The application of genomic and proteomic technologies in predictive, preventive and personalized medicine.

The long asymptomatic period before the onset of chronic diseases offers good opportunities for disease prevention. Indeed, many chronic diseases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction is vital for disease prevention so that therapy can be given to those individuals who are most likely to develop the disease. The utility of predictive markers is dependent on three parameters, which must be carefully assessed: sensitivity, specificity and positive predictive value. Specificity is important if a biomarker is to be used to identify individuals either for counseling or for preventive therapy. However, a reciprocal relationship exists between sensitivity and specificity. Thus, successful biomarkers will be highly specific without sacrificing sensitivity. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find for many diseases. One potential solution is to use the combinatorial power of a large number of biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer a great opportunity for biomarker discovery. The newly identified biomarkers have the potential to bring increased accuracy in disease diagnosis and classification, as well as therapeutic monitoring. In this review, we will use type 1 diabetes (T1D) as an example, when appropriate, to discuss pertinent issues related to high throughput biomarker discovery.

Molecular cloning and characterization of the mouse and human TUSP gene, a novel member of the tubby superfamily.

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.

A statistical method for flagging weak spots improves normalization and ratio estimates in microarrays.

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.

Rapid decrease of RNA level of a novel mouse mitochondria solute carrier protein (Mscp) gene at 4-5 weeks of age.

We cloned a novel mouse gene that encodes a protein with homology to the mitochondria solute carrier proteins (Mscp). The major full-length Mscp transcript contains 4112 bp of cDNA and a deduced protein of 338 amino acids. The Mscp protein shares 50%, 40%, and 39% sequence identity with the C. elegans hypothetical protein T26089 and the yeast mitochondria carrier proteins MRS3 and MRS4, respectively. It also showed homology with the uncoupling proteins (UCP1, UCP2, and UCP3; 22%, 24%, and 29% identity, respectively). The protein has six transmembrane domains and three mitochondria energy-transfer protein signature motifs, which are conserved among all the members of mitochondria carrier protein family. Northern analysis indicated that the Mscp gene is highly expressed in the spleen. Using cDNA microarray and Northern analysis, we have shown a significant decrease of the splenic Mscp mRNA levels around 4-5 weeks of age in several mouse strains including C57BL/6J, nonobese diabetic (NOD), and several NOD-congenic mice. These results suggest that the Mscp gene is decreased during splenic lymphocyte maturation in these mice.

Fine-mapping of the type 1 diabetes locus (IDDM4) on chromosome 11q and evaluation of two candidate genes (FADD and GALN) by affected sibpair and linkage-disequilibrium analyses.

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.

Genetic and physical mapping of a type 1 diabetes susceptibility gene (IDDM12) to a 100-kb phagemid artificial chromosome clone containing D2S72-CTLA4-D2S105 on chromosome 2q33.

Polymorphic markers within the CTLA4 gene on chromosome 2q33 have been shown to be associated with type 1 diabetes. Therefore, a gene responsible for the disease (IDDM12) most likely lies within a region of <1-2 cM of CTLA4. To define more precisely the IDDM12 interval, we genotyped a multiethnic (U.S. Caucasian, Mexican-American, French, Spanish, Korean, and Chinese) collection of 178 simplex and 350 multiplex families for 10 polymorphic markers within a genomic interval of approximately 300 kb, which contains the candidate genes CTLA4 and CD28. The order of these markers (D2S346, CD28, GGAA19E07, D2S307, D2S72, CTLA4, D2S105, and GATA52A04) was determined by sequence tagged site content mapping of bacterial artificial chromosome (BAC) and yeast artificial chromosome (YAC) clones. The transmission disequilibrium test (TDT) analyses of our data revealed significant association/linkage with three markers within CTLA4 and two immediate flanking markers (D2S72 and D2S105) on each side of CTLA4 but not with more distant markers including the candidate gene CD28. Tsp analyses revealed significant association only with the three polymorphic markers within the CTLA4 gene. The markers linked and associated with type 1 diabetes are contained within a phagemid artificial chromosome clone of 100 kb, suggesting that the IDDM12 locus is either CTLA4 or an unknown gene in very close proximity.

IL4 and IL4Ralpha genes are not linked or associated with type 1 diabetes.

Previous studies have shown the immunoregulatory functions IL-4 in type 1 diabetes mellitus. Therefore, the genes involved in the IL-4 regulatory pathway are candidates for diabetes susceptibility genes. Here we have evaluated IL4 and the alpha subunit of the IL-4 receptor (IL4Ralpha) genes using the affected sibpair (ASP) and transmission/disequilibrium test (TDT). We analyzed 309 diabetic families from the United States and 87 families from various European countries. There was no evidence that either of these two genes are linked or associated with type 1 diabetes. Means by which IL-4 directed signals could indirectly alter diabetes susceptibility are proposed.