Crystal structure of the NADP-dependent mannitol dehydrogenase from Cladosporium herbarum: Implications for oligomerisation and catalysis.

The ascomycete Cladosporium herbarum is a prominent fungal inducer of Type I allergy. The only major allergen identified so far is Cla h 8, a NADP-dependent mannitol dehydrogenase (MtDH). MtDH, a cytoplasmic protein of 28.5kDa, belongs to the Short chain Dehydrogenases/Reductases (SDR), acting as a NADP-dependent oxidoreductase. In this study, we found that C. herbarum MtDH can exist as monomers, dimers and tetramers in solution and, correspondingly, forms tetramers and higher oligomers in two crystal structures. Additionally, we identified a unique adaptive binding site for the metal ions Na(+) and Zn(2+) that were distinguished by an anomalous dispersion experiment. A Translation-Libration-Screw analysis confirmed the stabilising effect of Zn(2+) for the tetrameric assembly. Moreover, the zinc containing structure explains the mode of MtDH multimerisation by metal bridging of the tetramers. The formation of oligomers and higher multimers of MtDH provides a missing link to its allergenic properties. Based on the well defined active site region and a comparative analysis with related structures, we can also clarify the atypical enzymatic properties of MtDH by two alternative binding modes of the substrate to the active site.

Impact on reader performance for lesion-detection/ localization tasks of anatomical priors in SPECT reconstruction.

With increasing availability of multimodality imaging systems, high-resolution anatomical images can be used to guide the reconstruction of emission tomography studies. By measuring reader performance on a lesion detection task, this study investigates the improvement in image-quality due to use of prior anatomical knowledge, for example organ or lesion boundaries, during SPECT reconstruction. Simulated (67)Ga -citrate source and attenuation distributions were created from the mathematical cardiac-torso (MCAT) anthropomorphic digital phantom. The SIMIND Monte Carlo software was then used to generate SPECT projection data. The data were reconstructed using the De Pierro maximum a posteriori (MAP) algorithm and the rescaled-block-iterative (RBI) algorithm for comparison. We compared several degrees of prior knowledge about the anatomy: no knowledge about the anatomy; knowledge of organ boundaries; knowledge of organ and lesion boundaries; and knowledge of organ, lesion, and pseudo-lesion (non-emission uptake altering) boundaries. The MAP reconstructions used quadratic smoothing within anatomical regions, but not across any provided region boundaries. The reconstructed images were read by human observers searching for lesions in a localization receiver operating characteristic (LROC) study of the relative detection/localization accuracies of the reconstruction algorithms. Area under the LROC curve was computed for each algorithm as the comparison metric. We also had humans read images reconstructed using different prior strengths to determine the optimal trade-off between data consistency and the anatomical prior. Finally by mixing together images reconstructed with and without the prior, we tested to see if having an anatomical prior only some of the time changes the observer's detection/localization accuracy on lesions where no boundary prior is available. We found that anatomical priors including organ and lesion boundaries improve observer performance on the lesion detection/localization task. Use of just organ boundaries did not provide a statistically significant improvement in performance however. We also found that optimal prior strength depends on the level of anatomical knowledge, with a broad plateau in which observer performance is near optimal. We found no evidence that having anatomical priors use lesion boundaries only when available changes the observer's performance when they are not available. We conclude that use of anatomical priors with organ and lesion boundaries improves reader performance on a lesion-detection/localization task, and that pseudo-lesion boundaries do not hurt reader performance. However, we did not find evidence that a prior using only organ boundaries helps observer performance. Therefore we suggest prior strength should be tuned to the organ-only case, since a prior will likely not be available for all lesions.

Choosing anatomical-prior strength for MAP SPECT reconstruction to maximize lesion detectability.

With the widespread availability of SPECT/CT systems it has become feasible to incorporate prior knowledge about anatomical boundaries into the SPECT reconstruction process, thus improving observer performance on tasks of clinical interest. We determine the optimal anatomical-prior strength for lesion search by measuring area under the LROC curve using human observers. We conclude that prior strength should be chosen assuming that only organ boundaries are available, even if lesion boundaries will also be known some of the time. We also test whether or not the presence of anatomical priors affects the observer's strategy, and conclude that mixing images with and without priors does not hurt reader performance when priors are not available. Finally, we examine whether using an anatomical prior in SPECT reconstruction helps observer performance when the observer already knows the possible lesion location, and conclude for this task anatomical priors do not provide the same improvement seen in search tasks.

Human-observer LROC study of lesion detection in Ga-67 SPECT images reconstructed using MAP with anatomical priors.

We compare the image quality of SPECT reconstruction with and without an anatomical prior. Area under the localization-response operating characteristic (LROC) curve is our figure of merit. Simulated Ga-67 citrate images, a SPECT lymph-nodule imaging agent, were generated using the MCAT digital phantom. Reconstructed images were read by human observers.Several reconstruction strategies are compared, including rescaled block iterative (RBI) and maximum-a-posteriori (MAP) with various priors. We find that MAP reconstruction using prior knowledge of organ and lesion boundaries significantly improves lesion-detection performance (p < 0.05). Pseudo-lesion boundaries, regions without increased uptake which are incorrectly treated as prior knowledge of lesion boundaries, do not decrease performance.