The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform

Xinhua Chen - One of the best experts on this subject based on the ideXlab platform.

  • Transcriptome Analysis Reveals Comprehensive Insights into the Early Immune Response of Large Yellow Croaker (Larimichthys crocea) Induced by Trivalent Bacterial Vaccine.
    PloS one, 2017
    Co-Authors: Xin Zhang, Xinhua Chen
    Abstract:

    Vaccination is an effective and safe strategy for combating Bacterial diseases in fish, but the mechanisms underlying the early immune response after vaccination remain to be elucidated. In the present study, we used RNA-seq technology to perform transcriptome analysis of spleens from large yellow croaker (Larimichthys crocea) induced by inactivated trivalent Bacterial Vaccine (Vibrio parahaemolyticus, Vibrio alginolyticus and Aeromonas hydrophila). A total of 2,789 or 1,511 differentially expressed genes (DEGs) were obtained at 24 or 72 h after vaccination, including 1,132 or 842 remarkably up-regulated genes and 1,657 or 669 remarkably down-regulated genes, respectively. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichments revealed that numerous DEGs belong to immune-relevant genes, involved in many immune-relevant pathways. Most of the strongly up-regulated DEGs are innate defense molecules, such as antimicrobial peptides, complement components, lectins, and transferrins. Trivalent Bacterial Vaccine affected the expressions of many components associated with Bacterial ligand–depending Toll-like receptor signaling pathways and inflammasome formation, indicating that multiple innate immune processes were activated at the early period of vaccination in large yellow croaker. Moreover, the expression levels of genes involved in antigen processing were also up-regulated by Bacterial Vaccine. However, the expression levels of several T cell receptors and related CD molecules and signal transducers were down-regulated, suggesting that the T cell receptor signaling pathway was rapidly suppressed after vaccination. These results provide the comprehensive insights into the early immune response of large yellow croaker to vaccination and valuable information for developing a highly immunogenic Vaccine against Bacterial infection in teleosts.

  • Molecular characterization and bioactivity of a CXCL13 chemokine in large yellow croaker Pseudosciaena crocea
    Fish & shellfish immunology, 2009
    Co-Authors: Chen Tian, Yuanyuan Chen, Xinhua Chen
    Abstract:

    Abstract A CXCL13-like chemokine cDNA was isolated from large yellow croaker (Pseudosciaena crocea) by expressed sequence tag (EST) analysis (LycCXCL13). The full-length cDNA of LycCXCL13 is 796 nucleotides (nt) encoding a protein of 97 amino acids (aa), with a putative molecular weight of 10.7 kDa. The deduced LycCXCL13 contains a 24-aa signal peptide and a 73-aa mature polypeptide, which possesses the typical arrangement of four cysteines as found in other known CXC chemokines (C25, C27, C52 and C68). It shares 35, 36 and 39% aa sequence identities to green puffer CXCL13-like, Atlantic salmon CXCL13 and Japanese flounder CXCL13 chemokines, and 24–29% identities to CXCL13 chemokines in mammals, respectively. Phylogenetic analysis showed that LycCXCL13 is more closely related to the CXCL13 subgroup than to any other CXC chemokine subgroups. LycCXCL13 gene was constitutively expressed in all tissues examined, except for intestine. Upon induction with poly(I:C) or inactivated trivalent Bacterial Vaccine, LycCXCL13 gene expression was significantly up-regulated in spleen, head kidney, heart and gills at 24 h post-injection. Real-time PCR results showed that LycCXCL13 gene expression reached peak level in spleen and head kidney at 12 h after induction by poly(I:C), while its expression increased to the highest level in head kidney at 24 h or in spleen at 48 h by Bacterial Vaccine. Recombinant LycCXCL13 protein produced in E. coli BL21 exhibited obvious chemotaxis to the peripheral blood leucocytes (PBLs) from large yellow croaker. These results suggest that LycCXCL13 may be involved in inflammatory responses as well as homeostatic processes in large yellow croaker.

  • molecular characterization of goose type lysozyme homologue of large yellow croaker and its involvement in immune response induced by trivalent Bacterial Vaccine as an acute phase protein
    Immunology Letters, 2007
    Co-Authors: Wenbiao Zheng, Chen Tian, Xinhua Chen
    Abstract:

    Lysozyme acts as an innate immunity molecule against the invasion of Bacterial pathogens. Here, the cDNA of a goose-type lysozyme (g-lysozyme) was cloned from large yellow croaker (Pseudosciana crocea) by expressed sequence tags (EST) and RACE-PCR techniques. The full-length cDNA of large yellow croaker g-lysozyme (LycGL) is 716 nucleotides (nt) encoding a protein of 193 amino acids (aa), with a theoretical molecular weight of 21.3 kDa. The deduced LycGL possessed the typical structural features of g-lysozyme, including three catalytic residues (E71, D84, D101) and four substrate binding sites (L97, L121, L128, G152). Genomic analysis revealed that the LycGL gene consisting of 2383 nt, contained five exons interrupted by four introns and exhibited a similar exon-intron organization to its homologues in Japanese flounder and Chinese perch, except for having a much longer intron 1 in the LycGL gene. Recombinant LycGL produced in Pichia pastoris exhibited obvious lytic activity against Micrococcus lysodeikticus and several fish pathogenic bacteria such as Aeromonas sobria, Vibrio alginolyticus, Vibrio parahaemolyticus and Vibrio vulnficus. Tissue expression profile analysis showed that LycGL mRNA was constitutively expressed in all tissues examined, such as spleen, head kidney, intestine, liver, gills and heart, although at a different level. Upon stimulation with trivalent Bacterial Vaccine, LycGL mRNA levels in intestine, spleen and head kidney were quickly up-regulated and had 10.32-, 10.2- and 8.26-fold increases, respectively, and LycGL transcripts in intestine and head kidney reached their peak levels at 24 h post-induction and then decreased gradually while LycGL mRNA in spleen increased to its highest level at 48 h. These results suggest that LycGL may be involved in antiBacterial immune response activated by Bacterial Vaccine as an acute-phase molecule.

Rino Rappuoli - One of the best experts on this subject based on the ideXlab platform.

  • comparison of open source reverse vaccinology programs for Bacterial Vaccine antigen discovery
    Frontiers in Immunology, 2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990’s, several software programs have been developed implementing different flavours of the first RV protocol. However there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson and VacSol) against a set of eleven pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman in 2011 and enhanced by Heinson in 2017. Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results.

  • Table_1_Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery.docx
    2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results.

  • Table_2_Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery.XLSX
    2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results.

  • Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery
    Frontiers Media S.A., 2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results

Mattia Dalsass - One of the best experts on this subject based on the ideXlab platform.

  • comparison of open source reverse vaccinology programs for Bacterial Vaccine antigen discovery
    Frontiers in Immunology, 2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990’s, several software programs have been developed implementing different flavours of the first RV protocol. However there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson and VacSol) against a set of eleven pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman in 2011 and enhanced by Heinson in 2017. Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results.

  • Table_1_Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery.docx
    2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results.

  • Table_2_Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery.XLSX
    2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results.

  • Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery
    Frontiers Media S.A., 2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential Vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) Vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for Bacterial Vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known Bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize Vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of Bacterial proteins characterized by negative experimental results

Jason M Mwenda - One of the best experts on this subject based on the ideXlab platform.

  • pediatric Bacterial meningitis surveillance in the world health organization african region using the invasive Bacterial Vaccine preventable disease surveillance network 2011 2016
    Clinical Infectious Diseases, 2019
    Co-Authors: Jason M Mwenda, Elizabeth Soda, Goitom Weldegebriel, Regis Katsande, Joseph N Biey, Tieble Traore, Linda De Gouveia, Mignon Du Plessis, Anne Von Gottberg, Martin Antonio
    Abstract:

    BACKGROUND Bacterial meningitis is a major cause of morbidity and mortality in sub-Saharan Africa. We analyzed data from the World Health Organization's (WHO) Invasive Bacterial Vaccine-preventable Diseases Surveillance Network (2011-2016) to describe the epidemiology of laboratory-confirmed Streptococcus pneumoniae (Spn), Neisseria meningitidis, and Haemophilus influenzae meningitis within the WHO African Region. We also evaluated declines in Vaccine-type pneumococcal meningitis following pneumococcal conjugate Vaccine (PCV) introduction. METHODS Reports of meningitis in children <5 years old from sentinel surveillance hospitals in 26 countries were classified as suspected, probable, or confirmed. Confirmed meningitis cases were analyzed by age group and subregion (South-East and West-Central). We described case fatality ratios (CFRs), pathogen distribution, and annual changes in serotype and serogroup, including changes in Vaccine-type Spn meningitis following PCV introduction. RESULTS Among 49 844 reported meningitis cases, 1670 (3.3%) were laboratory-confirmed. Spn (1007/1670 [60.3%]) was the most commonly detected pathogen; Vaccine-type Spn meningitis cases declined over time. CFR was the highest for Spn meningitis: 12.9% (46/357) in the South-East subregion and 30.9% (89/288) in the West-Central subregion. Meningitis caused by N. meningitidis was more common in West-Central than South-East Africa (321/954 [33.6%] vs 110/716 [15.4%]; P < .0001). Haemophilus influenzae (232/1670 [13.9%]) was the least prevalent organism. CONCLUSIONS Spn was the most common cause of pediatric Bacterial meningitis in the African region even after reported cases declined following PCV introduction. Sustaining robust surveillance is essential to monitor changes in pathogen distribution and to inform and guide vaccination policies.

  • global invasive Bacterial Vaccine preventable diseases surveillance 2008 2014
    Morbidity and Mortality Weekly Report, 2014
    Co-Authors: Jason M Mwenda, Jillian Murray, Mary Agocs, Fatima Serhan, Simarjit Singh, Maria Deloriaknoll, Katherine L Obrien, Richard Mihigo, Lucia Helena De Oliveira
    Abstract:

    Meningitis and pneumonia are leading causes of morbidity and mortality in children globally infected with Streptococcus pneumoniae (pneumococcus), Neisseria meningitidis, and Haemophilus influenzae causing a large proportion of disease. Vaccines are available to prevent many of the common types of these infections. S. pneumoniae was estimated to have caused 11% of deaths in children aged <5 years globally in the pre-pneumococcal conjugate Vaccine (PCV) era. Since 2007, the World Health Organization (WHO) has recommended inclusion of PCV in childhood immunization programs worldwide, especially in countries with high child mortality. As of November 26, 2014, a total of 112 (58%) of all 194 WHO member states and 44 (58%) of the 76 member states ever eligible for support from Gavi, the Vaccine Alliance (Gavi), have introduced PCV. Invasive pneumococcal disease (IPD) surveillance that includes data on serotypes, along with meningitis and pneumonia syndromic surveillance, provides important data to guide decisions to introduce PCV and monitor its impact.

Martin Antonio - One of the best experts on this subject based on the ideXlab platform.

  • pediatric Bacterial meningitis surveillance in the world health organization african region using the invasive Bacterial Vaccine preventable disease surveillance network 2011 2016
    Clinical Infectious Diseases, 2019
    Co-Authors: Jason M Mwenda, Elizabeth Soda, Goitom Weldegebriel, Regis Katsande, Joseph N Biey, Tieble Traore, Linda De Gouveia, Mignon Du Plessis, Anne Von Gottberg, Martin Antonio
    Abstract:

    BACKGROUND Bacterial meningitis is a major cause of morbidity and mortality in sub-Saharan Africa. We analyzed data from the World Health Organization's (WHO) Invasive Bacterial Vaccine-preventable Diseases Surveillance Network (2011-2016) to describe the epidemiology of laboratory-confirmed Streptococcus pneumoniae (Spn), Neisseria meningitidis, and Haemophilus influenzae meningitis within the WHO African Region. We also evaluated declines in Vaccine-type pneumococcal meningitis following pneumococcal conjugate Vaccine (PCV) introduction. METHODS Reports of meningitis in children <5 years old from sentinel surveillance hospitals in 26 countries were classified as suspected, probable, or confirmed. Confirmed meningitis cases were analyzed by age group and subregion (South-East and West-Central). We described case fatality ratios (CFRs), pathogen distribution, and annual changes in serotype and serogroup, including changes in Vaccine-type Spn meningitis following PCV introduction. RESULTS Among 49 844 reported meningitis cases, 1670 (3.3%) were laboratory-confirmed. Spn (1007/1670 [60.3%]) was the most commonly detected pathogen; Vaccine-type Spn meningitis cases declined over time. CFR was the highest for Spn meningitis: 12.9% (46/357) in the South-East subregion and 30.9% (89/288) in the West-Central subregion. Meningitis caused by N. meningitidis was more common in West-Central than South-East Africa (321/954 [33.6%] vs 110/716 [15.4%]; P < .0001). Haemophilus influenzae (232/1670 [13.9%]) was the least prevalent organism. CONCLUSIONS Spn was the most common cause of pediatric Bacterial meningitis in the African region even after reported cases declined following PCV introduction. Sustaining robust surveillance is essential to monitor changes in pathogen distribution and to inform and guide vaccination policies.