Does minor histocompatibility antigen HA-1 disparity affect the occurrence of graft-versus-host disease in tunisian recipients of hematopoietic stem cells?

INTRODUCTION: Minor histocompatibility antigen HA-1 (MiHAg-HA-1) disparity between a patient and his or her human leukocyte antigen (HLA) genoidentical donor has been widely associated with an increased risk of graft-versus-host disease following allogeneic hematopoietic stem cell transplantation. OBJECTIVE: To examine the effect of HA-1 disparity on the incidence of both acute and chronic graft-versus-host disease in Tunisian recipients of hematopoietic stem cells. METHODS: A total of 60 patients and their 60 respective sibling hematopoietic stem cell donors were enrolled in this study. All patients prophylactically received cyclosporine A and/or methotrexate for graft-versus-host disease. An HA-1 genotyping assay was performed with the SSP-PCR method, and HLA-A*0201- and/or HLA-A*0206-positive samples were identified using the Luminex HLA typing method. RESULTS: The Luminex HLA typing assay showed that 54 patients were positive for either the HLA-A*0201 or HLA-A*0206 alleles. Among these cases, six pairs were mismatched for MiHAg-HA-1. Both acute and chronic graft-versus-host disease occurred in four mismatched patients (Fisher's p-values were 0.044 and 0.170, respectively). A univariate logistic regression model analysis showed that only acute graft-versus-host disease may be affected by recipient MiHAg-HA-1 disparity (p: 0.041, OR: 6.727), while chronic graft-versus-host disease correlates with both age and recipient/donor sex mismatch (p: 0.014, OR: 8.556 and p: 0.033, OR: 8.664, respectively). CONCLUSION: Our findings support previously reported data suggesting a significant association between HA-1 disparity and the risk of acute graft-versus-host disease following hematopoietic stem cell transplantation.

Evolutionary insights from suffix array-based genome sequence analysis.

Gene and protein sequence analyses, central components of studies in modern biology are easily amenable to string matching and pattern recognition algorithms. The growing need of analysing whole genome sequences more efficiently and thoroughly, has led to the emergence of new computational methods. Suffix trees and suffix arrays are data structures, well known in many other areas and are highly suited for sequence analysis too. Here we report an improvement to the design of construction of suffix arrays. Enhancement in versatility and scalability, enabled by this approach, is demonstrated through the use of real-life examples. The scalability of the algorithm to whole genomes renders it suitable to address many biologically interesting problems. One example is the evolutionary insight gained by analysing unigrams, bi-grams and higher n-grams, indicating that the genetic code has a direct influence on the overall composition of the genome. Further, different proteomes have been analysed for the coverage of the possible peptide space, which indicate that as much as a quarter of the total space at the tetra-peptide level is left un-sampled in prokaryotic organisms, although almost all tri-peptides can be seen in one protein or another in a proteome. Besides, distinct patterns begin to emerge for the counts of particular tetra and higher peptides, indicative of a 'meaning' for tetra and higher n-grams. The toolkit has also been used to demonstrate the usefulness of identifying repeats in whole proteomes efficiently. As an example, 16 members of one COG,coded by the genome of Mycobacterium tuberculosis H37Rv have been found to contain a repeating sequence of 300 amino acids.