Introduction Infectious diseases are causally related to a large array of non-communicable diseases (NCDs). Identifying genetic determinants of infections and antibody-mediated immune responses may shed light on this relationship and provide therapeutic targets for drug and vaccine development. Methods We used the UK biobank cohort of up to 10,000 serological measurements of infectious diseases and genome-wide genotyping. We used data on 13 pathogens to define 46 phenotypes: 15 seropositivity case-control phenotypes, and 31 quantitative antibody measurement phenotypes. For each of these, we performed genome-wide association studies (GWAS) using the fastGWA linear mixed model package, and human leukocyte antigen (HLA) classical allele and amino acid residue associations analyses using Lasso regression for variable selection. Results We included a total of 8735 individuals for case-control phenotypes, and an average of 4286 samples per quantitative analyses (range: 276 to 8555). Fourteen of the GWAS yielded a genome-wide significant (p<5x10 -8) loci at the major histocompatibility complex (MHC) on chromosome 6. Outside the MHC, we found a total of 60 loci, multiple associated with Epstein-Barr virus (EBV) related NCDs (e.g. RASA3, MED12L, and IRF4). FUT2 was also identified as an important gene for polyomaviridae. HLA analysis highlighted the importance DRB1*09:01, DQB1*02:01, DQA1*01:02, and DQA1*03:01 in EBV serologies, and of DRB1*15:01 in polyomaviridae. Conclusion We have identified multiple genetic variants associated with antibody immune response to 13 infections, many of which are biologically plausible therapeutic or vaccine targets. This may help prioritize future research and drug development.

In recent years, the Transformer architecture has proven to be very successful in sequence processing, but its application to other data structures, such as graphs, has remained limited due to the difficulty of properly defining positions. Here, we present the Spectral Attention Network (SAN), which uses a learned positional encoding (LPE) that can take advantage of the full Laplacian spectrum to learn the position of each node in a given graph. This LPE is then added to the node features of the graph and passed to a fully-connected Transformer. By leveraging the full spectrum of the Laplacian, our model is theoretically powerful in distinguishing graphs, and can better detect similar sub-structures from their resonance. Further, by fully connecting the graph, the Transformer does not suffer from over-squashing, an information bottleneck of most GNNs, and enables better modeling of physical phenomenons such as heat transfer and electric interaction. When tested empirically on a set of 4 standard datasets, our model performs on par or better than state-of-theart GNNs, and outperforms any attention-based model by a wide margin, becoming the first fully-connected architecture to perform well on graph benchmarks. * Equal contribution.Preprint. Under review.

The increased risk and persistence of infections in diabetic condition is probably associated with defects in the cellular immune responses. We have previously shown a decrease in the production of interferon (IFN)-α by dendritic cells (DCs) in diabetic subjects. The basal level of IFN-α in splenic plasmacytoid DCs (pDCs) is also lower in non-obese diabetic (NOD) mice compared to prediabetic mice. The objective of this study was to analyse the ability of diabetic mice to mobilize innate and CD8(+) T cell-mediated immune response to influenza A virus (IAV) with the live influenza A/Puerto Rico/8/1934 H1N1 (PR8) strain or with its immunodominant CD8(+) T cell epitopes. We found that following immunization with IAV, the level of IFN-α in diabetic mice was increased to the level in prediabetic mice. Immunization of NOD mice with the immunodominant IAV PR8 peptide induced clonal expansion of IFN-γ-producing CD8(+) T cells similar to the response observed in prediabetic mice. Thus, diabetic and prediabetic NOD mice have a similar capacity for IFN-α and IFN-γ production by pDCs and CD8(+) T cells, respectively. Therefore, the DC-related immune defect in diabetic NOD mice does not impair their capacity to develop an effective immune response to IAV. Our results suggest that reduced IFN-α production by diabetic human and mouse DCs is not an impediment to an effective immunity to IAV in type 1 diabetic subjects vaccinated with live attenuated influenza vaccine.

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