Search

Additional file 1: Figure S1. Association between cervicovaginal pH and relative abundance of bacterial L- and D-lactate dehydrogenase [LDH; Log2 intensity based absolute quantification (iBAQ) values]. Figure S2. Multidimensional scaling (MDS) plot of genes differentially expressed by ectocervical epithelial cells treated with 0.3% L-lactic acid (L-LA) pH 3.9 or D-lactic acid (D-LA) pH 3.9 (yellow and blue respectively), HCl pH 3.9 (green) or untreated cells (red). Figure S3. Primary cervicovaginal epithelial cells were apically treated with 0.3% L-lactic acid pH (L-LA, pH 3.9), or low pH media alone (HCl, pH 3.9) for 1 h and gene expression of tight junction factors claudin-1 (CLDN1) and claudin-4 (CLDN4) were assessed 4 h post-treatment by qRT-PCR.

Additional file 1: Supplementary Table S1. Details of the search terms used in the respective databases. Supplementary Table S2a. Additional summary of African Gut Microbiome studies. Supplementary Table S2b. Additional summary of African Urogenital Microbiome studies. Supplementary Table S2c. Additional summary of African Microbiome studies (Other body sites).

Additional file 1 : Table S1. Details of the search terms used in the respective databases. Table S2a. Additional summary of African Gut Microbiome studies. Table S2b. Additional summary of African Urogenital Microbiome studies. Table S2c. Additional summary of African Microbiome studies of other body sites.

COVID-19 is a pandemic infection of the respiratory system caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The viral ribonucleic acid (RNA) was found in many parts of the COVID-19 patients including the stool, suggesting a potential interaction with the host’s gut microbiome. The gut microbiome also plays major roles in immunity and inflammation. It also impacts pulmonary functions through the gut-lung axis. There have been recent reports of the importance of the host microbiome in infection and pathogenicity. The understanding of the gut and lung microbiomes would open the gate to new therapeutic approaches.

Background The role of the human microbiome in health and disease is an emerging and important area of research; however, there is a concern that African populations are under-represented in human microbiome studies. We, therefore, conducted a systematic survey of African human microbiome studies to provide an overview and identify research gaps. Our secondary objectives were: (i) to determine the number of peer-reviewed publications; (ii) to identify the extent to which the researches focused on diseases identified by the World Health Organization [WHO] State of Health in the African Region Report as being the leading causes of morbidity and mortality in 2018; (iii) to describe the extent and pattern of collaborations between researchers in Africa and the rest of the world; and (iv) to identify leadership and funders of the studies. Methodology We systematically searched Medline via PubMed, Scopus, CINAHL, Academic Search Premier, Africa-Wide Information through EBSCOhost, and Web of Science from inception through to 1st April 2020. We included studies that characterized samples from African populations using next-generation sequencing approaches. Two reviewers independently conducted the literature search, title and abstract, and full-text screening, as well as data extraction. Results We included 168 studies out of 5515 records retrieved. Most studies were published in PLoS One (13%; 22/168), and samples were collected from 33 of the 54 African countries. The country where most studies were conducted was South Africa (27/168), followed by Kenya (23/168) and Uganda (18/168). 26.8% (45/168) focused on diseases of significant public health concern in Africa. Collaboration between scientists from the United States of America and Africa was most common (96/168). The first and/or last authors of 79.8% of studies were not affiliated with institutions in Africa. Major funders were the United States of America National Institutes of Health (45.2%; 76/168), Bill and Melinda Gates Foundation (17.8%; 30/168), and the European Union (11.9%; 20/168). Conclusions There are significant gaps in microbiome research in Africa, especially those focusing on diseases of public health importance. There is a need for local leadership, capacity building, intra-continental collaboration, and national government investment in microbiome research within Africa.

Table S2. List of protein BLAST results against UniProt and NCBI nr databases (DBs). Liquid chromatography-tandem mass spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 113 women from Cape Town, South Africa. Primary assigned taxa, obtained based on MaxQuant version 1.5.7.4, are presented in the second column. The curated taxa (third column) were obtained after considering the top similar and frequent BLAST hits for each protein. IDs: UniProt Identity codes. Table S3. List of differentially abundant taxa distinguishing women with low and high inflammation. Liquid chromatography-tandem mass spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 113 women from Cape Town, South Africa. The false discovery rate (FDR) corrected p-values were obtained based on the aggregated log2-transformed intensity-based absolute quantification of all proteins assigned to the same taxa applying the moderated t-test (limma R package), after adjusting for confounding variables including age, contraceptives, prostate specific antigen (PSA) and sexually transmitted infections (STIs). Taxa with FDR adjusted p-values 1.2 or < -1.2 were considered as differentially abundant taxa. Table S4. List of differentially abundant proteins distinguishing women with low, medium and high inflammation. Liquid chromatography-tandem mass spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 113 women from Cape Town, South Africa. The false discovery rate (FDR) corrected p-values were obtained based on the log2-transformed intensity-based absolute quantification of each protein applying the moderated t-test (limma R package), after adjusting for confounding variables including age, contraceptives, prostate specific antigen (PSA) and sexually transmitted infections (STIs). Proteins with FDR adjusted p-values 1.2 or < -1.2 were considered as differentially abundant proteins. IDs: UniProt Identity codes. Table S5. List of differentially abundant molecular function (MF) gene ontology terms distinguishing women with low and high inflammation. Liquid chromatography-tandem mass spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 113 women from Cape Town, South Africa. The false discovery rate (FDR) corrected p-values were obtained based on the aggregated log2-transformed intensity-based absolute quantification of all proteins assigned to the same MF term applying the moderated t-test (limma R package), after adjusting for confounding variables including age, contraceptives, prostate specific antigen (PSA) and sexually transmitted infections (STIs). Taxa with FDR adjusted p-values 1.2 or < -1.2 were considered as significantly abundant MFs. (PDF 6094 kb)

Table S1. Demographic and clinical characteristics of study participants stratified by female genital tract pro-inflammatory cytokine levels. Table S6. Details of MaxQuant version 1.5.7.4 parameters used for analysis of metaproteomic data obtained from lateral vaginal wall swabs from 113 women from Cape Town, South Africa. Figure S1. Clustering of women according to overall metaproteomic profiles. Figure S2. Identification of women with low, medium and high levels of pro-inflammatory cytokines and chemokines. Figure S3. Human biological processes, microbial taxa and microbial biological processes in individual women with low, medium and high inflammation. Figure S4. Differentially abundant proteins in women with high versus low inflammatory cytokine profiles. Figure S5. Top 50 significantly differentially abundant human biological processes in women with low, and high inflammation. Figure S6. Relative abundance of gene ontologies in BV negative women with Lactobacillus dominant communities. Figure S7. Longitudinal changes in FGT microbial biological process and cellular component gene ontologies. (PDF 6091 kb)

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”. Los Grupos de Estudiantes Regionales (RSG) del Consejo de Estudiantes de la Sociedad Internacional de Biología Computacional (ISCB-SC) han sido fundamentales para conectar a los biólogos computacionales a nivel mundial y crear más conciencia sobre la educación bioinformática. Este artículo destaca las iniciativas llevadas a cabo por los RSG tanto a nivel nacional como internacional para fortalecer el presente y el futuro de la comunidad bioinformática. Además, discutimos las direcciones futuras que tomará la organización y los desafíos para avanzar más en la misión principal de ISCB-SC: “Nutrir a la nueva generación de biólogos computacionales”. Iowa State University, Estados Unidos European Molecular Biology Laboratory, Alemania Uppsala University, Suecia University of Padova, Italia Institute of Tropical Medicine, Bélgica University of Antwerp, Bélgica University of KwaZulu Natal, Sudáfrica Universidade Federal de Minas Gerais, Brazil Universitat Autònoma de Barcelona, España European Bioinformatics Institute, Reino Unido Universidad de Antioquía, Colombia Universidad CES, Colombia University of South Carolina, Estados Unidos Mohammed V University in Rabat, Marruecos University of Cape Town, Sudáfrica Jawaharlal Nehru University, India Universidad Andres, Chile Universidad de Talca, Chile Université libre de Bruxelles-Vrije Universiteit Brusse, Bélgica Technical University of Denmark, Dinamarca University of Copenhagen, Dinamarca University of Luxembourg, Luxemburgo University of Lausanne, Suiza APIN Public Health Initiatives, Nigeria Institute of Ecology, Argentina Lund University, Suecia Universidad Nacional, Costa Rica Max Planck Institute for Biophysical Chemistry, Alemania Rhodes University, Sudáfrica Dokuz Eylül University, Turquía Hacettepe University, Turquía University Medical Center Rostock, Alemania University Heidelberg, Alemania La Jolla Institute for Immunology, Estados Unidos Katholieke Universiteit Leuven, Belgica Roche, Alemania Carnegie Mellon University, Estados Unidos University of South Wales, Reino Unido Canterbury Christ Church University, Reino Unido Escuela de Ciencias Biológicas

Figure S1. Principal Coordinates Analysis PCoA (Unweighted UniFrac) plots of data generated by the three different platforms, analyzed by different bioinformatics pipelines and colored according to sequencing platform. PERMANOVA F and P values and ANOSIM R and P values are indicated. Figure S2. Differences in relative abundances of the most impacted taxa according to data generated by different platforms (indicated by different colors) and bioniformatic analysis pipelines (indicated across the top). (PPTX 4115 kb)