Chapter 1. Mirror Experiment Activity 27.4

Mirror Experiment Activity 27.4

The experiment described below explored the same concepts as the one described in Figure 27.4 in the textbook. Read the description of the experiment and answer the questions below the description to practice interpreting data and understanding experimental design.

Mirror Experiment activities practice skills described in the brief Experiment and Data Analysis Primers, which can be found by clicking on the “Resources” button on the upper right of your LaunchPad homepage. Certain questions in this activity draw on concepts described in the Experimental Design primer. Click on the “Key Terms” buttons to see definitions of terms used in the question, and click on the “Primer Section” button to pull up a relevant section from the primer.

Experiment

Background

Chloroplasts are believed to have originated through endosymbiosis—that is, by an ancestral cyanobacterium being engulfed by a eukaryotic cell. Over time, the engulfed cyanobacterium lost certain cellular structures and portions of its genome and became the modern-day chloroplast that cannot survive (independently) outside of a cell. Researchers have actively debated whether the chloroplasts of all photosynthetic organisms – green and red algae, vascular plants, and even photosynthetic amoebas – are derived from a single endosymbiotic event. Do all chloroplasts share the same cyanobacterial ancestor, or did a chloroplast-forming endosymbiosis occur more than once in the evolution of photosynthetic organisms?

Hypothesis

Unlike ancient and modern-day cyanobacteria, most chloroplasts do not possess a cell wall. An exception is the chloroplasts contained within P. chromatophora, a photosynthetic amoeba (recall Fig. 27.5). The chloroplasts of these organisms possess rudimentary cell walls. This observation lead Birger Marin and colleagues to hypothesize that the chloroplasts of P. chromatophora are the result of a recent endosymbiotic event, one that occurred after the formation of chloroplasts in algae. In other words, the chloroplasts of algae and P. chromatophora do not share a common cyanobacterial ancestor.

Experiment

Marin and colleagues compared the DNA sequences of ribosomal genes found in chloroplasts of red and green algae, cyanobacteria, and the chloroplasts of P. chromatophora. They also compared the amino acid sequences of the RNA products of these genes. Based on this collective data, researchers derived a phylogenetic tree detailing the evolutionary relationships among bacteria, cyanobacteria, and the chloroplasts found in different organisms.

Results

Cyanobacteria can be grouped into two categories depending on the type of rubisco enzyme they express. Some cyanobacteria produce rubisco 1A, and others synthesize rubisco 1B. Following genetic sequence analyses, it was determined that the chloroplasts of P. chromatophora are most closely related to cyanobacteria that express rubisco 1A. However, the genetic sequences of chloroplasts in red and green algae are remarkably similar to the sequence found in rubisco 1B cyanobacteria. These results provided evidence that the chloroplasts of P. chromatophora and those of red and green algae are the result of two separate endosymbiotic events (Fig. 1).

Source

Marin, B., et al. 2005. “A plastid in the making: evidence for a second primary endosymbiosis.” Protist 156: 425–32.

Figure 1

Question 1 of 9

Question

When researchers first discovered P. chromatophora, they noticed the appearance of two large, green objects within these amoebas. Given the presence of cell walls within these structures, scientists were unsure whether these objects were “true” chloroplasts. Marin and colleagues hypothesized that these structures were chloroplasts that resulted from a recent endosymbiotic event – an event separate and distinct from the endosymbiosis that resulted in the chloroplasts present in photosynthetic algae. What could have been suitable alternative hypotheses to explain what these green structures were in P. chromatophora?

These green structures could be food that the amoebas engulfed by endocytosis and are in the process of digesting.

These green structures could be chloroplasts that resulted from the same endosymbiotic event that formed algal chloroplasts; a mutation could have resulted in the cell walls being preserved.

These green structures may not be “true” chloroplasts, but symbiotic algae (or cyanobacteria) that contain a complete genome, and that associate with P. chromatophora.

These green structures could be new organelles unique to P. chromatophora, with as yet unknown functions.

All of the answer options could serve as suitable alternative hypotheses.

Alternative Hypotheses

Any hypothesis that differs from the null hypothesis (which predicts no effect).

Table

Experimental Design

Types of Hypotheses

A hypothesis, as we saw in Chapter 1, is a tentative answer to the question, an expectation of what the results might be. This might at first seem counterintuitive. Science, after all, is supposed to be unbiased, so why should you expect any particular result at all? The answer is that it helps to organize the experimental setup and interpretation of the data.

Let’s consider a simple example. We design a new medicine and hypothesize that it can be used to treat headaches. This hypothesis is not just a hunch—it is based on previous observations or experiments. For example, we might observe that the chemical structure of the medicine is similar to other drugs that we already know are used to treat headaches. If we went into the experiment with no expectation at all, it would be unclear what to measure.

A hypothesis is considered tentative because we don’t know what the answer is. The answer has to wait until we conduct the experiment and look at the data. When an experiment predicts a specific effect, as in the case of the new medicine, it is typical to also state a null hypothesis, which predicts no effect. Hypotheses are never proven, but it is possible based on statistical analysis to reject a hypothesis. When a null hypothesis is rejected, the hypothesis gains support.

Sometimes, we formulate several alternative hypotheses to answer a single question. This may be the case when researchers consider different explanations of their data. Let’s say for example that we discover a protein that represses the expression of a gene. Our question might be: How does the protein repress the expression of the gene? In this case, we might come up with several models—the protein might block transcription, it might block translation, or it might interfere with the function of the protein product of the gene. Each of these models is an alternative hypothesis, one or more of which might be correct.

Correct.

Incorrect.

Question 8 of 9

Question

Marin and colleagues used PCR to amplify DNA from chloroplasts or cyanobacteria, which they then sequenced. A typical PCR reaction requires researchers to add template DNA, primers specific for a gene of interest, DNA polymerase, and nucleotides to a tube. This tube is then exposed to cycles of heating and cooling, which results in a target DNA sequence being replicated multiple times. What could Marin and colleagues have used as negative controls for their PCR reactions?

A PCR reaction that contains template DNA derived from green algae chloroplasts, green algae-specific primers, DNA polymerase, and nucleotides.

A PCR reaction that contains template DNA derived from P. chromatophora nuclei, P. chromatophora-specific primers, DNA polymerase, and nucleotides.

A PCR reaction that contains template DNA derived from P. chromatophora chloroplasts, P. chromatophora-specific primers, DNA polymerase, and nucleotides.

A PCR reaction that contains template DNA derived from red algae chloroplasts, red-algae-specific primers, DNA polymerase, and nucleotides.

A PCR reaction that contains P. chromatophora-specific primers, DNA polymerase, and nucleotides – but no template DNA.

Correct.

Incorrect.

Negative Control

A group in which the variable is not changed and no effect is expected.

Table

Experimental Design

Testing Hypotheses: Controls

Hypotheses can be tested in various ways. One way is through additional observations. There are a large number of endemic species on the Galápagos Islands. We might ask why and hypothesize that it has something to do with the location of the islands relative to the mainland. To test our hypothesis, we might make additional observations. We could count the number of endemic species on many different islands, calculate the size of each of these islands, and measure the distance from the nearest mainland. From these observations, we can understand the conditions that lead to endemic species on islands.

Hypotheses can also be tested through controlled experiments. In a controlled experiment, several different groups are tested simultaneously, keeping as many variables the same among them. In one group, a single variable is changed, allowing the researcher to see if that variable has an effect on the results of the experiment. This is called the test group. In another group, the variable is not changed and no effect is expected. This group is called the negative control. Finally, in a third group, a variable is introduced that has a known effect to be sure that the experiment is working properly. This group is called the positive control.

Controls such as negative and positive control groups are operations or observations that are set up in such a way that the researcher knows in advance what result should be expected if everything in the study is working properly. Controls are performed at the same time and under the same conditions as an experiment to verify the reliability of the components of the experiment, the methods, and analysis.

For example, going back to our example of a new medicine that might be effective against headaches, you could design an experiment in which there are three groups of patients—one group receives the medicine (the test group), one group receives no medicine (the negative control group), and one group receives a medicine that is already known to be effective against headaches (the positive control group). All of the other variables, such as age, gender, and socioeconomic background, would be similar among the three groups.

These three groups help the researchers to make sense of the data. Imagine for a moment that there was just the test group with no control groups, and the headaches went away after treatment. You might conclude that the medicine alleviates headaches. But perhaps the headaches just went away on their own. The negative control group helps you to see what would happen without the medicine so you can determine which effects in the test group are due solely to the medicine.

In some cases, researchers control not just for the medicine (one group receives medicine and one does not), but also for the act of giving a medicine. In this case, one negative control involves giving no medicine, and another involves giving a placebo, which is a sugar pill with no physiological effect. In this way, the researchers control for the potential variable of taking medication. In general, for a controlled experiment, it is important to be sure that there is only one difference between the test and control groups.

Testing Hypotheses: Variables

When performing experiments, researchers manipulate the test group differently from the control groups. This difference is known as a variable. There are two types of variables. An independent variable is the manipulation performed on the test group by the researchers. It is considered “independent” because the researchers could choose any variable they wish. The dependent variable is the effect that is being measured. It is considered “dependent” because the expectation is that it depends on the variable that was changed. In our example of the headache medicine, the independent variable is the type of medicine (new medicine, no medicine, placebo, or medicine known to be effective). The dependent variable is the presence or absence of headache following treatment.

In designing experiments, there is an additional issue to consider: the size of each of our groups. In order to draw conclusions from our data, we need to make sure that our results are valid and reproducible, and not merely the result of chance. One way to minimize the effect of chance is to include a large number of patients in each group. How many? The sample size is the number of independent data points and is determined based on probability and statistics, the subject of the next primer.