Biology subjects

Kitaba, N. T.

Publications and source records attributed to Kitaba, N. T..

3 recordsLinked to original sources

bioRxiv2025

Immune transcriptomic differences in paediatric patients with SARS-CoV-2 compared to other lower respiratory tract infections

The clinical severity of SARS-CoV-2 infection in children varies, with asymptomatic or mild illness predominating and a minority developing severe disease. Understanding the immunological responses that underlie severity of disease may guide future development of preventive or therapeutic interventions. This study compared whole blood transcriptomes of healthy children (N=127), children with mild/asymptomatic SARS-CoV-2 infection (N=71) and children hospitalised with severe SARS-COV-2 (N=41), lower respiratory tract illness (LRTI) or LRTI due to Respiratory Syncytial Virus (RSV-LRTI) (N=47) or Pulmonary Tuberculosis (PTB) (N=47). We identified >5000 differentially expressed genes including: OLFM4, IFI27, CBX7, IGF2BP3, OTOF for severe SARS-CoV-2; IFI27, OTOF, SIGLEC1, IFI44L and USP18 for RSV-LRTI, and MMP8, LTF, IGF2BP3, GPR84, CD177, C1QC and DEFA4 for PTB, at false discovery rate (FDR) <0.05. Pathway analysis identified enrichment for neutrophil degranulation, interferon gamma signalling, overexpression of ribosomal proteins and depletion of immune response in severe SARS-CoV-2 compared to healthy (SAR-COV-2 uninfected) children. Weighted Gene Co-expression Network Analysis (WGCNA) identified 10 correlated gene modules shared between LRTI showing similar underlying response mechanisms. Cellular decomposition analysis identified the depletion of 22 cell types in severe SARS-CoV-2, 16 for RSV-LRTI and 21 for PTB compared to healthy SARS-CoV-2 uninfected control children. We identified 82 genes important for discriminating asymptomatic/mild from severe SARS-CoV-2 including CBX7, TRAF1, ZNF324 and CASS4; 93 healthy from severe SARS-CoV-2 including RORC, CBX7, NR3C2, MID2 and ADAMTS2; 110 genes for RSV-LRTI and 95 for PTB children which can be used for future therapeutic targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/687132v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@13db618org.highwire.dtl.DTLVardef@7df9e2org.highwire.dtl.DTLVardef@a0a448org.highwire.dtl.DTLVardef@818cef_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method

Prospective birth cohorts offer the potential to interrogate the relation between early life environment and embedded biological processes such as DNA methylation (DNAme). These association studies are frequently conducted in the context of blood, a heterogeneous tissue composed of diverse cell types. Accounting for this cellular heterogeneity across samples is essential, as it is a main contributor to inter-individual DNAme variation. Integrated blood cell deconvolution of pediatric and longitudinal birth cohorts poses a major challenge, as existing methods fail to account for the distinct cell population shift between birth and adolescence. In this paper, we critically evaluated the reference-based deconvolution procedure and optimized its prediction accuracy for longitudinal birth cohorts using DNAme data from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. The optimized algorithm, CellsPickMe, integrates cord and adult references and picks DNAme features for each population of cells with machine learning algorithms. It demonstrated improved deconvolution accuracy in cord, pediatric, and adult blood samples compared to existing benchmark methods. CellsPickMe supports blood cell deconvolution across early developmental periods under a single framework, enabling cross-time-point integration of longitudinal DNAme studies. Given the increased resolution of cell populations predicted by CellsPickMe, this R package empowers researchers to explore immune system dynamics using DNAme data in population studies across the life course.

bioinformatics↗

bioRxiv2023

Fathers' preconception smoking and offspring DNA methylation: A two generation study

RationaleExperimental studies suggest that exposures may impact respiratory health across generations via epigenetic changes transmitted specifically through male germ cells. Studies in humans are however limited. We aim to identify epigenetic marks in offspring associated with fathers preconception smoking. MethodsWe conducted epigenome-wide association studies (EWAS) in the RHINESSA cohort on fathers any preconception smoking (N=875 offspring) and fathers pubertal onset smoking <15 years (N=304), using Infinium MethylationEPIC Beadchip arrays, adjusting for offspring age, maternal smoking and personal smoking. EWAS of maternal and offspring personal smoking were performed for replication. ResultsFathers smoking commencing preconception was associated with methylation of blood DNA in offspring at two Cytosine-phosphate-Guanine sites (CpGs) (False Discovery Rate (FDR) <0.05) in PRR5 and CENPP. Fathers pubertal onset smoking was associated with 19 CpGs (FDR <0.05) mapped to 14 genes (TLR9, DNTT, FAM53B, NCAPG2, PSTPIP2, MBIP, C2orf39, NTRK2, DNAJC14, CDO1, PRAP1, TPCN1, IRS1 and CSF1R). These differentially methylated sites were hypermethylated and associated with promoter regions capable of gene silencing. Some of these sites were associated with offspring outcomes in this cohort including ever-asthma (NTRK2), ever-wheezing (DNAJC14, TPCN1), weight (FAM53B, NTRK2) and BMI (FAM53B, NTRK2) (P< 0.05). Pathway analysis showed enrichment for gene ontology pathways including regulation of gene expression, inflammation and innate immune responses. ConclusionFathers preconception smoking, particularly in puberty, is associated with offspring DNA methylation, providing evidence that epigenetic mechanisms may underly epidemiological observations that pubertal paternal smoking increases risk of offspring asthma, low lung function and obesity.

genomics↗