Screening for PTSD and functional impairment in trauma-exposed young children: evaluation of alternative CBCL-PTSD subscales

The Child Behavior Checklist (CBCL 1.5–5 years) posttraumatic stress disorder (PTSD) subscale by Dehon & Scheeringa (2006) as a screener for PTSD in trauma-exposed young children has yielded inconsistent results so far. Therefore, the aim of this study was to create and examine the validity of alternative CBCL-PTSD subscales and compare them to the existing CBCL-PTSD subscale based on the DSM-5 PTSD diagnostic criteria for children 6 years and younger. Further, the CBCL-PTSD subscales were examined regarding their usefulness in screening for posttraumatic stress-related functional impairment. The sample comprised 116 trauma-exposed young children (M$_{age}$ = 3.42 years, SD$_{age}$ = 1.21 years, female = 49.1%). The psychometric properties of the existing CBCL-PTSD subscale as well as the alternative subscales based on expert rating (CBCL-PTSD-17) and based on variable importance (CBCL-PTSD-6) were evaluated by means of receiver operating characteristic curves, sensitivity, specificity, positive predictive values, and negative predictive values. Area under the curves for all three investigated CBCL-PTSD subscales were good to excellent for PTSD and functional impairment. Further, all three CBCL-PTSD subscales showed high sensitivity for PTSD and functional impairment. Considering the length and the performance of the three investigated subscales, the CBCL-PTSD-6 appears to be a promising and clinically useful CBCL-PTSD subscale as a screener for PTSD and functional impairment due to the easiest and most practicable application. For purposes of discriminant validation of the CBCL-PTSD-6, young children without a history of trauma should be compared to young children with trauma history

Analyse der Einkaufsentscheidungen von Kantinen in Brandenburg im Hinblick auf Regionalität

Dieser Beitrag stellt Entscheidungskriterien für brandenburgische Kantinen hinsichtlich der Versorgung mit regionalen Produkten vor und zeigt Herausforderungen und Hemmnisse auf. Einschränkungen für Kantinen sind wirtschaftliche Rahmenbedingungen, Nachfrage sowie Versorgungssicherheit

Multi-level Strategy for Identifying Proteasome-Catalyzed Spliced Epitopes Targeted by CD8+ T Cells during Bacterial Infection

Proteasome-catalyzed peptide splicing (PCPS) generates peptides that are presented by MHC class I molecules, but because their identification is challenging, the immunological relevance of spliced peptides remains unclear. Here, we developed a reverse immunology-based multi-level approach to identify proteasome-generated spliced epitopes. Applying this strategy to a murine Listeria monocytogenes infection model, we identified two spliced epitopes within the secreted bacterial phospholipase PlcB that primed antigen-specific CD8+ T cells in L. monocytogenes-infected mice. While reacting to the spliced epitopes, these CD8+ T cells failed to recognize the non-spliced peptide parts in the context of their natural flanking sequences. Thus, we here show that PCPS expands the CD8+ T cell response against L. monocytogenes by exposing spliced epitopes on the cell surface. Moreover, our multi-level strategy opens up opportunities to systematically investigate proteins for spliced epitope candidates and thus strategies for immunotherapies or vaccine design

Extracellular proteasome-osteopontin circuit regulates cell migration with implications in multiple sclerosis

Osteopontin is a pleiotropic cytokine that is involved in several diseases including multiple sclerosis. Secreted osteopontin is cleaved by few known proteases, modulating its pro-inflammatory activities. Here we show by in vitro experiments that secreted osteopontin can be processed by extracellular proteasomes, thereby producing fragments with novel chemotactic activity. Furthermore, osteopontin reduces the release of proteasomes in the extracellular space. The latter phenomenon seems to occur in vivo in multiple sclerosis, where it reflects the remission/relapse alternation. The extracellular proteasome-mediated inflammatory pathway may represent a general mechanism to control inflammation in inflammatory diseases

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Visceral leishmaniasis is caused by a parasite called Leishmania donovani, which every year infects about half a million people and claims several thousand lives. Existing treatments are now becoming less effective due to the emergence of drug resistance. Improving our understanding of the mechanisms used by the parasite to adapt to drugs and achieve resistance is crucial for developing future treatment strategies. Unfortunately, the biological mechanism whereby Leishmania acquires drug resistance is poorly understood. Recent years have brought new technologies with the potential to increase greatly our understanding of drug resistance mechanisms. The latest mass spectrometry techniques allow the metabolome of parasites to be studied rapidly and in great detail. We have applied this approach to determine the metabolome of drug-sensitive and drug-resistant parasites isolated from patients with leishmaniasis. The data show that there are wholesale differences between the isolates and that the membrane composition has been drastically modified in drug-resistant parasites compared with drug-sensitive parasites. Our findings demonstrate that untargeted metabolomics has great potential to identify major metabolic differences between closely related parasite strains and thus should find many applications in distinguishing parasite phenotypes of clinical relevance

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

The 20S Proteasome Splicing Activity Discovered by SpliceMet

The identification of proteasome-generated spliced peptides (PSP) revealed a new unpredicted activity of the major cellular protease. However, so far characterization of PSP was entirely dependent on the availability of patient-derived cytotoxic CD8+ T lymphocytes (CTL) thus preventing a systematic investigation of proteasome-catalyzed peptide splicing (PCPS). For an unrestricted PSP identification we here developed SpliceMet, combining the computer-based algorithm ProteaJ with in vitro proteasomal degradation assays and mass spectrometry. By applying SpliceMet for the analysis of proteasomal processing products of four different substrate polypeptides, derived from human tumor as well as viral antigens, we identified fifteen new spliced peptides generated by PCPS either by cis or from two separate substrate molecules, i.e., by trans splicing. Our data suggest that 20S proteasomes represent a molecular machine that, due to its catalytic and structural properties, facilitates the generation of spliced peptides, thereby providing a pool of qualitatively new peptides from which functionally relevant products may be selected

Sensitivity and depth of investigation from Monte Carlo ensemble statistics

For many geophysical measurements, such as direct current or electromagnetic induction methods, information fades away with depth. This has to be taken into account when interpreting models estimated from such measurements. For that reason, a measurement sensitivity analysis and determining the depth of investigation are standard steps during geophysical data processing. In deterministic gradient-based inversion, the most used sensitivity measure, the differential sensitivity, is readily available since these inversions require the computation of Jacobian matrices. In contrast, differential sensitivity may not be readily available in Monte Carlo inversion methods, since these methods do not necessarily include a linearization of the forward problem. Instead, a prior ensemble is used to simulate an ensemble of forward responses. Then, the prior ensemble is updated according to Bayesian inference. We propose to use the covariance between the prior ensemble and the forward response ensemble for constructing sensitivity measures. In Monte Carlo approaches, the estimation of this covariance does not require additional computations of the forward model. Normalizing this covariance by the variance of the prior ensemble, one obtains a simplified regression coefficient. We investigate differences between this simplified regression coefficient and differential sensitivity using simple forward models. For linear forward models, the simplified regression coefficient is equal to differential sensitivity, except for the influences of the sampling error and of the correlation structure of the prior distribution. In the non-linear case, the behaviour of the simplified regression coefficient as sensitivity measure is analysed for a simple non-linear forward model and a frequency-domain electromagnetic forward model. Differential sensitivity and the simplified regression coefficient are similar for prior intervals on which the forward model response is approximately linear. Differences between the two sensitivity measures increase with the degree of non-linearity in the prior range. Additionally, we investigate the correlation between prior ensemble and forward response ensemble as sensitivity measure. Correlation yields a normalized version of the simplified regression coefficient. We propose to use this correlation and the simplified regression coefficient for determining depth of investigation in Monte Carlo inversions

Role of peptide processing predictions in T cell epitope identification : contribution of different prediction programs

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions