Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register

Metabolism of halophilic archaea

In spite of their common hypersaline environment, halophilic archaea are surprisingly different in their nutritional demands and metabolic pathways. The metabolic diversity of halophilic archaea was investigated at the genomic level through systematic metabolic reconstruction and comparative analysis of four completely sequenced species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and the haloalkaliphile Natronomonas pharaonis. The comparative study reveals different sets of enzyme genes amongst halophilic archaea, e.g. in glycerol degradation, pentose metabolism, and folate synthesis. The carefully assessed metabolic data represent a reliable resource for future system biology approaches as it also links to current experimental data on (halo)archaea from the literature

The Elephant in the Room: The Invisibility of Poverty in Research on Type 2 Diabetes

Over two hundred years of anecdotal, epidemiological, and experimental evidence indicate that poverty breeds disease. This holds true for type 2 diabetes, which both in the United States and other developed nations disproportionately occurs, cripples, and kills among the poor. In this article we examine rhetorical strategies used in 30 journal articles indexed under type 2 diabetes and poverty. As we show, poverty is rarely highlighted in this literature as a causal factor. Instead, explanations for diabetes among poor people overwhelmingly emphasize features of patients—their biology, behaviors, psychology, culture, or other “risk factors”—while ignoring, reframing or neglecting the links between poverty and disease. By so doing, these discursive strategies naturalize higher rates of diabetes among poor persons, legitimize relations of domination in the larger society, and encourage only research projects, treatment practices and health and social policies that do not challenge existing social relations. We discuss the implications of these discursive practices for medical research and care, and for social and public health policies

Comparison of CE-FDG-PET/CT with CE-FDG-PET/MR in the evaluation of osseous metastases in breast cancer patients

Background: Despite improvements in treatments, metastatic breast cancer remains difficult to cure. Bones constitute the most common site of first-time recurrence, occurring in 40–75% of cases. Therefore, evaluation for possible osseous metastases is crucial. Technetium 99 (99Tc) bone scintigraphy and fluorodexossyglucose (FDG) positron emission tomography (PET)-computed tomography (PET-CT) are the most commonly used techniques to assess osseous metastasis. PET magnetic resonance (PET-MR) imaging is an innovative technique still under investigation. We compared the capability of PET-MR to that of same-day PET-CT to assess osseous metastases in patients with breast cancer. Methods: One hundred and nine patients with breast cancer, who underwent same-day contrast enhanced (CE)-PET-CT and CE-PET-MR, were evaluated. CE-PET-CT and CE-PET-MR studies were interpreted by consensus by a radiologist and a nuclear medicine physician. Correlations with prior imaging and follow-up studies were used as the reference standard. Binomial confidence intervals and a χ2 test were used for categorical data, and paired t-test was used for the SUVmax data; a non-informative prior Bayesian approach was used to estimate and compare the specificities. Results: Osseous metastases affected 25 out 109 patients. Metastases were demonstrated by CE-PET-CT in 22 out of 25 patients (88%±7%), and by CE-PET-MR in 25 out of 25 patients (100%). CE-PET-CT revealed 90 osseous metastases and CE-PET-MR revealed 141 osseous metastases (P<0.001). The estimated sensitivity of CE-PET-CT and CE-PET-MR were 0.8519 and 0.9630, respectively. The estimated specificity for CE-FDG-PET-MR was 0.9884. The specificity of CE-PET-CT cannot be determined from patient-level data, because CE-PET-CT yielded a false-positive lesion in a patient who also had other, true metastases. Conclusions: CE-PET-MR detected a higher number of osseous metastases than did same-day CE-PET-CT, and was positive for 12% of the patients deemed osseous metastasis-negative on the basis of CE-PET-CT