Biology subjects

Holloway, A.

Publications and source records attributed to Holloway, A..

3 recordsLinked to original sources

bioRxiv2026

Defining influenza-specific B cells in vaccine responders, non-responders and influenza breakthrough infections

Although seasonal influenza vaccination programs are effective at a population level, our data from inactivated influenza vaccine (IIV) cohorts in years 2015-2022 reveal that 50-60% of individuals do not seroconvert following immunization. The underlying mechanisms of vaccine non-responsiveness are far from understood. In this study, we sought to define key determinants of optimal B cell immune responses elicited by seasonal influenza vaccination, and to explore why some individuals fail to elicit humoral immunity following immunization. Immune responses associated with seroconversion and vaccine failure from individuals immunized with IIVs were compared at cellular and molecular levels using single-cell transcriptomics. We analyzed HA-specific B cell immunity across vaccine-responders, breakthrough infections and patients hospitalized with acute influenza. Droplet-based single-cell RNA sequencing and VDJ-sequencing of influenza-specific B cells from stored PBMCs was performed using 10x Genomics. Our results show that atypical B cells are the major subset of B cell responses in vaccine non-responders on day 28 post-vaccination. Conversely, individuals who seroconvert had diverse B cell phenotypes. The use of recombinant influenza-specific HA probes allowed us to dissect expression patterns on influenza HA-specific B cells. We found that HA-specific B cells of vaccine non-responders for A/H1N1 and A/H3N2 components displayed elevated atypical-like markers (CD11c, FcRL-5) at baseline, compared to responders. Analysis of differentially expressed genes (DEGs) between responders and non-responders identified differential expression of HLA-DR, CD74, CD83, and CXCR3 genes. We subsequently demonstrated reduced frequencies of HLA-DR-, CD74- and CD83-expressing B cells in patients hospitalized with influenza, compared to healthy participants. Hospitalized influenza patients also had significantly higher proportions of atypical CD21-CD27- B cells. Overall, our data demonstrate an association between elevated frequencies of atypical-like B cells with both lack of seroconversion following immunization and severe influenza infection. These findings broaden our understanding of humoral immunity in influenza vaccination and infection, providing novel insights for vaccination strategies and design.

immunology↗

bioRxiv2025

Prenatal cannabis smoke exposure alters placental development in a murine model of pregnancy

Cannabis use during pregnancy continues to increase with smoking remaining the most common mode of consumption. While clinical studies highlight an association between prenatal cannabis use and adverse pregnancy outcomes, less is known about placental outcomes, even though many of the reported pregnancy outcomes are thought to be mediated via placental dysfunction. Here, we established a mouse model of gestational cannabis smoke exposure to investigate the impacts on fetal outcomes and placental structure and function. Pregnant CD1 mice were exposed daily to {Delta}9-tetrahydrocannabinol (THC)-dominant cannabis smoke (12-14% THC, 0-2% CBD) or filtered air from embryonic day (E)6.5 to E18.5 or parturition. Cannabinoid analyses in cannabis smoke-exposed, paired maternal and fetal livers revealed total THC and 11-Nor-9-carboxy-THC (THCA) concentrations of 135.95 {+/-} 13.60 ng/g and 30.84 {+/-} 4.68 ng/g, respectively. Moreover, Cyp1a1, a smoke-inducible enzyme, was induced by 4-fold in cannabis smoke-exposed placentae. No changes in offspring body weights were observed; however, there was a marked decrease in the brain-to-body weight ratio of exposed postnatal day 1 (PND1) offspring. Placentae from exposed dams were significantly reduced in size, with altered zonation marked by a significantly decreased junctional zone and increased labyrinth zone. Key trophoblast differentiation markers (Tfap2c, Tpbpa, Pcdh12) and placental endocrine regulators (Pl2, Igf1r) were significantly downregulated following cannabis smoke exposure in placentas. Furthermore, transcript levels of placental nutrient and vascularization markers, Glut1, Vegfa and Pparg were significantly decreased in cannabis smoke-exposed placentas. By employing a physiologically relevant platform of prenatal cannabis exposure in vivo we demonstrate the adverse effects of prenatal cannabis smoke exposure on placental structure and function as well as on fetal brain growth.

physiology↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗