Using Sewage to Estimate a City’s Gut Health?

A recent paper by Newton et al compares the microbial community composition in human stool to that of the sewage sludge that it inevitably ends up in. And surprise! The communities looked really similar. Sewage species recaptured most of the human stool species, and was essentially a medley of various gut microbes.

The really cool part is how the authors pitched the application of this information. For a long time now, researchers and doctors have been trying to find a way to diagnose a patient’s health using their gut microbiota. But this is an arduous and expensive task, and is made somewhat impossible by the sheer diversity between individual gut microbiota. Sewage provides something like an average of a city’s gut. This “average” can then be used to examine health trends among members of the community. The study found that percent obesity in the city explained a small but significant part of community variation. Although a loose association, this shows promise in using sewage to approximate the gut microbiota health of a specific population.

Hurrah for stool and sewage!

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Gizmodo article regarding discussing data uncertainty in microbiome studies

Really nice article from Sarah Zhang at Gizmodo “Not Even Science Could Explain the Bacteria In My Apartment“.  In it, she discusses the results of her participation in the Wildlife of Our Home study, the NY metagenomic data, and the fact that we’re still in the “exploration” phase of DNA-based microbial ecology.

I think it’s great to see an emphasis on what we don’t know… since in this field what we don’t know vastly exceeds what we do know.

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Cool: James Clemens High School-Hudson Alpha Classroom Microbiome Project

I love the idea behind this:

And see the associated story in AL.Com by Lee Roop Scientists study the microbiome – the bacteria, viruses and fungi – living in an Alabama high school.  Basically this is a partnership between Hudson Alpha and a local school on doing the microbiome of their classroom.

Though I would note – some the statements in the article are not quite accurate (e.g., “No one has ever looked at the types of bacteria that are present in a school, so this really gives insight into something that a lot of people of wondering but have never actually looked at.” and “It is one of the first examples of taking cutting-edge science and putting it into students’ hands to help them solve real-world science problems”).  But I will let these things slide here since it is great to see this happening in a high school class.

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Fungal amplicons

A recent post highlighted issues with analyzing fungal ITS data, and that inspired my labmate Sydney Glassman and me to want to share our experiences with using amplicons to characterize fungal communities. We are very excited that people are interested in delving into the wonderful world of fungi, and we wish to share our love of mycology with others! Fungi can have really interesting ecologies, and the field recently has had a lot of success with developing tools for studying environmental fungi.

  • First, the general. While ITS is the universal barcode for fungi, there are, of course, issues with it and the primers used to isolate it. There are multiple, potentially different, copies of ITS per species, and the typical primers show taxonomic bias in what they target – for example, Cantharellus may be lost. Plus, there are two variable sections within the ITS region. Given the sequence read length of current technologies, only one is typically targeted, but there is debate over whether it is best to amplify ITS1 or ITS2 . Our group amplifies ITS1, and we have had good luck with it. The primers we use as well as the specifics of the bioinformatics pipeline that we employ can be found in a recent paper by Smith and Peay.
  • ITS is variable in length, which can make it trickier to merge reads than, for example, 16S. We remove priming/adapter sites and low-quality sequences from the ends of reads, and we have found that this greatly improves the number of reads that can be paired.
  • The variable nature of ITS precludes any sort of alignment across broad groups of fungi, and thus fungal analyses are taxonomic rather than phylogenetic (i.e. no UniFrac). There are efforts afoot to change that, but currently, if you are interested in doing phylogenetics, you would need to target another region than ITS, most often the ribosomal small subunit (18S).  Meanwhile, for analyzing ITS, many of the default settings in QIIME, for instance, use a phylogenetically-informed process, so its important to use flags to mask alignments/trees.
  • The ITS database for fungi typically used is the UNITE database, which began as a database of ectomycorrhizal fungi. While it has expanded greatly over the years, there are still many lineages of fungi yet to be represented or named (beyond “uncultured environmental clone”) in the database.
  • After taxonomic assignment, many OTUs may be unassigned (for example, they appear as “No Blast Hit”). In our experience when we BLAST these OTUs by hand on GenBank, they are bacteriophage or chimeras. So, we tend to remove OTUs that are unassigned after taxonomic identification.
  • Some fungi have two taxonomic names: one for when they described in their sexual stage and another in the asexual stage, before molecular approaches revealed that this was one species, not two. This legacy remains in the database, so the richness of fungi can seem inflated. For example, the sexual stages of some Aspergillus species were named as Eurotium, and both of these genera appear in the database.

We hope that sharing our experiences facilitates the inclusion of fungi across broad ecological settings.

 

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Well, I guess this means microbiome engineering has arrived (sort of)

Just got pointed (by Laurie Garrett) to this call for proposals from the Bill and Melinda Gates Foundation: Addressing Newborn and Infant Gut Health Through Bacteriophage-Mediated Microbiome Engineering.

Some key lines from the call:

  • A growing body of evidence suggests that healthy gut function early in life plays a significant role in adult wellbeing.
  • It is further becoming increasingly clear that the gut microbiome in newborns and infants plays a significant role in gut health and therefore child development.
  • We are therefore looking for an innovative new way of manipulating and evaluating the gut microbiome in newborns and infants, with a particular focus on reducing environmental enteropathy in low-resource settings.
  • Such studies can be enabled by the development of a tool that would allow the specific perturbation of native microbiome communities in newborns and infants.
  • Bacteriophage-based strategies may address many of the challenges above,
  • The goal of this topic is to support all stages of development of bacteriophage-mediated strategies for microbiome engineering in children under two years of age, as a means to reduce the number of cases of environmental enteropathy in low-resource settings.

For more see the call.

I like many of hte ideas implied in this call.  I think they are right that there is enormous potential in using phage as a means to manipulate microbiomes.  There is still an enormous amount of work to be done in this area, but glad to see the Gates Foundation pushing it forward.  And though this call is focused on human microbiomes I think the same concept could be applied to other animal microbiomes, to plant microbiomes, to microbial communities including, for example, those of our built environment.

 

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Bad-omics

This is a short commentary on -omes, in the spirit of reminding.

-omes are the collections of all things in a class associated with an instance of another class. For example, all the genes in a single organism is the ‘genome.’ Because of a variety of complexities in the definition of a gene since 1920, ‘genome’ is tricky, but because it is one of the earliest ‘-omes’ the example is important.

Metabolomes are of all metabolites in an organism. Transcriptomes are all the transcripts. Microbiomes are all the microbes associated with an organism or location (at a specific time). A subset, viromes, are all the viruses.

Meta-omes are the collections of those things in a particular population of the second class. Metagenomes are all the genes in a population of organisms. Metamicrobiomes could be the set of microbes across a population of hosts or locations.

Badomics is making up -omes that are nonsense or pointless (http://www.gigasciencejournal.com/content/1/1/6). The usual motivation is that -ome and -omics sound cool and there is some authority by association. One could imagine that the toiletome is all the toilets in a building and a metatoiletome would be all the toilets in a development, town, or other community. However, this seems like a pretty silly nomenclature (for plenty of silliness, http://www.ark-genomics.org/badomics-generator).  Atmospheromics would be even sillier, because for any planet there is at most one atmosphere.  A mathematician can logically state that both the empty set and the set containing one element exist – ah, mathematicians (http://www.mathmos.net/misc/jokes.html) – but that doesn’t make the word useful.  It would be even worse to use the word atmospherome to refer to the set of all flying animals on a planet.

The ome that has me presently outraged is the ‘vaginome’ (http://motherboard.vice.com/read/enter-the-vaginome). I cannot denigrate this terminology sufficiently without being indecorous.  There is no sensible vaginome. Some humans have one vagina, many have none, and the cases of more than one are at best very few. To refer to the bacterial or microbial community of the vagina as the vaginome is at best a basic misunderstanding of scientific terminology.  If it were possible to retract a word from journalism, that would be the appropriate action at this time.

 

 

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Rob Knight’s lab moves to UCSD

Although many readers may already know, I’d like to announce that Dr. Rob Knight recently moved his laboratory from the University of Colorado at Boulder to the University of California at San Diego. This has been an exciting move for Rob and those in his group.

The laboratory is now physically located in the brand new Biomedical Research Facility on UCSD’s School of Medicine campus.  Several other high profile investigators are also located at UCSD, such as Larry Smarr, Pavel Pevzner, Trey Ideker, Pieter Dorrestein, and Karsten Zengler, presenting the Knight lab with the opportunity to collaborate on a number of different aspects of microbiome research, from bacterial culturing to metagenomic, metatranscriptomic, and metametabolimic data production and analysis. Notably, the Knight lab has access to the San Diego Supercomputer Center (SDSC), which routinely has systems on the Top 500! We’re very excited to keep the microbe.net community updated as we continue to study not only the built environment, but several microbial systems, in this amazing new location!

 

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Building science measurements in the Hospital Microbiome Project: Part 2

The University of Chicago Medicine’s new Center for Care and Discovery

 

Back in October 2013 I wrote a blog post here called “Building science measurements in the Hospital Microbiome Project: Part 1” where I described the types of building environmental and operational measurements we were making at the time as part of Jack Gilbert’s Sloan-funded Hospital Microbiome Project (Jeff Siegel at the University of Toronto also played a major role in these measurements and in bringing our team on to the project). Fast forward to present day and we’ve since collected all of our data — we actually finished data collection about a year ago — and published a paper in PLoS ONE, “Spatial and temporal variations in indoor environmental conditions, human occupancy, and operational characteristics in a new hospital building,” which is available online now. Here I will briefly summarize our key findings.

As a quick reminder, our team worked to measure a variety of indoor environmental conditions, human occupancy, and operational characteristics in 10 patient rooms and 2 nurse stations across 2 floors of the hospital, which we hypothesize may help explain some of the differences in microbial communities that are observed by Jack’s team over the course of the sample year. Building science measurements included environmental conditions (indoor dry-bulb temperature, relative humidity, humidity ratio, and illuminance) in the patient rooms and nurse stations; differential pressure between the patient rooms and hallways; surrogate measures for human occupancy and activity in the patient rooms using both indoor air CO2 concentrations and infrared doorway beam-break counters; and outdoor air fractions in the heating, ventilating, and air-conditioning systems serving the sampled spaces. We also utilized passive sampling of airborne microbes using a thin sheet of HVAC filter media installed over the return grilles in the rooms.

Hospital room layouts with building science sensor and microbial sampling sites

 

The data began pouring in February of 2013 and we finished collection in mid-January of 2014. At the end of the nearly yearlong monitoring period we ended up with approximately 100,000 time-series building science data points associated with about 80 different variables throughout the hospital and patient rooms for a total of over 8 million data points! This was a HUGE built environment data collection effort — one of the largest long-term building science data collection campaigns that I’ve seen. To give you an idea of how much and what kinds of data were collected, I’ve provided a figure below showing weekly averages of environmental conditions in the patient rooms and nurse stations measured over the duration of the project.

Weekly averages of environmental conditions in the patient rooms and nurse stations measured over the duration of the project.: (a) temperature, (b) relative humidity, (c) humidity ratio, and (d) illuminance. Rooms 101-105 are on the 9th floor; rooms 201-205 are on the 10th floor. Room 100 and 200 are the nurse station locations on the 9th and 10th floor, respectively. Weeks are counted from the week of hospital opening (i.e., week 0). White areas represent missing values. Values along the x-axes correspond to room identification numbers.

Briefly, the main findings from this work include:

  • Indoor temperature, illuminance, and human occupancy/activity were all weakly correlated between rooms, suggesting that occupants of otherwise very similar rooms maintained control of their indoor temperatures and lighting levels, and people entered and exited their rooms in very different patterns of intensity
  • Relative humidity, humidity ratio, and outdoor air fractions showed strong temporal (seasonal) patterns and strong spatial correlations between rooms, suggesting that humidity and ventilation rates were largely governed by the mechanical systems serving each floor and did not vary from room to room
  • Differential pressure measurements confirmed that all patient rooms were operated at neutral pressure, per the hospital’s design specifications
  • The patient rooms averaged about 100 combined entrances and exits per day, which suggests they were relatively lightly occupied compared to higher traffic environments (e.g., retail buildings) and more similar to lower traffic office environments; however, there was a broad distribution in these data
  • There were also clear differences in several parameters before and after the hospital was occupied with patients and staff: air temperatures, relative humidity, and occupant activity were all higher after the hospital opened

You can also find a blog post about this project on my Built Environment Research Group website and on the Hospital Microbiome Project website. It’s been a pleasure working on this project — stay tuned for future publications coming from this project, including results from the DNA sequencing efforts.

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New paper on diseased vs healthy infants in a NICU, possible impacts for future hospital microbiome work

This week in eLife, our lab published a study entitled Gut bacteria are rarely shared by co-hospitalized premature infants, regardless of necrotizing enterocolitis (NEC) development. Spearheaded by a talented Banfield Lab post-doc, Tali Raveh-Sadka, in collaboration with Michael Morowitz’s Lab, the study aimed to find the causative agent in an outbreak of NEC cases that happened last summer. NEC is a life threatening gastrointestinal disease that primarily afflicts preterm infants and the cause of disease remains cryptic. While the hypotheses and aims of the study were human focused, the results have a direct impact on how the readership of microBEnet might view built environment microbial communities. But first, the main findings from the study:

Hypotheses:

  1. The gut microbiome of infants that develop NEC will share a subset of microbial strains that likely cause or contribute to the onset of NEC, while co-housed healthy infants will not share these strains.
  1. The recovery of whole genomes necessary for comparison of microbial populations at the strain-level is achievable within a clinically relevant time frame.

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The Pittsburgh Water Microbiome Project

This is a quick post to introduce a project I have been developing over the past year and a half, called the Pittsburgh Water Microbiome (PWM) Project. I am aware of some other similar citizen science projects out there, so the goal of this post is to receive some feedback and advice, and open this idea up to potential collaboration and expansion.

In the PWM project, middle-school students from a Pittsburgh Public Schools enrichment program take water samples from throughout Pittsburgh. Originally, students were only sampling drinking water from their kitchen taps, but moving forward I hope to have students sample water from throughout Pittsburgh (e.g., our ‘Three Rivers’). Following sample collection, sample microbial ecology is analyzed by 16S rRNA sequencing by undergraduate students in my laboratory. This experience is not typical for engineering undergraduates, so it helps to train these students in biology but also requires a significant amount of instruction. Microbial ecology results are then shared with the middle-school students and integrated into a larger part of their curriculum on the ‘unseen world’. Our project is also highlighted in an interactive kiosk at our local science museum (The Carnegie Science Center) exhibit entitled H2Oh! Why Our Rivers Matter. On the kiosk, visitors can click through to explore the microbial ecology of samples from different locations, with some general information about the different microbes found at those locations.

A few lessons I have learned so far:

  1. You must be willing to adapt.

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