Quantitative gene expression profiles in real time from expressed sequence tag databases.

An accumulation of expressed sequence tag (EST) data in the public domain and the availability of bioinformatic programs have made EST gene expression profiling a common practice. However, the utility and validity of using EST databases (e.g., dbEST) has been criticized, particularly for quantitative assessment of gene expression. Problems with EST sequencing errors, library construction, EST annotation, and multiple paralogs make generation of specific and sensitive qualitative arid quantitative expression profiles a concern. In addition, most EST-derived expression data exists in previously assembled databases. The Virtual Northern Blot (VNB) (http: //tlab.bu.edu/vnb.html) allows generation, evaluation, and optimization of expression profiles in real time, which is especially important for alternatively spliced, novel, or poorly characterized genes. Representative gene families with variable nucleotide sequence identity, tissue specificity, and levels of expression (bcl-xl, aldoA, and cyp2d9) are used to assess the quality of VNB's output. The profiles generated by VNB are more sensitive and specific than those constructed with ESTs listed in preindexed databases at UCSC and NCBI. Moreover, quantitative expression profiles produced by VNB are comparable to quantization obtained from Northern blots and qPCR. The VNB pipeline generates real-time gene expression profiles for single-gene queries that are both qualitatively and quantitatively reliable.

Increased prevalence of mutant null alleles that cause hereditary fructose intolerance in the American population.

Mutations in the aldolase B gene (ALDOB) impairing enzyme activity toward fructose-1-phosphate cleavage cause hereditary fructose intolerance (HFI). Diagnosis of the disease is possible by identifying known mutant ALDOB alleles in suspected patients; however, the frequencies of mutant alleles can differ by population. Here, 153 American HFI patients with 268 independent alleles were analyzed to identify the prevalence of seven known HFI-causing alleles (A149P, A174D, N334K, Delta4E4, R59Op, A337V, and L256P) in this population. Allele-specific oligonucleotide hybridization analysis was performed on polymerase chain reaction (PCR)-amplified genomic DNA from these patients. In the American population, the missense mutations A149P and A174D are the two most common alleles, with frequencies of 44% and 9%, respectively. In addition, the nonsense mutations Delta4E4 and R59Op are the next most common alleles, with each having a frequency of 4%. Together, the frequencies of all seven alleles make up 65% of HFI-causing alleles in this population. Worldwide, these same alleles make up 82% of HFI-causing mutations. This difference indicates that screening for common HFI alleles is more difficult in the American population. Nevertheless, a genetic screen for diagnosing HFI in America can be improved by including all seven alleles studied here. Lastly, identification of HFI patients presenting with classic symptoms and who have homozygous null genotypes indicates that aldolase B is not required for proper development or metabolic maintenance.