Different Treatments For Crohns Disease In Pediatric Patients Have Distinct Effects On The Gut Microbiome
Changes in diet and application of antibiotics and/or anti-inflammatories are the typical interventions used as the standards of care for the treatment of Crohns disease , a subtype of inflammatory bowel disease. One major characteristic of CD is an imbalance in the normal composition of the microbiota in comparison to healthy controls. A recent study from Human Microbiome Project awardee Dr. Frederic Bushman and colleagues at the University of Pennsylvania sought to systematically separate the effects of these interventions on the gut microbiomes of a cohort of pediatric CD patients.
Each intervention independently affected the microbiome in CD patients. In particular, antibiotic use seemed to worsen dysbiosis by reducing the abundances of some microbes, increasing the abundances of fungi or both, thus aggravating the condition. Anti-inflammatories, on the other hand, reduced gut microbiota dysbiosis, thereby potentially supporting recovery from CD. Certain defined diets resulted in rapid changes in the gut microbiome suggesting diet may also be an effective treatment for CD.
References:
Inflammation, Antibiotics, and Diet as Environmental Stressors of the Gut Microbiome in Pediatric Crohnâs Disease. Lewis JD, Chen EZ, Baldassano RN, Otley AR, Griffiths AM, Lee D, Bittinger K, Bailey A, Friedman ES, Hoffmann C, Albenberg L, Sinha R, Compher C, Gilroy E, Nessel L, Grant A, Chehoud C, Li H, Wu GD, Bushman FD. Nature. 14 October 2015. 18: 489-500.
Creating Metabolite Species Maps And Obtaining The Frailty
We utilized the literature-curated experimentally annotated species to metabolite associations available as part of the Virtual Metabolic Human database as well as those obtained in a recent meta-analysis by Noronha et al. Sung et al. , to create a species-to-metabolite map of more than 300 metabolite production and consumption profile corresponding to 992 species in a 0 and 1 notation . For each microbiome, the metabolite production/consumption capability was then obtained as the matrix inner product of the abundance profile of the species and the species-to-metabolite map thus obtained.
Next, we identified the frailty-linked metabolites associated with the eight taxonomic markers of frailty using a two-step strategy. First, we performed a correlation analysis of each metabolite profile with FIM scores and identified metabolite profiles that showed significant association with FIM scores . Next, we identified which of these identified metabolite profiles were detected in the taxonomic markers of frailty at a rate significantly higher than the background detection (using our Fishers exact test approach with FDR corrected p< 0.25.
Importance Of The Human Microbiome Project
The human microbiome makes up about one to two percent of the body mass of an adult. It has been likened to a body organ. But, unlike say a heart or a liver, the importance and function of the microbiome is just starting to be appreciated.
It has long been known that bacteria are involved in certain body processes, such as digesting food and producing vitamins, but the microbiome appears have a much broader impact on our health than was previously realized. The community of microbes in an individual may influence the susceptibility to certain infectious diseases, as well as contribute to disorders such as obesity and diabetes. It may also contribute to the development of some chronic illnesses of the gastrointestinal system such as Crohnâs disease and irritable bowel syndrome. Some collections of microbes can determine how one responds to a particular drug treatment. The microbiome of the mother may even affect the health of her children.
A more complete understanding of the diversity of microbes that make up the human microbiome could lead to novel therapies. For example, it may be possible to treat a bacterial infection caused by a âbadâ bacterial species by promoting the growth of the âgoodâ bacteria. Microbiome transplants are already being used to combat certain illnesses, such as Clostridium difficile infections, to establish more healthful bacterial populations.
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What Is The Best Way To Collect A Sample For Microbiome Analysis
The first topic a clinician faces is the following: what is the optimal protocol for collecting a microbiome sample for analysis? There is still an ongoing debate on the best way to collect and store a sample for analysis of the microbiome. In short, there is no perfect method because the choice will depend on feasibility, cost, patient acceptance, and which methods will be used to read the microbiome downstream.
Culturomics approaches, in which large numbers of cells are isolated and cultured, show that metagenomics approaches miss many rare bacteria that are not well represented in the reference databases or that are below the filtering thresholds used to eliminate noise . They also suggest that even the most aggressive homogenizing procedure to break bacterial cell walls still may miss important organisms. On the other hand, approximately 85% of microbes in the human gut are anaerobic and therefore do not culture in an open Petri dish, although they can be grown in research laboratory anaerobic chambers. However, despite advances in culturing methods,, what can be cultured still is biased, especially because any given culture condition will allow some bacteria to grow much faster than others.
Normal Microbiota As Host Defense
The human microbiome is now recognized as a major host defense against bacterial pathogens by providing colonization resistance, maintaining a balance of commensals to pathogens, and by priming the immune system .5 Altering or disrupting the normal microbiota by antibiotics facilitates the expansion of enteric pathogens as Clostridium difficile and Salmonella typhimurium or selection of antibiotic-resistant members of the microbiome. Similarly, changes in human physiology, for example, exposure of skin to elevated temperatures and humidity, chronic stress, host immune suppression, or active behavioral changes, such as smoking, can cause a commensal-to-pathogen switch. Recent studies have demonstrated that certain resident microbiota can resist pathogen colonization and infection. For example, matched volunteers were inoculated with Haemophilus ducreyi into the arms, and the subsequent infection either resolved or resulted in formation of abscesses characterization of the skin microbiome before, during, and after the experimental inoculation showed that the microbiomes of those with pustule formation and of those with resolved infection were distinct and influenced the course of the H. ducreyi infection.6
Michael J. Orlich, ⦠Sarah Jung, in, 2017
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Alkek Center For Metagenomics And Microbiome Research
The Alkek Center for Metagenomics and Microbiome Research at Baylor, based in the Department of Molecular Virology and Microbiology, serves as an international hub for microbiome research including clinical and basic science applications and advanced bioinformatics analyses. The CMMR was established in 2011 and is directed by MVM faculty member Dr. Joseph F. Petrosino, a nationally recognized leader in metagenomic research. It was founded as an extension to Baylor’s involvement in the Human Microbiome Project and is supported in part by a generous donation from the Albert and Margaret Alkek Foundation.
The CMMR builds on the microbiology and virology expertise in the department and collaborates with the Human Genome Sequencing Center, headed by Dr. Richard Gibbs, and the Texas Children’s Microbiome Center for pediatric studies under the direction of Dr. James Versalovic.
CMMR researchers are developing molecular and informatics tools and resources to advance diverse clinical and basic research projects pertaining to the organisms that comprise the microbiome, the genetic makeup of these microbes, how these microorganisms interact with human cells and tissues during the course of life and their impact on health and disease. The CMMR provides metagenomic, informatics, model system and molecular biology support and guidance to other researchers and clinical collaborators engaging in these areas of study.
Common Fund Data Ecosystem
In addition to the HMP, the NIH Common Fund has supported numerous other programs that also generate large quantities of data and have associated Data Coordination Centers , LINCS ). A new Common Fund project, the Common Fund Data Ecosystem , has been developed to provide an overarching cloud-based data infrastructure and framework that will support past, present and future Common Fund project DCCs. The CFDE, in association with the NIH STRIDES program , is developing a cloud-based platform where DCCs can store, and users can access and compute on, Common Fund DCC metadata. Part of this effort is the development of a cross-cutting metadata model that will store metadata associated with all DCC assets. For DCCs that have reached the end of their funded time, not only metadata, but also primary and derived data, will be housed by the CFDE. Some of these data may be controlled-access. A CFDE data portal is under development that will provide controlled access, via portal query and API, to both public and protected data. This will be managed through a system that authenticates users based on whether they have been granted access permissions by the relevant NIH Data Access Committees.
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In One Study What Did Researchers Do To Help Build The Microbiomes Of C
They swabbed the babies with fluids from the mother’s birth canal. The study showed that there were tremendous differences between swabbed and unswabbed babies.
Along with fungi and bacteria, single-celled microorganisms make up the human microbiome.
Nonalcoholic Fatty Liver Disease
NAFLD encloses a spectrum of liver diseases from steatosis to nonalcoholic steatohepatitis, fibrosis, cirrhosis, and eventually hepatocellular carcinoma, in which liver fat deposition occurs. NAFLD requires the exclusion of a daily alcohol consumption of more than 30 g for men and 20 g for women. In recent years, NAFLD has emerged as one of the most common causes of liver disease worldwide .
The effectiveness of lifestyle changes, as modifications in diet and physical activity, have a great impact on metabolic control and liver histology , making clear the importance of environmental factors in this disease. However, there is a large variability in NAFLD progression that is not explained by genetics or environment.
Liver is the first organ to be exposed, through portal tract, to gut-origin metabolites, such as dietary nutrients and microbiota-related products. Due to this straight interaction between gut and liver, microbiota dysbiosis has been outlined as a major factor in the pathology of all stages of NAFLD . Dysbiosis can lead to increased intestinal permeability, promoting translocation of commensal metabolites through the vascular system into the liver , which directly contributes to hepatic lipid metabolism disruption and inflammatory processes in the liver .
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Microbiome Metabolism And Function
As the first study to include both marker gene and metagenomic data across body habitats from a large human population, we additionally assessed the ecology of microbial metabolic and functional pathways in these communities. We reconstructed the relative abundances of pathways in community metagenomes, which were much more constant and evenly diverse than were organismal abundances , confirming this as an ecological property of the entire human microbiome. We were likewise able to determine for the first time that taxonomic and functional alpha diversity across microbial communities significantly correlate , the latter within a more proscribed range of community configurations .
Personalised Nutrition And Future Directions
Given the variation in the gut microbiota between people, the optimal diet of a person may need to be tailored to their gut microbiota. Zeevi et al.94 obtained a multidimensional microbiota profile in 900 people and monitored food intake, continuous blood glucose levels, and physical activity for one week. The researchers devised a machine learning algorithm to predict personalised glucose responses after meals based on clinical and gut microbiome data and showed that it achieved significantly higher predictions than approaches such as carbohydrate counting or glycaemic index scores. In a follow-up double blinded randomised crossover trial of 26 participants, personalised dietary interventions based on the algorithm successfully normalised blood glucose levels.94
A study on response to bread68 using a randomised crossover trial of one week long dietary interventions showed significant interpersonal variability in the glycaemic response to different bread types. The type of bread that induced the lower glycaemic response in each person could be predicted based solely on microbiome data collected before the intervention.68 Much more research is needed to establish whether these kinds of personalised approaches are feasible, sustainable, and have a positive effect on clinical outcomes.
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Disease Classification Using Random Forest Models
If diseases have age-group specific signatures, then classifiers trained on the same age-group would have significantly better performance when tested on the same age-group as compared to that when tested on different age-groups. To evaluate the performance of disease classifiers trained on one age-group on disease prediction in either the same or different age-groups , we adopted the following strategy . For each disease-age group combination, we performed 100 iterations, such that in each iteration, we trained the classifier on a subset of disease and the same number of control samples . The evaluation of each of these disease classifiers for Same Age-group and Different Age-group classification was then performed using two approaches as mentioned below.
For a given disease, to ensure that the observed changes were not artefactual consequences of differences in sizes of training and testing subsets, we kept the training and testing subset sizes constant across all training age-groups. The classification AUCs, Specificities and Sensitivities were computed using the various modules in the pROC package.
The Human Microbiome Project
The Human Microbiome Project was supported by the National Institutes of Health Common Fund from 2007 through 2016, with the mission of generating resources that would enable the comprehensive characterization of the human microbiome and analysis of its role in human health and disease. This area of the website focuses on the first of a two-phase effort, frequently referred to as HMP1, which ran from 2008 through 2013. From the Common Fund website:
The Human Microbiome Project has transitioned from Common Fund support. Common Fund programs are strategic investments that achieve a set of high-impact goals within a 5-10 year timeframe. At the conclusion of each program, deliverables transition to other sources of support or use by the broader scientific community. The HMP was supported by the Common Fund from 2007 to 2016. Non-HMP investment in microbiome research at the NIH has increased over forty-fold since the inception of the HMP and spans over 20 of the NIH Institutes and Centers. Please note that since the HMP is no longer supported by the Common Fund, the program website is being maintained as an archive and will not be updated on a regular basis.
HMP1 characterized the microbial communities found at several different sites on the human body: nasal passages, oral cavity, skin, gastrointestinal tract, and urogenital tract, and examined the role of these microbes in human health and disease. The 5 stated aims of the project were
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New Approaches To Study Microbe
There is still much to understand about how microbe-microbe, inter-kingdom microbial interactions, and microbial community level interactions occur, and how these interactions may play a role in human health and disease. Some of these knowledge gaps would benefit from studies of cultured microorganisms of human-associated microbes but these are still not broadly available. For example, many microbial members identified in the HMP healthy cohort metagenomic reference database do not yet have known cultured representative strains or isolates , and this presents a significant technological gap for microbial physiology studies. This issue is being addressed to some degree in the oral microbiome field through new methods for laboratory cultivation of oral taxa. However, a broader range of microbiological and engineering approaches are needed to isolate and cultivate representative members of the human microbiome.
Reprocessing Of Phase 1 Hmp Sequence Data
A significant portion of HMP analysis data was generated with older tools that are no longer considered current state-of-the-art. Therefore, as part of our work under the CFDE, we will be re-processing all 16S and whole metagenome sequencing data from the first phase of the HMP using new pipelines incorporating state-of-the-art tools, including those described above in the Tools and Protocols Section. New analysis results will be made available through our HMPDACC resource and eventually the CFDE.
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What Is A Healthy Microbiome New Analysis Suggests That One Size Does Not Fit All
The typical healthy person is inhabited with trillions of microbes. To better understand the role of these organisms across our body sites, we must to catalog and analyze what organisms are there and how they interact with our own cells. A new analysis of healthy microbiomes has found that each persons microbiome is unique. Therefore, two healthy people may have very different microbial communities but still be healthy. Strikingly, the researchers found that although unique, certain communities could be used to predict characteristics. For example, whether you were breastfed as an infant and even your level of education could be predicted based on microbial communities across varying body sites. The analysis also showed that microbial communities from varying body sites on the same individual were predictive for others. For example, gut communities could be predicted by examining the oral community, even though these communities are vastly different from each other. Taken together, this new analysis will help pave the way for future studies that can begin to use microbial communities as a basis for personalizing therapies and possibly to assess the risk for certain diseases.
Read the University of Michigan press release here
Watch Dr. Schloss explain his research here
Reference
Ding T, Schloss PD. Dynamics and associations of microbial community types across the human body. Nature. 2014 Apr 16. PMID 24739969