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Dietary protein levels and cysteamine (CS) supplementation can affect growth performance and protein metabolism of pigs. However, the influence of dietary protein intake on the growth response of CS-treated pigs is unclear, and the mechanisms involved in protein metabolism remain unknown. Hence, we investigated the interactions between dietary protein levels and CS supplementation and the effects of dietary crude protein levels and CS supplementation on protein synthetic and degradative signaling in skeletal muscle of finishing pigs. One hundred twenty barrows (65.84 ± 0.61 kg) were allocated to a 2 × 2 factorial arrangement with five replicates of six pigs each. The primary variations were dietary crude protein (CP) levels (14% or 10%) and CS supplemental levels (0 or 700 mg/kg). The low-protein (LP) diets (10% CP) were supplemented with enough essential amino acids (EAA) to meet the NRC AA requirements of pigs and maintain the balanced supply of eight EAA including lysine, methionine, threonine, tryptophan, valine, phenylalanine, isoleucine, and leucine. After 41 days, 10 pigs per treatment were slaughtered. We found that LP diets supplemented with EAA resulted in decreased concentrations of plasma somatostatin (SS) (P

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptinob/ob mutation (Lepob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes., Author Summary Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.