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SCII > Microbiomics > Flashcards

Microbiomics Flashcards

1
Q

Name 3 intrinsic factors associated with shaping an individual’s microbiota profile

A

Age, genetics, immune system function

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2
Q

Name 5 extrinsic factors associated with shaping an individual’s microbiota profile.

A

Diet, antibiotic use, geographical location, exposure to pollutants, lifestyle (e.g., exercise or smoking)

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3
Q

Define the concept of ‘opportunism’ in the context of microbiota

A

Opportunism refers to a normally non-pathogenic organism becoming pathogenic when the host’s immune system is weakened or its environment is altered

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4
Q

Define the ‘keystone hypothesis’ in microbiota research

A

The keystone hypothesis suggests that specific species within a microbial community have a disproportionate impact on the health and stability of the overall microbiota

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5
Q

Name 3 mechanisms by which the microbiota can increase energy uptake, contributing to obesity.

A
  1. Enhanced short-chain fatty acid (SCFA) production, increasing energy absorption.
  2. Microbial enzymes that ferment dietary fiber, creating additional energy sources.
  3. Altered lipid synthesis and storage, promoting fat deposition.
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6
Q

Briefly describe the role of short-chain fatty acids (SCFA) in inflammatory bowel disease (IBD)

A

SCFAs, like acetate and butyrate, help reduce gut inflammation by promoting intestinal barrier integrity and downregulating inflammatory mediators

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7
Q

Name 4 mechanisms by which the human microbiota can affect cancer therapy using immune checkpoint inhibitors

A
  1. Synergizing with immune antibodies (PD-1 blockers).
  2. Enhancing production of interferon gamma.
  3. Modulating the immune response.
  4. Influencing T-cell activation and antitumor immunity.
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8
Q

Briefly describe how Koch’s postulates may not be relevant in human microbiota research.

A

Koch’s postulates focus on isolating and identifying single pathogens causing disease. However, many diseases in microbiota research are related to imbalances in microbial communities rather than a single organism, making it hard to apply these criteria

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9
Q

Name 3 advantages of using in vitro organoids in microbiota research

A
  1. More detailed and precise analysis.
  2. Fewer ethical concerns compared to animal models.
  3. Enhanced standardization and reproducibility.
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10
Q

Name 3 limitations of using in vitro organoids or ex vivo tissue biopsies in microbiota research

A
  1. Lack of complex interactions with immune systems.
  2. Difficulty in mimicking long-term physiological processes.
  3. Specialized equipment and expertise required.
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11
Q

Define the term ‘kitome’ in microbiota research

A

The kitome refers to contaminants introduced from laboratory kits, such as DNA extraction reagents, that may skew results.

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12
Q

Define the term ‘splashome’ in microbiota research

A

The splashome refers to cross-contamination from the laboratory environment or between samples during processing, affecting experimental results

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13
Q

Name 5 procedures that can reduce the effects of ‘kitome’ and ‘splashome’ in microbiota research

A
  1. Use of negative controls.
  2. Proper lab decontamination protocols.
  3. Regularly testing kits for contamination.
  4. Careful sample handling and storage.
  5. Using contamination-free kits.
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14
Q

Briefly describe 3 advantages associated with mouse models used for human microbiota research

A
  1. Low cost and short life cycle for efficient studies.
  2. Comprehensive knowledge of mouse genetics.
  3. High reproducibility due to inbred populations.
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15
Q

Briefly describe 3 disadvantages associated with mouse models used for human microbiota research

A
  1. Mouse microbiota differs significantly from humans.
  2. Dietary habits and physiology vary from humans.
  3. Mice do not have complex medical histories or social behaviors like humans.
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16
Q

Name and briefly describe 3 advantages of using ex vivo tissue biopsies in microbiota research.

A
  1. Controlled environment: Allows researchers to study isolated tissue without interference from the full immune system.
  2. Ethical benefits: Reduces the need for live animal testing, allowing use of waste tissue.
  3. Specific focus: Enables detailed study of microbial interactions with specific tissue types.
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17
Q

Name and briefly describe 3 limitations of using ex vivo tissue biopsies in microbiota research.

A
  1. Lack of immune interaction: No full immune system present to show complex interactions with microbes.
  2. Short viability: Tissues may not survive long, limiting the length of studies.
  3. Potential contamination: Handling can introduce external contaminants, affecting results.
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18
Q

What is 16S rRNA profiling?

A

16S rRNA profiling is a method used to identify and compare bacteria within a sample by sequencing the 16S ribosomal RNA gene. It focuses on conserved and variable regions of the gene to classify bacteria and determine their abundance.

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19
Q

Why is the 16S rRNA gene commonly used for bacterial profiling?

A

The 16S rRNA gene contains both highly conserved and variable regions, making it easy to amplify using PCR and useful for differentiating between bacterial species. It’s also present in almost all bacteria, making it ideal for taxonomy.

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20
Q

What are the key steps in 16S rRNA profiling?

A

Key steps in 16S rRNA profiling include DNA isolation, amplification of the 16S rRNA gene using PCR, sequencing of the amplified region, and bioinformatics analysis to assign taxonomy and quantify bacteria based on their sequences.

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21
Q

What is the role of bead beating in the 16S rRNA profiling process?

A

Bead beating is used to break open bacterial cells, particularly those with tough cell walls like gram-positive bacteria, to extract DNA for sequencing. It ensures that a wide variety of bacterial DNA is available for profiling.

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22
Q

What is an OTU (Operational Taxonomic Unit)?

A

An OTU is a cluster of closely related DNA sequences used to classify bacteria in 16S rRNA studies. It groups sequences with a high similarity, often set at 97%, and is used to approximate species or genera in microbial community profiling.

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23
Q

What is an ASV (Amplicon Sequence Variant)?

A

An ASV is a higher-resolution method for classifying bacteria, representing unique sequences in 16S rRNA profiling without clustering them by similarity. ASVs offer more precise identification of microbial taxa by treating each unique sequence as its own species-level unit.

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24
Q

How do OTUs and ASVs differ?

A

OTUs group similar sequences based on a set similarity threshold (usually 97%), while ASVs identify unique sequences without clustering. ASVs provide more resolution and accuracy, while OTUs are less precise but historically used.

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25
Q

What are the limitations of OTU-based analysis in 16S rRNA profiling?

A

OTU-based analysis can introduce errors by grouping distinct species into the same unit due to the 97% similarity threshold, potentially losing important distinctions between bacterial taxa.

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26
Q

What are some of the main challenges with 16S rRNA profiling?

A

Challenges include PCR amplification biases, sequencing errors, difficulty distinguishing closely related species, and contamination from kits or the environment (kitome and splashome).

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27
Q

What is the purpose of sequencing variable regions in the 16S rRNA gene?

A

Variable regions in the 16S rRNA gene allow researchers to differentiate between bacterial species because these regions vary significantly between different bacteria, helping to identify which bacteria are present in the sample.

28
Q

What is alpha diversity?

A

Alpha diversity refers to the measure of species variety within a single sample or environment. It combines both richness (the number of species) and evenness (the distribution of species).

29
Q

What is species richness in the context of alpha diversity?

A

Richness is the total number of different species present in a sample. It does not take into account how evenly these species are distributed.

30
Q

What is species evenness?

A

Evenness refers to how equally individuals are distributed among the species in a sample. Higher evenness means the species are more equally distributed.

31
Q

What is the relationship between richness, evenness, and diversity?

A

Diversity incorporates both richness (the number of species) and evenness (how equally species are distributed). A sample can have high richness but low diversity if one species dominates, whereas higher evenness increases diversity.

32
Q

How does evenness affect diversity?

A

Higher evenness increases diversity because species are more equally represented, leading to a more “chaotic” or complex system. A system with low evenness, where one species dominates, has lower diversity.

33
Q

What is the Simpson index?

A

The Simpson index is a measure of diversity that gives more weight to the abundance of the most common species in a sample. It is often used to measure dominance in a community.

34
Q

What is the Shannon index?

A

The Shannon index measures the uncertainty or entropy in predicting the next species in a sample. It is sensitive to both richness and evenness, making it a good measure of overall diversity.

35
Q

When is alpha diversity considered high?

A

Alpha diversity is high when a sample has both high richness (many species) and high evenness (species are evenly distributed). This indicates a complex and healthy ecosystem.

36
Q

How can alpha diversity be used as a health indicator?

A

In the gut microbiome, higher alpha diversity is often associated with a healthier system, while lower alpha diversity can indicate conditions like obesity or other diseases. However, some microbiomes (e.g., skin, vaginal) may function better with lower diversity.

37
Q

How is the Shannon index calculated conceptually?

A

The Shannon index measures the uncertainty in predicting the species of the next individual sampled. It considers both the richness (number of species) and evenness (how equally species are represented). The higher the uncertainty (i.e., more evenness), the higher the Shannon index.

38
Q

How is the Simpson index calculated conceptually?

A

The Simpson index focuses on the probability that two individuals randomly selected from a sample belong to the same species. It gives more weight to the most abundant species, meaning samples with high dominance (one species being much more common) will have lower diversity according to this index.

39
Q

What is the difference between how the Shannon and Simpson indexes measure diversity?

A

The Shannon index places more emphasis on evenness and considers how unpredictable it is to guess the species of the next individual. The Simpson index, on the other hand, emphasizes dominance, meaning it gives more weight to the most common species in a sample.

40
Q

What does alpha diversity represent in microbiome analysis?

A

Alpha diversity is a summary measure that represents the diversity of species within a single sample, often combining species richness and evenness.

41
Q

How does Shannon diversity index differ from a simple species count?

A

The Shannon index considers both the abundance and evenness of species in a sample, not just the number of species (richness), and gives more weight to even distributions.

42
Q

What is the main difference between alpha and beta diversity in microbiome research?

A

Alpha diversity measures diversity within a single sample, while beta diversity compares the differences or similarities between multiple samples.

43
Q

How is Shannon diversity index used in analyzing microbiome data?

A

The Shannon index helps quantify the uncertainty in predicting the species of an individual selected at random, with higher values indicating more even and diverse communities.

44
Q

What does beta diversity measure in microbiome studies?

A

Beta diversity measures the dissimilarity between two samples by comparing their species composition, often represented in a distance matrix.

45
Q

Why is it important to control for variables like age, sex, or BMI when analyzing alpha diversity?

A

Controlling for these variables helps isolate the effect of interest (e.g., diversity or bone density) by accounting for potential confounding factors, leading to more accurate results.

46
Q

What is a principal coordinate analysis (PCoA) and how is it used in beta diversity?

A

PCoA is a method that reduces the dimensionality of beta diversity data to visualize differences between samples, often used to cluster samples based on their similarity or dissimilarity.

47
Q

How can a linear model be used in alpha diversity analysis?

A

A linear model can analyze how alpha diversity relates to other variables (e.g., BMI), allowing for adjustment of confounders like age, sex, or technical factors.

48
Q

What is the Bray-Curtis dissimilarity index used for?

A

The Bray-Curtis index quantifies the compositional dissimilarity between two samples by comparing the number of shared species and their abundance.

49
Q

What is the role of ordination in beta diversity analysis?

A

Ordination techniques, like PCoA, help visualize the similarity or dissimilarity of samples in a lower-dimensional space, clustering similar samples together and separating different ones.

50
Q

Why do beta diversity analyses often include distance matrices?

A

Distance matrices allow for pairwise comparisons between all samples, helping visualize the overall similarity or dissimilarity in species composition.

51
Q

What is the role of visualization (e.g., heatmaps, bar charts) in microbiome data analysis?

A

Visualization methods like heatmaps or bar charts are used to display the relative abundance of different species across samples, helping to identify patterns or significant differences between groups.

52
Q

How do you analyze alpha diversity in microbiome research?

A

To analyze alpha diversity, you measure the diversity within a single sample using indices like Shannon or Simpson, and you can compare these values between groups using statistical tests or linear models, while controlling for confounding factors like age, sex, and BMI.

53
Q

Why might linear models be used in analyzing alpha diversity?

A

Linear models allow researchers to account for multiple variables (e.g., age, BMI, smoking) when analyzing alpha diversity, making it easier to assess the true relationship between diversity and an outcome of interest while adjusting for confounding factors.

54
Q

How do you analyze beta diversity in microbiome research?

A

Beta diversity is analyzed by comparing the species composition between two or more samples. This is done using distance metrics (e.g., Bray-Curtis dissimilarity or UniFrac), creating a matrix that shows pairwise differences, and visualizing these relationships using methods like PCoA or ordination.

55
Q

What is the purpose of beta diversity ordination (e.g., PCoA)?

A

Ordination methods like Principal Coordinate Analysis (PCoA) reduce the complexity of beta diversity data by plotting the samples in a lower-dimensional space, allowing researchers to visualize which samples are more similar or dissimilar based on their microbial profiles.

56
Q

How can single OTUs (Operational Taxonomic Units) be analyzed?

A

Single OTUs can be analyzed by comparing their abundance across samples or groups using statistical tests (e.g., linear models or transformation techniques), identifying which specific bacteria are associated with variables like disease status, age, or lifestyle factors.

57
Q

What challenges arise when analyzing single OTUs?

A

One challenge is the presence of many zeros (absence of OTUs in certain samples), which can make it difficult to use standard statistical methods. To overcome this, researchers may use a two-step method: first analyzing presence/absence, then analyzing abundance in samples where the OTU is present.

58
Q

What is a two-step model in single OTU analysis?

A

A two-step model involves first analyzing whether an OTU is present or absent in samples (binary analysis), then analyzing the abundance of that OTU in samples where it is present. This method helps deal with the issue of many zero values in microbiome data.

59
Q

What is the role of compositional data in analyzing single OTUs?

A

Microbiome data are compositional, meaning that the relative abundances of OTUs are interconnected. When one OTU increases, others must decrease. This requires special statistical transformations (e.g., centered log-ratio transformation) to ensure accurate interpretation.

60
Q

What is the overall workflow of microbiomics?

A

The overall workflow of microbiomics involves several key steps: 1) Sample collection (e.g., stool, skin, etc.), 2) DNA extraction from the sample, 3) Targeted sequencing of microbial DNA (often 16S rRNA gene for bacteria), 4) Bioinformatic analysis to group sequences into OTUs or ASVs, 5) Assign taxonomy to sequences, 6) Statistical analysis of microbial diversity (alpha and beta diversity) and other factors, and 7) Interpretation of microbial community structure and its relation to health or disease.

61
Q

What are the two Rotterdam population-based cohorts in microbiomics research?

A

The two Rotterdam-based cohorts are:

Generation R: A longitudinal cohort study following children from pregnancy up to 20 years old, focusing on normal and abnormal growth and development. Stool samples were collected at age 9, and now the Generation R Next cohort is starting before pregnancy.

Rotterdam Study: A cohort study that started with participants over 55 years old, examining chronic diseases in the elderly. The study has collected over 1,400 stool samples for microbiome analysis.

62
Q

Why is 16S rRNA profiling currently the preferred technique for microbiome studies?

A

16S rRNA profiling is preferred because it is cost-effective, allows for high-throughput analysis, and provides insights into bacterial taxonomy by targeting the 16S rRNA gene, which is present in all bacteria but varies enough to distinguish between different species. It is a well-established method for studying microbial communities.

63
Q

How is the field of microbiomics evolving with respect to sample types other than stool?

A

While stool samples have been the primary focus for gut microbiome studies, other niches such as the skin, oral cavity, lungs, and vaginal microbiota are gaining interest. These niches provide insights into localized microbial communities and their role in different health conditions beyond the gut, demonstrating the expanding scope of microbiomics.

64
Q

How is the field of microbiomics evolving rapidly?

A

The field of microbiomics is evolving rapidly due to advances in sequencing technologies, bioinformatic tools, and the growing availability of large-scale human cohort datasets. Studies are moving from focusing only on bacterial communities (16S rRNA) to examining entire microbial genomes (metagenomics) and functional outputs (metabolomics, proteomics), with increasing interest in niches beyond the gut.

65
Q

What are some of the advancements in microbiomics beyond 16S rRNA profiling?

A

Beyond 16S rRNA profiling, advancements include metagenomics, which involves sequencing all DNA in a sample to explore all microbes (bacteria, fungi, viruses) and their functions, metatranscriptomics for studying RNA activity, and metabolomics for analyzing metabolites produced by microbes, providing a more comprehensive view of microbial function and interaction in the host.

66
Q
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SCII (7 decks)

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