Dublin City University, Ireland
Fasciola hepatica is known to induce fascioliasis, an important zoonotic disease with annual global costs ranging over 3 billion USD. Besides the high prevalence in humans, its victims are foremost ruminants such as cattle and sheep.
While fascioliasis is manageable through the use of chemical agents, resistance is spiking. Thankfully, new potential surrounding exosomes, as key mediators of cellular communication, have recently surfaced as possible vaccine candidates. While tegument and excretory-secretory products of Fasciola hepatica have extensively been studied, a lot remains unknown about exosomes and its effect on the host.
In the conducted research, the immunomodulating effects of F. hepatica-derived exosomes were studied on bone marrow-derived macrophages (BMMФ). The focus was put on classical (M1) and alternative activation (M2) pathways, induced or inhibited by exosomes. Based on the secretion of cytokines (ELISA), the representation of immunological markers (flow-cytometry), presence of intracellular protein transcripts (PCR + gel electrophoresis), the interaction with Toll-like receptors (western blot) and cytotoxicity (Resazurin-assay), a clearer picture of the immunomodulatory effect could be painted.
A pro-inflammatory effect was detected as early as fifteen minutes after BMMФ stimulation, along with increased TNF-α secretion and iNOS expression, while M2-markers remained low and unaltered. This study is the first to report the immunomodulatory effect of F. hepatica-derived exosomes on macrophages, resulting in M1-polarization.
As cardio vascular disease is the most common cause of death in the European Union last year, the aim of this project is to produce nanoparticles loaded with compound E which are able to block gamma-secretase and thereby blocking the Notch Signalling pathway in stem cells.
It is know that compound E is able to block the Notch Signalling pathway in breast cancer cells. Many research has been performed of how nanoparticles can be made and how to characterize them using dynamic sight scattering (DLS).
To achieve the aim several minor aims need to be set. First of all, nanoparticles loaded with compound E have to be made. The nanoparticles are characterized by using DLS (size determination) and HPLC (the amount of drug incorporated). After being sure that the drug is incorporated, the compound E is tested to be sure that the drug is being released and is active. This done by comparing the Hey1 levels (gen influenced by Notch signaling pathway between mouse vascular smooth muscle cells (mVSMC) treated with blank and mVSMC treated with loaded nanoparticles (baseline experiment).
The results out of the DLS show use that the nanoparticles are not identical in size. But from HPLC results there can be determined that a high percentage (>90%) of the drug is incorporated in the nanoparticle. From the results out of the baseline experiment can be shown that compound E is indeed blocking the Notch signalling pathway.
The nanoparticles were not what was expected so further experimentation is needed to optimize the production of nanoparticles. The effects of compound E should be more research. Since only the effect of one concentration (55 µM) is research and only one time point has been seen. More research of the effect of time, dose and the present of a magnetic field to fully understand the release of the drug incorporated in nanoparticles.
Abstract traineeship (advanced bachelor of bioinformatics) 2017-2018: Integration of Gene Expression and Genome-Wide Association data
Alzheimer’s disease (AD) is a neurodegenerative disease that causes dementia. It affects the brain by forming Tau-tangles and amyloid-b plaques. There are three major groups of risk factors to the disease: vascular, psychosocial and genetic. The genetic side of late onset Alzheimer’s disease is not yet well understood.
To understand what genes are involved, genome wide association studies (GWAS) have been done in the past. These studies only provide information about the pure genetics, but do not tell anything tissue specific. As a solution for that limitation, Haky Im Lab’s PrediXcan and MetaXcan come in. These methods implement tissue specific gene expression data in GWAS data.
PrediXcan is a method that takes raw GWAS data, a gene expression model of a specific tissue and a phenotype file. The GWAS data should be provided as dosage files, preferably per chromosome. PrediXcan provides a script to convert the raw GWAS data to dosage files.
MetaXcan takes GWAS summary statistics and a gene expression model of a specific tissue. The GWAS summary statistics file should contain the phenotype data (Figure 1).
The aim of the project was to run a MetaXcan analysis on International Genomics of Alzheimer's Project (IGAP) data. To confirm that PrediXcan and MetaXcan provide similar results, a comparison of the methods was done on Genetic and Environmental Risk in Alzheimer's Disease (GERAD) data. The raw GWAS data is available for GERAD. The comparison was convincing. The advantage of MetaXcan is that GWAS summary statistics are publicly more available than the raw data.
10% of the genes that came up in the MetaXcan analysis on IGAP data were known as Alzheimer’s disease risk factor genes. 76% were genes in the same regions as known genes. The other 14% were genes outside of known regions. These genes could possibly be risk factors as well. To confirm these genes as being involved in AD, a differential expression analysis was done on data of the sequence read archive of NCBI. A dataset of 117 samples of AD and healthy brain samples of the fusiform gyrus was used for that(GSE95587). The results were not convincing.
The genes that have come up with the MetaXcan analysis will have to be confirmed with other data. On one side another gene expression reference will have to be used. There are gene expression datasets of the brain available on the database of Genotypes and Phenotypes (dbGAP) of the National Centre for Biotechnology Information (NCBI). An application will have to be filed for that. Haky Im Lab’s PredictDB has a pipeline available to train the prediction model, which has been adapted and tested to fit the server structure of DCU. On the other side more GWAS summary statistics data should be gathered.
In addition to the human AD project, there was a project about fascioliasis. A differential gene expression analysis was done on mice infected with Fasciola hepatica compared to control mice. Different pipelines were compared to test the reproducibility of the results that were already obtained by other people in the lab. The resulting genes were roughly the same, but the significance varied according to the statistical test that was used. The overall image of the gene set enrichment analysis showed that cell proliferation and digestion related pathways are downregulated in infected mice and immune system related pathways are upregulated.
Another small project was the comparison of Qiime and Qiime2. Both versions of microbiome analysis software. Qiime is being discontinued so the switch to Qiime2 has to be made.
Alzheimer’s disease is the most common form of dementia. During a genome-wide association study by the International Genomics of Alzheimer’s Project (IGAP), more than 20 loci were identified that influence risk of Alzheimer’s disease. This project will refine the known genetic association of variants at multiple loci.
This will be achieved by performing imputation of un-genotyped variants at the identified loci in the case/control dataset GERAD (a subset of IGAP). Using the imputed data, association testing will be performed for each locus. This will determine if an association exists between a variant and Alzheimer’s, and the strength of that association. If a novel lead variant is identified at a locus, conditional analysis will be performed to determine the independence of the association signal from the IGAP lead variant at that locus.
The whole process will be automated to analyse each locus quickly and efficiently. To do this, a pipeline script is written in bash that uses the command line tools Plink, gtool, impute2, snptest and awk.
The next step is functional annotation for each of the candidate variants, to determine the potential molecular mechanism through which the candidate variant acts. For functional annotation the webtools 3DSNP and LDProxy are used.
As a side project we tried to determine candidate pleiotropic variants impacting AD and other traits through meta-analysis. This includes other GWAS data from other traits and trying to identify variants which show significance in both diseases.
Septic shock is an important disease that can lead to mortality. It is a well-known disease caused by bacteria. Hereby, starts an inflammatory reaction of the body against the bacteria. Lipopolysaccharide is a surface marker on gram negative bacteria that makes cells go under stress. C3H-mesenchymal stem cells mimic stem cells in the adventitial layer of the vessel wall. The effect of LPS on these stem cells in normal conditions are studied. Also, the ability of the stem cell to differentiate to smooth muscle cells and there inflammatory response is under investigation.
There are four proteins under investigation. Tumor necrosis factor-alpha is a cytokine that is activated during an inflammatory response and stimulates this response. Inducible nitric oxygen synthase releases nitric oxygen in the blood vessel during inflammation. It causes the blood vessel to vasodilate. Alpha-actin is a protein that is mostly found in the process of cell contraction. This protein is very important because of the fall in blood pressure during septic shock. If cells create more actin, there can be a bigger cell contraction in the blood vessels so the fall of blood pressure reverses. Calponin-1 stimulates the α-actin to start the muscle contraction.
Alpha-actin and calponin-1 are studied proteins to look at the ability of the stem cell to differentiate to smooth muscle cell and the inflammatory response in normal and differentiating conditions. Calponin-1 is measured by immunocytochemistry. Alpha-actin is measured by immunocytochemistry and western blotting. Inducible nitric oxygen synthase and tumor necrosis factor-alpha are measured to investigate the inflammatory response of the stem cell. Tumor necrosis factor-alpha is measured by enzyme-linked immunosorbent assay. Inducible nitric oxygen synthase is measured by immunocytochemistry and western blotting.
Cells in differentiation media don’t grow much because they are focused on differentiating instead of growing. The calponin-1 is more present in maintenance media than in differentiating media but it increases in differencing conditions during inflammatory reaction while in maintenance media it decreases. This show that more cells want to differentiate to smooth muscle cells to reverse the septic shock. The alpha-actin increases in maintenance and differentiation media to stimulate muscle contraction to reverse the septic shocks blood fall. Inducible nitric oxygen synthase directly increases in maintenance media and cause a big blood fall and triggers the septic shock. Inducible nitric oxygen synthase decreases during differentiating circumstances. The results showed an increase of tumor necrosis factor-alpha at 1 µg/ml LPS and decreases after that.
C3H-mesenchymal stem cells can be differentiating to smooth muscle cells because calponin-1 and alpha-actin increases during differentiating and inflammation circumstances. The cells do produce inducible nitric oxygen synthase and TNF-alpha to trigger an inflammation reaction. So the cells in the adventitial layer are affect by the inflammation and produce proteins to block this reaction.
This thesis is about the optimization of the production and purification of a recombinant protein. The used recombinant protein for this work is Green fluorescent protein, produced by the Escherichia coli (E.coli) host.
The protocol lab BE383 forms the base for the experiments. To optimise that protocol several parameters will be changed and investigated. First the parameters for protein production are examined. An E.coli colony is incubated overnight and then inoculated in LB broth until the proper OD is reached. Afterwards, parameters can be changed and examined. Different E.coli hosts have first been tested to determine which one yields the best protein production. Once the right clone is chosen, colonies were grown for several hours to determine the optimal incubation time. To this end, samples were taken over a timespan of 72 hours. In the next step, different levels of IPTG were added to the broth with E.coli and samples were taken at different points in time. Next comes finding the right temperature to grow, since colonies grow at variable temperatures. Bacterial growth was monitored by taking the absorbance from the samples. The samples were set on a SDS-PAGE gel which was afterwards stained with instant blue.
The second part is the optimisation of the purification of the protein. A better cell lysis implies a better purification, which is why it was optimised. The original protocol was run with the improvements discovered in past experiments, one with the normal lysozyme to break the cells and the other one with the Bug buster. Samples were taken and then run on an IMAC column. The elusions were run on a SDS-PAGE gel and stained with instant blue. A protein assay, the Biuret method, is performed to calculate the total amount of the produced protein.
The new clone grows better and has a higher protein production than the old one. The results from the time-course show the highest amount of protein after 5 hours of incubation and cells still grow as the absorbance is high. The highest protein production and absorbance can be seen at 37°C, which means that the cell grows best at that temperature. 100 µM IPTG induces protein production best since other concentrations induce less production or even block the cell growth. In the last result the Bug buster implies the best cell lysis.
The results show that the following points can be optimised: use the new clone, with an incubation time up to 5 hours and Bug buster for the cell lysis. The IPTG level and temperature do not need to be optimised.