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 Complementary Methodologies To Investigate Human Gut Microbiota in Host Health, Working towards Integrative Systems Biology

2017 ◽  
Vol 200 (3) ◽  
Author(s):  
Celia Méndez-García ◽  
Coral Barbas ◽  
Manuel Ferrer ◽  
David Rojo
Keyword(s):  
Human Subjects ◽  
Human Microbiome ◽  
Fecal Microbiota ◽  
Microbiome Composition ◽  
Disease States ◽  
Microbial Product ◽  
Host Health ◽  
Health And Disease ◽  
The Cost ◽  
Biological State

ABSTRACT In 1680, Antonie van Leeuwenhoek noted compositional differences in his oral and fecal microbiota, pioneering the study of the diversity of the human microbiome. From Leeuwenhoek's time to successful modern attempts at changing the gut microbial landscape to cure disease, there has been an exponential increase in the recognition of our resident microbes as part of ourselves. Thus, the human host and microbiome have evolved in parallel to configure a balanced system in which microbes survive in homeostasis with our innate and acquired immune systems, unless disease occurs. A growing number of studies have demonstrated a correlation between the presence/absence of microbial taxa and some of their functional molecules (i.e., genes, proteins, and metabolites) with health and disease states. Nevertheless, misleading experimental design on human subjects and the cost and lack of standardized animal models pose challenges to answering the question of whether changes in microbiome composition are cause or consequence of a certain biological state. In this review, we evaluate the state of the art of methodologies that enable the study of the gut microbiome, encouraging a change in broadly used analytic strategies by choosing effector molecules (proteins and metabolites) in combination with coding nucleic acids. We further explore microbial and effector microbial product imbalances that relate to disease and health.

scholarly journals The Role of Microbiome in Cancer Cell Stimulation and Therapy

2021 ◽  
pp. 97-115
Author(s):  
Shahira Hassoubah
Keyword(s):  
Immune Response ◽  
Human Microbiome ◽  
Future Research ◽  
Published Data ◽  
Disease States ◽  
Life Threatening ◽  
Indigenous Microbiota ◽  
Symbiotic Microbiota ◽  
Insight Into

In recent times, the microbiome has been increasingly recognized as having a hand in various disease states that include cancer as a part. Our commensal and symbiotic microbiota, in addition to pathogens with oncogenesis features, have tumor-suppressive characteristics. Our nutrition and other environmental influences can modulate some microbial species representatives within our digestive system and other systems. The microbiota has recently shown a two-way link to cancer immunotherapy for both the prognosis and the therapeutic aspects. Preclinical results indicated that microbiota modification could be transformed into a novel technique to improve cancer therapy's effectiveness. This article aimed to review recent development in our understanding of the microbiome and its relationship to cancer cells and discuss how the microbiome stimulates cancer and its clinical and therapeutic applications. Such information was selected and extracted from the PubMed, Web of Science, and Google Scholar databases for published data from 2000 to 2020 using relevant keywords containing a combination of terms, including the microbiome, cancer, immune response, immune response, and microbiota. Finally, we concluded that studying the human microbiome is necessary because it provides a thorough understanding of humans' interaction and their indigenous microbiota. The microbiome provides useful insight into future research studies to optimize these species to fight life-threatening diseases such as cancer and has rendered the microbiome a successful cancer treatment strategy.

Abstract T P153: APOE e4 Increases Risk of Recurrent Non-lobar Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Miriam R Raffeld ◽  
Christopher D Anderson ◽  
Thomas W Battey ◽  
Alison M Ayers ◽  
Kristin Schwab ◽  
...  
Keyword(s):  
Risk Factors ◽  
Meta Analysis ◽  
Demographic Data ◽  
Apoe Genotype ◽  
Case Control ◽  
Published Data ◽  
Control Analysis ◽  
Apoe Ε4 ◽  
Lobar Hemorrhage ◽  
Case Control Analysis

Introduction: Genetic variants ε2 / ε4 within the APOE gene are established risk factors for lobar intracerebral Hemorrhage (ICH) and its recurrence. Data from a large case-control meta-analysis suggest a potential but not yet replicated role for APOE ε4 in risk of non-lobar ICH. Hypothesis: APOE ε4 increases risk of recurrent non-lobar ICH and, replicating prior results, increases risk of initial non-lobar ICH. Methods: Among consecutive non-lobar ICH cases from our single center series (n=468), 363 survivors were followed for recurrence. All subjects had clinical and demographic data, and APOE genotype determined at the time of index ICH. In a separate case-control analysis, 156 non-lobar ICH cases and 152 ICH-free controls, ascertained subsequent to all prior publications, were analyzed as replication of previously published case-control findings. These case-control results were then meta-analyzed with previously published data. Results: We observed 29 non-lobar-ICH recurrences among 363 survivors. APOE ε4 was associated with non-lobar-ICH recurrence (HR = 1.31, 95% CI =1.02 - 2.69, p = 0.038) after adjustment for age / gender / ethnicity and cardiovascular risk factors. Case-control analyses of newly ascertained subjects replicated the APOE ε4 association with risk of non-lobar ICH (OR = 1.31, 95% CI = 1.02-1.68, p = 0.035). Meta-analysis of these with published case-control data returned an association between ε4 and non-lobar ICH (OR = 1.21, 95% CI = 1.11-1.32, p = 1.5 x 10 -5 ). No associations were identified between APOE ε2 and non-lobar ICH risk or recurrence. Conclusions: APOE ε4, but not ε2 is associated with risk and recurrence of non-lobar ICH. Whether APOE’s role in non-lobar ICH involves beta-amyloid pathology, its presumed mechanism in lobar ICH, requires further study.

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.

Abstract P057: The Association Between Diabetes and Risk of Abdominal Aortic Aneurysm: A Meta-Analysis

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Lu Yao ◽  
Aaron Folsom ◽  
Alvaro Alonso ◽  
James Pankow ◽  
Weihua Guan ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Relative Risk ◽  
Aortic Aneurysm ◽  
Meta Analysis ◽  
Case Control ◽  
Outcome Variable ◽  
P Value ◽  
Published Data ◽  
Eligibility Criteria ◽  
Abdominal Aortic

Objectives: Data regarding the relationship between diabetes and abdominal aortic aneurysm (AAA) are inconsistent across studies: some studies showed an inverse relationship while others did not show an association. We conducted a meta-analysis to examine the association between diabetes and AAA based on published data from case-control and cohort studies. Methods: We searched literature in English from online databases including MEDLINE (1966-), EMBASE and Web of Science as of July 2017, plus a manual examination of references in selected articles. The eligibility criteria included (1) a case-control or cohort study conducted in adults; (2) diabetes is the exposure variable and AAA risk is the outcome variable; and (3) association estimates (hazard ratios, odds ratios or relative risks) and measurement of variance (P value, confidence interval, or standard error) were available. The literature review and data abstraction were conducted in duplicate by independent investigators. A DerSimonian and Laird random effects model was used to pool association estimates and their 95% confidence intervals from studies using STATA 13. The Cochran’s Q test was used to assess the presence of heterogeneity and the I-square index to quantify the extent of heterogeneity. Results: We included in the meta-analyses a total of 10 cohorts with 10,771 AAAs in 2,625,318 participants and 4 case-control studies with 1,065 AAAs and 11,009 controls that met the pre-determined eligibility criteria. The samples were predominantly white (88%). Study-specific relative risk and pooled relative risk as well as heterogeneity test results were shown in Figure. Diabetes was inversely associated with AAA risk (pooled relative risk: 0.55; 95%CI: 0.49 - 0.61, Figure) . Results were overall consistent by sex, study design and setting (hospital- vs community-based). Conclusions: The findings suggest that diabetes is strongly and inversely associated with the risk of AAA. Future studies are warranted to investigate the potential mechanisms.

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 Microbiome, Immunosenescence, and Chronic Kidney Disease

2021 ◽  
Vol 8 ◽  
Author(s):  
Elisavet Stavropoulou ◽  
Konstantia Kantartzi ◽  
Christina Tsigalou ◽  
Konstantina Aftzoglou ◽  
Chrysa Voidarou ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Diseases ◽  
Human Microbiome ◽  
Renal Diseases ◽  
Predictive Tool ◽  
Gut Dysbiosis ◽  
Multiple Factors ◽  
Disease States ◽  
Genetic Endowment ◽  
Health And Disease

The gut microbiome is known as an important predictive tool for perceiving characteristic shifts in disease states. Multiple renal diseases and pathologies seem to be associated with gut dysbiosis which directly affects host homeostasis. The gastrointestinal-kidney dialogue confers interesting information about the pathogenesis of multiple kidney diseases. Moreover, aging is followed by specific shifts in the human microbiome, and gradual elimination of physiological functions predisposes the microbiome to inflammaging, sarcopenia, and disease. Aging is characterized by a microbiota with an abundance of disease-associated pathobionts. Multiple factors such as the immune system, environment, medication, diet, and genetic endowment are involved in determining the age of the microbiome in health and disease. Our present review promotes recently acquired knowledge and is expected to inspire researchers to advance studies and investigations on the involved pathways of the gut microbiota and kidney axis.

scholarly journals Old and new models for studying host-microbe interactions in health and disease:C. difficileas an example

2017 ◽  
Vol 312 (6) ◽  
pp. G623-G627 ◽  
Author(s):  
Vincent B. Young
Keyword(s):  
Human Health ◽  
Bacterial Communities ◽  
Human Microbiome ◽  
Model Systems ◽  
Microbe Interactions ◽  
Health And Disease ◽  
Indigenous Microbiota ◽  
Pathogenic Microbes ◽  
Host Microbe Interactions ◽  
Insight Into

There has been an explosion of interest in studying the indigenous microbiota, which plays an important role in human health and disease. Traditionally, the study of microbes in relationship to human health involved consideration of individual microbial species that caused classical infectious diseases. With the interest in the human microbiome, an appreciation of the influence that complex communities of microbes can have on their environment has developed. When considering either individual pathogenic microbes or a symbiotic microbial community, researchers have employed a variety of model systems with which they can study the host-microbe interaction. With the use of studies of infections with the toxin-producing bacterium Clostridium difficile as a model for both a pathogen and beneficial bacterial communities as an example, this review will summarize and compare various model systems that can be used to gain insight into the host-microbe interaction.

scholarly journals GutCyc: a Multi-Study Collection of Human Gut Microbiome Metabolic Models

2016 ◽  
Author(s):  
Aria S. Hahn ◽  
Tomer Altman ◽  
Kishori M. Konwar ◽  
Niels W. Hanson ◽  
Dongjae Kim ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Human Microbiome ◽  
Therapeutic Interventions ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Disease States ◽  
Health And Disease ◽  
Inflammatory Bowel

AbstractAdvances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GUTCYC, a compendium of environmental pathway genome databases constructed from 418 assembled human microbiome datasets using METAPATHWAYS, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the PATHWAY TOOLS software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GUTCYC provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GUTCYC data products are searchable online, or may be downloaded and explored locally using METAPATHWAYS and PATHWAY TOOLS.

scholarly journals Biogeography & environmental conditions shape bacteriophage-bacteria networks across the human microbiome

2017 ◽  
Author(s):  
Geoffrey D Hannigan ◽  
Melissa B Duhaime ◽  
Danai Koutra ◽  
Patrick D Schloss
Keyword(s):  
Microbial Communities ◽  
Human Body ◽  
Complex Dynamics ◽  
Learning Algorithm ◽  
Human Microbiome ◽  
Metagenomic Sequence ◽  
Future Studies ◽  
Disease States ◽  
Health And Disease ◽  
Extinction Events

AbstractViruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.Author SummaryThe human microbiome, the collection of microbial communities that colonize the human body, is a crucial component to health and disease. Two major components of the human microbiome are the bacterial and viral communities. These communities have primarily been studied separately using metrics of community composition and diversity. These approaches have failed to capture the complex dynamics of interacting bacteria and phage communities, which frequently share genetic information and work together to maintain ecosystem homestatsis (e.g. kill-the-winner dynamics). Removal of bacteria or phage can disrupt or even collapse those ecosystems. Relationship-based network approaches allow us to capture this interaction information. Using this network-based approach with three independent human cohorts, we were able to present an initial understanding of how phage-bacteria networks differ throughout the human body, so as to provide a baseline for future studies of how and why microbiome networks differ in disease states.

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