'Hotspots' of Antigen Presentation Revealed by Human Leukocyte Antigen Ligandomics for Neoantigen Prioritization.

The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived from processing and presentation of mutated proteins are one of the leading targets for T-cell recognition of cancer cells. Currently, most studies attempt to identify neoantigens based on predicted affinity to HLA molecules, but the performance of such prediction algorithms is rather poor for rare HLA class I alleles and for HLA class II. Direct identification of neoantigens by mass spectrometry (MS) is becoming feasible; however, it is not yet applicable to most patients and lacks sensitivity. In an attempt to capitalize on existing immunopeptidomics data and extract information that could complement HLA-binding prediction, we first compiled a large HLA class I and class II immunopeptidomics database across dozens of cell types and HLA allotypes and detected hotspots that are subsequences of proteins frequently presented. About 3% of the peptidome was detected in both class I and class II. Based on the gene ontology of their source proteins and the peptide's length, we propose that their processing may partake by the cellular class II presentation machinery. Our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features such as binding affinity and stability or RNA abundance. We further introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens

Uncovering the topology of configuration space networks

The configuration space network (CSN) of a dynamical system is an effective approach to represent the ensemble of configurations sampled during a simulation and their dynamic connectivity. To elucidate the connection between the CSN topology and the underlying free-energy landscape governing the system dynamics and thermodynamics, an analytical soluti on is provided to explain the heavy tail of the degree distribution, neighbor co nnectivity and clustering coefficient. This derivation allows to understand the universal CSN network topology observed in systems ranging from a simple quadratic well to the native state of the beta3s peptide and a 2D lattice heteropolymer. Moreover CSN are shown to fall in the general class of complex networks describe d by the fitness model.Comment: 6 figure

Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival.

Natural killer (NK) cells have increasingly become a target of interest for immunotherapies. NK cells express killer immunoglobulin-like receptors (KIRs), which play a vital role in immune response to tumors by detecting cellular abnormalities. The genomic region encoding the 16 KIR genes displays high polymorphic variability in human populations, making it difficult to resolve individual genotypes based on next generation sequencing data. As a result, the impact of polymorphic KIR variation on cancer phenotypes has been understudied. Currently, labor-intensive, experimental techniques are used to determine an individual's KIR gene copy number profile. Here, we develop an algorithm to determine the germline copy number of KIR genes from whole exome sequencing data and apply it to a cohort of nearly 5000 cancer patients. We use a k-mer based approach to capture sequences unique to specific genes, count their occurrences in the set of reads derived from an individual and compare the individual's k-mer distribution to that of the population. Copy number results demonstrate high concordance with population copy number expectations. Our method reveals that the burden of inhibitory KIR genes is associated with survival in two tumor types, highlighting the potential importance of KIR variation in understanding tumor development and response to immunotherapy

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells

Exploring the Free Energy Landscape: From Dynamics to Networks and Back

The knowledge of the Free Energy Landscape topology is the essential key to understand many biochemical processes. The determination of the conformers of a protein and their basins of attraction takes a central role for studying molecular isomerization reactions. In this work, we present a novel framework to unveil the features of a Free Energy Landscape answering questions such as how many meta-stable conformers are, how the hierarchical relationship among them is, or what the structure and kinetics of the transition paths are. Exploring the landscape by molecular dynamics simulations, the microscopic data of the trajectory are encoded into a Conformational Markov Network. The structure of this graph reveals the regions of the conformational space corresponding to the basins of attraction. In addition, handling the Conformational Markov Network, relevant kinetic magnitudes as dwell times or rate constants, and the hierarchical relationship among basins, complete the global picture of the landscape. We show the power of the analysis studying a toy model of a funnel-like potential and computing efficiently the conformers of a short peptide, the dialanine, paving the way to a systematic study of the Free Energy Landscape in large peptides.Comment: PLoS Computational Biology (in press

Millennial changes in North American wildfire and soil activity over the last glacial cycle

Climate changes in the North Atlantic region during the last glacial cycle were dominated by the slow waxing and waning of the North American ice sheet as well as by intermittent, millennial-scale Dansgaard-Oeschger climate oscillations. However, prior to the last deglaciation, the responses of North American vegetation and biomass burning to these climate variations are uncertain. Ammonium in Greenland ice cores, a product from North American soil emissions and biomass burning events, can help to fill this gap. Here we use continuous, high-resolution measurements of ammonium concentrations between 110,000 to 10,000 years ago from the Greenland NGRIP and GRIP ice cores to reconstruct North American wildfire activity and soil ammonium emissions. We find that on orbital timescales soil emissions increased under warmer climate conditions when vegetation expanded northwards into previously ice-covered areas. For millennial-scale interstadial warm periods during Marine Isotope Stage 3, the fire recurrence rate increased in parallel to the rapid warmings, whereas soil emissions rose more slowly, reflecting slow ice shrinkage and delayed ecosystem changes. We conclude that sudden warming events had little impact on soil ammonium emissions and ammonium transport to Greenland, but did result in a substantial increase in the frequency of North American wildfires

Greenland records of aerosol source and atmospheric lifetime changes from the Eemian to the Holocene

The Northern Hemisphere experienced dramatic changes during the last glacial, featuring vast ice sheets and abrupt climate events, while high northern latitudes during the last interglacial (Eemian) were warmer than today. Here we use high-resolution aerosol records from the Greenland NEEM ice core to reconstruct the environmental alterations in aerosol source regions accompanying these changes. Separating source and transport effects, we find strongly reduced terrestrial biogenic emissions during glacial times reflecting net loss of vegetated area in North America. Rapid climate changes during the glacial have little effect on terrestrial biogenic aerosol emissions. A strong increase in terrestrial dust emissions during the coldest intervals indicates higher aridity and dust storm activity in East Asian deserts. Glacial sea salt aerosol emissions in the North Atlantic region increase only moderately (50%), likely due to sea ice expansion. Lower aerosol concentrations in Eemian ice compared to the Holocene are mainly due to shortened atmospheric residence time, while emissions changed little.It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO), Canada (NRCan/GSC), China (CAS), Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR), Korea (KOPRI), The Netherlands (NWO/ALW), Sweden (VR), Switzerland (SNF), United Kingdom (NERC), and the USA (US NSF, Office of Polar Programs). Long-term support of ice core research at the University of Bern by SNF is gratefully acknowledged

A Music-Related Quality of Life measure to guide music rehabilitation for adult CI users

Purpose: A music-related quality of life (MuRQoL) questionnaire was developed for the evaluation of music rehabilitation for adult cochlear implant (CI) users. The present studies were aimed at refinement and validation. Method: Twenty-four experts reviewed the MuRQoL items for face validity. A refined version was completed by 147 adult CI users and psychometric techniques were used for item selection, assessment of reliability and definition of the factor structure. The same participants completed the Short Form Health Survey for construct validation. MuRQoL responses from 68 CI users were compared with those of a matched group of normal-hearing (NH) adults. Results: Eighteen items measuring music perception & engagement and 18 items measuring their importance were selected; they grouped together into two domains. The final questionnaire has high internal consistency and repeatability. Significant differences between CI users and NH adults and a correlation between music engagement and quality of life (QoL) support construct validity. Scores of music perception & engagement and importance for the 18 items can be combined to assess the impact of music on the QoL. Conclusion: The MuRQoL questionnaire is a reliable and valid measure of self-reported music perception, engagement and their importance for adult CI users with potential to guide music aural rehabilitation

Inflammatory B cells correlate with failure to checkpoint blockade in melanoma patients.

The understanding of the role of B cells in patients with solid tumors remains insufficient. We found that circulating B cells produced TNFα and/or IL-6, associated with unresponsiveness and poor overall survival of melanoma patients treated with anti-CTLA4 antibody. Transcriptome analysis of B cells from melanoma metastases showed enriched expression of inflammatory response genes. Publicly available single B cell data from the tumor microenvironment revealed a negative correlation between TNFα expression and response to immune checkpoint blockade. These findings suggest that B cells contribute to tumor growth via the production of inflammatory cytokines. Possibly, these B cells are different from tertiary lymphoid structure-associated B cells, which have been described to correlate with favorable clinical outcome of cancer patients. Further studies are required to identify and characterize B cell subsets and their functions promoting or counteracting tumor growth, with the aim to identify biomarkers and novel treatment targets

Soil composition and plant genotype determine benzoxazinoid-mediated plant–soil feedbacks in cereals

Plant–soil feedbacks refer to effects on plants that are mediated by soil modifications caused by the previous plant generation. Maize conditions the surrounding soil by secretion of root exudates including benzoxazinoids (BXs), a class of bioactive secondary metabolites. Previous work found that a BX-conditioned soil microbiota enhances insect resistance while reducing biomass in the next generation of maize plants. Whether these BX-mediated and microbially driven feedbacks are conserved across different soils and response species is unknown. We found the BX-feedbacks on maize growth and insect resistance conserved between two arable soils, but absent in a more fertile grassland soil, suggesting a soil-type dependence of BX feedbacks. We demonstrated that wheat also responded to BX-feedbacks. While the negative growth response to BX-conditioning was conserved in both cereals, insect resistance showed opposite patterns, with an increase in maize and a decrease in wheat. Wheat pathogen resistance was not affected. Finally and consistent with maize, we found the BX-feedbacks to be cultivar-specific. Taken together, BX-feedbacks affected cereal growth and resistance in a soil and genotype-dependent manner. Cultivar-specificity of BX-feedbacks is a key finding, as it hides the potential to optimize crops that avoid negative plant–soil feedbacks in rotations