Biological validation of differentially expressed genes in chronic lymphocytic leukemia identified by applying multiple statistical methods to oligonucleotide microarrays.

Oligonucleotide microarrays are a powerful tool for profiling the expression levels of thousands of genes. Different statistical methods for identifying differentially expressed genes can yield different results. To our knowledge, no experimental test has been performed to decide which method best identifies genes that are truly differentially expressed. We applied three statistical methods (dChip, t-test on log-transformed data, and Wilcoxon test) to identify differentially expressed genes in previously untreated patients with chronic lymphocytic leukemia (CLL). We used a training set of Affymetrix Hu133A microarray data from 11 patients with unmutated immunoglobulin (Ig) heavy chain variable region (VH) genes and 8 patients with mutated Ig VH genes. Differential expression was validated using semiquantitative real-time polymerase chain reaction assays and by validating models to predict the somatic mutation status of an independent test set of nine CLL samples. The methods identified 144 genes that were differentially expressed between cases of CLL with unmutated compared with mutated Ig VH genes. Eighty genes were identified by Wilcoxon test, 60 by t-test, and 65 by dChip, but only 11 were identified by all three methods. Greater agreement was found between the t-test and the Wilcoxon test. Differential expression was validated by semiquantitative real-time polymerase chain reaction assays for 83% of individual genes, regardless of the statistical method. However, the Wilcoxon test gave the most accurate predictions on new samples, and dChip, the least accurate. We found that all three methods were equally good for finding differentially expressed genes, but they found different genes. The genes selected by the nonparametric Wilcoxon test are the most robust for predicting the status of new cases. A comprehensive list of all differentially expressed genes can only be obtained by combining the results of multiple statistical tests.

Compound heterozygosity with a novel S222N GALT mutation leads to atypical galactosemia with loss of GALT activity in erythrocytes but little evidence of clinical disease.

Galactosemia is an inborn error of galactose metabolism caused by mutations in the GALT gene. Though early detection and galactose restriction prevent severe liver disease, affected individuals have persistently elevated biomarkers and often neuro-developmental symptoms. We present a teenage compound heterozygote for a known pathogenic mutation (H132Q) and a novel variant of unknown significance (S222N), with nearly absent erythrocyte GALT enzyme activity but normal biomarkers and only mild anxiety despite diet non-adherence. This case is similar to a previously reported S135L mutation. In this report we investigate the novel S222N variant and critically evaluate a clinically puzzling case.

Biochemical and computational analyses of two phenotypically related GALT mutations (S222N and S135L) that lead to atypical galactosemia.

Galactosemia is a metabolic disorder caused by mutations in the GALT gene [1,2]. We encountered a patient heterozygous for a known pathogenic H132Q mutation and a novel S222N variant of unknown significance [3]. Reminiscent of patients with the S135L mutation, our patient had loss of GALT enzyme activity in erythrocytes but a very mild clinical phenotype [3-8]. We performed splicing experiments and computational structural analyses to investigate the role of the novel S222N variant. Alamut software data predicted loss of splicing enhancers for the S222N and S135L mutations [9,10]. A cDNA library was generated from our patient׳s RNA to investigate for splicing errors, but no change in transcript length was seen [3]. In silico structural analysis was performed to investigate enzyme stability and attempt to understand the mechanism of the atypical galactosemia phenotype. Stability results are publicly available in the GALT Protein Database 2.0 [11-14]. Animations were created to give the reader a dynamic view of the enzyme structure and mutation locations. Protein database files and python scripts are included for further investigation.

Real time PCR detection of the PI*Z and PI*S mutations associated with alpha-1 antitrypsin deficiency.

Alpha-1 antitrypsin (A1AT or AAT) is a serine protease inhibitor (PI) which, when present at low levels, can cause chronic obstructive pulmonary disease (COPD) and liver disease in both children and adults. Several mutations within the SERPINA1 gene have been found to cause this deficiency. The most common variants are PI*Z and PI*S, each caused by a single nucleotide polymorphism (SNP). We describe a real time polymerase chain reaction (PCR) assay for the rapid genotyping of these polymorphisms. DNA was extracted from fourteen EDTA-anticoagulated whole blood samples using the Qiagen EZ1 blood extraction kit. SNP genotyping was performed using primer/probe sets purchased from Applied Biosystems. These were evaluated for performance and assay conditions on the Applied Biosystems 7500 FAST System. The genotypes of these samples were compared with their phenotype results from isoelectric focusing assays, which were performed by an independent reference laboratory. In addition, twenty samples that were previously genotyped at another laboratory were obtained for accuracy studies. Thirty-four samples were tested; five genotypes were represented and the assay was able to discriminate these successfully. Only one genotype could not be correlated with its phenotype result, as the phenotype was reported as an "unidentified allele". All other genotyping results were concordant with previously determined genotypes and phenotypes. We describe a rapid real time PCR assay that is suitable for clinical use in genotyping AAT alleles and which can be used as the initial step in A1AT testing algorithms.