EBioMedicine最新文献

Background: BK polyomavirus (BKV) DNAaemia occurs in 10% of recipients of kidney transplants, contributing to premature allograft failure. Evidence suggests disease is donor derived. Hypothetically, recipient infection with a different BKV serotype increases risk due to poorer immunological control. Thus, understanding the composition and activity of the humoral anti-BKV responses in donor/recipient (D/R) pairs is critical.

Methods: Using 224 paired pre-transplant D/R samples, BKV VP1 genotype-specific pseudoviruses were employed to define the breadth of the antibody response against different serotypes (ELISA) and, to characterise specific neutralising activity (nAb) using the 50% inhibitory concentration (LogIC50). Mismatch (MM) ratios were calculated using the ratio of recipient ELISA or nAb reactive BKV serotypes relative to the number of donor reactive serotypes.

Findings: BKV DNAaemia was observed in 28/224 recipients of kidney transplants. These recipients had lower nAb titres against all the serotypes, with median logIC50 values of 1.19-2.91, compared to non-viraemic recipients' median logIC50 values of 2.13-3.30. nAb D/R MM ratios >0.67 associated with significantly higher risk of BKV viraemia, with an adjusted odds ratio of 5.12 (95% CI 2.07 to 13.04; p < 0.001). Notably, a mismatch against donor serotype Ic and II associated with adjusted odds ratios of 8.12 (95% CI 2.10 to 35.61; p = 0.002) and 4.52 (95% CI 1.19 to 19.23; p = 0.03) respectively. 21 recipients demonstrated broadly neutralising responses against all the serotypes, none of whom developed BKV DNAaemia post-transplant. In contrast, there was poor concordance with PsV-specific ELISA data that quantified the total antibody response against different serotypes.

Interpretation: BKV nAb mismatch predicts post-transplant BKV DNAaemia. Specific mismatches in nAb, rather than total seroreactivity, are key indicators of BKV risk post-transplant. This has the potential to risk-stratify individuals and improve clinical outcomes by influencing the frequency of monitoring and individualised tailoring of immunosuppression. Furthermore, detailed examination of individuals with broadly neutralising responses may provide future therapeutic strategies.

Funding: The research was funded by St. Peters Trust, Royal Free Hospital Charity and Wellcome Trust (grant numbers RFCG1718/05, SPT97 and 204870/Z/WT_/Wellcome Trust/United Kingdom).

Background: Alterations in ileal Paneth cell (PC) density have been described in gut inflammatory diseases such as Crohn's disease (CD) and could be used as a biomarker for disease prognosis. However, quantifying PCs is time-intensive, a barrier for clinical workflow. Deep learning (DL) has transformed the development of robust and accurate tools for complex image evaluation. Our aim was to use DL to quantify PCs for use as a quantitative biomarker.

Methods: A retrospective cohort of whole slide images (WSI) of ileal tissue samples from patients with/without inflammatory bowel disease (IBD) was used for the study. A pathologist-annotated training set of WSI were used to train a U-net two-stage DL model to quantify PC number, crypt number, and PC density. For validation, a cohort of 48 WSIs were manually quantified by study pathologists and compared to the DL algorithm, using root mean square error (RMSE) and the coefficient of determination (r²) as metrics. To test the value of PC quantification as a biomarker, resection specimens from patients with CD (n = 142) and without IBD (n = 48) patients were analysed with the DL model. Finally, we compared time to disease recurrence in patients with CD with low versus high DL-quantified PC density using Log-rank test.

Findings: Initial one-stage DL model showed moderate accuracy in predicting PC density in cross-validation tests (RMSE = 1.880, r² = 0.641), but adding a second stage significantly improved accuracy (RMSE = 0.802, r² = 0.748). In the validation of the two-stage model compared to expert pathologists, the algorithm showed good performance up to RMSE = 1.148, r² = 0.708. The retrospective cross-sectional cohort had mean ages of 62.1 years in the patients without IBD and 38.6 years for the patients with CD. In the non-IBD cohort, 43.75% of the patients were male, compared to 49.3% of the patients with CD. Analysis by the DL model showed significantly higher PC density in non-IBD controls compared to the patients with CD (4.04 versus 2.99 PC/crypt). Finally, the algorithm quantification of PCs density in patients with CD showed patients with the lowest 25% PC density (Quartile 1) have significantly shorter recurrence-free interval (p = 0.0399).

Interpretation: The current model performance demonstrates the feasibility of developing a DL-based tool to measure PC density as a predictive biomarker for future clinical practice.

Funding: This study was funded by the National Institutes of Health (NIH).

Background: This study aims to confirm the associations of air pollution with ulcerative colitis (UC) and Crohn's disease (CD); to explore interactions with genetics and lifestyle; and to characterize potential epigenetic mechanisms.

Methods: We identified over 450,000 individuals from the UK Biobank and investigated the relationship between air pollution and incident inflammatory bowel disease (IBD). Cox regression was utilized to calculate hazard ratios (HRs), while also exploring potential interactions with genetics and lifestyle factors. Additionally, we conducted epigenetic Mendelian randomization (MR) analyses to examine the association between air pollution-related DNA methylation and UC. Finally, our findings were validated through genome-wide DNA methylation analysis of UC, as well as co-localization and gene expression analyses.

Findings: Higher exposures to NO_x (HR = 1.20, 95% CI 1.05-1.38), NO₂ (HR = 1.19, 95% CI = 1.03-1.36), PM_2.5 (HR = 1.19, 95% CI = 1.05-1.36) and combined air pollution score (HR = 1.26, 95% CI = 1.11-1.45) were associated with incident UC but not CD. Interactions with genetic risk score and lifestyle were observed. In MR analysis, we found five and 22 methylated CpG sites related to PM_2.5 and NO₂ exposure to be significantly associated with UC. DNA methylation alterations at CXCR2 and sites within the MHC class III region, were validated in genome-wide DNA methylation analysis, co-localization analysis and analysis of colonic tissue.

Interpretation: We report a potential causal association between air pollution and UC, modified by lifestyle and genetic influences. Biological pathways implicated include epigenetic alterations in key genetic loci, including CXCR2 and susceptible loci within MHC class III region.

Funding: Xue Li was supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and the National Nature Science Foundation of China (No. 82204019). ET was supported by the CRUK Career Development Fellowship (C31250/A22804) and the Research Foundation Flanders (FWO). JW was supported by Belgium by a PhD Fellowship strategic basic research (SB) grant (1S06023N). JKN was supported by the National Science Center, Poland (No. 2020/39/D/NZ5/02720). The IBD Character was supported by the European Union's Seventh Framework Programme [FP7] grant IBD Character (No. 2858546).

Background: Omicron sub-variants breakthrough infections (BTIs) have led to millions of coronavirus disease 2019 (COVID-19) cases worldwide. The acute-phase immune status is critical for prognosis, however, the dynamic immune profiling of COVID-19 during the first month after BTIs remains unclear.

Methods: In this study, we monitored the immune dynamics at various timepoints in a longitudinal cohort during the first month post-BTIs through clinical evaluation, single-cell RNA sequencing (scRNA-seq), T cell receptor (TCR)/B cell receptor (BCR) sequencing, and antibody mass spectrometry.

Findings: Serological analysis revealed limited impairment to functions of major organs, active cellular and humoral immunity at 2 weeks post-BTI, with significant increases in cytokines (CKs) and neutralizing antibody levels. However, 1 month post-BTI, organ function parameters and CK levels reverted to pre-infection levels, whereas neutralizing antibody levels remained high. Notably, scRNA-seq showed that lymphocytes maintained strong antiviral activity and cell depletion at 2 weeks and 1 month post-BTI, with genes CD81, ABHD17A, CXCR4, DUSP1, etc. upregulated, and genes PFDN5, DYNLRB1, CD52, etc. downregulated, indicating that lymphocytes status take longer to recover to normal levels than that routine blood tests revealed. Additionally, T cell-exhaustion associated genes, including LAG3, TIGIT, PDCD1, CTLA4, HAVCR2, and TOX, were upregulated after BTI. TCRs and BCRs exhibited higher clonotypes, mainly in CD8Tem or plasmablast cells, at 2 weeks post-BTI comparing 1 month. More IgG and IgA-type BCRs were found in the groups of 1 month post-BTI, with higher somatic hypermutation, indicating greater maturity. Verification of monoclonal antibodies corresponding to amplified BCRs highlighted the antigen-specific and broad-spectrum characteristics.

Interpretation: Our study elucidated the dynamic immune profiling of individuals after Omicron BA.5 sublineages BTI. Strong immune activation, antiviral response, antibody maturation and class transition at 2 weeks and 1 month after BTI may provide essential insights into pathogenicity, sequential immune status, recovery mechanisms of Omicron sublineage BTI.

Funding: This study was supported by the National Key R&D Program of China, the China Postdoctoral Science Foundation, Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, CAS Project for Young Scientists in Basic Research, and the Air Force Special Medical Center Science and Technology Booster Program.

Background: Due to its late stage of diagnosis lung cancer is the commonest cause of death from cancer in the UK. Existing epidemiological risk models in clinical usage, which have Positive Predictive Values (PPV) of less than 10%, do not consider the temporal relations expressed in sequential electronic health record (EHR) data. We aimed to build a model for lung cancer early detection in primary care using machine learning with deep 'transformer' models on EHR data to learn from these complex sequential 'care pathways'.

Methods: We split the Whole Systems Integrated Care (WSIC) dataset into 70% training and 30% validation. Within the training set we created a case-control study with lung cancer cases and control cases of 'other' cancers or respiratory conditions or 'other' non cancer conditions. Based on 3,303,992 patients from January 1981 to December 2020 there were 11,847 lung cancer cases. 5789 cases and 7240 controls were used for training and 50,000 randomly selected patients out of the whole validation population of 368,906 for validation. GP EHR data going back three years from the date of diagnosis less the most recent one months were semantically pre-processed by mapping from more than 30,000 terms to 450. Model building was performed using ALBERT with a Logistic Regression Classifier (LRC) head. Clustering was explored using k-means. An additional regression model alone was built on the pre-processed data as a comparator.

Findings: Our model achieved an AUROC of 0.924 (95% CI 0.921-0.927) with a PPV of 3.6% (95% CI 3.5-3.7) and Sensitivity of 86.6% (95% CI 85.3-87.8) based on the three year's data prior to diagnosis less the immediate month before index diagnosis. The comparator regression model achieved a PPV of 3.1% (95% CI 3.0-3.1) and AUROC of 0.887 (95% CI 0.884-0.889). We interpreted our model using cluster analysis and have identified six groups of patients exhibiting similar lung cancer progression patterns and clinical investigation patterns.

Interpretation: Capturing temporal sequencing between cancer and non-cancer pathways to diagnosis enables much more accurate models. Future work will focus on external dataset validation and integration into GP clinical systems for evaluation.

Funding: Cancer Research UK.

Background: Global dissemination of SARS-CoV-2 Omicron sublineages has provided a sufficient opportunity for natural selection, thus enabling beneficial mutations to emerge. Characterisation of these mutations uncovers the underlying machinery responsible for the fast transmission of Omicron variants and guides vaccine development for combating the COVID-19 pandemic.

Methods: Through systematic bioinformatics analysis of 496,606 sequences of Omicron variants, we obtained 40 amino acid substitutions that occurred with high frequency in the S protein. Utilising pseudoviruses and a trans-complementation system of SARS-CoV-2, we identified the effect of high-frequency mutations on viral infectivity and elucidated the molecular mechanisms. Finally, we evaluated the impact of a key emerging mutation on the immune protection induced by the SARS-CoV-2 VLP mRNA vaccine in a murine model.

Findings: We identified a proline-to-leucine substitution at the 1263rd residue of the Spike protein, and upon investigating the relative frequencies across multiple Omicron sublineages, we found a trend of increasing frequency for P1263L. The substitution significantly enhances the capacity for S-mediated viral entry and improves the immunogenicity of a virus-like particle mRNA vaccine. Mechanistic studies showed that this mutation is located in the FERM binding motif of the cytoplasmic tail and impairs the interaction between the S protein and the Ezrin/Radixin/Moesin proteins. Additionally, this mutation facilitates the incorporation of S proteins into SARS-CoV-2 virions.

Interpretation: This study offers mechanistic insight into the constantly increasing transmissibility of SARS-CoV-2 Omicron variants and provides a meaningful optimisation strategy for vaccine development against SARS-CoV-2.

Funding: This study was supported by grants from the National Key Research and Development Plan of China (2021YFC2302405, 2022YFC2303200, 2021YFC2300200 and 2022YFC2303400), the National Natural Science Foundation of China (32188101, 32200772, 82422049, 82241082, 32270182, 82372254, 82271872, 82341046, 32100755 and 82102389), Shenzhen Medical Research Fund (B2404002, A2303036), the Shenzhen Bay Laboratory Startup Fund (21330111), Shenzhen San-Ming Project for Prevention and Research on Vector-borne Diseases (SZSM202211023), Yunnan Provincial Science and Technology Project at Southwest United Graduate School (202302AO370010). The New Cornerstone Science Foundation through the New Cornerstone Investigator Program, and the Xplorer Prize from Tencent Foundation.

Background: Physical activity is an adjunctive therapy that improves symptoms in people living with systemic lupus erythematosus (SLE), yet the mechanisms underlying this benefit remain unclear.

Methods: We carried out a cohort study of 123 patients with SLE enrolled in the California Lupus Epidemiology Study (CLUES). The primary predictor variable was self-reported physical activity, which was measured using a previously validated instrument. We analyzed peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing (scRNA-seq) data available from the cohort. From the scRNA-seq data, we compared immune cell frequencies, cell-specific gene expression, biological signalling pathways, and upstream cytokine activation states between physically active and inactive patients, adjusting for age, sex and race.

Findings: We found that physical activity influenced immune cell frequencies, with sedentary patients most notably demonstrating greater CD4+ T cell lymphopenia (P_adj = 0.028). Differential gene expression analysis identified a transcriptional signature of physical inactivity across five cell types. In CD4+ and CD8+ T cells, this signature was characterized by 686 and 445 differentially expressed genes (P_adj < 0.1). Gene set enrichment analysis demonstrated enrichment of proinflammatory genes in the TNF-α signalling through NF-kB, interferon-γ (IFN-γ), IL2/STAT5, and IL6/JAK/STAT3 signalling pathways. Computational prediction of upstream cytokine activation states suggested CD4+ T cells from physically inactive patients exhibited increased activation of TNF-α, IFN-γ, IL1Β, and other proinflammatory cytokines. Network analysis demonstrated interconnectivity of genes driving the proinflammatory state of sedentary patients. Findings were consistent in sensitivity analyses adjusting for corticosteroid treatment and physical function.

Interpretation: Taken together, our findings suggest a mechanistic explanation for the observed benefits of physical activity in patients with SLE. Specifically, we find that physical inactivity is associated with altered frequencies and transcriptional profiles of immune cell populations and may exacerbate pathologic inflammatory signalling via CD4+ and CD8+ T cells.

Funding: This work was supported by the US National Institutes of Health (NIH) (R01 AR069616, K23HL138461-01A1, K23AT011768) the US CDC (U01DP0670), and the CZ Biohub.

Background: The impact of the infecting SARS-CoV-2 variant of concern (VOC) and the vaccination status was determined on the magnitude, breadth, and durability of the neutralizing antibody (nAb) profile in a longitudinal multicentre cohort study.

Methods: 173 vaccinated and 56 non-vaccinated individuals were enrolled after SARS-CoV-2 Alpha, Delta, or Omicron infection and visited four times within 6 months and nAbs were measured for D614G, Alpha, Delta, BA.1, BA.2, BA.5, BQ.1.1, XBB.1.5 and JN.1.

Findings: Magnitude-breadth-analysis showed enhanced neutralization capacity in vaccinated individuals against multiple VOCs. Longitudinal analysis revealed sustained neutralization magnitude-breadth after antigenically distant Delta or Omicron breakthrough infection (BTI), with triple-vaccinated individuals showing significantly elevated titres and improved breadth. Antigenic mapping and antibody landscaping revealed initial boosting of vaccine-induced WT-specific responses after BTI, a shift in neutralization towards infecting VOCs at peak responses and an immune imprinted bias towards dominating WT immunity in the long-term. Despite that bias, machine-learning models confirmed a sustained shift of the immune-profiles following BTI.

Interpretation: In summary, our longitudinal analysis revealed delayed and short lived nAb shifts towards the infecting VOC, but an immune imprinted bias towards long-term vaccine induced immunity after BTI.

Funding: This work was funded by the Bavarian State Ministry of Science and the Arts for the CoVaKo study and the ForCovid project. The funders had no influence on the study design, data analysis or data interpretation.

Background: Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies such as cancer screening and tobacco treatment, which are currently under-utilised. Polygenic risk scores (PRSs) may further motivate health behaviour change in primary care for lung cancer in diverse populations. In this work, we introduce the GREAT care paradigm, which integrates PRSs within comprehensive patient risk profiles to motivate positive health behaviour changes.

Methods: We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardised PRS distributions across all ancestries. We validated our PRSs in 561,776 individuals of diverse ancestry from the GISC Trial, UK Biobank (UKBB), and All of Us Research Program (AoU).

Findings: Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58-2.18) in UKBB and 2.39 (95% CI: 1.93-2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32-1.41) in UKBB and 1.32 (95% CI: 1.28-1.36) in AoU.

Interpretation: Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations, which will be evaluated in two cluster-randomised clinical trials. This approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

Funding: National Institutes of Health, NIH Intramural Research Program, National Science Foundation.