DocuLearn4

Questions

    1. Over the past couple of weeks you have likely spent a noticeable amount of time working on your final project. What has been the most challenging aspect, and what helped you overcome this challenge? (You may think of things that you did, strategies that you employed, or tools and resources that you used).
      • Time management
        • I think a challenge that I faced over the weekend was the underestimation of the amount of time that would be required to complete the project draft. Because we had our outline completed, and we had been taking notes of the information that we would want to use on our draft, I thought a day would be sufficient. It took more time to synthesize of information from here and there and to write about them so that they flow well. We were able to complete our project by the extended deadline. For next time, I should allocate more time in writing the background and introduction section – or complete writing it before hand, while I am searching for information.
        • I think we were pretty good at continuously working on the project over the term. Shannah and I met once a week to discuss about our progress and the direction that we would want to go towards.
      • Being on track
        • I think I also spent a lot of time reading articles that were not super necessary for the progress of the project. I was able to later realize that I wouldn’t have to spend so much on such a topic, but the time to get to that realization was long. I hope I can develop a better skill for that. I’m not sure how – by experience?
    2. So far, what do you feel that you have learned (or, what skills have you improved) as a result of working on your project? Is this something that you expected? Have there been any surprises?
      • Reading papers
        • In the beginning, it took a long time to understand what the article was talking about, and what information would be useful for my project. Later on, as I got more familiar with the terms as well as an idea of what the general outline of the project would be, I became faster in gathering information (yet I would still sometimes get off track…).
        • Learning from research articles was one of the challenges that I listed in the beginning of the term. Through this experience, I learned that when I can’t understand the concept by reading one article, I could look into another article related to the topic. One article would have the details not provided in the other. Also, going into the referenced article also helped gain more information and understand what was going on.

DocuLearn3

      1. You have now spent almost two weeks tackling the issue of caste differentiation in honeybees. Is there anything that you learned throughout this ‘unit’, which you had not learned before? If so, briefly describe it; if there is nothing new, it is OK to say so.
        • I feel like everything was pretty new for me content wise with my case study paper, which is on the effect of miRNA on honey bee caste determination. One of the novel things that I encountered was learning how to describe a “just look experiment”. I had a formula in my mind that if it’s an adding experiment, then I would conclude that whatever is added is “sufficient to result in” something. If it’s a knock out experiment, then I would conclude that whatever that was knocked out is “necessary to result in” something. We were able to describe the data as well as what we can infer from the data by extensively looking back and forth of the explanation provided in the paper and the data. I’m not sure if I would have been able to come to the same answers without the help of the text. I think it might something that I should try to work on in order to prepare for the midterm.
        • Other things that I have learned: how a bioinformatics project of this kind is done (examining differential expression to figure out which miRNA might be important in caste determination, predicting what function they might have, and etc.) and that there are many different kinds of bioinformatics in biology. It was fun to have a discussion with my lab partner Shannah about what I know about bioinformatics (finding the function of  a gene) and what she knows about bioinformatics (related to computational biology and phylogenetics).
      2. What skills do you find that *you* have been using/practicing so far in this unit? Outside this unit and this class, do you think they may be useful skills to have
        • Skill: reflecting on what I didn’t do well in the past and improving for the next one
        • I received feedback on quiz 2 that I should include numbers and amounts when I describe the data. I practiced to incorporate numbers when doing the stage 1 assignment, and I was happy to receive a positive feedback about it! I think this skill is very important not only in academics but in life in general – to learn from mistakes 🙂
        • Skill: practicing presenting my ideas and trying to understand what others say

3. Thinking about challenges throughout this unit, do you find that they are similar to those you described in your previous DocuLearn assignment, or similar to the ones you were anticipating in your first DocuLearn assignment

  •  Summary of challenges: (1) reading journals and review articles (2) asking effective/ scientific questions to improve my understanding on the topic (3) following through the lecture material and extracting (?) information that is useful during discussion
  • (1) I think this is a less of a challenge than I anticipated! The honeybee paper was not as challenging as I thought it would be after having read through it a couple of times. Discussing with my partner and asking/answering questions to each other helped us fill in the gaps to understand the paper.
  • (2) I think this is still might be a challenge, but I am glad to have gotten more practice on this
  • (3) The integrative summary was a good practice for this. I think it still is a challenge.

 

 

 

Annotated Bibliography

Wang, F., Zhou, J., Xie, X., Hu, J., Chen, L., Hu, Q., . . . Yu, C. (2015). Involvement of

SRPK1 in cisplatin resistance related to long non-coding RNA UCA1 in human ovarian cancer cells. Neoplasma,62(03), 432-438. doi:10.4149/neo_2015_051

 

This article investigates UCA1’s effects on SRPK1 in cisplatin resistance in ovarian cancer. They started by looking at UCA1 expression in cancer and non cancer cells and its corresponding resistance to cisplatin treatment followed by assessing the expression of SRPK1 and apoptosis pathway proteins to explore the mechanism. Lastly, they looked at the effects of knocking out SRPK1 on cisplatin resistance. Results found an increased expression of SRPK1 and anti-apoptosis proteins in transgenic UCA1 cells, and knocking down SRPK1 could partly rescue the effect of UCA1 expression on cell migration, invasion and cisplatin resistance in cells. Based on these findings, they suggested that SRPK1 and apoptosis pathway proteins may be involved in the effect of UCA1.

 

Pan, J., Li, X., Wu, W., Xue, M., Hou, H., Zhai, W., & Chen, W. (2016). Long non-coding

RNA UCA1 promotes cisplatin/gemcitabine resistance through CREB modulating miR-196a-5p in bladder cancer cells. Cancer Letters,382(1), 64-76. doi:10.1016/j.canlet.2016.08.015

This paper described that UCA1 is highly overexpressed in bladder cancer cells, and that UCA1 promotes cell migration and invasion. The authors wanted to further study the mechanism of cisplatin and gemcitabine resistance in bladder cancer cells. Their experiment in vitro showed that an overexpression of UCA1 results in reduced cell apoptosis and enhanced cell viability, and a knockdown of UCA results in an opposite effect. They concluded that UCA1 activates miR-196a-5p through CREB, a transcription factor that can be activated by the AKT pathway.

 

Fan, Y., Shen, B., Tan, M., Mu, X., Qin, Y., Zhang, F., & Liu, Y. (2014). Long non-coding RNA UCA1 increases chemoresistance of bladder cancer cells by regulating Wnt signaling. FEBS Journal,281(7), 1750-1758. doi:10.1111/febs.12737

The authors of this research article investigated the role of UCA1 lncRNA in cisplatin resistance during chemotherapy for bladder cancer. They showed that cisplatin‐based chemotherapy results in up‐regulation of UCA1 expression in patients with bladder cancer. Their experiment sets the base to ours by showing that overexpression of UCA1 significantly increases cell viability during cisplatin treatment in bladder cancer. Furthermore, they demonstrated that UCA1 increases the cisplatin resistance of bladder cancer cells by enhancing the expression of Wnt6, and thus represents a potential target to overcome chemoresistance in bladder cancer. Our project arises from the question posed by this article.

 

Wang, F., Li, X., Xie, X., Zhao, L., & Chen, W. (2008). UCA1, a non-protein-coding RNA up-regulated in bladder carcinoma and embryo, influencing cell growth and promoting invasion. FEBS Letters,582(13), 1919-1927. doi:10.1016/j.febslet.2008.05.012

 

This is a research article that studied the role of lncRNA UCA1 in bladder carcinoma. They performed a gene expression analysis via RT-PCR to find potential targets of UCA1 in drug resistance. Some of the upregulated genes in high expression of UCA1 include WNT6, CYP1A1, AURKC. Some of the downregulated genes include: methyl CpG binding domain protein 3 (MBD3) and serine/arginine-rich protein specific kinase 1 (SRPK1). They confirmed these results with a microarray analysis. This paper gives important background information to our predictions and possible results.

 

Ayers, D., & Vandesompele, J. (2017). Influence of microRNAs and long non-coding RNAs in cancer chemoresistance. Genes, 8(3), 95. doi:10.3390/genes8030095

This review gives a thorough explanation of what miRNAs and lncRNAs are. They discuss about the cancer chemoresistance mechanisms and how miRNAs and lncRNAs are involved in chemoresistance. They created a model to show how miRNA and lncRNA might work in cisplatin resistance. However, the model seems more of a guess of the role of lncRNA by linking already known chemoresistance mechanisms rather than an informed model based on experimental results that test the role of lncRNA in chemoresistance. Nevertheless, this review article helps us set base on the clinical implications of our project: that validation of lncRNA as a biomarker for drug resistance could serve as novel drug targets and we can perform RNA directed therapy.

Wang, H., Guan, Z., He, K., Qian, J., Cao, J., & Teng, L. (2017). LncRNA UCA1 in anti-cancer drug resistance. Oncotarget, 8(38), 64638-64650. doi:10.18632/oncotarget.18344

This is a review article written by researchers of the Clinical Research Centre in Zhejiang University. They compile previous research performed on lncRNA UCA1 in various cancers including ovarian cancer, cervical cancer, and bladder cancer. Neuroblastoma has not been included in this article, however. This article introduces the role of lncRNA in general as well as specifically to UCA1. The role of UCA1 in drug resistance and the regulators of UCA1 expression have been discussed in various cancers in detail.

 

Piskareva, O., Harvey, H., Nolan, J., Conlon, R., Alcock, L., Buckley, P., . . . Stallings, R. L. (2015). Corrigendum to “The development of cisplatin resistance in neuroblastoma is accompanied by epithelial to mesenchymal transition in vitro” [Cancer Lett 364 (2015) 142–155]. Cancer Letters,369(2), 428. doi:10.1016/j.canlet.2015.09.010

This is a research article in which the authors identified the proteins that are involved in cisplatin resistant cell lines in neuroblastoma through proteomic profiling, a method to identify the proteins that are present. They showed that proteins that are differentially expressed in resistant cell lines are involved in cancer promoting mechanisms such as cell migration and proliferation. Proteins that were absent (or expressed in lower levels) in resistant cells were predicted to be due to miRNA that target specific genes. This article gives us background information on how cisplatin resistance mechanisms occur.

Luo, M. (2016). Methods to study long noncoding RNA biology in cancer. (pp. 69-107). SINGAPORE: SPRINGER-VERLAG SINGAPORE PTE LTD. doi:10.1007/978-981-10-1498-7_3

This is a review article that discusses the various methods of studying the function of lncRNA in cancer development. They provide how research is done in this area step by step starting from screening and identification of functional RNAs.They discuss in detail how manipulation of lncRNA expression is performed through various techniques including shRNA and transfection as a common way to investigate the function of lncRNA. Furthermore, they discuss about mapping of lncRNA binding targets in the genome to further study their function in regulating gene expression in cis. This article gives us background information such that we can design experiments for the purpose of our study.

 

Hu, Y., Zhu, Q., Deng, J., Li, Z., Wang, G., & Zhu, Y. (2018). Emerging role of long non-coding RNAs in cisplatin resistance. OncoTargets and Therapy, 11, 3185-3194. doi:10.2147/OTT.S158104

This is a review article published in dovepress which has a relatively low impact factor. However, this article discusses what cisplatin is as an anti-cancer drug and how cisplatin resistance evolves in detail. They provide a model how various lncRNAs affect pathways that are involved in the cisplatin resistance mechanisms. Some lncRNAs discussed are HOTAIR, HOTTIP, MEG3. This article helps us understand how lncRNAs might function in the context of cisplatin resistance as well as that one lncRNA can have multiple functions.

 

Wang, Q., Armenia, J., Zhang, C., Penson, A. V., Reznik, E., Zhang, L., . . . Schultz, N. (2018). Unifying cancer and normal RNA sequencing data from different sources. Scientific Data,5, 180061. doi:10.1038/sdata.2018.61

 

This is a call to action paper that aims to create a standard for RNA sequencing data generation based on inconsistent data generation currently used in the field. This is important so that RNA-seq data can be compared to one another for integrative analysis. This paper proposes a standard pipeline that should be used for processing and unifying RNA-seq data from different studies. They also include standards that should be used when verifying the tools that were used in the pipeline.

 

Honeybee model

After finishing the infographics, we formed new groups consisting of at least one person from each paper. We integrated the information provided by each paper to develop a model for how the different caste phenotypes develop. We separated parts that were explicitly given from the papers and parts that we made inferences to connect different ideas.

Top4: Lonfat et al.

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This is one of my top 4 assignments because the lonfat paper was the hardest paper in this class in that they had multiple techniques and had a complex way of studying gene interaction. I initially had no idea what they were doing with the inversions and what they were trying to say in the paper. After having discussions in class, it became clearer that the inversion would change the position of the genes, and therefore would allow us to study whether or not the position of enhancer affects gene expression. From this paper, I learned how to read the bisulfite sequencing paper, and how to think about different data (staining, bisulfite sequencing, experimental system, and chip) can come to one interpretation. It was interesting to learn that enhancers can be position dependent in some cases.

In-class small assignment: Lonfat et al. paper

Assigned paper: Lonfat, N., Montavon, T., Jebb, D., Tschopp, P., Nguyen Huynh, T.H., Zakany, J., Duboule, D. (2013). Transgene- and locus-dependent imprinting reveals allele-specific chromosome conformations.

 

Supporting PDF: http://www.pnas.org/content/pnas/suppl/2013/06/30/1310704110.DCSupplemental/pnas.201310704SI.pdf

 

Names of your group members:

Shannah Fisher, Joanne Lim, Erin Yang

 

Question 1

It is quite common for research papers to have Figure 1 be the “most important” figure in the article. Consider Figure 1:

 

 

  • What transgenic lines did the author use? Please briefly describe them (do these lines look somewhat familiar)?

 

The transgenic lines used were Hox9lacZ transgene inserted into the HoxD gene in both the forward direction and with an inversion in the region starting from ex26 to the end of Hoxd9lacZ insertion (contains ex26, Pdk1, digit enhancer sequences and the Hoxd9lacZ insertion).

(HoxD9lacZ transgene was inserted upstream of the HoxD gene. One transgenic line had HoxD9lacZ closely upstream of HoxD, and the other transgenic line had an inversion at Itga6loxP and rel5loxP such that the transgene is located further away from Hox D.

The transgenic lines shown in the Lonfat et al. paper is similar to that shown in class in that both are designed upstream of HoxD. However, the transgenic line from class includes Evx2 in the inversion.)

 

  1. What do the data in Panel C show?

In the normal HoxDrel5 embryo’s, there is no difference in expression of lacZ transgene between maternal and paternal inherited transgenes. However, in the inverted HoxDInv(rel5-Itga6) transgene, the embryo’s with paternal inherited transgenes showed higher expression of lacZ insert (compared to HoxDrel5 embryos) and maternally inherited trangenes showed no expression of lacZ.

 

  1. What is striking/unexpected about the data in Panel C?

When the Hoxd9lacZ inversion is inherited through the paternal line, fetuses show very strong staining from the transgene beyond its typical regions whereas the identical transgene inherited through the maternal almost entirely lacks staining. This suggests a very efficient regulatory mechanism that exists as separate inheritable paternal and maternal patterning.

 

  1. What direct conclusions do you make from the data?

Inversion of the region is sufficient to increase the expression of lacZ in the embryo with paternal inherited transgene as well as decrease expression of lacZ in maternal inherited transgenic embryos.

 

Question 2

How did the authors show that the peculiar effect observed is specific and position/site-specific?

  • The authors first inserted the lacZ transgene in the HoxDrel5 (rel5) site and observed no bias in expression based on parental inheritance.
  • After inverting the region using the loxP sites in rel5 and exon 5 of Itga6, they observed that the lacZ transgene was now biased in expression depending on which parent the transgene was inherited from.
  • The translocation results suggest regulatory effects influenced by parental inheritance that differ between Itga6 site and the rel5 site.
  • The authors showed that the result is transgene specific because the same effect was not observed when tested with SB lacZ.

 

Do you agree with their data interpretation and with their conclusion?

  • Yes, in the case of the lacZ transgene it seems that the effects are site specific. But the in vitro relevance of this data needs further development as a Hoxd11 inversion without the lacZ transgene didn’t produce the same bias in expression.

 

Question 3

Consider Figure 2 (please don’t hesitate to ask for clarification if you and your group have questions about it!):

  1. Briefly explain how to “read” the diagrams shown (i.e. what do the rows of circles represent, what do the white vs. black circles represent).
  • The white dots represent unmethylated regions, and the black dots represent methylated regions. Each dots represent DNA regions.
  1. What do the data in Figure 2 show?
  1. Embryos that paternally inherited transgenic line HoxDInv(rel5-Itga6) showed very low levels of methylation of DNA from E12.5 presumptive digits while those that maternally inherited HoxDInv(rel5-Itga6) showed extremly high methylation of DNA.
  2. The escapers showed varying methylation patterns.
  3. DNA from oocyte with the inverted allele is highly methylated while from the sperm isn’t. This suggests that the methylation patterns are carried through the oocyte and to maternally inherited offspring.
  4. Methylation patterns are not different in embryos that inherited rel5.
  1. Why aren’t there a “paternal/+” and a “maternal/+” groups for sperm and oocytes?

Sperm and oocytes are haploid gametes, each containing one allele. There is no paternal or maternal allele in the gametes.

  1. What are “escaper” embryos, and how were they identified prior to bisulphite sequencing?

Escaper embryos are maternal transgenic embryos that expressed variable levels of lacZ unlike other maternal inherited embryos that had no expression. These embryos expressed it despite the allele being silenced through maternal imprinting, thus “escaping” silencing. Expression of lacZ was studied through in situ hybridization and lacZ staining.

  1. What can we directly conclude from the data?

High levels of DNA methylation is inherited through the oocyte germline. Inversion in the transgenic line is sufficient to result in extremely different methylation patterns with paternal/sperm DNA having low DNA methylation while maternal/oocyte having high DNA methylation.

 

Question 4

Figure 3 depicts the results of a series of 4C experiments. Try to “read” the figure and see if you can identify the information described in the text.

  1. What did the authors do, and what are the results?

They use the lacZ sequence as bait for 4C experiments in normal HoxD-rel15 and inverted HoxDInv(rel-15-Itga6) for both paternal and maternal inherited transgenes.

Before inversion, there is no difference in interactions between lacZ and enhancer regions between the paternal and maternal inherited trangenes. However, after inversion, the paternal and maternal inherited transgenes, show a difference in interactions between enhancers and lacZ.The authors discuss that only the paternal allele interacts with the transgene and the digits enhancers, suggesting further effects on regulatory interactions based on the parental imprint.

  1. What can be directly concluded from the data?

The inversion of the  rel5-Itga6 region is sufficient to change the interactions between lacZ with maternal and paternal inherited transgenes.

 

Question 5

How does each figure support the statement in the title of the article? Which one supports it most?

Transgene dependent vs locus dependent imprinting → allele specific chromosome conformation

 

Figure 1 & 2: shows that maternal imprinting is dependent on the inversion, which brings the Hoxd9lacZ closer to Itga6. They showed transgene dependent imprinting by doing the same experiment with different transgenes.

 

Figure 3: reveals allele-specific chromosomal conformation by showing 3D conformation through 4C experiment, so this is figure supports the title the most.

 

Project Draft

Knowing that we have so much to do at the end of the term, we tried to make our draft as finalized as possible. I think this was a very wise decision that we have made since we still had more work to do near the end of the final deadline.

Long noncoding RNA UCA1: Function in Cisplatin Resistance in Neuroblastoma Cell Lines

Background

Long non-coding RNAs (lncRNAs) are evolutionary conserved gene transcripts of over 200 nucleotides that do not encode proteins (Geisler and Coller, 2013). lncRNAs are involved in regulation of gene expression at transcriptional, post-transcriptional, and translational levels (Moran et al., 2012) either by directly interacting with its target gene (cis-acting) or by interacting with transcription factors (trans-acting) (Ponting et al., 2009). lncRNAs affect chromatin remodeling and transcript modification in the nucleus and interact with other RNAs and proteins in the cytoplasm (Schmitt and Chang, 2016). They regulate diverse cellular functions such as embryonic development and cell cycle progression, and when expressed in abnormal amounts, carcinogenesis (Corra et al., 2018).

 

In recent studies, a variety of lncRNAs such as HOTAIR, MALAT1, and UCA1 have been found to play an important role in carcinogenesis, metastasis, and prognosis. The function of lncRNA in oncogenesis may vary depending on the identity of lncRNA and the cellular context of cancers. lncRNA can be involved in promoting cancer but also prohibit oncogenic phenotypes such as metastasis (Malik et. al, 2014). LncRNA Urothelial carcinoma associated 1 (UCA1) is the most common isoform of the UCA1 gene in bladder cancer, breast cancer, and hepatocellular carcinoma (Wang et al., 2017). The biological role of other isoforms such as lncRNA CUDR is not well studied (Wang et al., 2017). The oncogenic role of lncRNA UCA1 has been identified in various cancers including bladder cancer, breast cancer, hepatocellular carcinoma, ovarian cancer, and tongue squamous cell carcinoma (Wang et al., 2017). Furthermore, studies have shown that lncRNA UCA1 regulates chemoresistance in various cancers (Wang et al., 2017).

 

In a study by Wang et al. (2008), targeted expression of lncRNA UCA1 induced the bladder TCC cell line to become highly proliferative and invasive as well as more resistant to cisplatin (Wang et al., 2017). They also revealed the differential gene expression resulting from UCA1 overexpression including WNT6 and AURKC as upregulated genes and MBD3 and SPRK1 as downregulated genes. Pan et al. (2016) suggested that lncRNA UCA1 induces cisplatin resistance by upregulating miR-196a-5p through activation transcription factor CREB.

Cisplatin, a platinum based cytotoxic drug, is one of the most commonly used chemotherapy agent to treat cancer including neuroblastoma, lung cancer and bladder cancer (Dasari and Tchounwou, 2014). Although cisplatin is effective in many patients, a large proportion of patients are resistant to cisplatin-based therapies (Galluzzi, 2012). Furthermore, a large faction of initially sensitive cancers become resistant after frequent treatment with cisplatin (Galluzzi, 2012). Mechanisms of cisplatin resistance include change in the signaling pathways, silencing of certain genes by miRNA, changes to the cell cycle, development of an efflux system, and DNA repair (Chen, 2017). In addition, several studies have revealed that long non-coding RNAs (lncRNAs) are involved in chemoresistance to cisplatin by interacting with histone modification tools and with other chromatin regulatory factors (Guttman et al., 2011).

Relevance and Importance

Neuroblastoma is a childhood malignancy in the sympathetic nervous system and accounts for 15% of all deaths in pediatric cancer patients (Piskareva et al, 2015). Treatment of neuroblastoma is most commonly performed using cisplatin, a platinum based anticancer drug that induces apoptosis by activating various signal transduction pathways (Dasari and Tchounwou, 2014). Development of drug resistance has made it hard to effectively treat neuroblastoma patients, and doctors often resort to multi drug treatments (Piskareva et al, 2015). Despite these efforts, drug resistance in neuroblastoma is still prevalent, and the cause of the resistance is not fully understood. In order to effectively treat cancer, it is important to understand the mechanism of cancer resistance and find ways to prevent their effect (Ayers and Vandesompele, 2017). Recent studies have shown that lncRNA plays a major role in the development of cisplatin resistance in various cancer types, including breast cancer, cervical cancer, and lung cancer (Wang et al, 2017). In particular, Urothelial cancer-associated 1 (UCA1), a lncRNA that plays a regulatory role in proliferation of cells, has been found to play a major role in development of cisplatin resistance (Wang et al, 2017). However, UCA1 has never been studied in cisplatin resistance in neuroblastoma, posing a potential gap in knowledge that could be vital to finding effective treatment methods for patients who are resisting this drug. Although UCA1 has been studied in other cancers such as bladder and lung cancer, we cannot conclude that the effect of UCA1 will be conserved across all cancer types including neuroblastoma. For example, lncRNA MALAT1 was suggested to function as a tumor suppressor gene in breast cancer (Eastlack, 2018), but as a promoter of tumor growth and metastasis in oral squamous cell carcinoma (Zhou, 2015).  

 

Therefore, we propose a study that will determine if UCA1 is present in neuroblastoma cell lines that are resistant to cisplatin drug treatment and identify the changes in mRNA expression when UCA1 is present. If successful, this research would serve as a first step in understanding and finding better treatment methods for neuroblastoma patients who are resistant to cisplatin drug treatments. As a next step, the validation of UCA1 as a biomarker for drug resistance could serve as novel drug targets and could ultimately lead to the development of antagonists and/or mimics for adjunct therapy with traditional cisplatin treatment methods (Ayers and Vandesompele, 2017). Adjunct therapy methods have shown to increase susceptibility of the tumour, ultimately enhancing treatment effectiveness (Ayers and Vandesompele, 2017). In addition, use of RNA directed therapy could be used in patients who are highly resistant to the treatment due to dosage dependent resistance, reducing the discomfort of patients by permitting a lower treatment dosage (Ayers and Vandesompele, 2017). Lastly, use of UCA1 biomarker could be quantified in patients through RT-qPCR assays and provide pre-emptive knowledge to the oncologist on the best drug combination treatment for their patients (Ayers and Vandesompele, 2017).

Hypothesis

Several studies investigating the molecular mechanisms of lncRNA UCA1 in promoting cisplatin resistance have found that it is involved in silencing of tumor suppressor genes and inducing expression of multidrug resistant proteins (Want et. al, 2017). If the role of lncRNA UCA1 is conserved across different types of cancers that have been studied thus far, then we hypothesize that upregulated expression of lncRNA UCA1 causes increased resistance to cisplatin in neuroblastoma cell lines.

Experimental Plan

Level of cisplatin resistance after induced changes in lncRNA UCA1 expression

To assess the changes in cisplatin resistance after changes in expression of lncRNA UCA1, a cell viability assessment will be performed on both susceptible and resistant cells that were treated in various concentrations of cisplatin after either loss or further gain of expression of lncRNA UCA1. ()

 

Differential Gene Expression Analysis

 

To quantify changes in RNA expression levels between all conditions, an expression profile analysis will be performed on all RNA samples. RNA will be extracted in equal amounts from all conditions: lncRNA UCA1 KO, lncRNA UCA1 overexpressed, no change in lncRNA UCA1 (control) in both cisplatin resistant and cisplatin susceptible cell lines (control). All of the extracts will be sequenced and analysed. A summary of the steps and tools that will be used are outlined in Figure 2, with a detailed pipeline in Materials and Methods. To ensure that RNAi sufficiently knocked out UCA1, check the expression levels of UCA1 in the knockout.

 

Materials and Methods

Cell Lines

Cisplatin susceptible SK-N-AS and cisplatin resistant SK-N-ASrCDDP500 cells will be purchased from the Michaelis Lab, UK. The cisplatin resistant SK-N-ASrCDDP500 is a cell line that gained cisplatin resistance from its parental cell line SK-N-AS and therefore, is expected to exhibit characteristics similar to SK-N-AS compared to other cell lines with a different parental line.

Cisplatin treatment and Cell Viability Assay

Cisplatin treatment procedure will follow that outlined by Piskareva (2015). SK-N-AS neuroblastoma cells will be seeded at 10^4 cells/mL on a 96-well plate at 100µL medium per well. The plate will be incubated overnight at 37 °C in 5% CO2. Multiple concentrations of cisplatin was tested in serial dilution the following day. Cell proliferation will be monitored over 5 days. Cell viability will be assessed using the MTT assay as described by Wang et al (2008).   

lncRNA UCA1 Knockdown via RNAi

The scrambled siRNA control (Si-NC) and siRNA that targets lncRNA UCA1 will be purchased from Thermo Fisher Scientific. si-UCA1 lentivirus will be constructed and infected into SK-N-AS cells, which will then be screened with puromycin over 7 days (Fang et. al, 2017). Successful knockdown of lncRNA UCA1 will be assessed using RT-PCR.

lncRNA UCA1 Overexpression

A recombinant plasmid containing pcDNA-UCA1 will be constructed by inserting UCA1 gene with BamHI and EcoRI restriction enzymes. The recombinant plasmid will be transfected into SK-N-AS cells using Lipofectamine 2000 from Thermo Fisher Scientific (2018). Transfected cells that stably express UCA1 will be selected by RT-PCR. A negative control of cells transfected with pcDNA3.1 will also be tested (Wang et. al, 2014).

RNA Extraction

RNA will be extracted from each of the cell line conditions, with twelve replicates for each condition, as recommended by Mortazavi, et al (2008). Total RNA will be extracted with TRIzol reagent following the recommendations of the manufacturer. The purity of total RNA will be evaluated using the A260/A280 ratio of sample absorbance at 260 and 280 nm using NanoDrop ND-1000 (Thermal Fisher Scientific, 2011). Integrity of RNA samples will be measured using the 28S/18S ratio based on a densitometry plot using Agilent 2100 Bioanalyzer.

RNA Illumina Sequencing

RNA-seq will be performed using Illumina HiSeq™ 2000 Sequencing System with paired end sequences for improved accuracy. Standard library preparation procedure will occur using the manufacturer’s protocol for RNA library preparations. In order to have a high enough coverage, we will generate ~15-25 million reads per sample, as recommended by Mortazavi, et al (2008). Sequences will be open sourced on GenBank. Sequence read quality will be analyzed using FASTQC using the standard pass/fail metrics of the program.

Sequence Alignment

Sequence alignment will be performed using the program STAR and quality of alignment will be measured using RSeQC.

Gene-based Read Counting

Transcript quantification will be calculated using the R package, featureCounts, to generate integer-based read counts for each gene.

Differential Gene Expression Analysis

To analyze the differential gene expression between samples, we will use the R package, DESeq. Genes with a fold change > 2 and p-value < 0.05 will be considered to have a significant change in expression levels. To validate mRNA-seq data, 5 samples will be randomly chosen to run through qRT-PCR for analysis.

Predictions

Level of cisplatin resistance after induced changes in lncRNA UCA1 expression

The control experiment in which both cisplatin resistant and susceptible cells are treated with various concentrations of cisplatin will give us the concentration of cisplatin in which both types of neuroblastoma cells die. We predict that the IC50 of cisplatin in SK-N-AS cells will be extremely low at 0.4uM as provided by the supplier (Michaels Lab, UK). The IC50 in SK-N-ASrCDDP500 is predicted to be 9.6uM. If lncRNA UCA1 plays a significant role in inducing cisplatin resistance, then we expect the knockdown of lncRNA UCA1 to decrease percent cell viability and IC50in both cells. An overexpression of lncRNA UCA1 would increase the percent cell viability and IC50 in both cells.

 

Type of Experiment SK-N-ASrCDDP500 cells (resistant) SK-N-AS cells (susceptible)
lncRNA UCA1 KD % viability decrease % viability remains the same or slightly lower
lncRNA UCA1 Overexpressed % viability higher than control resistant cells % viability higher than control susceptible cells but lower than resistant cells
Control (no change) % viability higher than susceptible ells   Extremely to zero % viability in cisplatin treatment over IC50

Figure 3. Change in cisplatin resistance by knockdown or overexpression of lncRNA UCA1

Differential Gene Expression Analysis

Studies looking at cisplatin resistance in numerous cancers have found a relationship between lncRNA UCA1 and changes in expression levels of genes that are involved in the cells susceptibility to treatment (Wang et al, 2017). This includes a study performed by Wang et al, looking at microarray mRNA expression analysis of transfected UCA1 cells compared to control (2008). Expression levels of 42 genes were found to change by at least two-fold in the presence of UCA1 in bladder cancer, including an upregulation of Wnt signaling pathway member 6 (Wnt6), CYP1A1 (a cytochrome) and AURKC (kinase) and a downregulation of methyl‐CpG binding domain protein 3 (MBD3) and SR (serine/arginine‐rich) protein‐specific kinase 1 (SRPK1). These results are verified by several other studies looking further into the effects of UCA1 on gene expression in bladder cancer (Fan et al, 2014). Similar results have been found in ovarian cancer, including a study by Wang et al that focuses on the change in expression of SRPK1 in the presence of UCA1 and cisplatin resistance (2015). The gene expression analysis profiles continue to show similar changes in expression of genes in other cancers, including cervical, lung and bladder cancer (Wang et al, 2017). If our hypothesis is correct, then we would expect to see similar changes in expression of genes in neuroblastoma cell lines.

Therefore, we predict expression analysis will show an increase in expression levels in Wnt6, CYP1A1, and AURKC and a decrease in expression levels in MBD3 and SRPK1 when UCA1 is present in the cisplatin resistant cells compared to UCA1 knockdown cisplatin resistant cell lines and cisplatin susceptible cell lines, as shown in Figure 5.

Results

Potential Results 1: Hypothesis is not rejected and predictions are confirmed

Cell Viability

 

Expected cell viability results for potential results 1: relative % cell viability is in accordance with our prediction

Figure 4: percent cell viability of neuroblastoma cells measured in increasing concentration of cisplatin. Figures on the left show percent viability when NS-K-ASrCDDP500 cells are treated with si-UCA1 or overexpressed with lncRNA UCA1. Figures on the right show percent viability when NS-K-AS cells are treated with si-UCA1 or overexpressed with lncRNA UCA1.

 

We can conclude that high levels of expression of UCA1 is necessary and sufficient to increase the percent viability of both cisplatin resistant and susceptible cells when treated with various doses of cisplatin. We can also conclude that lncRNA UCA1 is not necessary for cellular functions other than resistance mechanism to cisplatin if there is no significant change in percent cell viability between control NS-K-AS cells and NS-K-AS with knockdown of UCA1. We may infer that lncRNA UCA1 regulates a molecular mechanism that induces cisplatin resistance such that higher levels of expression of lncRNA UCA1 results in resistant cells to become more resistant and susceptible cells to become resistant.

 

Our results support the conclusion made by Fan et al. (2014) that lncRNA UCA1 increases chemoresistance in cancer cells. We cannot conclude that the significance of UCA1 expression will be the same in vivo. An experiment could be done in vivo by performing lncRNA UCA1 knockdown and overexpression in neuroblastoma mouse models.  

Differential Gene Expression Analysis

In the case where the hypothesis is confirmed with an upregulated expression of lncRNA UCA1 causes increased resistance to cisplatin in neuroblastoma cell lines, we would expect to see changes in expression levels of RNA in both the knockdown and overexpression cases relative to control (Figure 5). From this, we can conclude that lncRNA UCA1 is both sufficient and necessary to change the expression levels of RNA in the cell lines studied. If we observe similar changes in RNA compared to expression profiles performed on other cancers (REFS), then we can infer that the molecular mechanism is the same.  For example, if we observe an upregulation of Wnt6 mRNA, we could infer that lncRNA UCA1 is involved in cisplatin resistance through interactions with the Wnt6 in the Wnt pathway (REF). However, we cannot make conclusions about this pathway without further experimentation. One potential experiment includes knocking down and over expressing the genes of interest (genes found in molecular pathway of UCA1 inducing cisplatin resistance in other cancers) and then performing a 3C experiment to see the gene interaction.

 

Type of Experiment Genes with fold change > 2 in cisplatin resistant cell lines Genes with fold change > 2 in cisplatin susceptible cell lines
lncRNA UCA1 Knocked down No change in gene expression No change in gene expression
lncRNA UCA1 Overexpressed Compared to both control and cisplatin resistant + no change in UCA1:

Highly Upregulated:

Wnt6, CYP1A1, AURKC

Highly Downregulated:

MBD3, SRPK1

Upregulated:

Wnt6, CYP1A1, AURKC

Downregulated:

MBD3, SRPK1

This may be in similar amounts to cisplatin resistant cell lines with no change to UCA1

No change

in lncRNA UCA1

Upregulated:

Wnt6, CYP1A1, AURKC

Downregulated compared to susceptible cell lines control:

MBD3, SRPK1

CONTROL for the normal expression of all genes

(including: Wnt6, CYP1A1, AURKC,

MBD3, SRPK1)

Figure 5: Table of possible results if the hypothesis and prediction is confirmed. All results are relative to the control for the normal expression of RNA with cisplatin susceptible cell lines. The genes described are a subset of the genes that were found to have >2 fold change in past experiments (REF), however, is not a complete set.

 

Result 2: Hypothesis rejected but prediction confirmed for overexpression of lncRNA UCA1 in susceptible neuroblastoma cells

Cell Viability

Induced lncRNA UCA1 expression increases cell viability in susceptible cells, but change in percent viability in other conditions is not significant.

Figure 6: percent cell viability of neuroblastoma cells measured in increasing concentration of cisplatin. Figures on the left show percent viability when NS-K-ASrCDDP500 cells are treated with si-UCA1 or overexpressed with lncRNA UCA1. Figures on the right show percent viability when NS-K-AS cells are treated with si-UCA1 or overexpressed with lncRNA UCA1.

 

We can conclude that high expression of lncRNA UCA1 is sufficient to increase percent cell viability of cisplatin susceptible NS-K-AS cells, but not in already resistant NS-K-ASrCDDP500 cells. Knockdown of UCA1 is not sufficient to reduce the percent cell viability in resistant cells.

 

Differential Gene Expression Analysis

In differential gene expression analysis, where the predictions are satisfied in the cases where genes are overexpressed but not in the knockdown, we would expect to see changes in expression outlined in figure 7. We can conclude that overexpressing lncRNA UCA1 is sufficient and knocking down lncRNA UCA1 is not sufficient, to change expression levels of RNA in cisplatin resistant cell lines compared to cisplatin susceptible cell lines.

Type of Experiment Genes with fold change > 2 in cisplatin resistant cell lines Genes with fold change > 2 in cisplatin susceptible cell lines
lncRNA UCA1 KD No change in expression No change in expression
lncRNA UCA1 OX Gene expression changes (upregulated or downregulated) Most likely will see no change in expression, similar to control for cisplatin resistant cell lines. Might see slight changes in gene expression.
No change

in lncRNA UCA1

No change in expression of one or more genes CONTROL for the normal expression of all genes

(including: Wnt6, CYP1A1, AURKC,

MBD3, SRPK1)

Figure 7: Table of possible results if the hypothesis is rejected but prediction is confirmed for changes in expression in lncRNA UCA1 overexpressed conditions. All results are relative to the control for the normal expression of RNA with cisplatin susceptible cell lines. The genes described are a subset of the genes that were found to have >2 fold change in past experiments (REF), however, is not a complete set.

Remarks

These results would suggest that there are mechanisms other than lncRNA UCA1 in cisplatin resistance such that the function of UCA1 is redundant. We cannot conclude, however, that lncRNA UCA1 has no effect in cisplatin resistance in control NS-K-ASr. It is possible that there are other lncRNA that have similar effects that are highly expressed in resistant cells. For example, the alteration of the wnt pathway is a common way of inducing cisplatin resistance and several lncRNA such as HOTTIP, MALAT, and MEG3 have been identified to affect this pathway (Hu et. al, 2018). Further investigation can be done by studying the pathways that lncRNA UCA1 may function in and compare with other lncRNAs.  

 

Result 3: Hypothesis rejected with no change in cell viability and expression profile

Cell Viability

Percentages of cell viability of neuroblastoma cells before and after induced changes in UCA1 expression are the same.

Figure 8: percent cell viability of neuroblastoma cells measured in increasing concentration of cisplatin. Figures on the left show percent viability when NS-K-ASrCDDP500 cells are treated with si-UCA1 or overexpressed with lncRNA UCA1. Figures on the right show percent viability when NS-K-AS cells are treated with si-UCA1 or overexpressed with lncRNA UCA1.

 

From these results, we could conclude that lncRNA UCA1 is not necessary or sufficient for percent cell viability of cells treated with cisplatin.

 

Differential Gene Expression Analysis

 

Type of Experiment Genes with fold change > 2 in cisplatin resistant cell lines Genes with fold change > 2 in cisplatin susceptible cell lines
lncRNA UCA1 KD No change in expression No change in expression
lncRNA UCA1 OX No change in expression No change in expression
No change

in lncRNA UCA1

No change in expression CONTROL for the normal expression of all RNA

(including: Wnt6, CYP1A1, AURKC,

MBD3, SRPK1)

Figure 9: Table of possible results if both the hypothesis and prediction are rejected. All results are relative to the control for the normal expression of RNA with cisplatin susceptible cell lines. The genes described are a subset of the genes that were found to have >2 fold change in past experiments, however, is not a complete set.

 

From these results, we could conclude that lncRNA UCA1 is not necessary or sufficient to change expression levels in the cell lines.

 

Remarks

We infer that lncRNA UCA1 is not involved in the pathways that induce or reduce cisplatin resistance in our neuroblastoma cell lines. We cannot conclude, however, that lncRNA UCA1 has no function in neuroblastoma, because it is still possible that it is involved in other ways, apart from cisplatin resistance. Further experiments could be done to test cisplatin resistance in other neuroblastoma cell lines as well as other potential functions of lncRNA UCA1 in neuroblastoma.

Citations

Wang, F., Zhou, J., Xie, X., Hu, J., Chen, L., Hu, Q., . . . Yu, C. (2015). Involvement of SRPK1 in cisplatin resistance related to long non-coding RNA UCA1 in human ovarian cancer cells. Neoplasma,62(03), 432-438. doi:10.4149/neo_2015_051

 

Pan, J., Li, X., Wu, W., Xue, M., Hou, H., Zhai, W., & Chen, W. (2016). Long non-coding

RNA UCA1 promotes cisplatin/gemcitabine resistance through CREB modulating miR-196a-5p in bladder cancer cells. Cancer Letters,382(1), 64-76. doi:10.1016/j.canlet.2016.08.015

 

Fan, Y., Shen, B., Tan, M., Mu, X., Qin, Y., Zhang, F., & Liu, Y. (2014). Long non-coding RNA UCA1 increases chemoresistance of bladder cancer cells by regulating Wnt signaling. FEBS Journal,281(7), 1750-1758. doi:10.1111/febs.12737

 

Wang, F., Li, X., Xie, X., Zhao, L., & Chen, W. (2008). UCA1, a non-protein-coding RNA up-regulated in bladder carcinoma and embryo, influencing cell growth and promoting invasion. FEBS Letters,582(13), 1919-1927. doi:10.1016/j.febslet.2008.05.012

 

Casinelli, G., Larosa, J., Sharma, M., Cherok, E., Banerjee, S., Branca, M., . . . Graves, J. A. (2016). N-Myc overexpression increases cisplatin resistance in neuroblastoma via deregulation of mitochondrial dynamics. Cell Death Discovery,2(1). doi:10.1038/cddiscovery.2016.82

 

Wang, B., Huang, Z., Gao, R., Zeng, Z., Yang, W., Sun, Y., . . . Zhou, S. (2017). Expression of Long Noncoding RNA Urothelial Cancer Associated 1 Promotes Cisplatin Resistance in Cervical Cancer. Cancer Biotherapy and Radiopharmaceuticals,32(3), 101-110. doi:10.1089/cbr.2016.2156

 

Xia, Y., He, Z., Liu, B., Wang, P., & Chen, Y. (2015). Downregulation of Meg3 enhances cisplatin resistance of lung cancer cells through activation of the WNT/β-catenin signaling pathway. Molecular Medicine Reports,12(3), 4530-4537. doi:10.3892/mmr.2015.3897

 

Piskareva, O., Harvey, H., Nolan, J., Conlon, R., Alcock, L., Buckley, P., . . . Stallings, R. L. (2015). Corrigendum to “The development of cisplatin resistance in neuroblastoma is accompanied by epithelial to mesenchymal transition in vitro” [Cancer Lett 364 (2015) 142–155]. Cancer Letters,369(2), 428. doi:10.1016/j.canlet.2015.09.010

 

REN D, LI H, LI R, et al. Novel insight into MALAT-1 in cancer: Therapeutic targets and clinical applications. Oncology Letters. 2016;11:1621-1630.

 

Wang, H., Guan, Z., He, K., Qian, J., Cao, J., & Teng, L. (2017). LncRNA UCA1 in anti-cancer drug resistance. Oncotarget, 8(38), 64638-64650. doi:10.18632/oncotarget.18344

 

Malik, R., Patel, L., Prensner, J., Shi, Y., Iyer, M., Subramaniyan, S., . . . Chinnaiyan, A. (2014). The lncRNA PCAT29 inhibits oncogenic phenotypes in prostate cancer. Molecular Cancer Research, 12(8), 1081-1087. doi:10.1158/1541-7786.MCR-14-0257

 

Guttman M, Donaghey J, Carey BW, Garber M, Grenier JK, Munson G, Young G, Lucas AB, Ach R, Bruhn L, Yang X, Amit I, Meissner A, et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature. 2011; 477:295–300.

 

Schmitt AM, Chang HY. Long noncoding RNAs in cancer pathways. Cancer Cell. 2016; 29:452–463.

 

Moran VA, Perera RJ, Khalil AM. Emerging functional and mechanistic paradigms of mammalian long non-coding RNAs. Nucleic Acids Res. 2012; 40:6391–6400.

 

Wang F, Li X, Xie X, Zhao L & Chen W(2008) UCA1, a non‐protein‐coding RNA up‐regulated in bladder carcinoma and embryo, influencing cell growth and promoting invasion. FEBS Lett 582, 1919–1927.

 

Hu, Y., Zhu, Q., Deng, J., Li, Z., Wang, G., & Zhu, Y. (2018). Emerging role of long non-coding RNAs in cisplatin resistance. OncoTargets and Therapy, 11, 3185-3194. doi:10.2147/OTT.S158104

 

Ayers, D., & Vandesompele, J. (2017). Influence of microRNAs and long non-coding RNAs in cancer chemoresistance. Genes, 8(3), 95. doi:10.3390/genes8030095

 

Cheng, N., Cai, W., Ren, S., Li, X., Wang, Q., Pan, H., . . . Hirsch, F. R. (2015). Long non-coding RNA UCA1 induces non-T790M acquired resistance to EGFR-TKIs by activating the AKT/mTOR pathway in EGFR-mutant non-small cell lung cancer. Oncotarget, 6(27), 23582. doi:10.18632/oncotarget.4361

 

Dasari, S., & Tchounwou, P. B. (2014). Cisplatin in cancer therapy: molecular mechanisms of action. European journal of pharmacology, 740, 364-78.

 

Chen, Q., Wei, C., Wang, Z., & Sun, M. (2017). Long non-coding RNAs in anti-cancer drug resistance. Oncotarget, 8(1), 1925-1936. doi:10.18632/oncotarget.12461

 

Galluzzi, L., Senovilla, L., Vitale, I., Michels, J., Martins, I., Kepp, O., . . . Kroemer, G. (2012). Molecular mechanisms of cisplatin resistance. Oncogene, 31(15), 1869-1883. doi:10.1038/onc.2011.384

 

Eastlack, S. C., Dong, S., Mo, Y. Y., & Alahari, S. K. (2018). Expression of long noncoding RNA MALAT1 correlates with increased levels of nischarin and inhibits oncogenic cell functions in breast cancer. PloS One, 13(6), e0198945. doi:10.1371/journal.pone.0198945

 

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNAseq. Nat Methods 5:621–628.

Thermo Fisher Scientific. (2011). NanoDrop 1000 Spectrophotometer V3.8 User’s Manual. Retrieved from: http://tools.thermofisher.com/content/sfs/manuals/nd-1000-v3.8-users-manual-8%205×11.pdf

 

Zhou, X., Liu, S., Cai, G., Kong, L., Zhang, T., Ren, Y., . . . Wang, X. (2015). Long non coding RNA MALAT1 promotes tumor growth and metastasis by inducing epithelial-mesenchymal transition in oral squamous cell carcinoma. Scientific Reports, 5(1), 15972. doi:10.1038/srep15972

 

Final Project Outline

Shannah and I finalized the topic of our project after talking with Pam and Evan. It was great that we decided on our topic early on so that we have a direction to work on. Not being able to find a direction was the most worried aspect of the final project for me, because I think I spend a lot of time on things that are not important. From this project I learned that it’s fine to reach for help at an early stage. I always thought that I should have a clear thought and specific questions to ask before approaching professors or TAs, but that’s not true. I wish I would have done that more during my undergrad, and I will in the future.

Topic chosen: long noncoding RNA and neuroblastoma cisplatin drug resistance

 

SPECIFIC QUESTION: What is the effect of lncRNA UCA1 in the development of cisplatin resistance in neuroblastoma?

 

HOW IS THIS QUESTION NOVEL AND ORIGINAL?

The effects of  long noncoding RNA (lncRNA) urothelial cancer‐associated 1 (UCA1) in the development of cisplatin resistance has been studied in many types of cancer, including cervical, bladder and ovarian cancer (REF). However, the effects of UCA1 on resistance to cisplatin treatment in neuroblastoma, a childhood malignancy in the sympathetic nervous system, has yet to be investigated. Studies have looked at the relationship between differential expression of proteins and development of cancer resistance.

 

Previous  research were done of the proteins that are expressed in resistant cancer cell lines. A study by _____ suggested the involvement of miRNA in silencing expression of proteins, thereby inducing chemoresistance. Recent studies have also demonstrated chemoresistance mechanisms beyond the protein level. For example, long non-coding RNA HOTAIR was shown to be involved in cancer metastasis.

 

POTENTIAL IMPACT OF THE PROPOSED QUESTION (WERE IT TO BE ANSWERED BY YOUR PROPOSED EXPERIMENT):

Neuroblastoma is a childhood malignancy in the sympathetic nervous system and accounts for 15% of all deaths in pediatric cancer patients (REF). Treatment of neuroblastoma is most commonly performed using cisplatin, a platinum based anticancer drug that induces apoptosis by activating various signal transduction pathways (REF). Development of drug resistance has made it hard to effectively treat neuroblastoma patients, and doctors often resort to multi drug treatments. Despite these efforts, drug resistance is still prevalent and the cause of the resistance is unknown. In order to effectively treat cancer it is important to understand the mechanism of cancer resistance and find ways to prevent their effect (REF). Recent studies have shown that long non-coding RNA plays a major role cisplatin drug capabilities in various cancer types, including breast cancer (REF), cervical cancer (REF), and lung cancer (REF). This includes urothelial cancer‐associated 1 (UCA1), a lncRNA that plays a regulatory role in the proliferation of cells and has been found to be upregulated in bladder cancer. However, UCA1 has never been studied in cisplatin resistance in neuroblastoma, posing a potential gap in knowledge that could be vital to finding effective treatment methods for patients who are resisting this drug. An investigation of the presence of UCA1 in cell lines that are resistant to drug treatment and identification of the changes in mRNA expression it promotes, will allow us to take a step further in understanding the mechanisms involved in resistance, a vital step in finding the best treatment methods.

 

HYPOTHESIS AND EVIDENCE ON WHICH THE HYPOTHESIS IS BASED (INCLUDE REFERENCES):

 

Experiment 1: If lncRNA UCA1 is crucial  in developing cancer resistance, then neuroblastoma cells without UCA1 expression should show little to no resistance. We predict that cisplatin sensitive neuroblastoma cells would have no or low levels or UCA1. Cisplatin resistant neuroblastoma cells will show reduced resistance after knockout of UCA1.

If high UCA1 is detected in cisplatin sensitive cells, then UCA1 may not be involved in cancer resistance or there are specific conditions required for UCA1 to be involved. If high levels of  resistance is shown even after knockout of UCA1 in cisplatin resistant neuroblastoma cells, then UCA1 may not be a crucial component of cisplatin resistance.

 

Study by Wang et al. (2008) has shown that UCA1 is correlated with cell proliferation and migration in bladder cancer. (rewording). Overexpression of UCA1 in bladder cancer cells resulted in invasion and migration of cells. The researchers identified that UCA1 may be  involved in activating the wnt signaling pathway. The wnt pathway is  known to contribute to chemoresistance development in various cancer cell lines including neuroblastoma, multiple myeloma, and hepatocellular carcinoma cells. Study by Fan et al. (2014) has demonstrated that UCA1 levels are higher in cisplatin resistant T24 cells, a type of bladder cancer cell line. Knock out of UCA 1 reduced cell proliferation

 

Experiment 2:

Studies looking at cisplatin resistance in numerous cancers have found a relationship between lncRNA UCA1 and changes in expression levels of genes that are involved in the cells susceptibility to treatment (REFS). This includes a study performed by …  looking at microarray mRNA expression analysis of transfected UCA1 cells compared to control. Expression levels of 42 genes were found to change by at least two-fold in the presence of UCA1 in bladder cancer, including an upregulation of Wnt signaling pathway member 6 (Wnt6), CYP1A1 (a cytochrome) and AURKC (kinase) and a downregulation of methyl‐CpG binding domain protein 3 (MBD3) and SR (serine/arginine‐rich) protein‐specific kinase 1 (SRPK1). These results are verified by several other studies looking further into the effects of UCA1 on gene expression in bladder cancer (REF’s). Similar results have been found in ovarian cancer, including a study by … that focuses on the change in expression of SRPK1 in the presence of UCA1 and cisplatin resistance. The gene expression analysis profiles continue to show similar changes in expression of genes in other cancers, including cervical, lung and bladder cancer [REFS]. Based on this continuing similarity between cancers, we hypothesize the expression levels of these genes will be similar in neuroblastoma cell lines [REF]. Among others, this includes an upregulation of Wnt6, CYP1A1, and AURKC and a downregulation of MBD3 and SRPK1.

PREDICTION(S):

Experiment 1:

Cisplatin Resistant Cisplatin Susceptible
UCA1 not knockout Cells grow Cells don’t grow
UCA1 knocked out Cells don’t grow Cells don’t grow

 

Experiment 2:

We predict RNA sequencing will show an increase in expression levels in Wnt6, CYP1A1, and AURKC and a decrease in expression levels in MBD3 and SRPK1 when UCA1 is present in the cisplatin resistant cells compared to UCA1 knockout cisplatin resistant cell lines and cisplatin susceptible cell lines.

 

EXPERIMENTAL APPROACH TO TEST PREDICTION (INCLUDE ANY DETAILS THAT YOU HAVE WORKED OUT SO FAR):

 

  1. Collect neuroblastoma cisplatin resistant and susceptible cell lines
  2. Knockout UCA1 using RNAi such that we have a control group and a knockout group
  3. Cell proliferation assay (MTT) of control (cisplatin resistant with UCA1) and UCA1 knockout cells (from cisplatin resistant).
    1. Seed cells separately in two 96 well plates for 24 hours
    2. Treat both plates with cisplatin and allow them to proliferate for 3-4 days
    3. Analyze cell proliferation. If our hypothesis is correct, then we expect to see less cell proliferation in the UCA1 knockout cells
    4. Follow the same protocol with cisplatin susceptible cells as a negative control

 

  1. Perform expression profile analysis using RNA sequencing of cell lines before and after knockout to compare their RNA expression levels. CONTROL: To ensure that RNAi sufficiently knocked out UCA1, check the expression levels of UCA1 in the knockout.

 

 

 

 

LIST OF RELEVANT PRIMARY AND REVIEW ARTICLES READ, AND SUMMARY OF RELEVANT INFORMATION FROM EACH (this is the start of the annotated bibliography that you will need to include in your portfolio)

 

Note to Pam: This will be a more fleshed out annotated bibliography in the future. Currently, we have point form for all of the

 

Wang, F., Zhou, J., Xie, X., Hu, J., Chen, L., Hu, Q., . . . Yu, C. (2015). Involvement of

SRPK1 in cisplatin resistance related to long non-coding RNA UCA1 in human ovarian cancer cells. Neoplasma,62(03), 432-438. doi:10.4149/neo_2015_051

 

This article investigates UCA1’s effects on SRPK1 in cisplatin resistance in ovarian cancer. They started by looking at UCA1 expression in cancer and non cancer cells and its corresponding resistance to cisplatin treatment followed by assessing the expression of SRPK1 and apoptosis pathway proteins to explore the mechanism. Lastly, they looked at the effects of knocking out SRPK1 on cisplatin resistance. Results found an increased expression of SRPK1 and anti-apoptosis proteins in transgenic UCA1 cells, and knocking down SRPK1 could partly rescue the effect of UCA1 expression on cell migration, invasion and cisplatin resistance in cells. Based on these findings, they suggested that SRPK1 and apoptosis pathway proteins may be involved in the effect of UCA1.

 

Pan, J., Li, X., Wu, W., Xue, M., Hou, H., Zhai, W., & Chen, W. (2016). Long non-coding

RNA UCA1 promotes cisplatin/gemcitabine resistance through CREB modulating miR-196a-5p in bladder cancer cells. Cancer Letters,382(1), 64-76. doi:10.1016/j.canlet.2016.08.015

 

Fan, Y., Shen, B., Tan, M., Mu, X., Qin, Y., Zhang, F., & Liu, Y. (2014). Long non-coding RNA UCA1 increases chemoresistance of bladder cancer cells by regulating Wnt signaling. FEBS Journal,281(7), 1750-1758. doi:10.1111/febs.12737

Investigated the role of UCA1 lncRNA in cisplatin resistance during chemotherapy for bladder cancer. We showed that cisplatin‐based chemotherapy results in up‐regulation of UCA1 expression in patients with bladder cancer. Similarly, UCA1 levels are increased in cisplatin‐resistant bladder cancer cells. Over‐expression of UCA1 significantly increases the cell viability during cisplatin treatment, whereas UCA1 knockdown reduces the cell viability during cisplatin treatment. We finally demonstrate that UCA1 increases the cisplatin resistance of bladder cancer cells by enhancing the expression of Wnt6, and thus represents a potential target to overcome chemoresistance in bladder cancer.

 

Wang, F., Li, X., Xie, X., Zhao, L., & Chen, W. (2008). UCA1, a non-protein-coding RNA up-regulated in bladder carcinoma and embryo, influencing cell growth and promoting invasion. FEBS Letters,582(13), 1919-1927. doi:10.1016/j.febslet.2008.05.012

In addition to other experiments, did an expression analysis of genes to find potential targets of UCA1 in drug resistance

    1. Results [this is copied from the paper but will be changed later]: The changes of expression of the several representative genes were confirmed through real time PCR (Fig. 4C3–C5), the up‐regulated genes including
      1. wingless‐type MMTV integration site family, member 6 (WNT6) 15
      2. CYP1A1 (cytochrome P450, 1A1) 16,
      3. AURKC (a urora kinase C) 17,
      4. and the down‐regulated genes including
      5. methyl‐CpG binding domain protein 3 (MBD3) 1820, and
      6. SR (serine/arginine‐rich) protein‐specific kinase 1 (SRPK1) 21, 22, which were identical with the microarray results (Tables 2 and 3).

 

Casinelli, G., Larosa, J., Sharma, M., Cherok, E., Banerjee, S., Branca, M., . . . Graves, J. A. (2016). N-Myc overexpression increases cisplatin resistance in neuroblastoma via deregulation of mitochondrial dynamics. Cell Death Discovery,2(1). doi:10.1038/cddiscovery.2016.82

 

Wang, B., Huang, Z., Gao, R., Zeng, Z., Yang, W., Sun, Y., . . . Zhou, S. (2017). Expression of Long Noncoding RNA Urothelial Cancer Associated 1 Promotes Cisplatin Resistance in Cervical Cancer. Cancer Biotherapy and Radiopharmaceuticals,32(3), 101-110. doi:10.1089/cbr.2016.2156

 

Xia, Y., He, Z., Liu, B., Wang, P., & Chen, Y. (2015). Downregulation of Meg3 enhances cisplatin resistance of lung cancer cells through activation of the WNT/β-catenin signaling pathway. Molecular Medicine Reports,12(3), 4530-4537. doi:10.3892/mmr.2015.3897

 

Piskareva, O., Harvey, H., Nolan, J., Conlon, R., Alcock, L., Buckley, P., . . . Stallings, R. L. (2015). Corrigendum to “The development of cisplatin resistance in neuroblastoma is accompanied by epithelial to mesenchymal transition in vitro” [Cancer Lett 364 (2015) 142–155]. Cancer Letters,369(2), 428. doi:10.1016/j.canlet.2015.09.010

 

https://www.sciencedirect.com/science/article/pii/S0304383515003250?via%3Dihub#bib0025

Identified the proteins that are involved in cisplatin resistant cell lines in neuroblastoma through proteomic profiling, a method to identify the proteins that are present. They showed that proteins that are differentially expressed in resistant cell lines are involved in cancer promoting mechanisms such as cell migration and proliferation. Proteins that were absent (or expressed in lower levels) in resistant cells were predicted to be due to miRNA that target specific genes.

 

 

 

POTENTIAL WAYS TO MAKE YOUR QUESTION KNOWN TO THE PUBLIC AT LARGE (e.g. TO YOUR NON-BIOLOGIST FAMILY AND FRIENDS):

  • Infographic of our findings and share on social media
  • Make a video that is easy for a non-biologist to understand and share on social media
  • Present at events that are intended for the public, for example: https://vancouver.nerdnite.com/

 

ANY OTHER PARTS OF THE PROJECT COMPLETED SO FAR:

 

 

ANYTHING YOU WOULD LIKE SPECIFIC FEEDBACK ON:

  • How much detail should we provide for the hypothesis, possible results and discussion of the expression analysis. There are a TON of genes that UCA1 could affect, as found in previous studies. Is it ok to mention a few and dive deeper into these? Or should we try to include all that we can find and not go into too much detail? Or some sort of combination of both?
  • Is our question novel enough? Is our evidence for our hypothesis valid and sufficient? How can we improve on this?

 

Top4: Case study stage 1

We were assigned the ashby paper that talked about the effect of miRNA on honeybee caste development. I chose this to be my top 4 because it gave us some ideas to our final project. This paper was based on a just look approach where they analyzed the upregulation and downregulation of miRNA during developement of honeybees and did a bioinformatics analysis, touching on the pathway that miRNA might act on. This paper was initially hard to understand since I was unfamiliar with bioinformatics (umm more like unfamiliar in everything like reading chip or methylation patterns ahaha but this one was harder). It was a good opportunity to introduce myself into the steps of analyzing expression patterns which further developed in my final project.

The worksheet that we have submitted is shown below:

Case study – Stage 1: Analysis and explanation of figures/article worksheet

Assigned article: Ashby et al., 2016

Focus for careful analysis: Table 2, Figure 2 and Figure 5

Questions

1.Your names and surnames:

Shannah Fisher & Joanne Lim

  1.  According to the author what was the overall purpose of the study presented in the paper?

The study investigates differences in the miRNA profiles for the drones, queen and workers during their development (after canalization) and how these differences in miRNA relate to the differences in transcriptional profiles and phenotypic output.

 

  1. This article includes a lot of bioinformatic analyses. If you are familiar with at least some of them, feel free to comment in more details; if you are not familiar with them, what are the general system and methods, and the main findings?

 

Bioinformatic Methods:

Upon collecting samples from honeybee larvae during stage L5, where commitment to a particular developmental trajectory is irreversible, the researchers extracted their RNA and prepared them illumina sequencing technology. The sequences produced were used in the remainder of the bioinformatics analyses, shown below:

Prediction of novel miRNAs:

  • METHOD: This involved inputting the RNA reads (short segments of RNA) from the illumina sequencing output into a software that will output the miRNA’s that are novel. (known miRNAs are found in a database storage  miRBase).
  • FINDINGS: They were able to identify 82 novel miRNAs in their samples from both intronic and intergenic regions.

Mapping mRNA:

  • METHOD: RNA sequencing reads (RNA prediction sequences from illumina) were taken and inputting into a software the aligns the reads to the reference sequence to determine in map the mRNA’s to the bee genome. This will result in the full mRNA sequence of the collected samples.

Differential gene expression analysis:

  • METHOD: RNA sequence reads were inputted into a program built in the programming language R that analyzes differential expression of the reads using statistical methods.
  • FINDINGS: Of the 164 miRNA’s detected, 120 show differential expression between at least two castes, with 27 showing differential gene expression amongst all three castes.
  • FINDINGS: Table 2 findings that compare each caste with each other to determine the changes in miRNA expression levels between castes.

Prediction of miRNA targets:

  • Target prediction using the 3’ UTR or for genes lacking 3’UTR, 500bp downstream of the stop codon.
  • FINDINGS: The predicted targets found were part of distinct gene pathways, including the steroid hormone pathway, and ion channel formation, to name a few. It was also found that many of the transcripts are targeted by more than one miRNA’s and many of the miRNA’s target more than one gene.

 

  1.       Carefully consider Table 2 (focus on the high-throughput sequencing side) and the part(s) of the article that talk about the data presented there, then answer the following:What did the author measure/detect/observe?Compared the miRNA expression of the 17 candidates in the 3 different castes during developmental canalization (L4/L5)
  2. What type of approach do these experiments fall under? Just look experiment in a sophisticated way: quantifying the expression of miRNA without manipulating the system other than extracting miRNA
  3. What do the data show for workers vs. queens? Expression levels of  ame-mir-13b, ame-mir-2, and ame-mir-252a were significantly higher in the queen than workers as shown by a positive fold change greater than 1.5. Expression levels of miRNA’s ame-mir-1175, ame-mir-276, ame-mir-315, ame-mir-375, ame-mir-750 and ame-mir-let7 were significantly lower in that of the queen compared to the worker, as shown by a negative fold change greater than -1.5.Expression levels of ame-bantam, ame-mir-2865-5p, ame-mir-283, ame-mir-2715m, ame-mir-6001-3p, ame-mir-87-1 and ame-mir-87-2 did not show a significant fold change, as indicated by a fold change between -1.5 and 1.5.
  4.    What can we indirectly infer from the data? The miRNAs that showed a significant fold change must be involved in the difference in transcriptional profile and phenotype of workers and queens. These miRNAs may act on the pathways that determine the phenotypic difference of workers and queens.
  5. What miRNAs may be interesting to study further, and why?

Out of the miRNAs that showed significant fold change, the ones that show higher fold change would be interesting to investigate further. The higher the fold change, the more effect miRNA might have in the differential expression of transcripts and phenotype. For example, by studying the function of a particular miRNA, we might be able to further investigate what pathway is affected by the miRNA to result in differential expression. If we have to pick one, we would study ame-mir-315 because it has the highest fold change (-2.9). Table 4 shows that ame-mir-315 may be involved in targeting pyruvate carboxylase and affect the catalysis of carboxylation of pyruvate to oxaloacetate. With the knowledge that ame-mir-315 expression is highly different in workers and queens, we can infer that the difference in pyruvate carboxylase function contributes to the phenotypic difference of workers and queens

9.     Carefully consider Figure 2 and answer the following questions: What type of approach does this experiment fall under?

A sophisticated just look experiment: looking at any correlations and patterns between the three castes in their miRNA and mRNA profiles by grouping the complex profiles together via the Principle Component Analysis (statistical technique).

10.  What can we directly conclude from these results?

Haploid drones have different non-coding RNA (miRNA) profile from the diploids at larval stage L4/L5 (developmental stage). mRNA profiles are distinct between the three castes at the larval stage L4/L5 (developmental stage). The difference in mRNA expression profiles in the three castes is more distinct than in the miRNA expression profiles, shown by clustering of samples.

11.  Figure 5 summarizes data from the literature and from an analysis for which the direct results are not shown in the main article. What did the author examine, and what do they show in the figure?

The authors examined all of the genes involved in the hippo signaling pathway, which is important in growth and development of the bees. They then categorized the genes into those that differ in expression in the three castes, those that are differentially methylated, and those that are targets of miRNA.

The figure shows:

  • which genes are expressed differently in the three castes (pink).
  • which genes are methylated in the queen or worker larvae (green) and of those, the differential DNA methylation between the two (green outline).
  • the predicted miRNA targets (brown).

12.  Overall, how do the data in Table 2 and Figure 2 and 5 support the statement made in the article’s title?

The article’s title is phrased in way that gives caste two meanings. The first meaning implied the true definition of a caste in a bee’s colony where the bees become a queen, worker or drone. The second meaning is traditionally spelt as ‘cast’ and is often used in a play to mean the director casting the actors to their roles. From this, the article’s title suggests that miRNAs are involved in casting the honey bees into their role of worker, drone or queen.

Figure 2 supports this statement by showing that there are overall differences between the miRNA levels of haploid and diploid bees and mRNA levels of all 3 castes. Table 2 then dives deeper by looking at 17 candidate miRNAs and comparing the miRNA expression levels between all 3 castes individually (compare each caste to each other for each miRNA). Figure 5 then shows an example of the differences in miRNA and mRNA levels and their effect on the genes involved in the hippo signaling pathway, one known to play a role in growth and development of the three castes. Overall, this progression of conclusions support the title by showing that the miRNA and mRNA are different overall, in all 3 castes and can cause a phenotypic difference.

Ashby, R., Forêt, S., Searle, I., & Maleszka, R. (2016). MicroRNAs in honey bee caste determination.Scientific Reports, 6(1), 18794. doi:10.1038/srep1879

 

Final Project Question

Final Project Question

members:Shannah Fisher, Joanne Lim, Erin Yang

What is the role of PAX3 in maintaining progenitor cell-like state in the cerebellum through comparisons of its lncRNAs and its comparisons to cancer cells? We will work in a group of 3 and will answer this question with three experiments: