SheWei-Peng Zheng, Yan Zhang, Leena Peltonen and Jin-XiongSetiady, Sarah Eckenrode, Bing Yi, Sharad Purohit, Qing-Guo Ruan, Kenneth Tung, Daniel Eisenman, Yuliushttp://www.jimmunol.org/content/178/11/7173J Immunol€2007; 178:7173-7180; ;Referenceshttp://www.jimmunol.org/content/178/11/7173.full#ref-list-1This article cites 39 articles, 16 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai.org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:

Previous studies have suggested more than 20 genetic intervals that are associated with susceptibility to type 1 diabetes (T1D) 1 , 2 , but identification of specific genes has been challenging and largely limited to known candidate genes. Here, we report evidence for an association between T1D and multiple single-nucleotide polymorphisms in 197 kb of genomic DNA in the IDDM5 interval. We cloned a new gene ( SUMO4 ), encoding small ubiquitin-like modifier 4 protein, in the interval. A substitution (M55V) at an evolutionarily conserved residue of the crucial CUE domain of SUMO4 was strongly associated with T1D ( P = 1.9 × 10 −7 ). SUMO4 conjugates to IκBα and negatively regulates NFκB transcriptional activity. The M55V substitution resulted in 5.5 times greater NFκB transcriptional activity and ∼2 times greater expression of IL12B , an NFκB-dependent gene. These findings suggest a new pathway that may be implicated in the pathogenesis of T1D.

Using cDNA microarrays we determined the gene expression patterns in the human acute promyelocytic leukemia (APL) cell line NB4 during all-trans retinoic acid (ATRA)-induced differentiation. We analyzed the expression of 12,288 genes in the NB4 cells after 12 hours, 24 hours, 48 hours, 72 hours, and 96 hours of ATRA exposure. During this time course, we found 168 up-regulated and more than 179 down-regulated genes, most of which have not been reported before. Many of the altered genes encode products that participate in signaling pathways, cell differentiation, programmed cell death, transcription regulation, and production of cytokines and chemokines. Of interest, the CD52 and protein kinase A regulatory subunit alpha (PKA-Rlalpha) genes, whose products are being used as therapeutic targets for certain human neoplasias in currently ongoing clinical trials, were among the genes observed to be markedly up-regulated after ATRA treatment. The present study provides valuable data to further understand the mechanism of ATRA-induced APL cell differentiation and suggests potential therapeutic alternatives for this leukemia.

Proteomics, the study of protein function on a global scale, will play an important role in furthering our understanding of gene functions, complex biological pathways, and discovery of novel drug targets. A number of techniques have been developed for proteomic studies to identify and analyze proteins, compare protein expression levels, and study protein–protein interactions. Recent developments have applied a DNA array-type approach to immobilize proteins on a surface for high-throughput analysis. Here we report the development and construction of protein chips using derivatized glass and nitrocellulose-coated slides and the employment of recombinant proteins fused with green and red fluorescent proteins for detection. Fluorescent signals were found to be proportional to the amount of arrayed proteins and could be readily detected with a conventional fluorescence slide scanner. This technique allows the investigation of protein–protein interactions without the need for additional labeling steps of probe proteins.

Over the last few years, there has been a dramatic increase in the use of cDNA microarrays to monitor gene expression changes in biological systems. Data from these experiments are usually transformed into expression ratios between experimental samples and a common reference sample for subsequent data analysis. The accuracy of this critical transformation depends on two major parameters: the signal intensities and the normalization of the experiment versus reference signal intensities. Here we describe and validate a new model for microarray signal intensity that has one multiplicative variation and one additive background variation. Using replicative experiments and simulated data, we found that the signal intensity is the most critical parameter that influences the performance of normalization, accuracy of ratio estimates, reproducibility, specificity and sensitivity of microarray experiments. Therefore, we developed a statistical procedure to flag spots with weak signal intensity based on the standard deviation (δ ij) of background differences between a spot and the neighboring spots, i.e. a spot is considered as too weak if the signal is weaker than cδ ij. Our studies suggest that normalization and ratio estimates were unacceptable when this threshold (c) is small. We further showed that when a reasonable compromise of c (c = 6) is applied, normalization using trimmed mean of log ratios performed slightly better than global intensity and mean of ratios. These studies suggest that decreasing the background noise is critical to improve the quality of microarray experiments. 3 The tremendous advance of the human genome project and development of new high-throughput technologies has created unparalleled opportunities to study the mechanism of disease, monitor disease progression and evaluate effective therapies. As more and more genes are being identified, it has become extremely important to understand the function of these genes and their pathways. Global gene expression analysis is a critical component of this ambitious endeavor. Microarray technologies offer investigators an opportunity to simultaneously monitor the expression of a large number of genes in the context of their biological system. For this study, we concentrated on microarray-based studies monitoring RNA expression levels using cDNA microarrays printed on glass microscope slides. Pat Brown and coworkers developed the protocols widely used to do this type of assay (1,2,3,4,5). The basic strategy for this type of analysis is to isolate RNA from two sources, a reference and an experimental sample. The RNA samples are converted to cDNA and labeled with a fluorophore, typically Cy3 …

We report here the cloning and characterization of a novel gene belonging to the tubby superfamily proteins (TUSP) in mouse and human. The mouse Tusp cDNA is 9120 bp in length and encodes a deduced protein of 1547 amino acids, while the human TUSP gene is 11,127 bp and encodes a deduced protein of 1544 amino acids. The human and mouse genes are 87% identical for their nucleotide sequences and 85% identical for their amino acid sequences. The protein sequences of these genes are 40–48% identical to other tubby family proteins at the C-terminal conserved ‘tubby domain’. In addition, the TUSP proteins contain a tubby signature motif (FXGRVTQ), two bipartite nuclear localization signals (NLSs) at the C-terminal, two proline-rich regions, one WD40 repeat region and one suppressor of cytokines signaling domain. Transfection assay with green fluorescent protein-tagged TUSP expression constructs showed that the complete TUSP protein and the N-terminal portion of TUSP are localized in the cytoplasm but the C-terminal portion with the two NLSs produced distinct dots or spots localized in the cytoplasm. Northern blotting analysis showed that the major transcript with the complete coding sequence is expressed mainly in the brain, skeletal muscle, testis and kidney. Radiation hybrid mapping localized the mouse gene to chromosome 17q13 and the human TUSP gene to chromosome 6q25-q26 near the type 1 diabetes gene IDDM5. However, association analysis in diabetic families with a polymorphic microsatellite marker did not show any evidence for association between TUSP and type 1 diabetes. The precise biological function of the tubby superfamily genes is still unknown; the highly conserved tubby domain in different species, however, suggests that these proteins must have fundamental biological functions in a wide range of multi-cellular organisms.

As the sequence of the entire human genome is completed, the paradigm of analyzing a single gene effect in a biological system has shifted to a global systems analysis. This change in perspective can be attributed, in part, to the recognition that complex diseases such as diabetes or other autoimmune disorders do not arise from a single gene defect. Rather, they arise from a complex interaction of genes in a system that changes over the life span of the patients. In the past, researchers had a limited resource for the discovery of new genes important in the etiology of the disease they investigated. They were forced to take a gene-by-gene approach, which only scratched the surface of their biological problem. The sheer magnitude of currently available gene sequences compels researchers to take a more global view of the etiology of disease. Global gene expression analyses at the RNA level offer the first glimpse into the future of organizing and using this type of information. Investigators can simultaneously monitor the RNA levels of a large number of genes, or even the entire genome, in the context of their biological system.

Over the last few years, there has been a dramatic increase in the use of cDNA microarrays to monitor gene expression changes in biological systems. Data from these experiments are usually transformed into expression ratios between experimental samples and a common reference sample for subsequent data analysis. The accuracy of this critical transformation depends on two major parameters: the signal intensities and the normalization of the experiment vs. reference signal intensities. Here we describe and validate a new model for microarray signal intensity that has one multiplicative variation and one additive background variation. Using replicative experiments and simulated data, we found that the signal intensity is the most critical parameter that influences the performance of normalization, accuracy of ratio estimates, reproducibility, specificity, and sensitivity of microarray experiments. Therefore, we developed a statistical procedure to flag spots with weak signal intensity based on the standard deviation (delta(ij)) of background differences between a spot and the neighboring spots, i.e., a spot is considered as too weak if the signal is weaker than cdelta(ij). Our studies suggest that normalization and ratio estimates were unacceptable when this threshold (c) is small. We further showed that when a reasonable compromise of c (c = 6) is applied, normalization using trimmed mean of log ratios performed slightly better than global intensity and mean of ratios. These studies suggest that decreasing the background noise is critical to improve the quality of microarray experiments.

. Previous studies have identified a susceptibility region for insulin-dependent (type 1) diabetes mellitus on chromosome 11q13 ( IDDM4 ). In this study, 15 polymorphic markers were analyzed for 382 affected sibpair (ASP) families with type 1 diabetes. Our analyses provided additional evidence for linkage for IDDM4 (a peak LOD score of 3.4 at D11S913 ). The markers with strong linkage evidence are located within an interval of approximately 6 cM between D11S4205 and GALN. We also identified polymorphisms in two candidate genes, Fas-associated death domain protein ( FADD ) and galanin ( GALN ). Analyses of the data by transmission/disequilibrium test (TDT) and extended TDT (ETDT) did not provide any evidence for association/linkage with these candidate genes. However, ETDT did reveal significant association/linkage with the marker D11S987 ( P =0.0004) within the IDDM4 interval defined by ASP analyses, suggesting that IDDM4 may be in the close proximity of D11S987 .