Epidemiological evaluation of rotavirus burden and potential impact of vaccination in Uganda

Rotavirus is an RNA virus that causes diarrhoeal disease and represents a significant cause of hospitalisation and death in children under five worldwide. The disease burden is particularly high in developing countries, including those in sub-Saharan Africa, where the population is young, sanitation is often poor and access to healthcare limited. Vaccination against the virus has been available since 2006, but population vaccination schedules have not yet been introduced worldwide. Vaccination programmes, including those in several sub-Saharan countries, have been successful in significantly relieving the burden of disease. Uganda, which has one of the highest rotavirus-associated death rates in the world, is scheduled to introduce the vaccine into its national schedule during 2016. As such, it seems an appropriate time to assess the current epidemiological status of rotavirus infection in Uganda, and to discuss important factors that must be considered in the implementation of the vaccine. These factors include cost-effectiveness and a dosing schedule for maximal efficacy, while minimising potential dangerous side effects like intussusception. If implemented properly and effectively, the rotavirus vaccine stands to save millions of lives in Uganda and around the world. </p

Medium Chain Acyl-CoA Dehydrogenase Deficiency (MCADD) in the Irish paediatric population

Aim  This study aims to investigate the disease frequency of Medium Chain Acyl-CoA Dehydrogenase Deficiency (MCADD) among the Irish population.  Methods  Children ( Results  From 1998 to 2016, 17 children (G mutation accounted for 88% of alleles.  Conclusion  The incidence of MCADD in Ireland is lower than global estimates. The potential for under-ascertainment and late diagnosis of cases exists in Ireland and is of concern for a treatable condition with a significant mortality when undiagnosed. The authors welcome the introduction of MCADD to the National Newborn Bloodspot Screening Program.</p

Flow-chart of integrative scheme between genomics and metabolomics to identify bacterial OTUs associated with cholesterol and coprostanol.

<p>Genomic DNA (gDNA) from longitudinal fecal samples emanating from Healthy or CDI subjects (over 90 days) was isolated and deep sequenced on the V1V3 hypervariable 16s rRNA gene before being classified to 2395 refOTUs (Right). The same longitudinal fecal sample was extracted with dichloromethane and injected on a GC-MS instrument where the retention time of discriminatory peaks were determined based on PLS-DA VIP scores (Left). Discriminatory peaks cholesterol and coprostanol were Spearman correlated to refOTUs based on NMDS and ANOVA. As a further step, ISA was used to determine whether refOTUs associated with high coprostanol or cholesterol were enriched in Healthy or CDI cohorts. Red arrows represent feedback and integration between chart items whereas black arrows are directional flow of the pipeline. Abbreviations: ANOVA: analysis of variance, ISA: indicator species analysis, NMDS: non-metric multidimensional scaling, PLS-DA: partial least squares discriminant analysis refOTUs: reference operational taxonomic units, RT: retention time, VIP scores: Variable importance in projection scores, CH2Cl2: dichloromethane.</p

Inverse relationship of cholesterol and coprostanol levels in fecal extracts from subjects with CDI and Healthy controls.

<p><b>(A)</b> The relationship between retention times for cholesterol and coprostanol as determined by mass spectrometry (x-axis) and their relative abundance (y-axis). The inverse relationship between the two compounds based on fold change in fecal composition is highlighted in blue and red circles. <b>(B)</b> Box-plots showing distribution of average total ion current of coprostanol (left) and cholesterol (right) for all fecal samples from the Healthy or the CDI group. The TIC of the two metabolites was normalized by auto-scaling before plotting. <b>(C)</b> Percentage of coprostanol TIC relative to the sum of coprostanol and cholesterol TIC for each subgroup. ANOVA on the ranked Coprostanol TIC values indicated a significant difference among the four cohorts (<i>F</i>_{3, 9} = 9.797, <i>p</i> < 0.01). For the 13 subjects, ranks were highest for Healthy (10) and HAbx (10), followed by Met (6) and Vanc (2); numbers in parentheses indicate mean ranks. Letters above whiskers indicate similar groups based on ranks according to the Tukey HSD test. Fecal samples from a Healthy, HAbx, or Metronidazole origin could be grouped together according to coprostanol levels. Likewise, Metronidazole and Vancomycin treated fecal derived samples could be grouped together based on coprostanol levels.</p

Subject characteristics used in this study.

<p>The number of fecal samples analyzed is shown in parenthesis.</p

Indicator species analysis (ISA) of the remaining 32 OTUs that were not previously assigned to one of the four individual cohorts.

<p>Groups were designated as “Health” for healthy volunteers (combining the two groups with and without prior antibiotic exposure, and as “Disease” for subjects with CDI (combining the metronidazole and vancomycin groups). A representative sequence accession number from Silva (release 108) database is shown for all uncultured species as a reference OTU. Indicator value of species <i>j</i> in group <i>k</i> is the product of the percent relative abundance of each organism to a specific cohort along with its percent relative frequency. Those species that are significant after 100,000 Monte-Carlo randomizations of ecological communities are listed below.</p

Flow-chart of integrative scheme between genomics and metabolomics to identify bacterial OTUs associated with cholesterol and coprostanol.

<p>Genomic DNA (gDNA) from longitudinal fecal samples emanating from Healthy or CDI subjects (over 90 days) was isolated and deep sequenced on the V1V3 hypervariable 16s rRNA gene before being classified to 2395 refOTUs (Right). The same longitudinal fecal sample was extracted with dichloromethane and injected on a GC-MS instrument where the retention time of discriminatory peaks were determined based on PLS-DA VIP scores (Left). Discriminatory peaks cholesterol and coprostanol were Spearman correlated to refOTUs based on NMDS and ANOVA. As a further step, ISA was used to determine whether refOTUs associated with high coprostanol or cholesterol were enriched in Healthy or CDI cohorts. Red arrows represent feedback and integration between chart items whereas black arrows are directional flow of the pipeline. Abbreviations: ANOVA: analysis of variance, ISA: indicator species analysis, NMDS: non-metric multidimensional scaling, PLS-DA: partial least squares discriminant analysis refOTUs: reference operational taxonomic units, RT: retention time, VIP scores: Variable importance in projection scores, CH2Cl2: dichloromethane.</p

Correlation between coprostanol total ion current (TIC) and 16S rRNA taxonomic sequences.

<p><b>(A)</b>: Spearman’s rank of 65 bacteria significantly correlated to coprostanol and cholesterol total ion current. Each taxon was grouped according to an indicator cohort (HAbx, Healthy, or Vancomycin) using indicator species analysis. No phylotypes were identified as an indicator for the Metronidazole (Met) cohort. <b>(B)</b> Nonmetric Multidimensional Scaling (NMDS) analysis of bacterial OTUs and relative coprostanol TICs. Fecal samples were assigned as either “High” or “Low” coprostanol formers. Data was reduced by the NMDS approach using Bray-Curtis distances, followed by Spearman rank correlation to identify OTUs associated with coprostanol TIC levels. Dimension 1 represents coprostanol levels; Dimension 2 represents CDI treatment or antibiotics exposure for each subject.</p

PLS-DA plots using a (left) two-state model, and (right) 4-state model.

<p>Antibiotic therapy for CDI (Met: Metronidazole, Vanc: Vancomycin) and antibiotic exposure history (HAbx: antibiotic exposure; Healthy: no antibiotic exposure) were used to distinguish groups. A matrix of retention time intensities were sum normalized and auto-scaled to generate both plots using a metabolomics pipeline established by Xia, et al. Each sphere represents a fecal chromatographic sample.</p

Agglomerative hierarchical clustering of 63 OTUs correlated with coprostanol and two correlated with high cholesterol.

<p>The dendrogram shows communities of bacteria more likely to co-localize with each other based on rank coprostanol and cholesterol GC-MS levels. Communities are shown in red boxes and are numbered from community 1 to 12 (C1-C12) (B) Boxplots for ranks of the abundances of community clusters (C1-C12). Highest abundances were given the lowest rankings (i.e., 1 = most abundant). The rank abundances were determined based on disease-drug combination (i.e., cohort). Cohort abbreviations are health (H, <i>n</i> = 3), healthy with prior antibiotics (HA, <i>n</i> = 3), CDI with metronidazole treatment (M, <i>n</i> = 4), and CDI with vancomycin treatment (V). Clusters are defined as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148824#pone.0148824.g006" target="_blank">Fig 6A</a> in red boxes. (C) Heatmaps for subject by community clusters scaled by column (left) and by row (right). Color scale goes from green (low value) to red (high value). Column scaling (left) indicates in which subject(s) a particular community cluster is dominant. Row scaling indicates which community clusters any given subject was dominated by, if any. Heatmap on the left indicates clustering based on the two-level model (CDI vs Health) and heatmap on the right is clustering based on a 4-level model (H vs. HA vs. M vs. V).</p