Extreme environments leave genomic traces in tiny organisms: study

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Frequency chaos game representation (fCGRk) demonstrate the global importance of different hexamers in classifying DNA sequences of each environmental category from the rest of the dataset. The top panel has fCGRk For the temperature dataset, the bottom panel contains fCGRk For pH datasets, both K=6. The color and intensity of each pixel represents the relative importance (relevance) of the corresponding 6-mer (dark blue pixels are the most relevant 6-mers, as explained in the color bar legend). -mer, etc.). credit: scientific report (2023). DOI: 10.1038/s41598-023-42518-y

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Frequency chaos game representation (fCGRk) demonstrate the global importance of different hexamers in classifying DNA sequences of each environmental category from the rest of the dataset. The top panel has fCGRk For the temperature dataset, the bottom panel contains fCGRk For pH datasets, both K=6. The color and intensity of each pixel represents the relative importance (relevance) of the corresponding 6-mer (dark blue pixels are the most relevant 6-mers, as explained in the color bar legend). -mer, etc.). credit: scientific report (2023). DOI: 10.1038/s41598-023-42518-y

Signatures written in genomic DNA have long been associated with ancestry rather than geographic location. However, recent research using AI from Western University scientific reportwe provide evidence that living in extreme temperature environments leaves a discernible imprint on the genomes of microbial extremophiles.

An interdisciplinary research team co-led by Western Associate Professor of Biology Kathleen Hill and Computer Science Adjunct Professor Lila Cali is using machine learning to identify the genomic signatures of extremophiles from microorganisms that live in similar extreme environments. Despite belonging to two environments, they found unexpected similarities. different areas of the tree of life: bacteria and archaea.

Hill, an expert in genetic variation, population genetics, and genome evolution, said: “This discovery overturns the traditional idea that genomic features, which are widespread throughout the genome, only convey information about naming, describing, and classifying organisms. It is a thing,” he said.

Extremophiles live in extremely harsh environments such as volcanoes, deep ocean trenches, and polar regions, all of which are characterized by extreme conditions (high temperature, radiation, pressure, or acidity) and where most other living organisms may pose an existential threat to For example, Pyrococcus furiosus is an archaea (single-celled organism) first discovered to thrive at 100 degrees near a volcanic crater in Italy, and Chryseobacterium greenlandensis is an archaea (single-celled organism) that was first discovered to thrive at 100 degrees near a volcanic crater in Italy, and Chryseobacterium greenlandensis This bacteria survived in ice for 120,000 years.

“This is similar to someone living in the Arctic realizing that their DNA resembles the algae that grows in the Arctic, rather than their cousin’s DNA,” he says. Kari, the expert, says: “DNA should primarily be about genetics, biological relatedness, and common ancestry, not about where you live, but we find that there is something entirely different about extremophiles. .”

In this study, Kari, Hill, and their collaborators used supervised and unsupervised machine learning to analyze the features of the genome. A supervised AI algorithm was trained on DNA sequences with taxonomic labels (bacteria or archaea). It has learned to recognize genomic patterns that characterize classification, and can now predict the classification of unknown DNA sequences with high accuracy.

Remarkably, when trained using the same DNA sequences labeled instead of the type of environment the organism lives in (hot or frigid), the AI ​​algorithm learns some genomic patterns associated with the type of environment. I learned. Additionally, we can now predict with moderate to high accuracy what extreme environments an unknown DNA sequence comes from.

“This result was unexpected, but it gave us the idea to continue our research,” said Kari, who is also a professor in the University of Waterloo’s computer science department.

Double check your results

To reconfirm these positive results, the team used unsupervised learning with the same dataset as input, only this time the DNA sequences had no labels at all.

In other words, a DNA sequence is fed to an unsupervised AI algorithm that knows nothing about its taxonomy or environment, and it asks a simple question: “Look at these DNA sequences, which one is more like you?” I did.

This blind AI algorithm successfully generates clusters of similar sequences, with each cluster containing sequences with similar genomic patterns. Surprisingly, some of the clusters that formed contained both bacterial and archaeal sequences, although bacteria and archaea are no more taxonomically similar than bears and fungi.

“Upon closer inspection, we discovered that these unlikely members of this population live in the same extreme environment,” said Kari. “This means that not only is an extremophile signal present in the very DNA structure of extremophiles, but in some cases this extremophile signal drowns out biologically relevant signals.”

The identification of environmental signals in the genomic signatures of extremophiles has significant implications for the future of space exploration, for example.

“By understanding how these resilient organisms adapt to extreme conditions on Earth, we may be able to take advantage of their unique abilities,” Hill said. . “This discovery brings us closer to unlocking the secrets of survival in harsh extraterrestrial environments, opening the door to new frontiers and expanding possibilities in space.”

For more information:
Pablo Millán Arias et al. Environment and taxonomy shape the genomic signature of prokaryotic extremophiles, scientific report (2023). DOI: 10.1038/s41598-023-42518-y

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