Hjorthaugaard8152

Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy. Many recent prediction tools developed upon the deep learning algorithms and mass spectrometry data have indeed showed improvement on the average predicting power for class I HLA-peptide interaction. However, their prediction performances show great variability over individual HLA alleles and peptides with different lengths, which is particularly the case for HLA-C alleles due to the limited amount of experimental data. To meet the increasing demand for attaining the most accurate HLA-peptide binding prediction for individual patient in the real-world clinical studies, more advanced deep learning framework with higher prediction accuracy for HLA-C alleles and longer peptides is highly desirable.

We present a pan-allele HLA-peptide binding prediction framework-MATHLA which integrates bi-directional long short-term memory network and multiple head attention mechanism. This model achieves better prediction accuracy in both fivefold cross-validation test and independent test dataset. In addition, this model is superior over existing tools regarding to the prediction accuracy for longer ligand ranging from 11 to 15 amino acids. Moreover, our model also shows a significant improvement for HLA-C-peptide-binding prediction. By investigating multiple-head attention weight scores, we depicted possible interaction patterns between three HLA I supergroups and their cognate peptides.

Our method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.

Our method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.

Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs.

In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and dect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

The genus Ehrlichia consists of tick-borne obligatory intracellular bacteria that can cause deadly diseases of medical and agricultural importance. Ehrlichia sp. HF, isolated from Ixodes ovatus ticks in Japan [also referred to as I. ovatus Ehrlichia (IOE) agent], causes acute fatal infection in laboratory mice that resembles acute fatal human monocytic ehrlichiosis caused by Ehrlichia chaffeensis. As there is no small laboratory animal model to study fatal human ehrlichiosis, Ehrlichia sp. HF provides a needed disease model. However, the inability to culture Ehrlichia sp. HF and the lack of genomic information have been a barrier to advance this animal model. In addition, Ehrlichia sp. HF has several designations in the literature as it lacks a taxonomically recognized name.

We stably cultured Ehrlichia sp. HF in canine histiocytic leukemia DH82 cells from the HF strain-infected mice, and determined its complete genome sequence. Ehrlichia sp. HF has a single double-stranded circular chromosome of 1,148,90matory responses. We propose to name Ehrlichia sp. HF as Ehrlichia japonica sp. nov. (type strain HF), to denote the geographic region where this bacterium was initially isolated.

The genome of Ehrlichia sp. HF encodes all known virulence factors found in E. chaffeensis, substantiating it as a model Ehrlichia species to study fatal human ehrlichiosis. Comparisons between Ehrlichia sp. HF and E. chaffeensis will enable identification of in vivo virulence factors that are related to host specificity, disease severity, and host inflammatory responses. We propose to name Ehrlichia sp. HF as Ehrlichia japonica sp. nov. Biricodar (type strain HF), to denote the geographic region where this bacterium was initially isolated.

Tumor-associated dendritic cells (TADCs) can interact with tumor cells to suppress anti-tumor T cell immunity. However, there is no information on whether and how TADCs can modulate programmed death-ligand 1 (PD-L1) expression by cancer cells.

Human peripheral blood monocytes were induced for DCs and immature DCs were cultured alone, or co-cultured with bladder cancer T24 or control SV-HUC-1 cells, followed by stimulating with LPS for DC activation. The activation status of DCs was characterized by flow cytometry and allogenic T cell proliferation. The levels of chemokines in the supernatants of co-cultured DCs were measured by CBA-based flow cytometry. The impacts of CXCL9 on PD-L1, STAT3 and Akt expression and STAT3 and Akt phosphorylation in T24 cells were determined by flow cytometry and Western blot.

Compared with the control DCs, TADCs exhibited immature phenotype and had significantly lower capacity to stimulate allogenic T cell proliferation, particularly in the presence of recombinant CXCL9. TADCs produced significantly higher levels of CXCL9, which enhanced PD-L1 expression in T24 cells.