Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults (2)

When we last left off, I was peering into the -80 freezer at the hundreds of stool samples I would need to analyze. In reality, a lot of experimental design work came on this project before I ever opened up the freezer!

Designing a good experiment was one of the most important things I learned in grad school. Science is already hard enough – you need to set yourself up for success from the beginning by designing a good experiment, whether it’s wet lab or computational. I like to think about what success in this project would look like, and work backwards from success to understand the data I need to collect.

To convincingly prove that a bacterium had transmitted from the microbiome of one patient to the microbiome of another, I needed the following pieces of evidence:

  1. At a given point in time, the bacterial genome was present in the microbiome of the source patient and undetectable in the microbiome of the recipient.
  2. At a future point in time, the bacterial genome was present in the microbiome of the recipient patient, and ideally persisted for multiple future time points.

Through Stanford Hospital, I also had access to a dataset of each patient’s room history. From this, I could find when two patients were roommates. Mapping the overlapping intervals, combined with the list of samples biobanked from each patient, was a challenging data science problem. It took me about a month of work to design an experiment that would give me the best chance of observing patient-patient microbiome transmission, if it was happening.

The wet lab work for this project was long and monotonous. You can read about it in the methods section of the paper, but we did DNA extraction and 10X Genomics linked read sequencing on all of the new samples.

When the new data came back, it was time to get cracking! The processing pipeline and data analysis I had planned would take too long to run on Stanford’s HPC cluster, so I turned to Google Cloud to get everything done with quick parallelization. The process of getting our workflows to run at scale in the cloud was certainly a learning experience, and I wrote a blog post about the effort (two years ago).

After assembling bacterial genomes from hundreds of microbiome samples, comparing strain-level populations with inStrain, and generating massive matrices comparing all sets of genomes in my samples, the true data analysis began. A few key lessons from the data analysis and writing experience have stuck with me, and the challenges made me a better scientist.

  1. Scrutinize your results! When I initially looked for identical bacterial genomes in samples from different patients, I found many “transmission events” that were simply the results of barcode swapping (when samples sequenced on an Illumina machine at the same time experience a small degree of contamination). I was prepared for this outcome, and developed a method to quantify when identical genomes were likely the result of barcode swapping in the linked read data.
  2. Carefully evaluate negative findings. After eliminating all the likely false positive results, I found very few identical genomes between patients, especially antibiotic resistant pathogens. At first, this was an upsetting result. I was really hoping to find lots of transmission between patients who were roommates! However, the lack of pathogen transmission findings allowed me to focus on the potentially more interesting cases of commensal bacteria transmitted between patients. The “negative” finding here turned out to make a more interesting story.


Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults (1)

My final project with the Bhatt Lab is now published! You can find the open access text at Nature Communications. I’m excited to bring this chapter of my research career to a close. The paper contains the full scientific results; here I’ll detail some of the journey and challenges along the way.

Hot off the success of my previous work studying mother-infant transmission of phages in the microbiome, I was eager to characterize other examples transmission between the microbiome of humans. While mother-infant transmission of both bacteria and phages was now understood, microbiome transmission between adults was less clear. There were some hints of it happening in the literature, but nobody had fully characterized the phenomenon at a genomic level of detail that I believed. I’m also not counting FMT as transmission here – while it certainly results in the transfer of microbiome components from donor to recipient, I was more interested in characterizing how this phenomenon happened naturally.

In our lab, we have a stool sample biobank from patients undergoing hematopoietic cell transplantation (HCT). We’ve been collecting weekly stool samples from patients undergoing transplant at Stanford Hospital, and to date we have thousands of samples from about one thousand patients. HCT patients are prime candidates to study gut-gut bacterial transmission, due to a few key factors:

  1. Long hospital stays. The conditioning, transplant and recovery process can leave a patient hospitalized for up to months at a time. The long stays provide many opportunities for transmission to occur and many longitudinal samples for us to analyze.
  2. Roommates when recovering from transplant. At Stanford Hospital, patients were placed in double occupancy rooms when there were not active contact precautions. These periods of roommate overlap could provide an increased chance for patient-patient transmission.
  3. Frequent antibiotic use. HCT patients are prescribed antibiotics both prophylactically and in response to infection. These antibiotics kill the natural colonizers of the gut microbiome, allowing antibiotic resistant pathogens to dominate, which may be more likely to be transmitted between patients. Antibiotic use may also empty the niche occupied by certain bacteria and make it more likely for new colonizers to engraft long-term.
  4. High burden of infection. HCT patients frequently have potentially life-threatening infections, and the causal bacteria can originate in the gut microbiome. However, it’s currently unknown where these antibiotic resistant bacteria originate from in the first place. Could transmission from another patient be responsible?

As we thought more about the cases of infection that were caused by gut-bloodstream transmission, we identified three possibilities:

  1. The microbes existed in the patient’s microbiome prior to entering the hospital for HCT. Then, due to antibiotic use and chemotherapy, these microbes could come to dominate the gut community.
  2. Patients acquired the microbe from the hospital environment. Many of the pathogens we’re interested in are Hospital Acquired Infections (HAIs) and known to persist for long periods of time on on hospital surfaces, in sinks, etc.
  3. Patients acquired the microbe via transmission from another patient. This was the most interesting possibility to us, as it would indicate direct gut-gut transmission.

While it’s likely that all three are responsible to some degree, finding evidence for (3) would have been the most interesting to us. Identifying patient-patient microbiome transmission would be both a slam dunk for my research, and would potentially help prevent infections in this patient population. With the clear goal in mind, I opened the door of the -80 freezer to pull out the hundreds of stool samples I would need to analyze…

More to come in part 2!