Is meningitis genetic

Is meningitis genetic DEFAULT


The research, led by Imperial College London and the Genome Institute of Singapore, is the largest ever genetic study of meningitis and septicaemia caused by meningococcal bacteria. It suggests that people who develop these diseases have innate differences in their natural defences that leave them unable to attack meningococcal bacteria successfully.

Although several different bacteria and viruses cause meningitis, meningococcal bacteria cause one of the most devastating forms of the disease -- meningococcal meningitis, which is fatal in approximately one in ten cases. Meningococcal septicaemia is a type of blood poisoning that often accompanies this form of meningitis.

Meningococcal meningitis and septicaemia most commonly affect babies, young children, teenagers and young adults. The diseases are frightening because they can strike rapidly, with people becoming critically ill within hours.

There are vaccines available against some strains of meningococcal bacteria but not others. The researchers hope that their new findings will boost the development of effective vaccines to combat the group B strain of the bacteria, for which there is currently no vaccine. Every year, this strain causes thousands of deaths in children and adults across the world.

Most people carry the meningococcal bacteria in their throat intermittently during their lives without ever developing the disease. Prior to the study, it has not been known why some people in the population develop meningococcal meningitis and septicaemia while others appear to be naturally immune to the bacteria.

This study compared the genetic makeup of 1,500 people who developed meningococcal meningitis, from the UK, Holland, Austria and Spain, with over 5,000 healthy controls from the Wellcome Trust Case Control Consortium. It was supported by the Wellcome Trust, Meningitis Research Foundation UK and the European Society for Paediatric Infectious Diseases.

Researchers looked at half a million common genetic variants scattered across each person's genome, and searched for differences between the patients with meningococcal disease and healthy controls. The results revealed that those who had developed meningococcal meningitis had genetic markers in a number of genes involved in attacking and killing invading bacteria.

Professor Michael Levin, from the Department of Paediatrics at Imperial College London, who led the international research effort, said: "Although most of us have carried the meningitis bacteria at some point, only around one in 40,000 people develop meningococcal meningitis. Our study set out to understand what causes this small group of people to become very ill whilst others remain immune. Our findings provide the strongest evidence so far that there are genetic factors that lead to people developing meningitis."

Dr Victoria Wright from the Department of Paediatrics at Imperial College London, who co-ordinated patient recruitment for the study across four European countries, added: "Meningococcal disease is a terrible illness as it strikes healthy children and adults suddenly, and can kill in a few hours. Improving our understanding of why some people get the disease and not others will help to identify those at risk and develop better vaccines. The success of the study was due to the willingness of patients and families to contribute their DNA for analysis, and it could not have been achieved without international collaboration."

The variations uncovered in the study were around the genes for Factor H and Factor H-related proteins. These proteins regulate a part of the body's immune system called the complement system, which recognises and kills invading bacteria.

Normally, Factor H and Factor H-related proteins ensure that the complement system does not cause excessive damage to the body's own cells. However, meningococcal bacteria can hijack the body's Factor H and use it to ensure that the body does not recognise the bacteria as foreign. The bacteria effectively use Factor H as a 'Trojan Horse,' enabling them to evade the body's defences and preventing the immune system from killing them.

The researchers are now keen to investigate precisely how the genetic variations that they have uncovered affect the activity of Factor H and Factor H-related proteins.

This study involved collaboration between researchers at Imperial College London and clinicians at Imperial College Healthcare NHS Trust, as part of the Academic Health Science Centre (AHSC), a unique kind of partnership between the College and the Trust, formed in October 2007. The AHSC's aim is to improve the quality of life of patients and populations by taking new discoveries and translating them into new therapies as quickly as possible.

Other institutions involved in the study were the Alder Hey Children's Hospital, the Genome Institute of Singapore, and other children's centres in the UK, Holland, Austria and Spain.

Story Source:

Materials provided by Imperial College London. Note: Content may be edited for style and length.

Journal Reference:

  1. Sonia Davila, Victoria J Wright, Chiea Chuen Khor, Kar Seng Sim, Alexander Binder, Willemijn B Breunis, David Inwald, Simon Nadel, Helen Betts, Enitan D Carrol, Ronald de Groot, Peter W M Hermans, Jan Hazelzet, Marieke Emonts, Chui Chin Lim, Taco W Kuijpers, Federico Martinon-Torres, Antonio Salas, Werner Zenz, Michael Levin & Martin L Hibberd for the International Meningococcal Genetics Consortium. Genome-wide association study identifies variants in the CFH region associated with host susceptibility to meningococcal disease. Nature Genetics, 2010; DOI: 10.1038/ng.640

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Genetic Variation in Neisseria meningitidis Does Not Influence Disease Severity in Meningococcal Meningitis


Neisseria meningitidis is a commensal to the human nasopharynx (1). Rarely, it crosses the mucosal barrier to cause invasive meningococcal disease, which can manifest as bacteremia, fulminant septicemia and meningitis (2). Meningitis occurs in over 60% of patients with invasive meningococcal disease, dependent on the income level of a country and patient age, and is invariably preceded by bacteremia (2, 3). In fulminant meningococcal septicemia, patients present with severe septic shock and no clinical signs of meningitis. In cerebrospinal fluid, few meningococci are present and white cell count is low (4, 5). Contrary, patients with meningococcal meningitis have lower concentrations of meningococci and endotoxin in blood, but higher concentrations in cerebrospinal fluid (CSF) (5, 6). For meningococcal septicemia, mortality rate is around 12% (7). For meningitis, mortality rate is lower, around 3%; and 10% of patients have neurological sequelae after disease (8). Unfavorable outcome in patients with meningococcal meningitis is the result of neurological or systemic disease complications such as multi-organ failure complicating bacteremia, peripheral vasculopathy and peripheral ischemia (9). Lipopolysaccharide (LPS) is a major component of the meningococcal outer membrane. In the host, LPS is recognized by Toll-like receptor 4 (TLR4). Activation of TLR4 results in induction of the innate immune system and activation of the coagulation system through upregulation of tissue-factor (10). Excessive activation of the coagulation system can result in disseminated intravascular coagulation (DIC). This leads to low blood thrombocyte counts through consumption. Low blood thrombocyte counts are associated with severe meningococcal disease and unfavorable outcome (11).

In meningococcal meningitis, host genes involved in inflammation and coagulation have been suggested to be involved in severity of disease (12). Polymorphisms in SERPINE1, a gene in the fibrinolysis pathway, and interleukin-1B (IL1B) and IL1RN, mediating cytokine production, were associated with mortality in a meta-analysis (12). Multiple meningococcal virulence factors have been identified (13). For a minority of these, an association with disease severity in humans has been determined. Mutations in the bacterial gene lpxL1, resulting in penta-acetylated LPS vs. wild-type hexa-acetylated LPS, have been shown to lead to decreased systemic inflammation and reduced coagulation, resulting in higher blood thrombocyte counts and less unfavorable outcome in patients (14). Meningococcal factor H binding protein (FHbp) inhibits complement activation by binding to human factor H. The fHbp gene has high sequence diversity and a study of this variability revealed an association with disease severity and outcome, but not with blood thrombocyte counts (15). Finally, gene variants encoding a two-partner secretion system have been implicated in disease severity, through an unknown mechanism of action (16).

We performed a pathogen genome-wide association study in meningococcal isolates causing meningitis to determine the contribution of genetic variants in explaining disease severity.


Nationwide Clinical Cohort

Adults aged 16 years or older who had N. meningitidis meningitis were identified by positive cerebrospinal fluid (CSF) culture, and were listed in the database of The Netherlands Reference Laboratory for Bacterial Meningitis (NRLBM) between 1982 and 2003 and between 2006 and 2013. Between 1 October 1998 and 1 April 2002 (17) and between 1 March 2006 and 1 April 2012 (8) two prospective national cohort studies ran in which patients with bacterial meningitis were included and their clinical characteristics were collected through a case record form. Patients with hospital-acquired bacterial meningitis, neurosurgical procedures, or those within 1 month following neurosurgical procedure or neurotrauma were excluded. Patients with an altered immune status owing to splenectomy, diabetes mellitus, cancer, alcoholism, or the use of immunosuppressive drugs were considered immunocompromised, as were patients infected with human immunodeficiency virus. Neurological examination was performed at discharge, and outcome was scored according to the Glasgow Outcome Scale, with scores varying from 1 (death) to 5 (good recovery) (18). A favorable outcome was defined as a score of 5, and an unfavorable outcome was defined as a score of 1–4. Seizures were defined as a clinical diagnosis of an epileptic seizure at or during hospital admittance, with or without electroencephalographic (EEG) confirmation. Thrombocyte count (109 per liter [L]), c-reactive protein (CRP; milligrams [mg] per L), cerebrospinal fluid (CSF) leucocytes (per microliter [uL]), CSF protein (grams [g] per L) and CSF glucose ratio were determined at admittance by the laboratory of the admitting hospital as part of routine clinical care. The NRLBM collects meningococcal isolates from cerebrospinal fluid, blood, and skin biopsy from clinical microbiology laboratories throughout the Netherlands. During the inclusion periods, notification for this disease was mandatory.

Ethical Approval

Written informed consent was obtained from all patients or their legally authorized representatives. The studies were approved by the Medical Ethics Committee of the Amsterdam UMC, Amsterdam, The Netherlands (approval number: NL43784.018.13).

Bacterial Whole Genome Sequencing

DNA from N. meningitidis strains was extracted using the Maxwell® RSC Cultured Cells DNA kit according to the manufacturer's protocol (Promega, Madison, WI, USA). Sequencing was performed using multiplexed libraries on the Illumina HiSeq platform to produce paired-end reads of 100 nucleotides in length (Illumina, San Diego, CA, USA). Quality control involved analysis of contamination, number and length of contigs, GC content and N50 parameter. Sequences for which one or more of these quality control parameters deviated by more than 3 standard deviations from the mean, were excluded. Sequences of the bacterial samples were assembled using a standard assembly pipeline (19). The median number of contigs was 85 (range 54–133), median GC content 53.83% (range 53.43–54.00%), average genome length 2,160,459 (range 2 066 672–2 389 876), and median coverage 204-fold (interquartile range 193–216). Serogroups and sequence types were determined from the whole genome sequence by in-house scripts. Clonal complexes were determined from sequence types.

Data Availability

Fastq sequences of bacterial isolates were deposited in the European Nucleotide Archive (ENA, accession numbers in Supplementary Table 1).

Pan-Genome Generation and Phylogenetic Tree

Genome sequences were annotated with PROKKA, version 1.11 (20). Roary (version 3.5.0) with default parameters was used to extract clusters of orthologous genes, and create a core gene alignment at a sequence identity threshold of 95% (21). A maximum likelihood phylogeny of single-nucleotide polymorphisms (SNPs) in the core genome of all sequenced isolates was produced with iqtree (version 1.6.5, including fast stochastic tree search algorithm) assuming a general time reversible model of nucleotide substitution with a discrete γ-distributed rate heterogeneity and the allowance of invariable sites (22).

Genetic Variants and Association Analysis

Sequence variation was determined in multiple ways. To obtain a reference free compilation of genetic variation, encompassing single and multiple base pair variants, we determined non-redundant k-mers from assembled sequences by counting nodes on compacted De Bruijn graphs with Unitig-counter (version 1.0.5, minimum k-mer length of 13) (23). SNPs and rare variants [deleterious variants with a minor allele frequency (MAF) <0.01] were called separately with Snippy (version 4.4.0, default parameters) from whole-genome sequence reads. Genetic variation was called against the N. meningitidis MC58 reference strain (24). Clusters of orthologous genes (COGs) were determined with Roary, with a sequence identity threshold of 95% (21). The association analysis for k-mers and SNPs was run as a linear mixed model in Pyseer (version 1.1.1), with a minor allele frequency of 0.05 (25). To correct for population structure, a similarity matrix, generated with Pyseer, was included. K-mers were mapped to the MC58 reference strain with bowtie-2 (version 2.2.3, with equal quality values and length of seed substrings 7 nucleotides). A p-value of 0.05 corrected for the number of independent tests defined as the number of unique k-mer patterns was selected as a threshold for association of the phenotype with k-mers. For SNPs, rare variants, and for COGs, a p-value of 0.05 divided by the number of statistical tests performed at the pre-specified minor allele frequency was selected as a threshold for association. Rare variants were considered as a burden test in which they were grouped per gene. Manhattan plots were generated in R, version 3.5.1, with the package ggplot2 (version 3.1.0). Quantile-quantile plots for SNP and k-mer data were generated in R using the qqman (version 0.1.7) package. The association with clinical characteristics was determined by Fisher's exact test for categorical data, Wald test for continuous data and binary logistic regression for multivariate analysis in R.

For the candidate gene analyses, results of the k-mer and SNP associations with phenotype in the genome-wide association analysis were extracted at the lpxL1 and fHbp gene locations in the MC58 reference genome. The number of independent tests was defined as the number of genetic variants in the respective locus and the threshold for statistical significance was set accordingly. Presence or absence of the tps variants were determined as COGs. Association analyses were performed in Pyseer (version 1.1.1) as linear mixed models, accounting for population stratification as described before. SNP-based heritability analyses were performed with a linear mixed model in Pyseer (version 1.1.1) using the genomic relatedness matrix as random effects (25). A confidence interval around these values was determined with ALBI (default parameters) based on the eigenvalue decomposed distances in the kinship matrix (26).

GWAS Simulation

The power this dataset had to find genetic variants was quantified by running genome-wide association studies with simulated phenotypes at heritability values of 0.1, 0.2, 0.3, 0.4, and 0.5 in dataset of 880 whole genome sequences from publicly available N. meningitidis genomes (27). Simulations of continuous phenotypes were generated using GCTA (version 1.93.2 beta), using ten simulation replicates and either 10 or 1,000 random causal SNPs with effect sizes randomly generated from a standard normal distribution). Plots were generated in R (version 4.0.0), with the package ggplot2 (version 3.1.0).


Clinical Characteristics

Between 1998 and 2002, 258 of 696 (37%) and between 2006 and 2013, 150 of 1,604 (9%) patients with meningitis had N. meningitidis as the causative pathogen. Meningococci from 78 patients out of an unknown total were included with isolation dates before 1998. Clinical data, including disease outcome, was available for 369 of 486 patients of whom meningococcal isolates were available. The median age was 29 years (interquartile range 19–51) and 167 (49%) were female (Table 1). Forty four patients (12%) had an unfavorable outcome and 24 (7%) died. Of the 486 isolates, 354 (73%) were serogroup B and 107 (22%) serogroup C. The remaining 25 isolates (5%) were serogroup A, W, X, Y, or E (28). The genotyping alleles of the major antigens have been described elsewhere (28). Unfavorable outcome was the result of serogroup C [13 out of 69 (19%)] and ST-11 complex [15 out of 85 (18%)] infection more often compared to other serogroups and clonal complexes (Supplementary Table 2). To increase the power to identify genetic variations associated with disease severity we investigated whether blood thrombocyte count on admission was a predictor of unfavorable outcome. We found that unfavorable outcome was highly correlated with a low thrombocyte count in blood (Wald test, p < 0.001, n = 342; Supplementary Figure 1). While age was also associated with unfavorable outcome (Wald test, p < 0.001, n = 364), age was not associated with thrombocyte count (Kruskal-Wallis test, p = 0.778). Sex was not associated with clinical outcome (chi-square test, p = 0.140) or thrombocyte count (Kruskal-Wallis test, p = 0.519). Clinical presentation was associated with unfavorable outcome, as well as thrombocyte count, because it is part of the same pathway of the phenotype under study (disease severity).

Table 1. Baseline characteristics of patients.

No Variants Surpass Threshold for Genome-Wide Significance in a Pathogen Genome-Wide Association Analysis

The association analysis was performed on 342 samples for which thrombocyte count as a continues variable was available. There were 611,389 unique k-mer patterns which varied in length from 13 to 46 nucleotides derived from bacterial sequences combined. After calling SNPs, there were 170,582 nucleotide variable positions (single nucleotide variants, insertions and deletions), of which 64,616 were present in between 5 and 95% of sequences. A p-value of 1.0 × 10−7 was selected as a threshold for association of the phenotype with k-mers and a p-value of 8.0 × 10−7 for association with SNPs. There were 1,445 rare variants called from the SNP data which were burdened in 260 genes. One thousand seven hundred and seventy five COGs were considered in association with the phenotype. None of the variants in the N. meningitidis genome surpassed the threshold for association after correction for multiple testing (Figure 1, Supplementary Figure 2). The top signal associated with thrombocyte count was a region at approximately 200,000 base pair positions in the N. meningitidis genome (p = 9.96e-07, Figure 1). This region held an isoprenyl trasferase (uppS) gene (Supplementary Figure 3).

Figure 1. Manhattan plot of combined k-mers and SNPs associated with thrombocyte count. The x-axis shows the base pair position on the N. meningitidis chromosome. The y-axis shows the minus log 10 transformed p-value. K-mers are shown in black, SNPs are shown in green. The region around 200,000 base pairs is the top signal for association with thrombocyte count.

Isoprenyl Trasferase (uppS) Gene Variation Is Associated With High Thrombocyte Count

The isoprenyl transferase gene is 746 nucleotides in length. The k-mer with the top signal in the uppS gene was 23 nucleotides long was annotated at position 146–168 nucleotides. The genetic variant of interest was absence of the k-mer sequence from the gene. Isolates from which the k-mer sequence was absent (which thus carried the variant of interest) had sequences which differed in one or two nucleotide positions (Table 2). These variant nucleotide positions were synonymous mutations. The variant (absence of the k-mer sequence) was rare and was found in 22 out of 342 isolates (6%). The variant was found more often in isolates from patients with a high blood thrombocyte count. In isolates from patients with a thrombocyte count in the highest quartile, the uppS variant was found in 14 of 86 isolates (16%), compared to 8 of 256 isolates (3%) from patients with thrombocyte counts in the lowest three quartiles. Isolates with the variant were detected as being evenly distributed over the phylogenetic tree (Figure 2).

Table 2. Nucleotide and amino acid sequence of the top associated k-mer sequence in uppS gene.

Figure 2. Phylogenetic tree of 486 N. meningitidis isolates colored by clonal complex. The inner circle shows isolates from patients with thrombocyte counts in blood (n = 342) in the lowest three quartiles in blue and the highest quartile in green. The outer circle shows isolates with wild-type uppS in blue and variant uppS in red. The variant uppS is found more often in isolates from patients with the highest thrombocyte quartile and is distributed evenly over the various clonal complexes in the tree.

Patients infected with a N. meningitidis isolate containing the uppS variant had lower rate of unfavorable outcome compared to wild-type isolates [1 of 22 patients (5%), vs. 40 of 320 patients (13%), Fisher's exact test p = 0.231], were of female sex less often [7 of 22 (32%), vs. 160 of 320 (50%), Fisher's exact test p = 0.075], and had lower CRP in blood levels at admission [128 mg L−1, interquartile range (IQR) 22–182; vs. 230, IQR 164–320; Wald test p < 0.001], lower CSF leucocyte levels at admission (2,363 uL−1, IQR 1,579–9,759; vs. 5,461, IQR 1,527–13,375; Wald test p = 0.132), and lower CSF protein levels at admission (3.1 g L−1, IQR 1.36–5.1; vs. 4.38, IQR 2.22–6.82; Wald test p = 0.037) (Table 3). CRP in blood was associated with uppS variant independently of thrombocyte count in a multivariate analysis (p < 0.001).

Table 3. Clinical characteristics in patients infected with meningococci with uppS wild-type or variant.

Genetic Variance Does Not Account for Variance in Disease Severity

To determine whether the lack of genome-wide significant results was more likely due to low statistical power or whether it represents a true biological feature, we calculated the proportion of variance in blood thrombocyte count that was attributable to genetics in the N. meningitidis genome by calculating h2. Heritability for this phenotype was zero, with an h2 of 0.0% (95% confidence interval: 0.0–0.9), based on SNPs mapped against a reference genome as well as pan-genome variation covered by k-mers. The wide confidence interval around the heritability value suggests low detection power for causative variants.

Phenotype Simulations Rule Out Large Contributions of Pathogen Genome Variation

To further quantify this, we simulated phenotypes, and ran multiple GWAS on these simulations to determine detection power at fictitious heritability values in a cohort of 880 N. meningococci genomes. From these results, it emerges that detection of causal variants is limited for continuous traits with heritability values around 0.25 or lower at the current sample size, irrespective of the number of causal variants assumed under a polygenic model (Figure 3). For a binary trait, detection is more or less precluded at a sample size of 880 samples, even at simulated heritability values of 0.5, resulting in a mean inferred h2 of 0.00 [standard deviation (sd) 0.007] for a model of 1,000 causal variants and an inferred h2 of 0.01 (sd 0.025) for a model of 10 causal variants.

Figure 3. Heritability plots for a simulated GWAS with 10 (A) and 1,000 (B) causal SNPs. On the x-axis is the simulated heritability (h2) value. On the y-axis the inferred h2. Colored lines represent different population sizes. The dashed line represents a perfect fit, x = y. Detection of causal variants is limited for continuous traits with heritability values around 0.25 or lower at the sample size in this study (342 genomes), irrespective of assuming 10 or 1,000 causal variants under a polygenic model.

No Replication of lpxL1, fHbp, and tps Gene Mutations on Outcome and Thrombocyte Count

In our cohort, we determined the association between published variants in lpxL1, fHbp, and tps genes and thrombocyte count and unfavorable outcome (14–16). In the lpxL1 gene, we detected 2 SNPs, 5 insertion/deletions and 46 k-mer variants at sites or spanning sites described to inactivate the lpxL1 gene in the previous publication (14). We did not observe an association between these variants with blood thrombocyte count or unfavorable outcome after correcting for multiple testing. In the fHbp gene, at the 184 amino acid position, we observed only lysine to arginine substitutions or deletions (15). We detected 24 SNPs, 23 insertions/deletions and 376 k-mer variants at other sites in the fHbp gene. The lysine to arginine variants were not associated with blood thrombocyte count or unfavorable outcome in a model accounting for bacterial population stratification. Presence of tps2 and tps3 gene was not associated with less severe disease or thrombocyte count in this cohort (16).


In this pathogen genome-wide association analysis, we could not detect genetic loci in the meningococcal genome predictive of blood thrombocyte count as a proxy for disease severity. Our results show that much larger sample sizes are needed to detect genetic variants for disease severity, a trait with low heritability. Together with the failed replication of previously published gene signals leads us to conclude that the contribution of genetic variants in the meningococcal genome to disease severity is limited. Presumably, environmental factors, host genetics or interacting factors play a much larger role in determining disease course.

We found a nucleotide sequence in the uppS gene as the top signal associated with thrombocyte count. This isoprenyl transferase gene is involved in biosynthesis of terpenoids. While this sequence did not surpass the genome-wide threshold for significance, patients infected with meningococci absent for this sequence (variant) in the uppS gene had lower CRP levels in blood, independent of thrombocyte count, in conjunction with higher blood thrombocyte counts. These measures can be indicators of less severe disease. The variant itself is not causal, as the translated alternative amino acid sequence is identical. Further studies are needed to determine whether the variant marks a sequence in uppS or in another gene and whether it determines virulence.

We were unable to confirm mutations in the lpxL1 gene associated with higher thrombocyte count in a previously published paper (14), both in a genome-wide approach and in a candidate gene approach. The study by Fransen et al. included samples from the same nationwide cohort from 1998 to 2002. The current study is larger and included the majority of samples from 2006 to 2013. By calling genetic variants as k-mers, SNPs, rare-variants and COGs we explored a broader range of genetic variability, increasing the multiple testing burden. Furthermore, in the study by Fransen et al., patients infected with meningococci having any lpxL1 variant resulting in an inactive gene were compared with the group of patients with meningocococci with the active lpxL1 gene. In contrast, in the present study, an association between thrombocyte count and each individual lpxL1 variant was explored. We could not replicate earlier studies showing associations between fHbp and tps and outcome (15, 16). Changes in clonal complex distribution among the meningococcal isolates in the present study population compared to that used in the earlier studies may explain replication failure of those results.

The epidemiology of N. meningitidis meningitis in the Netherlands has previously been described (29). For serogroup B, a hyperendemic period started in 1982, peaked in 1993 and incidence subsequently decreased until the last observation in 2012. For serogroup C disease, vaccination was introduced in 2001 in response to an epidemic, which resulted in a major decrease in incidence (29). The samples in this study encompass these periods and represent a national and longitudinal meningococcal population. Because we correct for phylogenetic clusters in the genome-wide association analysis, bacterial lineages do not confound the results.

By simulating phenotypes and running a pathogen GWAS using a larger set of 880 unphenotyped genomes, we were able to provide approximations of the sample sizes required to detect traits depending on the level of heritability, and provide an upper bound on the heritability detectable in our set of 342 phenotyped isolates. We show that much larger sample sizes are needed to detect genetic variants for disease severity, a trait with low heritability, and that these data can be of added value in future genetic studies of N. meningitidis, for example a meta-analysis to increase study power.

Similar to what is found in this study, pathogen genetics did not explain the variance in disease severity in pneumococcal meningitis and no loci were detected to be associated with disease severity in a pathogen genome-wide association study in pneumococcal meningitis (30). In contrast, genetic variance in the pathogen does play a major role in susceptibility to pneumococcal meningitis (30). One explanation for this is that contrary to nasopharyngeal carriage, invasive disease is an evolutionary dead-end for bacteria, in which mutations resulting in excessive disease severity are not selected for. Host genetic variance did explain a proportion of the variance in pneumococcal disease severity (30). For meningococcal meningitis, loci within the complement factor H (CFH) gene and CFH-related protein 3 (CFHR3) in the host were associated with susceptibility to disease (31). We were unable to investigate host genetics as a contribution to meningococcal disease severity in this study because of small sample size.

The study has several limitations. First, the sample size, although considerable for meningococcal meningitis, is low for a pathogen genome-wide association analysis. Second, there was no validation cohort available. Third, meningococci have various mechanisms for genetic variation, resulting in decreased correlation between the genetic sequence and protein expression or structure and function. Major mechanisms for genetic variation in meningococci are phase variation (reversible switching of gene expression), transformation and homologous recombination (13), restriction-modification and epigenetics (32). These mechanisms limit the genetic detection of disease modifying loci.

Whole genome sequencing together with detailed clinical metadata enabled us to quantify the contribution of meningococcal genetic variants to meningococcal meningitis disease severity. For future studies, much larger datasets would be required. To obtain these, we recommend international collaborations resulting in datasets combining microbial genomics and clinical patient data.

Data Availability Statement

The datasets generated for this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Ethics Statement

The studies involving human participants were reviewed and approved by Medical Ethics Committee of the Amsterdam UMC, Amsterdam (approval number: NL43784.018.13). The patients/participants provided their written informed consent to participate in this study.

Author Contributions

DB, SB, AE, and MB designed the study and collected the data. PK, JL, and BF performed the experiments. PK, JL, and BF analyzed the data. PK, JL, BF, MB, AE, SB, and DB interpreted the data. PK wrote the manuscript draft. JL, BF, MB, AE, SB, and DB provided comments on the manuscript. All authors contributed to the article and approved the submitted version.


This work was supported by grants from the European Research Council (ERC Starting Grant, proposal/contract 281156; and the Netherlands Organization for Health Research and Development (ZonMw; NWO-Vici grant, 91819627;, both to DB. Work at the Wellcome Trust Sanger Institute was supported by Wellcome Trust core funding (098051; JL was supported by a Medical Research Council studentship grant (1365620; The Netherlands Reference Laboratory for Bacterial Meningitis was supported by the National Institute for Health and Environmental Protection, Bilthoven (

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at:


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Keywords: severity, genome-wide association study, genome sequencing, Neisseria meningitidis, thrombocytes

Citation: Kremer PHC, Lees JA, Ferwerda B, van de Ende A, Brouwer MC, Bentley SD and van de Beek D (2020) Genetic Variation in Neisseria meningitidis Does Not Influence Disease Severity in Meningococcal Meningitis. Front. Med. 7:594769. doi: 10.3389/fmed.2020.594769

Received: 31 August 2020; Accepted: 23 October 2020;
Published: 11 November 2020.

Copyright © 2020 Kremer, Lees, Ferwerda, van de Ende, Brouwer, Bentley and van de Beek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Diederik van de Beek,

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Q-I often think back to the death of my sister at an early age from meningitis and wonder if the same fate still awaits me. Though I`m no longer a child, I`m concerned that I may soon show this disease. Is meningitis an inherited condition?

A-Stop worrying. You are never going to suffer from meningitis merely because your sister died of it years ago. Certain forms of meningitis (and there are many) are highly contagious. Historically, there have been dreadful meningitis epidemics, but be assured that it never is a hereditary disease. Different forms of relatively common meningitis can be caused either by viruses or bacteria. The word meningitis means an inflammation of the meninges (the three membranes that envelop a person`s brain and spinal cord).

Make no mistake about it: meningitis, which occurs in this country mainly during winter, spring and autumn, is always an extremely dangerous and frequently life-threatening infection, as are its possible aftereffects.

The disease is particularly serious and potentially deadly in the very old and the very young, and in patients who have another disease at the time they develop meningitis. Most commonly seen in children younger than 1, it may be even more serious in adults, especially those older than 50, for statistics reveal that when adults contract meningitis, 20 to 70 percent of them probably will die, even though they may be treated with effective, modern medicine. Therefore, when symptoms of the disease appear, treatment must be initiated as soon as possible.

Even with today`s highly developed medical techniques, physicians experience some difficulty in diagnosing meningitis. But medical treatments for the many different types of meningitis are frequently effective. Several forms of newly developed drugs, so-called super-antibiotics or super-anti-infectives (including the third-generation cephalosporins), are

administered, usually with impressive cure rates. To be safe, as soon as the disease is suspected, and even before diagnosis is complete, many doctors start a patient on antibiotics as a protective measure. While I never want to frighten anyone, I would be remiss not to tell you that neurological damage may occur in about 20 percent of all patients who have been cured of meningitis itself. Among the most frequently experienced effects are varying degrees of mental retardation, seizures, and noticeable defects in sensory activities, including hearing, taste and smell.

Q-It used to be that my doctor merely had my finger stuck for blood tests. Now, more and more, the nurse is taking the blood from a vein in my arm. I was wondering if there is any difference in the results of these tests, and if one method is better than the other.

A-Accurate and useful results can be obtained from many screening tests that require only a drop or two of blood. They have the advantage of using the blood obtained in the quick and easy method afforded by a finger stick, and also may be quite economical. Many people prefer this method to that of a needle in the vein. Actually, with new, very sharp, disposable needles used but one time, neither method is very painful. But more precise measurements may require more of a sample, so the vein is used to rapidly collect all that is necessary. With new developments in medical technology, Several tests may be run on a single blood sample, offering you some economies, and providing answers to more than one question. It is not a question of one method being better than the other, but of which type of test is required for your medical care.


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Dr. Bruckheim welcomes questions from readers. Although he cannot respond to each one individually, he will answer those of general interest in his column. Write to Dr. Bruckheim c/o the Chicago Tribune, P.O. Box 119, Orlando, Fla. 32802-0119.

The Signs and Symptoms of Meningitis

Gene link to meningitis infection

By Emma Wilkinson
Health reporter, BBC News

A set of genes which renders people more prone to meningitis has been pinpointed by researchers.

The international team compared DNA from 1,400 people with bacterial meningitis and 6,000 healthy individuals, Nature Genetics reports.

They found differences in a family of genes involved in the immune response seem to make people more or less susceptible to the infection.

It is hoped the findings will lead to the development of new vaccines.

The researchers were looking at meningitis caused by the Neisseria meningitidis bacterium, which leads to swelling of the lining of the brain and blood poisoning.

It is not the first time researchers have attempted to find out if some people are more likely to catch meningitis because of their genetic make-up.

But results have previously been unclear, probably because of the small number of people studied.

In the latest study, researchers first scanned the whole genetic code of 475 British patients with meningococcal disease and 4,700 healthy individuals.

They found a clear difference in a small set of genes known to be involved in the immune system response.

When they looked again in two other European populations they found the same result.

The genetic differences found means that, in some people, the bacteria is able to evade the immune system and cause infection, while other people' immune systems are better equipped to fight it off.

The genes encode for a protein called factor H, and factor H related proteins.

Where there are flaws, the meningococcal bacteria is able to bind to these proteins to prevent the immune system from recognising it - almost like a Trojan horse - enabling it to get a foothold.

Study author Professor Michael Levin, an expert in international child health at Imperial College London, said the findings would be particularly useful in developing a vaccine against meningitis B, which is now responsible for most cases in the UK.

There is already an effective vaccine against meningitis C.

"It seems that the genetic differences in factor H between people is what determines susceptibility or resistance.

"It suggests it may be an important protein to include in vaccines, and factor H is already one of the candidates for meningitis B vaccine."

He said the results will also help scientists better tailor vaccines to be effective in the whole population.

It may also open up avenues for improving treatment once people have bacterial meningitis, he said.

Sue Davie, chief executive of the Meningitis Trust, said: "This exciting work has thrown new light on factors that play a part in determining why some people get meningococcal disease and others do not.

"Further work will be needed to establish just what the genetic differences are in the genes which actually cause this susceptibility to invasive infection, but this is a promising start."

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Genetic is meningitis

Human genetics of meningococcal infections

Stephanie Hodeib

1Department of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

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Jethro A. Herberg

1Department of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

Find articles by Jethro A. Herberg

Michael Levin

1Department of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

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Vanessa Sancho-Shimizu

1Department of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

2Department of Virology, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

Find articles by Vanessa Sancho-Shimizu

Author informationArticle notesCopyright and License informationDisclaimer

1Department of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

2Department of Virology, Faculty of Medicine, Imperial College London, Norfolk Place, London, W2 1PG UK

Vanessa Sancho-Shimizu, Email: [email protected]

corresponding authorCorresponding author.

Received 2019 Nov 16; Accepted 2020 Feb 2.

Copyright © The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

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Neisseria meningitidis is a leading cause of bacterial septicaemia and meningitis worldwide. Meningococcal disease is rare but can be life threatening with a tendency to affect children. Many studies have investigated the role of human genetics in predisposition to N. meningitidis infection. These have identified both rare single-gene mutations as well as more common polymorphisms associated with meningococcal disease susceptibility and severity. These findings provide clues to the pathogenesis of N. meningitidis, the basis of host susceptibility to infection and to the aetiology of severe disease. From the multiple discoveries of monogenic complement deficiencies to the associations of complement factor H and complement factor H-related three polymorphisms to meningococcal disease, the complement pathway is highlighted as being central to the genetic control of meningococcal disease. This review aims to summarise the current understanding of the host genetic basis of meningococcal disease with respect to the different stages of meningococcal infection.


Neisseria meningitidis (Nm) is a common commensal bacterium that is paradoxically also a devastating human pathogen. It is a Gram-negative diplococcus that selectively colonises the human nasopharynx (Stephens et al. 1983). Nm is encapsulated with a polysaccharide capsule which can be classified into 13 capsular serogroups known to cause disease. The six major serogroups typically associated with disease are serogroups A, B, C, Y, W, and X (Rosenstein et al. 2001; Xie et al. 2013). Carriage of Nm refers to the commensal colonisation of the bacterium in the human nasopharynx, whereas invasive meningococcal disease (IMD) is a result of bacterial invasion of the mucosal layer leading to its dissemination throughout the body causing meningitis and/or septicaemia, and may result in purpura fulminans and/or death (Coureuil et al. 2012; Lecuyer et al. 2017; Pace and Pollard 2012). Carriage rates vary depending on multiple variables including age, geographical location, and living conditions but is estimated at 10% in the general population (Cartwright et al. 1987; Caugant et al. 1992). Whilst majority of carriers remain asymptomatic or can develop low-grade bacteraemia, carriage of Nm leads to the production of protective antibodies and development of acquired immunity, and very rarely leads to invasive disease (Caugant and Maiden 2009; Goldschneider et al. 1969a; b; Pace and Pollard 2012). Incidence of IMD resulting in meningitis and septicaemia is estimated at 1–3 cases per 100,000 individuals in Europe and North America (Parikh et al. 2018; Yazdankhah and Caugant 2004). However, in the “meningitis belt” of sub-Saharan Africa, attack rates during epidemics can reach 1000 cases per 100,000 individuals (Yazdankhah and Caugant 2004). The reasons for these regional differences in IMD rates are not fully understood; however, non-genetic environmental factors have been suggested to play a role (Agier et al. 2013; Omoleke et al. 2018). Young children are at particular risk of developing IMD due to the absence of protective antibodies. Whilst disease rates are high in those under 5 years of age, there are other peaks of IMD incidence seen in adolescents and in old age (Caugant and Maiden 2009; Cohn et al. 2013; Rosenstein et al. 2001). IMD is rare but it causes significant mortality at an overall rate of 10–15% with up to 19% of survivors suffering from severe life-long sequalae with a reduced quality of life (Cohn et al. 2013; Erickson and De Wals 1998; Kirsch et al. 1996; Pace and Pollard 2012).

Human genetics is known to influence response to pathogens. Nucleotide variants that alter or abolish the function of immune-related genes are important determinants of susceptibility to infection and course of disease (Casanova 2015a, b). Human genetic investigations are particularly pertinent to Nm infections as Nm is a human-host restricted pathogen resulting in a lack of suitable animal models. Due to this host restriction, it is anticipated that all evolutionary adaptations of the pathogen over time must be specific to human responses (Laver et al. 2015). Multiple genes have been identified via familial linkage, genome-wide association studies (GWASs), and candidate gene-based studies to influence the course of infection, elucidating the key pathways involved in IMD and the impact of the role of genetics (Brouwer et al. 2010; Casanova 2015b; Wright et al. 2009). A study of sibling risk ratio for IMD, comparing the risk of disease within family members to the general population, showed that host genetics contributed to approximately 30% of the total risk of developing disease (Haralambous et al. 2003). Monogenic disorders of the complement pathway have long been known to predispose to IMD (Westberg et al. 1995). Furthermore, GWASs for infection susceptibility are well established as a method for identification of more common polymorphisms for instance, polymorphisms of complement factor H (CFH) and complement factor H-related 3 (CFHR3) have been associated with IMD, highlighting the host genetic contribution to disease (Davila et al. 2010; Martinon-Torres et al. 2016).

This review describes the role of human genetics with respect to the different stages of Nm infection. This includes the initial meningococcal colonisation of the human nasopharynx, followed by penetration of the mucosal membrane and invasion of the bloodstream, ultimately leading to systemic complications that can arise from an abnormal inflammatory and coagulation response. We have considered aspects of the immune system that are functionally related and grouped together in themed sections, whilst we acknowledge that these categorizations are not definitive and some genes may be involved in various stages of meningococcal pathogenesis. This review will summarise the contribution of host genetics at each phase of meningococcal infection highlighting the genes either associated with IMD or responsible for the monogenic disorders that determine IMD (Fig. 1).

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Fig. 1

Monogenic disorders underlying Neisseria meningitidis infection. Nm is transmitted via droplets and selectively colonises the human nasopharynx. In susceptible hosts, meningococci can invade and cross the nasopharyngeal mucosal epithelium to gain access to the blood stream. Once inside the bloodstream the meningococci grow in number and are disseminated throughout the host. Uncontrolled growth in the blood, leads to high titres of Nm and septicaemia. In other patients, there is less Nm replication in the blood, but meningococci breach the blood brain barrier (BBB), multiply uncontrollably in the cerebrospinal fluid and infect the meninges, leading to meningitis. The genes highlighted in red are monogenic disorders almost exclusively associated with IMD. Highlighted in orange, are monogenic disorders associated with bacterial infections and, though not exclusive to Nm infection, has been observed in cases of IMD. SPLUNC1, highlighted in yellow, has recently been demonstrated as a monogenic disorder associated with IMD though its pathogen exclusivity is unknown. Image created with


Nm selectively colonises the epithelial surface of the nasopharynx. Initial adhesion is mediated by the meningococcal type IV pili and then further facilitated by interaction of its Opacity-associated adhesion (Opa) proteins with host cell surface proteins including carcino-embryonic antigen cell adhesion molecule (CEACAM) proteins found on the nasopharyngeal epithelium (Carbonnelle et al. 2006; Virji 2009; Virji et al. 1996). Host colonisation is commonly asymptomatic; however, in some cases, colonisation can lead to invasion of the protective mucosa and entry of meningococci into the bloodstream, resulting in IMD (Aycock and Mueller 1950; Virji 2009). A candidate gene-based study found specific haplotypes in CEACAM3 and CEACAM6 associated with IMD indicating that CEACAM proteins are key factors in initial meningococcal infection (Table ​2) (Callaghan et al. 2008). A recent study reported a novel heterozygous mutation in short palate, lung, and nasal epithelial clone 1 (SPLUNC1, also known as BPIFA1) in three IMD cases (Table ​1). This autosomal dominant SPLUNC1 mutation affected meningococcal biofilm formation, colonisation, and subsequent invasion, and is the first monogenic gene study demonstrating control of Nm colonisation (Mashbat et al. 2019).

Table 1

Monogenic disorders associated with meningococcal disease

GeneVariantInheritanceStudy typeGene-specific phenotypeInfection phenotypeDisease outcomeReferences
 SPLUNC1c.65G > A, p.G22EADFamilialIncreased bacterial adhesionIMDSusceptibilityMashbat et al. (2019)
 CFPc.481C > T, p.R161XXRFamilialReduced complement functionIMDSusceptibilityWestberg et al. (1995)
c.1240T > G, p.Y414DXRFamilialReduced complement functionIMDSusceptibilityFredrikson et al. (1996)
c.617C > G, p.S206XXRFamilialReduced complement functionIMDSusceptibilityvan den Bogaard et al. (2000)
c.893G > T p.G298VXRFamilialReduced complement functionIMDSusceptibilityvan den Bogaard et al. (2000)
c.1164G > A, p.W388XXRFamilialReduced complement functionIMDSusceptibilityHelminen et al. (2012)
 C5c.1055A > G, p.Y352CARFamilialReduced complement functionIMDSusceptibilityMarujo et al. (2019)
c.754G > A, p.A252TARFamilialReduced complement functionIMDSusceptibilityOwen et al. (2015)
c.55C > T, p.Q19X; c.4444C > T, p.R1482XaARFamilialReduced complement functionIMDSusceptibilityWang et al. (1995)
c.1115A > G, p.K372RARFamilialReduced complement functionIMDSusceptibilityPfarr et al. (2005)
c.4890-4891delinsG, p.L1631fsARFamilialReduced complement functionIMDSusceptibilityDelgado-Cervino et al. (2005)
 C6c.878delAARFamilialReduced complement functionIMDSusceptibilityParham et al. (2007)

c.1599T > A, p.Y493X

IVS3 + 3A > C

ARFamilialReduced complement functionIMDSusceptibilityParham et al. (2007)
c.1936delGARFamilialReduced complement functionIMDSusceptibilityNishizaka et al. (1996a)
c.879delGARFamilialReduced complement functionIMDSusceptibilityHobart et al. (1998)
c.1195delCARFamilialReduced complement functionIMDSusceptibilityZhu et al. (1998)
 C7c.2107C > T, p.Q681XARFamilialReduced complement functionIMDSusceptibilityBarroso et al. (2010)
c.2184T > A, p.C728XARFamilialReduced complement functionIMDSusceptibilityNishizaka et al. (1996b)

c.281-1G > T

c.1-?-2350 + ?del

ARFamilialReduced complement functionIMDSusceptibilityKi et al. (2005)
c.1135G > C, p. G379RARFamilialReduced complement functionIMDSusceptibilityFernie et al. (1997)
c.1922delAGARFamilialReduced complement functionIMDSusceptibilityBarroso et al. (2004)



ARFamilialReduced complement functionIMDSusceptibilityBarroso et al. (2006)
 C8Bc.1282C > T, p.R428XARFamilialReduced complement functionIMDSusceptibilityDellepiane et al. (2016)

c.271C > T, p.Q91X;

c.820C > T, p.R274X

ARFamilialReduced complement functionIMDSusceptibilitySaucedo et al. (1995)

c.1041_1047dup, p.L350fs

c.271C > T, p.Q91Xa

ARFamilialReduced complement functionIMDSusceptibilityArnold et al. (2009)
 C9c.346C > T, p.R116XARCohortReduced complement functionMMSusceptibilityKira et al. (1998)
c.162C > A, p.C54XARFamilialReduced complement functionIMDSusceptibilityZoppi et al. (1990)
 CFDc.638T > G, p.V213G; c.640T > C, p.C214RARFamilialReduced complement functionMSSusceptibilitySprong et al. (2006)
c.125C > A, p.S42XARFamilialReduced complement functionIMDSusceptibilityBiesma et al. (2001)
c.620G > C, p.R176PARFamilialReduced complement functionIMDSusceptibilitySng et al. (2018)

c. .677–678delinsTTCT

c.653T > C, p.L218Pa

ARFamilialReduced complement functionIMDSusceptibilityEl Sissy et al. (2019)

c.766C > T, p.Q256X

c.1894-1897delTTTG, p.F632CfsX8a

ARFamilialReduced complement functionMMSusceptibilitySlade et al. (2013)
 CFIc.1282A > T p.H400LARFamilialReduced complement functionIMDSusceptibilityVyse et al. (1996)

c.1282A > T p.H400L

c.801G > A p.del-exon 5a

ARFamilialReduced complement functionIMDSusceptibilityVyse et al. (1996)

c.266_?_536 + ?del

c.1420C > T, p.R474X

ARFamilialReduced complement functionIMDSusceptibilityAlba-Dominguez et al. (2012)

c.485G > A, p.G162D

c.1176_1177dupAT, p.W393Yfs*5

ARFamilialReduced complement functionIMDSusceptibilityAlba-Dominguez et al. (2012)
c.772G > A, p.A258TARFamilialReduced complement functionIMDSusceptibilityAlba-Dominguez et al. (2012)
 C3c.1716G > A, p.K552XARFamilialReduced complement functionIMDSusceptibilityDa Silva Reis et al. (2002)
 C2c.841_868del, p.Val281fsARCohortReduced complement functionIMDSusceptibilityJonsson et al. (2005)
 IRAK4c.877C > T, p.Q293XARCase studyImpaired IL-6 productionIMDSusceptibilityFrans et al. (2015)
 IKBKGc.1249T > C, p.C417RXRCase studyN/AIMDSusceptibilityHuppmann et al. (2015)
 GATA2c.988C > T, p.R330XN/ACohortN/AIMDN/ASpinner et al. (2014)

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Table 2

Polymorphisms associated with meningococcal disease

GeneVariantGenetic modelSignificanceStudy typeGene-specific phenotypeInfection phenotypeDisease outcomeReferences
 CEACAM3Haplotype CdAdditiveP < 0.001 OR = 0.52 (95% CI 0.35–0.075)Candidate geneN/AIMDProtectiveCallaghan et al. (2008)
 CEACAM6Haplotype BdAdditiveP < 0.001 OR = 0.29 (95% CI 0.14–0.61)Candidate geneN/AIMDProtectiveCallaghan et al. (2008)
Haplotype CdAdditiveP = 0.018 OR = 2.01 (95% CI 1.13–3.6)Candidate geneN/AIMDSusceptibilityCallaghan et al. (2008)
 SFTPA2rs1059046 (REF); rs17886395 (REF);rs1965707; rs1965708RecessiveP = 0.025 OR = 7.4 (95% CI 1.3–42.4)Candidate geneN/AIMDSusceptibilityJack et al. (2006)
rs1059046 (REF); rs17886395; rs1965707; rs1965708 (REF)DominantP = 0.045 OR = 0.3 (95% CI 0.1–0.97)Candidate geneN/AIMDProtectiveJack et al. (2006)
rs1965708RecessiveP = 0.016 OR = 6.7 (95% CI 1.4–31.5)Candidate geneN/AIMDSusceptibilityJack et al. (2006)
rs1965708RecessiveOR = 2.9 (95% CI 1.1–7.7)Candidate geneN/ADeathSusceptibilityJack et al. (2006)
 CFHR3rs426736AdditiveP = 4.6 × 10–13 OR = 0.63 (95% CI 0.55–0.71)GWASN/AIMDSusceptibilityDavila et al. (2010)
 CFHc.-496C > T (REF)RecessiveP = 0.001 OR = 2.0 (95% CI 1.3–3.2)Candidate geneHigh fH levels and reduced bactericidal activityIMDSusceptibilityHaralambous et al. (2006)
rs1065489AdditiveP = 2.2 × 10–11 OR = 0.64 (95% CI 0.56–0.73)GWASN/AIMDSusceptibilityDavila et al. (2010)
rs1061170DominantP = 5.3 × 10–3 OR = 1.26 (95% CI 1.07–1.49)Candidate geneN/AIMDSusceptibilityBradley et al. (2015)
rs3753396DominantP = 3.0 × 10–5 OR = 0.56 (95% CI 0.43–0.74)Candidate geneN/AIMDProtectiveBradley et al. (2015)
 MBL2rs5030737; rs1800450; rs1800451DominantP < 0.001Candidate geneN/AIMDSusceptibilityFaber et al. (2007)
rs5030737; rs1800450; rs1800451DominantP = 0.001 OR = 2.0 (95% CI 1.3–3.0)

Candidate gene

(hospital cohort)

N/AIMDSusceptibilityHibberd et al. (1999)
rs5030737; rs1800450; rs1800451DominantP = 0.008 OR = 2.4 (95% CI 1.2–4.6)

Candidate gene

(community-based study)

N/AIMDSusceptibilityHibberd et al. (1999)
 TLR4rs4986790DominantP = 0.021 OR = 3.3 (95% CI 1.14–9.73)Candidate geneN/ADeathSusceptibilityFaber et al. (2009)
rs4986790DominantP = 0.006 OR = 3.003 (95% CI 1.331–6.775)Candidate geneN/AIMDSusceptibilityFaber et al. (2006)
 TLR9rs352140DominantP = 0.0098 OR = 0.6 (95% CI 0.4–0.9)Candidate geneN/AMMProtectiveSanders et al. (2011)
 TNFrs1800629DominantP = 0.03 RR = 2.5 (95% CI 1.1–5.7)Candidate geneHigh TNF-αDeathSusceptibilityNadel et al. (1996)
rs1800629DominantP = 0.02 RR = 1.6 (95% CI 1.1–2.3)Candidate geneHigh TNF-αIMDSusceptibilityNadel et al. (1996)
rs1800629 (REF)DominantOR = 3.619 (95% CI 1.758–7.449)Candidate geneN/AIMDSusceptibilityTitmarsh et al. (2013)
rs1800629 (REF)RecessiveOR = 3.791 (95% CI 1.720–8.357)Candidate geneN/AIMDSusceptibilityTitmarsh et al. (2013)
rs1800629RecessiveOR = 1.93 (95% CI 1.08–3.46)Candidate geneHigh TNF-αIMDSusceptibilityRead et al. (2009)
 IL1Brs16944 (REF)RecessiveP < 0.001 OR = 3.39 (95% CI 1.39–8.29)Candidate geneN/ADeathIncreased severityRead et al. (2000)
rs16944RecessiveP < 0.001 OR = 7.35 (95% CI 2.51–21.45)Candidate geneN/ADeathIncreased severityRead et al. (2000)
rs16944 (REF)Dominant

P = 0.023 OR = 2.05

(95% CI 1.10–3.79)

Candidate geneN/ADeathProtectiveRead et al. (2003)
 IL1B/IL1RNrs16944/rs419598 (REF)Dominant/RecessiveP = 0.018 OR = 7.78 (95% CI 1.05–59.05)Candidate geneN/ADeathProtectiveRead et al. (2000)
 IL1B/IL1RNrs16944/rs419598Dominant/DominantOR = 0.61 (95% CI 0.38–0.993)Candidate geneN/ADeathSusceptibilityRead et al. (2003)
 IL1RN86-basepair VNTRRecessiveP = 0.033–0.043Candidate geneN/AIMDSusceptibilityBalding et al. (2003)
rs419598RecessiveOR = 2.0 (95% CI 1.1–3.4)Candidate geneN/AIMDSusceptibilityEndler et al. (2006)
 IL6rs1800795 (REF)RecessiveOR = 2.64 (95% CI 1.12–6.22)Candidate geneN/ADeathSusceptibilityBalding et al. (2003)
rs1800795 (REF)RecessiveOR = 4.395 (95% CI 1.900–10.162)Candidate geneN/AIMDSusceptibilityTitmarsh et al. (2013)
 IL10rs1800896RecessiveP = 0.0078 OR = 2.7 (95% CI 2.3–3.6)Candidate geneN/AIMDSusceptibilityBalding et al. (2003)
 FCGR2Ars1801274RecessiveP = 0.028 OR = 2.67 (95% CI 1.09–6.53)Candidate geneReduced phagocytosisMSSusceptibilityBredius et al. (1994)
rs1801274RecessiveP < 0.03 OR = 2.9 (95% CI 1.1–7.3)Candidate geneN/AIMDSusceptibilityPlatonov et al. (1997)
rs1801274 (REF)RecessiveP < 0.02 OR = 4.7 (95% CI 1.5–14.5)Candidate geneN/AIMDProtectivePlatonov et al. (1997)
rs1801274DominantP < 0.01 OR = 14Candidate geneN/AIMDSusceptibilityPlatonov et al. (1998)
rs1801274RecessiveP = 0.04 OR = 3.9 (95% CI1.0–16)Candidate geneN/AIMDSusceptibilityDomingo et al. (2002)
rs1801274RecessiveP = 0.004 OR = 3 (95% CI 1.4–7.8)Candidate geneN/AIMDSusceptibilityDomingo et al. (2002)
rs1801274RecessiveP = 0.03 OR = 3.5 (95% CI 1.1–11.7)Candidate geneN/AIMDSusceptibilityDomingo et al. (2004)
 FCGR2A/FCGR3Brs1801274/NA2 allotypeRecessiveP = 0.036 OR = 13.9 (95% CI 1.2–478)Candidate geneReduced phagocytosisIMDSusceptibilityFijen et al. (2000)
 F5rs6025DominantP < 0.03 RR = 3.1 (95% CI 1.2–7.9)Candidate geneIncreased thrombosisPurpuraIncreased severityKondaveeti et al. (1999)
rs6025RecessiveSingle case studyCandidate geneIncreased thrombosisPurpuraIncreased severitySackesen et al. (1998)
 SERPINE1rs1799889RecessiveRR = 2.0 (95% CI 1.0–3.8)Candidate geneHigher PAI-1 concentrationDeathIncreased severityaHermans et al. (1999)
rs1799889RecessiveP = 0.005 RR = 1.9 (95% CI 1.2–3.0)Candidate geneN/ADeathIncreased severityaHaralambous et al. (2003)
rs1799889RecessiveP < 0.0001 RR = 2.7 (95% CI 1.6–4.6)Candidate geneN/AMSIncreased severityaHaralambous et al. (2003)
rs1799889RecessiveP = 0.03 RR = 2.4 (95% CI 1.1–5.0)Candidate geneN/AVascular complicationsIncreased severityaHaralambous et al. (2003)
rs1799889RecessiveOR = 5.9 (95% CI 1.9–18.0)Candidate geneN/AMSIncreased severityaWestendorp et al. (1999)
rs1799889RecessiveP = 0.001Candidate geneN/AMMIncreased severitybWestendorp et al. (1999)
rs1799889RecessiveP = 0.037 OR = 2.31 (95% CI 1.04–5.14)Candidate geneN/ADeathIncreased severityaGeishofer et al. (2005)
rs1799889RecessiveP = 0.01 OR = 2.21 (95% CI 1.20–4.08)Candidate geneN/AMSIncreased severityaGeishofer et al. (2005)
rs1799889RecessiveP = 0.014 HR 1.5 (95% CI 1.1–2.1)Candidate geneN/ADisseminated intravascular coagulationIncreased severityaBinder et al. (2007a)
 PROCrs1799808 (REF), rs1799809RecessiveP = 0.04Candidate geneLow protein C levels cIMDSusceptibilityBinder et al. (2007b)
rs1799808, rs1799809DominantP = 0.036 OR = 3.43 (95% CI 1.05–11.20)Candidate geneN/AMSIncreased severityBinder et al. (2007b)
rs1799808, rs1799809 (REF)RecessiveP = 0.017 OR = 0.09 (95% CI 0.01–0.94)Candidate geneN/AMSProtectiveBinder et al. (2007b)
 CPB2rs779491029RecessiveP = 0.03 OR = 3.1 (95% CI 1.0–9.5)Candidate geneN/ADeathIncreased severityKremer Hovinga et al. (2004)
rs779491029RecessiveOR = 13.7 (95% CI 1.5–123)Candidate geneIncreased anti-fibrinolytic activityMSIncreased severityEmonts et al. (2008)

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Surfactant proteins are part of the collectins protein family involved in the innate immune system and in pathogen pattern recognition. They are expressed in the nasopharynx and respiratory tract and can activate inflammatory and phagocyte responses after binding to structures on the microbial cell wall (Pikaar et al. 1995). Surfactant proteins A1 and A2 (SP-A1 and SP-A2, respectively), encoded by SFTPA1 and SFTPA2, respectively, are expressed at the site of Nm colonisation. One candidate gene study has exhaustively investigated SP-A proteins in association with IMD describing various polymorphisms both increasing the risk of IMD and also showing a protective effect; however, these findings require further validation by other independent studies (Jack et al. 2006).


Invasion of the nasopharyngeal epithelium leads to dissemination of the bacterium in the bloodstream. The mechanisms that lead to invasion are poorly understood; however, Goldschneider and colleagues in the late 1960s suggested that complement-dependent killing by antibody was a key defence against meningococcal infection, with high antibody titres seen later in life (Goldschneider et al. 1969a). The majority of the population does not develop severe disease, even in those who lack pre-existing bactericidal antibodies, suggesting that the innate immune response plays a key role in preventing invasive disease after meningococcal colonisation of the nasopharynx (Welsch and Granoff 2007). Defects in genes involved in this stage of invasion can provide gaps in host defence and give rise to IMD.


Complement plays an important role in the innate immune response, assisting in a rapid response against invading pathogens (Lewis and Ram 2014). Complement is activated via three main pathways which all involve complement component 3 (C3): the classical antibody–antigen interaction, the mannose-binding lectin (MBL) interaction with the microbial cell walls and finally, the alternative pathway activated by C3 interacting with complement Factor B (CFB) and complement Factor D (CFD) (Fig. 2) (Janeway et al. 2001). The alternative pathway can also act as an amplification loop for the other two pathways (Janeway et al. 2001). All three pathways feed into the same final pathway of the formation of C3 convertase enzyme that can produce complement component C3b which can act as an opsonin and facilitate phagocytosis by binding to the bacteria. C3b and C3 can also bind to form C5, generating C5b which leads to the formation of the membrane-attack complex (MAC), comprised of complement components C5b–C9, creating pores in the membrane of the bacteria thereby causing bacterial death (Fig. 2) (Heesterbeek et al. 2019; Janeway et al. 2001). Host complement-dependent bactericidal activity is one of the key protective immune responses against meningococcal infection and its role was established early at the start of the twentieth century (Flexner 1913; Flexner and Jobling 1908). Later, Goldschneider and colleagues were able to decisively elucidate the protective role of complement and antibodies against invasive Nm infection (Goldschneider et al. 1969a, b). The role of complement as a vital part of host defence against Nm infection has been unequivocally established and further supported by the increased susceptibility to infection by complement-deficient individuals described further below (Figueroa et al. 1993).

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Fig. 2

Complement pathway. Overview of three main complement pathways that involves multiple cleavage events that converge to a cleavage of central component C3–C3b, which triggers a cascade that leads to the formation of the membrane-attack complex capable of cell lysis via pore formation. C3b is also an opsonin capable of tagging pathogens for phagocytosis and C3b formation can act as a positive feedback loop for the alternative pathway, necessitating the need for several negative regulators including CFI, and CFH. Properdin is a positive regulator of the alternative pathway, stabilising the C3 convertase. Those in red symbolises factors that have reported loss of function mutations that are associated with either chronic meningococcaemia or IMD. Image created with

Terminal complement deficiencies

Functional deficiencies of the terminal complement (C5–C9) were one of the first characterised defects associated with IMD in the 1970s and 1980s, whereas the identification of mutations underlying these deficiencies came about later (Lim et al. 1976; Nagata et al. 1989; Petersen et al. 1976; Snyderman et al. 1979). Complement deficiencies can be acquired or inherited, the latter being rarer occurring in 0.03% of the general population with frequencies depending on complement component and ethnicity (Lewis and Ram 2014). Mutations in any one of the terminal complement genes (C5, C6, C7, C8A, C8B, or C9) result in an autosomal recessive monogenic disorder leading to impaired function of the complement system and increased susceptibility to Nm infection (Table ​1) (Arnold et al. 2009; Barraso et al. 2004, 2006, 2010; Delgado-Cervino et al. 2005; Dellepiane et al. 2016; Fernie et al. 1997; Figueroa et al. 1993; Hobart et al. 1998; Kaufmann et al. 1993; Ki et al. 2005; Kira et al. 1998; Kojima et al. 1998; Lee et al. 1978; Lewis and Ram 2014; Marujo et al. 2019; Nishizaka et al. 1996a, b; Owen et al. 2015; Parham et al. 2007; Pfarr et al. 2005; Platonov et al. 1993; Saucedo et al. 1995; Wang et al. 1995; Wurzner et al. 1995; Zhu et al. 1998; Zoppi et al. 1990). Patients with deficiencies of the terminal complement are characteristically distinct as they typically present with recurrent meningococcal infection, with lower mortality rates per episode (Figueroa and Densen 1991; Fijen et al. 1999; Platonov et al. 1993).

Alternative pathway complement factors

Functional deficiencies of all the alternate pathway factors have been associated with IMD. Properdin is a positive regulator of the alternative complement pathway by binding to and stabilising C3b, prolonging its half-life and functional activity, as well as functioning as an initiator of the alternative pathway (Lewis and Ram 2014). Functional properdin deficiency resulting in impaired complement response and reduced bactericidal activity was first associated with IMD in a multiplex kindred in the 1980s (Braconier et al. 1983; Cunliffe et al. 1995; Densen et al. 1987; Figueroa and Densen 1991; Fijen et al. 1989; Genel et al. 2006; Nielsen and Koch 1987; Nielsen et al. 1989; Ross and Densen 1984; Schlesinger et al. 1990, 1993; Sjoholm et al. 1982; Spath et al. 1999). Genetic deficiency of properdin, encoded by CFP, is an X-linked recessive disorder and is typically associated with non-recurrent and rapidly progressive fatal meningococcaemia (Table ​1) (Fredrikson et al. 1996; Helminen et al. 2012; Sjoholm et al. 1982; Spath et al. 1999; van den Bogaard et al. 2000; Westberg et al. 1995). In properdin-deficient patients, around 50% of IMD is caused by uncommon serogroups of Nm such as W and Y (Figueroa and Densen 1991). Functional deficiency of complement factor D (CFD) was first reported in a patient with recurrent Nm infections (Hiemstra et al. 1989). The discovery of mutations in CFD resulting in an autosomal recessive disorder predisposing to IMD was subsequently reported in other unrelated kindreds (Table ​1) (Biesma et al. 2001; El Sissy et al. 2019; Sng et al. 2018; Sprong et al. 2006). There has been one report of autosomal recessive complement Factor B (CFB) deficiency with recurrent pneumococcal and meningococcal infections (Table ​1) (Slade et al. 2013). Complement Factor I (CFI) is a negative regulator of the alternative pathway that proteolytically inactivates C3b. CFI deficiency results in uncontrolled continuous activation of the alternative pathway and is associated with recurrent infections from encapsulated bacteria (Alba-Dominguez et al. 2012). In the 1990s, a study suggested CFI deficiency, resulting from recessive mutations, to be responsible for two cases of recurrent pyogenic infections, including Nm infection (Vyse et al. 1996). More recently several patients with autosomal recessive CFI deficiency have been associated with IMD (Table ​1) (Alba-Dominguez et al. 2012).

Finally, GWASs have identified polymorphisms in a broad region spanning complement factor H (CFH) and complement factor H-related 3 (CFHR3) as highly significantly associated with IMD (Table ​2) (Davila et al. 2010; Martinon-Torres et al. 2016). This association has been validated in different cohorts and represents the most significant genetic association with susceptibility to IMD (Table ​2) (Bradley et al. 2015; Davila et al. 2010; Haralambous et al. 2006; Martinon-Torres et al. 2016). CFH acts as a negative regulator and competes with CFB resulting in inactive C3b (Fig. 2) (Janeway et al. 2001).

The mechanisms underlying the association of polymorphisms in the CFH/CFHR3 region with IMD have begun to be clarified. Nm expresses a factor H-binding protein (fHBP) on its surface which binds human CFH. This binding assists the bacteria in evading complement-mediated killing in the blood stream (Schneider et al. 2009). CFHR proteins, which have partial homology to CFH, can antagonise the immune evasion through competition for fHBP binding (Caesar et al. 2014). However, the plasma concentrations of the CFHR proteins are low compared to that of CFH (Pouw et al. 2016), and patients with deletions in the CFHR region are not at increased risk of IMD (Davila et al. 2010). Therefore, other mechanisms are likely to contribute to the association of the CFH/CFHR3 region with IMD (Caesar et al. 2014). The EUCLIDS consortium (Martinon-Torres et al. 2018) is currently exploring the role of genetic polymorphisms in the CFH/CFHR3 region in determining CFH plasma concentrations.

Early complement components

Deficiencies of the early complement components, C1, C2, and C4, are inherited in an autosomal recessive manner and classically associated with autoimmune diseases, particularly systemic lupus erythematosus (SLE), although the impact on Nm infection is controversial (Fijen et al. 1989; Macedo and Isaac 2016; Tebruegge and Curtis 2008). The role of the C4 isoforms (C4A and C4B) in Nm infection is conflicting with reports that C4 deficiency alone is not significant enough to predispose individuals to bacterial infection (Bishof et al. 1990; Cates et al. 1992; Fasano et al. 1990). Until 1991, only six cases of C2 deficiency with incidence of IMD were reported (Figueroa and Densen 1991). Since then, few cases of IMD patients with C2 deficiency have been reported including a 4-year-old child from England, a 12-year-old child suffering from primary meningococcal arthritis as a result of Nm serogroup Y infection, and three patients from Sweden with homozygous C2 deficiency (Hoare et al. 2002; Hussain et al. 2007; Jonsson et al. 2005). Primary C3 deficiencies are rare, most likely due to the central role, it plays in the complement response, but these patients can suffer from recurrent bacterial infections, including from Nm (Da Silva Reis et al. 2002).

Mannose-binding lectin

MBL is a collectin, encoded by MBL2, that can recognise Nm and trigger the complement cascade by forming a complex and binding mannose residues present on pathogen surfaces (Janeway et al. 2001). In a candidate gene study, three functional variants in codon 52, 54, and 56 of MBL2 exon 1 show reduced plasma protein concentrations and have been previously associated with IMD (Table ​2) (Hibberd et al. 1999). MBL2 polymorphisms were also found to be significantly associated with IMD in a paediatric cohort with IMD incidence increasing with younger age (Faber et al. 2007); however, in a subsequent study, these polymorphisms were found to have no significant association with IMD (Lundbo et al. 2015). No association was also found between low serum MBL concentrations and serogroup B/C IMD in a Norwegian cohort (Garred et al. 1993).

Inflammatory response

The proper induction of the immune response including activation of immune cells and cytokines following infection is critical for preventing IMD as is demonstrated by the description of IMD in a patient with a mutation in GATA2, a hematopoietic transcription factor, resulting in cytopenias and associated with viral and bacterial infections and malignancies (Table ​1) (Spinner et al. 2014). Cytokine production is regulated by a complex system involving multiple factors and mediators (Westendorp et al. 1997). Some mutations can dysregulate this process and the immunological phenotype can vary. IRAK4 and NEMO deficiencies result in reduced cytokine levels including an abolished IL-6 response (Picard et al. 2011; von Bernuth et al. 2008). Conversely, some variants of IL1B and TNF, can result in an excessive inflammatory response that can increase risk of developing severe disease and even death in meningococcal infection (Nadel et al. 1996; Read et al. 2000, 2003). Key cytokines in IMD include the pro-inflammatory cytokines interleukin-1-beta (IL-1β), interleukin-6 (IL-6), tumour necrosis factor-alpha (TNF-α), and the anti-inflammatory cytokines IL-10 and IL-1 receptor antagonist (IL-1Ra) (Hackett et al. 2001; Pathan et al. 2003). These key cytokines have been investigated in candidate gene studies in relevance to IMD.

Toll-like receptors

The toll-like receptor (TLR) signalling pathway is a central part of the innate immune response as it recognises pathogens, triggering a signalling cascade that ends in production of cytokines and chemokines (Kawai and Akira 2011). Genetic deficiencies of key mediators of the innate immune response, autosomal recessive IRAK4, and X-linked recessive IKBKG (encoding for NEMO) deficiencies, underlie pyogenic bacterial infection with impaired interleukin-6 (IL-6) production and C-reactive protein (CRP) production (Ku et al. 2007; Picard et al. 2003, 2011). Deficiencies in these proteins are associated with impaired canonical TLR signalling pathway and typically predispose to pyogenic bacterial infections; however, cases of IMD have also been observed (Frans et al. 2015; Huppmann et al. 2015; Picard et al. 2010, 2011). Other polymorphisms associated with IMD have been identified in specific TLRs, including TLR4 and TLR9. TLR4 is a major transmembrane receptor expressed in immune cells and recognises bacterial lipopolysaccharides (LPS), a major outer membrane component of Gram-negative bacteria including Nm (Kawai and Akira 2011). Binding of LPS to the TLR4 complex initiates a signalling cascade leading to the activation of NF-κB-mediated transcription and production of pro-inflammatory cytokines (TNF, IL6, IL1 etc.) (Kawai and Akira 2011). A candidate gene-based study found an excess of rare heterozygous missense mutations in TLR4 in a cohort of patients with IMD (Smirnova et al. 2003). A TLR4 polymorphism, (rs4986790), results in hypo-responsiveness to LPS (Arbour et al. 2000) which has been associated with IMD, and mortality (Table ​2) (Faber et al. 2006, 2009), with conflicting findings (Biebl et al. 2009; Read et al. 2001). A candidate gene study-associated polymorphisms in TLR9, an intracellular, endosomal, receptor that recognises unmethylated CpG motifs in bacterial DNA, with meningococcal meningitis in a large paediatric cohort (Table ​2) (Kawai and Akira 2011; Sanders et al. 2011).

Cytokine response

TNF-α is central to the activation of the inflammatory response, it mediates septicaemia and septic shock and circulating TNF-α is correlated to severity, and mortality, in IMD (Waage et al. 1987). Possession of the rare TNF allele, resulting from a single nucleotide polymorphism (SNP) in the promoter region (rs1800629), was shown to increase constitutive and inducible secreted TNF-α and may predispose to susceptibility and severity to Nm infection (Table ​2) (Nadel et al. 1996; Read et al. 2009). However, another study has reported that it is the referent GG genotype that increases risk of IMD (Table ​2) (Titmarsh et al. 2013) whereas other studies report no association between TNF and IMD (Balding et al. 2003; Domingo et al. 2004; Read et al. 2000), showing that these results have not been reproducible and more work is needed to determine its effect. IL-6 is secreted by macrophages and T cells and has pro-coagulant effects that assist in the regulation of the immune response and haematopoiesis (Tanaka et al. 2014). High levels of circulating IL-6 are associated with poor outcome in meningococcal septic shock and septicaemia (Hack et al. 1989; Waage et al. 1989). A particular SNP in IL-6 (rs1800795) has been associated with an increased risk of death in IMD (Table ​2) (Balding et al. 2003; Titmarsh et al. 2013). IL-10 is an anti-inflammatory cytokine that suppresses the inflammatory response, upregulates IL-1Ra, and downregulates other pro-inflammatory cytokines. A SNP in IL-10 (rs1800896) has been significantly correlated with severe disease in IMD (Table ​2) (Balding et al. 2003).

IL-1α and IL-1β are pro-inflammatory cytokines, produced mainly by macrophages and monocytes that binds to the IL-1 receptor (IL-1R) complex and activates the acute phase response. IL-1Ra, encoded by IL-1RN, can also compete with the binding of IL-1α and IL-1β to the IL-1R complex. Increased levels of IL-1β and IL-1Ra have been associated with IMD. Allelic variation at the IL-1 gene cluster affects the inflammatory response and course of infection (Read et al. 2003). A SNP in IL1B (rs16944) has been associated with an increased risk of death in homozygous individuals (Table ​2) (Read et al. 2000, 2003). Furthermore, the presence of the heterozygous IL1B (rs16944) polymorphism combined with the homozygous IL-1RN (rs419598) polymorphism is also associated with outcome of IMD (Table ​2) (Read et al. 2000, 2003). However, another study described no association between the IL1B (rs16944) polymorphism and IMD outcome but showed that IMD outcome was associated with the IL1RN homozygous (rs419598) polymorphism (Table ​2) (Endler et al. 2006). An 86 base pair variable number tandem repeat (VNTR) in intron 2 of IL1RN has also been associated with mortality and severe septicaemia in IMD patients (Table ​2) (Balding et al. 2003).

Acquired immunity

Fc receptors for IgG (FcγR) are found on phagocytes and are a central component for phagocytosis. Three subclasses of FcγRs exhibit biallelic polymorphisms that influence the IgG-binding efficiency. FcγRIIa, FcγRIIIa, and FcγRIIIb, encoded by FCGR2A, FCGR3A, and FCGR3B, respectively, are shown to be important against meningococcal infection (Fijen et al. 2000; van der Pol et al. 2001). FcγRIIa is expressed on poly-morphonuclear cells and is the only subclass that can bind IgG2 (van der Pol et al. 2001). There are two FcγRIIa allotypes determined by rs1801274 (p.H131R), in humans the allotype FcγRIIa-H/H131 is more effective at IgG2-mediated phagocytosis of encapsulated bacteria (Sanders et al. 1995). Multiple candidate gene studies have shown correlation of the FCGR2A rs1801274 (p.H131R) polymorphism in development of severe IMD in patients (Table ​2) (Bredius et al. 1994; Domingo et al. 2002, 2004; Platonov et al. 1997, 1998). Patients with the FcγRIIa-H/H131 allotype have been reported to have higher antibody-mediated phagocytosis-dependent resistance to IMD compared to patients carrying the FcγRIIa-R/R131 allotype and were less likely to develop severe IMD (Platonov et al. 1997, 1998). There have also been conflicting studies showing no association between FcγRIIa p.H131R and IMD (Smith et al. 2003; van der Pol et al. 2001). FcγRIIIa is expressed on monocytes, macrophages and natural killer cells, and can bind IgG1, IgG3, and IgG4. There are two allotypes determined by FCGR3A rs396991 (p.V158F), with the V158 allotype able to increase binding of IgG (Koene et al. 1997). Finally, FcγRIIIb is expressed on neutrophils and binds IgG1, and IgG3. FcγRIIIb contains a neutrophil antigen polymorphism (NA1/NA2), attributed to a group of five base substitutions, with FcγRIIIb-NA1 shown to bind more efficiently than FcγRIIIa-NA2 (van der Pol et al. 2001). FcγRIIIb polymorphism alone is not associated with IMD but a combination of homozygous polymorphisms of all three FcγRs is observed in candidate gene studies to be significantly increased in relatives of IMD patients (Smith et al. 2003; van der Pol et al. 2001). Furthermore, a homozygous combination of FCGR2A p.H131R and FCGR3B NA2 was reported in a Dutch cohort of terminal complement deficient families to be associated with IMD (Table ​2) (Fijen et al. 2000).

Coagulation and fibrinolysis

Circulating meningococcal endotoxin is a strong activator of the coagulation pathway causing generation of thrombin (Lecuyer et al. 2017). Coagulopathy is a feature of severe IMD, resulting in meningococcal shock which can lead to the most severe complication, purpura fulminans, developing in 15–20% of cases (Kondaveeti et al. 1999; Powars et al. 1993). Purpura fulminans is primarily a thrombotic disorder that is characterised by widespread intravascular thrombosis and haemorrhagic lesions that can progress into skin necrosis requiring grafting or amputations (Kondaveeti et al. 1999; Lecuyer et al. 2017; Powars et al. 1993). The fibrinolytic system can regulate the coagulation response but polymorphisms in genes that are part of coagulation and fibrinolysis can deregulate this interaction and result in IMD (Lecuyer et al. 2017). Most candidate gene studies of genes involved in this pathway investigate the severity of IMD by comparing more severe manifestations of IMD, such as death or purpura fulminans, against non-severe IMD. The factor V Leiden mutation (FVL), a SNP in F5 rs6025, is associated with thrombotic events (Kondaveeti et al. 1999). This polymorphism also results in resistance to activated protein C, a key anti-coagulant that can inhibit plasminogen activator inhibitor (PAI) and deactivate factor V, and factor VIII, to downregulate a pro-coagulation signalling cascade. The FVL mutation has been associated with development of severe purpura fulminans in IMD as a homozygous mutation in a single case study and as a heterozygous mutation in a large paediatric cohort candidate gene study (Table ​2) (Kondaveeti et al. 1999; Sackesen et al. 1998). Protein C, encoded by PROC gene, is activated by thrombin and in meningococcal septicaemia low protein C plasma levels are associated with increased disease severity (Brandtzaeg et al. 1989). Two SNPs, in the PROC 5′UTR promoter region (rs1799808 and rs1799809), are known to affect activated protein C plasma levels (Spek et al. 1995). In a candidate gene study, a specific PROC haplotype was associated with IMD (Table ​2) (Binder et al. 2007b). Furthermore, the authors suggested that possession of the CG haplotype increased the risk of developing meningococcal septicaemia and that a homozygous TA haplotype conferred protection against meningococcal septicaemia (Table ​2) (Binder et al. 2007b).

A polymorphism in thrombin activatable fibrinolysis inhibitor (TAFI), encoded by CPB2 gene, rs779491029, has been shown to increase its anti-fibrinolytic potential (Emonts et al. 2008). This candidate gene study demonstrated an association of the polymorphism with an increased risk of developing septicaemia and was observed in the parents of IMD fatalities (Table ​2) (Emonts et al. 2008; Kremer Hovinga et al. 2004; Schneider et al. 2002). Furthermore, a 4G/5G insertion/deletion polymorphism in the SERPINE1 promoter region was found to determine plasma PAI-1 levels and promotes severe coagulopathy, with high levels of PAI-1 associated with severe meningococcal septic shock and poor outcome of IMD (Table ​2) (Binder et al. 2007a; Geishofer et al. 2005; Hermans et al. 1999; Westendorp et al. 1999). The 4G/4G genotype is associated with increased plasma PAI-1 levels and mortality in severe adult septicaemia (Lorente et al. 2015). A candidate gene study of the relatives of IMD patients reported that the homozygous 4G genotype was associated with meningococcal septic shock, whereas the 5G homozygous genotype was associated with meningococcal meningitis (Table ​2) (Westendorp et al. 1999). However, a meta-analysis study has shown no association to be found between the SERPINE1 promoter polymorphism and sepsis susceptibility (Shi et al. 2015).


There is strong evidence for a central role for host genetics in predisposition to meningococcal infection. Common polymorphisms, by dint of their frequency, may play a large role when the interaction between pathogen and host is considered at a population level. However, rare monogenic disorders are most significant for an individual, and they provide unprecedented insight into disease mechanisms. To date, mutations in genes involved in complement pathways continues to appear in all host genetic investigations of Nm infection, indicating a key role for complement in host defence against infection. Both rare monogenic traits, such as terminal complement deficiencies of C5-C9, CFD, CFB, CFI, and C3, and common polymorphisms in the CFH/CFHR3 region have been found in association with IMD. The large majority of genes discussed in this review were discovered through candidate gene studies, and most findings require validation in larger studies. Furthermore, caution must be taken interpreting SNP findings that have not been validated in other populations as significant findings resulting from candidate gene studies may in part relate to haplotype variation between populations where SNPs are found. Candidate gene studies are being largely replaced by the more robust GWAS and large-scale sequencing studies. Future genetic studies may focus on meningococcal strain-specificity, elaborate on disease-outcome specific associations, and include a better understanding of the effect size(s) contributed by a single or combination of variants/mutations in IMD which can help in estimating clinical risk of developing IMD at the individual level. In the UK, the recent introduction of the 4CMenB vaccine has reduced but not eliminated IMD (Parikh et al. 2016); however, the efficacy in protecting those with underlying immunodeficiencies remains unknown (Gianchecchi et al. 2015). The most vulnerable patients who develop IMD may contribute to vaccine failures due to the nature of their immunodeficiencies as observed in invasive pneumococcal disease (Maglione et al. 2014). Hence, the significance of understanding the underlying genetics of IMD is as relevant as ever. Given the increasing availability of patient-based genetic sequencing, we propose that children who have had a single severe episode of IMD should be considered for genetic investigations. Currently, the authors are exploring whether detailed genetic investigations on a patient by patient basis is a useful adjunct to the follow-up care of patients with IMD. Identification of key pathways for protection against meningococcal infection will contribute vital knowledge to our understanding of the pathogenesis of IMD.


The authors thank Dr Clive Hoggart and Dr Aubrey Cunnington for helpful discussions. We apologize to authors whose work could not be cited owing to space limitations.


This work was supported by the National Institute for Health Research Imperial Biomedical Research Centre at Imperial College (Grant number P76547), and the European Union Seventh Framework Programme for Research and Technological Development (European Union Childhood Life-Threatening Infectious Disease Study [EUCLIDS] Grant Agreement 279185). V.S.-S. is funded by a UK Research and Innovation Future Leader’s Fellowship (MR/S032304/1).

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On behalf of all authors, the corresponding author states that there is no conflict of interest.


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What is meningitis? - Signs \u0026 Symptoms - Meningitis Now

Host, Bacterial Genes Influence Meningitis Infection Susceptibility, Severity

NEW YORK (GenomeWeb) – Host genetic variants influence both susceptibility to and severity of meningitis infections, while bacterial variants only influence disease invasiveness, a new study has found.

While Streptococcus pneumoniae commonly colonizes the human throat and nose, it sometimes leads to life-threatening conditions including meningitis, which has a fatality rate between 17 percent and 20 percent.

An international team of researchers turned to sequencing data from both S. pneumoniae and people infected with the bacteria to tease out how genetic variation in each contributes to disease risk. Through a host genome-wide association study and a pathogen GWAS, the researchers uncovered genes in which variants may influence disease. These, they added, could represent targets for follow-up vaccine research.

"Our analyses define the role of genetic variation of host and pathogen in pneumococcal meningitis," researchers led by the University of Amsterdam's Diederik van de Beek wrote in their paper, which appeared this week in Nature Communications.

Using the Dutch MeninGene cohort, van de Beek and his colleagues calculated the heritability of meningitis susceptibility and severity that's due to bacterial genetics. Their model estimated that additive pneumococcal genetics account for about 70 percent of the variance in the tendency toward invasive disease, but none of the variance in disease severity. This, they said, suggests that while tendency toward invasive disease is affected by bacterial genetics, disease outcome is not.

Additionally, they noted that serotype contributes to about half of the variability in invasiveness.

The researchers also conducted a GWAS of pathogens' genetic effect on disease severity within the MeninGen cohort. As expected based on their heritability estimate, they uncovered no loci that were significantly linked with severity.

But when they then conducted a pooled analysis of carriage and invasive pneumococcal isolates — a total of 5,845 bacterial genomes — from the Dutch and a South African cohort and a meta-analysis of association studies within the individual cohorts, the researchers homed in on a number of genes linked with invasive disease, including the virulence genes pspC, dacB, and psrP.

Meanwhile, the researchers calculated that host genetics accounts for about 29 percent of the variability in meningitis susceptibility and about 49 percent of the variability in meningitis severity.

They also performed a GWAS of human hosts' contribution to disease susceptibility and severity in the Dutch cohort. They linked an intronic variant in UBE2U to disease severity and unfavorable outcomes. UBE2U, they noted, is involved in antigen presentation. Data from chromatin capture assays found that this variant further interacts with PGM1 and ROR1, while analysis using the GTex database found that this variant is linked to gene expression in a panel of tissues.

Additional association analyses in the Dutch and South African cohorts as well as using the UK Biobank further implicated a variant on chromosome 15 and an intronic SNP in CCDC33 in disease.

While CCDC33 hasn't before been linked to immunity the researchers noted that it could act at a distance on and affect the expression of ISLR2, an immunoglobulin family protein that is expressed in the brain.

Within the Dutch cohort, the researchers attempted to examine interactions between the host and pathogen genomes, but were only able to find speculative links due to the need for additional samples to boost power.

Still, the researchers noted that their approach enabled them to survey genomic variation within the host and pathogen that affect disease susceptibility and severity.

"These genes are potential candidates for the development of more broadly-acting pneumococcal vaccines," the researchers added.


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Researchers find 'strong evidence' of genetic link to contracting meningitis

Researchers have found "the strongest evidence so far" that genetic factors can increase the risk of a person developing meningitis. The discovery that gene mutations can determine whether a person develops the disease could help in the search for a vaccine for certain strains.

The findings were made after researchers studied the DNA of 1,500 people with the disease, and 5,000 without, to find anomalies between different groups.

Researchers looked at half a million common genetic variants scattered across each person's genome – which holds information on inheritable traits – and found that some people have differences in their natural defences that make them more vulnerable to contracting the disease.

The results of the study are published in the journal Nature Genetics.

They reveal that those who had developed meningococcal meningitis, a bacterial form of meningitis that affects the brain membrane and can cause death within hours, had markers on a number of genes that were involved in attacking and killing invasive bacteria.

Paediatrician Professor Michael Levin, of Imperial College London, said: "Although most of us have carried the meningitis bacteria at some point, only around one in 40,000 people develop it. Our study set out to understand what causes this small group of people to become very ill while others remain immune. Our findings provide the strongest evidence so far that there are genetic factors that lead to people developing meningitis."

While most people are exposed to the bacteria that causes meningitis at some point in their lives, the vast majority do not develop the disease, and the new research further suggests that a person's genetic makeup can determine whether or not they develop it.

It may also go some way to explaining why some people's immune systems are able to keep the bacteria in check while others contract the disease and deteriorate rapidly.

While antibiotics can treat some strains of meningococcal bacteria, the disease can become critical within hours.

Dr Victoria Wright, one of the study's co-researchers, said: "Improving our understanding of why some people get the disease and not others will help to identify those at risk and develop better vaccines." Researchers hope that their findings will help in developing effective vaccines to combat the group B strain of the virus which has so far proved elusive to treatment


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