Data science reveals universal rules for shaping cell power plants – ScienceDaily

Mitochondria are compartments – called “organelles” – in our cells that provide the chemical energy supply 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 look at two different worlds. But an international team of researchers, led by the University of Bergen, used data science and computational biology to show that the same “rules” shaped how each of the organelles — and more — evolved throughout life’s history.

Both types of organelles were independent organisms with a complete genome. 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, leaving only a few genes in modern-day mitochondrial and chloroplast DNA. These remaining genes are essential to life and important in many devastating diseases, but why they remain in the DNA of organelles—when many others were lost—has been debated for decades.

To get a fresh perspective on this question, the scientists took a data-driven approach. They collected data on all the DNA of the organelles that were sequenced through life. They then used modeling, biochemistry, and structural biology to represent a wide range of different hypotheses about gene retention as a set of numbers associated with each gene. Using tools from data science and statistics, they asked for ideas that could best explain the patterns of genes trapped in the data they collected — testing the results with unseen data to check their strength.

“Some clear patterns emerged from the modeling,” explains Costas Gianakis, a postdoctoral researcher in Bergen and co-first author on the paper. “Many of these genes encode subunits of larger cellular machinery, which are assembled like a jigsaw. The cut-off genes in the middle of the jigsaw are more likely to 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 respond quickly to change – a version of what’s called the “CoRR” model. They also found support for other existing, debated, and new ideas. For example, if the gene product is hydrophobic – and difficult to import into the organelle from the outside – the data shows that it is often retained there. The genes that are themselves encoded are often retained using stronger-linked chemical groups—perhaps because they are more robust in the harsh environment of the organelle.

“These different hypotheses were seen as competition in the past,” says Ian Johnston, professor at Bergen and team leader. “But in reality there is no single mechanism that can explain all observations – it requires a set. One of the strengths of this unbiased data-driven approach is that it can show that many ideas are partially correct, but none are exclusively so – perhaps Explains a long discussion 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 characteristics that make up mitochondrial DNA and chloroplasts also play a role in the evolution of other organisms — organisms recently captured by other hosts, from algae to insects.

“That was a wonderful moment,” Johnston says. “We – and others – have this idea that similar pressures might apply to the evolution of different organelles. But to see this global quantitative correlation – data from one organelle accurately predicting patterns in another, and in more recent endosymbiotics – – it was really amazing.”

The research is part of a broader project funded by the European Research Council, and the team is now working on a parallel question – how different organisms maintain the genes for the organelle they retain. Mutations in mitochondrial DNA can cause devastating genetic diseases; The team uses modeling, statistics and experiments to explore how to deal with these mutations in humans, plants, and more.

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Materials Introduction of University of Bergen. Note: Content can be modified according to style and length.

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