Measurement and assessment of grief in a large international sample

BACKGROUND

In 2022, the International Classification of Diseases (ICD-11) and an update of the Diagnostic Statistical Manual of Mental Disorders (DSM 5 TR) were released for implementation worldwide and now include the new Prolonged Grief Disorder (PGD). The newest definition of PGD is based on robust clinical research from the Global North yet until now has not been tested for global applicability.

METHODS

The current study assesses the new PGD ICD-11 criteria in a large international sample of 1393 bereaved adults. The majority of the sample was included from the USΑ. Additionally, we conduct a sub-sample analysis to evaluate the psychometric properties, probable caseness of PGD, and differences in network structure across three regions of residency (USA, Greece-Cyprus, Turkey-Iran).

RESULTS

The psychometric validity and reliability of the 33-item International Prolonged Grief Disorder Scale (IPGDS) were confirmed across the whole sample and for each regional group. Using the strict diagnostic algorithm, the probable caseness for PGD for the whole sample was 3.6 %. Probable caseness was highest for the Greece-Cyprus group (6.9 %) followed by Turkey-Iran (3.2 %) and the USA (2.8 %). Finally, the network structure of the IPGDS standard items and cultural supplement items (total of 33 items) confirmed the strong connection between central items of PGD, and revealed unique network connections within the regional groups.

LIMITATIONS

Future research is encouraged to include larger sample sizes and a more systematic assessment of culture.

CONCLUSION

Overall, our findings confirm the global applicability of the new ICD-11 PGD disorder definition as evaluated through the newly developed IPGDS. This scale includes culturally sensitive grief symptoms that may improve clinical precision and decision-making.

Correlating global trends in COVID-19 cases with online symptom checker self-assessments

BACKGROUND

Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user's symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced.

OBJECTIVES

Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections.

MATERIALS AND METHODS

In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma (www.symptoma.com) in 18 countries with suitably large user bases. We analyse this dataset's spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries.

RESULTS

We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries.

CONCLUSION

We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources.

Boost dynamic protocols for producing mammalian biopharmaceuticals with intensified DoE—a practical guide to analyses with OLS and hybrid modeling

Introduction: For the implementation of robust bioprocesses, understanding of temporal cell behavior with respect to relevant inputs is crucial. Intensified Design of Experiments (iDoE) is an efficient tool to assess the joint influence of input parameters by including intra-experimental changes.

Methods: We applied iDoE to the production phase of a monoclonal antibody in a mammalian bioprocess. The multidimensional design space spanned by temperature, dissolved oxygen (DO), timing of change, and growth category was investigated in 12 cultivations. We built ordinary least squares (OLS) and hybrid models (HM) on the iDoE-data, validated them with classical DoE (cDoE)-derived data, and used the models as in silico representation for process optimization.

Results: If the complexity of interactions between changing setpoints of inputs is sufficiently captured during planning and modeling, iDoE proved to be valid for characterizing the mammalian biopharmaceutical production phase. For local behavior and flexible composition of optimization goals, OLS regressions can easily be implemented. To predict global and interconnected dynamics while incorporating mass balances, HM holds potential.

Discussion: iDoE will boost protocols that optimize inputs for different bioprocess phases. The described key aspects of OLS- and HM-based analyses of iDoE-data shall guide future applications during manufacturing.

Optimising cell-based bioassays via integrated design of experiments (ixDoE) - A practical guide

For process optimisation Design of Experiments (DoE) has long been established as a more powerful strategy than a One Factor at a Time approach. Nevertheless, DoE is not widely used especially in the field of cell-based bioassay development although it is known that complex interactions often exist. We believe that biopharmaceutical manufacturers are reluctant to move beyond standard practices due to the perceived costs, efforts, and complexity. We therefore introduce the integrated DoE (ixDoE) approach to target a smarter use of DoEs in the bioassay setting, specifically in optimising resources and time. Where in a standard practice 3 to 4 separate DoEs would be performed, our ixDoE approach includes the necessary statistical inference from only a single experimental set. Hence, we advocate for an innovative, ixDoE approach accompanied by a suitable statistical analysis strategy and present this as a practical guide for a typical bioassay development from basic research to biopharmaceutical industry.

Efficacy of Retreatment After Failed Direct-acting Antiviral Therapy in Patients With HCV Genotype 1-3 Infections

Hepatitis C virus infection is causing chronic liver disease, cirrhosis, and hepatocellular carcinoma. By combining direct-acting antivirals (DAAs), high sustained virologic response rates (SVRs) can be achieved. Resistance-associated substitutions (RASs) are commonly observed after DAA failure, and especially nonstructural protein 5A (NS5A) RASs may impact retreatment options.^1-3 Data on retreatment of DAA failure patients using first-generation DAAs are limited.^4-7 Recently, a second-generation protease- and NS5A-inhibitor plus sofosbuvir (voxilaprevir/velpatasvir/sofosbuvir [VOX/VEL/SOF]) was approved for retreatment after DAA failure.⁸ However, this and other second-generation regimens are not available in many resource-limited countries or are not reimbursed by regular insurance, and recommendations regarding the selection of retreatment regimens using first-generation DAAs are very important. This study aimed to analyze patients who were re-treated with first-generation DAAs after failure of a DAA combination therapy.

An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot

To combat the pandemic of the coronavirus disease 2019 (COVID-19), numerous governments have established phone hotlines to prescreen potential cases. These hotlines have struggled with the volume of callers, leading to wait times of hours or, even, an inability to contact health authorities. Symptoma is a symptom-to-disease digital health assistant that can differentiate more than 20,000 diseases with an accuracy of more than 90%. We tested the accuracy of Symptoma to identify COVID-19 using a set of diverse clinical cases combined with case reports of COVID-19. We showed that Symptoma can accurately distinguish COVID-19 in 96.32% of clinical cases. When considering only COVID-19 symptoms and risk factors, Symptoma identified 100% of those infected when presented with only three signs. Lastly, we showed that Symptoma’s accuracy far exceeds that of simple “yes–no” questionnaires widely available online. In summary, Symptoma provides unparalleled accuracy in systematically identifying cases of COVID-19 while also considering over 20,000 other diseases. Furthermore, Symptoma allows free text input, furthered with disease-specific follow up questions, in 36 languages. Combined, these results and accessibility give Symptoma the potential to be a key tool in the global fight against COVID-19. The Symptoma predictor is freely available online at https://www.symptoma.com.

Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study

Background

A large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner.

Objective

The aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers.

Methods

We identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case reports alongside 410 non–COVID-19 control cases. A bootstrapping method was used to counter the unbalanced sample sizes and obtain confidence intervals (CIs). Results are reported as sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC).

Results

The classification task between COVID-19–positive and COVID-19–negative for “high risk” cases among the 460 test cases yielded (sorted by F1 score): Symptoma (F1=0.92, MCC=0.85), Infermedica (F1=0.80, MCC=0.61), US Centers for Disease Control and Prevention (CDC) (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Cleveland Clinic (F1=0.40, MCC=0.07), Providence (F1=0.40, MCC=0.05), Apple (F1=0.29, MCC=-0.10), Docyet (F1=0.27, MCC=0.29), Ada (F1=0.24, MCC=0.27) and Your.MD (F1=0.24, MCC=0.27). For “high risk” and “medium risk” combined the performance was: Symptoma (F1=0.91, MCC=0.83) Infermedica (F1=0.80, MCC=0.61), Cleveland Clinic (F1=0.76, MCC=0.47), Providence (F1=0.75, MCC=0.45), Your.MD (F1=0.72, MCC=0.33), CDC (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Apple (F1=0.70, MCC=0.25), Ada (F1=0.42, MCC=0.03), and Docyet (F1=0.27, MCC=0.29).

Conclusions

We found that the number of correctly assessed COVID-19 and control cases varies considerably between symptom checkers, with different symptom checkers showing different strengths with respect to sensitivity and specificity. A good balance between sensitivity and specificity was only achieved by two symptom checkers.

CD4 T Cell Determinants in West Nile Virus Disease and Asymptomatic Infection

West Nile (WN) virus infection of humans is frequently asymptomatic, but can also lead to WN fever or neuroinvasive disease. CD4 T cells and B cells are critical in the defense against WN virus, and neutralizing antibodies, which are directed against the viral glycoprotein E, are an accepted correlate of protection. For the efficient production of these antibodies, B cells interact directly with CD4 helper T cells that recognize peptides from E or the two other structural proteins (capsid-C and membrane-prM/M) of the virus. However, the specific protein sites yielding such helper epitopes remain unknown. Here, we explored the CD4 T cell response in humans after WN virus infection using a comprehensive library of overlapping peptides covering all three structural proteins. By measuring T cell responses in 29 individuals with either WN virus disease or asymptomatic infection, we showed that CD4 T cells focus on peptides in specific structural elements of C and at the exposed surface of the pre- and postfusion forms of the E protein. Our data indicate that these immunodominant epitopes are recognized in the context of multiple different HLA molecules. Furthermore, we observed that immunodominant antigen regions are structurally conserved and similarly targeted in other mosquito-borne flaviviruses, including dengue, yellow fever, and Zika viruses. Together, these findings indicate a strong impact of virion protein structure on epitope selection and antigenicity, which is an important issue to consider in future vaccine design.

MHC binding affects the dynamics of different T-cell receptors in different ways

T cells use their T-cell receptors (TCRs) to scan other cells for antigenic peptides presented by MHC molecules (pMHC). If a TCR encounters a pMHC, it can trigger a signalling pathway that could lead to the activation of the T cell and the initiation of an immune response. It is currently not clear how the binding of pMHC to the TCR initiates signalling within the T cell. One hypothesis is that conformational changes in the TCR lead to further downstream signalling. Here we investigate four different TCRs in their free state as well as in their pMHC bound state using large scale molecular simulations totalling 26 000 ns. We find that the dynamical features within TCRs differ significantly between unbound TCR and TCR/pMHC simulations. However, apart from expected results such as reduced solvent accessibility and flexibility of the interface residues, these features are not conserved among different TCR types. The presence of a pMHC alone is not sufficient to cause cross-TCR-conserved dynamical features within a TCR. Our results argue against models of TCR triggering involving conserved allosteric conformational changes.

The interaction between T-cells and other cells is one of the most important interactions in the human immune system. If T-cells are not triggered major parts of the immune system cannot be activated or are not working effectively. Despite many years of research the exact mechanism of how a T-cell is initially triggered is not clear. One hypothesis is that conformational changes within the T-cell receptor (TCR) can cause further downstream signalling within the T-cell. In this study we computationally investigate the dynamics of four different TCRs in their free and bound configuration. Our large scale simulations show that all four TCRs react to binding in different ways. In some TCRs mainly the areas close to the binding region are affected while in other TCRs areas further apart from the binding region are also affected. Our results argue against a conserved structural activation mechanism across different types of TCRs.

pyHVis3D: visualising molecular simulation deduced H-bond networks in 3D: application to T-cell receptor interactions

Motivation

Hydrogen bonds (H-bonds) play an essential role for many molecular interactions but are also often transient, making visualising them in a flexible system challenging.

Results

We provide pyHVis3D which allows for an easy to interpret 3D visualisation of H-bonds resulting from molecular simulations. We demonstrate the power of pyHVis3D by using it to explain the changes in experimentally measured binding affinities for three T-cell receptor/peptide/MHC complexes and mutants of each of these complexes.

Availability and implementation

pyHVis3D can be downloaded for free from http://opig.stats.ox.ac.uk/resources.

Contact

science.bernhard.knapp@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Proteomic identification of a marker signature for MAPKi resistance in melanoma

MAPK inhibitors (MAPKi) show outstanding clinical response rates in melanoma patients harbouring BRAF mutations, but resistance is common. The ability of melanoma cells to switch from melanocytic to mesenchymal phenotypes appears to be associated with therapeutic resistance. High-throughput, subcellular proteome analyses and RNAseq on two panels of primary melanoma cells that were either sensitive or resistant to MAPKi revealed that only 15 proteins were sufficient to distinguish between these phenotypes. The two proteins with the highest discriminatory power were PTRF and IGFBP7, which were both highly upregulated in the mesenchymal-resistant cells. Proteomic analysis of CRISPR/Cas-derived PTRF knockouts revealed targets involved in lysosomal activation, endocytosis, pH regulation, EMT, TGFβ signalling and cell migration and adhesion, as well as a significantly reduced invasive index and ability to form spheres in 3D culture. Overexpression of PTRF led to MAPKi resistance, increased cell adhesion and sphere formation. In addition, immunohistochemistry of patient samples showed that PTRF expression levels were a significant biomarker of poor progression-free survival, and IGFBP7 levels in patient sera were shown to be higher after relapse.

HLA-DM Stabilizes the Empty MHCII Binding Groove: A Model Using Customized Natural Move Monte Carlo

MHC class II molecules bind peptides derived from extracellular proteins that have been ingested by antigen-presenting cells and display them to the immune system. Peptide loading occurs within the antigen-presenting cell and is facilitated by HLA-DM. HLA-DM stabilizes the open conformation of the MHCII binding groove when no peptide is bound. While a structure of the MHCII/HLA-DM complex exists, the mechanism of stabilization is still largely unknown. Here, we applied customized Natural Move Monte Carlo to investigate this interaction. We found a possible long-range mechanism that implicates the configuration of the membrane-proximal globular domains in stabilizing the open state of the empty MHCII binding groove.

How peptide/MHC presence affects the dynamics of the LC13 T-cell receptor

The interaction between T-cell receptors (TCRs) of T-cells and potentially immunogenic peptides presented by MHCs of antigen presenting cells is one of the most important mechanisms of the adaptive human immune system. A large number of structural simulations of the TCR/peptide/MHC system have been carried out. However, to date no study has investigated the differences of the dynamics between free TCRs and pMHC bound TCRs on a large scale. Here we present a study totalling 37 100 ns investigating the LC13 TCR in its free form as well as in complex with HLA-B*08:01 and different peptides. Our results show that the dynamics of the bound and unbound LC13 TCR differ significantly. This is reflected in (a) expected results such as an increased flexibility and increased solvent accessible surface of the CDRs of unbound TCR simulations but also in (b) less expected results such as lower CDR distances and compactness as well as alteration in the hydrogen bond network around CDR3α of unbound TCR simulations. Our study further emphasises the structural flexibility of TCRs and confirms the importance of the CDR3 loops for the adoption to MHC.

Avoiding False Positive Conclusions in Molecular Simulation: The Importance of Replicas

Molecular simulations are a computational technique used to investigate the dynamics of proteins and other molecules. The free energy landscape of these simulations is often rugged, and minor differences in the initial velocities, floating-point precision, or underlying hardware can cause identical simulations (replicas) to take different paths in the landscape. In this study we investigated the magnitude of these effects based on 310 000 ns of simulation time. We performed 100 identically parametrized replicas of 3000 ns each for a small 10 amino acid system as well as 100 identically parametrized replicas of 100 ns each for an 827 residue T-cell receptor/MHC system. Comparing randomly chosen subgroups within these replica sets, we estimated the reproducibility and reliability that can be achieved by a given number of replicas at a given simulation time. These results demonstrate that conclusions drawn from single simulations are often not reproducible and that conclusions drawn from multiple shorter replicas are more reliable than those from a single longer simulation. The actual number of replicas needed will always depend on the question asked and the level of reliability sought. On the basis of our data, it appears that a good rule of thumb is to perform a minimum of five to 10 replicas.

Exploring Metabolic Configurations of Single Cells within Complex Tissue Microenvironments

Over the past years, plenty of evidence has emerged illustrating how metabolism supports many aspects of cellular function and how metabolic reprogramming can drive cell differentiation and fate. Here, we present a method to assess the metabolic configuration of single cells within their native tissue microenvironment via the visualization and quantification of multiple enzymatic activities measured at saturating substrate conditions combined with subsequent cell type identification. After careful validation of the approach and to demonstrate its potential, we assessed the intracellular metabolic configuration of different human immune cell populations in healthy and tumor colon tissue. Additionally, we analyzed the intercellular metabolic relationship between cancer cells and cancer-associated fibroblasts in a breast cancer tissue array. This study demonstrates that the determination of metabolic configurations in single cells could be a powerful complementary tool for every researcher interested to study metabolic networks in situ.

Variable Regions of Antibodies and T-Cell Receptors May Not Be Sufficient in Molecular Simulations Investigating Binding

Antibodies and T-cell receptors are important proteins of the immune system that share similar structures. Both contain variable and constant regions. Insight into the dynamics of their binding can be provided by computational simulations. For these simulations the constant regions are often removed to save runtime as binding occurs in the variable regions. Here we present the first study to investigate the effect of removing the constant regions from antibodies and T-cell receptors on such simulations. We performed simulations of an antibody/antigen and T-cell receptor/MHC system with and without constant regions using 10 replicas of 100 ns of each of the four setups. We found that simulations without constant regions show significantly different behavior compared to simulations with constant regions. If the constant regions are not included in the simulations alterations in the binding interface hydrogen bonds and even partial unbinding can occur. These results indicate that constant regions should be included in antibody and T-cell receptor simulations for reliable conclusions to be drawn.

Modeling Functional Motions of Biological Systems by Customized Natural Moves

Simulating the functional motions of biomolecular systems requires large computational resources. We introduce a computationally inexpensive protocol for the systematic testing of hypotheses regarding the dynamic behavior of proteins and nucleic acids. The protocol is based on natural move Monte Carlo, a highly efficient conformational sampling method with built-in customization capabilities that allows researchers to design and perform a large number of simulations to investigate functional motions in biological systems. We demonstrate the use of this protocol on both a protein and a DNA case study. Firstly, we investigate the plasticity of a class II major histocompatibility complex in the absence of a bound peptide. Secondly, we study the effects of the epigenetic mark 5-hydroxymethyl on cytosine on the structure of the Dickerson-Drew dodecamer. We show how our customized natural moves protocol can be used to investigate causal relationships of functional motions in biological systems.

Measurement of the WZ production cross section in pp collisions at [Formula: see text] and 8[Formula: see text] and search for anomalous triple gauge couplings at [Formula: see text]

The WZ production cross section is measured by the CMS experiment at the CERN LHC in proton-proton collision data samples corresponding to integrated luminosities of 4.9[Formula: see text] collected at [Formula: see text], and 19.6[Formula: see text] at [Formula: see text]. The measurements are performed using the fully-leptonic WZ decay modes with electrons and muons in the final state. The measured cross sections for [Formula: see text] are [Formula: see text] [Formula: see text] and [Formula: see text] [Formula: see text]. Differential cross sections with respect to the [Formula: see text] boson [Formula: see text], the leading jet [Formula: see text], and the number of jets are obtained using the [Formula: see text] data. The results are consistent with standard model predictions and constraints on anomalous triple gauge couplings are obtained.

Search for Resonant Production of High-Mass Photon Pairs in Proton-Proton Collisions at sqrt[s]=8 and 13 TeV

A search for the resonant production of high-mass photon pairs is presented. The analysis is based on samples of proton-proton collision data collected by the CMS experiment at center-of-mass energies of 8 and 13 TeV, corresponding to integrated luminosities of 19.7 and 3.3  fb^{-1}, respectively. The interpretation of the search results focuses on spin-0 and spin-2 resonances with masses between 0.5 and 4 TeV and with widths, relative to the mass, between 1.4×10^{-4} and 5.6×10^{-2}. Limits are set on scalar resonances produced through gluon-gluon fusion, and on Randall-Sundrum gravitons. A modest excess of events compatible with a narrow resonance with a mass of about 750 GeV is observed. The local significance of the excess is approximately 3.4 standard deviations. The significance is reduced to 1.6 standard deviations once the effect of searching under multiple signal hypotheses is considered. More data are required to determine the origin of this excess.

Interleukin-4 and interferon-γ orchestrate an epithelial polarization in the airways

Interferon-γ (IFN-γ) and interleukin-4 (IL-4) are key effector cytokines for the differentiation of T helper type 1 and 2 (Th1 and Th2) cells. Both cytokines induce fate-decisive transcription factors such as GATA3 and TBX21 that antagonize the polarized development of opposite phenotypes by direct regulation of each other's expression along with many other target genes. Although it is well established that mesenchymal cells directly respond to Th1 and Th2 cytokines, the nature of antagonistic differentiation programs in airway epithelial cells is only partially understood. In this study, primary normal human bronchial epithelial cells (NHBEs) were exposed to IL-4, IFN-γ, or both and genome-wide transcriptome analysis was performed. The study uncovers an antagonistic regulation pattern of IL-4 and IFN-γ in NHBEs, translating the Th1/Th2 antagonism directly in epithelial gene regulation. IL-4- and IFN-γ-induced transcription factor hubs form clusters, present in antagonistically and polarized gene regulation networks. Furthermore, the IL-4-dependent induction of IL-24 observed in rhinitis patients was downregulated by IFN-γ, and therefore IL-24 represents a potential biomarker of allergic inflammation and a Th2 polarized condition of the epithelium.

Exploring peptide/MHC detachment processes using hierarchical natural move Monte Carlo

MOTIVATION

The binding between a peptide and a major histocompatibility complex (MHC) is one of the most important processes for the induction of an adaptive immune response. Many algorithms have been developed to predict peptide/MHC (pMHC) binding. However, no approach has yet been able to give structural insight into how peptides detach from the MHC.

RESULTS

In this study, we used a combination of coarse graining, hierarchical natural move Monte Carlo and stochastic conformational optimization to explore the detachment processes of 32 different peptides from HLA-A*02:01. We performed 100 independent repeats of each stochastic simulation and found that the presence of experimentally known anchor amino acids affects the detachment trajectories of our peptides. Comparison with experimental binding affinity data indicates the reliability of our approach (area under the receiver operating characteristic curve 0.85). We also compared to a 1000 ns molecular dynamics simulation of a non-binding peptide (AAAKTPVIV) and HLA-A*02:01. Even in this simulation, the longest published for pMHC, the peptide does not fully detach. Our approach is orders of magnitude faster and as such allows us to explore pMHC detachment processes in a way not possible with all-atom molecular dynamics simulations.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available for download at http://www.cs.ox.ac.uk/mosaics/.

CONTACT

bernhard.knapp@stats.ox.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

T-Cell Receptor Binding Affects the Dynamics of the Peptide/MHC-I Complex

The recognition of peptide/MHC by T-cell receptors is one of the most important interactions in the adaptive immune system. A large number of computational studies have investigated the structural dynamics of this interaction. However, to date only limited attention has been paid to differences between the dynamics of peptide/MHC with the T-cell receptor bound and unbound. Here we present the first large-scale molecular dynamics simulation study of this type investigating HLA-B*08:01 in complex with the Epstein-Barr virus peptide FLRGRAYGL and all possible single-point mutations (n = 172). All of the simulations were performed with and without the LC 13 T-cell receptor for a simulation time of 100 ns, yielding 344 simulations and a total simulation time of 34 400 ns. Our study is 2 orders of magnitude larger than the average T-cell receptor/peptide/MHC molecular dynamics simulation study. This data set provides reliable insights into alterations of the peptide/MHC-I dynamics caused by the presence of the T-cell receptor. We found that simulations in the presence of T-cell receptors have more hydrogen bonds between the peptide and MHC, altered flexibility patterns in the MHC helices and the peptide, a lower MHC groove width range, and altered solvent-accessible surface areas. This indicates that without a T-cell receptor the MHC binding groove can open and close, while the presence of the T-cell receptor inhibits these breathing-like motions.

Rapid, Precise, and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment

The presentation of potentially pathogenic peptides by major histocompatibility complex (MHC) molecules is one of the most important processes in adaptive immune defense. Prediction of peptide-MHC (pMHC) binding affinities is therefore a principal objective of theoretical immunology. Machine learning techniques achieve good results if substantial experimental training data are available. Approaches based on structural information become necessary if sufficiently similar training data are unavailable for a specific MHC allele, although they have often been deemed to lack accuracy. In this study, we use a free energy method to rank the binding affinities of 12 diverse peptides bound by a class I MHC molecule HLA-A*02:01. The method is based on enhanced sampling of molecular dynamics calculations in combination with a continuum solvent approximation and includes estimates of the configurational entropy based on either a one or a three trajectory protocol. It produces precise and reproducible free energy estimates which correlate well with experimental measurements. If the results are combined with an amino acid hydrophobicity scale, then an extremely good ranking of peptide binding affinities emerges. Our approach is rapid, robust, and applicable to a wide range of ligand-receptor interactions without further adjustment.

Examining variable domain orientations in antigen receptors gives insight into TCR-like antibody design

The variable domains of antibodies and T-Cell receptors (TCRs) share similar structures. Both molecules act as sensors for the immune system but recognise their respective antigens in different ways. Antibodies bind to a diverse set of antigenic shapes whilst TCRs only recognise linear peptides presented by a major histocompatibility complex (MHC). The antigen specificity and affinity of both receptors is determined primarily by the sequence and structure of their complementarity determining regions (CDRs). In antibodies the binding site is also known to be affected by the relative orientation of the variable domains, VH and VL. Here, the corresponding property for TCRs, the Vβ-Vα orientation, is investigated and compared with that of antibodies. We find that TCR and antibody orientations are distinct. General antibody orientations are found to be incompatible with binding to the MHC in a canonical TCR-like mode. Finally, factors that cause the orientation of TCRs and antibodies to be different are investigated. Packing of the long Vα CDR3 in the domain-domain interface is found to be influential. In antibodies, a similar packing affect can be achieved using a bulky residue at IMGT position 50 on the VH domain. Along with IMGT VH 50, other positions are identified that may help to promote a TCR-like orientation in antibodies. These positions should provide useful considerations in the engineering of therapeutic TCR-like antibodies.

The immune system needs to be able to sense molecules that might be harmful to the organism. Such harmful molecules are known as antigens. Two classes of receptor proteins that mediate antigen recognition are antibodies and T-Cell receptors (TCRs). Antibodies are able to bind a diverse range of antigen shapes whilst TCRs are specialised to recognise a cell-surface protein, the pMHC. Antibodies that bind the pMHC are rarely created naturally. However, such TCR-like antibodies are of therapeutic importance. The binding regions of the TCR and the antibody have very similar three dimensional structures. Both consist of two independent units, domains, which associate and form the antigen binding site between them. This work examines how the two domains orientate with respect to one another in TCRs and antibodies. Our results show that the conformations that exist in TCRs and antibodies are distinct. Consequently it is difficult for an antibody to bind to a pMHC in the same way a TCR would. However, a similar conformation can be achieved in antibodies as in TCRs by the presence of certain amino-acids in the domain interface. This knowledge should aid the development of therapeutic TCR-like antibodies.

Early relaxation dynamics in the LC 13 T cell receptor in reaction to 172 altered peptide ligands: a molecular dynamics simulation study

The interaction between the T cell receptor and the major histocompatibility complex is one of the most important events in adaptive immunology. Although several different models for the activation process of the T cell via the T cell receptor have been proposed, it could not be shown that a structural mechanism, which discriminates between peptides of different immunogenicity levels, exists within the T cell receptor. In this study, we performed systematic molecular dynamics simulations of 172 closely related altered peptide ligands in the same T cell receptor/major histocompatibility complex system. Statistical evaluations yielded significant differences in the initial relaxation process between sets of peptides at four different immunogenicity levels.

Differential geometric analysis of alterations in MH α-helices

Antigen presenting cells present processed peptides via their major histocompatibility (MH) complex to the T cell receptors (TRs) of T cells. If a peptide is immunogenic, a signaling cascade can be triggered within the T cell. However, the binding of different peptides and/or different TRs to MH is also known to influence the spatial arrangement of the MH α-helices which could itself be an additional level of T cell regulation. In this study, we introduce a new methodology based on differential geometric parameters to describe MH deformations in a detailed and comparable way. For this purpose, we represent MH α-helices by curves. On the basis of these curves, we calculate in a first step the curvature and torsion to describe each α-helix independently. In a second step, we calculate the distribution parameter and the conical curvature of the ruled surface to describe the relative orientation of the two α-helices. On the basis of four different test sets, we show how these differential geometric parameters can be used to describe changes in the spatial arrangement of the MH α-helices for different biological challenges. In the first test set, we illustrate on the basis of all available crystal structures for (TR)/pMH complexes how the binding of TRs influences the MH helices. In the second test set, we show a cross evaluation of different MH alleles with the same peptide and the same MH allele with different peptides. In the third test set, we present the spatial effects of different TRs on the same peptide/MH complex. In the fourth test set, we illustrate how a severe conformational change in an α-helix can be described quantitatively. Taken together, we provide a novel structural methodology to numerically describe subtle and severe alterations in MH α-helices for a broad range of applications.

Relationship of pentraxin 3 with insulin sensitivity in gestational diabetes

BACKGROUND

Pentraxin 3 (PTX3) is a cytokine-inducible molecule expressed in different tissues, the levels of which increase in a response to a variety of inflammatory conditions. Recently, it has been linked to the serum glucose levels and some comorbidities in type 2 diabetes.

MATERIALS AND METHODS

Here, we aimed to investigate the role of PTX3 in gestational diabetes mellitus (GDM), which is considered a forerunner of type 2 diabetes. Fasting PTX3 serum levels were measured in 90 women [45 GDM, 45 normal glucose tolerance (NGT)] during pregnancy. In addition, PTX3 was measured during a 2 h, 75 g oral glucose tolerance test (OGTT) in 20 women (10 GDM, 10 NGT) at 24-28 weeks of gestation and in 16 of them after delivery (10GDM, 6 NGT).

RESULTS

A continuous increase in PTX3 levels was observed during the OGTT and reached in the GDM group a significant difference after 120 min compared with baseline (P < 0·05). Additionally, a rise in the PTX3 concentration was significantly higher in the GDM- compared with the NGT group, 120 min after glucose challenge (P < 0·01). During pregnancy, serum glucose and C-peptide were positively correlated with the PTX3 levels in the whole study group, whereas a negative association was found with the insulin sensitivity parameters QUICKI and OGIS.

CONCLUSIONS

Dependence of PTX3 on serum glucose levels was more pronounced in women with GDM than in the NGT group. This notion together with its inverse relation to the parameters of insulin sensitivity, suggests a potential involvement of PTX3 in GDM pathology.

Circulating progranulin levels in women with gestational diabetes mellitus and healthy controls during and after pregnancy

OBJECTIVE

Progranulin (PGRN) was recently introduced as a novel marker of chronic inflammatory response in obesity and type 2 diabetes capable of directly affecting the insulin signaling pathway. This study aimed to investigate the role of PGRN in gestational diabetes mellitus (GDM), which is regarded as a model for early type 2 diabetes.

METHODS

PGRN serum levels were measured in 90 pregnant women (45 GDM and 45 normal glucose tolerance (NGT)). In addition, PGRN was measured during a 2-h, 75 g oral glucose tolerance test in 20 pregnant women (ten GDM and ten NGT) and in 16 of them post partum (ten GDM and six NGT).

RESULTS

PGRN concentrations were significantly higher in pregnant women compared with post partum levels (536.79 ± 31.81 vs 241.53 ± 8.86, P<0.001). Multivariate regression analyses showed a strong positive correlation of PGRN with estrogen and progesterone. The insulinogenic index, a marker of early insulin secretion, displayed a positive correlation with PGRN, both during and after pregnancy (R=0.47, P=0.034; R=0.63, P=0.012). HbA1c and the oral glucose insulin sensitivity index showed significant post partum associations with PGRN (R=0.43, P=0.049; R=-0.65, P=0.009).

CONCLUSIONS

PGRN concentrations are markedly lower after pregnancy regardless of the gestational glucose tolerance state. PGRN levels per se do not discriminate between mild GDM and NGT in pregnant women. Therefore, the development of GDM appears to be due to impaired β-cell function that is not related to PGRN effect.

Association of HLA-DR1 with the allergic response to the major mugwort pollen allergen: molecular background

BACKGROUND

Mugwort pollen allergens represent the main cause of pollinosis in late summer. The major allergen, Art v 1, contains only one single immunodominant, solely HLA-DR-restricted T cell epitope (Art v 125-36). The frequency of HLA-DRB1*01 is highly increased in mugwort-allergic individuals and HLA-DR1 serves as restriction element for Art v 125-36. However, Art v 125-36 also binds to HLA-DR4 with high affinity and DR1-restricted Art v 125-36 -specific T cell receptors can be activated by HLA-DR4 molecules. To understand the predominance of HLA-DR1 in mugwort allergy in spite of the degeneracy in HLA/peptide-binding and TCR-recognition, we investigated the molecular background of Art v 125-36 /MHC/TCR interactions in the context of HLA-DR1 compared to -DR4.

RESULTS

The majority of Art v 125-36 -specific T cell lines and clones from HLA-DR1 carrying, mugwort pollen-allergic donors reacted to synthetic and naturally processed Art v 1-peptides when presented by HLA-DR1 or HLA-DR4 expressing antigen presenting cells. However, at limiting peptide concentrations DR1 was more effective in T cell stimulation. In addition, the minimal epitope for 50% of Art v 125-36 -specific T cells was shorter for DR1 than for DR4. In vitro binding assays of Art v 125-36 mutant peptides to isolated DR1- and DR4-molecules indicated similar binding capacities and use of the same register. In silico simulation of Art v 125-36 binding to HLA-DR1 and -DR4 suggested similar binding of the central part of the peptide to either molecule, but a higher flexibility of the N- and C-terminal amino acids and detachment at the C-terminus in HLA-DR1.

CONCLUSIONS

The predominance of HLA-DR1 in the response to Art v 125-36 may be explained by subtle conformation changes of the peptide bound to DR1 compared to DR4. Computer simulation supported our experimental data by demonstrating differences in peptide mobility within the HLA-DR complex that may influence TCR-binding. We suggest that the minor differences observed in vitro may be more relevant in the microenvironment in vivo, so that only presentation by HLA-DR1, but not -DR4 permits successful T cell activation.

MH2c: Characterization of major histocompatibility α-helices - an information criterion approach

Major histocompatibility proteins share a common overall structure or peptide binding groove. Two binding groove domains, on the same chain for major histocompatibility class I or on two different chains for major histocompatibility class II, contribute to that structure that consists of two α-helices ("wall") and a sheet of eight anti-parallel beta strands ("floor"). Apart from the peptide presented in the groove, the major histocompatibility α-helices play a central role for the interaction with the T cell receptor. This study presents a generalized mathematical approach for the characterization of these helices. We employed polynomials of degree 1 to 7 and splines with 1 to 2 nodes based on polynomials of degree 1 to 7 on the α-helices projected on their principal components. We evaluated all models with a corrected Akaike Information Criterion to determine which model represents the α-helices in the best way without overfitting the data. This method is applicable for both the stationary and the dynamic characterization of α-helices. By deriving differential geometric parameters from these models one obtains a reliable method to characterize and compare α-helices for a broad range of applications.

PROGRAM SUMMARY

Program title: MH²c (MH helix curves) Catalogue identifier: AELX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 327 565 No. of bytes in distributed program, including test data, etc.: 17 433 656 Distribution format: tar.gz Programming language: Matlab Computer: Personal computer architectures Operating system: Windows, Linux, Mac (all systems on which Matlab can be installed) RAM: Depends on the trajectory size, min. 1 GB (Matlab) Classification: 2.1, 4.9, 4.14 External routines: Curve Fitting Toolbox and Statistic Toolbox of Matlab Nature of problem: Major histocompatibility (MH) proteins share a similar overall structure. However, identical MH alleles which present different peptides differ by subtle conformational alterations. One hypothesis is that such conformational differences could be another level of T cell regulation. By this software package we present a reliable and systematic way to compare different MH structures to each other. Solution method: We tested several fitting approaches on all available experimental crystal structures of MH to obtain an overall picture of how to describe MH helices. For this purpose we transformed all complexes into the same space and applied splines and polynomials of several degrees to them. To draw a general conclusion which method fits them best we employed the "corrected Akaike Information Criterion". The software is applicable for all kinds of helices of biomolecules. Running time: Depends on the data, for a single stationary structure the runtime should not exceed a few seconds.

The sedoheptulose kinase CARKL directs macrophage polarization through control of glucose metabolism

Immune cells are somewhat unique in that activation responses can alter quantitative phenotypes upwards of 100,000-fold. To date little is known about the metabolic adaptations necessary to mount such dramatic phenotypic shifts. Screening for novel regulators of macrophage activation, we found nonprotein kinases of glucose metabolism among the most enriched classes of candidate immune modulators. We find that one of these, the carbohydrate kinase-like protein CARKL, is rapidly downregulated in vitro and in vivo upon LPS stimulation in both mice and humans. Interestingly, CARKL catalyzes an orphan reaction in the pentose phosphate pathway, refocusing cellular metabolism to a high-redox state upon physiological or artificial downregulation. We find that CARKL-dependent metabolic reprogramming is required for proper M1- and M2-like macrophage polarization and uncover a rate-limiting requirement for appropriate glucose flux in macrophage polarization.