Congratulations to the winners of the Paper of the Year Awards for 2020

Posted on Monday, February 22, 2021

The Winners are:

PDF Paper of the Year:

Emma Bondy-Chorney

New functions for an ancient molecule in human cells
Emma Bondy-Chorney (PDF, Downey Lab)
Polyphosphates (polyPs) are long chains of inorganic phosphates found in all kingdoms of life.
PolyP play important roles in how cells grow and what happens when cells get infected. PolyP
research is mostly in bacteria and yeast, and scientists know a lot about how polyP is made and
its various jobs in these organisms. Very little is known about how polyP works in mammals, like
humans. Often researchers test the importance of a molecule by engineering cells to make
more or less of it. Since in humans we don’t know what makes polyP, we borrowed machinery
from bacteria to make polyP inside human cells and observed what changed in those cells. High
amounts of polyP caused reprogramming of both the DNA and the proteins of the cell, including
turning on important pathways that help cells grow and survive. We saw that increased amounts
of polyP caused movement of certain proteins from one place in the cell to another, an event
that can have an important impact on how well proteins are able do their jobs. Our work is the
first showing that polyP made inside a human cell has big impacts on pathways in the cell that
impact human health.

Cell Reports

A Broad Response to Intracellular Long-Chain
Polyphosphate in Human Cells

PhD Paper of the Year

Yulong Wei

Wei Y, Silke JR, Aris P, Xia X. 2020. Coronavirus genomes carry the signatures of their habitats. PLoS One 15(12): e0244025 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244025

Non-technical summary: Different organs and tissues offer different cellular environments which serve as cellular habitats for invading pathogens such as RNA viruses. Different cellular habitats differ in RNA editing enzymes such as APOBEC3, and in concentrations of zinc-finger antiviral proteins (ZAP) that targets specific CpG dinucleotides in the viral RNA genome. RNA viruses that have specialized in certain cellular habitats often exhibit specific genomic changes corresponding to their specialized habitats. This paper compiled differential expression of APOBEC3 and ZAP proteins in different mammalian tissues and showed that RNA viruses in cellular environment with high APOBEC3 tend to have increased U in their RNA genome, and that RNA viruses in cellular environment  with high ZAP concentration tend to have reduced CpG dinucleotides in their RNA genomes presumably to evade the antiviral action of ZAP. The paper has already been cited twice, one by Cell Reports and one by Biochemical and Biophysical Research Communications.

MSc Paper of the Year:

Alexander Pelletier and Yun-En Chung

Pelletier, A. R.*, Chung, Y.-E.*, Ning, Z., Wong, N., Figeys, D., Lavallée-Adam, M. (2020)
MealTime-MS: A machine learning-guided real-time mass spectrometry analysis for protein
identification and efficient dynamic exclusion. Journal of the American Society for Mass
Spectrometry. 31(7), 1459-1472.
* These authors contributed equally to this work
Non-technical summary of the article
Proteins are molecules that play a key role in the cell and in most diseases. Identifying the
proteins present in a biological sample is critical in order to provide a better understanding of the
cell and diseases. Mass spectrometry is a technique that can detect thousands of proteins in a
biological sample. However, commonly used mass spectrometry approaches are unable to
identify proteins that are present at low levels in samples. Indeed, these mass spectrometry
protocols preferentially and redundantly collect data from proteins of higher abundance, leaving
low abundance proteins uncharacterized. In this paper, we presented a novel machine learningbased
algorithm, MealTime-MS, that addressed this issue by identifying proteins in real-time
during mass spectrometry analysis and excluding identified proteins from further redundant
analyses. Our study showed that 98% of the proteins that are identified by current mass
spectrometry protocols are identified by MealTime-MS using only 59% of the data normally
needed. Mass spectrometry resources saved can therefore be repurposed to characterize more
proteins. Most importantly, MealTime-MS is the first machine learning approach that guides the
acquisition of mass spectrometry data. It provides a more comprehensive protein
characterization in biological samples and, therefore, a better understanding of the cell and
diseases.

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