In a groundbreaking study, University of Toronto researchers have unveiled an “Enigma machine” program that can decode the messages of our very genes.

Like the German decoding device captured by the Allies during WW II, the U of T program can unlock the meaning of a garbled language – in this case, the cryptic orders that direct our genetic machinery.

“We are the first people to actually make predictions about which genetic message will be produced in different tissues,” said Brendan Frey, one of the paper’s senior authors.

“Prior to this, there was no way to predict that actually,” said Frey, who has a joint appointment in engineering and medicine at the school.

The paper was featured Wednesday on the cover of Nature, the world’s most prestigious science journal.

It introduces a readily accessible chart that can decode the messages that a gene will send out in any given type of cell.

The work helps explain the critical genetic process known as “alternative splicing” that accounts for the phenomenal complexity of human biology.

It also opens the door to a host of potential medical advancements, including deeper understandings of genetic diseases and the creation of replacement organs and tissues.

“From now on, it will be possible to predict the true meaning of a gene in a particular type of cell, “ said Spanish geneticist Juan Valcarel, one of several leading scientists already stepping up to praise the Toronto work.

“This will obviously be of great interest to understand how tissues work, i.e., what makes a muscle cell contract, an intestinal cell absorb nutrients, or a cell of the nervous system communicate with others through electrical signals,” said Valcarel, a researcher with the European Alternative Splicing Network.

Frey compared his computer decoder to the German Enigma encryption device, which helped the Allies defeat the Nazis after it fell into their hands.

“Just like in the old cryptographic systems in World War II, you’d have the Enigma machine…which would take an instruction and encode it in a complicated set of symbols,” he said.

“Well, biology works the same way. It turns out to control genetic messaging it makes use of a complicated set of symbols that are hidden in DNA.”

That genes had hidden messages that needed decoding only became apparent in the past decade.

Of all the genetic revelations produced by the human genome project, the most astonishing and vexing was surely this: we had far fewer genes than anyone had imagined.

Given the number of biological activities needed to grow and govern our bodies, scientists had believed humans must have 100,000 genes or more to direct those myriad functions.

But that genomic search of the 3 billion base pairs that make up the rungs of our twisting DNA ladders revealed a meagre 20,000 genes, about the same number as the lowly nematode worm boasts.

“The nematode has about 1,000 cells, and we have at least 1,000 different neuron (cells) in our brains alone,” said Benjamin Blencowe, a U of T biochemist and the study’s co-senior author.

To achieve this huge complexity, our genes must be monumental multi-taskers, with each one having the potential to do dozens or even hundreds of different things in different parts of the body.

And to be such adroit role switchers, each gene must have an immensely complex set of instructions – or a code – to tell them what to do in any of the different tissues they need to perform in.

Basically, the U of T team has broken that code – and proven their decryption works in four types of tissues.

Looking at central nervous system, muscle, embryonic and digestive system cells, the researchers showed they could predict which message the same genes would make in each of the different tissue types.

Here’s how it works:

Genes reside within the nucleus of a cell and do their work by acting as a template, throwing open their encoding DNA sequences as an assembly point for strands of messenger RNA.

This messenger strand then goes out into the cell and creates a protein, plucking out the correct, amino acid ingredients and stitching them together according to its genetically encoded instructions.

The resulting protein will then help to build an organ or direct a bodily function.

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Scientists had long thought that this messenger RNA building would occur along a continuous segment of a gene.

And if this had been the case, discovering what message a gene would make would be relatively simple.

“But it turns out that that view is actually wrong for over 95 per cent of genes,” Frey said.

What genes do instead, he says, is use different segments of their encoding surfaces to build separate segments of messenger RNA. Then they “splice” those segments together.

Different segments of the same gene would be spliced in different patterns in accordance to the needs of each different tissue.

These “alternative splicing” activities are directed by “code words” – short DNA sequences embedded along other parts of the gene – that dictate which segments will be used and how the messenger strand will be arranged.

Using about 400 code words, most of which they discovered themselves, the U of T team built up a computational algorithm that would determine which of those words would be used in any given tissue.

More importantly, the program was able to predict what message those code words would create in the form of the messenger RNA for each of the different tissues.

Frey says the code can be adapted to include all the body’s tissues once the relevant code words are found for them.

“Now that we have that framework in place, people can take that framework and they can expand it,” he said.

“Then they will be able to make predictions about what’s going to happen in terms of splicing in those other tissues.”

The code as it stands can only predict what the messenger RNA will look like, and the study does not look at the protein products those strands will actually assemble.

And while the code allows a deep understanding of a fundamental aspect of human biology – alternative splicing – its real value will come from the insight it can give into gene products.

“Now that we have the splicing code, doctors can actually dig in there and find out what’s going on with the actual proteins,” Frey said.

Chris Smith, a University of Cambridge geneticist, says many genetic diseases have been associated with splicing errors and that being able to predict what a message should look like will help medical researchers spot genetic errors that can lead to those ailments.

“It turns out that a large portion of complex human diseases have been associated with errors in splicing,” Frey said.

Also, any attempt to create replacement organs or tissues would need a deep understanding of the ways genes would splice during the creation of those parts, he said.