Background & Objective: Thioredoxin-interacting protein (TXNIP) also known as thioredoxin binding protein-2 is a ubiquitously expressed protein that interacts and negatively regulates expression and function of Thioredoxin (TXN). Over the last few years, TXNIP has attracted considerable attention due to its wide-ranging functions impacting several aspects of energy metabolism. TXNIP acts as an important regulator of glucose and lipid metabolism through pleiotropic actions including regulation of β-cell function, hepatic glucose production, peripheral glucose uptake, adipogenesis, and substrate utilization. Overexpression of TXNIP in animal models has been shown to induce apoptosis of pancreatic β-cells, reduce insulin sensitivity in peripheral tissues like skeletal muscle and adipose, and decrease energy expenditure. On the contrary, TXNIP deficient animals are protected from diet induced insulin resistance and type 2 diabetes.Summary:Consequently, targeting TXNIP is thought to offer novel therapeutic opportunity and TXNIP inhibitors have the potential to become a powerful therapeutic tool for the treatment of diabetes mellitus. Here we summarize the current state of our understanding of TXNIP biology, highlight its role in metabolic regulation and raise critical questions that could help future research to exploit TXNIP as a therapeutic target.

BackgroundHedgehog (HH) signaling plays a critical role in normal cellular processes, in normal mammalian gastrointestinal development and differentiation, and in oncogenesis and maintenance of the malignant phenotype in a variety of human cancers. Increasing evidence further implicates the involvement of HH signaling in oncogenesis and metastatic behavior of colon cancers. However, genomic approaches to elucidate the role of HH signaling in cancers in general are lacking, and data derived on HH signaling in colon cancer is extremely limited.Methodology/Principal FindingsTo identify unique downstream targets of the GLI genes, the transcriptional regulators of HH signaling, in the context of colon carcinoma, we employed a small molecule inhibitor of both GLI1 and GLI2, GANT61, in two human colon cancer cell lines, HT29 and GC3/c1. Cell cycle analysis demonstrated accumulation of GANT61-treated cells at the G1/S boundary. cDNA microarray gene expression profiling of 18,401 genes identified Differentially Expressed Genes (DEGs) both common and unique to HT29 and GC3/c1. Analyses using GenomeStudio (statistics), Matlab (heat map), Ingenuity (canonical pathway analysis), or by qRT-PCR, identified p21Cip1 (CDKN1A) and p15Ink4b (CDKN2B), which play a role in the G1/S checkpoint, as up-regulated genes at the G1/S boundary. Genes that determine further cell cycle progression at G1/S including E2F2, CYCLIN E2 (CCNE2), CDC25A and CDK2, and genes that regulate passage of cells through G2/M (CYCLIN A2 [CCNA2], CDC25C, CYCLIN B2 [CCNB2], CDC20 and CDC2 [CDK1], were down-regulated. In addition, novel genes involved in stress response, DNA damage response, DNA replication and DNA repair were identified following inhibition of HH signaling.Conclusions/SignificanceThis study identifies genes that are involved in HH-dependent cellular proliferation in colon cancer cells, and following its inhibition, genes that regulate cell cycle progression and events downstream of the G1/S boundary.

Genomic abnormalities leading to colorectal cancer (CRC) include somatic events causing copy number aberrations (CNAs) as well as copy neutral manifestations such as loss of heterozygosity (LOH) and uniparental disomy (UPD). We studied the causal effect of these events by analyzing high resolution cytogenetic microarray data of 15 tumor-normal paired samples. We detected 144 genes affected by CNAs. A subset of 91 genes are known to be CRC related yet high GISTIC scores indicate 24 genes on chromosomes 7, 8, 18 and 20 to be strongly relevant. Combining GISTIC ranking with functional analyses and degree of loss/gain we identify three genes in regions of significant loss (ATP8B1, NARS, and ATP5A1) and eight in regions of gain (CTCFL, SPO11, ZNF217, PLEKHA8, HOXA3, GPNMB, IGF2BP3 and PCAT1) as novel in their association with CRC. Pathway and target prediction analysis of CNA affected genes and microRNAs, respectively indicates TGF-β signaling pathway to be involved in causing CRC. Finally, LOH and UPD collectively affected nine cancer related genes. Transcription factor binding sites on regions of >35% copy number loss/gain influenced 16 CRC genes. Our analysis shows patient specific CRC manifestations at the genomic level and that these different events affect individual CRC patients differently.