Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets ( 20 samples per group) but also critically the only method tested that has a good control of false discovery rate. These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

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Normalization and microbial differential abundance strategies depend upon data characteristics

Disentangling Interactions in the Microbiome: A Network Perspective.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1310&context=foodsciefacpub

The Gut Microbiota of Rural Papua New Guineans: Composition, Diversity Patterns, and Ecological Processes

The appropriate treatment and control of infectious gastroenteritis depend on the ability to rapidly detect the wide range of etiologic agents associated with the disease. Clinical laboratories currently utilize an array of different methodologies to test for bacterial, parasitic, and viral causes of gastroenteritis, a strategy that suffers from poor sensitivity, potentially long turnaround times, and complicated ordering practices and workflows. Additionally, there are limited or no testing methods routinely available for most diarrheagenic Escherichia coli strains, astroviruses, and sapoviruses. This study assessed the performance of the FilmArray Gastrointestinal (GI) Panel for the simultaneous detection of 22 different enteric pathogens directly from stool specimens: Campylobacter spp., Clostridium difficile (toxin A/B), Plesiomonas shigelloides, Salmonella spp., Vibrio spp., Vibrio cholerae, Yersinia enterocolitica, enteroaggregative E. coli, enteropathogenic E. coli, enterotoxigenic E. coli, Shiga-like toxin-producing E. coli (stx1 and stx2) (including specific detection of E. coli O157), Shigella spp./enteroinvasive E. coli, Cryptosporidium spp., Cyclospora cayetanensis, Entamoeba histolytica, Giardia lamblia, adenovirus F 40/41, astrovirus, norovirus GI/GII, rotavirus A, and sapovirus. Prospectively collected stool specimens (n = 1,556) were evaluated using the BioFire FilmArray GI Panel and tested with conventional stool culture and molecular methods for comparison. The FilmArray GI Panel sensitivity was 100% for 12/22 targets and ≥94.5% for an additional 7/22 targets. For the remaining three targets, sensitivity could not be calculated due to the low prevalences in this study. The FilmArray GI Panel specificity was ≥97.1% for all panel targets. The FilmArray GI Panel provides a comprehensive, rapid, and streamlined alternative to conventional methods for the etiologic diagnosis of infectious gastroenteritis in the laboratory setting. The potential advantages include improved performance parameters, a more extensive menu of pathogens, and a turnaround time of as short as 1 h.

Multicenter Evaluation of the BioFire FilmArray Gastrointestinal Panel for Etiologic Diagnosis of Infectious Gastroenteritis

Oral Phage Therapy of Acute Bacterial Diarrhea With Two Coliphage Preparations: A Randomized Trial in Children From Bangladesh

Background: Diarrheal diseases continue to contribute significantly to morbidity and mortality in infants and young children in developing countries. There is an urgent need to better understand the contributions of novel, potentially uncultured, diarrheal pathogens to severe diarrheal disease, as well as distortions in normal gut microbiota composition that might facilitate severe disease. Results: We use high throughput 16S rRNA gene sequencing to compare fecal microbiota composition in children under five years of age who have been diagnosed with moderate to severe diarrhea (MSD) with the microbiota from diarrhea-free controls. Our study includes 992 children from four low-income countries in West and East Africa, and Southeast Asia. Known pathogens, as well as bacteria currently not considered as important diarrhea-causing pathogens, are positively associated with MSD, and these include Escherichia/Shigella, and Granulicatella species, and Streptococcus mitis/pneumoniae groups. In both cases and controls, there tend to be distinct negative correlations between facultative anaerobic lineages and obligate anaerobic lineages. Overall genus-level microbiota composition exhibit a shift in controls from low to high levels of Prevotella and in MSD cases from high to low levels of Escherichia/Shigella in younger versus older children; however, there was significant variation among many genera by both site and age. Conclusions: Our findings expand the current understanding of microbiota-associated diarrhea pathogenicity in young children from developing countries. Our findings are necessarily based on correlative analyses and must be further validated through epidemiological and molecular techniques.

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Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition

Cultivation-based assays combined with PCR or enzyme-linked immunosorbent assay (ELISA)-based methods for finding virulence factors are standard methods for detecting bacterial pathogens in stools; however, with emerging molecular technologies, new methods have become available. The aim of this study was to compare four distinct detection technologies for the identification of pathogens in stools from children under 5 years of age in The Gambia, Mali, Kenya, and Bangladesh. The children were identified, using currently accepted clinical protocols, as either controls or cases with moderate to severe diarrhea. A total of 3,610 stool samples were tested by established clinical culture techniques: 3,179 DNA samples by the Universal Biosensor assay (Ibis Biosciences, Inc.), 1,466 DNA samples by the GoldenGate assay (Illumina), and 1,006 DNA samples by sequencing of 16S rRNA genes. Each method detected different proportions of samples testing positive for each of seven enteric pathogens, enteroaggregative Escherichia coli (EAEC), enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), Shigella spp., Campylobacter jejuni, Salmonella enterica, and Aeromonas spp. The comparisons among detection methods included the frequency of positive stool samples and kappa values for making pairwise comparisons. Overall, the standard culture methods detected Shigella spp., EPEC, ETEC, and EAEC in smaller proportions of the samples than either of the methods based on detection of the virulence genes from DNA in whole stools. The GoldenGate method revealed the greatest agreement with the other methods. The agreement among methods was higher in cases than in controls. The new molecular technologies have a high potential for highly sensitive identification of bacterial diarrheal pathogens.

Sabbir Siddiqui

Papers

Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition

Survey of Culture, GoldenGate Assay, Universal Biosensor Assay, and 16S rRNA Gene Sequencing as Alternative Methods of Bacterial Pathogen Detection