IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis.

During these days of global emergency for the COVID-19 disease outbreak, there is an urgency to share reliable information able to help worldwide life scientists to get better insights and make sense of the large amount of data currently available. In this study we used the results presented in [1] to perform two different Systems Biology analyses on the HCoV-host interactome. In the first one, we reconstructed the interactome of the HCoV-host proteins, integrating it with highly reliable miRNA and drug interactions information. We then added the IL-6 gene, identified in recent publications [2] as heavily involved in the COVID-19 progression and, interestingly, we identified several interactions with the reconstructed interactome. In the second analysis, we performed a Gene Ontology and a Pathways enrichment analysis on the full set of the HCoV-host interactome proteins and on the ones belonging to a significantly dense cluster of interacting proteins identified in the first analysis. Results of the two analyses provide a compact but comprehensive glance on some of the current state-of-the-art regulations, GO, and pathways involved in the HCoV-host interactome, and that could support all scientists currently focusing on SARS-CoV-2 research.

Introduction

The sudden global emergency caused by the newly discovered SARS-CoV-2 requires a fast but reliable and comprehensive analysis of the virus interactions with the human genome. Coronaviruses (CoVs) typically affect the respiratory tract of mammals, including humans, and lead to mild to severe respiratory tract infections [3]. To provide fast results that could contribute to the fight against the epidemic, in this study we started from the results presented in [1] and performed two different Systems Biology analyses on the HCoV-host interactome. In the first one, relying on validated interaction only, we reconstructed the regulatory network linking the proteins identified in [1], integrating it with the IL-6 protein, miRNA, and drug interactions information. We would like to stress the fact that, while the original paper [1] focuses on Protein Interactions, this paper looks at gene regulatory mechanisms. We wanted to look at the same problem, starting from Zhou results, from a different point of view. We consider this contribution as a continuation or expansion of Zhou work, not something that in any way contradicts it.

Our first analysis has been done using the RING database [4], a data repository integrating 30 public databases designed for advanced biological networks reconstruction. This phase allowed to immediately identify a strongly connected cluster of proteins and drugs, as well as 3 miRNAs that the cluster produces. In the second analysis, executed using an R pipeline, we compared the full set of the HCoV-host interactome proteins with the ones belonging to the identified cluster. In particular, we performed a Gene Ontology [5] enrichment analysis, a Pathways enrichment analysis (with both KEGG [6] and WikiPathways [7]), as well as a final cluster analysis. The results of the latter analysis are publicly available at https://precious.polito.it/covid-19/ as an interactive website (the same data is also available in the S1_File of the supplemental materials).

As pure bioinformaticians we currently lack a valid experimental setup to prove the validity of results and the goal of this paper is not to propose a new methodology but to apply consolidated techniques to share and propagate knowledge that may help other scientists involved in SARS-CoV-2 research to better design their studies or uncover new hypothesis.

Methods and results

During these days of global emergency for the COVID-19 disease outbreak, there is an urgency to share reliable information able to help worldwide life scientists to get better insights and make sense of the large amount of data currently available. Based on these premise, we aim at providing at-a-glance insights, easy to read by life scientists, about: i) the current state-of-the-art knowledge available in terms of direct regulatory interactions taking place among gene/proteins included in the HCoV-host interactome [1] and IL-6, which resulted in the HCoV-Cluster network, and ii) the whole set of GOs and Pathways enriched in both the HCoV sets.

Network analysis

For this first analysis we used the Graph Tools of the RING database (https://precious.polito.it/theringdb/login) [4]. This tool integrates more than 30 publicly available data repositories and allows to reconstruct interaction networks between genes, Transcription Factors, miRNAs, Drugs, Diseases, and SNPs.

As a first step we performed a network reconstruction looking only for interactions among the proteins of the HCoV-host interactome as reported in S3 Table in the Supplemental material of [1]. In order to build the minimum set of most reliable interactions, we used the most conservative settings, where all interactions are validated, manually curated, and limited to signaling or regulatory actions like inhibition/activation (data sources: TRRUST [8] and SIGNOR [9]). This phase allowed to divide the original HCoV-host interactome proteins in two sets: one that shows no obvious regulatory interactions, and one made of 20 genes (see Table 1) out of the original 135 proteins, that forms a well-defined regulatory interactome. For all interactions we report (in S3 Table of the supplemental material), the Pubmed references of the supporting papers.

Table 1

List of highly-likely interacting genes within the HCoV-host interactome.

TYPE	ID	NAME	EXTRA INFO
TF	22809	ATF5	UniprotId: A0A024QZG3, Aliases: ATFX|HMFN0395, Map Location: A0A024QZG3
gene	596	BCL2	UniprotId: A0A024R2B3, Aliases: Bcl-2|PPP1R50, Map Location: A0A024R2B3
gene	598	BCL2L1	UniprotId: A0A0S2Z3C5, Aliases: BCL-XL/S|BCL2L|BCLX|Bcl-X|PPP1R52, Map Location: A0A0S2Z3C5
gene	2931	GSK3A	UniprotId: A0A024R0L5, Aliases: -, Map Location: A0A024R0L5
TF	2932	GSK3B	UniprotId: P49841, Aliases: -, Map Location: P49841
gene	3569	IL-6	UniprotId: B4DNQ5, Aliases: BSF-2|BSF2|CDF|HGF|HSF|IFN-beta-2|IFNB2|IL-6, Map Location: B4DNQ5
TF	3725	JUN	UniprotId: P05412, Aliases: AP-1|AP1|c-Jun|p39, Map Location: P05412
gene	3837	KPNB1	UniprotId: B7Z752, Aliases: IMB1|IPO1|IPOB|Impnb|NTF97, Map Location: B7Z752
gene	4170	MCL1	UniprotId: A0A087WT64, Aliases: BCL2L3|EAT|MCL1-ES|MCL1L|MCL1S|Mcl-1|TM|bcl2-L-3|mcl1/EAT, Map Location: A0A087WT64
gene	4666	NACA	UniprotId: A0A024RB41, Aliases: HSD48|NAC-alpha|NACA1|skNAC, Map Location: A0A024RB41
TF	4869	NPM1	UniprotId: A0A0S2Z491, Aliases: B23|NPM, Map Location: A0A0S2Z491
TF	142	PARP1	UniprotId: A0A024R3T8, Aliases: ADPRT|ADPRT 1|ADPRT1|ARTD1|PARP|PARP-1|PPOL|pADPRT-1, Map Location: A0A024R3T8
TF	5245	PHB	UniprotId: A8K401, Aliases: HEL-215|HEL-S-54e|PHB1, Map Location: A8K401
gene	5499	PPP1CA	UniprotId: A0A140VJS9, Aliases: PP-1A|PP1A|PP1alpha|PPP1A, Map Location: A0A140VJS9
gene	6502	SKP2	UniprotId: A0A024R069, Aliases: FBL1|FBXL1|FLB1|p45, Map Location: A0A024R069
TF	4088	SMAD3	UniprotId: A0A024R5Z3, Aliases: HSPC193|HsT17436|JV15-2|LDS1C|LDS3|MADH3, Map Location: A0A024R5Z3
TF	6774	STAT3	UniprotId: P40763, Aliases: ADMIO|ADMIO1|APRF|HIES, Map Location: P40763
TF	6776	STAT5A	UniprotId: A8K6I5, Aliases: MGF|STAT5, Map Location: A8K6I5
TF	7040	TGFB1	UniprotId: P01137, Aliases: CED|DPD1|LAP|TGFB|TGFbeta, Map Location: P01137

The HCoV-host Network has been then enhanced looking for co-expressed microRNAs (in this case we selected the MIRIAD data source [10], while relaxing the Validated and Manually curated filters). This operation highlighted that 3 miRNAs are co-expressed by the identified cluster: hsa-miR 3912-3p and hsa-miR 3912-5p hosted by the NPM1 gene, and hsa-miR 4751, hosted by ATF5 the gene. We decided to highlight miRna possibly involved in the Covid interactome because miRNAs are known to mediate several regulatory mechanisms, but also to be powerful biomarkers for several diseases. Finally, we added the IL-6 gene to see if it presented any interaction with the identified cluster.

The resulting network is presented in Fig 1. Yellow edges represent multiple interactions. For each node of the network, the S1 Table reports the node name, type, UniprotId, and possible aliases. For each interaction, the S2 Table reports the interaction type (to match the symbol with its meaning refer to [4]), the database of origin, and, where available, the PubMed Ids of the related papers.

Fig 1

HCoV-host and IL-6 interactome cluster.

Nodes are represented as follow: genes in violet, transcription factors in blue, and microRNAs in green. Edges are marked in: red for inhibition/silencing effects, green for activation/silencing, black for undirected/unknown and yellow when multiple regulations are available in literature (see the list of all the interactions reported in Supplemental Material in S2 Table).

As a third step, we further enhanced the network looking for drug interactions that had at least two targets in the identified cluster (data sources: DGIdB [11] and DRUGBANK[12]). In this way we limited the number of drugs to only the ones that possibly have a stronger effect on the HCoV-Cluster of proteins, by presenting multiple targets. The resulting network is presented in Fig 2.

Fig 2

HCoV-host and IL-6 interactome cluster enriched with drugs interaction data.

Nodes are represented as follow: genes in violet, transcription factors in blue, microRNAs in green, and drugs in cyan. Edges are marked in: red for inhibition/silencing effects, green for activation/silencing, black for undirected/unknown, fuchsia for drug-target association, and yellow when multiple regulations are available in literature (see the list of all the interactions reported.

For the sake of clarity and readability we kept the proposed HCoV interacting network as small as possible and we avoided including further possible regulations with weaker reliability. Custom enhancements are obviously possible for interested scientists, and instructions are reported in the S1 Instruction file.

The list of drugs that have at least two interactions with the cluster is reported in Table 2. Although, the list of drugs reported only shares Paroxetine with of [1], the others could still be of interest as possible repurposing candidates.

Table 2

List of possible drug interactions with the identified HCoV-host cluster.

Name	# targets	DB reference
Paroxetine	10	PubchemId: 43815, PharmGKB: PA450801, DrugBank: DB00715
Azd-1080	4
Isosorbide	3	PubchemId: 12597, DrugBank: DB09401
Imatinib	3	PubchemId: 5291, PharmGKB: PA10804, DrugBank: DB00619
Ponatinib	3	PubchemId: 24826799, PharmGKB: PA165980594, DrugBank: DB08901
Venetoclax	3	PubchemId: 49846579, PharmGKB: PA166153473, DrugBank: DB11581
Navitoclax	2	PubchemId: 24978538, DrugBank: DB12340
Lithium carbonate	2	PubchemId: 11125
Tideglusib	2	PubchemId: 11313622, DrugBank: DB12129
Docetaxel	2	PubchemId: 148124, PharmGKB: PA449383, DrugBank: DB01248
Carboplatin	2
Genistein	2	PubchemId: 5280961, PharmGKB: PA165109660, DrugBank: DB01645
Streptozotocin	2	PubchemId: 2733335
Lithium citrate hydrate	2	PubchemId: 2724118
Obatoclax mesylate	3
Ly-2090314	2	PubchemId: 10029385, DrugBank: DB11913
Chir-99021	2	PubchemId: 9956119
Doxorubicin	2	PubchemId: 31703, PharmGKB: PA449412, DrugBank: DB00997
Etoposide	2	PubchemId: 36462, PharmGKB: PA449552, DrugBank: DB00773

The proposed HCoV interacting network may be helpful to all scientists currently involved in SARS-CoV-2 research, since it provides a compact but comprehensive glance on some of the current state-of-the-art regulations that take place among the HCoV-host interactome.

Enrichment analysis

For this second analysis we resorted to an R [13] pipeline, taking advantage of clusterProfiler package [14], and we fed it with both the full-set of 135 HCoV-host proteins, and HCoV cluster of 20 proteins identified in the previous analysis.

With both the sets we performed statistically valid enrichment analysis on:

the three Gene Ontologies (GO [5]) (i.e., Molecular Function, Cellular Component and Biological Process);

two of the most widely used pathway repositories KEGG [6] and WikiPathways[7]);

Finally, we also added a one non-statistical GO classification.

The result is provided as an interactive website (available at https://precious.polito.it/covid-19) that collects and presents all results both in graphical and tabular form; all the tables are immediately available to download to, once again, provide scientists with immediately usable result for further analysis. All results are also available in the S1 Data file.

Each enrichment analysis result is provided with its entity (i.e., a GO or Pathway) defined with its ID and Description, the gene ratio coverage, a list of proteins involved in that pathway or GO, and its enrichment p-value adjusted by Benjamini & Hochberg (BH) method [14]. This value has been computed to better control the expected proportion of false discoveries amongst the rejected hypotheses (i.e., false discovery rate, FDR), thus allowing for a less stringent condition on false discoveries, and allowing more candidate options to be included as results. Furthermore, we also computed, only for GOs, a non-statistical classification, in this case, possibly helpful for preliminary evaluation or hypothesis definition. In this case we simply showcase the frequency of recurring GOs among the entities, with no statistical consideration. Because of that, data tables related to GO classification do not report any p-value.

Finally, a cluster analysis has been computed in order to selectively elucidate possible inner differences between the HCoV-host protein-set and the HCoV-host proteins cluster-set in all the computations performed in the previous steps (i.e., GO Classification GO Enrichment and Pathway Enrichment). The same statistical assumption and limits, previously discussed for enrichment analysis, do apply to cluster analysis.

As a proof of concept, we hereby show in Table 3, the top five Wiki Pathway enriched pathways.

Table 3

List of the top 5 enriched Wiki pathways.

To see the patway full content in the original wikipathway website use the following link https://www.wikipathways.org/index.php/Pathway: by replacing with the id reported in the table.

PathwayID	Name	Gene ratio	BG ratio	Adj. p-value	Genes
WP3872	Regulation of Apoptosis by Parathyroid Hormone-related Protein	7/70	22/6249	0	BCL2L1 BCL2L2 MCL1 BCL2A1 BCL2 GSK3A GSK3B
WP4298	Viral Acute Myocarditis	8/70	85/6249	0	STAT3 TGFB1 BCL2L1 BCL2 GSK3B PABPC1 PARP1 CAV1
WP127	IL-5 Signaling Pathway	6/70	40/6249	0	STAT5A JUN STAT3 BCL2 GSK3A GSK3B
WP286	IL-3 Signaling Pathway	6/70	49/6249	0.001	STAT5A JUN STAT3 TGFB1 BCL2L1 BCL2
WP3646	Hepatitis C and Hepatocellular Carcinoma	6/70	51/6249	0.001	JUN STAT3 TGFB1 SMAD3 BCL2L1 RRM2

WP3872 describes integrin mediated cell survival regulation induced by parathyroid hormone-related protein. Although the pathway may seem not of immediate applicability for SARS-CoV-2, it is interesting to highlight the involvement of the PI3-K/Akt pathway by increasing levels of integrin A6B4, which further modulate the pro/anti-apoptosis members in the Bcl-2 family. Bcl-2 family of genes have been shown to play an important role in the IL-6–mediated protective response to oxidative stress. Authors in [15] showed that IL-6 induced Bcl-2 expression, both in vivo and in vitro, disrupted interactions between proapoptotic and antiapoptotic factors, and suppressed H2O2-induced loss of mitochondrial membrane potential in vitro. Concluding that IL-6 induces Bcl-2 expression to perform cytoprotective functions in response to oxygen toxicity, and conclude that IL-6 induces Bcl-2 expression to perform cytoprotective functions in response to oxygen toxicity, and that this effect is mediated by alterations in the interactions between BAK and MFNS.

WP4298 refers to Viral Myocarditis (VM) pathway. VM is a rare cardiac disease associated with the inflammation and injury of the myocardium result of cooperation between viral processes and the adaptive as innate host's immune response (see [16-18]). Recent papers address SARS-CoV-2 as responsible for acute myocarditis or fulminant myocarditis nevertheless author state that the mechanism of cardiac pathology caused by SARS-CoV-2 needs further study [19, 20].

WP127 and WP286 are related to interleukine-mediated (IL-5, IL-3) inflammatory response.

Acute Respiratory Distress Syndrome (ARDS) induced by SARS-CoV-2, has been recently highlighted as mediated by high level of cytokine IL-6 [2, 21] that leads to excessive inflammatory response, which is further related to bad prognosis. While IL-6 may be considered as a therapeutic target on his own, common ILs regulatory traits (shared with IL-3 and IL-5, may be helpful to highlight more detailed mechanisms of action in the inflammatory response.

WP3646 pathway reports main hub genes and their related miRNAs. The pathways has been built on a set of differentially expressed genes in both chronic HCV (hepatitis C virus) and HCC (hepatocellular carcinoma) to highlight how Hepatitis C Virus leads to hepatocellular carcinoma [22]. This pathway suggests a possible similar behavior for corona family viruses and the liver involvement during infection has been recently highlighted, but still largely uncovered [23].

List of all nodes.

(CSV)

Click here for additional data file.

List of all the interaction identified in the cluster network.

(CSV)

Click here for additional data file.

List of all nodes of the cluster.

(CSV)

Click here for additional data file.

PDF form of the GO and Pathway enrichment analysis.

Available also as an interactive website at https://precious.polito.it/covid-19/.

(PDF)

Click here for additional data file.

Instructions on how to replicate the experiments.

(PDF)

Click here for additional data file.

Network in sif format.

(SIF)

Click here for additional data file.

22 May 2020

Dear Prof. Benso,

Thank you very much for submitting your manuscript "A Systems Biology study of an IL6-mediated HCoV-host interactome: Drug Repurposing and GO/Pathway Enrichment analysis" for consideration at PLOS Computational Biology.

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Deputy Editor

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this paper, the authors performed some preliminary analysis on the HCoV-host interactome.

I am aware that some of the provided results may be useful for the community. As stated by the authors, the computed enrichment analysis may allow scientists to better and more reliably infer candidate pathways and GOs for further inspection.

However, the present paper does not provide a significant improvement in terms of methodologies, nor provides a significant outcome in terms of results. Rather, it may be considered as the result of few queries performed on existing online repositories.

Therefore, although such results may deserve to be shown to the community, since the paper lacks both methodological originality and significance of results, I think that it cannot be published on PLOS Computational Biology in its current form.

Reviewer #2: This paper by politano and benso describes a computational approach to study the HCoV-host interactome. The purpose is to enable scientists with limited experience with compuational analyses to study the HCoV infections and potential drug repurposing/targeting. The paper elegantly shows how many of the results presented in a recent paper by Zhou et al can be achieved by non-programmers using extremely easy-to-use tools previously developed by the authors and adapted to this specific case. These types of workflows are really useful to speed up research in highly time sensitive cases such as the COVID-19 pandemic. The manuscript is well written, and with a few exceptions (listed below) the results are presented in a clear and concise manner.

One major issue that must be resolved before publication can be considered: The link https://precious.polito.itcovid-19/ doesn't work, so it's not really possible to evaluate the feature

Minor comments and suggestions:

It seems to me that the title promises a lot more than what is delivered in this paper. May I suggest a re-wording to more accurately reflect what I see as the ultimate value of this paper: the fact that it enables fast and easy reproduction of extremely advanced computational analyses by scientists with no explicit programming experience.

The first paragraph of materials and methods feels like it belongs in the introduction as it concisely describes the scope and provides essential context for the rest of the work presented.

Figure 1 and 2: Legends seem incomplete. What do the node and edge colors/shapes represent? I would also suggest to include the virus proteins in these graphs.

Figure 2: All of these drugs interact with human proteins, none with virus proteins. What do these drugs do to the proteins they target? They may not affect HCoV-host interaction at all (or they may even enhance it). I realize that it's not within the scope of the paper to dive into the biology, but these results should be discussed a bit more to highlight the value of this feature.

Section 3.2. "three main branches" - they are the only three ontologies in the gene ontology

Reviewer #3: In the manuscript titled “A Systems Biology study of an IL6-mediated HCoV-host interactome: Drug Repurposing and GO/Pathway Enrichment analysis” by Politano and Benso, the authors describe a computational systems biology analysis of the HCoV-host interactome aimed at the identification of candidate drugs with anti-SARS-CoV-2 effects.

The main and critical point that should be addressed is to provide a clear and adequate rationale for this study, highlighting the differences with the study that it is based on (Zhou et al, 2020). The authors have re-analyzed the HCoV-host interactome described in Zhou et al, and performed a different type of drug repurposing analysis whose results shared only one candidate drug with the original study.

More specifically:

1) Zhou et al generated their HCoV-host interactome based on validated published data. The authors of this novel manuscript found that 115 of the 135 proteins in the HCoV-host interactome showed no obvious interactions and focused their analysis on a set of 20 proteins forming a well-defined interactome. How are 115 proteins in the original HCoV-host interactome not connected? The authors should discuss their findings.

2) The added value and improvement over the original analysis is not clear, besides the addition of IL-6 (other cytokines might be considered as well). Zhou et al performed network-based drug repurposing and gene-set enrichment analysis. The authors of this manuscript should describe what weaknesses in the original work they address here with their analysis and compare and discuss the findings of the two analyses, highlighting the added value and improved accuracy of their study. Two almost completely different sets of drugs generated from the same data may raise more questions than provide answers. Thus, it is essential that the authors provide evidence that their analysis is, at the very least, significantly complementary to the original study.

3) What is the added value introduced by the integration of miRNA in the interaction map?

Two minor points:

1) Overall, the manuscript reads well but it should be carefully reviewed for syntax and incomplete sentences, e.g. “The resulting network is presented in” on Page 3 is incomplete. Similarly, the sentence “Concluding that IL-6 induces Bcl-2 expression to perform cytoprotective functions in response to oxygen toxicity, and that this effect is mediated by alterations in the interactions between BAK and MFNS” is incomplete as well.

2) The link http://precious.polito.it/covid-19 is broken.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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17 Jun 2020

Submitted filename: Responses to reviewers.doc

Click here for additional data file.

11 Aug 2020

Dear Prof. Benso,

We are pleased to inform you that your manuscript 'IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

James M. Briggs, Ph.D.

Associate Editor

PLOS Computational Biology

William Noble

Deputy Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: I am satisfied with the authors' responses to my comments. I still think that the computational approach to significantly speeding up hypothesis generation in a time of pandemics is the most valuable contribution here, and I think that the authors are still a bit too modest and underselling this point. That being said, in my opinion this manuscript is ready for publication.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #2: Yes

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Reviewer #2: No

24 Sep 2020

PCOMPBIOL-D-20-00555R1

IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis

Dear Dr Benso,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

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