Metabolic Pathways

TWiM #211: Bacteria, Colon Cancer, and Fire Blight

Podcast and Annotation Information
  • Annotation by Imandi Mendis, Julia Gaugel, Rebecca Seipelt-Thiemann, and Blythe Janowiak.
  • Podcast audio by TWiM: Listen to TWiM #211 Podcast
  • Podcast transcript by Otter.ai and edited by Harshita Sharma and Grace Helle: Access Podcast Transcripts
  • Papers Discussed:
    • Sobhani I, Bergsten E, Couffin S, Amiot A, Nebbad B, Barau C, de’Angelis N, Rabot S, Canoui-Poitrine F, Mestivier D, Pédron T, et al., 2019. Colorectal cancer-associated microbiota contributes to oncogenic epigenetic signatures. Proc Natl Acad Sci U S A. 116(48):24285-24295. doi: 10.1073/pnas.1912129116.
    • Klee SM, Sinn JP, Finley M, Allman EL, Smith PB, Aimufua O, Sitther V, Lehman BL, Krawczyk T, Peter KA, McNellis TW. 2019. Erwinia amylovora Auxotrophic Mutant Exometabolomics and Virulence on Apples. Appl Environ Microbiol. 85(15):e00935-19. doi: 10.1128/AEM.00935-19.

1. Paper Abstracts

1.1. Snippet paper; discussion starts at 2:44 minutes

The Most Interesting Things (according to students)

  • The microbes found in the crypts of the animal’s intestines accurately reflect the types of microbes present in the fecal matter that was transplanted. This demonstrates how interconnected our body is, allowing us to learn about what is going on inside of our body through less invasive routes of research.
  • Methylated sequences being used as markers for the CMI assay is very interesting. The detection of these methylation patterns has the potential to signal early stages of colorectal cancer, allowing for the ability to make earlier diagnoses. The ability to detect colorectal cancers earlier will allow patients to have more time to battle their cancer before it develops further.
  • The discovery of a test to determine methylation at certain gene promoters can be used to assess whether an individual needs further testing, such as a colonoscopy. This shows how advancements due to research are directly benefiting the population.
  • A carcinogen, azoxymethane, was used as a supplemental environmental stressor to simulate the conditions of those who develop colorectal cancer, that was interesting

“Sporadic colorectal cancer (CRC) is a result of complex interactions between the host and its environment. Environmental stressors act by causing host cell DNA alterations implicated in the onset of cancer. Here we investigate the stressor ability of CRC-associated gut dysbiosis as causal agent of host DNA alterations. The epigenetic nature of these alterations was investigated in humans and in mice. Germ-free mice receiving fecal samples from subjects with normal colonoscopy or from CRC patients were monitored for 7 or 14 wk. Aberrant crypt foci, luminal microbiota, and DNA alterations (colonic exome sequencing and methylation patterns) were monitored following human feces transfer. CRC-associated microbiota induced higher numbers of hypermethylated genes in murine colonic mucosa (vs. healthy controls’ microbiota recipients). Several gene promoters including SFRP1,2,3, PENK, NPY, ALX4, SEPT9, and WIF1 promoters were found hypermethylated in CRC but not in normal tissues or effluents from fecal donors. In a pilot study (n = 266), the blood methylation levels of 3 genes (Wif1, PENK, and NPY) were shown closely associated with CRC dysbiosis. In a validation study (n = 1,000), the cumulative methylation index (CMI) of these genes was significantly higher in CRCs than in controls. Further, CMI appeared as an independent risk factor for CRC diagnosis as shown by multivariate analysis that included fecal immunochemical blood test. Consequently, fecal bacterial species in individuals with higher CMI in blood were identified by whole metagenomic analysis. Thus, CRC-related dysbiosis induces methylation of host genes, and corresponding CMIs together with associated bacteria are potential biomarkers for CRC.” (Sobhani et al., 2019)

1.2. Main paper; discussion starts at 31:48 minutes

The Most Interesting Things (according to students)

  • Auxotrophic mutants, like E. coli strains that are unable to replicate, can still protect plants from harmful bacteria through competition of resources, like nutrients. Even though E. coli is not naturally found on apple blossoms, it was still effective against the wild-type disease-causing Erwina strains. This demonstrates the role that nutrient competition can play in biocontrol.
  • The use of apple juice as a growth medium for Erwinia to see which nutrients it can not make on its own and has to obtain from its environment. I had never thought about using apple juice as a growth medium before. This stood out to me because it reminded me of the biological tools we disregard in our daily lives.
  • Streptomycin was used on the apple blossoms as an antibiotic to prevent Fire Blight, but streptomycin-resistant Erwinia species are spreading exponentially due to a plasmid that transfers the resistance to other organisms. This is interesting because it highlights the growing concern of antibiotic resistance.
  • The process that the researchers used to discover the nutrients needed by the mutants can be compared to having someone watch your dog while you’re away and observing the contents of the fridge before leaving and after coming back. The “diet” of the mutants can be observed by analyzing the components that have changed from start to finish throughout the experiment. I thought this was an interesting analogy to make because it helped me to put the situation in perspective.

The abstract cannot be copied due to licensing restrictions. Please see licensing information and links to the article at the journal’s web page and/or PubMed in Section 8.1.

2. Vision and Change Core Concepts and 2024 ASM Fundamental Statements

Snippet Main
Vision and Change Topics
  • Information Flow and Genetics (V&C_IFG)
  • Impact of Microorganisms (V&C_IM)
  • Metabolic Pathways (V&C_MP)
  • Structure and Function (V&C_SF
ASM Fundamental Statements
  • Fundamental Statement 16 (ASM_16): Genetic variation can influence microbial structures and their functions.
  • Fundamental Statement (ASM_18): The regulation of gene expression is influenced by external and internal molecular cues and signals.
  • Fundamental Statement (ASM_25): Microbes are used as models that provide fundamental knowledge about life processes
  • Fundamental Statement (ASM_6): The distinct structures and processes in microbes can be targets for interspecies competition, antimicrobial treatments, and host immunity.
  • Fundamental Statement (ASM_13): Intrinsic factors, such as genotype, metabolism, and cell structures, impact the survival and growth of microbes.

3.  Potential Learning Objectives for the Podcast

The student will be able to: Paper1 Order2
  • Define cumulative methylation index (CMI).
  • Explain the role of DNA methylation in the identification of colorectal cancer.
 S L
  • Predict the results for a hypothetical experiment based on a hypothesis generated from the podcast discussion.
 S H
  • Define auxotroph.
 M L
  • Predict the impact of introducing auxotrophic bacteria into an apple orchard.
 M H

1Papers: Snippet (S) or Main (M)
2Learning Objectives: Lower Order or Higher Order (H)

4. Techniques Described (with Time Stamps)

Here is a link to a bio-dictionary that has many, but not all definitions if you need a definition: Explore Biology Bio-Dictionary

4.1. Snippet Paper

  • Polyethylene Glycol Control (8:10–8:23): The addition of PEG into the water of a gnotobiotic animal prevents the engulfment of the transplanted microbes from a fecal microbiome transplant.
  • Fecal Microbiome Transplant (FMT) (8:23–9:55): This technique involves transferring fecal material, containing a community of microbes, from a donor to a germ-free mouse. This technique allows researchers to study the effects of specific microbiomes on the recipient’s health over a stabilization period of about four weeks.
  • 16S Ribosomal Gene Sequencing (16S rDNA) (17:52–19:54): This technique is used to identify and analyze the composition of microbial communities in the gut, specifically the microbial dysbiosis associated with colorectal cancer. The 16S ribosomal gene has highly conserved regions across species of bacteria and variable regions that are unique to different bacteria. By sequencing the 16S ribosomal gene, researchers can identify the types and relative abundance of bacteria present in the gut microbiome. In this snippet, this technique is used to compare microbial populations between colorectal cancer patients and healthy controls.
  • Cumulative Methylation Index (CMI Assay) (19:54–21:34): This technique is a test that measures DNA methylation patterns in genes associated with colorectal cancer. This assay evaluates whether specific gene promoters are hyper-methylated or hypo-methylated. By assessing these methylation patterns in blood samples, this technique serves as a non-invasive early detection tool for colorectal cancer. In the snippet, this assay found higher cumulative methylation levels associated with colorectal cancer, which can potentially be used as a screening method to identify cancer at early stages.

4.2. Main Paper

  • Screening for Growth on Minimal Media (38:56–39:59): This technique involves screening strains of Erwinia amylovora to see if they can survive and grow on minimal media, which only provides the minimum amount of nutrients needed for bacterial growth. Through this screening process, researchers can identify which mutant strains are unable to grow under minimal nutrient conditions. This technique allows researchers to pinpoint the genes and metabolic pathways that are essential for Erwinia amylovora survival in nutrient-limited environments. These genes and metabolic pathways can then be targeted to control the growth of Erwinia amylovora in crops.
  • Apple Fruitlet Inoculation (40:53–41:34): A toothpick was dipped either into the wild-type virulent bacteria, or one of their auxotrophic mutants, and stuck into an open wound on an apple fruitlet. Researchers then waited a week and then analyzed the fruitlets to see if there was any evidence of disease.
  • Apple Juice Medium (42:56–43:55): Researchers mixed 50% apple juice with 50% bacteriological media, distributed this mixture across plates, and put each of the mutants in the apple juice mixture to see if they can grow when provided with extracts from the apples.
  • Auxotrophic Mutant Screening and Identification (47:26–50:28): In the main paper discussion, this technique involves selecting and testing a mutant strain of Erwinia amylovora that can not produce a necessary nutrient, like arginine, on its own. This mutant strain, an arginine auxotroph, is then applied to apple flowers to observe whether it can inhibit the growth of the virulent wild-type strain without harming the plant. Through this technique, researchers assess the auxotrophic mutant’s potential to act as a bio-control agent, as it may out-compete the pathogenic bacterial strains for space without causing disease.
  • Bio-control Experiment (48:34–51:45): Researchers created a heatmap of the results from the apple juice medium experiment to depict what components are consumed by the wild type as opposed to the mutant. They then took an auxotroph that is defective in one of its steps of arginine biosynthesis and they sprayed it onto apple flowers. They waited four hours, then dropped their wild type fully virulent Erwinia strain onto the flowers to observe whether the Erwinia strain had an effect.

5. Connections to General Microbiology Processes/Concepts (with Time Stamps)

5.1. Snippet Paper

  • Gut Dysbiosis (4:12–7:07): In the snippet, the term dysbiosis refers to the idea that the microbiome within the colon of an individual with colorectal cancer is fundamentally different from the microbiome within the colon of an individual without colorectal cancer.
  • Colorectal Cancer (11:27–12:16): Sporadic colorectal cancer is mechanistically thought to result from a complex set of interactions between the host and its environment. Environmental stressors, such as the diet of an individual, cause DNA alterations in the host cell that are linked to the onset of cancer.
  • Colorectal Cancer Associated Dysbiosis ad Hypermethylation  (18:01–19:13): Several gene promoters, including NPY, PENK, and WIF1, were used to create a cumulative methylation index to determine whether the host DNA was hyper-methylated or hypo-methylated based on the comparison to the control.
  • DNA Methylation (19:54–21:34): DNA methylation is an epigenetic modification where methyl groups are added to DNA. The addition of these methyl groups to DNA affects gene expression without altering the DNA sequence itself. In this study, DNA methylation is measured through the CMI assay as markers for early colorectal cancer detection.

5.2. Main Paper

  • Antibiotic Resistance (36:40–37:25): Antibiotic resistance occurs when bacteria evolve to become resistant to the antibiotics that were once able to kill them. In the discussion of the main paper, antibiotic resistance refers to the Erwinia species’ ability to survive a treatment of streptomycin, an antibiotic that has been used previously to control the disease. Through genetic adaptations, some Erwinia strains have acquired resistance to streptomycin, rendering the antibiotic ineffective.
  • Auxotroph (37:25–38:25): An auxotroph is a mutant strain of a particular bacteria that requires additional nutritional supplementation to grow. In the main paper discussion, an auxotroph refers to a mutant strain of Erwinia amylovora that requires a specific nutrient to grow because the strain can not produce it on its own. By creating auxotrophic strains, the goal is to develop strains that can inhibit the growth or colonization of wild-type, disease-causing strains of Erwinia amylovora without damaging the trees.
  • Erwinia Biosynthetic Pathways (42:10–43:08): Certain biosynthetic pathways are required by Erwinia in order to damage the apple fruitlets. Other biosynthetic pathways are dispensable, leading researchers to hypothesize that Erwinia is using the host fruit as its source of these dispensable pathways.
  • Environmental Niche (51:00–51:34): Erwinia’s natural habitat is in apple and pear orchards and therefore would be better adapted to persist on the apple blossoms. From this concept researchers concluded they would need to compose a mixture of Erwinia mutants to protect apples and pears in Erwinia’s ideal environment.

6. Podcast Questions

  1. How could DNA methylation analysis be used in identifying colorectal cancer?
    1. DNA methylation analysis can identify gene expression changes associated with cancer.
    2. DNA methylation analysis can identify membrane flexibility, which is high in cancer cells.
    3. DNA methylation analysis can identify changes in genome content indicative of cancer cells.
    4. DNA methylation analysis can identify cancer-related DNA mutations that have occurred.
  2. The cumulative methylation index (CMI) is a measure of whether the DNA was ________ compared to the control.
    1. hypomethylated during S phase of mitosis
    2. methylated at adenine or cytosine bases
    3. hypomethylated or hypermethylated
    4. hypermethylated at chromosomal telomeres
  3. Hypothetical probiotic therapies were tested for their effectiveness in reducing colon cancer by administering each probiotic or a control solution to groups of mice that are prone to colon cancer. Each group had cumulative methylation index (CMI) analysis performed on mouse blood.  Match the results you would expect for each hypothesis.
Hypothesis Results
a. ________ All of the tested probiotics are effective. 1. All mice show equally high levels of CMI levels regardless of probiotic, and the level is equivalent to the control group of mice.
b. ________ Some of the tested probiotics are effective. 2. All mice show equally low levels of CMI levels regardless of probiotic, and the level is low compared to the control group of mice.
c. ________ None of the tested probiotics are effective. 3. Different groups show different levels of CMI that is specific to a particular probiotic, and some are equivalent to the control group of mice.
  1. What is an auxotrophic microbe?
    1. A microbe that is sensitive to all known antibiotics and also bacteriophages.
    2. A microbe that requires a supplement in the minimal medium to be able to grow.
    3. A microbe that can synthesize all compounds for growth using minimal media.
    4. A microbe that is resistant to all forms of stress, including temperature and osmotic.
  2. Based on the research study discussed, what would be the impact of introducing the engineered, auxotrophic strains of bacteria to apple orchards? What is your reasoning?
    1. You would see an increase in Fire blight; auxotrophic bacteria would compete with apple trees for polysaccharide nutrients.
    2. You would see a reduction in Fire blight; auxotrophic bacteria would produce metabolites that inhibit pathogenic bacteria.
    3. You would see an increase in Fire blight; auxotrophic bacteria would likely promote the growth of pathogenic bacteria.
    4. You would see reduction in Fire blight; auxotrophic bacteria compete with the pathogenic bacteria so there is less disease.

7. Figure Reading Exercises

The following are two figure reading exercises, both from the snippet paper (Figure 1A-D,GH; Figure 3).

7.1. First Figure Reading Exercise

7.1.1. Learning Objectives

Students will be able to:

  • Identify the objective of this experiment.
  • Identify key features in the microscopy images, bar graphs, and experimental design for this experiment.
  • Analyze the impact of fecal microbiota transplants from healthy and colorectal cancer patients to make conclusions about their effect on tissue structure  and proliferation.
  • Analyze the impact of carcinogen-induced stress in combination with fecal microbiota transplants from healthy and colorectal cancer patients to make conclusions about their individual and combined effects on tissue structure  and proliferation.
  • Propose an early colon cancer indicator based on these data and provide reasoning for your choice.
Experimental Background (Sobhani et al., Figure 1A-D, GH)

The gut microbiome plays a significant role in health and its disruption (dysbiosis) has been correlated to a number of disease, including autoimmune disease and depression.  Here, Sobhani et al. (2019) investigate the relationship of gut dysbiosis and colorectal cancer.  To do this, they first seeded the mouse gut using fecal matter transfer (FMT) from either patients with colorectal cancer (CRC-μ; panels B, D, G) or healthy donors (N-μ; panels A, C, E, F).  To determine any possible effect of additional stress, some mice of each treatment were also given azoxymethane (AOM), a stress/cancer-inducing agent (panels C and D) or saline (panels A and B).  They first examined the structure (histology) of colonic mucosa of mice by looking for aberrant crypt foci (ACF; an early hallmark of colon cancer) using methylene blue staining (left panels of A-D) and hematoxylin/eosin stain (HES; right panels of A-D).  Ki67 staining, which is a marker for cell proliferation and hallmark of cancer is shown at higher resolution (panel G).  Cell proliferation and ACF formation were quantified at 7 and 14 weeks post FMT or AOM exposure (panel H).

Several microscopy images of mucosa cells and bar graphs showing change over time, Described in caption.
Figure 1. “Histological patterns of murine colonic mucosa following FMT. After the intestine was removed from cecum to anus, mucosa was carefully pinned flat, without folds, to examine the totality of the colonic mucosa which were stained with 0.2% methylene blue (left slides of coupled slides A to D) or HES (right slides of coupled slides A to D). Numbers of mice were as follows: n = 53 in 7-wk study (CRC-μ transfer, n = 30, and N-μ transfer, n = 23) and n = 132 in 14-wk study (CRC-μ transfer, n = 66, and N-μ transfer, n = 66). (Scale bar: 50 μm.) (A) After FMT from healthy human controls (N-μ), no ACF were visible in the colonic mucosa (here after 14 wk in Left), and pattern of crypts was normal by HES staining (Right). (B) Elevated numbers of ACF were observed after FMT from patients with CRC (CRC-µ) as compared to N-μ with multiple ACF (arrow in Left) as verified by HES (Right). (C) ACF counts were higher in the animals given AOM as compared to N-μ. Arrows indicate double ACF under blue coloration (Left) and illustration on HES slide of colonic mucosa. (D) The combination of CRC-µ and AOM increased the ACF count with dysplasia in rare cases (arrow). (E) No ACF nor inflammatory cell infiltrate was visible in mice given N-μ under NaCl (HES), although FISH staining showed density of bacteria trapped in the mucus layer (arrow, Left). (F) No ACF and no inflammatory cell infiltrate nor injury were noticed when mice received polyethylene glycol (PEG) in their drinking water as shown by H&S staining (HES) when FISH staining shows clear decrease in density of bacteria trapped in the proximal mucus layer (arrow). (G) Representative pictures of KI67 staining after human FMT from patients with CRC (CRC-µ) or from N-μ recipients. (H) Cell proliferation assessed by Ki67 staining and ACF quantification 7 and 14 wk after FMT in mice in the intestinal mucosa were higher in CRC-µ than in N-μ recipients. (I) The number of ACF counts were enhanced depending on the length of mucosa examined and the mice subgroups. (J) Comparative transcriptional levels of a set of inflammatory cytokines in the colonic mucosa assessed by murine cytokine qPCR quantification showing a trend to higher IL1, IL6, MIP2, and IL17 and lower IFNγ, IL10, and IL23 in mice given CRC-μ alone (fold vs. N-μ given mice) with AOM boosting this effect that reached significance for IL6, TNFα, and IL10 in mice given CRC-μ + AOM (vs. N-μ + AOM). (K) Inflammatory cell infiltrate in the colonic mucosa as assessed by semiquantification on HES stained slides (10 consecutive fields) under optic microscope magnification 20: a pathologist blinded to animal groups used a semiquantitative score to evaluate myeloid cell infiltrate in the colonic mucosa as 0, 1, and 2 indicating absence, scarce, and numerous inflammatory cells, respectively. The groups were compared by 1-way ANOVA followed by the Tukey–Kramer multiple comparisons post hoc test. No significant difference in between mice groups was observed. *P < 0.05; NS, not significant.” (Sobhani et al, 2019, no changes)

7.1.2. Questions

  1. What was the primary objective of examining cell and tissue organization in the colonic mucosa of individuals in this study?
    1. To determine the effects of various diets on colonic health
    2. To observe pathogen-induced, normal inflammation in the colon
    3. To compare microbial diversity of healthy and cancer patients
    4. To assess the impact of microbiome composition on the colon
  2. The researchers used methylene blue staining and hematoxylin/eosin staining to examine microscopic structures of the mouse colon when treated in different ways.  For the results shown in panels A-D, which sample(s) is/are the negative control and what does this tell you?
    1. Saline-treated with normal microbiome (NaCl + N-μ); this shows the normal colon structure.
    2. Saline-treated with cancer microbiome (NaCl + CRC-μ); this shows how colon cancer looks.
    3. Carcinogen-treated with normal microbiome (AOM + N-μ); this shows the normal stress conditions.
    4. Carcinogen-treated with cancer microbiome (AOM+ CRC-μ); this shows cancer and stress outcomes.
  3. What can you conclude about how the gut microbiome transplant from patients with colorectal cancer affects mouse colon histology? What is your evidence?
    1. It induces abnormal crypt formation; panel B has higher ACF than A.
    2. It protects from abnormal crypt formation; panel B has lower ACF than A .
    3. It induces abnormal crypt formation; panel B has higher ACF than A.
    4. It induces abnormal crypt formation in stress; only panel D shows ACF formation.
  4. What can you conclude about how the gut microbiome transplant from patients with colorectal cancer affects cell proliferation in the mouse colon (panel G)? What is your evidence?
    1. Proliferation is higher in mouse colon populated with normal microbiome; less brown staining in the right panel (CRC-μ) compared to the left panel N-μ).
    2. Proliferation is not different in mouse colon populated with cancer microbiome; the same brown staining in the right panel (CRC-μ) and the left panel N-μ).
    3. Proliferation is higher in mouse colon populated with cancer microbiome; more brown staining in the right panel (CRC-μ) compare to the left panel N-μ).
    4. Proliferation is equally high in mouse colon populated with either microbiome; high brown staining in the right panel (CRC-μ) and in the left panel N-μ).
  5. Cell proliferation and ACF formation results were quantified and averaged to generate the data in panel H. The data are displayed by bar graphs. What notation tells you the comparison being made is statistically significant, that is truly different?
    1. bar height
    2. whiskers
    3. NS
    4. asterisk
  6. At which time point is cell proliferation different for mice populated with normal microbiome and cancer microbiome? What is your evidence?
    1. 7 and 14 weeks; the bar heights for saline with N-μ and CRC-μ are different at both time points.
    2. 14 weeks; the bar heights for saline with N-μ and CRC-μ are different and statistically significant.
    3. Neither; the bar heights for saline N-μ and CRC-μ are different but are not statistically significant.
    4. Both; the bar heights for saline N-μ and CRC-μ are different and are statistically significant.
  7. What effect does populating the mouse with the different microbiomes have on carcinogen-stressed mice (panel H)? What is your evidence?
    1. Affects cell proliferation, but not ACF; the bar heights for AOM N-μ and CRC-μ are different for cell proliferation.
    2. Affects both ACF and cell proliferation; the bar heights for AOM N-μ and CRC-μ are different and significant.
    3. No effect; the bar heights for AOM N-μ and AOM CRC-μ are different but are not statistically significant.
    4. Affects ACF, but not cell proliferation; the bar heights for AOM N-μ and CRC-μ are much different for ACF.
  8. What effect does carcinogen-stress (AOM vs saline) mice have on mice populated the mouse with the cancer microbiomes (panel H)?
    1. Affects cell proliferation, but not ACF; the bar heights for saline CRC-μ and AOM CRC-μ are different for cell proliferation and statistically significant.
    2. Affects both ACF and cell proliferation; the bar heights for saline CRC-μ and AOM CRC-μ are both different different and also statistically significant.
    3. Affects neither ACF nor cell proliferation; the bar heights for saline CRC-μ and AOM CRC-μ are different but they are not statistically significant.
    4. Affects ACF, but not cell proliferation; the bar heights for saline CRC-μ and AOM CRC-μ are different for ACF and are also statistically significant.
  9. Based on these data, which would be the better early indicator for colon cancer, Ki67 staining or ACF formation? What is your reasoning?
    1. Ki67 staining; Ki67 staining shows an earlier response (at 7 weeks) than ACF.
    2. Abnormal crypt formation; ACF shows an earlier response (at 7 weeks) than ACF.
    3. Both are good indicators at 7 weeks, so either would be reasonable for early detection.
    4. Neither are good indicators at 7 weeks, both would be reasonable at 14 weeks.

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Identify key features in schematics, experimental design, and bi- and tri-dimensional correlation plots for this experiment.
  • Analyze the data to make conclusions about the number of hypermethylated genes in different samples and are common to all samples.
  • Evaluate the data and make conclusions about correlated methylation levels among the tissues.
  • Analyze the data to make reasonable arguments for the implications of these data.
Experimental Background (Sobhani et al., Figure 3)

The gut microbiome plays a significant role in health and its disruption (dysbiosis) has been correlated to a number of disease, including autoimmune disease and depression. Here, Sobhani et al. (2019) investigate the relationship of gut dysbiosis and colorectal cancer.  Many cancers utilize epigenetic alterations to change gene expression in a heritable way without changing the DNA sequence.  Epigenetic alterations instead use DNA methylation to cause the DNA to become tightly packaged and unavailable for transcription.  Sobhani et al. (2019) wanted to determine whether epigenetic effects in general, or for specific genes, were the way the microbiota were affecting colon cancer development.  To do this they investigated the methylation status and methylation level of DNA from individuals with colorectal cancer (and controls) in stool, serum, and tissue. Their workflow of data analysis included identifying highly methylated promoter regions by DNA methylation array (highly methylated in cancer vs. healthy) and comparing the methylation results among the three sources to find overlaps (panel A; Venn diagram). To determine which genes were most similarly and differentially hypermethylated across the three sources, they visualized the difference (D) in cancer and control methylation levels for specific genes in a dimensional/correlation analysis where the x-axis is for stool, the y-axis is for tissue, and z-axis is for sera (left of panel B).  Each pairwise comparison is also shown (panel B right side). For example, the pairwise comparison of gene methylation level for stool (x-axis) vs serum (z-axis) is at top middle of the 9 panel.

Two part figure. Part 1 (A) shows the process used to identify hypermethylated genes in humans. Part 2 (B) A figure is used to visualize genes in a 3d space.
Figure 3. “Identification of hypermethylated genes related to fecal microbiota in human. Overview on the strategy from experimental approach for the validation of gene methylated targets in human based on microbiota donors (CRC patients or controls) in germ-free mice experiments. Methylated genes in CRC-associated tissues and fluids were identified based on their power for showing differences between normal colonoscopy individuals and CRC patients. (A) Human tissues and effluents were submitted to methylation gene array. Based on significant differences of methylation values in CpG probes between control (n = 9) and CRC patient (n = 9) donors, genes were selected according to the promoter segments hypermethylated in CRC patients. (B) Bidimensional (Right) and tridimensional (Left) distribution of genes regarding the difference in methylation values are indicated; in red color are indicated 7 selected more discriminant genes regarding CRC patients and controls. D, difference.” (Sobhani et al. 2019, no changes).

7.2.2. Questions

  1. What types of samples were analyzed to identify hyper-methylated genes in CRC patients?
    1. Plasma, urine, tissue
    2. Stool, serum, tissue
    3. Stool, urine, plasma
    4. Serum, urine, plasma
  2. How many CpG probes were highly methylated in CRC tissues or effluents?
    1. 45
    2. 12
    3. 195
    4. 1505
  3. Which analysis technique was used to assess methylation patterns in the study?
    1. Protein immunoassay
    2. Gene knockout
    3. RNA sequencing
    4. DNA methylation array
  4. The methylated probes initially identified _______ genes as candidate genes.
    1. 257
    2. 49
    3. 21
    4. 7
  5. How many genes were commonly hypermethylated in tissue, stool, and serum?
    1. 257
    2. 49
    3. 21
    4. 7
  6. The differences in methylation levels for cancer and normal cells per “candidate” gene are most easily visualized in the pairwise correlation plots (right side of panel B).  If the genes were equally methylated in two tissues, what pattern would you expect to see in the data?
    1. a curved line that increases from left to right.
    2. a curved line that decreases from left to right.
    3. a straight line increasing in slope from left to right.
    4. a straight line decreasing in slope from left to right.
    5. a sigmoidal curve that increases steeply then levels.
  7. Based on the correlation/dimensional analyses, which pair of tissues/sources shows the best correlation of methylation differences among the candidate genes?
    1. stool-serum
    2. stool-tissue
    3. tissue-serum
    4. all are correlated
  8. Taken together, what conclusion(s) can be drawn from these data? [pick all that apply]
    1. Each sample type of the three tested has mostly unique hypermethylated genes.
    2. A few common hypermethylated genes could be used as biomarkers, but needs additional study.
    3. Methylation status and level in tissue samples alone is sufficient for colon cancer detection.
    4. This evidence supports epigenetics as a contributor to microbiome-related colon cancer.

8. Paper Information and Licensing

8.1. Snippet paper

8.2. Main paper

  • Klee SM, Sinn JP, Finley M, Allman EL, Smith PB, Aimufua O, Sitther V, Lehman BL, Krawczyk T, Peter KA, McNellis TW. 2019. Erwinia amylovora Auxotrophic Mutant Exometabolomics and Virulence on Apples. Appl Environ Microbiol. 85(15):e00935-19. doi: 10.1128/AEM.00935-19.
  • This article is not licensed for Creative Commons use; thus, the abstract and figures cannot be copied. Please see the article at the journal’s web page: https://journals.asm.org/doi/full/10.1128/aem.00935-19

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Podcast Annotation and Resources in Microbiology Copyright © 2025 by Rebecca Seipelt-Thiemann; Nancy Boury; Gwendowlyn S. Knapp; Amaya Garcia Costas; and Patrick Armstrong is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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