Browsing by Author "Smirnova, Ekaterina"

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    Development of Alcohol-Associated Hepatitis Is Associated With Specific Changes in Gut-Modified Bile Acids
    (Wolters Kluwer, 2022) Muthiah, Mark D.; Smirnova, Ekaterina; Puri, Puneet; Chalasani, Naga; Shah, Vijay H.; Kiani, Calvin; Taylor, Stephanie; Mirshahi, Faridoddin; Sanyal, Arun J.; Medicine, School of Medicine
    The perturbations in bile acids (BAs) in alcohol-associated hepatitis (AH) and its relationship to disease severity is not well defined. The aims of this study were to define (1) the effects of heavy alcohol consumption on BAs and related microbiome, (2) the additional changes with AH, and (3) the relationship of these changes to disease severity. In this multicenter study, plasma and fecal BAs and related microbiome were interrogated in healthy individuals, heavy drinking controls (HDCs) without overt liver disease, and AH. Compared to healthy controls, HDCs had increased glycine-conjugated 7α and 27α primary BAs and increased secondary BA glycocholenic sulfate (multiple-comparison adjusted P < 0.05 for all). Plasma-conjugated cholic and chenodeoxycholic acid increased in AH along with the secondary BAs ursodeoxycholic and lithocholic acid (P < 0.001 for all), whereas deoxycholic acid decreased; however fecal concentrations of both deoxycholic acid and lithocholic acid were decreased. Glycocholenic acid further increased significantly from HDCs to AH. HDCs and AH had distinct plasma and fecal BA profiles (area under the curve, 0.99 and 0.93, respectively). Plasma taurochenodeoxycholic acid and tauroursodeoxycholic acid were directly related to disease severity, whereas fecal ursodeoxycholic acid was inversely related. The fecal abundance of multiple taxa involved in formation of secondary BAs, especially deoxycholic acid (Clostridium cluster XIVa) was decreased in AH. Multiple genera containing taxa expressing 3α, 3β, 7α, and 7β epimerases were decreased with concordant changes in fecal BAs that required these functions for formation. Conclusion: There are distinct changes in BA-transforming microbiota and corresponding BAs in AH that are related to disease severity.
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    Effects of Rare Microbiome Taxa Filtering on Statistical Analysis
    (Frontiers Media, 2021-01-12) Cao, Quy; Sun, Xinxin; Rajesh, Karun; Chalasani, Naga; Gelow, Kayla; Katz, Barry; Shah, Vijay H.; Sanyal, Arun J.; Smirnova, Ekaterina; Medicine, School of Medicine
    Background: The 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. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the 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 the 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 the 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 abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. 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 the filtering and the 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.