New clues to a 500-year old mystery about the human heart

 Scientists show that muscular structures first described by Leonardo da Vinci are essential for heart function

Researchers have investigated the function of a complex mesh of muscle fibres that line the inner surface of the heart. The study, published in the journal Nature, sheds light on questions asked by Leonardo da Vinci 500 years ago, and shows how the shape of these muscles impacts heart performance and heart failure.

This project included collaborators at EMBL’s European Bioinformatics Institute (EMBL-EBI), Cold Spring Harbor Laboratory, the MRC London Institute of Medical Sciences, Heidelberg University, and the Politecnico di Milano.

In humans, the heart is the first functional organ to develop and starts beating spontaneously only four weeks after conception. Early in development, the heart grows an intricate network of muscle fibres – called trabeculae – that form geometric patterns on the heart’s inner surface. These are thought to help oxygenate the developing heart, but their function in adults has remained an unsolved puzzle since the 16th century.

To understand the roles and development of trabeculae, an international team of researchers used artificial intelligence to analyse 25 000 magnetic resonance imaging (MRI) scans of the heart, along with associated heart morphology and genetic data. The study reveals how trabeculae work and develop, and how their shape can influence heart disease. UK Biobank has made the study data openly available.

Solutions to da Vinci’s biological enigma

Leonardo da Vinci was the first to sketch trabeculae and their snowflake-like fractal patterns in the 16th century. He speculated that they warm the blood as it flows through the heart, but their true importance has not been recognised until now.

“Our findings answer very old questions in basic human biology. As large-scale genetic analyses and artificial intelligence progress, we’re rebooting our understanding of physiology to an unprecedented scale,” says Ewan Birney, Deputy Director General of EMBL. 

The research suggests that the rough surface of the heart ventricles allows blood to flow more efficiently during each heartbeat, just like the dimples on a golf ball reduce air resistance and help the ball travel further.

The study also highlights six regions in human DNA that affect how the fractal patterns in these muscle fibres develop. Intriguingly, the researchers found that two of these regions also regulate branching of nerve cells, suggesting a similar mechanism may be at work in the developing brain.

“Our work significantly advanced our understanding of the importance of myocardial trabeculae,” explains Hannah Meyer, Principal Investigator at Cold Spring Harbor Laboratory. “Perhaps even more importantly, we also showed the value of a truly multidisciplinary team of researchers. Only the combination of genetics, clinical research, and bioengineering led us to discover the unexpected role of myocardial trabeculae in the function of the adult heart.”

Trabeculae and the risk of heart failure

The researchers discovered that the shape of trabeculae affects the performance of the heart, suggesting a potential link to heart disease. To confirm this, they analysed genetic data from 50 000 patients and found that different fractal patterns in these muscle fibres affected the risk of developing heart failure.

Further research on trabeculae may help scientists better understand how common heart diseases develop and explore new approaches to treatment.

“Leonardo da Vinci sketched these intricate muscles inside the heart 500 years ago, and it’s only now that we’re beginning to understand how important they are to human health. This work offers an exciting new direction for research into heart failure, which affects the lives of nearly 1 million people in the UK,” says Declan O’Regan, Clinical Scientist and Consultant Radiologist at the MRC London Institute of Medical Sciences.

A machine learning approach for allocating gene function

 Researchers are making the most of machine learning methods to speed up genome annotation pipelines

EMBL’s European Bioinformatics Institute (EMBL-EBI) stores vast amounts of biological data and our researchers have expert knowledge of what these data are and how best to curate them. This makes EMBL-EBI well equipped to solve biological problems using machine learning – an artificial intelligence (AI) approach requiring extensive input of high-quality data to rapidly generate results. 

One project initiated in this way came from within the Ensembl team, who are using machine learning to help allocate a function to different genes in their newly-annotated genomes at an unprecedented rate. 

Adding gene function to genome annotations 

Annotating a genome means identifying and mapping the locations and structures of genes and other genomic features. Having access to genome annotation gives researchers information about the location of a gene but assigning a potential function requires additional work and experimental evidence. 

“We can start to extrapolate the function of a gene by looking at related genes in other species, but this can be costly, both computationally and in terms of  human effort to manually curate the results,” said Fergal Martin, Eukaryotic Annotation Team Leader at EMBL-EBI. “This led us to try a machine learning approach to streamline this process of allocating potential gene function to the genes in our new species annotations deployed through Ensembl Rapid Release. Currently we are focusing on vertebrate species, but we want to extend the approach across eukaryotes.”

Machine learning to allocate gene function 

The HUGO Gene Nomenclature Committee (HGNC) and the Vertebrate Gene Nomenclature Committee (VGNC) teams at EMBL-EBI work hard to manually assign gene symbols – a short-form abbreviation for a particular gene usually with an associated function – to a variety of genomes. While the manual assignment efforts continue to be streamlined and cover a growing number of vertebrate species, many species in Ensembl have the majority of their gene symbols assigned through automated methods. 

Historically gene symbols have been assigned through building gene trees, which describe the evolutionary relationships between genes both across and within species. This approach is computationally costly, especially with the recent rapid growth in the number of sequenced vertebrate genomes. Fergal and his team wanted to see if they could assign gene symbols and thereby infer function through a machine learning approach.

“We trained a neural network by feeding it roughly three and a half million protein sequences from a variety of different vertebrate species from Ensembl,” said Fergal. “For these sequences, we already had existing gene symbols with associated functions. The end result is that we have built a classifier that can replicate the existing assignments with around 94-97 percent accuracy, depending on the species. Crucially, it takes less than a minute to generate assignments and confidence values for a vertebrate gene set.”

Why use a machine learning approach?  

Using machine learning is saving the team a huge amount of computing time and the system is a lot less complex in terms of implementation than the existing approach that it replicates. Therefore, the team is looking at deploying it to the larger community. 

“While the system is not a replacement for the very high-quality manual assignments produced by teams like HGNC and VGNC, this approach could potentially be useful to curators as an additional tool to help manually validate assignments. It’s also something that individual users could use in assessing their own annotations,” said Fergal. 

The benefits of machine learning 

“As technology advances scientists are increasingly using machine learning to answer biological questions. One example is the protein structure predictions from AlphaFold. We didn’t have a highly accurate algorithmic approach to figure out how to fold proteins, but deep learning is helping to solve this complex biological mystery,” said Fergal. 

“That’s different from what we are trying to do,” he added. “AlphaFold is an example of solving a problem where we didn’t understand all the rules and variables of the system we were trying to model. What we’re doing here is replicating a system that we do understand, but which requires a lot of computing power to run. It’s exciting that deep learning approaches can provide such valuable solutions to challenges across the life sciences.”

Going forward, there is huge potential for using machine learning methods like this, both within Ensembl and across the organisation to benefit other data resources. Machine learning approaches can reduce both computational time and complexity. Large scale genomics projects such as the Darwin Tree of Life (DToL) and the Earth BioGenome Project (EBP) will also benefit greatly from these approaches as the new species annotations created for these can be deployed faster at a high-standard.

“If we are to annotate the genomes of all species on Earth, we need to think of where we can make computational savings,” said Fergal. “There’s an incredible wealth of both in-house knowledge and high quality training data at EMBL-EBI and it’s really exciting to think about how machine learning could not only improve the quality of our data but also drastically reduce the associated computational cost and environmental footprint.” 

This machine learning approach will be rolled out as part of Ensembl Rapid Release, Ensembl’s lightweight genome browser designed to allow fast access to the latest genome annotations for a large number of species.