Abstract

Innate immunity is an immunological process that encompasses the cells and the mechanisms that function as the first line of defense against foreign organisms. Innate immune cells including macrophages detect pathogens and apply various immune responses. Macrophages engulf the pathogens which in turn leads to activation and release of specific proinflammatory cytokines such as TNF-α to attract other cells to the site of inflammation. In this experiment, we investigated the process of phagocytosis in raw macrophages challenged with GFP-E coli through detection of TNF-α expression using anti-TNF-α antibody. Moreover, we investigated the viability of the cells using Annexin V-FITC and Propidium Iodide stains. Our results showed that TNF-α expression peaked 30 minutes after incubation (9.7%) with 1.7% and 2.4% at 15 and 30 minutes after incubation, respectively.

Materials and Methods

We followed the lab manual (MMI 490/590 – SECTION 4: MULTI-PARAMETRIC ANALYSIS OF INNATE IMMUNE CELL FUNCTION BY FLOW CYTOMETRY by Dr. Aja Rieger)

Experiment 1: Measurement of cell viability.

The experiment investigated the apoptotic effect of different concentrations of ethanol and cold shock on raw macrophage cells. We used eight samples, four of them had cells which were challenged with different concentrations of ethanol. One sample was used to observe the effects of cold shock. Then, we used the other three for compensation and to detect the auto-fluorescence of the cells (Figure 1). We used Annexin V-FITC (BioLegend Cat. # 6409063) as a marker to detect Annexin V (a cellular protein that binds Phosphatidylserine and is mainly used to visualize apoptotic cells). Moreover, we added Propidium Iodide (PI, BD Pharmingen Cat. # 51-66211E) which is an intercalating agent that binds to nucleic acids and is commonly used to detect necrotic cells. Cells were then incubated for 1 hour and then visualized using Attune NxT Flow Cytometer system.

Figure 1. Experimental design of Experiment 1. EtOH: ethanol, PI: Propidium Iodide.

Experiment 2. Detection of innate immune cell activation.

In the second experiment, we aimed to observe the expression levels of TNF-α as an immune response of challenging the cells with E. coli bacteria. We used eight samples. Three of them were cells that were challenged with GFP-E. coli (Provided by Dr. Daniel Barreda (CW325A1, Biological Sciences) at different incubation periods. GFP-E. coli was not added to one sample (no phagocytosis) to observe the amount of TNF-α secreted by the cells without phagocytosis. Other samples were used for compensation and to detect the auto-fluorescence of the cells (Figure 2). We added GolgiPlug (BD Cat. # 51-2301KZ), a protein transport inhibitor, to prevent intracellular cytokines from exiting the cells. We detected TNF-α using Anti-TNF-α (BioLegend Cat. # 506314). We analyzed samples visualized Attune NxT Flow Cytometer system.

Figure 2. Experimental design of Experiment 2.

Results

Experiment 1.

With increasing the concentration of ethanol, we noticed an increase in both necrotic and apoptotic cells and a decrease in healthy cells population (Figure 3). However, we noticed a decrease in apoptotic cells with increasing ethanol concentration which was against our expectations. It is worthy to mention that the sample containing 0% ethanol showed a high rate apoptotic cell (62.4%). Meanwhile, the number of healthy cells was the highest in the sample challenged with cold shock (Figure 3).

Figure 3. The percentage of necrotic, apoptotic, both necrotic/apoptotic, and healthy raw cells challenged with different concentrations of ethanol and cold shock.

Experiment 2.

The sample with no phagocytosis showed 0.006% of % gated TNF-α. On the other hand, % gated TNF-α of samples with 15, 30, and 60 minutes phagocytosis was 2.8%, 9.18%, and 1.72%, respectively (Figure 4). TNF-α expression was lower than expected and peaked in cells with 30 minutes phagocytosis.

Figure 4. % gated of TNF-α expression from raw macrophages at different time points of incubation with E. Coli.

Discussion

Ethanol can induce cell apoptosis through different mechanisms [1]. However, measurement of cell viability showed that with increasing ethanol concentration, the number of apoptotic cells decreased. This may be explained by the high number of apoptotic cells that was noticed at 0% ethanol which indicated that our raw cells were not in the expected healthy state before challenging them with ethanol. Meanwhile, both apoptosis and necrosis increased with increasing ethanol concentration. This may be explained by a previous study who noticed the role of ethanol in cell necrosis especially at high concentrations [2]. The amount of expressed TNF-α was lower than expected in addition to the low level of TNF-α which was detected at 60 minutes phagocytosis. We may contribute that to an experimental error which occurred during adding the permeabilization buffer which was essential to allow anti-TNF-α antibodies to go inside the cells to bind TNF-α proteins. This, in turn, would not allow proper labeling and visualization of TNF-α using the flow cytometry.

 

References

1. Asai K., Buurman W. A., Reutelingsperger C. P. M., Schutte B. and Kaminishi M. (2003) Low concentrations of ethanol induce apoptosis in human intestinal cells, Scandinavian. Journal of Gastroenterology, 38(11): 1154-1161, DOI: 10.1080/00365520310006252
2. Castilla R., González R., Fouad D., Fraga E., and Muntané J. (2004) Dual effect of ethanol on cell death in primary culture of human and rat hepatocytes. Alcohol Alcohol, 39(4):290-296, DOI: 10.1093/alcalc/agh065
3. Cole J., Aberdein J., Jubrail J., and Dockrell DH.(2014) The role of macrophages in the innate immune response to Streptococcus pneumoniae and Staphylococcus aureus: mechanisms and contrasts.Advanced Microbiology and Physiology, 65:125-202. DOI: 10.1016/bs.ampbs.2014.08.004.
4. Lab Manual. MMI 490/590 – SECTION 4: MULTI-PARAMETRIC ANALYSIS OF INNATE IMMUNE CELL FUNCTION BY FLOW CYTOMETRY by Dr. Aja Rieger.

 

Abstract

Interactions between the diet, the gut microbiome, and host characteristics that may influence the lifespan of the host are being investigated. Different kinds of food may have an impact on our health induced by the metabolic outputs of commensal bacteria living in our intestine. In this experiment, we aimed to the investigate the effects of different diet (high protein, moderate protein, and moderate carbohydrate diets) on the microbiome structure in the gut and the mortality rate of Drosophila melanogaster model. We identified the bacterial population composition through deep sequencing of the bacterial 16S DNA isolated from the bacteria associated with flies. Our results showed that different diets led to different bacterial populations. Moreover, we found out that high protein diet resulted in higher mortality rates. In conclusion, food may have effects on bacterial composition in the gut which in turn may affect the lifespan.

 

Materials and Methods

We followed steps in the lab manual (MMI 490/MMI 590 Advanced Techniques in Medical Microbiology and Immunology, Section 3: Deep Sequencing by Dr. Edan Foley).

A summary of the experimental steps is shown in Figure 1. Flies were maintained on different diets as shown in Table 1.

Figure 1. Schematic diagram of the key experimental steps.

 

Table 1. Dietary regimes tested in this experiment.

 

Results

After retrieving the data from MiSeq, we visualized it using an online software (https://view.qiime2.org). A summary of the quality of the sequencing libraries is shown in Figure 2. The quality dropped after base pair 260 so that base pair 260 resembled a threshold for quality trimming to reduce the noise and to improve the clustering of our sequences. Then, we managed to identify bacterial species and their frequency in each of the four groups (Figure 3). Based on our results, there is a change in the bacterial composition in relation to changing diets.

Figure 2. Quality scores for all sequences (reverse reads) at each position along their length.

Figure 3. A stacked bar plot showing the taxonomic abundance of bacteria in each group.

Following that, we conducted a Principal Coordinates Analysis (PCoA) of the four groups to detect any difference between them (Figure 4). Groups one and two seemed different from groups three and four.

 

 

Figure 4. A Principal Coordinates Analysis (PCoA) plot of the four groups. The plot was generated through Bray-Curtis distances using an online software (https://view.qiime2.org)  (g: group).

 

Discussion

Understanding human biology can be done only through analysis of both the host and the surrounding environment. The human gastrointestinal tract provides habitat for trillions of bacteria creating the gut microbiota which in turn perform essential biological functions necessary for human life [1]. Factors leading to changes in the bacterial composition may affect human health in different ways. In this experiment, we aimed to investigate the effects of changing diets on the bacteria in the gut of flies and how this may affect their lifespan. For instance, different diets lead to different bacterial composition (Figure 3). Moreover, we noticed that increasing protein content in the diet led to a higher mortality rate which was associated with high frequencies of certain species of bacteria (e.g. Firmicutes and OD1) as shown in Figure 3. We may consider this as a correlation. However, more investigations should be performed to link these changes in bacterial composition as a probable cause of high mortality rate.  Our results are supported by another study where they found alterations in bacteria associated with a high-fat diet, including a decrease in Bacteroidetes and an increase in both Firmicutes and Proteobacteria [2]. Based on our analysis, we assume that flies of group X were raised on a high carbohydrate diet (80% carbohydrate, 20% protein).

 

References

1- Herbert Tilg, Alexander R.Moschen. Food, immunity, and microbiome.  Gastroenterology.148 (6), 2015, P: 1107-1119.

2- Marie A. Hildebrandt, Christian Hoffmann, et al. High-Fat Diet Determines the Composition of the Murine Gut Microbiome Independently of Obesity. Gastroenterology.
137 (5), 2009, P: 1716-1724.e2.

3. Lab manual (MMI 490/MMI 590 Advanced Techniques in Medical Microbiology and Immunology, Section 3: Deep Sequencing by Dr. Edan Foley

Abstract:

Toll-Like Receptor 3 (TLR3) in macrophages can detect viral dsRNA in endosomes leading to activation of different immunological pathways. One of those pathways is the NF-kB pathway which in turn controls the expression of genes that regulate the antiviral response. Due to the established key rules of antiviral responses in different diseases, many researchers are keen to identify drugs that affect NF-kB activation. In our experiment, we aimed to investigate the effect of certain small molecules on the TLR3/NF-kB axis using a designed drug library. We screened 320 validated compounds as potential inhibitors of the pathway. We used an immunofluorescent anti-NF-κB antibody to detect the nuclear translocation of NF-κB. Following that, using high content imaging, we detected three preliminary hits which may represent potential drugs that inhibit the NF-κB pathway.

 

Material and Methods:

We followed the steps in the lab manual which included:

1. A cell culture of macrophages.
2. Using robots (video 1), we managed to perform an automated liquid handling for the 384 well plates (A and B) (including adding Poly I:C and the drug library).
3. We added a fluorescently labeled goat anti-rabbit antibody to visualize the anti-NF-κB antibody, and Hoechst to visualize nuclear DNA.
4. Using Operetta High-Content Imaging System (Figure 1), we managed to perform high-content imaging of the plates.
5. The last step included analysis of screen data:

• We exported the data in the form of excel sheet (Figure 2).
• We calculated the Z score for each well using this equation.

                       Z score for well x = (% translocated in well x-med_a)/stdev_a

• Then we plotted the Z score values of both plates A and B and we calculated the R² value.

 

Figure 1. Operetta High-Content Imaging System.

• We chose our preliminary hits based on the following criteria:

1- R² value

2- Average Z score (plate A and B) should be less than -2.

3- Images of the plate should show clearly failed NF-κB nuclear translocation (Figure 3).

4- Heat maps (Figure 4).

5- Selectivity of the drug.

6- Off-targets and side effects of the drugs.

Figure 2. Data of plate A exported from Operetta.

 

Figure 3. Image of the well containing Enoximone showing NF-κB (green) and nuclear DNA (blue) in plate A.

Figure 4. Heat maps of plate A and B.

Results:

According to Figure 5, the R² value of both plates was 0.1272 which was low compared to other groups. Based on the criteria mentioned in the methodology section, we chose three potential compounds:

Src/Syk Kinase Inhibitor

• Broad spectrum (would inhibit trafficking pathways and signal transduction of several pathways)
• Inhibits NLRP3 inflammasome.
• Average Z score: -3.00

MNS

• an opioid analgesic, a derivative of cyclohexanol. It is a non-selective agonist of mu-, delta- and kappa-receptors in the CNS.
• MNS causes sedation. At correct therapeutic doses, this medication almost does not cause respiratory depression.
• Average Z score: -3.6

Enoximone

• Enoximone is a selective phosphodiesterase inhibitor with vasodilating and positive inotropic activity that does not cause changes in myocardial oxygen consumption. It is used in patients with congestive heart failure. Trials were halted in the U.S., but the drug is used in various countries.
• Average Z score: -4.2

 

Figure 5. Scatter plot using  Z- score values in plate A (x-axis) and plate B (y-axis).

Due to the low value of R², we decided that our preliminary hits could not be used for further investigations as potential inhibitors of the NF-κB pathway. Meanwhile, we have selected three other compounds from the screening of other groups.

Discussion:

Nowadays, several diseases and health issues are related to antiviral responses. So that, many scientists are investigating different molecules which may act as modifiers of the NF-κB pathway. As shown in Figure 6, TLR 3 identifies viral dsRNA and then triggers the NF-κB signal transduction. Following that, a sequence of reactions occurs in the cell leading to the nuclear translocation of p50/p65 NF-kB heterodimers which in turn leads to activation of the expression of host genes that coordinate an antiviral response. This represents an ideal pathway for testing different compounds as potential inhibitors of the NF-kB pathway.

Figure 6. Schematic illustration of the TLR3/NF-kB signal transduction pathway.
Source: Mitchell, William M. et al. Discordant Biological and Toxicological Species Responses to TLR3 Activation.  The American Journal of Pathology , Volume 184 , Issue 4 , 1062 – 1072

We used Poly I:C to stimulate the cells and to trigger the reaction that mimics viral infection. Meanwhile, inhibitors were added to observe their effects. Analysis of the results revealed that we had a low R² value which may indicate technical errors and the two plates did not represent a valid replicate. That is the main reason why all groups decided to exclude our preliminary hits. Based on the selection criteria including the average Z score we managed to select three preliminary hits: Src/Syk Kinase Inhibitor, MNS, and Enoximone. Further investigations should be performed to validate hits from our screening including off targets and possible side effects.

Supplementary Material:

Video  1. A robot performing automated liquid handling for the 384 well plates (A and B) (including adding Poly I:C and the drug library).

https://www.dropbox.com/s/w7ugye8qd5pcr1r/VIDEO0084.mp4?dl=0

Abstract:
Fluorescence is re-emission of light at a higher wavelength than the excitation light which could be ‎detected using a special fluorescence microscope. Variant types of fluorophores have been established ‎to track different molecules inside the cells. The objective of this study was to apply these unique abilities of ‎the fluorophores to quantify and track the movements of mitochondria inside the cell with the help of a ‎fluorescence microscope. Endothelial cells were fixed and labeled with MitoTracker red, DAPI, and ‎AlexaFluor 488 Phalloidin. Meanwhile, cultured human cervical carcinoma Hela cells were labeled using ‎three different dyes (MitoTracker red CMXRoS, Hoechst 33258, and Picogreen). Both fixed and live cells ‎were viewed using OMX wide field V4 Microscopy. Images were acquired and analyzed. Using Image J ‎software we detected the number of mitochondria and through tracking one mitochondrion at different ‎time points, we managed to detect the nature and direction of its movement in addition ‎to calculation of its average velocity.

‎
Methods:‎
For fixed cell imaging, bovine endothelial cells were labeled with MitoTracker red CMXRoS (to ‎detect mitochondria), AlexaFluor 488 Phalloidin (for Actin), and DAPI (for Nuclear DNA). The cells were ‎examined using OMX wide field V4 Microscopy. Images were acquired and analyzed to obtain ‎quantitative data. Meanwhile, the cells which were used in live cell imaging were Human cervical ‎carcinoma Hela cells. They were cultured at ~ 20% confluence on coverslips which were added to the ‎bottom of a tissue culture dish. On the day of the experiment, cells were at the confluence of ~ 40%. ‎Four coverslips were added to a 6-well plate. Two mL of media containing DMEM, 10% FCS and ‎antibiotics were added into each well. The wells were labeled from one to four. We used three different ‎dyes for this experiment. MitoTracker red (MT red) CMX RoS accumulates specifically in mitochondria. ‎Hoechst 33258 intercalates into Nuclear DNA. Picogreen mainly stains mitochondrial DNA. Each of three dyes has its own ‎excitation and emission spectra (Figure 1). MT Red was prepared by diluting the stock solution ‎‎(1mM) to 100µM by adding 1 µL of MT Red to 9µL DMEM. Hoechst 33258 was prepared by diluting the ‎stock (16mM) to 1.6 mM by adding 1 µL of the Hoechst stock solution to 9µL DMEM. ‎

Figure 1. excitation (in blue) and emission (in red) spectra of  Hoechst 33258, picogreen, and Mitotracker red CMX RoS. modified from www.thermofisher.com

At time t=0 minute, 3 µl of picogreen were added to well number 1 and well number 4. Then, The plate was incubated at 37°C ‎and 5% CO2 conditions. At time t=30 minutes, the plate was removed from the incubator and 1 ‎‎µL of the MT red working solution was added to wells number two and four. The plate was placed again in ‎the incubator. At time t=45 minutes, 1µL of the Hoechst 33258 working solution was added to wells ‎number three and four. The plate was returned again to the incubator. At time t=60, each coverslip was ‎added into a live cell chamber that is designed specifically for the fluorescence microscope.‎
We used OMX wide field V4 Microscopy to view our sample and to acquire images. In the ‎beginning, we added immersion oil 1.514 to the lens prior to adding the chamber to the microscope. We ‎opened the software and we lowered down the lens using “Top 2400” setting. Following that, we added ‎the chamber containing the coverslips and adjusted the microscope lens back again to the proper ‎position. Then, we adjusted the viewing settings. We chose a red channel for MT red, a blue channel for ‎Hoechst and the green channel for picogreen. After that, we selected medium mode (recommended as it ‎indicates how fast the image readout is). We tried to lower the excitation value (20 is recommended) and ‎the exposure time to avoid damaging the cells. While using the microscope to image the cells, we had to ‎balance between increasing the intensity to get a good resolution and decreasing the intensity to keep ‎the cells alive. After adjusting all parameters, we took several images at different time points showing the ‎movements of both cells and mitochondria. Meanwhile, we could not detect any nuclear DNA using ‎Hoechst dye. ‎
Using Image J, we adjusted the threshold which allowed us to get more localization of the mitochondria ‎and through “analyze particles” option we managed to get a number of mitochondria and the size of each ‎mitochondrion. On the other hand, we have used a “manual tracker plugin” to track one mitochondrion ‎over 91 frames which were taken over the duration of six minutes. A video was created of the ‎mitochondrion tracking which gave us an idea about the nature of movements of mitochondria inside the ‎cell. Following that, Results from Image J were exported to an excel file to analyze the average speed of ‎the mitochondrion. ‎

Results:
Mitochondria, nuclear DNA, and actin filaments of fixed endothelial cells are shown clearly in Figure 2. ‎We have detected 141 mitochondria of different sizes ranging from 0.05 to 1 µm2 (Figure 3). A video was ‎created showing the movement of both mitochondria and nuclear DNA (Video 1). The movements ‎recorded were in different directions (forward, backward, and lateral movements) and speed. A second video was created demonstrating the tracking ‎of movements of one mitochondrion inside the living Hela cell (Video 2). Data were exported from the software ‎to excel sheet to be analyzed. The maximum speed detected was 2.337 µm/s while the lowest speed ‎detected was 0.111 µm/s. The average speed of the mitochondrion through 91 frames was 0.445 µm/s. ‎The velocity of the mitochondrion in each frame is shown in Figure 4.‎

‎ Figure 2. Bovine endothelial cells showing nuclear DNA (blue), mitochondria (red), and actin filaments ‎‎(green). Cells were fixed and labeled with MitoTracker red CMXRoS, Hoechst 33258, and Picogreen ‎dyes. The image was acquired using OMX wide field V4 Microscopy. ‎

‎ ‎Figure 3. The number of mitochondria detected in the endothelial cells and their sizes. ‎
Discussion:
Nowadays, Fluorescence microscopy possesses variant established implications on research. The ‎capability of fluorophores to detect different molecules made it easier for researchers to accomplish new ‎discoveries in different biological fields (Drummen, 2012). Mitochondria, nuclear DNA, and actin filaments of fixed ‎endothelial cells were clearly shown in Figure 1. This indicates that fixed cell imaging can provide us with ‎a detailed description of certain molecules inside the cell at the time they were fixed. For example, we ‎managed to get the number of mitochondria and their sizes using Image J. The same may apply to all ‎other different molecules that a researcher would be interested in. This may help scientists to visualize ‎those molecules and the mechanisms by which these molecules interact with different stimuli when ‎comparing them with controls. On the other hand, the videos that we collected during the live cell ‎imaging showed clearly the behavior of mitochondria which allowed us to track them and analyze their ‎movements. We observed that mitochondrion moves in different directions and with an average velocity ‎of 0.445 µm/second. Previous studies used different fluorophores for localization of mitochondria in ‎living cells (Johnson, 1980). Due to some technical issues, we acquired blurred images that could not allow us to do ‎tracking for the rest of the mitochondria and we were forced to perform the manual tracker.‎

Figure 4. The velocity of the mitochondrion at different frames. Frames were taken every 4 seconds. The analysis was performed using Image J software.‎
Supplementary material: ‎

Video 1. Movements of mitochondria and nuclei in Hela cells using live cell fluorescence imaging. URL: ‎
https://www.dropbox.com/s/vbirrhntldflbct/movement%20of%20mitochondria%20and%20nuclei.avi?dl=0‎

Video 2. Tracking of a mitochondrion inside a living Hela cell using Image J software. ‎
URL: https://www.dropbox.com/s/78pe02k5738cbj7/Mitochondria%20tracking.avi?dl=0‎

References:‎
‎1.‎ Drummen G. Fluorescent Probes and Fluorescence (Microscopy) Techniques – Illuminating ‎Biological and Biomedical Research. Molecules. 2012; 17(12): 14067.‎
‎2.‎ Johnson LV, Walsh ML, Chen LB. Localization of mitochondria in living cells with rhodamine 123. ‎Proceedings of the National Academy of Sciences. 1980; 77(2): 990-994.‎