Gelow, Kayla

Abstract Background: Accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. This approach reduces extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured "mock" datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation, as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs, while preserving between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially expressed across groups of samples, and random forest models to rank features with largest contribution towards disease classiffcation. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between filtering and contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data, while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

Background and Aims The role of the intestinal microbiome in alcoholic hepatitis is not established. The aims of this study were to (1) characterize the fecal microbial ecology associated with alcoholic hepatitis, (2) relate microbiome changes to disease severity, and (3) infer the functional relevance of shifts in microbial ecology. Approach and Results The fecal microbiome in patients with moderate alcoholic hepatitis (MAH) or severe alcoholic hepatitis (SAH) was compared with healthy controls (HCs) and heavy drinking controls (HDCs). Microbial taxa were identified by 16S pyrosequencing. Functional metagenomics was performed using PICRUSt. Fecal short chain fatty acids (SCFAs) were measured using a liquid chromatography–mass spectrometry platform. A total of 78 participants (HC, n = 24; HDC, n = 20; MAH, n = 10; SAH, n = 24) were studied. HDC had a distinct signature compared with HC with depletion of Bacteroidetes (46% vs. 26%; P = 0.01). Alcoholic hepatitis was associated with a distinct microbiome signature compared with HDC (area under the curve = 0.826); differential abundance of Ruminococcaceae , Veillonellaceae , Lachnospiraceae , Porphyromonadaceae , and Rikenellaceae families were the key contributors to these differences. The beta diversity was significantly different among the groups (permutational multivariate analysis of variance [PERMANOVA] P < 0.001). SAH was associated with increased Proteobacteria (SAH 14% vs. HDC 7% and SAH vs. HC 2%, P = 0.20 and 0.01, respectively). Firmicutes abundance declined from HDC to MAH to SAH (63% vs. 53% vs. 48%, respectively; P = 0.09, HDC vs. SAH). Microbial taxa did not distinguish between MAH and SAH (PERMANOVA P = 0.785). SCFAs producing bacteria (Lachnospiraceae and Ruminococcaceae) were decreased in alcoholic hepatitis, and a similar decrease was observed in fecal SCFAs among alcoholic hepatitis patients. Conclusions There are distinct changes in fecal microbiome associated with the development, but not severity, of alcoholic hepatitis.