The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both seen and unseen epitopes. In addition, we present a novel feature representation approach, which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are specific to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to unseen epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

Background After transplantation, cell-free deoxyribonucleic acid (DNA) derived from the donor organ (ddcfDNA) can be detected in the recipient’s circulation. We aimed to investigate the role of plasma ddcfDNA as biomarker for acute kidney rejection. Methods From 107 kidney transplant recipients, plasma samples were collected longitudinally after transplantation (Day 1 to 3 months) within a multicentre set-up. Cell-free DNA from the donor was quantified in plasma as a fraction of the total cell-free DNA by next generation sequencing using a targeted, multiplex polymerase chain reaction-based method for the analysis of single nucleotide polymorphisms. Results Increases of the ddcfDNA% above a threshold value of 0.88% were significantly associated with the occurrence of episodes of acute rejection (P = 0.017), acute tubular necrosis (P = 0.011) and acute pyelonephritis (P = 0.032). A receiver operating characteristic curve analysis revealed an equal area under the curve of the ddcfDNA% and serum creatinine of 0.64 for the diagnosis of acute rejection. Conclusions Although increases in plasma ddcfDNA% are associated with graft injury, plasma ddcfDNA does not outperform the diagnostic capacity of the serum creatinine in the diagnosis of acute rejection.

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STUDY QUESTION Does oocyte maturation under lipolytic conditions have detrimental carry-over effects on post-hatching embryo development of good-quality blastocysts after transfer? SUMMARY ANSWER Surviving, morphologically normal blastocysts derived from bovine oocytes that matured under lipotoxic conditions exhibit long-lasting cellular dysfunction at the transcriptomic and metabolic levels, which coincides with retarded post-hatching embryo development. WHAT IS KNOWN ALREADY There is increasing evidence showing that following maturation in pathophysiologically relevant lipotoxic conditions (as in obesity or metabolic syndrome), surviving blastocysts of good (transferable) morphological quality have persistent transcriptomic and epigenetic alteration even when in vitro embryo culture takes place under standard conditions. However, very little is known about subsequent development in the uterus after transfer. STUDY DESIGN, SIZE, DURATION Bovine oocytes were matured in vitro in the presence of pathophysiologically relevant, high non-esterified fatty acid (NEFA) concentrations (HIGH PA), or in basal NEFA concentrations (BASAL) as a physiological control. Eight healthy multiparous non-lactating Holstein cows were used for embryo transfers. Good-quality blastocysts (pools of eight) were transferred per cow, and cows were crossed over for treatments in the next replicate. Embryos were recovered 7 days later and assessed for post-hatching development, phenotypic features and gene expression profile. Blastocysts from solvent-free and NEFA-free maturation (CONTROL) were also tested for comparison. PARTICIPANTS/MATERIALS, SETTING, METHODS Recovered Day 14 embryos were morphologically assessed and dissected into embryonic disk (ED) and extraembryonic tissue (EXT). Samples of EXT were cultured for 24 h to assess cellular metabolic activity (glucose and pyruvate consumption and lactate production) and embryos’ ability to signal for maternal recognition of pregnancy (interferon-τ secretion; IFN-τ). ED and EXT samples were subjected to RNA sequencing to evaluate the genome-wide transcriptome patterns. MAIN RESULTS AND THE ROLE OF CHANCE The embryo recovery rate at Day 14 p.i. was not significantly different among treatment groups (P > 0.1). However, higher proportions of HIGH PA embryos were retarded in growth (in spherical stage) compared to the more elongated tubular stage embryos in the BASAL group (P < 0.05). Focusing on the normally developed tubular embryos in both groups, HIGH PA exposure resulted in altered cellular metabolism and altered transcriptome profile particularly in pathways related to redox-regulating mechanisms, apoptosis, cellular growth, interaction and differentiation, energy metabolism and epigenetic mechanisms, compared to BASAL embryos. Maturation under BASAL conditions did not have any significant effects on post-hatching development and cellular functions compared to CONTROL. LARGE-SCALE DATA The datasets of RNA sequencing analysis are available in the NCBI’s Gene Expression Omnibus (GEO) repository, series accession number GSE127889 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127889). Datasets of differentially expressed genes and their gene ontology functions are available in the Mendeley datasets at http://dx.doi.org/10.17632/my2z7dvk9j.2. LIMITATIONS, REASONS FOR CAUTION The bovine model was used here to allow non-invasive embryo transfer and post-hatching recovery on Day 14. There are physiological differences in some characteristics of post-hatching embryo development between human and cows, such as embryo elongation and trophoblastic invasion. However, the main carry-over effects of oocyte maturation under lipolytic conditions described here are evident at the cellular level and therefore may also occur during post-hatching development in other species including humans. In addition, post-hatching development was studied here under a healthy uterine environment to focus on carry-over effects originating from the oocyte, whereas additional detrimental effects may be induced by maternal metabolic disorders due to adverse changes in the uterine microenvironment. RNA sequencing results were not verified by qPCR, and no solvent control was included. WIDER IMPLICATIONS OF THE FINDINGS Our observations may increase the awareness of the importance of maternal metabolic stress at the level of the preovulatory oocyte in relation to carry-over effects that may persist in the transferrable embryos. It should further stimulate new research about preventive and protective strategies to optimize maternal metabolic health around conception to maximize embryo viability and thus fertility outcome. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the Flemish Research Fund (FWO grant 11L8716N and FWO project 42/FAO10300/6541). The authors declare there are no conflicts of interest.

Abstract:28 Current T-cell epitope prediction tools are a valuable resource in designing targeted immunogenicity 29 experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by 30 major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, 31 recognition of the peptide-MHC complex by a T-cell receptor is often not included in these tools. We developed 32 a classification approach based on random forest classifiers to predict recognition of a peptide by a T-cell and 33 discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) 34 distinguishing between two sets of T-cell receptors that each bind to a known peptide and (2) retrieving T-cell 35 receptors that bind to a given peptide from a large pool of T-cell receptors. Evaluation of the models on two 36 HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can 37 determine peptide immunogenicity. These results are of particularly importance as they show that prediction of 38 T-cell epitope and T-cell epitope recognition based on sequence data is a feasible approach. In addition, the 39 validity of our models not only serves as a proof of concept for the prediction of immunogenic T-cell epitopes 40 but also paves the way for more general and high performing models.

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Background Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure. Methods In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3′ mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile. Results Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples. Conclusions Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.