Bw4 ligand and direct T-cell receptor binding induced selection on HLA A and B alleles.

Introduction: The HLA region is the hallmark of balancing selection, argued to be driven by the pressure to present a wide variety of viral epitopes. As such selection on the peptide-binding positions has been proposed to drive HLA population genetics. MHC molecules also directly binds to the T-Cell Receptor and killer cell immunoglobulin-like receptors (KIR). Methods: We here combine the HLA allele frequencies in over six-million Hematopoietic Stem Cells (HSC) donors with a novel machine-learning-based method to predict allele frequency. Results: We show for the first time that allele frequency can be predicted from their sequences. This prediction yields a natural measure for selection. The strongest selection is affecting KIR binding regions, followed by the peptide-binding cleft. The selection from the direct interaction with the KIR and TCR is centered on positively charged residues (mainly Arginine), and some positions in the peptide-binding cleft are not associated with the allele frequency, especially Tyrosine residues. Discussion: These results suggest that the balancing selection for peptide presentation is combined with a positive selection for KIR and TCR binding.

Shared bias in H chain V-J pairing in naive and memory B cells.

Introduction: H chain rearrangement in B cells is a two-step process where first DH binds JH, and only then VH is joined to the complex. As such, there is no direct rearrangement between VH and JH. Results: Nevertheless, we here show that the VHJH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for DH genes or a preferred reading frame. Preferred VH JH combinations are shared between donors. Discussion: These results suggest a common structural mechanism for these biases. Through development, thepreferred VH JH combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain VH JH usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific VH-JH pairs.

Impact of Dietary Protein on the Management of Pediatric Short Bowel Syndrome.

Essential amino acids (AAs) play a key role in stimulating intestinal adaptation after massive small gut resection. The nutritional effect of dietary amino acids during intestinal regrowth has received considerable attention in recent years. This review explores the significance of dietary amino acids in the nutritional management of infants and children with intestinal failure and short bowel syndrome (SBS) as reported in the medical literature over the last three decades. A literature search was conducted using electronic databases. Breast milk emerged as the first-line enteral regimen recommended for infants with SBS. Hydrolyzed formulas (HFs) or amino acid formulas (AAFs) are recommended when breast milk is not available or if the infant cannot tolerate whole protein milk. The superiority of AAFs over HFs has never been demonstrated. Although glutamine (GLN) is the main fuel for enterocytes, GLN supplementation in infants with SBS showed no difference in the child's dependence upon parenteral nutrition (PN). Circulating citrulline is considered a major determinant of survival and nutritional prognosis of SBS patients. Early enteral nutrition and dietary supplementation of AAs following bowel resection in children are essential for the development of intestinal adaptation, thereby eliminating the need for PN.

Two Step Selection for Bias in ß Chain V-J Pairing.

The ß chain rearrangement in T cells is a two-step process where first Dß and Jß bind, and only then Vß is joined to the complex. We here show that the frequency of human and mouse Vß Jß combinations deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional (F) rearrangements, but also slightly in non-functional (NF) rearrangements. Preferred Vß Jß combinations in F clones are shared between donors and samples, suggesting a common structural mechanism for these biases in addition to any host-specific antigen-induced peripheral selection. The sharing holds even in clones with J ß 1 that share the same Dß 1 gene. Vß Jß usage is correlated with the Molecular Weight and Isoelectric Point in F clones. The pairing is also observed in the Double Positive cells in mice thymocytes, suggesting that the selection leading to such a pairing occurs before thymic selection. These results suggest an additional structural checkpoint in the beta chain development prior to thymic selection during the T cell receptor expression. Understanding this structural selection is important for the distinction between normal and aberrant T cell development, and crucial for the design of engineered TCRs.

Counting is almost all you need.

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.

Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.