Literature DB >> 29929468

Identification of genetic variants associated with dengue or West Nile virus disease: a systematic review and meta-analysis.

Megan E Cahill1, Samantha Conley2, Andrew T DeWan1, Ruth R Montgomery3.   

Abstract

BACKGROUND: Dengue and West Nile viruses are highly cross-reactive and have numerous parallels in geography, potential vector host (Aedes family of mosquitoes), and initial symptoms of infection. While the vast majority (> 80%) of both dengue and West Nile virus infections result in asymptomatic infections, a minority of individuals experience symptomatic infection and an even smaller proportion develop severe disease. The mechanisms by which these infections lead to severe disease in a subset of infected individuals is incompletely understood, but individual host differences including genetic factors and immune responses have been proposed. We sought to identify genetic risk factors that are associated with more severe disease outcomes for both viruses in order to shed light on possible shared mechanisms of resistance and potential therapeutic interventions.
METHODS: We applied a search strategy using four major databases (Medline, PubMed, Embase, and Global Health) to find all known genetic associations identified to date with dengue or West Nile virus disease. Here we present a review of our findings and a meta-analysis of genetic variants identified.
RESULTS: We found genetic variations that are significantly associated with infections of these viruses. In particular we found variation within the OAS1 (meta-OR = 0.83, 95% CI: 0.69-1.00) and CCR5 (meta-OR = 1.29, 95% CI: 1.08-1.53) genes is significantly associated with West Nile virus disease, while variation within MICB (meta-OR = 2.35, 95% CI: 1.68-3.29), PLCE1 (meta-OR = 0.55, 95% CI: 0.42-0.71), MBL2 (meta-OR = 1.54, 95% CI: 1.02-2.31), and IFN-γ (meta-OR = 2.48, 95% CI: 1.30-4.71), is associated with dengue disease.
CONCLUSIONS: Despite substantial heterogeneity in populations studied, genes examined, and methodology, significant associations with genetic variants were found across studies within both diseases. These gene associations suggest a key role for immune mechanisms in susceptibility to severe disease. Further research is needed to elucidate the role of these genes in disease pathogenesis and may reveal additional genetic factors associated with disease severity.

Entities:  

Keywords:  Dengue virus; Disease severity; Genetic variation; Meta-analysis; Single nucleotide polymorphism; West Nile virus

Mesh:

Substances:

Year:  2018        PMID: 29929468      PMCID: PMC6014009          DOI: 10.1186/s12879-018-3186-6

Source DB:  PubMed          Journal:  BMC Infect Dis        ISSN: 1471-2334            Impact factor:   3.090


Background

Dengue (DENV) and West Nile (WNV) viruses are mosquito-borne viruses in the Flaviviridae family, which also includes other viruses such as Zika and yellow fever. These viruses can cause disease with substantial public health impact. DENV and WNV are found in similar areas of the world, can be carried by the Aedes family of mosquitoes, have similar initial stages of infections and similar symptoms of mild febrile illness, and are highly cross-reactive; however, severe disease manifests differently for these two viruses [1-3]. West Nile Virus was first identified in Uganda in 1937, has been endemic in the United States since 1999 [4], and is estimated to have infected 3 million people [5]. While the majority of infections are asymptomatic, ~ 20% of infections lead to mild febrile disease in infected individuals and 1% of infected individual experience severe, neurological disease such as meningitis and encephalitis [6]. DENV has a vastly higher disease burden, with an estimated 50 million cases and 25,000 fatalities worldwide annually [7, 8]. The majority of DENV infections can be classified as asymptomatic or mild febrile illness, with approximately < 1% progressing to Dengue Hemorrhagic Fever (DHF) or Dengue Shock Syndrome (DSS). DHF is delineated from mild DENV febrile illness by the increase in vascular permeability, while DSS has the additional development of circulatory shock [7, 8]. For both WNV and DENV, known risk factors such as immune-compromised states or advanced age are associated with susceptibility to mild and severe disease [9, 10]. The mechanisms by which an infection leads to severe disease in a subset of all infected individuals is incompletely explained. Differing immune responses to infections, including elevated cytokine responses, have been proposed [11-13] and we have recently shown that geographic location is not a driver of severity of WNV infection in a localized region [14]. In addition to similarities in the early stages of infection [15-17], both viruses induce strong immune responses including chemokines (such as IL-8) and cytokines which up-regulate inflammatory reaction (such as TNF-α, IL-1, Il-6, and IFN-β) [18-21]. Renewed interest in understanding flaviviral infection and disease susceptibility comes as climate change expands the number of individuals at risk of exposure to WNV and DENV [3, 22], and with outbreaks of related flaviviruses, most notably Zika [23, 24]. Genetic differences are additional explanations of individual susceptibility to symptomatic disease, and previous genome-wide association studies (GWAS) and candidate-gene studies have identified genetic factors associated with DENV or WNV disease pathogenesis. To assess the current state of knowledge on genetic variation associated with these flaviviral diseases, and to identify any shared features of anti-viral responses, we conducted a systematic review and meta-analysis of the published associations to date between genetic variants and development of DENV or WNV disease.

Methods

A systematic review of genetic factors and WNV or DENV disease was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Additional file 1) [25].

Search strategy

Medline, PubMed, Embase, and Global Health databases were used to search the literature. Search terms included West Nile or DENV and genetic factors; the same set of text words was used for all databases in conjunction with subject headings that were tailored for each database. The text word search specified West Nile or Dengue in the title, a genetic term in the title or abstract, and a human-related term in the title or abstract (Table 1). A sample search strategy is included in the appendix (Additional file 2). Case-control studies which examined at least one genetic factor associated with either viral disease were included. Studies on non-human (e.g., viral, mosquito) genetics and case reports on single patients were excluded. Reports published prior to May 2017 were included in the review. An ancestry search was done of references of selected studies to collect additional potentially relevant references.
Table 1

Text word selection for search of selected databases. Text words used for the search strategy, with one term from each column required in the title for the viral term, or in the title or abstract for the genetic and human terms

Viral termsGenetic Factor termsHuman-related terms
• West Nile• Dengue▪ microsatellite(s)▪ genetic variation▪ genetic factor(s)▪ genetic marker(s)▪ genetic analysis/analyses▪ SNP(s)▪ single nucleotide polymorphism(s)▪ copy number variant(s)▪ genetic predisposition▪ genetic susceptibility▪ disease susceptibility▪ GWAS▪ genome-wide association study/studies▪ genetic association(s)▪ genetic association study/studies▪ candidate gene study/studies▪ genetic predisposition to disease▪ susceptibility to disease▪ genetic variability▪ gene identity▪ human▪ man/men▪ woman/women▪ child/children▪ teenager(s)▪ middle-aged▪ elderly▪ infant(s)▪ male(s)▪ female(s)▪ patient(s)▪ participant(s)▪ citizen(s)▪ subject(s)▪ case(s)▪ control(s)
Text word selection for search of selected databases. Text words used for the search strategy, with one term from each column required in the title for the viral term, or in the title or abstract for the genetic and human terms

Study selection and data extraction

Two researchers reviewed the titles and abstracts of all studies and identified potentially relevant articles within Covidence with 98.6% consistency [26]. Discrepancies were resolved through re-review and mutual consensus. Both researchers read the full text of all of the selected potentially relevant articles and identified the final reports to be included in this review. Data sets were extracted without personal identifiers and organized into literature tables. The main fields included authors, year of publication, country, sample size, case and control group definitions, genotyping method, genes and genetic variants analyzed, genotype count data when available, odds ratios (OR), and statistical analysis method. When two or more studies examined the same variants, we used the raw genotype data to calculate ORs with 95% confidence intervals using the R package Epitools [27]. When the raw genotype data were not available within the published papers, we requested the data sets from corresponding authors of the studies. Of the six authors contacted, three shared data, two indicated they no longer had access to the data, and one did not respond by date of submission. In order to make comparisons across the different DENV phenotypes used in the studies, we compared asymptomatic DENV infections and controls with all symptomatic infections (DENV fever, DENV hemorrhagic fever, and DENV shock syndrome). Using the genotype data, we calculated ORs for each study under a dominant model, recessive model, homozygote mutant versus homozygote wild-type, and heterozygote versus homozygote wild-type. We meta-analyzed the ORs using RevMan [28]. The genetic model with the most significant meta-OR is presented here. When this model was the homozygote mutant versus homozygote wild or the heterozygote versus homozygote wild, we included both of these models for that particular single nucleotide polymorphism (SNP).

Quality assessment

We assessed the quality of each study with the Newcastle-Ottawa Quality Assessment Scale for Case-Control Studies [29], which assesses each study’s selection, comparability, and exposure ascertainment approach.

Results

To identify all published research assessing the role of genetic variation with DENV or WNV disease, we executed the above search strategy and identified 633 published reports (Table 1, Fig. 1). Two researchers independently reviewed the titles and abstracts of these 633 papers and identified 104 papers for further full-text review in this meta-analysis (Additional file 3). One additional paper from 1987 was identified as pertinent during the ancestry search and was added to the review. The final analysis includes data from 87 of the 105 publications, following exclusion of 18 papers for cause (seven repeats, six conference abstracts, and five with an outcome other than disease severity). Reflecting the higher disease burden and longer research history of DENV virus, of these 87 papers selected, 74 studied DENV-infected populations and 13 focused on WNV.
Fig. 1

PRISMA Flowchart of strategy to identify papers assessing genetic variation and WNV or DENV disease

PRISMA Flowchart of strategy to identify papers assessing genetic variation and WNV or DENV disease

HLA genetic variation associated with disease severity

Notably, 27 separate HLA alleles were examined by two or more research groups for an association with severe DENV disease (Additional file 4). Four research groups analyzed HLA alleles for an association with WNV disease (Additional file 5), however there was no overlap in the alleles studied. Although HLA variants show substantial contribution to disease outcome, significant variations in study design, data analysis platforms, data availability and presentation precluded our in-depth meta-analysis of these data.

Multiple genes are associated with severity of WNV infection

Previous reports of genetic associations with WNV disease severity focused on U.S. or Canadian populations and compared severe and non-severe infections. Overall, these studies identified 12 gene variants and significant findings include SNPs of multiple immune-related genes such as RFC1, SCN1A, and IRF3 (Table 2).
Table 2

Genetic variation significantly associated with West Nile virus disease. We include in this table all variants studied by two or more research groups and variants found to have a significant association by one research group

GeneGenetic variantEntrez Gene ID [31]Major AlleleMinor AlleleNumber of CasesNumber in Comparison GroupCountryKey ResultsIncluded in meta-analysis
ANPEPrs25651290TC560 severe950 non-severe infectionsUS & Canada0.69 odds of severe disease [59]Noa
GA39 severe61 controlsIsraelNo significant association with disease [60]
CACNA1Hrs1138025948912AG1330 severe919 non-severe infectionsUS & Canada8.58 odds of encephalitis [61]No
CCR5CCR5 Δ321234Δ32 deletion560 severe950 non-Severe infectionsUS & CanadaNo significant association with disease [59]Yes
Δ32 deletion39 severe61 controlsIsraelNo significant association with disease [60]
Δ32 deletion422 symptomatic331 asymptomatic infectionsUS & CanadaNo significant association with disease [62]
Δ32 deletion634 infections422 controlsUSNo significant association with disease, but significant association with more severe disease (p = 0.0016) [46]
Δ32 deletion395 symptomatic (two cohorts of 247 and 148)1318 controlsUSSignificantly associated with disease in two cohorts (OR = 4.4 [1.6–11.8] and OR = 9.1 [3.4–24.8]), and fatal outcomes in one cohort with OR = 13.2 [1.9–89.9] [63]
HERC5rs14855630851,191AG1330 severe919 non-severe infectionsUS & CanadaSignificantly associated with severe disease (p-value = 6.5 × 10− 10) [61]No
IRF3rs23042073661GC422 severe331 asymptomatic infectionsUS0.52 odds of symptomatic infection under dominant model [62]Noa
GC39 severe61 controlsIsraelNo significant association with disease [60]
MIFrs584457242825 or 6 CATT repeats7 CATT repeats518 severe514 non-severeUS & Canada1.73 odds of encephalitis among patients with high-expression allele as compared to all other types of WNV disease [64]No
MX1rs72804224599CG39 severe61 controlsIsrael4.05 odds of infection associated with variant allele [60]Noa
CG422 severe331 asymptomatic infectionsUS0.25 odds of symptomatic infection under a recessive model [62]
OASLrs32135458638CT422 severe331 asymptomatic infectionsUSNo significant association with disease [62]Yes
CT39 severe61 controlsIsrael1.85 (1.03–3.3) odds of infection [60]
CT33 symptomatic60 controlsUSSignificantly associated with disease (P < 0.004) [65]
OAS1rs107746714938AG422 severe331 asymptomatic infectionsUSNo significant association with disease [62]Yes
AG39 severe61 controlsIsraelNo significant association with disease [60]
AG501 seropositive552 controlsUS1.6 [95% CI 1.2–2.0] odds of seroconversion [66]
OAS1rs341377424938CT422 severe331 asymptomatic infectionsUS9.79 [95% CI 3.60–26.61] odds of encephalitis and paralysis [62]Yes
CT39 severe61 controlsIsraelNo significant association with disease [60]
RFC1rs20667865981TC560 severe950 non-severe infectionsUS & Canada0.68 odds of severe disease associated with minor allele [59]Noa
GA39 severe61 controlsIsrael2.8 odds under dominant model [60]
SCN1Ars22987716323CT560 severe950 non-severe infectionsUS & Canada1.47 odds of severe disease associated with minor allele [59]Noa
AG39 severe61 controlsIsraelNo significant association with disease [60]
TFCP2L1rs1112285229,842AT1330 severe919 non-severe infectionsUS & Canada3.57 odds of severe disease and 4.94 odds of Acute Flaccid Paralysis than controls [61]No

agenotype data not available for meta-analysis

Genetic variation significantly associated with West Nile virus disease. We include in this table all variants studied by two or more research groups and variants found to have a significant association by one research group agenotype data not available for meta-analysis

OAS1 and CCR5 have significant associations with WNV disease across multiple studies

For genes with genotype count data available for ≥2 studies, we conducted a meta-analysis of genetic association to disease severity. Meta-analysis allows recognition of well-established genetic associations and identification of redundant studies for genes with null associations. We found that SNPs in MX1, OASL, OAS1, RFC1, and CCR5 were studied by multiple research groups for an association with WNV disease (Table 2). To assess the overall association of these SNPs with WNV disease, we calculated a combined OR for each gene based on the genotype counts under four different genetic models. Of these, CCR5 and OAS1 meta-ORs were significant under a dominant model, with meta-OR of 0.83 [95% CI: 0.69–1.00] and 1.29 [1.08–1.53], respectively (Fig. 2). The CCR5 meta-OR was also significant under an allelic model with a meta-OR of 1.22 [95% CI: 1.03–1.44]. The CCR5 delta 32 deletion is associated with more severe disease while the OAS1 allele G was associated with less severe disease.
Fig. 2

Significant meta-ORs for associations between OAS1 (rs10774671) and CCR5 (Δ32) and West Nile virus disease. Genotype count data from published reports of WNV subjects were meta-analyzed using RevMan. The meta-odds ratio (OR) for more severe disease is indicated with the genetic model for each gene. For each gene, the allele or genotype is shown which is associated with asymptomatic infection and controls (blue) or severe disease (yellow) outcome

Significant meta-ORs for associations between OAS1 (rs10774671) and CCR5 (Δ32) and West Nile virus disease. Genotype count data from published reports of WNV subjects were meta-analyzed using RevMan. The meta-odds ratio (OR) for more severe disease is indicated with the genetic model for each gene. For each gene, the allele or genotype is shown which is associated with asymptomatic infection and controls (blue) or severe disease (yellow) outcome

Multiple genes are associated with severity of DENV infection

Seventy-four studies have examined genetic associations with DENV disease severity and more than 30 genes have been implicated in DENV disease (Additional file 3). SNPs that were studied by only a single research group are presented in Table 3. We also include SNPs studied by multiple research groups, but for which genotype data was unavailable or the comparison groups of multiple studies could not be analyzed together.
Table 3

Genetic variation associated with DENV disease. We include in this table all variants studied by two or more research groups and variants found to have a significant association by one research group

GeneGenetic VariationEntrez Gene ID [31]Major AlleleMinor AlleleNumber of CasesNumber in Comparison GroupCountryKey ResultsMeta-Analyzed
BAK1rs5745568578GT509 DHF/DSS409 DFThailand1.32 (1.09–1.60) odds of severe disease associated with G allele [67]No
CCR5rs3331234Δ32 deletion56 DF91 controlsAustraliaNo significant association with disease [68]Yes
88 DHF/DSS335 controlsBrazilNo significant association with disease [69]
CD209/ DCSIGNrs48080330,835AG509 DHF/DSS409 DFThailandNo significant association with disease [67]Yes
AG88 DHF/DSS335 controlsBrazilNo significant association with disease [69]
AG112 symptomatic104 controlsIndiaNo significant association with disease [70]
AG103 symptomatic145 asymptomatic infectionsMexicoNo significant association with disease [71]
AG156 DF and 12 DHF72 controlsBrazilNo significant association with disease [72]
AG606 symptomatic696 controlsThailand5.84 (2.77–12.31) odds of DHF compared to DF and 0.204 (P = 2.0 × 10− 6) odds of symptomatic infection compared to controls [73]
AG286 symptomatic236 asymptomatic infectionsBrazilNo significant association with disease [74]
AG176 DF and 135 DH0046120 controlsTaiwan2.36 (1.12–4.97) odds of symptomatic infection compared to controls, 3.68 (1.67–8.09) odds of DHF compared to controls, and 2.46 (1.32–4.59) odds of DHF compared to DF [75]
CFHrs37533943075CT187 DHF121 DFThailandNo significant association with disease [76]Noa
CT87 DHF34 DFBrazil2.53 (1.38–4.69) odds of severe disease compared to mild disease under dominant model [77]
CLEC5Ars128593323,601TC88 DHF/DSS335 controlsBrazil2.25 (1.07–4.87) odds of severe disease for TT compared to CC genotype [69]No
CXCL8/ IL8rs49733576TA45 DHF108 controlsIndia0.43 (0.20–0.93) odds of severe disease [78]No
DDX58rs320516623,586TG120 DENV positive109 controlsIndia0.66 odds of disease associated with G allele for rs3205166 [79]No
rs11795343
rs669260
FCγRII-αrs18012742212A (H amino acid)G (R amino acid)103 symptomatic145 asymptomatic infectionsMexico0.51 (0.26–0.98) odds of symptomatic infection and 0.45 (0.21–0.96) odds of severe disease compared to controls [71]Yes
T (H)C (R)89 DF and 33 DHF107 controlsIndiaNo significant association with disease [80]
T (H)C (R)68 DF, 29 DHF/DSS42 asymptomatic infectionsCuba10.56 (2.33–54.64) odds of DHF compared to asymptomatic disease for under dominant model [81]
T (H)C (R)302 DHF238 controlsVietnamNo significant association with disease [82]
T (H)C (R)40 DF, 30 DHF/DSS40 asymptomatic infectionsPakistan3.21 (1.29–7.97) odds of symptomatic disease, 2.82 (1.00–7.97) odds of DF, and 3.90 (1.13–13.07) odds of DHF/DSS over asymptomatic infection [83]
HPAHPA 1a/1aNot available1a antigen1b antigen75 DHF90 DFIndia1.93 odds (p = 0.006) of severe disease [84]No
HPA 2a/2b2a antigen2b antigen75 DHF90 DFIndia2.8 odds (p = 0.007) of severe disease [84]No
IFN-γrs24305613458AT80 symptomatic100 DEN-negative febrile cases and 99 healthy controlsBrazil2.23 (p = 0.0255) odds compared to DEN-negative and 2.37 (p = 0.0165) odds compared to controls [85]Yes
AT25 DHF41 DFVenezuelaNo significant association with disease [86]
AT43 DHF99 controlsCubaNo significant association with disease [87]
IL-1Brs169443553AG45 DHF108 controlsIndiaNo significant association with disease [78]Yes
CT118 symptomatic80 controlsBrazilNo significant association with disease [88]
rs1143627CT367 secondary DHF and 74 secondary DSS313 secondary DFThailand3.49 (1.36–8.95) odds of DSS compared to DHF and 2.81 (1.12–7.06) odds of DSS compared to DF under dominant models [89]No
IL-1RA86 base pair tandem repeat35574 repeats2 repeats367 secondary DHF and 74 secondary DSS313 secondary DFThailand1.86 (1.05–3.26) odds of DSS compared to DHF and 1.86 (1.05–3.27) odds of DSS compared to DF for the 2/4 genotype [89]Noa
1 repeat2–4 repeats280 DHF229 controlsVietnamNo significant association between IL-1RA repeats and DHF [82]
IL-6rs18007953569GC25 DHF41 DFVenezuelaNo significant association with disease [86]Yes
GC43 DHF99 controlsCubaNo significant association with disease [87]
GC118 symptomatic80 controlsBrazilNo significant association with disease [88]
GC200 DF309 controlsBrazil0.62 (0.42–0.91) odds of disease among heterozygotes compared to homozygote wild-type [90]
IL-10rs18008713586CT88 DSS335 controlsBrazilNo significant association with disease [69]Yes
AG45 DHF108 controlsIndiaNo significant association with disease [78]
CT25 DHF41 DFVenezuelaNo significant association with disease [86]
CT43 DHF99 controlsCubaNo significant association with disease [87]
CT200 DF309 controlsBrazilNo significant association with disease [90]
TC86 DF, 182 DHF, 14 DSS120 controlsMalaysiaNo significant association with disease [91]
CT107 DHF62 controlsSri LankaNo significant association with disease [92]
rs1800872CA25 DHF41 DFVenezuelaNo significant association with disease [86]Yes
CA43 DHF99 controlsCubaNo significant association with disease [87]
CA200 DF309 controlsBrazilNo significant association with disease [90]
AC86 DF, 182 DHF, 14 DSS120 controlsMalaysiaNo significant association with disease [91]
AC107 DHF62 controlsSri LankaNo significant association with disease [92]
rs1800896AG25 DHF41 DFVenezuelaNo significant association with disease [86]Yes
AG43 DHF99 controlsCubaNo significant association with disease [87]
AG200 DF309 controlsBrazilNo significant association with disease [90]
AG86 DF, 182 DHF, 14 DSS120 controlsMalaysiaNo significant association with disease [91]
AG107 DHF62 controlsSri LankaNo significant association with disease [92]
JAK1rs112085343716TC50 DHF236 DFBrazil4.20 (1.7–10.4) odds of severe disease [74]No
rs2780831GA50 DHF236 DFBrazil2.1 (1.1–4.1) odds of severe disease [74]No
rs310196TG50 DHF236 DFBrazil0.4 (0.2–0.7) odds of severe disease [74]No
MBL2Exon 14153AO110 symptomatic150 controlsBrazilNo significant association with disease [93]Yes
AO57 DHF104 DFBrazil7.24 (1.38–38.02) odds of DHF among OO genotype compared to AA genotype [94]
MICBrs31324684277TC76 DSS409 DF, 432 DHFThailand1.58 (1.02–2.40) odds of DSS compared to non-DSS [95]Yes
TC2008 DSS2018 controlsVietnam1.34 (1.23–1.46) odds of DSS per allele [96]
TC3961 cases1068 controlsVietnam1.42 (1.20–1.64) odds of DSS per allele [97]
PLCE1rs374036051,196AC2008 DSS2018 controlsVietnam0.80 (0.75–0.86) odds per allele of DSS [96]Yes
AC3961 cases1068 controlsVietnam0.77 (0.59–0.99) odds per allele of DSS [97]
rs3765524CT76 DSS409 DF, 432 DHFThailand1.49 (1.00–2.26) odds of DSS compared to non-DSS [95]Yes
CT2008 DSS2018 controlsVietnam0.80 (0.75–0.86) odds per allele of DSS [96]
RXRArs123391636256GA60 DHF137 asymptomatic infections and controlsCuba0.36 (0.17–0.77) odds of severe disease [98]No
rs3118593AC60 DHF137 asymptomatic infections and controlsCuba0.44 (0.25–0.77) odds of severe disease [98]
rs4262378GA60 DHF137 asymptomatic infections and controlsCuba0.41 (0.24–0.72) odds of severe disease [98]
rs4424343AG60 DHF137 asymptomatic infections and controlsCuba0.43 [0.24–0.76] odds of severe disease [98]
rs62576287CA60 DHF137 asymptomatic infections and controlsCuba0.10 (0.01–0.83) odds of severe disease [98]
TAP1amino acid 3336890IleVal90 DF, 75 DHF, 32 DSS100 controlsIndia2.58 (p = 0.007) odds of DHF among heterozygotes compared to DF [84]Yes
IleVal107 DHF62 controlsSri LankaNo significant association with disease [92]
TAP2amino acid 3796891ValIle107 DHF62 controlsSri LankaNo significant association with disease [92]Yes
ValIle90 DF, 75 DHF, 32 DSS100 controlsIndia2.11 (p = 0.001) odds of DHF among heterozygotes [99]
TGFβ1rs18004717040GC25 DHF41 DFVenezuelaNo significant association with disease [86]
GC200 DF309 controlsBrazilNo significant association with disease [90]
rs1982073TC25 DHF41 DFVenezuelaNo significant association with disease [86]Yes
TC43 DHF99 controlsCubaNo significant association with disease [87]
TC200 DF309 controlsBrazilNo significant association with disease [90]
TIRAPrs8177374114,609CT33 DHF109 controlsIndia2.64 (1.17–5.99) odds of severe disease among heterozygotes [100]No
TLR3rs37752917098CT33 DHF87 DFIndia0.39 (0.16–0.88) odds of severe disease associated with T allele [100]No
TNF-αrs3615257124GA86 DF, 182 DHF, 14 DSS120 controlsMalaysia4.92 (1.10–21.90) odds of DHF compared to control group for heterozygotes [91]Yes
GA41 DF, 32 DHF169 controlsMexico0.19 (0.02–0.78) odds of disease with A allele [101]
rs1800629GA80 symptomatic100 DEN-negative febrile cases and 99 healthy controlsBrazilNo significant association with disease [85]Yes
GA25 DHF41 DFVenezuela2.5 (1.47–4.13) odds of severe disease [86]
GA43 DHF99 controlsCuba3.51 (1.77–7.00) odds of severe disease [87]
GA200 DF309 controlsBrazilNo significant association with disease [90]
GA86 DF, 182 DHF, 14 DSS120 controlsMalaysia0.43 (0.22–0.84) odds of DHF compared to control for heterozygotes [91]
GA107 DHF62 controlsSri Lanka2.53 (1.10–5.83) odds of disease for GG genotype [92]
GA85 DF, 29 DHF110 controlsIndiaNo significant association with disease [102]
GA19 DF, 82 DHF106 controlsThailandNo significant association with disease [103]
GA85 DF, 45 DHF163 controlsMexicoNo significant association with disease [101]
TLR4amino acids 299 and 3997099Asp299, Thr399Gly299, Ile399201 DHF179 controlsIndonesiaNo significant association with disease [104]Yes
Asp299, Thr399Gly299, Ile39963 DF, 57 DHF/DSS200 controlsIndia2.00 (1.17–3.43) odds associated with Gly299 for DF versus controls, and 2.38 (1.16–4.85) associated with Ile399 for DF versus controls [105]
VDRrs7312367421TC302 DHF238 controlsVietnamAssociated with more severe disease (p = 0.033) [82]Yes
TC83 DF, 29 DHF105 controlsIndiaNo significant association with disease [106]

a genotype data not available for meta-analysis

Genetic variation associated with DENV disease. We include in this table all variants studied by two or more research groups and variants found to have a significant association by one research group a genotype data not available for meta-analysis

Significant associations with DENV disease

Among the DENV studies, the same variant within 17 genes was studied by two or more research groups (Table 3). Four genes had significant meta-ORs (Fig. 3). For a SNP in MBL2 (exon 1), we calculated a meta-OR of 1.54 [1.02–2.31] under a dominant model and 1.65 [1.18–2.32] under an allelic model, with alleles other than the A allele being associated with more severe disease. The T allele for SNP rs2430561 in the IFN-γ gene was associated with severe disease under a recessive model with a meta-OR of 2.48 [0.30–4.71]. For a SNP located within MICB (rs3132468), we found the CC genotype had a significantly greater association with severe disease (meta-OR 2.35 [1.68–3.29]), but the heterozygote genotype showed no significant association with disease severity as compared to the TT genotype (meta-OR = 1.17 [0.86–1.59]). For this SNP, the C allele was also found to be significantly associated with disease as compared to the T allele (meta-OR = 1.35 [1.16–1.57]). For two SNPs located within the PLCE1 gene, every model tested was significant, with the most significant meta-ORs being 0.62 [0.48–0.79] for TT genotype as compared to CC genotype for rs3740360 and 0.55 [0.42–0.71] under a recessive model for rs3765524. TNF-α (rs1800629 and rs361525) was the most studied gene, but none of the models tested provided a significant meta-OR.
Fig. 3

Meta-analyzed genetic variation associated with DENV disease. Genotype count data from published reports of WNV subjects were meta-analyzed using RevMan. The meta-odds ratio (OR) for more severe disease is indicated with the genetic model for each gene: MBL2 (a), IFN-γ (b), MICB (c), PLCE1 (d and e). If multiple models were significant, we present the most significant model. The alleles or genotypes associated with asymptomatic infection and controls (blue) or with severe disease (yellow) outcome are shown for each gene

Meta-analyzed genetic variation associated with DENV disease. Genotype count data from published reports of WNV subjects were meta-analyzed using RevMan. The meta-odds ratio (OR) for more severe disease is indicated with the genetic model for each gene: MBL2 (a), IFN-γ (b), MICB (c), PLCE1 (d and e). If multiple models were significant, we present the most significant model. The alleles or genotypes associated with asymptomatic infection and controls (blue) or with severe disease (yellow) outcome are shown for each gene

Quality scores

Based on the Newcastle Ottawa Scoring System, the average quality score was 5.76 (range: 3–7) for the WNV publications and 5.10 (range: 2–7) for the DENV publications (Additional file 6). We also assessed whether the study authors corrected for multiple testing, and found less than half of both WNV and DENV studies provided corrected p-values when appropriate, indicating an inflated type I error rate.

Discussion

We have examined genetic variants that show association with DENV or WNV disease severity. This analysis was undertaken to identify genetic differences that are significant drivers of susceptibility to symptomatic disease that may shed light on mechanisms of immune resistance to these viruses. Among the 87 studies examined, a wide range of genetic targets was found to be significant, with many of the genes unsurprisingly playing a key role in the immune system defense against viral infections (Additional file 7). Despite the large number of studies, only 27 genes were studied by more than one research group for an association with either disease. Throughout these studies, several key genes rose to the forefront as the most studied and the most significant associations. Many studies focused on the HLA region of the genome, and, although inconsistencies in data presentation preclude a meta-analyze of these results, there were clear signs of the importance of this area for both diseases. With the central role of HLA for the immune system, polymorphisms in this region have been well studied for associations with disease. The area is highly polymorphic, however, leading to difficulties for comparing the diverse range of alleles. Adding to this complexity, DENV serotypes interact differently with HLA [30]. The regions identified in this systematic review, including DRB1, DQA1, DQB1, A, B, and C, are among the most diverse regions of the HLA region [31]. A recent study examined some of these regions by supertype, and found the B44 supertype could be protective against DHF during secondary infections and that the A02 and A01/03 supertypes could be associated with more severe disease [32]. KIR genes, which are expressed on the surface of natural killer cells, also have wide genetic variability as noted with HLA genes [33]. While several KIR alleles were studied in DENV-infected populations, only one publication to date has examined KIR genotypes in West Nile virus-infected individuals, and this study had a sample size of four [34]. The results suggested a possible association; this, in conjunction with the results of the DENV research in this area and the genes’ highly polymorphic nature, could be an area that should be explored further. Infection with WNV has been shown to lead to diversification of KIR receptor expression [35]. In addition to the research outlined above, researchers have examined the association of KIR genotypes with DENV infection in vitro. Within the in vitro research, the timing of natural killer cell activation has been linked to disease severity and interactions between KIR and HLA have been suggested [36-38]. Another key non-HLA gene identified to be associated with WNV disease was OASL, which codes for an enzyme that is induced by type 1 interferon and viruses [39]. OASL was first identified to have a potentially critical role in WNV disease pathogenesis in 2002, when researchers found that mice with a truncated form of the gene were more susceptible to disease [40]. Elevated activity of the OAS genes has also been associated with more severe DENV infection in vitro [41]. This data and the significance of variation within the OAS genes for WNV outcomes highlight the importance of the interferon pathways in response to flavivirus infections and suggest a need for further in depth examination the association of genetic variability within OAS and DENV severity. CCR5Δ32 was the only gene studied by two or more research groups for each disease. CCR5 was first identified as a co-receptor for HIV in 1996, and CCR5 deficiency, or a homozygous genotype of CCR5Δ32, was found to be protective against HIV infection [42-45]. In West Nile, CCR5 deficiency is not associated with incidence of infection, but is associated with severity of disease for infected individuals [46]. Subsequent research showed that CCR5 specifically plays a role in the ability of cortical neurons to combat West Nile virus infection of the brain [47]. In DENV, CCR5 deficiency has been linked with increased viral load and disease severity [48]. The study also found that the CCR5 receptor in macrophages is necessary for replication of DENV serotype 2, an early step in the infection process [49]. Given the similarities of these flaviviral diseases and the significant association of CCR5, the only gene looked at by research groups for both diseases, further research could be beneficial to further understanding the role of genetic variation in the development of severe flavivviral disease [50]. When we meta-analyzed the DENV studies, we found significant associations between DENV disease and genetic variation in MBL2, PLCE1, IFN-γ, and MICB. The role of many of these genes in disease pathogenesis has been characterized through in vivo and in vitro studies. MBL2, the mannose-binding lectin 2 gene, encodes a protein with a role in innate immunity and complement pathway, while PLCE1 encodes an enzyme critical to the generation of the inositol 1,4,5-triphosphate (IP3) and diacylglycerol (DAG) messengers [51]. MICB and IFN-y are both critical in the immune response, and thus variations within these genes could have strong effects on the initial response to the viral infection and the subsequent disease pathogenesis [51]. Our study is limited by several factors, most notably by the available literature. To ensure we found as many papers as possible, we constructed a search strategy that involved multiple databases, used both subject headings and text words, was not limited to English articles, and included an ancestry search [52]. Despite our focus on significant results and genes studied by at least two research groups, the wide heterogeneity among the populations studied limited our ability to interpret the meta-analyzed results. Lack of diversity in genetic studies is well-documented [53], and the absence of certain affected populations, particularly in Africa, among the identified studies further demonstrates this unmet research need [54]. The diversity of results among studies that examined the same SNP could be due to population heterogeneity, as well as to differences in study approach, including selection of control and comparison groups. Additionally, previous exposure history, DENV serotype, and WNV or DENV genotype are all factors that can affect disease severity, but were not accounted for in the included studies [4]. The number and type of genes examined varied greatly between studies, and we were limited by what genes researchers chose to sequence and include in publications. The unavailability of comparable genotype data and the incomparability of research groups across some studies preclude a more in depth analysis at present.

Conclusions

The genes found to be significantly associated with WNV or DENV disease pathogenesis varied in function, with most being linked to the immune response. As the regions of the world affected by WNV, DENV, and related viruses such as Zika, continue to expand due in part to climate change, an improved understanding of the association between genetic variation and disease severity will be valuable for all potentially affected populations [55-58]. Based on the growing incidence of these diseases, the paucity of consistency in the associations found, and the limited overlap in genetic targets studied to date, there is need for continued and deeper studies examining the role of genetic factors in WNV and DENV disease severity. In addition to conducting new studies such as whole-exome sequencing within larger population samples, further analyses could be conducted of existing data, to glean novel findings such as gene-gene or gene-environment interactions, rare and low frequency variants, and pathways of significant determinants of anti-viral resistance. PRISMA Checklist. This systematic review of genetic factors and WNV or dengue disease was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines outlined in this checklist. (DOC 95 kb) Sample Search Strategy for the Embase Database. Medline, PubMed, Embase, and Global Health databases were used to search the literature. Search terms included West Nile or Dengue and genetic factors; the same set of text words was used for all databases in conjunction with subject headings that were tailored for each database. As an example, this search strategy for the Embase database is provided. These subject headings, in conjunction with the text search (Table 1), were used to find relevant literature in Embase. (DOCX 13 kb) Included Papers. All publications included in this review are listed, with study details on authors, year of publication, country, sample size, case and control group definitions, and genotyping method. (XLSX 54 kb) HLA Associations with DENV Disease Severity. HLA alleles studied by two or more research groups for association with DENV disease severity. (DOCX 311 kb) HLA Associations with WNV Disease Severity. All examined targets from the analyzed publications are listed, with significant associations shown in bold. (XLSX 9 kb) Quality Scores. The quality of each study was evaluated with the Newcastle-Ottawa Quality Assessment Scale for Case-Control Studies, which assesses each study’s selection, comparability, and exposure ascertainment approach. (XLSX 16 kb) Extracted Data from Included Studies. All genotype data extracted from the manuscripts or collected from study authors is provided. (XLSX 54 kb)
  97 in total

1.  Acute West Nile virus neuroinvasive infections: cross-reactivity with dengue virus and tick-borne encephalitis virus.

Authors:  Anna Papa; Dimitra Karabaxoglou; Athina Kansouzidou
Journal:  J Med Virol       Date:  2011-10       Impact factor: 2.327

2.  Understanding systematic reviews and meta-analysis.

Authors:  A K Akobeng
Journal:  Arch Dis Child       Date:  2005-08       Impact factor: 3.791

3.  NK cells, displaying early activation, cytotoxicity and adhesion molecules, are associated with mild dengue disease.

Authors:  E L Azeredo; L M De Oliveira-Pinto; S M Zagne; D I S Cerqueira; R M R Nogueira; C F Kubelka
Journal:  Clin Exp Immunol       Date:  2006-02       Impact factor: 4.330

4.  RPLP1 and RPLP2 Are Essential Flavivirus Host Factors That Promote Early Viral Protein Accumulation.

Authors:  Rafael K Campos; Benjamin Wong; Xuping Xie; Yi-Fan Lu; Pei-Yong Shi; Julien Pompon; Mariano A Garcia-Blanco; Shelton S Bradrick
Journal:  J Virol       Date:  2017-01-31       Impact factor: 5.103

5.  Association of IL1B -31C/T and IL1RA variable number of an 86-bp tandem repeat with dengue shock syndrome in Thailand.

Authors:  Areerat Sa-Ngasang; Jun Ohashi; Izumi Naka; Surapee Anantapreecha; Pathom Sawanpanyalert; Jintana Patarapotikul
Journal:  J Infect Dis       Date:  2014-01-19       Impact factor: 5.226

6.  Tumor necrosis factor-alpha, transforming growth factor-β1, and interleukin-10 gene polymorphisms: implication in protection or susceptibility to dengue hemorrhagic fever.

Authors:  Ana B Perez; Beatriz Sierra; Gissel Garcia; Eglis Aguirre; Nina Babel; Mayling Alvarez; Licel Sanchez; Luis Valdes; Hans D Volk; Maria G Guzman
Journal:  Hum Immunol       Date:  2010-08-21       Impact factor: 2.850

7.  Killer cell immunoglobulin-like receptor genes in four human West Nile virus infections reported 2011 in the Republic of Macedonia.

Authors:  Mirko Spiroski; Zvonko Milenkovic; Aleksandar Petlichkovski; Ljubomir Ivanovski; Irena Kondova Topuzovska; Eli Djulejic
Journal:  Hum Immunol       Date:  2012-12-05       Impact factor: 2.850

8.  High producing tumor necrosis factor alpha gene alleles in protection against severe manifestations of dengue.

Authors:  Sing-Sin Sam; Boon-Teong Teoh; Karuthan Chinna; Sazaly AbuBakar
Journal:  Int J Med Sci       Date:  2015-01-12       Impact factor: 3.738

9.  Interaction of a dengue virus NS1-derived peptide with the inhibitory receptor KIR3DL1 on natural killer cells.

Authors:  E Townsley; G O'Connor; C Cosgrove; M Woda; M Co; S J Thomas; S Kalayanarooj; I-K Yoon; A Nisalak; A Srikiatkhachorn; S Green; H A F Stephens; E Gostick; D A Price; M Carrington; G Alter; D W McVicar; A L Rothman; A Mathew
Journal:  Clin Exp Immunol       Date:  2015-11-24       Impact factor: 4.330

10.  West Nile Virus Seroprevalence, Connecticut, USA, 2000-2014.

Authors:  Megan E Cahill; Yi Yao; David Nock; Philip M Armstrong; Theodore G Andreadis; Maria A Diuk-Wasser; Ruth R Montgomery
Journal:  Emerg Infect Dis       Date:  2017-04       Impact factor: 6.883

View more
  10 in total

1.  Associations between the presence of specific antibodies to the West Nile Virus infection and candidate genes in Romanian horses from the Danube delta.

Authors:  K Stejskalova; E Janova; C Horecky; E Horecka; P Vaclavek; Z Hubalek; K Relling; M Cvanova; G D'Amico; A D Mihalca; D Modry; A Knoll; P Horin
Journal:  Mol Biol Rep       Date:  2019-06-07       Impact factor: 2.316

2.  THE GORDON WILSON LECTURE: THE ETHICS OF HUMAN GENOME EDITING.

Authors:  Barry S Coller
Journal:  Trans Am Clin Climatol Assoc       Date:  2020

3.  Distribution pattern and prevalence of West Nile virus infection in Nigeria from 1950 to 2020: a systematic review.

Authors:  Idris Nasir Abdullahi; Anthony Uchenna Emeribe; Peter Elisha Ghamba; Pius Omoruyi Omosigho; Zakariyya Muhammad Bello; Bamidele Soji Oderinde; Samuel Ayobami Fasogbon; Lawal Olayemi; Isa Muhammad Daneji; Muhammad Hamis Musa; Justin Onyebuchi Nwofe; Nkechi Blessing Onukegbe; Chukwudi Crescent Okume; Sanusi Musa; Abubakar Muhammad Gwarzo; Odunayo Oyetola Rahmat Ajagbe
Journal:  Epidemiol Health       Date:  2020-11-26

4.  Engineering monocyte/macrophage-specific glucocerebrosidase expression in human hematopoietic stem cells using genome editing.

Authors:  Samantha G Scharenberg; Edina Poletto; Katherine L Lucot; Pasqualina Colella; Adam Sheikali; Thomas J Montine; Matthew H Porteus; Natalia Gomez-Ospina
Journal:  Nat Commun       Date:  2020-07-03       Impact factor: 14.919

5.  ImmuneRegulation: a web-based tool for identifying human immune regulatory elements.

Authors:  Selim Kalayci; Myvizhi Esai Selvan; Irene Ramos; Chris Cotsapas; Eva Harris; Eun-Young Kim; Ruth R Montgomery; Gregory Poland; Bali Pulendran; John S Tsang; Robert J Klein; Zeynep H Gümüş
Journal:  Nucleic Acids Res       Date:  2019-07-02       Impact factor: 16.971

Review 6.  West Nile virus vaccines - current situation and future directions.

Authors:  Sebastian Ulbert
Journal:  Hum Vaccin Immunother       Date:  2019-07-10       Impact factor: 3.452

Review 7.  Beyond HIV infection: Neglected and varied impacts of CCR5 and CCR5Δ32 on viral diseases.

Authors:  Joel Henrique Ellwanger; Bruna Kulmann-Leal; Valéria de Lima Kaminski; Andressa Gonçalves Rodrigues; Marcelo Alves de Souza Bragatte; José Artur Bogo Chies
Journal:  Virus Res       Date:  2020-05-30       Impact factor: 3.303

8.  In-Depth Analysis of Genetic Variation Associated with Severe West Nile Viral Disease.

Authors:  Megan E Cahill; Mark Loeb; Andrew T Dewan; Ruth R Montgomery
Journal:  Vaccines (Basel)       Date:  2020-12-08

9.  Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signatures.

Authors:  Yujiao Zhao; Matthew Amodio; Brent Vander Wyk; Bram Gerritsen; Mahesh M Kumar; David van Dijk; Kevin Moon; Xiaomei Wang; Anna Malawista; Monique M Richards; Megan E Cahill; Anita Desai; Jayasree Sivadasan; Manjunatha M Venkataswamy; Vasanthapuram Ravi; Erol Fikrig; Priti Kumar; Steven H Kleinstein; Smita Krishnaswamy; Ruth R Montgomery
Journal:  PLoS Negl Trop Dis       Date:  2020-03-09

10.  Use of Haematological Changes as a Predictor of Dengue Infection among Suspected Cases at Kairuki Hospital in Dar Es Salaam, Tanzania: A Retrospective Cross Sectional Study.

Authors:  Florence Salvatory Kalabamu; Shaaban Maliki
Journal:  East Afr Health Res J       Date:  2021-06-11
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.