Nutritional programming by maternal obesity: insights into the development of non‐alcoholic fatty liver disease
Bibliographic record
Abstract
Obesity has become an urgent global health concern. Individuals with obesity are at higher risk in developing numerous diseases including diabetes, metabolic syndrome and fatty liver disease. According to the World Health Organization (WHO, 2016), 39% of adults aged 18 years and over worldwide were overweight, 13% of them were obese, and these numbers had tripled over the past three decades. While obese adults are spotted for health concerns, childhood obesity has also become a serious health problem as obese children tend to remain so later in life. Increasing evidence suggests a link between maternal diet and childhood obesity. This is referred to as nutritional programming whereby maternal diet during pregnancy exerts lifelong effects on the developing fetus including predisposition to diseases. Of note, numerous studies have demonstrated maternal obesity (MO), resulting from diets comprising high fats, predisposes children and adolescents to the development of non-alcoholic fatty liver disease (NAFLD) (Brumbaugh et al. 2014). NAFLD is characterized by an excess fat deposition in the liver in the absence of significant alcohol consumption. It is the most prevalent form of chronic liver disease in children and adolescents; however, the underlying mechanisms by which NAFLD develops and progresses remain unknown. Given the devastating impact of NAFLD on the paediatric population, it is imperative to understand the effect of MO on the development of the disease. In a recent article by Lomas-Soria et al. (2018) in The Journal of Physiology, a cross-sectional study was conducted to investigate the effects of MO on the development of NAFLD in young adult rat offspring. This study in particular was designed in part to address previous results showing programming by MO displays a bias between sexes whereby disease severity is often more pronounced in male than female offspring. To elucidate the underlying cause of these observed differences, Lomas-Soria et al. (2018) examined the effects of MO on female and male F1 rat livers independently by analysing liver metabolites, structure and transcriptome changes. This study was performed on F1 progeny from mothers who were fed with either a high-fat or a control diet. To ensure F1 progeny homogeneity, Lomas-Soria and colleagues weaned all the F1 rats at postnatal day (PND) 21 and subjected them to experimental analyses at PND110. Measurements of liver metabolites and structure revealed clear differences in phenotypes between male and female F1 livers arising from MO. Livers of male MO F1 presented significantly increased levels of weight, total fat and triglycerides compared to controls whereas livers of female MO F1 showed only an increase in triglyceride levels that is much less pronounced than in male MO F1. These results reflect a previous study by Lomas-Soria and colleagues where they showed that male MO F1 displayed more severe phenotypes compared to female MO F1 at a younger age (PND36). To help understand the differences between male and female MO F1 phenotypes, the authors examined gene expression profiles of male and female MO F1 and control F1 rat livers by RNA-seq analysis. They found a complete separation in the gene expression profiles between male MO F1 and control F1 as opposed to female MO F1 where there is a significant overlap in gene expression profiles with the control F1. A closer look into the distribution of differentially expressed genes (DEGs) revealed that 1317 genes were down-regulated and 48 were up-regulated in MO F1 males when compared to controls and only 24 were down-regulated and 46 were up-regulated in MO F1 females when compared to controls. These novel findings illustrate a sex-dependent MO programming in the F1 liver transcriptome. While the current study provides novel insights into the effects of MO on F1 genetic expression, an interesting question arose as to whether the observed sexual dimorphism in DEGs is exclusively a consequence of MO. Sexual dimorphism has long been considered to arise during early development. Due to an imbalance in X and Y chromosomes, a number of sex-biased gene expressions can be observed throughout development (Rigby et al. 2015). In mammals, a sophisticated sex-determination system is adopted to ensure successful production of morphologically and physiologically distinct sexes. This system involves neatly regulated hierarchies which use sex-specific transcription factors to produce sex-biased gene expression. In addition to sex-specific gonadal tissues, evidence has also suggested sexual dimorphism in somatic tissues (Rigby et al. 2015). For example, Zhang et al. (2011) conducted a genome-wide scale study using human liver samples collected from 224 subjects and found that close to 4% of total genes in liver (1249/33250) had sex-biased expression. In addition, 70% of these genes were found to have higher expression in females. Therefore, according to this finding, it will be interesting to determine whether any of these genes have homologues that overlap with the DEGs found in the MO F1 rats reported by Lomas-Soria et al. (2018). Results from this study will have the potential to narrow down the genetic targets of F1 influenced by MO, which may further strengthen the current evidence of sexual dimorphism in the NAFLD F1 rats. Another factor that may also be implicated in MO programmed NAFLD in the F1 progeny is the difference in hormonal state between sexes. A study by Hogg et al. (2011) demonstrated that maternal exposure to androgens resulted in subclinical fatty liver in young adult sheep while oestrogens provided a protective role in the liver. Moreover, Villa et al. (2012) suggested that oestrogen receptor signalling plays an important role in regulating fat metabolism in the mouse liver. They found that oestrogen receptor oscillates in phase with the oestrous cycle and that a proper oestrous cycle is necessary to prevent fat deposition in the liver. These studies suggest a need to investigate whether hormones contribute a regulating role in the MO-programmed NAFLD. Future studies should also determine whether hormones underlie the cause of sexual dimorphic outcomes seen in the F1 rats shown by Lomas-Soria et al. (2018). Furthermore, Lomas-Soria et al. (2018) have shown that sex-specific changes in liver gene expression resulted in alteration of over 200 biological pathways suggesting complex mechanisms underlying MO-induced programming in NAFLD. To further elucidate the mechanisms, targeted approaches may be applied to facilitate narrowing down the number of biological pathways involved. It is believed that the development of NAFLD is due to complex interactions between different hepatic cells and recruited cells. The hepatic cells that may be involved include hepatic stellate cells, Kupffer cells and hepatic progenitor cells (Temple et al. 2016). RNA-seq analysis performed by Lomas-Soria et al. (2018) has shown compelling sex-specific changes in liver gene expression due to MO programming; however, such genome-wide scale analysis lacks the ability to ascertain whether the changes are cell specific or not, which could be informative for targeted NAFLD therapy. To narrow down the specific targets of MO, a single cell RNA-seq (scRNA-seq) approach may be employed. ScRNA-seq provides single cell resolution of gene expression profiles by isolating single cells from whole organs before cell lysis and RNA extraction. The advantages of using scRNA-seq are vast. An example of its application in the advancement of disease understanding can be seen in a recent study led by Tirosh et al. (2016). Their use of scRNA-seq on multiple malignant melanomas extracted from patients enabled them to identify a unique subgroup of dormant, therapy-resistant cells that otherwise would not have been possible to find using bulk RNA-seq methods. Therefore, performing scRNA-seq in female and male MO F1 and control livers will help reveal cells with changed gene expression profiles and provide a better context for the biological pathways that are altered by MO. Additionally, it will help determine whether the sex-dependent DEGs are found in the same subset of cells. Results from this approach will provide novel insights into potential targeted NAFLD therapeutics. In conclusion, Lomas-Soria et al. (2018) have demonstrated a sex-dependent MO-programmed NAFLD in young adult rats. Their findings provide direct evidence of maternal diet, when comprised with high fat, predisposes offspring to NAFLD through modifying liver transcriptome. Given a wide distribution of DEGs, future studies that focus on investigating the mechanisms should apply approaches that facilitate narrowing down the targets. As discussed, scRNA-seq would be a preferred approach. Ultimately, further consideration of other potential factors that may be implicated in MO programming, such as sexual dimorphism during development and hormonal state between sexes, will provide improved understanding of mechanisms by which obesity of mothers affects their offspring. None declared. The authors contributed equally to the article. Both authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. SH.L. is supported by a SickKids Hospital Research Training Competition (Restracomp) Award and a University of Toronto Open Fellowship Award from the Department of Molecular Genetics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".