A critical requirement for research using model organisms is an appropriate, well-defined and consistent diet. There is currently no complete chemically defined (holidic) diet available for Drosophila melanogaster. We describe a holidic medium that is equal in performance to an oligidic diet optimized for adult fecundity and lifespan. It is also sufficient to support development over multiple generations, but at a reduced rate. During seven years of experiments, the holidic diet yielded more consistent experimental outcomes than oligidic food for adult fitness traits. Furthermore, nutrients and drugs are more accessible to flies in holidic medium and, similar to dietary restriction on oligidic food, amino acid dilution increases fly lifespan. We also report amino acid specific effects on food choice behavior and that folic acid from the microbiota is sufficient for development. These insights could not be gained using oligidic or meridic diets.

SummaryBalancing the quantity and quality of dietary protein relative to other nutrients is a key determinant of evolutionary fitness. A theoretical framework for defining a balanced diet would both reduce the enormous workload to optimize diets empirically and represent a breakthrough toward tailoring diets to the needs of consumers. Here, we report a simple and powerful in silico technique that uses the genome information of an organism to define its dietary amino acid requirements. We show for the fruit fly Drosophila melanogaster that such “exome-matched” diets are more satiating, enhance growth, and increase reproduction relative to non-matched diets. Thus, early life fitness traits can be enhanced at low levels of dietary amino acids that do not impose a cost to lifespan. Exome matching also enhanced mouse growth, indicating that it can be applied to other organisms whose genome sequence is known.

Dietary restriction (DR), a moderate reduction in food intake, improves health during aging and extends life span across multiple species. Specific nutrients, rather than overall calories, mediate the effects of DR, with protein and specific amino acids (AAs) playing a key role. Modulations of single dietary AAs affect traits including growth, reproduction, physiology, health, and longevity in animals. Epidemiological data in humans also link the quality and quantity of dietary proteins to long-term health. Intricate nutrient-sensing pathways fine tune the metabolic responses to dietary AAs in a highly conserved manner. In turn, these metabolic responses can affect the onset of insulin resistance, obesity, neurodegenerative disease, and other age-related diseases. In this review we discuss how AA requirements are shaped and how ingested AAs regulate a spectrum of homeostatic processes. Finally, we highlight the resulting opportunity to develop nutritional strategies to improve human health during aging.

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IntroductionLipedema is a painful subcutaneous adipose tissue (SAT) disease characterized by adipocyte hypertrophy, immune cell recruitment, and fibrosis in the affected areas. These features are thought to contribute to the development and progression of the condition. However, the relationship between lipedema disease stage and the associated adipose tissue changes has not been determined so far.MethodsSAT biopsies of 32 lipedema patients, ranging across the pathological stages I to III, and 14 BMI- and age-matched controls were harvested from lipedema-affected thighs and non-symptomatic lower abdominal regions. Histological and immunohistochemical (IHC) staining and expression analysis of markers for adipogenesis, immunomodulation, and fibrosis were performed on the tissue biopsies.ResultsLipedema patients showed increased adipocyte areas and a stage-dependent shift towards larger cell sizes in the thighs. Lipedema SAT was linked with increased interstitial collagen accumulation in the thighs, but not the lower abdominal region when compared to controls. There was a trend toward progressive SAT fibrosis of the affected thighs with increasing lipedema stage. Elevated gene expression levels of macrophage markers were found for thigh SAT biopsies, but not in the abdominal region. IHC staining of lipedema thigh biopsies confirmed a transiently elevated macrophage polarization towards an M2-like (anti-inflammatory) phenotype.ConclusionsIn summary, lipedema SAT is associated with stage-dependent adipocyte hypertrophy, stage-progressive interstitial fibrosis and elevated proportion of M2-like macrophages. The character of the inflammatory response differs from primary obesity and may possess an essential role in the development of lipedema.

Pancreatic steatosis associates with β-cell failure and may participate in the development of type-2-diabetes. Our previous studies have shown that diabetes-susceptible mice accumulate more adipocytes in the pancreas than diabetes-resistant mice. In addition, we have demonstrated that the co-culture of pancreatic islets and adipocytes affect insulin secretion. The aim of this current study was to elucidate if and to what extent pancreas-resident mesenchymal stromal cells (MSCs) with adipogenic progenitor potential differ from the corresponding stromal-type cells of the inguinal white adipose tissue (iWAT). miRNA (miRNome) and mRNA expression (transcriptome) analyses of MSCs isolated by flow cytometry of both tissues revealed 121 differentially expressed miRNAs and 1,227 differentially expressed genes (DEGs). Target prediction analysis estimated 510 DEGs to be regulated by 58 differentially expressed miRNAs. Pathway analyses of DEGs and miRNA target genes showed unique transcriptional and miRNA signatures in pancreas (pMSCs) and iWAT MSCs (iwatMSCs), for instance fibrogenic and adipogenic differentiation, respectively. Accordingly, iwatMSCs revealed a higher adipogenic lineage commitment, whereas pMSCs showed an elevated fibrogenesis. As a low degree of adipogenesis was also observed in pMSCs of diabetes-susceptible mice, we conclude that the development of pancreatic steatosis has to be induced by other factors not related to cell-autonomous transcriptomic changes and miRNA-based signals.

In the version of this article initially published, 3D DAOSTORM was erroneously characterized in Table 2 as using least-squares fits. In fact, 3D DAOSTORM uses an implementation of maximum likelihood estimation (MLE). The error has been corrected in the HTML and PDF versions of the article.

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