scholarly journals Functional diversity of microbial ecologies estimated from ancient human coprolites and dental calculus

2020 ◽  
Vol 375 (1812) ◽  
pp. 20190586 ◽  
Author(s):  
David K. Jacobson ◽  
Tanvi P. Honap ◽  
Cara Monroe ◽  
Justin Lund ◽  
Brett A. Houk ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Fusobacterium Nucleatum ◽  
Dental Calculus ◽  
Hunter Gatherers ◽  
Associated Bacteria ◽  
Response Diversity ◽  
Health And Disease ◽  
Microbiome Data ◽  
Diversity Profiles ◽  
Ecological Profiles

Human microbiome studies are increasingly incorporating macroecological approaches, such as community assembly, network analysis and functional redundancy to more fully characterize the microbiome. Such analyses have not been applied to ancient human microbiomes, preventing insights into human microbiome evolution. We address this issue by analysing published ancient microbiome datasets: coprolites from Rio Zape ( n = 7; 700 CE Mexico) and historic dental calculus ( n = 44; 1770–1855 CE, UK), as well as two novel dental calculus datasets: Maya ( n = 7; 170 BCE-885 CE, Belize) and Nuragic Sardinians ( n = 11; 1400–850 BCE, Italy). Periodontitis-associated bacteria ( Treponema denticola , Fusobacterium nucleatum and Eubacterium saphenum ) were identified as keystone taxa in the dental calculus datasets. Coprolite keystone taxa included known short-chain fatty acid producers ( Eubacterium biforme, Phascolarctobacterium succinatutens ) and potentially disease-associated bacteria ( Escherichia , Brachyspira) . Overlap in ecological profiles between ancient and modern microbiomes was indicated by similarity in functional response diversity profiles between contemporary hunter–gatherers and ancient coprolites, as well as parallels between ancient Maya, historic UK, and modern Spanish dental calculus; however, the ancient Nuragic dental calculus shows a distinct ecological structure. We detected key ecological signatures from ancient microbiome data, paving the way to expand understanding of human microbiome evolution. This article is part of the theme issue ‘Insights into health and disease from ancient biomolecules’.

scholarly journals Microbial trend analysis for common dynamic trend, group comparison and classification in longitudinal microbiome study

2020 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J. Blaser ◽  
Huilin Li
Keyword(s):  
Trend Analysis ◽  
Critical Role ◽  
Human Microbiome ◽  
Real Data ◽  
Microbial Dynamics ◽  
Individual Subject ◽  
Microbial Profiling ◽  
Health And Disease ◽  
Microbiome Data

AbstractMotivationThe human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time.ResultsWe propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects on the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different in groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice.ConclusionsThe proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

scholarly journals Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J. Blaser ◽  
Huilin Li
Keyword(s):  
Trend Analysis ◽  
Critical Role ◽  
Human Microbiome ◽  
Real Data ◽  
Microbial Dynamics ◽  
Individual Subject ◽  
Microbial Profiling ◽  
Health And Disease ◽  
Microbiome Data

Abstract Background The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time. Results We propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice. Conclusions The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

scholarly journals Potential Use of Biotherapeutic Bacteria to Target Colorectal Cancer-Associated Taxa

2020 ◽  
Vol 21 (3) ◽  
pp. 924 ◽  
Author(s):  
Garreth W. Lawrence ◽  
Máire Begley ◽  
Paul D. Cotter ◽  
Caitriona M. Guinane
Keyword(s):  
Colorectal Cancer ◽  
Fusobacterium Nucleatum ◽  
Host Immunity ◽  
Associated Bacteria ◽  
Health And Disease ◽  
Inflammatory Bowel ◽  
Key Drivers ◽  
Potential Use

The role of the gut microbiome in human health and disease is the focus of much attention. It has been widely agreed upon that our gut bacteria play a role in host immunity, nutrient absorption, digestion, metabolism, and other key drivers of health. Furthermore, certain microbial signatures and specific taxa have also been associated with the development of diseases, such as obesity; inflammatory bowel disease; and, indeed, colorectal cancer (CRC), which is the focus of this review. By extension, such taxa represent potential therapeutic targets. In particular, the emerging human pathogen Fusobacterium nucleatum represents an important agent in CRC development and its control within the gastrointestinal tract is desirable. This paper reviews the principal bacterial pathogens that have been associated with CRC to date and discusses the in vitro and human studies that have shown the potential use of biotherapeutic strains as a means of targeting CRC-associated bacteria.

scholarly journals Meta analysis of microbiome studies identifies shared and disease-specific patterns

2017 ◽  
Author(s):  
Claire Duvallet ◽  
Sean Gibbons ◽  
Thomas Gurry ◽  
Rafael Irizarry ◽  
Eric Alm
Keyword(s):  
Meta Analysis ◽  
Human Microbiome ◽  
Case Control ◽  
Published Data ◽  
Associated Bacteria ◽  
Disease States ◽  
Microbial Response ◽  
Health And Disease ◽  
Pathogenic Microbes ◽  
Bacterial Associations

1AbstractHundreds of clinical studies have been published that demonstrate associations between the human microbiome and a variety of diseases. Yet, fundamental questions remain on how we can generalize this knowledge. For example, if diseases are mainly characterized by a small number of pathogenic species, then new targeted antimicrobial therapies may be called for. Alternatively, if diseases are characterized by a lack of healthy commensal bacteria, then new probiotic therapies might be a better option. Results from individual studies, however, can be inconsistent or in conflict, and comparing published data is further complicated by the lack of standard processing and analysis methods.Here, we introduce the MicrobiomeHD database, which includes 29 published case-control gut microbiome studies spanning ten different diseases. Using standardized data processing and analyses, we perform a comprehensive crossdisease meta-analysis of these studies. We find consistent and specific patterns of disease-associated microbiome changes. A few diseases are associated with many individual bacterial associations, while most show only around 20 genus-level changes. Some diseases are marked by the presence of pathogenic microbes whereas others are characterized by a depletion of health-associated bacteria. Furthermore, over 60% of microbes associated with individual diseases fall into a set of “core” health and disease-associated microbes, which are associated with multiple disease states. This suggests a universal microbial response to disease.

scholarly journals Health and disease imprinted in the time variability of the human microbiome

2015 ◽  
Author(s):  
Jose Manuel Marti ◽  
Daniel M Martinez ◽  
Manuel Pena ◽  
Cesar Gracia ◽  
Amparo Latorre ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Scaling Law ◽  
Human Microbiome ◽  
Time Variability ◽  
Clinical Use ◽  
Gut Development ◽  
Research Activity ◽  
Health And Disease ◽  
Fluctuation Scaling ◽  
Universal Law

Human microbiota plays an important role in determining changes from health to disease. Increasing research activity is dedicated to understand its diversity and variability. We analyse 16S rRNA and whole genome sequencing (WGS) data from the gut microbiota of 97 individuals monitored in time. Temporal fluctuations in the microbiome reveal significant differences due to factors that affect the microbiota such as dietary changes, antibiotic intake, early gut development or disease. Here we show that a fluctuation scaling law describes the temporal variability of the system and that a noise-induced phase transition is central in the route to disease. The universal law distinguishes healthy from sick microbiota and quantitatively characterizes the path in the phase space, which opens up its potential clinical use and, more generally, other technological applications where microbiota plays an important role.

scholarly journals Preface to the human microbiome in urologic health and disease

2017 ◽  
Vol 5 ◽  
pp. 27-27 ◽  
Author(s):  
Daniel A. Shoskes ◽  
Jill A. Macoska
Keyword(s):  
Human Microbiome ◽  
Health And Disease

scholarly journals G2S: A New Deep Learning Tool for Predicting Stool Microbiome Structure From Oral Microbiome Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Simone Rampelli ◽  
Marco Fabbrini ◽  
Marco Candela ◽  
Elena Biagi ◽  
Patrizia Brigidi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Microbiome ◽  
Oral Microbiome ◽  
Fecal Microbiome ◽  
The Family ◽  
Bioinformatic Tool ◽  
Microbial Metagenomics ◽  
Community Theory ◽  
Microbiome Data ◽  
Fecal Sampling

Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. Within the meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of the human microbiome. In this context, we developed G2S, a bioinformatic tool for taxonomic prediction of the human fecal microbiome directly from the oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on paired oral and fecal samples from populations across the globe, which allows inferring the stool microbiome at the family level more accurately than other available approaches. The tool can be used in retrospective studies, where fecal sampling was not performed, and especially in the field of paleomicrobiology, as a unique opportunity to recover data related to ancient gut microbiome configurations. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects.

scholarly journals Misclassification of a whole genome sequence reference defined by the Human Microbiome Project: a detrimental carryover effect to microbiome studies

2019 ◽  
Author(s):  
DJ Darwin R. Bandoy ◽  
B Carol Huang ◽  
Bart C. Weimer
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Reference Database ◽  
Whole Genome ◽  
Reference Species ◽  
Genome Sequences ◽  
Genome Homology ◽  
Microbiome Data

AbstractTaxonomic classification is an essential step in the analysis of microbiome data that depends on a reference database of whole genome sequences. Taxonomic classifiers are built on established reference species, such as the Human Microbiome Project database, that is growing rapidly. While constructing a population wide pangenome of the bacterium Hungatella, we discovered that the Human Microbiome Project reference species Hungatella hathewayi (WAL 18680) was significantly different to other members of this genus. Specifically, the reference lacked the core genome as compared to the other members. Further analysis, using average nucleotide identity (ANI) and 16s rRNA comparisons, indicated that WAL18680 was misclassified as Hungatella. The error in classification is being amplified in the taxonomic classifiers and will have a compounding effect as microbiome analyses are done, resulting in inaccurate assignment of community members and will lead to fallacious conclusions and possibly treatment. As automated genome homology assessment expands for microbiome analysis, outbreak detection, and public health reliance on whole genomes increases this issue will likely occur at an increasing rate. These observations highlight the need for developing reference free methods for epidemiological investigation using whole genome sequences and the criticality of accurate reference databases.

The Genetics and Genomics of Asthma

2018 ◽  
Vol 19 (1) ◽  
pp. 223-246 ◽  
Author(s):  
Saffron A.G. Willis-Owen ◽  
William O.C. Cookson ◽  
Miriam F. Moffatt
Keyword(s):  
Sequence Variation ◽  
Human Microbiome ◽  
Technological Advances ◽  
Genome Wide ◽  
A Genome ◽  
Wide Range ◽  
Health And Disease ◽  
Wide Scale ◽  
Genetics And Genomics ◽  
The Individual

Asthma is a common, clinically heterogeneous disease with strong evidence of heritability. Progress in defining the genetic underpinnings of asthma, however, has been slow and hampered by issues of inconsistency. Recent advances in the tools available for analysis—assaying transcription, sequence variation, and epigenetic marks on a genome-wide scale—have substantially altered this landscape. Applications of such approaches are consistent with heterogeneity at the level of causation and specify patterns of commonality with a wide range of alternative disease traits. Looking beyond the individual as the unit of study, advances in technology have also fostered comprehensive analysis of the human microbiome and its varied roles in health and disease. In this article, we consider the implications of these technological advances for our current understanding of the genetics and genomics of asthma.

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