TXNIP regulates peripheral glucose metabolism in humans.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is characterized by defects in insulin secretion and action. Impaired glucose uptake in skeletal muscle is believed to be one of the earliest features in the natural history of T2DM, although underlying mechanisms remain obscure. METHODS AND FINDINGS: We combined human insulin/glucose clamp physiological studies with genome-wide expression profiling to identify thioredoxin interacting protein (TXNIP) as a gene whose expression is powerfully suppressed by insulin yet stimulated by glucose. In healthy individuals, its expression was inversely correlated to total body measures of glucose uptake. Forced expression of TXNIP in cultured adipocytes significantly reduced glucose uptake, while silencing with RNA interference in adipocytes and in skeletal muscle enhanced glucose uptake, confirming that the gene product is also a regulator of glucose uptake. TXNIP expression is consistently elevated in the muscle of prediabetics and diabetics, although in a panel of 4,450 Scandinavian individuals, we found no evidence for association between common genetic variation in the TXNIP gene and T2DM. CONCLUSIONS: TXNIP regulates both insulin-dependent and insulin-independent pathways of glucose uptake in human skeletal muscle. Combined with recent studies that have implicated TXNIP in pancreatic beta-cell glucose toxicity, our data suggest that TXNIP might play a key role in defective glucose homeostasis preceding overt T2DM.

Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with recognized variability in clinical outcome, genetic features, and cells of origin. To date, transcriptional profiling has been used to highlight similarities between DLBCL tumor cells and normal B-cell subtypes and associate genes and pathways with unfavorable outcome. To identify robust and highly reproducible DL-BCL subtypes with comprehensive transcriptional signatures, we used a large series of newly diagnosed DLBCLs, whole genome arrays, and multiple clustering methods. Tumors were also analyzed for known common genetic abnormalities in DLBCL. There were 3 discrete subsets of DLBCL-"oxidative phosphorylation," "B-cell receptor/proliferation," and "host response" (HR)-identified characterized using gene set enrichment analysis and confirmed in an independent series. HR tumors had increased expression of T/natural killer cell receptor and activation pathway components, complement cascade members, macrophage/dendritic cell markers, and inflammatory mediators. HR DLB-CLs also contained significantly higher numbers of morphologically distinct CD2+/CD3+ tumor-infiltrating lymphocytes and interdigitating S100+/gamma interferon-induced lysosomal transferase-positive (GILT+) CD1a-/CD123- dendritic cells. The HR cluster shared features of histologically defined T-cell/histiocyte-rich B-cell lymphoma, including fewer genetic abnormalities, younger age at presentation, and frequent splenic and bone marrow involvement. These studies identify tumor microenvironment and host inflammatory response as defining features in DLBCL and suggest rational treatment targets in specific DLBCL subsets.

The molecular signature of mediastinal large B-cell lymphoma differs from that of other diffuse large B-cell lymphomas and shares features with classical Hodgkin lymphoma.

Mediastinal large B-cell lymphoma (MLBCL) is a recently identified subtype of diffuse large B-cell lymphoma (DLBCL) that characteristically presents as localized tumors in young female patients. Although MLBCL has distinctive pathologic features, it clinically resembles the nodular sclerosis subtype of classical Hodgkin lymphoma (cHL). To elucidate the molecular features of MLBCL, we compared the gene expression profiles of newly diagnosed MLBCL and DLBCL and developed a classifier of these diseases. MLBCLs had low levels of expression of multiple components of the B-cell receptor signaling cascade, a profile resembling that of Reed-Sternberg cells of cHL. Like cHLs, MLBCLs also had high levels of expression of the interleukin-13 (IL-13) receptor and downstream effectors of IL-13 signaling (Janus kinase-2 [JAK2] and signal transducer and activator of transcription-1 [STAT1]), tumor necrosis factor (TNF) family members, and TNF receptor-associated factor-1 (TRAF1). Increased expression of STAT1 and TRAF1 in MLBCL was confirmed by immunohistochemistry. Given the TRAF1 expression and known link to nuclear factor-kappa B (NF- kappa B), MLBCLs were also evaluated for nuclear translocation of c-REL protein. In almost all cases, c-REL was localized to the nucleus, consistent with activation of the NF-kappa B pathway. These studies identify a molecular link between MLBCL and cHL and a shared survival pathway.

Gene expression-based classification of malignant gliomas correlates better with survival than histological classification.

In modern clinical neuro-oncology, histopathological diagnosis affects therapeutic decisions and prognostic estimation more than any other variable. Among high-grade gliomas, histologically classic glioblastomas and anaplastic oligodendrogliomas follow markedly different clinical courses. Unfortunately, many malignant gliomas are diagnostically challenging; these nonclassic lesions are difficult to classify by histological features, generating considerable interobserver variability and limited diagnostic reproducibility. The resulting tentative pathological diagnoses create significant clinical confusion. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to classify high-grade gliomas in a manner more objective, explicit, and consistent than standard pathology. Microarray analysis was used to determine the expression of approximately 12000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology.

Profiling gene transcription in vivo reveals adipose tissue as an immediate target of tumor necrosis factor-alpha: implications for insulin resistance.

Despite extensive studies implicating tumor necrosis factor (TNF)-alpha as a contributing cause of insulin resistance, the mechanism(s) by which TNF-alpha alters energy metabolism in vivo and the tissue specificity of TNF-alpha action are unclear. Here, we investigated the effects of TNF-alpha infusion on gene expression and energy metabolism in adult rats. A 1-day TNF-alpha treatment decreased overall insulin sensitivity and caused a 70% increase (P = 0.005) in plasma levels of free fatty acids (FFAs) and a 46% decrease (P = 0.01) in ACRP30. A 4-day TNF-alpha infusion caused insulin resistance and significant elevation of plasma levels of FFAs and triglycerides and reduction of ACRP30. Plasma glucose concentration was not altered following TNF-alpha infusion for up to 4 days. As revealed by oligonucleotide microarrays, TNF-alpha evoked major and rapid changes in adipocyte gene expression, favoring FFA release and cytokine production, and fewer changes in liver gene expression, but favoring FFA and cholesterol synthesis and VLDL production. There was only a moderate repressive effect on skeletal muscle gene expression. We demonstrate that TNF-alpha antagonizes the actions of insulin, at least in part, through regulation of adipocyte gene expression including reduction in ACRP30 mRNA and induction of lipolysis resulting in increased plasma FFAs. TNF-alpha later alters systemic energy homeostasis that closely resembles the insulin resistance phenotype. Our data suggest that blockade of TNF-alpha action in adipose tissue may prevent TNF-alpha-induced insulin resistance in vivo.

Gene expression correlates of clinical prostate cancer behavior.

Prostate tumors are among the most heterogeneous of cancers, both histologically and clinically. Microarray expression analysis was used to determine whether global biological differences underlie common pathological features of prostate cancer and to identify genes that might anticipate the clinical behavior of this disease. While no expression correlates of age, serum prostate specific antigen (PSA), and measures of local invasion were found, a set of genes was identified that strongly correlated with the state of tumor differentiation as measured by Gleason score. Moreover, a model using gene expression data alone accurately predicted patient outcome following prostatectomy. These results support the notion that the clinical behavior of prostate cancer is linked to underlying gene expression differences that are detectable at the time of diagnosis.