Three students in Berkshire’s Advanced Math/Science Research (AMSR) program submitted their work to the Regeneron Science Talent Search (STS), the oldest and most prestigious high school science research competition in the nation. 
 
Clara Mollerus '22, Sierra Posey '22, and Sanjna Srinivasan '22 submitted their applications last month, which marked the culminating step of extensive research on a project of their own design. Applications require that scholars complete a number of essay questions, questions related to their project, and a 20-page original scientific paper, in addition to providing recommendation letters and their transcripts.

“The perseverance and dedication of these three young scholars is even more impressive considering the impact of the pandemic on young people,” said Dr. April Burch, who directs the AMSR program. “I couldn't be more proud of them and wish them all the best in the competition.” 

The top 300 scholars will be announced as semifinalists on January 6, 2022 at noon. From there, the top 40 finalists will be named on January 20. 

Keep reading below to learn more about each student’s project. 

Clara Mollerus '22
Chemical Analysis of Known Quorum Sensing Inhibitor in Vibrio Harveyi System

Bacteria use quorum sensing to communicate. They constantly release molecular signals that bind to other bacteria by chance. When the bacteria are in a higher concentration, like in an enclosed space, the likelihood that the signal reaches them instead of dissipating into the environment is greater. Upon receiving the signal, the bacterium turns on a gene, such as light production. This communication system allows bacteria to save energy by only producing light when in a high concentration and most effective. Inhibiting molecules block the reception of the signals and prevent this production of light. This serves as an alternative to antibiotics because it can control gene expression without contributing to antibiotic resistance. Although QStatin is a well known inhibitor to quorum sensing, there is little known about its chemical function. To investigate this, I tested chemically altered forms, or analogues, of the QStatin molecule on V. harveyi, a light producing bacteria. One of the analogues (Q-N), which lost a nitrogen atom, no longer blocked quorum sensing, and showed that the nitrogen was chemically essential for the efficacy of QStatin. This research aids in understanding why inhibitors are successful and contributes to work avoiding a worldwide catastrophe from antibiotic resistance.

Sierra Posey '22
Genetic Analysis of the Microviridae DNA Packaging Protein J: Studies to Elucidate the Role of the Terminal Aromatic Amino Acids

The bacteriophage PhiX174 is a virus that infects bacteria. With a small genomic structure, it is able to serve as a model system for more complex viruses through gaining understanding of it’s protein functions. My research examined the DNA packaging protein J of PhiX174 in the way that mutations in the aromatic ring structured amino acid Phenylalanine would affect the viability of the virus. Working with six different mutations in this specified location of the J protein, I was able to categorize their phenotypes based on the results of plating assay’s I completed. I completed plating assays at both restrictive and permissive conditions in terms of both temperature and bacterial cell line complementation and looked for the viability of all six mutants at these conditions. Isolating mutations in the J protein, I was able to locate break through plaque formation at restrictive conditions on one of the six mutants. This allowed me to hypothesize the presence of a second site suppressor somewhere else in the genome that allowed for viral viability. Isolate plaques were then picked for PCR and gel electrophoresis analysis to determine the presence of an additional mutation after DNA purification and sequencing.

Sanjna Srinivasan '22
Application of Exploratory Data Analysis to Prepare for Machine Learning: Investigation of the Effect of Anomalies in Autism Spectrum Disorder-Associated Gene Data

Outliers and anomalies may impact large amounts of data in unexpected ways. Exploring the data by employing methods of data mining allow us to better understand the data we are working with while detecting certain characteristics that are worth investigating further. Working with data of the phenotypic characteristics of the microscopic worms C.elegans with mutations in Autism-Spectrum-Disorder-associated genes, I found an anomaly in the data from the GNAI1 strain that hadn’t been taken into account prior to the analysis. I delved into the impacts of the anomaly on the characteristics of the strain and found that it was significant. As such, I was able to prove the importance of performing exploratory data analysis prior to performing a large scale analysis.