“Every man desires to live long; but no man would be old.” (Jonathan Swift)

In the coming decades, the populations of all developed countries will become substantially older. Not alone keeping older adults healthy will be of the highest importance in order to keep costs manageable for the health care system but also we personally want to age healthy. A special age group are centenarians (100-104 years old) and semi-supercentenarians (105-109 years old), who aged in a healthy way. Many studies are about the genetics of centenarians but less in know about their gut microbiota.

The study from Kong et al. 2016 combined gut microbiota data of Chinese long-living people and with results from a former Italian study of centenarians and semi-supercentenarians to identify gut-microbial signatures of healthy aging. We will discuss the question: Can gut microbiota help provide longevity?

Journal club will be this Friday November 25th from 3 – 4 pm in MUMC 3N10A.

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Working Group: New location

Our weekly, drop in Microbiome Working Group sessions now have a new home! From now until the foreseeable future, working group will be in MDCL 2249. Come by with your laptop to discuss issues, theory, or simply meet others working with microbiome data.

Hope to see you there!

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Special event: How to give a journal club.

UPDATE: Mark has provided us with his slides ahead of time in case you’d like to follow along on your own computer/handouts. The slides are available here: journal-club-tutorial

UPDATE: We have a room: this event will occur Nov 11th at 3pm in 3N44A.

Journal clubs, such as ours, are popular during Graduate and Postdoc studies. And for good reason: journal clubs help us learn how to read peer-reviewed manuscripts, interpret the results, and – sometimes – be critical of the data. However, learning how to effectively lead a group of peers in this activity is an important skill which is often not formally taught as part of our academic training.

Towards this end, the Human Microbiome Journal Club is very excited to announce that on November 11th at 3pm in 3N44A we will have a special guest seminar  given by Dr. Mark McDermott. Dr. McDermott is a recently retired Professor of Pathology and Molecular Medicine here at McMaster. Throughout his career, Dr. McDermott has published >40 peer-reviewed manuscripts, including a seminal paper in mucosal immunology. On the 11th, Dr. McDermott will use this seminal paper (McDermott MR, & Bienenstock J.”Evidence for a Common Mucosal Immunologic System.“) to demonstrate how to present an effective journal club. Don’t worry if you’re microbe-foccused project hasn’t come across the mucosa literature yet; this event will focus more about how to present, than the in’s and out’s of mucosal immunology.

We look forward to drawing on Dr. McDermott’s experience and expertise at this special HMJC event! Formal presentation will begin at 3:00pm sharp (room TBA), followed by a chance to chat with Dr. McDermott at the Phoenix.

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Human Phageome – Getting to know bacterial viruses inside o’me

Study of the human microbiota has been dominated by assessing the bacterial communities existing at different surfaces, primarily in the gastrointestinal tract.  While more recent studies have begun to assess fungal and viral communities, the extent to which these impact health are far from being known.  Bacteriophages are viruses that infect and complete their life cycle in bacterial cells.  While many biological tools have arisen from the examination of phages, the naturally occurring phages within our bacterial microbiota have not been extensively studied.  A recent article has examined the healthy human gut phageome, suggesting a core phageome in healthy people.

Please join us in MUMC 3N10A on October 28 from 3-4pm to discuss this article.  The objectives of this journal club will be: 1) to assess the methods involved in assessing the DNA phages living in the gut; 2) discussing their determination of a  “core” phageome, and 3) discussing the impacts of phage ecology in healthy and diseased people.

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Special event: How to give a journal club.

UPDATE: We have a room: this event will occur Nov 11th at 3pm in 3N44A.

Journal clubs, such as ours, are popular during Graduate and Postdoc studies. And for good reason: journal clubs help us learn how to read peer-reviewed manuscripts, interpret the results, and – sometimes – be critical of the data. However, learning how to effectively lead a group of peers in this activity is an important skill which is often not formally taught as part of our academic training.

Towards this end, the Human Microbiome Journal Club is very excited to announce that on November 11th at 3pm in 3N44A we will have a special guest seminar  given by Dr. Mark McDermott. Dr. McDermott is a recently retired Professor of Pathology and Molecular Medicine here at McMaster. Throughout his career, Dr. McDermott has published >40 peer-reviewed manuscripts, including a seminal paper in mucosal immunology. On the 11th, Dr. McDermott will use this seminal paper (McDermott MR, & Bienenstock J.”Evidence for a Common Mucosal Immunologic System.“) to demonstrate how to present an effective journal club. Don’t worry if you’re microbe-foccused project hasn’t come across the mucosa literature yet; this event will focus more about how to present, than the in’s and out’s of mucosal immunology.

We look forward to drawing on Dr. McDermott’s experience and expertise at this special HMJC event! Formal presentation will begin at 3:00pm sharp (room TBA), followed by a chance to chat with Dr. McDermott at the Phoenix.

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Follow-up: microbiome & obesity?

Last week, PhD student Daphnée Lamarche led us through Marc A. Sze’s and Patrick D. Schloss’s recent paper: “Looking for a Signal in the Noise: Revisiting Obesity and the Microbiome.” We really enjoyed how well-written this paper was; however, we had a hard time interpreting some of the Figures. For example, the text indicates that obese individuals had significantly lower alpha diversity scores in 7 instances in the original studies; however, the colouring of Figure 2 indicates that obese individuals had higher diversity in these 7 instances. Further, the AUC calculations for each study in Figure 4 don’t seem to match the coloured lines observed in the Figure. We spent a long time trying to understand these discrepancies and couldn’t come to an intelligent conclusion- we would love to hear from anyone who could help us understand these 2 Figures better!

More importantly to our group, however, was the message of this paper. We think that this type of study helps in moving the field of microbiome research forward. We hope that this marks the beginning of less descriptive studies with low n-values and instead on properly powered, well-designed studies of the human microbiome.

Update: With the help of@bykriscampbell, we got in touch with first-author @marc_sze last night and got a few answers to our questions which are summarized here.

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Is there really an association between microbiome and obesity?

Since the last decade, the microbiome has gained in popularity in human medicine among others. This popularity can be explained by the apparition of next generation sequencing technologies which have revolutionized our way to study the microbes inhabiting our body. To this day, several studies have proposed a correlation between the microbiota composition and various states and diseases in humans. However with the high variation between humans, obtaining a correlation which stands and could be reproduced in another study involving the same population of individuals is highly challenging.

During this week’s journal club, we will be discussing a recent article from Pat Schloss lab, “Looking for a signal in the noise: Revisiting obesity and the microbiome.” The authors of this paper have performed a meta-analysis of 10 studies involving the microbiome in obesity to re-assess the hypothesis that changes in the microbiota are occurring in those individuals. This article emphasizes the limitations of associating a phenotype to changes in the microbiome composition as well as demonstrating the lack of power of those studies to detect small differences in alpha diversity metrics.

Please join us September 30th at 3h in MUMC 3N10A to discuss the potential limitations of the microbiome studies involving human subject.

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Follow-up: What is the most appropriate method for associating microbiome data with health and disease?

At this month’s journal club, James, an undergraduate student in Jane Foster’s laboratory at McMaster University, led the discussion on how we can better associate microbiome data with clinical data on health and disease. Beginning with a brief introduction to PERMANOVA and GLM testing, we focused on a new regression-based testing method that was described in “An adaptive association test for microbiome data” by Wu et al. (2016), called aMiSPU, or the adaptive microbiome-based sum of powered score.

After an extensive buildup to understand the mathematics behind the method that involved several powerpoint slides, many questions, and a lot of eureka moments, the group was satisfied with much of the paper’s claims. The aMiSPU method provided better power in many cases, especially at detecting differences at low percentages.

Importantly, however, the paper acknowledged that there was no clear cut best method for all data, as there were situations where the aMiSPU was not the optimal test. This highlighted the importance of understanding the central questions being asked before analyzing microbiome data.

Of particular interest to the group was the authors’ analysis of simulated data to compare and contrast several methodologies, including DESeq2 and Kruskal-Wallis. We were surprised by the number of false positives that were created by each testing method, including the aMiSPU (although it did perform much better at detecting true positives and reducing false positives than other tests). An important area for future exploration could be the repetition of this experiment with new simulated data and across a wider span of published tests.

The paper communicated a new approach to detecting differences in the microbiome between clinical metadata and while we were unsure if it would be applicable to all datasets, we did find it an intriguing approach worthy of exploration in our own analyses.

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What is the most appropriate method for associating microbiome data with health and disease?

As DNA-sequencing technologies became cheaper to use, profiling the microbiomes of many different samples became efficient and feasible through the sequencing of highly variable regions on the bacterial 16S rRNA gene. A question that arises very quickly to novices in the domain of microbiome analysis is how to properly interpret 16S microbiome composition data. The counts within the OTU table to be analysed always vary greatly across samples, as an artefact of the sequencing technology. Additionally, the count data for a given bacterial group across samples is highly non-normal and at best somewhat close in distribution to that of a zero-inflated negative binomial random variable. Further complicating interpretation, the data is highly multidimensional in that the number of bacterial groupings (OTUs) greatly outnumbers the sample size. In order to determine if the microbiota are driving disease, regression-based analyses will need to be undertaken. In searching the literature this summer, I found that there is no consensus in how to do regression with 16S microbiome data. Issues arise due to the compositional nature of the data along with the high degree of dimensionality. One of the main benefits of regression is being able to take into account possible covariates and whether or not they, rather the microbiota, are the true drivers of observed differences. This becomes increasingly important in human studies where subjects have not been contained in environments controlled by the investigator.

At this week’s microbiome journal club at 3:00pm on Friday, August 26th in MUMC 3N10A, we will be discussing aMiSPU, a novel regression-based method for microbiome data presented in “An adaptive association test for microbiome data” by Wu et al. in 2016. The paper mainly compares aMiSPU to a similar method known as optimal MiRKAT in how well they perform on both simulated and real data*.

Questions for discussion:

  1. Is the aMiSPU test a valid statistical method of association for microbiome data and is it better than general linear modelling?
  2. Are significant alterations in rare microbes within microbiome studies repeatable and reliable? Should statistical tests of differential abundance be adjusted to detect differences in rare microbiota?
  3. Can microbiome data be accurately simulated, and if so, how important will methods papers on simulated data be for future developments in the field?

Come join for food and drinks afterwards at the Pheonix at 4!

*Note that within the explanation of the aMiSPU test on page 4 of 12 it is briefly mentioned that TaMiSPUu , TaMiSPUw , and TaMiSPU are no longer genuine p values and that a permutation method is used to estimate their p values. If you’re looking to wrap your head around the math, the authors explain this in more detail in a previous paper about aSPU available here under the section “A new class of tests and a data-adaptive test” at the bottom of page 4 and top of page 5 of the pdf. In brief, the permutation method involves randomly rearranging the subjects many times to literally create the null distribution of no association to be tested against. I will be also be explaining it in the presentation, mainly because I find it exciting, but also because aMiSPU involves multiple layers of permutations.

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Follow up: Why do people have such different microbiomes from one another?

This mid-summer human microbiome journal club was small but mighty, with representatives from 3 laboratories, undergraduates to post-doctoral fellows.

On Friday, Saad (future MD-PhD in the Surette lab) led us through a discussion of Universality of Human Microbial Dynamics, Bashan et al. We will admit that it took us a bit of effort to fully understand the author’s definitions of overlap and dissimilarity. However, after a few memorable quotes (“It’s pretty much like… I don’t know”; “…and then you take the square root of that for some f$#%ing reason”) and some pacing of the meeting room, the attendees worked together to truly understand the dissimilarity-overlap curve (DOC) method.

And I’m sure glad we did- because this method appears to be powerful. The first 2 figures of this paper are used to outline DOC and show that the application of this method to real and raw data indicates that there are universal underlying dynamics present within human-associated microbial communities. The authors then apply their method in two ways. First, they use Human Microbiome and Student Microbiome Project data to show that this universality holds in communities associated with the gastrointestinal tract and mouth but that there are less evident in communities of the skin.

Perhaps what the group found most interesting were the results of the last Figure. In Figure 4, the authors used their DOC measure to show a lack of universal dynamics in individuals with recurrent Clostridium difficile infection, citing their disrupted microbiomes as the culprit. However, after these individuals underwent Feacal Microbiota Transplantation, the DOC analysis produced a strong negative slope, suggesting universal dynamics.

This paper displayed a really interesting application of ecological methods and theories to microbiome research. We are excited to see the application of this method in future studying of microbial communities.

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