CD28 is one of the most important costimulatory receptors necessary for full T lymphocyte activation. The CD28 receptor can enhance T cell antigen receptor (TCR) signals, as well as deliver independent signals. Indeed, CD28 engagement by B7 can generate TCR-independent signals leading to IκB kinase and NF-κB activation. Here we demonstrate that the TCR-independent CD28 signal leads to the selective transcription of survival (Bcl-xL) and inflammatory (IL-8andB cell activation factor, but not proliferative (IL-2), genes, in a NF-κB-dependent manner. CD28-stimulated T cells actively secrete IL-8, and Bcl-xL up-regulation protects T cells from radiation-induced apoptosis. The transcription of CD28-induced genes is mediated by the specific recruitment of RelA and p52 NF-κB subunits to target promoters. In contrast, p50 and c-Rel, which preferentially bind NF-κB sites on theIL-2gene promoter after anti-CD3 stimulation, are not involved. Thus, we identify CD28 as a key regulator of genes important for both survival and inflammation.

Tumor necrosis factor α–converting enzyme (TACE, also known as ADAM17) was recently involved in the pathogenesis of insulin resistance. We observed that TACE activity was significantly higher in livers of mice fed a high-fat diet (HFD) for 1 month, and this activity was increased in liver > white adipose tissue > muscle after 5 months compared with chow control. In mouse hepatocytes, C2C12 myocytes, and 3T3F442A adipocytes, TACE activity was triggered by palmitic acid, lipolysaccharide, high glucose, and high insulin. TACE overexpression significantly impaired insulin-dependent phosphorylation of AKT, GSK3, and FoxO1 in mouse hepatocytes. To test the role of TACE activation in vivo , we used tissue inhibitor of metalloproteinase 3 (Timp3) null mice, because Timp3 is the specific inhibitor of TACE and Timp3 −/− mice have higher TACE activity compared with wild-type (WT) mice. Timp3 −/− mice fed a HFD for 5 months are glucose-intolerant and insulin-resistant; they showed macrovesicular steatosis and ballooning degeneration compared with WT mice, which presented only microvesicular steatosis. Shotgun proteomics analysis revealed that Timp3 −/− liver showed a significant differential expression of 38 proteins, including lower levels of adenosine kinase, methionine adenosysltransferase I/III, and glycine N -methyltransferase and higher levels of liver fatty acid-binding protein 1. These changes in protein levels were also observed in hepatocytes infected with adenovirus encoding TACE. All these proteins play a role in fatty acid uptake, triglyceride synthesis, and methionine metabolism, providing a molecular explanation for the increased hepatosteatosis observed in Timp3 −/− compared with WT mice. Conclusion: We have identified novel mechanisms, governed by the TACE–Timp3 interaction, involved in the determination of insulin resistance and liver steatosis during overfeeding in mice. (Hepatology 2009.)

The tissue inhibitor of metalloproteinase (TIMP)3, a stromal protein that restrains the activity of proteases and receptors, is reduced in inflammatory metabolic disorders such as type 2 diabetes mellitus (T2DM) and atherosclerosis. We overexpressed Timp3 in mouse macrophages (MacT3) to analyze its potential antidiabetic and antiatherosclerotic effects. Transgenic mice with myeloid cells targeting overexpression of TIMP3 were generated and fed a high-fat diet for 20 weeks. Physical and metabolic phenotypes were determined. Inflammatory markers, lipid accumulation, and insulin sensitivity were measured in white adipose tissue (WAT), liver, and skeletal muscle. In a model of insulin resistance, MacT3 mice were more glucose tolerant and insulin sensitive than wild-type mice in both in vitro and in vivo tests. Molecular and biochemical analyses revealed that increased expression of TIMP3 restrained metabolic inflammation and stress-related pathways, including Jun NH2-terminal kinase and p38 kinase activation, in WAT and liver. TIMP3 overexpression in macrophages resulted in reduced activation of oxidative stress signals related to lipid peroxidation, protein carbonylation, and nitration in WAT and liver. Our data show that macrophage-specific overexpression of TIMP3 protects from metabolic inflammation and related metabolic disorders such as insulin resistance, glucose intolerance, and nonalcoholic steatohepatitis.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.