Correlation of altered expression of a long non-coding RNA, NEAT1, in peripheral blood mononuclear cells with dengue disease progression

Accepted Manuscript Title: Correlation of altered expression of a long non-coding RNA, NEAT1, in peripheral blood mononuclear cells with dengue disease progression Author: Abhay Deep Pandey, Saptamita Goswami, Shweta Shukla, Shaoli Das, Suman Ghosal, Manisha Pal, Bhaswati Bandyopadhyay, Vishnampettai Ramachandran, Nandita Basu, Vikas Sood, Priyanka Pandey, Jayprokas Chakrabarti, Sudhanshu Vrati, Arup Banerjee PII: DOI: Reference:

S0163-4453(17)30308-0 https://doi.org/doi:10.1016/j.jinf.2017.09.016 YJINF 3990

To appear in:

Journal of Infection

Accepted date:

19-9-2017

Please cite this article as: Abhay Deep Pandey, Saptamita Goswami, Shweta Shukla, Shaoli Das, Suman Ghosal, Manisha Pal, Bhaswati Bandyopadhyay, Vishnampettai Ramachandran, Nandita Basu, Vikas Sood, Priyanka Pandey, Jayprokas Chakrabarti, Sudhanshu Vrati, Arup Banerjee, Correlation of altered expression of a long non-coding RNA, NEAT1, in peripheral blood mononuclear cells with dengue disease progression, Journal of Infection (2017), https://doi.org/doi:10.1016/j.jinf.2017.09.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Correlation of altered expression of a long non-coding RNA, NEAT1, in peripheral blood

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mononuclear cells with dengue disease progression

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Running Title: NEAT1 lncRNA in dengue infection

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Abhay Deep Pandey1#, Saptamita Goswami2#, Shweta Shukla3, Shaoli Das4, Suman Ghosal4,

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Manisha Pal5, Bhaswati Bandyopadhyay2, Vishnampettai Ramachandran3, Nandita Basu2, Vikas

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Sood1, Priyanka Pandey6, Jayprokas Chakrabarti4, Sudhanshu Vrati1, 7, Arup Banerjee1#

Comment [A1]: AU: Please verify the change to the title.

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1

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Technology Institute (THSTI), Faridabad-121001, India.

Vaccine and Infectious Disease Research Center (VIDRC), Translational Health Science and

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2

Calcutta School of Tropical Medicine (STM), Kolkata, West Bengal 700073, India.

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3

University College of Medical Sciences (UCMS) & Guru Teg Bahadur (GTB) Hospital, Delhi-

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110095, India.

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Indian Association for the Cultivation of Science, Jadavpur , Kolkata, India

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Department of Statistics, Calcutta University, Kolkata-700019, India.

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National Institute of Biomedical Genomics (NIBMG), Kalyani, West Bengal- 741251, India.

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Regional Center for Biotechnology (RCB), Faridabad, India

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Competing interests: The authors declare that they have no competing interests.

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Funding: This work was supported by the Department of Biotechnology (DBT), Government of

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India grant number (BT/PR8597/MED/29/764/2013) to AB, BB, VR. SV.

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# contributed equally

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Address correspondence to

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Arup Banerjee

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Vaccine and Infectious Disease Research Center (VIDRC)

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Translational Health Science and Technology Institute (THSTI) 1

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NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, PO Box

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#04, Faridabad -121001, E-mail: [email protected].

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Phone: +91-129 2876325 (O), Fax: +91-129 2876402

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Summary: 

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RNA-Seq analyses identify NEAT1 as common lncRNAs that are differentially expressed in dengue-infected patients.

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

Reduced expression of NEAT1 was observed in severe dengue patients.

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

The NEAT1 expression may be useful to monitor dengue disease progression.

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

Inverse relationship of NEAT1 and IFI27 as well as pro-apoptotic gene expression may

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link to severe dengue phenotype

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Abstract

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The association of long non-coding RNAs (lncRNAs) with dengue disease progression is currently

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unknown. Therefore, the present study aimed to identify lncRNAs in different categories of dengue

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patients and evaluate their association with dengue disease progression. Herein, we examined the

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expression profiles of lncRNAs and protein-coding genes between other febrile illness (OFI) and

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different grade of dengue patients through high-throughput RNA sequencing. We identified Nuclear

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Enriched Abundant Transcript 1 (NEAT1) as one of the differentially expressed lncRNAs

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(adjusted P ≤ 0.05 and log-fold change ≥ 2) and subsequently validated the expression by qRT-

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PCR. The co-expression analysis further revealed that NEAT1 and the coding gene IFI27 were

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highly co-expressed and negatively correlated with dengue severity. Using regression analysis, we

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observed that NEAT1 expression was significantly dependent on disease progression (Coefficient =

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-0.27750, SE Coefficient = 0.07145, and t = -3.88). Further, receiver operating characteristic (ROC)

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curve revealed that NEAT1 expression could discriminate DI from DS (sensitivity and specificity of

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100% (95%CI: 85.69 – 97.22) and area under the curve (AUC) = 0.97). Overall, the results of this 2

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study offer the first experimental evidence demonstrating the correlation between lncRNAs and

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severe dengue phenotype. Monitoring NEAT1and IFI27 expression in PBMC may be useful in

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understanding dengue virus-induced disease progression.

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Key words: LncRNAs, NEAT1, IFI27, Severe Dengue

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Introduction:

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Dengue fever is now recognized as one of the most important mosquito-borne human disease of the

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21st century, caused by the dengue virus. The global incidence of the dengue infection has now

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increased enormously. An estimated 50–100 million cases of dengue infections are reported

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annually from more than 100 tropical and sub-tropical countries in the world (1). The virus is

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known to develop vascular permeability and cerebral edema, leading to the most fatal severe

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dengue. The global epidemiology of dengue fever is changing fast. Dengue infection is endemic in

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India. In recent years, the disease has changed its course, manifesting in the severe form and with

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increasing frequency of outbreaks (2, 3). So far, 12 outbreaks of dengue virus infection have been

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reported in Delhi since 1997, with the last reported in 2015.

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To date, dengue research has primarily focused on the dysregulation of protein-coding genes.

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Recently, increasing evidence has confirmed the crucial roles of long non-coding RNAs (lncRNAs)

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in host antiviral response, indicating its role in modulating host immunity during viral infection (4-

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7). LncRNAs are endogenous cellular RNAs that are mRNA-like in length but lack coding

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potential. LncRNAs may exist in different forms (antisense, intronic, and intergenic) and may

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overlap with protein-coding loci (8, 9). According to GENCODE v24, the total estimated number of

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lncRNAs is likely ~20,000 transcripts; however, to date, only ~200 lncRNAs have been well

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characterized (10). LncRNAs are the functional end product, and the level of lncRNA expression

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correlates directly with the amount of the active molecule, thus providing natural advantages over

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the use of protein-coding RNAs. Moreover, lncRNAs show greater tissue specificity than miRNAs 3

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and protein-coding mRNAs, making them an attractive candidate to use as novel diagnostic and

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prognostic biomarkers for several diseases (11-13).

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Knowing the importance of lncRNAs in viral infection, studies on lncRNAs in dengue patients and

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their association with disease progression are largely unknown. Therefore, we undertook a study to

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identify the transcriptional signature in the peripheral blood mononuclear cells (PBMCs) isolated

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from clinically and virologically well-characterized patients with mild and severe dengue infection

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and establish their correlation with disease progression. We recently published the transcriptional

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data obtained from PBMCs of 31 different categories of dengue-infected patients (14). Analysis of

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RNA-Seq data also allowed us to identify the dysregulated lncRNAs and study their association

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with dengue disease progression.

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In this study, (1) we identified known lncRNAs/genes from RNA-Seq data of dengue patients and

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controls, (2) we predicted the possible functions of the identified lncRNAs by checking their co-

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expression pattern with protein-coding transcripts, (3) we also identified the pathways and

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biological processes that the candidate lncRNAs are associated with, and (4), finally, we evaluated

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the association between lncRNAs and co-expressed genes to predict clinical outcome.

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Our comprehensive study identified NEAT1 as one of the differentially expressed lncRNAs.

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Reduced expression of NEAT1 lncRNAs was observed in severe dengue patients. Further

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investigation revealed that NEAT1 and IFI27 expression inversely correlated with dengue virus-

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induced disease progression.

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Materials and Methods

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Ethics approval and consent to participate

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The Institutional ethical committee of three participating institutes (THSTI; Calcutta School of

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Tropical Medicine (STM), Kolkata; and University College of Medical Sciences (UCMS) & Guru

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Teg Bahadur (GTB) Hospital, Delhi) approved the study protocol. Before blood collection, all the

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patients (>12 years) had given their written consent and agreed to participate in this study. The

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experimental methods proceeded in compliance with standard guidelines.

Comment [A2]: AU: We are unsure what “their” is refering to. Do you mean “transcriptional signature”? If so, then please revise “their” to “its”

Comment [A3]: AU: Please check the italicization of gene terms, both in the manuscript and abstract/table.

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Sample collection and clinical data enrollment

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Following WHO criteria (2009) (15), we enrolled a total of 221 dengue suspected patients for this

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study. These patients registered at the outpatient clinic of STM (Kolkat) and UCMS & GTB

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Hospital (Delhi) for the treatment of febrile illness. Plasma samples were aliquoted and stored

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immediately at -80˚C for serological analysis and genotyping. Platelet count and PBMC isolation

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were done from the whole blood within 4 h of sample collection. PBMCs were separated using

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Lymphoprep solution in specialized 15-ml tubes (SepMate-15, # 15425, STEMCELL Technologies,

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Vancouver, Canada). Isolated PBMCs were lysed in TRIzol reagent (Invitrogen, Carlsbad, CA,

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USA). The total RNA was isolated using RNeasy kit (Qiagen, Hilden, Germany) and stored at -

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80ºC until use. Some of the dengue-positive patients were further followed up after an interval of 4

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days. Blood samples were collected both at day “0” (first visit) and day “5” (follow-up visit).

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Subsequently, platelet counts were measured for all samples. Along with sample collection, clinical

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and demographic data was also recorded.

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Dengue diagnosis

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Dengue virus infection was confirmed by the presence of viral nonstructural protein NS1 or the

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dengue-specific immunoglobulin M (IgM) by enzyme-linked immunosorbent assay (ELISA)

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(Panbio, supplied by National Institute of Virology, Pune). The infecting serotype was determined

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by using the CDC DENV 1-4 Real-Time RT-PCR assay kit and further confirmed by sequencing as

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described earlier (14).

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Library preparation, RNA sequencing, and analysis

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High-throughput RNA sequencing of PBMCs was done using HiSeq 2500 (Illumina, San Diego,

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CA, USA), as described earlier (14). Briefly, depletion of the rRNA was performed using Ribo-

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zero rRNA removal kit (Illumina, San Diego, CA, USA). The RNA-Seq library preparation was

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performed using TruSeq® RNA Library Prep Kit v2 (Illumina, San Diego, CA, USA) without a

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selection of the poly-A plus RNA. This process allowed us to sequence both mRNA and lncRNAs.

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Average fragment size of final libraries was found to be 306 ±10.5 bp, and paired-end sequencing 5

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(2 x 100 bp) was performed for all the RNA samples at National Institute of Biomedical Genomics

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(NIBMG), Kalyani.

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In silico analysis used for the identification of differentially expressed mRNAs and lncRNAs is

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depicted in figure 1. RNA-Seq data analysis was performed using the Tuxedo protocol (16). The

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known lncRNAs in this study were identified by comparing our data against the annotated lncRNAs

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available in GENCODE v24.

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Co-expression analysis

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We calculated Pearson’s correlation coefficient between the expression values of dysregulated

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lncRNAs and coding genes in different disease conditions. Statistically significant co-expressed

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pairs were filtered out with a threshold of >0.7 in disease conditions. Co-expression network was

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generated using Cytoscape (version 3.4.0). The lncRNA co-expressing mRNAs in different clinical

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stages further mapped for pathway analysis using MetaScape (17) (www.metascape.org/).

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Quantitative real-time PCR

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We used quantitative real-time PCR (qRT-PCR) to validate the expression of NEAT1 and

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associated genes. For each sample, total RNA input was of approximately 100 ng, and cDNA was

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synthesized by reverse transcription using the Prime Script reverse transcription kit (Takara Bio Inc.

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Otsu, Shiga, Japan). Expression levels of NEAT1, CCL2, CCL8, and IFI27 were detected by qRT-

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PCR

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Hs01086373_g1, respectively). The human GAPDH (Hs99999905_m1; Applied Biosystems, USA)

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was used to normalize the gene expression. The qRT-PCR was performed in 384 wells using teal-

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time PCR (Quant Studio™6 flex, Applied Biosystems, Foster City, CA, USA). Each qRT-PCR

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reaction was performed in duplicate, and the mean threshold cycle (Ct) value for each sample was

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used for data analysis. The δδct method was used for calculating the fold change.

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Statistical analysis

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All statistical analyses for RNA-Seq were performed using R software package. The significant

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differentially expressed genes (DEGs) and transcripts were analyzed using the Cuffdiff and

using

TaqMan

assay

(Hs01008264_s1,

Hs00234140_m1,

Hs04187715_m1,

and Comment [A4]: AU: Please verify the addition.

6

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cummeRbund package. A p-value of <0.05 and log fold change (LFC) value >2 was considered to

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indicate a significant difference. Statistical analyses were conducted using the statistical software

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MINITAB 15. Paired t-test was used to test for significant differences between mean platelet counts

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and means normalized NEAT1 for data collected on the same set of patients on two different days

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(day 0 and day 5). Fisher’s t-test was used to test whether the platelet count and normalized NEAT1

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are correlated. Non-parametric Mann-Whitney U test was used to evaluate statistical differences

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between lncRNA expression levels between different groups. A regression analysis was conducted

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to study the dependence of normalized NEAT1 on age, gender, and platelet count and disease

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progression. Receiver operating characteristic (ROC) analysis was performed to check the

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specificity and sensitivity of lncRNA.

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Availability of data and material: All the RNA-Seq data were submitted to GEO (accession

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number GSE94892). Dengue virus sequences were submitted to GenBank database (accession

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numbers KY404121-KY404147).

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Results

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Patients

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A total of 166 dengue-positive patients and 61 dengue-negative patients with other febrile illness

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(OFI) were included in this study. Among them, we evaluated the transcriptome of total 39 cases by

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performing RNA sequencing (discovery cohort; Table 1). The remaining 188 were included in the

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validation study of lncRNAs and genes. The validation cohort comprised of 53 dengue-negative

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patients with OFI and 135 dengue-infected patients. We classified dengue patients according to

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WHO guidelines as dengue infection (DI=31), dengue with a warning sign (DWS= 47), and severe

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dengue (DS=57). Clinical and virological parameters are listed in Table 1. We also included blood

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samples from dengue patients (n=12) who were followed up on day 0 (first visit) and day 5

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(subsequent visit). Blood samples were also collected at 28 days post infection from 18 DS patients

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with a platelet count <20,000/mm3 at the time of their first visit and included in this study. Blood

Comment [A5]: AU: Both “dengue-infected patients” and “dengue-positive patients” are used in the document. Please revise for consistency.

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samples from 11 apparently healthy individuals without history of dengue infection were included

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for comparisons.

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Differential expression pattern of lncRNAs in DI, DWS, and DS patients

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LncRNAs play a vital role in viral infection and disease progression. To confirm if such

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lncRNAs/genes exist with dengue virus-induced disease progression, we undertook high-throughput

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RNA sequencing approach to investigate the host transcriptional signature in dengue patients. When

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we compared each group against patients with OFI, we noted a 2-3 fold higher number of the

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transcripts differentially expressed in DS patients than in patients with uncomplicated DI (Figure

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2A). We further processed our data to look into lncRNAs that differentially expressed in our

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patient's group. Comparing our results against GENCODE v24, we identified a total of 100

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lncRNAs (Log fold change (LFC) > 2 and p-value < 0.05) that are differentially expressed in

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dengue patients (Figure 2B). Among them, 24 lncRNAs were common between DWS and DS.

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Heatmap analysis demonstrated a snapshot of separate clustering of dysregulated lncRNAs in

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disease group and control samples (Figure 2C, Table S1). The only lncRNA that was common in

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all the three groups was the Nuclear Enriched Abundant Transcript 1 (NEAT1).

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Co-expression study between differentially expressed lncRNAs and coding genes

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Coding–non-coding gene co-expression network analysis is considered a powerful method for

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functional analysis of lncRNAs (7). To decipher the transcriptional regulatory relationship between

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lncRNAs and coding genes, we undertook co-expression analysis between differentially expressed

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lncRNAs and coding genes in control and disease conditions. Statistically significant co-expressed

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pairs were filtered out, and pathway enrichment analysis was performed using the highly co-

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expressed genes.

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The functional analysis of the co-regulated genes suggested that cytokine- and chemokine-

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mediated signaling pathways were affected the most in DI patients (Figure 3A), whereas genes

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common for DWS and DS patients were associated with unfolded protein response, cytokine

Comment [A6]: AU: Please verify this addition throughout.

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response to a stimulus, and apoptotic signaling pathway (Figure 3B, C). Interestingly, leukocyte

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migration-associated genes were unique in DS.

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Because NEAT1 was common to all three groups, we also analyzed NEAT1-regulated pathways.

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Our study suggested that NEAT1 is probably involved in the regulation of nucleocytoplasmic

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transport and nicotinamide nucleotide metabolic process (Figure 3D). Co-expression network

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analysis further indicated a direct association between NEAT1 and IFI27 (Figure 3E).

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Validation of NEAT1 expression in dengue-infected patients

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Among dysregulated lncRNAs, we focused on NEAT1 in dengue patients. RNA-Seq data revealed

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that NEAT1 expression was downregulated in DS patients. Therefore, to confirm NEAT1

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expression in dengue patients, we performed qRT-PCR for the dengue-infected and uninfected

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PBMC samples. NEAT1 levels measured in this study refer to the expression of both NEAT1

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isoforms because NEAT1 primers were designed to hybridize to sequences common between

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NEAT1_1 and NEAT1_2 transcripts. Subsequently, we explored if there was an association between

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NEAT1 expression levels and other clinical factors in 135 NS1-positive dengue patients. As shown

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in Figure 4A, we observed a downregulation trend of NEAT1 expression in dengue-infected patients

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as compared to uninfected dengue patients. Further, this NEAT1 downregulation was more

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pronounced in patients with higher dengue severity (Figure 4B).

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It was recommended earlier that patients with <50,000/mm3 platelet count with dengue infection

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require constant monitoring and may need platelet transfusion (18, 19). In our study samples, we

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observed several patients with low platelet count (range between 10,000 and 49,000/mm3).

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Therefore, to check if NEAT1 expression had any association with platelet count, we further

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arbitrarily categorized our samples into three groups, Group 1, ≥50,000/mm3; Group 2, >20,000 to

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<50,000/mm3; and Group 3, ≤20,000/mm3, and further explored the association of platelet count

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and NEAT1 expression . As shown in Figure 4C, there were no significant changes observed in

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NEAT1 expression in patients with OFI, whereas suppression of NEAT1 expression was noted in

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dengue patients with platelet count <50,000/mm3.

Comment [A7]: AU: Please verify the change.

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Interestingly, dengue-positive patients with platelet count >50,000/mm3 showed significantly higher

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NEAT1 expression than patients with OFI having the same range platelet count. However, compared

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with patients with OFI (<50,000/mm3), significant suppression of NEAT1 expression was evident in

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dengue-infected patients having platelet count <50,000/mm3 (Figure 4D).

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We further segregated patients having platelet count <50,000/mm3 into two groups: (1) patients

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with platelet count >20,000 to <50,000/mm3 and (2) patients with platelet count ≤20000/mm3. We

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observed that even in the <50,000/mm3 platelet count group, suppression of NEAT1 expression was

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more significant in dengue-infected patients with platelet count ≤20,000/mm3 (Figure 4E).

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To examine the effects of age, gender, and platelet count and disease progression on normalized

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NEAT1, we performed a regression analysis as shown in Table 2. Our results suggested that age,

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gender, and platelet count of a patient did not affect his/her NEAT1 level significantly. However, the

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NEAT1 level was substantially dependent on disease progression (coefficient = -0.27750, SE

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coefficient = 0.07145, and t = -3.88). Further, negative coefficient of disease progression indicated

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that NEAT1 level diminished with the progression of the illness. Overall, these data suggest a strong

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association of NEAT1 suppression with dengue disease progression.

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NEAT1 and IFI27 expression negatively correlate with dengue disease progression

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Because NEAT1 was common and differentially expressed in the patient group, we assumed it

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might modulate the expression of the genes that were common to all dengue-infected groups. To

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identify genes that are co-expressed during dengue infection, we performed the co-expression

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network analysis considering all the dysregulated genes. Co-expression network analysis between

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dysregulated mRNAs revealed that IFI27, USP18, CCL2, and CCL8 highly interconnected with

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several inflammatory cytokines and interferon regulatory genes (Figure 5A). Previously, it was

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reported that NEAT1 could modulate inflammatory gene expression (20). Knockdown of NEAT1

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also suppressed the following gene expressions, namely of IFI27, CCL8, and CCL2, in THP1 cells

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(20). As all these genes were common in our transcriptome analysis (14), we assumed that the

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expression of these genes is associted with NEAT1. Thus, we next validated their expression in the 10

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three different disease groups and assessed their correlation with NEAT1 expression. Consistent

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with the transcriptome data and the data reported by Zhang et al. (20), CCL2 and CCL8 gene

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expression positively correlated with NEAT1 expression and was significantly suppressed in DS as

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compared to that in uncomplicated DI (Figure 5C-D). However, an inverse expression pattern of

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IFI27 was observed in the DS group (Figure 5B).

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We also assessed NEAT1 and other associated gene manifestations in follow-up patients (Figure 6).

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We followed up 12 patients whose platelet count drastically reduced at day 5. A pairwise analysis

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on the monitoring patients confirmed that suppression of NEAT1 expression was associated with the

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rapid decrease in platelet count in dengue-infected patients (Figure 6A). This was further supported

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by the correlation analysis shown in Figure 6B and Table 3 [between platelet count on day 0 and

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day 5, coefficient = -0.6199 (p-value=0.0158); normalized NEAT1 expression between day 0 and

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day 5, coefficient = 0.6546, (p-value=0.0104)]. Moreover, the IFI27 expression in most of the

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patients was upregulated as platelet count dropped (Figure 6C). However, a decreasing trend for

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other genes in follow-up patients was observed in this study (Figure 6 D, E). Overall, these data

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further confirm an inverse correlation between NEAT1 and IFI27, which may be linked to disease

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progression.

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Monitoring NEAT1 expression could be useful to comprehend dengue disease progression

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To know whether these genes truly correlate with disease progression, we checked their expression

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level in healthy donors and in patients recovering from severe dengue. NEAT1 expression was

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significantly higher in healthy donors than in OFI or DS group. Further, the NEAT1 expression in

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DS (n=18; platelet count <20,000/mm3) patients after recovery from the infection was shown to be

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upregulated, as observed in healthy donors (Figure 7A). In addition, when we measured IFI27

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expression, we found minimal expression in healthy donors as compared to disease control and

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dengue-infected patients. However, IFI27 expression in DS patients after recovery significantly

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reduced and became comparable with that in healthy donors (Figure 7B). Other genes did not show

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any such correlation (Figure 7C, D). 11

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To discriminate DI from DS, we generated ROC curve.

ROC analysis revealed that NEAT1

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expression could distinguish DI from DS (sensitivity and specificity of 100% (95%CI: 85.69–

3

97.22) and area under the curve (AUC) = 0.97). Subsequently, the specificity of IFI27 and NEAT1

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was assayed using ROC (Figure 8B, C). Our results suggested that NEAT1 and IFI27 expression

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level could distinguish between severe dengue (AUC=0.99 with 95%CI: 96.86-100.0) and

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recovering condition (AUC=0.89 with 95%CI: 78.44-100.0) when compared against patients with

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OFI. Overall, this data suggests that monitoring the co-expression pattern of lncRNA NEAT1 and

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IFI27 genes is useful in understanding dengue disease progression.

9

Discussion

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This study provides the first experimental evidence, demonstrating the association between lncRNA

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and dengue disease progression. The study is unique in many aspects. First, we used high-

12

throughput RNA sequencing method to identify the dysregulated lncRNAs and genes involved in

13

dengue disease progression. Second, co-expression analysis between lncRNA and protein-coding

14

genes allowed us to understand the biological processes that are possibly regulated through

15

dysregulated lncRNAs. Third, from the list of dysregulated lncRNAs, we specifically studied the

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NEAT1 lncRNA expression and its association with dengue disease progression. We observed a

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reduced expression of NEAT1 lncRNA in DS patients and could associate this with severe dengue

18

phenotype. Moreover, NEAT1 expression along with platelet count could be useful to monitor

19

dengue disease progression. Fourth, we also verified the gene expression that is co-expressed

20

during dengue infection in a different disease condition. We observed an inverse relationship

21

between NEAT1 and IFI27 as well as pro-apoptotic gene expression, and this may be associated

22

with disease pathogenesis

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In our study, we noted a dynamic expression pattern of lncRNAs during dengue viral infection.

24

With the co-expression analysis, which is considered a powerful tool to identify lncRNA-regulated

25

function (7, 21), we demonstrated that lncRNAs common to DWS and DS possibly regulate

26

apoptosis, response to cytokine stimulus, and UPR process. In a recent study, we reported that 12

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genes involved in leukocyte-mediated immunity and migration play an important role in the

2

development of DS (14). Here also, we found that leukocyte migration process uniquely enriched in

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DS could be potentially regulated through lncRNAs, suggesting the possible association of

4

dysregulated lncRNAs with the development of DS.

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The NEAT1 is encoded on chromosome 11q13.1 and formed from a single intergenic exon. The

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lncRNA NEAT1 is highly conserved within the mammalian lineage (22). In mice, in context to

7

HIV1, Japanese encephalitis, and rabies viral infection, NEAT1 lncRNA expression is upregulated

8

(23,

9

(doi: https://doi.org/10.1101/061788). In our study, we observed that DI patients with platelet count

10

>50,000/mm3 have a higher NEAT1 expression than patients with OFI (Figure 4B, D). However,

11

this expression pattern drastically reduced as the disease progressed. Therefore, it is be possible that

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NEAT1 expression varies depending on cell source, virus infection, and disease condition. As

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monocytes are the primary source for NEAT1 in PBMCs of healthy individuals (20), one can

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speculate that NEAT1 suppression in severe dengue cases is due to loss of monocytes, as observed

15

in some studies (25, 26). Vice versa, it may also be possible that low expression of NEAT1 can

16

induce apoptosis in the monocytes, as observed in other cells (27-29). NEAT1 can regulate

17

apoptosis through P53 (27) and is an important regulator of cell survival and differentiation in

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myeloid lineage cells (28). Downregulation of NEAT1 expression induces apoptosis in breast cancer

19

cell lines (29). Our co-expression analysis revealed that NEAT1 regulated genes mainly involved in

20

nicotinamide nucleotide metabolic process (Figure 3D). Dysregulation of this process is also

21

known to induce mitochondrial dysfunction and apoptosis in the cells (30).

22

IFI27, highly expressed in the PBMCs of DS patients,

23

contributes to IFN-induced apoptosis through perturbation of normal mitochondrial function.

24

Earlier studies suggested that dengue virus infection can cause mitochondrial dysfunction and

25

apoptosis in the infected monocytes (31, 32). In HIV-infected patients, upregulation of IFI27

26

expression in monocytes is considered as one of the determinants of constitutive apoptosis (33).

24),

whereas

upon

Zika

virus

infection,

it

is

downregulated

is also a mitochondrial protein that

13

Page 13 of 30

1

Studies also suggested that ectopic expression of IFI27, in various cell lines, leads to apoptosis of

2

the cells (34). In this study, we observed that IFI27 genes were highly expressed in severe dengue

3

and negatively correlated with NEAT1 expression. Considering that dengue virus infection induces

4

apoptosis in monocytes, further in vitro studies are required to understand the role of NEAT1 and

5

IFI27 association in this process.

6

In a study published earlier, Zhang et al. (20) showed a positive correlation between NEAT1 and

7

IFI27 expression in LPS-treated THP1 cells. They suggested that knockdown of NEAT1 in LPS-

8

treated monocytes downregulated various inflammatory genes’ expression, including IFI27. This

9

result is contrasting to what we have observed in our study. A fundamental difference between these

10

two studies is that while the results reported by Zhang et al. are based on LPS treatment in context

11

to NEAT1-mediated inflammatory responses on monocyte cell line, our data are from PBMCs of

12

dengue virus-infected patients. Other than monocytes, the PBMCs also include other cells such as

13

B-cell, T-cells, and NK cells. One of the limitations of our study is that we do not know which cell

14

population produced NEAT1 lncRNAs during dengue infection. Future research on different cell

15

populations from PBMCs will clarify this issue. Interestingly, in context to Hantaan virus (HTNV)

16

infection, which also causes hemorrhagic fever, NEAT1 downregulation in vitro or in vivo reported

17

delaying host innate immune responses and aggravated HTNV replication (35).

18

Overall, our data suggest that lncRNA expression may dynamically change with the dengue disease

19

progression. Our comprehensive study identified NEAT1 as one of the differentially expressed

20

lncRNAs. Reduced expression of NEAT1 lncRNAs is associated with severe dengue phenotype.

21

Examining the expression of NEAT1 and one of the co-expressed genes, IFI27, in healthy controls

22

and patients with dengue infection (during the acute and convalescent phases) further points toward

23

its potential association with dengue pathogenesis. However, to use lncRNA as a prognostic marker,

24

future studies will be required not only to evaluate the expression of NEAT1 in thye plasma of

25

different healthy and diseased groups but also to quantify it accurately.

14

Page 14 of 30

1

From our results, we conclude that monitoring NEAT1and IFI27 expression in dengue patients may

2

be useful in understanding dengue virus-induced disease progression.

3 4

Declarations

5

Ethics approval and consent to participate: The Institutional ethical committee of three

6

participating institutes (THSTI; STM, Kolkata; and UCMS & GTB Hospital, Delhi) approved the

7

study protocol. All patients (>12 years) enrolled in this study provided written consent form. The

8

experimental methods proceeded in compliance with recommended guidelines.

9

Consent for publication: All the authors have read the manuscript and given their approval for

10

submitting it to an appropriate journal.

11

Competing interests: The authors declare that they have no competing interests.

12

Funding: This work was supported by the grant (grant number: BT/PR8597/MED/29/764/2013,

13

Department of Biotechnology (DBT), Government of India) to AB, BB, VR. SV.

14

Authors' contributions: AB, VS, and SV conceived the idea. AB and SV were responsible for

15

experimental design, data analysis, and drafting the manuscript. SS and SG collected blood

16

samples, separated PBMCs, isolated total RNA, and performed the RNA extraction, RT-PCR,

17

genotyping. AP performed in silico analysis of RNA-Seq data processing under the guidance of PP.

18

AP, SD, and SG together developed lncRNA analysis pipeline under the supervision of JC. AP also

19

performed coding–non-coding gene co-expression network analysis. VR, BB, and NBa supervised

20

patients, sample collection process, collection of all relevant clinical information, and

21

categorization of patients into different groups; supervised the experiments; and participated in

22

drafting and editing the manuscript. MP did the statistical analysis and interpretation.

23

Acknowledgments: We are thankful to all technical staff of Department of Virology, School of

24

Tropical Medicine, and University College of Medical Sciences & Guru Teg Bahadur Hospital,

25

Delhi, for collecting and processing the blood samples. We sincerely acknowledge the people who 15

Page 15 of 30

1

gave their consent for participating in this study. We acknowledge the Department of Biotechnology

2

(DBT),

3

BT/PR8597/MED/29/764/2013.

Government

of

India,

for

their

financial

support

(grant

number:

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

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14. Banerjee A, Shukla S, Pandey AD, Goswami S, Bandyopadhyay B, Ramachandran V, et al. RNA-Seq analysis of peripheral blood mononuclear cells reveals unique transcriptional signatures associated with disease progression in dengue patients. Transl Res. 2017 Aug;186:62-78 e9. PubMed PMID: 28683259. 15. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control: New Edition. WHO Guidelines Approved by the Guidelines Review Committee. Geneva2009. 16. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012 Mar 01;7(3):562-78. PubMed PMID: 22383036. Pubmed Central PMCID: PMC3334321. 17. Tripathi S, Pohl MO, Zhou Y, Rodriguez-Frandsen A, Wang G, Stein DA, et al. Meta- and Orthogonal Integration of Influenza "OMICs" Data Defines a Role for UBR4 in Virus Budding. Cell Host Microbe. 2015 Dec 09;18(6):723-35. PubMed PMID: 26651948. Pubmed Central PMCID: PMC4829074. 18. Makroo RN, Raina V, Kumar P, Kanth RK. Role of platelet transfusion in the management of dengue patients in a tertiary care hospital. Asian J Transfus Sci. 2007 Jan;1(1):4-7. PubMed PMID: 21938225. Pubmed Central PMCID: PMC3168133. 19. de Azeredo EL, Monteiro RQ, de-Oliveira Pinto LM. Thrombocytopenia in Dengue: Interrelationship between Virus and the Imbalance between Coagulation and Fibrinolysis and Inflammatory Mediators. Mediators Inflamm. 2015;2015:313842. PubMed PMID: 25999666. Pubmed Central PMCID: PMC4427128. 20. Zhang F, Wu L, Qian J, Qu B, Xia S, La T, et al. Identification of the long noncoding RNA NEAT1 as a novel inflammatory regulator acting through MAPK pathway in human lupus. J Autoimmun. 2016 Dec;75:96-104. PubMed PMID: 27481557. 21. Guo X, Gao L, Liao Q, Xiao H, Ma X, Yang X, et al. Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks. Nucleic Acids Res. 2013 Jan;41(2):e35. PubMed PMID: 23132350. Pubmed Central PMCID: PMC3554231. 22. Hutchinson JN, Ensminger AW, Clemson CM, Lynch CR, Lawrence JB, Chess A. A screen for nuclear transcripts identifies two linked noncoding RNAs associated with SC35 splicing domains. BMC Genomics. 2007 Feb 01;8:39. PubMed PMID: 17270048. Pubmed Central PMCID: PMC1800850. 23. Saha S, Murthy S, Rangarajan PN. Identification and characterization of a virus-inducible non-coding RNA in mouse brain. J Gen Virol. 2006 Jul;87(Pt 7):1991-5. PubMed PMID: 16760401. 24. Zhang Q, Chen CY, Yedavalli VS, Jeang KT. NEAT1 long noncoding RNA and paraspeckle bodies modulate HIV-1 posttranscriptional expression. MBio. 2013 Jan 29;4(1):e00596-12. PubMed PMID: 23362321. Pubmed Central PMCID: PMC3560530. 25. Espina LM, Valero NJ, Hernandez JM, Mosquera JA. Increased apoptosis and expression of tumor necrosis factor-alpha caused by infection of cultured human monocytes with dengue virus. Am J Trop Med Hyg. 2003 Jan;68(1):48-53. PubMed PMID: 12556148. 26. Singla M, Kar M, Sethi T, Kabra SK, Lodha R, Chandele A, et al. Immune Response to Dengue Virus Infection in Pediatric Patients in New Delhi, India--Association of Viremia, Inflammatory Mediators and Monocytes with Disease Severity. PLoS Negl Trop Dis. 2016 Mar;10(3):e0004497. PubMed PMID: 26982706. Pubmed Central PMCID: PMC4794248. 27. Adriaens C, Standaert L, Barra J, Latil M, Verfaillie A, Kalev P, et al. p53 induces formation of NEAT1 lncRNA-containing paraspeckles that modulate replication stress response and chemosensitivity. Nat Med. 2016 Aug;22(8):861-8. PubMed PMID: 27376578. 28. Zeng C, Xu Y, Xu L, Yu X, Cheng J, Yang L, et al. Inhibition of long non-coding RNA NEAT1 impairs myeloid differentiation in acute promyelocytic leukemia cells. BMC Cancer. 2014 Sep 23;14:693. PubMed PMID: 25245097. Pubmed Central PMCID: PMC4180842. 29. Ke H, Zhao L, Feng X, Xu H, Zou L, Yang Q, et al. NEAT1 is Required for Survival of Breast Cancer Cells Through FUS and miR-548. Gene Regul Syst Bio. 2016;10(Suppl 1):11-7. PubMed PMID: 27147820. Pubmed Central PMCID: PMC4849421. 17

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30. Stein LR, Imai S. The dynamic regulation of NAD metabolism in mitochondria. Trends Endocrinol Metab. 2012 Sep;23(9):420-8. PubMed PMID: 22819213. Pubmed Central PMCID: PMC3683958. 31. Mosquera JA, Hernandez JP, Valero N, Espina LM, Anez GJ. Ultrastructural studies on dengue virus type 2 infection of cultured human monocytes. Virol J. 2005 Mar 31;2:26. PubMed PMID: 15801983. Pubmed Central PMCID: PMC1082913. 32. Hottz ED, Oliveira MF, Nunes PC, Nogueira RM, Valls-de-Souza R, Da Poian AT, et al. Dengue induces platelet activation, mitochondrial dysfunction and cell death through mechanisms that involve DC-SIGN and caspases. J Thromb Haemost. 2013 May;11(5):951-62. PubMed PMID: 23433144. Pubmed Central PMCID: PMC3971842. 33. Patro SC, Pal S, Bi Y, Lynn K, Mounzer KC, Kostman JR, et al. Shift in monocyte apoptosis with increasing viral load and change in apoptosis-related ISG/Bcl2 family gene expression in chronically HIV-1-infected subjects. J Virol. 2015 Jan;89(1):799-810. PubMed PMID: 25355877. Pubmed Central PMCID: PMC4301149. 34. Rosebeck S, Leaman DW. Mitochondrial localization and pro-apoptotic effects of the interferon-inducible protein ISG12a. Apoptosis. 2008 Apr;13(4):562-72. PubMed PMID: 18330707. 35. Ma H, Han P, Ye W, Chen H, Zheng X, Cheng L, et al. The Long Noncoding RNA NEAT1 Exerts Antihantaviral Effects by Acting as Positive Feedback for RIG-I Signaling. J Virol. 2017 May 01;91(9). PubMed PMID: 28202761. Pubmed Central PMCID: PMC5391460.

21 22 23 24

Figure 1 Outline for identification of differentially expressed known lncRNAs and genes from

25

RNA-Seq data

26 27

Figure 2 RNA-Seq analyses identifying NEAT1 as the common lncRNA that is differentially

28

expressed in dengue-infected patients. (A) Flowchart represents the number of differentially

29

expressed genes/lncRNAs in different categories of dengue patients (Log fold change (LFC) > 2, p

30

< 0.05). (B) Venn diagram represents shared and individual lncRNAs that are dysregulated in

31

different dengue groups. (C) Heat map shows the expression of known (annotated) lncRNAs that

32

are significantly dysregulated (FDR<0.05 and/ LFC>2.5, p<0.05) in rgw three different dengue

33

groups. Annotations of known lncRNAs were derived from GENCODE v24.

34

Figure 3 Co-expression analysis of lncRNAs and protein-coding genes in different categories

35

of dengue patients. (A-C) Pathway enrichment of co-expressed genes in various grades of dengue

36

patients represented are given in the bar diagram. (D) Genes that were highly co-expressed with

37

NEAT1 were analyzed through Metascape, and highly enriched pathways were plotted depending 18

Page 18 of 30

1

on their log10 (p-value). (E) Co-expression network between NEAT1 and other associated genes

2

and lncRNAs, as visualized by Cytoscape (version 3.4.0). Oval shape represents protein-coding

3

genes; red color denotes upregulated expression, while green represents downregulated expression.

4

Rectangle shape represents downregulated lncRNAs.

5 6

Figure 4 NEAT1 expression is suppressed in severe dengue patients and associated with low

7

platelet count. (A) Relative NEAT1 expression was compared between dengue-positive and

8

negative samples. (B) NEAT1 expression was examined in three different dengue groups (DI, DWS,

9

and DS) and compared with the OFI group. Relative fold change compared to the OFI group is

10

plotted. (C-D) Dengue-positive and negative PBMC samples are clustered according to their

11

platelet count. NEAT1 expression measured by qRT-PCR and normalized to GAPDH. (E) Dengue-

12

positive and negative samples with platelet count ≤50,000/mm3 were further classified into two

13

groups, namely those with paletelet count ≤20,000/mm3 and >20,000 to ≤50,000/mm3, and NEAT1

14

expression measured by qRT-PCR. Mann-Whitney test was used to compare gene expression

15

between different dengue groups.

16

number of samples in each panel.

17

Figure 5 Co-expression analysis and validation of dysregulated genes in dengue infection. (A)

18

Co-expression networks for genes significantly dysregulated (FDR<0.05 and absolute LFC>4) in

19

three groups of dengue patients depicted using Cytoscape 3.4.0. (B-D) Gene expression of 3

20

dysregulated genes (IFI27, CCL2, and CCL8) was examined in three different dengue groups (DI,

21

DWS, and DS) and compared with the OFI group. Relative fold change compared to the OFI group

22

is plotted. Mann-Whitney test was performed to calculate statistical significance. P-value <0.05 is

23

considered significant. “n” represents the number of samples in each cluster.

24

Figure 6 NEAT1 and co-expressed gene expression in follow-up dengue patients. (A, C-D) Data

25

show changes in lncRNA/gene expression levels for NEAT1, IFI27, and CCL8 in the PBMCs of

26

follow-up samples infected with dengue virus. Follow-up patients (n=12) had an average platelet

P-value <0.05 is considered significant. “n” represents the

19

Page 19 of 30

1

count of 118000 ± 33000 at their first visit to the hospital (designated as day 0) and drastically

2

decreased platelet count (54000 ± 16000) at day 5. Three dysregulated genes were examined in

3

follow-up samples both at day 0 and day 5 by qRT-PCR. Relative fold change was plotted as log2

4

scale. (B) Correlation analysis of normalized NEAT1 and platelet count between day 0 and day 5 is

5

depicted.

6

Figure 7 Inverse relationship of NEAT1 and IFI27 as well as pro-apoptotic gene expression

7

may be linked to disease outcome. Expression patterns of four dysregulated lncRNAs/genes were

8

measured by qRT-PCR in PBMCs of OFI and DS patients at day 0 and after recovery. PBMCs from

9

healthy donors were also included. A paired t-test was performed. P-value >0.05 is considered

10

significant. “n” represents the number of samples in each group

11

Figure 8 Receiver operating characteristics (ROC) curve analyses with relative expression

12

levels of NEAT1 and IFI27. (A) Relative expression of NEAT1 yielded an AUC of 0.9145 (95%

13

CI, 0.85-0.97), discriminating DI from DS. (B-C) ROC curve analysis with NEAT1 and IFI27

14

between day 0 and day 5 are shown.

15 16 17 18 19

20

Page 20 of 30

1

Table 1. Clinical characteristics of the dengue patients Total study samples (n=227) DI (n=38) DWS (n=61) RNA-Sequencing (n=39)

DS (n=67)

OFI (n=61)

7

14

10

8

Validation cohort (n=188)

31

47

57

53

Age, year median (range)

20 (12-50)

21 (12-75)

24 (12-55)

23 (14-59)

Gender, male/female

29/9

49/12

59/8

47/14

fever day, median (range)

2 (2-4)

3 (2-5)

4 (3-6)

2(2-4)

DENV-1

9

4

3

NA

DENV-2

23

41

50

NA

DENV-3

3

4

0

NA

Mix Infection

3

11

12

NA

Non-type able

0

1

2

NA

(Discovery cohort)

Serotype

Platelet count x 103/µl

72 (50-250)

25 (21-48)

17 (7-20)

110(15-126)

(median; range) Hematocrit level, median (range) 40.5 (32-43.5) 33 (20.2-40.3) 40 (19.6-54.5) 38 (29.3-48.3) 2

NA= Not applicable

3 4 5 6

21

Page 21 of 30

1

Table 2. Regression analysis for estimating relationship among variables Predictor Coeff. SE Coeff. T pvalue Constant

1.4125

0.2959

4.77

0.000

Age

0.001051

0.006597

0.16

0.874

Gender

0.0379

0.1527

0.25

0.804

1.51

0.133

-3.88

0.000

Platelet Count Progression of disease 2 3 4 5 6

7 8 9 10

0.00000357 0.00000237 -0.27750

0.07145

Table 3: The correlation between platelet count and normalized NEAT1 on day 0 and day 5 Platelet count Normalized NEAT 1 Patient Characteristic Day 5 Day 0 0.6414* -0.0538 Platelet count Day 0 (p-value=.0432) With high platelet count 0.3681 0.8804* Normalized NEAT 1 Day 5 on day 0 (p-value=0.002) -0.2319 0.9547* Platelet count Day 0 (p-value=0.0226) With low platelet count -0.3277 0.9856* Normalized NEAT 1 Day 5 on day 0 (p-value=0.0072) -0.6199** 0.2762 Platelet count Day 0 (p-value=0.0158) All 0.1652 0.6546* Normalized NEAT 1 Day5 (p-value=0.0104) * p-value <0.05 (correlation significantly greater than 0) ** p-value <0.05 (correlation significantly less than 0)

22

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