Are bacteria important? What do we get by analyzing microbiomes?

By Leila Lemos, PhD candidate, Fisheries and Wildlife Department, OSU

As previously mentioned in one of Florence’s blog posts, the GEMM Lab holds monthly lab meetings, where we share updates about our research and discuss articles and advances in our field, among other activities.

In a past lab meeting we were asked to bring an article to discuss that had inspired us in the past to conduct research in the marine field or in our current position. I brought to the meeting a literature review regarding methodologies to overcome the challenges of studying conservation physiology in large whales [1]. This article discusses different non-invasive or minimally invasive matrices (e.g., feces, blow, skin/blubber) that can be gathered from whales, and what types of analyses could be carried out, as well as their pros and cons.

One of the possible analyses that can be performed with fecal samples that was discussed in the article is the gut microflora (i.e., bacterial gut community) via genetic analysis. Since my PhD project analyzes fecal samples to determine/quantify stress responses in gray whales, we have since discussed the possibility of integrating this extra parameter to our analysis.

But… what is the importance of analyzing the gut microflora of a whale? What is the relationship between microflora and stress responses? Should we really use our limited sample size, time and money to work on this extra analysis? In order to be able to answer all of these questions, I began reading some articles of the field to better understand its importance and what kind of research questions this analysis can answer.

The gut of a mammal comprises a natural habitat for a large and dynamic community of bacteria [2] that is first developed in early life. Colonization of facultative bacteria (i.e., aerobic bacteria) begins at birth [3], and later, anaerobic bacteria also colonizes the gut. In humans, at the age of 1 year old, the microbiome should have a stable adult-like signature (Fig. 1).

Figure 01: Development of the microbiome in early life.
Source: [3]

The gut bacterial community is important for the physiology and pathology of its host and plays an important role in mammal digestion and health [2], responsible for many metabolic activities, including:

  • fermentation of non-digestible dietary residue and endogenous mucus [2];
  • recovery of energy [2];
  • recovery of absorbable nutrients [2];
  • cellulose digestion [4];
  • vitamin K synthesis [4];
  • important trophic effects on intestinal epithelia (cell proliferation and differentiation) [2];
  • angiogenesis promotion [4];
  • enteric nerve function [4];
  • immune structure [2];
  • immune function [2];
  • protection of the colonized host against invasion by alien microbes (barrier effect) [2];

Despite all the benefits, the bacterial community might also be potentially harmful when changes in the community composition (i.e., dysbiosis) occur due to the use of antibiotics, illness, stress, aging, lifestyle, bad dietary habits [4], and prolonged food and water deprivation [5]. Thus, potential pathological disorders might emerge when the microbiome community changes, such as allergy, obesity, diabetes, autism, multisystem organ failure, gastrointestinal and prostate cancers, inflammatory bowel diseases (IBD), and cardiovascular diseases [2, 4].

Changes in gut bacterial composition may also alter the brain-gut axis and the central nervous system (CNS) signaling [3]. More specifically, the core pathway affected is the hypothalamic-pituitary-adrenal (HPA) axis, which is activated by physical/psychological stressors. According to a previous study [6], the microbial community in the gut is critical for the development of an appropriate stress response. In addition, the microbial colonization in early life should occur within a certain time window, otherwise an abnormal development of the HPA axis might happen.

However, the gut microbiome can not only affect the HPA axis, but the opposite can also occur [3]. Signaling molecules released by the axis can alter the gastrointestinal (GIT) environment (i.e., motility, secretion, and permeability) [7]. Stress responses, as well as diseases, may also alter the gut permeability, causing the bacteria to cross the epithelial barrier (reducing the overall numbers of bacteria in the gut), activating immune responses that also alter the composition of the bacterial community in the gut [8, 9].

Figure 02: Communication between the brain, gut and microbiome in a healthily and in a stressed or diseased (mucosal inflammation) mammal.
Source: [3]

Thus, when thinking about whales, monitoring of the gut microflora might allow us to detect changes caused by factors such as aging, illness, prolonged food deprivation, and stressful events [2, 5]. However, since these are two-way factors, it is important to find an association between bacterial composition alterations and stressful events, such as the presence of predators (e.g., killer whales), illness (e.g., bad body condition), prolonged food deprivation (e.g., low prey availability and high competition), noise (e.g., noisy vessel traffic, fisheries opening and seismic surveys), and stressful reproductive status (e.g., pregnancy and lactating period). Examination of possible shifts in the gut microflora may be able to detect and be linked to many of these events, and also forecast possible chronic events within the population. In addition, the bacterial community monitoring study could aid in validating the hormone data (i.e., cortisol) we have been working with.

Therefore, the main research questions that arise in this context that can aid in elucidating the stress physiology in gray whales are:

  1. What is the microflora community content in guts of gray whales along the Oregon coast?
  2. Is it possible to detect shifts in the gut microflora from our gray fecal samples over time?
  3. How do gut microflora and cortisol levels correlate?
  4. Am I able to correlate shifts in gut microflora with any of the stressful events listed above?

We can answer so many other questions by analyzing the microbiome of baleen whales. Microbiomes are mainly correlated with host diet [10], so the composition of a microbiome can be associated with specific diets and functional gut capacity, and consequently, be linked to other animal populations, which helps to decode evolutionary questions. Results of a previous study on baleen whale microbiomes [10] point out that whales harbor unique gut microbiomes that are actually similar to those of terrestrial herbivores. Baleen whales and terrestrial herbivores have a shared physical structure of the GIT tract itself (i.e., multichambered foregut) and a shared hole for fermentative metabolisms. The multichambered foregut of baleen whales fosters the maintenance of the gut microbiome that is capable of extracting relatively unavailable nutrients from zooplankton (i.e., chitin, “sea cellulose”).

Figure 03: The similarities between whale and other terrestrial herbivore gut microbiomes: sea and land ruminants.
Source: [11]

Thus, the importance of studying the gut microbiome of a baleen whale is clear. Monitoring of the bacterial community and possible shifts can help us elucidate many questions regarding diet, overall health, stress physiology and evolution. Thinking about my PhD project, it may also help in validating our cortisol level results. I am confident that a microbiome analysis would significantly enhance my studies on the health and ecology of gray whales.



  1. Hunt, K.E., et al., Overcoming the challenges of studying conservation physiology in large whales: a review of available methods.Conservation Physiology, 2013. 1: p. 1-24.
  2. Guarner, F. and J.-R. Malagelada, Gut flora in health and disease.The Lancet, 2003. 360: p. 512–519.
  3. Grenham, S., et al., Brain–gut–microbe communication in health and disease.Frontiers in Physiology, 2011. 2: p. 1-15.
  4. Zhang, Y., et al., Impacts of Gut Bacteria on Human Health and Diseases.International Journal of Molecular Sciences, 2015. 16: p. 7493-7519.
  5. Bailey, M.T., et al., Stressor exposure disrupts commensal microbial populations in the intestines and leads to increased colonization by Citrobacter rodentium.Infection and Immunity, 2010. 78: p. 1509–1519.
  6. Sudo, N., et al., Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice.The Journal of Physiology, 2004. 558: p. 263–275.
  7. Rhee, S.H., C. Pothoulakis, and E.A. Mayer, Principles and clinical implications of the brain–gut–enteric microbiota axis Nature Reviews Gastroenterology & Hepatology, 2009. 6: p. 306–314.
  8. Kiliaan, A.J., et al., Stress stimulates transepithelial macromolecular uptake in rat jejunum.American Journal of Physiology, 1998. 275: p. G1037–G1044.
  9. Dinan, T.G. and J.F. Cryan, Regulation of the stress response by the gut microbiota: Implications for psychoneuroendocrinology.Psychoneuroendocrinology 2012. 37: p. 1369—1378.
  10. Sanders, J.G., et al., Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores.Nature Communications, 2015. 6(8285): p. 1-8.
  11. El Gamal, A. Of whales and cows: the baleen whale microbiome revealed. Oceanbites 2016[cited 2018 07/31/2018]; Available from:


Challenges of fecal hormone analyses (Round 2): finally in Seattle!

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU

In a previous blog of mine, you could read about the challenges I have been facing while I am learning to analyze the hormone content in fecal samples of gray whales (Eschrichtius robustus). New challenges appeared along the way over the last month, while I was doing my training at the Seattle Aquarium (Fig. 1).

Figure 1: View of the Seattle Aquarium.


My training lasted a week and I am truly grateful to the energy and time our collaborators Shawn Larson (research coordinator), Amy Green and Angela Smith (laboratory technicians) contributed. They accompanied me throughout my training to ensure I would be able to conduct hormonal analysis in the future, and to handle possible problems along the way.

The first step was weighing all of the fecal samples (Fig. 2A). Subsequently, the samples were transferred to appropriate glass tubes (Figs. 2B & 2C) for the next laboratorial step.

Figure 2: Analytical processes: (A) Sample weighing; (B) Transference of the sample to a glass tube; (C) Result from the performed steps.


The second conducted step was the hormone extraction. The extraction began with the addition of an organic solvent, called methanol (CH3OH), to the sample tubes (Fig. 3A & 3B). Hormones leach out from the samples and dissolve in the methanol, due to their affinity for this polar solvent.

Tubes were then placed on a plate shaker (Fig. 3C) for 30 minutes, which is used to mix the substances, in order extract the hormones from the fecal samples. The next step was to place the tubes in a centrifuge (Fig. 3D) for 20 minutes. The centrifuge uses the sedimentation principle, causing denser substances or particles to settle to the bottom of the tube, while the less dense substances rise to the top.

Figure 3: Analytical processes: (A) Methanol addition; (B) Sample + methanol; (C) Plate shaker; (D) Centrifuge.


After this process, the two different densities were separated: the high-density particles of the feces were in the bottom of the tube, while the methanol containing the extracted hormones was at the top. The top phase (methanol + hormones) was then pipetted into a different tube (Fig. 4A). The solvent was then evaporated, by using an air dryer apparatus (Fig. 4B), with only the hormones remaining in the tube.

The third performed step was dilution. A specific amount of water, measured in correlation with sample weight and to the amount of the methanol mixed with each sample, was added to each tube (Fig. 4C). Since the hormones were concentrated in the methanol, the readings would exceed the measurement limits of the equipment (plate reader). Thus, in order to prepare the extracts for the immunoassays, different dilutions were made.

Figure 4: Analytical processes: (A) Methanol transference; (B) Methanol drying; (C) Water addition.


The fourth and final step was to finally conduct the assays. Each assay kit is specific to the hormone to be analyzed with specified instructions for each kit. Since we were analyzing four different hormones (cortisol, testosterone, progesterone, and triiodothyronine – T3) we followed four different processes accordingly.

First, a table was filled with the identification numbers of the samples to be analyzed in that specific kit (Fig. 5A). The kit (Fig. 5B) includes the plate reader and several solutions that are used in the process to prepare standard curves, to initiate or stop chemical reactions, among other functions.

A standard curve, also known as calibration curve, is a common procedure in laboratory analysis for determining the concentration of an element in an unknown sample. The concentration of the element is determined by comparison with a set of standard samples of known concentration.

The plate contains several wells (Fig. 5C & 5D), which are filled with the samples and/or these other solutions. When the plate is ready, (Fig.5D) it is carried to the microplate reader that measures the intensity of the color of each of the wells. The intensity of the color is inversely proportional to the concentration of the hormone in both the standards and the samples.

Figure 5: (A) Filling the assay table with the samples to be analyzed; (B) Assay kit to be used; (C) Preparation of the plate; (D) Plate ready to be read.


Since this is the first fecal hormone analysis being performed in gray whales, a validation process of the method is required. Two different tests (parallelism and accuracy) were performed with a pool of three different samples. Parallelism tests that the assay is measuring the antigen (hormone) of interest and also identifies the most appropriate dilution factor to be used for the samples. Accuracy tests that the assay measurement of hormone concentration corresponds to the true concentration of the sample (Brown et al. 2005).

This validation process only needs to be done once. Once good parallelism and accuracy results are obtained, and we have identified the correct dilution factor and approximate concentration of the samples, the samples are ready to be analyzed. Below you can see examples of a good parallelism test (parallel displacement; Fig. 6) and bad parallelism tests (Fig. 7) that indicate no displacement, low concentration or non-parallel displacement; and a good accuracy test (Fig. 8).

Figure 6: Example of a good parallelism test. The dark blue line indicates the standard curve; the pink line indicates a good parallelism test, showing a parallel displacement; and the ratios in black indicate the dilution factors.
Source: Brown et al. (2005)


Figure 7: Examples of bad parallelism tests. The dark blue line indicates the standard curve; the light blue line is an example of no displacement; the pink line is an example of low concentration of the sample; and the green line is an example of non-parallel displacement.
Source: Brown et al. (2005)


Figure 8: Example of a good accuracy test while analyzing hormone levels of pregnanediol glucuronide (Pdg) in elephant urine. The graph shows good linearity (R2 of 0.9986) and would allow for accurate concentration calculations.
Source: Brown et al. (2005)


After the validation tests returned reliable results, the samples were also analyzed. However, many complications were encountered during the assay preparations and important lessons were learned that I know will allow this work to proceed more smoothly and quickly in the future. For instance, I now know to try to buy assay kits of the same brand, and to be extremely careful while reading the manual of the process to be performed with the assay kit. With practice over the coming years, my goal is to master these assay preparations.

Now, the next step will be to analyze all of the results obtained in these analyses and start linking the multiple variables we have from each individual, such as age, sex and body condition. The results of this analysis will lead to a better understanding of how reproductive and stress hormones vary in gray whales, and also link these hormone variations to nutritional status and noise events, one of my PhD research goals.


Cited Literature:

Brown J, Walker S and Steinman K. 2005. Endocrine manual for reproductive assessment of domestic and non-domestic species. Smithsonian’s National Zoological Park, Conservation and Research Center, Virginia 1-69.