Meta-Analysis of Transcriptomic Datasets Quantifies Differential Gene Expression, Affected Pathways, and Predicted Drugs from Hantavirus-Infected Human Material

doi:10.21203/rs.3.rs-1766515/v1

Download PDF

Research Article

Meta-Analysis of Transcriptomic Datasets Quantifies Differential Gene Expression, Affected Pathways, and Predicted Drugs from Hantavirus-Infected Human Material

https://doi.org/10.21203/rs.3.rs-1766515/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

We performed a meta-analysis that consisted of re-analyzing publicly available RNA-sequencing datasets across representative species within Orthohantavirus and across host cell types to identify differentially expressed genes associated with hantavirus infection. In addition, we determined signaling pathways that were significantly perturbed in human tissue, as well as predicted existing drugs that could be repurposed to potentially reduce the effects of acute hantavirus infection.

Methods

NCBI-GEO and ENA databases were searched to find datasets for this analysis, then processed using the Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR) to identify differentially expressed genes (DEGs). The results from this analysis were then processed using signaling pathway impact analysis (SPIA) to identify enriched signaling pathways. Finally, that data was processed using the Pathways2Targets program to identify existing therapeutics that target the impacted cellular pathways.

Results

Our results include affirming the roles of the previously identified interferon pathway and the MYBL1 and CCR7 genes, as well as newly identified genes, including SLC27A3, TSEN34 and neuron associated NOG and AMIGO1. We predicted dinaciclib, alvodicib, and ruxolitinib, among many other drugs, that could be useful as potential HFRS and HCPS treatments.

Conclusion

These data can be used in the design of future wet lab experiments to better characterize the human transcriptional response to infection, as well as to improve host-based diagnostics and/or therapeutics to reduce the severity of hantavirus infection.

Hantavirus

host response

RNA-sequencing

bioinformatics

meta-analysis

virology

Orthohantavirus is a viral genus that includes the hantaviruses that are pathogenic in humans. Members of this viral genus are transmitted by various rodent hosts throughout Asia, Europe, the Americas, and parts of Africa [1]. Hantavirus is generally well adapted to its rodent vector and is unlikely to cause disease in that host. However, when humans come into direct contact with infected rodent vectors or their feces, virions can enter host cells and potentially cause an infection [2]. Once human infection occurs, hantaviruses are far more likely to cause severe disease, permanent injury, or even death. Human-to-human spread of hantaviruses generally does not occur, with the exception of some rare cases of Andes virus [3]. However, as metropolitan areas continue to expand, the threat of hantavirus spillover into humans is increasingly becoming a threat. This is especially true in eastern Asia, where thousands of cases of hemorrhagic fever with renal syndrome (HFRS) due to hantavirus infection are reported each year [1]. New world hantavirus disease, known as hantavirus cardiopulmonary syndrome (HCPS), is far less common, but has been reported to have up to a 50% fatality rate [4]. It was the new world hantavirus Sin Nombre virus that was responsible for the 1993 hantavirus outbreak in the Four Corners region of the western United States [5].

Like all members of the Bunyavirales family, an Orthohantavirus virion is composed of a segmented negative-sense RNA genome enclosed by a spherical capsid. Each of its three segments encodes reading frames that code for the RNA-dependent RNA polymerase (RDRP), glycoprotein, and the nucleocapsid (L, M, and S segments respectively) [6]. The glycoprotein precursor is cleaved by endogenous proteases to form a heterodimeric protein receptor [7]. The integrin β3 surface protein serves as the primary receptor for all infectious hantaviruses [8]. Past work has shown that hantaviruses do not directly cause cytopathic effects in their host; rather that the host immune response to infection causes the breakdown of endothelial integrity, leading to severe symptoms or death. To better understand the pathogenesis of hantavirus, it is essential to characterize the underlying host intracellular transcriptional responses. Currently, there are no FDA-approved treatments for hantavirus infection besides supportive therapy, but effective vaccines are being investigated for several strains of hantavirus [9]. However, vaccination may be difficult to administer due to the sporadic and relatively limited nature of many hantavirus outbreaks.

Hantavirus infections are characterized by the loss of endothelial barrier integrity leading to severe symptoms and death, as well as high levels of T-cell proliferation in affected tissue [10]. Interestingly, several prior experiments have shown that hantavirus infection causes no cytopathic effects in vitro [7]. Instead, the highly elevated levels of cytokines along with T-cell levels in infected tissue indicate that host inflammatory responses are primarily responsible for the observed clinical signs and symptoms.

While several studies have examined the proteomics and health outcomes of infected individuals, relatively little has been done to transcriptionally characterize human infection. Furthermore, no studies have compared the in vitro and in vivo host transcriptional response during hantavirus infection. Evidence of differentially expressed genes (DEGs) across tissues would provide evidence that further research is required to understand how these DEGs affect differences in morbidity in human tissues. This study seeks to further examine how infection impacts host gene expression and intracellular signaling pathways by re-analyzing existing human RNA-seq data collected during hantavirus infection. We anticipate that this approach will enable us to better compare the DEGs across tissue types to predict more effective host-based therapeutics.

Data Acquisition

A search of the Gene Expression Omnibus (GEO) database, hosted at the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/geo/), was performed in mid-2021 to find RNA-sequencing datasets for hantaviruses using terms including “hantavirus”, “Seoul virus”, “andes virus”, “sin nombre virus” and “Hantaan virus”. In addition, these same terms were used to find relevant datasets within the European Nucleotide Archive (ENA) database. The corresponding RNA-sequencing data for five GEO series were retrieved from the Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) at NCBI: GSE158712, GSE161354, GSE133319, GSE133634, GSE133751[11–13]. Sequencing data with the experiment accession number PRJEB41624 was also downloaded from ENA and used in this analysis[14]. These datasets were generated from either human cell cultures, or patients who were infected with either Hantaan Virus (HTNV) or Puumala virus (PUUV). The SRA files were downloaded and converted to fastq format using GNU wget and version 2.10.1 of the NCBI sratools software package (https://github.com/ncbi/sra-tools).

Data Processing

Much of the data processing was managed by the Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR) [15]. Within this workflow the data was processed as follows: quality control of the reads was performed using fastQC, adapters and low-quality reads were trimmed using TrimGalore, reads were mapped and quantified to the GRCh38 human transcriptome using Salmon [16], and differential gene expression was calculated using edgeR [17]. The list of DEGs from this workflow was subsequently analyzed with the Signaling Pathway Impact Analysis (SPIA) algorithm [18], which uses pathway enrichment to identify cellular pathways using the Biocarta, KEGG, NCI, Panther, and Reactome databases [18–22]. The SPIA output file from this analysis was then analyzed with the Pathways2Targets program to identify existing therapeutics that target the impacted cellular pathways [23] from the opentargets.org database [24]. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to identify and visualize relevant protein-protein interactions [25].

We began by collecting the relevant fastq files for human-derived samples infected with Orthohantavirus strains. In total, we included 73 samples from six datasets in this meta-analysis, including 23 controls, and included all pertinent data and metadata that could be extracted from the NCBI-GEO and ENA databases [11–14]. Fastq files were divided into in vitro and in vivo groups for the analysis according to sample type and each group was separately input to ARMOR. The in vivo dataset consists of sequencing data from peripheral blood mononuclear cells (PBMC) of HFRS (Hantaan virus infected) patients, with relevant control fastq files. The in vitro dataset consisted of A549 and HUVEC cells infected with either HTNV or Puumala virus, with relevant control fastq files.

In Vivo Differentially Expressed Genes (DEGs)

We next used the automated ARMOR bioinformatics workflow to calculate genes with significantly altered expression from the raw fastq files. We observed approximately 5900 significant DEGs in the in vivo dataset (Fig. 1), which consisted of human PBMC samples (corrected p-value ≤ 0.05). This relatively high number of DEGs was expected since the infected material was being affected by the host immune response. Interestingly, four of the most significant 50 genes have been previously identified in earlier hantavirus studies including: OLFM1, CCR7, KLRG1, and PTGDR2–all of which were downregulated [26–29]. We observed the most significant upregulated genes found were NUSAP1, KIF20A, CDC25C, and DEPDC1.

In Vitro Differentially Expressed Genes (DEGs)

We then wanted to perform a similar analysis on the in vitro dataset. In this case, our analysis identified only 26 significant DEGs during infection including: IFIT1, OAS2, CMPK2, IFIT3, IFI6 (Additional file 1). We found that three genes were downregulated, while the remaining 23 significant genes were upregulated (Fig. 2).

To better understand whether these gene products directly interacted with each other, we used the STRING database to retrieve protein-protein interaction data [25]. We found that this protein network revealed most of the proteins directly interact with each other, with two proteins not connected to the rest of the graph and a third protein not represented in the database (Fig. 3).

Pathway Analysis

After identifying the significant DEGs, we used them as input to the signaling pathway impact analysis (SPIA) algorithm, which performs robust statistical analyses to identify cellular pathways that were significantly enriched in DEGs [18]. We identified 79 enriched pathways in the in vivo dataset, largely associated with transcription, cellular repair, homeostasis, and the cell cycle, with a few associated with immune response (Table 2). In contrast, the in vitro dataset enrichment identified 11 pathways, all of which are associated with the antiviral response (Table 3).

Table 1

Top 10 significantly affected Signaling Pathways during *in vivo* human hantavirus infection.
Pathway Name	Status	Corrected p-value	Source Database
Cytokine-cytokine receptor interaction	Inhibited	4.96E-12	KEGG
Systemic lupus erythematosus	Activated	0.000115	KEGG
Cell cycle	Activated	0.000276	KEGG
Carbohydrate digestion and absorption	Activated	0.000529	KEGG
Staphylococcus aureus infection	Activated	0.00062	KEGG
Alcoholism	Activated	0.006032	KEGG
Generic Transcription Pathway	Activated	7.52E-12	Reactome
Activated PKN1 stimulates transcription of AR (androgen receptor) regulated genes KLK2 and KLK3	Activated	5.01E-11	Reactome
RNA Polymerase I Promoter Opening	Inhibited	5.01E-11	Reactome
Meiotic recombination	Activated	5.26E-11	Reactome

Table 2

All 11 significantly affected Signaling Pathways during *in vitro* human hantavirus infection.
Pathway Name	Status	Corrected p-value	Source Database
Influenza A	Activated	1.13E-08	KEGG
Herpes simplex infection	Activated	9.39E-06	KEGG
Measles	Activated	0.000117	KEGG
RIG-I-like receptor signaling pathway	Activated	0.005427	KEGG
Interferon Signaling	Inhibited	3.68E-31	Reactome
Interferon alpha/beta signaling	Activated	1.21E-27	Reactome
Cytokine Signaling in Immune system	Inhibited	8.60E-18	Reactome
Antiviral mechanism by IFN-stimulated genes	Inhibited	5.06E-10	Reactome
ISG15 antiviral mechanism	Inhibited	5.06E-10	Reactome
Negative regulators of RIG-I/MDA5 signaling	Activated	5.49E-06	Reactome
RIG-I/MDA5 mediated induction of IFN-alpha/beta pathways	Inhibited	7.24E-05	Reactome

Potential Therapeutic Prediction

We next wanted to predict existing drugs that could be repurposed as host-based anti-viral therapies. We consequently used the enriched pathway data generated by SPIA as input to the Pathways2Targets algorithm [23]. This software identifies known drug targets in the significant pathways, then searches the public opentargets.org database to generate a list of potential therapeutics that could affect each pathway. This method was justified since reversing the phenotype of the significantly impacted pathways should reduce viral replication and/or pathogenesis. We sorted the results of this analysis to prioritize therapeutics that were predicted to affect multiple pathways. We intentionally selected this approach to maximize the potential antiviral effect of each drug by simultaneously interfering with multiple intracellular processes that the virus uses to replicate. We generated predictions for the top 20 drugs for the in vivo and in vitro datasets (Table 4). The top hits for the in vivo analysis included dinaciclib, alvocidib, and roniciclib.

Table 3

Top 20 therapeutics predicted for the *in vivo* datasets analyzed in this study.
Predicted Therapeutic Name	Number of Target Pathways
DINACICLIB	33
ALVOCIDIB	33
RONICICLIB	33
SELICICLIB	33
AT-7519	33
MILCICLIB	33
ATALUREN	33
ELX-02	33
MT-3724	33
UCN-01	24
PANOBINOSTAT LACTATE	23
ROMIDEPSIN	23
PANOBINOSTAT	23
BELINOSTAT	23
ENTINOSTAT	23
ABEXINOSTAT	23
VORINOSTAT	21
TACEDINALINE	19
AMG-232	17
APG115	17

Table 4

Top 20 therapeutics predicted for the *in vitro* datasets analyzed in this study
Predicted Therapeutic Name	Number of Target Pathways
RUXOLITINIB	7
BARICITINIB	7
RUXOLITINIB PHOSPHATE	7
TOFACITINIB	7
TOFACITINIB CITRATE	7
FILGOTINIB	7
UPADACITINIB	7
RONTALIZUMAB	6
SIFALIMUMAB	6
PF-06823859	6
PEGINTERFERON ALFA-2A	5
PEGINTERFERON ALFA-2B	5
INTERFERON BETA-1A	5
INTERFERON ALFA-2B	5
INTERFERON BETA-1B	5
INTERFERON ALFACON-1	5
ULIXERTINIB	5
LY3214996	5
RAVOXERTINIB	5
MK-8353	5

The purpose of this meta-analysis was to better characterize and compare the in vitro and in vivo human intracellular transcriptional response to infection with various hantaviruses. Specifically, we identified unique sets of DEGs in both comparisons that have been identified previously, as well as novel DEGs that could provide additional knowledge into the underlying mechanisms of viral pathogenesis. We determined statistically significant signaling pathways that were enriched with the identified DEGs for each set of samples, and subsequently used these pathways to predict potential therapeutics that could be relevant to natural infections.

We believe that this analysis was somewhat hindered by the relatively small number of samples in the public datasets. Specifically, the public data that we used came from HTNV infected tissue, rather than a variety of several hantaviruses. This is somewhat unsurprising due to various logistical reasons including the difficulty and danger of culturing hantavirus in a laboratory setting, or a perceived lack of interest in or access to hantavirus. Such studies can be quite useful in identifying potential causes or compounding factors in HFRS and HCPS. Other hantaviruses, such as Andes virus and Sin nombre virus, may cause far fewer infections each year, but infections by these pathogens have a higher mortality rate. Much research is still needed to determine whether the differential expression patterns vary between species of hantaviruses and between infected human tissue types, but this study attempts to address hantaviruses as a whole in an attempt to identify common expression patterns between different viral species. Our findings could potentially be applied across multiple hantaviruses.

In vivo, we found that of the top 10 genes found, only the NUSAP1 gene, which plays a role in microtubule organization [30], was upregulated. MYBL1 was the top hit, which is a protooncogene associated with promoting piRNA expression, was found to be strongly downregulated.

As stated earlier, of the top 100 identified genes, only OLFM1, CCR7, KLRG1, and PTGDR2 have been identified in previous hantavirus publications. OLFM1, which produces olfactomedin and was found to be associated with hantavirus in a previous study, is also found to be highly expressed in neuronal tissue and may aid in nervous system development; however, relatively little is understood about many of its functions in other cell types [31]. Interestingly, while this gene is expressed in monocytes, its function is unknown and may warrant future investigation [32]. Additionally, three of the top 10 genes found in this analysis, ZNF365, NOG and AMIGO1, regulate proper neuronal development [33–36]. Hantavirus rarely affects the nervous system and has not been observed to directly infect nervous tissue, but these genes may also play a role in development in other cell types which can be affected during hantavirus infection.

CCR7, or C-C Motif Chemokine Receptor seven, is upregulated in lymphocytes and monocytes during a variety of viral infections. A previous study showed that CCR7 induces monocyte egress during hantavirus infection, and therefore a decrease in the number of CCR7-expressing monocytes occurs in the bloodstream [27]. While this may account for the decrease in overall expression, it is unknown if lymphocyte expression of this gene is modulated by hantavirus infection. Similarly, KLRG1 is upregulated in T-cells during viral infection, but it is unclear if it is modulated by hantavirus infection [28].

PTGDR2, or the prostaglandin D2 receptor, promotes inflammation during a viral infection [37]. While no studies of this gene in association with hantavirus have been performed in humans, a 2014 study found that deer mice, a natural non-symptomatic host for the virus, upregulate this gene during active infection of Andes virus, a species of hantavirus [29]. Our analysis showed that this gene is strongly downregulated in humans during infection of HTNV, another hantavirus. Although our observation could be due to a difference in viral species or strain, the gene product may play a role in human pathogenesis of the virus that is not seen in murine species, and likely warrants additional investigation.

The most significant upregulated genes in vivo NUSAP1, KIF20A, CDC25C, and DEPDC1 were associated with cellular division, especially in rapidly dividing cells, which is expected during active viral infection and tissue repair [38–42]. In addition, CDC25C has been shown to play a role in viral replication of HSV, however there is no indication if this is true in other viruses such as hantavirus [43].

In vitro, of all the DEGs, only SLC27A3, AC090527.2 (a novel, uncharacterized gene not shown on figure STRING), and TSEN34 were not associated with interferon stimulation and are not part of the antiviral response. SLC27A3, which is a Very Long-Chain Acyl-CoA Synthetase, is both a synthetase and a transport protein. It has a role in brain development but has not previously been associated with viral pathogenesis or the immune response [44].The tRNA Splicing Endonuclease 34 (TSEN34) gene, and novel gene AC090527.2 similarly have no immune associated function. Finally, Immunoglobulin Heavy Constant Gamma 2 (IGHG2), is shown to be strongly downregulated. This is unexpected since the endothelial cells used in this analysis normally do not produce immunoglobulin. This may be a result of the cells being immortalized, or as an artifact of the analysis. However, at least one prior study has shown that primary endothelial cells can produce antibodies in vitro [45].

The pathway analysis revealed a much wider variety of genes and perturbed pathways in the in vivo dataset as compared to the in vivo dataset. We believe that one of the primary causes of this difference is likely the higher content and complexity of cellular signals that are present in vivo. As the putative cause for the disease is a cytokine storm, we expected to see cytokine signaling along with the interferon response in addition to repair pathways that would not be seen in vitro [46].

Using data from the pathway analysis, medications identified for the in vivo dataset generally treat rapidly dividing and surviving cells, such as in cancer. Specifically, the drugs we predicted would affect the highest number of in vivo pathways in this analysis included those that target cyclin-dependent kinases and JAK kinases such as dinaciclib, alvocidib, and roniciclib. All three of these therapeutics have shown promise as a host-based anti-viral in other pathogens [47–50], but none have been identified or tested for hantaviruses previously. Interestingly, our approach ranked these drugs higher than other potential therapeutics that were further down on the list that have previously been used as host-based anti-virals, such as vorinostat [51–53]. In contrast, the therapeutic prediction analysis from the in vitro dataset identified baricitinib and interferon derivatives, which are commonly used for viral infections [54–56]. As of now, there are no effective treatments for hantavirus infection, and the current suggested remedy is supportive therapy such as bed rest and IV fluids [9]. We believe that testing a subset of these drugs in a laboratory setting would be justified, and that laboratory validation of these findings could improve treatment options for patients in the clinic.

The primary purpose of this study was to analyze the existing public RNA-seq datasets to improve our understanding of the response of human cells during hantavirus infection and to predict existing drugs that could be repurposed as host-based therapeutics. Specifically, we identified seven genes that are novel to the human response to hantaviruses, as well as multiple signaling pathways and drugs that could be repurposed as therapeutics. Such treatments could benefit patients by reducing the occurrence of HFRS and HCPS. Further research is needed to understand how hantaviruses impact expression in the human cell and to improve the identification, development, and testing of effective treatments.

ARMOR: Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data

DEGs: differentially expressed genes

SPIA: signaling pathway impact analysis

HFRS: hemorrhagic fever with renal syndrome

HCPS: hantavirus cardiopulmonary syndrome

RDRP: RNA-dependent RNA polymerase

GEO: Gene Expression Omnibus

NCBI: National Center for Biotechnology Information

ENA: European Nucleotide Archive

SRA: Sequence Read Archive

HTNV: Hantaan Virus

PUUV: Puumala virus

PBMC: peripheral blood mononuclear cells

STRING: Search Tool for the Retrieval of Interacting Genes/Proteins

Ethics approval and consent to participate

Not applicable

Consent for Publication

Not applicable

Availability of data and materials

All data generated or analyzed during this study are included in this published article are including as additional file 1.

Competing Interests

The authors declare that they have no competing interests

Funding

This research was funded by the College of Life Sciences at Brigham Young University. No additional external funding was used.

Authors' contributions

BEP: Principal Investigator, Writing, Editing, Conceptualization. JK: Writing, Computational Analysis, Data Interpretation. All authors read and approved the final manuscript.

Acknowledgments

We would like to thank the Office of Research Computing at Brigham Young University for their support on this project.

Jonsson CB, Figueiredo LT, Vapalahti O: A global perspective on hantavirus ecology, epidemiology, and disease. Clin Microbiol Rev 2010, 23(2):412–441.
Ermonval M, Baychelier F, Tordo N: What Do We Know about How Hantaviruses Interact with Their Different Hosts? Viruses 2016, 8(8).
Martinez VP, Bellomo C, San Juan J, Pinna D, Forlenza R, Elder M, Padula PJ: Person-to-person transmission of Andes virus. Emerging infectious diseases 2005, 11(12):1848–1853.
Figueiredo LT, Souza WM, Ferrés M, Enria DA: Hantaviruses and cardiopulmonary syndrome in South America. Virus research 2014, 187:43–54.
Chapman LE, Khabbaz RF: Etiology and epidemiology of the Four Corners hantavirus outbreak. Infectious agents and disease 1994, 3(5):234–244.
Elliott RM: Emerging viruses: the Bunyaviridae. Molecular medicine (Cambridge, Mass) 1997, 3(9):572–577.
Muyangwa M, Martynova EV, Khaiboullina SF, Morzunov SP, Rizvanov AA: Hantaviral Proteins: Structure, Functions, and Role in Hantavirus Infection. Frontiers in microbiology 2015, 6:1326.
Gavrilovskaya IN, Shepley M, Shaw R, Ginsberg MH, Mackow ER: beta3 Integrins mediate the cellular entry of hantaviruses that cause respiratory failure. Proceedings of the National Academy of Sciences of the United States of America 1998, 95(12):7074–7079.
Liu R, Ma H, Shu J, Zhang Q, Han M, Liu Z, Jin X, Zhang F, Wu X: Vaccines and Therapeutics Against Hantaviruses. Frontiers in microbiology 2020, 10.
Vaheri A, Strandin T, Hepojoki J, Sironen T, Henttonen H, Mäkelä S, Mustonen J: Uncovering the mysteries of hantavirus infections. Nature reviews Microbiology 2013, 11(8):539–550.
Li Y, Quan C, Xing W, Wang P, Gao J, Zhang Z, Jiang X, Ma C, Carr MJ, He Q et al: Rapid humoral immune responses are required for recovery from haemorrhagic fever with renal syndrome patients. Emerging microbes & infections 2020, 9(1):2303–2314.
Lu S, Zhu N, Guo W, Wang X, Li K, Yan J, Jiang C, Han S, Xiang H, Wu X et al: RNA-Seq Revealed a Circular RNA-microRNA-mRNA Regulatory Network in Hantaan Virus Infection. Frontiers in cellular and infection microbiology 2020, 10:97.
Zhang J, Tang K, Zhang Y, Ma Y, Zhang C, Hu H, Jia X, Zhuang R, Jin B, Wang M et al: The Presence of Circulating Nucleated Red Blood Cells Is Associated With Disease Severity in Patients of Hemorrhagic Fever With Renal Syndrome. Frontiers in medicine 2021, 8:665410.
Gallo G, Caignard G, Badonnel K, Chevreux G, Terrier S, Szemiel A, Roman-Sosa G, Binder F, Gu Q, Da Silva Filipe A et al: Interactions of Viral Proteins from Pathogenic and Low or Non-Pathogenic Orthohantaviruses with Human Type I Interferon Signaling. Viruses 2021, 13(1).
Orjuela S, Huang R, Hembach KM, Robinson MD, Soneson C: ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data. G3 (Bethesda, Md) 2019, 9(7):2089–2096.
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C: Salmon provides fast and bias-aware quantification of transcript expression. Nature methods 2017, 14(4):417–419.
Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England) 2010, 26(1):139–140.
Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS, Kim CJ, Kusanovic JP, Romero R: A novel signaling pathway impact analysis. Bioinformatics (Oxford, England) 2009, 25(1):75–82.
Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 2000, 28(1):27–30.
Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway Interaction Database. Nucleic acids research 2009, 37(Database issue):D674-679.
Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD: PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic acids research 2017, 45(D1):D183-d189.
Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R et al: The reactome pathway knowledgebase. Nucleic acids research 2020, 48(D1):D498-d503.
Scott TM, Jensen S, Pickett BE: A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases. F1000Res 2021, 10:330.
Carvalho-Silva D, Pierleoni A, Pignatelli M, Ong C, Fumis L, Karamanis N, Carmona M, Faulconbridge A, Hercules A, McAuley E et al: Open Targets Platform: new developments and updates two years on. Nucleic acids research 2019, 47(D1):D1056-d1065.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P et al: STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research 2018, 47(D1):D607-D613.
Witkowski PT, Bourquain D, Bankov K, Auste B, Dabrowski PW, Nitsche A, Krüger DH, Schaade L: Infection of human airway epithelial cells by different subtypes of Dobrava-Belgrade virus reveals gene expression patterns corresponding to their virulence potential. Virology 2016, 493:189–201.
Scholz S, Baharom F, Rankin G, Maleki KT, Gupta S, Vangeti S, Pourazar J, Discacciati A, Höijer J, Bottai M et al: Human hantavirus infection elicits pronounced redistribution of mononuclear phagocytes in peripheral blood and airways. PLoS pathogens 2017, 13(6):e1006462.
Manigold T, Mori A, Graumann R, Llop E, Simon V, Ferrés M, Valdivieso F, Castillo C, Hjelle B, Vial P: Highly Differentiated, Resting Gn-Specific Memory CD8 + T Cells Persist Years after Infection by Andes Hantavirus. PLoS pathogens 2010, 6(2):e1000779.
Schountz T, Quackenbush S, Rovnak J, Haddock E, Black WCt, Feldmann H, Prescott J: Differential lymphocyte and antibody responses in deer mice infected with Sin Nombre hantavirus or Andes hantavirus. Journal of virology 2014, 88(15):8319–8331.
Raemaekers T, Ribbeck K, Beaudouin Jl, Annaert W, Van Camp M, Stockmans I, Smets N, Bouillon R, Ellenberg J, Carmeliet G: NuSAP, a novel microtubule-associated protein involved in mitotic spindle organization. Journal of Cell Biology 2003, 162(6):1017–1029.
Pronker MF, van den Hoek H, Janssen BJC: Design and structural characterisation of olfactomedin-1 variants as tools for functional studies. BMC Molecular and Cell Biology 2019, 20(1):50.
Pontén F, Schwenk JM, Asplund A, Edqvist PH: The Human Protein Atlas as a proteomic resource for biomarker discovery. Journal of internal medicine 2011, 270(5):428–446.
Hattori T, Baba K, Matsuzaki S, Honda A, Miyoshi K, Inoue K, Taniguchi M, Hashimoto H, Shintani N, Baba A et al: A novel DISC1-interacting partner DISC1-Binding Zinc-finger protein: implication in the modulation of DISC1-dependent neurite outgrowth. Molecular psychiatry 2007, 12(4):398–407.
Pera EM, Ikeda A, Eivers E, De Robertis EM: Integration of IGF, FGF, and anti-BMP signals via Smad1 phosphorylation in neural induction. Genes & development 2003, 17(24):3023–3028.
Kuja-Panula J, Kiiltomäki M, Yamashiro T, Rouhiainen A, Rauvala H: AMIGO, a transmembrane protein implicated in axon tract development, defines a novel protein family with leucine-rich repeats. The Journal of cell biology 2003, 160(6):963–973.
Bishop HI, Cobb MM, Kirmiz M, Parajuli LK, Mandikian D, Philp AM, Melnik M, Kuja-Panula J, Rauvala H, Shigemoto R et al: Kv2 Ion Channels Determine the Expression and Localization of the Associated AMIGO-1 Cell Adhesion Molecule in Adult Brain Neurons. Frontiers in Molecular Neuroscience 2018, 11.
Gupta A, Chander Chiang K: Prostaglandin D(2) as a mediator of lymphopenia and a therapeutic target in COVID-19 disease. Medical hypotheses 2020, 143:110122.
Zhu W, Xu J, Chen Z, Jiang J: Analyzing Roles of NUSAP1 From Clinical, Molecular Mechanism and Immune Perspectives in Hepatocellular Carcinoma. Frontiers in Genetics 2021, 12.
Qiu R, Wu J, Gudenas B, Northcott PA, Wechsler-Reya RJ, Lu Q: Depletion of kinesin motor KIF20A to target cell fate control suppresses medulloblastoma tumour growth. Communications Biology 2021, 4(1):552.
Nakamura M, Takano A, Thang PM, Tsevegjav B, Zhu M, Yokose T, Yamashita T, Miyagi Y, Daigo Y: Characterization of KIF20A as a prognostic biomarker and therapeutic target for different subtypes of breast cancer. International journal of oncology 2020, 57(1):277–288.
Liu K, Zheng M, Lu R, Du J, Zhao Q, Li Z, Li Y, Zhang S: The role of CDC25C in cell cycle regulation and clinical cancer therapy: a systematic review. Cancer Cell International 2020, 20(1):213.
Mi Y, Zhang C, Bu Y, Zhang Y, He L, Li H, Zhu H, Li Y, Lei Y, Zhu J: DEPDC1 is a novel cell cycle related gene that regulates mitotic progression. BMB reports 2015, 48(7):413–418.
Smith-Donald BA, Durand LO, Roizman B: Role of Cellular Phosphatase cdc25C in Herpes Simplex Virus 1 Replication. Journal of virology 2008, 82(9):4527–4532.
Maekawa M, Iwayama Y, Ohnishi T, Toyoshima M, Shimamoto C, Hisano Y, Toyota T, Balan S, Matsuzaki H, Iwata Y et al: Investigation of the fatty acid transporter-encoding genes SLC27A3 and SLC27A4 in autism. Scientific reports 2015, 5(1):16239.
Zhao Y, Liu Y, Chen Z, Korteweg C, Gu J: Immunoglobulin g (IgG) expression in human umbilical cord endothelial cells. The journal of histochemistry and cytochemistry: official journal of the Histochemistry Society 2011, 59(5):474–488.
Khaiboullina SF, Levis S, Morzunov SP, Martynova EV, Anokhin VA, Gusev OA, St Jeor SC, Lombardi VC, Rizvanov AA: Serum Cytokine Profiles Differentiating Hemorrhagic Fever with Renal Syndrome and Hantavirus Pulmonary Syndrome. Frontiers in immunology 2017, 8:567.
Perwitasari O, Yan X, O'Donnell J, Johnson S, Tripp RA: Repurposing Kinase Inhibitors as Antiviral Agents to Control Influenza A Virus Replication. Assay and drug development technologies 2015, 13(10):638–649.
Ahmad S, Pasha Km M, Raza K, Rafeeq MM, Habib AH, Eswaran M, Yadav MK: Reporting dinaciclib and theodrenaline as a multitargeted inhibitor against SARS-CoV-2: an in-silico study. Journal of biomolecular structure & dynamics 2022:1–11.
Wang S, Zhang J, Ye X: [Protein kinase inhibitor flavopiridol inhibits the replication of influenza virus in vitro]. Wei sheng wu xue bao = Acta microbiologica Sinica 2012, 52(9):1137–1142.
Yamamoto M, Onogi H, Kii I, Yoshida S, Iida K, Sakai H, Abe M, Tsubota T, Ito N, Hosoya T et al: CDK9 inhibitor FIT-039 prevents replication of multiple DNA viruses. The Journal of clinical investigation 2014, 124(8):3479–3488.
Nelson J, Roe K, Orillo B, Shi PY, Verma S: Combined treatment of adenosine nucleoside inhibitor NITD008 and histone deacetylase inhibitor vorinostat represents an immunotherapy strategy to ameliorate West Nile virus infection. Antiviral research 2015, 122:39–45.
Banerjee NS, Moore DW, Broker TR, Chow LT: Vorinostat, a pan-HDAC inhibitor, abrogates productive HPV-18 DNA amplification. Proceedings of the National Academy of Sciences of the United States of America 2018, 115(47):E11138-e11147.
Sung JA, Sholtis K, Kirchherr J, Kuruc JD, Gay CL, Nordstrom JL, Bollard CM, Archin NM, Margolis DM: Vorinostat Renders the Replication-Competent Latent Reservoir of Human Immunodeficiency Virus (HIV) Vulnerable to Clearance by CD8 T Cells. EBioMedicine 2017, 23:52–58.
Jorgensen SCJ, Tse CLY, Burry L, Dresser LD: Baricitinib: A Review of Pharmacology, Safety, and Emerging Clinical Experience in COVID-19. Pharmacotherapy 2020, 40(8):843–856.
Bernstein D, Tripathi R, Cohen DE: Ombitasvir, paritaprevir, and ritonavir with peginterferon-α2a plus ribavirin in treatment-experienced patients with chronic hepatitis C virus genotype 1 infection. Hepatic medicine: evidence and research 2019, 11:35–40.
Faquetti ML, Grisoni F, Schneider P, Schneider G, Burden AM: Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection. Scientific reports 2022, 12(1):7843.

No competing interests reported.

Additionalfile1.xlsx

Download PDF

Version 1

posted

You are reading this latest preprint version

Meta-Analysis of Transcriptomic Datasets Quantifies Differential Gene Expression, Affected Pathways, and Predicted Drugs from Hantavirus-Infected Human Material

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Data Acquisition

Data Processing

Results

In Vivo Differentially Expressed Genes (DEGs)

In Vitro Differentially Expressed Genes (DEGs)

Pathway Analysis

Potential Therapeutic Prediction

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1