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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Why do people have such different microbiomes from one another?

Discussing a computational approach to answering what controls inter-individual variability in the human microbiome.


Modulating human microbial communities continues to be an area of intense academic and entrepreneurial interest because of the role of the human microbiome in determining health and disease. However, these microbial communities often exhibit great inter-individual variability. Previously, it was unknown whether this was a result of these host-specific effects from a range of lifestyles, physiology, and genetics (i.e. host-specific) or whether it is a result of differences in the set of colonizing organisms (i.e. host-independent). An understanding of such dynamics would allow for improved modulation of the microbiome because we would then be able to determine whether therapies needed to be individually personalized or if they could be general, respectively.

At this week’s journal club at 3:00pm on Friday, July 29 in MUMC 3N10A we will discuss the “Universality of Human Microbial Dynamics” by Bashan et al. In their publication, they apply a new computational method to real data from the Human Microbiome Project and Student Microbiome Project to determine whether the ecological dynamics of several human microbial communities is host-specific or host-independent.

I look forward to discussing:

  1. The methodology behind their computation approach (DOC analysis)
  2. The authors’ application of DOC analysis to real data
  3. The application of DOC analysis to other microbiome data (e.g. cystic fibrosis)

We will continue the conversation at the Phoenix at 4!

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Ten percent scientist: Developing your science communication skill set

What does it take to be a scientist in addition to collecting and analyzing data?

Since most people aren’t looking in on your lab mice or peering at the graphs on your computer screen, they rely on you to shape the meaning of your work. In the competitive world of academia, where the ‘impact’ of publications is becoming just as important as the quantity of publications, communication skills are becoming a key part of a scientist’s job.

Scientists have traditionally been reluctant to engage with the media and the public. Yet if scientists step back and leave communication in the hands of the media, their work is too often reduced to ‘morning show gossip’. (See John Oliver’s entertaining clip on science hype here.) It’s incumbent on the scientists themselves to communicate about their work responsibly and lead others do the same.

Because of the general excitement over microbiome science and its potential to impact so many aspects of how humans live, results from this field are particularly prone to distortion in the media. Here are some examples of how science communication can go awry (keeping in mind that I’m not trying to pick on any particular news outlet):

Obesity could be contagious, scientists say (based on this study)

Gut bacteria affecting both our cravings and moods to get us to eat what they want: University of New Mexico Study (based on this review/opinion)

Exercise is good for your gut bacteria, too (based on this study)

In the next Human Microbiome Journal Club, I’ll be visiting to lead a discussion on why the next generation of scientists will need to become ten percent scientist, ninety percent communicator. It’s not that you need to spend ninety percent of your time communicating — but that you’ll benefit throughout your career if you constantly think about the role of your work in the wider world.

From my perspective as a science writer, I’ll give you some tips on how to get others interested in your research without resorting to cheap tricks. We’ll cover the different audiences who need to know about your work and how to target each one. I’ll give you some examples of microbiome scientists who are great communicators,  and we’ll talk about a few guidelines on how to start engaging with a minimal commitment of time.

Join us in MUMC 3N10A on Friday, June 3rd, at 3:00 pm. We’ll continue the discussion over drinks afterward!

Science writer Kristina Campbell, from Victoria, BC, is a freelancer whose work has appeared in publications throughout North America and Europe. She consults on microbiome-related medical education projects and works as a web editor for the Gut Microbiota for Health website. Kristina is also co-author of an upcoming cookbook and an Elsevier textbook on nutrition & gut microbiota.

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A network based approach to longitudinal analysis of the microbiota

tileshopAnalysis of the microbiome over time is hard. You can treat the abundance of each bacterial taxa as a separate outcome and look at them all individually but then you lose interactions between them. Worse yet, you can treat each measurement time point as separate, to get a picture of the interactions, but lose the connections between time points.

This week in journal club we will explore an alternative to longitudinal gut microbiome modeling – Network-based methods. After a simple introduction to longitudinal modeling and the different types of network methods we will look at a new implementation of one network method for longitudinal microbiome analysis from McGeachie et al. 2016 called Dynamic Bayesian Networks. In this paper they present the method and try it out on real data (16S rRNA gene profiles) from infants sampled every day in a neonatal intensive care unit. It’s important that you attempt the paper and google some of the terms before attending but I’ll do my best to explain the concepts used so that we can have a lively discussion.

Journal club will be this Friday April 29th from 3 – 4 pm in MUMC 3N10A. Afterward we will retire to the pub for a pint and to talk about a new view of the tree of life or maybe What Is the Tree of Life?

McGeachie MJ, Sordillo JE, Gibson T, Weinstock GM, Liu YY, Gold DR, Weiss ST, Litonjua A. (2016) Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks. Scientific Reports 6.

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April journal club: genomes from metagenomes.

Research into the composition and effect that the human microbiome can have on its host has advanced significantly with the application of 16S rRNA gene sequencing techniques and improvements in next generation sequencing technology. Using this marker gene, we can get a pretty good picture of “who” is there using standard, easy-to-use techniques.

However, in order to move forward with mechanistic, functional research, we need to know more than the coarse outline of the bacteria that are present in these communities. First, 16S studies only allow for the identification of the bacterial portion of the microbiome (though we have looked at ways of assessing fungal communities as well in past JCs). Additionally, 16S sequencing does not have the ability to differentiate between bacterial strains, and at the length that most high-throughput methods are currently optimized for, sequencing of specific regions of the 16S rRNA gene generally can only identify bacteria accurately to the genus level.

Inevitable advances in the biology and sequencing will improve 16S sequencing in the coming years; however, another interesting avenue for studying the composition of the (human) microbiome is by whole genome shotgun metagenomics. This method is advantageous in that they give us more biological information about a sample by identifying the genes that are present, and non-bacterial components such as viruses, phage, and fungi. However, if we do not have the computational tools to be able to re-assemble the metagenomic jigsaw puzzle into individual genomes, mechanistic and functional research will still be difficult.

This Friday, we will be examining a semi-recent paper entitled “Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes” where Nielsen et al. design a bioinformatic approach for sub-dividing the jigsaw puzzle of metagenomics into neat little piles, each representing a genome present within the sample.

Together we’ll investigate:

-the what of this algorithm (for a non-bioinformatic audience): what it is, and how it works to disentangle the input information into pseudo-genomes

-the howhow the authors applied this algorithm to a set of gut microbiome samples from which they identified and assembled 238 microbial genomes

For those who stick around to philosophize over beer, we can discuss the future of metagenomic technologies, how it might interact with 16S sequencing for the microbiome centre-stage, and how algorithms like these could be used to explore the human microbiome further.

Details: I will give a short presentation with discussion Friday, April 1st (no joke) at 3:00pm in HSC-3N10A (just outside of the Farncombe Institute). Following, all are welcome to continue discussions at the Phoenix.

Note: You MUST read the paper! This is a more technical paper than we usually pick, but you must make an honest effort to go through the manuscript to attend HMBJC. Highlight areas you are unsure of, and we’ll go through them together!

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Sign up now for the human microbiome journal club

Journal club continues this year with a powerpoint presentation and discussion from 3 – 4 pm in MUMC 3N10A, then continuing the discussion at the Phoenix for those who are interested. See the schedule here and sign up for empty spots! A blog post introducing each paper will go up one week before the presentation and notes on each journal club discussion will be turned into a brief post on the pros and cons of the research discussed. See you all there!

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