Surpassing Time Restraints: The Search for Viruses That Kill Many Bacteria

AJAS · 2022 Microbiology

Gabrielle Shapiro

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Overview

Mycobacteriophages are viruses that are parasitic to the fungus-like Mycobacterium, and with the recent increase in antibiotic resistance in these bacteria such as M. tuberculosis, more research into using phages as a treatment to bacterial infections is imperative. A larger host range is preferred when searching for the specific phage to use in the treatment “cocktails” because it vastly reduces the amount of time it takes to find the right phage. However, testing for host range has many challenges, from plating dangerous bacteria to time constraints, thus it is crucial to find a way to test host range without the need for numerous in-person plaque tests. I investigated how host range can be determined on a genetic basis. Minor tail proteins contain some of the most diverse sequences which contribute to adsorption, penetration, and importantly recognition and have been consistently correlated to host range. Indeed, Cluster K host range is known to be large and connected to its tail proteins, thus I compared their tail protein amino acid sequences to tail proteins in other Mycobacteriophages. I used several online technologies: first, the fully sequenced phage genomes were found on Actinobacteriophage Database (PhagesDB.org), then the Phamerator compared genes between different phages. BLASTP (P for protein) found related sequences, 1e-20 was used as a cut-off value, defining a “strong” relationship. The resulting hits were then compared with Cluster data in previous research including host range. Using MUSCLE on MEGAX, sequences underwent multiple sequence alignment (.FASTA sequences from Batch Entrez). Neighbor-Joining phylogenetic trees were built for each tail protein BLAST and one for the tape measure protein, a representation of their cluster designation, of the same phages. Finally, a Mann-Whitney U test was used to evaluate whether the sum of branch lengths (evolutionary distance) were statistically significant. I resulted with statistically significant correlations in select phages which indicate larger host ranges; phages that were previously known to have large host ranges were congruent with my correlations.

From the student

Hello! My name is Gabrielle Shapiro. From a young age, I have been fascinated with genetics and molecular biology. I started this research project as a Junior in my school’s Science Research Seminar Class. Choosing a topic was incredibly hard: I had been keeping a list for 10 years of science research ideas, pages and pages of science research ideas. I knew I wanted to study genetics, and due to the pandemic, I was able to cut my list to only projects that I could complete fully online and in my house. I had an idea linked with an article from the WHO about the antibiotic resistance crisis: “BACTERIOPHAGES! lots of real-world consequences.” Reading more literature, I realized how relatively new this field was its necessary impact. I had found my topic. Choosing a research question was just as difficult; zoom calls with Dr. Fillman directed me to look at the limitations of Phage Therapy. Through a humorous email chain with Dr. Hatfull, one of the leading experts in this field, I was referred back to Colorado with Dr. Guild. With more zoom calls and reading her student’s phage posters, I was able to settle down on Cluster K tail proteins and host range.

From there, the demanding yet satisfying part began; collecting and analyzing data. (I could fill pages with all the problem solving). I had never coded before so using APIs, sorting, and parsing data on google sheets was a fun challenge. However, my main issue was that there was no basis for comparison; the BLAST hits could not be shown to be statistically significant as there were no E-values from the entire genome to compare. After learning MEGAX and multiple sequence alignment, my entire genome phylogenetic tree consistently ran for 120hrs+ and then crashed. Finding solutions such as switching to Tape Measure proteins took several months of sheer will. When I finally created and analyzed my phylogenetic trees, I was ecstatic.

I presented my research to the Regional science tournament and unfortunately did not move on to the State tournament. I was not deterred and continued to work on defining to others the necessity of my research and thus signed up for the Colorado-Wyoming Junior Academy of Sciences. Strangely, I was one of the few competitors who watched the other presentations; I found them captivating. Results took about a month to come back. During that time, I realized that losing earlier had not changed anything about my passion; this entire experience only made me fall in love with research (from problem-solving to the one-day life-saving applications) and defined my career goals. When I opened the results page I was astonished; I qualified for The American Junior Academy of Science. I am excited to once again present my research and watch more engrossing presentations.

Results

Page 5. Gordonia Splitstree:

Welkin H. Pope, Travis N. Mavrich, Rebecca A. Garlena, Carlos A. Guerrero-Bustamante, Deborah Jacobs-Sera, Matthew T. Montgomery, Daniel A. Russell, Marcie H. Warner, Science Education Alliance-Phage Hunters Advancing Genomics and Evolutionary Science (SEA-PHAGES), Graham F. Hatfull, mBio Aug 2017, 8 (4) e01069-17; DOI: 10.1128/mBio.01069-17

Page 7. TM4 Phylogenetic Trees:

Neighbor Joining phylogenetic trees for TM4: Tape Measure protein and minor tail proteins 18-25. Scale 0.35 substitutions per site. Some trees are larger or smaller and this depends on the number of blast hits along with the sequence lengths. Each tree rooted on TM4 to allow for a basis of comparison. Top trees have been compared while the bottom are solely portrayed.

Page 8. Median Branch Lengths: Y axis describes median branch lengths in substitutions per site, while the X axis shows each cluster. The legend is tail proteins 18-27.

Acknowledgments

I would like to thank my teacher, Dr. Strode, for mentoring me throughout this entire process. I also thank Dr. Fillman and Dr. Guild at the Phage Geonomics Lab at the University of Colorado Boulder for their help directing me towards the limitations of Phage therapy and defining host range on a genetic basis. I want to thank my dad for teaching me to program in google sheets and helping me to work out the multitudes of bugs. Finally, I would like to thank the hundreds of dedicated scientists and programmers who created the many bioinformatic technologies I used; their dedication to open source technologies is absolutely critical to scientific research and discovery.

References

Links to Online Software:

Actinobacteriophage database: https://phagesdb.org/

Phamerator: https://phamerator.org/

BLAST: https://blast.ncbi.nlm.nih.gov/Blast.cgi

Batch Entrez: https://www.ncbi.nlm.nih.gov/sites/batchentrez

MEGAX: https://www.megasoftware.net/

Phylo.io: http://phylo.io/index.html#

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