Impairment of host adaptive immune response to Candida albicans infection: an in-silico insight on SARS-CoV-2 conspiracy

doi:10.21203/rs.3.rs-1644512/v1

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Impairment of host adaptive immune response to Candida albicans infection: an in-silico insight on SARS-CoV-2 conspiracy

https://doi.org/10.21203/rs.3.rs-1644512/v1

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Recent evidence reported that SARS-CoV-2 infections might trigger serious consequences in otherwise asymptomatic Candida albicans infections. To elucidate the mechanism of such co-infections, we have reconstructed Protein-protein interaction network (PPIN_res)with the upregulated genes in blood of C. albicans infected asymptomatic patients and identified 55 Core Genes (CGs). Functional enrichment of CGs evidenced their potential to resist fungal infection. With CGs, Gene Regulatory Network (GRN_res) was reconstructed with TF-TF, TF-CG, CG-CG interactions and it exhibited 7 Articulation Points (APs). Mapping these TF-CG interactions with DEGs of COVID-19 revealed that 460 TF-CG pairs and 4 APs are downregulated. Subsequently, functional and pathway enrichment analysis of these gene pairs was performed. Our overall results suggest that weakening of adaptive immune response might pave the path for C. albicans superinfections in COVID-19 patients.

SARS-CoV-2

Candida albicans

Differentially expressed genes

Transcription Factors

Module core genes

Articulation point

immune response

Since the emergence of SARS-CoV-2 infections, research has been focused on organotropism of SARS-CoV-2 beyond the lungs to identify several co-morbidities that might aggravate serious consequences during the disease (Richardson et al. 2020; Das and Podder 2021). But recently, case studies and hospital data reported an alarming increase in fungal co-infections associated with SARS-CoV-2 (Chen et al. 2020). Invasive fungal infections (IFI) have a high mortality rate with approximately 90% of the deaths being caused by infections with Aspergillus spp., Candida spp., Cryptococcus spp. and Pneumocystis spp. (Brown et al. 2012). For hospital admitted COVID-19 patients, the cases of IFI have been ranging between 4–27% (Chen et al. 2020) and it was reported that 14% of SARS-CoV-2 infected patients were tested positive for C. albicans and C. parapsilosis (Segrelles-Calvo et al. 2021). Invasive candidiasis has a high mortality rate ranging between 36–63% (Kullberg et al. 2011) and thus systemic co-infections of Candida in patients with COVID-19 might be lethal.

C. albicans are polymorphic fungi that thrive without any pathological relevance in majority of people (Neville et al. 2015). However, under certain circumstances, C. albicans can cause life-threatening systemic infections (Tsui et al. 2016). It was reported a distinctive host immunological pathway prevents development of systemic candidiasis (Richardson and Moyes 2015). During C. albicans infections, binding of major histocompatibility complexes (MHCs) on antigen presenting cells (APCs) to T-cell receptors (TCRs) causes release of IL-6 IL-23, IL-1β and TGFβ which differentiates naïve T helper (Th) cells to mature Th17 cells (Richardson and Moyes 2015). This MHC-TCR binding is reinforced by binding of CD28 of naïve Th cells with CD80 on APCs. Thus, dysregulation of this T-cell differentiation pathway in immunocompromised individuals leads to progression of life threatening systemic candidiasis (Lanternier et al. 2013). Concurrently, experiments conducted by Moser et al. showed co-infection with C. albicans in the blood from SARS-CoV-2 infected patients resulted in downregulation of CD80 on monocytes and also lessened the release of IL-10, IL-6, TNF, IL-1α, and IL-1β like cytokines that are responsible for T-cell differentiation (Moser et al. 2021). This observation indicates that immune response is dampened due to viral infections which facilitate systemic candidiasis. Thus, we sought to identify impaired immunomodulatory landscape which might be paving the path for systemic candidiasis in SARS-CoV-2 infected individuals. Using integrated omics and network biology approach, we have revealed that during COVID-19 downregulation of several key molecular players implicating host defense against fungal infection might enhance the possibilities of life-threatening systemic candidiasis. Prospective therapeutic strategies targeting network APs and key players of immunological pathway could restrain the fatalities of such superinfections.

2.1. Data processing and identification of DEGs

We used 4 datasets for the extraction of upregulated DEGs in C. albicans infected human blood- GSE154911(Bruno et al. 2020), GSE42606(Smeekens et al. 2013), GSE162746(Bruno et al. 2021)deposited in NCBIand supplementary data from Kammer et al.(Kämmer et al. 2020). For GSE154911, we considered two each control and test samples (4 hours postC.albicans infection) from donors B and C. In GSE42606, we considered 24 and 30 replicates for test and control samples respectively. For GSE162746, we considered 8 samples each for test and control.Lastly we considered upregulated DEGs reported in the study by Kammer et al. (Kämmer et al. 2020) for our analysis. For DEGs identification from each dataset, we have considered only 240 min post C.albicans infection samples for the analysis.

In case of COVID-19, we have extracted downregulated DEGs from the blood of COVID-19 patients using microarray data deposited in GSE164805(Zhang et al. 2021), GSE171110(Lévy et al. 2021), and GSE152418(Arunachalam et al. 2020). In GSE164805, we have considered 5 healthy and 5 each samples from mild and severe cases of COVID-19. While in GSE171110, we have considered 10 healthy and 44 severe COVID-19 patient samples and in GSE152418, we have considered samples from 4 moderate, 8 severe and 4 ICU admitted COVID-19 patients with 17 healthy samples for DEGanalysis. The DEGs from RNA-Seq and microarray data were identified using DESeq2 (Love et al. 2014) in R v4.1.0 and GEO2R analyzer in NCBI (Davis and Meltzer 2007) respectively. A cut off of |log₂FC| >1.0 and adj. P value < 0.05 were considered across all thedatasets.

2.2. Reconstruction of PPIN_res and identification of module core genes (CGs)

To build PPIN_reswith upregulated DEGs, we have used physical interactions with a confidence level > 0.4 in STRING v11.5(Szklarczyk et al. 2019). For identifying the CGs in the PPIN_res, we used ModuLand (Szalay-Bekő et al. 2012) plugin in Cytoscape 3.8.2 (Shannon et al. 2003). The modularity of a network is determined by adding together impact zones encompassing a certain edge and calculating community centrality in the ModuLand framework. (Kovács et al. 2010; Szalay-Bekő et al. 2012).

2.3. Reconstruction of GRN_res and determining articulation points (APs)

The GRN_res was reconstructed using three sets of interactions: TF-CG, TF-TF and CG-CG interactions. Physical interactions for TF-TF and CG-CG with confidence level > 0.4 were retrieved from STRING v11.5(Szklarczyk et al. 2019) while TF-CG interactions were retrieved from three databases containing experimental TF binding evidences i.e., TRRUST V.2 (Han et al. 2018), RegNetwork(Liu et al. 2015) and hTFtarget(Zhang et al. 2020). Next, articulation points (APs) in GRN_res, were identified using igraph package (Csardi and Nepusz 2005) in R v4.1.0.

2.4. Functional and pathway enrichment analysis

Functional and pathway enrichment analysis were performed using Gene Ontology Annotation (GOA)(Camon et al. 2004), EnrichR(Kuleshov et al. 2016; Xie et al. 2021) and Database for Annotation, Visualization and Integrated Discovery (DAVID)(Huang et al. 2009; Sherman et al. 2022), clusterProfiler (Wu et al. 2021)and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (Kanehisa et al. 2017) databases by considering P value/adj. P value/FDR < 0.05.For functional enrichment analysis we used commonly enriched GO Biological Processes from DAVID, GOA database, clusterProfiler v4.0 and for pathway enrichment we considered common KEGG pathways from DAVID and enrichR.

3.1. Reconstruction of PPIN_res with differentially expressed genes in C. albicans infected human blood

Since, the host immune response keeps infections of C. albicans under control (Kumamoto et al. 2020), so we initially aimed to investigate the genes that are being upregulated during C. albicans infection to provide resistance against it. To proceed with our analysis, we have analyzed three GEO datasets from peripheral blood mononuclear cells (PBMC)- GSE154911(Bruno et al. 2020), GSE42606(Smeekens et al. 2013) and GSE162746(Bruno et al. 2021) and one supplementary data extracted from Kämmer et. al.(Kämmer et al. 2020) of C. albicans infected healthy individuals by considering a cut off of log₂FC > 1.0 and adj. P value < 0.05 (Supplementary File 1). As it was reported that human immune response is maximally elicited within 4 hours of C. albicans infection (Kämmer et al. 2020), so we have identified upregulated genes in blood after 4 hrs. of C. albicans infection across all datasets. Then, we have screened upregulated DEGs from all these datasets and prepared an exhaustive list of 1282 upregulated DEGs for our downstream analysis. It could be speculated that these upregulated genes might be involved in complex signaling cascade for providing host resistance against C. albicans infection. Subsequently, we have reconstructed a protein-protein interaction network (PPIN_res) consisting of 556 nodes and 1007 edges (Fig. 1A). Since the PPIN_resis implicated for host defense, perturbation of key nodes that depict function of the whole network would alter immunological response of healthy individuals against candidiasis (Mihalik and Csermely 2011). To identify such key nodes in PPIN_res, we have identified 55 module core genes (CGs) (Fig. 1A; Supplementary File 1). Functional enrichment analysis of the CGsshowed that GO Biological Processes like – “cellular response to interleukin-1” (GO:0071347) and “response to stress” (GO:0006950) (Fig. 1B) were enriched. So, it could be inferred that the module core genes in PPIN_res play significant roles in eliciting effective host immune response to Candida infections.

3.2. Reconstruction of Gene Regulatory network (GRN_res) associated with module core genes of PPIN_res

In response to the environmental signals, cells modulate gene expression via transcriptional regulatory pathways. Thus, initially we have identified 167 transcription factors (TFs) that might control the expression of 52 module core genes (CGs) of PPIN_resby integrating transcriptional regulatory elements data of genes from three databases - TRRUST(Han et al. 2018), RegNetwork(Liu et al. 2015) and hTFtarget(Zhang et al. 2020). Next, we have considered three sets of interactions i.e., TF-CG interactions along with TF-TF and CG-CG interactions to reconstruct a gene regulatory network (GRN_res). The GRN_res (Fig. 2) consisting of 218 nodes and 2421 edges. Now, it was observed that specific nodes within this GRN could act as articulation points (APs) because they are highly connected in a network and so perturbation of these nodes disturb network topologysignificantly (Italiano et al. 2012). Consequently, we have identified 7 APs within GRN_res (Fig. 2) and hypothesized that perturbation of these 7 APs might weaken the defense mechanism of healthy individuals against C. albicans infections.

3.3. Investigating the impact of SARS-CoV-2 infection on host immune response to C. albicans superinfections

Recent evidences suggest that SARS-CoV-2 infection aggravates fatal outcomes of the otherwise asymptomatic cases of C. albicans like opportunistic pathogens (Salehi et al. 2020). To unravel the reason of symptomatic candidiasis in COVID-19 cases, we have analyzed GSE164805(Zhang et al. 2021), GSE171110(Lévy et al. 2021), and GSE152418(Arunachalam et al. 2020). In GSE164805, we have considered 5 samples each from healthy, mild and severe conditions of COVID-19. In GSE171110,we have considered 10 control and 44 test samples from severe conditionof COVID-19. For GSE152418, we have considered17 control samples with4, 8 and 4 samples from moderate, severe and ICU patients admitted due to COVID-19. For delving into the genomic landscape which is being suppressed in COVID-19 cases, we have identified downregulated DEGs by considering a cut off of log₂FC < -1.0 and adj.P value < 0.05 in above mentioned datasets and then merged them to make an exhaustive list of 11,390 DEGs(Supplementary File 2).

Since proper orchestration of immunological pathways safeguards healthy individuals from C. albicans infections (Richardson and Moyes 2015), so it could be assumed that jeopardization of immunological response due to downregulation of vital regulatory interactions during SARS-CoV-2 infection might be responsible for triggering magnified fungal superinfections. Thus, to explicate whether any of the crucial genes of host defense cascade against C. albicanscan become downregulated during COVID-19, we have mapped TF-CG pairs from GRN_reswith COVID-19 down DEGs. We have found that 68 TFs and 51 CGs which are involved in 460 TF-CG interactions are downregulated in COVID-19 cases (Fig. 3). We performed functional and pathway enrichment analyses of downregulated TF-CG pairs to infer whichimmunological functions and pathways are hampered during SARS-CoV-2 infection(Supplementary File 3). For functional enrichment analysis we used commonly enriched GO Biological Processes from DAVID (Sherman et al. 2022), GOA database (Camon et al. 2004), clusterProfiler v4.0 (Wu et al. 2021) and common pathway enrichment results from DAVID (Sherman et al. 2022) and enrichR (Xie et al. 2021).The downregulated TF-CG pairs showed enrichment of GO Biological Processes like “regulation of T cell mediated immunity” (GO:0002711), “regulation of adaptive immune response” (GO:0002819), “CD4-positive, alpha-beta T cell differentiation” (GO:0043370), “T cell differentiation involved in immune response” (GO:0002292) and KEGG pathways like “NF-kappa B signaling pathway”(hsa04064), “Notch signaling pathway” (hsa04330), “IL-17 signalling pathway” (hsa04657) and “Toll-like receptor signaling pathway” (hsa04620) (Fig. 4). So, it could be inferred that T lymphocyte mediated adaptive immune response is being jeopardized due to SARS-CoV-2 infection. Interestingly, we also have found that out of 7APs in GRN_res, 4 APs i.e., HES1, CCR4, CEBPB and NFKB1are down regulated in COVID-19. Together these results demonstrate that deregulation of key genes in COVID-19 patients might impair T-cell differentiation pathway which in turn could weaken the host adaptive immune response to Candida infections.

After the advent of COVID-19, the rate of invasive candidiasis in SARS-CoV-2 infected patients has increased drastically and this has led to severe fatalities associated with co-infections (Pemán et al. 2020; Song et al. 2020). As the host immune response normally keeps development of systemic candidiasis at bay, thus it could be hypothesized that alterations in the host immune response during COVID-19 might be enhancing possibilities of this fungal co-infections. To proceed with our analysis, first we have extracted the upregulated genes in the blood of healthy humans which helps to combat C. albicans infections. Then, we have reconstructed PPIN_res(Fig. 1A) and determined module core genes (CGs) (Fig. 1B; Supplementary File 1). As CGs determine the function of PPIN_res rather than its centrality (Kovács et al. 2010), so functional enrichment of CGs with immune response related functions like- “cytokine response” (Fig. 1A) resembled that PPIN_resconstitute an intricate network of immunological signaling that confers resistance against Candida infections (Chin Kin et al. 2014; Griffiths et al. 2021). Notable CGs included -SRC, which has the ability to activate NFAT (nuclear factor of activated T cells) that aids T cell development (Kalia et al. 2021) and TRAF3 which elicits IL-17-induced signaling to prevent mucosal C. albicans infection (Zhu et al. 2010; Mengesha and Conti 2017). Thus, the CGs could be regarded as key players in the immunological signaling against C. albicans infections. Now, this immune response is being governed by interplay between TFs that regulate pathogen specific response. So, we reconstructed GRN_res, to identify transcription factors (TFs) regulating the CGs (Fig. 2). Important TF-CG interactions include IRF1 and its interactor SLP1 which are required for CD8⁺ T cell differentiation (Zhang and Tsung 2017) and functioning of innate immune response (Williams et al. 2006) respectively. We also have detected a total of 7 network APs among which HES1, aids early T cell development in the thymus via the Notch signaling pathway(Seo and Taniuchi 2016). Since perturbation of APs might affect the network topology significantly (Tian et al. 2017), so subtle alterations in any of them might dampen the immune response against C. albicans infections to a greater amount.

Now, downregulation of these TF-CG interactions in GRN_resduring COVID-19 could weaken host immune response and thus permit C. albicans co-infections. So, we initially identified downregulated genes in PBMCs of COVID-19 patients (Supplementary File 2) and mapped the immunomodulatory interactions of GRN_reswith the downregulated gene pool of COVID-19. Among the TF-CG interactions (Fig. 3), notable interactions like ATF3-STX4 pair is found to be downregulated. Downregulation of such immunological mediators during COVID-19 could be altering host immune response because ATF3 was reported to regulate Th1 differentiation (Filén et al. 2010),while knockdown of STX4 reduces the cytotoxicity of human cytotoxic T cells (Spessott et al. 2017). Thus, we subsequently performed functional and pathway enrichment analysis of downregulated TF-CG pairs during COVID-19 (Fig. 4; Supplementary File 3). Our analysis showed that COVID-19 might disturb cell-mediated adaptive immune responseswhich could increase host’s susceptibility to Candida albicans infections.

Th17 and Treg subtypes particularly are indispensable for adaptive immunological response against candidiasis (Scheffold and Bacher 2020).Immunomodulatory signaling by IL-2R/STAT pathway, TGF-β/Smad and Notch pathway were evidenced to regulate Treg function for clearing of Candida albicans(Wang et al. 2022). To differentiate into Th17 subtype, TCRs activate IP₃-DAG, MAPK and notch pathway to subsequently localize NF-κB (Krishna and Zhong 2013). IP₃-DAG and MAPK pathway was also evidenced to activate AP-1 to mediate downstream signaling for T-cell differentiation. Activation of NF-κB and AP-1 mediated signalling triggers secretion of IL23, IL1β, TGFβ (Fujioka et al. 2004) which terminally diffrentiates naïve T-cells to Th17 cell type. We found in our analyses that both the subunits of AP-1 i.e. JUN and FOS, and NFKB1 are downregulated during COVID-19. Moreover, the chemokine receptor for Th17 i.e. CCR4(Acosta-Rodriguez et al. 2007) is also downregulated in PBMCs of COVID-19 patients (Fig. 5).

Tregs are also an important class of Th cells that regulate tissue specific diffrentiation of Th17 cells in candidiasis (Whibley and Gaffen 2014). Differentiation into this subtype requires stimulation of STAT5 by TCR (Welte et al. 1999). Activation of STAT5 activates FOXP3 which in turn forms a complex with RUNX1 to terminally differentiate naïve T-cells into Treg cells by secretion of IL2, IL15, IL16, IL-34and TGFβ(Zhang et al. 2008; Bézie et al. 2015; Apert et al. 2018; Hu et al. 2020). Moreover, RORγtcompetes with FOXP3 to bind with RUNX1 which if formed induces Th17 differentiation by releasing cytokines IL-17A, IL-17F, and IL-23(Ivanov et al. 2006; McGeachy and Cua 2008). Unusually, cytokines IL2, IL15, IL16, IL-34 and TGFβ for Treg differentiation and IL-17A, IL-17F, and IL-23A for Th17 differentiation are found to be downregulated during COVID-19 (Fig. 5). Not only the cytokines but other genes involved in these two T-cell differentiationsi.e., STAT5, FOXP3, RUNX1 and RORγt are also downregulated during COVID-19. Thus, weconcludethat Treg and Th17 differentiation pathways might be severely affected during COVID-19 which could make the patient palatable for Candida infections.

Our conclusions were further strengthened when we found that Notch signaling pathway, which alsohas the ability to decide fate of T cell differentiation to Treg and Th17 subtypes (Fig. 5)(Hosokawa and Rothenberg 2021), is affected due to COVID-19. T-cell differentiation by Notch signaling requires binding of JAG1 of APC to notch receptors Notch1/2 in T cell (Amsen et al. 2015).This activates GATA3 which in turn helps T-cell to differentiate into Tregby IL2 secretion(Wohlfert et al. 2011). Additionally, activation of NFκB and HES1 by notch signaling activates FOXP3 mediatedTreg function and development of Th17 cells by secreting IL17A respectively (Shin et al. 2014; Amsen et al. 2015; Ferrandino et al. 2018). Consequently, JAG1 ligand of APC along with GATA3 and HES1 from the notch signaling pathway were also found to be downregulated in COVID-19, Thus, disruption of IP₃-DAG, MAPK and Notch signaling mediated Th17 and Treg differentiation pathway due to COVID-19 might aggravate chances of invasive candidiasis.

Additionally, we also noticed that expressions of APs in GRN_res are suppressed during COVID-19. Strikingly, downregulated AP CEBPB was evidenced to be critical for pathogen clearance and host cell survival (Muñoz et al. 2019) in C. albicans infections. We also found NFKB1, HES1, CEBPB, CCR4 as downregulated APs which have already been shown in Fig. 5 to be involved in T-cell mediated adaptive immune response. Thus, during COVID-19, downregulation of these APs which exert important immunological functions to resist invasive candidiasis might be causing severity. Thus, experimental studies to elucidate their roles in conferring immunity against such co-infections should be conducted in future and also therapeutic procedures to optimize their expression should be designed to combat the disease.

In this communication, we have revealed an array of key molecular players in GRN_res of C. albicans infected healthy individual implicating for host defense mechanism against the microbe. In COVID-19, downregulation of some of these key players might jeopardize the otherwise healthy adaptive immune response that safeguards against C. albicans infection. Although this study identifies relevant targets for developing therapeutic strategies to combat such superinfection, but the study is limited as detailed experiments with co-infected C. albicans and SARS-CoV-2 infected models must be performed to validate these targets. This study also doesn’t consider patient heterogeneity i.e., sex ratio, age group and thus could be biased due to these factors.

DEG: Differentially Expressed Genes; CG: Module Core Gene; TF: Transcription Factor; AP: Articulation Point; PPIN: Protein- Protein Interaction Network; GRN: Gene Regulatory Network.

Author Contribution

SP designed the study and ARK performed the experiments. DD generated the figures. ARK and DD wrote the manuscript. SP helped in manuscript preparation and the work was completed under her supervision.

Acknowledgements

We are grateful to Raiganj University for providing the infrastructure to carry out this work.

Declaration of Competing Interest

The authors declare that there isn’t any conflict of interest with any organization or financial entity.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acosta-Rodriguez E V, Rivino L, Geginat J, et al (2007) Surface phenotype and antigenic specificity of human interleukin 17–producing T helper memory cells. Nat Immunol 8:639–646. https://doi.org/10.1038/ni1467
Amsen D, Helbig C, Backer RA (2015) Notch in T Cell Differentiation: All Things Considered. Trends Immunol 36:802–814. https://doi.org/10.1016/j.it.2015.10.007
Apert C, Romagnoli P, van Meerwijk JPM (2018) IL-2 and IL-15 dependent thymic development of Foxp3-expressing regulatory T lymphocytes. Protein Cell 9:322–332. https://doi.org/10.1007/s13238-017-0425-3
Arunachalam PS, Wimmers F, Mok CKP, et al (2020) Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science 369:1210–1220. https://doi.org/10.1126/science.abc6261
Bézie S, Picarda E, Ossart J, et al (2015) IL-34 is a Treg-specific cytokine and mediates transplant tolerance. J Clin Invest 125:3952–3964. https://doi.org/10.1172/JCI81227
Brown GD, Denning DW, Gow NAR, et al (2012) Hidden Killers: Human Fungal Infections. Sci Transl Med 4:165rv13-165rv13. https://doi.org/10.1126/scitranslmed.3004404
Bruno M, Dewi IMW, Matzaraki V, et al (2021) Comparative host transcriptome in response to pathogenic fungi identifies common and species-specific transcriptional antifungal host response pathways. Comput Struct Biotechnol J 19:647–663. https://doi.org/10.1016/j.csbj.2020.12.036
Bruno M, Kersten S, Bain JM, et al (2020) Transcriptional and functional insights into the host immune response against the emerging fungal pathogen Candida auris. Nat Microbiol 5:1516–1531. https://doi.org/10.1038/s41564-020-0780-3
Camon E, Magrane M, Barrell D, et al (2004) The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 32:D262-6. https://doi.org/10.1093/nar/gkh021
Chen N, Zhou M, Dong X, et al (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395:507–513. https://doi.org/10.1016/S0140-6736(20)30211-7
Chin Kin V, Foong Jeang K, Maha A, et al (2014) Early expression of local cytokines during systemic Candida albicans infection in a murine intravenous challenge model. Biomed Rep 2:869–874. https://doi.org/10.3892/br.2014.365
Csardi G, Nepusz T (2005) The Igraph Software Package for Complex Network Research. InterJournal Complex Sy:1695
Das D, Podder S (2021) Unraveling the molecular crosstalk between Atherosclerosis and COVID-19 comorbidity. Comput Biol Med 134:104459. https://doi.org/https://doi.org/10.1016/j.compbiomed.2021.104459
Davis S, Meltzer PS (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23:1846–1847. https://doi.org/10.1093/bioinformatics/btm254
Ferrandino F, Grazioli P, Bellavia D, et al (2018) Notch and NF-κB: Coach and Players of Regulatory T-Cell Response in Cancer. Front Immunol 9:2165. https://doi.org/10.3389/fimmu.2018.02165
Filén S, Ylikoski E, Tripathi S, et al (2010) Activating Transcription Factor 3 Is a Positive Regulator of Human <em>IFNG</em> Gene Expression. J Immunol 184:4990 LP – 4999. https://doi.org/10.4049/jimmunol.0903106
Fujioka S, Niu J, Schmidt C, et al (2004) NF-kappaB and AP-1 connection: mechanism of NF-kappaB-dependent regulation of AP-1 activity. Mol Cell Biol 24:7806–7819. https://doi.org/10.1128/MCB.24.17.7806-7819.2004
Griffiths JS, Camilli G, Kotowicz NK, et al (2021) Role for IL-1 Family Cytokines in Fungal Infections. Front Microbiol 12:633047. https://doi.org/10.3389/fmicb.2021.633047
Han H, Cho J-W, Lee S, et al (2018) TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46:D380–D386. https://doi.org/10.1093/nar/gkx1013
Hosokawa H, Rothenberg E V (2021) How transcription factors drive choice of the T cell fate. Nat Rev Immunol 21:162–176. https://doi.org/10.1038/s41577-020-00426-6
Hu X, Huang F, Deng Y, et al (2020) The multifactorial roles of IL-34 in immune responses. Ann Blood; Vol 5 (March 2020) Ann Blood
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211
Italiano GF, Laura L, Santaroni F (2012) Finding strong bridges and strong articulation points in linear time. Theor Comput Sci 447:74–84. https://doi.org/https://doi.org/10.1016/j.tcs.2011.11.011
Ivanov II, McKenzie BS, Zhou L, et al (2006) The orphan nuclear receptor RORgammat directs the differentiation program of proinflammatory IL-17 + T helper cells. Cell 126:1121–1133. https://doi.org/10.1016/j.cell.2006.07.035
Kalia N, Singh J, Kaur M (2021) The role of dectin-1 in health and disease. Immunobiology 226:152071. https://doi.org/https://doi.org/10.1016/j.imbio.2021.152071
Kämmer P, McNamara S, Wolf T, et al (2020) Survival Strategies of Pathogenic Candida Species in Human Blood Show Independent and Specific Adaptations. MBio 11:. https://doi.org/10.1128/mBio.02435-20
Kanehisa M, Furumichi M, Tanabe M, et al (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092
Kovács IA, Palotai R, Szalay MS, Csermely P (2010) Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics. PLoS One 5:. https://doi.org/10.1371/journal.pone.0012528
Krishna S, Zhong X (2013) Role of diacylglycerol kinases in T cell development and function. Crit Rev Immunol 33:97–118. https://doi.org/10.1615/critrevimmunol.2013006696
Kuleshov M V, Jones MR, Rouillard AD, et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90-7. https://doi.org/10.1093/nar/gkw377
Kullberg BJ, Verweij PE, Akova M, et al (2011) European expert opinion on the management of invasive candidiasis in adults. Clin Microbiol Infect 17:1–12. https://doi.org/10.1111/j.1469-0691.2011.03615.x
Kumamoto CA, Gresnigt MS, Hube B (2020) The gut, the bad and the harmless: Candida albicans as a commensal and opportunistic pathogen in the intestine. Curr Opin Microbiol 56:7–15. https://doi.org/https://doi.org/10.1016/j.mib.2020.05.006
Lanternier F, Cypowyj S, Picard C, et al (2013) Primary immunodeficiencies underlying fungal infections. Curr Opin Pediatr 25:736–747. https://doi.org/10.1097/MOP.0000000000000031
Lévy Y, Wiedemann A, Hejblum BP, et al (2021) CD177, a specific marker of neutrophil activation, is associated with coronavirus disease 2019 severity and death. iScience 24:102711. https://doi.org/10.1016/j.isci.2021.102711
Liu Z-P, Wu C, Miao H, Wu H (2015) RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database 2015:. https://doi.org/10.1093/database/bav095
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8
McGeachy MJ, Cua DJ (2008) Th17 cell differentiation: the long and winding road. Immunity 28:445–453. https://doi.org/10.1016/j.immuni.2008.03.001
Mengesha BG, Conti HR (2017) The Role of IL-17 in Protection against Mucosal Candida Infections. J fungi (Basel, Switzerland) 3:. https://doi.org/10.3390/jof3040052
Mihalik Á, Csermely P (2011) Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation. PLoS Comput Biol 7:e1002187. https://doi.org/10.1371/journal.pcbi.1002187
Moser D, Biere K, Han B, et al (2021) COVID-19 Impairs Immune Response to Candida albicans. Front. Immunol. 12
Muñoz JF, Delorey T, Ford CB, et al (2019) Coordinated host-pathogen transcriptional dynamics revealed using sorted subpopulations and single macrophages infected with Candida albicans. Nat Commun 10:1607. https://doi.org/10.1038/s41467-019-09599-8
Neville BA, d’Enfert C, Bougnoux M-E (2015) Candida albicans commensalism in the gastrointestinal tract. FEMS Yeast Res 15:. https://doi.org/10.1093/femsyr/fov081
Pemán J, Ruiz-Gaitán A, García-Vidal C, et al (2020) Fungal co-infection in COVID-19 patients: Should we be concerned? Rev Iberoam Micol 37:41–46. https://doi.org/10.1016/j.riam.2020.07.001
Richardson JP, Moyes DL (2015) Adaptive immune responses to Candida albicans infection. Virulence 6:327–337. https://doi.org/10.1080/21505594.2015.1004977
Richardson S, Hirsch JS, Narasimhan M, et al (2020) Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA 323:2052–2059. https://doi.org/10.1001/jama.2020.6775
Salehi M, Ahmadikia K, Badali H, Khodavaisy S (2020) Opportunistic Fungal Infections in the Epidemic Area of COVID-19: A Clinical and Diagnostic Perspective from Iran. Mycopathologia 185:607–611. https://doi.org/10.1007/s11046-020-00472-7
Scheffold A, Bacher P (2020) Anti-fungal T cell responses in the lung and modulation by the gut-lung axis. Curr Opin Microbiol 56:67–73. https://doi.org/https://doi.org/10.1016/j.mib.2020.06.006
Segrelles-Calvo G, de S Araújo GR, Llopis-Pastor E, et al (2021) < em > Candida</em > spp. co-infection in COVID-19 patients with severe pneumonia: Prevalence study and associated risk factors. Respir Med 188:. https://doi.org/10.1016/j.rmed.2021.106619
Seo W, Taniuchi I (2016) Transcriptional regulation of early T-cell development in the thymus. Eur J Immunol 46:531–538. https://doi.org/https://doi.org/10.1002/eji.201545821
Shannon P, Markiel A, Ozier O, et al (2003) Cytoscape: A Software Environment for Integrated Models. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303.metabolite
Sherman BT, Hao M, Qiu J, et al (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. https://doi.org/10.1093/nar/gkac194
Shin HM, Tilahun ME, Cho OH, et al (2014) NOTCH1 Can Initiate NF-κB Activation via Cytosolic Interactions with Components of the T Cell Signalosome. Front Immunol 5:. https://doi.org/10.3389/fimmu.2014.00249
Smeekens SP, Ng A, Kumar V, et al (2013) Functional genomics identifies type I interferon pathway as central for host defense against Candida albicans. Nat Commun 4:1342. https://doi.org/10.1038/ncomms2343
Song G, Liang G, Liu W (2020) Fungal Co-infections Associated with Global COVID-19 Pandemic: A Clinical and Diagnostic Perspective from China. Mycopathologia 185:599–606
Spessott WA, Sanmillan ML, Kulkarni V V, et al (2017) Syntaxin 4 mediates endosome recycling for lytic granule exocytosis in cytotoxic T-lymphocytes. Traffic 18:442–452. https://doi.org/10.1111/tra.12490
Szalay-Bekő M, Palotai R, Szappanos B, et al (2012) ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality. Bioinformatics 28:2202–2204. https://doi.org/10.1093/bioinformatics/bts352
Szklarczyk D, Gable AL, Lyon D, et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613. https://doi.org/10.1093/nar/gky1131
Tian L, Bashan A, Shi D-N, Liu Y-Y (2017) Articulation points in complex networks. Nat Commun 8:14223. https://doi.org/10.1038/ncomms14223
Tsui C, Kong EF, Jabra-Rizk MA (2016) Pathogenesis of Candida albicans biofilm. Pathog Dis 74:ftw018. https://doi.org/10.1093/femspd/ftw018
Wang F, Yang Y, Li Z, et al (2022) Mannan-Binding Lectin Regulates the Th17/Treg Axis Through JAK/STAT and TGF-β/SMAD Signaling Against Candida albicans Infection. J Inflamm Res 15:1797–1810. https://doi.org/10.2147/JIR.S344489
Welte T, Leitenberg D, Dittel BN, et al (1999) STAT5 Interaction with the T Cell Receptor Complex and Stimulation of T Cell Proliferation. Science (80-) 283:222–225. https://doi.org/10.1126/science.283.5399.222
Whibley N, Gaffen SL (2014) Brothers in arms: Th17 and Treg responses in Candida albicans immunity. PLoS Pathog 10:e1004456. https://doi.org/10.1371/journal.ppat.1004456
Williams SE, Brown TI, Roghanian A, Sallenave J-M (2006) SLPI and elafin: one glove, many fingers. Clin Sci (Lond) 110:21–35. https://doi.org/10.1042/CS20050115
Wohlfert EA, Grainger JR, Bouladoux N, et al (2011) GATA3 controls Foxp3 + regulatory T cell fate during inflammation in mice. J Clin Invest 121:4503–4515. https://doi.org/10.1172/JCI57456
Wu T, Hu E, Xu S, et al (2021) clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov 2:100141. https://doi.org/https://doi.org/10.1016/j.xinn.2021.100141
Xie Z, Bailey A, Kuleshov M V, et al (2021) Gene Set Knowledge Discovery with Enrichr. Curr Protoc 1:e90. https://doi.org/https://doi.org/10.1002/cpz1.90
Zhang F, Meng G, Strober W (2008) Interactions among the transcription factors Runx1, RORgammat and Foxp3 regulate the differentiation of interleukin 17-producing T cells. Nat Immunol 9:1297–1306. https://doi.org/10.1038/ni.1663
Zhang L, Tsung A (2017) Chap. 8 - Role of the Transcription Factor Interferon Regulatory Factor-1 in Regulating Autophagy in Lipopolysaccharide-Stimulated Macrophages. In: Hayat Other Pathologies, Inflammation, Immunity, Infection, and Aging MABT-AC (ed). Academic Press, pp 211–230
Zhang Q, Liu W, Zhang H-M, et al (2020) hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets. Genomics Proteomics Bioinformatics 18:120–128. https://doi.org/https://doi.org/10.1016/j.gpb.2019.09.006
Zhang Q, Meng Y, Wang K, et al (2021) Inflammation and Antiviral Immune Response Associated With Severe Progression of COVID-19. Front Immunol 12:631226. https://doi.org/10.3389/fimmu.2021.631226
Zhu S, Pan W, Shi P, et al (2010) Modulation of experimental autoimmune encephalomyelitis through TRAF3-mediated suppression of interleukin 17 receptor signaling. J Exp Med 207:2647–2662. https://doi.org/10.1084/jem.20100703

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Impairment of host adaptive immune response to Candida albicans infection: an in-silico insight on SARS-CoV-2 conspiracy

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Data processing and identification of DEGs

2.2. Reconstruction of PPIN_res and identification of module core genes (CGs)

2.3. Reconstruction of GRN_res and determining articulation points (APs)

2.4. Functional and pathway enrichment analysis

3. Results

3.1. Reconstruction of PPIN_res with differentially expressed genes in C. albicans infected human blood

3.2. Reconstruction of Gene Regulatory network (GRN_res) associated with module core genes of PPIN_res

3.3. Investigating the impact of SARS-CoV-2 infection on host immune response to C. albicans superinfections

4. Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Impairment of host adaptive immune response to Candida albicans infection: an in-silico insight on SARS-CoV-2 conspiracy

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Data processing and identification of DEGs

2.2. Reconstruction of PPINres and identification of module core genes (CGs)

2.3. Reconstruction of GRNres and determining articulation points (APs)

2.4. Functional and pathway enrichment analysis

3. Results

3.1. Reconstruction of PPINres with differentially expressed genes in C. albicans infected human blood

3.2. Reconstruction of Gene Regulatory network (GRNres) associated with module core genes of PPINres

3.3. Investigating the impact of SARS-CoV-2 infection on host immune response to C. albicans superinfections

4. Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

2.2. Reconstruction of PPIN_res and identification of module core genes (CGs)

2.3. Reconstruction of GRN_res and determining articulation points (APs)

3.1. Reconstruction of PPIN_res with differentially expressed genes in C. albicans infected human blood

3.2. Reconstruction of Gene Regulatory network (GRN_res) associated with module core genes of PPIN_res