SARS-CoV-2 Infected Pediatric Cerebral Cortical Neurons: Transcriptomic Analysis and Potential Role of Toll-like Receptors in Pathogenesis

Abstract

Different mechanisms were proposed as responsible for COVID-19 neurological symptoms but a clear one has not been established yet. In this work we aimed to study SARS-CoV-2 capacity to infect pediatric human cortical neuronal HCN-2 cells, studying the changes in the transcriptomic profile by next generation sequencing. SARS-CoV-2 was able to replicate in HCN-2 cells, that did not express ACE2, confirmed also with Western blot, and TMPRSS2. Looking for pattern recognition receptor expression, we found the deregulation of scavenger receptors, such as SR-B1, and the downregulation of genes encoding for Nod-like receptors. On the other hand, TLR1, TLR4 and TLR6 encoding for Toll-like receptors (TLRs) were upregulated. We also found the upregulation of genes encoding for ERK, JNK, NF-κB and Caspase 8 in our transcriptomic analysis. Regarding the expression of known receptors for viral RNA, only RIG-1 showed an increased expression; downstream RIG-1, the genes encoding for TRAF3, IKKε and IRF3 were downregulated. We also found the upregulation of genes encoding for chemokines and accordingly we found an increase in cytokine/chemokine levels in the medium. According to our results, it is possible to speculate that additionally to ACE2 and TMPRSS2, also other receptors may interact with SARS-CoV-2 proteins and mediate its entry or pathogenesis in pediatric cortical neurons infected with SARS-CoV-2. In particular, TLRs signaling could be crucial for the neurological involvement related to SARS-CoV-2 infection.

Keywords: SARS-CoV-2; Toll-like receptors; children; human neuronal cells; nervous system; transcriptomic analysis.

Figure 1

Virus replication in HCN-2 cortical neurons. N1 and N2 copy number increased in a time dependent manner. * p < 0.05; ** p < 0.01; **** p < 0.0001.

Figure 2

Bubble plot of enriched KEGG maps of DEGs in transcriptomic analysis of HCN2-CTR against HCN2-SARS-CoV-2. The pathways are vertically sorted by the ratio of the number of DEGs observed in the map over the total amount of genes that take place there. Ideally, if all the genes in the map are deregulated, the value is 1, while no DEGs in the map is 0. The size of the circle gives information about the number of DEGs included in the pathway. From the left to the right we represented a score for each pathway obtained by −log(q-Value). Interestingly, the “Coronavirus disease—COVID-19” (hsa05171) is among the first 10 overrepresented maps. Then, “Shigellosis” (hsa05131), “Protein processing in endoplasmic reticulum” (hsa04141), “Endocytosis” (hsa04144), “AGE-RAGE signaling pathway in diabetic complications” (hsa04933), “Focal adhesion” (hsa04510), “Viral carcinogenesis” (hsa05203), “Ribosome” (hsa03010), “Salmonella infection” (hsa05132) and “Chronic myeloid leukemia” (hsa05220) have the highest score.

Figure 3

Western blot for ACE2 and TLR4. HCN-2 cells expressed TLR4 but not ACE2. A549 and A549-hACE2 expressed ACE2 and TLR4.

Figure 4

The interactomic analysis was focused on DEGs that have a fold change magnitude higher than 2 (blue DEGs on the right). Nevertheless, we also included in the analysis DEGs that are already known to be associated with immunity response selected on the bases of KEGG analysis even if only some of them have higher fold change differences (green DEGs on the left). The size of the nodes is dependent on the stress parameter so the bigger the node size, the higher the stress of that node in the network. The stress parameter gives an index of the centrality of the node in the network. Among the blue DEGs, EGR1, IRF7, PPARG, SDC1 and CHD4 in diamond shape had the five highest stress levels. For this reason, they may have a key role in immunity response to SARS-CoV-2.

Figure 5

Concentration of cytokines/chemokines in cell culture supernatants 6 days post infection. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001 compared to HCN2-CTR.