Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2020 Dec 21;15(12):e0243375. doi: 10.1371/journal.pone.0243375

Genomic surveillance of Neisseria meningitidis serogroup B invasive strains: Diversity of vaccine antigen types, Brazil, 2016-2018

Ana Paula Silva de Lemos 1,*, Claudio Tavares Sacchi 2, Claudia Regina Gonçalves 2, Carlos Henrique Camargo 1, Ana Lúcia Andrade 3
Editor: Yung-Fu Chang4
PMCID: PMC7751880  PMID: 33347452

Abstract

Background

Neisseria meningitidis serogroup B remains a prominent cause of invasive meningococcal disease (IMD) in Brazil. Because two novel protein-based vaccines against serogroup B are available, the main purpose of this study was to provide data on the diversity and distribution of meningococcal vaccine antigen types circulating in Brazil.

Methodology

Genetic lineages, vaccine antigen types, and allele types of antimicrobial-associated resistance genes based on whole-genome sequencing of a collection of 145 Neisseria meningitidis serogroup B invasive strains recovered in Brazil from 2016 to 2018 were collected.

Results

A total of 11 clonal complexes (ccs) were identified among the 145 isolates, four of which were predominant, namely, cc461, cc35, cc32, and cc213, accounting for 72.0% of isolates. The most prevalent fHbp peptides were 24 (subfamily A/variant 2), 47 (subfamily A/variant 3), 1 (subfamily B/variant 1) and 45 (subfamily A/variant 3), which were predominantly associated with cc35, cc461, cc32, and cc213, respectively. The NadA peptide was detected in only 26.2% of the isolates. The most frequent NadA peptide 1 was found almost exclusively in cc32. We found seven NHBA peptides that accounted for 74.5% of isolates, and the newly described peptide 1390 was the most prevalent peptide exclusively associated with cc461. Mutated penA alleles were detected in 56.5% of the isolates, whereas no rpoB and gyrA mutant alleles were found.

Conclusion

During the study period, changes in the clonal structure of circulating strains were observed, without a predominance of a single hyperinvasive lineage, indicating that an epidemiologic shift has occurred that led to a diversity of vaccine antigen types in recent years in Brazil.

Introduction

Invasive meningococcal disease (IMD) remains a major health problem and is a major cause of morbidity and mortality worldwide. Globally, most IMD cases are caused by serogroups A, B, C, W, Y, and X [1].

In the past three decades, IMD in Brazil has been primarily caused by meningococcal strains of serogroups B and C. During the 2000s, the proportion of IMD cases caused by serogroup B meningococcal strains declined from 74.5% in 2000 to 25.7% in 2008, corresponding to a decline from 0.54 to 0.14 per 100,000 inhabitants in the average annual incidence of disease due to this serogroup (S1 Fig). At the same time, serogroup C IMD cases increased from 22.4% to 67.7% at the national level, except for in the South Region, where serogroup C IMD surpassed serogroup B only in 2014 [2]. Indeed, the annual incidence of serogroup C IMD increased from 0.2 cases in 2000 to 0.46 cases per 100,000 habitants in 2008, making C the prevalent serogroup circulating in Brazil from 2005 onward (S1 Fig). In late 2010, the routine meningococcal C conjugate (MCC) vaccination was introduced in the National Immunization Program (NIP), which targeted only infants, and MCC has been included in the routine immunization calendar for adolescents since 2017. After its introduction by the NIP, MCC vaccination was considered to be highly effective in Brazil based on the reduction of IMD caused by serogroup C in infants [3]. Although we observed a downward trend of IMD following the introduction of the MCC vaccine, from 1.54 per 100,000 in 2010 to 0.54 per 100,000 habitants in 2018, and observed a significant declining trend in incidence rates of serogroup B IMD cases [4] (S1 Fig), meningococcal serogroup B (MenB) remains the prominent cause of IMD in Brazil, particularly in infants, with 1.5 cases per 100,000 habitants in 2018 (S2 Fig).

The structural similarity between the serogroup B polysaccharide and fetal brain-cell adhesion molecules makes the B polysaccharide a poor immunogen [5]. This similarity reduces the ability to elicit bactericidal antibodies and raises concerns about possible autoimmunity. Therefore, several noncapsular outer membrane vesicle vaccines were used against Neisseria meningitidis serogroup B, and vaccines based on recombinant proteins identified by proteomic and reverse vaccinology approaches are currently available [6, 7].

Since late 2013, two novel protein-based vaccines against serogroup B have been licensed in Europe and the Americas (MenB-FHbp, Trumenba, Pfizer, and 4CMenB, Bexsero, GlaxoSmithKline). The MenB-FHbp vaccine is composed of two recombinant lipidated factor H-binding proteins (fHbp) from subfamilies A (A05) and B (B01). The 4CMenB multicomponent vaccine is composed of multiple recombinant antigens: a nonlipidated fHbp peptide 1 from subfamily B/variant 1, Neisserial adhesin A (NadA) peptide 8 variant NadA-2/3, Neisserial heparin-binding antigen (NHBA) peptide 2 and PorA P1.7–2,4 from the New Zealand epidemic strain [8, 9]. Studies in different countries have detailed the distribution and diversity of these vaccine proteins [10–16]. Since 2015, the Men-FHbp and 4CMenB vaccines have been implemented in 12 countries in children and adolescents in NIPs [17–19], for the control of outbreaks in the United States and Canada [20, 21] or individual clinical decisions in the United States [20].

Nonetheless, data on the distribution and diversity of these vaccine antigens in Latin America are scarce [13, 22, 23]. Therefore, we aimed to evaluate the genetic lineages, the diversity of vaccine antigens, and the alleles types of antimicrobial resistance-associated genes in serogroup B IMD strains isolated in Brazil from 2016 to 2018 using whole-genome sequencing. The ultimate goal was to support the NIP in the future decision to implement a MenB vaccination.

Material and methods

Collection of strains

Epidemiological data and bacterial isolates

During the period from 2016 to 2018, 3,317 IMD cases were reported to the Ministry of Health in Brazil from all geographic regions of the country [2]. IMD isolates collected by the Brazilian meningococcal surveillance network are routinely sent to Adolfo Lutz Institute (IAL)–the National Reference Laboratory for serogrouping and other tests. A total of 2,518 (76.0%) of 3,317 cases reported between 2016 and 2018 were confirmed in the IAL by either (i) culture (882 cases; 35.0%), (ii) detection of meningococcal DNA in cerebrospinal fluid, serum, or tissue by quantitative polymerase chain reaction (qPCR) (935 cases; 37.1%), (iii) detection of meningococcal antigens in cerebrospinal fluid or serum by counterimmunoelectrophoresis and/or latex agglutination (401 cases; 16.0%), or (iv) identification of the presence of gram-negative diplococci by Gram staining of cerebrospinal fluid (300 cases; 11.9%). Of the 2,218 cases confirmed in the IAL by culture and antigen or DNA detection, the serogroup was determined for 1,536 cases (69.2%), with 403 cases (26.2%) caused by serogroup B. In this study, we analyzed the genetic profile of 145 serogroup B IMD isolates received at the IAL from 2016 to 2018 (S1 Table).

Laboratory procedures

Extraction of DNA

Genomic DNA extraction and purification were performed using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions.

Whole-genome sequencing

Genome library preparation was performed using the Ion Xpress Plus Fragment Kit (Thermo Fisher Scientific, Waltham, MA, USA), and genome libraries corresponding to 400 base pairs were prepared using E-Gel SizeSelect Agarose Gel, 2% (Thermo Fisher Scientific, Waltham, MA, USA). Genome sequencing was performed using the Ion Torrent S5 sequencer (Thermo Fisher Scientific, Waltham, MA, USA), and the reads were assembled de novo using SPAdes v.5.6.0.1.

Genome analysis

The assembled contigs were functionally annotated by the NCBI Prokaryotic Genome Annotation Pipeline. Sequence assignment was performed using the gene-by-gene approach with the PubMLST Neisseria database (https://pubmlst.org/neisseria/).

Nucleotide sequences of fHbp, nadA, and nhba were extracted from fasta files, converted to deduced protein sequences with BioNumerics v.7.6.2, and then aligned by using ClustalW (CLC Genomics Workbench 7). SplitsTree v.5.0 was employed to create phylogenetic networks. Genome reference sequence(s) were included for each antigen as follows: peptide 1 (B24/1.1; accession number NZ_AEQZ00000000.1), peptide 45 (A05/3.45; accession number AY330361.1), and peptide 55 (B01/1.55; accession number AY330406.01) for fHbp; peptide 8 (NadA-2/3; accession number GQ302859.1) for NadA; and peptide 2 for NHBA; accession number AF226445.1).

High-quality single nucleotide polymorphisms (SNPs) were called using the mpileup part of SAMTools v. 0.1.18 [24] from assembled genomes, and a phylogenetic tree based on the concatenated alignments was built in CSI Phylogeny 1.4 (https://cge.cbs.dtu.dk/services/CSIPhylogeny/) [25]. The genome of Neisseria meningitidis H44/76 (GenBank accession number CP002420.1) was included as a reference. The Newick generated file was uploaded to the Microreact platform [26] along with metadata for tree visualization.

Meningococcal characterization by multilocus sequence typing (MLST)

Multilocus sequence typing (MLST) enables the identification of meningococcal hyperinvasive lineages that are particularly associated with disease [27]. Arbitrary integer values are assigned to unique alleles at each of the defined seven housekeeping gene loci. Sequence types (STs) are unique combinations of these seven alleles from the defined locus. Groups of related STs were grouped into clonal complexes (ccs) identified by MLST [27].

Meningococcal scheme nomenclature for antigenic variants

Different classification schemes have been proposed to describe allelic variation in the fhbp gene and the corresponding peptides: the Novartis/GlaxoSmithKline nomenclature classifies the protein variants of fHbp into three variant families, variants 1, 2, and 3 [28]; the Pfizer nomenclature classifies it into subfamilies A and B [29]; and the fHbp database at PubMLST proposes a unified nomenclature in which unique fHbp peptide and nucleotide sequences are arbitrarily assigned sequential numbers in order of discovery independent of subfamily/variant [30]. For this study, a combination of these systems was used: subB/var1 correlates to subfamily B or variant 1, and subA/var2-3 correlates to subfamily or variants 2 and 3. The NadA classification is based on peptide sequence homology in one of the four variants described: NadA-1, NadA-2/3, NadA-4/5, and NadA-6 [31]. The NHBA peptide is assigned arbitrarily integer values to unique peptide sequences. PorA classification is based on nucleotide and peptide sequence homology and recognized the previous serologic classification: the prefix "P1." followed by the VR1 family name, followed by a dash and then the variant number, and the VR2 variant name in the same format [32]. All nomenclature is available on the PubMLST website (https://pubmlst.org/).

Statistical analysis

The association of fHbp subfamily/variant with patient age was evaluated using the chi-square test, and a p-value lower than 0.05 was used to denote statistical significance. Information on patient age is provided in S1 Table.

Data availability statement

Nucleotide sequences have been deposited in GenBank under the accession numbers listed in S2 Table.

This study was approved by the scientific (CTC 39-K/2018) and ethical committees (n. 3.448.268) of the Institute Adolfo Lutz (IAL), São Paulo, Brazil, and Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Brazil.

Results

Clonal complex distribution

By MLST, a total of 41 different STs were identified among the 145 isolates, of which 138 isolates could be assigned to 11 ccs: cc461 (n = 34/145; 23.4%), cc35 (n = 27/145; 18.6%), cc32 (n = 25/145; 17.2%), cc213 (n = 18/145; 12.4%), cc41/44 (n = 12/145; 8.3%), cc865 (n = 8/145; 5.5%), cc162 (n = 7/145; 4.8%), cc103 (n = 3/145; 2.1%), cc60 (n = 2/145; 1.4%), cc269 (n = 1/145; 0.7%), and cc254 (n = 1/145; 0.7%). Overall, 104 isolates (72.0%) were found to belong to cc213, cc32, cc35 and cc461 (Fig 1). The remaining 7 isolates, STs 1768, 4221, 14576, 14577, 14578, 14630 and 15345, were not assigned to ccs (S2 Table).

Fig 1. Distribution of clonal complexes among 145 serogroup B invasive meningococcal disease isolates, Brazil, 2016–2018.

Fig 1

Open in a new tab

Genetic variability and distribution of PorA, PorB, and FetA

Among 142 isolates, we found 17 alleles of PorA VR1 and 28 alleles of PorA VR2. A total of 31 different combinations of VR1/VR2 PorA were identified. For the remaining three isolates, no nucleotide sequence allele was assigned because of the presence of a premature stop codon. Five combinations, P1.19,15 (n = 25/145; 17.3%), P1.19,15–1 (n = 23/145; 15.9%), P1.22,14 (n = 17/145; 11.7%), P1.7–2,4 (n = 15/145; 10.3%) and P1.19–2,13–1 (n = 14/145; 9.6%), accounted for 64.8% (n = 94) of the isolates. The remaining combinations represented 5% or less of the isolates. In addition, 17 combinations were represented by only a single isolate. The above five combinations were strongly associated with the cc detected. P1.19 and 15 belong to cc32 (n = 21/25; 84.0%), P1.19 and 15–1 to cc35 (n = 20/23; 87.0%), P1.22 and 14 to cc213 (n = 16/18; 94.0%), P1.19–2 and 13–1 to cc461 (n = 14/34; 41.2%), and P1.7–2 and 4 to cc41/44 (n = 8/15; 53.0%) and cc162 (n = 7/15; 47.0%) (S3 Fig). PorA VR2 variant 4, included as an antigen in the 4CMenB vaccine, was found in 15 (10.3%) isolates and was associated exclusively with PorA VR1 variant 7–2 belonging to different ccs (cc41/44, n = 8/15; 53.0% and cc162, n = 7/15; 47.0%) (S2 Table).

Thirty-five alleles of PorB2 (n = 2) and PorB3 (n = 33) were found among the variants. The most prevalent alleles of PorB3 were 3–14 (30/145; 20.7%), 3–1 (29/145; 20.0%), and 3–45 (24/145; 16.6%), which were distributed in the five predominant ccs (32, 35, 41/44, 213 and 461) (S3 Fig). Some PorB3 alleles (985, 986, 987, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1190) had not been previously assigned (S2 Table).

Among 145 isolates, 17 alleles for FetA variants were grouped into four families of FetA. The most prevalent variants were F5-5 (n = 46/145, 31.7%), F1-7 (n = 31/145, 21.4%), F5-1 (n = 19/145, 13.1%), F1-5 (n = 12/145, 8.3%), and F5-9 (n = 11/145, 7.6%), which were distributed among the five predominant cc (32, 35, 41/44, 213 and 461) (S2 Table, S3 Fig). The remaining combinations represented 4% or less of the isolates. FetA variants F4-78 and F5-194 have not been previously assigned (S2 Table).

Genetic variability and distribution of NadA

Most isolates (n = 107/145, 73.8%) were unable to present the potential for expression of the NadA peptide due to the absence of the nadA gene, the presence of frameshift mutations, or the presence of insertion elements. The NadA peptide was identified in only 38 (38/145, 26.2%) isolates. Among the 38 isolates, NadA peptides 1 and 118 were grouped as variant 1 (n = 25/38, 65.8%), peptide 3 as variant 2/3 (n = 2/38, 5.3%) and peptides 79 and 122 as variant 4/5 (n = 11/38, 28.9%). Four isolates were assigned to variant 4/5, but the nadA nucleotide allele (allele 34) did not predict the expression of any peptide due to the shifted reading frame. The NadA variants were mainly observed in cc213 (n = 15) and cc32 (n = 25). cc32 was the only lineage in which the isolates presented the potential to express NadA peptide. The NadA peptide was absent from all isolates belonging to cc162, cc254, cc269, cc41/44, cc461, cc60, and cc865 (Fig 2). The maximum pairwise amino acid identity among peptides between NadA-1 and NadA2-3 was 97.0%; between NadA-1 and NadA-4/5, it was 70.0%, and between NadA-2/3 and NadA-4/5, it was 73.0%. Additionally, the pairwise amino acid identity within NadA-1, NadA2-3, and NadA4-5 was at least 99.0%, 100.0% and 95.0%, respectively (Fig 3).

Fig 2. Frequency distribution of NadA peptides by clonal complex among 145 serogroup B invasive meningococcal disease isolates, Brazil, 2016–2018.

Fig 2

Open in a new tab

Fig 3. Phylogenetic analysis of (A) fHbp, (B) NadA, and (C) NHBA peptides.

Fig 3

Open in a new tab

Reference peptide sequences fHbp 45 and 55 correspond to antigens in MenB-FHbp, and fHbp 1, NadA3-8, NHBA 2 correspond to antigens in 4CMenB vaccines. The reference peptides are indicated by red circles in the figures, and the prevalent ones are indicated by blue circles.

Genetic variability and distribution of NHBA

Most of the isolates (n = 142/145, 98.0%) contained the intact nhbA gene, and three isolates were unable to present the potential for expression of the NHBA peptide due to the frameshift mutations or premature stop codon. A total of 28 unique NHBA peptides were found, and of these, 13 had not been previously assigned (S2 Table). The pairwise amino acid sequence identity was at least 76.0% among the 28 different NHBA peptides (Fig 3). Twelve newly described NHBA peptides were detected only once. Despite the observed diversity of the predicted NHBA peptides, we found seven peptides, 3 (n = 16/145, 11.0%), 18 (n = 10/145, 6.9%), 20 (n = 7/145, 4.8%), 21 (n = 30/145, 20.7%), 24 (n = 13/145, 9.0%), 115 (n = 8/145, 5.5%), and 1390 (n = 24/145, 16.6%), that accounted for 74.5% of the isolates (n = 108/145). NHBA peptide 2, included as an antigen in the 4CMenB vaccine, was found in only one (0.7%) isolate. The five most common peptides were exclusively associated with unique ccs; peptides 3 (cc32), 18 (cc213), 20 (cc162), 115 (cc213), and newly described 1390 (cc461) (Fig 4).

Fig 4. Frequency distribution of NHBA peptides by clonal complex among 145 serogroup B invasive meningococcal disease isolates, Brazil, 2016–2018.

Fig 4

Open in a new tab

Genetic variability and distribution of fHbp

All 145 isolates contained the fHbp gene. A total of 34 unique fHbp peptides were identified: 15 belonging to subB/var1, 9 to subA/var2, and 10 to subA/var3. A total of 23 (67,6%) fHbp peptides were detected only once. Six fHbp peptides have not been previously assigned (S2 Table). A total of 54 (37.3%) isolates were predicted to encode the fHbp peptide of subB/var1, 46 (31.7%) of subA/var2 and 45 (31.0%) of subA/var3, clustering in 12 ccs. Most subB/var1 alleles belong to cc32 (n = 23/54, 42.6%), subA/var2 to cc35 (n = 26/46, 56.5%) and subA/var3 to cc461 (n = 28/45, 62.2%) (Fig 5). Moreover, we found only 12 alleles which were present in more than one isolate, despite the observed diversity of the predicted fHbp peptides (n = 35). The most prevalent fHbp peptides were 24 (subA/var2), 47 (subA/var3), 1 (subB/var1; the 4CMenB vaccine antigen), and 45 (subA/var3), accounting for 18.6% (27/145), 16.5% (24/145), 14.5% (21/145), and 6.9% (10/145) of the isolates, respectively (Fig 6). The maximum pairwise amino acid sequence identity between fHbp subA/var2-3 and subB/var1 was 61.0%. Additionally, the pairwise amino acid identity within fHbp subA/var2-3 and subB/var1 was least 87.0% and 92.0%, respectively (Fig 3).

Fig 5. Frequency distribution of fHbp subfamily/variant among 145 serogroup B invasive meningococcal disease isolates, Brazil, 2016–2018 by clonal complex.

Fig 5

Open in a new tab

Fig 6. Frequency distribution of fHbp peptides among 145 serogroup B invasive meningococcal disease isolates, Brazil, 2016–2018.

Fig 6

Open in a new tab

Others: fHbp peptides represented by only one isolate.

Genetic variability and distribution of antimicrobial resistance-associated genes

All isolates were found to present the gyrA gene without mutations. A total of seven gyrA alleles were identified; of these, only one (allele 314) had not been previously assigned (S2 Table). The gyrA alleles 2 (n = 57/145; 39.3%), 3 (n = 21/145; 14.5%) and 4 (n = 58/145; 40.0%) accounted for 93.8% (n = 136/145) of the isolates distributed in almost all ccs, except cc254 and cc269.

A total of 17 rpoB alleles were found among the 145 isolates. Five rpoB alleles had not been previously assigned, and four of them were found only once. The four most common rpoB alleles (n = 100/145, 69.0%) were almost exclusively associated with a unique cc: alleles 2, 5, 34, and 72 were related to cc32, cc461, cc213, and cc35, respectively (S2 Table).

A total of 26 penA alleles were identified among the 145 isolates. Half of the alleles identified were found only once. In addition, 82/145 (56.5%) isolates carried penA alleles (9, 10, 12, 14, 19, 20, 36, 37, 52, 90, 203, 282, 600, 873, 874, 875, 876, 879, 880, 881 and 995) harboring reduced susceptibility-associated mutations (amino acid substitutions: F504L, A510V, I515V, G541N, and I566V). The mutated alleles were observed among all described ccs, except cc269. The nonmutated alleles 22, 27 and 420 were exclusively associated with cc162 (n = 4), cc269 (n = 1) and cc461 (n = 30), respectively. The most prevalent penA alleles were 14, 420, 3, and 9, which accounted for 71.0% (n = 103/145) of the isolates (Fig 7). The remaining isolates represented 4% or less. Eight mutated alleles had not been previously assigned (S2 Table).

Fig 7. Frequency distribution of penA alleles among 145 serogroup B invasive meningococcal disease isolates, Brazil, 2016–2018, by clonal complex.

Fig 7

Open in a new tab

Discussion

Here, we present the detailed genomic analysis of a collection of MenB invasive strains from Brazil, focusing on vaccine antigens, antimicrobial resistance genes, and hyperinvasive lineage distribution. After three decades of the predominance of cc32 in the MenB clonal structure in Brazil [13, 33], we observed the important circulation of cc213, cc35, and cc461, indicating that an epidemiological change has occurred.

Our data show that there is no prevalent circulation of a single MenB cc in Brazil, which has currently been observed for serogroup B IMD epidemiology in some European countries and not in North America [15, 17, 34]. cc213, cc35, and cc461 have previously been reported in Argentina, Australia, Colombia, Paraguay, Tunisia, Turkey, and Europe [23, 22, 35–41]. Additionally, clonal diversity exhibiting a decline in cc41/44 and cc32 and an increase in cc269 and cc461 was observed in the Republic of Ireland over 19 years [39]. Variations over time that led to a change in the distribution of cc were also reported in western Australia, England, Wales, and northern Spain [14, 37, 39, 42, 43]. It is well-documented in meningococcal epidemiology that certain meningococcal lineages are known to transmit globally [44], and this may be a possible explanation for the increase in cc213, cc35, and cc461 in Brazil. Thus, further studies are needed to better understand the emergence of these clones in Brazil and how they can impact transmission in the near future.

Previous studies have demonstrated the strong and persistent association of surface antigen variants with ccs over time [45–47]. The genetic characterization among MenB strains isolated from Brazil in 2004 and among additional strains from São Paulo from 1988, 1996, and 2006 revealed that the meningococcal strain B:4,7:P1.19,15 cc32 predominated throughout Brazil [13]. The outer membrane protein (OMP) type was significantly associated with the cc. Among cc32 strains, PorB 4, 7, 11, 9, 5; PorA 19, 15, 36; and F5-1 prevailed across Brazil, except in the North Region, where F5-13 was the most prevalent [13]. In this study, we reinforced the strong association of the cc32 strain carrying PorB 3–1 (21/25, 84,5%), PorA 19 and 15 (21/25, 84,5%) and F5-1 (18/25, 72,0%). We did not detect F5-13, but only one strain from the North Region was available.

Additionally, the predominant circulation of serogroup B strains carrying fHbp peptide 1 for decades in Brazil has been described [13, 22], and a strong association between fHbp subB/var1 and peptide 1 within cc32 is well described in the literature [48]. We have shown the circulation of fHbp subB/A (var1/2–3) in almost equal proportions in recent years, in contrast to reports in China, where subA/var2 is prevalent [49], and in the United States and some European countries, where subB/var1 has prevailed among serogroup B IMD strains [50–52]. The epidemiology of fHbp subfamilies/variants has been related to different age groups. IMD cases caused by strains with fHbp subA/var2-3 have caused more disease in infants (< 1 year) than adolescents and young adults [53, 54]. Our data reinforce the previous observation; 80.5% (p = 0,005) of IMD cases in patients less than 1 year of age were due to fHbp subA/var2-3 strains. Strains with fHbp peptides 24 (subA/var2) and 47 (subA/var3) were frequent causes of IMD cases in infants <1 year of age.

Additionally, our data on fHbp gene variability can be explained by the emergence and expansion of new ccs in Brazil. For example, strains belonging to cc213 harbor fHbp subA/var3 (77.8%), cc35 harbors fHbp subA/var2 (96.3%), cc461 harbors fHbp subA/var3 (82.3%), and cc32 harbors fHbp subB/var1 (92.0%). In this study, we found 35 different fHbp peptides among all subfamilies/variants. Despite a high diversity, we have shown the prevalence of fHbp peptides 1, 24, 45, and 47 among the studied strains, which were predominantly associated with cc32, cc35, cc213, and cc461, respectively, as previously reported [36, 40, 41, 51].

Regarding the nadA gene, only 26.2% (38/145) of the studied strains presented the potential for expression of a functional peptide; this low percentage has also been reported by others, including in a study on a collection of Brazilian MenB strains [13, 43, 51]. The majority (27/38, 71.0%) of these strains had the potential of expressing NadA peptide and were also predicted to encode the fHbp subB/var1 peptide. Only 11 strains harbored fHbp subA/var2/3 in the presence of the NadA peptide. Such an observation of a close correlation of NadA and fHbp subB/var1 peptide within cc32 has also been described previously, including among Brazilian meningococcal strains [13, 43, 47, 52, 56]. cc32 was the only lineage-clustering isolate (25/38, 66.0%) presenting the potential for expression of NadA peptide, as prevalent peptide 1, as previously reported [36, 47, 55, 56]. All strains belonging to cc213 were assigned to variant 4/5, peptide 3 or 79, but we found only four frameshift peptides, an event frequently reported for cc213 [16, 36, 43].

We also detected high heterogeneity in NHBA peptides, but these prevalent NHBA peptides have been widely reported; an exception is NHBA peptide 1390, which is first described in this study. We observed a close association with types of NHBA peptides within ccs as well as those already described [36, 41, 51, 57]. In our study, cc213 includes strains that also present NHBA peptide 115, which was not observed in Spain [36]. Interestingly, we showed for the first time the association of cc461 with NHBA peptide 1390.

Considering the vaccine antigen components of the 4CMenB vaccine, fHbp peptide 1 (sub/var1) was present in 21 strains (14.5%), NHBA peptide 2 was present in 1 strain (0.7%), PorAVR2-4 was present in 15 strains (10.3%), and no strain presented NadA peptide 8. On the other hand, 10 strains (6.9%) carried fHbp peptide 45 (subA/var3), matching with one of the antigens of the MenB-FHbp vaccine. The genetic characterization of vaccine antigens is not sufficient to predict the strain coverage. In an attempt to fill this gap, two methods (MATS and MEASURE) were developed to measure the level of antigenic expression and provide predicted strain coverage by 4CMenB and MenB-FHbp vaccines, respectively [50, 58]. Recently, a genetic MATS (gMATS) was proposed for predicting strain coverage by an association of antigen genotyping and MATS results [59].

Temporal changes in the prevalence of vaccine antigen variants can impact predicted MenB strain coverage. Recently, a decrease in the potential predicted coverage of MenB strains by the 4CMenB vaccine was observed in England, Wales, Northern Ireland, North Spain, and The Netherlands [37, 39, 42]. MATS was performed in a Brazilian collection of 99 serogroup B IMD strains isolated in 2010, and the strain coverage predicted by MATS was 80.8%. There is no information about fHbp, NHBA, or NadA peptides in this studied collection. However, we can assume that the predicted coverage of MenB strains can be attributed to the predominance of cc32 mostly associated with fHbp peptide 1 (subB/v1) and to the prevalence of NHBA peptide cross-reactivity with the 4CMenB vaccine [60].

Half of the studied MenB strains belonged to three antigenic distinct emerging ccs: cc461, 35, and 213. Therefore, we identified a shift in the prevalent repertoire of the antigens of the serogroup B IMD strains. The predominance of fHbp subB/var1 decreased between 2004 (86.7%) and 2016–2018 (31.7%) due to circulation of cc461, cc35, and 213, which predominantly harbored fHbp subA/var2–3. It was observed that fHbp subA/var2-3 or subB/var1 antigens elicit mainly subfamily specific responses and that these are cross-protective against strains expressing variants from the same subfamily [58].

The emergence of these ccs carrying completely unknown peptides makes it difficult to estimate the real vaccine predicted coverage, since no experimental data are available. Most strains belonging to cc461 harbored fHbp peptide 47 (subA/var3), which is not covered by gMATS and had NHBA peptide 1390, which is unpredictable by gMATS or MATS (S2 Table) [59]. Most strains belonging to cc35 harbored fHbp peptide 24 (subA/var2), which is not covered by gMATS but did have NHBA peptide 21, which is covered by gMATS (S2 Table) [59]. All strains belonging to cc461, and cc35 did not have NadA or PorAVR2-4. Most strains belonging to cc213 harbor fHbp peptide 45 (subA/var3) and NHBA peptide 18, which are not covered by gMATS, or had NHBA peptide 115, which is unpredictable by gMATS (S2 Table) [59]. All strains belonging to cc213 had NadA 4/5, which has no cross-reactivity between NadA variant 2/3 [56]. On the other hand, the fHbp peptides 47 and 45 are indicated as being potentially susceptible to the MenB-FHbp vaccine, and there is insufficient data on fHbp peptide 24 to predict coverage by this vaccine (S2 Table) [61]. In light of the large antigenic variability found in this study, an ongoing investigation will provide more contribution to the cross-reactivity of this studied strain collection with available MenB vaccines based on genomic data. However, our data will be limited by the lack of cross-reactivity data from the new peptides with the available vaccines.

Although meningococcal resistance to ciprofloxacin remains uncommon, strains not susceptible to the drug have been reported sporadically in other countries, including Brazil [62–65]. Our results showed that Men strains remain extremely susceptible to ciprofloxacin, and we did not find a mutated gyrA allele. Currently, rifampicin is one of the recommended antimicrobials for chemoprophylaxis for IMD in Brazil [65]. Previous studies have already shown rifampicin resistance [66]. However, no mutated rpoB gene was characterized in our study. Therefore, our data strengthen the indication for the use of rifampicin as a drug that can be used for IMD chemoprophylaxis in Brazil.

Alterations in the penA gene encoding penicillin-binding protein 2 (PBP2) seem to be the most common mechanism of penicillin resistance [67]. Our findings indicate that the frequency of reduced susceptibility to penicillin remains high in Brazil. An upward trend of reduced susceptibility to penicillin has also been reported in other countries [68, 69]. Interestingly, we observed the exclusive association of nonmutated penA allele 420 with cc461, which is in contrast to what was observed in Tunisia, where cc461 is associated with the mutated penA allele 33 [40]. Our data suggest a clonal expansion of cc461 associated with reduced susceptibility to penicillin. An association of serotype 19 within Brazilian serogroup B strains with this type of resistance has been previously described [70]. Other mechanisms for inducing reduced susceptibility to penicillin without mutation of the penA gene have been described [71]. Overall, more research is required to determine which type of mechanism is involved in cc461.

Important limitations of this study should be considered. The quality of epidemiological surveillance and meningococcal diagnosis differ among various geographic regions of the country. Although there has been an improvement with the incorporation of qPCR in our surveillance system, approximately one-third of IMD cases are still confirmed by clinical criteria, and only 47% of the IMD cases reported during the study period had serogroup information [2]. Therefore, we might have underestimated the real burden of IMD cases in Brazil. Nonetheless, we are aware that in the era of the dramatic spread of the COVID-19 pandemic in Latin America, continuous surveillance of IMD cases is key in monitoring the emergence of possible cases of IMD secondary to COVID-19, as recently observed in France [72].

A considerable amount of additional work will be needed to describe the antigenic diversity of the N. meningitidis strains circulating in Latin America. However, this study makes several noteworthy contributions to understanding the temporal changes in vaccine antigens as well as knowledge of emerging hyperinvasive lineages of N. meningitidis strains circulating in our region. The present data reinforce that continued laboratory surveillance over time supports local epidemiological data as the driver in planning and preparedness in making decisions about the best vaccination schedule strategy against serogroup B IMD.

Supporting information

S1 Fig. Incidence rates of invasive meningococcal disease by serogroup: Brazil, 2000–2018.

(TIF)

Click here for additional data file. (78.7KB, tif)
S2 Fig. Incidence rates of serogroup B invasive meningococcal disease caused by age group: Brazil, 2000–2018.

(TIF)

Click here for additional data file. (92.6KB, tif)
S3 Fig. Circular tree representation based on high-quality SNPs (CSIPhylogeny) of 145 serogroup B invasive meningococcal disease Brazilian isolates. Colored circles represent the ST based on the seven housekeeping genes (as determined at neisseria.org (https://pubmlst.org/organisms/neisseria-spp/).

The rings (from the inner) represent the following: 1. presence of NadA peptide; 2. fHbp peptide (subfamily/variant); 3. most frequent NHBA peptides found in this study (115, 1390, 18, 20, 3, and the remaining ones grouped as ‘other’); 4. most frequent PorA variants; 5. most frequent PorB variants; and 6. most frequent FetA variants.

(TIF)

Click here for additional data file. (613.2KB, tif)
S1 Table. Epidemiological data for 145 studied Neisseria meningitidis serogroup B invasive isolates from Brazil, 2016–2018.

(XLSX)

Click here for additional data file. (18.9KB, xlsx)
S2 Table. Genomic characterization of serogroup B invasive meningococcal disease strains (n = 145) isolated in Brazil from 2016 to 2018.

(DOCX)

Click here for additional data file. (86.5KB, docx)

Acknowledgments

The authors thank all microbiologists who provided isolates; Igor Gonçalves Ribeiro from Meningitis Divison, Ministry of Health, Brasília, Brazil, for providing epidemiological invasive meningococcal disease data; Maria Vaneide de Paiva and Rosemeire Capoane Almendros for microbiological assistance; Monique Tiba for assistance using the software CLC Genomics Workbench; Roberto Dias for secretarial support; and Research Support Foundation of the Federal University of Goiás, Goiânia, Goiás (FUNAPE-UFG) for the financial management of the project. This publication made use of the Neisseria Multi Locus Sequence Typing website (https://pubmlst.org/organisms/neisseria-spp/) at the University of Oxford (Jolley et al. Welcome Open Res 2018,3:124 [version 1; referees:2 approved]).

Data Availability

All 145 files have been deposited in NCBI under project accession number PRJNA508311.

Funding Statement

This research was supported by an Investigator-Initiated Research award from Pfizer. The authors were responsible for the content of the manuscript and the decision to submit for publication. Pfizer had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Chang Q, Tzeng YL, Stephens DS. Meningococcal disease: changes in epidemiology and prevention. Clin Epidemiol. 2012; 4: 237–45. 10.2147/CLEP.S28410 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Ministério da Saúde. Datasus. http://www2.datasus.gov.br/DATASUS/index.php?area=0205
  • 3.Andrade SL, Minamisava R, Tomich LM, Lemos AP, Gorla MC, Brandileone MCC et al. Impact of meningococcal C conjugate vaccination four years after introduction of routine childhood immunization in Brazil. Vaccine. 2017; 35(16): 2025–33. 10.1016/j.vaccine.2017.03.010 [DOI] [PubMed] [Google Scholar]
  • 4.Chicuto LAD, Moraes C, Moraes JC, Safadi MAP. A critical analysis of serogroup B meningococcal disease burden in Brazil (2001–2015): Implications for public health decisions. Hum Vaccin Immunother. 2020. 10.1080/21645515.2019.1700710 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Finne J, Leinonen M, Mäkelä PH. Antigenic similarities between brain components and bacteria causing meningitis. Implications for vaccine development and pathogenesis. Lancet. 1983; 13:2(8346):355–7. 10.1016/s0140-6736(83)90340-9 [DOI] [PubMed] [Google Scholar]
  • 6.Tan LKK, Carlone GM, Borrow R. Advances in the Development of Vaccines against Neisseria meningitidis. N Engl J Med. 2010; 362:1511–20. 10.1056/NEJMra0906357 [DOI] [PubMed] [Google Scholar]
  • 7.Christodoulides M, Heckels J. Novel approaches to Neisseria meningitidis vaccine design. Pathog Dis. 2017; 75(3):1–16. 10.1093/femspd/ftx033 [DOI] [PubMed] [Google Scholar]
  • 8.Serruto D, Bottomley MJ, Ram S, Giuliani MM, Rappuoli R. The new multicomponent vaccine against meningococcal serogroup B, 4CMenB: Immunological, functional and structural characterization of the antigens. Vaccine. 2012; 30 Suppl 2: B87–B97. 10.1016/j.vaccine.2012.01.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zlotnick GW, Jones TR, Liberator P, Hao L, Harris S, McNeil LK, et al. The discovery and development of a novel vaccine to protect against Neisseria meningitidis Serogroup B Disease. Hum Vaccin Immunother. 2015; 11(1): 5–13. 10.4161/hv.34293 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Jacobsson S, Thulin S, Molling P, Unemoa M, Comanducci M, Rappuoli R, et al. Sequence constancies and variations in genes encoding three new meningococcal vaccine candidate antigens. Vaccine. 2006; 24(12): 2161–68. 10.1016/j.vaccine.2005.11.006 [DOI] [PubMed] [Google Scholar]
  • 11.Lucidarme J, Newbold LS, Findlow J, Gilchrist S, Gray SJ, Carr AD, et al. Molecular Targets in Meningococci: Efficient Routine Characterization and Optimal Outbreak Investigation in Conjunction with Routine Surveillance of the Meningococcal Group B Vaccine Candidate, fHBP. Clin Vaccine Immunol. 2011; 2(18): 194–202. https://cvi.asm.org/content/18/2/194 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Mothibeli KM, Plessis M, Gottberg A, Murphy E, Hoiseth SK, Zlotnick G, et al. Distribution of factor H binding protein beyond serogroup B: Variation among five serogroups of invasive Neisseria meningitidis in South Africa. Vaccine. 2011; 29: 2187–92. 10.1016/j.vaccine.2010.11.072 [DOI] [PubMed] [Google Scholar]
  • 13.Filippis I, Lemos APS, Hostetler JB, Wollenberg K, Sacchi CT, Harrison LH, et al. Molecular Epidemiology of Neisseria meningitidis Serogroup B in Brazil. PLoS ONE. 2012; 7(3): 3e33016 10.1371/journal.pone.0033016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Brehony C, Rodrigues CMS, Borrow R, Smith A, Cunney R, Moxon ER, et al. Distribution of Bexsero® Antigen Sequence Types (BASTs) in invasive meningococcal disease isolates: Implications for immunisation. Vaccine. 2016; 34(39): 4690–97. 10.1016/j.vaccine.2016.08.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Rodrigues CMC, Lucidarme J, Borrow R, Smith A, Cameron JC, Moxon R, et al. Genomic Surveillance of 4CMenB Vaccine Antigenic Variants among Disease-Causing Neisseria meningitidis Isolates, United Kingdom, 2010–2016. Emerg Infect Dis. 2018; 24(4): 673–82. 10.3201/eid2404.171480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Waśko I, Gołębiewska A, Kiedrowska M, Ronkiewicz P, Wróbel-Pawelczyk I, Kuch A, et al. Genetic variability of Polish serogroup B meningococci (2010–2016) including the 4CMenB vaccine component genes. Vaccine. 2020; 38(8): 1943–52. 10.1016/j.vaccine.2020.01.021 [DOI] [PubMed] [Google Scholar]
  • 17.Parikh SR, Andrews NJ, Beebeejaun K, Campbell H, Ribeiro S, Ward C, et al. Effectiveness and impact of a reduced infant schedule of 4CMenB vaccine against group B meningococcal disease in England: a national observational cohort study. Lancet. 2016; 388(10061): 2775–82. 10.1016/S0140-6736(16)31921-3 [DOI] [PubMed] [Google Scholar]
  • 18.European Centre for Disease Prevention and Control (ECDC). Expert opinion on the introduction of the meningococcal B (4CMenB) vaccine in the EU/EEA. Cited 6 Dec 2017. https://www.ecdc.europa.eu/en/publications-data/expert-opinion-introduction-meningococcal-b-4cmenb-vaccine-eueea
  • 19.Government of South Australia, South Australian Meningococcal B expert working group. A meningococcal B program for South Australia public report, 2018. https://www.sahealth.sa.gov.au/wps/wcm/connect/b82a9fb7-061a-48b9-be37-54e88a1907d1/2018-06+Optimal+Men+B+Program+for+SA+Public+Report+%282%29.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-b82a9fb7-061a-48b9-be37-54e88a1907d1-mMzmzGg.pdf
  • 20.MacNeil JR, Rubin L, Folaranmi T, Ortega-Sanchez IR, Patel M, Martin SW. Use of Serogroup B Meningococcal Vaccines in Adolescents and Young Adults: Recommendations of the Advisory Committee on Immunization Practices, 2015. MMWR Morb Mortal Wkly Rep. 2015; 64(41): 1171–76. 10.15585/mmwr.mm6441a3 [DOI] [PubMed] [Google Scholar]
  • 21.Deceuninck G, Lefebvre B, Tsang R, Betala-Belinga JF, De Serres G, De Wals P. Impact of a mass vaccination campaign against Serogroup B meningococcal disease in the Saguenay-Lac-Saint-Jean region of Quebec four years after its launch. Vaccine. 2019; 37(31): 4243–45. 10.1016/j.vaccine.2019.06.021 [DOI] [PubMed] [Google Scholar]
  • 22.Silva LA, Coronato B, Schlackman J, Marsh JA, Ezeonwukac C, Fernandes ACL, et al. Neisseria meningitidis disease-associated clones in Amazonas State, Brazil. Infect Dis (Lond). 2018; 50(9): 697–704. 10.1080/23744235.2018.1459829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Moreno J, Alarcon Z, Parra E, Duarte C, Sanabria O, Prada D, et al. Molecular characterization of Neisseria meningitidis isolates recovered from patients with invasive meningococcal disease in Colombia from 2013 to 2016. PLoS ONE. 2020;15(7): e0234475 10.1371/journal.pone.0234475 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. 10.1093/bioinformatics/btp352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kaas RS, Leekitcharoenphon P, Aarestrup FM, Lund O. Solving the Problem of Comparing Whole Bacterial Genomes across Different Sequencing Platforms. PLoS One. 2014;9:e104984 10.1371/journal.pone.0104984 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J, Glasner C, et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genomics. 2016;2:e000093 10.1099/mgen.0.000093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Maiden MC, Bygraves JA, Feil E, Morelli G, Russell JE, Urwin R, et al. Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. Proc Natl Acad Sci U S A. 1998; 95(6):3140–5. 10.1073/pnas.95.6.3140 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Masignani V, Comanducci M, Giuliani MM, Bambini S, Adu-Bobie J, Aricò B, et al. Vaccination against Neisseria meningitidis Using Three Variants of the Lipoprotein GNA1870. J. Exp. Med. 2003; 197(6): 789–99. https://doi/10.1084/jem.20021911 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Fletcher LD, Bernfield L, Barniak V, Farley JE, Howell A, Knauf M, et al. Vaccine potential of the Neisseria meningitidis 2086 lipoprotein. Infect Immun. 2004; 72(4):2088–100. Erratum in: Infect Immun. 2017; 85(6): 10.1128/iai.72.4.2088-2100.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Brehony C, Wilson DJ, Maiden MCJ. Variation of the factor H-binding protein of Neisseria meningitidis. Microbiology (Reading). 2009; 155:4155–69. 10.1099/mic.0.027995-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Bambini S, De Chiara M, Muzzi A, Mora M, Lucidarme J, Brehony C, et al. Neisseria adhesin A variation and revised nomenclature scheme. Clin Vaccine Immunol. 2014;21(7):966–71. 10.1128/CVI.00825-13 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Russell JE, Jolley KA, Feavers IM, Maiden MC, Suker J. PorA variable regions of Neisseria meningitidis. Emerg Infect Dis. 2004;10(4):674–78. 10.3201/eid1004.030247 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Sacchi CT, Pessoa LL, Ramos SR, Milagres LG, Camargo MC, Hidalgo NT, et al. Ongoing group B Neisseria meningitidis epidemic in São Paulo, Brazil, due to increased prevalence of a single clone of the ET-5 complex. J Clin Microbiol. 1992; 30(7): 1734–8. https://jcm.asm.org/content/30/7/1734 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Tsang RSW, Law DKS, De Paola R, Giuliani M, Stella M, Zhou J, et al. Culture-Confirmed Invasive Meningococcal Disease in Canada, 2010 to 2014: Characterization of Serogroup B Neisseria meningitidis Strains and Their Predicted Coverage by the 4CMenB Vaccine. mSphere. 2020; 5 (2) e00883–19. 10.1128/mSphere.00883-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Sorhouet PC, Efron A, Gagetti P, Faccone D, Regueira M, Corso A, et al. Phenotypic and genotypic characteristics of Neisseria meningitidis disease-causing strains in Argentina, 2010. PLoS One. 2013; 8(3): e58065 10.1371/journal.pone.0058065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Abad R, Medina V, Stella M, Boccadifuoco G, Comanducci M, Bambini S, et al. Predicted Strain Coverage of a New Meningococcal Multicomponent Vaccine (4CMenB) in Spain: Analysis of the Differences with Other European Countries. PLoS One. 2016; 11(3): e0150721 10.1371/journal.pone.0150721 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Parikh SR, Newbold L, Slater S, Stella M, Moschioni M, Lucidarme J, et al. Meningococcal serogroup B strain coverage of the multicomponent 4CMenB vaccine with corresponding regional distribution and clinical characteristics in England, Wales, and Northern Ireland, 2007–08 and 2014–15: a qualitative and quantitative assessment. Lancet Infect Dis. 2017; 17(7): 754–62. 10.1016/S1473-3099(17)30170-6 [DOI] [PubMed] [Google Scholar]
  • 38.León MR, Kawabata A, Nagai M, Rojas L, Chamorro G, Grupo del laboratorio VIMENE Paraguay. Genotipos de Neisseria meningitidis aislados de pacientes con enfermedad meningocócica en Paraguay, 1996–2015. Rev Panam Salud Publica. 2019; 43: e10 10.26633/RPSP.2019.10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Bennett DE, Meyler KL, Cafferkey MT, Cunney RJ. Diversity of meningococci associated with invasive meningococcal disease in the Republic of Ireland over a 19—year period, 1996–2015. PloS one. 2020; 15(2), e0228629 10.1371/journal.pone.0228629 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Brik A, Terrade A, Hong E, Taha MK, Bouafsoun A, Khmit M, et al. Phenotypic and genotypic characterization of meningococcal isolates in Tunis, Tunisia: High diversity and impact on vaccination strategies. Int J Infect Dis. 2020; 91: 73–8. 10.1016/j.ijid.2019.11.013 [DOI] [PubMed] [Google Scholar]
  • 41.Ceyhan M, Ozsurekci Y, Lucidarme J, Borrow R, Gürler N, Meningitis Surveillance Group. Characterization of invasive Neisseria meningitidis isolates recovered from children in Turkey during a period of increased serogroup B disease, 2013–2017. Vaccine. 2020; 38(19): 3545–52. 10.1016/j.vaccine.2020.03.024 [DOI] [PubMed] [Google Scholar]
  • 42.Pérez-Trallero E, Esnal O, Marimón JM. Progressive decrease in the potential usefulness of meningococcal serogroup B vaccine (4CMenB, Bexsero®) in Gipuzkoa, Northern Spain. PLoS ONE. 2014; 9(12): e116024 10.1371/journal.pone.0116024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Mowlaboccus S, Perkins TT, Smith H, Sloots T, Tozer S, Prempeh LJ, et al. Temporal Changes in BEXSERO® Antigen Sequence Type Associated with Genetic Lineages of Neisseria meningitidis over a 15-Year Period in Western Australia. PLoS ONE. 2016; 11(6): e0158315 10.1371/journal.pone.0158315 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Caugant DA. Population genetics and molecular epidemiology of Neisseria meningitidis. APMIS. 1998; 106(1–6):505–25. 10.1111/j.1699-0463.1998.tb01379.x [DOI] [PubMed] [Google Scholar]
  • 45.Urwin R, Russell JE, Thompson EA, Holmes EC, Feavers IM, Maiden MC. Distribution of surface protein variants among hyperinvasive meningococci: implications for vaccine design. Infect Immun. 2004; 72(10): 5955–62. 10.1128/iai.72.10.5955-5962.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Russell JE, Urwin R, Gray SJ, Fox AJ, Feavers IM, Maiden MCJ. Molecular epidemiology of meningococcal disease in England and Wales 1975–1995, before the introduction of serogroup C conjugate vaccines. Microbiology. 2008; 154: 1170–77. 10.1099/mic.0.2007/014761-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Bambini S, Piet J, Muzzi A, Keijzers W, Comandi S, De Tora L, et al. An analysis of the sequence variability of meningococcal fHbp, NadA and NHBA over a 50-year period in the Netherlands. PLoS ONE. 2013; 8(5), e65043 10.1371/journal.pone.0065043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Murphy E, Andrew L, Lee KL, Dilts DA, Nunez L, Fink PS, et al. Sequence diversity of the factor H binding protein vaccine candidate in epidemiologically relevant strains of serogroup B Neisseria meningitidis. J Infect Dis. 2009; 200(3): 379–89. 10.1086/600141 [DOI] [PubMed] [Google Scholar]
  • 49.Shi F, Zhang A, Zhu B, Gao Y, Xu L, Li Y, et al. Prevalence of factor H Binding Protein sub-variants among Neisseria meningitidis in China. Vaccine. 2017; 35(18): 2343–50. 10.1016/j.vaccine.2017.03.057 [DOI] [PubMed] [Google Scholar]
  • 50.Donnelly J, Medini D, Boccadifuoco G, Biolchi A, Ward J, Frasch C, et al. Qualitative and quantitative assessment of meningococcal antigens to evaluate the potential strain coverage of protein-based vaccines. Proc Natl Acad Sci U S A. 2010; 107(45): 19490–95. 10.1073/pnas.1013758107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Vogel U, Taha MK, Vazquez JA, Findlow J, Claus H, Stefanelli P, et al. Predicted strain coverage of a meningococcal multicomponent vaccine (4CMenB) in Europe: a qualitative and quantitative assessment. Lancet Infect Dis. 2013; 13(5): 416–425. 10.1016/S1473-3099(13)70006-9 [DOI] [PubMed] [Google Scholar]
  • 52.Chang H-Y, Vuong J, Hu F, Liberator P, Chen A, Kretz CB, et al. Distribution of Neisseria meningitidis serogroup B (NmB) vaccine antigens in meningococcal disease causing isolates in the United States during 2009–2014, prior to NmB vaccine licensure. J Infect. 2019; 79(5): 426–34. 10.1016/j.jinf.2019.09.001 [DOI] [PubMed] [Google Scholar]
  • 53.Wang X, Cohn A, Comanducci M, Andrew L, Zhao X, MacNeil JR, et al. Prevalence and genetic diversity of candidate vaccine antigens among invasive Neisseria meningitidis isolates in the United States. Vaccine. 2011; 29(29–30):4739–44. 10.1016/j.vaccine.2011.04.092 [DOI] [PubMed] [Google Scholar]
  • 54.Hoiseth SK, Murphy E, Andrew L, Vogel U, Frosch M, Hellenbrand W, et al. A Multi-country Evaluation of Neisseria meningitidis Serogroup B Factor H–Binding Proteins and Implications for Vaccine Coverage in Different Age Groups, Pediatr. Infect. Dis. J. 2013; 32(10):1096–1101. [DOI] [PubMed] [Google Scholar]
  • 55.Green LR, Lucidarme J, Dave N, Chan H, Clark S, Borrow R, et al. Phase Variation of NadA in Invasive Neisseria meningitidis Isolates Impacts on Coverage Estimates for 4C-MenB, a MenB Vaccine. J Clin Microbiol. 2018;56(9): e00204–18. 10.1128/JCM.00204-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Comanducci M, Bambini S, Brunelli B, Jeannette A-B, Beatrice A, Barbara C, et al. NadA, a Novel Vaccine Candidate of Neisseria meningitidis. J Exp Med. 2002; 195(11): 1445–54. 10.1084/jem.20020407 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Tzanakaki G, Hong E, Kesanopoulos K, Xirogianni A, Bambini S, Orlandi L, et al. Diversity of greek meningococcal serogroup B isolates and estimated coverage of the 4CMenB meningococcal vaccine. BMC Microbiol. 2014; 14(111):1–7. 10.1186/1471-2180-14-111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.McNeil LK, Donald RGK, Gribenko A, French R, Lambert N, Harris SL, et al. Predicting the Susceptibility of Meningococcal Serogroup B Isolates to Bactericidal Antibodies Elicited by Bivalent rLP2086, a Novel Prophylactic Vaccine. mBio. 2018; 13:9(2): e00036–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Muzzi A, Brozzi A, Serino L, Bodino M, Abad R, Caugant D, et al. Genetic Meningococcal Antigen Typing System (gMATS): A genotyping tool that predicts 4CMenB strain coverage worldide. Vaccine. 2019; 37(7):991–1000. 10.1016/j.vaccine.2018.12.061 [DOI] [PubMed] [Google Scholar]
  • 60.Lemos A, Gorla M, Brandileone M, Orlandi L, Rigat F, Boccadifuoco G, et al. MATS based coverage prediction for the 4CMenB vaccine on Neisseria meningitidis B (MenB) Brazilian invasive strains. In: Poster presented at 18th international pathogenic Neisseria conference (IPNC) meeting. 2012. Poster P272
  • 61.Rodrigues CMS, Jolley KA, Smith A, Cameron JC, Feavers IM, Maiden MCJ. Meningococcal Deduced Vaccine Antigen Reactivity (MenDeVAR) Index: a Rapid and Accessible Tool that Exploits Genomic Data in Public Health and Clinical Microbiology Applications. BioRxiv [Preprint]. bioRxiv 56834 [posted 2020 Oct 20]:[33 p.]. https://www.biorxiv.org/content/10.1101/2020.08.18.256834v1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Shultz T, Tapsall J, White P. An invasive isolate of Neisseria meningitidis showing decreased susceptibility to quinolones. Antimicrob Agents Chemother. 2000; 44(4): 1116 10.1128/aac.44.4.1116-1116.2000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Enríquez R, Abad R, Salcedo C, Perez S, Vazquez JA. Fluoroquinolone resistance in Neisseria meningitidis in Spain. J Antimicrob Chemother. 2008; 61: 286–90. 10.1093/jac/dkm452 [DOI] [PubMed] [Google Scholar]
  • 64.Tsang RS, Law DK, Deng S, Hoang L. Ciprofloxacin-resistant Neisseria meningitidis in Canada: likely imported strains. Can J Microbiol. 2017; 63:265–8. 10.1139/cjm-2016-0716 [DOI] [PubMed] [Google Scholar]
  • 65.Gorla MC, Cassiolato AP, Pinhata JMW, Moraes C, Corso A, Gagetti P, et al. Emergence of resistance to ciprofloxacin in Neisseria meningitidis in Brazil. J Med Microbiol. 2018; 67: 286–8. 10.1099/jmm.0.000685 [DOI] [PubMed] [Google Scholar]
  • 66.Taha MK, Hedberg ST, Szatanik M, Hong E, Ruckly C, Abad R, et al. Multicenter study for defining the breakpoint for rifampicin resistance in Neisseria meningitidis by rpoB sequencing. Antimicrob Agents Chemother. 2010; 54: 3651–8. 10.1128/AAC.00315-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Spratt B. G. 1994. Resistance to antibiotics mediated by target alterations. Science. 1994; 264(5157): 388–93. 10.1126/science.8153626 [DOI] [PubMed] [Google Scholar]
  • 68.Harcourt BH, Anderson RD, Wu HM, Cohn AC, MacNeil JR, Taylor TH, et al. Population-based surveillance of Neisseria meningitidis antimicrobial resistance in the United States. Open Forum Infect Dis. 2015; 2(3): ofv117 10.1093/ofid/ofv117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Vacca P, Fazio C, Neri A, Ambrosio L, Palmieri A, Stefanelli P. Neisseria meningitidis. Antimicrobial Resistance in Italy, 2006 to 2016. Antimicrob Agents Chemother. 2018; 62(9): e00207–18. 10.1128/AAC.00207-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Gorla MC, Brandão AP, Pinhata JMWP, Moraes C, Pereira G, Lemos AP. Phenotypic characterization of Neisseria meningitidis strains isolated from invasive meningococcal disease in Brazil from 2002 to 2017; Acess Microbiol. 2020; 2(1): 34–43. 10.1099/acmi.0.000079 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Orús P, Viñas M. Mechanisms other than penicillin-binding protein-2 alterations may contribute to moderate penicillin resistance in Neisseria meningitidis. Int J of Antimicrob Agents. 2001; 18(2): 113–9. 10.1016/S0924-8579(01)00362-4 [DOI] [PubMed] [Google Scholar]
  • 72.Taha M, Deghmane A. Impact of COVID-19 pandemic and the lockdown on invasive meningococcal disease. Research Square. 2020. 10.21203/rs.3.rs-37726/v1 [DOI] [PMC free article] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Incidence rates of invasive meningococcal disease by serogroup: Brazil, 2000–2018.

(TIF)

Click here for additional data file. (78.7KB, tif)
S2 Fig. Incidence rates of serogroup B invasive meningococcal disease caused by age group: Brazil, 2000–2018.

(TIF)

Click here for additional data file. (92.6KB, tif)
S3 Fig. Circular tree representation based on high-quality SNPs (CSIPhylogeny) of 145 serogroup B invasive meningococcal disease Brazilian isolates. Colored circles represent the ST based on the seven housekeeping genes (as determined at neisseria.org (https://pubmlst.org/organisms/neisseria-spp/).

The rings (from the inner) represent the following: 1. presence of NadA peptide; 2. fHbp peptide (subfamily/variant); 3. most frequent NHBA peptides found in this study (115, 1390, 18, 20, 3, and the remaining ones grouped as ‘other’); 4. most frequent PorA variants; 5. most frequent PorB variants; and 6. most frequent FetA variants.

(TIF)

Click here for additional data file. (613.2KB, tif)
S1 Table. Epidemiological data for 145 studied Neisseria meningitidis serogroup B invasive isolates from Brazil, 2016–2018.

(XLSX)

Click here for additional data file. (18.9KB, xlsx)
S2 Table. Genomic characterization of serogroup B invasive meningococcal disease strains (n = 145) isolated in Brazil from 2016 to 2018.

(DOCX)

Click here for additional data file. (86.5KB, docx)

Data Availability Statement

All 145 files have been deposited in NCBI under project accession number PRJNA508311.

Nucleotide sequences have been deposited in GenBank under the accession numbers listed in S2 Table.

This study was approved by the scientific (CTC 39-K/2018) and ethical committees (n. 3.448.268) of the Institute Adolfo Lutz (IAL), São Paulo, Brazil, and Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Brazil.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES