Abstract Background Crohn’s disease (CD) pathogenesis involves complex interactions between immune cells and intestinal tissues. We investigated the heterogeneity of CD8 effector T cells (Teff) and their role in CD inflammation at the single-cell level. Methods T cells were isolated from blood, inflamed, and uninflamed intestinal tissues of 15 patients with CD. Various combinations of single-cell RNA sequencing, TCR sequencing, and spatial transcriptomics were performed on subsets of these samples to comprehensively analyze the cellular landscape. Results Analysis of 41,699 CD8 T cells revealed two main CD8 Teff subsets: granzyme B (GZMB+) Teff cells, predominantly in blood with high cytotoxic potential, and granzyme K (GZMK+) Teff cells, enriched in intestinal tissue with lower cytotoxic potential. In the intestine, GZMK+ and GZMB+ Teff cells exhibited distinct transcriptional profiles. GZMB+ Teff cells showed higher expression of KLF2 and its target genes, including tissue egress markers S1PR1 and S1PR5, suggesting circulation propensity. In contrast, GZMK+ Teff cells displayed a tissue residency signature, with reduced egress-related gene expression and increased AP-1 transcription factors (FOS, FOSB, JUN). TCR clonotype analysis revealed substantial clonal expansion of GZMK+ Teff cells and high clonal overlap with CD8+ tissue-resident memory T (Trm) cells. Cell-cell communication analysis demonstrated GZMK+ Teff cell interactions with myeloid cells through the CXCR3/CXCL9,-10 axis, which was confirmed by spatial transcriptomics. Delving deeper into the heterogeneity of GZMK+ Teff cells, we identified three distinct subpopulations, each characterized by unique functional attributes: cytotoxic GZMK+ cells expressing high levels of PRF1, GNLY, and GZMB; stem-like GZMK+ cells expressing TCF7, a key regulator of T cell stemness; and Trm-like GZMK+ cells with high CXCR6 expression, indicative of long-term tissue residence. Notably, the Trm-like GZMK+ T cells displayed elevated levels of pro-inflammatory cytokines (IFNG, TNF), suggesting their potential role in tissue inflammation. Conclusion Our study reveals the transcriptional heterogeneity of CD8 Teff cells in blood and intestinal tissues of patients with CD. We identified intestinal GZMK+ CD8 Teff cells that may contribute to local inflammation through myeloid cell interactions. These findings provide new insights into CD pathogenesis and may guide the development of targeted therapies modulating tissue-specific immune responses.
Pancreatic ductal adenocarcinoma (PDAC) is the most challenging type of cancer to treat, with a 5-year survival rate of <10%. Furthermore, because of the large portion of the inoperable cases, it is difficult to obtain specimens to study the biology of the tumors. Therefore, a patient-derived xenograft (PDX) model is an attractive option for preserving and expanding these tumors for translational research. Here we report the generation and characterization of 20 PDX models of PDAC. The success rate of the initial graft was 74% and most tumors were re-transplantable. Histological analysis of the PDXs and primary tumors revealed a conserved expression pattern of p53 and SMAD4; an exome single nucleotide polymorphism (SNP) array and Comprehensive Cancer Panel showed that PDXs retained over 94% of cancer-associated variants. In addition, Polyphen2 and the Sorting Intolerant from Tolerant (SIFT) prediction identified 623 variants among the functional SNPs, highlighting the heterologous nature of pancreatic PDXs; an analysis of 409 tumor suppressor genes and oncogenes in Comprehensive Cancer Panel revealed heterologous cancer gene mutation profiles for each PDX-primary tumor pair. Altogether, we expect these PDX models are a promising platform for screening novel therapeutic agents and diagnostic markers for the detection and eradication of PDAC.
Abstract Background and Aim Tobacco smoking is a risk factor for gastrointestinal disorders, causing mucosal damage and impairing immune responses. However, smoking has been found to be protective against ulcerative colitis (UC). Human leukocyte antigen (HLA) is a major susceptibility locus for UC, and HLA‐DRB1*15:02 has the strongest effect in Asians. This study investigated the effects of smoking on the association between HLA and UC. Methods The study enrolled 882 patients with UC, including 526 never, 151 current, and 205 former smokers, and 3091 healthy controls, including 2124 never, 502 current, and 465 former smokers. Smoking‐stratified analyses of HLA data were performed using a case–control approach. Results In a case–control approach, HLA‐DRB1*15:02 was associated with UC in never smokers (OR never smokers = 3.20, P never smokers = 7.88 × 10 −23 ) but not in current or former smokers ( P current smokers = 0.72 and P former smokers = 0.33, respectively). In current smokers, HLA‐DQB1*06 was associated with UC (OR current smokers = 2.59, P current smokers = 6.39 × 10 −12 ). No variants reached genome‐wide significance in former smokers. Conclusions An association between UC and HLA‐DRB1*15:02 was limited to never smokers. Our findings highlight that tobacco smoking modifies the effects of HLA on the risk of UC.
Although B cells and T cells are integral players of the adaptive immune system and act in co-dependent ways to orchestrate immune responses, existing methods to study the immune repertoire have largely focused on separate analyses of B cell receptor (BCR) and T cell receptor (TCR) repertoires. Based on our hypothesis that the shared history of immune exposures and the shared cellular machinery for recombination result in similarities between BCR and TCR repertoires in an individual, we examine any commonalities and interrelationships between BCR and TCR repertoires. We find that the BCR and TCR repertoires have covarying clonal architecture and diversity, and that the pattern of correlations appears to be altered in immune-mediated diseases. Furthermore, hierarchical clustering of public B and T cell clonotypes in both health and disease based on correlation of clonal proportion revealed distinct clusters of B and T cell clonotypes that exhibit increased sequence similarity, share motifs, and have distinct amino acid characteristics. Our findings point to common principles governing memory formation, recombination, and clonal expansion to antigens in B and T cells within an individual. A significant proportion of public BCR and TCR repertoire can be clustered into nonoverlapping and correlated clusters, suggesting a novel way of grouping B and T cell clonotypes.
The shared epitope (SE) is a group of alleles of the HLA DRB1 gene and was thought to have the strongest effect on rheumatoid arthritis (RA) susceptibility. However, recently, HLA-DRB1 position 11,outside of the classical SE, has been shown to be a stronger predictor of RA susceptibility. Positions 11,71 and 74 define 16 haplotypes, the effect of which ranges from risk to protective on RA susceptibility. Their effect on RA severity, treatment response or mortality in patients has not previously been studied.
Objectives
To assess whether HLA-DRB1 positions 11,71,74 can also be used to predict radiological outcome, anti-TNF response and mortality in patients with RA.
Methods
We used 3 independent prospective cohort studies: the Norfolk Arthritis Register-NOAR (1691 patients with 2811 x-rays); the Early Rheumatoid Arthritis Study-ERAS (421 patients with 3758 x-rays); a cohort from 57 UK centres-BRAGGSS (1846 patients with treatment response). HLA typing was determined using a reverse dot-blot method or dense genotyping of the HLA region by the ImmunoChip array, followed by imputation.Longitudinal modelling of the presence of erosions was performed with generalized estimating equation (GEE) models whilst the Larsen score was modelled with Generalized Linear Latent and Mixed Modelling (GLLAMM). Change in Disease Activity Score28 was modelled with linear regression and EULAR response with ordinal logistic regression. Cox proportional hazard models were used for all cause and cardiovascular mortality studies.
Results
Valine at position 11 of HLA-DRB1 (Val11) is a new and the strongest independent genetic determinant of radiological damage in RA and this finding has been replicated in separate cohorts (OR in NOAR: 1.75, 95%CI 1.52 to 2.01, p=8.7E-15). Positions 71 and 74 represent independent predictors and the 3 positions define 16 haplotypes strongly associated with disease outcome (multivariate p=2.83E-12), superseding the SE. The hierarchy, ranging from risk to protective effects, is perfectly correlated with that observed for disease susceptibility. HLA-DRB1 haplotypes associated with RA susceptibility and severe outcome are also predictors of good treatment response with anti-TNF therapy. For example, the Val11Lys71Ala74-haplotype, carried by 52% of patients, is associated with good EULAR response (OR: 1.24 95%CI 1.07 to 1.44 p=5.31E-03). On average, 17 patients need to be treated with anti-TNF to see one more patient responding better, based solely on the carriage of this haplotype. Both all-cause and cardiovascular mortality are also predicted by the 16 haplotypes.
Conclusions
Combinations of amino-acids at positions 11, 71 and 74 of the HLA-DRB1 gene predict severe disease, treatment response and mortality in RA, superseding the classical SE. At disease onset, this allows stratification of patients to identify those at risk of joint damage and early death but who are more likely to respond to anti-TNF biologic therapy.
The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time-consuming and inaccurate for complicated pedigrees, such as pedigrees with inbreeding. In this work, we propose an efficient algorithm that is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation, backward in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path-based approach implemented with an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations, and therefore, it can reconstruct pedigrees that have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data.
Systemic juvenile idiopathic arthritis (sJIA) is an often severe, potentially life-threatening childhood inflammatory disease, the pathophysiology of which is poorly understood. To determine whether genetic variation within the MHC locus on chromosome 6 influences sJIA susceptibility, we performed an association study of 982 children with sJIA and 8,010 healthy control subjects from nine countries. Using meta-analysis of directly observed and imputed SNP genotypes and imputed classic HLA types, we identified the MHC locus as a bona fide susceptibility locus with effects on sJIA risk that transcended geographically defined strata. The strongest sJIA-associated SNP, rs151043342 [P = 2.8 × 10(-17), odds ratio (OR) 2.6 (2.1, 3.3)], was part of a cluster of 482 sJIA-associated SNPs that spanned a 400-kb region and included the class II HLA region. Conditional analysis controlling for the effect of rs151043342 found that rs12722051 independently influenced sJIA risk [P = 1.0 × 10(-5), OR 0.7 (0.6, 0.8)]. Meta-analysis of imputed classic HLA-type associations in six study populations of Western European ancestry revealed that HLA-DRB1*11 and its defining amino acid residue, glutamate 58, were strongly associated with sJIA [P = 2.7 × 10(-16), OR 2.3 (1.9, 2.8)], as was the HLA-DRB1*11-HLA-DQA1*05-HLA-DQB1*03 haplotype [6.4 × 10(-17), OR 2.3 (1.9, 2.9)]. By examining the MHC locus in the largest collection of sJIA patients assembled to date, this study solidifies the relationship between the class II HLA region and sJIA, implicating adaptive immune molecules in the pathogenesis of sJIA.
Meta-analysis of genome-wide association studies is increasingly popular and many meta-analytic methods have been recently proposed. A majority of meta-analytic methods combine information from multiple studies by assuming that studies are independent since individuals collected in one study are unlikely to be collected again by another study. However, it has become increasingly common to utilize the same control individuals among multiple studies to reduce genotyping or sequencing cost. This causes those studies that share the same individuals to be dependent, and spurious associations may arise if overlapping subjects are not taken into account in a meta-analysis. In this paper, we propose a general framework for meta-analyzing dependent studies with overlapping subjects. Given dependent studies, our approach "decouples" the studies into independent studies such that meta-analysis methods assuming independent studies can be applied. This enables many meta-analysis methods, such as the random effects model, to account for overlapping subjects. Another advantage is that one can continue to use preferred software in the analysis pipeline which may not support overlapping subjects. Using simulations and the Wellcome Trust Case Control Consortium data, we show that our decoupling approach allows both the fixed and the random effects models to account for overlapping subjects while retaining desirable false positive rate and power.
1 Abstract The identification of pleiotropic loci and the interpretation of the associations at these loci are essential to understand the shared etiology of related traits. A common approach to map pleiotropic loci is to use an existing meta-analysis method to combine summary statistics of multiple traits. This strategy does not take into account the complex genetic architectures of traits such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO, a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple traits. Our method maximizes power by systematically accounting for the genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with differing units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 20 novel pleiotropic loci, which showed five different patterns of associations. Our method is available at https://github.com/hanlab-SNU/PLEIO .
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