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Amino acid (AA)-related inherited metabolic disorders (IMDs) and urea cycle disorders (UCDs) require strict dietary management including foods low in protein such as fruits, vegetables and starchy roots. Despite this recommendation, there are limited data on the AA content of many of these foods. The aim of this study is to describe an analysis of the protein and AA content of a range of fruits, vegetables and starchy roots, specifically focusing on amino acids (AAs) relevant to AA-related IMDs such as phenylalanine (Phe), methionine (Met), leucine (Leu), lysine (Lys) and tyrosine (Tyr). AA analysis was performed using high-performance liquid chromatography (HPLC) on 165 food samples. Protein analysis was also carried out using the Dumas method. Foods were classified as either 'Fruits', 'Dried fruits', 'Cruciferous vegetables', 'Legumes', 'Other vegetables' or 'Starchy roots'. 'Dried fruits' and 'Legumes' had the highest median values of protein, while 'Fruits' and 'Cruciferous vegetables' contained the lowest median results. 'Legumes' contained the highest and 'Fruits' had the lowest median values for all five AAs. Variations were seen in AA content for individual foods. The results presented in this study provide useful data on the protein and AA content of fruits, vegetables and starchy roots which can be used in clinical practice. This further expansion of the current literature will help to improve diet quality and metabolic control among individuals with AA-related IMDs and UCDs. [ABSTRACT FROM AUTHOR]

Pregnancy represents a period of immense maternal physiological adaptation, with progressive increases in lipid storage potential and insulin resistance to support fetal/placental growth. This requires significant change in the adipose tissue. Women living with obesity/overweight are more susceptible to these changes causing complications such as gestational diabetes. This is particularly worrying as up to 60% of European women are living with overweight/obesity at the onset of pregnancy. Furthermore, less than 1% meet all nutrition guidelines. There is now evidence that these deep metabolic changes can result in a predisposition to metabolic disease in both the mother and child in later life. Health and nutrition status during this period therefore represents a window to future health. This period offers a valuable opportunity for intervention to prevent the negative consequences of poor in utero environments and increases the long‐term quality of life for mother and offspring. This review will examine a range of in utero factors which determine adipose tissue development, the impact of these factors on later‐life obesity and metabolic health and the therapeutic value of dietary anti‐inflammatory nutritional interventions during pregnancy and early life. When it comes to early life nutrition, a 'one size fits all' approach is not always appropriate. Understanding the mechanisms of adipose tissue development in response to differing nutritional strategies may be important in the context of complicated or adverse in utero environments and represents a substantial step towards a more personalised nutritional approach for the prevention of obesity, metabolic syndrome and related non‐communicable diseases in future generations. What is the topic of this review?The influence of adversein utero environments (obesogenic, undernutrition, gestational diabetes) onadipocyte capacity and the associated risk of obesity and metabolicdysregulation in later life.What advances does it highlight?Novel anti‐inflammatory dietary intervention strategies duringpregnancy that aim prevent obesity and metabolic dysfunction in the offspring. Despiteoverwhelming evidence of inflammatory involvement in obesity and associatedcomorbidities, the involvement of metabolic inflammation on pregnancy andearly‐life programming of metabolic disease has not been investigatedcomprehensively. Studies identifying novel intervention strategies to combat apoor start to life are pivotal in the developmental programming field given thedual approach of both identifying valid nutritional interventions while at thesame time aiding in the identification of molecular targets that influenceadipose tissue function early in the lifecourse. [ABSTRACT FROM AUTHOR]

Abstract Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways - selenoamino acid metabolism and steroid biosynthesis - illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease.