scholarly journals On the robustness of inference of association with the gut microbiota in stool, rectal swab and mucosal tissue samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shan Sun ◽  
Xiangzhu Zhu ◽  
Xiang Huang ◽  
Harvey J. Murff ◽  
Reid M. Ness ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Rectal Swab ◽  
Taxonomic Composition ◽  
Mucosal Tissue ◽  
Tissue Samples ◽  
Health And Disease ◽  
Inflammatory Drugs ◽  
Metagenome Sequencing ◽  
Functional Profiles

AbstractThe gut microbiota plays an important role in human health and disease. Stool, rectal swab and rectal mucosal tissue samples have been used in individual studies to survey the microbial community but the consequences of using these different sample types are not completely understood. In this study, we report differences in stool, rectal swab and rectal mucosal tissue microbial communities with shotgun metagenome sequencing of 1397 stool, swab and mucosal tissue samples from 240 participants. The taxonomic composition of stool and swab samples was distinct, but less different to each other than mucosal tissue samples. Functional profile differences between stool and swab samples are smaller, but mucosal tissue samples remained distinct from the other two types. When the taxonomic and functional profiles were used for inference in association with host phenotypes of age, sex, body mass index (BMI), antibiotics or non-steroidal anti-inflammatory drugs (NSAIDs) use, hypothesis testing using either stool or rectal swab gave broadly significantly correlated results, but inference performed on mucosal tissue samples gave results that were generally less consistent with either stool or swab. Our study represents an important resource for determination of how inference can change for taxa and pathways depending on the choice of where to sample within the human gut.

scholarly journals On the robustness of inference of association with the gut microbiota in stool, swab and mucosal tissue samples

2021 ◽  
Author(s):  
Shan Sun ◽  
Xiangzhu Zhu ◽  
Xiang Huang ◽  
Harvey J. Murff ◽  
Reid M. Ness ◽  
...  
Keyword(s):  
Microbial Community ◽  
Gut Microbiota ◽  
Community Composition ◽  
Microbial Community Composition ◽  
Amplicon Sequencing ◽  
Taxonomic Composition ◽  
Mucosal Tissue ◽  
Tissue Samples ◽  
16S Rrna Amplicon Sequencing ◽  
Functional Profiles

AbstractThe gut microbiota plays an important role in human health and disease. Stool, swab and mucosal tissue samples have been used in individual studies to survey the microbial community but the consequences of using these different sample types are not completely understood. We previously reported differences in microbial community composition with 16S rRNA amplicon sequencing between stool, swab and mucosal tissue samples. Here, we extended the previous study to a larger cohort and performed shotgun metagenome sequencing of 1,397 stool, swab and mucosal tissue samples from 240 participants. Consistent with previous results, taxonomic composition of stool and swab samples was distinct, but still more similar to each other than mucosal tissue samples, which had a substantially different community composition, characterized by a high relative abundance of the mucus metabolizers Bacteroides and Subdoligranulum, as well as bacteria with higher tolerance for oxidative stress such as Escherichia. As has been previously reported, functional profiles were more uniform across sample types than taxonomic profiles with differences between stool and swab samples smaller, but mucosal tissue samples remained distinct from the other two types. When the taxonomic and functional profiles of different sample types were used for inference in association with host phenotypes of age, sex, body mass index (BMI), antibiotics or non-steroidal anti-inflammatory drugs (NSAIDs) use, hypothesis testing using either stool or swab gave broadly similar results, but inference performed on mucosal tissue samples gave results that were generally less consistent with either stool or swab. Our study represents an important resource for the experimental design of studies aimed to understand microbiota perturbations specific to defined micro niches within the human intestinal tract.

scholarly journals Inference based PICRUSt accuracy varies across sample types and functional categories

2019 ◽  
Author(s):  
Shan Sun ◽  
Roshonda B. Jones ◽  
Anthony A. Fodor
Keyword(s):  
16S Rrna ◽  
Gene Prediction ◽  
Amplicon Sequencing ◽  
Taxonomic Composition ◽  
Inference Models ◽  
16S Rrna Amplicon Sequencing ◽  
Gene Profiles ◽  
Metagenome Sequencing ◽  
The Cost ◽  
Functional Profiles

AbstractBackgroundDespite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition, and PICRUSt is the most widely used of these techniques. In this study, we evaluated the performance of PICRUSt by comparing the significance of the differential abundance of functional gene profiles predicted with PICRUSt to those from shotgun metagenome sequencing across different environments.ResultsWe selected 7 datasets of human, non-human animal and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between gene compositions predicted with PICRUSt and measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the sample labels were shuffled. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of algorithms like PICRUSt. As an alternative, we compared the performance of PICRUSt predicted genes to metagenome genes in inference models associated with metadata within each dataset. With this method, we found reasonable performance for human datasets, with PICRUSt performing better for inference on genes related to “house-keeping” functions. However, the performance of PICRUSt degraded sharply outside of human datasets when used for inference.ConclusionWe conclude that the utility of PICRUSt for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.

scholarly journals Inference based accuracy of metagenome prediction tools varies across sample types and functional categories

2020 ◽  
Author(s):  
Shan Sun ◽  
Roshonda B. Jones ◽  
Anthony A. Fodor
Keyword(s):  
16S Rrna ◽  
Gene Prediction ◽  
Amplicon Sequencing ◽  
Taxonomic Composition ◽  
Inference Models ◽  
Prediction Tools ◽  
16S Rrna Amplicon Sequencing ◽  
Metagenome Sequencing ◽  
The Cost ◽  
Functional Profiles

Abstract Background: Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2 and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments. Results: We selected 7 datasets of human, non-human animal and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2 and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to “house-keeping” functions. However, their performance degraded sharply outside of human datasets when used for inference. Conclusion: We conclude that the utility of PICRUSt, PICRUSt2 and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.

A Method for the Colorimetric Determination of β-Glucuronidase in Urine, Serum, Tissue; Assay of Enzymatic Activity in Health and Disease

1959 ◽  
Vol 36 (2) ◽  
pp. 193-201 ◽  
Author(s):  
Julius A. Goldbarg ◽  
Esteban P. Pineda ◽  
Benjamin M. Banks ◽  
Alexander M. Rutenburg
Keyword(s):  
Enzymatic Activity ◽  
Colorimetric Determination ◽  
Health And Disease ◽  
Urine Serum

Sampling, Preservation and Counting of Samples II: Zooplankton

2017 ◽  
Author(s):  
Peter H. Wiebe ◽  
Ann Bucklin ◽  
Mark Benfield
Keyword(s):  
Genetic Analysis ◽  
High Speed ◽  
Morphological Analysis ◽  
Taxonomic Composition ◽  
Sampling Techniques ◽  
Sample Analysis ◽  
Sample Preservation ◽  
The Past ◽  
Plankton Collection

This chapter reviews traditional and new zooplankton sampling techniques, sample preservation, and sample analysis, and provides the sources where in-depth discussion of these topics is addressed. The net systems that have been developed over the past 100+ years, many of which are still in use today, can be categorized into eight groups: non-opening/closing nets, simple opening/closing nets, high-speed samplers, neuston samplers, planktobenthos plankton nets, closing cod-end samplers, multiple net systems, and moored plankton collection systems. Methods of sample preservation include preservation for sample enumeration and taxonomic morphological analysis, and preservation of samples for genetic analysis. Methods of analysis of zooplankton samples include determination of biomass, taxonomic composition, and size by traditional methods; and genetic analysis of zooplankton samples.

scholarly journals BOARD INVITED REVIEW: The pig microbiota and the potential for harnessing the power of the microbiome to improve growth and health1

2019 ◽  
Vol 97 (9) ◽  
pp. 3741-3757 ◽  
Author(s):  
Nirosh D Aluthge ◽  
Dana M Van Sambeek ◽  
Erin E Carney-Hinkle ◽  
Yanshuo S Li ◽  
Samodha C Fernando ◽  
...  
Keyword(s):  
Gastrointestinal Tract ◽  
Gut Microbiota ◽  
Therapeutic Potential ◽  
Animal Health ◽  
Microbial Colonization ◽  
Critical Importance ◽  
Specific Host ◽  
Microbiome Research ◽  
Health And Disease ◽  
The Impact

Abstract A variety of microorganisms inhabit the gastrointestinal tract of animals including bacteria, archaea, fungi, protozoa, and viruses. Pioneers in gut microbiology have stressed the critical importance of diet:microbe interactions and how these interactions may contribute to health status. As scientists have overcome the limitations of culture-based microbiology, the importance of these interactions has become more clear even to the extent that the gut microbiota has emerged as an important immunologic and metabolic organ. Recent advances in metagenomics and metabolomics have helped scientists to demonstrate that interactions among the diet, the gut microbiota, and the host to have profound effects on animal health and disease. However, although scientists have now accumulated a great deal of data with respect to what organisms comprise the gastrointestinal landscape, there is a need to look more closely at causative effects of the microbiome. The objective of this review is intended to provide: 1) a review of what is currently known with respect to the dynamics of microbial colonization of the porcine gastrointestinal tract; 2) a review of the impact of nutrient:microbe effects on growth and health; 3) examples of the therapeutic potential of prebiotics, probiotics, and synbiotics; and 4) a discussion about what the future holds with respect to microbiome research opportunities and challenges. Taken together, by considering what is currently known in the four aforementioned areas, our overarching goal is to set the stage for narrowing the path towards discovering how the porcine gut microbiota (individually and collectively) may affect specific host phenotypes.

scholarly journals Gut Microbiota and Host Metabolism: From Proof of Concept to Therapeutic Intervention

2021 ◽  
Vol 9 (6) ◽  
pp. 1302
Author(s):  
Patrice D. Cani ◽  
Emilie Moens de Hase ◽  
Matthias Van Hul
Keyword(s):  
Gut Microbiota ◽  
Research Field ◽  
Proof Of Concept ◽  
Clinical Implementation ◽  
Science Area ◽  
Microbiome Research ◽  
Fast Pace ◽  
Health And Disease ◽  
Underlying Mechanisms ◽  
Host Metabolism

The field of the gut microbiota is still a relatively young science area, yet many studies have already highlighted the translational potential of microbiome research in the context of human health and disease. However, like in many new fields, discoveries are occurring at a fast pace and have provided new hope for the development of novel clinical applications in many different medical conditions, not in the least in metabolic disorders. This rapid progress has left the field vulnerable to premature claims, misconceptions and criticism, both from within and outside the sector. Tackling these issues requires a broad collaborative effort within the research field and is only possible by acknowledging the difficulties and challenges that are faced and that are currently hindering clinical implementation. These issues include: the primarily descriptive nature of evidence, methodological concerns, disagreements in analysis techniques, lack of causality, and a rather limited molecular-based understanding of underlying mechanisms. In this review, we discuss various studies and models that helped identifying the microbiota as an attractive tool or target for developing various translational applications. We also discuss some of the limitations and try to clarify some common misconceptions that are still prevalent in the field.

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