ABSTRACT
Background: SARS-CoV-2 primarily infects the lung but may also damage other organs including the brain, heart, kidney, and intestine. Central nervous system (CNS) disorders include loss of smell and taste, headache, delirium, acute psychosis, seizures, and stroke. Pathological loss of gray matter occurs in SARS-CoV-2 infection but it is unclear whether this is due to direct viral infection, indirect effects associated with systemic inflammation, or both. Methods: We used iPSC-derived brain organoids and primary human astrocytes from cerebral cortex to study direct SARS-CoV-2 infection, as confirmed by Spike and Nucleocapsid immunostaining and RT-qPCR. siRNAs, blocking antibodies, and small molecule inhibitors were used to assess SARS-CoV-2 receptor candidates. Bulk RNA-seq, DNA methylation seq, and Nanostring GeoMx digital spatial profiling were utilized to identify virus-induced changes in host gene expression. Results: Astrocytes were robustly infected by SARS-CoV-2 in brain organoids while neurons and neuroprogenitor cells supported only low-level infection. Based on siRNA knockdowns, Neuropilin-1, not ACE2, functioned as the primary receptor for SARS-CoV-2 in astrocytes. The endolysosomal two-pore channel protein, TPC, also facilitated infection likely through its regulatory effects on endocytosis. Other alternative receptors, including the AXL tyrosine kinase, CD147, and dipeptidyl protease 4 (DPP4), did not function as SARS-CoV-2 receptors in astrocytes. SARS-CoV-2 infection dynamically induced type I, II, and III interferons, and genes involved in Toll-like receptor signaling, MDA5 and RIG-I sensing of double-stranded RNA, and production of inflammatory cytokines. Genes activating apoptosis were also increased. Down-regulated genes included those involved in water, ion and lipid transport, synaptic transmission, and formation of cell junctions. Epigenetic analyses revealed transcriptional changes related to DNA methylation states, particularly decreased DNA methylation in interferon-related genes. Long-term viral infection of brain organoids resulted in progressive neuronal degeneration and death. Conclusion: Our findings support a model where SARS-CoV-2 infection of astrocytes produces a panoply of changes in the expression of genes regulating innate immune signaling and inflammatory responses. Deregulation of these genes in astrocytes produces a microenvironment within the CNS that ultimately disrupts normal neuron function, promoting neuronal cell death and CNS deficits.
ABSTRACT
Background: SARS-CoV-2 infection has resulted in over 219 million confirmed cases of COVID-19 with 4.5 million fatalities, highlighting the importance of elucidating mechanisms of severe disease. Here we utilized machine learning (ML) technologies to identify DNA methylation footprints of COVID-19 disease from publicly available data. Methods: Genome-wide DNA methylation of SARS-CoV-2 infected and uninfected patients using Illumina HumanMethylationEPIC microarray platform from whole blood was publicly available through NCBI Gene Expression Omnibus. A training cohort (GSE167202) consisting of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset (GSE174818) consisting of 128 individuals (102 COVID-19-infected and 26 non-COVID with pneumonia diagnosis) were obtained. COVID-19 severity score (SS) was classified as follows: 0. uninfected;1. released from department to home;2. admitted to in-patient care;3. progressed to ICU;and 4. death. Participants were then dichotomized by SS=0 or SS≥3. Raw data was processed using ChAMP in R 4.1.1, resulting in over 850,000 methylation sites per sample for analysis. Beta values were logit transformed to M values using CpGTools in Python 3.8.8. JADBio AutoML platform was leveraged to analyze these datasets with the goal of identifying a methylation signature indicative of COVID-19 disease. Results: From our training cohort, JADBio utilized LASSO feature selection (penalty=1.5) to identify 4 unique methylation sites capable of carrying the predictive weight of a classification random forest trained on 100 trees with Deviance splitting criterion (minimum leaf size=3). The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 (95% confidence interval [0.885, 0.970]), while the average area under the precision-recall curve (AUC-PRC) of 0.965 [0.932, 0.986]. When COVID-19 mild infections (SS = 1 or 2) were returned to the training dataset as an internal control, the model retained its predictive power (AUC-ROC=0.985, AUC-PRC=0.992). When applied to our external validation, this model produced an AUC-ROC of 0.901 with an AUC-PRC of 0.748. Conclusion: We developed a Random Forest Classification model capable of accurately predicting COVID-19 infection leveraging JADBio AutoML platform. These results enhance our understanding of epigenetic mechanisms used by SARS-CoV-2 in disease pathogenesis and identify potential therapeutic targets.