Association of pre-natal maternal HIV with cord gene expression linking with lung function
HIV-exposed uninfected (HEU) children are observed to have altered lung function at 6 weeks and 2 years of age. However, the molecular mechanisms accounting for this effect of maternal HIV on offspring lung function are poorly understood. A brief description of this research problem has already been discussed in the Chapter 2 - Section 2.8.1. This chapter describes the methods and techniques applied to explore the underlying mechanism, presents the results, and finally, discusses the research findings including feature identification and interprets the results from the biological point of view.
tious, nutritional, genetic, psychosocial, maternal and immunological risk factors, considering their impacts on child health (Figure 3.1).
Figure 3.1: Drakenstein Child Health Study (DCHS) Schema. This figure displays the study plan of the DCHS. Figure borrowed from [43].
Mothers were enrolled during their second trimester of pregnancy and mother-child pairs were followed from birth to 5 years. Parents provided informed, written consent in their first language for their infants to participate. Longitudinal assessment of a wide variety of exposures with potential impact on respiratory illness and the evolution of chronic disease was measured, as previously described [39, 42].
3.1.2 Maternal HIV exposure
Maternal HIV infection was ascertained at enrolment through self-report as previously described [48]. All HIV-infected mothers were on highly active antiretroviral therapy (HAART) or received antiretroviral therapy according to the Western Cape Department of Health Guidelines for preven- tion of mother-to-child transmission (PMTCT) at the time. HIV data were gathered from folder reviews of mothers and children and accessing electronic laboratory data from the National Health Laboratory Service as well as self-report interviews antenatally and postnatally. In the case of multiple measures, the lowest recorded CD4+ cell count (collected 1 year before to 3 months after birth to maximise numbers) and highest viral load during pregnancy were used. HIV-exposed children were tested for HIV at 6 weeks (by PCR), 9 months (by PCR, ELISA or rapid antibody testing) and 18 months (by rapid antibody testing), as per provincial PMTCT guidelines.
3.1.3 Lung Function
Child lung function testing was undertaken at the local hospital at the age of 6 weeks, and 2 years as previously described [48]. Lung function testing was undertaken first at 6 weeks of age corrected for prematurity (<37 weeks) and then at 1 year and 2 years in unsedated and, behaviourally assessed quiet sleep. Tidal breathing lung function was measures were collected using the Exhalyser D and ultrasonic flow meter (Ecomedics, Deurnton, Switzerland) [42, 48, 49]. As tidal volume was reported to be the only measure associated with HEU exposure at 6 weeks, we investigated the association of gene expression and tidal volume outcomes at the age of 6 weeks and 2 years.
3.1.4 Gene Expression in Cord Blood
This study used quality controlled normalized gene expression data previously described by Breen et al.[41]. Briefly, umbilical cord blood (UCB) was collected in PAXgene RNA tubes and stored at
−80◦Cafter offspring delivery but before delivery of the placenta. RNA was extracted and 168 sam- ples had a Bioanalyzer RNA Integrity Number (RIN)>= 7. Complementary DNA (cDNA) was reverse transcribed from RNA and raw probe intensities were generated on Illumina HumanHT-12 v4 BeadChip arrays. Samples were randomized with respect to sex, maternal diagnosis, maternal alcohol and tobacco use, and mode of delivery (i.e. natural delivery, elective C-section, or emergent C-section) to reduce the chance of batch effects. After background correction and quantile normal- ization, a log base-2 transformation were applied using theneqc function of limma [50] and quality control filters applied. Probes with detectable expression (p-value < 0.05) in at least 50 arrays were kept and outlying samples identified using unsupervised hierarchical clustering of samples and principal component analysis. From the 168 samples (withRIN >= 7), a total of 144 samples passing quality control (Qc) and with HIV exposure information and 10,705 genes were retained for subsequent analysis [41].
3.1.5 Prenatal Maternal Diagnoses
Maternal smoking history was measured prenatally using a quantitative analysis of maternal urine cotinine (IMMULITE 1000 Nicotine Metabolite Kit; Siemens Medical Solutions Diagnostics, Glyn
Rhonwy, UK). Smoking exposure was categorized based on the value of urine cotinine and are defined as follows: active smoker if the value of urine cotinine>500 ng/mL, passive smoker if the value of urine cotinine 10–500 ng/mL and non-smoker if urine cotinine <10 ng/mL [44].
Maternal alcohol use was also assessed prenatally with the alcohol subscale of the ASSIST or the women’s responses on the positive history on the alcohol exposure questionnaire (AEQ) of alcohol use during three trimesters of pregnancy. Full description of this measurement is available in [41]. For example, any women was categorized as ’alcohol-exposed’ if they scored >= 11 on the alcohol subscale of ASSIST or they drunk 2 or more times in a week or 2 or more drinks per occasion.
Maternal trauma exposure was also calculated prenatally with the modified PTSD Symptom Scale (mPSS) as previously described [41]. Any woman with a trauma history fulfilling the re- quirement of DSM-IV criteria (reported in [45]) for PTSD based on the mPSS during the prenatal period was identified as a PTSD case.
3.1.6 Differential Gene Expression Analysis in Cord Blood
Differential gene expression (DGE) analysis was performed to investigate the impact of HIV expo- sure on UCB gene expression by linear regression using the limma package [50]. Maternal smoking (urine cotinine), maternal alcohol use (self-report), biological sex, gestational age, mode of delivery, ethnicity, PTSD, RIN, and batch were considered as covariates for all models. Multiple testing correction was undertaken using the Benjamini-Hochberg False Discovery Rate (FDR) method and FDR adjusted p-value<0.05 was considered as significant.
3.1.7 Enrichment Analysis of Differentially Expressed Genes for Maternal HIV
Fast Gene Set Enrichment Analysis (fGSEA) [6] was performed on the differentially expressed genes (DEGs) in HEU infants. The DEGs were ranked by -log10 of p-value divided by sign of the log2 fold-change for each gene. This ranked list was used in fGSEA to assess enrichment for GO (Gene Ontology) biological processes, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways [6, 51]. Pathways were considered significant based on the hyper-geometric test p-value<0.05.
3.1.8 Weighted Gene Co-expression Network Analysis
Weighted Gene Co-expression Network Analysis (WGCNA) [5] was performed on 10,705 genes expressed in UCB to form a signed co-expression network. A consensus network was generated using all 144 samples in order to increase the power, the confidence and the certainty of identifying biologically meaningful clusters (i.e., modules) among all genes. Pearson correlation coefficients were calculated for all possible gene pairs and then absolute values thereof were taken. To ensure that the matrix of the values follows an approximate scale-free topology (R2 > 0.94), the values are transformed with the default value of exponential weight (β = 12)) to get the final matrix.
The dynamic tree-cut algorithm was applied in WGCNA to detect the network modules (tree-cut height = 0.99; deep-split = 3; merge module height = 0.25; minimum module size = 50) following Breenet al. [41].
Once the modules were detected, these were investigated further to identify significant associ- ations with HEU as well as with confounding biological and technical factors. In order to identify modules, and their biological processes that were highly associated with HEU, singular value de- composition (SVD) was computed on each module’s expression matrix resulting module eigengene (ME). These MEs present the overall expression profiles for these modules.
Gene significance (GS), representing the correlation of the differentially expressed gene to ma- ternal HIV, was calculated for each gene using the -log10 of the p-value of the DGE analysis. Mod- ule significance (MS) was computed by taking the average GS within each module. An ANOVA (ANalysis Of VAriance) testing was applied with post hoc Tukey correction to perform differential analysis of MEs. Finally, intra-modular membership (kME), the correlation between ME expres- sion and gene expression values, was calculated for each module. In these co-expressed modules, the genes with kME>0.7 were treated as hub genes. These co-expressed modules were investigated further with the output of DGE to identify significant modules for maternal HIV.
3.1.9 Overrepresentation Analysis for Identifying Significant Modules from WGCNA
An over-representation analysis of the gene network modules, identified from the WGCNA anal- ysis, on the DEGs (based on p-value<0.01) for maternal HIV was performed using a hyper- geometric test. The modules with FDR corrected p-value<0.05 were considered as significantly over-represented modules for HEU.
3.1.10 HEU-Associated Highly Co-Expressed Genes and Lung Function
To explore the association of HEU associated co-expressed hub genes and child lung function linear regression analysis was conducted on tidal volumes (TV) at 6 weeks and 2 years of age for each hub gene (kME>0.7) from the HEU highly associated modules. Weight-for-age z score, gestational age, male gender, and active smoking were used as covariates to adjust the models for tidal volume based on previously reported association between maternal HIV and child lung function [39]. As the linear regression for tidal volume was carried out a priori on hub genes from the HEU co-expressed modules with p-values<0.05 were considered as significant.
Additionally, enrichment analysis was performed using tidal volume associated hub genes from the HEU highly associated co-expressed modules using Enrichr [8] to explore enrichment in REAC- TOME pathways [7] with significant pathways identified as p-value<0.05 using a hyper-geometric test.
The proportions of immune cell types were measured for each sample using CIBERSORT cell type de-convolution [52] using the LM22 signature matrix to identify specific immune cell types: B cells, cytotoxic T cells (CD8+), helper- and regulatory T cells (CD4+), dendritic cells, macrophages, mast cells, natural killer (NK) cells (CD56+), monocytes (CD14+) and neutrophils etc. Differences in proportions of cell types between HEU and unexposed offspring were assessed using t-tests.