Information Flow and Genetics

TWiM #288: Cancer and E. coli

Podcast and Annotation Information
  • Annotation by Gabriela Garcia Molina, Neelam Zala, Angelina Fahey, Ethan Pacis, Yiorgios Spinos, Owen Cannon, Jeremy T. Ritzert, Rebecca Seipelt-Thiemann, and Karen P. York
  • Podcast audio by TWiM: Listen to TWiM #288 Podcast
  • Podcast transcript by Otter.ai and edited by Eden Anderson and Rebecca Seipelt-Thiemann: Access Podcast Transcripts
  • Papers Discussed:
    • Pleguezuelos-Manzano C, Puschhof J, Rosendahl Huber A, van Hoeck A, Wood HM, Nombur J, Gurjao C, Manders F,  Dalmasso G, Stege PB, et al. 2020. Mutational signature in colorectal cancer caused by genotoxic pks+ E. coli. Nature. 580:269-273. https://doi.org/10.1038/s41586-020-2080-8.
    • Klapper M, Hübner A, Ibrahim A, Wasmuth I, Borry M, Haensch VG, Zhang S, Al-Jammal WK, Suma H, Fellows Yates JA, et al. 2023. Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic. Science. 380(6645):619-624. DOI: 10.1126/science.adf5300.

1. Paper Abstracts

1.1. Snippet paper discussion starts at 3:33 minutes

The Most Interesting Things (according to students)

  • Intestinal organoids from stem cells were used to mimic the structure and function of the intestines. A distinctive pattern of DNA damage was observed in the organoid cells after co-culturing with colibactin-producing pks+ E. coli. Biopsy tissue from patient colorectal tumors had the same distinctive signature patterns. Thus colibactin-producing pks+ E. coli naturally found in the gut microbiome can contribute to colorectal cancer development.
  • Some strains of E. coli in the gut can produce toxins that cause DNA damage leading to cancer. Over 20% of healthy people unknowingly carry these potentially harmful bacteria in their intestines.

This article is not licensed for Creative Commons use; see licensing information for the article. Thus the abstract and figures cannot be copied here. Please see the article at the journal’s web page: https://doi.org/10.1038/s41586-020-2080-8. Alternate open access link: https://pmc.ncbi.nlm.nih.gov/articles/pmid/32106218/

1.2. Main paper discussion starts at 28:07 minutes

The Most Interesting Things (according to students)

  • Scientists reconstructed bacterial genomes from 100,000-year-old Neanderthal dental plaque to learn about ancient microbes. An unexpected green sulfur bacteria was found in seven of the paleolithic individuals. This fresh water bacteria was most likely acquired from the paleolithic humans’ fresh water source.
  • The researchers also describe synthesizing enzymes of a biosynthetic gene cluster found in the ancient DNA sequences. The biosynthetic enzymes were functional and they produced unique natural products that were called “paleofurans.”  They discovered completely new chemicals from ancient bacteria that might have cool future uses in medicine or biotech.

This article is not licensed for Creative Commons use. See Article copyright information. Thus, the abstract and figures cannot be copied here. Please see the article at the journal’s web page; 10.1126/science.adf5300

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

 Snippet Main

Vision and Change Topics
  • Information Flow and Genetics (V&C_IFG)
  • Microbial Ecology (V&C_ME)
  • Evolution (V&C_E)
  • Information Flow and Genetics (V&C_IFG)
  • Impact of Microorganisms (V&C_IM)

ASM Fundamental Statements
  • Fundamental Statement 16 (ASM_16): Genetic variation can influence microbial structures and their functions.
  • Fundamental Statement 22 (ASM_22): Most microbes interact with hosts in beneficial or neutral ways, with a minority having a detrimental impact on their host.
  • Fundamental Statement 3 (ASM_3): The evolution of microbes is impacted by their interactions with the environment and a variety of ecological forces, including other microbes, humans, and habitats.
  • Fundamental Statement 16 (ASM_16): Genetic variation can influence microbial structures and their functions.
  • Fundamental Statement 26 (ASM_26): Humans leverage microbes and their products to address problems and improve quality of life.

3.  Potential Learning Objectives for the Podcast

The student will be able to:

Paper1

Order2
  • Explain how the intestinal organoid was used in this study.
  • Describe the effects of colibactin producing pks+ E. coli on human intestinal cells.
  • Recall where the mutational signature found in the organoid cultures was also found.

S

L
  • Propose reasons why some healthy people have pks+ E. coli.

S

H
  • Describe conditions relevant for the successfully recovery of ancient DNA.
  • Recall the unusual features found in ancient microbiome.
  • Identify how the tool PyDamage contributed to the study.

M

L
  • Propose an experiment based on a hypothesis generated in the podcast discussion.

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

  • Human Genome Sequencing Analysis (5:43 –13:05): This is an analysis of the human genome sequence to find mutations.  Here, the researchers used it to find mutations that are associated with one condition (i.e. tumor or exposure to the mutagenic E. coli) but not found in the control.  Mutational signatures associated with the pks+ E. coli compared to the control were determined by whole genome sequencing of intestinal cells. A database of whole genome sequences from tumors removed from patients also informed this study.
  • Intestinal Organoids (6:25–8:18): These are cell cultures made from stem cells that are used to simulate a functional intestine. The effect of E. coli strains on the intestinal cells was studied using organoids.

4.2. Main Paper

  • Metagenome-assembled Genome (MAG) (33:31–34:03; 38:25–43:34): Metagenomes are a collection of genomes where DNA is isolated as a mixture from an environmental source, such as soil.  Bioinformatic analysis is used to identify and piece together (assemble) genetic sequences and identify separate functional groups (a genus, or species or gene clusters). In this research the genomes were assembled from millions of 30 base pair fragments to reconstruct the ancient microbial metagenome.
  • Heterologous Gene Expression (36:52–38:24; 51:26–53:10): In this method, a gene from one species (here, ancient Chlorobium) is expressed in another species (here, modern day bacteria). In this research ancient biosynthetic genes were introduced into the genomes of two different modern bacteria using the “chassis independent recombinase assisted genome engineering system” (CRAGE). The production of novel metabolites (furans) in both modern bacteria that the ancient genes were introduced into indicated the ancient biosynthetic genes were being expressed and the enzymes produced were functional.
  • PyDamage (42:53–43:37): This is a python-based computer algorithm that detects age-related DNA damage in a DNA sequence dataset. It is used to differentiate ancient DNA sequences from contaminating modern environmental DNA contaminants. This validates the assembly of sequences that are truly ancient DNA in origin based on characteristic age related patterns of DNA damage.
  • Antibiotics & Secondary Metabolite Analysis Shell (AntiSMASH) (49:10–51:45; 1:03:44–1:04:11): AntiSMASH is a bioinformatics tool to identify known biosynthetic gene clusters in genomic DNA sequence. This is a way to assign function to newly discovered genes. The biosynthetic gene clusters often help to define natural bacterial metabolites that may have unique bioactivity and useful biomedical or other applications.
  • Chassis-independent Recombination-assisted Genome Engineering (CRAGE) System (51:27–52:14): This is a cloning method.  Here, the researchers used the CRAGE system to construct strains for the heterologous expression of the ancient gene cluster.

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

5.1. Snippet Paper

  • DNA Damage (4:11–5:42): The podcasters describe the DNA damage caused by genotoxic strains of pks+ E. coli
  • Development of Cancer (16:56–18:38; 18:44–20:27; 20:34–21:32): These sections describe how repair of double stranded breaks in DNA can lead to mutations and how mutations over time can ultimately lead to tumors and cancer.

5.2. Main Paper

  • Dental Calculus (34:04–34:47): This is calcified dental plaque that contains the oral microbial biofilm from the teeth. In this paper, ancient DNA was extracted from dental plaque found on the teeth of Neanderthals and anatomical humans that were up to 100,000 years old.
  • Winogradsky Column (34:13–36:34): A method for culturing diverse microbes from pond water and mud. Invented by Sergei Winogradsky in the 1880s. Layers of microbes thrive at different levels within the column depending on their metabolic capacities and the availability of nutrients, carbon-sources, sulfur, oxygen, and sunlight within the column.
  • Paleogenomics (40:30–43:37): This section focuses on the challenges of applying next generation sequencing (NGS), genomics and bioinformatics in working with ancient DNA samples. It is critical to include techniques to validate that the DNA is of ancient origin and not a recent environmental contaminant. This section includes the description of PyDamage.

6. Podcast Questions

  1. Which of the following describes how the intestinal organoid is used in the snippet paper study?
    1. It was transplanted into a mouse to mimic drug delivery.
    2. It is used to test the effects of E. coli on intestinal cells.
    3. Their formation assayed the presence of tight junctions.
    4. It is used to test the effects of anti-cancer medications.

2. What did the researchers conclude about colibactin’s effects on the intestine?

    1. Colibactin supports gut bacteria cell growth by nutrient activation.
    2. Colibactin inactivates oncogenes in intestinal epithelia cells.
    3. Colibactin kills intestinal epithelial cells to force genome DNA repair.
    4. Colibactin damages the DNA of the intestinal epithelial cells.

3. The mutational signatures found in the organoid experiments were frequently also found in  genome(s) from __________________. [pick all that apply]

    1. E. coli cultured with stem cells
    2. a broad array of cancer cell types
    3. colorectal tumor biopsies
    4. antimicrobial resistant fungi

4. It was estimated that about 20% of healthy individuals have pks+ E. coli in their gut microbiome.  What are some valid reasons that these individuals do not have colon cancer? [pick all that apply]

    1. Other host factors such as highly effective DNA repair may play a role.
    2. Other microbiome species may play a role in limiting intestinal cancer.
    3. The pks+ E. coli do not express pks all the time, only in quorum conditions.
    4. The pks+ E. coli are protective and play a role in limiting intestinal cancer.

5. In the main paper, how was the ancient DNA preserved for so long, that is up to 100,000 years?

    1. It was preserved by the cold temperatures in the arctic tundra.
    2. It was preserved by the anaerobic conditions found in a peat bog.
    3. It was preserved by the DNase-free environment of fossilized amber.
    4. It was preserved by mineralization of the dental calculus on the teeth.

6. The bioinformatics tool “PyDamage”, that identifies characteristic signatures of age-related DNA damage, is critical in analysis of paleogenomic data because:

    1. PyDamage validates the DNA sequences that are from an ancient source.
    2. PyDamage identifies metabolites that are from a modern contaminant.
    3. PyDamage indicates the DNA is too damaged to include in the analysis.
    4. PyDamage scores the relative evolutionary history of specific sequences.

7. Finding Chlorobium in the ancient microbiome DNA was unexpected because ___________ .

    1. the Paleolithic samples had very little bacterial DNA.
    2. it is not a commonly found in modern microbial communities.
    3. Chlorobium was a previously undescribed genus of bacteria.
    4. there was no plausible reason why Neanderthals had Chlorobium.

8. Bacterial metabolites can often inhibit the growth of other bacteria, which is how researchers found metabolites with antibiotic properties.  If the researchers wanted to test their metabolites for antibiotic function, what would be a good experiment for them to do?

    1. A bacterial competition assay; grow bacteria in a co-culture, one expressing and one not expressing the metabolite, and monitor the ratio of survivors.
    2. A reporter activation assay; use a reporter gene fused to the promoter for common antibiotic biosynthesis genes, look for activation with metabolite exposure.
    3. A disk diffusion assay; grow bacteria in a lawn on an agar plate with paper disks impregnated with different metabolites and monitor the zone of clearing.
    4. An enzyme-linked immunosorbent assay; make dilutions of the metabolite and incubate with antibodies to common antibiotics, look for a loss of signal.

7. Figure Reading Exercises

The following are two figure reading exercises, one from the snippet paper (Figure 1) and one from the main paper (Figure 3A-C).

7.1. First Figure Reading Exercise

7.1.1. Learning Objectives

Students will be able to:

  • Identify key features including experimental design, controls, variables for the experiment.
  • Identify key features of microscopy and strip plot data visualizations.
  • Analyze growth data to identify common phases of a bacterial growth curve.
  • Evaluate immunofluorescence microscopy images to identify conditions where DNA damage has occurred.
Experimental Background (Pleguezuelos-Manzano et al. 2020, Figure 1)

The gut microbiome has been implicated in human health and disease, including autoimmune diseases and cancer.  Here, Pleguezuelos-Manzano et al. (2020) explore a direct role for bacterial-encoded mutagenesis in colon cancer.  They began their study knowing that Escherichia coli, a common gut bacterium, can carry a pathogenicity island (pks) that encodes genes for synthesis of colibactin, which alkylates DNA and induces double-stranded DNA breaks. To investigate the impact of bacterial-encoded colibactin exposure on human intestinal cells, two strains of E. coli were used: one that can produce colibactin (pks+) and one that cannot produce colibactin because the required genes have been deleted (pksΔclbQ). They injected pks+ or pksΔclbQ E. coli into the lumen of human intestinal organoids (panel b).  They used electron microscopy to evaluate the direct contact of organoid and E. coli (panel b). To evaluate the lumenal growth of both E. coli types, living bacteria were quantified and the data are displayed as colony forming units (CFU) for the first three days (panel c). To investigate the organoid DNA damage being induced by the E. coli, they stained organoids at day 1 of co-culture with a blue-fluorescent dye (4′,6-diamidino-2-phenylindole (DAPI; to stain DNA) and γH2AX (a biomarker of double-strand DNA breaks) (panel d).  They also stained organoids that had either been injected with dye (dye) or a known DNA damage-inducing agent, mitomycin C (MMC) (panel d). The final panel (e) is the quantification of the data collected from the fluorescent microscopy observations in panel d.

7.1.2. Questions

  1. The researchers determined the viability and growth of E. coli cultured in the organoids by quantifying colony forming units (CFU) and the data are displayed as a strip plot (panel c).  Match the strip plot feature with its description.

1 = mean; 2 = median; 3 = outlier; 4 = standard deviation; 5 = individual measures; 6= standard error; 7 = measure of statistical significance (or not)

    1. ________ horizontal bar height
    2. ________ whiskers
    3. ________ circles

2. Which color notes the data for organoids injected with the E. coli strain carrying the wild-type pathogenicity island (panel c)?

    1. black
    2. gray
    3. blue
    4. red

3. The researchers determined the viability and growth of E. coli cultured in the organoids by quantifying colony forming units (panel c).  In what part of the typical microbial growth curve is each strain one day after injection into the organoids?  What is your evidence?

    1. The pks+ are in the logarithmic phase, but the pksΔclbQ are in the lag phase; pks+ have higher growth at day 0.
    2. Both strains are in the lag phase because the cell numbers (CFU) are similar to each other day 0 and at day 1.
    3. The pksΔclbQ are in the logarithmic phase, but the pks+ are in the lag phase; pksΔclbQ have higher growth at day 1.
    4. Both strains are in the logarithmic phase because the cell number (CFU) increased dramatically from day 0.

4. At what co-culture timepoint does the cell number (CFU) indicate the E. coli strains have entered stationary phase growth?

    1. Between day 0 and day 1
    2. Between day 1 and day 2
    3. Between day 2 and day 3
    4. They do not reach stationary phase.

5. Based on the data, does having the pathogenicity island or not having the pathogenicity island confer a growth advantage to E. coli in the organoids? What is your evidence?

    1. Yes; because the absence of the pathogenicity island (pksΔclbQ) is an advantage as this strain grows better at all times.
    2. No; because both strains (with or without the pathogenicity island) grow to about the same cell numbers at all time points.
    3. No; because neither strain (with or without the pathogenicity island) grows particularly well, reaching stationary phase early.
    4. Yes; because the strain with the pathogenicity island is wild-type and that is always an advantage to make strains grow better.

6. What does the red color in the fluorescent microscopy results (panel d) indicate?

    1. double-stranded DNA breaks
    2. double-stranded DNA
    3. cell membranes
    4. mitochondrial DNA

7. In this experiment to quantify DNA damage in organoids, ______ is the negative control and _______ is the positive control.

    1. pks+; mitomycin C
    2. pksΔclbQ; pks+
    3. dye; mitomycin C
    4. dye; pksΔclbQ

8. The average percentage of intestinal organoid cells damaged in the presence of pksΔclbQ E. coli is ____________ (panel e).

    1. 25%
    2. 50%
    3. 75%
    4. 99%

9.  What impact does each strain of E. coli have on organoid cells, and what does this suggest about the pks pathogenicity island (panels d and e)?

    1. Both E. coli strains have equally damaged DNA; therefore, it is E. coli and not the pathogenicity island contributes to colon cancer.
    2. pks+ E. coli damages the DNA more extensively than pksΔclbQ E. coli; therefore, both the E. coli and pks may contribute to colon cancer.
    3. pks+ E. coli damages the DNA, but pksΔclbQ E. coli does not; therefore, the pathogenicity island contributes to colon cancer.
    4. pksΔclbQ E. coli damages the DNA but pks+ E. coli does not; therefore, the pathogenicity island is protective for colon cancer.
    5. Neither E. coli strains damaged DNA; therefore, neither the gut microbiome nor the pathogenicity island contributes to colon cancer.

7.2. Second Figure Reading Exercise

7.2.1. Learning Objectives

Students will be able to:

  • Identify key features in the network visualization, gene structure diagrams, high pressure liquid chromatography (HPLC) traces.
  • Interpret network analysis results to identify clusters and relationships among the members.
  • Evaluate biosynthetic gene cluster structure schematic to identify members that have or lack specific genes.
  • Analyze high pressure liquid chromatography (HPLC) data to identify which strains successfully produce a metabolite.
Experimental Background (Klapper et al., Figure 3A-C)

Microbial products are widely varied and play roles in microbial communities.  A great number of these products also have therapeutic value, such as antimicrobial compounds.  A typical workflow for finding these microbial compounds is to screen environmental microbes.  In this study, Klapper et al. (2023) add a new spin to this typical workflow by investigating ancient microbial metagenomes to identify potential biosynthetic pathways missing in modern bacterial communities.  To do this the scientists reconstructed bacterial genomes from biofilms of teeth from Neanderthals. Several biosynthetic gene clusters (BGC) were identified in the metagenomic analysis including one for synthesis of butyrolactone.  To get a better idea of how similar ancient (labeled with letters and numbers) and modern (labeled with species names) butyrolactone BGC were to each other, the researchers performed cluster analysis (panel A).  In this network analysis using a tool called BiG-SCAPE*, a node (circle) is a particular BGC and the line connecting two nodes shows similarity with short lines indicating more similarity than long lines.  Therefore, a cluster of close circles with lines interconnecting have very similar BGC.  The nodes are usually color-coded to highlight clusters of particular types.  Following identification of similar ancient butyrolactone BGC, the researchers looked more closely at the gene structure within the operons (panel B) and identified a group of genes to work with in some additional “proof of principle” gene expression experiments.  To determine whether these select genes (plfA and adjacent plfB) could be expressed and produce a novel metabolite, they cloned these two genes into a plasmid, expressed the proteins, and analyzed lysates of the engineered strain and the host strain alone (Pf-5) by high pressure liquid chromatography.  The data for 190 nm are shown in the HPLC chromatogram (panel C). Putative structures of the novel metabolites, paleofuran, are shown in panel D.

 

7.2.2. Questions

  1. In the network analysis, which color dot and letter indicates the gene cluster family where most of the ancient butyrolactone BGCs are found?
    1. black; a
    2. gold; b
    3. sky blue; c
    4. green; d
    5. yellow; e
    6. blue; f
    7. orange; g

2.  In the network analysis, which species group is the most similar to the ancient bacteria, at least with respect to their butyrolactone gene cluster?

    1. Candidatus
    2. Chlorobiaceae
    3. Ferredoxins
    4.  Prostecochloris

3. The genome structure of putative ancient butyrolactone biosynthetic gene clusters are shown with each gene noted as a colored shape.  The key that notes the color, shape, protein name, and gene name is shown in panel B (right side).  Each shape has an arrow point indicating a direction of transcription for the gene. Which one of the following ancient bacteria genomes is missing the gene that encodes the copper transporting ATPase PacS?

    1. EMN001_21
    2. RIG001_014
    3. PES001_018
    4. PLV001_001

4. If direction of transcription is an indicator for an operon (panel B), how many operons are likely present for bacterium EMN001_121?

    1. 14 operons
    2. 10 operons
    3. 7 operons
    4. 1 operon

5. Genes were experimentally expressed in the laboratory to produce functioning enzymes for butyrolactone synthesis (panel B). Which enzymes produced?

    1. Copper transporting ATPase PacS, Serine O-acetyltransferase
    2. A-factor-like synthase, oxidoreductase, sensor kinase/phosphatase
    3. Chorismate synthase, Porphobilinogen synthase, hypothetical protein
    4. Sensor kinase/phosphatase, hypothetical protein, chorismate synthase

6. What do the peaks shown in the high-pressure liquid (HPLC; panel C) chromatograph results indicate?

    1. Peaks are the engineered DNA of the ancient biosynthetic genes (top line) and not present in non-modified modern bacteria (bottom line).
    2. Peaks are RNAs encoded by the ancient biosynthetic genes (top line) and not present in non-modified modern bacteria (bottom line).
    3. Peaks are the proteins produced using ancient biosynthetic genes (top line) and not present in non-modified modern bacteria (bottom line).
    4. Peaks are metabolites produced in modern bacteria using ancient biosynthetic genes (top line) and not in non-modified modern bacteria (bottom line).

8. Paper Information and Licensing

8.1. Snippet paper

8.2. Main paper

  • Klapper M, Hübner A, Ibrahim A, Wasmuth I, Borry M, Haensch VG, Zhang S, Al-Jammal WK, Suma H, Fellows Yates JA, Frangenberg J, et al. 2023. Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic. Science. 380(6645):619-624. DOI: 10.1126/science.adf5300.
  • This article is not licensed for Creative Commons use. See article copyright information. Thus, the abstract and figures cannot be copied here. Please see the article at the journal’s web page.

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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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