BIOINFORMATICS REVIEW- APRIL 2018

APRIL 2018 VOL 4 ISSUE 4

“When radium was discovered, no one knew that it would prove useful in hospitals. The work was one of pure science. And this is a proof that scientific work must not be considered from the point of view of the direct usefulness of it.�

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Marie Curie

Genome editing of human embryos using CRISPR/Cas9crossing the ethics of gene editing?

Machine learning in prediction of ageing-related genes/proteins

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Contents

April 2018

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Topics Editorial....

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03 Featured Genome editing of human embryos using CRISPR/Cas9- crossing the ethics of gene editing?

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04 Machine Learning Machine learning in prediction of ageingrelated genes/proteins 09

FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR NABAJIT DAS

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BACK ISSUE Bioinformatics Review back issues can be downloaded in digital format from bioinformaticsreview.com at $5 per issue. Back issue in print format cost $2 for India delivery and $11 for international delivery, subject to availability. Pre-payment is required CONTACT PHONE +91. 991 1942-428 / 852 7572-667 MAIL Editorial: 101 FF Main Road Zakir Nagar, Okhla New Delhi IN 110025 STAFF ADDRESS To contact any of the Bioinformatics Review staff member, simply format the address as firstname@bioinformaticsreview.com PUBLICATION INFORMATION Volume 1, Number 1, Bioinformatics Reviewâ„¢ is published monthly for one year (12 issues) by Social and Educational Welfare Association (SEWA)trust (Registered under Trust Act 1882). Copyright 2015 Sewa Trust. All rights reserved. Bioinformatics Review is a trademark of Idea Quotient Labs and used under license by SEWA trust. Published in India

Bioinformatics- A broad future ahead: Editorial

EDITORIAL

It has been a wonderful time since BiR came into existence. As we enter a new year, BiR tries to look forward towards the development and wonderful achievements and providing the best knowledge regarding bioinformatics. In the past two years, BiR has hit a long road from a few readers to several thousand.

Muniba Faiza

Founding Editor

Every complimentary and appreciation mail we get feels like an achievement for us. Bioinformatics has got a great future ahead of it with a better understanding and precise methodologies for both dry and the wet lab experimentations.

In the last two years, BiR has advanced in many aspects. We have come up with an android app which helps our readers to stay connected with the latest updates, our articles have started to appear in Google Scholar, we get a lot of cherishing emails, and collaboration proposals. BiR is trying to broaden the horizons by covering different domains of bioinformatics. Since bioinformatics is multidisciplinary, to date, the team of BiR has tried to go through almost every aspect of it including big data, sequence analysis, structural bioinformatics, data mining, tools, software, biostatistics, and so on. This year BiR is more focused to provide a rich content to our readers and help to understand the concepts of bioinformatics more easily. The team of BiR is trying to reach to the students to encourage them for their career in bioinformatics and to the researchers currently working in the same area. The last internship at BiR was a great success and we got an amazing response from our interns. We are looking forward to presenting our work at school and college level to introduce this to the young minds who are more fascinated by the technology.

We have such a long road to drive on which is not possible without the support of our readers, subscribers, and contributors. We are thankful to our readers wholeheartedly for their support and suggestions and wish them a very happy and prosperous new year

Letters and responses: info@bioinformaticsreview.com

with new hopes and great achievements. We would like to hear your thoughts and feedback about BiR, and what other kinds of articles you would like to read.

EDITORIAL

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FEATURED

Genome editing of human embryos using CRISPR/Cas9crossing the ethics of gene editing?

Image Credit: Google Images

“A research group at Sun Yat-Sen University, Guangzhou led by Dr. Junjiu Huang have attempted to cure a fatal blood disorder caused by removing a gene known as β-thalassemia responsible for this disorder. This is done by using a geneediting technique called CRISPR/Cas9, which can recognize specific region in a DNA and cut and replace it with some other residues which do not code for any disorder.”

RISPR/Cas9 system is a recently developed multipurpose technology for genome editing [1,2] and its tool CRISPR-ERA/Cas9 is widely used as explained in the previous article [3].

The possible applications of this system have been discussed by its developers [4] and have been successfully applied for genome editing, gene function identification, and for gene therapy in animals and human cells [5-9]. Recently, a group of Chinese researchers has reported the editing of a genome using the CRISPR/Cas9 system in human embryo for the first time in history [5].

The team has attempted to remove

C

'harmful' genetic codes to be potentially replaced by the 'good' ones. The results are published in Protein & Cell journal [10] but have opened a topic for debate over crossing the ethics of gene editing.

A research group at Sun Yat-Sen University, Guangzhou led by Dr. Junjiu Huang have attempted to cure a fatal blood disorder caused by removing a gene known as βthalassemia responsible for this disorder. This is done by using a geneediting technique called CRISPR/Cas9, which can recognize specific region in a DNA and cut and replace it with some other residues which do not code for any disorder. The researchers

have modified this gene in a nonviable embryo but they observed some serious obstacles during the application of this method in human embryos.

The team applied this technique to 86 embryos and then waited for 48 hours to act. Out of 86 embryos, 71 survived, out of which 54 were genetically tested. According to their results, only 28 embryos were successfully spliced and only a fraction of them contained the replaced genetic material [10,11].

At this point, the team stopped experimenting quoting "If you want to do it in normal embryos, you need to Bioinformatics Review | 7

be close to 100%. That’s why we stopped. We still think it’s too immature." says Dr. Huang [10].

There are several ethical concerns regarding gene editing in the human genome. Modifying the DNA of viable embryos may lead to unpredictable results in the future generation. Most of the researchers think that this technique is not ready to be used for tweaking human embryos. Some concerned scientists have published an article addressing the consequences of editing human germ line using current technologies [12]. A stem-cell biologist George Daley at Harvard Medical School in Boston, Massachusetts, says "I believe this is the first report of CRISPR/Cas9 applied to human pre-implantation embryos and as such the study is a landmark, as well as a cautionary tale. Their study should be a stern warning to any practitioner who thinks the technology is ready for testing to eradicate disease genes." [10] Due to ethical concerns, this paper was not published by Nature and Science journal, says Dr. Huang [10,13]. The major concern which is most feared by some researchers is that once this technique is employed to tweak human embryos, it will be applied later to create humans with desired traits. This could be a huge step.

This research is questionable due to ethical issues but still being experimented with. Dr. Huang's team is trying to improve the technique using CRISPR/Cas9 in animal models for now [13].

References 1.

2.

Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., ... & Zhang, F. (2013). Multiplex genome engineering using CRISPR/Cas systems. Science, 1231143.

Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., ... & Church, G. M. (2013).

RNA-guided human genome engineering via Cas9. Science, 339(6121), 823-826.

3.

https://bioinformaticsreview.com/20151105 /explained-crispr-era-and-crisprcas9system/?format=pdf

4.

https://ideas.ted.com/the-promising-andperilous-science-of-gene-editing/

5.

Cho SW, Kim S, Kim JM, Kim JS (2013) Targeted genome engineering in human cells with the Cas9 RNA-guided endonuclease. Nat Biotechnol 31:230–232

6.

Jinek M, East A, Cheng A, Lin S, Ma E, Doudna J (2013) RNAprogrammed genome editing in human cells. Elife 2:e00471

7.

Li D, Qiu Z, Shao Y, Chen Y, Guan Y, Liu M, Li Y, Gao N, Wang L, Lu X et al (2013) Heritable gene targeting in the mouse and rat using a CRISPR-Cas system. Nat Biotechnol 31:681– 683

8.

Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, Norville JE, Church GM (2013) RNA-guided human genome engineering via Cas9. Science 339:823–826

9.

Smith C, Gore A, Yan W, Abalde-Atristain L, Li Z, He C, Wang Y, Brodsky RA, Zhang K, Cheng L et al (2014) Whole-genome sequencing analysis reveals high specificity of

CRISPR/Cas9 and TALEN-Based Genome Editing in Human iPSCs. Cell Stem Cell 15:12– 13 10. Cyranoski, D., & Reardon, S. (2015). Chinese scientists genetically modify human embryos. Nature News.

11. Liang, P., Xu, Y., Zhang, X., Ding, C., Huang, R., Zhang, Z., ... & Sun, Y. (2015). CRISPR/Cas9mediated gene editing in human tripronuclear zygotes. Protein & cell, 6(5), 363-372.

12. Lanphier, E., Urnov, F., Haecker, S. E., Werner, M., & Smolenski, J. (2015). Don’t edit the human germline. Nature News, 519(7544), 410.

13. http://uk.businessinsider.com/chinesescientists-genetic-modification-humanembryo-crispr-2015-4?r=US&IR=T

Further reading Liang, P., Xu, Y., Zhang, X., Ding, C., Huang, R., Zhang, Z., ... & Sun, Y. (2015). CRISPR/Cas9-mediated gene editing in human tripronuclear zygotes. Protein & cell, 6(5), 363-372. https://www.nature.com/news/chine se-scientists-genetically-modifyhuman-embryos-1.17378#/b1

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MACHINE LEARNING

Machine learning in prediction of ageing-related genes/proteins Image Credit: Stock photos

“Machine learning is being rapidly applied in the field of computational biology, which works on the human-designed algorithms that can learn from and use to make predictions on data. Besides, there are several databases has been developed for studying ageing-related genes/proteins.” geing has a great impact on human health, when people's age advance towards 80 years, approximately half of the proteins in the body get damaged through oxidation. The chemical degradations occurring in our body produce energy by the consumed food via oxidation in the presence of oxygen.

A

There are some proteins which have been found to be associated with ageing and age-related diseases such as Alzheimer's disease [1], which makes them relevant to explore their functionality and characteristics. The regulation and molecular basis of ageing are still poorly understood.

More than three hundred ageingrelated genes have been associated

with human ageing so far. Many studies have revealed that ageing has genetic components [2-5].

Machine learning is being rapidly applied in the field of computational biology, which works on the humandesigned algorithms that can learn from and use to make predictions on data. Besides, there are several databases has been developed for studying ageing-related genes/proteins. For example, GenAge is a highly maintained, manually curated benchmark database which is composed of ageing-related genes [6].

The genes in this database are related to longevity and/or ageing in humans and some model organisms such as yeast, mice, flies, worms, etc. This database summarizes 305 human ageing-related genes (version 18)

amongst which some of the genes are found directly linked with the human ageing. The other ageing-related genes/proteins databases include AGEMAP [7], NetAge [8], LongevityMap [9], and so on.

Features of genes/proteins [10]:

ageing-related



more protein-protein interaction partners.



more ageing-related proteinprotein interaction partners.



higher co-expression coefficients with other genes.

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higher K-core value; K-core or coreness value of a node is a maximal subgraph in which each vertex has a degree K [11,12].

Bioinformatics Review | 9

Some of the supervised machine learning methods such as support vector machines (SVMs), k-nearest neighbor (KNN), and decision tree classifiers have been applied in identifying and understanding ageingrelated genes and/or proteins in Caenorhabditis elegans [13], Drosophila melanogaster [14], and mice [15]. Recently, a simple classification model has been introduced based on different protein features such as response to oxidative stress, number of ageing-related protein interaction partners, and so on [16]. They have applied three different filtering algorithms: a scalable tree boosting system, regression analysis, and SVM, to identify ageing-related proteins, discover characteristic ageing-related features in humans, and quantify the relevance of the identified proteins in the process of ageing.

The approaches and methods of machine learning in computational biology will be discussed in upcoming articles in detail. Though machine learning is making some advancements in identifying new ageing-related genes and proteins, the metabolic understanding of ageing is still not understood well, it needs

some other new and better approaches and models to identify maximum proteins related to ageing and their pathways.

References 1.

https://www.sciencedaily.com/releases/2016/ 01/160129171322.htm

10. Li, Y.-H., Zhang, G.-G. & Guo, Z. Computational Prediction of Aging Genes in Human. In Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on, 1–4 (IEEE 2010) 11. Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F. (2006). K-core organization of complex networks. Physical review letters, 96(4), 040601.

12. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.

2.

de Magalhães, J. P. (2003). Is mammalian aging genetically controlled?. Biogerontology, 4(2), 119-120.

3.

de Magalhães, J. P., Cabral, J. A., & Magalhães, D. (2005). The influence of genes on the aging process of mice: a statistical assessment of the genetics of aging. Genetics, 169(1), 265-274.

4.

Vellai, T., Takács-Vellai, K., Sass, M., & Klionsky, D. J. (2009). The regulation of aging: does autophagy underlie longevity?. Trends in cell biology, 19(10), 487-494.

5.

Kenyon, C. J. (2010). The genetics of ageing. Nature, 464(7288), 504.

6.

Tacutu, R., Thornton, D., Johnson, E., Budovsky, A., Barardo, D., Craig, T., Diana, E., Lehmann, G., Toren, D., Wang, J., Fraifeld, V. E., de Magalhaes, J. P. (2018) "Human Ageing Genomic Resources: new and updated databases." Nucleic Acids Research 46(D1):D1083-D1090.

15. Feng, K., Song, X., Tan, F., Li, Y. H., Zhou, Y. C., & Li, J. H. (2012, May). Topological analysis and prediction of aging genes in Mus musculus. In Systems and Informatics (ICSAI), 2012 International Conference on (pp. 2268-2271). IEEE.

7.

Zahn, J. M., Poosala, S., Owen, A. B., Ingram, D. K., Lustig, A., Carter, A., ... & Lakatta, E. G. (2007). AGEMAP: a gene expression database for aging in mice. PLoS genetics, 3(11), e201.

16. Kerepesi, C., Daróczy, B., Sturm, Á., Vellai, T., & Benczúr, A. (2018). Prediction and characterization of human ageing-related proteins by using machine learning. Scientific reports, 8(1), 4094.

8.

Tacutu, R., Budovsky, A., & Fraifeld, V. E. (2010).

The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes. Biogerontology, 11(4), 513-522.

9.

Budovsky, A., Craig, T., Wang, J., Tacutu, R., Csordas, A., Lourenço, J., ... & de Magalhães, J. P. (2013). LongevityMap: a database of human genetic variants associated with longevity. Trends in Genetics, 29(10), 559-560.

13. Li, Y. H., Dong, M. Q., & Guo, Z. (2010).

Systematic analysis and prediction of longevity genes in Caenorhabditis elegans. Mechanisms of ageing and development, 131(11), 700-709.

14. Song, X., Zhou, Y. C., Feng, K., Li, Y. H., & Li, J. H. (2012, December). Discovering aginggenes by topological features in Drosophila melanogaster protein-protein interaction network. In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on (pp. 94-98). IEEE.

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