Data science reveals universal rules shaping cells’ powerhouses – ScienceDaily

Mitochondria are compartments – so-called “organelles” – in our cells that provide the supply of chemical energy we need to move, think and live. Chloroplasts are organelles in plants and algae that capture sunlight and carry out photosynthesis. At first glance, they may seem worlds apart. But an international team of researchers led by the University of Bergen have used data science and computational biology to show that the same “rules” have shaped how both organelles — and more — have evolved throughout the history of life.

Both types of organelles were once independent organisms with their own complete genomes. Billions of years ago, these organisms were captured and imprisoned by other cells – the ancestors of modern species. Since then, the organelles have lost most of their genomes, with only a handful of genes remaining in modern mitochondrial and chloroplast DNA. These remaining genes are essential for life and important in many devastating diseases, but why they remain in the organelle’s DNA – when so many others have been lost – has been debated for decades.

For a new perspective on this issue, scientists took a data-driven approach. They collected data on all organelle DNA that has been sequenced throughout life. They then used modeling, biochemistry, and structural biology to present a wide range of different hypotheses about the retention of a gene as a set of numbers associated with each gene. Using tools from data science and statistics, they asked which ideas could best explain the patterns of conserved genes in the data they collected—testing the results with unseen data to check their strength.

“Some clear patterns emerged from the modeling,” explains Kostas Janakis, a postdoctoral researcher in Bergen and joint first author of the paper. “Many of these genes code for subunits of larger cellular machines that assemble like a jigsaw puzzle. The genes for the pieces in the middle of the puzzle will most likely remain in the DNA of the organelles.”

The team believes this is because maintaining local control over the production of such central subunits helps the organelle to quickly respond to change – a version of the so-called “CoRR” model. They also found support for other existing, discussed and new ideas. For example, if a gene product is hydrophobic—and difficult to bring into the organelle from the outside—the data show that it often gets stuck there. Genes that themselves are encoded using stronger binding chemical groups are also more often conserved—perhaps because they are more resilient in the organelle’s harsh environment.

“These different hypotheses have generally been considered competing in the past,” says Ian Johnston, professor at Bergen and team leader. “But really, no one mechanism can explain all the observations – a combination is needed.” The strength of this unbiased, data-driven approach is that it can show that many ideas are partially correct, but none exclusively—perhaps explaining a long debate on these topics.”

To their surprise, the team also found that their models trained to describe mitochondrial genes also predicted the retention of chloroplast genes, and vice versa. They also found that the same genetic features that shape mitochondrial and chloroplast DNA also appear to play a role in the evolution of other endosymbionts—organisms that have recently been captured by other hosts, from algae to insects.

“It was a wow moment,” says Johnston. “We — and others — had this idea that similar pressures might apply to the evolution of different organelles. But to see this universal, quantitative relationship — data from one organelle precisely predicting patterns in another, and in more recent endosymbionts — was truly astounding.”

The research is part of a wider project funded by the European Research Council, and the team is now working on a parallel question – how different organisms maintain the genes of the organelles they retain. Mutations in mitochondrial DNA can cause devastating inherited diseases; the team uses modeling, statistics, and experiments to study how these mutations are handled in humans, plants, and more.

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Materials provided by University of Bergen. Note: Content may be edited for style and length.

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