Degree of predicted minor histocompatibility antigen mismatch correlates with poorer clinical outcomes in nonmyeloablative allogeneic hematopoietic cell transplantation.

In fully HLA-matched allogeneic hematopoietic cell transplantation (HCT), the main mechanism of the beneficial graft-versus-tumor (GVT) effect and of detrimental graft-versus-host disease (GVHD) is believed to be caused by donor cytotoxic T cells directed against disparate recipient minor histocompatibility antigens (miHAs). The most common origin of disparate miHAs is nonsynonymous single nucleotide polymorphism (nsSNP) differences between donors and patients. To date, only some 30 miHAs have been identified and registered, but considering the many different HLA types in the human population, as well as all the possible nsSNP differences between any 2 individuals, it is likely that many miHAs have yet to be discovered. The objective of the current study was to predict novel HLA-A- and HLA-B-restricted miHAs in a cohort of patients treated with nonmyeloablative conditioning allogeneic HCT (matched related donor, n = 70; matched unrelated donor, n = 56) for a hematologic malignancy. Initially, the cohort was genotyped for 53 nsSNPs in 11 known miHA source proteins. Twenty-three nsSNPs within 6 miHA source proteins showed variation in the graft-versus-host (GVH) direction. No correlation between the number of disparate nsSNPs and clinical outcome was seen. Next, miHAs in the GVH direction were predicted for each patient-donor pair. Using the NetMHCpan predictor, we identified peptides encompassing an nsSNP variant uniquely expressed by the patient and with predicted binding to any of the HLA-A or -B molecules expressed by the patient and donor. Patients with more than the median of 3 predicted miHAs had a significantly lower 5-year overall survival (42% vs 70%, P = .0060; adjusted hazard ratio [HR], 2.6, P = .0047) and significantly higher treatment-related mortality (39% vs 10%, P = .0094; adjusted HR, 4.6, P = .0038). No association between the number of predicted miHAs and any other clinical outcome parameters was observed. Collectively, our data suggest that the clinical outcome of HCT is affected not by disparate nsSNPs per se, but rather by the HLA-restricted presentation and recognition of peptides encompassing these. Our data also suggest that 6 of the 11 proteins included in the current study could contain more miHAs yet to be identified, and that the presence of multiple miHAs confers a higher risk of mortality after nonmyeloablative conditioning HCT. Furthermore, our data suggest a possible role for in silico based miHA predictions in donor selection as well as in selecting candidate miHAs for further evaluation in in vitro and in vivo experiments.

A probabilistic model of RNA conformational space.

The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling procedure. Both are only partly solved problems. Here, we focus on the problem of conformational sampling. The current state of the art solution is based on fragment assembly methods, which construct plausible conformations by stringing together short fragments obtained from experimental structures. However, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.

Asap: a framework for over-representation statistics for transcription factor binding sites.

BACKGROUND: In studies of gene regulation the efficient computational detection of over-represented transcription factor binding sites is an increasingly important aspect. Several published methods can be used for testing whether a set of hypothesised co-regulated genes share a common regulatory regime based on the occurrence of the modelled transcription factor binding sites. However there is little or no information available for guiding the end users choice of method. Furthermore it would be necessary to obtain several different software programs from various sources to make a well-founded choice. METHODOLOGY: We introduce a software package, Asap, for fast searching with position weight matrices that include several standard methods for assessing over-representation. We have compared the ability of these methods to detect over-represented transcription factor binding sites in artificial promoter sequences. Controlling all aspects of our input data we are able to identify the optimal statistics across multiple threshold values and for sequence sets containing different distributions of transcription factor binding sites. CONCLUSIONS: We show that our implementation is significantly faster than more naïve scanning algorithms when searching with many weight matrices in large sequence sets. When comparing the various statistics, we show that those based on binomial over-representation and Fisher's exact test performs almost equally good and better than the others. An online server is available at http://servers.binf.ku.dk/asap/.