Abstract Background A hyperinflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection gravely worsens the clinical progression of coronavirus disease 2019 (COVID-19). Although the undesirable effects of inflammasome activation have been correlated to the severity of COVID-19, the mechanisms of this process in the asymptomatic infection and disease progression have not yet been clearly elucidated. Methods We performed strand-specific RNA sequencing in 39 peripheral blood mononuclear cell (PBMC) samples from asymptomatic individuals（n = 10）, symptomatic patients（n = 16） and healthy donors（n = 13）. Results Dysregulation of pyrin inflammasomes along with the proline-serine-threonine phosphatase-interacting protein 1 (PSTPIP1) gene was identified in SARS-COV-2 infection. Notably, the PSTPIP1 expression level showed a significant negative correlation with an adjacent long-noncoding RNA (lncRNA) RP11-797A18.6 in the asymptomatic individuals compared with the healthy controls. In addition, a decline in the nuclear factor kappa B subunit 1 (NFKB1) gene expression was observed in asymptomatic infection, followed by a rise in the mild and moderate disease stages, suggesting that altered NFKB1 expression and associated proinflammatory signals may trigger a disease progression. Conclusions Overall, our results indicate that PSTPIP1-dependent pyrin inflammasomes-mediated pyroptosis and NF-κB activation might be potential preventive targets for COVID-19 disease development and progression. Keywords: COVID-19, Proinflammatory responses, Pyroptosis, lncRNA 1. Introduction Background The severity of coronavirus disease 2019 (COVID-19) ranges widely, from asymptomatic to lethal. The majority of research on gene expression profiling to date has focused on the identification of risk factors for severe diseases in COVID-19 patients [[45][1], [46][2], [47][3], [48][4]]. Numerous asymptomatic COVID-19 person-to-person transmission cases have been reported [[49][5], [50][6], [51][7], [52][8], [53][9], [54][10], [55][11]]. However, little is known regarding the molecular characteristics related to asymptomatic COVID-19 individuals and the risk factors associated with the progression from asymptomatic to mild and moderate disease stages. Investigations of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections might lead to the identification of key regulators protecting the infected individuals from more severe symptoms. A rising amount of clinical evidence indicates that an excessive immune response known as a cytokine storm is associated with COVID-19 severity and is perhaps one of the critical hallmarks of mortality from COVID-19 [[56]12,[57]13]. However, the immunological mechanism for asymptomatic individuals remains incompletely understood. Previous studies indicated that inadequate immune activation from asymptomatic infections may have delayed viral clearance [[58]14,[59]15]. On the contrary, some reports also provided evidence that asymptomatic SARS-CoV-2 infected individuals do not exhibit weakened antiviral immunity, but develop effective virus-specific immune responses instead [[60]16]. Innate immunity, as a front-line guard of alerting the host defensive mechanism, sensing of SARS-CoV-2 by a variety of host pattern recognition receptors (PRRs) that include membrane receptors such as the Toll-like receptors (TLRs), and cytosolic receptors such as the NOD-like receptors (NLRs), which trigger the production of proinflammatory cytokines such as interferons (IFNs), and inflammatory cell death such as pyroptosis [[61]17]. Pyroptosis is characterized by the loss of plasma membrane integrity, which results in lytic cell death and prevents microbial spread, and is caused by the activation of inflammasome sensors. Inflammasomes are cytoplasmic multiprotein complexes that are assembled by a sensor protein, in some situations, an adapter protein, and inflammatory caspases. Inflammasome complexes are classified by their protein domain structures into the NLR family such as (NLR family pyrin domain containing) NLRP subfamily, and non-NLR inflammasomes such as pyrin, absent in melanoma 2 (AIM2) and interferon gamma-inducible protein 16 (IFI16) inflammasomes [[62]18,[63]19]. Recently, there have been numerous reports of the NLRP3 inflammasome as a critical mediator in the activation of inflammatory macrophages, which releases mature forms of the pro-inflammatory cytokines interleukin-1 beta (IL-1β) and IL-18 and triggers pyroptosis through the activation of caspase-1 (CASP1) in severe COVID-19 [[64][20], [65][21], [66][22]]. In general, early innate immune-mediated inflammatory responses play a critical role in protecting hosts from viral infections, while late inflammatory responses can cause tissue damage and organ dysfunction if not controlled [[67]17]. Besides, many models have been proposed to explain how immune cell and molecule dynamics affect SARS-CoV-2 infiltration and disease progression [[68][23], [69][24], [70][25]]. Apart from proteins, long-noncoding RNAs (lncRNAs) are now known to regulate immune responses through a variety of mechanisms to eradicate viral infections [[71]26,[72]27]. For instance, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1) have been reported dysregulated in the lung and peripheral blood mononuclear cell (PBMC) samples of severe COVID-19 patients and SARS-CoV-2 infected cells could be associated with innate immune responses and inflammation development [[73][28], [74][29], [75][30], [76][31]]. However, the functions and roles of lncRNAs in the asymptomatic SARS-CoV-2 infection remain uncharacterized. In this study, to explore the immunological features associated with the asymptomatic and mild to moderate SARS-CoV-2 infection, we performed strand-specific RNA-seq using PBMCs from 26 SARS-CoV-2 infected individuals and 13 healthy donors. Our results for the first time reveal that pyroptosis is mediated by proline-serine-threonine phosphatase-interacting protein 1 (PSTPIP1)-dependent pyrin inflammasomes in asymptomatic COVID-19. In addition, lncRNA RP11-797A18.6 was found negatively correlated with the expression of PSTPIP1. Functional prediction revealed that RP11-797A18.6 might regulate PSTPIP1 through a direct binding on its genomic sequences, indicating RP11-797A18.6 might be considered as a regulator controlling PSTPIP1 transcription and pyroptosis. 2. Methods 2.1. Patients Twenty-six individuals diagnosed with SARS-CoV-2 infection and 13 healthy donors were enrolled from the three hospitals including Guangzhou Eighth People's Hospital, Shunde Hospital of Guangzhou University of Chinese Medicine, and Fourth People's Hospital of Foshan from April to May in 2020. All participants or their surrogates provided written informed consent. According to the Guidelines for the Diagnosis and Treatment of COVID-19 (7th edition) of National Health Commission of China, patients who had positive SARS-CoV-2 RT-PCR tests or specific blood IgM antibodies but without clinically recognizable symptoms, were diagnosed with asymptomatic COVID-19, and patients with mild cases displayed mild clinical symptoms with no radiological findings of pneumonia. Moderate cases are characterized by fever (temperature above 37.3 °C) and respiratory symptoms with radiological findings of pneumonia. In our study, ten cases were diagnosed with asymptomatic COVID-19, and 5 cases were defined as mild symptoms, 11 cases were defined as moderate symptoms. The demographics and clinical features of all the groups are summarized in Additional file 1: [77]Table S1. Among the 26 cases, ten asymptomatic individuals at a median age of 36.5, and 16 symptomatic patients at a median age of 33. No significant differences in age or gender were found between the healthy control group and any of the infection groups. The PBMC samples were collected from all the infected individuals and healthy donors within 4 days following the exact diagnosis. 2.2. The strand-specific RNA sequencing and data processing PBMCs were isolated by density gradient centrifugation, followed by total RNA isolation and purification according to the manufacturer's procedure. The quality and yield of the isolated RNA were evaluated using an Agilent 2100 Bioanalyzer. After ribosomal RNA removal, strand-specific RNA sequencing libraries were generated using VAHTS® Universal V6 RNA-seq Library Prep Kit for Illumina (Vazyme, cat.NR604-02, Nanjing, China) and sequenced in 150 bp paired-end mode on Illumina NovaSeq 6000 at Guangzhou Huayin Medical Laboratory Center Ltd. (Guangzhou, China). The summary of sample quality control and RNA-seq outcomes were presented in Additional file 2: [78]Table S2. Cutadapt (v1.16) and SOAPnuke were used to trim the adaptor reads, repeat reads and low-quality reads [[79]32,[80]33], followed by rRNA removal using bowtie 2 (v2.2.9) with default settings [[81]34]. Trimmed reads were mapped to human genome annotation hg19 using Hisat (v2-2.0.5) [[82]35]. The expression levels of transcripts were calculated using StringTie-eB (v1.3.3b) and normalized to Fragments Per Kilobase Per Million reads (FPKM) [[83]36], and the low-abundance transcripts with FPKM value < 0.5 were discarded. LncRNA identification was based on the criteria that more than two exons, bases greater than 200 bp, and locations do not overlap with mRNAs. The known lncRNAs were annotated using the Cuffcompare (v2.1.1). Further, CPC (cpc-0.9-r2) and Pfam-scan (v1.3) were used to predict the unknown transcripts [[84]37]; the transcripts identified by one tool as having coding potential were classified as the transcripts of uncertain coding potential (TUCPs), while those identified by both tools as having no coding potential were predicted as novel lncRNAs. The Ballgown package in R was used to filter the differentially expressed transcripts with a fold change of >1.5 (|log 2 fold change| > 0.5849625) and a p-value of <0.05 [[85]38]. 2.3. Construction of Co-expression modules and networks analysis For constructing co-expression networks, we have used the weighted gene co-expression network analysis (WGCNA) method [[86]39], which integrates modules with similar eigengene expression profiles. The option for the soft threshold parameter was set at 14 to achieve a scale-free topology fit threshold >0.85. Then, we obtained 43 modules of genes, followed by module merging with a dissimilarity threshold of 0.25. A heatmap was used to find modules of interest that were significantly related to clinical traits, Modules correlated with Asy traits with |cor| >0.35 and p-value <0.05 were considered as the hub modules of As. After the identification of the hub modules, transcripts derived from the collected hub modules with gene significance (GS) > 0.2 and module membership (MM) > 0.8 were marked as hub transcripts including hub protein coding genes (PCGs) and hub lncRNAs. The hub transcripts that related to the five key pathways of each six hub modules were used to visualize the PCGs-lncRNAs-pathways networks using the Cytoscape (v3.9.1). 2.4. Functional enrichment of protein coding genes Gene functional enrichment analysis was performed using clusterProfiler R package to map DE PCGs to pathways and terms against the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) database, respectively [[87]40]. A cut-off of p-value <0.05 was applied to select the significant KEGG pathways and GO terms, of which top enriched pathways and terms were visualized using the ggplot 2 R package. 2.5. Prediction of the interplay between protein coding genes and lncRNAs The co-located target genes of cis-acting lncRNAs were predicted by searching for genomic regions at 100,000 bp upstream and downstream of the lncRNAs. Silencers with the nearest PCGs were predicted using the SilencerDB tool. The lncRNA-DNA interaction prediction of lncRNA RP11-797A18.6 and its co-located gene PSTPIP1 was based on RNAplex. 2.6. Statistical analysis Statistical analysis was performed using R version 4.1.2. Demographic data on age and gender were compared using Fisher's exact test. Pearson's correlation coefficient was used to calculate the relationship between the expression of lncRNAs and that of PCGs. A p-value <0.05 was considered significant. 3. Results 3.1. Differential transcription profiles of healthy controls, asymptomatic and symptomatic COVID-19 individuals To define the transcriptome responses to the severity of COVID-19, we conducted strand-specific RNA-seq on PBMCs collected from asymptomatic and symptomatic individuals with SARS-CoV-2 infection and healthy donors. An overview of the study design, consisting of the sample distribution and transcriptomic analysis, is shown in [88]Fig. 1. The differentially expressed transcripts among different groups were identified based on the Ballgown R package under the conditions of foldchange >1.5 and p-value <0.05. The landscape of transcriptional dysregulation in individuals with different disease severity associated with infections by SARS-CoV-2 was presented in [89]Fig. 2. Globally, severer disease resulted in an increase in the number of both up-regulated and down-regulated transcripts ([90]Fig. 2A), displaying a preferential up-regulation pattern where the PCGs were more abundant, according to an analysis of the distinct types of transcripts ([91]Fig. 2B–C). Additionally, both the asymptomatic and symptomatic groups exhibited a larger fraction of down-regulated transcripts represented by lncRNAs in comparison with the healthy controls (Additional file 3: [92]Fig. S1). A Venn diagram of the transcripts of PCGs and lncRNA detected for each of the three comparison groups was created ([93]Fig. 2D–E). The results showed that common differentially expressed transcripts including 131 PCGs and six lncRNAs were defined to be associated with asymptomatic SARS-CoV-2 infections at high risk for progression to symptomatic infections. Fig. 1. [94]Fig. 1 [95]Open in a new tab Research workflow. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PCG, protein coding genes; WGCNA, weighted gene co-expression network analysis. Fig. 2. [96]Fig. 2 [97]Open in a new tab Differential expression patterns of protein coding genes and lncRNAs in COVID-19 infections. A, number of differentially expressed transcripts observed in Asy and Sym individuals in comparison with the Con group, and that in Sym comparing to Asy individuals. Number of the different types of up-regulated (B) and down-regulated (C) transcripts in Asy and Sym individuals in comparison with the Con group, and that in Sym comparing to Asy individuals. Venn diagrams considering DE PCGs (D) and lncRNAs (E) comparing the groups “Con vs Asy”, “Con vs Sym” and “Asy vs Sym”. Asy, asymptomatic; Con, healthy controls; DE PCGs, differentially expressed protein coding genes; Sym, symptomatic; TUCPs, transcripts of uncertain coding potential. We performed KEGG pathway enrichment analysis of the differentially expressed protein coding genes (DE PCGs) in the three comparison groups to explore the specific changes in different disease severity after SARS-CoV-2 infections ([98]Fig. 3A–C). The most represented enrichments that DE PCGs involved in among the top 20 pathways according to the p-value influenced by the asymptomatic and symptomatic infections were associated with pathway clusters such as Folding, sorting and degradation, Development and regeneration, and Immune system. In particular, pathways of the Immune system including the C-type lectin receptor signaling pathway and NOD-like receptor signaling pathway were more robust in both the asymptomatic and symptomatic infections. Interestingly, the NOD-like receptor signaling pathway is the only pathway for the Immune system among the top 20 enrichments in the symptomatic compared with the asymptomatic COVID-19 individuals ([99]Fig. 3C). Furthermore, GO biological processes enrichment analysis of the DE PCGs in the three comparison groups was also conducted. We found that both the asymptomatic and symptomatic groups shared the same terms of biological processes when compared to the healthy controls, such as neutrophil activation, neutrophil degranulation, and neutrophil mediated immunity ([100]Fig. 3D–F). Besides, biological processes of viral gene expression/transcription and I-kappaB kinase/NF-kappaB signaling were indeed changed in the asymptomatic infections in comparison to the healthy controls. Instead, when we compared the asymptomatic and symptomatic infections, we found that enrichment in terms of translational initiation, protein targeting to endoplasmic reticulum, and SRP-dependent cotranslational protein targeting to membrane were more robust ([101]Fig. 3F). Overall, these functional analysis results above suggested that immune dysregulation is prevalent not only in symptomatic COVID-19 patients but also in asymptomatic individuals. Fig. 3. [102]Fig. 3 [103]Open in a new tab KEGG and GO enrichment analysis of differentially expressed protein coding genes (DE PCGs) in COVID-19 infections. A-C, the top 20 significantly enriched KEGG pathways of DE PCGs in the “Con vs Asy”, “Con vs Sym” and “Asy vs Sym” groups, respectively. D-F, the top 20 significantly enriched GO terms of DE PCGs in the “Con vs Asy”, “Con vs Sym” and “Asy vs Sym” groups, respectively. Asy, asymptomatic; Con, healthy controls; DE PCGs, differentially expressed protein coding genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; Sym, symptomatic. 3.2. Weighted co-expression network and gene function associated with asymptomatic SARS-CoV-2 infections In addition, to obtain the asymptomatic associated characteristics depending on multi-methods, we further used the WGCNA package to identify the patterns of gene enrichment and connectivity. The optimal soft threshold parameter was set as 14 due to it met an approximate scale-free topology. A cluster dendrogram depicts the original clustered modules that each represented a collection of eigengenes, as well as the 43 merged modules, which are detailed in the Additional file 4: [104]Fig. S2. The correlation coefficients between the modules and the traits under the degree of COVID-19 severity are shown in the form of a heatmap, in which red denoted a positive relationship and green denoted a negative one ([105]Fig. 4A). Six modules including MEpaleturquoise, MEpalevioletred3, MEdarkslateblue, MEblack, MEwhite, and MEsalmon2 were selected as hub modules correlated with the asymptomatic trait following the criteria: |cor| >0.35 and p-value <0.05. The MEblack module was composed of more highly correlated mRNAs and lncRNAs with the asymptomatic trait ([106]Fig. 4B). Then, we extracted the transcripts from the collected six hub modules and performed KEGG pathways enrichment analysis. To investigate the key pathways associated with asymptomatic infections, the KEGG pathway enrichment patterns of Ballgown and WGCNA methods screened asymptomatic-related gene sets were then merged in a combined bubble plot ([107]Fig. 4C). Both methods revealed a relationship between asymptomatic and enrichment in pathways of Coronavirus disease-COVID-19, Protein processing in endoplasmic reticulum, Spliceosome, NOD-like receptor signaling pathway, and Ubiquitin mediated proteolysis, excluding other disease-related pathways. Fig. 4. [108]Fig. 4 [109]Open in a new tab Weighted specific gene-modules, co-expression network, and gene function associated with asymptomatic SARS-CoV-2 infections. A, heatmap of the correlation between module eigengenes and traits including Con, Asy, and Sym; Modules significantly associated with the Asy traits with identified with |cor| > 0.35 and p-value <0.05, and are indicated by asterisks. B, number of the different types of transcripts observed in six hub modules. C, As-associated enrichment in the top 20 significant KEGG pathways identified by the Ballgown and WGCNA methods. D, a PCGs-lncRNAs-pathway network was constructed with 56 hub lncRNAs and 48 hub PCGs that were involved in five KEGG pathways; triangle nodes represent hub lncRNAs, square nodes represent hub PCGs and circle nodes represent pathways; the purple, pink, orange, blue and green nodes represent MEblack, MEdarkslateblue, MEpaleturquoise and MEwhite modules, and all pathways, respectively. Asy, asymptomatic; Con, healthy controls; PCGs, protein coding genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; Sym, symptomatic; TUCPs, transcripts of uncertain coding potential; WGCNA, weighted gene co-expression network analysis. (For interpretation of the references to color in this figure legend,