Transcriptomic profile of adverse neurodevelopmental outcomes after neonatal encephalopathy

Abstract

A rapid and early diagnostic test to identify the encephalopathic babies at risk of adverse outcome may accelerate the development of neuroprotectants. We examined if a whole blood transcriptomic signature measured soon after birth, predicts adverse neurodevelopmental outcome eighteen months after neonatal encephalopathy. We performed next generation sequencing on whole blood ribonucleic acid obtained within six hours of birth from the first 47 encephalopathic babies recruited to the Hypothermia for Encephalopathy in Low and middle-income countries (HELIX) trial. Two infants with blood culture positive sepsis were excluded, and the data from remaining 45 were analysed. A total of 855 genes were significantly differentially expressed between the good and adverse outcome groups, of which RGS1 and SMC4 were the most significant. Biological pathway analysis adjusted for gender, trial randomisation allocation (cooling therapy versus usual care) and estimated blood leukocyte proportions revealed over-representation of genes from pathways related to melatonin and polo-like kinase in babies with adverse outcome. These preliminary data suggest that transcriptomic profiling may be a promising tool for rapid risk stratification in neonatal encephalopathy. It may provide insights into biological mechanisms and identify novel therapeutic targets for neuroprotection.

Figure 1

(A) Volcano plot showing the significant genes identified in the comparison of neonates with adverse versus good outcome, plotted according to log₂ fold-change (x axis) and log₁₀ p value (y axis). In green are genes with false discovery rate (FDR) < 0.05 and log₂ fold change < 0.4 in red are genes with FDR < 0.05 and log₂ fold change > 0.4. (B) Box plot (median, IQR) of gene count values expressed as Fragments Per Kilobase of transcript per Million mapped reads (FPKM) (y axis) of the 6 most significant genes for children with normal (blue) compared with abnormal neurodevelopmental outcome at 2 years (orange). (C) Brain hypoxia leads to Ca²⁺ influx with activation of the Ca²⁺/calmodulin dependent protein kinase IV (CaMK-IV) cascade. CaMK-IV in the cytosol has a proapoptotic effect and is responsible for hypoxic neural cell death both through activation of MAP kinases signalling in the cytosol and through phosphorylation of cAMP response element-binding protein (CREB) in the nucleus, which enhances the expression of pro-apoptotic proteins. Melatonin binds to its plasma membrane receptor MTNR1, to calmodulin and to nuclear receptor retinoid-related receptor alpha (RORA) increasing its expression. RORA is also considered a downstream target of HIF-1α and its levels have been found upregulated in the cellular response to hypoxia. MTNR1A and MTNR1B activation increases PKC activity through activation of Gαq, which stimulates the PLC signalling cascade and leads to inhibition of Ca²⁺/calmodulin dependent protein kinase (CAMK). Both MTNR1A and MTNR1B activation by melatonin inhibits cAMP formation. Furthermore, activation of MTNR1B decreases the expression of the glucose transporter GLUT4, which in turn decreases glucose uptake. The upregulated genes in our analysis are shown in red, while the downregulated genes are shown in green. CALM Calmodulin, CAMK4 calcium/calmodulin dependent protein kinase 4, CAMKII calcium/calmodulin dependent protein kinase II, CREB cAMP-response element binding protein, DAG diacylglycerol, ERK extracellularly regulated kinase, Gαq Gq protein alpha subunit, Gαi α subunit of the heterotrimeric G protein complex, GLUT4 Glucose transporter type 4, Hif-1 alpha Hypoxia-inducible factor 1-alpha, PIP2 Phosphatidylinositol biphosphate, PKA protein kinase A, PKC Protein Kinase C signalling, MAPK mitogen-activated protein kinases, MTNR1A Melatonin receptor type 1A, MTNR1B Melatonin receptor type 1B, IP3 Inositol trisphosphate, NMDAR N-methyl-D-aspartate receptor, RORA Retinoid-related receptor alpha. A,B were created using R (version 4.0.0) (https://cran.r-project.org/). C Was created through the use of Ingenuity pathway software (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis).