The Fieldston Science Bulletin

By: Ellie Usdin

Abstract:

This experiment was conducted to determine the immune system effects of unidentified lectins produced by microbiota in the gastrointestinal region. The functions of the lectins are useful in identifying possible triggers of Crohn’s disease and inflammation in the GI tract. Several previously unidentified lectins were produced by E. coli bacteria and were then mixed with human PBMC immune cells to observe interleukin responses to the different types of lectins. Analysis of the interleukin heat maps demonstrated that there were no significant differences among the reaction of PBMCs to the different lectins. This could be attributed to systematic gating errors in relation to the data analysis, or it could signify that all lectins present in the experiment from different microbiota were similar in composition. Future experiments will expand on this idea to further understand the functions of the lectins and the effects of various microbiota in the gastrointestinal region.

Introduction:

Background of the Human Microbiome

The human body is filled with microbiota communities in all parts of the body including the skin and gut, creating the human microbiome. Many estimates even predict that there are more bacterial cells in the body compared to human cells (Sender et al., 2016). Until technological advancements in the mid-2000s, biologists rarely researched the gastrointestinal microbiome. Many labs did not have access to materials to properly identify microbiota and were therefore unable to properly diagnose and treat gastroenterological diseases. This has led to a lack of previous experiments examining microbiota.

The Human Microbiome Project (HMP) was a program that worked with labs around the United States from 2007 through 2016 to find and classify gastrointestinal microbiota (Turnbaugh et al., 2007). The characterizations found are helpful in understanding the significance of the microbiota in the gastrointestinal system and developing further research and treatment plans for gastrointestinal diseases. Additionally, the project helped identify which gastrointestinal systems appear “healthy” compared to ones that are inflamed (Cho & Blaser, 2012).

The HMP and other projects found mutualistic interactions between microbiota and their hosts. Many microbiota provide hosts with additional defense mechanisms, aid in their digestion of nutrients, and more. Microbiota are also known to produce metabolites such as amino acids and vitamins that their hosts use for a variety of purposes. Additionally, certain groupings of microbiota may be used by scientists to classify the species of their host. Gut bacteria have been observed to vary between individuals. For example, a study conducted with groups of twins found that identical twins had the same average similarity between their clusters of gut bacteria compared to fraternal twins (Turnbaugh et al., 2008). In relation to the human microbiome, different areas of the body are home to different clusters of microbiota. For example, the concentrations of microbiota in the gut differ from the concentrations on the skin.

Additionally. concentrations of microbiota may change in the human body over time. Diseases such as cancer, drug treatments, and other conditions can cause disruption in the human body microbiome. For example, it is hypothesized that certain concentrations of bacteria such as Fusobacterium nucleatum in the colon are involved in the development of colon cancer, impacting cell development and pathogenesis. F. nucleatum has been observed to be more common in patients with colon cancer compared to patients with a healthy colon (Cho & Blaser, 2012).

Bacteria may affect symptoms of Crohn’s disease and Ulcerative Colitis. Crohn’s disease is an autoimmune disease in the gastrointestinal system that is greatly altered by the presence of different concentrations of bacteria. Crohn’s disease makes the stomach lining and intestines inflamed, causing discomfort and occasionally creating ulcers. In patients with Crohn’s, immune cells around the intestine attack the body in order to mitigate a response that they perceive is unwanted from the bacteria in the gut. Ulcerative Colitis is a disease similar to Crohn’s except with symptoms in a centralized area of the intestines compared to symptoms present all around the gastrointestinal tract. Numerous experiments have found that the lack of diversity in gut microbiota may trigger Crohn’s symptoms (Manichanh, 2006). Additionally, certain concentrations of bacteria may be linked to these gastrointestinal autoimmune diseases (Mondot et al., 2011).

Unfortunately, there are no current cures for Crohn’s disease according to the U.S. Department of Health and Human Services (National Institute of Diabetes and Digestive and Kidney Diseases, 2019). Treatment plans for Crohn’s include immunosuppressant drugs, IV therapies, and in extreme cases, surgery. Many medications have been approved to alleviate symptoms of the disease including forms of aminosalicylates and corticosteroids (Gade et al., 2020). However, most treatment options cause many unwanted side effects such as metabolic issues, nausea, and more long-term medical issues (Kumar et al., 2022). Further research on understanding these reactions between gastrointestinal microbiota and their human hosts allows for the development of effective treatments to gastrointestinal diseases with less side effects.

Lectin Identification

One of the important tools in identifying microbiota are lectins. Lectins have been useful in classifying bacteria and determining functions of microbiota in the gastrointestinal region. Lectins are protein identifiers that bind to carbohydrate receptors on cells. This interaction causes multiple functions including aiding in cell signaling, interacting with microbiota, and stimulating immune responses (Song et al., 2013). Lectins are specifically present on gastrointestinal microbiota (Cohen et al., 2022). Commensal bacteria effector genes (Cbegs) trigger types of lectins that can be used to identify bacteria based on their genetic makeup. Through using genetic screening techniques, Cbegs on bacteria can be recognized to help determine bacterial identities.

Specific lectins studied

Specifically, past experiments conducted by Dr. Louis J. Cohen identified a lectin titled Cbeg5 which activates myeloid cells (a group of red blood cells) and other immune cells (Cohen et al., 2022). To confirm Cbeg5’s job, bacteria with Cbeg5 were placed onto Peripheral blood mononuclear cells or PBMCs. The Cbeg5 lectins were observed to trigger immune cells including CD14+ monocytes.

This experiment examined multiple lectins that have not been previously studied in order to understand PBMC reactions to them. The lab selected random lectins that were unidentified by past experiments to test their impact on the cells. Additionally, Cbeg5 was tested again as the positive control to set a basis for reactions, as it has been proven that PBMCs react to it. The lab hypothesized that different lectins would trigger different inflammatory reactions which then identifies their function in the microbiome.

Methods:

In order to conduct this experiment, E. coli cells were inoculated with specific lectin genes and cloned. After, the presence of the lectins was confirmed through sequencing and the proteins were purified and separated. Peripheral blood mononuclear cells (PBMCs) were then separated from blood samples and were run through flow cytometry protocol with the various lectins. The PBMCs were tagged with antibodies that highlighted when cell immune responses were triggered from the presence of the lectins. All samples were then analyzed via Cytobank (Cytobank, n.d.). Below is a description of each method. The following methods are abstracted from Cohen, 2022.

Cloning and Insertion of Lectins

First, primer was used to amplify the DNA sequences of all lectin genes present in the experiment. After the genes were sequenced, they were inserted into plasmids. The plasmids created by the primer sequencing were then amplified by PCR protocols by New England Biolabs. After PCR, the plasmids were inserted into electrocompetent E. coli cells. The E. coli cells were inoculated into LB medium with kanamycin, and the presence of the lectin plasmids was confirmed through Sanger sequencing. For detailed information on the cloning process, see the methods section of Cohen, 2022.

Purification of Proteins

After the presence of the lectin plasmids were confirmed in the E. coli, a protocol was run to isolate the lectins produced by the plasmids. First, the E. coli were inoculated in the LB medium again and treated with IPTG, a molecule that triggers protein expression (Meridian Bioscience, n.d.). The proteins were separated through polyacrylamide gel electrophoresis. The protocol was followed for all E. coli colonies described in Cohen, 2022 which allowed the proteins to be separated and purified.

PBMC Isolation

PBMCs were used to test the immune system response to the lectins. The PBMCs were obtained from healthy consentual patients (for more information about the consent process, see Cohen, 2022). PBMCs were purified from blood cells using ficoll and then separated the blood into its components of plasma, red blood cells, and PBMCs (Kuhns, 2008). The PBMC cell layer was obtained through a pipette and was resuspended in PBS buffer. The cell density of the PBMC cells was then determined via a CelloMeter cell counter (Mitchell, 2023). For an in depth explanation on PBMC isolation, see the Cohen paper.

Flow Cytometry/CyTOF

After the PBMCs were obtained, they were plated in wells at a density of one-million cells per well in a ninety-six well microplate. Each lectin was then added to the wells at a concentration of 500nM and was incubated with the PBMCs for six hours at 37 degrees Celsius. After six hours, the PBMCs were obtained and analyzed using CyTOF software (Iyer et al., 2022). The PBMCs were prepared for CyTOF by being stained with antibodies that correspond to specific immune cell types. The antibodies bound to certain cell surface markers, allowing differentiation to which types of immune cells were triggered by the addition of the lectin via cytokine reactions. The samples were processed in a CyTOF2 Mass Cytometer and all data was sent to Cytobank for analysis (Penn Institute for Technology, n.d.).

CyTOF Analysis

All samples were analyzed through Cytobank software (Cytobank, n.d.). Immune cell types were identified through their cell surface markers and immune cell responses were identified through the expression of cytokines. Specific PBMCs that had reactions were gated by the research team based on cytokine expression. Data were analyzed through looking at the increase in cytokine expression in each specific immune cell population via heat maps.

Results and Analysis:

Overall, it was observed that there were no significant differences in reaction to the various lectins among all tested immune cells. The amount of interleukins secreted by the immune cells seemed to be similar in response to all lectins. The exact numbers of the concentrations are not given but are depicted by differences in shading.

This image depicts a heat map of the reactions of CD14+ monocytes to the lectins listed on the left.

The CD14+ reaction is characterized by the releases of interleukins, a form of cytokines that are produced by immune cells. The numbers represented in the heat map within the range of 0.48 to 940.1 are the calculations of the average number of interleukins bound to each cell across all immune cells that are secreting the particular interleukin. The positive control group is Cbeg5 in the first row whose impact has been observed in the past through other experiments. The negative control group is the last row with PBMCs that did not have any lectins present.

As observed in the heat map, all lectins triggered similar immune responses in their interleukin production. The varying colors on the graph are similar throughout all lectins and show analogous responses by the immune cells to each lectin. Out of all of the immune responses, the CD14+ population represents these results the most accurately because the range of values for the average number of interleukins is very large. Although there are some visible differences between the lectins, for example the reaction to IL-6 between Lectin 19 and Lectin 1, the interleukin expression in the graph is based on subjective visual opinions as to what defines a “significant difference” in the expression of interleukins. Because the graphs are analyzed visually, the subjectivity of the test based on the gating makes it unable to determine accurate numbers. The researchers decided to choose this form of testing because there were no other further statistical tests that they had access to (pers. com.).

The two types of interleukins in the CD14+ heat map that showed a larger reaction to the lectins, Il-1b and Il-6, are general inflammatory reactors. Unlike the other interleukins such as Il-17 which specifically relates to the triggering of T cells by other communicating immune cells, these two interleukins that were most present only related to general responses by the immune system. This means that there can be a variety of factors as to why the immune system is being triggered.

The CD14+ graph produced the most useful data for analysis because it has the highest range of values for the average interleukin expression. Although other graphs depict similar results (as in Figure 2), the interleukin expression in the graphs are much lower in range. For example, the interleukin expression of natural killer (NK) cells is shown below:

Although there seem to be some trends of the interleukin secretion by the NK cells, the range of values for the interleukin expression is extremely low (varying from an average of 0.18 to an average of 3.98). This low range means that the colors on the heat map are more sensitive from changes in the concentrations of interleukins.

Discussion and Conclusion:

The experiment found that there were no significant differences among the amounts of interleukin released among all of the lectins. These results may be due to two main reasons: all cells are reacting to very similar lectins, or there is a systematic error throughout the experiment. First, there is a possibility that all lectins present happened to be very structurally similar to one another which then triggered similar responses. This could be possible because all of the lectins found are secreted in bacteria inhabiting the same region of the GI system. In a future experiment, sequencing of the bacteria can be examined to compare how the different types of bacteria compare to one another related to the lectins that they produce. This can be helpful in understanding the cell reactions to the different lectins. This experiment was unable to fully understand the function of each specific lectin because all of them triggered similar reactions.

In relation to the data analysis, the manual gating mechanisms for the interleukin expression can be extremely subjective. The researchers are the people who determine what is considered to be an interleukin response. Because this process is not automated, it is prone to error as many people may have their own definitions of what is considered interleukin secretion. For example, there may be errors associated with the steps of the experiment that would then have created inaccurate gates compared to the actual number of interleukins expressed for each type of lectin. If this is true, systematic errors biasing data in one direction may occur. Inaccurate gating may remove the small differences between different interleukin responses which may have been significant in determining the functions of the lectins.

In the future, a computational analysis will be conducted to see if computer generation and machine learning can detect differences in the interleukin reactions in the immune cells that human interpretations of the data may be missing. Through using resources such as viSNE (Amir et al., 2013) and SPADE (Hesler, 2019), a computer can organize all of the cells into groupings related to their expression of the specific interleukins. This can then allow for analysis that is focused more on an algorithm rather than a subjective gating experience.

In the future, more experiments will be conducted with these lectins to further determine their impacts on cells in the human gastrointestinal microbiome. Additionally, the lectins will be inserted into mouse models to test their impacts on the mice gastrointestinal system. Overall, future experiments will attempt to identify the function of these lectins and other lectins present in bacteria in the gastrointestinal region to examine the immune responses of GI cells. The similarities found among the lectins has created a strong starting point for future experiments as it offers insights into the relationship between microbiota and their human environment.

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Acknowledgements:

Thank you so much to Dr. Louis J. Cohen for allowing me to work in your lab and collect data. Thank you to Shishir Singh for guiding me through the data collection process and helping conduct the experiment. Finally, thank you to Mr. Waldman for assisting me through the writing process of the paper.