Insulin activates sterol regulatory element-binding protein-1c (SREBP-1c) in liver, thereby increasing fatty acid and triglyceride synthesis. We created a line of transgenic rats that produce epitope-tagged human SREBP-1c in liver under control of the constitutive apolipoprotein E promoter/enhancer. This system allows us to dissect the pathway by which insulin stimulates SREBP-1c processing without interference by the insulin-mediated increase in SREBP-1c mRNA. Rats are used because freshly isolated rat hepatocytes respond much more robustly to insulin than do mouse hepatocytes. The data reveal that insulin-mediated stimulation of SREBP-1c processing requires the mechanistic target of rapamycin complex 1 (mTORC1), which also is required for insulin-mediated SREBP-1c mRNA induction. However, in contrast to mRNA induction, insulin stimulation of SREBP-1c processing is blocked by an inhibitor of p70 S6-kinase. The data indicate that the pathways for insulin enhancement of SREBP-1c mRNA and proteolytic processing diverge after mTORC1. Stimulation of processing requires the mTORC1 target p70 S6-kinase, whereas induction of mRNA bypasses this enzyme. Insulin stimulation of both processes is blocked by glucagon. The transgenic rat system will be useful in further defining the molecular mechanism for insulin stimulation of lipid synthesis in liver in normal and diabetic states.

Most kidney cancers are metabolically dysfunctional^1–4, but how this dysfunction affects cancer progression in humans is unknown. We infused ¹³C-labelled nutrients in over 80 patients with kidney cancer during surgical tumour resection. Labelling from [U-¹³C]glucose varies across subtypes, indicating that the kidney environment alone cannot account for all tumour metabolic reprogramming. Compared with the adjacent kidney, clear cell renal cell carcinomas (ccRCCs) display suppressed labelling of tricarboxylic acid (TCA) cycle intermediates in vivo and in ex vivo organotypic cultures, indicating that suppressed labelling is tissue intrinsic. [1,2-¹³C]acetate and [U-¹³C]glutamine infusions in patients, coupled with measurements of respiration in isolated human kidney and tumour mitochondria, reveal lower electron transport chain activity in ccRCCs that contributes to decreased oxidative and enhanced reductive TCA cycle labelling. However, ccRCC metastases unexpectedly have enhanced TCA cycle labelling compared with that of primary ccRCCs, indicating a divergent metabolic program during metastasis in patients. In mice, stimulating respiration or NADH recycling in kidney cancer cells is sufficient to promote metastasis, whereas inhibiting electron transport chain complex I decreases metastasis. These findings in humans and mice indicate that metabolic properties and liabilities evolve during kidney cancer progression, and that mitochondrial function is limiting for metastasis but not growth at the original site.

Significance: Famine kills millions of people each year. Survival requires the maintenance of blood glucose. Famine depletes body fat, thereby removing a source of energy for hepatic glucose production. Here we used a mouse model of fat depletion to show that growth hormone (GH) maintains blood sugar by stimulating hepatic autophagy, the process by which the liver digests its organelles to provide energy and substrates for producing glucose. When fat-depleted mice are fasted, their stomachs secrete ghrelin, a GH secretagogue. The resultant elevation in GH stimulates hepatic autophagy. In mice lacking ghrelin, GH fails to rise appropriately, hepatic autophagy is reduced, and mice die from hypoglycemia. These studies demonstrate a ghrelin-GH-autophagy axis that is required for survival in famine.

Stereotactic body radiation therapy (SBRT) has demonstrated high local control rates in early stage non-small cell lung cancer patients who are not ideal surgical candidates. However, distant failure after SBRT is still common. For patients at high risk of early distant failure after SBRT treatment, additional systemic therapy may reduce the risk of distant relapse and improve overall survival. Therefore, a strategy that can correctly stratify patients at high risk of failure is needed. The field of radiomics holds great potential in predicting treatment outcomes by using high-throughput extraction of quantitative imaging features. The construction of predictive models in radiomics is typically based on a single objective such as overall accuracy or the area under the curve (AUC). However, because of imbalanced positive and negative events in the training datasets, a single objective may not be ideal to guide model construction. To overcome these limitations, we propose a multi-objective radiomics model that simultaneously considers sensitivity and specificity as objective functions. To design a more accurate and reliable model, an iterative multi-objective immune algorithm (IMIA) was proposed to optimize these objective functions. The multi-objective radiomics model is more sensitive than the single-objective model, while maintaining the same levels of specificity and AUC. The IMIA performs better than the traditional immune-inspired multi-objective algorithm.