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Bacterial colonisation of surface and core of palatine tonsils among Tanzanian children with recurrent chronic tonsillitis and obstructive sleep apnoea who underwent (adeno)tonsillectomy
research-article
Author(s):
Denis R Katundu
1
,
2
,
3
,
,
Desderius Chussi
1
,
2 ,
Christa E van der Gaast-de Jongh
4 ,
Maroeska M Rovers
5 ,
Marien I de Jonge
4 ,
Gerjon Hannink
5 ,
Niels van Heerbeek
1
,
2
,
3
Publication date (Electronic, pub):
19 June 2023
Publisher:
Cambridge University Press
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Abstract
Objective
Acute and chronic tonsillitis are frequently treated with antibiotics. This study aimed to understand the presence of pathogenic micro-organisms on the surface and core of chronically infected tonsils among Tanzanian children.
Methods
The study enrolled children undergoing adenotonsillectomy. Surface and core tonsillar swabs were taken. Quantitative polymerase chain reaction was performed for Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, Neisseria meningitidis and Pseudomonas aeruginosa.
Results
Surface and core combined, isolated N meningitidis (86.1 per cent) was found the most, followed by H influenzae (74.9 per cent), S pneumoniae (42.6 per cent) and S aureus (28.7 per cent). M catarrhalis and P aeruginosa were only found in a few patients, 5.6 per cent and 0.8 per cent respectively.
Related collections
Most cited references33
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Comparative Analyses of the Bacterial Microbiota of the Human Nostril and Oropharynx
INTRODUCTION The outermost segment of the nose, the nostrils or anterior nares, is a transition zone from the skin to the nasal cavity. Like skin, the nostrils contain sebaceous glands, sweat glands, and hairs and are lined by a keratinized, stratified squamous epithelium more similar to that of skin than to the mucus-producing, ciliated, columnar epithelium of the nasal cavity (1). The nostrils help filter inhaled air, which contains low numbers of extremely diverse microbes (2, 3). In addition, the nostrils are exposed to microbes present in the drainage from the nasal cavity and sinuses. The throat, or pharynx, can be divided into three sections. Like the nasal cavity, the nasopharynx (the upper region of the throat behind the nose) is lined by a ciliated, columnar epithelium. The oropharynx, located immediately behind the mouth, is lined by a nonkeratinized stratified squamous epithelium, as is the more distal laryngopharynx. The oropharynx is constantly exposed to both inhaled and ingested microbes, those cleared by mucociliary mechanisms from both the upper and lower respiratory tracts and those contained in saliva. The nostril and oropharynx are distinct habitats. While the pathogen Staphylococcus aureus colonizes both sites (1, 4–6), cultivation-based studies suggest that these sites share few other common bacterial residents. This led us to explore the bacterial community compositions of the microbiota of these two habitats in conjunction with each other. As mentioned above, most of the knowledge on nose and throat microbiota has been generated via cultivation and has focused on pathogen carriage. The nostrils are known to harbor bacteria from the genera Corynebacterium, Propionibacterium, and Staphylococcus, including the important pathogen Staphylococcus aureus (1). The adjacent nasal cavity appears dominated (at least by cultivation) by Corynebacterium spp. and Staphylococcus spp. (7). The oropharynx harbors species from the genera Streptococcus, Haemophilus, Neisseria, and to a lesser extent Staphylococcus and various anaerobic bacteria (1). It is the site of carriage of many important human pathogens, including Streptococcus pneumoniae, Streptococcus pyogenes, Haemophilus influenzae, Neisseria meningitidis, Moraxella catarrhalis, and Staphylococcus aureus (1, 4, 6). Three recent culture-independent surveys focused on skin or gastrointestinal microbiota included either the nostril or the throat (8–10), though none compared the microbiota of the nostril to that of the throat. The recent application of culture-independent analyses to the healthy adult human mouth (11, 12), saliva (13), gastrointestinal tract (8, 14–17), vagina (18–20), outer ear (21), and skin (9, 10, 22–25) has revealed that hundreds of types of bacteria colonize various human body niches. These surveys indicate that a limited number of phyla account for the majority of bacteria present at each site, with phylum-level conservation among healthy humans (26). They also show a high degree of interpersonal variation in species-level bacterial community composition at each site. A more complete understanding of human microbiota begins with in-depth surveys of the bacterial community present in each niche. In identifying the bacteria present and determining their relative abundances, such surveys provide fundamental information on aspects of the microbiota that correlate with human health. For example, correlations are reported between health and microbiota compositions in obesity (27, 28) Crohn’s disease (29, 30), periodontitis (31), or bacterial vaginosis (32, 33). Such surveys also serve as the foundation for identifying bacteria that might have significant influence on overall community composition and dynamics. The construction and sequencing of clone libraries of 16S rRNA genes from myriad sources have uncovered an immense diversity of bacteria. However, due to economic constraints, clone libraries cannot be feasibly applied for in-depth sampling of microbial communities. 16S rRNA gene microarrays offer an alternate approach. One such microarray, the PhyloChip (34, 35), possesses 500,000 probes and can detect approximately 8,500 bacterial taxa in a single experiment. On this array, a taxon is broadly defined as a cluster of 16S rRNA gene sequences with ≤3% divergence (34). The PhyloChip has been used to examine bacterial community profiles from a number of different sample types, including mouse gastrointestinal tract (36) and human (37–39) samples. Comparison between the PhyloChip and 16S rRNA clone libraries indicates that the array is orders of magnitude more sensitive in its ability to identify diversity, detecting low-abundance taxa (0.01% of the community) even when the community is dominated by a small number of highly abundant microbes (2, 35). Here we describe the application of the PhyloChip to profile the bacterial community composition of nostril and oropharyngeal samples from seven healthy adults. In addition, we constructed and sequenced parallel 16S rRNA gene clone libraries from the samples of the first four participants to identify the most prevalent bacteria by 16S rRNA gene sequencing, as well as to provide a comparative method. RESULTS Phylum-level comparison of nostril and oropharyngeal bacterial communities. Paired mucosal surface swabs (one swab from each site) were collected from the nostril and the posterior wall of the oropharynx of seven healthy adults aged 26 through 45 years who had not taken antimicrobials in the preceding 2 months, were not pregnant, and were not acutely ill. Taxonomy previously defined for the PhyloChip was used to classify bacteria detected using both methods (40, 41). Microarray analyses detected a total of 39 phyla from both sites, with 34 from the nostril and 38 from the oropharynx (see Fig. S1 in the supplemental material). 16S rRNA gene clone library analyses of samples from four of the seven participants identified eight phyla, six from the nostril and seven from the oropharynx (filled circles in Fig. S1 in the supplemental material). An averaged phylum-level distribution pattern for each site demonstrated that both nostril and oropharyngeal microbiota have a phylum-level distribution distinct from those of other body sites (26). At both sites, a few phyla accounted for both the majority of the hybridization signal from the microarrays and the majority of cloned 16S rRNA gene sequences, with similar phylogenetic distribution patterns (Fig. 1; see Fig. S2A to C in the supplemental material), suggesting good concordance between these profiling approaches. From the nostril samples, these were Firmicutes and Actinobacteria (light blue and dark blue, respectively, in Fig. 1 and see Fig. S2C in the supplemental material). In the oropharynx, the most prevalent phyla were Firmicutes, Proteobacteria, and Bacteroidetes (light blue, maroon, and yellow, respectively, in Fig. 1 and see Fig. S2C). From the microarrays, the 16S rRNA copy number was estimated based on the fluorescence intensity of each taxon deemed present, to permit calculation of the relative ratio of each phylum detected relative to the total bacteria detected (2). Interpersonal variation at the phylum level was evident, with the relative abundances of the core phyla at each site varying across samples. FIG 1 Bar graph showing the relative distributions of the major bacterial phyla in the nostril and oropharyngeal samples as detected with a PhyloChip. We used the microarray hybridization intensity to estimate the 16S rRNA gene copy number for each taxon detected on the array and then summed these to estimate the relative prevalence of each phylum in order to compare communities from all participants. Each bar labeled sample 1 to 7 represents 100% of the bacteria detected in a sample by the microarray analysis. Bars labeled AV 1-7 represent the average community composition detected from all 7 seven samples for a site by the microarray. Bars labeled AV 1-4 represent the average community composition detected by the microarray from samples 1 to 4. Bars labeled CL 1-4 represent the average of the relative abundances of phyla in the 16S rRNA gene clone libraries from samples 1 to 4. Family-level comparison of nostril and oropharyngeal bacterial communities. Firmicutes accounted for a large percentage of the bacteria present in both the nostril and oropharynx; however, the most abundant families of this phylum varied by site. In the nostrils, the Staphylococcaceae and Lachnospiraceae accounted for the majority of the Firmicutes detected by the array, while in the oropharynx, the majority of the signal was due to the Streptococcaceae, Lachnospiraceae, and an unclassified group of Clostridia (Fig. 2). Similarly, in the clone libraries, sequences from the family Staphylococcaceae were abundant in the nostril samples, and sequences from the families Streptococcaceae and the clostridial families Acidaminococcaceae and Lachnospiraceae were abundant in the oropharyngeal samples (see Fig. S3 in the supplemental material). FIG 2 Relative abundances of the most common Firmicutes families compared to relative abundances of Actinobacteria families in nostril samples (A) and compared to relative abundances of Proteobacteria families in the oropharynx samples (B) as detected by PhyloChip analysis. For comparison, back-to-back graphs are shown for each site, each with the families from the specified phylum colored as indicated. We used the microarray hybridization intensity to estimate the 16S rRNA gene copy number for each taxon detected on the array and then summed these to estimate the relative abundance of each phylum. Inverse correlation between Firmicutes and another phylum in both sites. There was a strong inverse correlation in the relative prevalences of Actinobacteria and Firmicutes in nostril communities (Fig. 3A) (Pearson correlation coefficient = −0.95, P 60%) from the family Propionibacteriaceae (9), whereas we observed a large number of both Corynebacteriaceae and Propionibacteriaceae among the Actinobacteria detected using both methods (Fig. 2, and see Fig. S3 in the supplemental material). Again, methodological differences might account for this. The phylum-level bacterial composition in the oropharynx differs from that in the esophagus and mouth but is similar to that in saliva. The increased presence of Gram-negative bacteria, particularly from the phylum Proteobacteria, in the oropharynx compared to their presence in the nostril is consistent with cultivation data. Compared to other human sites analyzed with culture-independent methods, the Proteobacteria signal from the oropharynx is rivaled only by those from skin (10, 22, 24–26) and saliva (13). The distal esophagus microbiota is numerically dominated by Firmicutes and Bacteroidetes of genera similar to those found in the oropharynx but with many fewer Proteobacteria (16). The healthy mouth likewise is host to an abundance of Firmicutes; in one study, Firmicutes were out of proportion to any other phylum present (11, 26), and in another, the phylum-level distribution pattern was similar to that of saliva (12). Of the human body niches analyzed with culture-independent methods, our clone library results from the oropharyngeal microbiota showed a phylum-level composition pattern most similar to that of saliva. The composition of phyla in the salivary microbiota detected by 16S rRNA gene clone libraries from 120 individuals (with ~120 16S rRNA gene sequences per person) is as follows: Firmicutes, ~37.8%; Proteobacteria, ~28%; Bacteroidetes, ~20%; Actinobacteria, ~7%; and others, 7.2% (13). A recent survey of gut microbiota using bar-coded tag pyrosequencing of 16S rRNA gene amplicon pools included the throat (8). Only ~5% of the sequences from their throat samples (4.7% ± 3.4) cluster in the phylum Proteobacteria, whereas ~15% (14.5 ± 3.9) cluster within the Actinobacteria (8). Differences in methods are likely to account for these different observations. Another possible source of variation is differences in the sampled populations. The six participants in the study by Andersson and colleagues both were older and had underlying medical conditions (three aged 42 to 73 years with duodenal ulcer and three controls aged 70 to 75 years with dyspepsia) (8). These differences suggest a need for surveys of healthy respiratory tract microbiota from a greater number of individuals with a broad age range. At the phylum level, from both the nostril and oropharynx, Firmicutes were detected as a greater proportion of the total community by using the microarrays than by using the clone libraries. For both, we followed the same protocols, used the same bacterial DNA mixture, and, in most cases, used the same amplicon pool. The differences observed suggest either that the microarray overrepresented Firmicutes or that the clone libraries underrepresented them for these sites. The relative proportions of probes on the microarray for Firmicutes versus Bacteroidetes and Actinobacteria might have contributed to the difference in prevalence of these phyla as detected by each method. Alternatively, some have speculated that cloning through Escherichia coli might lead to a slight decrease in detection of AT-rich organisms, i.e., Firmicutes, though to our knowledge, this has never been directly demonstrated. In fact, a recent assessment of the underrepresentation of marine SAR11 biodiversity based on techniques that rely on cloning through E. coli (fosmid and bacterial artificial chromosome [BAC] libraries) suggests that the underrepresentation of this low-GC-content group using these methods is unlikely to be due to its AT richness (47). Combined 16S rRNA gene-based approaches to study microbiota composition. All of the 16S rRNA gene-based techniques likely have specific biases and strengths, though generally similar patterns are expected from each; thus, a combined approach offers advantages. Most molecular analyses of human microbiota to date have utilized 16S rRNA gene clone libraries, and we used both this method and the PhyloChip to analyze samples from the first four participants. The number of clones per person per site was similar to those of other human body site clone libraries reported (10, 13, 14, 16, 22, 24). The number of taxa identified by the PhyloChip was much greater than the number identified from 16S rRNA gene clone libraries and was at least comparable to what might be expected using 454 pyrosequencing of the 16S rRNA gene. However, by using the microarray, we were able to analyze a larger number of individuals than would have been possible at the time for a cost comparable with pyrosequencing. While the microarray does not identify previously unreported taxa, the combined approach with the microarrays and 16S rRNA gene clone libraries enabled identification of predominant members of sampled communities via their 16S rRNA gene sequence. Furthermore, the total hybridization pattern of each chip can be used as a community “signature” for analyses comparing the communities (e.g., beta diversity). As mentioned in the results, the exact number of taxa detected by the microarray is best viewed as an estimate. That said, both the nostril and oropharynx are continually exposed to a large number of environmental bacteria via inhaled air. Additionally, the oropharynx is exposed to microbes present in food and liquids. Thus, it is not surprising that a wide variety of bacteria associated with outside sources were detected at very low levels in samples from each site. Both a larger data set and validation of the presence of rare taxa via other methods will be required to discern if any of these rare taxa are long-term residents of these sites, or if these are simply transiently present. Bacterial microbiota of the nostril compared to that of the oropharynx. Analyses of the microbiota sampled from the nostril and oropharynx revealed that the bacterial communities grouped by site and not by individual, similar to what has been observed for other body sites (9). The differences we observed largely reflected disparities in each taxon’s signal abundance at each site. As both the oropharynx and nostril receive drainage from common sources (nasopharynx, sinuses, and nasal cavity), it is not surprising that there was a large overlap in the taxa detected. Variation might arise then from different sources; bacteria in the oropharynx could be introduced via the mouth, saliva, and ingestions, whereas the nostrils filter air before it reaches the oropharynx. We postulate that the differences in bacterial microbiota compositions from the two sites are largely due to differences in niche environments, such as substrate and surface differences (e.g., keratin and sebum in the nostril), the slightly lower temperature of the nostrils, and the expected variations in pH between the sites (not measured in this study) (1). Among the seven adults sampled, there was more conservation among the oropharyngeal microbiota compositions than among the nostril microbiota compositions based on a comparison of UniFrac distances within all the nostril and oropharynx samples (Fig. 5D). Previously, throat microbiota was shown to have less interpersonal variation than the stomach or fecal microbiota (8). Studies analyzing bacterial communities from a number of different body sites from a larger number of individuals will be required to determine if, in general, oropharyngeal microbiota demonstrate more interpersonal compositional conservation. Unlike with the gender differences in palmar skin microbiota compositions (23), we did not observe any differences in the nostril or oropharyngeal microbiota compositions that correlated with gender among the individuals sampled, although our sample size might have impacted this assessment. This survey of the microbiota of the nostril and oropharynx from seven healthy adults contributes to the growing understanding of the composition of healthy human microbiota and its interpersonal variation. Such surveys are a necessary foundation for future research aimed at identifying the impact of various perturbations, e.g., antibiotics, vaccines, infections, diseases, and medical interventions, on the ecology of human-associated microbial communities and correlations between human health and microbiota composition. MATERIALS AND METHODS Participant enrollment. We enrolled seven healthy adult volunteers, four male and three female, whose ages were 26 through 45 years. After receiving an explanation of the study and details about sample collection, all provided verbal consent prior to participation. Exclusion criteria for this pilot study were as follows: (i) use of any antimicrobials within the past 2 months, (ii) pregnancy, (iii) any intercurrent illness, and (iv) an age less than 21 years or greater than 65 years. The Institutional Review Board (IRB) at Children’s Hospital Boston ceded review to the IRB at Harvard Medical School, which approved this study. Sample collection and DNA extraction. Separate mucosal swabs were collected from one nostril and from the posterior wall of the oropharynx of each participant and rapidly frozen at −80°C (BBL CultureSwab; Becton, Dickinson and Co.). The posterior wall of the oropharynx was swabbed without touching the tonsils, uvula, tongue, or other oral structures. For nucleic acid extraction, the top of the swab was aseptically snipped off into a sterile 2-ml lysing matrix B tube (MP Biomedicals) containing 600 µl buffer RLT Plus (Qiagen) with 2-mercaptoethanol (Sigma-Aldrich, Inc.). After bead beating (30 s at 5.5 m/s), genomic DNA was purified from sample supernatants using the AllPrep DNA/RNA kit (Qiagen, 2005). PCR amplification and purification of 16S rRNA genes. To minimize potential PCR amplification bias, we amplified 16S rRNA genes from DNA extracts using a temperature gradient (48°C to 56°C) in eight replicate reactions with the bacteria-specific 16S primer set 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTACGACTT-3′) (48) as previously described (39). For each sample, amplified products were pooled, purified by isopropanol precipitation, and quantified by gel electrophoresis using a 2% E-gel with a low-mass-DNA quantification ladder (Invitrogen Corp.). 16S rRNA gene clone library construction and analysis. To construct the clone libraries for nostril and oropharyngeal samples from the first four participants, amplicon pools were ligated and cloned using the standard protocol from the TOPO TA cloning kit for sequencing (Invitrogen). Individual cloned 16S rRNA gene sequences were first amplified using the M13F and M13R primers (TOPO TA cloning kit for sequencing manual) and then sequenced from the 5′ end with the 27F primer using an ABI3700 (Applied Biosystems, Inc.). After primer and vector sequences were removed, the 16S rRNA gene sequences were trimmed by removing any leading and trailing bases that contained ambiguities and for which confidence was less than 25%, and the chromatogram of each sequence was manually inspected for any remaining base caller errors by using Sequencher (Gene Codes Corp.). Sequences with a minimal length of 500 bp were then grouped based on ≥97% sequence identity. The 97% clustering was done to facilitate comparison with taxa detected by the PhyloChip, for which a taxon is broadly defined as a cluster of 16S rRNA gene sequences with ≤3% divergence (34). Sequences were aligned using NAST on the Greengenes website (40, 49). Putative chimeras were identified using ChimeraCheck in RDP and Bellerophon and discarded from the data set (50). Grouped 16S rRNA gene cloned sequences were compared to sequences in two databases, NCBI and RDP, using sequence alignment (BLAST) to identify the best-named matches. The NAST-aligned sequences, along with the best BLAST matches retrieved from the two databases for individual cloned sequences, were imported into the Greengenes database using the ARB software suite (40, 51). Sequences were added to the universal ARB dendrogram using the ARB parsimony algorithm with a Lane mask filter (48). Cloned sequences and their closest named reference sequence(s) were then retrieved and assembled using parsimony into the trees shown for the clone libraries. Ultimately, there were 141, 261, 176, and 141 clones from individual nostril samples and 199, 217, 171, and 79 clones from individual oropharyngeal samples, for a total of 719 nostril-derived sequences and 666 oropharynx-derived sequences. Hybridization of pooled PCR amplicons to the PhyloChip. We spiked 250 ng of pooled 16S rRNA gene amplicon from each sample with a mix containing known concentrations of control amplicons to permit normalization of interarray variation (2). The combined mixture for each sample was then fragmented, biotin labeled, and hybridized to the PhyloChip (version G2; Affymetrix) as previously described (2, 34, 35). PhyloChips were washed, stained, and scanned using a GeneArray scanner (Affymetrix) as previously described (34). Each scan was captured using standard Affymetrix software (GeneChip Microarray Analysis Suite, version 5.1), and array data were processed as previously described (2, 34, 35). On the PhyloChip, each taxon was represented with a minimum of 11 probe pairs, and some were represented with up to 55. As previously described, a taxon was considered to be “present” in a sample when the number of positive probe pairs divided by the total number of probe pairs in a probe set was equal to or greater than 0.9 (34). Hybridization values (fluorescence intensity) for each taxon were calculated as a trimmed average (with maximum and minimum values removed before averaging) (34). Hybridization values were converted to estimated gene copy numbers using a formula derived from a Latin square assay as described previously (2). Analysis of PhyloChip data. All of the taxa detected by the PhyloChip from all 14 samples were added using parsimony to the existing phylogenetic tree based on the ARB parsimony tree delivered with the Greengenes ARB database (October 2006 release) (40). To compare the numerically dominant phyla from each body site, correlation coefficients and linear regression were performed using Sigma Plot 11. We performed weighted and unweighted UniFrac analyses using the neighbor-joining tree of all taxa represented on the PhyloChip that have >1,200-bp 16S rRNA sequences (40, 43, 52, 53; file bacteria.6190.tree at http://greengenes.lbl.gov/Download/Taxonomic_Outlines/). A t test on the UniFrac distance matrix was used to determine if the UniFrac distances were on average significantly different for the bacterial communities detected in the two body sites. To determine if the community compositions within and between oropharynx and nostril groups were different, we used one-way analysis of variance (ANOVA) with Tukey’s test (set to 0.05) on pair-wise UniFrac values. To determine how bacterial community compositions varied across samples, we also compared total hybridization profiles for each sample using correspondence analysis (CoA) in MeV v4.4 (54). Analysis was done on the log2-transformed hybridization intensity data for each sample, with a percentage cutoff filter set to 0.02%. 16S rRNA gene sequence accession numbers. Sequences from clone libraries grouped at 99% identity (53 from nostril and 109 from oropharynx) were deposited in the NCBI database with GenBank accession numbers HM172637 to HM172798. SUPPLEMENTAL MATERIAL TABLE SA1 Taxa detected from nostril and oropharyngeal samples using the PhyloChip. Taxa unique to the oropharynx are highlighted in pink, and those unique to the nostril are highlighted in blue. The hybridization value is shown for each taxon that was considered to be present. FIG S1 Phylogenetic tree of the bacterial microbiota of the nostril and oropharynx showing the phyla detected using 16S rRNA gene microarrays (lettering) and clone libraries (circles). Black type, the 10 phyla present in all samples from both nostril and oropharynx; black circles, five phyla detected by clone library analysis at both sites; red type, the additional 11 phyla present in all oropharyngeal samples; red circles, two phyla detected by clone libraries only from the oropharynx; blue type, an additional phylum present in all nostrils; blue circles, in the clone libraries, the phylum detected only from the nostril samples; purple type, 11 phyla present in a subset of both nostril samples and oropharyngeal samples; green type, five phyla present only in a subset of oropharyngeal samples; aqua type, organisms present only in a subset of nostril samples. The tree was generated in ARB with Archaea as the outgroup. Branch length reflects diversity except that the Firmicutes and Proteobacteria braches were shortened to fit. Download FIG S2 Analyses of nostril and oropharyngeal clone libraries. (A) Phylogenetic tree of bacterial taxa identified in 16S rRNA gene clone libraries from the nostrils of participants 1 to 4 (719 clones). (B) Phylogenetic tree of bacterial taxa identified in 16S rRNA gene clone libraries from the oropharynges of participants 1 to 4 (666 clones). The trees were generated in ARB. (C) Relative abundances of the major phyla detected by the 16S rRNA gene clone libraries in the nostril and oropharynx from samples 1 to 4. (D) Rarefaction analysis of ≥97% (open symbols)- and ≥99% (closed symbols)-similarity groups of 16S rRNA genes recovered from the nostril (black squares) and oropharyngeal (gray circles) clone libraries. Download FIG S3 Relative abundances of the most common Firmicutes families detected by the 16S rRNA gene clone libraries from samples 1 to 4 compared (A) to Actinobacteria families in the nostril and (B) to Proteobacteria families in the oropharynx. For comparison, back-to-back graphs are shown for each site, each with coloring of the families from the specified phylum. Download FIG S4 To visualize the large amount of data generated by the microarray for each sample, hybridization intensity, which has a log relationship to the 16S rRNA gene copy number (2), is shown for each taxon identified in at least 1 of the 14 samples. The 1,325 taxa detected are arranged alphabetically by phylum along the x axes, and each vertical line represents a taxon. Each row is from one participant, with nostril samples in the left column and oropharyngeal samples in the right column. The four major phyla are highlighted in color. Download FIG S5 Taxonomic diversity detected with 16S rRNA gene clone libraries from nostril and oropharyngeal samples 1 to 4. (A) Total number of taxa detected from each nostril sample (dark-gray bars) and each oropharyngeal sample (light-gray bars) and average number (AV) of taxa detected for each site. *, the average number of taxa per oropharynx was statistically different from that per nostril (t test, P < 0.05). Error bars represent the standard errors of the means. (B) Simpson’s index of diversity (1 − D) for the 16S rRNA gene clone library from each nostril sample (dark-gray bars) and each oropharyngeal sample (light-gray bars) and on average for each site (AV). Data are graphed as 1 − D, such that the higher the bar, the greater the diversity. Error bars represent the standard errors of the means. Download
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Use of antibiotics in children younger than two years in eight countries: a prospective cohort study
Abstract Objective To describe the frequency and factors associated with antibiotic use in early childhood, and estimate the proportion of diarrhoea and respiratory illnesses episodes treated with antibiotics. Methods Between 2009 and 2014, we followed 2134 children from eight sites in Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa and the United Republic of Tanzania, enrolled in the MAL-ED birth cohort study. We documented all antibiotic use from mothers’ reports at twice-weekly visits over the children’s first two years of life. We estimated the incidence of antibiotic use and the associations of antibiotic use with child and household characteristics. We described treatment patterns for diarrhoea and respiratory illnesses, and identified factors associated with treatment and antibiotic class. Findings Over 1 346 388 total days of observation, 16 913 courses of antibiotics were recorded (an incidence of 4.9 courses per child per year), with the highest use in South Asia. Antibiotic treatment was given for 375/499 (75.2%) episodes of bloody diarrhoea and for 4274/9661 (44.2%) episodes of diarrhoea without bloody stools. Antibiotics were used in 2384/3943 (60.5%) episodes of fieldworker-confirmed acute lower respiratory tract illness as well as in 6608/16742 (39.5%) episodes of upper respiratory illness. Penicillins were used most frequently for respiratory illness, while antibiotic classes for diarrhoea treatment varied within and between sites. Conclusion Repeated antibiotic exposure was common early in life, and treatment of non-bloody diarrhoea and non-specific respiratory illnesses was not consistent with international recommendations. Rational antibiotic use programmes may have the most impact in South Asia, where antibiotic use was highest.
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Antibiotic prescribing practice in management of cough and/or diarrhoea in Moshi Municipality, Northern Tanzania: cross-sectional descriptive study
Introduction The increase in resistance of many pathogens to currently available antibiotics has been recognized as life-threatening problem. The development of drug resistance is promoted by irrational prescribing behavior. Inappropriate use of antibiotics is attributed by over-prescription, inadequate dosage and use for non-bacterial infections. The purpose of this study was to assess antibiotic prescribing practices in the management of diarrhoea and cough among children attending hospitals in Moshi municipal, Tanzania. Methods We conducted a cross-sectional descriptive hospital based study, from September 2010 to March 2011. All children presenting with diarrhoea and cough, aged between one month and 5years attended at the two hospitals were enrolled. Data were collected by a standard questionnaire. Information on the prescribed drugs was obtained from patient files. Results A total of 384 children were enrolled. Of these, 326 (84.9%) received antibiotics; common prescribed antibiotics were penicillins, sulphonamides, aminoglycosides and macrolides. Eighty percent of children with acute watery diarrhoea and 68.9% with common cold were given antibiotics inappropriately. Inappropriate antibiotic prescription was significantly associated with prescriber being a clinical officer and assistant medical officer, and child having diarrhoea. Inappropriate antibiotic dosage was significantly occurred when prescriber was clinical officer with reference to medical officer. Conclusion This study observed a high antibiotic prescription rate by clinicians and treatment guidelines for management of patients who presented with cough and/or diarrhoea are followed. Continuing professional development programmes for clinicians on prescription would help in reducing irrational prescribing practices.
Author and article information
Contributors
Denis R Katundu:
ORCID: https://orcid.org/0000-0003-0771-9192
Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole:
Project administrationRole: Writing – original draftRole: Writing – review & editing
Desderius Chussi: Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
Christa E van der Gaast-de Jongh: Role: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: Writing –
review & editing
Maroeska M Rovers: Role: ConceptualizationRole: MethodologyRole: Project administrationRole: Writing
– original draftRole: Writing – review & editing
Marien I de Jonge: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing
– review & editing
Gerjon Hannink: Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing
– original draftRole: Writing – review & editing
Niels van Heerbeek: Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole:
Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing
– original draftRole: Writing – review & editing
Journal
Journal ID (nlm-ta): J Laryngol Otol
Journal ID (iso-abbrev): J Laryngol Otol
Journal ID (publisher-id): JLO
Title:
The Journal of Laryngology and Otology
Publisher:
Cambridge University Press
(Cambridge, UK
)
ISSN
(Print):
0022-2151
ISSN
(Electronic):
1748-5460
Publication date
(Print, pub):
January
2024
Publication date
(Electronic, pub):
19
June
2023
Volume: 138
Issue: 1
Pages: 89-92
Affiliations
[2
]Department of Otolaryngology, Kilimanjaro Christian Medical University College , Kilimanjaro, Tanzania
[3
]Department of Otolaryngology, Head and Neck Surgery, Radboud University Medical Center , Nijmegen, the Netherlands
Author notes
Corresponding author: Denis Robert Katundu; Email:
katundu101@
123456gmail.com
Author information
Denis R Katundu
https://orcid.org/0000-0003-0771-9192
Article
Publisher Item ID: S0022215123001147DOI: 10.1017/S0022215123001147
PMC ID: 10772025
PubMed ID: 37332170
SO-VID: 6fc68918-166b-4463-9d8e-eec3e1b5aff4
Copyright © © The Author(s) 2023
License:
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
History
Date
received
: 19
February
2023
Date
revision received
: 13
May
2023
Date
accepted
: 30
May
2023
Page count
Tables: 2,
References: 33,
Pages: 4
Categories
Subject:
Main Article