The Microbiome and Longevity: How gut microbes may impact lifespan

In recent years, the microbiome has been recognized as a major factor affecting the functioning of host organisms, influencing both health and disease. At the same time, aging research has provided many possible interventional strategies aimed at extending the human lifespan. Several studies have shown a link between the human gut microbiota and aging, as well as how the aging process can affect the structure of the microbiota and its homeostasis with the host immune system. Over the past few decades, the nematode C. elegans has become an important organism for studying aging but more recently, it has been used as a model for microbiome research. C. elegans’ short and easily observable lifespan as well as its defined microbiota can be used as a model to further understand the interactions between microbes and host aging.

In their recent paper, Han et al. used a C. elegans model to identify specific mechanisms by which gut bacteria may influence mitochondrial dynamics and aging. They screened for bacterial mutants that affected aging in their C. elegans model, and identified a specific link between a bacterial polysaccharide and worm mitochondrial dynamics. Their screen was able to identify 29 bacterial mutants out of 3983 that led to an extended lifespan in the worms. Two bacterial mutants which increase production of the polysaccharide colanic acid were chosen for further study because they acted independently of certain pathways associated with longevity and aging. The findings of this paper indicate that the makeup of the microbiota may influence aging in the host organism. This raises the possibility of identifying and using bacterial variants associated with healthy aging with the goal of enhancing human longevity.

On July 28th at 3pm in HSC 3N10A I hope to examine the findings and methods presented in this paper as well as discuss a few key points:

  • The advantages and disadvantages of the C. elegans model for microbiome research
  • Microbiome modulation of the aging process
  • The influence of the microbiome on mitochondrial function


Paper Citation: Han, B., Sivaramakrishnan, P., Lin, C. C. J., Neve, I. A., He, J., Tay, L. W. R., … & Herman, C. (2017). Microbial Genetic Composition Tunes Host Longevity. Cell169(7), 1249-1262.

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2 for 1 deal: a response to Waste Not, Want Not and the benchmarking of ANCOM

This week’s microbiome journal club will look at the recent paper by Rob Knight’s group (Weiss et al., 2017) that simultaneously responded to “Waste Not, Want Not” and confirmed ANCOM as an effective differential abundance analysis technique.  More details about these two contributions below.

Questions to be answered and discussed:

  • When to rarefy 16S data
  • The assumptions of various normalisation techniques (DeSeq2, EdgeR, cumulative sum scaling [metagenomeSeq], relative abundance)
  • Is ANCOM an effective tool?
  • Can we trust these simulations?
  • ANCOM versus other tools that adjust for compositional effects: ALDEx2 (Greg Gloor’s group)
  • Potential conflicts of interest in tool development (more users = more funding? – perhaps true of all research outcomes as more citations tends to get more funding)

Come to HSC 3N10A from 3-4pm on Friday, June 23rd to join in the discussion. I will do my best to make this session entertaining so that everyone can have enough energy to actually come to the Phoenix afterwards.

The paper can be found here.

More details:

A response by Rob Knight’s group to “Waste Not, Want Not” finally gets published, three years* after that paper put the very common practice of rarefying (subsampling to common sequencing depth) in microbiome studies into controversial territory. The impact of “Waste Not, Want Not” was very large and created a divide between two groups that develop competitive microbiome analysis pipelines, those behind QIIME (Rob Knight’s group) and those behind the phyloseq R package (Paul McMurdie and Susan Holmes, authors of “Waste Not, Want Not“). McMurdie and Holmes criticise QIIME as a “one size fits all” pipeline, since “it is often encouraged to rarefy counts as a normalizing transformation prior to any/all analysis” (phyloseq FAQ). Weiss et al, 2017 clears up most confusion about the utility of rarefying, suggesting it is the best method to correct for sequencing depth effects when using presence/absence beta diversity metrics such as unweighted UniFrac and binary Jaccard. Weighted metrics such as Bray-Curtis and weighted UniFrac are not affected as much by differences in sequencing depth and can be used after normalizing by relative abundance. Any effect of sequencing depth can be seen by rarefying many times and doing multiple rarefaction PCoA, which is the best way to “Waste Not” and still fix sequencing depth bias.

*This response was originally submitted in October, 2015

The impact of this paper does not stop there, as Weiss and colleagues also benchmarked ANCOM against many other popular differential abundance analysis techniques (DeSeq2, EdgeR, metagenomeSeq, etc.). ANCOM was shown to be the only technique examined to control false discovery rate in the presence of spurious differences associated with the compositional nature of 16S microbiome data. Can we trust their simulations?


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So you want to be a MicrobiomeAnalyst?

Analysis of microbiota composition has been an ongoing pursuit for decades. However, the improved feasibility of next-generation sequencing techniques have greatly increased access to gigabytes of microbiome data. Whether you look at the hypervariable regions of bacterial 16S rRNA gene or metagenomic samples, the amount of data that can be overwhelming. Furthermore, researchers not trained in bioinformatic analysis, including biologists, immunologists, geologists and ecologists are struggling to analyze their own data. While several different platforms have been developed to improve microbiome analysis (ie. QIIME, mothur), these tools still require bioinformatic training.

Researchers from McGill University have attempted to bridge that gap by releasing MicrobiomeAnalyst, a free, web-based tool for in-depth microbiome analysis. This website allows for uploading your own files and give results including bacterial composition, community dynamics, and some statistical comparisons.  This will surely pique the interest of non-bioinformaticians to enhance their microbiome analysis throughput, while also potentially allowing standardization of analysis.  Nonetheless, it is important to assess this tool for its value to the community, especially whether bioinformaticians should: 1) fully endorse this as a tool, 2) “proceed at your own risk”, or 3) “You should not use this because…”.

The purpose of this journal club will be to assess this tool from several different angles:

  1. Who should be using this?
  2. What are the necessary data inputs for this tool?
  3. When should this be used? Hypothesis generation? Publication quality figures?
  4. How easy is this to use?
  5. Are the tools incorporated into the best tools to use?

Please join us Friday, May 26 3-4pm in HSC3N10A for the discussion of this tool.

Dhariwal, A., Chong, J., Habib, S., King, I., Agellon, LB., and Xia. J. (2017) “MicrobiomeAnalyst – a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data”
Nucliec Acids Research


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Get That Weak Staph Outta Here!

Certain skin commensal bacteria protect individuals against Staphylococcus aureus colonization

We’re all covered in skin (hopefully) and our skin is covered with microbes. For the most part, this skin microbiome coexists with us and remains stable, despite our constant contact with other people and our environment.

However, when a person has atopic dermatitis (a subtype of dermatitis, or what’s commonly referred to as eczema), their skin microbiome is different than non-atopic dermatitis subjects. Coincidentally, atopic dermatitis patients are much more likely to be colonized with Staphylococcus aureusStaphylococcus aureus is not only the preeminent cause of skin infections in these patients, but has also been linked to the immune dysfunction intrinsic to atopic dermatitis.

Gallo and colleagues recently showed that certain bacteria on the skin of non-atopic dermatitis subjects secrete antimicrobial peptides that selectively targeted S. aureus. These microbes were significantly reduced on the skin of atopic dermatitis patients. Culturing of these low abundance strains from the patients and a re-application of them on their respective arms at higher abundance decreased S. aureus colonization. Utilizing a range of techniques, the authors elucidate a role of the healthy skin microbiome in pathogen defence and apply their findings to carry out a pilot precision medicine trial on atopic dermatitis patients.

On April 28, 2017 at 3PM in HSC 3N10A, I will discuss this paper and its implications for microbiome research. I hope to:

  • Critically appraise the findings of the journal article
  • Discuss pathways to translation of microbiome research into clinical practice and expectations of the public, policymakers, researchers, industry, and clinicians

Paper Citation: Nakatsuji, T. et al. Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Sci. Transl. Med. 9, 1–12 (2017).

P.S. Go Raptors!

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Genomes from metagenomics: pulling the needles from the haystack.

Shotgun whole genome sequencing revolutionized how we study single, microbial isolates. By breaking the genome into small reads in vitro, we are able to parallelize sequencing and decrease costs before bioinformatic assemblers put the puzzle back together again in silico. However, re-building the genomic puzzle gets more complicated in metagenomic samples and bioinformatic tools are still being developed in order to improve our abilities to re-compile multiple genomic puzzles from a given sample.

Nadel im Heuhaufen

One way of doing this is to separate the puzzles from each other by organizing metagenomic information into bins which can each be dealt with independently. Many tools exist to separate metagenomic information based on the composition of sequences, and the relative abundance within and across samples; however, we have found that the output of these tools can vary substantially, making biological interpretation of the data difficult.

Recently, Sieber et al. released a possible improvement to these approaches in the DAS Tool. This tool takes the output of multiple binning strategies and dereplicates, aggregates, and scores these to produce an optimal binning output. On March 31st at 3pm in 3N10A, I will lead the Club through this approach. The goals of this journal club will be:

  1. To provide amble background information. Shotgun metagenomic sequencing is not yet as universal as 16S rRNA gene sequencing approaches, so I will make sure to spend time explaining this technique and the accompanying literature to-date.
  2. To assess the DAS Tool compared to other binning strategies in terms of (i) accuracy, (ii) ease-of-use, and (iii) feasibility on our own human microbiota datasets.

Afterwards, I hope to continue with whiteboard discussions of metagenomic sequencing strategies in general… which may relocate to the Phoenix as necessary.

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Introducing ANCOM a new (hopefully better) tool for microbiome analysis

The most frequently asked question in current analyses of different microbiomes is what organism(s) differentiates these two groups. As microbiologist and aspiring bioinformaticians we are all aware that due to the complex community structures of microbiomes, and the resulting complex sequence data, finding the appropriate tools to answer this question isn’t straight forward. Accordingly, this week’s human microbiome journal club will look at a paper from Peddada and colleagues detailing their new tool ANCOM (ANalysis Of Composition Of Microbiomes).
In the paper ANCOM is compared to two other potential methods of group comparison, the t-test and the Zero Inflated Gaussian or ZIG method.
Please come out to the journal club as we will aim to
– Better understand how ANCOM works (including the supplement)
– Assess its strength at understanding some of our results
– Learn to implement ANCOM for our uses
Conversation starts at 3pm in HSC 3N10A and will likely continue at the Phoenix afterwards.
Siddhartha Mandal, Will Van Treuren, Richard A. White, Merete Eggesbø, Rob Knight, Shyamal D. Peddada
Microb Ecol Health Dis. 2015; 26: 10.3402/mehd.v26.27663. Published online 2015 May 29. doi: 10.3402/mehd.v26.27663
PMCID: PMC4450248
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Delivery by C-section Has No Effect on the Infant Microbiome… Wait What?

baby-1867222_960_720Every paper on the infant microbiome starts with known determinants… infant diet (formula or breastfeeding), oral antibiotic use and delivery method. But a new paper just out from Kjersti Aagaard’s group, who you may remember as the placental microbiome folks, looks at the effect of delivery method on the infant microbiome across multiple body sites. Unlike a previous study, where there was an interruption of the transfer of microbes from mom to infant, this paper found no association between delivery by C-section and differences in the infant gut microbiome.

This Friday, January 27th at 3pm in HSC 3N10A, we will take a critical eye to the evidence presented in Chu et al. 2017. Our, no doubt, lively discussion will include:

  • What is a confounder, modifier and covariate?
  • How did they account for confounders and modifiers?
  • How microbial community structure differed in this paper from previous reports?

The discussion will continue at the Phoenix afterward.

Chu, D. M., Ma, J., Prince, A. L., Antony, K. M., Seferovic, M. D., & Aagaard, K. M. (2017). Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat Med, advance online publication. Retrieved from


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A new year for our journal club!

Happy New Year, microbiome fanatics! With a new year, comes more interesting microbiome research for us to tackle in our journal club. Please take the opportunity now to sign up for an available spot on our 2017 calendar by commenting here, or emailing Jen or myself.

We look forward to the improvements in microbiome research that 2017 will have to offer!


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“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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