ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
Frequency of Meningococcal Meningitis Susceptibility Associated TLR4 +896 A/G (rs4986790) Allele in the Saudi Population
Ali Alisaac
Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Albaha University, Albaha, Saudi Arabia.
Article Number: 8019 | © The Author(s). 2022
J Pure Appl Microbiol. 2022;16(4):2923-2933. https://doi.org/10.22207/JPAM.16.4.67
Received: 08 August 2022 | Accepted: 23 November 2022 | Published online: 01 December 2022
Issue online: December 2022
Abstract

Meningococcal meningitis (MM) is a severe central nervous system (CNS) infection that occurs primarily in children. MM can damage brain areas associated with hearing, learning, reasoning, focus, and memory. Genetic changes, including single nucleotide polymorphisms (SNPs), which compromise pathogen recognition increase the risk and severity of MM. There is little data on how the variation in the frequency of the rs4986790 polymorphism in the Toll-like receptor 4 (TLR4) gene may affect the population of Saudi Arabia. This study sought to determine the allelic frequency and distribution of the TLR4 rs4986790 A/G polymorphism in the Saudi population and compare the data to other global populations. Data from epidemiological studies conducted in various ethnic groups were extracted using PUBMED (Medline) and similar web databases. An estimated 5.88% of the Saudi population harbors the TLR4 rs4986790 G variant allele. This differed significantly from the frequencies in populations in China (p=0.0002), Japan (p=0.0001), Korea (p=0.0001), and Mexico (p=0.01). The TLR4 rs4986790 polymorphism variant allele has a unique pattern in the Saudi population, which may be the result of racial differences. These findings could assist in the risk assessment of people harboring the TLR4 +896 GG genotype susceptible to MM in the Saudi population.

Keywords

Meningococcal Meningitis, Toll-like Receptor 4, rs4986790 Allele, Saudi Population, Single Nucleotide Polymorphism (SNP)

Introduction

Genetic epidemiological studies have shown that genetic variations in human groups influence susceptibility to infections. There are several obstacles to overcome to identify the relevant genes and translate these results into biological mechanistic explanations.1,2 Meningococcal Meningitis (MM), a severe infection of the central nervous system (CNS) that affects hearing and learning capacities, frequently occurs in childhood.3-5 The main objective of the immune response is to neutralize the pathogen by recognizing microbial ligands and then induce the release of certain cytokines. However, these cytokine reactions may also incidentally harm healthy brain tissue, which would be
detrimental. 6,7

Mutations in pathogen recognizing receptors (PRRs) including Toll-like receptors (TLRs) and nucleotide oligomerization domain like receptors (NLRs) in macrophages and epithelial cells critically modulate the inflammatory response.8 These receptors are also expressed by neuro-epithelial cells, resident macrophages in the CNS, and microglia. Thus, any mutation of these receptors significantly increases risk and severity of MM.

Early reports showed that single nucleotide polymorphisms (SNPs) located in genes responsible for the development of innate immunity increase meningococcal, pneumococcal, and meningitis susceptibility.9-11 A severity analysis linked SNPs located in TLR2, TLR4, and TLR9 with deafness in MM patients.12 MM usually begins with Neisseria meningitidis and Streptococcus pneumoniae growth in the nasopharynx and epithelium, progressing to bacteremia in the blood circulation. Bacteria may eventually cross the blood–brain barrier and proliferate in the subarachnoid area.13

Microglia, astrocytes, and non-neuronal structures near the cerebrospinal fluid (CSF), including dendritic cells and macrophages, detect the presence of bacteria in the CNS and activate the immune response. PRR activation causes the production of inflammatory cytokines and chemokines, which are also present in the CNS.8 Brain edema, infarction, increased intracranial pressure, and neuronal damage result from the local inflammatory response within the brain, which is exacerbated by cytokine-induced increased blood–brain barrier permeability and entry of inflammatory cells into the CNS 13. To clear these microbes, the host must be able to recognize microbial CNS invasion in order to clear the infection. However, the ensuing inflammatory response produces few cytotoxic mediators that affect healthy bystander neurons, ultimately resulting in poor prognosis. 13,14

Immune cells recognize gram-positive and gram-negative bacteria with the participation of TLR2 and TLR4 surface receptors. Animal studies have established that a lack of TLR2 and TLR4 reduces the ability of the CNS to remove germs after an infection with S. pneumoniae.15

Although the rs4986790 SNP is located in a critical genomic region for MM susceptibility, its prevalence and impact in Saudi Arabia populations is unclear.  The present study sought to determine the frequency of genetic variation in TLR4 +896 A/G (rs4986790) that is associated with an increased risk of MM. The frequency distribution of the TLR4 rs4986790 polymorphism among healthy Saudi Arabians was compared with data from multiple epidemiological studies conducted worldwide.

Materials and Methods

Search criteria of gene variants
The PUBMED (Medline), Web of Science, and EGEMS databases were searched using the keywords “TLR4,” “rs4986790,” and “polymorphism”. Studies on human subjects written in any language were included in the search. Studies reporting genotype frequencies for the control population were included. Studies that reported only allele frequencies and no genotype frequencies were excluded. For every study that met the requirements, the first author’s name, year of publication, subjects’ country, number of controls, research type, inclusion/exclusion criteria, and subjects’ allele and genotype frequencies were all abstracted. The most recent publication data were used for the Saudi population. The prevalence of the TLR4 rs4986790 polymorphism was extracted from 48 studies and included in the current analysis and compared to the Saudi population (Table 1). 16

Table (1):
Studies included in the TLR4 +896 A/G (rs4986790) gene variant analysis in different populations.

S. No.
Study
Year
Ethnicity
Reference
1
Semlali
2019
Arab
16
2
Martinez-Rios
2013
Mexican
17
3
Ameziane
2003
Caucasian
18
4
O’Halloran
2006
Caucasian
19
5
Edfeldt
2004
Caucasian
20
6
Zee
2005
Caucasian
21
7
Koch
2006
Caucasian
22
8
Dzumhur
2012
Caucasian
23
9
Nebel
2007
Caucasian
24
10
Balistreri
2004
Caucasian
25
11
Morange
2004
Caucasian
26
12
Golovkin
2014
Caucasian
27
13
Guven
2015
Turks
28
14
Van well
2013
Caucasian
29
15
Sargın
2017
European
30
16
Machado
2016
Mixed
31
17
Qin
2009
Asian (China)
32
18
Na
2008
Asian (Korea)
33
19
Burton
2007
European
34
20
Snelgrove
2007
European
35
21
Adam
2006
European
36
22
Gergely
2006
European
37
23
van der
2005
European
38
24
van Well
2013
European
29
25
Ahmad-Nejad
2011
Caucasian
39
26
Nakada
2005
Asian (Japan)
40
27
Agnese
2002
Multi-ethnic
41
28
Bronkhorst
2013
Caucasian
42
29
Carregaro
2010
Multi-ethnic
43
30
Elkilany Atia
2015
Caucasian
44
31
Everett
2007
Undefined
45
32
Feterowski
2003
Caucasian
46
33
Guarner-Argente
2010
Undefined
47
34
Henckaerts
2009
Caucasian
48
35
Horcajada
2009
Caucasian
49
36
Kompoti
2015
Caucasian
50
37
Kumpf
2010
Caucasian
51
38
Lorenz
2002
Caucasian
52
39
Mensah
2009
Multi-ethnic
53
40
Ozgur
2009
Undefined
54
41
Rodriguez-Osorio
2013
Mexican-Mestizo
55
42
Read
2001
Caucasian
56
43
Sampath
2013
Multi-ethnic
57
44
Schnetzke
2015
Caucasian
58
45
Shalhub
2009
Caucasian
59
46
Tellería-Orriols
2014
Caucasian
60
47
Van der Graaf
2006
Undefined
61
48
Yoon
2006
Asian (Korea)
62
49
Yuan
2008
Caucasian
63

Statistical analysis
SPSS version 21 software was used for the Pearson’s χ2 test to match the genotype and allelic frequencies of various populations. The Hardy-Weinberg equilibrium (HWE) was investigated using Court-Lab. A p-value <0.05 denoted statistical significance.

RESULTS

The minor allele frequency (MAF) of the TLR4 rs4986790 polymorphism in the Saudi population was 5.88%, according to the genotype distribution. The value was in accordance with HWE (Table 2). Different minor allele frequencies were found in the genotypic (A/A, A/G, and G/G) and allelic frequency distributions of the studied polymorphisms in various populations (Table 3). When the frequency in Saudi Arabia was compared to that of other populations, a substantially different MAF was observed for the ethnicities of populations of China (p=0.0002), Japan (p <0.0001), Korea (p <0.0001), and Mexico (p=0.01).

Table (2):
Observed and expected genotypic frequencies of TLR4 +896 A/G (rs4986790) polymorphism in the control group.

Study Genotype observed (n) Genotype Expected (n) MAF p-value (HWE)
A/A A/G G/G A/A A/G G/G
Semlali et al, 2019 166 20 1 166 21 1 0.059 0.83

Table (3):
TLR4 +896 A/G (rs4986790) gene variant genotype and allele frequency distribution in different populations and p-values in contrast to Saudi Arabian population.

    Genotype distribution of TLR4 +896 A/G  
  Study Year Ethnicity  Total no. of subjects AA AG GG Allele A Allele G Total Alleles G allele frequency A Allele frequency p-value MAF
1 Semlali 2019 Arab 187 166 20 1 352 22 374 0.059 0.941176 Ref 5.88
2 Martinez-Rios 2013 Mexican 283 267 16 0 550 16 566 0.028 0.971731 0.01* 2.83
3 Ameziane 2003 Caucasian 216 187 28 1 402 30 432 0.069 0.930556 0.54 6.94
4 O’Halloran 2006 Caucasian 386 343 42 1 728 44 772 0.057 0.943005 0.88 5.70
5 Edfeldt 2004 Caucasian 1508 1,374 133 1 2881 135 3016 0.045 0.955239 0.22 4.48
6 Zee 2005 Caucasian 695 605 87 3 1297 93 1390 0.067 0.933094 0.57 6.69
7 Koch 2006 Caucasian 1211 1,069 138 4 2276 146 2422 0.060 0.939719 0.92 6.03
8 Dzumhur 2012 Caucasian 120 98 22 0 218 22 240 0.092 0.908333 0.12 9.17
9 Nebel 2007 Caucasian 323 293 30 0 616 30 646 0.046 0.95356 0.38 4.64
10 Balistreri 2004 Caucasian 182 155 23 4 333 31 364 0.085 0.914835 0.16 8.52
11 Morange 2004 Caucasian 490 439 50 1 928 52 980 0.053 0.946939 0.68 5.31
12 Golovkin 2014 Caucasian 300 253 46 1 552 48 600 0.080 0.92 0.21 8.00
13 Guven 2015 Turks 150 134 14 2 282 18 300 0.060 0.94 1 6.00
14 Van well 2013 Caucasian 1141 1001 136 4 2138 144 2282 0.063 0.936897 0.75 6.31
15 Sargın 2017 European 41 41 0 0 82 0 82 0.000 1 not calculated 0.00
16 Machado 2016 Mixed 200 178 22 0 378 22 400 0.055 0.945 0.82 5.50
17 Qin 2009 Asian (China) 112 112 0 0 224 0 224 0.000 1 0.0002* 0.00
18 Na 2008 Asian (Korea) 197 197 0 0 394 0 394 0.000 1 <.0001* 0.00
19 Burton 2007 European 1465 1,335 123 7 2793 137 2930 0.047 0.953242 0.30 4.68
20 Snelgrove 2007 European 98 93 5 0 191 5 196 0.026 0.97449 0.07 2.55
21 Adam 2006 European 125 107 17 1 231 19 250 0.076 0.924 0.39 7.60
22 Gergely 2006 European 140 127 12 1 266 14 280 0.050 0.95 0.62 5.00
23 van der 2005 European 170 153 16 1 322 18 340 0.053 0.947059 0.72 5.29
24 van Well 2013 European 1141 1001 136 4 2138 144 2282 0.063 0.936897 0.75 6.31
25 Ahmad-Nejad 2011 Caucasian 112 99 12 1 210 14 224 0.063 0.9375 0.86 6.25
26 Nakada 2005 Asian (Japan) 214 214 0 0 428 0 428 0.000 1 <.0001* 0.00
27 Agnese 2002 Multi-ethnic 39 34 5 0 73 5 78 0.064 0.935897 not calculated 6.41
28 Bronkhorst 2013 Caucasian 139 118 20 1 256 22 278 0.079 0.920863 0.30 7.91
29 Carregaro 2010 Multi-ethnic 205 178 26 1 382 28 410 0.068 0.931707 0.59 6.83
30 Elkilany Atia 2015 Caucasian 21 19 2 0 40 2 42 0.048 0.952381 not calculated 4.76
31 Everett 2007 Undefined 167 145 22 0 312 22 334 0.066 0.934132 0.69 6.59
32 Feterowski 2003 Caucasian 154 135 19 0 289 19 308 0.062 0.938312 0.88 6.17
33 Guarner-Argente 2010 Undefined 105 97 8 0 202 8 210 0.038 0.961905 0.27 3.81
34 Henckaerts- 2009 Caucasian 293 264 27 2 555 31 586 0.053 0.947099 0.69 5.29
35 Horcajada 2009 Caucasian 114 100 14 0 214 14 228 0.061 0.938596 0.88 6.14
36 Kompoti- 2015 Caucasian 245 213 30 2 456 34 490 0.069 0.930612 0.53 6.94
37 Kumpf 2010 Caucasian 176 150 24 2 324 28 352 0.080 0.920455 0.27 7.95
38 Lorenz 2002 Caucasian 73 65 8 0 138 8 146 0.055 0.945205 0.86 5.48
39 Mensah 2009 Multi-ethnic 48 42 6 0 90 6 96 0.063 0.9375 0.88 6.25
40 Ozgur 2009 Undefined 70 66 4 0 136 4 140 0.029 0.971429 0.16 2.86
41 Rodriguez-Osorio 2013 Mexican-Mestizo 126 122 4 0 248 4 252 0.016 0.984127 0.008* 1.59
42 Read 2001 Caucasian 879 787 81 11 1655 103 1758 0.059 0.941411 1 5.86
43 Sampath 2013 Multi-ethnic 318 287 31 0 605 31 636 0.049 0.951258 0.48 4.87
44 Schnetzke 2015 Caucasian 81 76 5 0 157 5 162 0.031 0.969136 0.17 3.09
45 Shalhub 2009 Caucasian 451 400 50 1 850 52 902 0.058 0.94235 0.92 5.76
46 Tellería-Orriols 2014 Caucasian 66 60 4 2 124 8 132 0.061 0.939394 0.92 6.06
47 Van der Graaf 2006 Undefined 166 148 17 1 313 19 332 0.057 0.942771 0.92 5.72
48 Yoon 2006 Asian (Korea) 179 179 0 0 358 0 358 0.000 1 <.0001* 0.00
49 Yuan 2008 Caucasian 409 364 44 1 772 46 818 0.056 0.943765 0.86 5.62
DISCUSSION

Many human diseases, including multiple sclerosis, diabetes, asthma, cancer, and birth abnormalities exhibit multifactorial inheritance patterns. A complex interplay between genetic factors, including copy number variation, epistatic interactions, and modifier effects, as well as numerous environmental factors, results in disease onset and progression. It is difficult to predict whether a disease will develop in situations where there is discontinuous trait variation due to the number of factors that may or may not exceed the liability threshold. Common alleles that contribute to the hereditary component of widespread multifactorial disorders can be identified using genome-wide association studies (GWAS). The alleles discovered using this method typically have small impact sizes and cannot fully explain the disease susceptibility.

This gap might emerge as a result of the difficulty in utilizing GWAS to find rare variants with low to medium penetrance. The percentage of people in a group that has a specific allele and displays an associated phenotype signifies penetration. Mendelian diseases, in contrast to multifactorial illnesses, have strong penetrance and a very low allele frequency.

Several techniques have been developed to study complicated illnesses. GWAS have identified the common genetic variables underlying the most severe complex illnesses. However, much remains to be discovered regarding the origins and characteristics of many multifactorial illnesses.

The majority of diseases are multifactorial, and the consequences of an intricate web of hereditary and environmental factors affect how the disease develops over the course of a person’s lifetime. A growing body of research suggests that genetic variation makes people more susceptible to conditions such as diabetes, cardiovascular disease, and cancer.64-66 Therefore, a primary priority in understanding the pathophysiological mechanisms underlying common human illnesses is the detection of genetic variations associated with common complicated diseases. The possible impact of common functional germline polymorphisms on disease risk, development, and prognosis has attracted increasing attention.

Genetic variety refers to the genomic variation present within a population or species.67 Given the richness of the human genome, genetic variation is recognized as a factor that affects a person’s phenotype.68 Individual gene variation is referred to as genetic diversity and serves as a mechanism for population survival by enabling adaptation to a dynamic environment. The key to understanding the biology of human diseases has long been thought to be genetic heterogeneity within and between populations.69-71

TLRs are central to the activation of the innate immune system and its response to CNS infections. 72 Early studies have linked SNPs located in TLR4 with meningitis, tuberculosis, malaria, and lupus risk.73 TLR2 and TLR4 activation leads to variable gene expression through nuclear factor-kappa B (NF-κB) regulated transcription.74 Toll/interleukin 1-domain-containing adapter inducing interferon-beta (TRIF) also contributes to TLR signaling. When TLR4 is activated, MyD88 and TRIF are recruited. When TLR2 is activated, only MyD88 is recruited. Due to variations in the timing of NF-κB activation, MyD88 and TRIF are believed to coordinate distinct intracellular pathways.74 TLR2 and TLR4 activation also leads to the production of pro-inflammatory TNF-α in murine macrophages.75,76 Previous genetic studies have shown a strong association between TLR4 and Crohn’s disease in the pediatric population.77

Experimental studies have shown that TLR4+896 SNP is associated with a reduced response to lipopolysaccharide (LPS) in mice and humans.78,79 Compared to healthy volunteers, adult surgical intensive care unit patients have a higher risk of developing gram-negative infections owing to the same TLR4 SNP 41. TLR4 +896 has also been associated with mortality, greater need for respiratory assistance, use of inotropic agents, skin grafting, and limb loss in a pediatric population with meningococcal infections.80 Decreased pro-inflammatory intracellular signaling and impaired TLR4-mediated LPS responses are probable mechanisms.

Identifying genetic variations that predispose individuals to the development of MM is important because it helps to clarify the specifics of MM pathogenesis. Additionally, this knowledge makes it possible to forecast a person’s risk of developing MM and may help in identifying people at the highest risk of developing serious complications from their condition and needing specialized care. Furthermore, the outcome can be useful in the identification and immunization of individuals with the highest MM risk. Another possibility is to supplement existing prediction models for difficulties in hearing, memory, or behavior after MM with genetic risk factors.81-83

Global human genome variation is a product of numerous evolutionary processes, including population separation, mixing, migration, selective pressure, and genetic drift. 84-86 Footprints conserved throughout the genomes of multiple groups provide evidence to support our understanding of health and disease.87,88 The Human Genome Diversity Project has recently made significant contributions to the development of a single nucleotide alteration database by identifying genetic differences between and within individuals of various ethnic groups worldwide. 89-91 The likely heterogeneous genetic diversity of the Saudi population could be investigated to help develop early preventative and intervention techniques. This study compared the frequency distribution of the TLR4 +896 A/G polymorphism variant in the Saudi population with that of other populations worldwide.

TLR4 detects bacterial LPS on the surface of gram-negative bacteria. Previous research has revealed a connection between TLR4 and bacterial-related phenotypes such as Crohn’s disease, ascites, scrub typhus, and tuberculosis. 92,93 Similarly, the rs4986790 SNP located in TLR4 has been used to assess variable manifestations of disease.94,95 These results suggest that the rs4986790 SNP of the TLR4 gene modulates the antibacterial actions of TLR4 because genetic changes result in functional alterations.96,97

The present study involving the Saudi population revealed a 5.88% frequency of variant allele (G) of rs4986790. This frequency is substantially different from China, Japan, Korea, and Mexico. Differences in allele frequencies among separate datasets can affect the ultimate SNP effect because most SNPs are less penetrant, and diseases are polygenic in nature. A change in MAF of 0.02 will result in significant statistical changes in genetic association studies. Any change, even as small as <0.1, in a particular allelic prevalence will significantly influence the individual effect of one SNP in the case of interaction between two SNPs.98

Variations in allelic frequencies in genetic association studies can be attributed to racial variance, demographic heterogeneity, and varying sample sizes. The TLR4 gene exhibits a wide range of patterns compared to other people worldwide.99 The varying incidence of these SNPs in various populations shows that different groups are differently affected by susceptibility factors. It is important to note that the genotype and allele frequencies examined in this analysis may not accurately represent all possible variants at a location. However, such investigations can inform the subsequent creation of epidemiological and clinical databases. Large data repositories have been created over the past ten years as a result of GWAS and genetic association studies.100 Multiple genetic association tests are required to identify important genes and/or their SNPs involved in the development of early disease prevention programs and treatments. However, before novel genetic biomarkers for application in gene-disease-association research can be identified, a number of bottlenecks must be solved. These include statistical and computational trials as well as the repeatability factor.101

CONCLUSION

The TLR4 rs4986790 polymorphism variant allele in the Saudi population differs significantly from that of many other populations worldwide. These findings may help with population screening and evaluation of the relevance and propensity of MM. The evaluation of diseases may be aided by variations in the frequency distribution of important MM-related genes in healthy Saudi populations and other racial groups. Better management of the affected pediatric cohort in the Saudi population may result from the identification of susceptibility factors linked to individual susceptibility and predisposition to increased frequencies of support for artificial breathing, use of inotropic agents, skin grafting, and limb loss. To utilize this polymorphism as a biomarker, future large-scale research investigating gene-gene and gene-environment interactions is necessary.

Declarations

ACKNOWLEDGMENTS
None.

FUNDING
None.

DATA AVAILABILITY
All datasets generated or analyzed during this study are included in the manuscript.

ETHICS STATEMENT
Not applicable.

References
  1. Burgner D, Jamieson SE, Blackwell JM. Genetic susceptibility to infectious diseases: big is beautiful, but will bigger be even better? The Lancet infect dis. 2006;6(10):653-663.
    Crossref
  2. Haralambous E, Weiss H, Radalowicz A, Hibberd M, Booy R, Levin M. Sibling familial risk ratio of meningococcal disease in UK Caucasians. Epidemiol. Infect. 2003;130(3):413-418.
    Crossref
  3. Somand D, Meurer W. Central nervous system infections. Emerg. med.clin. North Am. 2009;27(1):89-100.
    Crossref
  4. Giovane RA, Lavender PD. Central nervous system infections. Prim Care: Clin Office Pract. 2018;45(3):505-518.
    Crossref
  5. de Jonge RC, Van Furth A, Wassenaar M, Gemke RJ, Terwee CB. Predicting sequelae and death after bacterial meningitis in childhood: a systematic review of prognostic studies. BMC Infect. Dis. 2010;10(1):1-14.
    Crossref
  6. Tunkel AR, Wispelwey B, Scheld WM. Bacterial meningitis: recent advances in pathophysiology and treatment. Ann. intern. med. 1990;112(8):610-623.
    Crossref
  7. Lucas MJ, Brouwer MC, van de Beek D. Neurological sequelae of bacterial meningitis. J. Infect. 2016;73(1):18-27.
    Crossref
  8. Becker CE, O’Neill LA. Inflammasomes in inflammatory disorders: the role of TLRs and their interactions with NLRs. Paper presented at: Sem immunopath. 2007.
    Crossref
  9. Sanders MS, van Well GTJ, Ouburg S, Morré SA, van Furth AM. Genetic variation of innate immune response genes in invasive pneumococcal and meningococcal disease applied to the pathogenesis of meningitis. Genes & Imm. 2011;12(5):321-334.
    Crossref
  10. Brouwer MC, de Gans J, Heckenberg SG, Zwinderman AH, van der Poll T, van de Beek D. Host genetic susceptibility to pneumococcal and meningococcal disease: a systematic review and meta-analysis. The Lancet infect. dis. 2009;9(1):31-44.
    Crossref
  11. Dale AP, Read RC. Genetic susceptibility to meningococcal infection. Exp. Rev Anti-infect. ther. 2013;11(2):187-199.
    Crossref
  12. Sanders MS, van Well GTJ, Ouburg S, Lundberg PS, van Furth AM, Morré SA. Single nucleotide polymorphisms in TLR9 are highly associated with susceptibility to bacterial meningitis in children. Clin. Infect. Dis. 2011;52(4):475-480.
    Crossref
  13. Kim KS. Pathogenesis of bacterial meningitis: from bacteraemia to neuronal injury. Nat Rev Neurosci. 2003;4(5):376-385.
    Crossref
  14. McGill F, Heyderman RS, Panagiotou S, Tunkel AR, Solomon T. Acute bacterial meningitis in adults. The Lancet. 2016;388(10063):3036-3047.
    Crossref
  15. Klein M, Obermaier B, Angele B, et al. Innate immunity to pneumococcal infection of the central nervous system depends on toll-like receptor (TLR) 2 and TLR4. The J infect dis. 2008;198(7):1028-1036.
    Crossref
  16. Semlali A, Al Mutairi M, Oqla Alanazi I, et al. Toll-like receptor 4 polymorphisms in Saudi population with cardiovascular diseases. Mol genet genom med. 2019;7(9):e852.
    Crossref
  17. Martínez-Ríos MA, Vargas-Alarcón G, Vallejo M, et al. Toll-like receptor 4 gene polymorphisms and acute coronary syndrome: no association in a Mexican population. Archivos de cardiología de México. 2013;83(4):257-262.
    Crossref
  18. Ameziane N, Beillat T, Verpillat P, et al. Association of the Toll-like receptor 4 gene Asp299Gly polymorphism with acute coronary events. Artertio. thromb. vas. biol. 2003;23(12):e61-e64.
    Crossref
  19. O’Halloran A, Stanton A, O’Brien E, Shields D. The impact on coronary artery disease of common polymorphisms known to modulate responses to pathogens. Ann. hum. genet. 2006;70(6):934-945.
    Crossref
  20. Edfeldt K, Bennet AM, Eriksson P, et al. Association of hypo-responsive toll-like receptor 4 variants with risk of myocardial infarction. Eur. heart J. 2004;25(16):1447-1453.
    Crossref
  21. Zee RY, Hegener HH, Gould J, Ridker PM. Toll-like receptor 4 Asp299Gly gene polymorphism and risk of atherothrombosis. Stroke. 2005;36(1):154-157.
    Crossref
  22. Koch W, Hoppmann P, Pfeufer A, Schömig A, Kastrati A. Toll-like receptor 4 gene polymorphisms and myocardial infarction: no association in a Caucasian population. Eur. heart J. 2006;27(21):2524-2529.
    Crossref
  23. Džumhur A, Zibar L, Wagner J, Šimundić T, Dembić Z, Barbić J. Association studies of gene polymorphisms in toll-like receptors 2 and 4 in Croatian patients with acute myocardial infarction. Scand. J. Immunol. 2012;75(5):517-523.
    Crossref
  24. Nebel A, Flachsbart F, Schäfer A, et al. Role of the toll-like receptor 4 polymorphism Asp299Gly in longevity and myocardial infarction in German men. Mech. ageing dev. 2007;128(5-6):409-411.
    Crossref
  25. Balistreri CR, Candore G, Colonna-Romano G, et al. Role of Toll-like receptor 4 in acute myocardial infarction and longevity. Jama. 2004;292(19):2335-2340.
    Crossref
  26. Morange P, Tiret L, Saut N, et al. TLR4/Asp299Gly, CD14/C-260T, plasma levels of the soluble receptor CD14 and the risk of coronary heart disease: The PRIME Study. Europ. J. hum. genet. 2004;12(12):1041-1049.
    Crossref
  27. Golovkin AS, Ponasenko AV, Khutornaya MV, et al. Association of TLR and TREM-1 gene polymorphisms with risk of coronary artery disease in a Russian population. Gene. 2014;550(1):101-109.
    Crossref
  28. Guven M, İsmailoğlu Z, Batar B, et al. The effect of genetic polymorphisms of TLR2 and TLR4 in Turkish patients with coronary artery disease. Gene. 2015;568(2):229-232.
    Crossref
  29. van Well GTJ, Sanders MS, Ouburg S, Kumar V, van Furth AM, Morre SA. Single nucleotide polymorphisms in pathogen recognition receptor genes are associated with susceptibility to meningococcal meningitis in a pediatric cohort. PLoS One. 2013;8(5):e64252.
    Crossref
  30. Sargın B, Akbal A, Resorlu H, et al. The frequency of toll-like receptor 4 gene polymorphism in ankylosing spondylitis and its relationship between disease activity. Eu Res J. 2017;4(2):106-111.
    Crossref
  31. Machado NP, Nogueira E, Oseki K, et al. Clinical characteristics and frequency of TLR4 polymorphisms in Brazilian patients with ankylosing spondylitis. Revista brasileira de reumatologia. 2016;56:432-440.
    Crossref
  32. Zheng B, Li Q, Wei C, et al. Lack of association of TLR4 gene Asp299Gly and Thr399Ile polymorphisms with rheumatoid arthritis in Chinese Han population of Yunnan Province. Rheumatol. Int. 2010;30(9):1249-1252.
    Crossref
  33. Na K-S, Kim T-H, Rahman P, Peddle L, Choi C-B, Inman RD. Analysis of single nucleotide polymorphisms in Toll-like receptor 4 shows no association with ankylosing spondylitis in a Korean population. Rheumatol. Int. 2008;28(7):627-630.
    Crossref
  34. Wellcome Trust Case Control Consortium; Australo-Anglo-American Spondylitis Consortium (TASC), Burton PR, et al. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat. Genet. 2007;39(11):1329-1337.
  35. Snelgrove T, Lim S, Greenwood C, et al. Association of toll-like receptor 4 variants and ankylosing spondylitis: a case-control study. J. Rheumat. 2007;34(2):368-370.
  36. Adam R, Sturrock R, Gracie J. TLR4 mutations (Asp299Gly and Thr399Ile) are not associated with ankylosing spondylitis. Ann. rheum. dis. 2006;65(8):1099-1101.
    Crossref
  37. Gergely Jr P, Blazsek A, Weiszhar Z, Pazar B, Poor G. Lack of genetic association of the Toll-like receptor 4 (TLR4) Asp299Gly and Thr399Ile polymorphisms with spondylarthropathies in a Hungarian population. Rheum. 2006;45(10):1194-1196.
    Crossref
  38. Van der Paardt M, Crusius J, De Koning M, et al. No evidence for involvement of the Toll-like receptor 4 (TLR4) A896G and CD14-C260T polymorphisms in susceptibility to ankylosing spondylitis. Ann. rheum. dis. 2005;64(2):235-238.
    Crossref
  39. Ahmad-Nejad P, Denz C, Zimmer W, et al. The presence of functionally relevant toll-like receptor polymorphisms does not significantly correlate with development or outcome of sepsis. Genet test mol bio. 2011;15(9):645-651.
    Crossref
  40. Nakada T-a, Hirasawa H, Oda S, et al. Influence of toll-like receptor 4, CD14, tumor necrosis factor, and interleukine-10 gene polymorphisms on clinical outcome in Japanese critically ill patients. J. Surg. Res. 2005;129(2):322-328.
    Crossref
  41. Agnese DM, Calvano JE, Hahm SJ, et al. Human toll-like receptor 4 mutations but not CD14 polymorphisms are associated with an increased risk of gram-negative infections. J. infect. dis. 2002;186(10):1522-1525.
    Crossref
  42. Bronkhorst MW, Boyé ND, Lomax MA, et al. Single-nucleotide polymorphisms in the Toll-like receptor pathway increase susceptibility to infections in severely injured trauma patients. J. Tr. Ac. CareSurg. 2013;74(3):862-870.
    Crossref
  43. Carregaro F, Carta A, Cordeiro JA, Lobo SM, Silva EH, Leopoldino AM. Polymorphisms IL10-819 and TLR-2 are potentially associated with sepsis in Brazilian patients. Mem. Inst. Oswaldo Cruz. 2010;105:649-656.
    Crossref
  44. Elkilany A, Zeljić K, Surbatović M, Đorđević D, Magić Z, Božić B. Toll-like receptors (TLR) 2, 3, and 4 gene polymorphisms in critically ill patients. Arch. Biol. Sc. 2015;67(1):261-273.
    Crossref
  45. Everett B, Cameron B, Li H, et al. Polymorphisms in Toll-like receptors-2 and-4 are not associated with disease manifestations in acute Q fever. Genes Imm. 2007;8(8):699-702.
    Crossref
  46. Feterowski C, Emmanuilidis K, Miethke T, et al. Effects of functional Toll-like receptor-4 mutations on the immune response to human and experimental sepsis. Immunology. 2003;109(3):426-431.
    Crossref
  47. GUARNER-ARGENTE C, Sánchez E, Vidal S, et al. Toll-like receptor 4 D299G polymorphism and the incidence of infections in cirrhotic patients. Aliment.pharmacol. ther. 2010;31(11):1192-1199.
    Crossref
  48. Henckaerts L, Nielsen KR, Steffensen R, et al. Polymorphisms in innate immunity genes predispose to bacteremia and death in the medical intensive care unit. Crit. care med. 2009;37(1):192-e193.
    Crossref
  49. Horcajada JP, Lozano F, Muñoz A, et al. Polymorphic receptors of the innate immune system (MBL/MASP-2 and TLR2/4) and susceptibility to pneumococcal bacteremia in HIV-infected patients: a case-control study. Curr. HIV res. 2009;7(2):218-223.
    Crossref
  50. Kompoti M, Michopoulos A, Michalia M, Clouva-Molyvdas P-M, Germenis AE, Speletas M. Genetic polymorphisms of innate and adaptive immunity as predictors of outcome in critically ill patients. Immunobiology. 2015;220(3):414-421.
    Crossref
  51. Kumpf O, Giamarellos-Bourboulis EJ, Koch A, et al. Influence of genetic variations in TLR4 and TIRAP/Mal on the course of sepsis and pneumonia and cytokine release: an observational study in three cohorts. Crit. care. 2010;14(3):1-11.
    Crossref
  52. Lorenz E, Mira JP, Frees KL, Schwartz DA. Relevance of mutations in the TLR4 receptor in patients with gram-negative septic shock. Arch. intern. med. 2002;162(9):1028-1032.
    Crossref
  53. Yaa MN, Paolo P, Peter S, Eric PG, Jaya S, Papanicolaou GA. Toll-like receptor 4 polymorphisms and risk of gram-negative bacteremia after allogeneic stem cell transplantation. A prospective pilot study. Biol. Blood Marrow Transplant. 2009;15(9):1130-1133.
    Crossref
  54. Özgür TT, Yel L, YİĞİT Ş, et al. Lack of association between TLR4 polymorphism and severe gram-negative bacterial infection in neonates. Turk. J of MedSci. 2009;39(3):423-427.
    Crossref
  55. Rodriguez-Osorio CA, Lima G, Herrera-Caceres JO, et al. Genetic variations in toll-like receptor 4 in Mexican-Mestizo patients with intra-abdominal infection and/or pneumonia. Immunol Lett. 2013;153(1-2):41-46.
    Crossref
  56. Read RC, Pullin J, Gregory S, et al. A functional polymorphism of toll-like receptor 4 is not associated with likelihood or severity of meningococcal disease. The J. infect. dis. 2001;184(5):640-642.
    Crossref
  57. Sampath V, Mulrooney NP, Garland JS, et al. Toll-like receptor genetic variants are associated with Gram-negative infections in VLBW infants. J. Perinatol. 2013;33(10):772-777.
    Crossref
  58. Schnetzke U, Spies-Weisshart B, Yomade O, et al. Polymorphisms of Toll-like receptors (TLR2 and TLR4) are associated with the risk of infectious complications in acute myeloid leukemia. Genes & Immunity. 2015;16(1):83-88.
    Crossref
  59. Shalhub S, Junker CE, Imahara SD, Mindrinos MN, Dissanaike S, O’Keefe GE. Variation in the TLR4 gene influences the risk of organ failure and shock post-trauma: a cohort study. The J. of trauma. 2009;66(1):115.
    Crossref
  60. Tellería-Orriols J, García-Salido A, Varillas D, Serrano-González A, Casado-Flores J. TLR2-TLR4/CD14 polymorphisms and predisposition to severe invasive infections by Neisseria meningitidis and Streptococcus pneumoniae. Medicina intensiva. 2014;38(6):356-362.
    Crossref
  61. Van der Graaf CA, Netea MG, Morré SA, et al. Toll-like receptor 4 Asp299Gly/Thr399Ile polymorphisms are a risk factor for Candida bloodstream infection. Eur. cytokine netw. 2006;17(1):29-34.
  62. Yoon HJ, Choi JY, Kim CO, et al. Lack of Toll-like receptor 4 and 2 polymorphisms in Korean patients with bacteremia. J. Korean med. sci. 2006;21(6):979-982.
    Crossref
  63. Yuan FF, Marks K, Wong M, et al. Clinical relevance of TLR2, TLR4, CD14 and FcγRIIA gene polymorphisms in Streptococcus pneumoniae infection. Immunol. cell biol. 2008;86(3):268-270.
    Crossref
  64. Schmith VD, Campbell DA, Sehgal S, et al. Pharmacogenetics and disease genetics of complex diseases. Cell. mol. life sci: 2003;60(8):1636-1646.
    Crossref
  65. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57-70.
    Crossref
  66. Eccles D, Tapper W. The influence of common polymorphisms on breast cancer. Cancer treat. res. 2010;155:15-32.
    Crossref
  67. Nevo E. Genetic variation in natural populations: patterns and theory. Theor. popul. biol. 1978;13(1):121-177.
    Crossref
  68. Kaneko K, Furusawa C. An evolutionary relationship between genetic variation and phenotypic fluctuation. J. theor. biol. 2006;240(1):78-86.
    Crossref
  69. McKeigue PM. Mapping genes underlying ethnic differences in disease risk by linkage disequilibrium in recently admixed populations. Am. J hum. genet. 1997;60(1):188-196.
  70. Shriver MD. Ethnic variation as a key to the biology of human disease. Ann Intern Med. 1997;127(5):401-403.
    Crossref
  71. Shriver MD, Mei R, Parra EJ, et al. Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation. Hum. genomics. 2005;2(2):81-89.
    Crossref
  72. Seok J, Warren HS, Cuenca AG, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc. Natl. Acad. Sci . 2013;110(9):3507-3512.
    Crossref
  73. Corr SC, O’Neill LA. Genetic variation in Toll-like receptor signalling and the risk of inflammatory and immune diseases. J of innate immunity. 2009;1(4):350-357.
    Crossref
  74. Toshchakov V, Jones BW, Perera P-Y, et al. TLR4, but not TLR2, mediates IFN-β-induced STAT1α/β-dependent gene expression in macrophages. Nat. immunol. 2002;3(4):392-398.
    Crossref
  75. Sato S, Nomura F, Kawai T, et al. Synergy and cross-tolerance between toll-like receptor (TLR) 2-and TLR4-mediated signaling pathways. The J. Immunol. 2000;165(12):7096-7101.
    Crossref
  76. Beutler E, Gelbart T, West C. Synergy between TLR2 and TLR4: a safety mechanism. Blood Cells. Mol. Dis. 2001;27(4):728-730.
    Crossref
  77. Wagner J, Sim WH, Ellis JA, et al. Interaction of Crohn’s disease susceptibility genes in an Australian paediatric cohort. PLoS One. 2010;5(11):e15376.
    Crossref
  78. Hoshino K, Takeuchi O, Kawai T, et al. Cutting edge: Toll-like receptor 4 (TLR4)-deficient mice are hyporesponsive to lipopolysaccharide: evidence for TLR4 as the Lps gene product. The J. of Immunol. 1999;162(7):3749-3752.
  79. Arbour NC, Lorenz E, Schutte BC, et al. TLR4 mutations are associated with endotoxin hyporesponsiveness in humans. Nat. genet. 2000;25(2):187-191.
    Crossref
  80. Faber J, Henninger N, Finn A, Zenz W, Zepp F, Knuf M. A toll-like receptor 4 variant is associated with fatal outcome in children with invasive meningococcal disease. Acta Pædiatrica. 2009;98(3):548-552.
    Crossref
  81. Koomen I, Grobbee DE, Roord JJ, Donders R, Jennekens-Schinkel A, Van Furth A. Hearing loss at school age in survivors of bacterial meningitis: assessment, incidence, and prediction. Pediatrics. 2003;112(5):1049-1053.
    Crossref
  82. de Jonge RC, Sanders MS, Terwee CB, et al. Independent validation of an existing model enables prediction of hearing loss after childhood bacterial meningitis. PLoS One. 2013;8(3):e58707.
    Crossref
  83. Koomen I, Grobbee D, Roord J, et al. Prediction of academic and behavioural limitations in school-age survivors of bacterial meningitis. Acta Paediatr. 2004;93(10):1378-1385.
    Crossref
  84. Barbujani G, Colonna V. Human genome diversity: frequently asked questions. Trends Genet. 2010;26(7):285-295.
    Crossref
  85. Henn BM, Cavalli-Sforza LL, Feldman MW. The great human expansion. Proc Natl Acad Sci U S A. 2012;109(44):17758-17764.
    Crossref
  86. Balaresque PL, Ballereau SJ, Jobling MA. Challenges in human genetic diversity: demographic history and adaptation. Hum Mol Genet. 2007; 2:R134-139.
    Crossref
  87. Scheinfeldt LB, Tishkoff SA. Recent human adaptation: genomic approaches, interpretation and insights. Nat Rev Genet. 2013;14(10):692-702.
    Crossref
  88. Hancock AM, Witonsky DB, Alkorta-Aranburu G, et al. Adaptations to climate-mediated selective pressures in humans. PLoS Genet. 2011;7(4):e1001375.
    Crossref
  89. The International HapMap C. A haplotype map of the human genome. Nature. 2005;437(7063):1299-1320.
    Crossref
  90. Rosenberg NA, Pritchard JK, Weber JL, et al. Genetic structure of human populations. Science. 2002;298(5602):2381-2385.
    Crossref
  91. Li JZ, Absher DM, Tang H, et al. Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008;319(5866):1100-1104.
    Crossref
  92. Mukherjee S, Karmakar S, Babu SPS. TLR2 and TLR4 mediated host immune responses in major infectious diseases: a review. Braz J of Infect Dis. 2016;20:193-204.
    Crossref
  93. Janardhanan J, Joseph Martin S, Astrup E, et al. Single-nucleotide polymorphisms in Toll-like receptor (TLR)-2, TLR4 and heat shock protein 70 genes and susceptibility to scrub typhus. J. hum genet. 2013;58(11):707-710.
    Crossref
  94. Ohto U, Yamakawa N, Akashi-Takamura S, Miyake K, Shimizu T. Structural analyses of human Toll-like receptor 4 polymorphisms D299G and T399I. J. Biol. Chem. 2012;287(48):40611-40617.
    Crossref
  95. Anwar MA, Choi S. Structure-activity relationship in TLR4 mutations: atomistic molecular dynamics simulations and residue interaction network analysis. Sci rep. 2017;7(1):1-14.
    Crossref
  96. Kim Y-C, Won S-Y, Jeong B-H. Identification of prion disease-related somatic mutations in the prion protein gene (PRNP) in cancer patients. Cells. 2020;9(6):1480.
    Crossref
  97. Kim Y-C, Jeong M-J, Jeong B-H. Strong association of regulatory single nucleotide polymorphisms (SNPs) of the IFITM3 gene with influenza H1N1 2009 pandemic virus infection. Cell & mol immunol. 2020;17(6):662-664.
    Crossref
  98. Greene CS, Penrod NM, Williams SM, Moore JH. Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One. 2009;4(6):e5639.
    Crossref
  99. Huang X, Zhang W, Shao Z. Association between long non-coding RNA polymorphisms and cancer risk: a meta-analysis. Biosci rep. 2018;38(4).
    Crossref
  100. Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA. 2008;299(11):1335-1344.
    Crossref
  101. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005;6(2):95-108.
    Crossref
Cite This Article

Article Metrics

Article View: 1512
JPAM.16.4.67 (1072 downloads)

Share This Article

© The Author(s) 2022. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright © 2024 Journal of Pure and Applied Microbiology. All Rights Reserved.