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 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 Revealing the Bacterial Butyrate Synthesis Pathways by Analyzing (Meta)genomic Data

mBio ◽  
2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Marius Vital ◽  
Adina Chuang Howe ◽  
James M. Tiedje
Keyword(s):  
Gut Microbiota ◽  
Critical Role ◽  
Human Microbiome ◽  
Genomic Analysis ◽  
16S Rrna Gene Sequencing ◽  
Emerging Diseases ◽  
Rrna Gene ◽  
Data Set ◽  
Genome Database ◽  
Health And Disease

ABSTRACTButyrate-producing bacteria have recently gained attention, since they are important for a healthy colon and when altered contribute to emerging diseases, such as ulcerative colitis and type II diabetes. This guild is polyphyletic and cannot be accurately detected by 16S rRNA gene sequencing. Consequently, approaches targeting the terminal genes of the main butyrate-producing pathway have been developed. However, since additional pathways exist and alternative, newly recognized enzymes catalyzing the terminal reaction have been described, previous investigations are often incomplete. We undertook a broad analysis of butyrate-producing pathways and individual genes by screening 3,184 sequenced bacterial genomes from the Integrated Microbial Genome database. Genomes of 225 bacteria with a potential to produce butyrate were identified, including many previously unknown candidates. The majority of candidates belong to distinct families within theFirmicutes, but members of nine other phyla, especially fromActinobacteria,Bacteroidetes,Fusobacteria,Proteobacteria,Spirochaetes, andThermotogae, were also identified as potential butyrate producers. The established gene catalogue (3,055 entries) was used to screen for butyrate synthesis pathways in 15 metagenomes derived from stool samples of healthy individuals provided by the HMP (Human Microbiome Project) consortium. A high percentage of total genomes exhibited a butyrate-producing pathway (mean, 19.1%; range, 3.2% to 39.4%), where the acetyl-coenzyme A (CoA) pathway was the most prevalent (mean, 79.7% of all pathways), followed by the lysine pathway (mean, 11.2%). Diversity analysis for the acetyl-CoA pathway showed that the same few firmicute groups associated with severalLachnospiraceaeandRuminococcaceaewere dominating in most individuals, whereas the other pathways were associated primarily withBacteroidetes.IMPORTANCEMicrobiome research has revealed new, important roles of our gut microbiota for maintaining health, but an understanding of effects of specific microbial functions on the host is in its infancy, partly because in-depth functional microbial analyses are rare and publicly available databases are often incomplete/misannotated. In this study, we focused on production of butyrate, the main energy source for colonocytes, which plays a critical role in health and disease. We have provided a complete database of genes from major known butyrate-producing pathways, using in-depth genomic analysis of publicly available genomes, filling an important gap to accurately assess the butyrate-producing potential of complex microbial communities from “-omics”-derived data. Furthermore, a reference data set containing the abundance and diversity of butyrate synthesis pathways from the healthy gut microbiota was established through a metagenomics-based assessment. This study will help in understanding the role of butyrate producers in health and disease and may assist the development of treatments for functional dysbiosis.

scholarly journals A Mechano-Activated Cell Reporter System as a Proxy for Flow-Dependent Endothelial Atheroprotection

2018 ◽  
Vol 23 (8) ◽  
pp. 869-876
Author(s):  
Bendix R. Slegtenhorst ◽  
Oscar R. Fajardo Ramirez ◽  
Yuzhi Zhang ◽  
Zahra Dhanerawala ◽  
Stefan G. Tullius ◽  
...  
Keyword(s):  
Vascular Endothelium ◽  
Critical Role ◽  
Green Fluorescence Protein ◽  
Dependent Manner ◽  
Reporter System ◽  
Health And Disease ◽  
Fluorescence Protein ◽  
Biomechanical Stimuli ◽  
Dose Dependent

The vascular endothelium plays a critical role in the health and disease of the cardiovascular system. Importantly, biomechanical stimuli generated by blood flow and sensed by the endothelium constitute important local inputs that are translated into transcriptional programs and functional endothelial phenotypes. Pulsatile, laminar flow, characteristic of regions in the vasculature that are resistant to atherosclerosis, evokes an atheroprotective endothelial phenotype. This atheroprotective phenotype is integrated by the transcription factor Kruppel-like factor-2 (KLF2), and therefore the expression of KLF2 can be used as a proxy for endothelial atheroprotection. Here, we report the generation and characterization of a cellular KLF2 reporter system, based on green fluorescence protein (GFP) expression driven by the human KLF2 promoter. This reporter is induced selectively by an atheroprotective shear stress waveform in human endothelial cells, is regulated by endogenous signaling events, and is activated by the pharmacological inducer of KLF2, simvastatin, in a dose-dependent manner. This reporter system can now be used to probe KLF2 signaling and for the discovery of a novel chemical-biological space capable of acting as the “pharmacomimetics of atheroprotective flow” on the vascular endothelium.

Approaches and developments in studying the human microbiome network

2015 ◽  
Vol 61 (2) ◽  
pp. 90-94 ◽  
Author(s):  
Mor Nitzan ◽  
Sefi Mintzer ◽  
Hanah Margalit
Keyword(s):  
Bacterial Population ◽  
Bacterial Species ◽  
Human Microbiome ◽  
Structure And Function ◽  
Complex Relationship ◽  
Human Host ◽  
Competition And Cooperation ◽  
Health And Disease ◽  
And Function

The human microbiome is dynamic and unique to each individual, and its role is being increasingly recognized in healthy physiology and in disease, including gastrointestinal and neuropsychiatric disorders. Therefore, characterizing the human microbiome and the factors that shape its bacterial population, how they are related to host-specific attributes, and understanding the ways in which it can be manipulated and the phenotypic consequences of such manipulations are of great importance. Characterization of the microbiome so far has been mostly based on compositional studies alone, where relative abundances of different species are compared in different conditions, such as health and disease. However, inter-relationships among the bacterial species, such as competition and cooperation over metabolic resources, may be an important factor that affects the structure and function of the microbiome. Here we review the network-based approaches in answering such questions and explore the first attempts that focus on the interactions facet, complementing compositional studies, towards understanding the microbiome structure and its complex relationship with the human host.

scholarly journals Improving characterization of understudied human microbiomes using targeted phylogenetics

2019 ◽  
Author(s):  
Bruce A Rosa ◽  
Kathie Mihindukulasuriya ◽  
Kymberlie Hallsworth-Pepin ◽  
Aye Wollam ◽  
John Martin ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Genomic Sequences ◽  
Bacterial Strains ◽  
Bacterial Genomes ◽  
Genomic Databases ◽  
Disease States ◽  
Health And Disease ◽  
Taxonomic Groups

AbstractWhole genome bacterial sequences are required to better understand microbial functions, niches-pecific bacterial metabolism, and disease states. Although genomic sequences are available for many of the human-associated bacteria from commonly tested body habitats (e.g. stool), as few as 13% of bacterial-derived reads from other sites such as the skin map to known bacterial genomes. To facilitate a better characterization of metagenomic shotgun reads from under-represented body sites, we collected over 10,000 bacterial isolates originating from 14 human body habitats, identified novel taxonomic groups based on full length 16S rRNA sequences, clustered the sequences to ensure that no individual taxonomic group was over-selected for sequencing, prioritized bacteria from under-represented body sites (such as skin, respiratory and urinary tract), and sequenced and assembled genomes for 665 new bacterial strains. Here we show that addition of these genomes improved read mapping rates of HMP metagenomic samples by nearly 30% for the previously under-represented phylum Fusobacteria, and 27.5% of the novel genomes generated here had high representation in at least one of the tested HMP samples, compared to 12.5% of the sequences in the public databases, indicating an enrichment of useful novel genomic sequences resulting from the prioritization procedure. As our understanding of the human microbiome continues to improve and to enter the realm of therapy developments, targeted approaches such as this to improve genomic databases will increase in importance from both an academic and clinical perspective.ImportanceThe human microbiome plays a critically important role in health and disease, but current understanding of the mechanisms underlying the interactions between the varying microbiome and the different host environments is lacking. Having access to a database of fully sequenced bacterial genomes provides invaluable insights into microbial functions, but currently sequenced genomes for the human microbiome have largely come from a limited number of body sites (primarily stool), while other sites such as the skin, respiratory tract and urinary tracts are under-represented, resulting in as little as 13% of bacterial-derived reads mapping to known bacterial genomes. Here, we sequenced and assembled 665 new bacterial genomes, prioritized from a larger database to select under-represented body sites and bacterial taxa in the existing databases. As a result, we substantially improve mapping rates for samples from the Human Microbiome Project and provide an important contribution to human bacterial genomic databases for future studies.

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 A broken promise: microbiome differential abundance methods do not control the false discovery rate

2017 ◽  
Vol 20 (1) ◽  
pp. 210-221 ◽  
Author(s):  
Stijn Hawinkel ◽  
Federico Mattiello ◽  
Luc Bijnens ◽  
Olivier Thas
Keyword(s):  
Statistical Methods ◽  
High Throughput Sequencing ◽  
Bacterial Species ◽  
Human Microbiome ◽  
Real Data ◽  
Differential Abundance ◽  
Sequencing Technologies ◽  
Antibiotic Drugs ◽  
Count Distribution ◽  
Microbiome Data

Abstract High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution; the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

2020 ◽  
Vol 3 (1) ◽  
pp. 411-432
Author(s):  
Chengsheng Zhu ◽  
Maximilian Miller ◽  
Zishuo Zeng ◽  
Yanran Wang ◽  
Yannick Mahlich ◽  
...  
Keyword(s):  
Human Genome ◽  
Critical Role ◽  
Human Microbiome ◽  
Healthy Individuals ◽  
Computational Tools ◽  
The Past ◽  
Disease States ◽  
Health And Disease ◽  
Taxonomic Groups ◽  
Genome Variants

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

scholarly journals mbImpute: an accurate and robust imputation method for microbiome data

2020 ◽  
Author(s):  
Ruochen Jiang ◽  
Wei Vivian Li ◽  
Jingyi Jessica Li
Keyword(s):  
State Of The Art ◽  
Imputation Accuracy ◽  
Human Microbiome ◽  
Real Data ◽  
Imputation Method ◽  
Imputation Methods ◽  
Microbiome Research ◽  
Microbiome Data ◽  
Critical Challenge

AbstractMicrobiome studies have gained increased attention since many discoveries revealed connections between human microbiome compositions and diseases. A critical challenge in microbiome research is that excess non-biological zeros distort taxon abundances, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method, mbImpute, to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. Comprehensive simulations verified that mbImpute achieved better imputation accuracy under multiple measures than five state-of-the-art imputation methods designed for non-microbiome data. In real data applications, we demonstrate that mbImpute improved the power and reproducibility of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer.

scholarly journals Microbiome Changes during Tuberculosis and Antituberculous Therapy

2016 ◽  
Vol 29 (4) ◽  
pp. 915-926 ◽  
Author(s):  
Bo-Young Hong ◽  
Nancy Paula Maulén ◽  
Alexander J. Adami ◽  
Hector Granados ◽  
María Elvira Balcells ◽  
...  
Keyword(s):  
Potential Role ◽  
Critical Role ◽  
Human Microbiome ◽  
Content Type ◽  
Microbiome Composition ◽  
Commensal Microbiota ◽  
Lung Microbiome ◽  
Antituberculous Drug ◽  
Health And Disease

SUMMARYThe critical role of commensal microbiota in the human body has been increasingly recognized, and our understanding of its implications in human health and disease has expanded rapidly. The lower respiratory tract contains diverse communities of microbes known as lung microbiota, which are present in healthy individuals and in individuals with respiratory diseases. The dysbiosis of the airway microbiota in pulmonary tuberculosis (TB) may play a role in the pathophysiological processes associated with TB disease. Recent studies of the lung microbiome have pointed out changes in lung microbial communities associated with TB and other lung diseases and have also begun to elucidate the profound effects that antituberculous drug therapy can have on the human lung microbiome composition. In this review, the potential role of the human microbiome in TB pathogenesis and the changes in the human microbiome withMycobacterium tuberculosisinfection and TB therapy are presented and discussed.

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