EBioMedicine最新文献

Background: Despite the importance of vaccination- and infection-elicited antibodies (Abs) to SARS-CoV-2 immunity, current mouse models do not fully capture the dynamics of Ab-mediated immunity in vivo, including potential contributions of the neonatal Fc receptor, encoded by FCGRT.

Methods: We generated triple knock-in (TKI) mice expressing human ACE2, TMPRSS2, and FCGRT; and evaluated the protective efficacy of anti-SARS-CoV-2 monoclonal Abs (mAbs) and plasma from individuals with immunity elicited by vaccination alone plus SARS-CoV-2 infection-induced (hybrid) immunity.

Findings: A human anti-SARS-CoV-2 mAb harbouring a half-life-extending mutation, but not the wild-type mAb, exhibited prolonged half-life in TKI mice and protected against lung infection with Omicron BA.2, validating the utility of these mice for evaluating therapeutic Abs. Pooled plasma from individuals with hybrid immunity to Delta, but not from vaccinated-only individuals, cleared infectious Delta from the lungs of TKI mice (P < 0.01), even though the two plasma pools had similar Delta-binding and -neutralising Ab titres in vitro. Similarly, plasma from individuals with hybrid Omicron BA.1/2 immunity, but not hybrid Delta immunity, decreased lung infection (P < 0.05) with BA.5 in TKI mice, despite the plasma pools having comparable BA.5-binding and -neutralising titres in vitro. Depletion of receptor-binding domain-targeting Abs from hybrid immune plasma abrogated their protection against infection.

Interpretation: These results demonstrate the utility of TKI mice as a tool for the development of anti-SARS-CoV-2 mAb therapeutics, show that in vitro neutralisation assays do not accurately predict in vivo protection, and highlight the importance of hybrid immunity for eliciting protective anti-receptor-binding domain Abs.

Funding: This work was funded by grants from the e-Asia Joint Research Program (N10A650706 and N10A660577 to MLM, in collaboration with SS); the NIH (U19 AI142790-02S1 to EOS and SS and R44 AI157900 to KJ); the GHR Foundation (to SS and EOS); the Overton family (to SS and EOS); the Arvin Gottlieb Foundation (to SS and EOS), the Prebys Foundation (to SS); and the American Association of Immunologists Fellowship Program for Career Reentry (to FASB).

Background: HIV-1 infection impacts biological ageing, and epigenetic clocks highlight epigenetic age acceleration in people with HIV. Despite evidence indicating sex differences in clinical, immunological, and virological measures, females have been underrepresented in most HIV epigenetic studies. Hence, we generated a more representative epigenetic dataset to examine sex differences in epigenetic ageing and relationships to clinical phenotypes and proteomics.

Methods: We calculated first, second, and third-generation epigenetic ages using DNA methylation data in an observational cohort of 52 females and 106 males with HIV age 50 and over. We profiled plasma biomarkers with Olink high-throughput proteomics to test associations with epigenetic age acceleration. Survival was ascertained over 5 years.

Findings: Epigenetic age acceleration measured by three principal-component based chronological epigenetic age clocks (p = 0.0029, 0.021, 0.010) and one epigenetic mortality risk clock was significantly lower in females living with HIV compared to males (p = 0.0011). Additionally, sex was significantly associated with epigenetic biomarker scores for proportion of naïve CD4+ T cells (p = 0.0006), physical fitness including DNAmGait (p = 0.0010), DNAmGrip (p < 0.0001), and DNAmV02 max (p < 0.0001). We found epigenetic age acceleration associated with plasma proteomic markers involved in inflammation, senescence, immune regulation, kidney function, and tissue homoeostasis (p < 0.0001). Higher epigenetic frailty risk scores were associated with lower CD4 T cell counts (p = 0.0072) and lower CD4/CD8 ratio (p = 0.0017). Slower gait (p = 0.0017), greater frailty (p = 0.0074), and history of smoking (p = 0.042) were associated with increased DNAmFitAge. Risk of death was increased in females with PCPhenoAge acceleration over a 5-year timespan compared to men with PCPhenoAge acceleration (p = 0.03).

Interpretation: These results highlight the importance of studying sex-specific differences in epigenetic ageing biomarkers for HIV-related geroscience research.

Funding: National Institute on Aging (K23AG072960), National Center for Advancing Translational Sciences (UL1TR000457), National Institute of Mental Health (R21 MH115821).

Background: The association between childhood obesity and language development may be confounded by socio-environmental factors and attributed to comorbid pathways.

Methods: In a longitudinal Singaporean mother-offspring cohort, we leveraged trans-ancestry polygenic predictions of body mass index (BMI) to interrogate the causal effects of early-life BMI on child language development and its effects on molecular and neuroimaging measures. Leveraging large genome-wide association studies, we examined whether the link between obesity and language development is causal or due to a shared genetic basis.

Findings: We found an inverse association between polygenic risk for obesity, which is less susceptible to confounding, and language ability assessed at age 9. Our findings suggested a shared genetic basis between obesity and language development rather than a causal effect of obesity on language development. Interrogating early-life mechanisms including neurology-related proteomics and language-related white matter microstructure, we found that EFNA4 and VWC2 expressions were associated with language ability as well as fractional anisotropy of language-related white matter tracts, suggesting a role in brain myelination. Additionally, the expression of the EPH-Ephrin signalling pathway in the hippocampus might contribute to language development. Polygenic risk for obesity was nominally associated with EFNA4 and VWC2 expression. However, we did not find support for mediating mechanisms via these proteins.

Interpretation: This study demonstrates the potential of examining early-life proteomics in conjunction with deep genotyping and phenotyping and provides biological insights into the shared genomic links between obesity and language development.

Funding: Singapore National Research Foundation and Agency for Science, Technology and Research.

Background: Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a susceptible main population contains small subpopulations of resistant cells. Mathematical models, animal experiments and clinical studies associate HR with treatment failure. Currently used susceptibility tests do not detect heteroresistance reliably, which can result in misclassification of heteroresistant isolates as susceptible which might lead to treatment failure. Here we examined if whole genome sequence (WGS) data and machine learning (ML) can be used to detect bacterial HR.

Methods: We classified 467 Escherichia coli clinical isolates as HR or non-HR to the often used β-lactam/inhibitor combination piperacillin-tazobactam using pre-screening and Population Analysis Profiling tests. We sequenced the isolates, assembled the whole genomes and created a set of predictors based on current knowledge of HR mechanisms. Then we trained several machine learning models on 80% of this data set aiming to detect HR isolates. We compared performance of the best ML models on the remaining 20% of the data set with a baseline model based solely on the presence of β-lactamase genes. Furthermore, we sequenced the resistant sub-populations in order to analyse the genetic mechanisms underlying HR.

Findings: The best ML model achieved 100% sensitivity and 84.6% specificity, outperforming the baseline model. The strongest predictors of HR were the total number of β-lactamase genes, β-lactamase gene variants and presence of IS elements flanking them. Genetic analysis of HR strains confirmed that HR is caused by an increased copy number of resistance genes via gene amplification or plasmid copy number increase. This aligns with the ML model's findings, reinforcing the hypothesis that this mechanism underlies HR in Gram-negative bacteria.

Interpretation: We demonstrate that a combination of WGS and ML can identify HR in bacteria with perfect sensitivity and high specificity. This improved detection would allow for better-informed treatment decisions and potentially reduce the occurrence of treatment failures associated with HR.

Funding: Funding provided to DIA from the Swedish Research Council (2021-02091) and NIH (1U19AI158080-01).

Chimeric Antigen Receptor (CAR) T-cell therapy is characterised by the heterogeneous cellular kinetic profile seen across patients. Unlike traditional chemotherapy, which displays predictable dose-exposure relationships resulting from well-understood pharmacokinetic processes, CAR T-cell dynamics rely on complex biologic factors that condition treatment response. Computational approaches hold potential to explore the intricate cellular dynamics arising from CAR T therapy, yet their ability to improve cancer treatment remains elusive. Here we present a comprehensive framework through which to understand, construct, and classify CAR T-cell kinetics models. Current approaches often rely on adapted empirical pharmacokinetic methods that overlook dynamics emerging from cellular interactions, or intricate theoretical multi-population models with limited clinical applicability. Our review shows that the utility of a model does not depend on the complexity of its design but on the strategic selection of its biological constituents, implementation of suitable mathematical tools, and the availability of biological measures from which to fit the model.

Background: Chronic Obstructive Pulmonary Disease (COPD) has a broad spectrum of clinical characteristics. The aetiology of these differences is not well understood. The objective of this study is to assess whether respiratory genetic variants cluster by phenotype and associate with COPD heterogeneity.

Methods: We clustered genome-wide association studies of COPD, lung function, and asthma and phenotypes from the UK Biobank using non-negative matrix factorization. We constructed cluster-specific genetic risk scores and tested these scores for association with phenotypes in non-Hispanic white subjects in the COPDGene study.

Findings: We identified three clusters from 482 variants and 44 traits from genetic associations in 379,337 UK Biobank participants. Variants from asthma, COPD, and lung function were found in all three clusters. Clusters displayed varying effects on white blood cell counts, height, and body mass index (BMI)-related phenotypes in the UK Biobank. In the COPDGene cohort, cluster-specific genetic risk scores were associated with differences in steroid use, BMI, lymphocyte counts, and chronic bronchitis, as well as variations in gene and protein expression.

Interpretation: Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD.

Funding: MHC was supported by R01HL149861, R01HL135142, R01HL137927, R01HL147148, and R01HL089856. HA and HJ were supported by ANR-20-CE36-0009-02 and ANR-16-CONV-0005. The COPDGene study (NCT00608764) is supported by grants from the NHLBI (U01HL089897 and U01HL089856), by NIH contract 75N92023D00011, and by the COPD Foundation through contributions made to an Industry Advisory Committee that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

Background: Maternal smoking and foetal exposure to nicotine and other harmful chemicals in utero remains a serious public health issue with little knowledge about the underlying genetics and consequences of maternal smoking in ageing individuals. Here, we investigated the epidemiology and genomic architecture of maternal smoking in a middle-aged population and compare the results to effects observed in the developing foetus.

Methods: In the current project, we included 351,562 participants from the UK Biobank (UKB) and estimated exposure to maternal smoking status during pregnancy through self-reporting from the UKB participants about the mother's smoking status around their birth. In addition, we analysed 64 foetal liver transcriptomic expression datasets collected from women seeking elective pregnancy terminations. Foetal maternal smoking exposure was confirmed through measurement of foetal plasma cotinine levels.

Findings: Foetal exposure to maternal smoking had a greater impact on males than females, with more differentially expressed genes in liver tissue (3313 vs. 1163) and higher liver pathway activation. In the UKB, maternal smoking exposure was linked to an unhealthy lifestyle, lower education, and liver damage. In a genome-wide analysis in the UKB, we leveraged the shared genetic basis between affected offspring and their mothers and identified five genome-wide significant regions. We found a low heritability of the trait (∼4%) and implicated several disease-related genes in a transcriptome-wide association study. Maternal smoking increased all-cause mortality risk (Hazard ratio and 95% CI: 1.10 [1.04; 1.16], P = 4.04 × 10^-4), which was attenuated in non-smoking males.

Interpretation: Although male foetuses are more affected than females by maternal smoking in pregnancy, this effect was largely reduced in middle-aged individuals. Importantly, our results highlight that the overall 10% increased mortality due to maternal smoking in pregnancy was greatly attenuated in non-smokers. This study demonstrates the importance of campaigns promoting offspring smoking prevention in families where the parent(s) smoke.

Funding: Funding for this project was provided by the University of Aberdeen, the Science Initiative Panel of the Institute of Medical Science, the UK Medical Research Council, the Seventh Framework Programme of the European Union under Grant Agreement 212885 (REEF) and by NHS Grampian Endowments grants.