (150 bp, paired-end). See Additional file 1: Table S3 for the primer sequences. Small RNA libraries were initially sequenced using Illumina MiSeq Nano sequencing (75 bp, single-end) and read counts for each samples were used to re-balance the library pool. The final pool was then sequenced using Illumina NextSeq sequencing (75 bp, single-end).
Bismark (v0.7.12) was used to align Bis-PCR-Seq data to the mm10 reference genome (imprinting control region data; Additional file 1: Figure S4) or to the adjusted consensus rDNA reference, using Bowtie2 (v2.1.0). Only reads that mapped to the correct starting position and perfectly matched the consensus were used for further analysis. For rDNA analysis, the R package RSamtools was used to identify each read as either having an A or a C at position −104 bp and to determine the methylation status at position −133 bp of each read. Reads could therefore be assigned to either Am, Au, Cm, or Cu. The total number of reads in each group was summed and %A ((Am + Au)/(Cm + Cu)) and CpG –133 A meth % (Am/(Am + Au)) calculated for each sample. Methylation of imprinting control regions were assessed using a custom program (https://bitbucket.org/lowelabqmul/methylation-extractor).
Small RNA sequencing data was mapped to the whole genome (UCSC, mm10), piRNA, tRNA, miRNA and rRNA databases using the SPORTS 1.0 pipeline (https://github.com/junchaoshi/sports1.0.git). Total read counts for each small RNA class were expressed as percentages of number of reads mapped to the genome for each sample in the composition analysis (Fig. 4a). Differential expression analysis of tRNA fragments was performed using edgeR (glmQLFTest) using the number of reads mapping to the genome as the library sizes for normalisation.
All statistical analysis and plotting were performed using R (v3.2.3). For all phenotype plots, a linear model was run on individuals and P values were derived by using robust standard errors to account for the relatedness between siblings in each diet group (R packages plm  and lmtest ) and corrected for n = 3 tests using Bonferroni’s correction. A Pearson’s product moment correlation coefficient was calculated to describe the relationship between %A and CpG –133 A methylation percentage in each diet group (cor) and a linear model with robust standard errors was then run to obtain P values, which were then corrected for n = 4 comparisons using Bonferroni’s correction (P lin ). All mice were used in the analyses instead of litter averages and linear models with robust standard errors were used to correct for any biases due to sibling relatedness (a full justification is provided in Additional file 1: Supplementary methods). Fisher’s exact test was used to assess the differences in distribution of the small RNA compositions compared to CTCT, using percentages of mapped reads corresponding to each species in each sample and these were rounded to the nearest integer (corrected for n = 3 tests using Bonferroni’s correction). ANOVA was used to assess whether the %A or %C and CpG –133 A meth % or CpG –133 C meth % were different between the diet groups.
We thank the BSU technicians for their help with the animal work and the Bart’s and the London Genome Centre staff for performing the high through-put sequencing. We also thank Féaron Cassidy, Philip Howard and Gabriel Rosser for their help and advice.
AFD is funded by an MRC Studentship (MR/K501372/1) and a Life Sciences Initiative Small Grant and the work was supported by a Biotechnology and Biological Sciences Research Council, UK (BB/M012494/1) grant awarded to VKR.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the GEO repository, Series GSE107541.
AFD, MLH and VKR conceived the project and carried out the experiments. AFD analysed the data with the help of RL and SJM advised on statistical analyses. All authors discussed the results and interpretation and approved the final manuscript.
All animal procedures were conducted in accordance with the Home Office Animals (Scientific Procedures) Act 1986 (Project License number: 70/6693).
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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